Keywords: metabolomic data

Characterization of the gene expression changes associated with the age-related Smurf phenotype in Drosophila melanogaster

Ageing is a process affecting a broad range of living organisms. In humans and multiple model organisms, it is characterized by an age-dependent decrease in functional efficiency and increased vulnerability to death. Even if deregulations in specific pathways and mechanisms have been identified as associated with ageing, the so-called hallmarks of ageing, a comprehensive picture of the process is still missing. 2.1 Experimental design . . . . . .

Most of the studies conducted to understand ageing associated changes are performed at a population level, assuming individuals at a given time point in a synchronous population to be a homogenous group. Although this assumption simplifies reality too much, as individuals in the population can die at very different time, and therefore carry a biological age that does not match with their chronological one, it is often used for practical reasons. Indeed, even if the definition of biological clocks to monitor biological age in humans and model organisms is helping in this matter, it is still challenging to have non-invasive biomarkers that could allow following individual ageing in a research context. We believe this inability to be a limiting factor in ageing research.

In 2012, Rera and collaborators described a new age-dependent and deathrelated phenotype in Drosophila melanogaster. By looking at a physiological increase in the intestinal permeability to a non-toxic and non-absorbed blue food dye, they were able to identify individuals committed to death in a few days. Interestingly, these individuals, called "Smurfs", are the only ones, among a population, to exhibit various ageing-related changes and high-risk of impending death whatever their chronological age; furthermore, all the flies studied so far underwent the Smurf transition prior to death. Smurfness has been hypothesized to be a good proxy to follow the physiological age of single individuals.

During my PhD, I have focused on characterizing the gene expression changes associated with the Smurf phenotype. In particular, my initial research question was to test whether Smurfness is a valid method to detect i biological age in vivo. Through whole-body RNA-sequencing on Smurf and age-matched non-Smurf flies of different chronological ages, I demonstrated that Smurfs carry a stereotypical transcriptional signature independently of their chronological age, mostly overlapping with what was described so far as ageing. Furthermore, by studying time-related changes and Smurf-related changes in gene expression concomitantly, I identified genes moving through time but not necessarily associated with the physiological collapse of the organism and death (Smurf phase). This led to the detection of putative pathways affected by time. Lastly, by looking at the genes changing their expression with Smurfness, I functionally validated new genes affecting longevity in Drosophila.

In conclusion, rather than to focus on a specific aspect or mechanism of ageing, my PhD takes place in a more general context in the field, with the investigation of a new phenotype biomarking the biological age. The results, which I will present in the manuscript, point at Smurfs as an excellent tool to look at the physiological age of D. melanogaster, and possibly as a powerful method for deconvolving the changes related to physiological and chronological time.

ii Abstract Caractérisation des changements d'expression génétique associés au phénotype Smurf lié à l'âge chez Drosophila melanogaster Le vieillissement est un processus qui affecte un large éventail d'organismes vivants. Chez l'homme et de nombreux organismes modèles, il est caractérisé par une diminution liée à l'âge de l'efficacité fonctionnelle et une vulnérabilité accrue à la mort. Même si la dérégulation de certaines voies et de certains mécanismes a été identifiée comme étant associée au vieillissement (ce que l'on appelle les marqueurs du vieillissement) il manque encore une image complète du processus.

La plupart des études menées pour comprendre les changements associés au vieillissement sont réalisées au niveau de la population, en supposant que les individus à un moment donné dans une population synchrone constituent un groupe homogène. Bien que cette hypothèse simplifie trop la réalité, puisque les individus de la population peuvent mourir à des moments très différents et donc avoir un âge biologique qui ne correspond pas à leur âge chronologique, elle est souvent utilisée pour des raisons pratiques. En effet, même si le développement d'horloges biologiques pour suivre l'âge biologique chez l'homme et les organismes modèles aide en la matière, il est encore difficile de disposer de biomarqueurs non-invasifs qui pourraient permettre de suivre le vieillissement individuel dans un contexte de recherche. Nous pensons que cette incapacité est un facteur limitant dans la recherche sur le vieillissement.

En 2012, Rera et ses collaborateurs ont décrit un nouveau phénotype dépendant de l'âge et lié à la mort chez Drosophila melanogaster. En observant une augmentation physiologique de la perméabilité intestinale à un colorant alimentaire bleu, ils ont pu identifier des individus voués à la mort en quelques jours. Il est intéressant de noter que ces individus, appelés "Smurfs", sont les seuls, au sein d'une population, à présenter divers changements normalement décrits comme liés à l'âge et un risque élevé de mort imminente, quel que soit leur âge chronologique. De plus, toutes les mouches étudiées jusqu'à présent ont subi la transition Smurf avant de mourir. Nous avons donc émis l'hypothèse que le phénotype Smurf était un bon indicateur pour suivre l'âge iii physiologique d'individus.

Au cours de mon doctorat, je me suis concentrée sur la caractérisation des changements d'expression génique associés au phénotype Smurf. En particulier, ma question de recherche initiale était de vérifier si le phénotype est un bon outil pour détecter l'âge biologique in vivo. Grâce au séquençage des ARN du corps entier sur des mouches Smurfs et des mouches non-Smurfs appariées à différents âges chronologiques, j'ai démontré que les Smurfs portent une signature transcriptionnelle stéréotypée indépendamment de leur âge, qui recouvre en grande partie ce qui a été décrit jusqu'à présent comme étant la signature du vieillissement. En étudiant concomitamment les changements liés au temps et les changements liés aux Smurfs dans l'expression des gènes, j'ai essayé d'identifier les gènes qui sont affectés par le temps mais qui ne sont pas nécessairement associés à l'effondrement physiologique de l'organisme et à la mort (phase Smurf); ceci a conduit à la détection de voies putatives affectées par le temps. Enfin, en examinant les gènes affectés par la "Smurfness", j'ai essayé de cribler de nouveaux gènes affectant la longévité chez la drosophile.

En conclusion, plutôt que de se concentrer sur un aspect ou un mécanisme spécifique du vieillissement, ma thèse s'inscrit dans un contexte plus général dans le domaine, avec l'étude de nouveaux phénotypes biomarqueurs de l'âge biologique. Les résultats, que je présente dans mon manuscrit, indiquent que les Smurfs sont un modèle efficace pour étudier l'âge physiologique de D. melanogaster, et peut-être un outil puissant pour déconvoluer les changements liés au temps physiologique et chronologique.

General introduction and guidelines

If asked to define ageing, our reply would probably be similar to "the progressive decline in health and functionality that our body experiences in late adulthood, and that is eventually leading to death". The perception of our body's ageing is that of a continuous process, the beginning of which is not readily identifiable, and that is eventually -at least partially -causally linked to death.

If we now ask the same question to the scientific literature, the answer would not be so different. A standard definition is the one of progressive loss of physiological integrity, leading to impaired function and increased vulnerability to death [START_REF] López-Otín | The Hallmarks of Aging[END_REF]. Ageing is acknowledged as a process occurring progressively over time, causing a decline in an organism's efficiency, and that correlates with increasing mortality. Establishing a causal link between ageing and death could seem intuitive to us. However, ageing is never listed as a cause of death itself. If we visit the Center for Disease Control (CDC) online page regarding the leading causes of death -currently updated to 2020 -we find in the podium three killers: heart diseases, cancer and Covid-19. All of these diseases have age as a significant risk factor. We could therefore say that ageing is an indirect cause of death.

The definition of ageing that I introduced as standard is correct but not exhaustive. Does every organism age this way? Do all organisms age? These are complex questions to which whole books can be dedicated. The idea of ageing as a process that reduces the ability of an organism to survive goes back to unicellular organisms like bacteria or the yeast model organism Saccharomyces cerevisiae. For them, two different ageing features can be described: chronological lifespan (CLS) -the time the cell lives without dividing -and replicative lifespan (RLS) -the times the cell can divide. It is difficult to imagine these unicellular organisms sharing our same ageing patterns; indeed, not all the ageing-associated phenotypes are conserved across evolution. [START_REF] Lemoine | The Evolution of the Hallmarks of Aging[END_REF] interestingly tries to reconstruct the evolution of the hallmarks of ageing, as they are identified by [START_REF] López-Otín | The Hallmarks of Aging[END_REF], from prokaryotes to bilaterian animals like us. He provides a fascinating journey through time that shows how adding parts and pieces to a machine can make it more complex and elegant but can, on the other side of it, add potential problems that the machine needs to cope with.

Even leaving on the side unicellular organisms, it would be a mistake to believe that all multicellular organisms age the way we do. Plants, which organization and life cycle is different from the one of animals, show a different ageing pattern [START_REF] Thomas | Ageing in plants[END_REF]. Even inside the animal kingdom, we find organisms not showing an over time decline. The jellyfish Turritopsis dohrnii can revert to an early, pre-reproductive stage of life when subjected to stress, and restart all over the cycle [START_REF] Martell | Life cycle, morphology and medusa ontogenesis of Turritopsis dohrnii (Cnidaria: Hydrozoa)[END_REF]. The cells of the small freshwater animal Hydra show an indefinite capacity of self-renewal [START_REF] Boehm | FoxO is a critical regulator of stem cell maintenance in immortal Hydra[END_REF]. The lobsters, even if not potentially immortal, show a different ageing pattern with no decline of functionality over time and expression in adult cells of the telomerese enzyme, which continuously works to protect and preserve the chromosomes [START_REF] Klapper | Longevity of lobsters is linked to ubiquitous telomerase expression[END_REF].

The scientific definition that I provided earlier is therefore biased towards human-like ageing, which, as a society, we are interested in studying for apparent reasons. Therefore, in this manuscript, I will focus on human-like ageing, which followed by Drosophila melanogaster, the model organism I used in my research.

As my PhD takes place in the field of ageing, I will provide a general introduction to the topic in section 1.2. I will then move to discuss the difference between biological and chronological ageing (section 1.3), and what has been done so far to try to deconvolve the first from the second. I will then present the Smurf model, which I have investigated during the past three years to validate it as a tool for identifying biologically old individuals in fruitflies.

The reader can see the introduction chapter as a guide towards the biological problem I investigated. I then divided the rest of the manuscript in three different chapters. Chapter 2 is dedicated to the bioinformatic part of the project. In Chapter 3, I describe the experimental work I conducted. Chapter 4 is dedicated to the final discussion of the results and the conclusions. I divided each chapter into different and clearly identifiable sections so that the reader can quickly jump from one to the other if needed.

Ageing: how and why

Ageing, as a process strongly decreasing an individual's fitness and eventually associated with its end, seems counterintuitive from an evolutionary point of view. Why would evolution select for something that damages the individual? On this subject, there are two leading schools of thought. Supporters of the programmed ageing point of view affirm that evolution is not blind with respect to ageing and that such mechanism is intrinsic to each organism; some of the associated theories explain why this would be selected by evolution, and some not. An example of a programmed ageing theory that explains ageing in the light of evolution is altruistic ageing (see [START_REF] Longo | Programmed and altruistic ageing[END_REF] for a more extensive review on the subject). We can reconduct somehow the origin of this view of ageing to Hamilton's kin selection theory in the 1960's [START_REF] Hamilton | The genetical evolution of social behaviour[END_REF] (though the conceptualization of kin selection goes back to Darwin), which affirms that an individual, in a given social context, can act in a way that reduces its fitness if such behaviour maximizes the fitness of its relative. Altruistic ageing brings this to the extreme, as ageing would be sort of an altruistic suicide of the individual in favour of the population's survival. Behaviours that could be interpreted in such a sense have been observed in unicellular organisms as Saccharomyces cerevisiae [START_REF] Longo | Programmed and altruistic ageing[END_REF]). The most obvious example of this in the animal kingdom is the behaviour of the Pacific salmons, which show fast senescence and death just after reproduction; this would be a case of phenoptosis, a programmed death of an organism when not needed anymore by the population (malfunction or competition, as in this case, with the progeny).

On the other side, we have the non-programmed ageing theories, which affirm that in different ways, ageing results from a passive action of evolution. The first theory proposed in this context is the wear and tear (Weismann, 1882), which generally affirms that the organism ages and collapses due to damage accumulation over time. Other theories have been proposed along the past century, some on the trend of scientific discoveries (as the Reactive Oxygen Species -ROS-in association with ageing in the 1950's). Whether ageing is programmed or not, is still a matter of debate.

For quite a long time now, what cannot be debated is that ageing is a plastic process that can be modulated. Indeed, back in the 1930's, it was shown that caloric restriction (CR) induced lifespan extension in rats [START_REF] Mccay | The Effect of Retarded Growth Upon the Length of Life Span and Upon the Ultimate Body Size: One Figure[END_REF][START_REF] Mccay | Retarded Growth, Life Span, Ultimate Body Size and Age Changes in the Albino Rat after Feeding Diets Restricted in Calories: Four Figures[END_REF]. The CR modulation of ageing has been since then demonstrated in other species, such as Caenorhabditis elegans [START_REF] Klass | Aging in the nematode Caenorhabditis elegans: Major biological and environmental factors influencing life span[END_REF], and D. melanogaster [START_REF] Chippindale | Phenotypic plasticity and selection in Drosophila life-history evolution. I. Nutrition and the cost of reproduction[END_REF]. More recently, it has been confirmed that CR improves healthspan (i.e. the amount of time the organism spend in good health) in primates [START_REF] Mattison | Caloric restriction improves health and survival of rhesus monkeys[END_REF].

In addition, mutations in specific genes can modulate ageing and extend lifespan. [START_REF] Friedman | A mutation in the age-1 gene in Caenorhabditis elegans lengthens life and reduces hermaphrodite fertility[END_REF] showed for the first time that a mutation in the age-1 gene of C. elegans was extending lifespan in both males and females. The gene codes for a domain of catalytic subunit of class-I phosphatidylinositol 3-kinase (PI3K). It is involved in the Insulin/IGF-1 Signaling (a well-known ageing pathway which I will further discuss in 1.2.1). Ten years later, the first genetic mutation increasing lifespan was identified in D. melanogaster [START_REF] Lin | Extended life-span and stress resistance in the Drosophila mutant methuselah[END_REF], while in the same years lifespan extension was shown for mice with hereditary dwarfism [START_REF] Brown-Borg | Dwarf mice and the ageing process[END_REF].

The biological mechanisms associated with ageing have been extensively studied in the past century. Although a complete picture is still missing, [START_REF] López-Otín | The Hallmarks of Aging[END_REF] have proposed, by summarizing what is known so far, nine hallmarks of ageing. For the hallmarks selection, they have considered three criteria: 1) the process manifests during normal ageing; 2) if experimentally induced or aggravated, it accelerates ageing; 3) if experimentally ameliorated, it retards ageing.

I will now present the different hallmarks as a general state of the art of ageing. With each hallmark, I will describe, if any, the associated theories. Please note that the theory was proposed before the presented experimental evidence in some cases, but I am grouping them to allow a more fluid reading. At the end of the section, I will present the theories that do not fit in a specific hallmark. In figure 1.1, you can follow the chronological order of the theories and discoveries that I am presenting all along the Introduction.

Hallmarks of ageing

The nine hallmarks of ageing span from DNA to the systemic level, and they are summarized in figure 1.2. They can indeed be divided in three categories: nuclear, cellular and systemic. I will review them from the nuclear to the systemic ones, following the same order as the paper.

Genomic instability

Numerous diseases that show accelerated ageing phenotypes present defects in DNA repair mechanisms, as well summarized in [START_REF] Niedernhofer | Nuclear Genomic Instability and Aging[END_REF]. For example, progeria syndrome, which causes premature ageing in humans, is linked to a mutation in the lamin A gene, involved in nuclear architecture [START_REF] Eriksson | Recurrent de novo point mutations in lamin A cause Hutchinson-Gilford progeria syndrome[END_REF]. Furthermore, DNA lesion markers have been shown to increase with age in different cells isolated from mice and rats [START_REF] Helbock | DNA oxidation matters: The HPLC-electrochemical detection assay of 8-oxo-deoxyguanosine and8-oxo-guanine[END_REF][START_REF] Wong | Elevation of oxidativedamage biomarkers during aging in F2 hybrid mice: protection by chronic oral intake of resveratrol[END_REF][START_REF] Nie | Age-dependent accumulation of 8-oxoguanine in the DNA and RNA in various rat tissues[END_REF]. On the other hand, DNA repair mechanisms are indeed observed to decline in efficiency with age [START_REF] Ren | Non-homologous DNA end joining in the mature rat brain[END_REF][START_REF] Intano | Age-related base excision repair activity in mouse brain and liver nuclear extracts[END_REF][START_REF] Yamada | Aged human skin removes UVB-induced pyrimidine dimers from the epidermis more slowly than younger adult skin in vivo[END_REF]. In addition, [START_REF] Baker | Increased expression of BubR1 protects against aneuploidy and cancer and extends healthy lifespan[END_REF] showed how overexpression of BubR1, involved in guaranteeing the correct segregation of chromosomes during mitosis, extends lifespan in mice. Is this a simple correlation, or does genomic instability drive ageing? [START_REF] Niedernhofer | Nuclear Genomic Instability and Aging[END_REF] argue that although there are pieces of evidence supporting that, some studies in humans and mice show a weak correlation with increased DNA stability in long-living mammal models; an improved response to oxidative stress could be a confounding factor in this matter. Overall, cause or effect, genomic instability is a well-conserved hallmark of ageing. Interestingly, mitochondrial DNA (mtDNA) too has been shown to accumulate mutations over time in mice [START_REF] Ameur | Ultra-deep sequencing of mouse mitochondrial DNA: mutational patterns and their origins[END_REF].

Mutation-accumulation theory. This theory is part of the non-programmed view of ageing. First proposed by P. Medawar in 1952, it affirms that ageing is the result of the random accumulation of mutations that are only expressed later in life, after the reproduction time of the organism. Therefore, such mutations escape evolution.

Antagonistic pleiotropy theory. G. C. Williams proposed it in 1957, and it falls as well under the non-programmed theories. Although it does not refer to genomic instability, it refers to genetic variants, and, therefore, I place it in the genome hallmark. It proposes that a gene controlling different traits (pleiotropy) can provide benefits to the organism early in life but results in a disadvantage late in life (antagonistic). Therefore, such genes would be actively selected for by evolution but driving ageing as a side effect. Experiments conducting artificial selection in D. melanogaster showed that the lines obtained as the progeny of young flies have a mortality rate increasing more rapidly with age compared to that of the lines obtained as the progeny of old flies. This seems to be the cost of a benefit in early life, with higher fertility observed in "young" lines, as explained in [START_REF] Partridge | Mechanisms of aging: public or private?[END_REF].

Programmed longevity. This theory falls in the programmed ageing view. It affirms that the genome is intrinsically meant to fail over time, and no organism can escape such mechanism. Genomic instability manifesting over time would therefore be the leading cause of ageing [START_REF] Davidovic | Old age as a privilege of the "selfish ones[END_REF]. In this context, long-living individuals are the "privileged ones", carrying an intrinsically more stable genome, which delays their ageing. This group is opposed to the "normal ones", who carry a normal genome meant to a normal ageing pattern. To support their hypothesis, the authors cite a retrospective study showing that siblings of centenarians experienced a mortality advantage throughout their lives relative to the reference cohort [START_REF] Perls | Life-long sustained mortality advantage of siblings of centenarians[END_REF]. However, it is unclear if such an effect is due to a more stable genome.

Telomere attrition

Telomeres are the end of linear DNA molecules in cells. In most eukaryotic cells, they are made of repeated sequences, polymerized by a dedicated enzyme (telomerase). Not all the animals show such telomere structure. Drosophila telomeres, for instance, are constituted by an array of retrotransposons [START_REF] Mason | Drosophila telomeres: an exception providing new insights[END_REF]. Telomeres shortening over time, due to the non-expression of telomerase in somatic cells, has beeen shown to play an essential role in cellular senescence, with artificial expression of the enzyme being sufficient to immortalize the cells without undergoing malignant transformation [START_REF] Bodnar | Extension of Life-Span by Introduction of Telomerase into Normal Human Cells[END_REF]. Moreover, it has been shown in mice that ageing can be delayed by telomerase induction, without cost in healthspan (Bernardes de [START_REF] Bernardes De Jesus | Telomerase gene therapy in adult and old mice delays aging and increases longevity without increasing cancer[END_REF]. On the other hand, maybe due to their different structure, telomere length seems uncoupled from longevity in Drosophila [START_REF] Walter | Effects of telomere length in Drosophila melanogaster on life span, fecundity and fertility[END_REF].

Epigenetic alterations

Epigenetic is a broad and still not wholly understood world. Some changes associated with genome regulation and stabilization through epigenetic mechanisms have been seen as associated with ageing. Changes in histone modifications (both methylation and acetylation) have been observed. In particular, inhibition of histone methylation has been shown to extend longevity in nematodes and flies [START_REF] Greer | Members of the Histone H3 Lysine 4 Trimethylation Complex Regulate Lifespan in a Germline-dependent Manner in C. elegans[END_REF][START_REF] Siebold | Polycomb Repressive Complex 2 and Trithorax modulate Drosophila longevity and stress resistance[END_REF].

The sirtuin gene family (NAD-dependent protein deacetylases) acts on histones -amongst other targets-and has been extensively studied for its role in ageing. Indeed, Sir2 has been shown to have pro-longevity effects when overexpressed in yeast (replicative lifespan), nematodes, flies and mice [START_REF] Zhao | Sirtuins and their Biological Relevance in Aging and Age-Related Diseases[END_REF]. DNA methylation is also described as changing patterns with age, with a decreased global methylation favouring an increased local one. Even if its causal role -if any -is not clear yet, DNA methylation changes are a wellestablished biomarker of age. They have indeed been used as a base for the definition of the first biological clock (see section 1.3) [START_REF] Horvath | DNA methylation age of human tissues and cell types[END_REF][START_REF] Horvath | DNA methylation-based biomarkers and the epigenetic clock theory of ageing[END_REF]. Epigenetic changes can lead to transcriptional alterations and reduced genome protection, linking the first three described hallmarks in a more general and interconnected process associated with ageing.

Loss of proteostasis

Cells maintain proteostasis through two mechanisms: stabilization of unfolded proteins (chaperone-mediated folding) and degradation by the proteasome (ubiquitin-mediated) and lysosomes (autophagy-mediated). The heat-shock chaperones family (Hsp) seems to play an important role, as the activation of their regulator (called Hsf1 in humans) has been shown to cause lifespan shortening when knocked down in C. elegans, and an extension with its overexpression [START_REF] Hsu | Regulation of aging and age-related disease by DAF-16 and heat-shock factor[END_REF]. On the other hand, Hsp genes appear to be upregulated in nematodes [START_REF] Frenk | Gene expression hallmarks of cellular ageing[END_REF], and in flies [START_REF] Tower | Heat shock proteins and Drosophila aging[END_REF]. Therefore, it seems that cells undergo a stress condition with increasing age, which results in unfolded proteins and protein aggregation. Degradation mechanisms are impaired with age [START_REF] Vilchez | The role of protein clearance mechanisms in organismal ageing and age-related diseases[END_REF]. Proteasome mediated clearance has been described as less effective with ageing [START_REF] Vernace | Aging perturbs 26S proteasome assembly in Drosophila melanogaster[END_REF][START_REF] Ferrington | Altered proteasome structure, function, and oxidation in aged muscle[END_REF][START_REF] Andersson | Enhancing protein disaggregation restores proteasome activity in aged cells[END_REF]. Autophagy has also been shown to work less efficiently, having effects also on organelles clearance -particularly mitochondria (mitophagy) [START_REF] Vilchez | The role of protein clearance mechanisms in organismal ageing and age-related diseases[END_REF]. Interestingly, promoting autophagy by overexpressing its regulating genes increases lifespan in mice, nematodes, and fruit flies [START_REF] Hansen | Autophagy as a promoter of longevity: insights from model organisms[END_REF]. Autophagy is promoted by inactive insulin signal (see section 1.2.1.5), and its stable function is therefore involved in CR-mediated lifespan extension.

Cross-linking theory. Proposed by J. Bjorksten in 1942, states that the decline observed with ageing is due to accidental cross-linking between proteins, which eventually damages cells and tissues.

Deregulated nutrient sensing

Our organism needs to finely perceive the level of nutrients available to respond to high and low energy states correctly. The IIS pathway -mentioned at the beginning of section 1.2 -is the mediator of high energy states response. I schematized its effect on ageing in figure 1.3. The axis mediated by insulin and insulin-like growth factor (IGF1) represses the transcription factor FOXO activity, with a final positive regulation of glucose uptake and fatty acid storage. The other important player of the response is the kinase mTOR, which is activated by the IIS pathway, but it also senses amino acids availability, positively regulating protein synthesis while inhibiting autophagy [START_REF] Sabatini | Twenty-five years of mTOR: Uncovering the link from nutrients to growth[END_REF]. It has been reported that artificial attenuation of the IIS pathway extends lifespan in worms, flies and mice [START_REF] Fontana | Dietary Restriction, Growth Factors and Aging: from yeast to humans[END_REF] -which justifies the effect of CR on longevity. Inhibition by rapamycin of mTORC1 (one of the two subunits of mTOR) is also a well-documented lifespan increasing treatment [START_REF] Harrison | Rapamycin fed late in life extends lifespan in genetically heterogeneous mice[END_REF]. What is even more interesting is that IGF-1 and GH decrease during normal ageing and have been shown to do so in mice models with accelerated ageing [START_REF] Schumacher | Delayed and Accelerated Aging Share Common Longevity Assurance Mechanisms[END_REF] -why so? It has been proposed that a constitutive decrease in IIS signalling, instead of a later-life one, decreases the rates in cell growth and metabolism, therefore decreasing the rate in cellular damage that comes with it as a side effect [START_REF] Garinis | DNA damage and ageing: new-age ideas for an age-old problem[END_REF]. Concerning the signal related to low nutrients sensing, we find the AMP-activated protein kinase (AMPK) -which inhibits mTOR -and once again a protein of the sirtuin family (Sirt1), which acts downstream of PGC-1α. The latest, when activated, stimulates mitochondriogenesis and fatty acid catabolism. AMPK activation through metformin extends lifespan in worm and mice [START_REF] Anisimov | Metformin slows down aging and extends life span of female SHR mice[END_REF][START_REF] Onken | Metformin induces a dietary restriction-like state and the oxidative stress response to extend C. elegans Healthspan via AMPK, LKB1, and SKN-1[END_REF].

Rate-of-living theory. It was proposed by M. Rubner in 1908, and affirms that the lifespan of an organism is inversely proportional to its metabolic rate. This formulation came from observing that larger animals outlive smaller animals with a slower metabolism. The theory was further supported in 1928 by R. Pearl in his book "The rate of living". However, the slow ageing observed The IIS pathway and its effects on ageing. In green the genes/treatments which result in a positive modulation of ageing. Vice versa for the yellow nodes. The IGF-1 activated inside the cell a kinase signaling cascade, which results in the inactivation of the transcription factor FOXO and the activation of the kinase mTOR. This provokes positive regulation of glucose and fatty acid storage, as well as protein synthesis, while inhibiting autophagy. This is the high nutrients level sensing axis. On the other hand, the kinase AMPK, together with Sirt1 and PGC-1α, mediates the low nutrient sensing axis. Their activity stimulates catabolism and mitochondriogenesis. By working as a calmer on the high nutrient sensing axis, CR extends lifespan. Picture inspired from [START_REF] López-Otín | The Hallmarks of Aging[END_REF].

in small animals like birds [START_REF] Holmes | Comparative biology of aging in birds: an update[END_REF] contradicts this theory or, at least, it questions its universality.

Disposable soma theory. It was proposed by T. Kirkwood in 1977 and focuses on hypothetical necessity of the organism to allocate resources. In particular, the individual needs to balance the energies it spends for its own needs and reproduction. For instance, an increase in investment in reproduction would lead the organism to "neglect" its maintenance, bringing to a decline in functionality and eventually death. It has been shown in D. melanogaster that not only virgin flies live longer than mated flies, but that a late-reproducing line has a longer lifespan than an early-reproducing line, and that this difference disappears when a mutation in the oogenesis gene ovoD1 is introduced [START_REF] Sgrò | A delayed wave of death from reproduction in Drosophila[END_REF]. More recent results in flies support the theory [START_REF] Travers | Live fast die young life history in females: evolutionary trade-off between early life mating and lifespan in female Drosophila melanogaster[END_REF]. On the other hand, retrospective studies trying to correlate female lifespan with reproduction have been attempted in humans, with unclear results [START_REF] Kuningas | The relationship between fertility and lifespan in humans[END_REF][START_REF] Kaptijn | The Trade-Off between Female Fertility and Longevity during the Epidemiological Transition in the Netherlands[END_REF][START_REF] Gagnon | Natural fertility and longevity[END_REF].

Overall, the antagonistic pleiotropy and the disposable soma theories could be more generally summarized in one category, the trade-off theories.

Mitochondrial dysfunction

Mitochondria play an essential role in the metabolism and energy production of cells. They carry their own genome, partially coding for the proteins operating in the organelle [START_REF] Anderson | Sequence and organization of the human mitochondrial genome[END_REF]. They are also mediators of numerous stress responses in the cell. For instance, permeabilization of their outer membrane starts the intrinsic apoptosis pathway. Or again, in case of dysfunction, translation is attenuated in the cell through the action of the kinase GCN2 and can also mediate the unfolded protein response through the transcription factor ATFS-1 [START_REF] Melber | UPRmt regulation and output: a stress response mediated by mitochondrialnuclear communication[END_REF]. Given their importance in the cellular network and the fact that they are the main source of ROS production in the cell (see theories below), mitochondria have been intensively studied in ageing. Downregulation of genes acting in mitochondria has been listed as one of the gene expression hallmarks of ageing [START_REF] Frenk | Gene expression hallmarks of cellular ageing[END_REF]. [START_REF] Webb | Intimate Relations-Mitochondria and Ageing[END_REF] and [START_REF] Sharma | Causal roles of mitochondrial dynamics in longevity and healthy aging[END_REF] provide a good overview of the mitochondrial changes associated with ageing. Global mitochondrial proteomic changes are observed, with further evidence pointing at a reduced electron transport chain (ETC) efficiency. Senescence induction in cells also correlated with deterioration of mitochondrial oxidative phosphorylation. The clearance of dysfunctional mitochondria through mitophagy has also been shown as impaired with ageing.

As mentioned in the previous section 1.2.1.1, mtDNA damage increases over time -with the accumulation of erroneous copy of the mtDNA inside mitochondria. However, this last point is debated, as mitochondria present multiple copies of their genome inside the organelle and can therefore carry different versions of it (heteroplasmy). Therefore, for a mutation to affirm and have an effect, it needs to be carried by a sufficient proportion of copies.

Free-radical and ROS theory of ageing. It was first proposed in the 1950s by D. Harman as the free-radical theory of ageing, and it has later been expanded to oxidizing agents in general, such as ROS (reactive oxygen species). According to this theory, cells (and the organism they form) age due to the accumulation of oxidative damage to their components, caused by highly reactive free radicals. Over time, the accumulation of such compounds overcome cells reductive capability, leading to unrepaired damage. Such theory gained trend following the discovery of the first superoxide dismutase (SOD) [START_REF] Mccord | Superoxide dismutase. An enzymic function for erythrocuprein (hemocuprein)[END_REF], which plays a ROS scavenging role in the cell. This theory has been central in the ageing field during the second half of the 1900s. However, more recent findings do not support it systematically. Although there is evidence supporting an increased resistance to oxidative stress in long-lived Drosophila strains [START_REF] Deepashree | Oxidative stress resistance as a factor in aging: evidence from an extended longevity phenotype of Drosophila melanogaster[END_REF], some studies show how increases in oxidative damage is not always linked to lifespan reduction (Van Raamsdonk and Hekimi, 2009;[START_REF] Pérez | Is the Oxidative Stress Theory of Aging Dead?[END_REF]. In addition, it is now clear that the mitochondrial ROS do not play only a "destructive" role, but below a certain threshold, they trigger signalling pathways that are important in cells and organism homeostasis [START_REF] Ristow | Mitohormesis: Promoting Health and Lifespan by Increased Levels of Reactive Oxygen Species (ROS)[END_REF].

Cellular senescence

Cellular senescence is a growth arrest state in which cells cannot proliferate, even under optimal conditions and mitogenic stimuli. Initially observed in dividing cultured cells, there is evidence that postmitotic cells can show a senescent phenotype. Senescence is thought to be induced by DNA damage and telomere shortening, and it is associated with changes in chromatin structure and mitochondrial dysfunctions. When looking deeper into the described changes [START_REF] Di Micco | Cellular senescence in ageing: from mechanisms to therapeutic opportunities[END_REF], they indeed overlap with some of the described hallmarks of ageing. Interestingly, the level of cyclin-dependent kinase inhibitor p16 -a senescent cell biomarker-, has been shown to increase with age in different rat tissues [START_REF] Krishnamurthy | Ink4a/Arf expression is a biomarker of aging[END_REF] and human skin [START_REF] Ressler | p16INK4A is a robust in vivo biomarker of cellular aging in human skin[END_REF]. This could lead us to a chicken-egg problem: are the previously hallmarks of ageing triggering cell senescence, or are -at least partially -hallmarks of senescence itself? Studies in mice have shown that accumulation of senescent cells due to genetic manipulation leads to early ageing tissues [START_REF] Baker | BubR1 insufficiency causes early onset of aging-associated phenotypes and infertility in mice[END_REF], with the clearance of such cells delaying the early ageing phenotype [START_REF] Baker | Clearance of p16Ink4a-positive senescent cells delays ageing-associated disorders[END_REF]. While this might not be enough to affirm that the accumulation of senescent cells is triggering ageing, it proves the association between the two phenotypes. Senescent cells also have specific secretory patterns (SASP -Senescence-associated secretory phenotype), mainly composed of pro-inflammatory cytokines and extracellular matrix (ECM) re-modelling enzymes. Di [START_REF] Di Micco | Cellular senescence in ageing: from mechanisms to therapeutic opportunities[END_REF] argue that it is yet to be deciphered if the ageing improvements observed in the literature upon senescent cells clearance are due to the removal of the cells themselves or their SASP instead.

Stem cell exhaustion

Adult stem cells contribute to tissue renewal all along life. As part of the organism, they are not immune from the alteration I described so far. Indeed, aged stem cells show a higher level of DNA damage and impaired metabolic regulation, with an altered balance between glycolysis and mitochondrial respiration [START_REF] Ermolaeva | Cellular and epigenetic drivers of stem cell ageing[END_REF]. The latter is also linked to an impaired production of epigenetic co-factors such as acetyl-coA, possibly cooperating with the changes observed in the epigenome. Over time, stem cells are also subjected to stress from changes in their niche. For instance, in skeletal muscle with age, we observed the so-called fibrosis, which is an excessive accumulation of ECM components, primarily collagen, due to excessive ECM production or alteration in ECM-degrading activities, or both [START_REF] Mahdy | Skeletal muscle fibrosis: an overview[END_REF]. This phenomenon changes the mechanical forces to which stem cells are subjected, resulting in reduced self-renewal and regenerative capacity [START_REF] Blau | The central role of muscle stem cells in regenerative failure with aging[END_REF]. It has also been observed that stem cells misregulation with age can cause over-proliferation and misdifferentiation, as in D. melanogaster intestine; [START_REF] Biteau | Lifespan Extension by Preserving Proliferative Homeostasis in Drosophila[END_REF] and [START_REF] Rera | Modulation of Longevity and Tissue Homeostasis by the Drosophila PGC-1 Homolog[END_REF] show that genetic manipulation improving stem cells homeostasis delays the dysplasia observed in the gut and extends lifespan.

Altered intercellular communication

This hallmark mainly refers to the chronic inflammation experienced by older individuals. As discussed in [START_REF] Calder | Health relevance of the modification of low grade inflammation in ageing (inflammageing) and the role of nutrition[END_REF], different studies in humans over the last decade have shown an increase in the blood of inflammatory biomarkers. Production of pro-inflammatory cytokines was found increased in old -and apparently healthy -individuals [START_REF] Fagiolo | Increased cytokine production in mononuclear cells of healthy elderly people[END_REF][START_REF] Franceschi | The immunology of exceptional individuals: the lesson of centenarians[END_REF]. Dysregulation of immune systems genes is also annotated amongst the hallmarks of transcriptional ageing [START_REF] Frenk | Gene expression hallmarks of cellular ageing[END_REF]. Does lowering the level of inflammation postpone ageing? It has been shown that NF-κB inhibition in the skin of old mice leads to the rejuvenation of the tissue, bringing back to a transcriptional signature more similar to the young mice [START_REF] Adler | Motif module map reveals enforcement of aging by continual NF-kappaB activity[END_REF]; once again, we also find involvement of a member of the sirtuin family. Active Sirt1 inhibits Nf-κB by deacetylation, and its downregulation (which has been described in mice cells with senescence and ageing [START_REF] Xu | SIRT1 is downregulated by autophagy in senescence and ageing[END_REF]), could therefore have an impact on the inflammatory status. In flies, it has been shown that light ubiquitous or gut-only upregulation of anti microbial peptides (AMPs, produced downstream of the immune pathways) extend mean lifespan (not maximum) by rectangularization of the survival curve of the population [START_REF] Loch | Antimicrobial peptides extend lifespan in Drosophila[END_REF]. However, the authors show that such flies present decreased expression of immune pathways. The slight artificial overexpression of the downstream product could therefore help maintain a negative feedback loop slowing down immune response; this could be the actual cause of lifespan extension.

Immunological theory of ageing. This theory assigns the leading ageing role to the intrinsic deregulation of the immune system over time, which leaves the organisms more susceptible to infection and cancer, and therefore death. Firstly proposed by R. Walford [START_REF] Walford | The Immunologic Theory of Aging[END_REF], it laid the foundations for the study of the importance of immune system deregulation with ageing.

