The doctoral thesis you are about to read would not have been possible without the hard work and support of many people. Whether through financial, professional or other support, these people have left their mark on the work presented here.

I would like to thank the university of Évry Val d'Essonne, the university of Paris-Saclay and the doctoral school SDSV for the financial support that made this work possible as well as the administrative support they provided.

Additionally, I would like to thank my reporters, Hannu Myllykallio and Nathalie Declerck for taking the time to read this long document and providing feedback and I would like to express the hope that they will the read stimulating. I would also like to thank Jean-Luc Ferat, Dominique Le Coq and Bianca Sclavi for taking the time to be on the jury of the thesis defence and express the hope that they will consider this time well spent.

A doctoral thesis is not produced in a vacuum, but requires scientific input from many sources. I would like to thank François Képès for accepting me as his PhD student and the many stimulating discussions we had about science. I would also like to thank Bianca Sclavi and Olivier Espéli for being on my thesis committee and guiding me through these last three years. I would like to thank the many collaborators, who have significantly enriched this project. Specifically, I thank Alexandria Holland, Matthaios Pitoulias and Panos Soultanas, our collaborators from Nottingham who have contributed the biochemical work presented here. Next, I would like to thank our collaborators from Genoscope, namely Gabor Gyapay, Nathalie Vega-Czarny and Marcel Salanoubat who have contributed the flow cytometry results and Alain Perret, Christophe Lechaplais and Marcel Salanoubat who are going to contribute metabolomic studies.

The work presented here would not be half as good without them and I would like to thank Marcel Salanoubat specifically for enabling these crucial collaborations.

But mostly, I am extremely grateful to current supervisor, Laurent Jannière without whom this work would not have been possible. He graciously accepted me as his student on his project after the retirement of François, taught me everything I needed to know and worked alongside me througout this final year. I feel blessed to have had the opportunity to push the boundaries of science just a little further with him.

I also would like to thank my current colleagues Nazim Sarica and Morgane Champleboux for their help on my project and the many discussions we had on life and other topics. I would also like to thank my former colleagues and especially Daniel Trejo Banos and François Bucchini for their support both inside and outside the lab. I would like to thank Sylvie Bobelet for guiding me through the many administrative messes I found myself in during my tenure here. All these people have been instrumental in 2 achieving this succes and helping me maintain my sanity.

In my personal life, I am extremely grateful to my girlfriend Hanne Verhaegen, my parents Frank Horemans and Carina Vermeulen and my brother Stijn Horemans, who have all supported me through the most difficult moments of this PhD.

To conclude, I would like to extend a big thank you to all the people whose vital contribution I am inevitably forgetting.

Table of figures

Résumé français de la thèse

La réplication de l'ADN est un processus biologique complexe et universel qui a pour fonction de transmettre entièrement et fidèlement le patrimoine génétique au cours des générations cellulaires. Pour assurer cette tâche, la réplication doit être étroitement coordonnée au cycle cellulaire. De la bactérie à l'homme, cette coordination est principalement assurée par l'action concertée de nombreux systèmes partiellement redondants qui, en contrôlant l'activité d'éléments spécifiques d'initiation de la réplication (protéines et origines de réplication), assurent une duplication du génome une et une seule par cycle cellulaire. Cependant, la réplication de l'ADN est aussi sous le contrôle du métabolisme. Chez les bactéries, ce niveau supérieur de régulation couple le taux global de réplication au taux de croissance. Il est assuré en modulant la fréquence d'initiation de la réplication et la vitesse des fourches de réplication en fonction de la richesse du milieu de culture. Le résultat de ce niveau supérieur de régulation est un fenêtrage temporel précis et reproductible de la synthèse d'ADN dans le cycle cellulaire. Chez la levure de boulanger (et peut-être tous les eucaryotes), le contrôle métabolique de la réplication confine la synthèse d'ADN dans une phase particulière d'un cycle métabolique intracellulaire qui est répété entièrement plusieurs fois par cycle cellulaire.

En dépit d'efforts importants depuis les années 60, le mécanisme et les éléments du contrôle métabolique de la réplication restent pour l'essentiel inconnus. Les hypothèses classiques stipulant que ce contrôle chez les bactéries dépend de la concentration de formes actives de facteurs d'initiation, d'une carence partielle des ADN polymérases en précurseurs ou de la concentration d'une alarmone (ppGpp) qui signale l'état métabolique des cellules et bloque la réplication à haute concentration, ont été tour à tour critiquées.

Des hypothèses alternatives ont alors vu le jour dans lesquelles il est proposé que le contrôle de la réplication est un phénomène multifactoriel qui varie avec le milieu et qui dépendrait de systèmes capables de sentir le métabolisme et de communiquer cette information à la machinerie de réplication.

Parmi les différentes processus cellulaires connus ou supposés reliés à la réplication se trouve le métabolisme central carboné (MCC), un groupe de réaction hautement conservé dans le monde vivant dont la fonction première est de brûler les sources de carbone pour générer l'énergie et les briques nécessaires à la production de biomasse. Puisque le MCC sent les ressources disponibles dans l'environnement et alimente de façon adaptée les principales voies de macrosynthèse, il est supposé que cette région métabolique pourrait également envoyer des signaux que les cellules utiliseraient pour ajuster de façon concertée le taux de réplication à la vitesse de croissance. Un tel processus de régulation a été récemment découvert pour la division cellulaire.

Plusieurs études suggèrent un rôle du MCC dans la réplication de l'ADN de la bactérie à l'homme. Chez la bactérie B. subtilis, qui est un organisme de référence dans ce domaine, il a été mis en évidence dès les années 80 qu'une sous unité de la pyruvate déshydrogénase, une enzyme du MCC qui produit de l'acétyl-CoA à partir du pyruvate, inhibait l'initiation de la réplication dans un extrait membranaire en se fixant à une région riche en AT située près de l'origine de réplication. De plus, cette sous-unité et d'autres provenant d'enzymes métaboliques apparentées, ont été vues interagir avec des enzymes de réplication.

Ces études suggèrent donc qu'une enzyme du MCC peut agir sur la réplication de l'ADN grâce à des interactions directes entre protéines métaboliques et enzymes de réplication.

Plus récemment, il a été montré chez B. subtilis que le contrôle métabolique de la réplication agit pour une bonne part sur l'étape d'initiation de la réplication et qu'il impliquerait des liens entre réplication et plusieurs fonctions cellulaires principales dont le MCC. Une étude assez large conduite précédemment au laboratoire a également montré que 11 gènes du MCC sont important pour la réplication. Ces gènes sont groupés dans la partie terminale de la glycolyse et dans un groupe de réactions situé juste en aval et appelé overflow. L'implication de ces gènes dans la réplication est dépendante du milieu de culture: si certains gènes sont nécessaires dans un seul milieu, d'autres sont requis dans deux ou trois milieux différents. De plus, un même gène peut être important pour l'initiation dans un milieu et pour l'élongation (la phase de synthèse d'ADN) dans un autre milieu. Dans un autre travail, l'équipe a démontré que les gènes de la partie terminale de la glycolyse sont génétiquement liés à trois enzymes de réplication qui jouent un rôle important dans l'initiation et l'élongation de la réplication. Ensemble, ces résultats suggèrent que le MCC est important pour un fenêtrage optimale de la réplication dans le cycle cellulaire et que ce processus dépend de nombreux liens entre MCC et réplication. Le travail a été réalisé dans un milieu néoglucogénique où l'activité catalytique de PykA ne joue pas de rôle important dans la croissance et le métabolisme cellulaire. Des expériences contrôles ont permis de confirmer ces points dans le milieu utilisé (milieu minimum complémenté en malate et hydrolysat de caséine) et pour l'ensemble des mutants testés. Dans un premier temps, nous avons étudié la réplication de l'ADN dans une souche délétée du gène pykA. Les différentes technologies utilisées ont permis de déterminer (i) le taux global de réplication (ratio ori/ter déterminé par PCR quantitative (qPCR)), (ii) le temps nécessaire pour répliquer le chromosome bactérien (période C ; par analyse de fréquence de marqueurs par qPCR), (iii) la vitesse de progression des fourches de réplication (en utilisant une formule qui prend en compte la taille du chromosome et la durée de la période C), (iv) le nombre de séquence origines par cellule (par cytométrie en flux) et (v) l'âge d'initiation de la réplication (en utilisant les données de cytométrie et le temps de génération). La mesure de ces paramètres de cycle cellulaire dans des cultures en phase exponentielle de la souche sauvage et du mutant ont révélé de nombreuses différences : dans le mutant, le ratio ori/ter est plus élevé, la période C est allongée, la vitesse des fourches de réplication et le nombre de séquences origines par cellules sont réduites et l'âge d'initiation est retardé. Tout cela montre que le système qui positionne dans le temps la réplication de l'ADN dans le cycle cellulaire est perturbé en absence de la pyruvate kinase. Ainsi, une enzyme du MCC est importante pour le contrôle temporel de la réplication.

Nous avons ensuite étudié la réplication dans des mutants du site catalytique de PykA. L'une des souches était délétée du domaine catalytique alors que d' autres portaient une mutation ponctuelle qui empêche la fixation du phosphoénolpyruvate, de l'ADP ou, simultanément, du phosphoénolpyruvate et du Mg 2+ Une souche mutée également ponctuellement dans un élément de changement conformationnel a également été utilisée ainsi qu'une autre qui porte une petite délétion de 27 acides-aminés dans la région impliquée dans le changement conformationnel. Cette petite délétion a pour particularité d'être liée génétiquement à trois enzymes de réplication impliquées dans l'initiation et l'élongation de la réplication. Des expériences contrôles de croissance et de métabolomiques ont confirmé que toutes ces mutations affectent profondément l'activité catalytique de PykA. La mesure des paramètres de réplication ont montré que les mutations ponctuelles dans le site de fixation à l'ATP et dans l'élément de changement conformationnel n'ont pas d'effet sur le contrôle temporel de la réplication. Ce contrôle est par contre perturbé dans les autres mutants qui présentent une augmentation du ratio ori/ter, un allongement de la période C, une diminution de la vitesse de réplication ainsi que probablement, une avancée de l'âge d'initiation. Comme toutes les mutations inhibent l'activité catalytique de façon similaire, ces résultats montrent que c'est la présence de la protéine PykA et non son activité métabolique ou son rôle dans le métabolisme cellulaire qui est importante pour le contrôle temporel de la réplication. De plus, ces travaux identifient trois régions dans PykA importantes pour la réplication. Deux d'entre elles affectent la fixation du phosphoénolpyruvate indiquant que le contrôle temporel de la réplication pourrait dépendre de la concentration de ce métabolite du MCC.

Nous avons ensuite déterminé les paramètres de réplication dans des mutants de PEPut. Ces mutants étaient soit délétées de PEPut, soit mutées ponctuellement dans le motif TSH de PEPut. Dans ces dernières souches, les résidus Thr, Ser et His étaient individuellement mutés en Ala ou Asp, un acideaminé utilisé pour mimer la phosphorylation dans les protéines. L'analyse des paramètres de réplication montre que la mutation Thr> Asp a une réplication fortement perturbée : le ratio ori/ter et la période C sont fortement réduits, la vitesse de réplication est fortement accélérée, le nombre de séquences origines par cellule est légèrement réduit et l'âge d'initiation est extrêmement retardé. Tout ceci montre une forte perturbation du contrôle temporel de la réplication dans ce mutant. Puisque le mutant Thr>Ala montre une augmentation à peine détectable du ratio ori/ter et puisque le résidu Asp mime la phosphorylation, ces résultats suggèrent (i) qu'une phosphorylation permanente du résidu Thr dans le motif TSH interfère dramatiquement avec le contrôle temporel de la réplication alors que (ii) une phosphorylation transitoire de ce résidu est requise pour permettre un contrôle temporel sauvage de la réplication. Par ailleurs, les résultats suggèrent que les résidus Ser et His du motif TSH ne sont pas impliqués dans la réplication dans les conditions métaboliques utilisées puisque les mutants Ser>Ala et His>Ala ont un ratio ori/ter sauvage.

Cette déduction est contredite par la légère réduction du ratio observée dans les mutants Ser>Asp et His>Asp à moins de supposer que la présence d'un signal de phosphorylation au niveau des résidus Ser et His est perçue de façon erronée par la cellule comme une phosphorylation partielle du résidu Thr, provoquant une faible réduction du ratio. Enfin, dans le mutant délété de PEPut, une réduction modéré du ratio ori/ter est détectée suggérant que l'absence de PEPut est moins délétère pour la réplication qu'une phosphorylation permanente du résidu Thr dans le motif TSH.

Dans leur ensemble, les résultats montrent que les domaines Cat et PEPut fusionnés dans la protéine

PykA ont des influences opposées sur le contrôle temporel de la réplication : le domaine Cat prévient une initiation précoce et une vitesse lente des fourches de réplication alors que la limitation du niveau de phosphorylation de la Thr dans le motif TSH de PEPut prévient une initiation tardive et une vitesse très élevée des fourches de réplication. De plus, puisqu'aucun rôle métabolique n'a été assigné à PEPut, ces résultats confirment que l'engagement de PykA dans le contrôle temporel de la réplication dépend de la protéine elle-même et non de son activité métabolique ni de son rôle dans le métabolisme cellulaire.

Pour mieux comprendre le fonctionnement de PEPut et son interface avec Cat, nous avons ensuite construit des souches codant pour des molécules de PEPut non fusionnées à Cat (molécules PEPut libres).

Pour cela, la phase ouverte de lecture de PEPut et son site de fixation au ribosome ont été mis sous le contrôle d'un promoteur inductible à l'IPTG et la construction appelée HyPEPut a été insérée dans un locus du chromosome éloigné de pykA. Cette insertion a été effectuée dans la souche sauvage et dans différents mutants de pykA. Des souches contenant des formes mutées de HyPEPut ont également été construites. Nous avons dans un premier observé que la synthèse de molécules de PEPut libres est associée à une nette réduction du ratio ori/ter dans le contexte sauvage, délété de pykA et délété de PEPut (c'est à dire codant pour Cat seulement). Cet effet inhibiteur, qui augmente avec le niveau d'expression de HyPEPut, n'est par contre pas observé dans un contexte produisant PEPut à partir du promoteur naturel de pykA. Comme le promoteur inductible de HyPEPut permet la production de beaucoup moins de protéines que le promoteur naturel de pykA (environ 3.000 contre 30.000 molécules), ce résultat montre que la molécule PEPut non fusionnée à Cat a un effet négatif sur le ratio ori/ter à des concentrations modérées mais pas à des concentrations élevées et que cet effet est dominant sur de fortes concentrations de PEPut quand ce peptide est fusionné à Cat (comme dans le contexte sauvage). Ils montrent également que les formes libres de PEPut ne complémentent pas l'absence de PEPut dans PykA puisque le ratio ori/ter est faible dans la souche produisant séparément Cat et PEPut. Ceci suggère que PEPut et Cat doivent être fusionnés dans une molécule unique pour un contrôle temporel sauvage de la réplication. Les effets opposés de l'expression de HyPEPut sur le ratio ori/ter dans des contextes où PEPut est massivement produite à partir du promoteur naturel de pykA dans un état fusionné à Cat (inhibition du ratio) ou libre (pas d'inhibition du ratio) suggèrent de plus que les molécules libres de PEPut sentent un facteur limitant inhibiteur du ratio mais pas les molécules fusionnées à Cat. On peut alors proposer qu'à des fortes concentrations, seulement une proportion des molécules PEPut libres reçoivent un signal du facteur limitant inhibiteur rendant le système de signalisant inefficace alors que la majorité de ces molécules reçoivent le signal quand elles sont à faible concentration, activant efficacement le système de signalisation pour in fine réduire le ratio ori/ter.

Nous avons ensuite

Introduction to the cell cycle in eukaryotes, prokaryotes and archaea

The cell cycle in eukaryotes

The purpose of the cell cycle is to ensure that key periodic biological processes for reproduction are performed every cycle in a timely fashion. These key processes include (i) build-up of the necessary energy and building blocks (ii) faithful and efficient replication of DNA (iii) segregation of DNA to the cell poles and (iv) division.

In eukaryotic cells, the cell cycle is divided into temporally separated phases, each responsible for a specific task. The cell cycle in eukaryotes consists of two main phases: interphase and mitosis and is reviewed in 1,2 . These different phases of the eukaryotic cell cycle are summarized in figure 1A.

The interphase itself is divided into three phases: Gap 1 (G1), Synthesis (S) and Gap 2 (G2).

The G1 phase is used to build up energy and building blocks such as nucleotides, proteins and many more required for the next phase of the cycle. During S phase, eukaryotic cells replicate their DNA. DNA is replicated from specific sequences called origins of replication and each eukaryotic chromosome has multiple origins and is linear. Specific regulatory mechanisms ensure that DNA replication occurs once and only once each cell cycle. Likewise, G2 is used to prepare for mitosis and to make sure that DNA replication has been completed before going into the M-phase.

Mitosis covers the process of nuclear division. Mitosis itself is divided into (i) prophase, (ii) metaphase, (iii) anaphase and finally (iv) telophase. During the prophase, the nuclear membrane and nucleolus are disassembled into vesicles, the mitotic spindle (consisting of two centromeres and a network of microtubules) is assembled and the DNA is condensed into chromatids.

During metaphase, the condensed chromatids are aligned along the cell equator; equidistant from the centromeres. During the anaphase, the spindle complex pulls the sister chromatids to opposite sides of the cell and segregates them. Finally, the effects of prophase are reversed during telophase. DNA decondenses, the nuclear membrane and the nucleolus are reformed and the spindle complex is disassembled. Additionally, cells that are not committed to the cell cycle are said to be in the phase of G0. In short, the eukaryotic cell cycle is a multiphase ordered process culminating in the duplication of the cell.

Correct progression through these different phases is regulated mainly by a network of threonine-serine kinases called cyclin dependent kinases (CDK) 1,2 . Three classes of CDK's exist depending on their time of activity: G1, S phase and Mitotic CDK's. [START_REF] Lodish | Overview of the Cell cycle and its control[END_REF] CDK's themselves are continuously expressed throughout the cell cycle, but require the binding of cyclin proteins to be activated. The cyclin proteins however are periodically expressed in distinct phases of the cell cycle, hence rendering CDK's active at specific timepoints during the cell cycle.

Additionally, the timing of activity of CDK's is further regulated by inhibitors of CDK activity that either prevent CDK-cyclin binding or inactivate the CDK-cyclin complexes 1 . Active CDK's then exert their control over the cell cycle by phosphorylating key target proteins that are important during each specific phase of the cell cycle 1 . For instance, phosphorylation of the protein Rb during early G1 ultimately leads to the activation of two transcription factors that promote gene transcription of targets required for the S-phase, including cyclin A and E 3 .

Cyclin A dependent CDK's stimulate replication initiation by phosphorylating DNA polymerases 4 and Cyclin E-CDK2 phosphorylates histone H1, which may be important for controlling chromosome condensation during replication 1 . Using this phosphorylation system, the CDK network assures timely progression through the cell cycle, as shown in figure 2.

Additionally, the eukaryotic cell cycle contains 3 major checkpoints that make sure that the previous steps have been executed correctly 1,2 . The restriction point is the point where a cell transitions from G0 to G1. After this point, the cell is committed to completing the entire cell cycle, regardless of any environmental signals it receives. The checkpoint at the transition between G1/S ascertains that the cell has enough building blocks to finish replication and to check for DNA damage.

Likewise, the checkpoint at the transition between G2/M makes sure that mitosis can proceed without delay and that no DNA damage was accumulated after replication and G2. Finally, spindle checkpoint makes sure that chromatids are properly aligned with the spindle complex during the metaphase-anaphase transition. During these checkpoints, specific protein kinases capable of detecting the presence of DNA damage induce the expression of CDK inhibitors to arrest the cell cycle 1 . For instance, DNA damage during the G1/S checkpoint is detected by the ATM protein, whose kinase activity depends on the presence of DNA damage. Next, ATM phosphorylates p53 which in turn activates transcription of p21, an inhibitor of CDK activity 5 .

Together, the CDK network and checkpoint controls enable the orderly progression of the eukaryotic cell cycle through its different phases and ensure correct reproduction of the cell. The eukaryotic cell cycle is an ordered sequence of temporally separated events. First, the cell enters the G1 phase, the phase between the birth of the cell and the initiation of DNA replication. Next, the cell duplicates its chromosomes during the S phase and enters G2 with twice as many chromosomes. During mitosis, the nucleoid is disassembled into vesicles, DNA is condensed and the spindle structure is formed (prophase) chromatids are aligned at the cell equator (metaphase) chromatids are segregated by the spindle network (anaphase) the events of prophase are reversed (telophase) and the cell divides. Taken from 2 Regulation of the B A eukaryotic cell cycle using cyclin dependent kinases (CDK's): The eukaryotic cell cycle is regulated using a network of CDK's, which fall in three classes: G1 CDK's, S-phase CDK's and Mitotic CDK's. CDK's require the activity of cyclins. Taken from 2

The cell cycle in bacteria

In contrast to the eukaryotic cell cycle, the key biological processes of the bacterial cell cycle (preparation, replication, segregation and division) are not completely temporally separated into different phases. The bacterial cell cycle is reviewed in [6][7][8] and shown in figure 2A. Historically, the cell cycle in bacteria has been divided into three phases. The first phase, called the B-period, is the time between cell birth and the initiation of DNA replication and is akin to the G1 phase in eukaryotes. The second phase, called the C-period, encompasses the time needed to fully replicate the chromosome. The DNA replication process in bacteria differs from the one in eukaryotes in a number of important ways. First, bacterial chromosomes are usually circular as opposed to linear chromosomes in eukaryotes. This allows DNA replication in bacteria to start from a single origin of replication (oriC) where it proceeds bidirectionally until a termination region in the opposite half of the chromosome is reache (terC). Additionally, bacterial chromosomes are segregated as they are replicated, with oriC moving to different sides of the cell and the bulk of the chromosome following. The ParAB system (Soj) and the SMC complex are responsible for this segregation. Two models of segregation have been observed. In the model bacterium Escherichia coli (E. coli), replichores separate along the transversal axis of the cell and occupy two different halves of the cell with oriC and terC located at the poles or mid-cell (depicted in figure 2B). Alternatively, replichores separate along the longitudinal axis and oriC and terC end up at different cell poles in the model bacterium Bacillus subtilis (B. subtilis). The replicated chromosomes are concatenated at the end of the C-period 7 .

Chromosome separation is achieved using the DNA translocase FtsK, which colocalizes at the division site and recruits topoisomerase IV to decatenate the DNA and XerCD to promote homologous recombination between diff sites and chromosome separation. Finally the third phase, called the D-period, is the time between finalizing DNA replication and the end of cell division 8 . During this phase, bacteria start forming a Z ring made up of a tubulin homologue FtsZ when the middle of the cell is free of chromosomal DNA. The Z-ring defines the division site and recruits other cell division proteins, membrane invagination proteins and cell wall biosynthetic enzymes to complete division 8 . Another feature of the bacterial cell cycle that differs from the eukaryotic one is that no overarching regulatory network has been discovered that ensures orderly progression through the cell cycle 9 . CDK analogues do not seem to exist in bacteria 9 . Instead, a multitude of regulatory mechanisms that control the timing of DNA replication initiation, DNA replication elongation, cell division have been described in detail without a unifying principle that ensures the orderly progression of the cell cycle 9 . Hence, the cell cycle in bacteria is thought to consist of a series of independent steps, that are coordinated by a multitude of mostly unknown signaling systems 10 . Nevertheless, bacteria also have DNA damage response mechanisms like the SOS response (reviewed in 11 ) that can arrest progression of the cell cycle and nucleoid occlusion factors like SlmA in E.coli and Noc in B. subtilis prevent the formation of the Z-ring before the chromosomes are segregated 8 , akin to checkpoint control in eukaryotes.

B A

The cell cycle in archaea

The cell cycle in archaea contains features of both eukaryotic and bacterial systems as well as unique features to the phylum 12 . The archaeal cell cycle is summarized in figure 1D. Like in eukaryotes, the cell cycle in archaea consists of a temporally ordered sequence of events. The archaeal cell cycle generally consists of a G1 phase, an S phase, a G2 phase and an M phase.

The G1 phase in archaea is generally short, whereas the S (30-35%) and the G2 phase (>50%) take up the bulk of the cell cycle. Most archaea have a circular chromosome that is replicated from 1 or more origins of replication. During S-phase, all the origins fire synchronously. The replication machinery of all known archaea consists of homologues to eukaryotic proteins.

After the completion of replication and during the G2 phase, the chromosomes are resolved using the XerA homologous recombination system similar to the one in bacteria. This is remarkable since the multi origin replication of most archaea makes the decatenation of the replicated chromosomes more complex. This might be the reason why G2 takes up the bulk of the cell cycle. During the M phase, the duplicated genomes are segregated and the cell divides.

In the Sulfolobales order of archaea, chromosome segregation is achieved using the SegAB system, akin to ParAB in bacteria. Other segregation systems are currently unknown, but some species show rapid segregation of chromosomes, akin to the anaphase in eukaryotes, whereas others show a more gradual separation akin to the one in bacteria. Cell division systems are diverse in archaea. Some archaea divide using FtsZ, akin to bacteria. Others divide using the recently discovered Cdv system, which forms a spiral inside the membrane that constricts the cell similar to the activity of VP4, a vesicle forming protein in eukaryotes. Still other species have neither system, suggesting that other division systems remain to be discovered in archaea.

The mechanisms that enable correct progression through the phases of the cell cycle remain poorly understood in archaea. However, checkpoint control systems have been reported in archaea 13,14 .

Figure 3: The cell cycle in archaea

Like the eukaryotic cell cycle, the key biological cell cycle processes of the archaeal cell cycle are separated into a G1 phase, S phase, G2 phase and M phase. During S phase, the circular archaeal chromosomes are replicated from multiple origins of replication using eukaryotic protein homologues. During G2 phase, the multi-origin replicated chromosomes are decatenated using recombination systems homologous to bacterial ones and the cell is prepared for mitosis. During M phase, the archaeal chromosomes are segregated using segregation systems akin to bacterial ones. Finally, the cell is divided using a division machinery that is akin to either bacterial or eukaryotic systems or is entirely unique to archaea depending on the species studied. Taken from 12

Proper timing of cell cycle events

The proper timing and execution of events throughout the cell cycle is vital for proper survival in any organism. Temporal defects in the cell cycle could lead to the formation of anucleate, inviable mini cells or cause division through the chromosomal DNA. Each of these defects reduces cellular viability. Furthermore, in multicellular organisms such as humans, temporal disruption of the cell cycle is associated with unrestrained cell proliferation and therefore cancer. Mutations in cyclins, CDK activators or inhibitors and CDK substrates are frequently observed in tumors 1 and several anti-tumor drugs have been developed to control CDK activity 115 . In all organisms, proper progression of the cell cycle starts by restricting the The replication process is reviewed here 16 and consists of three phases: initiation, elongation and termination, depicted in fig. 4A. What follows is a model for the replication process in the bacterium B. subtilis.

DNA replication initiation itself consists of three major steps: (i) the assembly of the DnaA initiator oligomer at the origin of replication, (ii) the opening of the DNA and (iii) the recruitment of the other parts of the replication machinery at the unwound start site. First, the initiator protein DnaA binds to specific DNA sequences in the origin of replication called the DnaA boxes 17 . When bound to ATP, DnaA assembles into a right handed oligomer, thus forming the nucleoprotein complex 18,19 . The assembly of DnaA-ATP into the nucleoprotein complex triggers the unwinding of a nearby AT rich region called the DNA Unwinding Element (DUE) 20,21 . Next, the initiator proteins DnaB and DnaD bind to the origin 22 and remodel the DNA 23 prior to replisome loading 24 . DnaA-ATP then recruits the processive helicase DnaC in B. subtilis 24 , which is loaded on the DNA by the helicase loader protein DnaI 24 . Afterwards, DnaC recruits the primase DnaG and the polymerase β-clamp DnaN, which then recruits the other elements required for replication such as the lagging strand polymerase DnaE and the leading strand polymerase PolC 25 . The process of DNA replication initiation is summarized in figure 4B.

During elongation, new DNA is synthesized by a molecular machine called the replisome.

Single molecule studies suggest that the replisome consists of three DNA polymerase complexes PolC, DnaE and the primase DnaG, the hexamer DNA helicase DnaC, the β-clamp DnaN, three processivity clamps and a pentameric clamp loader 26 . DNA replication in any organism only occurs in one direction (5'end to 3'end) which results in one strand being continuously replicated (leading strand) and one strand being discontinuously replicated (lagging strand). The helicase DnaC forms a homohexameric ring that sits at the head of the replication fork on the lagging strand. Using the energy obtained from ATP hydrolysis, the DnaC ring mechanistically unwinds dsDNA. Separated DNA strands are then coated by the single stranded DNA binding protein (SSB) . This prevents both reassembly of the DNA duplex and protects the single stranded DNA against degradation by nucleases [27][28][29] . DnaG is bound to the helicase ring and catches single stranded DNA from the lagging strand for RNA primer synthesis right after duplex unwinding 30,31 . The single stranded DNA with its RNA primer is then handed off to the DNA polymerases for further extension 30 . The DNA polymerases DnaE and PolC use deoxyribonucleotides (dNTP's) to replicate the chromosome. DnaE was found to be required for lagging strand synthesis, whereas PolC is required for both 32 . Furthermore, it was found that both PolC and DnaE can extend DNA primers, but only DnaE can extend RNA primers 32 . For this reason, it is assumed that PolC is responsible for continuous synthesis of the leading strand and DnaE is responsible for extending the RNA primers on the lagging strand, before handing it over to PolC 32 .

Replication termination is controlled by a polar mechanism, where there is a permissive and non-permissive direction in which the terminus, the region directly opposed to the origin, can be approached. In B. subtilis, the binding of two homodimers of the replication termination protein (RTP) at A and B sites is necessary to stop replication 33,34 . Crystal studies have shown that the binding of RTP is different at the A site compared to the B site 35 , which may give rise to the establishment of a permissive and non-permissive direction. However, RTP binding to the A site is also required 36 .

This general mechanism of DNA replication is highly conserved in E. coli, but there are a few differences 16 . First, the origin of replication in B. subtilis is bipartite i.e. it consists of two intergenic regions, separated by dnaA 37 . In contrast, the E. coli origin of replication is continuous i.e. consist of only one intergenic region 16 . Second, E. coli does not have the B. subtilis DNA remodellers DnaB and DnaD 16 . Third, in E. coli, the DNA helicase loader is called DnaC and the DNA helicase is called DnaB 16 . Fourth, elongation in E.coli is done solely by DNA polymerase III (PolIII) [27][28][29] as opposed to B. subtilis leading strand polymerase PolC and lagging strand polymerase DnaE 32,38 . Fifth, while the principle of termination is maintained in E. coli, the termination protein is monomeric and called Tus in E.coli [39][40][41] as opposed to the homodimeric RTP from B. subtilis 16 . These small differences highlight the bacterial diversity in the specifics of the replication process as well as the conservation of the general process. In B. subtilis, only a handful of proteins are known to regulate timing of DNA replication and all act by regulating the assembly of the DnaA oligomer 16 .

YabA is the major regulator of DNA replication timing during vegetative growth 43 . Deletion of yabA leads to over initiation phenotypes, growth defects and asynchronous replication 43 . YabA consists of an N-terminal tetramerization domain and a C-terminal domain that can interact with both DnaA and DnaN 44 . Despite a lot of research however, the mechanistic details of how YabA controls DNA replication timing remain elusive. To date, two models have been proposed. In the first model, YabA tethers DnaA to DnaN at the replisome, thereby preventing DnaA to initiate new rounds of replication when the first one is finished 45 , consistent with an observed change in DnaA localization inside ΔyabA cells 45 . In the second model, YabA binds DnaA and prevents assembly of the DnaA oligomer 46 . This inhibition is relieved by competitive binding with DnaN 46 , consistent with the observation that DnaN concentrations are correlated with initiation frequency 46 .

Similarly, the segregation protein Soj (ParA) is another regulator that influences timing of DNA replication by modulating the assembly of DnaA 47,48 . Soj is an ATPase and its effect on replication depends on whether it is bound to ADP or ATP 49 . Soj-ADP is monomeric 50 and its interaction with DnaA prevents the assembly of DnaA at the origin. Conversely, Soj-ATP is dimeric and stimulates assembly of the DnaA helix 48 .

Additionally, six DnaA box clusters have been found outside the origin region of B. subtilis and their deletion causes over initiation 51 . This could mean that these regions titrate DnaA away from the origin. However, the effect was only strong when all six were deleted, casting doubt on how important this mechanism is for controlling the timing of DNA replication 51 .

Comparatively, more diverse mechanisms of controlling DNA replication timing have been discovered in E.coli 16 .

Like YabA and Soj in B. subtilis, the replication control protein DiaA influences DNA replication timing in E. coli 52 by affecting the oligomerization of DnaA at the origin of replication 53 . Deletion of diaA is associated with replication timing defects 52 and the DiaA tetramer is thought to act as a molecular scaffold that supports DnaA oligomerization at the origin of replication 53 .

Furthermore, binding of the nucleoid associated proteins IHF and Fis in the origin region also replication timing 54,55 . IHF binding in the origin region is thought to bring two DnaA boxes closer together and hence stimulate the assembly of the DnaA oligomer 56 . Conversely, Fis binding in the origin region inhibits DNA replication initiation by preventing the binding of both IHF and DnaA 54 .