Inflammageing. Over the years, increasing evidence was showing that the organism seems to enter a state of chronic inflammation with age. The immunological theory of ageing was then updated to what is now called inflammageing [START_REF] Franceschi | Inflammaging. An evolutionary perspective on immunosenescence[END_REF], according to which a state of chronic inflammation characterizes old individuals. This new formulation adds to what was hypothesized by [START_REF] Kirkwood | Is aging as complex as it would appear? New perspectives in aging research[END_REF] as the "ageing network". According to the ageing network, the anti-ageing cellular defence is based on: a) heat shock response, b) oxygen-free radical scavenging, c) UV-stress recovery, and d) DNA damage repair system. The state of chronic inflammation incorporates this picture as a first background step: it represents a first hit in the ageing process, while the second hit that leads to failure of the network depends on individual genetic background or specific environmental stimuli. Therefore, chronic inflammation would be a shared mechanism of ageing. At the same time, different individuals could be less or more resistant to the other kinds of stresses represented in the ageing network.

Healthy ageing first hit inflammation susceptible time second hit

Unhealthy ageing Figure 1.4: According to the Inflammageing theory proposed by [START_REF] Franceschi | Inflammaging. An evolutionary perspective on immunosenescence[END_REF], chronic inflammation would be a first hit (shared by all the individuals) towards unhealthy ageing. The second hit, which leads to the manifestation of the unhealthy ageing phenotype, can instead be different in different invidividuals, depending on their genetic background or the environmental stresses they undergo.

Final remarks

As already hinted by the discussion on the single hallmarks, it seems clear that no process is completely independent of the other, as cells are systems that base their life on well-regulated interactions and cross-talks. Indeed, in the

Antagonist hallmarks

Integrative hallmarks

Response to damage, beneficial below a certain treshold propose to divide the hallmarks into three categories: primary, antagonistic and integrative. The primary hallmarks would be the first cause of damage, and they are uniquely negative. The antagonist hallmarks would occur at a second time. They are characterized by having contrasting effects depending on their intensity. If mild, they can have a protective role or trigger a protective signal. When exacerbated, they are detrimental for the organism. The authors propose that the primary hallmarks might play a role in the chronicization of such processes. Last, we have the integrative hallmarks, which emerge from the previous alterations and directly affects tissues function.

In this section, I provided a general overview of the processes that have been shown as associated with ageing. I will now discuss the way we study ageing, focusing on the difference between the biological age and the chronological age of an organism. (2013). According to the study, the first hallmark appearing in prokaryotes is the accumulation of misfolded proteins. It follows the chromatin-related hallmarks with the appearance of Archea. After that, we find hallmarks related to organelles (mitochondria) and eukaryotic nuclear complexity. The metabolic hallmarks are the last to appear in unicellular organisms. As the organism's complexity increases with multicellularity, the rest of the hallmarks appear.

Biological and chronological age

The longevity curve (i.e the proportion of alive individuals plotted over time) of a synchronous population with human-like ageing is exemplified in figure 1.7. After a time characterized by an almost null mortality (initial survival plateau), the population progressively decreases in size as individuals die. Over time, the population experiences an apparently progressive increase of the hallmarks of ageing.

What happens instead on an individual's level? If we look at the curve, we will immediately understand that all individuals are not biologically ageing at the same rate. A given population is characterized by a mean lifespan (ML, average time of death of its individuals), meaning that most individuals will die at an age around this number. However, some individuals will die earlier (short-lived) and some later (long-lived). The population is made of subpopulations with a different remaining lifespan at each moment. Individuals of different subpopulations are possibly ageing at a different rate, therefore experiencing ageing-related changes at different levels, even if they are the same age.

There is indeed a distinction between the chronological age of an individual and its biological age. Chronological age is defined as the time passed since birth, and, therefore, it is equal for all the individuals born at the same time. The biological age is the individual's actual age in terms of its physiology and is reflected by its mortality risk.

Different ageing rates amongst a population. [START_REF] Zhang | Extended Twilight among Isogenic C. elegans Causes a Disproportionate Scaling between Lifespan and Health[END_REF] conducted an experiment on C. elegans to understand how different individuals age in a synchronous population. They studied a cohort of 734 worms followed for all their lives in an automated way. They also recorded and kept track of different parameters (such as movements, body size, and tissue integrity) to assess their health status. They considered three different hyphotesis: 1) starting point hypothesis, according to which each individual would be born with a given status intrinsically deciding its lifespan; 2) rate of ageing hypothesis, according to which all the individuals are born with the same potential but then starting to age at a different pace; 3) premature death hypothesis, If we could distinguish the biological age of the individuals in a population, we would observe that they actually age at a different rate. In this example, the 10 individuals present at the beginning in the population are a mixture of individuals that will age at a different speed and die in different moments. The blue individuals are short-lived and will die off first. The most numerous red individuals will die at an age around the average lifespan of the population. But a few individuals (yellow) will die off last, at a higher age than the mean lifespan. In the heterogeneous population, we observe an increase, on average, of the hallmarks of ageing carried by individuals.

according to which individuals are following the same ageing path, and dying stochastically on the way. Their results point at the second (rate of ageing) hypothesis as the one supported by the data, with a hint for the third hypothesis (premature death) for individuals with a similar ageing rate.

This would mean that at day 0 in the population, all the individuals are equal, but some experience a faster ageing rate than others. Interestingly, their data also suggest that individuals living longer spend on average a more significant proportion of their life in bad health compared to individuals dying prematurely. So the latter would indeed experience a "faster senescence" compared to the first ones.

Different ageing rates can create a sampling bias. [START_REF] Meyer | BiT age: A transcriptome-based aging clock near the theoretical limit of accuracy[END_REF] exemplifies the kind of bias we can face when sampling from a synchronous population ageing a different rates. of the individuals at a given chronological age as normally distributed. When the population is young (in the survival plateau), we expect the median biological age to overlap with chronological age for most of the individuals. However, some will be biologically younger and some biologically older -with a maximum biological age in the population. The biologically older individuals will die off first, shifting the mean of the distribution towards biologically younger individuals. The authors, therefore, suggest that, as the population advances in age, we experience an increase in the probability of sampling biologically younger individuals. This implies that each individual, once it diverges from the others in a way similar to the "rate of ageing" hypothesis explained in the paragraph before, carries a gap in its biological age relative to the other individuals -a "negative" one if it is ageing faster, a "positive" one otherwise.

Ageing clocks. In order to identify at each moment individuals that are physiologically old or young, we need biomarkers that correctly represent their biological age. Then we would be able to distinguish the different biological subpopulations present in a group.

The frailty index (FI34) was defined by [START_REF] Kim | Association of healthy aging with parental longevity[END_REF] The ageing clocks are instead predictors of the ageing rate of an organism given a set of biological variables. The first developed clock is the epigenetic Horvath's clock [START_REF] Horvath | DNA methylation age of human tissues and cell types[END_REF], which estimates a DNA methylation age (DNAm age) by using the cytosine-5 methylation pattern of 353 CpG sites (regions with repetitions of cytosine and guanine) in the human genome. The clock has been proven to almost perfectly correlate with age, with an error in estimation of 3.6 years. The estimation performs well also on blood cells, affirming the epigenetic clock as a non-invasive way to predict age in humans.

Recently, attention has been focused on identifying a transcriptomic ageing clock that could be applied to organisms with no DNA methylation (as C. elegans) or a low level of CpG methylation as D. melanogaster. [START_REF] Tarkhov | A universal transcriptomic signature of age reveals the temporal scaling of Caenorhabditis elegans aging trajectories[END_REF] have identified a set of 327 genes whose expression is potentially a biomarker of ageing; amongst them, there are targets of genes that are involved in longevity regulation as daf-16 (ortholog of FOXO transcription factor). The authors rescale the samples' age to the mean lifespan to correct the difference between chronological and biological age during the estimation process. This leads to predicting a "bioage" expressed as an absolute number.

Last year [START_REF] Meyer | BiT age: A transcriptome-based aging clock near the theoretical limit of accuracy[END_REF] published a new transcriptome based clock in C. elegans (BiT age). It selects 576 genes that predict the individual's biological age with a correlation of 0.98 (Pearson), and a r 2 of 0.96. The biological age is here computed by using a correcting factor, defined as the mean lifespan of the strain divided by the mean lifespan of the N-2 wild-type strain. Functional enrichment of the 576 selected genes predicting age with their expression reveals, amongst others, genes involved in innate immune response, detoxification and neuronal communication.

Although these results are an essential step forward in identifying an individual's biological age, they are not yet easily applicable in an experimental setting. The recording of the expression of a numerous set of genes is indeed invasive in model organisms such as nematodes or fruit flies, and would not be feasible prior to sampling. [START_REF] Rera | Intestinal barrier dysfunction links metabolic and inflammatory markers of aging to death in Drosophila[END_REF] described a phenotype in D. melanogaster that can be useful in this matter. I will describe this phenotype, called Smurf, in more detail in the next section.

The Smurf phenotype

The Smurf phenotype is an age-associated phenotype characterized by the increase in intestinal permeability to the blue food dye FD&C #1 in D. melanogaster. The dye, normally not absorbed by the digestive tract, spreads throughout the body in flies with impaired intestinal permeability, turning them completed blue. Hence, the name Smurf. A Smurf and a non-Smurf D. melanogaster (females and males) are shown in figure 1.9. I will start by describing the standard protocol for identification of Smurfs [START_REF] Rera | Intestinal barrier dysfunction links metabolic and inflammatory markers of aging to death in Drosophila[END_REF][START_REF] Martins | In vivo Assessment of Intestinal Permeability in Multiple Model Organisms[END_REF], and I will subsequently move to illustrate the phenotype characterization that has been carried out so far.

Smurf assay

The Smurf assay allows in vivo assessment of gut permeability in Drosophila.

It consists of a co-digestion of classic food mixed with the blue dye FD&C #1.

It is important to specify that the dye is:

1. non-toxic. Two synchronous populations of Drosophila of the same strain, one aged on food + blue dye, the other aged on food only, do not show differences in their lifespan [START_REF] Rera | Intestinal barrier dysfunction links metabolic and inflammatory markers of aging to death in Drosophila[END_REF][START_REF] Tricoire | A New, Discontinuous 2 Phases of Aging Model: Lessons from Drosophila melanogaster[END_REF];

2. non-toxic when the intestinal permeability is impaired. It does not cause a reduction in lifespan when continuously ingested after the increase in intestinal permeability [START_REF] Martins | In vivo Assessment of Intestinal Permeability in Multiple Model Organisms[END_REF]. It has been shown that two synchronous population of the same genetic background do not present differences in lifespan when a) one of them is continuously exposed to blue food; b) the other have the Smurf flies switched back to normal food as soon as detected, therefore limiting their exposure to the dye in a condition of impaired intestinal permeability.

The most common way of detecting the presence of the Smurf flies is by eye, with the Smurf defined as such when the fly presents a complete blue colouration. However, the phenotype is not binary but instead presents continuous variations, with flies possibly showing an uncertain status or "light Smurf" phenotype. The authors propose three different explanations for this: 1) the dye might take some time to diffuse through limited gut permeability, generating a gradient phenotype depending on the permeability; 2) biological and environmental factors could cause variations in the phenotype observed; 3) different perception by different observers.

The scoring "by eye" presents an additional bias related to the proportion of Smurf at the moment of scoring: in the presence of many non-Smurfs and only a few Smurfs, even light Smurfs could be scored. In order to prevent this, the authors suggest that, when the experimental conditions allow it, single individuals could be photographed for independent validation.

As a general rule, only Smurfs presenting the blue colouration all over their body have to be scored as such, if not stated otherwise.

Smurf phenotype in an ageing population

Interestingly, the phenotype assessed by the Smurf assay shows an age-dependent pattern. Figure 1.10 illustrates what happens when we age flies with food where the dye is present. As soon as the population exits the survival plateau, blue flies appear in the population. Interestingly, the proportion of Smurfs (computed as the number of Smurfs over the total number of flies in the population at the given time point) is increasing with time. The Smurf Increasing Rate (SIR) depends on the way the population ages; a population with a higher mean lifespan and a slower decay in the number of individuals will show a lower SIR (figure 1.10). Smurfness is therefore an age-related phenotype. The absolute number of Smurfs in the population at each time point follows a different trend, which will be discussed in section 1.4.5. Last but not least, in the experiments conducted so far, every fly has been shown to turn Smurf prior to death [START_REF] Rera | Intestinal barrier dysfunction links metabolic and inflammatory markers of aging to death in Drosophila[END_REF][START_REF] Tricoire | A New, Discontinuous 2 Phases of Aging Model: Lessons from Drosophila melanogaster[END_REF].

At a given time point in a population, we can distinguish two subpopulations, Smurfs and non-Smurfs. Let's suppose we separate them and monitor their remaining lifespan independently. In that case, we will see that the median lifespan (T 50 ) of the non-Smurfs is around the same as the one of the whole population at the given age; the one of the Smurfs is instead low and constant independently of their age (figure 1.11). An independent study [START_REF] Tricoire | A New, Discontinuous 2 Phases of Aging Model: Lessons from Drosophila melanogaster[END_REF] confirmed that Smurfs have a T 50 ∼ 2.04 days, independently of their age, even if a trend is noticeable with young Smurfs living longer than old Smurfs.

The relatively constant time that individuals spend as Smurf before dying suggests that the flies find themselves in a stereotyped condition characterized by similar (if not equal) molecular changes. In the attempt of understanding if Smurfs, given their high risk of impeding death, carry the burden of ageing associated changes independently of their age, [START_REF] Rera | Intestinal barrier dysfunction links metabolic and inflammatory markers of aging to death in Drosophila[END_REF] tested the expression of a few genes described as ageing-related in Smurfs and non-Smurfs at different ages. They observed that changes are occurring with Smurfness rather than with age (figure 1.11). Summary. To conclude, if we age a population in the presence of FD&C #1, we will observe that: 1) after a certain time t, corresponding to the survival plateau, Smurfs are detectable at each time point in the population; 2) every fly turns blue before dying, and its probability of doing so increases with age; 3) once Smurf, the fly shows, independently of its age, some gene expression changes that have been so far associated with ageing.

What is interesting if we look at figure 1.11 is the absence of a detectable trend for gene expression changes in non-Smurf. It seems that the signal is merely carried by the Smurf flies at each age, without an age-dependent pattern in the non-Smurfs. This suggests a biphasic behaviour of the ageing markers.

Those observations led to hypothesize that the Smurf phenotype could be a good proxy to follow biological age in flies. The first row show the expression of three antimicrobial peptides (AMPs) expressed downstream of the inflammatory pathway, known to be overactivated with ageing. The AMPs is significantly lower in the non-Smurfs than in the Smurfs independently of their age, hinting that the Smurfs could be the one actually carrying the "ageing inflammation signal" in the population. On the second row, the same analysis is done on three FOXO targets, induced when the IIS is repressed, as it normally occurs with ageing. The same pattern of the inflammatory genes is observed, with a significant overexpression of the genes with Smurfness, independently of the age.

Conservation of the Smurf phenotype

In order to assess if Smurfness is specific to D. melanogaster, [START_REF] Dambroise | Two phases of aging separated by the Smurf transition as a public path to death[END_REF] have investigated the presence of the Smurf phenotype in other two Drosophila species (D. mojavensis and D. virilis), as well as in the nematode C.elegans and the vertebrate Danio rerio, commonly known as zebrafish.

They have checked for three main features: 1) identification of the Smurf phenotype through Smurf assay; 2) age-related trend of the proportion of Smurf in a synchronous population and 3) association between the Smurf phenotype and high risk of death. I will briefly report the results obtained for the three points.

1. Smurf identification. With some adaptation of the protocol, the presence of Smurfs was tested for the four species in old individuals. This corresponded to 40 days old flies, 12 days old worms and 48 months old fishes. In all of them, Smurfs individuals, showing the dye colouration all over the body, were detected (figure 1.12).

2. Age dependency. The two species of Drosophila, wild-type strains presenting different lifespans, show an age-dependent SIR with Smurf assay performed every ten days. As in D. melanogaster, the strain with the shortest lifespan shows the highest SIR. Three wild-type strains were tested in nematodes. In all three, the age-dependency of the phenotype was confirmed. Furthermore, the two strains with similar lifespans have also a non-significantly different SIR. In contrast, with a shorter lifespan, the remained strain presents a higher SIR, significantly different from the others. In zebrafish, given the relatively long mean lifespan of the animal (∼ 42 months), fishes of the same strain were collected at four different ages (4, 8, 12 and 23 months), and each group was assayed for Smurfs. The same was repeated 3 and 7 months after. The results show a significant trend for males, and a non-significant but detectable trend for females. The authors suggest this could be due to the low number of individuals used.

3. Death risk. The Smurf population isolated from D. mojavensis and the one isolated from D. virilis show the same survival curve, with ex-Figure 1.12: Smurf phenotype conservation in different model organisms, with their associated phylogenetic tree. The pictures present females Drosophila, hermaphrodite nematodes and females zebrafish. From [START_REF] Dambroise | Two phases of aging separated by the Smurf transition as a public path to death[END_REF] ponential decay of the number of individuals and a T 50 of 2.5 days. This overlaps with what was shown in D. melanogaster [START_REF] Tricoire | A New, Discontinuous 2 Phases of Aging Model: Lessons from Drosophila melanogaster[END_REF]. Similar results were obtained for the nematodes. Smurfs scored at 2 and 2.5 years in zebrafish were monitored for 7 months, with their aged-matched non-Smurfs. Again, Smurfs show a higher risk of death, with 75% dying within the seven months against the 24% amongst the non-Smurfs.

Altogether, those results suggest that not only the Smurf phenotype first described in D. melanogaster is conserved amongst different species, even nonclosely related, but also that the amount of time the individuals spend in this phase could be function of the life expectancy at birth.

Preliminary work in mammals

Preliminary studies have been conducted on mice to validate the Smurf model in mammals. The work (Cansell et al., under review) gathers a cohort of 44 mice and follows them from the end of the survival plateau of the population until death. For each mouse, parameters such as body temperature, glycemia, intestinal permeability and activity are recorded at regular intervals. Figure 1.13 presents some the results. Interestingly, the authors cannot identify a changing trend with age for most of the recorded parameters but can instead identify a biphasic behaviour when grouping the mice by remaining time before death. For example, intestinal permeability increases around 22 days before death, while glycemia drops around 24 days before death. The presence of a biphasic behaviour hints at the possibility of transposing the Smurf model to mammals.

2-phase model of ageing

Given the results pointing at the biphasic behaviour of age-related changes in non-Smurfs and Smurfs, [START_REF] Tricoire | A New, Discontinuous 2 Phases of Aging Model: Lessons from Drosophila melanogaster[END_REF] proposed to reinterpret ageing as a sharp transition made of a non-Smurf and a Smurf phase rather than a continuous process (figure 1.14). According to such model, in phase 1 (non-Smurf) the individual is apparently healthy and has a null probability of dying but experiences an increasing probability with time to enter phase 2.

In phase 2 (Smurf) the individual is committed to death independently of its chronological age, and it experiences the decline of the physiological function that has been so far described as hallmarks of ageing. [START_REF] Tricoire | A New, Discontinuous 2 Phases of Aging Model: Lessons from Drosophila melanogaster[END_REF]. Each individual is undergoing two phases in his life: non-Smurf (phase 1) and Smurf (phase 2). The length of phase 1 represents the healthspan of the organism (i.e. the time the individual spends in good health). During such phase, the individual experiences null mortality, but an increasing probability with time of entering phase 2, or Smurf phase. In the Smurf phase, individuals experience the degradation in functional efficiency that we commonly refer to as ageing. While phase 1 length can differ in individuals, phase 2 seems to have a stereotyped short duration.

In this view, the progressive increase in the ageing markers that have been seen so far as occurring in individuals when studying ageing at a population level could be reinterpreted as a progressive increase with time in the Smurf individuals carrying such changes.

So, according to this model, we have two subgroups co-existing in the general population, the non-Smurfs and the Smurfs. The authors propose a mathematical formulation to model how their evolution shapes the survival curve of the whole population.

The authors propose that three parameters with easy biological interpretation can explain the dynamics of an ageing population :

• a. The rate at which Smurfs appear in the population;

• k. The rate at which the Smurfs population decay.

• t 0 . The time at which the first Smurf appears in the population.

The proportion of Smurfs is modeled as increasing linearly over time, in the following way:

p = at + b (1.1)
where p is the proportion of new Smurfs at a given time point t, a is the slope of the line -the rate at which Smurfs appear in the population -, and b is the intercept with the y axis. t 0 is instead graphically the intercept with the x-axis (time) and can be computed as -b a . The decay of the Smurf population is instead modelled exponentially as follows:

S = S 0 + e -kt (1.2)
Where S is the number of Smurfs at a given time t, S 0 is the number of Smurfs at t0, and k is the parameter defining the exponential decay at which the Smurfs die.

The whole population of flies is built at each moment by two subpopulations, Smurfs and non-Smurfs. As long as Smurfs do not appear in the population (i.e t < t 0 ), the number of individuals is equal to that of non-Smurfs. At t 0 , the two subpopulations (Smurfs and non-Smurfs) start to co-exist in the general population.

How is the absolute number of individuals belonging to the non-Smurfs and Smurfs group evolving? For simplification, let's take t 0 as a starting point. The number of non-Smurfs N will change as following:

dN dt = -atN t (1.3)
where at is describing the rate of Smurfs appearance, and N t is the number of non-Smurfs at t.

The number of Smurfs at a given time point is instead the number of flies turning smurfs from the N t flies group, summed to the number of flies dying (therefore leaving the Smurf population):

dS dt = atN t -kS t (1.4)
where k is the decay parameter of the smurf population and S t is the number of Smurfs in the population at time t. By solving the equations (for details, see [START_REF] Tricoire | A New, Discontinuous 2 Phases of Aging Model: Lessons from Drosophila melanogaster[END_REF]), we obtain the behavior shown in figure 1.15. The model has been shown to fit as well as models commonly used to describe human-like ageing, as the Gompertz and Weibull [START_REF] Tricoire | A New, Discontinuous 2 Phases of Aging Model: Lessons from Drosophila melanogaster[END_REF]. Furthermore, it is built on parameters that are easy to interpret biologically and are experimentally measurable. I am here simulating how the tuning of the a and k parameter affects the longevity of a population (black curve) and the evolution of its non-Smurf (red) and Smurfs (blue) subpopulations, when t0 is fixed (in this case 10 days). Parameters are used are extracted from the [START_REF] Tricoire | A New, Discontinuous 2 Phases of Aging Model: Lessons from Drosophila melanogaster[END_REF] paper. I am using three values of k and a: 0.05, 0.2, 0.5. a governs the rate at which Smurfs appear in the population; k the rate at which the Smurfs die. If we fix k and tune a (reading the graph by column) we can notice that the total population is decaying faster with a higher value of a, and a higher number of Smurfs is accumulating in the population, with a peak around the T 50 . If we fix a and we tune k, we will observe the total population decaying faster once again with higher values of k, but the Smurfs, although increasing at the same rate, do not accumulate in the population, as they die faster. For high values of k and low values of a (top right), we will barely observe Smurf in the population, as they are on average dying as soon as they become Smurf. For low values of k and high values of a we observe instead the accumulation of Smurfs in the population.

1.4.6 Critics to the model [START_REF] Bitner | Predicting death by the loss of intestinal function[END_REF] found contrasting results to the ones described above. More specifically, they affirm that 1) the dye has a negative effect on the flies' longevity and 2) only a fraction of flies turns blue prior to death.

The study follows five different wild-type populations of Drosophila -males and females -on a control banana molasses food and five different blue dyes. Amongst them is the FD&C #1, reported under the name of Alfachem blue. Every fly of each population is placed individually in plastic straws closed with a pipette tip and scored daily for Smurf phenotype appearance and death. I will now discuss separately the two claims reported.

• The dye affects longevity. I will discuss only the results regarding the FD&C #1, as it is the one used in the results illustrated above. [START_REF] Bitner | Predicting death by the loss of intestinal function[END_REF] show that the mean lifespan of control flies is higher than the one of the flies exposed to the dye across the five populations tested, with a difference that varies between 4.9 and 9.8 days. However, some points need to be elucidated: 1) by testing the significant effect of the longevity on sex through a linear model, they found that sex does not influence longevity. Therefore, they say they would not consider the sex for further analysis. However, this could be a risky choice given the number of individuals of different sex per line (∼ 20-30 flies) used for the estimation. Male and females have been shown to present a lifespan gap in multiple species [START_REF] Austad | Sex Differences in Lifespan[END_REF], and it is standard procedure to monitor their survival curve separately. Therefore, I assume that the data shown for the mean lifespan of the populations refer to a mixture of males and females (not clarified by the authors). Longevity curves are also not shown. 2) If males and females are mixed, the total number of flies used per population per condition varies between 54 and 58. Without any replication, it is difficult to firmly assess any significant effect on longevity with such a low number of individuals.

• Smurfness is not necessary to die. The results presented in [START_REF] Bitner | Predicting death by the loss of intestinal function[END_REF] show how only a percentage of flies turns Smurfs prior to death. This percentage seems to depend on the population and the dye, never reaching 60%. This is in contrast with the studies described in the previous section. Reproducibility is essential to prove a scientific experiment as valid, but it needs to be pursued following the same conditions of the experiment to validate. In this case, two main issues arise in the way the experiment is conducted by [START_REF] Bitner | Predicting death by the loss of intestinal function[END_REF]. First, the flies are stored individually in thin tubes closed with pipette tips. The food is stored at the end of the tube, in quantity per tube not specified, and changed every three days. Such procedure differs from the standard longevity assay where the flies, in a group or individually, are kept in larger and closed vial [START_REF] Tricoire | A New, Discontinuous 2 Phases of Aging Model: Lessons from Drosophila melanogaster[END_REF]. Although this could seem a detail, it is an important factor to control. Using thin tubes, though long enough for the flies to fly from one side to another, increases the chances for the insect to stick on the side of the tube with its wings and introduces therefore a stress factor. Furthermore, using open tubes allows ventilation, which could cause dry stress for the fly. In particular, when the food dries, it becomes more challenging to eat, introducing a second possible bias in the experiment. The two described factors possibly affect how the flies are dying and the food availability prior to death -both important for Smurf phenotype detection -, but are not commented in the paper.

Interestingly, the authors claim that the Smurf population's demographic and the phenotype's dependency from chronological age has never been investigated at the date of their publication (2020), which is false. They also add that, according to their study, most of the Smurfs are dying within one day from transition, which limits its application in studies. Once more, this is no surprise, as it has been shown in the tested populations that Smurfs have an exponential decay with a T 50 of 2.04 days. Studies that imply Smurfs sampling need to consider that and generate sufficient N flies, with N depending on the number of Smurfs that need to be collected, taking into account the ageing pattern of the population.

This study shows interesting results, but they look too weak to support their claim that the model is not working. The authors also show a scarce knowledge of the Smurf model as described so far.

Aim of the study

To recapitulate, the Smurf phenotype has been proposed as a non-invasive biomarker for biological age in three commonly used model organisms. Furthermore, preliminary studies aiming at detecting a biphasic behaviour similar to that of the Smurfs have also been conducted in mice. Nevertheless, the phenotype has been characterized more in Drosophila, where the expression of some age-related genes has been investigated. In order to prove the biphasic non-Smurf/Smurf behavior regarding ageing-related changes, and therefore validate the ability of the model to predict the individual's biological age, further investigations are needed.

The laboratory in which I conducted my PhD research focuses on characterizing the Smurf phenotype and how to exploit it for better understanding ageing. My work integrates the one of the laboratory, focusing on characterizing the gene expression changes associated with the Smurf phenotype. More specifically, my PhD aims to characterize the Smurf transcriptome compared to the non-Smurf. I divide my research into three main questions:

1. Can we detect a biphasic behaviour in the gene expression of Smurfs and non-Smurfs?

2. How is the gene expression of the Smurfs and non-Smurfs affected by time?

3. Can we detect novel genes affecting longevity by looking at Smurfness? Chapter 2 will be dedicated to the results concerning the first two questions. Chapter 3 will instead be dedicated to the experimental work conducted for the third question. I will then discuss the results and propose further development of the project in Chapter 4.

Chapter 2

Smurf transcriptional signature and ageing

We decided to assess the behaviour of the transcriptome in Smurfs and non-Smurfs at different ages by performing bulk RNA-sequencing (RNA-Seq). The data collection and the sequencing were done prior to my PhD. I, therefore, did not take part in the generation and sampling of the flies, with my contribution starting from the pre-processing of the data (section 2.2.1).

Experimental design

RNA-Seq was performed on the whole body of the flies. Preliminary data suggested Smurfness as a systemic transition. Therefore, before going to a tissue-specific level, we wanted to characterize the core changes detectable at the level of the whole organism. drs-GFP Drosophila line was used for the transcriptomic experiment. This line is a GFP reporter for the expression of the AMP drosomycin (drs). Three main reasons are behind this choice.

First, AMPs are overexpressed in Smurfs, with apparently no trend with age for the non-Smurfs (figure 1.11). It has been therefore thought that looking at the expression of inflammatory genes could be a good alternative method to select Smurfs and get rid of the bias introduced by a "by-eye" selection (see section 1.4.1). Preliminary data have shown that Smurf selection by drs reporter leads to identification of the same pattern of expression for the genes tested and shown in figure 1.11 [START_REF] Rera | Intestinal barrier dysfunction links metabolic and inflammatory markers of aging to death in Drosophila[END_REF]. In case of the detection of a biphasic signal in the transcriptomic data from Smurf and non-Smurf, it would be possible then to check if the same signal is found when sampling flies by drs expression instead than "by-eye". The use of drs-GFP line allows to perform this experiment on the same genotype, without therefore introducing a possible line-related bias in the experiment.

Secondly, at the same time, and for the same reason, proteomic data were generated from the same line. Last, the line presents a relatively short lifespan, which has a T 5 0 ∼ 30 days.

The experiment was performed using mated females for coherence with previous data generated by the laboratory.

Six vials of 30 mated females each (N = 180) were generated to follow the line's longevity. In parallel, ∼ 1200 flies belonging to the same population, and divided into vials of 30 individuals each, were used for the Smurfs and non-Smurfs sampling. Smurfs and non-Smurfs were sampled at three time points corresponding to ∼ 90%, 50% and 10% survival of the population, which corresponds to 20, 30 and 40 days old flies for the given line. In figure 2.1 I report the longevity curve of the population used for the experiment and the sampling times.

Each sample corresponds to a group of 8 flies. From now on, when I refer to one sample, the actual number of individuals sampled has to be multiplied by 8. Flies were sampled at different ages to investigate the effect of time on the transcriptome of non-Smurfs and Smurfs. Smurfs were also sampled at different moments after transition to explore the dynamics of phase 2 of life. The sampling followed the procedure here detailed.

• Transfer of the flies on blue food. All the flies -the ones used for longevity as the ones used for sampling -are placed on blue food overnight.

• First sampling: mixed samples. The next morning (9 a.m.) 1 Smurf sample and 1 age-matched non-Smurf are collected. These samples are called "mixed", given the uncertainty regarding the time individuals spent as Smurfs. Indeed, the moment we place flies on blue food, we can detect individuals with impaired intestinal permeability. However, those might have lost their intestinal functionality before, without us being able to see it in the absence of the dye. In the mixed samples, we have no information about the time passed from the entrance in Smurf phase. After sampling, all the Smurfs left were removed from the vials and flies were kept on blue.

• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •
• Second sampling: 5 hours Smurfs. At 2 p.m., 5 hours after the Smurfs clearance, 2 additional samples were collected. Those flies underwent the transition within the past 5 hours. At 20 days, no samples were collected; indeed, even with the large cohort of flies available, at 90% survival is not easy to obtain 5 hours Smurfs. In the specific case, the population has an expected presence of ∼ 5% of Smurfs at the given age, which corresponds to ∼ 60 flies given the number used for sampling. After the sampling, Smurfs were cleared from the population and flies were left on blue.

• Second sampling: 24 hours Smurfs. 24 hours after (2 p.m. of the following day) Smurfs and age-matched non-Smurfs were sampled. Therefore, these flies are individuals who turned Smurfs within 24 hours from the sampling moment. For all time points, 3 samples per condition were collected.

After sampling, flies were immediately frozen in liquid N 2 and stored at -80°C up to the RNA extraction. RNA was extracted following the standard Trizol protocol (see Appendix A.2), and samples were subsequently sent for sequencing. The final data consists of 32 samples. The data structure is schematized in figure 2.1.

RNA-Seq analysis 2.2.1 Data preprocessing

Samples were sequenced by paired-end using Illumina technology. The library preparation and the sequencing were externalized (Fasteris company), and my work started from the fastq files obtained from the sequencer. fastq is the file format used to store the information about the reads of one sample. In the case of paired-end sequencing, two fastq files are associated with each sample, as each carries the information about one read of the pair for each fragment sequenced. I performed the pre-processing of the data in collaboration with the bioinformatic platform of the Institute Biologie Paris-Seine (IBPS). The analysis was performed on their Galaxy [START_REF] Afgan | The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update[END_REF] server.

Prior to any manipulation, to assess the quality of the reads and eventual contamination from other species or Illumina adapters, we ran FastQC on the whole set of raw data. FastQC is a program that checks for different features of the reads, as the quality of the single basis inside the read or the GC content of the sample. For instance, a bimodal distribution in the GC content of the sequenced reads can be a sign of contamination with another species. As the FastQC report showed good results for the quality of our samples, we decided to perform no reads filtering prior to alignment.

Alignment was performed using Hisat2 (Kim et al., 2019) on the reference Drosophila genome BDGP6.95. Alignments statistics proved good, with each sample having no less than 83% of the sequences identified as paired-end; amongst them, no less than 85% are uniquely mapping on the genome. After the alignment, each read is assigned to a certain position on the genome. In order to quantify gene expression, we need to annotate the localization of the genes and see how many reads colocalize with a specific gene. Such information is stored in a specific file (.gtf) that needs to match the genome release used for the alignment. The software featureCounts [START_REF] Liao | featureCounts: an efficient general purpose program for assigning sequence reads to genomic features[END_REF] was used to perform the counting. It inputs the Hisat2 output files (BAM extension) and the gtf file, and returns a matrix with the gene counts for each sample. It also returns the assignment statistics; in this case, the "worst" sample sees 86% of the reads assigned. This means that most of the reads for all samples are assigned to genes. The quality of the alignment and the counting was conveniently visualized by using the program MultiQC [START_REF] Ewels | MultiQC: summarize analysis results for multiple tools and samples in a single report[END_REF] (see figures B.1 and B.2 in Appendix B.1).

At the end of this procedure, we obtained the raw counts matrix of 15364 genes which is the initial step for the biological analysis. The data preprocessing steps are schematized in figure 2.2. ; each fastq file contains information about one of the paired reads (so for each sample we have 2 fastq files). The quality of the reads is checked using FastQC. The reads passing the control go as input for the alignment step. This step, that we performed using Hisat2, also requires as input the reference genome on which the reads will be aligned. The algorithm output files are called BAM. These files store the information about the genomic coordinates on which each read is mapping. By inputing the BAM files and the .gtf file of the genome used for the alignment (containing the information about the genes coordinates), featureCounts counts the number of reads mapping to a certain genes. This gives as output the raw counts matrix, where for each samples are annotated the number of reads mapping to a certain gene. The quality of the alignement and counting can be conveniently checked by using the online tool MultiQC. The raw counts matrix needs to undergo normalization before performing further analysis.

Normalization

Normalization is essential to correct the technical biases present in the data.

In the case of Illumina RNA-Seq, the normalization needs to correct for a bias related to the sequencing lane: certain samples might present a different library size (total number of reads) compared to the others, and the expression of their genes will therefore be consistently higher or lower. There are different normalization methods [START_REF] Dillies | A comprehensive evaluation of normalization methods for Illumina highthroughput RNA sequencing data analysis[END_REF], and the choice requires us to consider the type of analysis we want to perform afterwards. DESeq2 R package [START_REF] Love | Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2[END_REF], which is one of the standards for RNA-Seq analysis nowadays, provides normalization for inter-sample comparison. The method is based on the assumption that most genes are not differentially expressed. DESeq2 computes a ratio between each sample count and the geometric mean of the counts across the sample for each gene. Subsequently, it estimates a correction factor for a given sample by taking the median of the ratios for each gene. If most of the genes are not differentially expressed, the median of the ratios should be ∼ 1. Deviations from this value are due to a technical bias consistently increasing or decreasing the number of counts in the sample.

Another commonly used program for RNA-Seq analysis is edgeR (Robinson et al., 2010). edgeR provides a normalization similar to DESeq2, assuming that most of the genes in the samples are not differentially expressed. The method is called Trimmed Mean of M-values (TMM) [START_REF] Robinson | A scaling normalization method for differential expression analysis of RNAseq data[END_REF]. For calculating the correcting factor, it takes one sample as reference and the others as tests, and computes the weighted mean of log ratios between the test and the reference. The most expressed genes and the ones with the largest log ratios are excluded from the computation. Similarly to what I previously explained, the TMM should be ∼ 1 in the absence of technical bias. The TMM is then used to rescale the library sizes and the raw counts.

Exploratory analysis

In order to first explore the data and see how samples group together, I performed a Principal Component Analysis (PCA). PCA is a dimensionality reduction technique that helps to visualize high-dimensional data by creating new features grouping the existing ones. In the RNA-Seq case, a sample can be seen as a vector of n features, where the features are the expressions of the genes. In order to visualize if samples have similar expression profiles, we would need to plot them in the space of n dimensions. This soon becomes not convenient, and impossible when we deal with thousands and thousands of genes. With the PCA, genes with similar expressions are "grouped" together in a single principal component (PC). The first PC explains the most variance in the dataset, and it is theoretically formed by genes whose expression is relevant for a particular biological condition.

In the case of RNA-Seq data, prior to performing PCA we need to apply a transformation to stabilize the variance. Indeed, the counts follow a Poisson distribution, where the variance is equal to the mean. So genes with higher expression will also have a higher variance. This has no biological meaning and would therefore create a bias in the analysis. I used the variance stabilized transformation (vst) proposed by the DESeq2, which runs fast on large datasets. I subsequently selected the top 5000 genes in terms of expression variance (normalized counts) and performed the analysis on this group. Results are shown in figure 2.3.

The samples are here plotted in the space of the first two principal components (PCs). The first component explains 38% of the variance against the 15% explained by the second. Colour indicates the Smurf status of the sample, while the symbols the age. Each sample is associated to an acronym, that indicate the time after transition and a letter or a number specifying the sample -see picture caption for the exact information -. From this PCA, we can conclude the following:

• Smurfs and non-Smurfs transcriptomes are different. The first PC separates almost perfectly the Smurfs samples from the non-Smurfs. Therefore, we can affirm that, from a qualitative point of view, Smurfs and non-Smurfs are characterized by different gene expression patterns. The fact that the first PC summarizes this suggests that Smurfness is the biological variable affecting the most the transcriptome in our dataset.

• Age has an effect. If we now focus on the single groups, the non-Smurfs tend to cluster by age and distribute along with the second PC. There and symbols the age. Each sample is associated to an acronym specifying the time after the transition (5h = 5 hours, 1d = 1 day and M = mixed, for which the information is not available) and an unique letter or number identifying the sample itself. For tracing the samples easily, such index has been retained from the original fastq data. PC1 highlights the separation between the Smurf and non-Smurfs samples, while PC2 looks associated to the age and seems to have a minor effect.

M-1 M-4 M-7 M-10 M-13 M-16 1d-a 1d-b 5h-c1 5h-c2 5h-c3 5h-c4 5h-c5 5h-c6 5h-c7 5h-c8 1d-c 1d-d 1d-e 1d-f 1d-g 1d-h 1d-i 1d-j 1d-k 1d-l 1d-m 1d-n 1d-o 1d-p 1d-q 1d-
could be a trend for the Smurf with age also along the first component, with older samples having values closer to 0. The situation is slightly different for the Smurfs, where the young and mid-life samples group homogeneously, while the 40 days present higher values of the second PC (possibly a age-related signal) and lower values of the first PC. This suggests them being similar to the old non-Smurfs samples.

To conclude, the PCA suggests that both Smurfness and age impact the gene expression of the flies, with Smurfness contributing the most. Indeed, at least for all the young and mid-life samples, a Smurf is more similar to a Smurf of different ages than an age-matched non-Smurf (and vice-versa).

In order to confirm that those results are not somehow specific to the PCA approach, I performed another dimensionality-reduction analysis (tSNE or t-distributed stochastic neighbour embedding). Contrary to PCA, tSNE is not deterministic (a seed needs to be set to assure reproducibility) and results are sensitive to the tuning of the perplexity parameter. Briefly, this parameter 1) has to be lower than the number of samples and 2) tells how to balance attention between local and global aspects of the data. Low perplexity values correspond to a smaller amount of "neighbours", vice-versa for higher values. Local clusters will be therefore preferred when perplexity is low. tSNE is widely used in single cells analysis. It has the advantage of highlighting sample separation; this feature needs to be used carefully to avoid creating "artificial" patterns. I ran the analysis using three different values for perplexity (5,10,15). Even if with an expected difference in local separation, results show the same pattern of the PCA, particularly the same 40 days Smurf outliers. Results are reported in figure B.3 (Appendix B.1).

For further confirmation, I performed unsupervised hierarchical clustering on the samples by taking all the genes into account. The results are shown in figure 2.4. It is interesting to see how also in this case I obtain a good Smurf/non-Smurf separation, except for the old samples. Most of them cluster in an independent group, mixing between the two categories. These results, already suggested by the PCA, shows that non-Smurfs individuals share on average between them more similarities in gene expression than a Smurf and a non-Smurf of the same age. This reasoning does not apply to old individuals who appear to follow a distinct behaviour.

20s1d-d 20s1d-e 20s1d-f 20sM-4 30s1d-l 32sM-10 30s1d-k 30s5h-c3 30s5h-c4 40s1d-q 30n5h-c1 40n5h-c5 40n5h-c6 30n5h-c2 30n1d-h 32nM-7 30n1d-i 20n1d-c 20n1d-a 20n1d-b 20nM-1 40nM-13

Differential gene expression analysis

Once assessed that differences are indeed present between Smurfs and non-Smurfs, I moved to quantify them through differential gene expression analysis.

To this end, I used the R package DESeq2.

Even if the old Smurf samples seemed like behaving differently than the rest, I decided to retain them for the analysis and group them with the Smurfs. My initial question is indeed to understand whether samples present or not a biphasic behaviour with Smurfness. By removing the 40 days samples that do not seem to differentiate from non-Smurfs as much as the others, I would introduce a bias towards my hyphotesis. By instead retaining them, I can understand if there is a core Smurf gene expression signal, independently of some heterogeneity.

Comparing the whole group of 16 non-Smurfs to the whole group of 16 Smurfs results in 3049 genes differential expressed (DEGs) with a FDR (false discovery rate, adjusted p-value for multiple testing) cutoff of 5%. The volcano plot in figure 2.5 allows a fast visualization of the results. By plotting the negative logarithm of the p-value (in this case, the FDR) against the log2 fold change (log 2 FC, computed for Smurfs/non-Smurfs), we can immediately visualize genes that show a change in the condition of interest. In particular, in the right part of the graph, we find genes upregulated in Smurfs, and vice versa. The height of the points gives information on the significance level. In the plot, DEGs are in red. While most of them show a relatively contained change (between 0 and |1| for the fold change), some present extreme values. In order to have a general overview of such genes, I plotted the names of the ones presenting a log 2 FC greater than |2|.

Amongst the overexpressed ones, we can recognize immediately two groups: AMPs (the antimicrobial peptides, amongst which also the three shown in figure 1.11) and Hsp genes. The active immune response in Smurfs previously observed (figure 1.11) is therefore confirmed. It adds to this a possible response to unfolded protein (UPR) suggested by the Hsp strong upregulation. The most overexpressed gene is Tim17a1, a component of the TIM/TOM complex for importing protein to mitochondria. However, this gene is lowly expressed, and this might influence the log 2 FC estimation. With a lower fold change showing high significance we find upd2 and Fst.

Upd-2, together with upd-3 (also upregulated in Smurfs), is the activator ligand for the JAK/STAT pathway. In the adult Drosophila gut, this pathway is involved in keeping homeostasis and triggering tissue regeneration in case of damage [START_REF] Herrera | JAK/STAT signaling in stem cells and regeneration: from Drosophila to vertebrates[END_REF]. Its chronic activation is involved in the gut dysplasia observed with ageing in flies [START_REF] Li | Preventing Age-Related Decline of Gut Compartmentalization Limits Microbiota Dysbiosis and Extends Lifespan[END_REF]; the same study shows how the modulation of such pathway in the middle part of the midgut (copper cell regione, CCR) extends lifespan. The ligand vein, produced downstream of the JAK/STAT pathway, is significantly upregulated in Smurfs, suggesting an actual activity of the pathway.

Frost is involved in the cold response. However, it is also overexpressed in adult diapause -a condition of dormancy triggered by environmental stress and characterized by stress resistance, somatic maintenance and reallocated energy resources, together with reduced senescence and lifespan extension [START_REF] Kučerová | Slowed aging during reproductive dormancy is reflected in genome-wide transcriptome changes in Drosophila melanogaster[END_REF]. The activation of both those signal peptides could support a stressful condition for the individual.

Regarding the most downregulated genes, we find genes involved in egg formation (Vm26 and psd ). NimC4 is a transmemebrane phagocytic glial receptor, reported as expressed during development but not in the adult [START_REF] Hakim-Mishnaevski | Glial Phagocytic Receptors Promote Neuronal Loss in Adult Drosophila Brain[END_REF]. LManIV is an enzyme predicted to be active in different metabolic pathways and the lysosomes (information retrieved from the database Flybase [START_REF] Larkin | FlyBase: updates to the Drosophila melanogaster knowledge base[END_REF]).

TotX belongs to the Turandot gene family, counting 8 members. Out of the seven retrieved in my dataset, five are significantly downregulated (TotX, Totb, Totc, Tota, TotM ). This family of gene is activated by a broad range of stress conditions (bacterial infection, heat shock, oxidative stress) [START_REF] Ekengren | A Family of Turandot-Related Genes in the Humoral Stress Response of Drosophila[END_REF]. Their downregulation in Smurfs seems to contradict the response to stress hinted by the strongly upregulated genes.

Obp99a is a small peptide thought to be involved in odorant perception. The odorant-binding protein (Obp) family counts in Drosophila 51 genes. I detect 44 of them in my dataset, out of which 9 are significantly downregulated. Even if the name suggests an expression mostly in the nervous system, Obp genes seem to act in different tissues [START_REF] Arya | Natural Variation, Functional Pleiotropy and Transcriptional Contexts of Odorant Binding Protein Genes in Drosophila melanogaster[END_REF]. Interestingly, in the adult head of Drosophila males, a group of Obps, amongst which Obp99a have been found as downregulated in midlife flies compared to young ones [START_REF] Ratliff | Aging and Autophagic Function Influences the Progressive Decline of Adult Drosophila Behaviors[END_REF]. [START_REF] Arya | Natural Variation, Functional Pleiotropy and Transcriptional Contexts of Odorant Binding Protein Genes in Drosophila melanogaster[END_REF] also demonstrated a significant association between polymorphisms in the Obp19d gene and lifespan. The latter is not significantly downregulated in Smurfs, but this preliminary evidence from the literature may suggest an involvement of this gene family in ageing.

The set of DEGs identified is a Smurf specific signature. Indeed, as shown in figure 2.6, they cluster all the Smurfs together independently on their age. Once again, I find the same three outlier samples of the PCA, while the other old Smurfs sample cluster with the younger ones. From the heatmap, we can see a shift in expression between Smurfs and non-Smurfs, with around half of the genes deregulated and half upregulated.

The deregulation of a gene alone might not give us enough information. Therefore, to better investigate the list of DEGs, I proceed with an enrichment analysis.

Confirmation of the results via edgeR

In order to confirm the results obtained with DESeq2, I performed the DEG analysis by using another standard package, edgeR. As explained in 2.2.2, edgeR uses a different normalization (even if based on the same assumptions than DESeq2) and also has a different estimation procedure. I performed the analysis and set the same FDR cut-off of 5%. I obtained 2609 DEGs, 2377 of which overlap with the 3049 detected by DESeq2. The fold change estimation for all the genes is also correlating, as shown in figure B.4 (Appendix B.1).

Enrichment analysis

Enrichment analysis aims at extracting functional sense from the list of DEGs. In particular, it tests whether in a specific list we find genes belonging to a certain gene set or biological pathway in a higher number than expected. The well-known Gene Ontology (GO) annotation, for instance, can be considered as a gene set.

It is important to clarify the difference between a gene set and a pathway, as they might sometimes erroneously be used as synonymous. A gene set is an unordered list of genes, classified together given their involvement in a similar biological process; each gene can belong to multiple gene sets if it has multiple functions. A pathway is instead an ordered group of genes, where their interactions are annotated; in a pathway, we have topological information, i.e. we know the spatial organization of the group of genes. A gene can as well belong to multiple pathways. All the gene set analysis can be performed using the gene set or the pathways if we consider them as a simple list of genes; but not all the pathways analysis can be performed on the gene sets. I will talk more about the pathway analysis in section 2.2.6.

Gene set enrichment analysis (GSEA) [START_REF] Subramanian | Gene set enrichment analysis: A knowledgebased approach for interpreting genome-wide expression profiles[END_REF] is nowadays a well-known standard for performing enrichment analysis. It takes as input a pre-ranked list of genes (either the whole set of genes or just a subset, as the DEGs) and tests for the significant presence of gene sets at the top or the bottom of the list. If the list is ranked by FC, this estimates gene sets that are upregulated and downregulated. I performed the analysis using fgsea [START_REF] Korotkevich | Fast gene set enrichment analysis[END_REF], a recent and fast implementation in R of GSEA.

I ran the analysis by ranking the DEGs list by FC and using the GO "biological process" (BP) categories as gene sets. The analysis provides an enriched score (whose sign represents the direction of the deregulation), the p-value (normal and adjusted) and the set of the core genes providing the enrichment.

The method contains randomness, as it estimates the p-value empirically by random permutations of the gene labels (to check how many times a random permutation is more extreme than the obtained result). Therefore, the output can be slightly different if the analysis is repeated starting from the same input.

The authors recommend therefore to set a seed for reproducibility. In addition, I decided to perform the analysis 50 times and retain only the categories that appear 80% of the times. I chose manually such thresholds, which proved sufficient for considering the analysis reliable without being too restrictive on the cutoff.

Results are shown in figures 2.7 and 2.8.In the represented network, each node is a significantly deregulated GO BP category. Edges are connecting categories sharing the same genes to facilitate interpretation. The hubs can be therefore interpreted as a group of strictly related processes. Please note that I separated the results into two figures for enhancing nodes' readability, but that the two were generated as a single graph and therefore share the same colour scale for the enrichment score. More details on the score and the level of significance of the categories are reported in tables B. 1 and B.2 (Appendix B.1).

• • • • • • • • • • • • • • • • • 'de novo'

Smurfs upregulated biological processes

Results for upregulated categories are presented in figure 2.7 and table B.1 (Appendix B.1).

Amongst the biological processes that are upregulated in Smurfs, we find the two main groups that the volcano plot has already suggested: immune response and response to unfolded protein.

As far as we know, Drosophila lacks an adaptative immune response, and therefore it is not a proper model to investigate the change with ageing in adaptative immunity. On the other hand, it has two pathways mediating the innate immune response: Toll (Gram-positive and fungi response) [START_REF] Lemaitre | The dorsoventral regulatory gene cassette spätzle/Toll/cactus controls the potent antifungal response in Drosophila adults[END_REF], and Immuno Deficiency (Imd, Gram-negative response) [START_REF] Lemaitre | A recessive mutation, immune deficiency (imd), defines two distinct control pathways in the Drosophila host defense[END_REF][START_REF] Dushay | Origins of immunity: Relish, a compound Rel-like gene in the antibacterial defense of Drosophila[END_REF]. In order to have a clearer idea of the genes pushing the group to be upregulated, I extracted the upregulated genes belonging to the largest groups of "immune system process" and "defence process". I expected those groups to contain the more specific categories such as "defence against Gram-negative bacteria" etc. This resulted in 178 DEGs being selected, out of which 121 were upregulated. This gives an idea of the extension of such deregulation. In order to visualize in a simple way the core genes related to the inflammation categories I mentioned above, I extracted from database KEGG the Toll and Imd pathway (dme04624) and plotted the alterations on the pathway itself. The result is presented in figure 2.9. We can observe a general upregulation of the pathway at different levels, from the activators to the transcription factors (both rel -the homologous of Nf-κBand dl -which plays the same role in the Toll pathway-) and the downstream AMPs targets. Interestingly, we find also some genes which negatively modulates the pathway, such as PGRP-SC and pirk. Those are genes expressed downstream of the pathway and play a negative feedback role. PGRP proteins have a double role, as they recognize and attack the peptidoglycans of the bacteria's wall. However, from the over-expression pattern of the pathway, the modulation carried out by those factors does not look sufficient to mitigate the inflammation.

As I have discussed in section 1.2.1, a heightened and chronic inflammation state have been listed amongst the hallmarks of ageing. In our dataset, we can 58 detect it as a core Smurf signal. The other main group is the response to unfolded protein, which I more generally indicated with the name "stress response" in figure 2.7. I extracted the genes associated to all the categories. This procedure leads to 50 genes, out of which 12 are heat shock genes (11 upregulated). Interestingly, 7 CCT genes (Chaperonin Containing Tailless complex polypeptide 1) are lowly but consistently downregulated in the Smurfs. The 8 CCT subunits, coded by as many genes, act together as an oligomer of two rings. Contrarily to the heat shock family, which acts on a wide range of substrates, CCT proteins are more specific. In particular, they are involved in the polymerization of actin and tubulin [START_REF] Sternlicht | The t-complex polypeptide 1 complex is a chaperonin for tubulin and actin in vivo[END_REF], but CCT5 is also playing a role in actin and actin-binding proteins transcription [START_REF] Elliott | A novel function of the monomeric CCTε subunit connects the serum response factor pathway to chaperone-mediated actin folding[END_REF]. Only CCT2 is not significantly downregulated in Smurfs. However, it is important to specify that we are looking at the mRNA expression level. Therefore, this might not translate to a protein level, especially with a low downregulation. It is nevertheless worth mentioning that [START_REF] Kim | TRiC/CCT chaperonins are essential for organ growth by interacting with insulin/TOR signaling in Drosophila[END_REF] have shown how the CCT transcripts level is regulated by the insulin/TOR signalling activity, with rapamycin treatment reducing the level of their expression. Another upregulated gene interesting to mention is the Xbp1 transcription factor, which is involved in the UPR in the endoplasmatic reticulum (ER).

Inside the stress response group, we can also notice a few categories related to the response to changes in oxygen levels, with three categories related. By looking at the DEGs present there, I find genes encountered already in other categories, such as AMPs and Hsp, making it difficult to understand if this is a "off-target" category. I find in this category also chico, slightly upregulated in Smurfs. The protein chico is phosphorylated by the insulin receptor (InR) as the first step in the insulin pathway. Chico is a longevity gene in Drosophila, with mutations reducing its activity has been shown to extend lifespan [START_REF] Clancy | Extension of life-span by loss of CHICO, a Drosophila insulin receptor substrate protein[END_REF].

Another group of categories that we find upregulated is the protein localization to mitochondria. This category is not straightforward to interpret. I find genes of the TIM/TOM complex, some with a negative and some with a positive FC, in all cases close to 0. Here, the group seems to mainly be driven by the Tim17a1, already discussed in section 2.2.4. In the following section (2.2.5.2) I will illustrate how genes involved in mitochondrial metabolism show general downregulation in Smurf. The upregulation of the genes involved in the protein import system to this organelle could be a way to try to cope with a decreased presence of mitochondrial related proteins.

Even if it does not appear as an enriched category, I will briefly mention the glutathione metabolism. Indeed, multiple Gst (Glutathione S-transferase) genes are upregulated. Glutathione (GSH) is a molecule used by animals and plants as reducing power in oxidation-reduction (redox) reactions, and it is a tool for detoxification case of oxidative stress. It can exist in two forms: oxidized and reduced. Gst is the enzyme in charge of oxidizing the GSH to detoxificate another substrate [START_REF] Hopkins | On an Autoxidisable Constituent of the Cell[END_REF][START_REF] Deponte | Glutathione catalysis and the reaction mechanisms of glutathione-dependent enzymes[END_REF]. In Smurfs, 29 genes belonging to this category are deregulated, amongst which 20 upregulated Gst and one upregulated enzyme involved in the first step of glutathione synthesis (Glcl ). Even if the fold changes are low, the consistent upregulation of the group could suggest an oxidative stress condition faced by the organism.

Smurf down-regulated biological processes

There are several processes that appears downregulated in Smurfs. Results are presented in figure 2.8 and B.2 in Appendix B.1.

The first group is related to fertility. This confirms what was shown by [START_REF] Rera | The Smurf transition: new insights on ageing from end-of-life studies in animal models[END_REF] regarding a significant decrease in fertility in Smurfs midlife individuals compared to age-matched non-Smurfs. These categories group 33 genes, mostly downregulated. We find genes playing a structural role in the formation of the egg, such as the Vm26Aa and Vm26Ab, and genes having a regulatory function. An interesting one is armi, which shows a light downregulation in Smurfs. This gene encodes for a helicase involved in the PIWI-interacting RNAs (piRNAs), a class of small RNAs suppressing transposable elements in the germline.

Connected to these subcategories, we also have nodes related to extracellular matrix (ECM) organization. Apart from some genes involved in the eggshell formation that I have mentioned before, we find downregulation of laminin (LanB1 and LanB2 ), a structural component of the nucleus and collagen, Col4a1 and vkg. Interestingly, there is the upregulation of the two metalloproteinases Mmp1 and Mmp2, whose function is degradation and remodelling of the ECM. As the data are coming from the whole body of the flies, we cannot know if this is a general signal or a tissue-specific one. However, the Smurf phenotype is assessed by an alteration in intestinal selective permeability, suggesting an alteration on the gut epithelium. [START_REF] Kiss | Drosophila type IV collagen mutation associates with immune system activation and intestinal dysfunction[END_REF] have shown how Col4a1 mutants present a premature loss of intestinal integrity and increase of inflammation markers in the gut.

We then find a broad metabolic group. The first subgroup is the ecdysteroid hormones metabolisms. Ecdysteroids (hormones produced starting from cholesterol) are synthesized throughout the insect's life. It has been suggested that ecdysone, together with juvenile hormone (JH), is involved in the lifespan increase observed under reduced insulin signal; indeed, the production of ecdysone is impaired in flies with insulin receptor mutants [START_REF] Tu | Deletion of the Mitochondrial Superoxide Dismutase sod-2 Extends Lifespan in Caenorhabditis elegans[END_REF]. Ecdysone levels are modulating lifespan in a sex-specific manner: lifespan extension has been observed in males, but not in females [START_REF] Tricoire | The steroid hormone receptor EcR finely modulates Drosophila lifespan during adulthood in a sex-specific manner[END_REF]. In our case, we observe a consistent deregulation of genes involved in ecdysone and other ecdysteroid hormones, such as sad, spo, and phm. However, ecdysone in adult females is well characterized for its role in the ovaries [START_REF] Niwa | Enzymes for ecdysteroid biosynthesis: their biological functions in insects and beyond[END_REF] : the downregulation of the enzymes involved in its construction could therefore be due a general impairment in the gonads of the Smurf flies.

Lipid biosynthesis also has a negative enrichment score, with 69 genes belonging to the category, out of which 47 downregulated. It is a low but consistent downregulation that affects various genes involved in fatty acid biosynthesis. Amongst them, it is worth mentioning: FASN1, fatty acid synthetase catalyzing the de novo biosynthesis of long-chain saturated fatty acids; ACC, catalyzing the synthesis of malonyl-CoA from acetyl-coA, the first step of lipid biosynthesis; eloF, involved in the chain elongation. We also find the downregulation of Acsl, Acyl-CoA synthetase long chain: this enzyme is involved in fatty acid degradation, as it catalyzes the activation of the fatty acid prior to the entrance in the mitochondria through the carnitine shuttle. Fatty acids synthesis occurs in the cytosol, and depends on the availability of acetyl-coA, which is also an essential step in the Krebs cycle, by reacting with the pyruvate produced by glycolysis. Furthermore, acetyl-coA levels have also an effect on the epigenome, as acetyl-coA is used as substrate for histone acetylation [START_REF] Etchegaray | Interplay between Metabolism and Epigenetics: A Nuclear Adaptation to Environmental Changes[END_REF].

Interestingly, we also have mitochondrial metabolism, in particular cellular respiration. These categories contain 28 downregulated genes. The electron transport chain (ETC) in the mitochondrial inner matrix is the mediator of cellular respiration. It is composed of five complexes: the first four (NADH dehydrogenase -Complex I -, succinate dehydrogenase -Complex II -, cytochrome c reductase -Complex III -, cytochrome c oxidase -Complex IV -) mediates the H + gradient formation between the mitochondrial membranes. The last one (ATP synthase) exploits such a gradient to produce ATP. All those complexes are composed of multiple subunits and are partially encoded by the mitochondrial genome. We find 27 downregulated genes coding for subunits of Complex I, 2 of Complex II, 3 of Complex III and 3 of Complex IV. Let's suppose these changes actually correspond to a protein downregulation. In that case, Smurf metabolism is probably suffering from mitochondrial dysfunction. If the aerobic metabolism of mitochondria is impaired, the cell could switch to an anaerobic one, with the conversion of pyruvate (the final product of glycolysis) to lactate. Interestingly, the enzyme catalyzing such reaction (Ldh, lactate dehydrogenase) is upregulated in Smurfs. Interestingly, this reminds of the Warburg effect observed in tumors, where cells, even in presence of enough oxygen, switch their glucose metabolism from aerobic to anaerobic [START_REF] Warburg | THE METABOLISM OF TUMORS IN THE BODY[END_REF][START_REF] Deberardinis | We need to talk about the Warburg effect[END_REF]. The changes in metabolism occurring with ageing could contribute to the increase risk of cancer: ageing and cancer do indeed show overlapping metabolic changes [START_REF] Tidwell | Aging, Metabolism, and Cancer Development: from Peto's Paradox to the Warburg Effect[END_REF].

More details on the metabolism and Smurfness will be discussed in section 2.2.7.

I grouped the ribosome biogenesis and proteolysis under the general name of proteostasis. The proteolysis is a large category, where we find 59% of genes downregulated. Even if this does not account for a vast majority of the group, there are some family of genes that are consistently downregulated. Interestingly, 16 out of 16 detected Jonah genes in our dataset are significantly downregulated in Smurfs. Jonah proteins are a family of serine endopeptidases. Not well characterized, these genes are expressed in larval and adult Drosophila gut [START_REF] Carlson | Developmental and functional analysis of Jonah gene expression[END_REF]. Similarly, we find another compact group of 10 trypsin-like endopeptidases. Although many other genes with similar activity are present in the list, those are the only two big families consistently deregulated in Smurfs. It is not easy to make sense or propose a hypothesis on the rest of the list by looking at the single genes. The categories of ribosome biogenesis present 47 genes, 45 out of which downregulated. In all cases, it is a low but consistent deregulation. It affects genes having a structural function in the ribosomes and genes coding for proteins processing rRNA. Also three mitochondrial ribosomal proteins are downregulated.

Before integrating and commenting on these results, I will describe a complementary pathway analysis performed to 1) validate such results and 2) see possible deregulation that the DEG analysis might have missed.

Pathway analysis

As briefly described in section 2.2.5, pathway analysis aims at discovering the deregulation in biological pathways (as the ones annotated in KEGG or Reac-tome) in a particular biological condition. Such analysis can be topological or non-topological, depending on whether the structure of the pathway and the gene-gene interactions are taken into account. Pathway analysis has the advantage of taking as input the whole dataset without a pre-filter or any cut-off applied. Pathway analysis does not operate at the level of a single gene and is therefore more sensitive to detecting biological pathways whose genes move all together in a specific direction, even if lightly. This could be still of biological relevance and be missed by the DEG analysis, which considers one gene as an isolated entity.

Here I will present the results of a non-topological pathway analysis performed by the GAGE [START_REF] Luo | GAGE: generally applicable gene set enrichment for pathway analysis[END_REF] method.

The analysis returns 9 upregulated and 27 down-regulated pathways (FDR cut-off 5%). As shown in picture 2.10, in the downregulated pathways, we find similar terms to what already explored in the enrichment analysis. In addition, the list suggests a broader deregulation of metabolism that what previously seen: the glucose metabolism looks generally downregulated from the glycolysis to the respiration; fatty acid degradation and not only biosynthesis is affected; and many amino-acid metabolic pathways present genes less transcribed in Smurfs; also the pentose phosphate pathway, important for the synthesis of NADPH and pentoses that will be then used for the building of the nucleotides. Moreover, NADPH is involved, amongst others, in the anabolism of GSH (glutathione) mentioned above. These results confirm that Smurf flies experience a broad downregulation of genes coding for metabolic enzymes. The analysis also provides an extension of the previous results.

Concerning the pathways upregulated in the Smurfs, some hits did not appear in the enrichment analysis. In particular, the endocytosis pathway, the autophagy and the glycerol phospholipid metabolism (which is partially overlapping to the "ether lipid metabolism"). Concerning autophagy, there is a light general upregulation of the genes involved. However, the log 2 FC estimated by DESeq2 is in most of the cases extremely low (∼ |0.01|, in grey in the map), which might hint for a non-relevant biological result concerning a broad upregulation of the pathway (figure B.5 in Appendix B.1). The endocytosis hit is mainly composed by genes involved in endosomal formation and func- The genes coding for the receptor (InR), the kinase chico and Thor do present a significant upregulation also in the DEG analysis. It is interesting as InR and Thor (indicated in the pathway as 4E-BP) was detected as upregulated also in previous Smurfs studies (figure 1.11). As explained in [START_REF] Rera | Intestinal barrier dysfunction links metabolic and inflammatory markers of aging to death in Drosophila[END_REF], they are a target of dFOXO and activated when the insulin signal is reduced. Therefore, my results validate previous work and suggest a reduced IIS in the Smurfs.

Integration with metabolomic data 2.2.7.1 The data

The laboratory post-doc Céline Cansell, in collaboration with the Gustave Roussy Institute Metabolomic platform, has recently generated metabolomic data (mass spectrometry) from a large cohort of flies, males and females of different lines. Amongst them, we find males and females, non-Smurf and Smurf, from drs-GFP (at a single age, corresponding to the T 50 ). As the whole data set has not been fully analyzed yet, I decided to proceed with a simple check on a few metabolites corresponding to the transcriptome results. I focused only on the line corresponding to my RNA-Seq data, and on females.

I had 14 samples available, 7 non-Smurfs and 7 Smurfs. Most of the samples are a mixture of 20-30 flies, apart from one Smurf sample consisting of only 8 flies. As in a first preliminary check, this sample behaved like an outlier and as the difference in the number of flies compared to the others was high, I decided to remove it and to continue with 7 non-Smurf and 6 Smurf samples.

Exploratory analysis

I was provided with a dataset of 202 metabolites, with already preprocessed values estimating their concentration. To see if I could detect a separation in the samples as in the transcriptomic data, I performed a PCA (figure 2.12). Once again, we observe a separation between Smurfs and non-Smurfs, which looks like a different metabolic profile. The separation between the two categories is clear, as I previously showed with the transcriptome for young and midlife flies.

Results

The gene expression analysis highlighted a broad metabolic deregulation in Smurfs. Different enzymes involved in fatty acid biosynthesis and degradation, and in the oxidative phosphorylation resulted as DEGs. The GAGE analysis has also detected the Krebs cycle (or TCA cycle) as downregulated. Among the metabolites detected in the analysis, I tried to look for the ones directly connected to the genes showing deregulation. Results are shown in figure 2.13 and described hereafter. The metabolic processes in which the genes and the metabolites here discussed are involved are schematized in figure 2.14.

• Lactate. As briefly described in section 2.2.5.2, pyruvate can undergo aerobic metabolism (Krebs cycle and oxidative phosphorylation with consequent NADH and ATP production) or anaerobic metabolism (only NADH production). The second reaction occurs in the cytoplasm, while mitochondria carry out the aerobic metabolism. Somatic cells in the adult organism normally rely on aerobic metabolism. The fermentation path is preferred in case of immediate need of fast ATP production by the glycolysis, as for instance happens in muscles during exercise. In Smurfs, the gene Ldh, coding for the lactate dehydrogenase, is significantly upregulated; interestingly, the lactate is also significantly overrepresented in Smurfs. So a possible metabolic shift, speculated in section 2.2.5, could be confirmed by these results. However, the glycolytic genes are not upreguleted in Smurfs, and the pathway is even generally found as downregulated (section 2.2.6). Therefore, the active fermentation that we observe in Smurfs could be a simple response to mitochondrial dysfunction rather than a metabolic switch.

• Electron transport chain. The transcriptomic data suggest downregulation of the ETC and the Krebs cycle. The succinate dehydrogenase enzyme is the Complex II of ETC, but it is also an enzyme of the Krebs cycle. In figure 2.13 (point b), I show the downregulation of two subunits of the succynate dehydrogenase enzyme (Complex II of the ETC) and its upstream metabolite, succinate, which is accumulating in Smurfs. The downregulation of the Sdh genes is low, but it can be reliable as 1.3 for the general pathway). In both cases, the data are noisy and I cannot detect a change in the Smurfs; further analysis might be needed.

• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • *** 9 10 11 NS S Ldh expression • • • • • • • • • • • • • ** 1.0e+10 1.5e+10 2.0e+10 2.5e+10 3.0e+10 NS S • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • ** ** • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • * SdhA SdhC NS S NS S 10 11 12 • • • • • • • • • • • • • ** 2e+08 4e+08 6e+08 8e+08 1e+09 NS S • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • *** *** • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • ** • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • *** *** ACC Acsl FASN1 NS S NS S NS S 13 14 15 expression • • • • • • • • • • • • • n.s. • • • • • • • • • • • • • ** • • • • • • • • • • • • • ** Acetyl-CoA L-
• Fatty acid metabolism. Another signal coming from the transcriptional data is the downregulation of fatty acid metabolism. I have tried here to look at genes and metabolites that interact directly. I considered three already discussed genes (section 2.2.5.2) ACC, Acsl and FASN1. The three present a significant downregulation in Smurfs. I then looked at the concentration of three metabolites: acetyl-coA, L-Carnitine, Palmitic acid. acetyl-coA is the connection between glucose and fatty acid metabolism: its conversion to malonyl-CoA (catalyzed by ACC) is the entry point of the fatty acid biosynthesis; vice versa, fatty acid degradation releases one acetyl-coA molecule for every carbon of the chain; the acetyl-coA produced in such a way fuels the Krebs cycle. Acetyl-coA levels are noisy in our data, and no significant change can be assessed.

The carnitine shuttle mediates the entrance of fatty acids to mitochondria prior to degradation. Such mechanism required the activation of the fatty acid, performed -amongst others-by Acsl. We observe in Smurfs both a downregulation of the gene and a decrease in the concentration level of carnitine, suggesting the the first steps of the lipid degradation might be impacted in Smurfs. Palmitic acid is a saturated long-chain fatty acid with a 16-carbon backbone. We see a significant decrease in its concentration in Smurfs, which could confirm the downregulation of fatty acid biosynthesis suggested by the decreased level of ACC and FASN1. This last results also confirm the decreased level of palmitic acid in Smurfs observed in [START_REF] Rera | Intestinal barrier dysfunction links metabolic and inflammatory markers of aging to death in Drosophila[END_REF].

These preliminary results are encouraging, but I would like to highlight that these results are indicative, and a more exhaustive and general analysis is required to integrate the two datasets. This could restrict the hypothesis generated by the transcriptomic dataset to fewer processes that can be tested experimentally. producing the NAD + that will be then re-used by glycolysis. Alternatively, the pyruvate enters the mitochondria and is converted to acetyl-CoA, entering the Krebs cycle and producing ATP and NADH through the ETC. This aerobic pathway is downregulated in Smurfs.

Lipid biosynthesis (top left) starts from acetyl-CoA, which is converted to malonyl-CoA by ACC. The elongation step follows (involvement of the FASN1 enzyme), and the process ends with the complete synthesis of a fatty acid, as palmitic acid. When fatty acids need to be consumed, they undergo a degradation pathway that partially occurs in mitochondria. Prior to the entrance, the fatty acid needs to be activated, a process in which the enzyme Acsl is involved. It then enters the mitochondria through the carnitine shuttle, and undergoes the final steps of degradation. Those will produce acetyl-CoA, which will enter the Krebs cycle.

Gene expression noise and ageing

It has been reported that gene expression becomes heterogeneous with age [START_REF] Bahar | Increased cell-to-cell variation in gene expression in ageing mouse heart[END_REF][START_REF] Perez-Gomez | The Aging Transcriptome: Read Between the Lines[END_REF]. However, not all the studies point in such a direction [START_REF] Rogina | Regulation of gene expression is preserved in aging Drosophila melanogaster[END_REF][START_REF] Pletcher | Genome-Wide Transcript Profiles in Aging and Calorically Restricted Drosophila melanogaster[END_REF].

In our case, we were interested in understanding if we could observe any change in gene expression variance with age or Smurfness. We used an easy way to visualize it. I computed the standard deviation for each condition (Smurf/non-Smurf) within each age group. I then obtained a relative standard deviation by dividing the group one for each sample. I plotted the distribution of such values and compared them within age and condition (Kolmogorov-Smirnov statistic to compare unidimensional distributions -KS). Results are presented in figure 2.15.

As you can observe, age has a clear effect on the distribution. For both non-Smurfs and Smurfs, age shifts the peak towards higher values of relative standard deviation while lowering the peak itself (increase noise in the distribution). Such changes are significant to the KS statistic. Therefore, we can affirm that, in our dataset, we detect an effect of age on the gene expression noise. This is interesting, as it hints are the existence of changes that are occurring with time and normally associated with ageing, but not necessary to die. More will be discussed in section 2.2.9 and 2.5.

Interestingly, it seems that there is also a Smurf-related effect. Indeed, young and midlife Smurfs present a higher peak and less noisy distribution than their age-matched non-Smurfs siblings. However, this difference is diluted by age and, although still significant at 40 days, it looks not relevant anymore. This could be due to the fact that non-Smurfs might be at the moment of sampling at a different biological age (different time before the Smurf transition) and this could lead to the presence of heterogeneity in the transcriptome across the samples. The fact that the age dilutes the difference could show how, by sampling at later ages, we enrich our samples in individuals that are closer and closer to the Smurf transition. More about the re-interpretation of non-Smurfs as pre-Smurfs will be discussed in section 2.4. Genes belonging to the 1st quartile of the mean expression distribution are removed from the analysis. For the sake of visualization, the distribution are cut at 0.5 in the plot, but all the values of relative standard deviation are taken into account for the statistic.

Results discussion

In the previous sections, I have presented the results of a standard RNA-Seq analysis to characterize the Smurf samples. I will discuss the effect of age on our samples in section 2.3. Before moving to that, I would like to conclude this section by discussing the results that I have so far listed. In particular, can we reply to the initial questions of 1) is Smurfness showing a biphasic behaviour and 2) is Smurfness a good proxy for biological age?