Like Fis, the SeqA protein in E. coli affects DNA replication timing by preventing the binding of DnaA at the origin of replication [57][58][59] . Concretely, SeqA binds hemimethylated GATC sequences in the origin region (and beyond) 60 , which prevents both binding of DnaA there as well as the transcription of dnaA [57][58][59] .. The purpose of SeqA is to prevent immediate reinitiation after a new round of replication was launched. SeqA inhibition is relieved by methylation of the newly replicated strand using the Dam methyl transferase 61 .

In contrast to B. subtilis however, oligomerization and binding of DnaA in E. coli is actively regulated by controlling the conversion between DnaA-ATP and DnaA-ADP 16 . DnaA in S. aureus (a close relative of B. subtilis) was found to exchange ATP for ADP 10 times more frequently, making the conversion between DnaA-ATP and DnaA-ADP less viable as a regulatory mechanism in these organisms 62 .

The E.coli replication control protein Hda, the main E.coli regulator of DNA replication timing during vegetative growth, functions in this way. Hda is an ATPase whose effect on replication depends on whether it is bound by a nucleotide. Hda-ADP binds to DnaA and stimulates its ATPase activity, converting it from DnaA-ATP to DnaA-ADP 63 . Conversely, apo-Hda forms oligomers and multimers, which may suggest that Hda oligomerization is important for its activity 63 . Interestingly, Hda also binds DnaN and the replisome and this binding is required for Hda-DnaA binding 63 . This has led to the model that the role of Hda is to prevent immediate reinitiation after a new round of replication by disassembling the DnaA oligomer after the replisome is loaded 64 .

DnaA box clusters in E. coli outside the origin region not only titrate DnaA, but also control the conversion between DnaA-ATP and DnaA-ADP 16 . To date, three major DnaA box clusters have been discovered in E.coli. The first locus datA is bound right after initiation by IHF, which is essential for datA function 65 . When bound by IHF, datA binds DnaA-ATP and stimulates assembly and hydrolysis of ATP there, thereby preventing immediate reinitiation 66 .

Conversely, binding of DnaA to the DnaA reactivating sequence (DARS) 1 or 2, stimulates nucleotide exchange and recharges DnaA-ADP with ATP 67 . This process of recharging of DnaA has been shown to be influenced by IHF and Fis binding in the DARS region as well 68 .

Recently, DnaA in E. coli was also found to be acetylated on a lysine residue that is crucial for nucleotide binding in a growth phase dependent way 69 .

A summary of the different strategies to regulate DnaA activity is given in figure 5. The classical mechanisms described in this section assure that replication happens once and only once during each cell cycle during steady state conditions. However, most bacteria and lower eukaryotes must also be able to adapt their growth rate to varying environmental conditions 70 . These changes in growth rate require coordinated changes in the timing of the cell cycle 70 . Despite decades of research, the mechanisms that enable organisms to adjust their cell cycle dynamics, including DNA replication timing, to changing nutritional conditions remain poorly understood 70 71 . At low growth rates, the B, C and D period lengths increase with decreasing growth rates 72 . At high growth rates, the C and D periods remain virtually constant with increasing growth rates 71 . Paradoxically, both E. coli and B. subtilis achieve growth rates that are faster than their C and D periods at a certain threshold of nutrient availability. In order to resolve this issue, these bacteria start multiple replication cycles within one cell, a phenomenon called multifork replication 71,73 . Following these observations, Cooper and Helmstetter elaborated a model in which the timing of replication initiation is under growth rate control and that the timing of all other events in the cell cycle followed at a fixed time interval 71 . This widely influential model successfully explained how bacteria balance largely constant C and D periods at high growth rates with nutrient dependent changes in growth rate 71 .

Not much later, the hypothesis that the accumulation of cell mass regulates the timing of DNA replication became popular. Donachie 74 observed that the ratio of the average cell initiation mass to number of origins per cell was constant throughout many different media that enable fast growth in E. coli 71 and Salmonella 75 . More recently, single cell studies have demonstrated that the accumulation of cell mass since birth, not cell mass itself, before the onset of DNA replication is constant 76,77 . This model of cell mass accumulation controlling DNA replication timing has been criticized 10,[78][79][80][81][82] . Briefly, the studies mentioned above correctly noted a correlation between cell mass and DNA replication timing, but incorrectly concluded a causal relationship 10 . Moreover, recent studies in B. subtilis and E. coli have shown that cell size and DNA replication timing can be uncoupled, suggesting that there is no causal relationship 83,84,85 . For instance, mutants in B. subtilis that are 35% shorter than wild type still initiate with normal timing 83 . This means that replication can be uncoupled from cell size in B. subtilis 83 . Additionally, initiation mass in E.coli can vary 1,6 to 2 fold in slow growing cells, despite being constant in cells growing at a normal rate 78,84,85 . Finally, this view of the cell cycle as a single process controlled through DNA replication timing by the accumulation of cell mass doesn't explain why changes in nutrient availability can also affect the subsequent steps in the cell cycle 70 (See 1.4.2).

For these reasons, several groups have started to argue that growth rate dependent control of replication timing is a multifactorial process, which may involve sensing cellular metabolism and communicating this information to the replication machinery 10,70,[86][87][88] . Currently, this view is favored, since accumulating evidence suggests that the necessary molecular factors that govern replication timing may depend on the growth rate 78,80,85 .

Additionally, some evidence points to a direct role for metabolic signals to control DNA replication timing. It has been known for many years that yeast cultured in continuous culture condition creates synchronized oscillations of oxygen consumption 89 . These oscillations are called the yeast metabolic cycle (YMC) and are accompanied with oscillations in gene expression of over half of the yeast genome 90 . Interestingly, the proteins necessary for DNA replication (polymerases, primases etc) reach peak expression at the same time during the reductive phase of the YMC and DNA replication was also restricted to this phase 90 . This temporal compartmentalization of DNA replication to the reductive phase within the YMC is thought to protect DNA from damage caused by oxidative agents produced during the oxidative phase [90][91][92] . This example highlights both the importance of proper timing of DNA replication within the cell cycle as well as the fine-tuned way this timing responds to the metabolic state of the cell.

In the next section, the known mechanisms coupling DNA replication timing to growth rate are discussed.

Mechanisms of metabolic control of replication

Metabolic control of initiation: classical explanations

The concentration of DnaA has been proposed to couple DNA replication initiation timing to growth rate. First, the concentration of DnaA correlates well with the growth rate in bacteria during rapid growth 93 . Conversely, cells starved from amino acids stop expressing DnaA and don't initiate new rounds of replication 94 . Second, the concentration of DnaA has been shown to directly impact replication initiation timing. Artificial overexpression of DnaA leads to overinitiation [95][96][97] . By contrast, artificial inhibition of DnaA leads to underinitiation phenotypes 98 . Third, DnaA expression levels in vivo have been demonstrated to be under metabolic control. Specifically, DnaA expression is under the control of ppGpp 88 . ppGpp is a small nucleotide that can inhibit the transcription of many genes by interfering with the transcription complex 99,100 . DnaA is among the genes whose expression is affected by ppGpp, although it is currently unknown if this effect is direct or indirect 101 . Interestingly, ppGpp synthesis is stimulated upon amino acid or carbon starvation and therefore couples DnaA expression levels to nutrient availability 100 .

Metabolic control of elongation: classical mechanisms

DNA replication elongation is known to be subjected to metabolic control as well.

The most obvious mechanism by which elongation rate is controlled by nutrition is through the availability of deoxynucleotide (dNTP) precursors 70 . The replication rate in bacteria that require exogeneous addition of the nucleotide thymine varies with the concentration of thymine 104 . Additionally, the expression of ribonucleotide reductase, an enzyme required for the production of nucleotides, is regulated by DnaA to adapt the production of nucleotides to the requirements for elongation 105 . This suggest that the levels of dNTP influence replication rates under limiting conditions. Interestingly, the expression of nrdAB (main gene for RNR) is also under the control of the transcription factor NrdR 106 , whose activity is itself controlled by the binding of ATP 107 .

In addition to controlling DNA replication initiation rate, ppGpp has been shown to control DNA replication elongation rate under starvation conditions. ppGpp inhibits primase activity in both E. coli and B. subtilis 108 . Curiously enough though, halting DNA replication through inhibition of primase activity does not induce the SOS response 109 . The classical mechanisms involved in coupling DNA elongation rates to metabolism are summarized in figure 7. Ribonucleotide reductase, an enzyme that is crucial for the production of nucleotides in E. coli, is under metabolic control of both DnaA-ATP and NdrR-ATP. This ensures the timely production of nucleotides when there is enough energy and when there is need for them. Nucleotides are rate limiting for the replication machinery. Additionally, ppGpp can block primase activity and stall replication fork on top of its effects on initiation Taken from 8,16

Beyond classical mechanisms of metabolic control of replication: a role for direct metabolic signaling

However neither classical regulators of replication timing nor the known metabolic regulatory pathways for control of DNA replication suffice seem to fully explain coupling. In both E. coli and B. subtilis, the notion that the concentration of DnaA-ATP is a limiting factor for proper timing of DNA replication during fast growth conditions has been challenged 110,111 .

While the effect of ppGpp on replication at high concentrations during nutritional stress is well established, its role during exponential growth remains up for debate 110,112 . Deletion of the key classical regulators of DNA replication timing yabA and soj in B. subtilis impacted replication frequency, but not the coupling between DNA replication and metabolism 110 . In short, few mechanisms have been proposed that satisfactorily couple DNA replication timing to metabolism, especially under fast growth conditions in bacteria.

Recent studies suggest that key cellular metabolites and metabolic enzymes might directly send signals that regulate the cell cycle, even under fast growth conditions.

In E. coli, acidic phospholipids like phosphotidylglycerol (PG) and cardiolipin (CL) have been shown to regulate DNA replication initiation reviewed in 113 . In vitro studies have shown that CL and PG can stimulate the exchange of ADP for ATP in the E. coli DnaA [114][115][116] . The speed of this release depends strongly on the fluidity of the membrane 115,116 . Acidic phospholipids have also been known to inhibit binding of DnaA to oriC 117 . Interestingly, the presence of phospholipids in the membrane has been directly connected to cellular growth 118,119 , making this particular signaling system well suited for coupling DNA replication timing to growth rate.

During the yeast metabolic cycle (YMC), acetyl-CoA levels, which is a crucial intermediate in cellular metabolism (see below), were found to respond to the addition of rich carbon sources. Increased acetyl-CoA levels caused increased acetylation levels of histones that were specifically associated with genes important for growth through the activity of an acetyltransferase Gcn5 120 . This also led to increased entry in the cell cycle 120 . The authors also pointed out that under rich growth conditions, acetyl-CoA levels are constitutively high, which may be the reason why this type of metabolic control of the cell cycle has not been observed before.

Other recent studies have suggested that the metabolic enzyme GADPH is involved in regulating viral replication in yeast and plants 121,122 . Specifically, GADPH was found to stimulate plus strand synthesis of the RNA virus Tombusvirus both using in vitro assays and cell extracts 121 . Moreover, a negative mutant of GADPH inhibited viral replication in vitro and viral build-up in vivo 121 . GADPH was also found to bind the RNA virus Bamboo Mosaic Virus and negatively regulate its replication in vivo 122 .

These examples illustrate that key metabolites and key metabolic enzymes might signal metabolic cues that regulate the cell cycle and DNA replication timing, even in fast growth conditions and in a diverse array of organisms.Incidentally, both acetyl-CoA and GADPH belong to a highly conserved set of reactions called central carbon metabolism (CCM), which lies at the hearth of both energy and building block production.

The main hypothesis pursued in this work is that key metabolites and/or enzymes in CCM couple DNA replication timing to the metabolic state of the cell in bacteria. In the next chapter, the metabolic reactions that constitute CCM are reviewed with an emphasis on their importance for cellular energy and building block production. Next, the regulation of CCM activity in B. subtilis is discussed. Finally, an overview is presented of the accumulating evidence that CCM is committed to replication control in a wide range of organisms. 3 Central carbon metabolism (CCM) and its commitment in DNA replication

3.1 Central carbon metabolism (CCM): brief introduction

CCM in eukaryotes

Central carbon metabolism (CCM) is a collection of reactions aimed at extracting energy and precursors for macromolecular synthesis from nutrients 86 . It consists of about 30 key reactions that function by oxidizing glucose 123 and are highly conserved throughout all phyla 86 .

Central carbon metabolism in higher eukaryotes consists of four major pathways: glycolysis, the Krebs cycle, the pentose phosphate pathway and overflow metabolism and is reviewed in 124 .

Glycolysis comprises the set of reactions that breakdown glucose into pyruvate. In human cells, glycolysis is performed by the Embden-Meyerhof-Parnas pathway (EMP) pathway 124 . The EMP pathway is shown in figure 6. The EMP pathway net produces 2 ATP and 2 NADH in terms of energy and reductive power 124 . Additionally, the EMP pathway also produces the intermediates required for the Krebs cycle (pyruvate) and the pentose phosphate pathway (glucose-6-phosphate, fructose-6-phosphate and glycerldehyde-3-phosphate) (See below). On top of that, the EMP pathway fulfills important anabolic needs by providing key intermediates for non-essential amino acid production in humans 124 . Specifically, key intermediates of the EMP pathway include 3-phosphoglycerate (serine, glycine, cysteine) and pyruvate (alanine) 124 .

The EMP pathway is shown in figure 9. This top-down flux regimen is called a glycolytic growth condition. In human cells, glutamine, which enters the TCA cycle via oxaloacetate, can support growth as well 124 . In this case, the flux through the CCM is reversed

CCM in bacteria and archaea

Compared to eukaryotes, central carbon metabolism in bacteria is more diverse. In bacteria, breakdown of glucose into pyruvate is predominantly, but not exclusively achieved using the EMP pathway. The model bacteria B. subtilis, E.coli and many others use the EMP pathway as their major pathway for glycolysis 123 and most bacteria can make all amino acids using CCM intermediates 123 . However, a widespread alternative to the EMP pathway is the Entner-Doudoroff pathway (ED), which is shown in figure 7. The ED pathway converts glucose-6phosphate into pyruvate and glyceraldehyde-3-phosphate, which is turned into pyruvate using the same final reactions as EMP 123 . The ED pathway is the main glycolytic pathway in Pseudomonas, Zymomonas and Azotobacter and is a minor pathway in E. coli, that enables it to use gluconate as a carbon source 123 . The ED pathway has a net yield of 1 ATP and 2

NADH 123 . The ED pathway is shown in figure 12. The TCA cycle and PPP pathway are universally present in some form in all bacteria due to their importance for producing building blocks 123 . Full complements of both are present in the model bacteria B. subtilis and E. coli, but this isn't the case for all bacteria. The PPP pathway becomes the major pathway of energy production in bacteria that utilize pentoses and don't have the alternative EMP or ED pathways 123 . In some bacteria lacking a full TCA cycle, PPP pathway also helps fully oxidize pyruvate 123 .

Archaea have similar metabolic complexity to their bacterial counterparts, but demonstrate even greater variability in their CCM 125 . Only modified versions of EMP and ED pathways have been identified so far in Archeae 125 . Pentose sugars are broken down differently from their bacterial counterparts and PPP pathway is not fully present if at all 125 . Some key CCM enzymes/reactions in bacteria and eukaryotes have been replaced in Archeae 125 .

Regardless of the form it takes in any particular organism, CCM as a set of reactions that serve both catabolic and anabolic cellular needs, is universally conserved. Given its importance for both energy production and biosynthesis of important precursors of macromolecular synthesis, CCM is uniquely well positioned to sense both supply and demand of cellular energy and building blocks 86 . All organisms, including the model organism B. subtilis expend a lot of energy to produce CCM enzymes in high copy numbers 126 and some studies suggest the tradeoff between enzyme cost and energy efficiency is the reason for the prevalence of both EMP and ED 127 . It is therefore important to tightly regulate the flux through these pathways. The mechanisms responsible for controlling CCM activity in B. subtilis, the object of this study, are discussed in the next section.

3.2 Regulation of CCM activity: the PTS system and carbon catabolite repression

The phosphoenolpyruvate:carbohydrate phosphotransferase (PTS) system

In a mixture of carbon sources, B. subtilis preferentially uses the carbon source that most readily supports growth before moving onto others, creating a hierarchy of carbon sources. To make this sophisticated phenotype possible, B. subtilis has developed a very complex regulatory network capable of controlling CCM activity. For good reviews on this topic, the reader is referred to 128,129 .

In bacteria, the phosphoenolpyruvate:carbohydrate phosphotransferase system (PTS) is a key player in regulating CCM activity. First and foremost, the PTS system is responsible for the uptake of a large variety of carbohydrates, including sugars and sugar derivatives such as sugar alcohols, amino sugars, disaccharides and many others 129 . Equally important though are the many regulatory functions the components of the PTS system have in the regulation of CCM activity 129 .

The PTS system consists of four soluble and one membrane spanning protein that end up transporting PTS carbohydrates and phosphorylating them upon entry. The PTS system uses phosphoenolpyruvate (PEP) as both an energy source and a phosphoryl donor 129 . At the first step, PEP is bound by the protein EI, the first component of the PTS system 130 . Next, EI is phosphorylated on a conserved histidine by PEP 130 , which will subsequently transfer this phosphorylation to histidine 15 of the protein HPr 131 . This protein subsequently transfers the phosphate to the sugar specific PTS components EIIA on a conserved histidine residue 132,133 .

EIIA then transfers the phosphate onto EIIB on a conserved cysteine residue 134 . EIIC is the membrane spanning part of the PTS system that holds the carbohydrate, which will ultimately receive the phosphate from PEP and this phosphorylation event lowers the affinity of EIIC for the carbohydrate 135 .

The different parts of the PTS system form a phosphorylation cascade that transfers a phosphate from PEP to the incoming carbohydrate 129 . This cascade is shown in figure 13. The phosphorylation status of the proteins forming this cascade depends on two things. First, the uptake of more efficiently metabolized sugars leads to increased dephosphorylation of the PTS components 129 . Second, the PEP to pyruvate ratio also affects the phosphorylation state 136,137 . A high PEP to pyruvate ratio is indicative of starving conditions or growth on non-PTS sugars 137,138 , whereas a low PEP to pyruvate ratio is indicative of metabolically active cells 138 .

These phosphorylation signals can then be used to regulate CCM activity. It controls catabolic gene expression through (i) carbon catabolite repression (CCR), (ii) operon-specific inducer exclusion and (iii) induction prevention. These are discussed in the next section. This binding is stimulated by the glycolytic indicators glucose-6-phosphate and fructose-1,6bisphosphate 148,149 . The HPr-P-CcpA complex then binds to cre sites and represses the transcription of catabolic genes of carbon sources secondary to glucose 146,147 . PTS mediated carbon catabolite repression is summarized in figure 14.

Figure 14: Carbon catabolite repression

In the presence of glucose, a phosphorylation cascade from PEP through the PTS system results in the import of glucose-6-phoaphate, which is transformed into fructose-1, 6bisphosphate (FBP). FBP stimulates the kinase activity of HprK, resulting in the phosphorylation of HPr on serine 46. HPr-S46-P then interacts with the global regulator CcpA, that binds to cre sites and prevents transcription of any gene involved in the catabolism of secondary carbon sources. Based on 129 .

Regulation of central carbon metabolism: operon specific control

In addition to CCR regulation, the expression of catabolic genes for many secondary carbon sources is also controlled by an operon-specific transcription factor that is induced when it detects the secondary carbon source 128 . In order to prevent activation of these genes in a Alternatively, specific transcription factors can be inactivated by sequestration near the membrane. This is achieved through the fusion of EIIB to its cognate sugar specific transporter and interaction of the cognate transcription factor with the unphosphorylated EIIB.

An example of this regulatory mechanism includes the MtlR from B. subtilis 159 . Conversely, CCM activity can also be regulated by interactions with phosphorylated PTS components 129 .

The HPr-CcpA system described above is one example. Other examples include the stimulation of adenylyl cyclase through interaction with phosphorylated EIIA Gluc 160 and the antiterminator LicT in B. subtilis through interaction with phosphorylated EIIB for βglucoside 161 . . Collectively, these different mechanisms are responsible for optimizing CCM activity under different growth conditions at all times based on the measurement of a few key metabolites.

This principle is summarized in figure 16. Thanks to this and CCM's unique position in cellular metabolism (see section 2.1), the activity of CCM perfectly encapsulates the metabolic state of the cell. This makes sensing CCM activity theoretically ideal to couple DNA replication timing to the metabolic state of the cell. In the next section, the evidence will be reviewed that CCM is indeed involved in gating DNA replication timing Figure 16: CCM perfectly reflects the metabolic state of the cell : CCM senses both supply and demand. Furthermore, the activity of CCM is dynamically modulated by a complex regulatory system that involves sensing key CCM metabolites such as PEP and regulating metabolic gene expression, thus ensuring optimal flux through CCM in every metabolic condition.

Commitment of central carbon metabolism in replication

Functional connections between CCM enzymes and DNA replication

In recent years, evidence has been building that CCM is involved in the control of replication timing in prokaryotes and eukaryotes alike. This evidence is summarized in figure 17.

In and elongation (gapA and ackA) directly 86,110 . Both papers suggested the existence of multiple medium dependent links between CCM and replication 86,110 .

Similar experiments in E.coli also revealed that several CCM (pta, ackA, tkt and gpmA) genes are functionally connected to DNA replication proteins as CCM deletions could both restore viability at elevated temperatures 163 and could reverse cell filamentation and improper nucleoid positioning in Ts sensitive backgrounds at elevated temperatures 164 .

Bioinformatic analysis in prokaryotes supports a role for CCM in control of replication timing as well. In silico analysis of fully sequenced bacterial genomes found a highly conserved physical linkage between two glycolytic enzymes (pyruvate kinase and phosphofructokinase)

and the DNA polymerase DnaE 165 . This conserved proximity on the genome sequence is thought to indicate related functions.

The connection between CCM and replication doesn't seem to be limited to prokaryotes.

In budding yeast, the following results were obtained: (i) three CCM enzymes (HXK2, ENO1

and GPM1) were found to be functionally linked to MCM1 166 . MCM1 is a multifunctional protein that can bind DNA to stimulate replication initiation and also controls the expression of many genes, including some involved in DNA replication 167,168 . (ii) Inactivation of glycolytic genes in yeast arrested them at the G1 phase (PYK1) or at the S-phase (PGI1) [169][170][171] . (iii)

Mutations in Cdc40p, a splicing factor required for both G1/S and G2/S transition are suppressed by overexpressing the glycolytic enzymes GAPDH, PGK1, GPM1 and ENO2 172 .

(iv) The origin recognition complex (ORC) interacted with CCM genes near replication origins 173 .

In human fibroblasts, recent studies found that silencing of several glycolytic (HK2, TPI, GADPH, ENO1 and LDHA) ,TCA cycle (CS1, ACO2, SUCLG2, SDHA, FH and MDH2) and PPP genes (H6PD, PRPS1, G6PD, RBKS and TALDO) impacted either the delay for entry into S-phase or its efficiency or both. 174,175 . Given its strong interconnection with all cellular processes, it is highly likely that CCM influences replication indirectly. First, CCM is responsible for the production of nucleotides through PPP, which are necessary for successful completion of replication as discussed earlier. 123,176 . Second, deletion of CCM activities can sometimes lead to the activation of stress responses that can influence replication. For instance, a study in E.coli found that the suppression of temperature sensitivity in DnaA by the previously found ackA-pta pathway 163,164 were due to an activation of the stress response pathway 177 . Furthermore, the deletion of the ackA-pta pathways were associated with an accumulation of pyruvate and resulted in an altered activity of pyruvate dehydrogenase (PDH) 177 . Deletion of parts of pdh also resulted in suppression of Ts DnaA 177 . These results underscore the difficulty of interpreting these deletion studies of CCM enzymes due to pleiotropic effects.

Nevertheless, evidence is accumulating that CCM may play a more direct role in regulation of replication timing as well.

Direct protein-protein interactions (PPI) have been shown to modulate the activity of replication proteins. DnaE, DnaC and DnaG were shown to interact in B.subtilis in a ternary complex and this interaction was shown to improve the fidelity of DnaE 178 . Later, it was found that PPI between DnaE and other replication proteins also improved its fidelity considerably and that DnaE was essential for both initiation and elongation 179 . Similar PPI interactions between replisome components through the Ctf4 hub protein are important in yeast and human replisomes for proper DNA replication 180 .This suggests that the replisome is a potential target for controlling the timing of replication through direct PPI.

In bacteria, some CCM enzymes directly interact with replication proteins and modulate their activity. A yeast-two-hybrid screen in B.subtilis revealed that DnaC and DnaG interact with subunits of the pyruvate dehydrogenase, branched ketoacid dehydrogenase and acetoin dehydrogenase 181 . The binding of the primase DnaG to pyruvate dehydrogenase was thought to inhibit replication 182 .

Direct interactions between CCM enzymes and replication proteins have been observed in eukaryotes as well.

In humans, nearly all CCM enzymes enter the nucleus, making direct interactions in vivo possible 183,184 .Additionally, some human CCM enzymes (LDH, PGK, GADPH) bind DNA and stimulate replication in vitro [185][186][187][188] and in vivo (PGK) 189,190 .

In plants, both GADPH and PGK influence viral replication through direct interaction with replisomes of several viruses 121,122,191,192 .

Post-translational modifications of replication proteins constitute another method to control DNA replication timing within the cell cycle. It is a well-known system for regulating cellular activities in eukaryotes, including replication (See the CDK system for regulating eukaryotic cell cycle: section 1.1) More recent lines of evidence suggest that post-translational modifications may play a key role in regulating replication activity in prokaryotes as well. First, proteome technologies have revealed post-translational modifications in many bacterial proteins, including replication proteins [193][194][195][196] . Second, independent studies have shown that modifications of replication proteins in prokaryotes happen and alter their activity. For instance, DnaA is acetylated in E.coli and this acetylation is important for its activity 69 . Additionally, phosphorylation of the bacterial single stranded DNA binding protein (SSB) is also important for replication 197,198 . However, direct post-translational modifications of replication proteins by CCM effectors have not yet been described to the best of our knowledge.

By contrast, histone modification and cell cycle dependent histone expression regulate gene expression, control the timing of DNA replication and therefore constitute another level of control on DNA replication timing (for review, see 199 ).

Indeed, CCM effectors directly influence the (modification) status of histones to promote growth and DNA replication.

In yeast, the CCM metabolite acetyl-CoA stimulates growth by increasing histone acetylation near genes involved in growth 120 . One of these genes is CLN3, an early G1 cyclin that permits the subsequent expression of hundreds of cell cycle genes 200 .

In humans, the following results were obtained: (i) GAPDH proved to be an essential part of the OCA-S complex. This complex binds tightly to the Oct-1 transcription activator, which is responsible for the expression of H2B, an essential histone for S-phase progression 201 . (ii)

PDH translocates in a cell cycle dependent manner into the nucleus and generates the acetyl-CoA needed for correct acetylation of histones involved in progression through the G1/S transition and S-phase 202 . (iii) PKM2 phosphorylates histone H3 and changes expression of cyclin D1 among others, which is important for cell cycle progression in cancer cells 203 .

In summary, a lot of evidence has emerged that CCM activity is indeed coupled to replication control. In this work, the role of the glycolytic enzyme pyruvate kinase in DNA replication timing control is evaluated. enter the TCA cycle to replenish molecules withdrawn for anabolic purposes using pyruvate kinase (pykA) and pyruvate carboxylase (pycA) 204 . Under gluconeogenesis, the TCA cycle intermediates oxaloacetate or malate are converted into the intermediates of glycolysis that were used for anabolic conditions using the enzyme PEP carboxykinase (pckA) 204 . Thus, the enzymes of the PEP-pyruvate-oxalate node in general are crucial for the switch between glycolysis and gluconeogenesis.

The activities of these enzymes during the switch between gluconeogenesis/glycolysis is partially transcriptionally and partially post-transcriptionally regulated. pckA expression is repressed under glycolytic conditions [205][206][207] , but pykA and pycA expression is more or less constitutive [207][208][209] . This constitutive investment in PykA production under gluconeogenic conditions is remarkable, since pykA is one of the most highly expressed genes in the cell. By contrast, PycA is strongly allosterically regulated using acetyl-CoA 209 , but PykA activity is only known to respond to the concentrations of its substrate PEP 208 .

A role for PykA in cell cycle control

Recent evidence in the model bacterium B. subtilis suggests that pyruvate kinase (pykA) may be involved in control of DNA replication timing.

First, a mutation in pyruvate kinase (pykA) in B. subtilis was able to suppress Ts mutations in the dnaE gene in LB and this suppression was proposed to occur through a post-translational mechanism 162 . In a follow-up study, pykA deletion was also found to affect DNA replication initiation in LB and LB + malate 86 . In the same organism, pykA deletion rescues an assembly defect in a Ts sensitive FtsZ ring assembly required for cell division and causes a defect in cell division in wild type backgrounds, which can be rescued by exogenous addition of pyruvate 210 .

Taken together, these results indicate that PykA is required for proper cell cycle and replication control in B.subtilis. However, given their reliance on deletion of pykA , it is not clear from these studies if it is required for its catalytic activity or if the PykA protein itself plays a role in replication control. In the next section, the structure of the PykA protein is reviewed.

Structure of pyruvate kinase (PykA) in B. subtilis

The structure of pyruvate kinase has been solved for many organisms [211][212][213][214][215][216][217][218] , but unfortunately not for B. subtilis. However, the structure of a near identical pyruvate kinase from the closely related species Geobacillus staerothermophilus has been solved 214 and is shown in figure 18A.

The overall structure of pyruvate kinase is strongly conserved throughout the tree of life and reviewed in 219 . Most pyruvate kinases exist in the cell as homotetramers 214 .

Pyruvate kinase monomers consist of three domains (A,B and C) that are highly conserved throughout the tree of life and together constitute the catalytic domain [211][212][213][214][215][216][217][218] . Eukaryotic pyruvate kinases have an additional N-terminal domain 211,216,217 , which bacterial pyruvate kinases lack [212][213][214]218 . However, the pyruvate kinases of B. subtilis and G. staerothermophilus have an extra C-terminal sequence ECTS 214,220 , which is conserved among the Bacilli 220 .

In general, pyruvate kinases are formed by monomer-monomer and dimer-dimer interactions 219 . Monomer-monomer interactions are mediated through interactions of the Adomain along a 2-fold axis, also called the r-axis or large interface 219 . The dimer-dimer interface of PykA is mediated by the C domains along a 2-fold axis called the q-axis or small interface 219 . Additionally, the extra ECTS domain is predicted to interact with a neighboring A domain and is thought to stabilize the tetramer, even though its deletion does not affect catalysis 214 .

Objectives

The major goal of this work is to demonstrate for the first time that (i) PykA moonlights in the control of DNA replication gating and to generate insights into the moonlighting mechanism and (ii) that it can couple DNA replication gating to the metabolic state of the cell.

The first goal of this work was to demonstrate that PykA plays a role in control of DNA replication gating beyond its catalytic activity and to identify the regions by which it may exert control over replication timing. This was achieved by (i) analysing the effect of pykA mutations in the PEP utilizer and catalytic domains on replication and cell cycle control in a medium in which PykA metabolic activity is dispensable and (ii) by integrating in vivo analysis methods (qPCR, flow cytometry and microscopy) with in vitro replication assays to get a holistic view of the effects of PykA on replication. These results are described in Chapter 2 and will be published in Spring 2020.

The second goal of this work was to demonstrate that PykA is not just a replication protein, but also couples DNA replication timing to the metabolic state of the cell. This was achieved by analysing the effects of pykA mutations during a shift from a rich gluconeogenic to a rich glycolytic medium using the analysis methods from the first part. These results are described in Chapter 3.

Strain construction was performed as indicated in Supplementary table I. Growth rate and qPCR experiments (ori-ter, marker frequency) were performed by Steff Horemans (except for mutated hyPEPut constructs). Flow cytometry was performed by Laurent Jannière, Gabor Gyapay, Nathalie Vega-Czarny and Marcel Salanoubat . In vitro experiments were performed by Alexandria Holland, Matthaios Pitoulias and Panos Soultanas.

Key methodology used in this thesis

6.1 Marker frequency analysis and (ori-ter ratio) for determining replication speed Ori-ter ratio analysis is a method that enables the detection of the average number of ongoing replication events(Fig. 19A) 82 . It is based on the quantification of the number of DNA copies close to the origin versus the number of DNA copies close to the terminus 82 . For instance, if a cell is in the process of replicating its chromosome (single replication), this cell will contain twice as many origin sequences compared to terminus sequences. Conversely, the ratio becomes one again when the chromosome is fully replicated. Hence, the ori-ter ratio reflects the time it takes to complete chromosome replication and is used in literature to calculate the C-period 82 . In this work, the ori-ter ratio is used as a general replication parameter to see if replication is impaired in a particular strain. The C-period and replication speed are determined using the quantification of about 10 DNA sequences between the origin and terminus, which allows for a more accurate determination.

Quantification of DNA sequences is done using qPCR in this work, but could also be done by and the more active it is, the more DNA product is produced. The DNA product produced by DnaE is radioactive due to the radioactive primer, which is used to quantify the DNA product using gel electrophoresis and radiography.

Helicase activity is measured in a similar way (Fig 20B) 178 . First, a radioactive primer is annealed to MS13 DNA. Helicase is then incubated with that. Helicase then displaces the radioactive primer from MS13 as part of its activity, which leads to its dissociation from the column. The amount of dissociated primer versus bound primer is determined from using gel electrophoresis and radiography.