I have demonstrated that the Smurf transcriptome is more similar to the one of another Smurf than to the one of an age-matched non-Smurf. However, we can see an effect of time, especially for the non-Smurfs and the old Smurfs.

By looking at the DEG analysis mixing all the Smurfs independently of their age, I had identified a core Smurf signal. I would therefore affirm that, with the element collected so far, Smurfs show at least a partial biphasic behaviour, with a stereotyped transcriptome and changes always affecting them independently of the age. A more complete answer can be given with the results of 2.3.

Regarding the second question, we need to compare the Smurf changes to what has been so far described as ageing-related changes in order to reply. As I am analyzing mainly transcriptomic data, I will focus on what has been described as the ageing transcriptional signature to proceed for a fair comparison. I will refer to two main studies: Stegeman and Weake (2017) and [START_REF] Frenk | Gene expression hallmarks of cellular ageing[END_REF], two recent studies reviewing the transcriptional changes identified with ageing in different species.

Both the cited reviews agree on the difficulty of easily categorizing a transcriptional signature of ageing. Different studies performed in different organisms, tissues, and ages often do not show an overlap. Indeed, different deregulation occur in different tissues, as the genes are physiologically expressed at a different level. Nevertheless, there is a conserved set of six changes that Frenk and Houseley (2018) have called "hallmarks of ageing gene expression".

• Downregulation of genes encoding mitochondrial protein. This hallmark can be linked to the general mitochondrial dysfunction described in section 1.2.1. This hallmark is present in the Smurfs with the deregulation of the Krebs cycle, ETC, and some mitochondrial ribosomal proteins.

• Downregulation of the protein synthesis machinery. The ribosome hallmark is described as more challenging to detect in highthroughput experiments due to their high expression, possibly leading to a non-negligent difference in RNA molecules and resulting in minor FC, missing DEGs cut-off. However, in the Smurf case, we have detected GO terms and KEGG pathways related to ribosomes and their biogenesis as downregulated, even if single genes present a low FC. In addition, proteolysis is downregulated, which, together with the upregulation of In the Smurf transcriptome, three of the four hallmarks are detected (downregulation of mitochondrial proteins, downregulation of ribosomes, dyresgulation of immuno system). A fourth hallmarks is partially detected (stress response), while no hits are found to affirm an overexpression of DNA damage related genes or a reduction in growth factors.

On the other hand, the dysregulation in gene expression seems a time-related signal rather than a Smurf related one.

the heat shock proteins, hints at the impaired proteostasis described as a general hallmark of ageing [START_REF] Lopez | Analysis of immune-related genes during Nora virus infection of Drosophila melanogaster using next generation sequencing[END_REF].

• Dysregulation of immune system genes. As discussed before, flies are missing -for what we know -adaptive immunity but present the innate one. In Smurfs, we find the over-activation of the inflammation that is described both as transcriptional and general ageing hallmark.

• Reduction in growth factor signalling. This hallmark mainly refers to two categories: cell cycle and "cell growth" (as the nutrient sensing insulin/mTOR pathway). The provided examples mainly refer to rodent models and humans, but it is still interesting to see if some of these deregulations can be found in the Smurfs. Concerning genes involved in DNA replication and cell cycle, being these categories well annotated in GO and pathway, the fact that they did not appear in the analysis is per se a sign of no deregulation. Concerning the growth factors, I proceeded with a manual check. I downloaded from Flybase the genes annotated in three different "growth" pathways: EGFR (epidermal growth factor receptor) signalling pathway, FGFR (fibroblast growth factor receptor) signalling and insulin signalling. I then checked if genes belonging to these pathways were differentially expressed in my dataset. As described in section 2.2.6, some genes belonging to the insulin pathway and regulated by FOXO are upregulated, suggesting a reduced IIS. However, the insulin-like peptides (Ilp) do not present significant change. We cannot detect a trend or change in core genes for the other pathways. Therefore, I would not affirm that we can detect a reduction in growth factor signalling in Smurfs, even if the general downregulation of metabolism does not point at such pathways being active.

• Constitutive responses to stress and DNA damage. This hallmark is actually of no straightforward interpretation, as Frenk and Houseley (2018) report a well-conserved upregulation of heat shock proteins. In contrast, Stegeman and Weake (2017) are more careful and show an organism dependent pattern, reporting heat shock proteins upregulation in the whole body of the worms, and downregulation for the whole body of the fruit flies and some rodent's tissues. However, as discussed in the Introduction, various studied showed upregulation of Hsp with ageing in the fruit fly [START_REF] Tower | Heat shock proteins and Drosophila aging[END_REF]. On the other hand, the DNA damage response is treated oppositely. In the hallmarks paper, it is reported as a less conserved process, with an increase of DNA damage in old individuals often not correlating with an increase in transcription of DNA repaired genes. In the second paper, the upregulation of DNA repair genes is reported as a conserved feature. The contrasting results might dependent on the specific experiments and tissues (survival curve of the population and sampling time, food, etc). Overall, in Smurfs, we find indeed upregulation of heat shock protein, UPR and glutathione trans-ferases, suggesting a stress response. On the other hand, DNA repair is detected in none of the analyses.

• Dysregulation of gene expression and mRNA processing. This hallmark refers to a general increase in gene expression heterogeneity (i.e. noise) with age and dysfunction in the RNA pre-processing steps and maturation. In the DEG and pathway analysis, categories such as "Splicing" or "mRNA-preprocessing" are annotated but never significant. Concerning the increase in noise in gene expression, as described in section 2.2.8, we can detect a difference between Smurfs and non-Smurfs, with non-Smurfs presenting a higher heterogeneity. As briefly discussed, this could be due to the non-Smurfs being a different moment of their biological age at the moment of sampling, compared to the Smurfs, which are in a late and stereotyped end-of-life phase. However, a relevant and significant difference is appreciated with time in Smurfs and non-Smurfs.

Our analysis suggests that the increased noise in gene expression could be a time-related effect. Therefore, it could be an ageing-related change not necessary for death (i.e. entrance in the Smurf phase).

The results presented in this section show how not only Smurfs present a core signal independent from age, but then such signal recapitulates more than half of the hallmarks of transcriptional ageing. On the other hand, the analysis performed have also hinted at an effect of age on the gene expression of the flies. In the next part of the chapter, I will focus on the age-related changes in Smurfs and non-Smurfs.

Smurfs and non-Smurfs through time

An intuitive solution to explore age-associated changes is to look at the differences between young and old samples, separating them into the respective Smurfs and non-Smurf categories. I will first present the results of such comparisons. I will then move to discuss how to interpret the changes occurring over time in the non-Smurfs (section 2.4). I will finally show the results obtained in the attempt of identifying an age-specific signal differing from the Smurf one (section 2.5).

Effect of age Smurf DEGs: an example

As a start, I looked at what happens for different groups of Smurfs DEGs. By dividing them by biological pathways, I plotted the expression of the related genes through heatmaps to easily visualize if differences were noticeable. An example is in figure 2.6, where the Smurf DEGs belonging to the KEGG "Toll and Imd pathway" are represented (dme04624).

Figure 2.17: The heatmap of the DEGs mapping to the KEGG "Toll and Imd pathway"

shows how the expression of these genes in old Smurfs tends to converge to the one of the Smurfs and vice versa.

The plot shows how the genes related to the inflammatory pathway present on average a lower upregulation in the 40 days Smurfs compared to the young and midlife samples. On the other hand, the old non-Smurfs seem to have an increased expression of these genes compared to the young and mid-life samples. I am here showing only the inflammatory genes, which well exemplify the general behaviour I observed by looking at the different groups of genes. To investigate the extent of those changes in non-Smurfs and Smurfs over time, I proceeded on differential gene expression analysis within each group.

Non-Smurf samples over time

I first compared the differences occurring over time in the transcriptome of the non-Smurfs. This comparison is also essential to prove whether Smurfness has a biphasic behaviour and is a better ageing predictor than chronological age. If indeed the old non-Smurfs present the ageing changes listed in section 2.2.9, our Smurf model could still be good for the identification of young flies presenting ageing changes but would fail to be proved as an actual ageing model.

Similarly for what done in 2.2.4, I performed a pairwise comparison between the different non-Smurfs group. The number of DEGs for each comparison in show in table B.3 (Appendix B.1).

The old vs young comparison (40 vs 20 days) results in "only" 510 DEGs. Note that the only is placed into quotation marks, as it is a relative statement compared to the 3049 DEGs identified with Smurfness. I proceeded on performing the enrichment analysis with the same package and parameters as the one presented before (section 2.2.5). Results are shown in figure 2.18 and table B.4. Old non-Smurfs present an upregulated immune response compared to younger flies and a decrease in the expression of oogenesis genes.

Therefore, changes are occurring with time in the non-Smurfs. However, these changes are less numerous than those occurring with Smurfness and are included in the Smurf changes (both inflammation and oogenesis are deregulated in Smurfs). So interestingly, we can detect age-related transcriptional changes (as downregulation in mitochondrial genes and ribosomes related genes) in Smurfs, regardless of their age, but not in non-Smurfs individuals sampled at 10% survival. This proves that Smurfness can identify -at a transcriptional level -biologically old individ- uals better than age. It also shows that most of what has been described as transcriptional marker of ageing is actually a Smurf related signal. Smurf could therefore be a confounding factor for many ageing studies, at least in Drosophila.

• • • • • • • • • • • • • • • • • • • • • • antibacterial humoral response
The fact that the DEGs found in the old non-Smurfs are associated with categories that we find deregulated in the Smurfs made me advance the following hypothesis: are those changes simply age-related, or are they indicative of pre-Smurfs changes? The 40 days samples are very close to the transition, as they are sampled at 10% survival and might present changes that are essential for the Smurf transition. Inflammation and decreased fertility might be amongst them. However, additional light changes might occur and possibly miss the p-value cut-off in the DEG analysis. I will further develop this hypothesis in section 2.4.

Smurf samples over time

I used the same workflow to explore the differences occurring in the Smurfs over time. As expected given the results obtained so far, the young and midlife individuals do not present many differences (see table B.3 in Appendix B.1). Surprisingly, when comparing young Smurfs to old Smurfs I find 2320 DEGs. Once again, I followed the same procedure as for the other comparisons and performed the enrichment analysis. The results are presented in table B.5 and in figure 2.19 and 2.20. Note that the two figures were generated together and afterwards split to improve readability. The colour scale is therefore the same.

The first thing worth mentioning is that we do not find specific Smurf categories as upregulated in the old Smurfs, if not for some metabolic genes; if the trend I have noticed for inflammation is legit, it might be too weak to be detected through differential gene expression. The majority of nodes of the graph in figures 2.19 and 2.19 are associated with RNA processing (as splicing factors), cell cycle, chromatin organization and response to DNA damage. This signal is not detected in any of the previous comparisons, but is present in the transcriptional hallmarks of ageing as presented in figure 2.16. Regarding the response to DNA damage, as reported before, the hallmark refers to a constitutive upregulation, although the authors are careful and consider it as an uncertain hallmark. In Smurf, however, we find the opposite result. The dysregulation of RNA processing goes instead in the same direction of what was proposed by [START_REF] Frenk | Gene expression hallmarks of cellular ageing[END_REF].

Why are the old Smurfs carrying such a signal? I mainly thought about three hypothesis:

1. this is an age-related signal, not necessary to enter the Smurf phase. It is therefore carried by the old samples, with an exacerbation in Smurfs that allows its detection;

2. it is somehow specific only to old Smurfs;

3. it is an artifact of the data, even if this is less probable as the signal seems quite strong.

I will further discuss this point in section 2.5. In particular, I find terms related to RNA processing and DNA regulation (amongst which chromatin regulation) which are not found when comparing the Smurfs to the non-Smurfs or the old non-Smurfs to the young non-Smurfs. The only upregulated terms concerns metabolism, and are deregulations before associated to Smurfs when compared to non-Smurfs. This suggests that, for such categories, the "dilution" of the Smurf signal with time discussed in figure 2.17 could be not strong enough to be detected by DESeq2. Each node is a GO term, and edges are connecting categories with overlapping genes. In particular, I find terms related to RNA processing and DNA regulation (amongst which chromatin regulation) which are not found when comparing the Smurfs to the non-Smurfs or the old non-Smurfs to the young non-Smurfs. The only upregulated terms concerns metabolism, and are deregulations before associated to Smurfs when compared to non-Smurfs. This suggests that, for such categories, the "dilution" of the Smurf signal with time discussed in figure 2.17 could be not strong enough to be detected by DESeq2. Each node is a GO term, and edges are connecting categories with overlapping genes.

• • • • • • • • • • • • • • • • • • • • • • • • cytoplasmic
• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • cell cycle checkpoint cell cycle
• • • • • • • • • • • • • • • • • • • • • • • • • • ATP-dependent

A pre-Smurf phenotype

As explained in section 2.3.2 not many changes are detected over time in the non-Smurfs. However, the changes found (reduced oogenesis genes and increased expression of inflammatory genes) are shared with the Smurfs. This led me to hypothesize that, rather than age-related changes, these could be pre-Smurf related changes. In other words, changes that are already occurring before we can detect the transition to the Smurf phase with the current biomarker of intestinal permeability.

The results presented in this chapter have shown how Smurfs present quite a biphasic and drastic change in gene expression, even at a whole-body level. Furthermore, some ageing-related changes occurring with Smurfness cannot be detected when looking only at non-Smurfs over time. This suggests that given the features currently used to define an ageing transcriptome [START_REF] Frenk | Gene expression hallmarks of cellular ageing[END_REF];Stegeman and Weake (2017)), Smurf transcriptome is an ageing transcriptome, while that of an old non-Smurf is not.

When we look at Smurfness, as described in the Introduction, we are looking at a very late phase of life that has been not shown to be reversible so far. A possible way to see it is that Smurfness represents a final collapse of the organism, when the organism itself has lost its resiliency and is irreparably committed to death. If that is the case, Smurfness would be a non-plastic pre-death stage. Interventions increasing lifespan would delay the entrance in the Smurf state by prolonging the non-Smurf one rather than acting on the Smurf phase. In Chapter 3, I will comment more on this latest hyphotesis, with experimental evidence pointing in that direction.

In such a scenario, it would be interesting to understand the changes leading to Smurfness by looking at what is happening to non-Smurfs before the transition. This would mean looking at the biological time ticking in a single individual and finding features (such as expression of genes, for instance) that are a strong predictor of impending Smurfness. How to do this? It would be ideal to follow and record data from an organism every day as it lives. This would allow us to have data to analyze retrospectively once the organism turns Smurf. Unfortunately, in practice, this kind of experiment is impossible, not only for the cost and amount of work required but also because the data col-lection is usually invasive and, in some cases, kills the organism (for instance, in the fruit fly). Please note that an approximation of such an experiment is currently ongoing in the lab in mice for translation of the Smurf model in such organism (section 1.4.4).

I tried to approximate the problem to find a possible answer using my data. The young non-Smurf samples I have are sampled at 90% survival; the old at 10% survival. Given the random sampling, the 20 days non-Smurfs are enriched in flies being further from the Smurf transition than the old ones. The ones sampled at 30 days would be in a middle point. Thus, I thought to reconstruct the Smurf genes' expression through physiological time by looking at the timecourse non-Smurf signal as a pre-Smurf signal. This idea is schematized in figure 2.21. As in the population from which my data come from 20 days corresponds to 90% survival, I can imagine that, on average, the non-Smurfs samples at this point are representative of an early phase of life. The same reasoning applies to the samples at 40 days (10%) survival, which could represents a pre-Smurf phase of life. Samples at 30 days are at the T 50 , and could therefore be a good approximation of a non-Smurfs midlife.

I assumed that the changes preceding the Smurfs must be probably conserved in the Smurfs themselves. I therefore focused on the genes that are DEGs in Smurfs. I discarded genes lowly expressed (mean expression across samples <50 counts, which discards around the lowest 20% of the DEGs), as the signal could be noiser and genes lowly expressed would not be the best targets to find a biomarker of pre-Smurfs. The expression of each gene is centered on the mean across conditions and time and divided for its standard deviation, in order to highlight differences. Each point is the gene average expression for a certain condition and a certain age. The lines in the background are connecting the same gene over time, and the violin plots help visualize the distribution of the points. (a) Genes upregulated in Smurfs. Over time, the change in expression of those genes in the Smurfs seems noisy, and suggests that, although an increase in the expression compared to the non-Smurfs is always present, such increase is not necessarily always the same for the same gene over age. Regarding the non-Smurfs, there is a noticeable increasing trend over time for the peaks of the violin plot, hinting that those genes are already experiencing an increase prior to the Smurf transition. (b) Genes downregulated in Smurfs. Regarding the genes down-regulated in Smurfs, the first thing that comes to the eye is the very noisy distribution at 40 days for the Smurfs. Some of the genes might therefore not experience a downregulation if we compare simply the non-Smurfs with the old Smurfs: this might also be due to the outliers showed in picture 2.6. In the non-Smurfs, a general downregulation trend over time is less noticeable. However, it might be that some genes show and interesting trend if considered alone.

Figure 2.22 shows the evolution over time of the considered genes in Smurfs and non-Smurfs; upregulated and downregulated genes are separated. By looking at the peak of the distribution in the violin plots, a shift towards higher values is noticeable in the non-Smurfs over time for Smurfs upregulated genes. On the other hand, we can see how the expression in the Smurfs is quite noisy, indicating that not all the genes are experiencing the same increase in expression. In the Smurfs' downregulated DEGs, no clear trend is visible for the non-Smurfs over time.

This general representation could be already informative in the case in which a trend is noticeable over the whole set of genes, but that could be misleading if no general trend is visible. Indeed, it might be that only some genes having an important function display such a trend.

To investigate this, I opted for a simple approach, by inferring the following linear model for the expression of the genes in the non-Smurfs:

g = β0 + β1 t + εi (2.1)
where g is the expression of the gene, t the time (20, 30, 40 days) and εi the noise (i.e. what of the g variable cannot be explained with time). If time does not affect the expression, I expect its coefficient β1 not to be significant. I obtained 355 genes (out of 2482 tested) for which the time coefficient is significant. 240 belongs to the genes upregulated in Smurfs, the remaining 115 to the genes downregulated in Smurfs.

I explored this list by mapping the genes to each KEGG pathway and counting how many genes map to a particular pathway. There is in this method a bias towards large pathways, that could be mapped by chance by more genes than more minor pathways. However, the pathways where most of the genes are mapped might be good indicators of overall processes. I initially tried to map the genes on the GO biological process terms, but the redundancy and the vastness of each category made the interpretation more challenging than the simple list of genes.

In figure 2.23 I show the top five of the KEGG categories for the upregulated and downregulated Smurfs groups. We can see how only a few genes are mapped in both cases. The first pathway ranked is the "Metabolic pathways" (dme01100), which groups all the different metabolic pathways noted for the organism. It is a large category, where two genes can map far and be involved in different processes. This is the case for the category of upregulated genes, where the mapping genes are dispersed throughout different groups. For the downregulated genes, some are found grouped in the same pathways, and reappear in the categories from the presented list. For these reasons, in the following subsections, I will discuss the other pathways found, leaving on the side this first hit. You can find all the genes mapping retrieved from the analysis in table B showing a significant trend over time in the non-Smurfs. The ones belonging to the upregulated Smurf group seems to have more genes mapping to the pathways.

Upregulation

The inflammation category confirms the results found in the DEG analysis over time. Only 9 genes map to related KEGG pathway, but I can retrieve up to 28 genes if I filter for the same GO inflammatory-related categories as in section 2.2.5. This is interesting, as it represents a compact group of genes upregulated in Smurfs, which also follows an increase in the non-Smurfs prior to the transition. Interestingly, I do find the two transcription factors rel and dl. This adds as a confirmation of the results of 2.3.2, where inflammatory genes are present amongst the DEGs. Results from some of the inflammatory genes are presented in figure 2.24.

In the ER and endocytosis related categories (dme04141 and dme04144) we find genes involved in protein folding and genes involved in vesicle mediated transport or polymerization of the actin cytoskeleton. However, some of these q q q q q q q q q q q q q q q q 8.6 8.8 9.0 20 25 30 35 40 time expression PGRP-LA q q q q q q q q q q q q q q q q 9.0 PGRP-SD q q q q q q q q q q q q q q q q 11.8 Rel q q q q q q q q q q q q q q q q 11.2 11.3 11.4

11.5 20 25 30 35 40 time expression dl q q q q q q q q q q q q q q q q 8.0 Listericin q q q q q q q q q q q q q q q q 8 9 10 11 12 13 20 25 30 35 40 time expression DptA q q q q q q q q q q q q q q q q 8 9 10 11 1.0 1.5 2.0 2.5 3.0 time expression Jon99Fi q q q q q q q q q q q q q q q q 9 10 11 12 1.0 1.5 2.0 2.5 3.0 time expression Jon99Fii q q q q q q q q q q q q q q q q 14.0 14.5 15.0 15.5 16.0 16.5

1.0 1.5 2.0 2.5 3.0 time expression Yp3 q q q q q q q q q q q q q q q q 8.5 9.0 9.5 10.0 1.0 1.5 2.0 2.5 3.0 time expression Jon66Ci q q q q q q q q q q q q q q q q 7.5 8.0 8.5 9.0 1.0 1.5 2.0 2.5 3.0 time expression Npc2d q q q q q q q q q q q q q q q q 7.0 genes might have different functions depending on the cells and the conditions, so it is difficult to hypothesize on them.

Regarding the MAPK pathway, it is worth mentioning that amongst the genes, we find the transcription factor Jra and its downstream product puc, which mediate a feedback loop regulation. kay, the other transcription factor of the pathway, is also upregulated in Smurfs (as it was displayed already in figure 2.9. The JNK pathway, in which these genes act, has been shown to have an effect on lifespan [START_REF] Wang | JNK Signaling Confers Tolerance to Oxidative Stress and Extends Lifespan in Drosophila[END_REF][START_REF] Wang | JNK Extends Life Span and Limits Growth by Antagonizing Cellular and Organism-Wide Responses to Insulin Signaling[END_REF].

The table detailed the results of this section is B.6 (Appendix B.1).

Downregulation

In this group, the metabolism makes most of the mapping results. The last four categories of the list indeed contain genes overlapping with the bigger "Metabolic pathways". Overall, there is no biological process that seems more represented than the others, as it is for the immune response in the case of the upregulated genes. Furthermore, the trend at the level of single genes seems weaker than the previous group. As most of the mapped genes showed a rather weak slope (∼ 0.2), I decided to focus instead on the ones showing the bestinferred slopes. Those are represented in figure 2.24. Amongst them, I have three proteins of the Jonah family, which I discussed briefly already in section 2.2.5, as they were consistently present in the genes downregulated in Smurfs.

Jon99Fi and Jon99Fii are arranged in a cluster on chromosome 3R, and it could therefore be the case that the activation of one implies the activation of the other. I would discharge the hypothesis that their activation is a side-effect on chromatin opening due to another gene activation as I find downregulation of other Jonah genes which are not in the same region (for instance, Jon66Ci is on chromosome 3L). Yp3 is involved in egg formation, while Npc2d and Lsp1beta have predicted roles, respectively in sterol transport and amino acids storage. The Jonah genes might be the most interesting result in this context as they are present in a group.

An interesting pattern for the Smurf downregulated genes is that 20% of the genes present a significantly positive trend over time in non-Smurfs. On the other hand, in the upregulated group, the genes showing significantly showing a negative trend were only 4%, having, in addition, a slope close to zero. Hereafter is an example of genes showing this behaviour, which I comment in the caption: q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q 10 11 12 20 25 30 35 40 time expression wat q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q 8.0 ). The linear model refers to only the grey points, representing the non-Smurfs samples. The blue points are the expression of the Smurfs at different age. As you can notice, even if the genes were detected as differentially expressed, the points are quite overlapping, suggesting that the signal in the Smurfs themselves might be weak. In particular, in this case of wat we can see how non-Smurfs and Smurfs converge at old age. Note that mthl2 belongs to the same receptor family as mth, the first gene identified as extending lifespan in fruit fly, when mutated.

To conclude, I can indeed detect a trend over time for some of the genes that are DEGs in Smurfs. In particular, many of the immune-related genes that are overexpressed in Smurfs follow such trend. One can speculate that inflammation might therefore be an early mechanism of the Smurf transition, growing over the organism own biological time and enhancing the probability of entrance in the Smurfs phase. On the other hand, isolated genes might of course have an important role as well in the determining the entrance of the transition, and putative targets might be hidden amongst the ones showing a better trend over time in the non-Smurfs. By filtering on these genes, we can go down to a list of targets to handle experimentally. An idea could be to build a report (as GFP report) that can follow the expression of the gene of interest over time in vivo in the flies, without affecting the expression and the function of the gene itself. I will further comment on these hypotheses in Chapter 4 (4.1.3).

Age and Smurfness: a distinct effect?

In the previous section, I discussed the possibility of re-interpreting (given my data structure) the chronological age of the non-Smurfs as an approximation of the biological age of the samples.

By investigating this, and given the results of section 2.3.3, I wondered whether some genes may be affected by age only and not by the time left to the Smurf transition. In other words, these genes might not be strictly associated with the fly's biological age. However, they might be a mere result of the exposition of the organism to time and the environment.

If those effects existed, they would be specific to the old individuals, whether Smurfs or non-Smurfs. I thought that a simple way of visualizing such changes could be inferring the correlation of each gene with Smurfness and age. A gene whose regulation changes in Smurfness but that is not affected by age would present a good correlation with Smurfness but not with age. A pre-Smurf gene (as the ones investigated in section 2.4) would present a good correlation with both. A gene affected by time but not by Smurfness would correlate with the first but not with the second. Finally, a gene not affected by time or age will not present a correlation with any of them. The correlation with Smurfness increases when those samples are removed, suggesting immediately that age has an effect also on genes well correlating with Smurfness.

91

I estimated the correlation with both Smurfness and age for all the genes. Results are shown in figure 2.26. I did not display all the points, but only their density maps for visualization purposes. Each point would represent a gene, plotted in the space of the two computed correlations. Out of curiosity, I plotted all the samples (density in black) and all the samples but the 40 days (density in the background, red dotted line). The correlation with Smurfness increases drastically when the 40 days samples are removed, again showing that the old Smurfs indeed have a "diluted signal" compared to the young Smurfs.

I tried to follow the behaviour of the genes differentially expressed in all the different comparisons I have done so far. Results are shown in figure 2.27. The genes differentially expressed in Smurfs plot on the side of the graph showing a high correlation with Smurfness. Some also show a correlation with age, and are probably overlapping with the ones illustrated in section 2.4. The genes differentially expressed when comparing young and old non-Smurfs distributes interestingly amongst the ones correlating with both age and Smurfness. This would suggest that amongst the DEGs in the non-Smurf, we cannot detect genes affected by age only, if any. When we look at the graph for the young- They map on the side of the plot, suggesting a high correlation with Smurfness as expected. Though the peak of the density is on the space of higher Smurfness correlation than age, some genes also display correlation with age. (b)DEGs in old non-Smurfs compared to young non-Smurfs. Those genes have a good correlation with both age and Smurfness, as expected from the analysis in section 2.3.2. (c) DEGs in old Smurfs compared to young Smurfs. The downregulated genes of this group are interesting, as they show a compact behavior in a zone of higher correlation with age than with Smurfness.

old Smurfs comparison, the distribution of the DEGs is different compared to the ones of the non-Smurfs. The genes overexpressed at old age in the Smurfs (mostly given by a dilution of the Smurf signal) distributes along the x-axis; the genes downregulated in old Smurfs instead are a more compact group, with peaks at low correlation with Smurfness and high negative correlation with age. The genes downregulated with Smurfness could therefore be a putative age-related signal, as already suggested in section 2.3.3.

In figures 2.19 and 2.20 I have seen how the genes related to cell cycle, RNA processing and DNA repair were overrepresented in the genes downregulated in old Smurfs. I therefore tried to plot the genes related to those pathways in the correlation plot, to check if there were distributed in the lower part (good negative correlation with age).

To select the specific process and genes, I referred to the list of KEGG pathways in Drosophila, which can be easily retrieved from the website. I selected the pathways indexed under the groups "Transcription" and "Replication and repair". The specific name are listed in following table: As a "control", I extracted the genes belonging to the inflammation pathway, which I expect to correlate with Smurfness mostly. Results are shown in figure 2.28.

Replication and repair Transcription

The genes related to DNA replication, repair, and transcription show an age-dependent behaviour rather than Smurf dependent one. As expected, the inflammation shows mainly a correlation with Smurfs, although some noise is present. I compared the two dimensional distributions using the Fasano-94 -0.5 0.0 0.5 -0.5 0.0 0.5 gene expression correlation with smurfness gene expression correlation with age Figure 2.28: All the genes detected in my dataset are here plotted, as in figure 2.27. The red points and density lines refer to different group of genes that I will describe in the specific caption point. In all cases the difference in distribution between the genes considered and the background is significant (Fasano-Franceschini test, p-value ¡ 0.05). (a)Toll and Imd pathway As expected, the genes mapping to this pathway mostly map in a zone of high correlation with Smurfness. (b). Replication and repair. Those genes, taken from the pathways listed in table 2.1, show a compact behavior in a zone of higher correlation with age rather than Smurfness. (c). Transcription The same reasoning as in point (b) applies for this category of genes, for which the zone of major density is at higher correlation with age than Smurfness.

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Franceschini test, an implementation of the KS statistic for multidimensional distributions. In all cases, the distributions resulted significantly different from the background ones. The results obtained for the "Transcription" pathways deregulated with time could be linked to the increased noise in gene expression observed with age in our dataset (section 2.2.8).

Interstingly, this analysis suggests that we can indeed discern a "chronological old" signal from a "biologically old" (Smurf) signal when we include the Smurfness information in our data. Smurf identification would then be a powerful tool for deconvolving chronological and biological ageing. Indeed, studying ageing at a population level rather than at an individual level does not allow us to distinguish changes associated with time from changes associated with physiological age. Although our common sense tends to mix them, these two processes may differ.

The Smurf model provides us with a tool to identify an individual's advanced physiological age, independent of its chronological age. If we were unable to separate Smurf samples from non-Smurf samples, we would likely have identified an "aging transcriptome" only by comparing samples at 40 days versus samples at 20 days, due to the higher proportion of Smurfs individuals at 10% survival. We would then have identified what is shown in figure 2.16 as an ageing signal, labelling under the name of "ageing" both Smurf specific changes and age-specific changes.

Interestingly, the age-related changes might give us hints on why the probability of becoming Smurf increases with time. For example, one could speculate that an increased transcriptional heterogeneity would increase the gene networks instability up to the moment when the resilience of the network fails, leading to a general collapse that would correspond to the Smurf phase. specific, confirming the presence of a (at least partial) biphasic behaviour and pointing at Smurfness as a better proxy for physiological age of the organism than age itself, and redefines most of the so-called ageing signature as an end-of-life one.

2. Effect of age on the expression of the genes As already suggested by the PCA in figure 2.3, age affects the transcriptome, but its effects are lower than the ones of Smurfness. Interestingly, the age-effects detectable in the non-Smurfs overlap with Smurfs' alterations. This led me to hypothesize that the non-Smurfs age-related changes might be pre-Smurfs alterations (figure 2.21). By individually looking at the time trend of the gene expression in non-Smurf, I found inflammation as a process consistently showing an increase over time in non-Smurfs. Those results will be further discussed in Chapter 4.

On the other hand, the Smurfs show a specific signal over time, especially in the downregulation of genes related to DNA replication and repair and transcription. These results are interesting, as DNA repair genes have been shown (even if not consistently) to be upregulated with ageing, while deregulation mRNA processing is indeed one of the hallmarks of transcriptional ageing that was not found when comparing the Smurfs to the non-Smurfs. Moreover, when looking at the correlation of these genes with age, independently of the Smurfness status of the sample, they show an age-dependent behaviour. Interestingly, by analyzing how the heterogeneity in gene expression evolves in the samples, I can detect a trend of increasing variance with age in both Smurfs and non-Smurfs 2.15. Those results open the way to deconvolving time-specific signals (based on chronological age, independently of the Smurf status) and ageing-specific signals (by looking at the non-Smurfs over their own physiological time, prior to the Smurf phase).

In addition, I investigated the differences between Smurfs sampled at different moments after transition, but I could not detect a difference in the signal (results not shown). Therefore, Smurfs carry the same changes at any moment after the transition. However, the study presents here a limited amount of samples (no 5h Smurfs for the 20 days samples, and only two samples each for 5h Smurfs at 30 days and 5h Smurfs at 40 days), which considerably lowers the statistical power, especially for the detection of light changes.

Chapter 3

Identifying genes affecting longevity using Smurfness

Since the identification of the mth mutation as extending lifespan in Drosophila, alterations in many genes have been detected as increasing lifespan in the fruit fly. Those are conveniently annotated in the GenAge database [START_REF] Tacutu | Human Ageing Genomic Resources: new and updated databases[END_REF]. Amongst the annotated genes (202), we find 58 present in the Smurfs DEGs (figure 3 We wondered if, amongst the Smurfs DEGs, were hiding new genes whose artificial manipulation could affect longevity.

With Smurfness we are looking at a late phase of life, where causes and effects of ageing are probably mixed. In other words, a gene that is altered in Smurfs could hypothetically present such deregulation for the following reasons:

1. The alteration of the gene is cause of entrance in the Smurf phase. Suppose the upregulation or downregulation of a gene played a role in the entrance in the Smurf phase. In that case, we could think that artificially inducing the opposite alteration (for instance a physiological downregulation for a gene upregulated in Smurfs) could delay the entrance in the Smurf phase. This would prolong the non-Smurf phase and the lifespan of the population.

2. The alteration of the gene is an effect of the entry in the Smurf phase. For instance, the general downregulation of metabolism observed in Smurfs could be a late process, occurring after the organism is already committed to death. As another example, some genes could have a rescue mechanism, meaning that they are activated to try to compensate for other stresses affecting the organism. The alteration of an "effector" gene might likely not affect the lifespan, except possibly in the "rescue genes" case, if those carry out their action early enough in the transition process.

We decided to screen for the genes deregulated in Smurfs by altering their expression (overexpression or downregulation) and monitoring the effect on the population's lifespan. We selected the genes to experimentally test by trying to focus on genes possibly belonging to the first category (cause of Smurfness rather than effect). I will start by illustrating the selection process, then moving to the experimental plan and finally illustrating the results.

Genes selection process

As the genes deregulated in Smurfs are numerous, I needed to apply some selection criteria before moving to the bench to test them. Such selection criteria were meant to maximize the probability of the gene selected to be actively involved in the "Smurf process" rather than being an effect of it.

I considered that an intuitive category to focus on was transcription factors (TFs). Indeed, the response that I am observing is an alteration in gene expression. Being time and spatial regulators of transcription, TFs might explain the changes observed, acting upstream of the deregulation.

I downloaded the list of Drosophila TFs from the database Flybase. I subsequently intersected this list with the list of Smurf DEGs. This led to the identification of 102 deregulated TFs out of the 624 in the Flybase list. 77 are upregulated and 25 are downregulated. For the list of Smurf deregulated TFs, see table B.9 (Appendix B.2).

As a hundred genes to test was still a too large number, I tried to reduce it by using two additional selection criteria. First, by looking for upstream regulators of the genes present in the list to possibly condense a redundant signal and secondly, selecting genes involved in pathways found as deregulated in Smurfs, or having a function of particular interest.

The idea behind this approach is schematized in figure 3.2.

Identification of upstream regulators: i-cisTarget. We reckoned that identifying putative upstream regulators of the Smurf TFs selected could have been a good way to shrink the available list of genes. For this purpose, I used the online software i-cisTarget [START_REF] Herrmann | i-cisTarget: an integrative genomics method for the prediction of regulatory features and cis-regulatory modules[END_REF][START_REF] Imrichová | i-cisTarget 2015 update: generalized cis-regulatory enrichment analysis in human, mouse and fly[END_REF]. i-cisTarget takes as input a list of genes and, by looking at common patterns in their promoters, returns as output a list of TFs that are putative regulators. Each proposed TF is associated with a score.

I split the list of Smurf TFs in two, dividing the TFs upregulated from the ones downregulated. The two lists were submitted independently.

In a parallel analysis, I used i-cisTarget to look for regulators of the most deregulated Smurf genes, TFs or not. I took all the genes deregulated in Smurfs with a log 2 FC > |2|.

The results of the i-cisTarget analysis are shown in figure 3.3. In particular, or downregulated (in green). These TFs might be partially responsible for the expression profile in point 3. By extracting them from the rest of the list, we possibly go one step back in the biological regulation of our final expression landscape. This brings us to point 2. Those TFs are in turn regulated by other genes, which could be present in the group itself or not be deregulated in the final expression profile (as it can happen in case of activation by post-translational modification). To move one step further in our backwards path to upstream regulators, we can try to predict which genes are regulating the TFs in point 2. This could be done by looking at the presence of shared binding motifs for specific TFs in the promoters of the genes. The software i-cisTarget conveniently performs this job, and brings us to point 1. The orange circles represent the predicted TFs not present in the final expression profile. As putative upstream regulators of the Smurf transition, the genes in point 1 could be good targets for experimental validation. From right to left, dashed lines reconstruct the hypothetical regulation path.

I have selected the hits that were occurring more than one time (possible different binding motifs) and presented a score > 4 (a score of 3 is indicated as the minimum threshold).

As you can notice from figure 3.3, the results are often redundant in terms of putative targets. This could be due to the output genes interacting and playing a role in the same processes. On the other hand, it could be that only one or a few of them are involved in the process we are investigating, and the others are an "off-target" signal.