INTRODUCTION

DNA replication is gated to a specific timeslot within the cell cycle to ensure that chromosome replication occurs once and only once every cell cycle. This gating is maintained by two distinct systems. First, overlapping mechanisms control the activity of replication initiators and replication origins. These are responsible for initiating DNA strand opening and recruiting the replication machinery at origins every cell cycle. (Jameson & Wilkinson, 2017; Siddiqui et al., 2013) and prevent re-initiation before the end of the cell cycle. Second, the timing of DNA replication is also connected to the availability of nutrients. (Buchakjian & Kornbluth, 2010; Ewald, 2018; Helmstetter, 1996). In bacteria, this higher level of regulation couples the rate of replication to the growth rate by modulating the initiation frequency and/or the speed of replication (Bipatnath et al., 1998; Schaechter et al., 1958; Sharpe et al., 1998; Skarstad et al., 1986). In Saccharomyces cerevisiae (and possibly higher eukaryotes), metabolic control confines DNA synthesis to a specific phase of a metabolic cycle (Burnetti et al., 2016; Klevecz et al., 2004; Papagiannakis et al., 2017; Slavov & Botstein, 2011; Tu, 2005). In either case, the net result of these two systems is a precise and reproducible timing of DNA synthesis within the cell cycle across a wide range of nutritional conditions.

In contrast to the classical regulatory mechanisms, the exact nature of determinants involved in the metabolic control of replication remains elusive. In bacteria, long-standing hypotheses postulating that this control depends on (i) the concentration of the active form of the replication initiator DnaA (DnaA-ATP), (ii) on restricting DNA polymerases activity by limiting precursor concentrations or (iii)

on the concentration of the alarmon (p)ppGpp have been challenged (Flåtten et al., 2015; Hernandez & Bremer, 1993; Mathews, 2015; Murray & Koh, 2014). For this reason, several groups have started to argue that metabolic control of replication is a multifactorial process where the effectors vary with nutrient richness and which may involve directly sensing cellular metabolism and communicating this information to the replication machinery (see for instance (Barańska et al., 2013; Boye & Nordström, 2003; Wang & Levin, 2009).

Central carbon metabolism (CCM) is a collection of metabolic reactions thought to be particularly involved in communicating information about the metabolic state of the cell (Nouri et al., 2018). In most organisms, it consists of glycolysis/gluconeogenesis, tricarboxylic acid (TCA) cycle, the overflow pathway and penthose phosphate pathway (PPP) (Kim & Gadd, 2008). Collectively, these reactions convert nutrients into the energy and the nucleotides, sugars, lipids and amino acids the cell needs for survival (Kim & Gadd, 2008). Moreover, the activity of CCM is dynamically modulated by a complex regulatory system that ensures optimal CCM activity across different nutritional conditions (Deutscher et al., 2014; Görke & Stülke, 2008). This regulatory system involves sensing the concentration of key CCM metabolites such as PEP and adjusting the expression of metabolic enzymes accordingly (Deutscher et al., 2014). This group of highly conserved reactions is therefore in a strategic position for producing signals that cells may use to couple key cellular decisions to the metabolic state of the cell, as was recently demonstrated for cell division in Escherichia coli and Bacillus subtilis (Chien et al., 2012; Monahan et al., 2014; Weart et al., 2007).

In addition, several lines of evidence have suggested that CCM might be involved in the gating of DNA replication in different organisms. In B. subtilis, deletion of the metabolic enzymes responsible for the terminal four reactions of glycolysis rescued temperature sensitive mutations in the primase DnaG, the helicase DnaC and the lagging strand polymerase DnaE in the LB medium (Jannière et al., 2007). Furthermore, follow-up studies showed that these mutations also impacted the gating of DNA replication within the cell cycle (Murray & Koh, 2014; Nouri et al., 2018). Moreover, similar connections between CCM and DNA replication gating have been found in E. coli (Maciąg et al., 2011, 2012), yeast (V. K. Chang et al., 2004; Victoria K. Chang et al., 2003; Chen & Tye, 1995; Dickinson & Williams, 1987; Hartwell, 1973; Kaplan & Kupiec, 2007; Shor et al., 2009; Sprague, 1977) and human cells (Fornalewicz et al., 2017; Konieczna et al., 2015) albeit involving different metabolic reactions, suggesting a conserved functional link between CCM and the gating of DNA replication.

However, the main limitation of the studies connecting CCM to gating is the drastic physiological effects of depletion of metabolic enzymes, complicating their interpretation. In E. coli, deletion of the overflow pathway enzymes ackA-pta has been consistently functionally connected to DNA replication and repair (Maciąg et al., 2011, 2012, 2012). However, a follow-up study found that deletion of ackA-pta lead to an activation of stress responses, an accumulation of pyruvate and an altered activity of another metabolic enzyme (pdh) in LB (Tymecka-Mulik et al., 2017). At present, it is unclear whether CCM indirectly influences DNA replication through its metabolic importance for the cell, or whether some CCM enzymes/metabolites have an additional moonlighting function in gating DNA replication. In spite of this limitation, it is likely that at least some CCM enzymes moonlight in DNA replication gating. First, CCM enzymes are well-documented to have several moonlighting functions in bacteria to higher eukaryotes (see for review (Commichau & Stülke, 2007; Snaebjornsson & Schulze, 2018)). Second, several CCM enzymes from different organisms directly interfere with DNA replication in vitro (Baxi & Vishwanatha, 1995; Grosse et al., 1986; Huang & Nagy, 2011; Jindal & Vishwanatha, 1990; Kumble et al., 1992; Li et al., 2018, p. 1; Popanda et al., 1998; K. R. Prasanth et al., 2011; K. Reddisiva Prasanth et al., 2017; Tian et al., 2015).

One of the CCM enzymes most frequently associated with both DNA replication gating and moonlighting is pyruvate kinase (Pyk). Deletion of pyk suppressed temperature sensitive mutations in replication enzymes in B. subtilis (Nouri et al., 2018) and S. cerevisae (Dickinson & Williams, 1987; Hartwell, 1973; Sprague, 1977). Additionally, pyruvate kinase isoform 2 (PKM2) in human cells is known to moonlight in a wide variety of cellular processes, including the regulation of S-phase histone expression, DNA repair, DNA segregation, transcription and cell-cell communication (Snaebjornsson & Schulze, 2018).

Here, we report on a possible moonlighting role of pyruvate kinase (PykA) in DNA replication gating in B. subtilis. PykA catalyzes the final reaction of glycolysis by converting phosphoenolpyruvate and ADP into pyruvate and ATP and is an important regulator of glycolytic flux (Schormann et al., 2019).

The 3D structure of pyruvate kinase has been solved for many organisms (Cheng et al., 1996; Larsen et al., 1998; Mattevi et al., 1995; Muirhead et al., 1986; Rigden et al., 1999; Suzuki et al., 2008; Zhong et al., 2017), but not for PykA . However, a structure has been obtained for the one of G.

staerothermophilus, which only differs by two amino acids (Suzuki et al., 2008).

In general, pyruvate kinase is a fairly well conserved protein across all different organisms (Schormann et al., 2019). Pyruvate kinase exists as a homotetramer in most known organisms, although monomers and dimers have been observed (Schormann et al., 2019). Additionally, the overall structure and active site configuration are also strongly conserved in all known organisms.

On the other hand, the activity of pyruvate kinases is regulated by a variety of effectors, most notable fructose-1,6-bisphosphate (FBP) or AMP and sugar monophosphates like ribose-5phosphate (Schormann et al., 2019). The mechanisms behind this regulation shows remarkable versatility, making pyruvate kinase an interesting drug target, despite the large degree of conservation of the active site (Schormann et al., 2019).

Pyruvate kinase in B. subtilis consists of 4 domains: the A, B, C and Extra C-terminal sequence (ECTS) (Suzuki et al., 2008). The A, B and C domains collectively make up the catalytic site, are strongly conserved and are sufficient for catalysis (Suzuki et al., 2008). The monomer-monomer interface of pyruvate kinases is mediated by the A domains along a 2-fold axis called the r-axis or large interface (Schormann et al., 2019). The dimer-dimer interface of PykA is mediated by the C domains along a 2-fold axis called the q-axis or small interface (Schormann et al., 2019). Additionally, the extra ECTS domain is predicted to interact with the A domain of a neighboring monomer through the amino acids E209 and L508 (Suzuki et al., 2008). This interaction is thought to stabilize the tetramer (Suzuki et al., 2008). The catalytic domain also contains the catalytic site and the effector binding site that regulates PykA catalytic activity.

The catalytic site is situated at the interface of the A and B domain in most Pyk, including PykA. The residues important for catalysis in the active site are strongly conserved. (Schormann et al., 2019).

The corresponding amino-acids in the B. subtilis protein are listed Fig. S1A. Amino acids N34, S36, D66 and T67 coordinate K + while amino acids E222 and G245D246 coordinate Mg 2+ (Schormann et al., 2019). The substrates PEP and ADP are partly bound by these cations, with Mg 2+ being essential for PEP binding (Schormann et al., 2019). PEP is additionally bound by R32, G245D246 and T278. ADP, on the other hand, is bound by R73 and K156. Finally, phosphate transfer from PEP to ADP is stabilized by K220. Mutation of this amino acid completely abolishes catalytic activity, though its effect on substrate binding is less clear (Schormann et al., 2019).

The effector binding site in most Pyk is situated in the C-domain and usually consists of a phosphate binding loop, a mobile loop and an effector loop (Schormann et al., 2019). In general, the effector loop remains buried in a pocket formed by the phosphate binding loop (Schormann et al., 2019). Upon binding of the phosphate binding loop to the effector, the effector loop becomes ordered (Schormann et al., 2019). The effector binding site in Pyk of G. staerothermophilus is thought to interact with R5P and AMP through the amino acids T381, S383, T386 and H425 (Suzuki et al., 2008), but no structural data is available of Pyk of G. staerothermophilus complexed with an effector (Schormann et al., 2019). However, mutation of H425 to alanine did lead to a decrease in AMP and to a lesser extent R5P binding (Suzuki et al., 2008).

In addition to the catalytic domain, the pyruvate kinases of B. subtilis and related species have a conserved extra C-terminal sequence (Nguyen & Saier, 1995; Sakai, 2004). The function of this Cterminal peptide is not known and its deletion in the related G. staerotermophilus pyruvate kinase has no effect on its catalytic activity (Sakai, 2004). However, the sequence of the extra peptide is homologous to the one of PEP utilizer (PEPut) domains found in the phosphoenolpyruvate:carbohydrate phosphotransferase (PTS) component EI and the metabolic enzymes pyruvate phosphate dikinase (PPDK) and PEP synthase (PEPS) (Fig. S1B) (Sakai, 2004) In these proteins, the PEPut domain transfers phosphates from one side of a protein to another. In the component EI of PTS, the mobile PEPut hosts a conserved histidyl that transfers phosphate abstracted from PEP to HPr (Teplyakov et al., 2006). By contrast, in the metabolic enzymes PPDK and PEPS, PEPut is a key part of the catalytic activity. It is responsible for turning ATP and pyruvate into AMP, pyrophosphate and PEP and the conserved histidyl transfers the phosphate groups between the nucleotide binding domain and the substrate binding domain (Goss et al., 1980; Herzberg et al., 1996).

Interestingly, a TSH motif around the catalytic H is conserved in PEPut of EI, PEPS, PPDK and PykA (Fig. S1B) (Sakai, 2004). In PEPS and PPDK, the T residue of the motif is phosphorylated at the expense of ADP to inhibit the catalytic activity of H-P. The phosphorylation/dephosphorylation of this residue is catalyzed by serine/threonine kinases of the DUF299 family (Jim N Burnell, 2010; Jim N. Burnell & Chastain, 2006; J.N. Burnell & Hatch, 1984; Tolentino et al., 2013). In contrast, the middle S has no effect on catalytic activity or substrate specificity (Tolentino et al., 2013). However, this residue may be phosphorylated in PykA as well (Eymann et al., 2004; Mader et al., 2012; Reiland et al., 2009).

As mentioned above, PykA has been functionally connected to DNA replication gating, but it is not clear whether these results indicate a moonlighting activity in the regulation of DNA replication gating or an indirect consequence of its metabolic activity (Jannière et al., 2007; Murray & Koh, 2014; Nouri et al., 2018). The goal of this work is therefore to demonstrate that PykA indeed moonlights in the gating of DNA replication and to identify regions of PykA involved in this moonlighting activity.

To achieve this, (i) the effect of mutations in the catalytic and PEPut domains of pykA on DNA replication timing and cell physiology were assessed in a medium where the metabolic activity of PykA is dispensable and (ii) the effect of the protein PykA on DnaE, DnaG and DnaC replication activities was analysed in vitro. This work (i) demonstrates that PykA moonlights in the gating of DNA replication via direct and indirect pathways and (ii) identifies regions in both the cat and PEPut domain important for this activity, assigning a concrete role to PEPut domain for the first time.

RESULTS

The temporal control of DNA replication is compromised in pykA null cells

PykA is a glycolytic enzyme that has no major function in neoglucogenic media (ref). To gain insights into its commitment in DNA replication, we thus analyzed replication in pykA mutants cultivated in the rich neoglucogenic medium MC (malate + casein hydrolysate). Control experiments confirm that PykA is dispensable in this medium as the size and growth rate of cells deleted for pykA (DGRM25, DpykA) are indistinguishable to that of the wild-type strain growing in the same medium (Fig. 1A). Moreover, metabolomic studies showed similar metabolite profiles in both strains in a minimal medium supplemented with malate (Fig. S2). Despite this global similarity, detailed qPCR and flow cytometry analyses showed that the mutant exhibits significant replication phenotypes in comparison to the wild-type strain: the ori/ter ratio is higher, the C period is extended, the speed of the replication forks is reduced, the concentration of origin sequences is lower and the age of initiation is pushed back (Fig. 1B). As microscope studies showed no major DNA segregation defect (Fig. 1C), we compare the cell cycle of the mutant and the wild-type strain (Fig. 1D).

Results clearly show that the temporal gating of replication in the cell cycle is compromised in pykA null cells growing in the MC medium and that this phenotype may not be due to the loss of PykA metabolic activity.

Note: the published (Nouri et al) ori/ter and C period of DpykA in mal+casa are nicely confirmed here if the latter value is corrected for the growth rate difference (previously 2.36 db/h and here 2 db/h).

Different regions in the catalytic domain of PykA but not its catalytic activity prevent reduction in replication fork velocity and early initiation

To gain insights into how pykA deletion alters replication gating, we first analyzed cell cycle parameters in mutants of the catalytic domain (Cat). To that end, we used a strain deleted for Cat (pykAcat) and mutants that affect the binding of phosphoenolpyruvate (pykAR32A), ADP (pykAR73A), phosphoenolpyruvate and Mg 2+ (pykADG245-6AA) or the transition state stabilizer (pykAK220A) (Fig. S1) (Schormann et al., 2019). We also used a 27 amino-acid deletion (208-234) (pykAJP) that overlaps the pykAK220A mutation and that suppresses a thermosensitive mutation in the lagging strand polymerase DnaE that affects DNA elongation (dnaE2.4) (Jannière et al., 2007; Paschalis et al., 2017). Control experiments confirmed that the Cat mutations inactivate or strongly inhibit PykA metabolic activity as the corresponding strains grow on a glycolytic medium as poorly as the strain lacking PykA (Fig. S3A). They also confirmed that disruption of the catalytic activity of PykA has no major outcome on cellular metabolism in MC as Cat mutants have a growth rate in MC and a metabolomic profile in a minimal media supplemented with malate similar to the wild-type strain (Fig. S3B andS4).

Moreover, Cat mutations have no detectable effect on segregation/compaction of DNA (Fig. S3C).

Determination of cell cycle parameters showed that two Cat mutations cause no significant cell cycle alteration (pykAR73A and pykAK220A) while the remaining four (pykAcat, pykAJP, pykAR32A and pykADG245-6AA) are associated with a high ori/ter ratio, an extended C period, a drop in fork velocity and, when they give nicely resolved peaks in run out experiments (pykAR32A), an increase in the number of origins/cell and the age of initiation is brought forward (Fig. 2 &S5). As all the tested mutations abrogate or strongly reduce PykA metabolic activity, this result indicates that the commitment of PykA in replication gating depends on the protein itself rather than on its metabolic activity and/or its role in cellular metabolism. PykA/dNTP synthesis/dNTP pools by metabolomic. We infer from this that the PykA domain encoding the catalytic activity (but not the catalytic activity on its on) is required to prevent slow replication fork and possibly early initiation in the MC medium. Three separate regions in the Cat domain appeared to be important for these activities. They encompass amino-acids R32, DG245-246 and residues 208-234 that are deleted in PykAJP.

Interestingly, the R32 and DG245-246 mutations affect the binding of phosphoenolpyruvate (Fig. S1)

suggesting that proper replication gating may depend on phosphoenolpyruvate binding to PykA. Given the strong genetic link between amino-acids 208-234 in one hand and DnaC, DnaG and DnaE in the other hand, and given that this link may depend on conformational changes in replication enzymes (Jannière et al., 2007), S6A and S7). This is consistent with previously data showing that PEPut plays no major function in the B.

our
subtilis pyruvate kinase metabolic activity (Sakai, 2004). Additionally, PEPut mutations have no detectable effect on DNA segregation/compaction (Fig. S6B).

The moderate ori/ter ratio decrease found in pykAΔPEP cells showed that PEPut is also important for replication (Fig. 3A). As this domain has no detectable metabolic activity (Fig. S7) (Sakai, 2004), this result confirms that PykA commitment in replication gating depends on the protein itself rather than on its metabolic activity and/or its effect on cellular metabolism. Interestingly, the pykATSH>AAA mutant exhibits a similar ori/ter ratio decrease than pykAΔPEP cells indicating that this phenotype mostly depends on the TSH motif (Fig. 3A). As individual mutations have no (pykAT>A and pykAH>A) or a marginal effect on the ratio (pykAS>A), 2 or the 3 residues of the motif are important for proper ori/ter ratio in the MC medium. Surprisingly, a dramatic drop in the ratio was found in pykATSH>DDD cells and the pykAT>D mutation is sufficient to account for this drop (Fig. 3A). The D residue mimicking amino-acid phosphorylation, this suggests that permanent phosphorylation of T in the TSH motif dramatically interferes with replication. The moderate ratio decrease observed in pykAS>D and pykAH>D cells indicates that permanent phosphorylation of the S and H residues of the TSH motif may also impact on the ratio however this effect is much less pronounced than in the pykAT>D mutant.

Determination of the remaining cell cycle parameters showed that the pykATSH>DDD mutation strongly delays initiation and dramatically stimulates replication speed while the pykATSH>AAA mutation only slightly increases fork velocity (Fig. 3BC and Fig. S8). Strong elongation phenotype/ dNTP pools by metabolomic in TSH mutants.

Overall, results show that the Cat and PEPut domains have opposite effects on replication timing in the MC medium. Where the Cat domain prevents early initiation and slow replication forks, PEPut, and more specifically transient phosphorylation of the T residue of TSH, prevents initiation delay and fast replication forks. The S and H residues of the TSH motif are also engaged in replication.

Free PEPut molecules have an inhibitory activity on the ori/ter ratio that depends on the H residue of the TSH motif, on amino-acids 208-234 of the Cat domain and on a limiting inhibitor

In order to better understand the functioning of PEPut and its interplay with Cat, this peptide was expressed from an IPTG inducible construct cloned at an ectopic position (in the amyE gene). The construction, termed hyPEPut hereafter, was introduced in wild-type cells and in various pykA mutants. Cells with key mutations in the TSH motif of hyPEPut (hyPEPutH>A and hyPEPutT>D) were also constructed.

Measurements of the ori/ter ratio in the wild-type, pykA and PykAPEPut contexts showed that hyPEPut expression is associated with a ratio decrease and that this phenotype is accentuated at high expression level (Fig. 4A). In contrast, this expression has no effect on the ratio in pykAcat cells (Fig. 4A). As the inducible promoter of hyPEPut drives synthesis of much less molecules per cell than the native promoter of pykA (about 3.000 versus about 30.000) (Eymann et al., 2004; Le Chatelier et al., 2004), this result showed that free PEPut molecules have a negative effect on the ori/ter ratio at moderate concentration (strains hyPEPut, hyPEPut pykA and hyPEPut pykAPEPut) but not at high concentration (strains hyPEPut pykAcat and pykAcat) and that this effect is dominant over high concentration of Cat (strain hyPEPut pykAPEPut) and high concentration of PEPut fused to Cat (WT strain). They also show that free PEPut does not complement pykAPEPut as the ori/ter

A B C

ratio in the pykAPEPut hyPEPut double mutant is lower than in the wild-type strain (Fig. 4A). These results suggest that the Cat and PEPut domain must be fused in a single macromolecule for proper functioning in replication. The opposite outcome of hyPEPut expression in WT (inhibition) and pykAcat (no inhibition) cells further suggests that B. subtilis encodes a replication factor (unidentified so far) that free but not Cat-fused PEPut molecules can sense. This factor is likely limiting as it is able to convey a signal for reducing the ori/ter ratio at mid but not high concentration of free PEPut molecules. We thus propose that most of the PEPut peptides receive the signal from the limiting factor at mid concentration switching on a system causing a decrease in the ori/ter ratio while, at high concentration, the system remains in the off status because only a weak proportion of free PEPut molecules receive the signal from the limiting factor.

Next, the effect on the ori/ter ratio of TSH mutations in hyPEPut was analyzed (Fig. 4C). This work, carried out in a WT context, showed a low ratio in hyPEPutT>A cells and an even lower value in the hyPEPutT>D mutant.

However, the ratio is nearly wild-type in hyPEPutH>A and hyPEPutH>D strains. This suggests that the H residue of the TSH motif in free PEPut molecules is essential for receiving and/or conveying the signal from the limiting inhibitor to the ori/ter ratio. This assigns a function to this residue that was not clearly detected in PykA (see Fig. 3A). Alike in the Cat-fused context, the T>D mutation in the TSH motif enhanced the inhibitory effect of free PEPut molecules further highlighting the importance of T phosphorylation in PEPut functioning.

Taken together these results suggest that the T and H residues of the TSH motif of PEPut ensure two separate functions important for replication.

We then analyzed the effect of hyPEPut expression in catalytic mutants of PykA (Fig. 4D). We found that, alike in the WT strain, expression of hyPEPut is associated with a reduction in the ori/ter ratio in the pykAR32A, pykAR73A, pykAK220A and pykADG245-6AA contexts. However, this inhibitory activity is abrogated in the pykAJP context and this abrogation is very strong as it still operates in the pykAJP hyPEPutT>D double mutant (Fig. 4D).

This established a tight functional relationship between amino-acids 208-234 of the Cat domain of PykA (these residues are deleted in PykAJP) and the H residue of the TSH motif of PEPut.

Overall, data show that PEPut can affect replication (likely initiation) on its on (i.e. independently of its fusion to Cat) and suggest that this activity is regulated by Cat (test pykA at low concentration). Although it is unclear how PEPut affects replication, this activity somehow depends on the H and T residues of the TSH motif of PEPut, on the level of T phosphorylation in the TSH motif, on residues 208-234 of Cat and on a limiting inhibitor. Potential targets in the replication machinery of PEPut are the DnaC, DnaG and DnaE replication enzymes as these enzymes are genetically linked to the 208-234 residues of the Cat domain of PykA (Jannière et al., 2007). Interestingly, these proteins form a ternary complex and play key functions in initiation and elongation of replication (Dervyn et al., 2001; Paschalis et al., 2017; Rannou et al., 2013; Sanders et al., 2010).

The purified PykA protein modulates replication activities of DnaE and DnaC through proteinprotein interactions

The results presented above uncovered metabolic determinants important for replication gating. They however do not provide clear insights on the mechanism at play. To investigate this mechanism, the effect of PykA on replication enzymes activities was investigated in vitro. To that end, the PykA protein was purified and controlled experiments showed that the isolated protein is metabolically active and forms the expected stable tetramer (Fig. S9). The effect of PykA was then tested on DnaC, DnaG and DnaE as these replication enzymes are genetically linked to PykA (Jannière et al., 2007). Previous studies showed that DnaC, DnaG and DnaE form a ternary complex that ensures important roles during replication initiation and elongation (Dervyn et al., 2001; Paschalis et al., 2017; Rannou et al., 2013; Sanders et al., 2010). The DnaC helicase melts the duplex DNA at oriC during initiation and separates the DNA strands in replication forks during elongation.

The DnaG primase synthesizes RNA primers at the origin and in replication forks. These primers are used by DNA polymerases for polymerizing DNA. The DnaE polymerase extends RNA primers produced by DnaG at the origin as well as in replication forks to ensure partial or complete synthesis of the lagging strand. The three replication enzymes were purified and their activities were tested in the presence or absence of PykA as previously (Paschalis et al., 2017; Rannou et al., 2013).

Replication assays with polymerase DnaE were first conducted. These assays were carried at a DnaE concentration (10 nM) that produces low amount of replication products in order to ease the detection of stimulatory effects. In reaction mixtures containing M13 ssDNA annealed to a 20-mer DNA oligonucleotide and DnaE in combination or not with equimolar concentrations of PykA, a substantial stimulation of DnaE polymerase activity by PykA was found in term of both product sizes and product amounts (Fig. 5A, left panel). A similar stimulation was observed with different templates (M13 ssDNA annealed to a 60mer DNA primer and a 110-mer annealed to a 15-mer primer) and at a lower DnaE concentration (2 nM) even though nearly no DNA synthesis was observed at this low DnaE concentration in the absence of PykA (not shown).

The stimulation was specific to PykA as it was not observed with equimolar amount of BSA (Fig. 5A, right panel). The lack of DnaE stimulation by BSA was confirmed at a 10-fold excess concentration over DnaE (Fig. S10A). The marginal stimulation observed at very high (100-fold) BSA excess is likely artifactual. Finally, titration experiments and gel shift assays showed that the stimulation of DNA synthesis by PykA was not due to a stimulation of DnaE binding to primed template (Fig. S10B). It is inferred from these experiments that PykA stimulates DnaE polymerase activity probably via a direct interaction between these two proteins. Since the purified PEPut domain does not change DnaE activity (not shown), the stimulation probably depends on a direct contact between DnaE and the Cat domain of PykA.

The helicase activity of DnaC was assayed by monitoring the displacement of a labelled 104-mer oligonucleotide annealed onto M13 ssDNA and forming a double-fork substrate with poly(dT) tails as previously (Rannou et al., 2013). To assemble a functional DnaC hexamer onto the DNA substrate, the helicase loader DnaI was added to reaction mixtures at equimolar concentrations. Results showed that DnaC is marginally inhibited in the presence of PykA (Fig. 5B, panel (1)). As we previously showed that DnaC activity is stimulated by DnaG (Rannou et al., 2013), DnaG was added to the above-mentioned reaction mixtures at equimolar concentrations. This analysis confirmed the stimulation of helicase activity by DnaG and showed that this stimulation is cancelled by PykA. Thus, in some contexts, PykA can significantly inhibit the helicase activity of DnaC. All together, these results suggest that PykA can modulate replication enzyme activities likely through direct interactions with replication enzymes.

Possible new result sections: suppression of dnaTs mutants by

DISCUSSION

The pyruvate kinase is a highly conserved and highly abundant protein that catalyzes the last step of glycolysis by converting phosphoenolpyruvate and ADP into pyruvate and ATP. In B. subtilis, this enzyme is composed of a N-terminal domain that encodes the catalytic activity and a C-terminal domain that is dispensable for PykA catalytic and has no known function. Interestingly, this peptide is strongly homologous to PEP utilizers imbedded in metabolic enzyme and endowed with kinase activity (Herzberg et al., 1996; Sakai, 2004; Teplyakov et al., 2006). Different evidences suggest that PykA is important for DNA replication. First, its deletion suppress thermosensitive mutations in enzymes essential for replication initiation and elongation in LB (the DnaC helicase, the DnaG primase and the DnaE DNA polymerase) and this suppression was proposed to occur through conformational changes in replication enzymes (Jannière et al., 2007; Paschalis et al., 2017).

Second, the metabolic control that mainly depends on DnaA and oriC in wild-type cells, was found DnaAdependent and oriC-independent in cells lacking PykA and grown in LB (Murray & Koh, 2014). Third, pykA deletion was found to stimulate replication initiation in rich media and this phenotype was proposed to result from a modulation of the functional recruitment of DnaC, DnaG and/or DnaE to oriC (Nouri et al., 2018).

Fourth, the pykA and dnaE genes are supposed functionally related as they are synthenic along a significant range of bacterial species (Overbeek et al., 1999).

To further investigate the link between pykA and replication in B. subtilis, we analyzed here different replication parameters in cells encoding a PykA protein mutated in the Cat or PEPut domain. This study was carried out in a gluconeogenic medium where the enzyme is strongly expressed but not required for metabolism and growth (Nicolas et al., 2012). Significant replication phenotypes were found in 13 of the 19 mutants tested (Fig. 2 and3). These phenotypes are not due to changes in pyruvate kinase activity as (i) some catalytic mutations that disrupt the metabolic activity of PykA are not associated with replication defect and as (ii) PEPut mutations do not affect PykA metabolic activity (Sakai, 2004). In contrast, these results suggest that replication phenotypes in PykA mutants result from changes in the PykA protein on its own. It is inferred from these data that the B. subtilis PykA protein encodes non-metabolic function for moonlighting in DNA replication. A literature survey shows that PykA and other CCM enzymes are multifunctional proteins that moonlight in various key cellular processes all along the tree of life (Boukouris et al., 2016; Commichau & Stülke, 2007; Snaebjornsson & Schulze, 2018).

Depending on the position of the mutations in the protein, opposite replication defects are generally observed (Fig. 2 and3). In mutants of the catalytic site, the speed of replication forks is significantly decreased (from 830 nt/s in the wild-type strain to 600 nt/s in mutants) causing a dramatic enlargement of the C period (from 40 to 55 min). In at least one of these mutants, the age of initiation is also put forward. Conversely, in PEPut mutants, the speed of the replication forks is remarkably accelerated (up to 1060 nt/s) causing an impressive reduction of the C period (up to 31 min) and the age initiation can be strongly delayed (from 9.2 to 17.8 min in a generation time of 30 min). It is inferred from this that both the Cat and PEPut domain of PykA moonlight in replication. Given that cells encoding only the Cat or the PEPut domain have different phenotypes, the results also suggest that the moonlighting activity of each domain is, at least in part, independent of each other. Finally, as replication defects in PykA mutants change the speed of replication forks and the age of initiation, our result shows that the moonlighting activity of PykA helps the cells to temporalize DNA replication in the cell cycle.

Results uncover amino-acids or groups of amino-acids in the PykA protein important for its moonlighting activity in replication. In the Cat domain, three such elements were identified (Fig. 2). They correspond to amino-acids R32, DG245-246 and residues at position 208-234. Interestingly, residues R32 and DG245-246 are important for phosphoenolpyruvate binding (Schormann et al., 2019) (Fig. S1) suggesting that replication gating may depend on phosphoenolpyruvate binding to PykA. Moreover, the moonlighting activity of Cat may operate through replication enzymes DnaG, DnaC and/or DnaE as residues 208-234 are genetically linked to these enzymes and may impact their conformation (Jannière et al., 2007). The in vitro modulation of replication activity of DnaC and DnaE by Cat (Fig. 5) strongly enforce this hypothesis and suggests that the temporalization of replication involves direct physical interactions between Cat and DnaC, DnaG and/or DnaE. Test colocalization Cat-replication machinery in vivo.

Point mutations in the PEPut domain clearly showed that the TSH motif play a key role in the moonlighting activity of PykA (Fig. 3). As these residues have been reported in the literature to be phosphorylated and as the phosphorylated H residue can transfer its phosphoryl group to a target protein (Burnell and Hatch, 1984; Herzberg et al., 1996; Mäder et al., 2012; Teplyakov et al., 2006; Tolentino et al., 2013), this result strongly argues that the moonlighting activity of PEPut may involve phosphorylation events. This hypothesis is supported by results showing that mutations mimicking phosphorylation at T and, to a lesser extent, S and H residues affects replication (Fig. 3). Alike in the PTS system, the H residue may be phosphorylated at the expense of phosphoenolpyruvate. As PykA binds this metabolite to catalyze the conversion of phosphoenolpyruvate to pyruvate, and as PEPut peptides are mobile, H phosphorylation may occur through an interaction between the PEPut and Cat domain of PykA. This hypothesis is supported by data suggesting that the binding of phosphoenolpyruvate to Cat is important for PykA moonlighting activity. However, despite our efforts, we were unable to detect autophosphorylation of PykA in the presence phosphoenolpyruvate in vitro (not shown). We speculate that something was missing in our reaction mixtures or that phosphorylation of the H residue of PEPut is carried out through trans interaction with other metabolic enzymes containing PEPut, namely the pyruvate phosphate dikinase and the PEP synthase. So far, we do not known how the T and S residues of PEPut may be phosphorylated. A survey of the literature however suggests that this phosphorylation may be carried out by one or another serine-threonine kinases encoded by B. subtilis (Cousin et al., 2013).

Additional determinants of the moonlighting activity of PykA have been uncovered when cells express free PEPut molecules from an ectopic locus at a moderate concentration (about 3.000 molecules/cell in comparison to about 30.000 molecules/cell for PykA; the construction is termed hyPEPut hereafter) (Fig. 4).

First, we found that the production of free PEPut molecules from hyPEPut is associated with an ori/ter ratio decrease in pykA context confirming that PEPut can express a moonlighting activity in replication independently of Cat. Interestingly, the ratio decrease observed at moderate concentrations of free PEPut molecules in cells lacking pykA is still expressed in cells producing PykA (the Cat-PEPut fusion) at high concentration, but not in cells producing high concentration of free PEPut molecules from the natural expression signals of pykA. This indicates that free PEPut molecules are dominant over PEPut molecules fused to Cat and that the activity of PEPut is modulated by its fusion to Cat. The contrasted response of the ori/ter ratio to the concentration of free PEPut molecules suggests that B. subtilis encodes a replication factor (unidentified so far) that free but not Cat-fused PEPut molecules can sense and that this factor is limiting as it is able to convey a signal for reducing the ori/ter ratio at mid but not high concentration of free PEPut molecules. We thus propose that most of the PEPut peptides receive the signal from the limiting factor at mid concentration, switching on a system causing a decrease in the ori/ter ratio while, at high concentration, the system remains in the off status because only a weak proportion of free PEPut molecules receive the signal from the limiting factor. The identification of the limiting factor is awaited to determine whether it can modulate the moonlighting activity of PEPut when fused to Cat.

The effect of moderate concentration of wild-type or mutated free PEPut molecules in various genetic contexts was also investigated. While, the negative effect of free PEPut on the ori/ter was found in most of the tested contexts, this phenotype was abolished in cells encoding a PykA protein deleted for amino-acids 208-234 (PykAJP) and when PEPut contains the H>A or H>D mutation (Fig. 4). Hence, the inhibitory activity of PEPut at moderate concentration on the ori/ter ratio depends on residues 208-234 of the Cat domain and on the H residue of the TSH motif in addition to the limiting factor. This functional link may also include DnaC, DnaG and DnaE as residues 208-234 of Cat are genetically connected to these replication enzymes and as the Cat domain of PykA modulates in vitro the activity of DnaE and DnaC likely through direct protein-protein interactions (Fig. 5). Additionally, the moonlighting activity of PykA may also depend on the binding of phosphoenolpyruvate to Cat as mutations known to disrupt this interaction affect PykA activity on replication and as the H residue in metabolic enzymes containing a PEP utilizer is phosphorylated at the expense of phosphoenolpyruvate. The phosphorylation of the T residue of the TSH motif may also contributes to PykA moonlighting activity as the T>D mutation has dramatic effect on replication (Fig. 3 and4). Interestingly, results suggest that the T and H residues operate independently indicating that the PEPut is the site of more than one signaling system. Further studies are awaited to better understand how the determinants uncovered here operate for conferring a moonlighting activity to PykA in DNA replication.

Results reported here show that PykA in B. subtilis moonlights in replication to help the temporal control of replication in the cell cycle. This may assign a function to PykA in genetic stability as an impressive core of data shows that defects in replication control increase the risk of double-stranded breaks which are chromosomal lesions with a high mutagenic potential (Michel et al., 2001; Rodgers & McVey, 2016). In cells with aberrant timing of initiation, re-initiation of replication may increase the risk of double-stranded breaks by collision between forks or by forks encountering gaps in the DNA template. Such a risk may also increase in cells with a lengthened C period in consequence of a higher probability of fork breakage over time and when replication forks are fast by reducing the time for error corrections. The potential involvement of CCM mutations in genetic stability through their effect on the temporal control of replication may be important in the field of cancer. Indeed, two hallmarks of cancer cells are aerobic glycolysis and genetic instability (Gatenby & Gillies, 2004; Loeb et al., 2003; Tubbs & Nussenzweig, 2017). As early as 1956, Otto Warburg has postulated that aerobic glycolysis is the root cause of cancer (Warburg, 1956). However, more recent hypotheses suggest that mutations in oncogenes and tumor suppressor genes are responsible for malignant transformation, and that aerobic glycolysis is a result of these mutations rather than their cause (Macheret & Halazonetis, 2015). Although the Warburg hypothesis (i.e. cancer results from impaired mitochondrial metabolism) has proven incorrect (aerobic glycolysis results from stimulation of glycolysis and acetate production), the hypothesis that changes in metabolism pave the way to mutations and tumorigenesis has been rekindled by a series of studies from bacteria to human cells. By assigning a moonlighting activity in replication control to CCM enzymes, this work suggests that CCM mutations can increase genetic instability and pave the way to tumorigenesis by altering replication control.

MATERIAL AND METHODS

Strains and plasmids

Strains and plasmids are listed in Supplementary Table S1. Table S2 presents primers used for strain constructions. In order to ease the construction of pykA mutants, we first inserted the tet gene of plasmid pTB19 few bp downstream of pykA. To that goal, we amplified the tet gene and two chromosomal sequences flanking the site of insertion. Next, we separated the PCR products from the parental genomic DNA by gel electrophoresis and the PCR fragments were gel purified using the Monarch DNA Gel Extraction Kit (New England Biolabs, Evry, Fr). The purified fragments were then mixed and fused together using assembly PCR. This reaction depends on 20 bp sequences that were added to insertion site primers and that are homologous to the 5' and 3' tails of the tet fragment. Competent cells of the wild-type strain cured of prophages TF8A were then transformed with the PCR product and double cross-over integrating events were selected on Tet plates after induction of Tet resistance (before plating, cells were incubated 1h at 37°C in the presence of 2.5 µg/mL Tet). A representative transformant was selected by sequencing and named DGRM295. To generate pykA mutants, two PCR reactions were performed on strain DGRM295 (pykA-tet). In each reaction, one external primer and one mutagenic specific primer were used to generate PCR products with the desired pykA mutation at one end. Upon gel purification, the products were fused by assembly PCR and used to transformed TF8A competent cells as above (however, transformants thought to encode an inactive pyruvate kinase were selected on LB + Tet + Malate 0,4%). The DGRM1009 strain containing the Lactococcus lactis pykA open reading frame in place off and downstream from the expression signals of the native pykA gene was constructed using the same principal as for generating the pykA-tet (DGRM295) strain. First, we generated 3 PCR fragments: one contained sequences upstream of and ending at the start codon of pykA;

another one contained the tet gene and downstream B. subtilis sequences (they were both generated using as template the DNA of the pykA-tet strain); and the last one corresponded to the L. lactis pykA open reading frame (it was generated using as template the L. lactis genome and primers having short homologies with the flanking PCR products). The fragments were then fused and the combined PCR product was used to transform TF8A competent cells as above. The DGRM1010 strain that contains the pykA L. lactis fused to the PEPut of PykA was constructed in a similar way. However, in that case the tet PCR fragment contained 5' to the tet gene the sequences of the PEPut domain. The genotype of transformants was verified by PCR and DNA sequencing. A slow growth on the rich LB medium which confers a glycolytic regimen confirmed that mutations in the catalytic domain of PykA (R32A, R73A, K220A and DG245-246AA) inactivate (or strongly reduce) its metabolic activity.

The GM2840 strain (provided by D. Le Coq, INRA, Jouy en Josas) contains at the amyE locus a DNA sequence called here HyPEPut that encodes (i) the PEPut domain of PykA under the control of the IPTG inducible Hyspank promoter and (ii) a CmR marker. The genomic DNA of this strain was used to construct strain DGRM722. DGRM722 derivatives with mutations in HyPEPut were generated as pykA mutants. For generating double mutants, competent cells of pykA mutants were transformed with the genomic DNA of HyPEP strains. The genotype of CmR transformants were verified by PCR and DNA sequencing.

Growth conditions

Routinely, B. subtilis cells were grown at 37⁰C in LB supplement or not with antibiotics at the following concentrations: spectinomycin (Sp, 60 µg/mL); tetracycline (Tet, 5 µg/ml); chloramphenicol (Cm, 5 µg/mL); phleomycin (Phl, 10 µg/mL). When cells are deficient in pyruvate kinase activity, 0.4 % malate was added to LB. For replication analysis, cells were grown in MC, a minimal medium (K2HPO4: 80 mM; KH2PO4: 44 mM; (NH4)2SO4: 15 mM; C6H5Na3O7 2H20: 3, 4 mM; CaCl2: 50 mM; MgSO4: 2 mM; FeIII citrate: 11 µg/mL; MnCl2: 10 µM; FeSO4: 1 µM; FeCl3:4 µg/mL; Trp 50 µg/mL) supplemented with casein hydrolysate 0.2%, malate 0.4% and tryptophan 0.01%. Other genetic and molecular biology procedures were standards.

Quantitative PCR

Cells were grown overnight at 30°C in MC medium supplemented with antibiotic. For accurate measurement, > 4 cultures were inoculated for ori/ter ratio analysis and 3 for marker frequency analysis.

With a very few exceptions, three independent constructs were used for the cultures. Saturated overnight cultures were then diluted 1000-fold in MC without antibiotic and growth at 37°C was carefully monitored using spectrophotometry. Samples for qPCR analysis were collected at low cell concentration (OD600 nm 0.06 to 0.25) to ensure that cell cycle parameters are determined in steady-state cells and are not affected by the approach to the stationary phase or by changes in medium composition. The genomic DNA was extracted as previously (Nouri et al., 2018) or using the PureLink Genomic DNA mini kit (Invitrogen by Thermo Fisher Scientific, Courtaboeuf, Fr). Every qPCR reaction was carried out using two technical repeats of 4 serial dilutions. For ori/ter ratio monitoring, a non-replicating control DNA (stage II sporlets 230 ) was analyzed simultaneously with the samples in about 1/4 of the qPCR plates. Reactions of qPCR for ori/ter ratio and C period measurements were carried out as previously (Nouri et al., 2018). The C period was extracted from marker frequency data and the slope of the straight line of the plot (log2Nm) against m where Nm is the relative copy number of a sequence N at position m (with 0 < m < 1 along the ori-ter axis) and  the generation time in minutes (Zheng et al., 2016). The mean fork speed was calculated using the C period (min) and the actual size of the TF8A genome (4,000,631 bp; this genome had been reduced compared to the reference 168 strain by deleting prophages SP, PBSX and skin).

Flow cytometry analysis

Strains were grown as indicated in the previous section and at OD600nm = 0.1-0.15, chloramphenicol (200 µg/mL final) was added to the cultures to impede replication initiation, cell division and allow completion of ongoing rounds of replication (Séror et al., 1994). After 4 hours of drug treatment, 10 8 cells were fixed in filtered ethanol 70% and stored at 4⁰C. Stored cells were then washed in filtered 0.1M of phosphate buffer (PB) pH 9, resuspended in 1mL of Tris-buffered saline buffer (TBS 150, filtered) (20mM Tris-HCl pH 7.5, 150mM NaCl) and stained with Hoechst 33258 (1.5 µg/mL) for at least 30 min as described elsewhere (Morigen et al., 2009). Flow cytometry analysis was performed using a MoFlow Astrios cell sorter (Beckman Coulter, Life Sciences) equipped with a 355 nm krypton laser and a 448/59 nm bandpass filter used to collect Hoechst 33258 fluorescence. Data were analyzed with the Kaluza software (Beckman Coulter, Life Sciences).

We counted 50,000 to 100,000 events. In most of the tested samples, DNA histograms showed 2 main peaks with a 2 n distribution. The number of origins/cell was obtained from the proportion of cells with 1, 2, 4, 8 and 16 chromosomes. The age of initiation was obtained from the fraction F of non-initiated cells (left peak in DNA histograms) using the formula F = 2 -2 (( -ai)/) were  is the generation time expressed in minutes (Morigen et al., 2009).

Microscopy analysis

In order to analyze cell size and nucleoid compaction and distribution, cells grown as above were collected at OD650nm = 0.08-0.1. The nucleoid and membrane of 1mL of cells were then stained with X µL of 4',6'diamidino-2-phenylindole (DAPI, Sigma) 10 mg/ mL and X µL of XXXX (FM4-64, Sigma) XXX µg/mL. Stained cells were then centrifuged gently and resuspended in 10 µL and mounted on glass slides (VWR) covered with 1.2 % agarose (low melting point, Sigma) in MC and a 0.17 mm glass coverslip (VWR) was placed on top.

Images were acquired on an epifluorescence microscope (Zeiss, Axio Observer.Z1) with a 100x magnification oil-immersion objective (Plan-APOCHROMAT Ph3) and a CMOS camera (Orca Flash 4.0 LT Hamamatsu). The images were analyzed using the Zen software (ref) and FiJi (Ref). Cell lengths and DAPI fluorescence were exported to Excel for statistical analysis. All experiments were independently performed at least three times and representative pictures are shown.

Biochemistry

Replication enzymes DnaC, DnaG and DnaE were purified an tested as previously (Paschalis et al., 2017; Rannou et al., 2013). PykA expression, purification and biochemistry.

Metabolomic by mass spectrometry

Materials and methods

In general, experiments were performed as in 86 . What follows is a brief explanation of the experimental procedure.

Strains and plasmids

Strains and plasmids are listed in Supplementary Table S1. B. subtilis cells were grown at 37⁰C in LB supplement or not with malate 0.4%. Unless stated otherwise, antibiotics were used at the following concentrations: spectinomycin (Sp, 60 µg/mL); tetracycline (Tet, 10 µg/ml); chloramphenicol (Cm, 5 µg/mL); phleomycin (Pm, 10 µg/mL); ampicillin (100 µg/ml). Liquid cultures were performed in an incubator under strong shaking (200-230rpm).

In order to obtain most point mutations and deletions in the pykA gene, two PCR reactions were performed on strain 295 (pykA::Tet). In each reaction, one external primer and one construction specific primer was used to introduce the desired mutation and later refuse the PCR products to a mutated version of pykA. A list of primers for each construct is found in Supplementary table 2.

Next, the PCR reaction products were purified from gel using the Monarch Gel Extraction Kit (NEB) to remove parental genomic DNA. The purified PCR products were then fused together using assembly PCR and the parental PCR fragments were removed using gel purification with the Monarch Gel Extraction Kit. Competent B. subtilis strains (TF8A) were then transformed with the PCR products and they were integrated into the chromosome of B. subtilis using double cross-over.

Positive transformants were selected on Tet. The genotype of the transformants was verified by PCR, phenotypic analysis and/or DNA sequencing. Mutants thought to inhibit PykA activity were selected on LB + Tet + Malate (0,4%) and slow growth on LB (which is glycolytic) was verified.

Strains 722 (hyPEP) and 1019 ( pykATSH>DDD) were obtained by transformation with linearized plasmids pIC610 and TSH>DDD-pJET respectively. TSH>DDD-pJET was obtained as follows.

First, two PCR reactions were performed on strain 295 (pykA::Tet). In each reaction, one external primer (LJ621 and LJ622) and one construction specific primer (pSH277 and pSH278) was used to introduce the desired mutation and refuse the PCR products to a mutated version of pykA. The external primers had overhangs homologous to pJET. Next, the PCR reaction products were purified from gel using the Monarch Gel Extraction Kit (NEB) to remove parental genomic DNA.

The purified PCR products were then fused together using assembly PCR and the parental PCR fragments were removed using gel purification with the Monarch Gel Extraction Kit. The purified PCR products were then cloned into a linearized E. coli plasmid pJET and used for transformation in Stellar E. coli competent cells (Clontech, Saint-Germain-en-Laye, France). Plasmids from positive transformants were selected on Amp, extracted using the Monarch Plasmid Miniprep Kit (NEB) and verified using agarose gel electrophoresis and DNA sequencing.

Plasmids with correct sequences were linearized by digestion with the restriction enzyme FspI Competent B. subtilis cells were transformed with the assembled and gel purified PCR fragments.

Positive transformants were selected on Tet and verified by PCR, phenotypic analysis and sequencing.

Mutated versions of hyPEP were obtained as described for pykA mutants using the plasmid pIC610 as a template for generating PCR fragments for assembly PCR. Positive transformants were selected on Cm and verified using PCR verification, DNA sequencing and phenotypic analysis.

Quantitative PCR

Measurement of the ori-ter ratio during metabolic shift

Wild-type cells cured of prophages (TF8A) and containing or not CCM mutations were grown overnight in minimal medium (K2HPO4: 80 mM; KH2PO4: 44 mM; (NH4)2SO4: 15 mM; C6H5Na3O7 2H20: 3, 4 mM; CaCl2: 50 mM; MgSO4: 2 mM; FeIII citrate: 11 µg/mL; MnCl2: 10 µM; FeSO4: 1 µM; FeCl3:4 µg/mL; Trp 50 µg/mL) supplemented with casein hydrolysate 0.2%, malate 0.4% and tryptophan 0.01% (MC medium) in triplicates (with a very few exceptions, the three independent cultures were inoculated with different constructs). Saturated cultures were then diluted 1000-fold in the same media and growth was carefully monitored using spectrophotometry.

At OD 0.08-0.1, the t0 sample was taken and 0.8% glucose was added to the MC medium (MCG).

In general, samples were collected every 15 min until timepoint 75 min, unless stated otherwise.

Cells were generally diluted 1/3 in prewarmed MCG medium after 45 min to maintain exponential growth. The genomic DNA was extracted using the GeneJET genomic DNA extraction kit (Thermoscientific) and every qPCR reaction was carried out using two technical repeats of 4-5

Hoechst 33258 fluorescence. Data were analysed with the Kaluza software (Beckman Coulter, Life Sciences). We counted 50,000 to 100,000 events. In most of the tested samples, DNA histograms showed 1-2 main peaks with a 2 n distribution. The number of origins/cell was obtained from the proportion of cells with 1, 2, 4, 8 and/or 16 chromosomes.

Age of initiation

The age of initiation was calculated from the equation ai=𝜏 -𝜏 * ln(2 -𝐹) ln( 2) ⁄ where τ is the generation time and F the fraction of uninitiated cells (Fraction of cells in the lowest peak). 82

Microscopy analysis

In order to analyze cell size and the presence of a nucleoid, cells were grown overnight at 30⁰C in LB and diluted 1000-fold in the same medium for further growth. At OD 0.080-0,1 ,cells were collected and the nucleoid was stained with 4',6'-diamidino-2-phenylindole (DAPI, 10mg/ mL) (Sigma). Stained cells were then mounted on glass slides (VWR) covered with 1.2% agarose (low melting point, Sigma) in minimal medium supplemented with malate 0.4% and casa 0.2% and a 0.17mm glass coverslip (VWR) was placed on top. Images were acquired on an epifluorescence microscope (Zeiss, Axio Observer.Z1) with a 100x magnification oil-immersion objective (Plan-APOCHROMAT Ph3) and a CMOS camera (Orca Flash 4.0 LT Hamamatsu). The images were analysed using FiJi. Cell lengths and DAPI fluorescence were exported to Excel for statistical analysis. All experiments were independently performed at least three times.

Results

Dynamic adaptation of cell cycle parameters in B. subtilis to a shift from MC medium to MCG medium

The goal of this work is to demonstrate that PykA communicates information about cellular metabolism to the replication machinery. To achieve this, the effect of a metabolic shift from rich gluconeogenic medium (MC: see previous chapter) to rich glycolytic medium (MC + 0.8% Glucose =MCG) on replication parameters was studied in wild type (TF8A) and pykA mutants. Concretely, 0.8% of glucose was added to cell cultures growing in MC and samples were collected every 15 min for ori-ter ratio analysis, marker frequency analysis and run-out flow cytometry.

Ori-ter ratio analysis in TF8A during the shift reveals that the ori-ter ratio remains constant for 15 min after the shift and starts to increase afterwards to reach a plateau at 45-60 min after the shift (Fig. 21A). This plateau ori-ter ratio is the same as the ori-ter ratio observed of TF8A in steady state MCG.

However, determination of replication parameters by qPCR and flow cytometry reveals a complex A caveat to this study is that many of the cell cycle parameters require the growth rate to be calculated and it is not possible to quantify growth rate changes during the shift. The generation time of TF8A

in MC is about 30 min and this drops to 23 min in MCG (data not shown). Therefore, the growth rate might change during the shift experiment.

However, it is known that the average cell size of B. subtilis is connected to the growth rate 75 .

Therefore, the cell size at specific time points was used as a proxy to assess whether the growth rate changes after a specific time point. This analysis demonstrated that the cell size of TF8A is smaller in steady state MC (5.67µm) than in steady state MCG (6.2 µm), consistent with the known connection between cell size and growth rate 75 . Interestingly though, the cell size didn't change after 45 min. Therefore, the growth rate was assumed constant between 0 min and 45 min after the shift.

In summary, TF8A (wild type) cells adapt their replication parameters to a metabolic shift from MC to MCG medium in a complex, multiphase manner involving in order (i) a short period in which replication doesn't change (ii) a temporary slowdown of replication fork speed (iii) increase in initiation frequency and advancement of the initiation age and finally (iii) increase of the cell size/growth rate. Ori-ter ratio t (min) TF8A In the next sections, the ori-ter ratio curves are examined for different pykA mutants using this framework. Other replication parameters are scheduled to be measured but were not yet finished at the time of writing. Fig. 24A shows examples of cat and PEPut mutation that were not determinants i.e. they neither affected the ori-ter ratio in MC nor at later stages of the shift. Hence, these results do not provide evidence for coupling.

By contrast, many of the cat and PEPut mutations that were determinants in MC are no longer involved at later stages of the shift (fig. 23B). This is the case for the rest of the cat mutants, as well as the T>D and TSH>DDD mutants. As argued in the previous chapter, these results therefore strongly suggest PykA mediated coupling between DNA replication timing and metabolism.

2.3

The signalling mechanism behind PykA mediated DNA replication gating responds to metabolism: a new role for phosphorylated H539.

A few mutations in the TSH motif did not fall in the categories from section 2.2 i.e. their effect on replication changed during the shift. These results are summarized in figure 25.

A

Chapter summary

In the previous chapter, PykA was demonstrated to moonlight in DNA replication gating in ways that could not be attributed to its metabolic activity. Here, it was shown that the moonlighting PykA, unlike its traditional counterparts YabA and Soj, couples its DNA replication gating to indicating that PykA regulatory activity is coupled to metabolism. Furthermore, the role of the TSH motif changed during the shift and phosphorylation of H539 became important at later stages of the shift. This suggests that the signalling mechanism behind PykA mediated DNA replication gating also depends on metabolism. Taken together, these results suggest for the first time that PykA, unlike its classical counterparts YabA and Soj, couples its activity to the metabolic state of the cell.

The goal of this research project was to demonstrate that (i) PykA moonlights in DNA replication gating beyond its metabolic activity and (ii) that PykA can communicate information about the metabolic state of the cell to the replication machinery.

PykA moonlights in DNA replication gating

To demonstrate that PykA has a role in regulating replication gating beyond its metabolic activity, the effect of pykA mutations on replication control was analyzed in a rich gluconeogenic medium (MC) in which PykA is strongly expressed [207][208][209] , but not required for metabolism. Using this approach, the results presented in this work put forward 5 main arguments that the effect of PykA on DNA replication gating can't be explained by its metabolic activity.

First, deletion of pykA impacted replication speed and initiation parameters without impacting growth rate in the MC medium. Second, mutations in the PEPut domain and its conserved TSH motif, which are dispensable for the metabolic activity of PykA 220 , affected replication speed and initiation parameters. Third, ectotopic expression of free PEPut affected ori-ter ratio, even in a wild type context. Fourth, precise mutagenesis in the pykA catalytic site revealed that some, but not all mutations that inhibit catalytic activity also lead to defects in replication gating. Finally, purified

PykA could modulate DnaE polymerase and DnaC helicase activity in vitro. Taken together, these results strongly suggest that the effect of PykA on DNA replication gating is not due to its metabolic activity, but is indicative of a moonlighting function, similar to the main function of the proteins YabA 43 and Soj 47,48 .

In support of this hypothesis, non-canonical functions of CCM enzymes (moonlighting functions) are increasingly being found to play a major role in cellular decision making across the tree of life, in particular in the development of cancer (see for a review 234,235 ).

For example, the moonlighting functions of pyruvate kinase isoform 2 (PKM2) in humans have been subject of intense research due to its perceived connections with tumorigenesis (for a recent review, see 236 ). Recent results have directly associated PKM2 to the regulation of cell cycle progression 203,237,238 , and DNA repair 239 , although direct stimulation of replication has not yet been demonstrated to the best of our knowledge. Additionally, PKM2 is involved in the other hallmarks of cancer as well including promotion of angiogenesis 240 , regulation of apoptosis 241 , immunity 242 or growth factor regulation 243 . As a side note, several studies also indicate that PKM2 is excreted and used for cell-cell signalling in humans, promoting growth, migration and matrix adhesion of endothelial cells 244 , enhancing cell migration in colon cancer cells 245 , promoting wound healing 246 and associating with MHC complex of B-cells 242 . PKM2 is but one of many examples of moonlighting CCM enzymes 234 , but it illustrates the extent to which CCM enzymes are increasingly found at the heart of cellular decision making. Hence, a moonlighting role for PykA in DNA replication gating is consistent with this growing body of work.

Mechanisms behind PykA moonlighting

Though still incomplete, this work has also generated insight into the mechanisms behind PykA moonlighting.

Mechanisms behind PEPut moonlighting

PykA moonlighting in DNA replication gating occurs in part through its PEPut domain. Despite many speculations about its role prior to this work 129,219 , this is the first work to identify a role for the domain, hence it is also the first to provide some insights into its functioning. Our results confirmed the speculated importance of the conserved TSH motif and the effects of phosphomimic mutations in H, S and especially T suggest that phosphorylation of the TSH motif may play a role in PEPut functioning. This is consistent with observations in PEPut from EI, PEPS and PPDK, where these residues were found to be phosphorylated as well [223][224][225][226] . In these examples, H is (auto)phosphorylated using PEP as a donor 223 and T using AMP 226 . However, the absence of any strong effects for alanine mutations in the T, S and H residues suggests that the PEP utilizer is only phosphorylated in a limited way under MC conditions.

Ectotopic expression of mutated hyPEPut largely confirmed these results. Mutation of the T in hyPEPut confirmed the results obtained for wild type (hyPEPutT>D and hyPEPutT>A). Additionally, hyPEPut does require H in its TSH motif (hyPEPutH>A and hyPEPutH>D), underscoring the importance of this residue as well.

Importantly, H>A does not have the same effect on replication gating than T>D. This suggests that T>D affects replication beyond regulating the phosphorylation on H as is observed for plant PPDK and PEPS PEP utilizers 225 . Even though more research is needed to fully understand the mechanisms behind PEP utilizer functioning, these initial results indicate that the mechanism might be different from what is known from EI, PPDK and PEPS.

In summary, several lines of evidence suggest that PEPut is involved in the regulation of DNA replication gating and that its activity depends on phosphorylation events of its TSH motif, particularly involving T and H. This is visualized in figure 26.

Mechanisms behind cat moonlighting

Additionally, the cat domain of PykA also seems to have independent moonlighting functions in DNA replication gating. PEP binding mutants and deletion of the catalytic domain affected replication speed, initiation frequency and the age of initiation. Therefore, these results might mean that PEP binding is required for cat moonlighting. As pointed out before, the PEP/pyruvate ratio is an important indicator of metabolic activity 137,138 that controls PTS activity as well 136,137 . These results thus suggest, but do not prove, that the activity of the catalytic domain in replication control is coupled to the PEP/pyruvate ratio and the metabolic state of the cell. Furthermore, the effect of the JP mutation on DNA replication parameters confirms its previously observed involvement in DNA replication 162 .

Additionally, the in vitro effects of PykA on DNA replication suggest that PykA might modulate DNA replication through direct protein-protein interactions. Like PykA, there are indications that PKM2 can regulate its targets through protein-protein interactions. PKM2 interacts with important actors for signal transduction and gene expression such as tyrosine kinase receptors like A-Raf and Fibroblast growth factor receptor 1 (FGFR1) 243,247,248 and RNA binding proteins such as HuR and tritetraprolin 249,250 with documented consequences for tumour development. Additionally, PKM2

is also known to translocate into the nucleus and interact with transcription factors like NF-kB and HIF-1α to promote angiogenesis there 240 .

In summary, the catalytic domain of PykA is also though to moonlight in replication through direct protein-protein interactions and this activity is thought to depend on PEP binding.

Functional interplay between cat and PEPut moonlighting

Conflicting results were obtained about the interplay between cat and PEPut moonlighting. On the one hand, ectotopically expressed PEPut does not require the cat domain to interfere with replication (hyPEPut ΔpykA). However, ectotopic expression of PEPut in a background with only the catalytic domain did not recover the gating phenotype, suggesting that PEPut needs to be fused to the cat domain to function properly (hyPEPut pykAΔPEPut). In line with that, the JP mutation could fully suppress the effects of ectotopic expression of hyPEPut, even when the latter was mutated T>D ( pykAJP hyPEPut and pykAJP hyPEPutT>D). This functional connection between JP and the TSH motif might suggest that the contact between PEPut and the cat domain is also important for moonlighting, since both mutations are near the interface between PEPut and the cat domain 214 . More research is needed to better understand these results. To demonstrate moonlighting more strongly, the effect of pykA mutations on the physiology of B.

subtilis in MC needs to be examined in greater detail. The goal of this work was to demonstrate that the effect of PykA on DNA replication gating was due to moonlighting beyond its metabolic activity. OD measurements of all pykA mutations as well as a competitive growth experiment of ΔpykA suggest that no mutation significantly affects B. subtilis physiology under MC conditions, but small effects on physiology can't be ruled out by this method and might explain some of the weaker effect sizes, especially for mutants in the cat domain. However, the fact that some mutants, but not all affect replication suggests that the replication phenotype is distinct from catalysis and strong effect size PEPut mutations are unlikely to be explained through possible weak effects on physiology anyway. Regardless, the metabolic state of several key pykA mutants will be assayed using mass spectrometry to strengthen this point, which is reviewed here 251 .

Additionally, the PykA signaling pathway remains largely unknown. This work has not clearly identified the targets of PykA nor the metabolic sensing mechanism.

First, the replication target of PykA (initiation/elongation) needs to be established. The genetic analysis presented in this work was very effective at (i) demonstrating the existence of the moonlighting system (ii) identifying important regions in the PykA protein for replication control and (iii) determining how they are functionally connected. However, as stated in section 1.1 of the introduction, proper temporal control of cell cycle events is vital for cell fitness. Therefore, defects in the timing of cell cycle events create an evolutionary advantage for secondary effects that restore proper timing. For this reason, the genetic analysis presented here is inherently insufficient to figure out the exact replication target and effects of PykA. This limitation can be overcome by testing the effect of different pykA mutations on ori-ter ratio in an oriN ΔoriC and oriN ΔdnaA background, as done in our previous work. 86 Next, concrete molecular regulatory targets of PykA need to be established as well. These can partially be found through temperature sensitive complementation assays in pykA mutants 162 .

Moreover, performing these experiments in MC bypasses any potential physiological effects of pykA mutations, making it a more powerful tool than before. Since our in vitro results suggest that PykA moonlighting might operate through direct protein-protein interactions, a good start would also be to identify potential interaction partners. Protein-protein interactions can be inferred from evolutionary information, sequence features and structural features using a variety of computational methods, reviewed in 252 . Additionally, many experimental procedures have been developed to detect and validate protein-protein interactions, both in vitro (NMR, isothermal calorimetry, analytical ultracentrifugation etc) and in vivo (fluorescence colocalization, FRET, FCCS, yeast-2hybrid, etc) 253 .

Finally, the fact that PykA senses metabolic information (i.e. coupling between DNA replication gating and metabolism) and the mechanisms by which it does this remain to be established as well.

Coupling DNA replication gating can be demonstrated either by studying the effect of pykA mutations in different growth conditions 110 or by studying their effect on a metabolic shift (see below). In vitro and in vivo tests of protein-protein interactions in PEP binding mutants could be used to further establish a sentinel role for PEP. Alternatively, studying the effect of varying PEP concentration on replication in vitro demonstrates that as well. TSH phosphorylation has to be established, using in vivo and in vitro methods 254 and the mechanisms responsible for this phosphorylation have to be identified.

In the next part, the question about PykA coupling DNA replication gating to metabolism is taken up.

3. PykA couples its moonlighting activity in DNA replication gating control to metabolism

The goal of the second part of the thesis was (i) to demonstrate that PykA couples metabolism to replication and (ii) identify the role that the different replication determinants play in this coupling.

To determine coupling, the effect of a metabolic shift from the gluconeogenic MC to the glycolytic MCG medium on ori-ter ratio in wild type and pykA mutants was assessed. As stated before, the importance of replication control proteins for cellular fitness may cause secondary compensatory mutations to be accumulated during overnight growth in liquid cultures. The metabolic shift approach presented here avoids this problem by studying in real time the effect of the metabolic shift, before secondary mutations can occur.

Before analyzing the effect of the metabolic shift on ori-ter ratio in pykA mutants, it was determined in a wild type strain cured from prophages (TF8A). Surprisingly, the adaptation of DNA replication parameters did not occur instantaneously after the addition of glucose, but consisted of four overlapping phases. (I) Between 0 and 15 min after the shift, none of the cell cycle parameters changed. (II) Between 15 and 45 minutes, the replication speed dropped and ori-ter ratio increases.

(III) Between 30 and 45 minutes after shift, the initiation frequency increased and the age of initiation advanced. Finally, (IV) sometime after 45 min, the cell size (a proxy for growth rate in this work) increased. To the best of our knowledge, this complex response of DNA replication parameters to a metabolic shift has not been described in literature and merits further investigation.