Other TFs selection. Apart from the results of i-cisTarget, I manually screened the list of TFs to check for other genes to test. I focused on genes possibly fulfilling both these criteria: 1) showing an important deregulation (log 2 FC > |1|); 2) not having been investigated so far in longevity. The inflam- (2) Putative regulators of TFs down in Smurfs. Note that the second hit is a group of GATA family genes, and comes as a compact category in the output. (3) Putative regulators of genes up in Smurfs (log 2 FC > 2). The results reflect the immune and stress response identified as upregulated signal in Smurf. (4) Putative regulators of genes downregulated in Smurfs (log 2 FC < -2). Similarly to what happened in point 3, this output reflected the results found by the enrichment analysis, as the decrease in fertility genes (Blimp-1 and ken). Interestingly, maf-S has been shown to ameliorate age-associated phenotypes when overexpressed [START_REF] Rahman | Declining signal dependence of Nrf2-MafS-regulated gene expression correlates with aging phenotypes[END_REF]. matory genes are, for instance strongly deregulated in Smurfs, but the action of the pathways TFs has been the object of studies in Drosophila longevity, even if sometimes with contrasting results [START_REF] Khor | Control of lifespan and survival by Drosophila NF-κB signaling through neuroendocrine cells and neuroblasts[END_REF][START_REF] Moskalev | Pharmacological inhibition of NF-κB prolongs lifespan of Drosophila melanogaster[END_REF].

In the next section I will discuss the final list of selected genes.

The genes

The final list of selected genes is shown in figure 3.4. I will now provide a brief description for each of the selected and tested genes. Otherwise cited, the information presented here are retrieved from Flybase. Drosophila longevity. Some of them have been identified starting from the DEG analysis, while some of them are putative upstream regulators (i-cisTarget).

From i-cisTarget output

Aef1. Adult enhancer factor 1, it reported the best score with i-cisTarget for putative regulators of TFs upregulated in Smurfs. It is annotated as acting at the level of the fat body, but seems overall poorly characterized. Together with Adf1 and Trl, it has been predicted to cobind in regions of increased FOXO activity with ageing in Drosophila [START_REF] Birnbaum | Age-Dependent Changes in Transcription Factor FOXO Targeting in Female Drosophila[END_REF].

Adf1. Adh transcription factor 1 is named after its activation role in alcohol dehydrogenase gene (Adh), but it is poorly characterized and might therefore present other functions.

CG4360. Its function or role is not characterized. Interestingly, it is orthologous to the human ZSCAN10, an embryonic stem cell-specific TFs (from Uniprot annotation).

Trl. Trithorax-like plays a role in gene expression through chromatin modification. It has been reported as having an effect on the expression of various genes, amongst which homeotic and Hsp genes [START_REF] Katokhin | Molecular genetic analysis of Thrithorax-like gene encoded transcriptional factor GAGA in Drosophila melanogaster[END_REF].

FoxP. Forkhead box P has been shown as working in the nervous system. Interestingly, it is also differentially expressed in Smurfs (log 2 FC = 0.46).

Mef2.

Initially characterized for its role in muscle development, it looks now as being functional in a larger number of tissues. [START_REF] Clark | MEF2 is an in vivo immune-metabolic switch[END_REF] show how Mef-2 plays a role in both metabolic regulation and immune response, and negatively affect the survival of the flies under infection when KD. Since in Smurfs both these processes are deregulated, we decided to retain this gene.

It is also lightly upregulated in Smurfs (log 2 FC ∼ 0.07, which might not be relevant).

Hsf. Even if its role has been already investigated with longevity (with the overexpression of the gene giving lifespan increase, as seen in section 1.2.1), I included it in the list of the genes to test as the Hsp genes are strongly upregulated in Smurfs.

Nf-YB. Nuclear factor Y-box B (Nf-YB) codes for a subunit of the Y transcription factor. It has been mostly investigated for its role during develop-ment, but it is interestingly both upregulated with Smurfness (even if lowly, log 2 FC of 0.2) and a putative regulator of TFs downregulated in Smurfs.

GATAe,GATAd, srp, grn, prn. Those genes are all part of the GATA group, a family of TFs named after its binding sequence. The human GATA TFs are known mostly for their developmental role. Recently [START_REF] Dobson | Tissue-specific transcriptome profiling of Drosophila reveals roles for GATA transcription factors in longevity by dietary restriction[END_REF], they have been shown to be involved in the CR-related lifespan extension. Not all those genes have been characterized yet in Drosophila, but I initially planned on testing them all. For practical reasons, I had to leave aside grn and prn.

From DEseq2 analysis dmrt93B. Even if not well characterized, I selected this gene as it presented a log 2 FC > 2 in Smurfs.

rib. It presents a medium-level upregulation in Smurfs (log 2 FC of 1.5) and has not been studied yet in ageing. I therefore selected it for testing.

Ets21C. It has a log 2 FC of ∼ 1.7 and has been reported as involved in stress response, especially in the gut [START_REF] Mundorf | Ets21c Governs Tissue Renewal, Stress Tolerance, and Aging in the Drosophila Intestine[END_REF].

Hey. It is annotated as being involved in nervous system development, and is upregulated in Smurfs (log 2 FC of 1.45). Selected for the same reasons as rib.

kay. It has been shown to be involved in different processes, from cell cycle to stress response, and its pathway to be involved in longevity (see section 2.4). It presents a light upregulation in Smurfs (log 2 FC ∼ 0.5), but I decided to add it as it was often present in the analysis together with other genes, as for Jra in section 2.4 and Ets21C in [START_REF] Mundorf | Ets21c Governs Tissue Renewal, Stress Tolerance, and Aging in the Drosophila Intestine[END_REF].

Ets96B. Part of the same family as Ets21C, it is downregulated in Smurfs (log 2 FC of -1.2). It is mainly annotated as having a role in dopamine signaling in the nervous system.

Experimental plan

For each of the selected genes, depending on the availability of the transgenic line, I performed longevity experiments after knock-down (KD) and overexpression (OX) of the gene. The gene alteration was performed using the GeneSwitch (GS) [START_REF] Osterwalder | A conditional tissue-specific transgene expression system using inducible GAL4[END_REF][START_REF] Roman | P{Switch}, a system for spatial and temporal control of gene expression in Drosophila melanogaster[END_REF]. This technique, widely used as KD and OX system in the fruit fly, allows tuning spatially (through the use of tissue-specific promoters) and temporally (by controlling the system's activation with the RU486 drug) the manipulation of the gene of interest. Its mechanism is exemplified in figure 3.5. .5: GAL4 is a yeast TF binding the yeast promoter sequence called UAS. This system has been engineered to manipulate gene expression in Drosophila in a specific way, as GAL4 and UAS are not present in the genome of the fly. The TF GAL4 is cloned downstream of a promoter of interest. GAL4 is produced every time that the promoter is active. Therefore, the promoter will allow for spatial and temporal control of its expression -for instance, a promoter active in a particular tissue only during development -. GAL4 binds the UAS sequence as a dimer. In the GS system, GAL4 is engineered so that it can dimerize (and therefore be functional) only when the drug RU486 is present. Adding or removing the drug allows for temporal control of the gene alteration, switching on and off the system. When GAL4 is active, it activates the transcription of the gene downstream of the UAS sequence. If we want to overexpress a gene, its cDNA is cloned downstream of the UAS; if we want to knock it down, a double-strand RNA (dsRNA) against its sequence is cloned downstream the UAS.

As the fly research community widely uses the GS, it is likely to find the flies already engineered for the gene of interest in stock centres such as Bloomington, FlyORF or VRDC. In particular, we need two "types" of flies: the driver, carrying the GAL4 under the control of a chosen promoter, and the flies carrying the UAS part of the construct.

As the RNA-Seq data from which the list of genes to test was extracted are from the whole body of the flies, we chose to start our screening with a ubiquitous promoter, active both during development and adulthood. We used the daughterless-GS (daGS) line [START_REF] Tricoire | The steroid hormone receptor EcR finely modulates Drosophila lifespan during adulthood in a sex-specific manner[END_REF]. The information about the retrievement of each line is provided in Appendix B.2.

Once both the driver and the UAS line are retrieved, the preparation of the experiment follows the scheme in figure 3.6. Virgin driver females are crossed with males from the UAS line. It is essential to use virgin females in the cross. Indeed, Drosophila females have a spermatheca, an organ dedicated to storing the sperm after mating. A female can lay fertilized eggs up to more than two weeks after a mating event. Therefore, the use of virgin females is essential for controlling for the genetic background of the flies we want to use in the experiment.

The scheme in figure 3.6 represents an experimental set up in which the gene alteration's is induced the first day after eclosion. On day 1 of adulthood, flies are placed in vials containing the RU486 drug. We used 5 different drug concentrations, including the controls at 0 µg/mL. We then diluted the drug in the food at 10 µg/mL, 50 µg/mL, 100 µg/mL and 200 µg/mL (as done in the study describing the line [START_REF] Tricoire | The steroid hormone receptor EcR finely modulates Drosophila lifespan during adulthood in a sex-specific manner[END_REF]). Depending on the strength of the promoter and the correct function of the system (i.e. the GAL4GS is actually not binding in the absence of the drug), this will create a gradient of activation of the gene alteration, allowing to test from "light" alterations to stronger ones. Once the flies are exposed to the assigned drug condition, males and females are kept in the same vial to mate for 48 hours. As I discussed in the Introduction, in particular with the disposable soma theory of ageing, fertility and mating are debated to have a role on longevity. I therefore controlled for this variable possibly affecting lifespan by mating all the flies used for the experiment for the same amount of time. Subsequently, 150 females for each condition were sorted and divided into 5 vials of 30 flies each, for a total of 25 Figure 3.6: Exemplification of the experimental protocol followed for the experiment.

Virgin females from the driver daGS are crossed with males carrying the UAS construct. The F1 are the flies containing both transgenic constructs and will be used for the longevity experiment. At the moment of eclosion (day 1 of adulthood), flies are placed in new vials with different concentrations of drug and let mate for two days. After that, 150 females per condition are chosen for the longevity experiment and placed in 5 vials of 30 females each. On the other hand, if we want to alter the gene of interest since development, the cross will take place on vials with the drug. The rest of the protocol will not differ.

vials and 750 flies used per line tested.

Development and adulthood. The protocol illustrated allows the alteration of the gene only during adulthood. As a complementary check, I also monitored if differences in lifespan can be observed when the gene is altered during both development and lifespan. This implies two main changes in the workflow. First, parents of the F1 are mating on food already containing the drug (note that as these flies do not carry the complete GS system, the expression of their genes is not affected). [START_REF] Osterwalder | A conditional tissue-specific transgene expression system using inducible GAL4[END_REF] recommend not to use a concentration > 100 µg/mL during development, to avoid possible toxic effects. In order to reduce as much as possible the possibility of drugdependent toxicity, while keeping the same fold difference in the concentrations used, we divided by 10 the concentrations of drug used for the adults (concentrations already tested during development in Drosophila in [START_REF] Rera | Mitochondrial electron transport chain dysfunction during development does not extend lifespan in Drosophila melanogaster[END_REF]) . Therefore, during development flies are exposed to RU 0 µg/mL (control), 1 µg/mL, 5 µg/mL, 10 µg/mL and 20 µg/mL. On day 1 of adulthood, flies are placed in vials with the drug concentrations listed in the paragraph before.

In order to reduce the number of crosses to make, I used the same parents when testing for lifespan effect of alteration of the gene in adulthood only and development and adulthood. After mating for two days in food without drug, where the eggs will develop for the adulthood only (AO) experiment, the flies were placed in food with the reduced amount of drug for additional 48 hours. The parents will then be removed, and the eggs will develop with the alteration of the gene of interest already occurring. No other differences are present in the two experiments. From now on, I will call the experiment where the gene alteration is tested during both development and adulthood whole life experiment (WL).

Longevity experiment: the work load. As I stated before, for one experiment (i.e. five conditions of RU486 on one gene), I need to generate the final amount of 750 flies. It doubles to 1500 when we consider both AO and WL experiments. To generate the final number of 5 vials of 30 females each, I usually proceed on preparing 8 vials of crosses per concentration of drug (8 virgins females drivers with 5 UAS males each). So, for testing one gene in all the five conditions, I need to collect 320 virgins females and 200 UAS males. This requires some prior work of amplification of the single lines (UAS and drivers), and a consistent work of virgins collection and fly sorting for the experimental preparation. Once the experiment is launched, there is the need to score the vials every other day and prepare new food to replace them (with the same frequency as the scoring).

The 48 longevity experiments performed in the first screening (section 3.3) implies the use of 36,000 flies. As the management at the same time of the such a high amount of flies is not possible, I split the experiments in 3 rounds. Considering that a longevity experiment (preparation phase + following of the longevity) takes between 3 and 4 months, the first screening took around a year to be performed.

For more details regarding the lines used and the food preparation, refer to Appendix A.1.

First screening

Results

I carried a first round of screening on the selected genes, OX and KD in most cases, for total amount of 48 longevity experiments (1 longevity experiment = the monitoring of 5 populations of the same genetic background exposed to the 5 RU486 conditions). Results are summarized in a more qualitative way in figure 3.7. For more details on the data, I refer to table B.10 in Appendix A.2.

As shown in figure 3.7, most of the experiments result in a negative effect on lifespan (blue points). However, there are lines showing an increased lifespan due to the gene alteration. In particular, the genes selected thanks to i-cisTarget show positive results in multiple cases. Before moving to that, I would like to spend a few words concerning Aef1 that, as seen in figure 3.7, causes a significant decrease in lifespan when downregulated. Figure 3.8 represents the five longevity curves of the experiment. The gradient in the gene downregulation causes a gradient in the decrease in lifespan in the flies. This is interesting, as in other cases (as in figure 3.8), we observe the KD causing a sharp decrease in lifespan, suggesting that the intervention is toxic for the

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Genes regulating genes down in Smurf

Figure 3.7: Summary of the results on the longevity screen carried out on the genes listed in figure 3.4. For each gene alteration, I have the AO and the WL experiment, and 5 RU486 conditions. The controls are not present as they are the reference for the statistical test (log-rank). The size of the point indicates the significance of the difference in the longevity curves of the population, while the colour indicates the direction of the change -decrease or increase in mean lifespan. In most cases, even if we detect a significant difference, this is due to the gene alteration negatively affecting the population's lifespan. Interestingly, most of the red hits are present in the group of the genes found by i-cisTarget as putative regulators of TFs up in Smurfs.

flies. We wondered if, by overexpressing Aef1, we could have seen the opposite trend. Unfortunately, an UAS-Aef1 was not already available. We initially thought of creating the line from scratch. Unfortunately, given the impact on the project of the Covid-19 pandemic and other practical issues that delayed experiments (see section 4.2), we had to postpone this idea, which we could not finally realize. However, it could be interesting to further investigate more on this gene in the future. We initially considered validating all the positive results by re-running the longevity experiments. Once again, we had to lower the number of experiments to perform for practical reasons. We then focused our attention on the genes reported in figure 3.9. We did not proceed further with the results of dmrt93B KD, Hey KD, rib KD, FoxP, NF-yB OX, and srp KD. For dmrt93B, rib, NF-yB and srp it is mostly linked to the fact that the hits were isolated (the other drug conditions were killing the flies or showing no effect at all) or not relevant (an increase in lifespan of 3%, even if significant, is less reliable and possibly given by the condition in which the given experiment is carried). Concerning Hey KD, it was showing consistent results when downregulated during the WL. However, we decided to exclude it as we preferred to focus on genes showing an effect when also altered during AO. However, as for Aef1, Hey could be a possible target for further investigation. The same reasoning applies for FoxP. In addition, the lifespan of the controls in the WL is ∼ 6% lower than in the AO, suggesting that the lifespan increase of ∼ 5.5% detected for its KD during the WL could be an artifact.

Genes selected for validation

We therefore finally selected for further validation the genes shown in figure 3.9. The longevity curves on the right of the table refer to the experiment carried out on the first screening, showing in black the controls and in red the population exposed to the RU486 condition reported in the table. Figure 3.9: Summary table showing the genetic alterations selected for final validation at the end of the first round of experiments. In the figure, I show the longevity curve for the RU condition presenting the best results (the condition is reported on the RU column). In the survival curves, the controls are black, while the treated population is red. Note that, even if only one condition is reported here, I then validated the experiments on all the RU. The genes selected in red are the ones that, after the validation round, still showed an effect on longevity when knocked-down. I will presented them in section 3.4.

Trl, CG4360 and Adf1 showed anyway a consistent trend along RU486 concentrations, as seen in figure 3.7. Ets96B is interesting as it shows the increase in lifespan only for the RU200 condition, in both AO and WL experiments. In the figure, I reported the one resulting from the AO experiment, but the WL experiment curve shows a similar trend. Hsf OX shows the best increase in lifespan in the whole set of experiments (∼ 10%). However, I encountered here a similar problem to the one before presented for FoxP : the WL controls live around 11% less than the AO control. The observed increase could therefore be an artifact. Nevertheless, since it was the best result and the gene was interestingly from a biological point of view, given the Hsp overexpression signal found in Smurfs, I decided to re-perform the experiment.

Following the same protocol, I performed again the longevity experiments for all these 5 genes in females. I could not see again an effect on longevity for Ets96B and Hsf. Hsf OX (second round) simply showed no effect on lifespan, while Ets96 KD (second round) was toxic. In order to exclude the hypothesis of a problem related to the specific experiment, I performed the longevity on Ets96B a third time, obtaining the same results. I therefore left this gene on the side. The cases of these two genes exemplify the importance of performing the longevity experiments more than once, even if they are long and timeconsuming.

I will not present the results for Ets96B and Hsf. I will focus instead on the results for the validation of Trl, CG4360 and Adf1.

Validation experiments

The validation experiments were performed following the same protocol as in figure 3.2. In addition, I also recorded the evolution of the Smurf proportion with time in the population. Indeed, as suggested before, if the gene alteration causes a lifespan increase, this could translate to a delayed appearance of Smurfs in the population. For more details on the Smurf recording protocol, see Appendix A.2. I divided the results into three sections, one for each gene investigated. I also performed the experiments in males, and I will briefly describe these results in section 3.4.4.

Adf1

Adf1 KD is confirmed to affect lifespan, even if this time I observed it with the RU 50 µg/mL (+10.7%) instead than with the RU 10 µg/mL as the experiment before.

In figure 3.10, I show the results for the RU concentration extending the lifespan, as well as the proportion of Smurfs recorded overtime on the same flies on which the longevity was performed. Smurf proportion data are analyzed by fitting the following linear model:

Smurf s ∼ time + dose + time * dose + ϵ (3.1)
where Smurf s is the proportion of Smurfs recorded, time the time interval over which we are fitting the model, and dose is the RU486 concentration, and ϵ the noise (i.e. what of the dependent variable Smurf s cannot be explained by the explanatory variables). The final term consists in the interaction between the time and the dose of drug, and its significance (F-test) proves that the drug concentration has an effect on the Smurf proportion increase.

By looking at the survival curve (figure 3.10a) we observe that the population exposed to RU 50 µg/mL (in red) exits the survival plateau later, while it shows a faster decay once the individuals start to die. The control and the treated population are converging at the end. In figure (figure 3.10b) I report the increase in Smurf proportion in the two populations, as well as the number of alive flies on which the proportion is computed. Both the population show a significant time-dependent increase in the proportion of Smurfs. However, the treated population shows a significant slower increase of Smurf proportion. This confirms that the lifespan extension delays the entrance in the Smurf phase, prolonging phase 1 of life. As I did not monitor the lifespan of the Smurfs, I cannot affirm that the gene alteration does not affect its length.

I could not detect an increase in lifespan for the other RU concentrations. The quantification of the KD gene through qPCR was performed but resulted in non-interpretable outcome due to technical issues, and will have to be repeated. Each point represents the proportion of Smurfs in an independent vial. The fitted linear model results in a significant coefficient for the time variable (p-value < 0.0001), proving that the proportion of Smurfs is time-dependent. The interaction coefficient between time and drug dose is also significant (p-value < 0.05), proving that the drug dose significantly affect the time-dependence of the Smurf proportion. The graph at the bottom represents the number of alive flies at the moment of the Smurf counting.

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Trl

Trl also confirms its positive effect on lifespan. In figure 3.11, I report the results for the RU 50 µg/mL, which shows the highest effect in this experiment (+ 10.5%). The control curve looks in this case less "clean", with a light drop around day 50. However, even after, flies continue to die faster in the controls than in the treated population, hinting that the lifespan extension observed is not due to unhealthy controls. The Smurf recording mimics the behavior of the survival curve, with the proportion of Smurfs in the control increasing faster than in the treated flies. As for Adf1, we can therefore conclude that the treated population spends on average longer time in the non-Smurf phase. The quantification of the KD for the RU 50 µg/mL concentration is reported in figure B.7 (Appendix B.2).

The WL experiment proved a significant lifespan increase for for RU 100 µg/mL and RU 200 µg/mL. However, shortly after day 75 the control curve shows a sharp drop which corresponds to a single day of scoring in the data. As the curves are quite close, it might be that such a drop is exacerbating the lifespan extension observed. Even if the log-rank test is significant, I would therefore be careful in affirming that in this case we are looking at a real lifespan extension. Data are reported in figure B.9 (Appendix B.2). As explained before, the RNA-Seq study presented identified a female-Smurf transcriptional signature. We therefore experimentally tested the genes presented in this chapter in females. However, for the ones showing a longevity effect, we wanted to address if the effect was conserved in males. I performed and completed the experiments for Trl and CG4360 in daGS males. The experiment for Adf1 is ongoing, together with a third validation on daGS females. Its estimated end is mid-April 2022.

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In figure 3.15 I show the results for the completed experiment. The T 50 (dashed line in the figures) of the lifespan in males does not differ much from the ones of the females of the same line (precedent figures for Trl and CG4360 ).

All the longevity curves on males show, on average, a similar shape. They present a faster exit from the survival plateau than the females' ones. However, this has no significant effect on the mean lifespan of the flies when comparing males and females (data not shown). Indeed, by looking at the curves, the flies exit the survival plateau faster but decay slower than the females.

The WL experiment for Trl showed a significant lifespan increase for RU 10 µg/mL (log-rank p-value 0.02). The effect translates to an increase in mean lifespan of 6%, probably due to the initial rectangularization of the curve. However, the two curves do not show a clear separation and remind more of what was observed in figure 3.14 for the KD during adulthood of CG4360. I would therefore not claim a lifespan extension in this case.

The fact that we do not detect an increase in lifespan in males does not invalidate the results shown in females. However, it highlights the issue of physiological sexual dimorphism of Drosophila, which has been recently more investigated. Males and females indeed present differences even in processes not intuitively sex-related, such as the immune response [START_REF] Belmonte | Sexual Dimorphisms in Innate Immunity and Responses to Infection in Drosophila melanogaster[END_REF], and they can respond differently to lifespan-extending treatment (Garratt, 2020; [START_REF] Regan | Sex difference in pathology of the ageing gut mediates the greater response of female lifespan to dietary restriction[END_REF]. Furthermore, even if the Smurf phenotype was proven to exist in males, most of the studies conducted so far for its characterization were performed on females. Therefore, we cannot exclude that the transcriptional signature identified in Smurf females does not entirely reflect the transcriptional signature of Smurf males. The type of experiment (AO or WL) is specified below each graph. Note that the RU200 in the CG4360 WL experiment is missing, due to a problem in the growth of the flies during the experiment preparation. None of the population show lifespan increase but for the Trl KD in the WL experiment. The extension is however low, and even if significant it might not be relevant.

Complementary experiments: preliminary data

The experiments performed in section 3.4 confirmed the effect of the KD of Adf1, Trl and CG4360 on lifespan.

Investigating the mechanism through which those genes act could represent a whole research project. We decided to proceed with preliminary experiments to investigate those genes and narrow their possible range of action on longevity.

The proposed experiments are the following:

1. Tissue-specific KD by using the same UAS line and a different driver;

2. test the resistance of flies to different kinds of stress, to check if the KD helps the individuals to cope with a stressful event. In particular, we chose heat shock, starvation and oxidative stress;

3. monitor if the KD affects the fertility of the flies, to check if the lifespan extension could be a case of a trade-off between energy investment in reproduction and energy investment in its maintenance, as suggested by the disposable soma theory.

In the following sections I will present the results obtained so far in this context. I do not have yet any additional result for Adf1 yet, as chronologically was the last gene for which I confirmed the lifespan extension, meanwhile additional experiments for CG4360 and Trl were already ongoing. However, tissue-specific experiments are ongoing for Adf1. The estimated end is mid-April 2022.

Tissue specific GS experiments

Tissue-specific longevity experiments have been completed so far only for Trl. We performed the experiments with four different drivers targeting different cell types/systems. Below I report the information for each driver and the associated experiments. All the experiments were performed on the AO and WL settings.

Ti GS. It is a pan-gut driver, i.e. expressed in all the different cell types of the gut. The AO experiment shows a significant lifespan extension, as shown in figure 3.16a (+8% for RU 200 µg/mL). Unfortunately, the WL experiment does not present a clean curve for the controls. The results are therefore not interpretable.

5961 GS. Given the positive result for the Ti GS experiment, we decided to look more specifically at the level of only certain cell types. This driver targets the gut's intestinal stem cells (ISCs) and enteroendocrine (EE) cells. It does not drive in the enterocytes (EC). The results could hint for a lifespan extension in the AO experiment, RU 100 µg/mL (the +6% showed in figure 3.16b) is significant to the log-rank test. However, the two curves are close and I would therefore not be confident in claiming a lifespan extension in this case. The WL experiment presented the same problem for the controls as in Ti GS.

elav GS. It is the most commonly used GS driver for targeting neurons. In particular, we used the driver version kindly provided by Tricoire laboratory (UMR 8521), which had mobilized the construct to enhance its expression. elav GS showed toxicity during development, with only the RU 1µg/mL having flies developing in sufficient number to perform the experiments. However, the treated flies died faster during adulthood. This must be an aftermath of the developmental toxicity, as such an effect was not seen in the AO experiment, where the lifespan of the treated flies was not altered (data not shown).

repoGS. It is the most commonly used GS driver for targeting glial cells. Flies subjected to Trl KD driven by repo were looking unhealthy since early age. Moving slower in the vials and flying with more difficulty, they were extremely slow in regaining motility after tapping during vial transfer. All the RU concentrations, temporally starting from 200 µg/mL, showed such behaviour and eventually died. However, also the controls started to look sick and eventually die (mean lifespan ∼ 60 days, lower than the ones observed in all the other longevity experiments). We therefore hypothesized that the toxic effect was line-related rather than coming from the KD of the gene. Although the RU 100 µg/mL effect is significant, it might not be relevant and mostly caused by the initial difference in the two curves.

Control experiments for drivers' toxicity

Given the issue experienced with the repoGS, we decided to control for possible toxic effects of the drivers themselves on longevity and development. [START_REF] Robles-Murguia | Tissue-specific alteration of gene expression and function by RU486 and the GeneSwitch system[END_REF] have shown that the RU486 can affect the flies gene expression and physiology in a line-dependent manner. The results observed could therefore be driver specific rather than biologically relevant. To check for this, we crossed the drivers with UAS lines altering genes supposedly not affecting longevity or development.

By following the same procedure used for all the experiments presented (figure 3.6), we crossed all the drivers used in our experimental set with two different UAS lines. The first UAS line is mediating white KD, which should not have any effect on longevity. The second UAS line is driving the expression of lacZ, a gene not present in eukaryotes and which expression should therefore not alter the fly physiology.

Interestingly, repoGS showed, for both the UAS lines, the same phenotype as in the Trl experiment -toxicity during development and adulthood. Therefore, the results of the previous experiments are an artefact and do not have any biological meaning regarding the gene of interest. The same applies to elav GS, which resulted toxic during development for both the control lines.

Concerning Ti GS, no significant changes in lifespan or development were detected in the control experiments (data not shown).

5961 GS and daGS showed, for some RU concentrations, an initial drop in the longevity curve, with a subsequent plateau. However, this is likely due to a problem of the specific cross or experiment. Indeed, no such effect was seen consistently in the numerous daGS experiments performed, and in none of the 5961 GS experiments. This therefore suggests that, as in the case of Ti GS, the driver itself, in presence of the drug, is not altering lifespan. Nevertheless, the control experiments for daGS and 5961 GS should be repeated to obtain statistically valid results.

Fertility and heat shock assay

The protocol chosen for such assays is explained in Appendix A.2. Unfortunately, both the assays did not output explainable results due to technical problems.

Regarding the fertility assay, I monitored it as the number of eggs laid per female in a vial. We approximated the number of eggs by the count of the pupae after development. However, some vials experienced growth issues and dry medium -even with daily manual watering. Therefore, the experiments will need to be performed a second time to obtain interpretable results.

Smurf flies present a strong upregulation of the heat shock proteins. We were wondering therefore if the KD of the genes of interest was somehow affecting such response. The heat shock assay was performed, as explained in Appendix A.2, at in an incubator at 37°C degrees, on day 5 of life. From the beginning to the end of the assay, the flies were scored every hour. The protocol was built based on previous experiments run by the laboratory. In the ideal setting, the flies needed to be followed up to the death of the whole population. Unfortunately, given the fixed opening hours of the institute (shortened during the time of Covid-19 emergency), it was not possible to complete the experiment. On average, half of the flies were still alive for some of the RU concentrations in the evening. However, they were all dead the morning after, suggesting the experiment with such protocol needs to be carried out over 24 hours.

In addition, there was a strong effect of the position of the vial on the death of the flies. Flies in vials positioned in the outer part of the rack were experiencing a higher death rate than the ones positioned internally. As they were ordered by concentration, some were strongly affected and it was impossible to draw biological conclusions from the data. I would therefore change the protocol of the assay for the next experiments. Flies could be kept at high temperature for a given amount of time, and monitor the mortality after, as done by [START_REF] Moghadam | Heat hardening capacity in Drosophila melanogaster is life stage-specific and juveniles show the highest plasticity[END_REF]. In order to try to minimize the vial specific effect, vials can be placed individually in small racks (or equally separated in bigger racks) in the incubator or a water bath [START_REF] Moghadam | Heat hardening capacity in Drosophila melanogaster is life stage-specific and juveniles show the highest plasticity[END_REF].

Starvation assay

Starvation resistance can be associated to increased energy and fat stores in the organism. Indeed, the resistance to starvation shows in Drosophila a dependency from the sex and the diet to which the flies have been exposed prior to the assay [START_REF] Chandegra | Sexually dimorphic effects of dietary sugar on lifespan, feeding and starvation resistance in Drosophila[END_REF]. As in Smurfs we observed a downregulation of fatty acid anabolism, and reduced fat storages were already shown in Smurfs compared to non-Smurfs [START_REF] Rera | Intestinal barrier dysfunction links metabolic and inflammatory markers of aging to death in Drosophila[END_REF], we included the starvation amongst the stress assays to perform. I performed the assay twice in order to set the final protocol explained in Appendix A.2. On day 7 of life, flies were switched from the longevity food to 1% agarose. Flies were scored three times a day (morning, mid-day, afternoon) up to death.

The results of the final experiment for Trl and CG4360 KD are shown in figure 3.17.

The only case in which we can detect an increase in lifespan is the CG4360 KD over the whole life of the flies. In this case, we have an increase of survival up to +15% (figure 3.18). Interestingly, the WL experiment is also the one for which CG4360 showed lifespan extension.

Concerning the CG4360 AO, the treated conditions live less than the controls. However, the treated conditions present less clean curves at the beginning and the results, even if significant, need to be interpreted carefully (figure 3.18).

• • • • • RU0 RU10 RU50 RU100 RU200
Figure 3.17: Longevity curves for the starvation experiments. The experiment corresponding to the curve is reported below the graph. Time is reported in hours; time 0 is the time at which the starvation assay starts. For some curves, we can observe a initial drop for some of the drug concentrations, due to deaths overnight before the chance to score them regularly. CG4360 KD over whole life shows increased lifespan for the treated conditions. 3.17. Mean lifespan is reported in hours after the start of the starvation assay. There is no difference in the lifespan of the controls. In the WL experiment, the treated flies experience an increase in lifespan up to 15%.

For Trl the two experiments show similar results, with the curves overlapping but for RU 10 µg/mL. This concentration presents a starting drop that is probably experiment-specific rather than biologically relevant.

The fact that we observe an increased survival to starvation stress does not imply that is the mechanism through which CG4360 extends lifespan in physiological condition. However, in the light of such results it could be relevant to check for an age-dependent and Smurf-dependent evolution of energy stores in the CG4360 KD line compared to the controls.

Oxidative stress assay

As explained in the Introduction, mitochondrial dysfunction is a hallmark of ageing. For a long time, it has been thought to be the main cause of ageingrelated damage (ROS theory). In Smurfs, we indeed observe a strong decrease in the expression of genes involved in mitochondrial metabolism, suggesting a decrease in mitochondrial efficiency. On the other hand, there is upregulation of Gst genes, which are used for detoxification. Furthermore, the UPR detected in the Smurfs could also hint at a condition of oxidative stress. We therefore included the oxidative stress in the assays to perform, to check if the KD in the genes extending lifespan was helping the flies cope with such condition.

H 2 0 2 at four different concentrations (0% -control-, 0.5%, 1%, 2%) was used to test the resistance of the flies to oxidative stress.

For each concentration, the five usual RU486 concentrations were tested. As the number of flies to generate for one experiment became consistent, I lowered the N from the usual 150 per concentration used in the previous experiments (5 vials, 30 females each) to 120 (4 vials, 30 females each). For more details concerning the protocol and the food preparation, see Appendix A.2. The experiment has been performed so far only in the AO setting. Trl, while a significant positive effect is found for CG4360 all along the RU concentrations. However, the RU controls were in this case showing a initial drop that could influence the results. H 2 0 2 1% show lifespan extension for both the KD, with an extension of 30% for the RU 10 µg/mL of Trl KD. H 2 0 2 2% is instead toxic in all cases, with no distinctions across the RU concentrations. Indeed, even if some significant changes are found, they are not relevant from the point of view of the effect. Interestingly, it seems that both Trl and CG4360 KD confer resistance to oxidative stress for 0.5% and 1% H 2 0 2 concentrations. It would be interesting to see, especially for CG4360, if the same happens when the KD occurs during the whole life of the fly. Indeed, in the case of Trl the conferred resistance to oxidative stress is associated with a lifespan extension (AO experiment, figure 3.11). In the case of CG4360 instead, it does not match with the longevity results observed so far (AO experiments, figure 3.14).

As discussed for the starvation results, the fact that the KD of the investigated genes improves resistance to oxidative stress, does not automatically prove that this is the mechanism through which lifespan is extended in physiological condition. It could be however relevant to check for oxidative markers (as protein glycation, lipid oxidation and DNA damage) over time and with Smurfs for the CG4360 and Trl KD.

Validation using independent transgenic lines

The experiments reported were carried on specific lines bought by fly stocks such as Bloomington or VRDC (see Appendix A.2). We decided to perform the longevity experiments on independent UAS lines targeting the same genes as an additional check. We proceeded by buying at least one additional UAS line for the genes showing lifespan increase. The information about such lines are reported in appendix A. For CG4360 only one additional line was found (VRDC stock number 10500). However, it had a reported off-target (CG42277, gene symbol rn). The experiment resulted in toxicity even for the control flies (data not shown). This led us to think that the cross with daGS is probably somehow toxic, as it happened with repoGS.

In the case of the Adf1, I performed the validation on two independent Bloomington lines (line showed the initial effect was bought from VRDC). While for one I could not detect lifespan alteration, for the other I could detect a significant lifespan extension (+11%) for the RU 10 µg/mL in the WL experiment (while for the AO there was no detectable lifespan alteration). The results are shown figure 3.22, point b.

I tested another Trl KD GS line. The line showed a strong toxicity during development, with flies not growing in food containing drug. Surprisingly, also the AO experiment showed a quite strong toxicity, with a reduction in lifespan of 20%. Results are shown in figure 3.22, point a. I performed the experiment a second time, obtaining the same results. Since the control have a clean longevity curve, the toxicity of the treated samples must be due to: 1) the Trl KD itself, which might invalidate the previous experiments; 2) the activation of the GS itself, with for instance no-reported off targets.

I made the hypothesis of a stronger KD mediated by the second line, which I will for simplicity refer to as line 2. I quantified the KD of Trl in line 2, but it did not show a stronger effect than line 1. However, the RU 100 µg/mL presented no difference compared to the controls, suggesting that there might be a problem in the analysis (either at the level of the RNA used for the retrotranscription or the qPCR preparation). Therefore, flies of line 2 will have to be regenerated and the quantification re-performed.

As the effect is line-specific, I read more in details the information concerning the lines and the vectors used for their construction (TRiP project, see A.2). The sequence targeted by the two dsRNAs differs. Line 1, showing the effect, targets exon 2; line 2, which shows toxicity, targets exon 4. However, both those exons are always retained in the different isoforms of the gene, suggesting that the different effect does not come from a isoform-specific KD. Trl presents an ovelapping ORF (CG42507 ). This gene, not characterized, should be equally targeted by the two dsRNAs given its position in the genome.

However, line 1 (Bloomington 41582) uses the vector "VALIUM 22", while line 2 (Bloomingotn 67265) uses the "VALIUM 20" vector. As reported in the TRiP project website (see Appendix B.2 for the link to the online page) concerning the RNAi lines generation, the "VALIUM 22" should drive a stronger expression in the germline and a light expression on the soma, and viceversa for the "VALIUM 20". The results of the qPCR show how the KD is however detectable for the line 41582 (whole flies). Furthermore, a lifespan extension for the same line is detected in Ti GS, which suggests that the KD is active at the level of the gut. Unfortunately, the qPCR performed on the gut samples to confirm the KD of Trl presented technical problems and will therefore have to be repeated.