It is also worth pointing out that the assumption that cell size is a good proxy for cell growth (i) is a law derived in steady state conditions 75 and thus(i) may not hold and (ii) may not hold in pykA mutants. For this reason, this work uses the shift as a tool to investigate coupling between DNA replication timing and metabolism by PykA, even though a full investigation of the phenotype is of interest.

Next, a framework was established to interpret ori-ter ratio curves of pykA mutants and determine whether or not they indicate that PykA couples DNA replication gating to metabolism. The work of Murray and Koh was used as an inspiration to decide whether or not PykA couples DNA replication timing to metabolism 110 . In order to decide whether or not YabA and Soj were able to couple DNA replication timing to metabolism, the ori-ter ratio of deletion mutants of these two proteins was analyzed in different media by Murray and Koh 110 . They found that the effect of the deletions remained coupled to the growth rate in the media they tested and concluded that therefore the function of YabA and Soj was independent from metabolism 110 . Thus, if the ori-ter ratio curve of a pykA mutant is either identical to wild type or runs parallel to it, the ori-ter ratio curve does not count as evidence for coupling and any other situation suggests coupling.

Using this framework, we classified the ori-ter ratio curves of all the pykA mutants in different categories.

The first category held pykA mutants that had curves identical to wild type and did not have a moonlighting role in MC nor at any other point during the shift. This included the cat mutants K220A and R73A and the PEPut mutants T>A and S>A. Hence, these mutants do not provide evidence for coupling. The second category held pykA mutants that had an effect in MC, but lost that effect at later stages of the shift. This group included ΔpykA, Δcat, the PEP binding mutants in the cat domain and T>D and TSH>DDD in the PEPut domain. The third category held pykA mutants that acquired a new effect during the metabolic shift. This group included TSH>AAA and H>A. Based on our framework, we decided that the second and third category provided strong evidence for PykA mediated coupling between DNA replication gating and metabolism. This behavior is not described for any replication control protein in literature to our knowledge. All in all, this work presents evidence that PykA can communicate metabolic signals to the replication machinery, making PykA a new type of regulator of DNA replication.

Mechanisms behind PykA coupling metabolism to DNA replication gating

While this part of the research is in its infancy, the coupling experiments have generated some new insights into the PykA moonlighting mechanism.

H phosphorylation becomes important during the shift

Interestingly, coupling experiments showed that TSH>AAA and H>A acquire a new effect at later stages of the shift and that H>D suppresses this. This suggests that, while H phosphorylation doesn't play a major role in MC, it does at later stages of the shift and confirms that H is important for PykA moonlighting.

Some observations suggest that PKM2 moonlights as a protein kinase and phosphorylate histones 255 , transcription factors 256 and other proteins in the nucleus 237,238,257 . These last results however are in conflict with other studies in mouse embryonic cells, casting doubt on the protein kinase function of PKM2 258 . Additionally, PEP utilizers like the one in EI, PPDK or PEP synthase are known to transfer phosphates from one side of the protein to another using their conserved histidine 223 . However, this work does not support a role for PykA as a protein kinase, since H539D

seems to be able to suppress the effect of H539A. If the mechanism were linked to phosphoryl transfer, H539D was expected to have the same effect as H539A. Phosphoryl transfer by H therefore doesn't seem to be required for the effect of H on replication during the shift. The effects of phosphomimic mutants do however indicate that phosphorylation plays an important role in PykA moonlighting activity and these results still establish a metabolic condition in which H is likely phosphorylated.

Future directions

Despite the advances made in this chapter, much work remains to be done.

A weakness of this coupling part of the work is that the effect of the metabolic shift on cell physiology in general and the growth rate in particular are insufficiently understood. This is particularly problematic for catalytic mutations of pykA, since they are expected to impact physiology in glycolytic media such as MCG. However, given that PEPut mutations are not expected to impact metabolism under any condition 220 , it is reasonable to assume that comparison of the curves with wild type still yields meaningful insights, even if the consequences of the metabolic shift are not fully understood. Particularly, the effect of the metabolic shift on initiation parameters and growth rate needs to be established.

The majority of ori-ter ratio curves presented in this work seem to indicate that the outcome of the PykA signaling system depends on metabolic signals. It is currently unclear how this is achieved, but it may involve a structural transition in the PykA monomer/dimer/tetramer. The translocation of PKM2 inside the nucleus is accompanied by a switch in oligomerization state from metabolically active tetramer to non-metabolically active dimer 259 . A structural transition in PykA induced by metabolism could also explain why the same mutation (e.g. T>D or H>A) has an enormous effect in one medium, but not the other.

5. Theoretical implications of CCM enzymes coupling DNA replication gating to metabolism 5.1 Implications for the coordination between replication, growth and the availability of nutrients

Currently, several groups have started to argue that growth rate dependent control of replication timing is a multifactorial process, which may involve sensing cellular metabolism and communicating this information to the replication machinery 10,70,[86][87][88] . However, all of the mechanisms that were proposed to be responsible for this coupling have been challenged in some form [110][111][112] , especially under fast growth conditions (see introduction section 1.4 for a discussion).

The discovery that PykA communicates metabolic signals to the replication machinery in fast growth conditions therefore supports the view that growth rate dependent control of replication over a wide range of nutritional conditions involves sensing cellular metabolism 10,70,[86][87][88] and establishes for the first time a mechanism by which this may occur. Furthermore, a previous publication of our group identified multiple, medium dependent links between CCM enzymes and replication 86 . This suggests that other CCM enzymes may also communicate metabolic information to the replication machinery, leading to the hypothesis that growth rate dependent control of DNA replication is achieved at least in part through non-metabolic activities of CCM enzymes. This hypothesis is likely important for understanding replication in a wide range of organisms since (i) CCM enzymes are strongly conserved throughout the tree of life, (ii) CCM-replication links have been found in diverse model organisms, including E. coli, S. cerevisae and even human cells (see introduction section 2.3 for a full discussion) and (iii) numerous non-metabolic functions of CCM enzymes are described in a wide range of organisms (for reviews, see 234,235 ). This hypothesis will later be referred to as the CCM-coupling hypothesis. Hence, this work extends the knowledge on the long enigmatic coupling between DNA replication gating and metabolism by providing the first example of a CCM enzyme responsible for it and pointing to other CCM enzymes as likely future targets.

Furthermore, the CCM-coupling hypothesis has important implications for understanding and treating cancer, which is discussed in the next section.

Theoretical implications of the CCM coupling hypothesis for the origin of cancer

The CCM-coupling hypothesis has important implications for our understanding of the origin of cancer. The Warburg hypothesis was the first major theory about the origin of cancer 260 . It was based on the observation that many tumors have a high rate of glucose consumption and excrete lactate, instead of fully oxidizing it using the TCA cycle 260 . This led him to conclude that cancer cells originate from dysfunctions in the mitochondria 260 . This theory has later been disproven on the basis that most tumor cells also have functional respiration, but the debate on the reason behind the observation of disrupted metabolism in most tumor cells continues to date 261,262 . Nowadays, cancer is thought to require the simultaneous acquisition of many mutations to obtain the so-called hallmarks of cancer 263 . These include sustained proliferation, evasion of immune responses, evasion of growth factors, evasion of induced cell-death, induction of angiogenesis and activation of metastasis 264 . The chance of this occurring in a healthy cell is incredibly small, which is why cancer nowadays is thought to originate from mutations that promote further mutagenesis i.e. genetic instability 263,265 . Genetic instability in turn is caused by mutations in any process that is required for faithful transmission of genetic information, such as DNA repair, DNA replication (see 266 ), chromosome segregation, cell cycle checkpoint control and others 265 . The CCM-coupling hypothesis therefore suggests that cancer could originate from defects in the metabolic signaling pathways that couple these processes to metabolism as shown in figure 28. Figure 1 was assembled from figures in 270,[278][279][280] . 270,[278][279][280] Interestingly, several features of chromosomal structural organization have been shown to profoundly impact DNA transcription and replication. For example, supercoiling has been shown to directly affect the expression of many genes 281 . Moreover, constraining supercoiling to a localized domain on the chromosome is thought to cause genes within such microdomain to have correlated transcription and transcription bursting profiles, underlining the importance of these microdomains for gene expression control 282 . Macrodomains on the other hand seem to contribute to efficient replication 283 284 and limit genome plasticity 285 . To date, there is no known evidence suggesting a direct link between macrodomain organization and gene expression.

The dependence of gene expression on its position in the chromosome sequence is a consequence of this link between chromosome structure and gene expression 286 . This implies that there is an evolutionary pressure to organize the positioning of genes in ways that optimize their expression.

Several in silico analyses have shown that most genes are not randomly scattered along the chromosome sequence, but follow distinct chromosome layouts that reflect structural features of the chromosome 287 288 289 .

This leads to a three way relationship between gene position, gene expression and chromosome structure that evolves to optimize gene expression and function. This relationship is depicted in figure 2. In this work, the TBS framework is tested by analyzing the periodic organization of stress response genes and by studying its implications for stress response regulation.

Stress response systems in E.coli

As most bacteria, E. coli has to cope with a rapidly changing and often stressful environment. To this end, E. coli has evolved mechanisms capable of sensing and coping with environmental stress. Firstly, E. coli has acquired a diverse set of transcription factors dedicated to sensing environmental stress and altering gene expression to increase stress resistance and/or decrease the stress threat. These transcription factors include OxyR (H2O2 stress) 300 , SoxR and SoxS 300 (redox cycling compound stress), Fur (iron deprivation) 301 , GadE (acid stress) 302 and OmpR (osmotic stress) 303 . Additionally, E. coli has also evolved a set of alternative σ factors that change the specificity of its unique RNA polymerase toward stress response genes. These include σ 24 (Extreme heat shock), σ 28 (Flagellar synthesis/chemotaxis), σ 32 (Heat shock), σ 38 (General starvation) and σ 54 (Nitrogen regulated genes) 304 . Interestingly, E. coli has also been shown to drastically change the structure of its chromosome under stress conditions with global impacts on gene expression 281 . For example, the degree of DNA supercoiling has been found to respond to several stresses including oxidative stress 305 , osmotic stress 306 , heat shock 307 , cold shock 308 and nutrient starvation 309 . As an extreme example, the NAP Dps self-aggregates under starvation conditions while bound to DNA and thus condenses the chromosome and physically protects it from damage 310 .

Despite recent advances in the elucidation of the E. coli chromosome structure under non-stressed conditions 311 , the 3D structure of the chromosome under stress conditions, its impact on gene expression and the switching mechanisms between different chromosomal structures have all been far less understood. This knowledge is indispensable for increased understanding of the functioning of natural chromosomes under stress conditions.

Objectives of Annex I

The goal of this project was to test the Transcription Based Soleonoid framework. This was done in four steps: (i) defining periodicity evaluation criteria using control datasets (Section 3.1.1) (ii)

analyzing gene periodicity in the OxyR, SoxS, Fur, GadE and OmpR regulons (Section 3.1.2) iii) using these results to choose a suitable candidate regulon for further investigation (Section 3.2.1) and iv) testing the effect of displacing the stress response regulator on the stress response (Section 3.2.2).

Materials and methods

2.1 Periodicity analysis 2.1.1 Periodicity analysis using GREAT :SCAN :patterns

In order to investigate the interplay between periodic chromosome organization, chromosome expression and chromosome structure, our group has developed GREAT : SCAN :patterns, the main computational tool used in this work. GREAT : SCAN :patterns is an online tool that exhaustively analyzes all periodic patterns that can be detected from the positions of coregulated genes in the genome 312 . The explanation here is largely based on 312 and 313 .

The input of GREAT : SCAN :patterns simply consists of a file with gene identifiers and their chromosome positions 312 . Depending on the nature of the data that was used to create the input file, chromosome positions may mean the 5' end of the genes (defined by transcriptome data and 3C interaction data) or the middle of a transcription factor binding site (ChIP data).

Briefly, GREAT: SCAN: patterns works in two steps for our purposes, as shown in figure 4.

The first step involves the detection and visualization of all periods along the full length chromosome.

Initially, the data is preprocessed to remove the effects of proximal gene identifiers 312 . This is necessary, because proximal gene identifiers can artificially increase the p-value of the periods when paired with another, sufficiently distant gene identifier 312 . This can lead to false positive periods. 313 To avoid this problem, the program replaces a set of proximal gene identifiers by its barycentre.

Proximity is defined as two times the average gene distance (E. coli:1000 bp) 313 . Next, the p-value of each period is calculated. First, the program produces test datasets containing randomly generated chromosome positions 312 . Positions are drawn from a uniform distribution 312 , i.e. every position between 0 and the end of the chromosome has equal probability to be drawn. Next, periodicity scores are calculated as in 314 . The p-value is then defined as the number of times the test dataset had a higher periodicity score than the real dataset divided by the total number of test datasets 312 . Next, the pvalues are corrected for multiple hypothesis testing using a period-length-dependent correction 312 .

Finally, if at least one period meets the preprogrammed threshold (standard threshold: p-value < 0,05 or S=-log(p-value) > 1,3), all of the periods are plotted in a periodogram 312 .

In a second step, the chromosome is scanned to determine the length of each region over which a periodicity occurs 312 . To do this, GREAT : SCAN: patterns uses a variable size sliding window that scans the chromosome for regions in which periodic organization is found, starting with a window size of 10 kb that geometrically increases with a constant factor and ends with a sliding window that

Origin and preparation of datasets for periodicity analysis

Three different types of datasets were used as input for GREAT :SCAN :patterns, namely : synthetic datasets, ChIP-exo datasets and RegulonDB datasets.

Synthetic control datasets

Synthetic datasets are datasets created to test the signal-to-noise of the periodicity analysis program used in this work. These datasets fall into two categories: negative control datasets and positive control datasets. Figure 5 gives a visual overview of them. Both negative and positive control datasets were made using the open-source statistical tool R 315,316 . These datasets were both .csv files consisting of a column of unique identifiers ('mock_position') and a column of numbers representing chromosomal coordinates. In the case of negative control datasets, chromosomal coordinates were generated using the following formula : 2) Positive control datasets consist of randomly generated points that are always n*P separated where n is a whole number. They should therefore display periodicity of P. These datasets are generated using the indicated formulas in the statistical program R 315,316 .

iPCR product of plasmid 35 and assembled using HiFi assembly (NEB, 50℃, 15 min). The assembly product was transformed in E. coli strain DH5α and positive transformants were selected for KanR.

Plasmid 62 was created by introducing terminators on both sides of the oxyRS-Kan cassette in plasmid 61 using two rounds of iPCR. The terminators were chosen from 322 to have >98% termination efficiency in the direction they were inserted. Briefly, iPCR was performed on plasmid 61 to introduce terminators on the left side of the cassette (pSH178-179, Q5, NEB, standard reaction conditions). Template plasmid 61 was removed using DpnI digestion (NEB, 20 U, CutSmart, 2h, 37℃) and the PCR product was purified using the Monarch DNA cleanup kit (NEB). Next, the PCR product was phosphorylated using T4 polynucleotide kinase (NEB, 10 U, 30 min, 37℃). The T4 polynucleotide kinase was then heath inactivated (20 min, 65℃). The PCR product was chilled on ice for 2 min and T4 DNA ligase was added (NEB, 20 U, 2h, room temperature). The ligation products were then transformed in DH5α and positive transformants were selected for KanR. The intermediate plasmid (with left side terminators) was checked by sequencing and used as a template for introducing the right side terminators using the same procedure (pSH182-183). was mixed with the purified iPCR product of plasmid 91 and assembled using HiFi assembly (NEB, 50℃, 15 min). The assembly product was transformed in E. coli strain DH5α and positive transformants were selected for KanR.

Since deletion of oxyRS leads to increased mutagenesis 323,324 , oxyRS movant strains (ORS31, ORS32, ORSFT8, ORSFT9 and ORSFT13) were constructed without a ΔoxyRS intermediate. This was done through two rounds of recombination with PCR product and KanR cassette removal.

During the first round, the oxyRS-Kan-terminator was amplified from plasmid 92 using primers with overhangs homologous to their insertion location (see supplementary table II, Q5, NEB, standard reaction conditions). Template plasmid 92 was removed using DpnI digestion (NEB, 20 U, CutSmart, 2h, 37℃) and the PCR product was purified using the Monarch DNA cleanup kit (NEB). The purified PCR product was then electroporated in recombination competent strain 74 (MG1655 + pTKRED, 1800 V) and grown overnight on LB. Each transformation was performed three times. The liquid culture was plated on LB + Kan and grown overnight on 37 ℃. Correct integration was then screened to promote homologous recombination inside the cassette (2h, LB + Sp + IPTG) and (ii) excising the cassette with I-SceI (overnight, LB + Sp + IPTG + ara) using the strategy outlined in 321 . Removal of the KanR cassette was screened for using grid plating to check for loss of Kan resistance.

The genotype of final transformants was verified by PCR and subsequent DNA sequencing.

Zone of inhibition experiments for measuring oxidative stress

Oxidative stress sensitivity was measured using the Kirby-Bauer method 325 . The E. coli strains were incubated overnight from glycerol stocks in Luria Broth medium at 37℃ shaking at 220 rpm. The next day, they were re diluted in LB to an OD of 0.040 and incubated in a water bath at 37 °C, shaking at 200 rpm. At an OD of around 0.2, the cells were diluted 1/100 in 5 mL of LB and spread evenly on 3 Petri dishes containing LB with agar. Excess medium was subsequently removed. Next, a pipette tip was used to punch a hole in the center of the LB + agar layer. H2O2 dilutions (0 mM, and 100 mM and 1M) were subsequently added to the holes and the plates were incubated overnight at 37 °C.

The next day, the zone of inhibition of the plates were photographed using G-box with a ruler as internal control and saved under a loss-less tiff format. Diameters of inhibition were measured in Fiji using the ruler to set the scale.

Results

3.1 Periodicity analysis of stress response genes in E. coli

Defining periodicity evaluation criteria based on analysis of synthetic datasets

Before analyzing TF binding site datasets, GREAT :SCAN: patterns was first tested on synthetic datasets in order to define periodicity evaluation criteria. The synthetic datasets fell into two categories : positive control and negative control datasets. Positive control datasets consisted of coordinates that were programmed to always have a multiple of a distance P between them and therefore should give rise to period P. Negative control datasets consisted of coordinates that were drawn randomly with equal probability between 1 and the total length of the genome. Therefore, they should not give rise to a periodic signal. However, other periods that are not obviously multiples of 8kb (6 kb and 6.4 kb etc) are also observed with great significance (S > 30). This result suggests that dominant periodic signals can be identified by their p-value, but a true secondary periodic signal in a dataset will be hard to distinguish from one of these non-obvious multiples of the primary period.

Analysis of a positive and a negative control dataset: qualitative insights

By contrast, figure 8 (C+D) shows that GREAT :SCAN :patterns can still find significant periods in a random dataset albeit with a much lower S score. Nevertheless, S scores above 2-3 are readily observed in this dataset (compare to the threshold S=1,3).

X-axis depicts period size and y-axis period significance as log p-value. (B) Local periods in tabular format with from left to right (i) period length (ii) Left coordinate of the periodic region (iii) Right coordinate of the periodic region (iv) Length of periodic region (v) p-value (vi) S-score of the period and (vii) Periodic region coverage: the fraction of the number of coordinates inside the periodic region vs the total number of coordinates (C+D) Output for a negative control dataset

Definition of periodicity evaluation criteria: global periods

To get an idea of the generality of these observations, these experiments with synthetic datasets were repeated 10-20 times for datasets of different sizes. The investigated size range was between 5 and 100 datapoints, which was a relevant range for the datasets investigated in this work (see next sections). Figure 9 shows the global properties of dataset controls. Figure 9A shows that regardless of dataset size, significant periods with S > 1,3 are often observed in negative control datasets. Figure 9B shows the same analysis for positive control datasets. Here, we see that the average S-score of the programmed period observed in these datasets reaches a minimum for datasets with 20 coordinates and then increases again as dataset size increases. Nevertheless, the average S score in positive control datasets of different sizes ranges between 150 and 300, demonstrating that GREAT SCAN :patterns has a good signal to noise ratio. Figure 9C shows that with increasing dataset size, the number of datasets that don't reach the significance threshold steadily increases for negative control datasets. Still, around 60% of the datasets with 100 coordinates produces a period with a Sscore > 1.3.

These results suggested that the preprogrammed threshold of S > 1.3 was not sufficient to exclude false positives. This lead to the following evaluation criterium which was also used in 287 :

A global period is considered significant if it has an S-score > 4.

Definition of periodicity evaluation criteria: local periods

Next, the same analysis was performed to define criteria for evaluating local periods. Local periods can be evaluated both on their p-value and their coverage (Number of periodic points within the region). Figure 10 shows the results of this. Figure 10A Coverage (defined as the ratio of genes within the periodic region to total number of genes) of top period when the top period is evaluated based on coverage.

Conversely, figure 10C and D show the average p-value and coverage of periods selected for their coverage. Together, these pictures show that local periods with either a decent S-score (>4) or a good coverage occur in negative control datasets.

Local periods must therefore be evaluated on both criteria at the same time. This leads us to the local period evaluation criterium :

A local period is considered significant if it has an S-score >4 and a coverage > 50%. In this section, evaluation criteria for periods were defined using insights gained from the analysis of synthetic datasets. These will be applied to query datasets in the next sections. and analyzed with GREAT:SCAN:patterns. Neither global nor local periodicity analysis produced periods that would be considered significant using the evaluation criteria mentioned above although some local periods come close. This includes the 8.9 kb local period of Fur (Fe replete), the 5.8 kb local period of Fur (Fe starv) and the 28.5 kb local period of GadE. Nevertheless, these results were not considered enough to support the conclusion that these regulons are periodically organized.

Table 1 shows a summary of the periods observed in each dataset. Table 1 : Periodicity analysis of key stress response regulators from ChIP-exo binding data. (Left) Length and S-scores of the best global periods that were observed. (Right) Length, S-scores and coverage within the best local periodic regions that were observed. The color codes denote the regulon in which the period was observed.

Therefore, two in silico approaches were undertaken to analyze further this study. In the first, new datasets for these same regulons were obtained from RegulonDB to see if significant periods could be obtained from them or if the same periods would reoccur in them (Section 3.1.2.2). In the second approach, harmonic analysis was used to see if different stress response systems that are activated under the same conditions also have a common periodic organization (Section 3.1.2.3).

Periodicity analysis of RegulonDB datasets

Periodicity was analyzed in RegulonDB datasets of the same stress response regulons as in section 3.1.2 and compared. Only binding sites with a strong degree of evidence were analyzed. This includes binding sites with 2 strong or 3 weak evidences 312 , 1 strong +1 in vivo evidence and 1 in vivo evidence.

Table 2 shows the results of this for global periodicity analysis and table 3 shows the results for local periodicity analysis. These results show that the same periods are rarely found in more than one dataset of the same stress response regulon. Nevertheless, we do observe that the 40.5 kb SoxS global period, the 7.9 kb OxyR global period, the 25.7 kb OxyR local period and the 8.6 kb Fur global period (which is 1/10 th of the 86-87 kb periods observed) are found in more than 1 dataset, providing some extra support for these periods.

Harmonic analysis of ChIP-exo data

To better understand the relationships between the found periods, a harmonic analysis was performed to see which periods were multiples of other periods and therefore shared common chromosomal organizations. Harmonic periods were allowed to differ 1% maximally from anyone of its multiples.

Periods were considered to be part of the same harmonic family if they were a harmonic of at least one period in the family. Related harmonic periods and their corresponding regulons are shown for each period in tables 4 and 5. Cis-harmonic periods are periods that were harmonic within a dataset and trans-harmonic periods were periods that were harmonic between datasets and could point to common periodic organizations. Using this analysis, we found a family of 8,1kb periods connecting the OxyR, SoxS and Fur (Fe replete) regulons. Other observed families are the 5,8 kb family in SoxS and Fur (Fe starv). These common periodic themes mirror known crosstalk between these regulons.

Fe 2+ is known to exacerbate oxidative stress, making its regulation a key part of the oxidative stress defense (See section 3.2.1 for more information on the oxidative stress response).

Table 4: Harmonic analysis of global periods obtained from analysis of ChIP-exo data of key stress regulators. Data is depicted as in table 1, with the addition of cis-harmonic periods (Period multiples observed within a dataset) and trans-harmonic periods (Multiples of periods observed in other datasets).

Table 5: Harmonic analysis of local periods obtained from analysis of ChIP-exo data of key stress regulators. Data is depicted as in table 1, with the addition of cis-harmonic periods (Period multiples observed within a dataset) and trans-harmonic periods (Multiples of periods observed in other datasets).

Summary

To sum up, GREAT found periods for the stress response systems from ChIP-exo data, but the Sscores were deemed insignificant based on our insights from the analysis of synthetic control datasets.

Analysis of datasets for these same regulons extracted from RegulonDB provided some additional support for OxyR, SoxS and Fur periods. On top of that, harmonic analysis also found common periodic layouts that might be reflective of known crosstalk between stress response systems, including OxyR-SoxS-Fur. It is therefore possible that GREAT:SCAN:patterns is not sensitive enough to detect periods in biological datasets with reasonable certainty, but that the periods found in the ChIP-exo datasets are still biologically relevant.

To test this possibility, one of these transcription factors was moved to different positions on the chromosome to test if the position of the gene impacts its regulation of the stress response. The goal of this section is to decide which transcription factor (OxyR, SoxS or Fur) is best suited for in vivo testing. First, the biology of oxidative stress and the oxidative stress response is discussed. Second, experimental and theoretical criteria are proposed to decide which transcription factor should be moved.

Introduction to oxidative stress and the oxidative stress response

Oxidative stress is caused by oxidants. Oxidants are substances that accept electrons from other molecules during oxido-reduction reactions. The most commonly encountered oxidant in biological systems since the advent of photosynthesis is oxygen (O2). While O2 itself is not very reactive with biological molecules, it can give rise to highly reactive partially reduced Reactive Oxygen Species (ROS) 327 .

These damaging ROS molecules are the superoxide ion (O2 -), hydrogen peroxide (H2O2) and the hydroxyl radical (OH . ) 328 These molecules can react with (i) DNA, causing DNA damage and mutagenesis 329 , (ii) amino acids 330 and lipids and most importantly (iii) Fe 2+ , causing its oxidation into Fe 3+ and the inactivation of Fe-S dehydratases 331 and mononuclear iron proteins 332 , which fulfill critical roles in central carbon metabolism (CCM) 328 .

Due to their damaging effects, bacteria often encounter exogeneous ROS molecules as part of an effective immune response in bacteria, plants and animals 328 . H2O2 is uncharged and can diffuse past the membrane of the bacterium 328 . O2 -, on the other hand, is produced by redox-cycling molecules that can diffuse past the membrane and create O2 -inside the targets cytoplasm 328 .

Oxidative stress is then defined as an imbalance between the amount of ROS molecules in the cell versus the cell's ability to detoxify them. For this reason, all living aerobic cells have evolved defense mechanisms against oxidative stress and these defense mechanisms are collectively called the oxidative stress response 328 .

In E. coli, the response to oxidative stress is controlled by the transcriptional regulators OxyR, OxyS, SoxR and SoxS 328 . OxyR can sense H2O2 through oxidation of two sensory cysteine residues 333,334 , which leads to a conformational change and the activation of transcription of about 20-25 operons 300 .

SoxR on the other hand, can sense the presence of redox cycling compounds, which leads to expression of SoxS and the activation of another 20 operons. 300 The effect of the members of the OxyR and SoxRS regulons are summarized in figure 11. From this figure, it is clear that the maintenance of iron metabolism is essential for an effective oxidative stress response. Consequently, the master regulator of iron metabolism Fur is both a target of OxyR and SoxS 300 .

Selection of regulon for testing the TBS framework

According to the TBS model factor oligomerization seems to be a necessary requirement for colocalization. For experimental purposes, the best suited test regulon maximally impacts the oxidative stress response and minimally impacts cell function under non-experimental conditions.

SoxS is a member of the AraC family of regulators, but lacks its dimerization domain 336,337 . It induces gene expression by binding to the α subunit of the RNA polymerase binding as a monomer and scanning the DNA for a 20 bp degenerate sequence called the soxbox that is at the correct distance from the promoter [338][339][340][341] . Given this mechanism, it is unlikely that SoxS is vital for recruiting periodic genes of the regulon and thereby establishing a theoretical transcription factory and it was ruled out as an experimental test system.

Fur is part of its own family of transcriptional regulators, called the FUR family 342 . In general, Fur represses gene expression by binding to a palindromic AT rich sequence as a dimer. The binding of Fe 2+ stabilizes the interaction of the Fur dimer with its cognate binding sequence 343 . This would make the Fur regulon a suitable candidate for testing. However, Fur is a global regulator that directly controls not only iron transport and metabolism, but also the expression of key metabolic genes that use Fe 2+ involved in the TCA cycle and deoxyribonucleotide synthesis 301,342 . Disruption of Fur has been shown to profoundly change the transcriptional program of the cell under non-oxidative conditions 301 . These features make Fur a suitable, but not ideal regulon for testing the periodicity framework.

OxyR is a member of the LysR family of transcriptional regulators 323,344,345 . OxyR forms a homotetramer in E.coli 346 that undergoes a major conformational change when its sensory cysteine residues are oxidized 323,333 . In its oxidized form, OxyR recognizes four adjacent ATAG elements with 10 bp spacing 347 , whereas it only binds to two in its reduced form 323,348 . It then cooperatively binds with RNA polymerase to stimulate gene expression of its target genes 323,349 . The OxyR core regulon consists of 20-25 operons and is extended to about 40 operons when the targets of its divergently transcribed oxyS RNA molecule is included 323,350 . OxyR mutants are hypersensitive to oxidative stress 351 and unfortunately prone to mutagenesis as a result 323,324 . Nevertheless, thanks to (i) the ability of OxyR to oligomerize, (ii) its profound importance for oxidative stress defense, (iii) its relatively small regulon that minimally impacts cell metabolism in non-experimental conditions apart from increased mutagenesis and (iv) the connections between oxidative stress, iron maintenance that were reflected in the periodicities observed in this work, the OxyR regulon was chosen as our test regulon.

Zone of inhibition experiments in oxyRS repositioned mutants

The goal of this experiment was to test if the repositioning of oxyRS impacts the oxidative stress response. In this first experiment, 3 strains with oxyRS near the origin (ORS_init, ORS31 and ORS32)

are compared to 3 strains with oxyRS near the terminus (ORS_FT8, ORS_FT9 and ORS_FT13) when grown in LB. This is an extreme repositioning which should not only disrupt periodic patterns, but also drastically alter copy number of oxyRS [352][353][354] .

The effect of repositioning on the OxyRS stress response was tested using an initial set of zone of inhibition experiments with 0, 100 and 1000 mM H2O2. The results of these experiments are found in figure 10. Next, oxidative stress sensitivity is quantified by measuring the ZOI of MG1655, ΔoxyRS and the oxyRS movant strains at 100 mM H2O2. Figure 10C shows these results. ΔoxyRS has a much higher ZOI diameter, indicating an increased sensitivity to H2O2, as expected 355 . However, the ZOI diameter of the oxyRS movants is similar to the ZOI diameter of MG1655, showing no observable difference in oxidative stress sensitivity.

In order to improve the sensitivity of this test, the ZOI experiment was repeated when 1M was added to the central hole. These results are shown in figure 10D. Again, the differences observed are in the range of 1 to 2 mm, which is more likely explained by measurement inaccuracy than a real difference in oxidative stress sensitivity.

In conclusion, oxyRS repositioning does not seem to strongly alter sensitivity to oxidative stress, not even when the TF is moved to a location near the terminus. and three near the terminus to test the maximal effect of oxyRS repositioning on the stress response before testing periodicity [352][353][354] .

The oxidative stress response of the oxyRS movants was tested using the Kirby Bauer test 325 . While

ΔoxyRS showed drastically increased sensitivity to oxidative stress, the oxyRS movants were equally sensitive to it as wild type. These results suggest that the position of a transcriptional regulator does not impact gene regulation of its targets.

In this work, negative autoregulation of OxyR expression might have played a role in buffering the effects of gene translocation 345 . Nevertheless, 5 out of 6 insertion locations were out of phase with the OxyR targets, which should have disturbed gene regulation according to the TBS framework.

Moreover, while gene displacement has been shown to impact the expression level of a moved gene in bacteria 286,353,354,[361][362][363][364][365][366][367][368][369][370] , displacement of a transcription factor gene does not always measurably affect gene regulation 354,366 (see 371 for counterexample).

Conflicting results on the role of gene colocalization in gene expression in bacteria(another key prediction of the TBS model) have emerged in recent years. Recent results from single molecule imaging partially support the TBS model 372 . The NAP H-NS was shown to form dense foci that colocalized with their regulated genes in E.coli 373 , despite no observed effect of its displacement 366 .

The transcription factor GalR also formed dense foci in stationary phase 374 . Additionally, 3C studies detected physical interactions between distant members of the GalR regulon that were abolished in a strain deleted for GalR 374 . Also, dimerization mutants of GalR and H-NS did not cluster anymore and yielded reduced gene regulation 373,374 . By contrast, other NAP's such as Fis and HU are uniformly distributed on the E. coli chromosome 373 and other transcription factors like LacI cluster near their target genes, but do not come together in space 375 . Finally, genes from the arabinose operon do not change cellular localization upon induction of gene expression 376 . These results suggest that oligomerization of the transcription factor is a necessary, but not sufficient condition for spatial clustering and an effect on gene expression 372 .

Moreover, the more recent studies seem to suggest that chromosome structure and its effect on gene expression in bacteria is shaped by only a handful of global regulators. In a very recent study, the effect of gene position on transcriptional propensity (RNA expression/DNA) in E. coli was studied for the first time at a very large scale (144000 insertion sites) 370 . These results revealed that locations of high propensity corresponded to Fis and HU binding sites, two global regulators 370 . Interestingly, Fis is the only known transcriptional regulator for which target transcription is affected when its gene is displaced 371 . Additionally, deletion of the hu genes impacts the location-to-location variability in gene expression when a gene is displaced 367 .

In conclusion, the results of this work suggest that gene position of oxyRS plays no major role in the regulation of its targets. This is in line with conflicting results on the importance of gene position for gene regulation. Moreover, recent studies suggest that only a handful, abundantly expressed proteins shape chromosome architecture in E. coli.

Conclusion and future directions

In the end, this work was unable to demonstrate an effect of gene periodicity on oxidative stress response in E. coli. Periods of key stress response regulators were deemed insignificant using our evaluation criteria and displacement of the oxyRS stress response regulator failed to produce significant increases in stress sensitivity, consistent with some recent results in literature. Given these results and the untimely retirement of the supervisor that had started this project, the project was stopped in accordance with the thesis committee.

Future work on a possible role of gene periodicity in its regulation would benefit from several aspects of the approach taken in this work, namely (i) establish periodicity evaluation criteria using control datasets (ii) support periodicity results through the analysis of multiple datasets per regulon and through cross-referencing common periodic themes between regulons with known regulatory crosstalk and finally (iii) direct testing of the hypothesis by displacement of a reporter in and out of phase.

Additionally, the experiment would best be done in eukaryotes (e.g. yeast). As stated above, periodicity results in yeast, while not significant on their own, are supported by the identification of common periodic themes in strongly different datasets 291 . Additionally, gene position effects are generally stronger in eukaryotes and their existence has not been controversial for many years [377][378][379][380] .

Moreover, the existence of transcription neighborhoods (colocalization of active cofunctional genes inside a transcription focus) is not controversial in eukaryotes either (for numerous examples: see 377 ).

Therefore, a similar study to the one presented here in eukaryotes might shed an interesting new light on the three-way relationship between gene position, 3D structure and gene expression in eukaryotes. To demonstrate that PykA moonlights as a replication protein, the effect of pykA mutations on DNA replication parameters was assessed using run-out flow cytometry and marker frequency analysis by qPCR in growth conditions where its metabolic activity is dispensable. Deletion of pykA did not affect the growth rate during these conditions, but it affected the replication speed and timing of initiation, altering therefore the temporal control of replication. The deletion of either the catalytic domain or the PEP utilizer domain of PykA showed that both domains play a role in the temporal control of replication, identifying for the first time a replication function to these domains. Mutagenesis of specific amino acids inside the catalytic domain showed that some, but not all key catalytic amino acids are involved in replication. Mutagenesis of conserved amino acids in the TSH motif of the PEP utilizer on the other hand suggests an important role for threonine phosphorylation. Expression of the PEP utilizer detached from the catalytic domain in trans unexpectedly affected replication control at medium copy numbers. This phenotype depended strictly on H of the TSH motif, establishing a role for H in replication. Furthermore, the effect of expression of free PEP utilizer may depend of an unidentified inhibitor effector and is fully suppressed by a mutation in the catalytic domain. Moreover, expression of the catalytic domain in cis and the PEP utilizer in trans did not restore proper replication control. Finally, in vitro assays demonstrated that PykA can directly stimulate DnaE polymerase activity and inhibit DnaC helicase activity. Together, these results imply that PykA moonlights in replication.

To demonstrate that PykA communicates information about the metabolic state of the cell to the replication machinery, the effect of pykA mutations on DNA replication parameters during a metabolic shift was assessed using the techniques described above. The adaptation of replication parameters during the metabolic shift in wild type cells unexpectedly consisted of three phases: (i) a decrease of replication fork speed between 15 and 45 min after the shift, (ii) an advancement of initiation timing and increase in initiation frequency from 30 min onwards and (iii) an adaptation of the growth rate sometime after 45 min after the shift. Mutations in the catalytic domain did not clearly demonstrate a change in the replication response to the shift. By contrast, the effect of the PEP utilizer domain (both in cis and in trans) on replication parameters did change during the shift. Moreover, the H amino acid, not T, was important for replication control during later time points of the shift. This suggests that the regulatory behaviour of the PEP utilizer and more generally PykA changes in response to metabolism. Overall, we conclude that PykA is a new type of regulator of DNA replication that helps the temporal positioning of replication in the cell cycle through processes that varies with the richness of the carbon sources provided in the environment. This work, combined with previous work in this field suggest that the temporal control of replication in a large range of metabolic growth conditions is achieved at least in part via a network of moonlighting CCM enzymes with important implications for our basic understanding of cell biology and human health.

Figure 1 18 Figure 2 : 20 Figure 3 : 22 Figure 4 : 26 Figure 5 : 29 Figure 6 : 32 Figure 7 : 33 Figure 8 : 35 Figure 9 : 38 Figure 11 : 42 Figure 14 : 58 Figure 20 : 59 Figure 21 :

 11822032242652963273383593811421458205921 Figure 1 The cell cycle in eukaryotes ......................................................................................................... 18 Figure 2 : The cell cycle in bacteria ............................................................................................................ 20 Figure 3: The cell cycle in archaea ............................................................................................................. 22 Figure 4 : DNA replication initiation and elongation in the model bacteria E. coli and B. subtilis: ......... 26 Figure 5: Regulation of DnaA mediated initiation in E.coli and B. subtilis ............................................... 29 Figure 6: Classical mechanisms of metabolic regulation of DNA replication initiation ............................ 32 Figure 7 : Classical mechanisms of metabolic regulation of DNA replication elongation: ....................... 33 Figure 8 : Recent examples of direct metabolic signalling that impacts replication and growth. .............. 35 Figure 9 : The Embden-Mayerhof-Parnas pathway for glycolysis + the overflow pathway ...................... 37 Figure 10 : The Tricarboxyic acid (TCA) or Krebs cycle........................................................................... 38 Figure 11: The pentose phosphate pathway ................................................................................................ 38 Figure 12: The Entner-Douderoff pathway ................................................................................................. 40 Figure 13: The PTS system in B. subtilis. Shown here are the PTS systems for fructose (EIIX Lev ), mannitol (EIIX Mtl )and cellobiose (EIIX Lic ). Taken from 128 ....................................................................... 42 Figure 14: Carbon catabolite repression ..................................................................................................... 43 Figure 15: Local control of gene expression by the PTS system ................................................................ 45 Figure 16: Connections between CCM and replication in the model organisms B. subtilis, E. coli, S. cerevisiae and higher eukaryotes (human/plant) ......................................................................................... 49 Figure 17: The structure of pyruvate kinase ............................................................................................... 53 Figure 18: Principles of marker frequency analysis for determining replication fork speed ...................... 57 Figure 19: Principles of run-out flow cytometry for determining initiation parameters ............................ 58 Figure 20: DnaE polymerase activity and DnaC helicase activity assays .................................................. 59 Figure 21: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A ................................................................................................................................................................... 102 Figure 22: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A and ΔpykA ........................................................................................... Fout! Bladwijzer niet gedefinieerd.Figure 23: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A and cat mutants..................................................................................... Fout! Bladwijzer niet gedefinieerd.Figure 24: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A and pykAΔPEP ........................................................................................ Fout! Bladwijzer niet gedefinieerd.Figure 25: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A and TSH triple mutants ........................................................................ Fout! Bladwijzer niet gedefinieerd.Figure 26: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A and T and H single mutants.................................................................. Fout! Bladwijzer niet gedefinieerd.Figure 27: The effect of hyPEPut on the shift ..................................... Fout! Bladwijzer niet gedefinieerd.Figure 28: The effect of mutated hyPEPut on the shift ........................ Fout! Bladwijzer niet gedefinieerd.Figure 29: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A and LLPEP ........................................................................................... Fout! Bladwijzer niet gedefinieerd.

Figure 23 :

 23 Figure 1 The cell cycle in eukaryotes ......................................................................................................... 18 Figure 2 : The cell cycle in bacteria ............................................................................................................ 20 Figure 3: The cell cycle in archaea ............................................................................................................. 22 Figure 4 : DNA replication initiation and elongation in the model bacteria E. coli and B. subtilis: ......... 26 Figure 5: Regulation of DnaA mediated initiation in E.coli and B. subtilis ............................................... 29 Figure 6: Classical mechanisms of metabolic regulation of DNA replication initiation ............................ 32 Figure 7 : Classical mechanisms of metabolic regulation of DNA replication elongation: ....................... 33 Figure 8 : Recent examples of direct metabolic signalling that impacts replication and growth. .............. 35 Figure 9 : The Embden-Mayerhof-Parnas pathway for glycolysis + the overflow pathway ...................... 37 Figure 10 : The Tricarboxyic acid (TCA) or Krebs cycle........................................................................... 38 Figure 11: The pentose phosphate pathway ................................................................................................ 38 Figure 12: The Entner-Douderoff pathway ................................................................................................. 40 Figure 13: The PTS system in B. subtilis. Shown here are the PTS systems for fructose (EIIX Lev ), mannitol (EIIX Mtl )and cellobiose (EIIX Lic ). Taken from 128 ....................................................................... 42 Figure 14: Carbon catabolite repression ..................................................................................................... 43 Figure 15: Local control of gene expression by the PTS system ................................................................ 45 Figure 16: Connections between CCM and replication in the model organisms B. subtilis, E. coli, S. cerevisiae and higher eukaryotes (human/plant) ......................................................................................... 49 Figure 17: The structure of pyruvate kinase ............................................................................................... 53 Figure 18: Principles of marker frequency analysis for determining replication fork speed ...................... 57 Figure 19: Principles of run-out flow cytometry for determining initiation parameters ............................ 58 Figure 20: DnaE polymerase activity and DnaC helicase activity assays .................................................. 59 Figure 21: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A ................................................................................................................................................................... 102 Figure 22: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A and ΔpykA ........................................................................................... Fout! Bladwijzer niet gedefinieerd.Figure 23: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A and cat mutants..................................................................................... Fout! Bladwijzer niet gedefinieerd.Figure 24: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A and pykAΔPEP ........................................................................................ Fout! Bladwijzer niet gedefinieerd.Figure 25: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A and TSH triple mutants ........................................................................ Fout! Bladwijzer niet gedefinieerd.Figure 26: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A and T and H single mutants.................................................................. Fout! Bladwijzer niet gedefinieerd.Figure 27: The effect of hyPEPut on the shift ..................................... Fout! Bladwijzer niet gedefinieerd.Figure 28: The effect of mutated hyPEPut on the shift ........................ Fout! Bladwijzer niet gedefinieerd.Figure 29: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A and LLPEP ........................................................................................... Fout! Bladwijzer niet gedefinieerd.

Figure 24 :

 24 Figure 1 The cell cycle in eukaryotes ......................................................................................................... 18 Figure 2 : The cell cycle in bacteria ............................................................................................................ 20 Figure 3: The cell cycle in archaea ............................................................................................................. 22 Figure 4 : DNA replication initiation and elongation in the model bacteria E. coli and B. subtilis: ......... 26 Figure 5: Regulation of DnaA mediated initiation in E.coli and B. subtilis ............................................... 29 Figure 6: Classical mechanisms of metabolic regulation of DNA replication initiation ............................ 32 Figure 7 : Classical mechanisms of metabolic regulation of DNA replication elongation: ....................... 33 Figure 8 : Recent examples of direct metabolic signalling that impacts replication and growth. .............. 35 Figure 9 : The Embden-Mayerhof-Parnas pathway for glycolysis + the overflow pathway ...................... 37 Figure 10 : The Tricarboxyic acid (TCA) or Krebs cycle........................................................................... 38 Figure 11: The pentose phosphate pathway ................................................................................................ 38 Figure 12: The Entner-Douderoff pathway ................................................................................................. 40 Figure 13: The PTS system in B. subtilis. Shown here are the PTS systems for fructose (EIIX Lev ), mannitol (EIIX Mtl )and cellobiose (EIIX Lic ). Taken from 128 ....................................................................... 42 Figure 14: Carbon catabolite repression ..................................................................................................... 43 Figure 15: Local control of gene expression by the PTS system ................................................................ 45 Figure 16: Connections between CCM and replication in the model organisms B. subtilis, E. coli, S. cerevisiae and higher eukaryotes (human/plant) ......................................................................................... 49 Figure 17: The structure of pyruvate kinase ............................................................................................... 53 Figure 18: Principles of marker frequency analysis for determining replication fork speed ...................... 57 Figure 19: Principles of run-out flow cytometry for determining initiation parameters ............................ 58 Figure 20: DnaE polymerase activity and DnaC helicase activity assays .................................................. 59 Figure 21: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A ................................................................................................................................................................... 102 Figure 22: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A and ΔpykA ........................................................................................... Fout! Bladwijzer niet gedefinieerd.Figure 23: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A and cat mutants..................................................................................... Fout! Bladwijzer niet gedefinieerd.Figure 24: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A and pykAΔPEP ........................................................................................ Fout! Bladwijzer niet gedefinieerd.Figure 25: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A and TSH triple mutants ........................................................................ Fout! Bladwijzer niet gedefinieerd.Figure 26: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A and T and H single mutants.................................................................. Fout! Bladwijzer niet gedefinieerd.Figure 27: The effect of hyPEPut on the shift ..................................... Fout! Bladwijzer niet gedefinieerd.Figure 28: The effect of mutated hyPEPut on the shift ........................ Fout! Bladwijzer niet gedefinieerd.Figure 29: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A and LLPEP ........................................................................................... Fout! Bladwijzer niet gedefinieerd.

Figure 25 :

 25 Figure 1 The cell cycle in eukaryotes ......................................................................................................... 18 Figure 2 : The cell cycle in bacteria ............................................................................................................ 20 Figure 3: The cell cycle in archaea ............................................................................................................. 22 Figure 4 : DNA replication initiation and elongation in the model bacteria E. coli and B. subtilis: ......... 26 Figure 5: Regulation of DnaA mediated initiation in E.coli and B. subtilis ............................................... 29 Figure 6: Classical mechanisms of metabolic regulation of DNA replication initiation ............................ 32 Figure 7 : Classical mechanisms of metabolic regulation of DNA replication elongation: ....................... 33 Figure 8 : Recent examples of direct metabolic signalling that impacts replication and growth. .............. 35 Figure 9 : The Embden-Mayerhof-Parnas pathway for glycolysis + the overflow pathway ...................... 37 Figure 10 : The Tricarboxyic acid (TCA) or Krebs cycle........................................................................... 38 Figure 11: The pentose phosphate pathway ................................................................................................ 38 Figure 12: The Entner-Douderoff pathway ................................................................................................. 40 Figure 13: The PTS system in B. subtilis. Shown here are the PTS systems for fructose (EIIX Lev ), mannitol (EIIX Mtl )and cellobiose (EIIX Lic ). Taken from 128 ....................................................................... 42 Figure 14: Carbon catabolite repression ..................................................................................................... 43 Figure 15: Local control of gene expression by the PTS system ................................................................ 45 Figure 16: Connections between CCM and replication in the model organisms B. subtilis, E. coli, S. cerevisiae and higher eukaryotes (human/plant) ......................................................................................... 49 Figure 17: The structure of pyruvate kinase ............................................................................................... 53 Figure 18: Principles of marker frequency analysis for determining replication fork speed ...................... 57 Figure 19: Principles of run-out flow cytometry for determining initiation parameters ............................ 58 Figure 20: DnaE polymerase activity and DnaC helicase activity assays .................................................. 59 Figure 21: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A ................................................................................................................................................................... 102 Figure 22: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A and ΔpykA ........................................................................................... Fout! Bladwijzer niet gedefinieerd.Figure 23: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A and cat mutants..................................................................................... Fout! Bladwijzer niet gedefinieerd.Figure 24: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A and pykAΔPEP ........................................................................................ Fout! Bladwijzer niet gedefinieerd.Figure 25: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A and TSH triple mutants ........................................................................ Fout! Bladwijzer niet gedefinieerd.Figure 26: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A and T and H single mutants.................................................................. Fout! Bladwijzer niet gedefinieerd.Figure 27: The effect of hyPEPut on the shift ..................................... Fout! Bladwijzer niet gedefinieerd.Figure 28: The effect of mutated hyPEPut on the shift ........................ Fout! Bladwijzer niet gedefinieerd.Figure 29: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A and LLPEP ........................................................................................... Fout! Bladwijzer niet gedefinieerd.

Figure 26 :

 26 Figure 1 The cell cycle in eukaryotes ......................................................................................................... 18 Figure 2 : The cell cycle in bacteria ............................................................................................................ 20 Figure 3: The cell cycle in archaea ............................................................................................................. 22 Figure 4 : DNA replication initiation and elongation in the model bacteria E. coli and B. subtilis: ......... 26 Figure 5: Regulation of DnaA mediated initiation in E.coli and B. subtilis ............................................... 29 Figure 6: Classical mechanisms of metabolic regulation of DNA replication initiation ............................ 32 Figure 7 : Classical mechanisms of metabolic regulation of DNA replication elongation: ....................... 33 Figure 8 : Recent examples of direct metabolic signalling that impacts replication and growth. .............. 35 Figure 9 : The Embden-Mayerhof-Parnas pathway for glycolysis + the overflow pathway ...................... 37 Figure 10 : The Tricarboxyic acid (TCA) or Krebs cycle........................................................................... 38 Figure 11: The pentose phosphate pathway ................................................................................................ 38 Figure 12: The Entner-Douderoff pathway ................................................................................................. 40 Figure 13: The PTS system in B. subtilis. Shown here are the PTS systems for fructose (EIIX Lev ), mannitol (EIIX Mtl )and cellobiose (EIIX Lic ). Taken from 128 ....................................................................... 42 Figure 14: Carbon catabolite repression ..................................................................................................... 43 Figure 15: Local control of gene expression by the PTS system ................................................................ 45 Figure 16: Connections between CCM and replication in the model organisms B. subtilis, E. coli, S. cerevisiae and higher eukaryotes (human/plant) ......................................................................................... 49 Figure 17: The structure of pyruvate kinase ............................................................................................... 53 Figure 18: Principles of marker frequency analysis for determining replication fork speed ...................... 57 Figure 19: Principles of run-out flow cytometry for determining initiation parameters ............................ 58 Figure 20: DnaE polymerase activity and DnaC helicase activity assays .................................................. 59 Figure 21: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A ................................................................................................................................................................... 102 Figure 22: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A and ΔpykA ........................................................................................... Fout! Bladwijzer niet gedefinieerd.Figure 23: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A and cat mutants..................................................................................... Fout! Bladwijzer niet gedefinieerd.Figure 24: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A and pykAΔPEP ........................................................................................ Fout! Bladwijzer niet gedefinieerd.Figure 25: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A and TSH triple mutants ........................................................................ Fout! Bladwijzer niet gedefinieerd.Figure 26: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A and T and H single mutants.................................................................. Fout! Bladwijzer niet gedefinieerd.Figure 27: The effect of hyPEPut on the shift ..................................... Fout! Bladwijzer niet gedefinieerd.Figure 28: The effect of mutated hyPEPut on the shift ........................ Fout! Bladwijzer niet gedefinieerd.Figure 29: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A and LLPEP ........................................................................................... Fout! Bladwijzer niet gedefinieerd.

Figure 27 :

 27 Figure 1 The cell cycle in eukaryotes ......................................................................................................... 18 Figure 2 : The cell cycle in bacteria ............................................................................................................ 20 Figure 3: The cell cycle in archaea ............................................................................................................. 22 Figure 4 : DNA replication initiation and elongation in the model bacteria E. coli and B. subtilis: ......... 26 Figure 5: Regulation of DnaA mediated initiation in E.coli and B. subtilis ............................................... 29 Figure 6: Classical mechanisms of metabolic regulation of DNA replication initiation ............................ 32 Figure 7 : Classical mechanisms of metabolic regulation of DNA replication elongation: ....................... 33 Figure 8 : Recent examples of direct metabolic signalling that impacts replication and growth. .............. 35 Figure 9 : The Embden-Mayerhof-Parnas pathway for glycolysis + the overflow pathway ...................... 37 Figure 10 : The Tricarboxyic acid (TCA) or Krebs cycle........................................................................... 38 Figure 11: The pentose phosphate pathway ................................................................................................ 38 Figure 12: The Entner-Douderoff pathway ................................................................................................. 40 Figure 13: The PTS system in B. subtilis. Shown here are the PTS systems for fructose (EIIX Lev ), mannitol (EIIX Mtl )and cellobiose (EIIX Lic ). Taken from 128 ....................................................................... 42 Figure 14: Carbon catabolite repression ..................................................................................................... 43 Figure 15: Local control of gene expression by the PTS system ................................................................ 45 Figure 16: Connections between CCM and replication in the model organisms B. subtilis, E. coli, S. cerevisiae and higher eukaryotes (human/plant) ......................................................................................... 49 Figure 17: The structure of pyruvate kinase ............................................................................................... 53 Figure 18: Principles of marker frequency analysis for determining replication fork speed ...................... 57 Figure 19: Principles of run-out flow cytometry for determining initiation parameters ............................ 58 Figure 20: DnaE polymerase activity and DnaC helicase activity assays .................................................. 59 Figure 21: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A ................................................................................................................................................................... 102 Figure 22: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A and ΔpykA ........................................................................................... Fout! Bladwijzer niet gedefinieerd.Figure 23: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A and cat mutants..................................................................................... Fout! Bladwijzer niet gedefinieerd.Figure 24: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A and pykAΔPEP ........................................................................................ Fout! Bladwijzer niet gedefinieerd.Figure 25: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A and TSH triple mutants ........................................................................ Fout! Bladwijzer niet gedefinieerd.Figure 26: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A and T and H single mutants.................................................................. Fout! Bladwijzer niet gedefinieerd.Figure 27: The effect of hyPEPut on the shift ..................................... Fout! Bladwijzer niet gedefinieerd.Figure 28: The effect of mutated hyPEPut on the shift ........................ Fout! Bladwijzer niet gedefinieerd.Figure 29: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A and LLPEP ........................................................................................... Fout! Bladwijzer niet gedefinieerd.

Figure 28 :

 28 Figure 1 The cell cycle in eukaryotes ......................................................................................................... 18 Figure 2 : The cell cycle in bacteria ............................................................................................................ 20 Figure 3: The cell cycle in archaea ............................................................................................................. 22 Figure 4 : DNA replication initiation and elongation in the model bacteria E. coli and B. subtilis: ......... 26 Figure 5: Regulation of DnaA mediated initiation in E.coli and B. subtilis ............................................... 29 Figure 6: Classical mechanisms of metabolic regulation of DNA replication initiation ............................ 32 Figure 7 : Classical mechanisms of metabolic regulation of DNA replication elongation: ....................... 33 Figure 8 : Recent examples of direct metabolic signalling that impacts replication and growth. .............. 35 Figure 9 : The Embden-Mayerhof-Parnas pathway for glycolysis + the overflow pathway ...................... 37 Figure 10 : The Tricarboxyic acid (TCA) or Krebs cycle........................................................................... 38 Figure 11: The pentose phosphate pathway ................................................................................................ 38 Figure 12: The Entner-Douderoff pathway ................................................................................................. 40 Figure 13: The PTS system in B. subtilis. Shown here are the PTS systems for fructose (EIIX Lev ), mannitol (EIIX Mtl )and cellobiose (EIIX Lic ). Taken from 128 ....................................................................... 42 Figure 14: Carbon catabolite repression ..................................................................................................... 43 Figure 15: Local control of gene expression by the PTS system ................................................................ 45 Figure 16: Connections between CCM and replication in the model organisms B. subtilis, E. coli, S. cerevisiae and higher eukaryotes (human/plant) ......................................................................................... 49 Figure 17: The structure of pyruvate kinase ............................................................................................... 53 Figure 18: Principles of marker frequency analysis for determining replication fork speed ...................... 57 Figure 19: Principles of run-out flow cytometry for determining initiation parameters ............................ 58 Figure 20: DnaE polymerase activity and DnaC helicase activity assays .................................................. 59 Figure 21: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A ................................................................................................................................................................... 102 Figure 22: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A and ΔpykA ........................................................................................... Fout! Bladwijzer niet gedefinieerd.Figure 23: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A and cat mutants..................................................................................... Fout! Bladwijzer niet gedefinieerd.Figure 24: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A and pykAΔPEP ........................................................................................ Fout! Bladwijzer niet gedefinieerd.Figure 25: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A and TSH triple mutants ........................................................................ Fout! Bladwijzer niet gedefinieerd.Figure 26: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A and T and H single mutants.................................................................. Fout! Bladwijzer niet gedefinieerd.Figure 27: The effect of hyPEPut on the shift ..................................... Fout! Bladwijzer niet gedefinieerd.Figure 28: The effect of mutated hyPEPut on the shift ........................ Fout! Bladwijzer niet gedefinieerd.Figure 29: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A and LLPEP ........................................................................................... Fout! Bladwijzer niet gedefinieerd.

Figure 29 :

 29 Figure 1 The cell cycle in eukaryotes ......................................................................................................... 18 Figure 2 : The cell cycle in bacteria ............................................................................................................ 20 Figure 3: The cell cycle in archaea ............................................................................................................. 22 Figure 4 : DNA replication initiation and elongation in the model bacteria E. coli and B. subtilis: ......... 26 Figure 5: Regulation of DnaA mediated initiation in E.coli and B. subtilis ............................................... 29 Figure 6: Classical mechanisms of metabolic regulation of DNA replication initiation ............................ 32 Figure 7 : Classical mechanisms of metabolic regulation of DNA replication elongation: ....................... 33 Figure 8 : Recent examples of direct metabolic signalling that impacts replication and growth. .............. 35 Figure 9 : The Embden-Mayerhof-Parnas pathway for glycolysis + the overflow pathway ...................... 37 Figure 10 : The Tricarboxyic acid (TCA) or Krebs cycle........................................................................... 38 Figure 11: The pentose phosphate pathway ................................................................................................ 38 Figure 12: The Entner-Douderoff pathway ................................................................................................. 40 Figure 13: The PTS system in B. subtilis. Shown here are the PTS systems for fructose (EIIX Lev ), mannitol (EIIX Mtl )and cellobiose (EIIX Lic ). Taken from 128 ....................................................................... 42 Figure 14: Carbon catabolite repression ..................................................................................................... 43 Figure 15: Local control of gene expression by the PTS system ................................................................ 45 Figure 16: Connections between CCM and replication in the model organisms B. subtilis, E. coli, S. cerevisiae and higher eukaryotes (human/plant) ......................................................................................... 49 Figure 17: The structure of pyruvate kinase ............................................................................................... 53 Figure 18: Principles of marker frequency analysis for determining replication fork speed ...................... 57 Figure 19: Principles of run-out flow cytometry for determining initiation parameters ............................ 58 Figure 20: DnaE polymerase activity and DnaC helicase activity assays .................................................. 59 Figure 21: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A ................................................................................................................................................................... 102 Figure 22: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A and ΔpykA ........................................................................................... Fout! Bladwijzer niet gedefinieerd.Figure 23: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A and cat mutants..................................................................................... Fout! Bladwijzer niet gedefinieerd.Figure 24: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A and pykAΔPEP ........................................................................................ Fout! Bladwijzer niet gedefinieerd.Figure 25: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A and TSH triple mutants ........................................................................ Fout! Bladwijzer niet gedefinieerd.Figure 26: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A and T and H single mutants.................................................................. Fout! Bladwijzer niet gedefinieerd.Figure 27: The effect of hyPEPut on the shift ..................................... Fout! Bladwijzer niet gedefinieerd.Figure 28: The effect of mutated hyPEPut on the shift ........................ Fout! Bladwijzer niet gedefinieerd.Figure 29: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A and LLPEP ........................................................................................... Fout! Bladwijzer niet gedefinieerd.

  Dans ce travail de thèse, nous avons étudié chez B. subtilis le rôle de la pyruvate kinase PykA dans la réplication. Ce travail a été motivé par les diverses publications résumées ci-dessus qui suggèrent que PykA est un déterminant important du lien entre métabolisme et réplication. PykA est une enzyme du MCC qui catalyse la dernière réaction de la glycolyse et convertit le phosphoénolpyruvate et l'ADN en pyruvate et ATP. Chez B. subtilis, cette enzyme comprend deux domaines. Le premier, N-terminal, correspond au domaine catalytique classique. Le second, C-terminal, n'est pas essentiel à l'activité catalytique. De façon intéressante, il est fortement homologue au domaine « PEP utilizer » (nommé ici PEPut) présent dans d'autres enzymes métaboliques. Dans le système principal de transport des sucres (le système PTS pour phoshpoenolpyruvate:carbohydrate phosphotransferase), l'enzyme EI se lie au phosphoénolpyruvate qui est utilisé comme donneur pour phosphoryler de façon transitoire un résidu histidine dans le domaine PEPut de EI. Le groupement phosphoryl est ensuite successivement transféré aux protéines Hpr, EII pour être finalement transféré sur la molécule de sucre entrante. De façon remarquable, le résidu histidine phosphorylé dans EI est inclus dans un motif Thr-Ser-His (TSH) hautement conservé dans les différentes formes de PEPut et dans PykA. Puisque, dans une premier temps, PykA comme EI a pour substrat le phosphoénolpyruvate et porte un domaine PEPut potentiellement fonctionnel et puisque, dans un second temps, PykA est importante pour la réplication, nous avons étudié la réplication dans différents mutants de PykA afin de rechercher des éléments dans PykA impliqués dans la communication avec la synthèse d'ADN.