In order to investigate the reason behind the line-specific difference, I would follow the following steps:

1. perform the qPCR on ovaries only for line 1. In parallel, perform the qPCR on other body-parts (as the head, for an approximation of the nervous system). Detecting the KD in the heads would prove that the line is driving not only in the germline;

2. validate the KD for Ti GS gut;

3. re-perform the qPCR on the line 2, given the unexpected results for RU 100 µg/mL, which suggest a technical problem. In addition, perform the quantification in the ovaries only, to compare the signal found in line 1. If there is not detectable difference in the whole body signal between the two lines, but there is in the ovaries, it could be that the effect is due to the germline specificity of the vector. If on the other hand a stronger KD is detected for line 2 in the whole body, a model in which a light KD of Trl is pro-longevity, while a stronger one is toxic, can be proposed. A way to finally confirm this would be to decrease the RU concentrations used for the experiment in line 2, in order to drive a KD of similar strenght compared to line 1.

Conclusions

In this chapter, I have described the experiments aimed at identifying new genes affecting lifespan using the Smurf phenotype. Those experiments were carried out during my PhD's second and third years. Part of the complementary experiments, aiming at further investigating the genes having a positive effect on lifespan, are still ongoing.

The starting point of this part of my project is the DEG analysis presented in section 2.2.4. Given the presence of numerous genes annotated as affecting longevity amongst the Smurf genes (figure 3.1 and table B.8), we decided to screen for new genes affecting longevity amongst the ones detected as DEGs in Smurfs. We chose to focus on TFs, as amongst them may lie genes responsible for the signature observed. Starting from a selection at the level of the TFs detected as DEGs and their putative upstream regulators (through the software i-cisTarget), we selected 17 genes to test. Then, through GS, I performed longevity experiments to assess the effect on the longevity of their KD or OX.

The first screening resulted in five genes putatively extending lifespan (figure 3.7). Amongst the ones not extending lifespan, Aef1 showed interesting results, with its KD at different levels showing a gradient effect on the longevity of the flies (figure 3.8). This hints at a putative role of this gene in shaping longevity. Unfortunately, the OX line was not available yet and, therefore, I could not carry the complementary OX experiment. However, it will be interesting to do so in the future and further investigate this gene.

I carried a second round of experiments to validate the results observed in the first screening, confirming the effect for three of the five genes initially identified (figure 3.4): Trl, CG4360 and Adf1. This proves that we can identify new genes affecting longevity by looking at the Smurf phenotype.

For all the genes showing an effect on lifespan, I recorded the change in Smurf proportion over time. For all the experiments performed ((figures 3.10, 3.11, 3.12, 3.13, 3.14), I confirmed the time-dependence increase of the Smurf proportion in a population. Interestingly, the lifespan extension (figures 3.10, 3.11, 3.12, 3.13) is accompanied by a slower increase of Smurfs in the population, proving that the lifespan extension corresponds to an increase in length of the non-Smurf phase in the flies.

The KD of Trl and CG4360 in males do not show any effect on lifespan. Longevity experiment on Adf1 KD in males are ongoing.

To further investigate the three genes showing a positive effect, we planned a series of complementary experiments. More specifically: 1) performing tissue-specific KD using tissue-specific GS drivers; 2) performing longevity experiments under stress conditions (starvation, oxidative stress and heat shock) to test if the KD of the gene of interest increases the flies' resistance; 3) monitor the fertility of the flies, to check if the increase in lifespan is accompanied by alteration in fertility.

Only part of these experiments has been completed. Amongst the ones performed, the only positive hit is given by lifespan extension of Trl KD in the gut. Tissue-specific experiments for Adf1 are ongoing. The heat shock and fertility assays will have to be carried out again, as the experiments did not return explainable results.

CG4360 KD confers resistance to starvation in the WL experiment, with an extension in lifespan up to +15%. CG4360 KD also confers resistance to oxidative stress, with treated flies living longer than controls when exposed to food at H 2 O 2 0.5% and 1%. Interestingly, also Trl KD, which does not show any effect for the starvation assay, confers resistance to oxidative stress. Flies with Trl KD live 30% more than the controls when exposed to food at 0.5% H 2 O 2 .

The exploratory experiments conducted suggest that Trl and CG4360 have a role in stress resistance (starvation and oxidative stress). However, additional experiments will be required to clarify the role of these genes in establishing lifespan extension. The Smurf phenotype is assessed through the loss of selective intestinal permeability in the organism [START_REF] Rera | Intestinal barrier dysfunction links metabolic and inflammatory markers of aging to death in Drosophila[END_REF]. In the studies conducted so far, flies have been shown to always turn Smurf prior to death and to spend around a constant amount of time in such phase [START_REF] Rera | Intestinal barrier dysfunction links metabolic and inflammatory markers of aging to death in Drosophila[END_REF][START_REF] Tricoire | A New, Discontinuous 2 Phases of Aging Model: Lessons from Drosophila melanogaster[END_REF]. The proportion of Smurf flies in a synchronous population increases with time [START_REF] Rera | Intestinal barrier dysfunction links metabolic and inflammatory markers of aging to death in Drosophila[END_REF]. Therefore, individuals dying off early in a population will turn Smurf faster and spend less time as non-Smurfs than those who die later. Furthermore, it has been shown that Smurfs are the only ones in the population, independently of their age, to carry some markers that have been previously associated with ageing [START_REF] Rera | Intestinal barrier dysfunction links metabolic and inflammatory markers of aging to death in Drosophila[END_REF][START_REF] Rera | The Smurf transition: new insights on ageing from end-of-life studies in animal models[END_REF]. Therefore, it has been proposed that ageing could be reinterpreted as a biphasic path instead of a progressive one [START_REF] Tricoire | A New, Discontinuous 2 Phases of Aging Model: Lessons from Drosophila melanogaster[END_REF]. Individuals would then undergo two different phases in their life. In phase 1 (non-Smurf) the organism is seemingly healthy and experiences a null mortality rate. However, with time their probability of transition to phase 2 increases. In phase 2 (Smurf), the individuals experience health degradation and decreased functionality that is typically described as ageing, together with a high risk of impending death [START_REF] López-Otín | The Hallmarks of Aging[END_REF].

If this model holds, then the ability to identify Smurfs in a population would allow for identifying physiologically old individuals, independently of the age, in vivo and in a non-invasive way.

In order to validate the model and its implication in ageing, I investigated the changes occurring in the transcriptome in Smurfs at different ages.

Smurfs: a biphasic core signal summarizing ageing

The results presented in my work validated the Smurfs (transcriptomewide) as a powerful model to identify biologically old individuals in vivo. Indeed, Smurfs present a core gene expression signal that define them as such in comparison to the non-Smurfs and independently of their age (sections 2.2.4, 2.2.5 and figures 2.6, 2.7, 2.8). Furthermore, these Smurf specific changes summarize more than half of the transcriptional hallmarks of ageing (Frenk and Houseley, 2018) (figure 2.16). These founds demonstrated that most of what has been so far described as ageing-related signal, is a Smurf specific signal.

Inflammation and stress response (UPR, together with oxidative stress suggested by the Gst family activation) are found as consistently upregulated in Smurfs on the different analyses performed. On the other hand, Smurfs present a broad downregulation of metabolic pathways, especially mitochondrial-related, and genes involved in oogenesis. Also, downregulation of ribosome-related genes is detected.

When I compare old non-Smurfs (sampled at 10% survival) to young non-Smurfs (90% survival), 3% of the genes result as DEGs, in opposition to the 20% detected with Smurfness. Those genes are mainly connected to increased inflammation and a decrease in fertility. These results show that the old non-Smurf one is not an ageing transcriptome. Therefore, Smurfness establishes itself, transcriptome-wide, a better predictor of ageing than chronological age.

Effect of chronological age per se

However, it would be too simplistic to confine ageing to a binary vision of life. Indeed, the mere fact that time increases the probability of transitioning to Smurf is a confirmation of its effects on individuals.

To investigate the effect of time on my samples, I proceeded with a timecourse comparison within conditions (non-Smurfs and Smurfs). As mentioned in the previous section, the old non-Smurfs carry a partial Smurf signal (increased inflammation and reduced fertility). However, the old Smurfs carry a strong signal not observed in the previous analysis. That can be summarized in a decreased expression for genes involved in DNA replication and repair and RNA processing and transcription. The latter is one of the hallmarks of ageing that was not detected in Smurfs 2.3.3. By looking at how the genes involved in such processes correlate with age and Smurfness, I showed that they negatively correlate with age (section 2.5 and figure 2.28). This suggests that we are looking at an age-related signal rather than an old Smurf one. The fact that DESeq2 detects it only in old Smurfs might be due to a possible exacerbation of these changes after the old flies turn Smurfs.

Another transcriptional hallmark of ageing, falling in the same category as RNA processing, is a general misregulation of transcription, provoking an increase in heterogeneity in gene expression. In section 2.2.8 I show that, although we can also detect Smurf/non-Smurf differences, the noise associated with gene expression increases with age for both conditions. This result is probably not independent of the fact that I observe a decrease in transcription-related genes: a malfunction in these pathways might generate variability in gene expression. This could also be one of the mechanisms through which the probability of turning Smurf increaes over time. The increased hetereogeneity in gene expression could also be a concomitant mechanism in explaining the increased probability of entering the Smurf phase with time, as I speculated in section 2.2.9. By using Smurfness as biomarker of physiological age, we can have a tool to deconvolve markers associate to the biological age of the organism and markers associated to its chronological age. In the literature, this signal might be mixed, as for what shown with the transcriptional hallmarks of ageing [START_REF] Frenk | Gene expression hallmarks of cellular ageing[END_REF] (see section 2.2.9).

My results point at the model proposed in 4.1, where short-lived and longlived individuals present the same physiological ageing hallmarks, with the difference of experiencing them at different rates. However, they differ in the chronological ageing ones, not experienced only by long-lived individuals in their last phase of life.

As a continuation of the study, it would be interesting to experimentally validate such results. To start, I would confirm by qPCR the decrease of expression of some of these putative age-only related genes in Smurfs and non-Smurfs (possibly mimicking the same time points of the current experiment, i.e. 90%, 50% and 10% survival). Suitable targets for this validation could be the genes showing the best negative correlation with age in figure 2.28. The experimental validation of these results would add evidence to the model proposed here (figure 4.1). Non-Smurf and Smurf represents two distinct phases in the life of the organism. The non-Smurf is a seemingly healthy phase, where individuals do not present ageing-related gene expression changes. However, before turning Smurfs, flies experience one of the transcriptional hallmarks of ageing, the disregulation of immune genes. This brings the flies in a "susceptible" phase, or pre-Smurf phase. The transition to Smurf marks the beginning of phase 2 of life, characterized by the degradation of the body's health and a high risk of death. From a transcriptional point of view, individuals show many of the markers associated with ageing (downregulation of mitochondrial metabolism genes, downregulation of genes involved in translation, deregulation of immune response genes and upregulation of genes involved in the response to different types of stress). The presence of such changes is independent from the time at which the transition happen, as they are related to the physiological time of the organism. Short-lived individuals (i.e. dying before the mean lifespan of the population) will have a shorter non-Smurf phase compare to long-lived individuals (i.e. dying after the mean lifespan of the population). The long-lived individuals will have a "slower" physiological time compared to the short living individuals. However, at old ages they will experience time related changes not-observed in the short living individuals and that accounts for an hallmark of transcriptional ageing that is not otherwise found in Smurfs: increased noise in gene expression and RNA processing.

Studying the pre-Smurfs

The Smurf phenotype is an late stage of life, where an individual is committed to death within a specific time frame (on average 2 days in flies, [START_REF] Tricoire | A New, Discontinuous 2 Phases of Aging Model: Lessons from Drosophila melanogaster[END_REF]). The fact that, at least from a transcriptional point of view, Smurfs summarize much of what has been described as the markers of ageing, may suggest that the phenotype represents a critical confounding factor in ageing studies. Indeed, if we randomly sample from a population over time, we will sample, on average, an increasing number of non-Smurfs. Older time points would therefore experience a Smurf bias in the sampling. In this case, the progressive increase in the ageing markers would be actually due to an increase in the sampling of Smurf individuals carrying them.

Smurfness being a late phase of life, the changes we identify studying it are probably a mixture of causes and effects of ageing. Interventions extending lifespan delay the entrance in this phase, prolonging the non-Smurf phase instead (results presented in 3.4). This suggests that the Smurf phase might not be a plastic phase of ageing, but rather a non-returning one that commits the organism to death.

As I discussed in section 2.4, the ability to identify flies before they turn Smurf could give us powerful insights on the early mechanism of ageing. My results show how inflammation is probably one of the pre-Smurf processes. This support the inflammageing hypothesis [START_REF] Franceschi | Inflammaging. An evolutionary perspective on immunosenescence[END_REF] as illustrated in figure 1.4, according to which inflammation would be the first hit of ageing, leading to entering a susceptibility state before a second hit brings to the unhealthy stage of life. The second hit could be reinterpreted as the entrance in the Smurf transition.

Building a Smurf reporter would be a non-invasive way to identify pre-Smurfs. The idea would be to observe the physiological time passing by, in vivo. This could be achieved by expressing a fluorescent reporter (as GFP) for a gene showing an increase in expression with time in the non-Smurfs (figure 4.2). By testing putative reporters, and collecting data on the expression of the gene and the entrance in the Smurf phase, we would possibly be able to find a good predictor for the transition and set a tool for pre-Smurf identification. Such a tool would allow identification of individuals at different physiological times, following their ageing.

The pre-Smurf reporter project was started last summer by Sofia Sosa Marmol, an undergraduate student in the laboratory. However, the work was preliminary, and the project is currently on hold.

In the Introduction, I presented the work of [START_REF] Zhang | Extended Twilight among Isogenic C. elegans Causes a Disproportionate Scaling between Lifespan and Health[END_REF] in C. elegans, where the authors were investigating the different individual ageing rates in a large population of nematodes (section 1.3). Their results pointed to a model supporting the "rate of ageing hyphotesis". Individuals are biologically equal at birth (in terms of life expectancy) but diverge early in life, starting to experience a different ageing rate and dying at a different time. Furthermore, their results suggest that long-lived individuals spend, in proportion, more time in a senescent or "bad health" phase compared to short-lived individuals.

Flies have been shown, on the other hand, to spend around the same time in the Smurf state [START_REF] Rera | Intestinal barrier dysfunction links metabolic and inflammatory markers of aging to death in Drosophila[END_REF][START_REF] Tricoire | A New, Discontinuous 2 Phases of Aging Model: Lessons from Drosophila melanogaster[END_REF], which would correspond to the late/bad health phase of life. However, given the model proposed in 4.1, we could hypothesize that the chronological ageing experienced by the long-lived individuals brings them to experience a more extended susceptibility phase (which would translate in a longer pre-Smurf phase compared to short-lived individuals). Of course, this is no more than speculation, but it could be an interesting way to investigate and combine those two models in the future.

Comparison with the BiT transcriptome

In the Introduction, I also discussed the recent published BiT transcriptome [START_REF] Meyer | BiT age: A transcriptome-based aging clock near the theoretical limit of accuracy[END_REF], which identifies a transcriptomic ageing clock in C. elegans.

We tried to compare the signature found in our data with the C. elegans BiT. The enrichment analysis performed by the authors on the 576 genes forming the ageing clock found enriched categories as immune response, neuropeptide signalling, and genes related to stress response. The biological processes cited recall of what identified in section 2.2.5 for the Smurfs.

As independent validation, I downloaded the list of 576 genes constituting the clock and the corresponding coefficients (negative if the gene is decreasing its expression with ageing, and viceversa). I then submitted the two lists independently to the enrichment analysis tool avaiable at WormBase [START_REF] Davis | WormBase in 2022-data, processes, and tools for analyzing Caenorhabditis elegans[END_REF]. The immune response was the primary signal that I could detect for genes with a positive coefficient. In contrast, the downregulated genes were enriched in germline/mating categories, motility, neuropeptides signalling and extracellular matrix components. Those results are interesting, as they show good overlap with what was observed in Smurfs.

Thus, I tried to compare at a gene level. In order to compare the two datasets, I first retrieved the Drosophila orthologs corresponding to the BiT transcriptome (with the online tool g:Profiler [START_REF] Raudvere | Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update)[END_REF]). I found 316 Drosophila genes, corresponding to 129 genes of the BiT. The numbers do not correspond as for certains genes more than one ortholog is retrieved. Interestingly, the TF Adf1 is a BiT gene, which supports its role in longevity as described in the manuscript.

I first tried to plot the genes retrieved in a similar manner of what done in section 2.5. As shown in figure 4.3, I cannot detect a significant difference in the gene distribution compared to the background. This means that the genes retrieved do not specifically correlate with age or Smurfness in my dataset.

Comparing the list of genes retrieved with the Smurf DEGs do not increase the interpretability. I could detect only 140 Drosophila genes, retrieved from In order to exclude the possibility that the lack of correlation between my dataset and the BiT was due simply to a conversion issue with the tool used, I performed the conversion using the homologene R package. Also in this case, I could not detect an overlap.

However, the lack of immune related genes in the Smurf DEGs overlapping with the BiT genes suggests a problem at the level of conversion or the presence of homologous of BiT genes in Drosophila. It might indeed be the case that a Drosophila BiT implies genes involved in similar processes of the nematode BiT, but not directly identifiable as homologous.

Smurfness and new genes affecting longevity

Chapter 3 was dedicated to the illustration of the experiments carried during the second and third year of PhD. The aim of such experiments was to investigate the possibility of finding new genes extending lifespan by studying ageing through the Smurf phenotype. A large screening carried by longevity experiments showed lifespan extension effect for the KD of three TFs: Adf1, Trl and CG4360.

Those genes have been found as putative regulators of TFs upregulated in Smurfs (see section 3.1). As shown in figure 3.3, the genes predicted as targets amongst the ones selected in Smurfs do show a non-negligent overlap. The products of these genes might therefore directly or indirectly interact and share a similar action in their lifespan extending effect.

In order to investigate the possible interactions amongst the three genes validated as having a longevity effect, I used the STRING database [START_REF] Szklarczyk | The STRING database in 2021: customizable proteinprotein networks, and functional characterization of user-uploaded gene/measurement sets[END_REF]. STRING is a database for protein-protein interactions, which conveniently groups and maps them in a weighted network (i.e. a network where each node is a protein and each edge an interaction, weighted with a score summarizing the evidences provided for it) for each query. The annotated protein-protein interactions can be known (from curated databases and experimental evidences), predicted (gene neighborhood, gene fusion, gene co-occurrence), or coming from textmining (as co-presence in abstracts).

In figure 4.4 I reported the STRING interaction network of Adf1. We can retrieve some familiar interactors encountered along Chapter 3: Trl, Aef1, CG4360. The interaction with Aef1 comes from textmining, in particular from the paper briefly discussed in 3.1 [START_REF] Birnbaum | Age-Dependent Changes in Transcription Factor FOXO Targeting in Female Drosophila[END_REF], that finds Adf1, Aef1 and Trl as putative co-actors of FOXO (binding site are found enriched in FOXO binding regions). Interestingly, the interaction between Adf1 and Trl has been experimentally demonstrated [START_REF] Lomaev | The GAGA factor regulatory network: Identification of GAGA factor associated proteins[END_REF]. In this study, the authors screens for Trl (which protein is mostly known as GAGA factor, or GAF) interacting proteins. In addition, although not demonstrating a physical interaction between the two proteins, [START_REF] Talamillo | Expression of the Drosophila melanogaster ATP synthase α subunit gene is regulated by a transcriptional element containing GAF and Adf-1 binding sites[END_REF] show the requirement of both of them for the expression of the ATP synthase α subunit.

However, the two genes have mostly been studied in different contexts. The GAF protein coded by Trl works as an antirepressor, inducing gene expression by promoting open chromatin configuration [START_REF] Farkas | The Trithorax-like gene encodes the Drosophila GAGA factor[END_REF][START_REF] Okada | Chromatin Remodeling Mediated by Drosophila GAGA Factor and ISWI Activates fushi tarazu Gene Transcription In Vitro[END_REF]. Its action has been documented as influencing disparate biological processes, as dosage compensation in males [START_REF] Greenberg | The Drosophila GAGA factor is required for dosage compensation in males and for the formation of the male-specific-lethal complex chromatin entry site at 12DE[END_REF], activation of genes of Hsp family [START_REF] Weber | Molecular architecture of the hsp70 promoter after deletion of the TATA box or the upstream regulation region[END_REF][START_REF] Leibovitch | GAGA factor and the TFIID complex collaborate in generating an open chromatin structure at the Drosophila melanogaster hsp26 promoter[END_REF], and both oogenesis [START_REF] Fedorova | GAGA protein is required for multiple aspects of Drosophila oogenesis and female fertility[END_REF] and spermatogenesis [START_REF] Dorogova | GAGA protein is essential for male germ cell development in Drosophila[END_REF]. Its role in the germline has been studied also during development [START_REF] Dorogova | Role of GAGA Factor in Drosophila Primordial Germ Cell Migration and Gonad Development[END_REF]. The role of Trl in the germline is interesting in the light of what discussed in section 3.6 concerning the germline specific KD of Trl in the line showing lifespan extension. However, according to the studies cited above, a reduction in GAF has negative effects on the oogenesis, negatevely impacting fertility. Even if a proper quantification of the fertility has not been obtained yet, the line never presented noticeable sterility in all the experiments carried.

Adf1 was instead first studied through its mutant nalyot. nalyot flies present defects in long-term memory, with impaired learning capacity when undergoing Pavlovian training [START_REF] Dezazzo | nalyot, a Mutation of the Drosophila Myb-Related Adf1 Transcription Factor, Disrupts Synapse Formation and Olfactory Memory[END_REF]. Adf1 has been since then mostly studied in the nervous system. Its expression has been confirmed in the motor neurons of the thoracic abdominal ganglion in the adult [START_REF] Timmerman | The Drosophila Transcription Factor Adf-1 (nalyot) Regulates Dendrite Growth by Controlling FasII and Staufen Expression Downstream of CaMKII and Neural Activity[END_REF]. The same study shows its implication in dendritic branching in motor neurons. Interestingly, in none of my experiments, I observed visible differences in locomotor activity compared to other lines. In addition, at a developmental level, I could not notice toxicity for the treated flies, if not maybe a slight reduced growth for the higher concentrations of drugs. The longevity assay on elav GS -Adf1 KD is ongoing, and it is not showing toxicity so far (estimated end: mid-April 2022).

It is possible that, given their experimentally demonstrated interaction, the mechanism through which Trl and Adf1 are acting on longevity is the same.

In the context of further studies addressing such point, I would start by investigating in which tissues these genes show co-expression. If any, the interaction between the two proteins in those tissues will have to be verified by using techniques such as pull-down assay. Identifying the tissue or tissues of action will narrow the space of investigation. As a subsequent step, I would then return to the list of putative Smurf genes target returned by i-cisTarget and evaluate which ones present a different expression in the Smurfs in the tissues of interest. At that point, we would have a better idea of the pathways through which the genes are acting.

CG4360 is uncharacterized. Flybase reports it as an orthologous of the human zinc TFs ZSCAN10, ZNF157 and ZNF182, which show as well poor characterization. I tried to blast the sequence of CG4360 against Drosophila genome using blastn [START_REF] Altschul | Basic local alignment search tool[END_REF]. The gene shows 70% of identity with Aef1, which also belongs to the zinc finger family. However, according to presented results, the two genes have the opposite effect on longevity. The available elements are not enough to affirm a possible interaction of the two, as it happens on the other hand for Adf1 and Trl. However, it could be interesting to better characterize them and their possible role in longevity. The network in figure 4.4 presents an edge between CG4360 and Adf1, which corresponds to interaction of the homologous genes in C. elegans [START_REF] Reece-Hoyes | Extensive Rewiring and Complex Evolutionary Dynamics in a C. elegans Multiparameter Transcription Factor Network[END_REF].

Limits of the study

The main limit presented by the study is the use of only females for the Smurf signature characterization. Indeed, the transcriptional signature of the Smurf males might be different. In order to describe an universal Smurf signature, the validation on males will be required.

More generally, most of the Smurf studies performed so far has been done in females. The metabolomics data, briefly presented in section 2.2.7, will start to fill this gap as they will allow to compare the metabolomic Smurf female signature with the male's one.

Concerning the understanding on the Smurf model, another limitation of the study is the characterization of the transcriptional state only. Indeed, gene expression changes can give us extensive information about the the physiological state of the organism; on the other hand, a change observed in the gene expression might not translate to protein level. Furthermore, a process regulated at a post-translation level might not be possible to detect with transcriptomic data. The integration with the metabolomic data and proteomic data collected in Smurfs will help to adress this limitation. The genes that are found as deregulated on many levels could be interesting ageing targets.

An example of such gene is ref( 2

Impact of the Covid-19 pandemic on the project

The Covid-19 pandemic had an important impact on my research project.

When the first lockdown (March-May 2020) arrived, I had started since a few weeks the first experimental screening 3.3. The lockdown delayed the experiments, exacerbated by the loss of our fly stocks. All the lines that were initially bought in October 2019 were re-bought. The delivery of flies from stock centres was also slowed down over the summer 2020, with flies arriving sometimes in bad conditions and needed to be re-order. Over the same summer, the moving of the laboratory from Institut Biologie Paris-Seine (IBPS) to the Center for Reaserch and Interdisciplinarity (CRI, now Learning Planet Institute LPI) blocked the experiments for a few weeks.

Over the academic year 2020-2021, the laboratory remained accessible during the second and third lockdowns for those who could not work from home, but was subjected to curfew times (closing at 8p.m. for most of the year) and having short opening times over the weekend. Unfortunately, working with animals often required flexibility, even with good planning. The restricted opening hours especially affected the stress resistance assays, with the impossibility to complete the heat shock experiment and the need to carry the starvation experiment three times before obtaining a protocol adapted to the institute accessibility.

The pandemic also affected my work in other ways. Spring 2020 and the time from fall 2020 to late spring 2021, when restrictions were eased and then lifted, was a time characterized by the impossibility of doing not much more but working. Over time, the isolation and lack of any other activity that was not work-related, together with the general non-standard situation, dramatically increased my level of stress and eventually decreased my productivity, creativity and lucidity at work.

In addition to the pandemic, the development of carpal syndrome pathology on both hands badly affected my capacity to perform experiments and work several hours on my computer during my second and third years of PhD. At the end of the first year, starting from the bioinformatic analysis performed during that time, we had many ideas for complementary experiments to carry.

Amongst them, we wanted, for instance, to investigate changes in structural genes downregulated in Smurfs in the gut through immuno-staining. I did some preliminary work in January 2020 on this, but the lockdown and the worsening of my hands condition prevented me from pursuing additional experiments apart from the longevities. In this regard, I want to take my supervisor Michael Rera and the students of the laboratory (Hayet Bouzid, Sofia Sosa Marmol and Jasmina Yeung) who helped me in the maintenance and carrying of my experiments when it was needed.

Final conclusions

I started this manuscript by referring to the words of Heraclitus about the in becoming state of animated and non-animated matter.

Ageing has always been studied as a becoming (or better, unbecoming) condition of the organism, progressively occurring through time. The Smurf model apparently contradicts such a view, proposing a transition between 2 phases of life.

The Smurf phenotype allows us to identify a very late, stereotyped stage of life, where the ageing process (in the sense of unbecoming of the organism through time) might be already concluded. However, as I have illustrated throughout the manuscript, many ageing gene expression hallmarks are Smurfspecific, rather than age-specific.

So, then, is Smurfness ageing? If with ageing we mean the changes that have been so far described as such, my answer is yes. Smurfness does summarize most of ageing. If we instead refer to ageing as the process that brings to the organism's failure, my answer is no. In this context, I would define as ageing what occurs through time before the Smurf transition.

The critical information that the biphasic ageing model carries is that ageing might be a more discrete process than what we initially thought. The organism undergoes a first healthy state of life, then enters a susceptibility phase, and then a late stage (Smurfness). The plastic steps of ageing are probably hidden in the first healthy and susceptibility phases. Therefore, the Smurf model can provide a critical implementation to the field of ageing by not only allowing to follow ageing individually, but by opening the way to studying it

A.2 Experiments

General fly maintenance. All the flies are kept in closed vials in incubators at controlled temperature, humidity and 12 hours light cycle. All the experiments are carried at 26°C. Line in stocks are kept at 18°C. Longevity experiment food. All the longevity experiments were run on the following food composition: 5.14% (w/v) yeast, 2.91% (w/v) corn, 4.28% (w/v) sugar, 0.57% (w/v) agar and Methyl 4-hydroxybenzoate (Moldex) at a final concentration of 5.3 g/L to prevent fungi contamination. All components are mixed as powders in the respective amount and water is then added. The solution is mixed up to the moment it is homogeneous. The subsequent step is boiling. Boiling is essential for agar polymerization. After the first boiling, the food is mixed and then boiled a second time. We perform the additional boiling to be sure that the whole batch of food is correctly boiled and not only the surface. After that, the Moldex is added and the solution remixed. Food is then poured in the vial, in the amount of 1.25 mL each.

Maintenance and developmental. Stocks and developmental part of the experiments are performed on vials with 5 mL of food each. Food composition is the following: 1% (w/v) agar, 9% (w/v) corn, 7% (w/v) yeast, and Moldex (final concentration 5.3 g/L). This food was bought from the UMR7622.

Use of RU486 drug. The drug is stocked in ethanol 96% at 20 mg/mL (stored at -20°C). At the moment of the food preparation, the batch of food is divided into 5 batches where the drug is diluted. Ethanol is added in the batches with a lower drug concentration to compensate for the otherwise higher ethanol presence in the highest drug conditions. driver lines used (GS). Lab based. daGS; Kindly provided. Ti GS was provided by the UMR9197; elav GS, repoGS and 5961 GS were provided by the UMR8521. TRiP project. Information about the 41582 and 67265 lines and the vectors used for their construction were retrieved from the official website of the TRiP project (https://fgr.hms.harvard.edu/fly-in-vivo-rnai).

UAS lines used (GS).

Longevity assay. Just after eclosion, flies are collected in tubes with food and RU486. Males and females are left together to mate for 48 hours. After that time, males or females (depending on the experiment) are sorted in number of 30 per vial, with 5 vials for each RU concentration. The total N for each RU concentration is therefore 150. Flies are transferred to new vials with fresh food and scored three times per week (Monday, Wednesday, Friday). An exception are the first two weeks of the experiment, when females undergo an additional transfer on Saturday or Sunday due to the fertilized eggs altering the food composition. The food is prepared normally the day before the scoring.

Smurf assay. Flies were transferred from normal food to food containing the blue dye FD&C #1 at 2.5% (w/v) 24 hours prior to Smurfs counting. The dye is added as last component in the food, and dissolved in it. At the moment of the counting, flies were transferred back on normal food. All the flies are therefore spending the same amount of time on blue food, in order not to introduce bias in the counts. Note that with the following method we are not having information about the time at which the Smurfs are becoming such. However, as the Smurfs spend on average the same amount of time in this phase, recording the presence of a mixed Smurf population gives us a good estimation of their appearance in the population. Smurf counting was performing every two weeks while the population was in the survival plateau, and every week once it exited it.

Fertility assay. The F1 generated as in figure 3.6 is left to mate for 48 hours (in vials with the specific drug concentration). After that time, females of each group are divided in five vials, in number of 20 females per vial. I lowered the number of flies per vial from 30 to 20 as the aim of the experiment is to count the number of eggs laid, and having a high number of flies could cause overcrowding and possible underestimation of the number of eggs laid. I perform the assay at day 10 and day 17 of age. For 8 hours, flies were placed in vials with 5 mL of food and no drug. After the 8 hours, they are switched back on vials with drug and 2.5 mL of food each (the normal conditions of the longevity assay). After 10 days -development time for the lines at 26°C -the pupae are manually counted for each vial. The eggs are laid and develop on food without drug in order to interfere as less as possible with the development of flies.

Starvation assay. After the development and the 48 hours mating, females flies are separated, in number of 30 per vial, and 5 vials per concentration of drug. Flies are placed on starvation food at day 7 and scored three times per day, every day. The starvation food consists of 1% agarose in water.

Oxidative stress assay. Flies are generated and kept on 48 hours mating as in figure 3.6. Females are then separated, in number of 30 per vial, and 16 vials per concentration of drug. Flies are kept for five days on condition equals to the one of the longevity experiment. At day 5, the assay starts. For each drug concentration, I test 4 concentration of H 2 O 2 (0% -controls-, 0.5%, 1%, 2%). For each concentration of drug and H 2 O 2 I have therefore 4 vials of 30 females each (N = 120). Survival is scored every day up to the end of the assay. Food is prepared following the protocol used for the longevity assay. The food is prepared in a single batch, then divided in 5 sub-batches for the 5 drug concentrations, and subsequently each drug batch is divided in 4 sub-batches for the H 2 O 2 concentration. It is important to add the H 2 O 2 once the food reaches 40-50°C, in order to preserve the molecule integrity. It is also important to correct for the amount of water when preparing the initial batch. Indeed, H 2 O 2 stock solution is diluted in water. In order to have the same amount of water desired at the end, the % of water we are adding with the H 2 O 2 needs to be removed from the preparation of the initial batch.

Heat shock assay. Flies are generated and kept on 48 hours mating as in figure 3.6. Females are then separated, in number of 30 per vial, and 5 vials per concentration of drug. The conditions are the same of the longevity experiment. After one week, the assay is performed. Flies are put in the morning in vials with no drug, 5 mL of food each. The vials are placed in a incubator at 37°C, and a bucket of water in order to enhance humidity and avoid the food to dry because of the heat. Survival is scored every hour.

RNA extraction Extraction of RNA was performed using the Trizol protocol as in [START_REF] Rio | Purification of RNA using TRIzol (TRI reagent)[END_REF], adapted to the amount of tissue used. Each sample corresponds to a mixture of three flies. For RNA-Seq, each sample was obtained by homogenization of 8 flies. For the qPCR performed on the daGS-UAS lines, 3 flies were homogenized together for each biological replicate.

RT and qPCR RNA was retro transcribed using the Applied Biosystems cDNA Reverse Transcription Kit. RT-qPCR was subsequently performed using the Applied Biosystem PowerTrack SYBR Master Mix on Applied Biosystem QuantStudio-1 Real Time PCR System. Primers were designed on Benchling.

Primers sequence.

Adf1 Fw: ACAGCCCTTCAACGGCA Adf1 Rw: CGGCTCGTAGAAGTATGGCT CG4360 Fw: CAGCAGAGCACCCTTACCAA CG4360 Rw: GGAGCGGGCATTGAGTGAT Trl Fw: TCCTATCCACGCCAAAGGCAAA Trl Rw: TAGCAAATGGGGCAAGTAGCAGG Act Fw: CCATCAGCCAGCAGTCGTCTA Act Rw: ACCAGAGCAGCAACTTCTTCG . This is probably due to the low number of samples, and the quantification will have to be repeated with higher sample size. Less than half of the initial expression is present in the treated samples, but the difference is not significance to wilcoxon test (p-value = 0.1). This might be due to the low number of samples used, and the quantification will have to be repeated with higher sample size.

M-1 M-4 M-7 M-10 M-13 M-16 1d-a 1d-b 5h-c1 5h-c2 5h-c3 5h-c4 5h-c5 5h-c6 5h-c7 5h-c8 1d-c 1d-d 1d-e 1d-f 1d-g 1d-h 1d-i 1d-j 1d-k 1d-l 1d-m 1d-n 1d-o 1d-p 1d-q 1d-
Table B.8: Drosophila longevity genes (annotated in GenAge) which are present in the list of Smurf DEGs. I report here the gene symbol, the log 2 FC in Smurfs, the annotated effect in GenAge (direction and % of change on the ML), the experiment to which the GenAge annotation refers to and its reference.

Note that for the same gene different experiments could be reported.

Figure 1 . 1 :

 11 Figure 1.1: Chronological representation of the theories, discoveries and important advancements in the field of ageing across the last century, as presented in this chapter.

Figure 1 . 2 :

 12 Figure 1.2: The 9 hallmarks discussed by López-Otín et al. (2013). They can be spatially divided in three main categories: nuclear hallmarks, cellular hallmarks, and systemic hallmarks -involving cell-cell communication and tissue alterations -.

  Figure 1.3: The IIS pathway and its effects on ageing. In green the genes/treatments

  past years, theories imputing ageing to a single mechanism are progressively abandoned in favour of models integrating different processes. López-Otín et al. (2013) try to incorporate the hallmarks just explained in the attempt of reconstructing what chronologically happens in the organism. Their proposition is illustrated in figure 1.5. On the other hand, Lemoine (2021) proposes an interesting evolutionary view of such hallmarks, illustrated in figure 1.6.

Figure 1 . 5 :

 15 Figure 1.5: Temporal classification of the hallmarks of ageing. López-Otín et al. (2013)

Figure 1

 1 Figure 1.6: Lemoine (2021) proposes an evolutionary view of the hallmarks of ageing presented in[START_REF] López-Otín | The Hallmarks of Aging[END_REF]. According to the study, the first hallmark appearing in prokaryotes is the accumulation of misfolded proteins. It follows the chromatin-related hallmarks with the appearance of Archea. After that, we find hallmarks related to organelles (mitochondria) and eukaryotic nuclear complexity. The metabolic hallmarks are the last to appear in unicellular organisms. As the organism's complexity increases with multicellularity, the rest of the hallmarks appear.

Figure 1 . 7 :

 17 Figure 1.7: Longevity curves of a synchronous population with human-like ageing. (a)Population based view. After experiencing a phase of no-mortality, the population decreases progressively with time. A population is characterized by a mean lifespan, i.e. the average time of deaths of the individuals. As it ages, individuals in the population progressively experience an increase in the hallmarks of ageing. (b) Individual based view. If we could distinguish the biological age of the individuals in a population, we would observe that they actually age at a different rate. In this example, the 10 individuals present at the beginning in the population are a mixture of individuals that will age at a different speed and die in different moments. The blue individuals are short-lived and will die off first. The most numerous red individuals will die at an age around the average lifespan of the population. But a few individuals (yellow) will die off last, at a higher age than the mean lifespan. In the heterogeneous population, we observe an increase, on average, of the hallmarks of ageing carried by individuals.