  analysé l'effet sur le ratio ori/ter de mutations dans le motif TSH de HyPEPut en contexte sauvage. Les résultats montrent que le ratio est aussi faible dans les cellules exprimant HyPEPut sauvage qu'avec la mutation Thr>Ala, alors qu'il est encore plus réduit dans en contexte HyPEPut avec la mutation Thr>Asp. Par contre, le ratio n'est plus inhibé quand le résidu His de HyPEPut est remplacé par Ala ou Asp. Ceci montre que le résidu His des molécules libres de PEPut est essentiel pour recevoir et envoyer le signal du facteur limitant inhibiteur. Ceci attribue une fonction au résidu His du motif TSH de PEPut qui n'avait pas été détectée quand ce domaine est fusionné à Cat. Par ailleurs, comme dans le contexte où PEPut et Cat sont fusionnés, la mutation Thr>Asp augmente l'effet inhibiteur de PEPut libre. Ceci renforce l'hypothèse selon laquelle la phosphorylation du résidu Thr est importante pour le bon fonctionnement de PEPut et suggère que les résidus Thr et His du motif TSH de PEPut assurent des fonctions importantes distinctes dans le contrôle temporel de la réplication. Enfin, nous avons étudié l'effet de l'expression de HyPEPut dans des mutants catalytiques de PykA. Nous avons observé que, comme dans une souche sauvage, l'expression de HyPEPut est associée à une nette réduction du ratio ori/ter dans tous les mutants catalytiques de PykA sauf dans celui contenant une petite délétion de 27 acides-aminés. Dans ce cas, l'effet inhibiteur de HyPEPut est abrogé. Ceci établit un lien fonctionnel étroit entre 27 acides aminés du domaine Cat de PykA (coordonnées 208 à 234), le résidu His de PEPut, le facteur limitant inhibiteur et les trois enzymes de réplications liées génétiquement à ces 27 résidus de PykA. Dans une collaboration avec Panos Soutanas (Nottingham, UK), nous avons ensuite recherché d'éventuels effets directes de PykA sur l'activité d'enzymes de réplication in vitro. Ce travail a été centré sur les trois enzymes de réplication liées génétiquement à PykA : l'hélicase DnaC, la primase DnaG et l'ADN polymérase DnaE. Ces trois enzymes forment un complexe qui joue des rôles importants dans l'initiation et l'élongation de la réplication. L'hélicase DnaC ouvre l'ADN dans la région origine au moment de l'initiation de la réplication et sépare les brins d'ADN dans la fourche de réplication pendant la phase d'élongation. La primase DnaG synthétise des amorces ARN au moment de l'initiation et dans les fourches de réplication. Ces amorces sont utilisées pour démarrer la synthèse d'ADN à l'origine de réplication et pour synthétiser le brins discontinu dans la fourche de réplication. L'ADN polymérase DnaE allonge les amorces ARN produites par DnaG à l'origine ainsi que dans les fourches de réplication pour assurer une synthèse partielle ou totale du brin discontinu. Les enzymes de réplication ont été purifiées et leurs activités testées en présence ou en absence de PykA comme dans nos études précédentes. En utilisant un système d'expression et de purification de E. coli classiquement utilisé en biologie moléculaire, nous avons purifié la forme native de PykA de B. subtilis. A l'aide de tests biochimiques (test de Michalis Menten, test de Hill et spectroscopie de masse), nous avons montré dans un premier temps que la forme purifiée de PykA est métaboliquement active et forme, comme attendu, un tétramère très stable. Des tests de réplication ont ensuite été conduits avec l'ADN polymérase DnaE. Dans des mélanges réactionnelles contenant différentes matrices de réplication, nous avons ajouté des concentrations faibles de DnaE en combinaison ou non avec des concentrations équimolaires de PykA. Les tests montrent une stimulation de la synthèse d'ADN en présence de PykA dans toutes les conditions utilisées. Des expériences contrôles ont montré que cette stimulation était spécifique de PykA car elle n'était pas observée quand l'enzyme métabolique était remplacée par la BSA dans les mélanges réactionnels. De plus, des expériences de titration associées à des tests de décalage sur gel (gel shift) ont montré que la stimulation de la synthèse d'ADN par PykA n'était pas due à une stimulation de la fixation de DnaE sur la matrice d'ADN. Il est conclu de ces expériences que PykA stimule l'activité ADN polymérase de DnaE probablement via une interaction directe entre ces deux protéines. Puisque le domaine PEPut purifié ne provoque pas cette stimulation, celle-ci dépend probablement d'un contact direct entre DnaE et le domaine Cat de PykA. Des tests permettant de mesurer l'activité hélicase de DnaC en présence ou en absence de PykA ont ensuite été conduits. Ils montrent que PykA a un effet inhibiteur marginal sur DnaC. Cependant, la stimulation de l'activité hélicase de DnaC par la primase DnaG n'est pas observée en présence de PykA. Ainsi, dans certains contextes, PykA peut inhiber significativement DnaC. Ensemble, ces résultats suggèrent que PykA peut modifier positivement ou négativement les activités d'enzymes de réplication de façon spécifique grâce à des interactions directes avec les enzymes de réplication. En conclusion, nos résultats chez B. subtilis mettent en évidence des phénotypes et des activités nonmétaboliques à une enzyme du MCC qui catalyse la dernière étape de la glycolyse : la pyruvate kinase. Cette enzyme semble donc dotée de fonctions cryptiques. Les phénotypes ont été observés dans des conditions de culture néoglucogénique où l'enzyme est produite en abondance (environ 30.000 molécules par cellule) mais où sa fonction métabolique n'est pas requise pour la croissance cellulaire. Leur analyse met systématiquement en évidence des défauts de réplication. Suivant la position des mutations dans la protéine, des défauts opposés sont observés. Dans les mutants du site catalytique, l'initiation de la réplication est avancée dans le cycle cellulaire et la vitesse des fourches de réplication est ralentie provoquant un allongement du temps nécessaire à la réplication du chromosome. A l'inverse, dans les mutants du domaine PEPut, l'initiation est retardée et la vitesse des fourches de réplication accélérée. Ces défauts de réplication qui changent le positionnement dans le temps de la réplication, indiquent donc que les activités cryptiques de la pyruvate kinase chez B. subtilis sont importantes pour un bon contrôle temporel de la réplication dans le cycle cellulaire. Le mécanisme sous-jacent reste inconnu mais il semble dépendre de plusieurs facteurs. L'un de ces facteurs pourrait être le phosphoenolpyruvate puisqu'au moins deux des trois mutations catalytiques affectant la réplication inhibent la fixation de ce métabolite à PykA. Un autre facteur pourrait être le niveau de phosphorylation du résidu Thr situé dans le motif TSH de PEPut. Il semble que plus ce résidu est phosphorylé, plus l'initiation est retardée et la synthèse d'ADN rapide. Un troisième facteur serait un inhibiteur limitant non identifié. Enfin, l'étude des phénotypes associés à une production modérée de PEPut non fusionné au domaine Cat indique des liens fonctionnels étroits entre 27 acides aminés du domaine Cat, le résidu His du motif TSH de PEPut et le facteur inhibiteur non identifié. Des études effectuées dans des conditions métaboliques différentes (un shift métabolique) indiquent que la réponse de la réplication aux variations nutritionnelles peut faire appel à des déterminants et donc des activités cryptiques différentes de PykA. En effet, dans ces nouvelles conditions de culture, le changement de réplication dépend du domaine PEPut, et plus particulièrement du résidu His de son motif TSH, mais pas du domaine Cat. Ceci montre que le comportement des activités cryptiques de PykA change avec le métabolisme cellulaire et donc que PykA agit comme une plateforme qui évalue le métabolisme pour envoyer des signaux adaptés à la réplication afin de positionner dans le temps la réplication de l'ADN dans le cycle cellulaire. Plusieurs données indiquent que PykA agit sur le contrôle de la réplication en ayant une action directe ou indirecte sur trois enzymes clé de réplication : l'hélicase DnaC, la primase DnaG et l'ADN polymérase DnaE. Premièrement, nous avons montré précédemment que, dans un milieu glycolytique riche (LB), (i) le gène pykA est important pour l'initiation de la réplication, (ii) que 27 acides aminés du site catalytique de PykA sont génétiquement liés à DnaC, DnaG et DnaE et que (iii) ce lien génétique pouvait être accompagné de changements conformationnels dans ces enzymes de réplication. Ici, nous montrons in vitro que la protéine PykA purifiée change les propriétés de réplication de DnaE et DnaC probablement via des interactions directes et spécifiques entre PykA et ces enzymes de réplication. Dans son ensemble, ce travail apporte toute une série d'évidences conduisant à la conclusion que, chez la bactérie B. subtilis, la pyruvate kinase est dotée de plusieurs activités cryptiques qui jouent un rôle direct ou indirect dans le contrôle temporel de la réplication. Ce système de régulation peut impliquer des effets opposés orchestrés par les domaines Cat et PEPut de l'enzyme et ces effets seraient médiés par des voies de signalisations pouvant impliquer la concentration intracellulaire du phosphoénolpyruvate et des événements de phosphorylation. Ces observations attribuent donc pour la première fois une fonction de régulation temporelle de la réplication de l'ADN à une enzyme métabolique. Etant donné que le contrôle temporel de la réplication est universel, que les enzymes du MCC sont fortement conservées et que de nombreuses études mettent en évidence des liens entre le MCC et la réplication, nous proposons ici que des enzymes du MCC présentent des activités cryptiques de réplication nécessaires au contrôle temporel de la réplication dans la plupart des organismes. Par ailleurs, il est bien établi que l'altération du contrôle temporel de la réplication augmente l'instabilité génétique et que le cancer présente deux caractéristiques précoces et essentielles : une altération du MCC (effet Warburg) et une instabilité génétique. Nous proposons donc ici que l'émergence de cancers pourrait résulter de changements dans le MCC qui, en altérant le contrôle temporel de la réplication, augmenterait l'instabilité génétique pour engager les cellules dans le processus de cancérisation.

Figure 1

 1 Figure 1 The cell cycle in eukaryotes (A) The eukaryotic cell cycle: The fate of a single chromosome is depicted during the cell cycle.The eukaryotic cell cycle is an ordered sequence of temporally separated events. First, the cell enters the G1 phase, the phase between the birth of the cell and the initiation of DNA replication. Next, the cell duplicates its chromosomes during the S phase and enters G2 with twice as many chromosomes. During mitosis, the nucleoid is disassembled into vesicles, DNA is condensed and the spindle structure is formed (prophase) chromatids are aligned at the cell equator (metaphase) chromatids are segregated by the spindle network (anaphase) the events of prophase are reversed (telophase) and the cell divides. Taken from 2 Regulation of the

Figure 2 :

 2 Figure 2 : The cell cycle in bacteria (A) Events of the cell cycle in bacteria : The bacterial cell cycle (slow growing) consists of three periods. During the B period, bacteria grow and accumulate nutrients to initiate the cell cycle. During the C period, the bacterial chromosome is fully replicated bidirectionally from a single origin of replication (oriC) and simultaneously segregated to different halves of the cell (see figure 2B) The replicated chromosomes are concatenated which is resolved by homologous recombination at the end of the C period. During the D period, a Z-ring starts to form at mid-cell when it is free of genomic DNA. This Z-ring defines the division site and becomes the anchor point for proteins that will ultimately lead to division. Taken from 8 . (B) Orientation of chromosome segregation Top: Segregation orientation in the model organism E. coli Bottom: Segregation in the model organisms B. subtilis and C. crescentus. Taken from 8 .

  process of DNA replication to a specific time window in the cell cycle, also called DNA replication gating. The focus of this work is on the mechanisms that regulate DNA replication gating in the model bacterium B. subtilis. In the next section, the process of DNA replication in B. subtilis is described and contrasted with the one in the other model bacterium Escherichia coli. 2. DNA replication in B. subtilis and E. coli 2.1 Basic mechanisms of DNA replication in B. subtilis and E.coli

Figure 4 :

 4 Figure 4 : DNA replication initiation and elongation in the model bacterium B. subtilis: (A) The three phases of DNA replication (B) DNA replication initiation: DNA replication initiation: When bound to ATP, the initiator protein DnaA binds to DnaA boxes in the origin of replication and oligomerizes. This oligomerization triggers the binding of DnaD, that loops the origin and recruits DnaB. DnaB binding leads to the unwinding of the DNA unwinding element (DUE), which triggers the loading of the DNA helicase loader, the DNA helicase and all the other proteins that collectively form the replication machine (replisome). Taken from 42 (C) DNA replication elongation in B. subtilis. First, the helicase DnaC unwinds dsDNA. ssDNA is immediately coated with single stranded binding proteins (SSB) to prevent reassembly and degradation. Next, the leading strand DNA is handed off to the leading strand polymerase (PolIII vs PolC), which is bound to the DNA by the β-clamp DnaN. The lagging strand is handed off to the primase DnaG, which synthesizes an RNA primer. This RNA

Figure 5 :

 5 Figure 5: Regulation of DnaA mediated initiation in E.coli and B. subtilis Regulation of DnaA acts on (i) the nucleotide bound state of DnaA (ATP or ADP) done by the E.coli regulators Hda and the datA/DARS loci and (ii) the binding of DnaA and its assembly into the initiation competent DnaA oligomer, done by the other mechanisms.

Finally,

  DNA gyrase has been shown to influence DnaA dependent replication initiation in B. subtilis by inhibiting DnaA binding 102 . The sensitivity to DNA topology was partly mediated by YabA 102 . Interestingly, the activity of DNA gyrase has long been known to depend on the metabolic state of the cell 103 allowing it to potentially couple DNA replication timing to metabolism. These classical mechanisms coupling DNA replication to metabolism are shown in figure 6.

Figure 6 :

 6 Figure 6: Classical mechanisms of metabolic regulation of DNA replication initiation Metabolic regulation of initiation acts primarily on DnaA. DnaA expression increases with increasing growth rates, yielding higher initiation frequencies. Under AA starvation conditions, ppGpp prevents expression of DnaA and inhibits new rounds of replication. Additionally, DNA gyrase activity has recently been shown to impact DNA initiation frequency in a DnaA dependent manner and DNA gyrase activity has long been known to be influenced by nutrition.

Figure 7 :

 7 Figure 7 : Classical mechanisms of metabolic regulation of DNA replication elongation:

Figure 8 :

 8 Figure 8 : Recent examples of direct metabolic signalling that impacts replication and growth. (Top) Acidic phospholipids like phosphatidylglycerol (PG) or cardiolipin (CL) are increasingly recognized to have a role in DNA replication initiation in E. coli. PG and CL regulate replication by (i) stimulating nucleotide exchange and recharging DnaA and (ii) sequestering DnaA away from oriC. The presence of these lipids in the membrane is associated with growth. (Bottom Left) During the Yeast Metabolic Cycle, acetyl-CoA levels have been associated with increases acetylation of histones associated with genes important for growth. Histone acetylation causes the histones to dissociate and expression of growth genes is no longer repressed. (Bottom right) The metabolic enzyme GADPH has been found to influence DNA replication speed of several plant viruses through direct interactions.

Figure 9 :

 9 Figure 9 : The Embden-Mayerhof-Parnas pathway for glycolysis + the overflow pathway Glossary: (Molecules) GLU: glucose, G6P: Glucose-6-phosphate, F6P: Fructose-6phosphate, FBP: Fructose-1,6-bisphosphate, DHAP: Dihidroxyacetonephosphate, G3P: Glyceraldehyde-3-phosphate, 1,3PG: 1,3 bisphosphoglycerate, 3PG: 3-phosphoglycerate, 2PG: 2-phosphoglycerate, PEP: phosphoenolpyruvate, PYR: pyruvate, LAC: lactate, ALA: alanine, SER: Serine, GLY: glycine, CYS: cysteine (Enzymes): HK:hexokinase, PGI: phosphoglucose isomerase, PFK: phosphofructokinase, ALDO: aldolase, TPI: triosephosphate isomerase, GADPH: glyceraldehyde phosphate dehydrogenase, PGK: phosphoglycerate kinase, PGM: phosphoglycerate mutase, ENO: enolase, PK: pyruvate kinase, LDH: lactate dehydrogenase

Figure 10 :

 10 Figure 10 : The Tricarboxyic acid (TCA) or Krebs cycle Glossary : (Molecules) PYR: pyruvate, LAC: lactate, ALA: alanine, SER: Serine, GLY: glycine, CYS: cysteine, Ac-CoA: Acetyl-CoA, CIT: citrate, ISOCIT: isocitrate, α-KG: α-ketoglutarate, SUC-CoA: succinyl-CoA, SUC: succinate, FUM: fumarate, MAL: malate, OX: oxaloacetate, (Enzymes) PDH: pyruvate dehydrogenase, CS: citrate synthase, ACO: aconitase, IDH: isocitrate dehydrogenase, KGDH: ketoglutarate dehydrogenase, SCS: succinyl-CoA synthetase, SDH: succinate dehydrogenase, FH: fumarase, MDH: malate dehydrogenase, PC: pyruvate carboxylase

Figure 11 :

 11 Figure 11: The pentose phosphate pathway Glossary : (Molecules) 6PGL: 6-phosphogluconolactone, 6PG: 6-phosphogluconate, RU5P:

Figure 12 :

 12 Figure 12: The Entner-Douderoff pathway Glossary : KDPG: 2-keto-3-deoxy-phosphogluconate, 6PGDH: 6-phosphogluconate dehydrogenase, KDPGALDO: 2-keto-3-deoxy-6-phosphogluconate aldolase

Figure 13 :

 13 Figure 13: The PTS system in B. subtilis. Shown here are the PTS systems for fructose (EIIX Lev ), mannitol (EIIX Mtl )and cellobiose (EIIX Lic ). Taken from 129

  mixture of carbon sources, CCR prevents the induction of the transcription factor, either by excluding the secondary carbon source (inducer exclusion) or by preventing induction of the transcription factor (induction prevention) 128 . Both mechanisms have been described for Gram positive bacteria like B. subtilis.Inducer exclusion in Gram positive bacteria is achieved by inactivating the transporter proteins of secondary carbon sources in the presence of glucose using phosphorylation dependent interactions with PTS components. In Lactobacillus brevis, HPr-Ser-P can bind and inactivate the galactose permease 150 . In S. thermophilus, HPr-His-P can activate the lactose permease 151 . Phosphorylation on the Ser of HPr prevents this activation 152 . Similarly, in Enterobacteriae, the PTS glucose specific component EIIA Gluc is known to interact with and inactivate lactose permease, glycerol kinase, the maltose, melibiose, galactose and arabinose transporters when it is phosphorylated 129 . Induction prevention can be achieved in a variety of ways.Many secondary carbon source specific transcription factors have duplicated PTS regulatory domains (PRD's) that can be phosphorylated by at least two PTS components 129 . These domains enable hierarchical usage of secondary carbon sources, as the activity of the transcription factors depends both on the availability of the carbon source and on the absence of glucose 153 . In B. subtilis, the LevR transcription factor requires phosphorylation by HPr to achieve full activity 154 . Another well-studied example is the LicT antiterminator[155][156][157][158] .

Figure 15 :

 15 Figure 15: Local control of gene expression by the PTS system PTS mediated signalling also influences gene expression more locally, enabling hierarchical usage of carbon sources. (Left) Inducer exclusion: Interaction of HPr-S46-P ( a sign of the presence of glucose) with the galactose transporter (GalT) prevents the import of galactose in the presence of glucose. This prevents gene expression of galactose specific genes. (Induction prevention) Induction prevention can be achieved through (i) direct phosphorylation dependent interactions with PTS components (ii) phosphorylation of secondary carbon source specific regulators and (iii) sequestration of the regulators at the membrane. Shown is the example of LicT. LicT has two PTS regulatory domain (PRD), which are phosphorylated using EIIB and HPr. When phosphorylated, LicT has full antiterminator activity. When not phosphorylated, it is sequestered near the membrane.

  2007, Janniere et al found that mutations in the four terminal genes of CCM (pgk, pgm, eno and pykA) could suppress the effects of temperature sensitive (Ts) mutations in the replication proteins DnaG, DnaE and DnaC on survival at elevated temperatures in B. subtilis.162 This restoration of viability to Ts mutants in replication enzymes occurred over a narrow range of concentrations of the CCM enzymes and did not appear to be due to defects in growth rate or an activation of major stress responses 162 . Rather, the restoration phenotype depended at least in part on maintenance of high activity of temperature sensitive mutants at elevated temperatures 162 . The authors therefore proposed that this suppression is evidence of a genetic system connecting CCM to the gating of DNA replication 162 . In follow-up studies, it was shown that multiple CCM deletions in B. subtilis can also affect replication initiation (pykA and pgk)

Figure 17 :

 17 Figure 17: Connections between CCM and replication in the model organisms B. subtilis, E. coli, S. cerevisiae and higher eukaryotes (human/plant)

Figure 18 :

 18 Figure 18: The structure of pyruvate kinase (A) Domain structure of the monomer pyruvate kinase in G. staerothermophilus .Shown is

57 Figure 19 :

 5719 Figure 19: Principles of marker frequency analysis for determining replication fork speed (A) Basic idea behind ori-ter ratio and marker frequency analysis (B) Quantification of gene copy number by qPCR using SybR Green Taken from 227

Figure 20 :

 20 Figure 20: Principles of run-out flow cytometry for determining initiation parameters (A) Basic principle of using flow cytometry for determining initiation parameters (B) Basic principle of quantification through flow cytometry Taken from 229

Figure 21 :B

 21 Figure 21: DnaE polymerase activity and DnaC helicase activity assays (A) Principle of DnaE polymerase activity measurement (B) Principle of DnaC helicase activity measurement

Fig. 1 :

 1 Fig. 1: The temporal gating of DNA replication is disturbed in pykA null cells. (A) Growth rate and size of wild-type and DpykA cells in MC. Cells were grown exponentially in the neoglucogenic MC medium for more than 20 generations using successive dilutions. All along the experiment, growth was assessed by spectrophotometry (OD650nm, a typical experiment is shown) and cell size was analyzed at a representative timepoint by microscopy upon FM4-64 membrane staining to ease the measurement of pole-to-pole distances. (B) Cell cycle parameters analysis. The ori/ter ratio, C period and fork speed were determined by qPCR from cells collected at OD650nm = 0.08 to 0.25 after exponential growth in MC for more than 10 generations. For determining the number of origins/cell and the age of initiation, chloramphenicol was

  division but allows completion of ongoing rounds of replication. After 4 hours of drug treatment, cells were analyzed by flow cytometry after DNA staining with Hoechst 33258. Top panel: typical runout histograms with the % of cells containing 4 or 8 chromosomes. Mid panel: typical marker frequency slot. The nearly monotonous decrease of marker frequencies from the origin to the terminus showed that there is no pause site along the chromosome and that the velocity of replication forks in the two strains are different. Bottom panel: cell cycle parameters (mean and SD are from 3 to 8 independent biological repeats in most instances). The cell cycle parameters were determined as described in Material and Method. (C) Microscope study of chromosome segregation. Cells growing exponentially in MC were incubated with FM4-64 and DAPI to stain the membrane (colored in red) and the nucleoid (colored in blue), respectively. Shown are the phase contrast and the FM4-64 and DAPI merge of a typical analysis. (D) Schematic representation of cell cycles. The triangles represent two successive cell cycles. Replication of sister cells initiated in mother cells. Numbers are in min; ai: age of initiation; at: age of termination.

  results further suggest that replication gating may involve conformational changes in DnaC, DnaG and/or DnaE orchestrated somehow by residues 208-234 of the Cat domain of PykA.

Fig. 2 : 3 .

 23 Fig.2: Distinct regions in the catalytic domain of PykA are important for proper replication gating. A. Ori/ter ratio analysis. B. Cell cycle parameters. C. Schematic representation of cell cycles. See Fig. 1 for details.

Fig. 3 :

 3 Fig. 3: The PEPut sequence and phosphorylation of the T residue in the TSH motif of PEPut are important for proper replication gating. A. Ori/ter ratio analysis. B. Cell cycle parameters. C. Schematic representation of cell cycles. See Fig. 1 for details.

Fig. 5 :

 5 Fig. 5: PykA stimulates the DNA polymerase activity of DnaE and inhibits the helicase activity of the helicase DnaC via the primase DnaG. A. Representative alkaline agarose gels showing DnaE primer extension time courses alone and in the presence or absence of PykA or BSA. 10 nM DnaE were premixed with equimolar concentrations of pykA or BSA. Reactions were carried out as described in Material and Methods. B. Monitoring of DnaC helicase activity. The helicase activity of DnaC/DnaI (500 nM each protein) in the presence or absence of PykA (x nM) and/or DnaG (250 nM) is shown as a function of time. It was assayed by monitoring the displacement of a primer annealed to M13 ssDNA onto non-denaturing polyacrylamide gel as described in Material and Method. Data were plotted as a percentage of displaced primer versus time.

Fig. S1 :

 S1 Fig. S1 : important residues in PykA in B. subtilis (A) Coordinates of key catalytic residues in pyruvate kinases in B. subtilis, human PKM2 and M. tuberculosis Taken from UniProt (B) The conserved TSH motif in the PEPut domain Fig. S2 : Metabolomic analysis of WT and ΔpykA

(

  NEB) and used to transform competent B. subtilis strains. The TSH>DDD mutations was obtained by double crossover of the linearized plasmid into the chromosome. Positive transformants were selected on Tet. The genotype of the transformants was verified by PCR, phenotypic analysis and/or DNA sequencing. Strains containing hyPEP in other genetic backgrounds were obtained by transforming competent cells of these genetic backgrounds with genomic DNA of strain 722. Positive transformants were selected on Cm and verified using PCR verification and phenotypic analysis.Strains 1009 (pykA Lactococcus Lactis) and 1010 (pykA Lactococcus Lactis fused to PEP utilizer from B. subtilis) were obtained by fusing pykA from L. Lactis to DNA sequences homologous to the pykA region and containing a Tet marker using assembly PCR. Detailed protocol is as described for the point mutations/deletions in pykA. pykA L.Lactis was amplified from plasmid pLJH63.

  multiphase adaptation of replication parameters to the shift (fig 21B-D). Between 0 and 15 min after the shift, none of the cell cycle parameters changes in TF8A. Between 15 min and 45 minutes, the Cperiod (time to replicate the chromosome) increases from 40 to 55 min (fig. 21B). Next, from 30-45 minutes onwards, the initiation frequency (number of origins/cell) increases (fig. 21C) and the age of initiation is pushed forward (fig. 21D). To our knowledge, this behaviour has not yet been described in literature.

Figure 22 : 2 .

 222 Figure 22: Response of replication parameters during a metabolic shift from MC to MCG media in TF8A (A) Ori-ter ratio analysis during the shift (B) C-period during the shift (C) Number of origins per cell during the shift (D) Initiation age during the shift. C-period and initiation age were calculated assuming a constant growth rate during the shift, which may not hold true, especially at later stages of the shift.

Figure 23 :

 23 Figure 23: Theoretical framework for evaluating replication parameter curves obtained from metabolic shift experiments (Blue curves) Theoretical WT curve (Red curves) pykA mutant curves. Examples of curves that don't provide evidence for coupling are shown in fig. 22A, whereas examples of curves that doe provide evidence for coupling are shown fig. 22B. Two types of curves are shown in figure 23 that do not provide evidence for coupling (fig.23A). First, if the effect of a pykA mutation does not change throughout the metabolic shift, the replication parameter curve does not provide evidence for a coupling role of PykA. This is similar to the observed effects of yabA or soj deletion, which have been shown to impact DNA replication similarly in different growth conditions 110 . Trivially, if the curve of a pykA mutant is completely identical to wild type, it does not provide evidence for coupling either. Conversely, any other pykA mutant curve would suggest coupling and examples are shown in Fig. 23B and C. Loss of function of a mutation or an effect on the timing of the replication response both provide evidence that the regulatory behaviour of PykA changes in response to the metabolic shift (fig 23B). Moreover, a positive new role for a pykA mutant at later stages of the shift indicates that the regions involved in DNA replication timing respond to metabolism and that the PykA signals through a different molecular mechanism in different metabolic contexts (fig. 23C).

2. 2

 2 Several cat and PEPut mutants lose their moonlighting role at later stages of the metabolic shift, indicating coupling.The ori-ter ratio curves of most cat and PEPut mutants were measured during the metabolic shift and type and classified into the categories mentioned in the previous section. These results are shown in figure24.

Figure 24 :

 24 Figure 24: Effect of cat and PEPut mutations on ori-ter ratio during shift : (A) Not determinants : pykA mutation that neither had an effect in MC nor acquired one during the shift. These do not indicate coupling. (B) Determinant no longer involved : these mutations affected ori-ter ratio in MC, but gradually lost their effect during the shift, indicating coupling. In both cases, cat mutations are shown at the top, whereas PEPut mutations were shown at the bottom.

Figure 25 :

 25 Figure 25: A changing role for the TSH motif on DNA replication gating during the shift Surprisingly, the effect of TSH>AAA lowers the ori-ter ratio in MC, but causes an overshoot at later stages of the shift, indicating a changing involvement of the TSH motif at later stages of the shift. Replacement of individual amino acids by alanine revealed that H539 and not T537 was responsible for the overshoot phenotype, i.e. H539A is a new determinant at late stages of the shift. Replacement of H539 by aspartate suppressed the effect of H539A, indicating that H539 phosphorylation plays a key role in DNA replication gating during the shift. Furthermore, the effect of H539D does not change during the shift.

  metabolic signals. This was demonstrated by studying the effect of pykA mutations on DNA replication parameters during a metabolic shift from the rich gluconeogenic medium MC to the MCG medium. First, the dynamic response of DNA replication parameters in wild type TF8A was assessed. This response was found to consists of multiple phases involving (i) no change of replication parameters during the first 15 min of the shift, (ii) an increase of the C-period between 15 and 45 min, (iii) a change in initiation parameters after 30 min and (iv) a change in cell size/growth rate sometime after 45 min after the shift. B 106 Next, a theoretical framework was developed to infer coupling by comparing the ori-ter ratio curves of different pykA mutants to wild type. Using this framework, most cat and PEPut mutants were shown to lose their moonlighting effects on DNA replication gating at later stages of the shift,

Figure 26 :

 26 Figure 26: Scheme of PEPut regulation

2. 4 A

 4 model for PykA moonlighting in DNA replication gating controlThe extensive genetic analysis performed in this work has enabled the creation of a working model of the PykA moonlighting system, shown in figure27. In this model, PEP and possibly AMP are reused as sentinel molecules to determine PykA moonlighting activity. This is consistent with the role of PEP in regulating CCM activity129 . Both the cat and the PEP domains act as sensors of metabolic activity. They convey the metabolic status of the cell sent by the sentinel molecules to the initiation and elongation machinery via phosphorylation events and protein-protein interactions, similar to how CcpA/HpR senses metabolic information to drive metabolic gene expression 129 . This regulatory system involves PEP binding and the JP region of Cat, the TSH residues of PEPut and kinases. Together, these regulatory signals precisely gate replication in the cell cycle and may be part of the metabolic control of replication.

Figure 27 :

 27 Figure 27: A model for PykA moonlighting in DNA replication gating : PykA senses metabolic information using the sentinel metabolites AMP and PEP. The cat and PEPut domain then transduce this signal to the replication machinery using PPI and phosphorylation events, leading to precise DNA gating

Figure 28 :

 28 Figure 28: Coupling CCM-DNA replication may lead to cancer To conclude, the work presented in this thesis provides evidence for the first time not only that PykA in B. subtilis is a replication control protein, but also that it couples DNA replication timing to metabolism. It also identified for the first time a role for the long enigmatic PEP utilizer domain. While more research is needed to understand the details of PykA signalling to replication, these results are consistent with an emerging body of work that puts non-metabolic functions of CCM enzymes at the heart of cellular decision making and the development of cancer. This emerging understanding of cell biology will likely lead to exciting new treatment options, such as the therapeutic targeting of non-metabolic functions of CCM enzymes (See 267 ) or the improvement of innovative treatments such as immunotherapy 268 and manipulation of the microbiome 269 .

  connection between chromosome structure, gene position and gene expression Bacterial chromosomes have to be compacted 500-1000 times to fit inside the bacterial cell, whilst staying accessible to the action of DNA processing proteins such as DNA polymerases, RNA polymerases and other DNA binding proteins 270 . This is achieved by folding the chromosome into a remarkably intricate structure called the nucleoid of which the most detailed features are only now being rapidly discovered 270 . This intricate structure is summarized in figure 1. The lowest level of chromosome structure is defined by supercoiling or the twisting of the DNA helix 270 . DNA supercoiling is balanced by dedicated enzymes called topoisomerases and gyrases, that can relax and add DNA twists, respectively. DNA topology is continuously altered by the action of DNA processing enzymes like DNA and RNA polymerases, which overwind the DNA in front of them and underwind the DNA behind 271 . On a higher structural level, abundant nucleoid associated proteins (NAP's) organize the chromosome by locally constraining supercoiling in topologically isolated domains called microdomains. Experimental studies in E. coli have shown that its chromosome has 400 of these microdomains of average length between 8-12 kb 272 . Recently, new experimental studies have revealed that the E. coli chromosome is organized in domains of 100-200 kb long, in which DNA elements physically interact more frequently 273 These domains represent yet another level of chromosome structural organization. At the highest level of organization (~1 Mbp), the chromosome is divided in four structured macrodomains (Ori, Left, Right, Ter) and two non-structured ones (NS-Left, NS-Right) 274 . The Ter macrodomain is organized by the MatP protein 275 and the Ori one by the MaoP protein 276 , but the other macrodomains seem to be defined by the replication process itself 277 .

Figure 1 :

 1 Figure 1 : Structural organization of the E.coli chromosome: (Level 1) The action of DNA processing enzymes such as RNA and DNA polymerases causes twisting of the DNA double helix. Their action causes overwinding in front (+ supercoiling) and underwinding behind (-supercoiling) These twisted structures are called plectonemes and can be relaxed using the dedicated enzymes topisomerases or introduced using gyrases. (Level 2) DNA is further compacted through the action of abundant DNA binding proteins called nucleoid associated proteins (NAP's). Their binding creates barriers for supercoiling diffusion and leads to the establishment of 8-12kb topologically insulated domains called microdomains. Additionally, their binding causes specific deformations depending on the NAP such as DNA bending, bridging, wrapping and coating. This leads to a 'bottlebrush' of microdomains. (Level 3) The microdomains organize into 100-200kb chromatin interaction domains (CID) in which physical interactions happen more frequently than outside. These CID's are then organized into Mb size macrodomains : (Green) Ori macrodomain (Black) Non-Structured Right macrodomain (Orange) Right macrodomain (Red) Ter macrodomain (Blue)Left macrodomain (Yellow) Non-structured left macrodomain. The figure was assembled from figures in270,[278][279][280]

Figure 2 :

 2 Figure 2 : A three way relation between chromosome structure, gene expression and gene position Gene position determines in which local chromosome structure is located. This in turn influences gene expression. Since gene expression is tightly related to gene function, the position of a gene is under evolutionary pressure to permit optimal expression.

Figure 3 :

 3 Figure 3 : The Transcription Based Solenoid model : Imagine a regulon consisting of a regulatory gene (green), a structural operon A (orange) and a structural operon B (blue). Next, imagine that the length of a plectoneme is P and that the genes are separated by distances that are a multiple of P (N*P and M*P) a) When depicted in a 1D conformation, the genes of the regulon are separated by a long distance. b) When depicted in a 3D confirmation with plectonemes (bacteria) or solenoids (eukaryotes), the genes of the regulon are now in close proximity. The TBS model predicts that this confirmation facilitates the search of the regulatory gene for its targets and also the assembly of gene products A and B into a complex. Picture taken from 1

encompasses 95 %BFigure 4 :

 954 Figure 4: GREAT: SCAN: patterns (A) Flowchart of the algorithm : GREAT : SCAN : patterns takes a file with two columns as input. Next, the program scans for periods and calculates p-value. First, the program produces test datasets containing randomly generated chromosome positions. Positions are drawn from a uniform distribution, i.e. every position between 0 and the end of the chromosome has equal probability to be drawn. Next, periodicity scores are calculated as in 314 . Finally, a p-value is calculated. The p-value is defined as the number of times the test dataset had a higher periodicity score than the real dataset divided by the total number of test datasets. Next, the p-values are corrected for multiple hypothesis testing using a period-length-dependent correction. Finally, if at least one period meets the preprogrammed threshold (standard threshold : p-value < 0,05 or -log(pvalue) > 1,3), all of the periods are plotted in a periodogram. The periodogram contains a significance score (-log(p-value)) on the y-axis and a log(period length) on the x-axis (B) Example output for a global periodogram for the CRP genes. Taken from 312 (C) Example output for a local periodogram for the CRP genes. Taken from 312

  coordinate for the negative control dataset GL : The genome length of E.coli MG1655, i.e. 4641652 Runif : A randomly drawn number from a uniform distribution between two numbers i.e 1 and GL Floor: The result is rounded down to the closest whole number.Chromosomal coordinates for the positive control datasets on the other hand were generated using the following formula : coordinate for the positive control dataset GL : The genome length of E. coli MG1655, i.e. 4641652 P : The period that is programmed in the positive control dataset, i.e. 8000Runif : A randomly drawn number from a uniform distribution between two numbers i.e 1 and GL/P Floor: The result is rounded down to the closest whole number.