Figure 1 . 9 :

 19 Figure 1.9: Smurf phenotype identified in both males and females in D.melanogaster. In non-Smurf flies, the dye is restricted at the level of the digestive tract, while the Smurf flies show the blue coloration all over the body. The non-Smurf coloration at the level of the gut is easier to visualize in females, given their larger abdomen. Adapted from Martins et al. (2018).

Figure 1 .

 1 Figure 1.10: Smurfs evolution over time in a population. (a): If we age a populationon stardard food and blue food dye, we will notice the first blue flies appearing as soon as the population exits the mortality plateau. From that moment, the proportion of Smurfs at each time point will increase over time. (b): two wild-type Drosophila population with different lifespan. The longevity curve was reconstructed by following the population over time, and the proportion of Smurfs was recorded at 5 or 6 time points, depending on the lifespan of the population. In both cases the proportion of Smurfs increases linearly, but we can see how w 1118 , with a shorter lifespan, presents a faster increase in Smurfs appearance. Adapted from[START_REF] Rera | Intestinal barrier dysfunction links metabolic and inflammatory markers of aging to death in Drosophila[END_REF]

Figure 1 .

 1 Figure 1.11: Smurf characterization, adapted from Rera et al. (2012). (a) Non-Smurfs and Smurfs from w 1118 synchronous population were sampled at 4 different time points, and their remained lifespan monitored. We can notice how the Smurfs present a constant and low T 50 independently of their age, always significantly different from the one of the non-Smurfs. The remaining lifespan of the non-Smurfs at a given age is similar to the one of the general population. (b) mRNA level (quantified by qPCR) of genes involved in inflammation and IIS pathway in Drosophila.The first row show the expression of three antimicrobial peptides (AMPs) expressed downstream of the inflammatory pathway, known to be overactivated with ageing. The AMPs is significantly lower in the non-Smurfs than in the Smurfs independently of their age, hinting that the Smurfs could be the one actually carrying the "ageing inflammation signal" in the population. On the second row, the same analysis is done on three FOXO targets, induced when the IIS is repressed, as it normally occurs with ageing. The same pattern of the inflammatory genes is observed, with a significant overexpression of the genes with Smurfness, independently of the age.

Figure 1 . 13 :

 113 Figure 1.13: The biological features recorded over time in the mice cohort are analyzed both by age and remaining lifespan. I am here showing the the intestinal permeability (3 hours after dye gavage) -first row-, glycemia -second row-, body weight -last row-. In the first column data points are analyzed by age, in the second by remaining lifespan. The aim is to see if a transition point can be detected. The dashed blue line shows the transition point in the data identified by multivariate adaptive regression splines (MARS) analysis. Full black line depicts the nonparametric local regression (LOWESS) on the data. Green and red lines represent linear regression analysis of longitudinal intestinal permeability values before and after the transition point respectively. If MARS analysis does not identify a transition point (as in the case of the intestinal permeability by age), linear regression of longitudinal values does not apply. $ p < 0.05 by the Mann-Whitney U test on mean intestinal permeability values before and after the transition point. p < 0.05 by the Ftest performed on linear regression slope of before and after the transition point. Intestinal permeability and body weight show a significant biphasic behavior only when analyzed by remaining lifespan. Glycemia instead, show a significant decrease in both analysis. However, the analysis by remaining lifespan identifies a sharper transition.

Figure 1 .

 1 Figure 1.14: 2-phase model of ageing as proposed in[START_REF] Tricoire | A New, Discontinuous 2 Phases of Aging Model: Lessons from Drosophila melanogaster[END_REF]. Each

  Figure 1.15: I am here simulating how the tuning of the a and k parameter affects the

Figure 2 . 1 :

 21 Figure 2.1: Experimental design for RNA-Seq sampling. (a) Longevity curve of the drs-GFP line used for the experiment. Sampling points are highlighted in red. (b) Data structure, with the number of sample per condition and age.

Figure 2 . 2 :

 22 Figure2.2: Workflow used for the RNA-seq preprocessing. After sequencing, reads are stored in fastq files (raw data); each fastq file contains information about one of the paired reads (so for each sample we have 2 fastq files). The quality of the reads is checked using FastQC. The reads passing the control go as input for the alignment step. This step, that we performed using Hisat2, also requires as input the reference genome on which the reads will be aligned. The algorithm output files are called BAM. These files store the information about the genomic coordinates on which each read is mapping. By inputing the BAM files and the .gtf file of the genome used for the alignment (containing the information about the genes coordinates), featureCounts counts the number of reads mapping to a certain genes. This gives as output the raw counts matrix, where for each samples are annotated the number of reads mapping to a certain gene. The quality of the alignement and counting can be conveniently checked by using the online tool MultiQC. The raw counts matrix needs to undergo normalization before performing further analysis.

Figure 2 . 3 :

 23 Figure 2.3: PCA on the top 5000 genes of the dataset. Color indicates the Smurf status

Figure 2 . 4 :

 24 Figure 2.4: Unsupervised hierarchical clustering on sample-to-sample distance. Three main clusters are identified, showing a good separation amongst Smurfs and non-Smurfs but for the old sample, which appears to either correlate with one of the two groups or form a third cluster independently of the Smurf status.

Figure 2 . 5 :

 25 Figure2.5: Volcano plot of the differential gene expression analysis. The negative logarithm of the adjusted p-value (FDR) is plotted as a function of the log 2 FC for each gene. DEGs are highlighted in red. Genes with a log 2 FC > |2| are mapped with the their gene symbol, or Flybase identifier when the symbol could not be retrieved.

Figure 2 . 7 :Figure 2 . 8 :

 2728 Figure 2.7: Smurfs upregulated GO terms in fgsea analysis. Edges connects terms with overlapping genes, allowing hub identification. The name of the hubs are manually annotated.

  Figure 2.9: Log2 fold change of DEGs plotted on the KEGG Toll and Imd pathway (dme04624) show a general upregulation of the process in Smurfs.

Figure 2

 2 Figure 2.10: Pathways deregulated according to the GAGE analysis, plotted as a function of the negative log10 of the adjusted p-value. The colors manually map the processes the pathways belong to. We can notice a consistent representation of metabolism amongst the downregulated pathways.

Figure 2

 2 Figure 2.12: PCA on the 202 metabolites detected for Smurfs and non-Smurfs. The two conditions are separated along the first component.

Figure 2 . 15 :

 215 Figure 2.15: Transcriptional noise with age and Smurfness. The distribution of the relative variance is plotted for the different ages and condition. (a,b,c) Smurf and non-Smurfs relative standard deviation is plotted for the groups at 20 days (a), 30 days (b), 40 days (c).There is a clear shift with time of the peak of the distribution in both Smurfs and non-Smurfs, confirming an effect of age on the both categories. Interestingly, the difference in the distributions observed between Smurfs and non-Smurfs is reduced with age. (d,e) Smurfs (d) and non-Smurfs (e) are this time plotted in separated graphs, so to visually compare the effect of age within each group. *** p-value < 0.001 for Kolmogrov-Smirnov statistic. Genes belonging to the 1st quartile of the mean expression distribution are removed from the analysis. For the sake of visualization, the distribution are cut at 0.5 in the plot, but all the values of relative standard deviation are taken into account for the statistic.

Figure 2

 2 Figure 2.16: (a) The six hallmarks of cellular transcriptional ageing across multiple organisms, as identified in[START_REF] Frenk | Gene expression hallmarks of cellular ageing[END_REF]. (b) In the Smurf transcriptome, three of the four hallmarks are detected (downregulation of mitochondrial proteins, downregulation of ribosomes, dyresgulation of immuno system). A fourth hallmarks is partially detected (stress response), while no hits are found to affirm an overexpression of DNA damage related genes or a reduction in growth factors. On the other hand, the dysregulation in gene expression seems a time-related signal rather than a Smurf related one.

Figure 2

 2 Figure 2.18: Enrichment analysis in the old non-Smurfs DEGs show upregulation of immune response related categories and downregulation of oogenesis genes.

Figure 2 . 19 :

 219 Figure2.19: Enrichment analysis in the old non-Smurfs DEGs shows a surprisingly high number of terms. In particular, I find terms related to RNA processing and DNA regulation (amongst which chromatin regulation) which are not found when comparing the Smurfs to the non-Smurfs or the old non-Smurfs to the young non-Smurfs. The only upregulated terms concerns metabolism, and are deregulations before associated to Smurfs when compared to non-Smurfs. This suggests that, for such categories, the "dilution" of the Smurf signal with time discussed in figure 2.17 could be not strong enough to be detected by DESeq2. Each node is a GO term, and edges are connecting categories with overlapping genes.

Figure 2 . 20 :

 220 Figure2.20: Enrichment analysis in the old non-Smurfs DEGs shows a surprisingly high number of terms. In particular, I find terms related to RNA processing and DNA regulation (amongst which chromatin regulation) which are not found when comparing the Smurfs to the non-Smurfs or the old non-Smurfs to the young non-Smurfs. The only upregulated terms concerns metabolism, and are deregulations before associated to Smurfs when compared to non-Smurfs. This suggests that, for such categories, the "dilution" of the Smurf signal with time discussed in figure 2.17 could be not strong enough to be detected by DESeq2. Each node is a GO term, and edges are connecting categories with overlapping genes.

Figure 2

 2 Figure 2.21: (a) Each individual follows its physiological time, experiencing ageing at adifferent rate and entering the Smurf phase at a different age (therefore dying at a different age). Being able to follow the physiological time of the organism prior to Smurf transition might help to put a chronological order in the events occurring in the Smurfs, and identify early ageing mechanisms. (b) As we do not know yet how to identify pre-Smurfs, we can try to approximate the physiological time of an individual by averaging at a population level. As in the population from which my data come from 20 days corresponds to 90% survival, I can imagine that, on average, the non-Smurfs samples at this point are representative of an early phase of life. The same reasoning applies to the samples at 40 days (10%) survival, which could represents a pre-Smurf phase of life. Samples at 30 days are at the T 50 , and could therefore be a good approximation of a non-Smurfs midlife.

Figure 2 . 22 :

 222 Figure 2.22: Expression of the Smurfs' DEGs, plotted over time in non-Smurfs and Smurfs.

  Figure 2.24: (a) The effect of time on six different genes belonging to the Toll and Imd pathway: two upstream regulators (PGRP-LA and PGRP-SD), the two transcription factors (Rel and dl ), and two downstream effectors (Listericin and DptA). Each point is the gene expression of a non-Smurf sample at given age, and the trend show the inferred linear model as in equation 2.1. (b) The genes showing the best decreasing trend in the non-Smurfs over time. We find three genes of the Jonah family, which was already overrepresented in the genes downregulated in Smurfs.

Figure 2 . 25 :

 225 Figure 2.25: Example of two genes showing a positive trend ( β1 ∼ 0.83 for wat and ∼ 0.74 for mthl2). The linear model refers to only the grey points, representing the non-Smurfs samples. The blue points are the expression of the Smurfs at different age. As you can notice, even if the genes were detected as differentially expressed, the points are quite overlapping, suggesting that the signal in the Smurfs themselves might be weak. In particular, in this case of wat we can see how non-Smurfs and Smurfs converge at old age. Note that mthl2 belongs to the same receptor family as mth, the first gene identified as extending lifespan in fruit fly, when mutated.

Figure 2

 2 Figure2.26: Density plot for the correlation of all the genes with Smurfness and age. In black all the data are considered, in red (dotted lines) the data without 40 days samples. The correlation with Smurfness increases when those samples are removed, suggesting immediately that age has an effect also on genes well correlating with Smurfness.

Figure 2

 2 Figure 2.27: All the genes detected are here plotted, while the densities refer to specific DEGs that I will specify in each point of the caption. Red always represents upregulated genes, green downregulated. (a) DEGs in the comparison non-Smurfs -Smurfs. They map on the side of the plot, suggesting a high correlation with Smurfness as expected. Though the peak of the density is on the space of higher Smurfness correlation than age, some genes also display correlation with age. (b)DEGs in old non-Smurfs compared to young non-Smurfs. Those genes have a good correlation with both age and Smurfness, as expected from the analysis in section 2.3.2. (c) DEGs in old Smurfs compared to young Smurfs. The downregulated genes of this group are interesting, as they show a compact behavior in a zone of higher correlation with age than with Smurfness.

Figure 3 . 1 :

 31 Figure 3.1: Venn diagram representing the intersection between the genes annotated inGenAge as altering lifespan in the fruit fly and the Smurf DEGs. Around 24% of the genes annotated in GenAge are found as deregulated in Smurfness, highlighting the validity of the model for the identification of genes affecting longevity. Amongst the rest of the Smurf DEGs could hide new genes playing a role in longevity.

Figure 3 . 2 :

 32 Figure3.2: My data consists of the final expression profile of the flies (3). Some of the deregulated genes (circles of different colours in the figure) are TFs, upregulated (in red) or downregulated (in green). These TFs might be partially responsible for the expression profile in point 3. By extracting them from the rest of the list, we possibly go one step back in the biological regulation of our final expression landscape. This brings us to point 2. Those TFs are in turn regulated by other genes, which could be present in the group itself or not be deregulated in the final expression profile (as it can happen in case of activation by post-translational modification). To move one step further in our backwards path to upstream regulators, we can try to predict which genes are regulating the TFs in point 2. This could be done by looking at the presence of shared binding motifs for specific TFs in the promoters of the genes. The software i-cisTarget conveniently performs this job, and brings us to point 1. The orange circles represent the predicted TFs not present in the final expression profile. As putative upstream regulators of the Smurf transition, the genes in point 1 could be good targets for experimental validation. From right to left, dashed lines reconstruct the hypothetical regulation path.

Figure 3

 3 Figure 3.3: i-cisTarget results for the four cases considered. (1) Putative regulators of TFs up in Smurfs.(2) Putative regulators of TFs down in Smurfs. Note that the second hit is a group of GATA family genes, and comes as a compact category in the output. (3) Putative regulators of genes up in Smurfs (log 2 FC > 2). The results reflect the immune and stress response identified as upregulated signal in Smurf. (4) Putative regulators of genes downregulated in Smurfs (log 2 FC < -2). Similarly to what happened in point 3, this output reflected the results found by the enrichment analysis, as the decrease in fertility genes (Blimp-1 and ken). Interestingly, maf-S has been shown to ameliorate age-associated phenotypes when overexpressed[START_REF] Rahman | Declining signal dependence of Nrf2-MafS-regulated gene expression correlates with aging phenotypes[END_REF].

Figure 3 . 4 :

 34 Figure 3.4: Genes selected for testing experimentally the effect of their alteration on

  Figure 3.5: GAL4 is a yeast TF binding the yeast promoter sequence called UAS. This

Figure 3 . 8 :

 38 Figure 3.8: Longevity curves for the AO experiment on the Aef1 KD (a) and Nf-yB KD (b). Controls are in black. Colors indicate the level of KD, as reported in the legend. (a)Aef1 KD shows a grandient decrease in the lifespan of the flies, corresponding to the gradient of the drug. This might hint for a mild effect of the gene on longevity. (b) Nf-yB KD kills the flies independently from the concentration, suggesting a toxicity of the KD itself more than an effect on ageing.

Figure 3 . 10 :

 310 Figure 3.10: Validation results for Adf1. (a) Longevity curve for the control population and the KD showing lifespan extension (ML RU0 = 71 days, ML RU50 = 79.5 days). We observe a rectangularization of the curve, with the treated flies leaving the mortality plateau later but decaying faster afterwards. *** p-value < 0.0001 (log-rank test). (b) In the upper part, we have the proportion of Smurfs recorded over time in controls (grey) and treated (red).Each point represents the proportion of Smurfs in an independent vial. The fitted linear model results in a significant coefficient for the time variable (p-value < 0.0001), proving that the proportion of Smurfs is time-dependent. The interaction coefficient between time and drug dose is also significant (p-value < 0.05), proving that the drug dose significantly affect the time-dependence of the Smurf proportion. The graph at the bottom represents the number of alive flies at the moment of the Smurf counting.

Figure 3 . 11 :

 311 Figure 3.11: Validation results for Trl, AO experiment. (a) I am showing here the results for the RU 50 µg/mL treatment, as amongst the four treatment is the one showing the higher effect (ML RU0 = 72.2 days, ML RU50 = 79.8 days). Contrary to what results with Adf1, the curves here are diverge from the survival plateau to the end of the curve. *** p-value < 0.0001 (log-rank test). (b) In the upper part the proportion of Smurfs recorded over time.The analysis resulted in a significant time-dependence of the proportion of Smurfs (p-value < 0.0001) and a significant effect of the drug dose on such dependence (p-value < 0.01). The lifespan extension is therefore significantly associated to a slower increase in Smurf proportion. The graph at the bottom indicates the number of alive flies at each moment of the Smurf recording.

Figure 3 . 12 :

 312 Figure 3.12: First validation results for CG4360. (a) I am showing here the results for the RU 10 µg/mL treatment in the WL CG4360 KD experiment (ML RU0 = 71.7 days, ML RU10 = 78.5 days). Here the two curves are initially overlapping, but diverge around day 60. *** p-value < 0.001 (log-rank test). (b) In the upper part the proportion of Smurfs recorded over time, each point representing the proportion in an independent vial. Time-dependence increase of the Smurf proportion is significance (p-value < 0.001), as well as the effect of the RU486 dose on it (p-value < 0.05), proving a significant slower increase of Smurf proportion in the treated population. The graph on the bottom of point (b) shows the number of alive flies at each Smurf recording.

Figure 3 . 13 :

 313 Figure 3.13: Second validation results for CG4360, WL experiment. (a) I am showing here the results for the RU 10 µg/mL treatment that for a second time showed a significant lifespan extension (ML RU0 = 68.5 days, MLRU10 = 77.0 days). The curves are here diverging before what happened in the experiment in figure 3.12, with the treated population staying longer in the survival plateau. *** p-value < 0.001. (b) In the upper part the proportion of Smurfs recorded over time, each point representing the proportion in an independent vial.The analysis results in a significant time-dependence of the proportion of Smurfs (p-value < 0.001), and a significant interaction term (p-value < 0.01) between the RU486 concentration and the time-dependence. This proves the slower increase in Smurf proportion in the treated flies. The graph at the bottom of point (b) shows the number of alive flies at each Smurf recording time point.

Figure 3 . 15 :

 315 Figure 3.15: Survival curves for all the concentrations of RU tested in males for Trl and CG4360.The type of experiment (AO or WL) is specified below each graph. Note that the RU200 in the CG4360 WL experiment is missing, due to a problem in the growth of the flies during the experiment preparation. None of the population show lifespan increase but for the Trl KD in the WL experiment. The extension is however low, and even if significant it might not be relevant.

Figure 3

 3 Figure 3.16: (a) Trl KD driven by Ti GS in the gut, AO experiment. Higher RU486 concentrations led to lifespan extension, with the best effect (+8%) shown by RU µg/mL. (b) Trl KD driven by 5961 GS in the intestinal stem cells and enteroendocrine cells, AO experiment.Although the RU 100 µg/mL effect is significant, it might not be relevant and mostly caused by the initial difference in the two curves.

Figure 3 .

 3 Figure 3.19 illustrate the results for Trl KD, while figure 3.20 the results for CG4360. In figure 3.21, I report the mean lifespan and statistical analysis for each of the H 2 0 2 and RU concentrations.

Figure 3

 3 Figure 3.22: (a) Trl KD using an independent line (referred as line 2 in the text, it is the Bloomington 67265.) The plot longevity curves show how the KD is toxic all across the different RU concentrations. (b) Lifespan extension for Adf1 KD with an independent line (Bloomington 31500).

  discussion Throughout this manuscript, I tried to guide the reader through the work I have done during my PhD. As explained in the Introduction, my research project focused on validating a new age-related phenotype (Smurf) in Drosophila as a model for following biological age in vivo.

Figure 4 . 1 :

 41 Figure 4.1: Proposed model for transcriptomic behaviour with Smurfness and ageing.

  Figure 4.2: A reporter mimicking the expression of a pre-Smurf gene is a non-invasive way to identify flies before the transition. A pre-Smurf gene (in red) is putatevely changing its expression gradually before to Smurf transition, experiencing then an exacerbation of such change once Smurf. The reporter will mimick the action, by being activated by the same promoter as the pre-Smurf gene. In this case, I simulate a pre-Smurf gene increasing its expression with time.

Figure 4 . 3 :

 43 Figure 4.3: The behaviour of the BiT genes retrieved in Drosophila do not show a distribution skewed towards age or Smurfs correlation in our dataset.

  Figure 4.4: STRING protein-protein interaction network result for Adf1. We can notice the presence of Trl, Aef1 and CG4360.

  )P. This gene is upregulated in Smurf, upregulated in old non-Smurfs, with a significant trend in the non-Smurfs over time. Ref(2)P, involved in autophagy, is also upregulated in the Smurf proteome. Its KD with daGS has been shown to increase lifespan (unpublished data by the laboratory, figure 4.5). Recently, its KD in neurons has been shown to increase lifespan in Drosophila[START_REF] Hurley | Inhibition of Ref(2)P, the Drosophila homologue of the p62/SQSTM1 gene, increases lifespan and leads to a decline in motor function[END_REF].

Figure 4

 4 Figure 4.5: ref(2)P KD through daGS increases lifespan (unpublished data by the laboratory).

Figure B. 4 :

 4 Figure B.4:The fold changes obtained by edgeR and DESEq2 for the 2377 overlapping genes found as DEGs by both the analysis are plotted against each other. The correlation between the two is 0.99 (pearson).

Figure B. 5 :

 5 Figure B.5: Autophagy KEGG pathway in Drosophila (dme04136), identified from GAGE analysis. Most of the genes present an almost null fold change in Smurfs (in grey). The log 2 FC plotted refers to DESeq2 estimation (independently from the significance of the deregulation of the single gene).

Figure B. 6 :

 6 Figure B.6: Endocytosis KEGG pathway in Drosophila (dme04144), identified from GAGE analysis. Smurfs present positive regulation of genes involved in endosomal function.

Figure B. 7 :

 7 Figure B.7: RU50 µg/mL Trl samples fold change, relative to controls. n = 3 for controlsand treated. Half of the initial expression is present in the treated samples, but the difference is not significance to wilcoxon test (p-value = 0.1). This is probably due to the low number of samples, and the quantification will have to be repeated with higher sample size.

Figure B. 8 :

 8 Figure B.8: RU10 µg/mL CG4360 samples fold change, relative to controls. n = 3 for controls and treated. Less than half of the initial expression is present in the treated samples, but the difference is not significance to wilcoxon test (p-value = 0.1). This might be due to the low number of samples used, and the quantification will have to be repeated with higher sample size.

Figure B. 10 :

 10 Figure B.10: Data collected for the first screening of longevity experiments. Results are organized as in figure 3.7. The % effect refers in each case to the controls. NA indicate a missing value, due to missing population. This is due to developmental toxicity of the gene alteration, not allowing flies to grow.

  They model the biological age[START_REF] Zhang | Extended Twilight among Isogenic C. elegans Causes a Disproportionate Scaling between Lifespan and Health[END_REF] illustrate three possible hypothesis regarding the individuals different ageing rates. In a population we can distinguish three kind of individuals: short-lived (blue), average-lived (red) and long-lived (yellow). Colors are the same as in figure 1.7. In the starting point hypothesis, the three kind of individuals are distinguishable since birth, with a prognosis reflecting the age of death. In the rate of ageing hypothesis, the prognosis at birth is instead the same for the three kind of individuals. However, soon after birth their age rate diverge, and they die off at different moments. The premature death hypothesis proposes instead a situation in which not only individuals have the same prognosis at birth, but they also age at the same rate. Death is here a stochastic event that randomly hits individuals in a population.

	short-lived	average-lived	long-lived
	Prognosis at birth	Prognosis at birth		Prognosis at birth
	Age		Age	Age
	Starting Point Hypothesis	Rate of Ageing Hypothesis	Premature Death Hypothesis
	Figure 1.8:

  in order to target this problem late in life, from a clinical point of view. It consists of 34 parameters linked to age-related conditions (as for instance cataracts, high cholerestol, need of assistance) that can predict mortality independently of age. However, it is human-restricted and impossible to apply to other organisms, especially in research.

  Smurfs present downregulation of enzymes involved in fatty acid metabolism; interestingly, although we cannot observe a difference in the level of acetyl-coA, we observe a reduction in carnitin levels -playing an important role in the fatty acid degradation pathway -and palmitic acid. Significance refers to DEG analysis for the genes and wilcoxon test for the metabolites. *P < 0.05; **P < 0.001;***P < 0.0001 it comes in a more general context of downregulation. The accumulation of succinate could reinforce the hypothesis of a dysfunction, even if the downstream metabolite of the enzyme (fumarate) is not significantly changing in Smurfs. I also looked at the ratio NAD/NADH and ATP/ADP (results not shown). The first is an indicator of the cell's reduction power and the correct function of the Krebs cycle, which is the leading NADH producer. The second is an indicator of the energy state and the correct functioning of the electron transport chain that produces ATP. High level of ADP/ATP values indicates a low energy state, and activate, for instance, the kinase AMPK (see figure

	condition	NS	S	age	•	20	•	30	•	40
	Lactate concentration		expression							Succinate concentration
			Metabolites concentration

Figure 2.13: (a) Ldh overexpression and lactate accumulation suggest anaerobic metabolism in Smurfs. (b) Light downregulation of two subunits of the enzyme succinate dehydrogenase and succinate accumulation in Smurfs might hint for dysfunction of Complex II. (c)

  .6 and B.7 (Appendix B.1).

	Upregulated in Smurfs	Downregulated in Smurfs
	pathway	counts pathway	counts
	dme01100 Metabolic pathways	21 dme01100 Metabolic pathways	20
	dme04144 Endocytosis	9 dme00020 Citrate cycle (TCA cycle)	4
	dme04624 Toll and Imd signaling pathway	9 dme01200 Carbon metabolism	4
	dme04013 MAPK signaling pathway -fly	6 dme04142 Lysosome	4
	dme04141 Protein processing in endoplasmic reticulum	6 dme00010 Glycolysis / Gluconeogenesis	3

Figure 2.23: First 5 pathways for numbers of genes mapping, respectively for the genes

Table 2 .

 2

1: Pathways selected from KEGG, putatevely changing with age and not influenced by Smurfness. In total we have 6 pathways for the group "Replication and repair" and three pathway for the group "Transcription".

  .1 and table B.8 in Appendix B.2, for more details concerning the longevity effect and the Smurf deregulation.).

	longevity		Smurf genes
	annotated genes
	143	58	2991
	201		3049

  Oxidative stress longevity assay on flies carrying Trl KD and controls. The arrow indicates the starting time of the assay. H 2 0 2 0% flies are not dying during the experimental time, proving that the the deaths that we observe in the other conditions are indeed due to the H 2 0 2 presence. Flies exposed to 2% start to die the same day of the assay, with little or no difference amongst the RU concentrations (see 3.21). The intermediate concentrations show instead interesting results, with RU 100 µg/mL extending lifespan for the 0.5% concentration and a more general resistance effect noticed in the 1% concentration.

		1.0
		0.8
	% Survival	0.4 0.6
		0.2
		0.0
		0	5	10	15	20	25	30	35
					Time (days)
		H 2 0 2 0% H 2 0 2 1% Figure 3.19: H 2 0 2 0% H 2 0 2 1%		H 2 0 2 0.5% H 2 0 2 2% H 2 0 2 0.5% H 2 0 2 2%	• • • • •	RU0 RU10 RU50 RU100 RU200

  Oxidative stress longevity assay on flies carrying CG4360 KD and controls.The arrow indicates the starting time of the assay. H 2 0 2 0% shows only a couple of sporadic deaths, proving that the the deaths that we observe in the other conditions are indeed due to the H 2 0 2 presence. Flies exposed to 2% start to die the same day of the assay, with little or no difference amongst the RU concentrations (see 3.21). Concerning the intermediate concentrations, 0.5% shows a potentially interesting effect, but unfortunately the controls show a initial drop that makes the interpretation of the results less reliable. On the other hand, the 1% show an extension that looks like following the RU gradient (from 10 µg/mL to 200 µg/mL), as also reported in figure3.21. Mean lifespan (ML), % effect in lifespan change and log-rank p-value for the different H 2 0 2 and RU concentrations. H 2 0 2 0.5% has basically no significant effect for

	RU0 ML 22.8 18.6 RU0 ML 7.3 7.4 RU0 ML 10.7 11.5 Figure 3.20: H 2 0 2 0.5% RU10 RU50 ML effect % p-val ML effect % p-val Trl KD 22.3 -2.2 n.s. 22.5 -1.3 n.s CG4360 KD 21.4 15.0 *** 20.7 11.3 *** H 2 0 2 2% RU10 RU50 ML effect % p-val ML effect % p-val Trl KD 7.3 0 n.s. 7.0 -4.1 ** CG4360 KD 7.7 4 n.s. 7.6 2.7 n.s. H 2 0 2 1% RU10 RU50 ML effect % p-val ML effect % p-val Trl KD 14.0 30.8 *** 12.8 19.6 *** CG4360 KD 13.5 17.4 *** 13.5 17.4 *** Figure 3.21:	ML 24.0 22.1 ML 7.7 7.3 ML 11.4 12.3	RU100 effect % p-val 5.2 * 18.8 *** RU100 effect % p-val 5.6 ** -1.3 n.s. RU100 effect % p-val 6.5 *** 6.9 ***	ML 22.8 20.8 ML 7.7 7.9 ML 11.6 13.4	• • • • • RU200 RU0 RU10 RU50 RU100 RU200 effect % p-val 0.0 n.s 11.8 *** RU200 effect % p-val 5.6 n.s. 6.7 *** RU200 effect % p-val 8.4 *** 15.5 ***

  tSNE (perplexity = 10) on the samples. Colour indicates Smurfness and symbols the age. Similarly to the PCA results in figure2.3, the Smurfs and non-Smurfs form two separate groups. In the non-Smurfs group, we can notice the samples clearly distributing by age, while the Smurfs appear more mixed. The same outliers as in the PCA analysis are identified.

		Age	20 days	30 days	40 days	Condition a	non-Smurf	a	Smurf
		50			r
	Dimension 2	0
		-50
		-60	-30		0		30		60
					Dimension 1
	Figure B.3:

Table B . 1 :

 B1 GO BP categories significantly upregulated when performing the analysis on the list of DEGs from Smurf/non-Smurf comparison. ** adjusted p-value < 0.01; * adjusted p-value < 0.01 Table B.2: GO BP categories significantly downregulated when performing the analysis on the list of DEGs from Smurf/non-Smurf comparison. ** adjusted p-value < 0.01; * adjusted p-value < 0.01 Table B.3: Number of DEGs detected in the different over time comparisons (DESeq2).

	ID	ID	Description Description 30 vs 20 days 40 vs 30 days		Score adj p-value Score adj p-value 40 vs 20 days
	GO:0019731 GO:0008202	antibacterial humoral response steroid metabolic process (number DEGs) (number DEGs)	2.34 (number DEGs) ** -2.32	**
	GO:0050829 GO:0030703 non-Smurf	defense response to Gram-negative bacterium eggshell formation 59 158		2.31	510	** -2.2	**
	GO:0070482 GO:0019730 GO:0042445 GO:0007304 Smurf	response to oxygen levels antimicrobial humoral response hormone metabolic process chorion-containing eggshell formation 4 915		2.3 2.28	** ** -2.19 -2.17 2320	** **
	GO:0006457 GO:0034754	protein folding cellular hormone metabolic process		2.26		** -2.16	*
	GO:0006458 GO:0045455	'de novo' protein folding ecdysteroid metabolic process		2.24		** -2.16	*
	GO:0051084 GO:0016125	'de novo' posttranslational protein folding sterol metabolic process		2.24		** -2.1	*
	GO:0051085 GO:0006694	chaperone cofactor-dependent protein refolding steroid biosynthetic process		2.24		** -2.1	**
	GO:0061077 GO:0006508	chaperone-mediated protein folding proteolysis		2.24		** -2.09	**
	GO:0034620 GO:0042180	cellular response to unfolded protein cellular ketone metabolic process		2.22		** -2.06	*
	GO:0035967 GO:0007305 vitelline membrane formation involved in chorion-containing eggshell formation cellular response to topologically incorrect protein 2.22	** -2.03	**
	GO:0006986 GO:0030704	response to unfolded protein vitelline membrane formation		2.22		** -2.03	**
	GO:0035966 GO:0035803	response to topologically incorrect protein egg coat formation		2.22		** -2.03	**
	GO:0042026 GO:0085029	protein refolding extracellular matrix assembly		2.11		* -2.02	*
	GO:0001666 GO:0030198	response to hypoxia extracellular matrix organization		2.1		** -2.02	*
	GO:0036293 GO:0043062	response to decreased oxygen levels extracellular structure organization		2.09		** -2.01	*
	GO:0006959 GO:0045333	humoral immune response cellular respiration		2.07		** -1.98	*
	GO:0042742 GO:0022900	defense response to bacterium electron transport chain		2.03		** -1.94	*
	GO:0051276 GO:0022904	chromosome organization respiratory electron transport chain		2.02		** -1.93	*
	GO:0050830 GO:0042773	defense response to Gram-positive bacterium ATP synthesis coupled electron transport		2		* -1.93	*
	GO:0031347 GO:0010817	regulation of defense response regulation of hormone levels		1.95		* -1.91	*
	GO:0006955 GO:0042775	immune response mitochondrial ATP synthesis coupled electron transport	1.93		** -1.91	*
	GO:0061057 GO:0044282	peptidoglycan recognition protein signaling pathway small molecule catabolic process		1.93		* -1.88	*
	GO:0006952 GO:0042254	defense response ribosome biogenesis		1.89		** -1.86	*
	GO:0017038 GO:0044281	protein import small molecule metabolic process		1.87		* -1.85	**
	GO:0098542 GO:0016054	defense response to other organism organic acid catabolic process		1.85		** -1.79	*
	GO:0006626 GO:0046395	protein targeting to mitochondrion carboxylic acid catabolic process		1.85		* -1.79	*
	GO:0070585 GO:0015980	protein localization to mitochondrion energy derivation by oxidation of organic compounds		1.85		* -1.79	*
	GO:0072655 establishment of protein localization to mitochondrion GO:0006091 generation of precursor metabolites and energy		1.85		* -1.77	*
	GO:0006575 GO:0008610	cellular modified amino acid metabolic process lipid biosynthetic process		1.79		* -1.76	*
	GO:0009617 GO:0022613	response to bacterium ribonucleoprotein complex biogenesis		1.76		* -1.75	*
	GO:0002376 GO:0044283	immune system process small molecule biosynthetic process		1.76		** -1.65	*
	GO:0009266 GO:0055114	response to temperature stimulus oxidation-reduction process		1.76		* -1.57	*
	GO:0006950 GO:0019538	response to stress protein metabolic process		1.72		** -1.45	*
		GO:0071310	cellular response to organic substance		1.72		*
		GO:0009628	response to abiotic stimulus		1.71		*
		GO:0009607	response to biotic stimulus		1.66		*
		GO:0043207	response to external biotic stimulus		1.66		*
		GO:0051707	response to other organism		1.66		*

Table B

 B .4: GO BP categories significantly enriched when performing the analysis on the list of DEGs from non-Smurfs at 40 days compared to non-Smurfs at 20 days. ** adjusted p-value < 0.01; * adjusted p-value < 0.01Table B.5: GO BP categories significantly enriched when performing the analysis on the list of DEGs from Smurfs at 40 days compared to Smurfs at 20 days. * adjusted p-value < 0.01 Table B.5: GO BP categories significantly enriched when performing the analysis on the list of DEGs from Smurfs at 40 days compared to Smurfs at 20 days. * adjusted p-value < 0.01 Table B.5: GO BP categories significantly enriched when performing the analysis on the list of DEGs from Smurfs at 40 days compared to Smurfs at 20 days. * adjusted p-value < 0.01 TableB.6: Genes presenting a significant positive slope in linear regression over the three time points in non-Smurfs and discussed in 2.4. *p-value < 0.05; **p-value < 0.01 (F-test). All these genes are significantly upregulated in Smurfs. Smurfs and discussed in 2.4. *p-value < 0.05; **p-value < 0.01 (F-test). All these genes are significantly upregulated in Smurfs.TableB.7: Genes presenting a significant negative slope in linear regression over the three time points in non-Smurfs and discussed in 2.4. P-value * < 0.05, ** < 0.01 (F-test). All these genes are significantly downregulated in Smurfs.

	ID ID B.2 Chapter 3 ID Gene symbol Slope Description Description Description r 2 time points in non-Gene symbol Slope r 2 p-value p-value Mapping Mapping Score adj p-value Score adj p-value Score adj p-value Gene symbol Slope r 2 p-value Mapping 1.00
	GO:0010038 GO:1902275 GO:0044093	response to metal ion regulation of chromatin organization positive regulation of molecular function CG10814 1.574 0.733 ** Hsc70-3 0.15 0.396 * ER Cbs -0.087 0.512 * 0.75 n.s.	1.98 -2.52 Metabolism Metabolism	-2.26	* *	*
	GO:0010035 GO:0006030 GO:1901071 GO:0061640 GO:0000910 GO:2001252 GO:0006475 GO:0016573 GO:0018393	response to inorganic substance chitin metabolic process glucosamine-containing compound metabolic process cytoskeleton-dependent cytokinesis cytokinesis positive regulation of chromosome organization internal protein amino acid acetylation histone acetylation internal peptidyl-lysine acetylation Gnmt 1.463 0.853 ** Metabolism 1.78 1.78 1.78 -2.53 -2.53 -2.53 Nmdmc 0.482 0.495 * Metabolism yellow-f 0.457 0.57 * Metabolism alpha-Man-Ia 0.144 0.464 ** ER CG8042 0.115 0.551 * ER CG13887 0.106 0.701 ** ER cN-IIIB -0.088 0.435 * Metabolism Aldh-III -0.09 0.366 * Metabolism muc -0.095 0.332 * Metabolism 0.50 foldchange	-2.27 -2.27 -2.27	* * * * * *	* * *
	GO:0006022 GO:0006511 GO:0018394	aminoglycan metabolic process ubiquitin-dependent protein catabolic process peptidyl-lysine acetylation Prx2540-2 0.37 0.545 ** Metabolism 1.75 -2.53 Mulk -0.101 0.407 * Metabolism 0.25	-2.27	* *	*
	GO:0006040 GO:0006302 GO:0071897	amino sugar metabolic process double-strand break repair DNA biosynthetic process CG12896 0.356 0.709 ** bgm -0.121 0.587 ** 0.00			1.74 -2.56 Metabolism Metabolism	-2.29	* *	*
	GO:0055114 GO:0005975 GO:0010948 GO:0034968 GO:0043044 GO:2001251	oxidation-reduction process carbohydrate metabolic process negative regulation of cell cycle process histone lysine methylation ATP-dependent chromatin remodeling negative regulation of chromosome organization LKRSDH 0.352 0.546 * Prx2540-1 0.282 0.603 ** spidey -0.123 0.349 * CG5991 -0.131 0.451 * RU50 sample	1.53 1.52 -2.57 -2.58 Metabolism Metabolism Metabolism Metabolism	-2.3 -2.3	* * * *	* *
	GO:0017144 GO:0010639 GO:0006479	drug metabolic process negative regulation of organelle organization protein methylation aay 0.266 0.765 ** Metabolism 1.51 -2.59 l(1)G0334 -0.152 0.372 * Metabolism	-2.3	* *	*
	ID GO:0032984 GO:1901988 GO:0008213	Description negative regulation of cell cycle phase transition protein-containing complex disassembly protein alkylation iPLA2-VIA 0.224 0.516 ** ATPsynD -0.159 0.302 *	-1.98 -2.59 Metabolism Metabolism	Score adj p-value * -2.31 * *
	GO:0009617 GO:1901991 GO:0000086 GO:0061982	response to bacterium negative regulation of mitotic cell cycle phase transition G2/M transition of mitotic cell cycle meiosis I cell cycle process CG30022 0.207 0.508 * ND-B16.6 -0.168 0.351 *	-1.98 -2.64 Metabolism Metabolism	2.08 -2.33	* *	** *
	GO:0009607 GO:0034470 GO:0044839 GO:0006473	response to biotic stimulus ncRNA processing cell cycle G2/M phase transition protein acetylation alpha-Man-Ia 0.144 0.464 ** l(1)G0156 -0.169 0.278 *			-2 -2.65 Metabolism Metabolism	2.01 -2.33	* *	** *
	GO:0043207 GO:0051129 GO:0051303 GO:0006277	response to external biotic stimulus negative regulation of cellular component organization establishment of chromosome localization DNA amplification LPCAT 0.142 0.363 * Ugt86Dh -0.202 0.309 *	-2 -2.65 Metabolism Metabolism	2.01 -2.33	* *	** *
	GO:0051707 GO:0045930 GO:0065004 GO:0050684	response to other organism negative regulation of mitotic cell cycle protein-DNA complex assembly regulation of mRNA processing Gclc 0.129 0.701 * SdhA -0.205 0.294 *			-2.01 -2.67 Metabolism Metabolism	2.01 -2.33	* *	** *
	GO:0050830 GO:0043254 GO:0007062 GO:0006270	defense response to Gram-positive bacterium regulation of protein complex assembly sister chromatid cohesion DNA replication initiation Hsepi 0.122 0.466 * Metabolism -2.01 -2.68 CG11241 -0.213 0.532 * Metabolism	1.97 -2.34	* *	** *
	GO:0009605 GO:0022613 GO:0000380 GO:0007127	response to external stimulus ribonucleoprotein complex biogenesis alternative mRNA splicing, via spliceosome meiosis I Pi4KIIalpha 0.1 0.39 * Metabolism -2.03 -2.71 CG3940 -0.239 0.566 ** Metabolism	1.97 -2.35	* *	** *
	GO:0019731 GO:0010769 regulation of cell morphogenesis involved in differentiation antibacterial humoral response GO:0000381 regulation of alternative mRNA splicing, via spliceosome GO:0007088 regulation of mitotic nuclear division wol 0.092 0.479 * Metabolism -2.04 -2.72 CG12262 -0.352 0.31 * Metabolism	1.96 -2.35	* *	** *
	GO:0042742 GO:0006342 GO:0034728 GO:0031123	ifc Gal	defense response to bacterium chromatin silencing nucleosome organization RNA 3'-end processing 0.077 0.553 ** -0.402 0.365 *			-2.04 -2.73 Metabolism Metabolism	1.96 -2.35	* *	** *
	GO:0050829 GO:0006952 GO:0045814 GO:0030951 establishment or maintenance of microtubule cytoskeleton polarity defense response to Gram-negative bacterium defense response negative regulation of gene expression, epigenetic GO:0043161 proteasome-mediated ubiquitin-dependent protein catabolic process GO:1903311 regulation of mRNA metabolic process CG5724 -0.587 0.479 * Metabolism -2.04 -2.05 -2.74 NT5E-2 0.214 0.443 * Metabolism GO:0006281 DNA repair -2.75 Glycogenin -0.121 0.396 * Metabolism GstE9 0.487 0.363 * Metabolism	1.93 1.9 -2.35	* * * *	** ** *
	GO:0098542 GO:0050770 GO:0031109 GO:0051783	defense response to other organism regulation of axonogenesis microtubule polymerization or depolymerization regulation of nuclear division PIG-L -0.063 0.44 * Metabolism -2.06 -2.8 muc -0.095 0.332 * TCA cycle	1.89 -2.36	* *	** *
	GO:0002376 GO:0019730 GO:0060810 GO:0071826 GO:0006323 GO:0016571 GO:0043543	immune system process antimicrobial humoral response intracellular mRNA localization involved in pattern specification process ribonucleoprotein complex subunit organization DNA packaging ALiX 0.247 0.478 ** Endocytosis -2.08 -2.82 l(1)G0334 -0.152 0.372 * TCA cycle histone methylation protein acylation -2.89 CG10103 0.202 0.411 * Endocytosis l(1)G0156 -0.169 0.278 * TCA cycle	1.87 1.82 -2.37 -2.37	* * *	** * * *
	GO:0006955 GO:0051704 GO:0060811 GO:0031056 GO:0007143 GO:0007307 GO:0006352	immune response multi-organism process intracellular mRNA localization involved in anterior/posterior axis specification regulation of histone modification female meiotic nuclear division CHMP2B 0.187 0.76 ** SdhA -0.205 0.294 * eggshell chorion gene amplification DNA-templated transcription, initiation Rho1 0.161 0.414 * muc -0.095 0.332 * Carbon metabolism -2.08 -2.95 Endocytosis TCA cycle -2.97 Endocytosis	1.81 1.8 -2.37 -2.38	* * *	** ** * *
	GO:0006950 GO:0043085 GO:0006333 GO:0006367 transcription initiation from RNA polymerase II promoter response to stress positive regulation of catalytic activity chromatin assembly or disassembly Cdc42 0.145 0.405 * Endocytosis -2.08 -2.97 l(1)G0334 -0.152 0.372 * Carbon metabolism	1.8 -2.38	* *	** *
	GO:0006959 GO:0007338 GO:0045786 GO:0006310	Sara l(1)G0156	humoral immune response single fertilization negative regulation of cell cycle DNA recombination 0.127 0.316 * -0.169 0.278 *		-2.09 -2.97 Endocytosis Carbon metabolism	1.78 -2.38	* *	* *
	GO:0007292 GO:0009566 GO:0072657 GO:0032259	Asap SdhA		female gamete generation fertilization protein localization to membrane methylation 0.112 0.554 ** -0.205 0.294 *		-2.12 -2.39 Carbon metabolism -2.09 -3.03 Endocytosis	* *	* *
	GO:0048477 GO:1990778 GO:0007052 GO:0043414	oogenesis protein localization to cell periphery mitotic spindle organization macromolecule methylation TSG101 0.091 0.6 * LManI -0.29 0.597 **			-2.1 -3.03 Endocytosis Lysosome	-2.12 -2.39	* *	* *
	GO:0022412 cellular process involved in reproduction in multicellular organism GO:0031400 negative regulation of protein modification process -2.1 GO:0034660 ncRNA metabolic process GO:0071103 DNA conformation change -3.04 Vps4 0.059 0.399 * Endocytosis CG4847 -0.315 0.403 * Lysosome	-2.19 -2.4	* *	* *
	GO:0007276 GO:0030952 GO:0010498 GO:0045132	gamete generation establishment or maintenance of cytoskeleton polarity proteasomal protein catabolic process meiotic chromosome segregation DptA 1.391 0.442 * Immune response -2.11 -3.05 Gal -0.402 0.365 * Lysosome	-2.25 -2.4	* *	* *
	GO:0007281 GO:0007265 GO:0033045 GO:0006261	germ cell development Ras protein signal transduction regulation of sister chromatid segregation DNA-dependent DNA replication pirk 0.717 0.576 ** Immune response -2.12 -3.08 Bace -0.718 0.368 * Lysosome	-2.29 -2.4	* *	** *
	GO:0051052 GO:1901987 GO:0000070	regulation of DNA metabolic process regulation of cell cycle phase transition mitotic sister chromatid segregation PGRP-SD 0.519 0.381 * Immune response -2.13 -3.24 Aldh-III -0.09 0.366 * Glucose metabolism	-2.41	* *	*
	GO:0046578 GO:1901990	regulation of Ras protein signal transduction regulation of mitotic cell cycle phase transition PGRP-LF 0.33 0.495 * Immune response -2.15 muc -0.095 0.332 * Glucose metabolism	-2.41	*	*
	GO:0044786 GO:0050000	cell cycle DNA replication chromosome localization Rel 0.328 0.482 * l(1)G0334 -0.152 0.372 *	-2.15 Immune response Glucose metabolism	-2.41	*	*
	GO:0006399 GO:0006470	tRNA metabolic process protein dephosphorylation PGRP-LA 0.289 0.55 **		-2.16 Immune response	-2.45	*	*
	GO:0008298 GO:0007098	intracellular mRNA localization centrosome cycle dl 0.172 0.523	**		-2.16 Immune response	-2.45	*	*
	GO:0033047 GO:0018022	regulation of mitotic sister chromatid segregation peptidyl-lysine methylation key 0.156 0.349 *	-2.17 Immune response	-2.46	*	*
	GO:0006402 GO:0044772	Jra	mRNA catabolic process mitotic cell cycle phase transition 0.11 0.411 *		-2.17 Immune response	-2.47	*	*
	GO:0051056 GO:0044770	regulation of small GTPase mediated signal transduction cell cycle phase transition edl 0.243 0.467 * MAPK signalling -2.18	-2.49	*	*
	GO:0007093 GO:0051983	mitotic cell cycle checkpoint regulation of chromosome segregation puc 0.236 0.416 * MAPK signalling -2.19	-2.49	*	*
	GO:0010638 GO:0002181	positive regulation of organelle organization cytoplasmic translation raw 0.177 0.421 *		-2.2 MAPK signalling	-2.49	*	*
	GO:0043087 GO:0030261	regulation of GTPase activity chromosome condensation msn 0.136 0.547 **		-2.23 MAPK signalling	-2.51	*	*
	GO:0000075 GO:0043484	vap		cell cycle checkpoint regulation of RNA splicing 0.132 0.507 **		-2.23 MAPK signalling	-2.51	*	*
	GO:0022618 GO:0048024	ribonucleoprotein complex assembly regulation of mRNA splicing, via spliceosome Jra 0.11 0.411 * MAPK signalling -2.24	-2.51	*	*
	GO:0031124 GO:1902850	mRNA 3'-end processing microtubule cytoskeleton organization involved in mitosis P58IPK 0.203 0.497 ** ER	-2.25	-2.51	*	*
	GO:0000281 GO:0006401	CG10420	mitotic cytokinesis RNA catabolic process 0.156 0.359 *			ER	-2.25	-2.52	*	*

  TableB.8: Drosophila longevity genes (annotated in GenAge) which are present in the list of Smurf DEGs. I report here the gene symbol, the log 2 FC in Smurfs, the annotated effect in GenAge (direction and % of change on the ML), the experiment to which the GenAge annotation refers to and its reference. Note that for the same gene different experiments could be reported. TableB.9: TFs differentially expressed in Smurfs. I reported the Flybase ID and gene symbol, together with the log 2 FC (Smurf/non-Smurf) and the level of significance (*** p-value < 0.001; ** p-value < 0.01; * p-value < 0.05). TableB.9: TFs differentially expressed in Smurfs. I reported the Flybase ID and gene symbol, together with the log 2 FC (Smurf/non-Smurf) and the level of significance (*** p-value < 0.001; ** p-value < 0.01; * p-value < 0.05). TableB.9: TFs differentially expressed in Smurfs. I reported the Flybase ID and gene symbol, together with the log 2 FC (Smurf/non-Smurf) and the level of significance (*** p-value < 0.001; ** p-value < 0.01; * p-value < 0.05).

	Flybase ID Flybase ID Symbol Symbol log 2 FC adj p-value log 2 FC adj p-value Flybase ID Symbol log 2 FC adj p-value
	FBgn0000097 FBgn0027364 FBgn0261283 SREBP aop	Six4	0.11 0.29	-0.21	* ***	*
	Symbol AhcyL1 Akt1 alpha-Man-Ia Atg1 Atg8a Atg8a ATPCL Cbs Pcyt1 CG14207 Tspo ATPsynbetaL ND-20 chico Coq2 Myc Eip71CD Eip71CD elav esg fabp Gadd45 Gclc GlyP GlyS Gpdh GstS1 Hsc70-3 Hsp27 Hsp67Bc Hsp68 Hsp70Ba Hsp70Ba Hsp70Ba Hsp70Ba Hsp70Ba Hsp70Ba Ilk Symbol log 2 FC 0.05 0.18 0.29 0.17 0.26 0.26 -0.28 -0.11 0.46 0.31 0.59 1.61 -0.15 0.23 0.19 0.29 -0.4 -0.4 0.31 0.31 -0.21 1.3 0.21 -0.13 -0.1 -0.41 -0.09 0.26 -0.22 1.48 1.95 2.55 3.31 2.87 2.55 3.31 2.87 0.09 FBgn0025525 Effect increase decrease increase increase increase increase increase increase increase increase increase increase increase increase increase decrease increase increase decrease increase increase increase increase increase increase increase increase increase increase increase increase decrease decrease decrease increase increase increase increase FBgn0260632 % effect 13 11 39 25 56 50 32 43 8 5 27 11 46 48 30.8 47 70 20 66 21.1 81 77 50 17.1 10 20 33 27 30 6 20 30 30 30 25 25 25 63 bab2 FBgn0024244 drm FBgn0259938 log 2 FC Effect % effect Experiment RNA interference RNA interference RNA interference, Mutations Overexpression Overexpression Overexpression Mutation Overexpression Overexpression Overexpression RNA interference RNA interference RNA interference Knockout Mutation Overexpression Overexpression Overexpression Mutation Mutation Overexpression Overexpression Overexpression Post developmental RNA interference Post developmental RNA interference Mutation Overexpression Overexpression Overexpression Overexpression Overexpression Overexpression Overexpression Overexpression Epigenetic modification Epigenetic modification Mutation 0.22 dl 0.26 modification 0.32 cwo 0.21 Epigenetic Experiment * ** FBgn0000287 salr 0.22 * FBgn0027788 Hey 1.46 FBgn0261930 vnd -0.24 * ImpL2 0.91 increase 23 Overexpression FBgn0000448 Hr3 0.3 * FBgn0028789 Doc1 0.4 FBgn0261963 mid -0.63 *** InR 0.42 increase 85 Mutation FBgn0000567 Eip74EF 0.27 ** FBgn0028979 tio 0.32 FBgn0262477 FoxP 0.46 ** Keap1 0.27 increase 10 Mutations FBgn0000964 tj -0.64 *** FBgn0028996 onecut 0.21 FBgn0262656 Myc 0.27 *** ND-SGDH -0.13 increase 24 RNA interference FBgn0001150 gt 0.35 * FBgn0029504 CHES-1-like 0.17 FBgn0263118 tx 0.38 *** Lnk 0.19 increase 17.5 Mutations FBgn0001168 h 0.51 *** FBgn0030005 CG2120 -0.65 FBgn0264490 Eip93F 0.23 * Lnk 0.19 increase 33 Mutation FBgn0001291 Jra 0.25 *** FBgn0030505 NFAT 0.15 FBgn0265784 CrebB 0.09 ** loco 0.56 increase 20 Knockout FBgn0001297 kay 0.49 *** FBgn0030899 Hesr -0.46 FBgn0267978 ap -0.32 * loco 0.56 decrease 20 Overexpression p38a 0.11 decrease 40 Mutation Mrp4 0.48 decrease 47 Mutation Mrp4 0.48 increase 16 Overexpression MTF-1 0.13 increase 40 Overexpression mth -0.15 increase 35 Mutation mth -0.15 increase 21 Overexpression mth -0.15 increase 29 RNA interference mys 0.36 increase 20 Mutation mys 0.36 increase 44 Mutation Naam 0.36 increase 30 Overexpression ND-75 -0.3 increase 15 RNA interference Nmdmc 1.13 increase 120 Overexpression NPF 0.39 decrease 25 Overexpression p38b -0.07 decrease 60 Mutation p53 0.11 increase 58 p53 0.11 increase 19 Mutation Prp19 -0.08 increase 25 Overexpression Rbp9 0.2 decrease 33 Mutation SdhC -0.17 decrease 22 Sirt1 0.11 increase 57 Overexpression Sirt1 0.11 decrease 30 RNA interference Sirt1 0.11 increase 13 Overexpression Sod1 -0.2 increase 33 Overexpression Sod1 -0.2 increase 48 Overexpression Sod2 -0.15 increase 20 Overexpression teq -0.54 increase 31 Mutation Thor 0.77 increase 20 Overexpression Thor 0.77 increase 22 Overexpression Tsp42Ef 0.29 increase 18.2 Zw -0.22 increase 38 Overexpression FBgn0023489 Pph13 0.3 * FBgn0259211 grh 0.18 FBgn0022740 HLH54F 0.74 * FBgn0259176 bun 0.09 RNA interference FBgn0021872 Xbp1 0.19 ** FBgn0085432 pan 0.22 Post developmental Parkhitko et al. (2016) Reference Biteau et al. (2010) Liu et al. (2009) Ulgherait et al. (2014) Simonsen et al. (2008) Park et al. (2009) Peleg et al. (2016) Kabil et al. (2011) Landis et al. (2003) Vos et al. (2016) Lin et al. (2014) Copeland et al. (2009) Copeland et al. (2009) Clancy et al. (2001) Liu et al. (2011) Greer et al. (2013) Ruan et al. (2002) Chung et al. (2010) Toba et al. (2010) Magwire et al. (2010) Lee et al. (2012) Plyusnina et al. (2011) Orr et al. (2005) Bai et al. (2013) Sinadinos et al. (2014) Talbert et al. (2015) Simonsen et al. (2008) Simonsen et al. (2008) Wang et al. (2004) Vos et al. (2016) Biteau et al. (2010) Yang and Tower (2009) Yang and Tower (2009) Yang and Tower (2009) Zhao et al. (2005) Zhao et al. (2005) Nishimura et al. (2014) *** Zhao et al. (2005) *** ** Reference ** Alic et al. (2011) *** Tatar et al. (2001) * Sykiotis and Bohmann (2008) ** Copeland et al. (2009) *** Slack et al. (2010) *** Song et al. (2010) * Lin et al. (2011) * Legan et al. (2008) * Bai et al. (2013) * FBgn0020912 Ptx1 -0.24 * FBgn0085424 nub 0.18 * Zid et al. (2009) FBgn0016076 vri 0.28 *** FBgn0085405 CG34376 -0.18 * Demontis and Perrimon (2010) FBgn0015919 caup -0.54 *** FBgn0052296 Mrtf 0.13 * Huang et al. (2015) FBgn0015904 ara -0.57 *** FBgn0052121 CG32121 0.29 ** Curtis et al. (2007) FBgn0014931 CG2678 -0.11 * FBgn0050431 CG30431 -1.08 *** Sun and Tower (1999) FBgn0014859 Hr38 0.49 ** FBgn0050401 dany 0.51 * Orr and Sohal (1994) FBgn0014343 mirr -0.3 * FBgn0043364 cbt 0.19 *** Hoffmann et al. (2013) FBgn0014018 Rel 0.57 *** FBgn0042696 NfI 0.21 * Kusama et al. (2006) FBgn0013751 Awh -0.39 * FBgn0041156 exex 0.61 *** Rogina and Helfand (2004) FBgn0011656 Mef2 0.08 * FBgn0040305 MTF-1 0.12 * mutation Tsuda et al. (2007) FBgn0005660 Ets21C 1.68 *** FBgn0039808 CG12071 0.41 ** Dominant negative FBgn0005659 Ets98B 0.64 *** FBgn0039225 Ets96B -1.2 *** Toba et al. (2010) FBgn0005638 slbo 0.53 ** FBgn0039209 REPTOR 0.26 *** Garschall et al. (2017) FBgn0005561 sv -0.24 * FBgn0039044 p53 0.1 * Bauer et al. (2007) FBgn0004914 Hnf4 0.08 * FBgn0039039 lmd 0.68 * mutation Bauer et al. (2005) FBgn0004865 Eip78C 0.42 ** FBgn0038851 dmrt93B 2.66 *** Dominant negative FBgn0004858 elB 0.2 * FBgn0038418 pad 0.18 * Vrailas-Mortimer et al. (2011) FBgn0004666 sim -0.46 *** FBgn0037877 CG6689 0.14 *** Gendron et al. (2014) FBgn0004567 slp2 0.36 * FBgn0037617 nom 0.12 * Yu et al. (2015) FBgn0004396 CrebA 0.23 * FBgn0037275 CG14655 0.53 *** Owusu-Ansah et al. (2013) FBgn0004394 pdm2 -0.32 ** FBgn0036294 CG10654 -0.27 * Balan et al. (2008) FBgn0003963 ush -0.19 ** FBgn0036126 Irbp18 0.3 *** Nishimura et al. (2014) FBgn0003900 twi 0.74 ** FBgn0035903 CG6765 0.33 * Goddeeris et al. (2003) FBgn0003499 sr 0.75 *** FBgn0035691 CG7386 0.24 * Gimenez et al. (2013) FBgn0003459 stwl 0.17 * FBgn0035157 CG13894 1.2 *** Gimenez et al. (2013) FBgn0003448 sna 0.63 * FBgn0035144 Kah 0.58 *** Lin et al. (1998) FBgn0003254 rib 1.51 ** FBgn0035137 CG1233 0.1 ** Bahadorani et al. (2010) FBgn0003117 pnr -0.36 * FBgn0034012 Hr51 0.58 * Huang et al. (2014) FBgn0002609 E(spl)m3-HLH -0.24 * FBgn0033252 CG12769 0.16 * Huang et al. (2014) FBgn0002576 lz 0.34 * FBgn0032816 Nf-YB 0.19 *** Vrailas-Mortimer et al. (2011) FBgn0001981 esg 0.27 ** FBgn0032512 Bdp1 0.11 * Lin et al. (2011) FBgn0001319 kn -0.47 * FBgn0032202 REPTOR-BP 0.16 * FBgn0283451 br -0.4 **

Table B .

 B 10: Smurf DEGs amongst the BiT genes. The fold change (log 2 FC) refers to the DESeq2 analysis.Table B.10: Smurf DEGs amongst the BiT genes. The fold change (log 2 FC) refers to the DESeq2 analysis. Table B.10: Smurf DEGs amongst the BiT genes. The fold change (log 2 FC) refers to the DESeq2 analysis.

	Worbase ID Worbase ID Symbol Drosophila Symbol Drosophila log 2 FC BiT coefficient log 2 FC BiT coefficient
	WBGene00003843 WBGene00000778	CG14691	Mp20	1.37	0.244	-1.063	-2.595
	WBGene00003843 WBGene00002259	CG17752	fabp	-0.452	-0.207	-1.063	-3.442
	Worbase ID WBGene00003843 WBGene00012475 WBGene00003843 WBGene00077490 WBGene00003843 WBGene00013739 WBGene00003843 WBGene00018946 WBGene00003843 WBGene00012467 WBGene00003843 WBGene00012467 WBGene00016664 WBGene00012467 WBGene00016664 WBGene00012467 WBGene00008018 WBGene00012467 WBGene00011095 WBGene00012467 WBGene00007509 WBGene00001150 WBGene00007509 WBGene00013900 WBGene00019105 WBGene00009226 WBGene00019105 WBGene00007963 WBGene00019105 WBGene00007963 WBGene00019105 WBGene00007963 WBGene00008127 WBGene00007963 WBGene00010585 WBGene00007963 WBGene00010585 WBGene00014052 WBGene00044459 WBGene00002252 WBGene00044459 WBGene00001592 WBGene00016863 WBGene00011161 WBGene00016863 WBGene00011161 WBGene00016863 WBGene00011161 WBGene00016863 WBGene00011161 WBGene00016863 WBGene00011161 WBGene00016863 WBGene00011161 WBGene00016863 WBGene00011161 WBGene00016863 WBGene00018210 WBGene00016863 WBGene00017948 WBGene00016863 WBGene00013717 WBGene00006679 WBGene00011760 WBGene00006679 WBGene00018569 WBGene00016943 WBGene00002260 WBGene00016943 WBGene00016595 WBGene00016943 WBGene00016595 WBGene00016266 WBGene00016595 WBGene00009146 WBGene00016595 WBGene00009146 WBGene00016595 WBGene00018877 WBGene00016595 WBGene00018877 WBGene00016595 WBGene00018877 WBGene00016595 WBGene00018877 WBGene00016595 WBGene00018877 WBGene00016595 WBGene00018877	Symbol Drosophila CG6231 GILT2 CG4462 l(2)34Fc CG6356 spin CG3690 axed CG31103 CG10086 CG31272 CG3014 CG11291 CG2993 CG5577 Leash tbc CG18745 CG7997 CG18748 CG6465 Mtpalpha CG6733 Ugt36D1 Bace Cyp4s3 CG6508 Cyp9b1 CG31661 Cyp9b2 CG31928 Cyp9h1 CG12338 Cyp9f3 Fbxl7 Cyp9f2 CG32085 GILT2 Fbxl7 mthl10 CG32085 GluClalpha Est-6 Idgf6 Glt Idgf3 Jhe Cht4 alpha-Est1 Cht8 alpha-Est10 Cht9 alpha-Est2 Cht12 alpha-Est3 Idgf5 alpha-Est6 nemy alpha-Est7 mthl14 alpha-Est8 brwl Task6 NKAIN CG34396 Pka-C3 Acox57D-d fabp CG4860 Est-6 Arc42 Glt CG5028 Jhe CG9449 alpha-Est1 CG9452 alpha-Est10 LManII alpha-Est2 LManIII alpha-Est3 LManIV alpha-Est6 LManV alpha-Est7 LManVI alpha-Est8 LManI	0.349 log 2 FC BiT coefficient -1.063 -0.172 -0.466 -1.063 2.504 -0.727 -0.331 -1.063 2.251 0.196 0.37 -1.063 2.022 0.286 -0.248 -1.063 2.002 0.404 0.688 -1.063 1.922 -0.497 1.939 -1.106 1.922 0.114 -0.343 -1.106 1.922 0.122 0.503 -1.107 1.922 0.22 -0.486 -1.227 1.922 0.353 -0.32 -1.432 1.922 -0.148 -0.782 -1.432 1.866 0.401 -0.567 -1.671 1.861 -0.7 -2.609 -1.671 1.784 0.591 -1.447 -1.671 1.731 0.617 -1.745 -1.671 1.731 -0.515 -0.24 -1.713 1.731 -0.178 0.456 -1.863 1.731 -0.278 0.315 -1.863 1.731 -0.172 0.456 -1.956 1.668 0.484 0.315 -1.956 1.539 0.251 -0.753 -2.062 1.398 -0.206 -0.924 -2.062 1.106 0.65 -1.231 -2.062 1.106 1.265 -0.425 -2.062 1.106 -1 0.258 -2.062 1.106 0.647 -0.939 -2.062 1.106 1.297 0.224 -2.062 1.106 -0.54 -1.395 -2.062 1.106 -0.395 -0.583 -2.062 0.9 0.385 -0.338 -2.062 0.884 0.156 0.385 -2.313 0.806 0.103 0.388 -2.313 0.757 0.355 -0.352 -2.356 0.378 -0.207 0.17 -2.356 0 -0.753 -0.105 -2.356 -0.264 -0.924 -0.132 -2.371 -0.264 -1.231 -0.377 -2.407 -0.264 -0.425 1.358 -2.407 -0.264 0.258 -0.569 -2.437 -0.264 -0.939 -2.891 -2.437 -0.264 0.224 -3.13 -2.437 -0.264 -1.395 -2.01 -2.437 -0.264 -0.583 -1.694 -2.437 -0.264 -0.338 -0.264 -0.863 -2.437

Table B.10: Smurf DEGs amongst the BiT genes. The fold change (log 2 FC) refers to the DESeq2 analysis. Worbase ID Symbol Drosophila log 2 FC BiT coefficient WBGene00017069 Tim17a1 3.736 -0.443 WBGene00015284 Est-6 -0.753 -0.561 WBGene00015284 Glt -0.924 -0.561 WBGene00015284 Jhe -1.231 -0.561 WBGene00015284 alpha-Est1 -0.425 -0.561 WBGene00015284 alpha-Est10 0.258 -0.561 WBGene00015284 alpha-Est2 -0.939 -0.561 WBGene00015284 alpha-Est3 0.224 -0.561 WBGene00015284 alpha-Est6 -1.395 -0.561 WBGene00015284 alpha-Est7 -0.583 -0.561 WBGene00015284 alpha-Est8 -0.338 -0.561 WBGene00023423 CG4593 -0.114 -0.731 WBGene00017013 CG30280 -1.529 -0.802 WBGene00010703 Asph 0.323 -0.854 WBGene00006468 RhoGEF2 0.097 -0.875 WBGene00019227 Scgalpha 0.348 -0.892 WBGene00021412 Cyp4d1 -0.816 -0.916 WBGene00021412 Cyp4d2 -0.218 -0.916 WBGene00021412 Cyp4d8 -0.548 -0.916 WBGene00021412 Cyp4e1 -0.539 -0.916 WBGene00021412 Cyp4ae1 -0.275 -0.916 WBGene00021412 Cyp4p1 0.854 -0.916 WBGene00021412 Cyp4d14 -0.489 -0.916 WBGene00021412 Cyp4ac1 -0.702 -0.916 WBGene00021412 Cyp4ac2 -1.482 -0.916 WBGene00021412 Cyp4ac3 0.342 -0.916 WBGene00021412 Cyp4d21 -0.949 -0.916 WBGene00021412 Cyp4ad1 -0.318 -0.916 WBGene00021412 Cyp4p2 0.7 -0.916 WBGene00021412 Cyp4p3 0.443 -0.916 WBGene00021412 Cyp4d20 -0.552 -0.916 WBGene00011362 Est-6 -0.753 -0.987 WBGene00011362 Glt -0.924 -0.987 WBGene00011362 Jhe -1.231 -0.987 WBGene00011362 alpha-Est1 -0.425 -0.987 WBGene00011362 alpha-Est10 0.258 -0.987 WBGene00011362 alpha-Est2 -0.939 -0.987 WBGene00011362 alpha-Est3 0.224 -0.987 WBGene00011362 alpha-Est6 -1.395 -0.987 WBGene00011362 alpha-Est7 -0.583 -0.987 WBGene00011362 alpha-Est8 -0.338 -0.987 WBGene00007072 Ugt36D1 0.401 -1.003 WBGene00001433 Fkbp39 -0.166 -1.017 WBGene00003843 Orct -0.336 -1.063 WBGene00003843 CG15221 -0.537 -1.063 WBGene00003843 SLC22A 0.703 -1.063 WBGene00003843 CG7458 -0.141 -1.063

Smurfs. The 3049 genes are sufficient to obtain an almost perfect separation between Smurfs and non-Smurfs, with the same three outliers identified in previous analysis. Those can be visually recognized as presenting a mixed pattern in gene expression. The rest of the Smurfs and non-Smurfs, independently of the age, show a compact expression pattern. Enrichment analysis will be used to investigate the genes overexpressed and downregulated in Smurfs.

Summary of the results

In this chapter, I have discussed the bioinformatic analysis performed in order to answer the following biological questions:

1. Can we detect a biphasic behaviour in the gene expression of Smurfs and non-Smurfs?

2. How is the gene expression of the Smurfs and non-Smurfs affected by time?

1. Biphasic behaviour of Smurfs A Smurf is more similar -transcriptionwide -to another Smurf than to an age-matched non-Smurf. PCA analysis (figure 2.3) shows a separation along the first component (38% of variance) of Smurfs and non-Smurfs, and along the second (15% of variance) a separation along age groups, especially for the non-Smurfs. Differential gene expression analysis identified almost 20% of genes deregulated with a p-value cut-off of 5%. Expression of these genes defines the Smurf status in opposition to the non-Smurf (figure 2.6, suggesting the presence of a core Smurf signal. Enrichment analysis on the deregulated genes found upregulation of innate immunity and UPR genes, as well as genes involved in glutathione metabolism, suggesting a condition triggering a general stress response in the Smurfs. On the other hand, metabolic categories dominate downregulated genes, especially mitochondrial related. Such results also found confirmation in preliminary analysis conducted on metabolic data on Smurfs 2.2.7. In addition, Smurfs present a downregulation of genes involved in oogenesis, confirming a drop in fertility that has already been experimentally demonstrated [START_REF] Rera | The Smurf transition: new insights on ageing from end-of-life studies in animal models[END_REF].

The Smurf signature recapitulates more than half of the transcriptional ageing hallmarks 2.16. When I compared the young non-Smurfs to the old non-Smurfs through the same analysis, in order to see weather the effect of age was overlapping the effect of Smurfness, I could only detect the increase in inflammation and the decrease in expression of genes involved in reproduction. Those changes account for only one hallmark of transcriptional ageing. This proves the deregulations found in Smurfs independently of their age as Smurf

CG4360

When repeating the experiment for CG4360, I obtained an unexpected result. Indeed, I could not detect a clear lifespan extensions in the case of the AO, while WL showed a good effect on the RU10 µg/mL concentration (+9.5%), mirrored by a slower increase in Smurf proportion. Results are reported in figure 3.12. In the previous experiment (see figure 3.7 and figure B.10), the lifespan extension in the WL was moderated, and looked not relevant.

In order to clarify such results, I performed the experiment a third time. This additional validation confirmed the results obtained in the second experiment, with a lifespan extension for the RU10 µg/mL in the WL (+12.4%, figure 3.13). No relevant lifespan extension was detectable for RU 10 µg/mL (figure 3.14) of the AO experiment. Therefore, two independent experiments did not confirm the lifespan extension for CG4360 KD, while confirming an effect for the same dose in the WL set up. Interestingly, for the AO experiment (see figure 3.14) we can detect a significant time-dependence increase of the Smurfs proportion, but we cannot detect a significant difference amongst the controls and the treated group. This proves that two populations ageing at the same rate present the same increase in Smurfs proportion over time.

The quantification of the KD for the RU 10 µg/mL concentration is reported in figure B.8 (Appendix B.2). for the RU 10 µg/mL treatment in the AO CG4360 KD experiment (ML RU0 = 71.6 days, ML RU10 = 75.6 days), as it is the one that initially gave lifespan extension. However, log-rank test is not significant (p-value < 0.05). Therefore, the results of the first screening for the AO KD of CG4360 were not confirmed in two independent experiments. (b) In the upper part the proportion of Smurfs recorded over time, each point representing the proportion in an independent vial. The analysis proved the time-dependence of the Smurf proportion (p-value < 0.001), but rejected the hypothesis of an interaction between the drug dose and the time-dependence (p-value > 0.05). Therefore, in a condition in which we cannot detect a significant lifespan extension, we cannot detect a significant difference in the Smurf proportion increase. This is an additional proof of the relation between the Smurf increase rate and the population ageing. In the graph at the bottom of point (b), I report the number of alive flies at each time point for the Smurf recording.

by focusing on the time window where it is is plastic, and can be modulated. This ability can undoubtedly helping us move forward in the understanding of ageing.

To conclude, I do not believe that any model is entirely wrong or right. Instead, I reckon that the continuous ageing and a biphasic ageing views should result in a synergistic combination rather than in an unfruitful competition.

Appendix A Materials and Methods

A.1 Bionformatics

Unless stated otherwise, analysis have been performed on R 3.5.3. Mentioned packages are therefore to be interpreted as R packages. When not stated otherwise, plots have been generated using ggplot2 3.3.5. Exploratory analysis. PCA was performed using package DESeq2 1.22.2. tSNE was performed on package Rtsne 0.15. Sample-to-sample clustering was performed used the function dist from Rbase and package pheatmap 1.0.12.

Differential gene expression analysis. The main analysis has been perfomed on DESeq2 1.22.2. Validation analysis on edgeR 3.24.3. Enrichment analysis. Enrichmend analysis was performed with the Bioconductor package clusterProfiler 3.10.1, which calls fgsea 1.8.0. Analysis was ran with the following parameters: nPerm = 15000, minGSSize = 10, maxGSSize = 600. Plot was generated with the function emmaplot from the same package.

Pathway analysis. Performed on R package gage 2.32.