Figure 5 :

 5 Figure 5 : Synthethic datasets are generated to test signal to noise ratio of GREAT. Two datasets are generated : negative and positive control datasets. (1) Negative control datasets consist of points randomly distributed along the chromosome and should not display periodicity. (2) Positive control datasets consist of randomly generated points that are always n*P separated where n is a whole number. They should therefore display periodicity of P. These datasets are generated using the indicated formulas in the statistical program R315,316 .

  Plasmid 92 was created by transferring the oxyRS-Kan-terminator cassette from plasmid 62 to the backbone of plasmid 91. First oxyRS-Kan-terminator was amplified using primers with overhangs homologous to plasmid 91 (pSH220-221, Q5, NEB, standard reaction conditions). Template plasmid 62 was removed using DpnI digestion (NEB, 20 U, CutSmart, 2h, 37℃) and the PCR product was purified using the Monarch DNA cleanup kit (NEB). Next, plasmid 91 was linearized using inverse PCR (iPCR) (pSH222-223, Q5, NEB, standard reaction conditions). Template plasmid 91 was again removed using DpnI digestion (NEB, 20 U, CutSmart, 2h, 37℃) and the PCR product was purified using the Monarch DNA cleanup kit (NEB). Next, the purified oxyRS-Kan-terminator PCR product

Figure 8

 8 Figure 8 contains an example output for a positive control dataset of 50 coordinates with a preprogrammed period of 8kb (A+B) and an example output of a negative control dataset of equal size (C+D). The y-axis shows the significance score (-log(p-value)) and the period length (also depicted in log scale).

Fig 8

 8 Fig 8 A+B shows that GREAT :SCAN :patterns can find the 8 kb period with a high degree of significance in the positive control dataset. The 8 kb period has a significance score S > 200 and local periods of 8 kb with an S > 50 are readily observed. This is way more significant than the threshold ( S = 1,3 corresponding to a p-value = 0.05).

Figure 8 :

 8 Figure 8: Example output for synthetic datasets: (A+B) Output for a positive control dataset with 50 positions and a preprogrammed period of 8 kb (A) Periodogram of global periods.X-axis depicts period size and y-axis period significance as log p-value. (B) Local periods in tabular format with from left to right (i) period length (ii) Left coordinate of the periodic region (iii) Right coordinate of the periodic region (iv) Length of periodic region (v) p-value (vi) S-score of the period and (vii) Periodic region coverage: the fraction of the number of coordinates inside the periodic region vs the total number of coordinates (C+D) Output for a negative control dataset

Figure 9 :

 9 Figure 9 : Definition of global periodicity criteria by analyzing positive and negative control datasets of different sizes : (A) Average S-score (negative logarithm of p-value) of negative control datasets of 5 to 100 positions (B) Average S-score of the preprogrammed periods in positive control datasets between 5 and 100 positions (c) Ratio of datasets between 5 and 100 positions that failed to produce periods with S > 1,3 ( p-value < 0,05).

Figure 10 :

 10 Figure 10 : Definition of local periodicity criteria by analyzing negative control datasets of different sizes. (A) Average S score of top period observed in negative control datasets between 5 and 100 points when the top period is evaluated based on S-score (B) Coverage (defined as the ratio of genes within the periodic region to total number of genes) of top period when the top period is evaluated based on S-score. (C) Average S-score of top period observed in negative control datasets between 5 and 100 points when the top period is evaluated based on coverage. (D)Coverage (defined as the ratio of genes within the periodic region to total number of genes) of top period when the top period is evaluated based on coverage.

3. 2

 2 Effect of transcription factor positioning on the oxidative stress response in E.coli 3.2.1 Choosing a suitable test regulon : OxyR-SoxS-Fur and the oxidative stress response

Figure 11 :

 11 Figure 11: Biological processes affected by OxyRS and SoxRS targets. (A) Detoxification of ROS molecules. (B) Protection and repair of DNA and protein after oxidative stress. (C) Rerouting central metabolism to replace or overexpress enzymes sensitive to ROS or to produce reducing agents. (D) Cell wall synthesis. (E) Metal ion transport to replace Fe 2+ by other divalent cations Picture was taken from 335

  Figure 10A and B show examples of plates without (A) and with (B) 1M H2O2 added the central hole for the wild type MG1655. This shows that 1M H2O2 can impede MG1655 growth in a ring around it, creating a zone of inhibition (ZOI).

Figure 12 :

 12 Figure 12 : Oxidative stress sensitivity of oxyRS movant strains using the Kirby-Bauer zone of inhibition test. (A-B) Comparison of the zone of inhibition of MG1655 when challenged with (A) 0 M or (B) 1 M of H2O2. (C) Diameter of the Zone of Inhibition (cm) of MG1655, ΔoxyRS and the oxyRS movants when challenged with 100 mM H2O2. (D) Diameter of the Zone of Inhibition (cm) of MG1655 and the oxyRS movants when challenged with 1M H2O2.

Titre:

  Découverte d'un nouveau type de régulateur de réplication d'ADN : l'enzyme glycolytique pyruvate kinase en Bacillus subtilis Mots clés : B. subtilis, pyruvate kinase, réplication d'ADNRésumé : On sait depuis longtemps que le fenêtrage de la réplication de l'ADN dans le cycle cellulaire chez les bactéries varie avec la richesse en nutriments. Cependant, le mécanisme sousjacent reste pour l'instant inconnu. Le but de ce travail est de déterminer si PykA, une enzyme du métabolisme central carboné (MCC), est impliquée dans ce contrôle temporel de la réplication chez la bactérie Bacillus subtilis et si, pour assurer cette fonction, PykA utilise une activité non-métabolique cryptique qui lui permet d'agir en tant qu'effecteur temporel de la réplication en transmettant des informations sur l'état métabolique de la cellule à l'appareil de réplication.Pour tester ces hypothèses, l'effet de mutations dans le gène pykA sur la réplication a été évalué dans un milieu de culture où son activité métabolique n'est pas nécessaire. Les paramètres de réplication dans ces mutants ont alors été mesurés par des approches de cytométrie en flux et de PCR quantitative. Nos résultats ont confirmé que la délétion de pykA n'a pas d'effet sur la croissance des cellules dans le milieu utilisé mais que cette mutation affecte le contrôle temporel de la réplication en modifiant l'âge d'initiation de la réplication et la vitesse de réplication. La délétion du domaine catalytique ou du domaine « PEP utilizer » de PykA a montré que chacun de ces domaines joue un rôle dans le contrôle temporel de la réplication, assignant pour la première fois une fonction de réplication à ces domaines. Une mutagenèse ciblée du domaine catalytique a montré que seulement certains acides aminés impliqués dans l'activité métabolique de PykA sont importants pour la réplication. La mutagenèse des acides aminés conservés dans le motif Thr-Ser-His (TSH) du PEP utilizer suggère un rôle important de la thréonine dans le contrôle temporel de la réplication. L'expression du PEP utilizer seul (non fusionné au domaine catalytique), a permis de découvrir plusieurs principes de réciprocité entre les deux domains.

Title:

  Discovery of a new type of regulator of DNA replication: the glycolytic enzyme pyruvate kinase in Bacillus subtilis Keywords : B. subtilis, pyruvate kinase, DNA replication Abstract : It has been known for decades that the timing of DNA replication within the cell cycle in bacteria is coupled to nutrient richness.However, the mechanisms that enable this temporal gating have remained elusive. The goal of this work was to test whether the CCM enzyme PykA in the bacterium B. subtilis helps achieve this gating by moonlighting as a temporal effector of replication that communicates information about the metabolic state of the cell to the replication machinery.

Table 1 :

 1 Summary table with the effects of pykA mutations on different replication parameters .......

Fout! Bladwijzer niet gedefinieerd.Table 2 :

 2 Summary of the role changes pykA replication determinants during the shift :

Fout! Bladwijzer niet gedefinieerd.

  . In the next section, the coupling of growth rate and DNA replication timing in bacteria is discussed. Next, the possible molecular mechanisms that might underlie this coupling between DNA replication timing and metabolism are critically evaluated in the model organisms B. subtilis and E. coli.

	2.3 Metabolic control of DNA replication: coupling DNA replication timing to
	nutrient richness and growth rate
	2.3.1 Evidence for a metabolic control of DNA replication in bacteria and lower
	eukaryotes
	Early studies in the model bacteria E. coli and B. subtilis have revealed that the growth rate
	increases with increased nutrient availability and that this has consequences for DNA
	replication timing

  The overflow pathway is an alternative route for pyruvate oxidation 124 . Instead of being completely oxidized, pyruvate is converted into lactate, thereby regenerating 1 NADH per pyruvate molecule124 . Despite being less efficient in terms of energy production and production of intermediates, overflow pathways regenerate NAD + much faster, allowing for a faster flux through glycolysis 124 . For this reason, overflow pathways are often active in highly proliferative cells such as tumors 124 .

ribulose

-5-phosphate, R5P: ribose-5-phosphate, X5P: xylose-5-phosphate, SH7P: sedoheptulose-7-phosphate, E4P: erythrose-4-phosphate (Enzymes)

GP6D: glucose-6phosphate dehydrogenase, 6PGL: 6-phosphogluconolactonase, 6PGD: 6-phosphogluconate dehydrogenase, RPI: ribose-5-phosphate isomerase, UlaE: Ribose-5-phosphate-3-epimerase, TKT: transketolase, TALDO: transaldolase Despite this top-down description, CCM can be fed at multiple points and most cells can use several organic molecules as carbon source for growth. Sugar molecules like glucose or fructose enter the EMP pathway at the top and are broken down throughout the EMP and TCA pathways.

  domain A (cyan), domain B, (magenta) domain C (green) and the ECTS Picture taken from214 (Red) (B) Catalytic site of human PKM2 complexed with substrates Picture was made using the visualization software Chimera. Crystal structure obtained from 214 (C) Important and corresponding catalytic sites and their function in B. subtilis and PKM2. List obtained from UniProt. (D) Structure of the PEPut domain histidyl that transfers phosphate abstracted from PEP to HPr 223 . This similarity might suggest that it is responsible for PTS regulation of its activity. However, deletion of the PEP utilizer domain in G. staerotermophilus pyruvate kinase had no effect on its metabolic activity220 . By contrast, in the metabolic enzyme PPDK, the PEP utilizer domain is a key part of the catalytic activity. It is responsible for turning ATP and pyruvate into AMP, pyrophosphate and PEP and the conserved histidyl transfers the phosphate groups between the nucleotide binding domain and the pyruvate binding domain224 . Interestingly, a TSH motif around the catalytic H is conserved in plant PPDK and in Bacilli PEP utilizers. The T in this motif regulates the catalytic activity of H during the light/dark transition in plant PPDK 225 , whereas the middle S has no effect on catalytic activity or substrate specificity 226 .

	The catalytic site of pyruvate kinase has been studied in detail and conserved residues important
	for catalysis are summarized in fig. 18B and C. The pyruvate kinase catalytic site requires one
	divalent cation (Mg 2+ ) and one monovalent kation (K + ). Other key residues are 3 positively
	charged amino acids (2 R and 1 K) and 4 negatively charged residues (2 D and 2 E). In B.
	subtilis, K + is coordinated by the amino acids N34, S36, D66 and T67. Mg 2+ is coordinated by
	the amino acids E222 and G245D246. The substrates PEP and ADP are partly bound by these
	cations, with Mg staerothermophilus is thought to interact with R5P and AMP through the amino acids T381,
	S383, T386 and H425 214 , but no structural data is available of Pyk of G. staerothermophilus
	complexed with an effector 219 . Mutation of H425 to alanine led to a decrease in AMP and to
	a lesser extent R5P binding 214

2+

being essential for PEP binding. PEP is additionally bound by R32, G245D246 and T278. ADP, on the other hand, is bound by R73 and K156. Incidentally, mutation of the equivalent amino acid of R73 in rabbit pyruvate kinase has been shown to decrease ADP binding 215 . Finally, phosphate transfer from PEP to ADP is stabilized by K220.

Mutation of this amino acid in pyruvate kinase of G. staerothermophilus or yeast has been shown to completely abolish catalytic activity, though its effect on substrate binding is less clear

221,222

.

In contrast to the highly conserved catalytic mechanism, regulation of Pyk in different organisms is much more variable

219

. Most Pyk are either regulated by FBP or by AMP and sugar monophosphates 219 . PykA is regulated by ribose-5-phosphate and by AMP 214 . The effector binding site in most Pyk is situated in the C-domain and usually consists of a phosphate binding loop, a mobile loop and an effector loop 219 . In general, the effector loop remains buried in a pocket formed by the phosphate binding loop 219 . Upon binding of the phosphate binding loop to the effector, the effector loop becomes ordered. The effector binding site in Pyk of G. Currently, the function of the ECTS of pyruvate kinase in Bacilli remains unknown, but its structure is shown in figure 18D. The sequence is homologous to the one of PEP utilizer domains found in the PTS component EI and the metabolic enzymes pyruvate phosphate dikinase (PPDK) and PEP synthase 220 . In the PTS component EI, the PEP utilizer domain hosts the conserved

Table S2 : Primers for strain construction Table S3: Primers for qPCR analysis

 S2

	Name	Primer name	Chromosome arm	Position (degree) Orientation	Sequence	Product size
	Ori	LJ114	oriC	0	Forward	TCCGAGATAATAAAGCCGTCGA
		LJ115			Reverse	CTGGGTTTGTTCTTTCCCCG
	R05	LJ518		9	Forward CGCGAGCAAGTAGAGGATAC
		LJ519			Reverse CGCTACAACTGCTTCATCCT
	R1	LJ520 LJ521	Primer name CS3 CS4	Construction H→ A mutation 18 H → A mutation	Orientation Forward GGGATTTTAACACCAGCCGA Sequence Forward GGT TTG ACT AGC gcT GCT GCG GTA G Reverse CTA CCG CAG C AGC GCT AGT CAA ACC Reverse ACCATCACTCGTTCACCTGT
	R2	LJ480	LJ101	T → A mutation 36	Forward gaaggcggtttgGcCagccatgctgcg Forward TGACCGGGACACAGAATTCA
	R3	LJ481 LJ482	LJ102 LJ109 LJ110	T → A mutation TSH → AAA mutation Forward GGT TTG gCT gct gcT GCT GCG GTA G Reverse CGC AGC ATG GCT GGC CAA ACC GCC TTC Reverse ATTGATTTTGACCACCCGCC TSH → AAA mutation Reverse CTA CCG CAG C Agc agc AGc CAA ACC 54 Forward CGACGAACTCAATCATGGGG
		LJ483	LJ103	S → A mutation	Forward ggcggtttgactGCGcatgctgcgg Reverse ATGTGCCAAGTCTCTCTCCC
	R4 Construction primers PEP utilizer mutants LJ484 LJ485 R5 LJ486	LJ104 LJ563 LJ564 pSH262	S → A mutation H-→ D mutation H → D mutation 72 S → D mutation 90	Reverse CCG CAG CAT GCG CAG TCA AAC CGC C Forward GGTTTGACTAGCgATGCTGCGGTAG Forward TCATTGCAAGACCTCCTCGT Reverse CAAGACGCCGGATAAAAGCA Reverse CTACCGCAGCATCGCTAGTCAAACC Forward ggcggtttgactGATcatgctgcgg Forward CCACAATGCTTCCGGAGATG
	R7	LJ487 LJ522	pSH263 pSH268 pSH269	S → D mutation PEP PEP 126	Reverse CCGCAGCATGATCAGTCAAACCGCC Reverse TCGGGGAGATTGGTTTCGAT Forward CTTGGCCTTTAGCGATGATATCCATTTGGTTCACTTCCTTCTGAAATCTTCA Reverse GATATCATCGCTAAAGGCCAAG Forward TCCAATTCTCCAACCGGTCA
		LJ523	pSH260 pSH261	T-→ D mutation T-→ D mutation	Forward gaaggcggtttggaTagccatgctgcg Reverse CGCAGCATGGCTATCCAAACCGCCTTC Reverse ACCGGCGTGACTTTATCTGA
	R8	LJ524	pSH277	TSH-→ DDD mutation Forward gaaggcggtttggaTGATGatGCTGCGGTAG 144 Forward ATCTATTGGATCAGGCCGCT
		LJ525	pSH278 LJ610	TSH-→ DDD mutation Reverse CTACCGCAGCATCATCATCCAAACCGCCTTC PykAR32A Forward TGGCTGCATTAAACTTTTCTCACGGAG Reverse ATAAGCAGCAAGTACACGCG
	terC	BD280 BD281	LJ611 LJ612 TerC LJ613	PykAR32A PykAR73A 180 PykAR73A	Reverse AGAAAAGTTTAATGCAGCCACGTTCATTCCTGACTC TTTCAGTTGTGCCACCATGT Forward Forward TGAAATCGCTACACATACAATGGAAAACGG Reverse TTGTATGTGTTGCGATTTCAGGACCTTTTGTATC Reverse
			LJ614	PykAK220A	Forward AATCATCCCTGCAATCGAAAACCAAGAGG
			LJ615	PykAK220A	Reverse TTTTCGATTGCAGGGATGATTTGAATATCC
	Construction primers catalytic mutants	LJ616	PykAG245AD246A	Forward GTTGCACGCGCAGCTTTAGGTGTGGAAATTCCAGCTG
			LJ617	PykAG245AD246A	Reverse CCTAAAGCTGCGCGTGCAACCATTAAGCCGTCAG
			LJ604	PykALL/PykALLPEP Forward GATTTCAGAAGGAAGTGAACCAAATGAATAAACGTGTAAAAATCGTCTC
			LJ618	PykALL	Forward CTCTACTGAATTTAAAACTTAG
			LJ619	PykALL	Reverse CTAAGTTTTAAATTCAGTAGAGTTATTTAACAGTGCGGATACGC
			LJ608	PykALLPEP	Forward GATATCATCGCTAAAGGCC
			LJ609	PykALLPEP	Reverse GGCCTTTAGCGATGATATCTTTAACAGTGCGGATACGC
			CS1	PykA constructions	Reverse CATACAATGGAAAACGGCGG
	External primers PykA region	CS10	PykA constructions	Forward GCCGGATCGTGTGACAGCTAAC
			BD154	PykA constructions	Reverse ATGAAACGAATAGGGGTATTAACG
	External primers amyE locus	LJ161 LJ165	hyPEP mutations hyPEP mutations	Forward TTCTCCAGTCTTCACATCGGTTTGA Reverse TGCCGCTTCCAATCACCC
			LJ621	PykA constructions	Forward ATGGCTCGAGTTTTTCAGCAAGATGCC GGA TCG TGT GAC AGC TAA C
	External primers PykA region HiFi assembly	LJ622	PykA constructions	Reverse TGTAGGAGATCTTCTAGAAAGATTTATCGGTGGAGACGGTTCC
			LJ623	PykA constructions	Forward ATGGCTCGAGTTTTTCAGCAAGATTTAGGACTGATTGCGGGAC

ATTTGCCGTTCTCGGGTTA Right Université Paris-Saclay Espace Technologique / Immeuble Discovery Route de l'Orme aux Merisiers RD 128 / 91190 Saint-Aubin, France Chapter 3:

PykA couples DNA replication timing to metabolism during a shift from gluconeogenic to glycolytic media

Université Paris-Saclay

Espace Technologique / Immeuble Discovery Route de l'Orme aux Merisiers RD 128 / 91190 Saint-Aubin, France

Table 2 :

 2 Comparison

of the global periods observed for key stress response regulators in (Left to Right) ChIP-exo results (Section 3.1.2), RegulonDB datapoints with either 2 strong or 3 weak interactions, RegulonDB with 1 strong and 1 in vivo interaction and RegulonDB with 1 in vivo interaction. Data is depicted as in table 1. Matches between periods within different datasets of the same regulon are marked in red.

Table 3 :

 3 Comparison of the local periods observed for key stress response regulators in (Left to Right) ChIP-exo results (Section 3.1.2), RegulonDB datapoints with either 2 strong or 3 weak interactions, RegulonDB with 1 strong and 1 in vivo interaction and RegulonDB with 1 in vivo interaction. Data is depicted as in table 1. Matches between periods within different datasets of the same regulon are highlighted in red.

  Enfin, des tests in vitro ont démontré que PykA peut directement stimuler l'activité de la polymérase DnaE et inhiber l'activité de l'hélicase DnaC. Ensemble, ces résultats indiquent que PykA a une activité cryptique de réplication. Pour démontrer que PykA transmet l'état métabolique de la cellule à la machinerie de réplication, l'effet de mutations dans pykA sur les paramètres de réplication de cellules subissant un changement métabolique a été évalué à l'aide des techniques décrites ci-dessus. Nous avons observé que l'adaptation des paramètres de réplication au cours du changement métabolique dans les cellules sauvages se déroule en trois phases: (i) une diminution progressive de la vitesse de la fourche de réplication de 15 à 45 minutes après le changement métabolique, (ii) une stimulation de l'initiation à partir du point 30 minutes et (iii) finalement une adaptation du taux de croissance après 45 minutes. Les mutations dans le domaine catalytique ne semblent pas avoir d'effet sur l'évolution de ces paramètres. En revanche, le PEP utilizer, fusionné ou non au domaine catalytique, affecte l'adaptation des paramètres de réplication par un processus nouveau dépendant de l'acide aminé His et non Thr du motif TSH. Ainsi donc, le PEP utilizer et plus généralement PykA, agit sur la réplication selon des voies particulières en fonction du métabolisme.Nous concluons que PykA est un nouveau type de régulateur de la réplication de l'ADN qui facilite le positionnement temporel de la réplication par le biais de processus variant avec la richesse des sources de carbone fournies dans l'environnement. Ces travaux, combinés à des travaux antérieurs dans ce domaine, suggèrent que le contrôle temporel de la réplication dans un large éventail de conditions de croissance métabolique est réalisé au moins en partie via un réseau d'enzymes MCC ayant des activités cryptiques. Cette conclusion a des implications importantes dans notre compréhension de la biologie cellulaire.

Acknowledgements

For every qPCR reaction, the primer efficiency (97 to 103%) was controlled. The ori/ter ratio of the non-replicating sporlets was 0.585þ/-0.019, a value consistent with the size of the PCR products (Supplementary Table S3). The amplifications were carried out on a Mastercycler ® ep realplex (Eppendorf, Le Pecq, Fr) and the ΔCq were determined automatically from the cycle threshold.

Measurement of the C-period during metabolic shift

Cell culture, genomic DNA extraction and qPCR amplification reactions were as above. Primer pairs, their positions on the chromosome map and the size of the PCR products are given in Supplementary Table S3. Results were reported to the terC signal and corrected for the size of the PCR products. The C period was extracted from the slope of the straight line of the plot (log2Nm)τ against m where Nm is the relative copy number of a sequence N at position m (with 0<m<1 along the ori-ter axis) and τ the generation time in minutes. 231 Control experiments showed that the marker frequency of the different loci in the non-replicating DNA was close to 1 (1.011 þ/-0.045) as expected. Similar data were obtained with primers on the right and left arm of the chromosome.

Flow cytometry analysis

3.1 Measurement of the number of origins/cell during metabolic shift Strains were grown as indicated in the previous section. At OD 0.08-0.1, 10 ml of cell culture, corresponding to t0, was added to chloramphenicol (200 µg/mL final) to impede replication initiation, cell division and allow completion of ongoing rounds of replication. 232 0.8% glucose was added to the rest of the culture, changing the MC medium into an MCG medium. In general, 5 ml of cell culture samples were added to chloramphenicol (200 µg/mL final) every 15 min until timepoint 75 min unless stated otherwise. Cells were generally diluted 1/3 in prewarmed MCG medium after 45 min to maintain exponential growth.

After 4-6 hours of drug treatment, 10 8 cells were fixed in filtered ethanol 70% and stored at 4⁰C. When the TBS model was formulated, it explained a lot of observations at the time, particularly in yeast 292 . First, transcription factors are remarkably specific, despite the presence of a multitude of unused protein binding motifs [293][294][295][296] . The TBS explains this by making gene position a discriminator between used and unused sites 292 . Second, transcription factors find their targets incredibly fast 297,298 and the TBS framework explains this by suggesting that the distance between transcription factors and its targets is decreased under a periodic organization 292 . Finally, the TBS model suggests that by changing the chromosomal structure and bringing together different genes, organisms can rapidly alter their gene expression programs 299 . However, the TBS framework has never been directly tested.

Chromation Immuno Precipitation -exonuclease (ChIP-exo) datasets

Chromatin Immuno Precipitation-exonuclease (ChIP-exo) is an experimental technique used to determine the exact binding position of a transcription factor on the chromosome 317 . Figure 6 gives a detailed overview of the approach 317 . Since this approach allows for the identification of direct regulatory targets of a transcription factor, these datasets would likely be free of contamination of indirect targets that may complicate the periodicity analysis. Hence, ChIP-exo datasets were picked as the datasets of choice and extracted from the following publications 300 301 302 303 . The exact binding positions of OxyR, SoxS, GadE, Fur and OmpR were calculated by taking the average of the begin and end coordinates of the binding region. These coordinates were remapped on the newest version of the E. coli K12 MG1655 chromosome (NC_000913.3) and saved with a gene identifier (name of the closest gene) as a .csv file for further analysis.

RegulonDB datasets

RegulonDB is a database that collects and organizes all the experimental knowledge on regulation of transcription in E.coli 318 . Among other things, this database contains a dataset with all known or predicted transcription factor binding sites 319 . First, the dataset with transcription factor binding sites was downloaded. Next, the dataset was curated using a home-made R-script to only keep binding sites that have strong enough experimental support using the RegulonDB evidence classification code (introduced in 320 ). Different criteria for the strength of evidence for a binding site were compared.

As a first criterion, only binding sites that had 2 strong or 3 weak forms of evidence in favor of it were considered, as used in 312 . As a second criterion, only binding sites that had at least 1 or 2 strong pieces of evidence combined with evidence from at least one in vivo experiment were considered.

Since the theory of periodicity predicts that well-positioned weak binding sites may outperform illpositioned strong binding sites, evidence from in vivo experiments was given more weight for this analysis than RegulonDB generally does. After this curation step, the R-script was used to split the curated datasets by transcription factor and put them in a format amenable to GREAT : SCAN: patterns analysis.

Figure 7 : RegulonDB datasets : RegulonDB is a database collecting all information on gene regulation in E.coli. It takes in data from gene expression analysis, DNA binding analysis, computational prediction etc and makes a list of TF binding sites with the evidence that supports it. This list was downloaded and curated. First, TF supported by weak evidence was removed (see text for criteria). Second, the list was reduced to two columns (feature identifier and chromosome position) in order to comply with GREAT. Finally, the list was split by TF in order to create datasets ready for periodicity analysis.

Bacterial strains and culture conditions

A list of the strains used in this work can be found in Annex I supplementary Table 1. A list of primers used can be found in Annex I supplementary table II. E. coli cells were grown at 37 ℃ in liquid LB under strong shaking (200-230 rpm). Antibiotics were used at the following concentration unless specified otherwise: spectinomycin (Sp, 60 µg/ml), kanamycin (Kan, 50 µg/ml), ampicillin (Amp, 100 µg/ml) and phleomycin (Ph , 10 µg/ml in low salt LB). Induction molecules were used at the Removal of the PhleoR cassette was screened for using grid plating to check for loss of Ph resistance.

pTKRED was cleared from this background by overnight growth on LB at 37℃ and a grid plate screen to check for loss of Sp resistance. for by colony PCR using external primers to each integration location (see supplementary table II, Dreamtaq, Thermoscientific, standard reaction conditions). The KanR marker was then removed by (i) λ RED induction to promote homologous recombination inside the cassette (2h, LB + Sp + IPTG) and (ii) excising the cassette with I-SceI (overnight, LB + Sp + IPTG + ara) using the strategy outlined in 321 . Removal of the KanR cassette was screened for using grid plating to check for loss of Kan resistance.

During the second round, the oxyRS genes in the original location were removed using the same procedure as for the ΔoxyRS strain (although using plasmid 35 and Kan selection).

ORS_init is a strain where the oxyRS genes are replaced by oxyRS-Kan-terminator cassette. This was constructed through two rounds of recombination with PCR product and one round of KanR cassette removal.

During the first round, the oxyRS-Kan-terminator was amplified from plasmid 92 using primers with overhangs homologous to the oxyRS location (pSH148-149, Q5, NEB, standard reaction conditions).

Template plasmid 92 was removed using DpnI digestion (NEB, 20 U, CutSmart, 2h, 37℃) and the PCR product was purified using the Monarch DNA cleanup kit (NEB). Recombination of the PCR product into a recombination competent 74 strain (MG1655 + pTKRED ) lead to misrecombination with the native oxyRS genes and loss of the right side terminators (Data not shown). Therefore, the purified PCR product was then electroporated in recombination competent strain ΔoxyRS + pTKRED (1800 V) and grown overnight on LB. Each transformation was performed 3 times. The liquid culture was plated on LB + Kan and grown overnight on 37 ℃. Correct integration was then screened for by colony PCR using external primers to each integration location (pSH03-04, Dreamtaq, Thermoscientific, standard reaction conditions) and one external primer combined with a primer inside the region with the right side terminator (pSH04-156, Dreamtaq, Thermoscientific, standard reaction conditions).

During the second round, the oxyRS-Kan-terminator was amplified from the strain obtained during the first round using external primers that created a 1kb overhang on the left and a 2 kb overhang on the right to increase the probability of a proper recombination event (pSH03-240, Q5, NEB, standard reaction conditions). The PCR product was purified using the Monarch DNA cleanup kit (NEB). The purified PCR product was then electroporated in recombination competent strain 74 (MG1655 + pTKRED, 1800 V) and grown overnight on LB. Each transformation was performed three times. The liquid culture was plated on LB + Kan and grown overnight on 37 ℃. Correct integration was then screened for by colony PCR using (pSH03-04, Dreamtaq, Thermoscientific, standard reaction conditions) and one external primer combined with a primer inside the region with the right side terminator (pSH04-156, Dreamtaq, Thermoscientific, standard reaction conditions). About 1/3 of the clones carried the correct integration. The KanR marker was then removed by (i) λ RED induction

Discussion

The aim of this project was to test the Transcription Based Solenoidal (TBS) framework by assessing the impact of periodic gene organization on the stress response in E. coli. To this end, the presence of periodic gene organisations of various stress response regulons was analysed using our in-house software GREAT : SCAN :Patterns. These results were then used to pick the OxyRS regulon as a suitable experimental system for in vivo testing. Finally, the effect of displacing oxyRS on the stress response was assessed.

Periodic organization of (stress response) genes

Before analysing periodicity in stress response regulons, a set of synthetic datasets were analysed using GREAT : SCAN: Patterns to define periodicity evaluation criteria.

First, these experiments demonstrated that GREAT : SCAN: patterns has an excellent signal to noise, as the S-score (negative logarithm of the p-value of the period) for periods from the positive control dataset were at least 50 times higher than the ones from the negative control dataset. However, the results from the negative control datasets demonstrated that datasets with random positions still give rise to periods with an S-score of max 4. These last results are similar to the periodicity scores obtained by Junier et al, who used the same core algorithm as the one in GREAT : SCAN :Patterns 287 .

For this reason, the same periodicity evaluation criterion of not considering periods with S score < 4

for global and local periods was used in this work. In the case of local periods, an extra coverage criterion of > 50% of all genes was added as it was observed that local periods over short stretches of the chromosome can give rise to highly significant periods in negative control datasets.

Armed with the evaluation criteria, ChIP-exo datasets of key stress regulons OxyR, SoxS, GadE, Fur and OmpR were analyzed and compared to the periodicity evaluation criteria. The ChIP-exo technique gives the direct regulatory targets of a transcription factor with near-nucleotide precision 317 .

Hence, there is no need for applying any data clean-up procedures to prepare the data for periodicity analysis, other than the technical cut-offs performed in the publications. Unfortunately, while many periodic layouts were observed, the p-value of any of them did not meet the threshold set by the evaluation criteria. In fact, applying the periodicity evaluation criteria to previous works, suggests that none of the previously observed periods would be considered significant 290,291,312,313,356 , except for the ones from Junier et al 287 . In addition, other methods have been applied to periodicity analysis of gene position as well [357][358][359] . It would be interesting to see if the interpretation of the results of those papers would change when tested using the synthetic control dataset approach. Regardless, the results from this work suggest more caution in the interpretation of periodic patterns.

On the other hand, a non-significant p-value does not rule out that the periodic pattern is relevant.

Query biological datasets might just be too noisy to give statistically significant results, while still providing the correct period. This could be revealed by analyzing different kinds of datasets and demonstrating that they point to the same conclusion. For instance, common periodic layouts between 3 different kinds of ChIP experiments for each chromosome arm were found in yeast 291 . In one case, the periodicities found on the different chromosome arms even corresponded well with the periodicity observed for the distribution of replication origins 291 . These results suggest that the lack of significant periods observed in the ChIP-exo datasets may be due to a lack of accuracy of the analysis program, rather than the absence of a periodic signal. It therefore suggests that additional evidence is needed to support these periods.

Additional support came from two sources in this work: (i) comparison of the ChIP-exo results with periodicity results from datasets obtained from RegulonDB and (ii) harmonic analysis to find common periodic organizations between different stress response organizations.

The RegulonDB datasets contain manually curated binding sites of the stress response regulators that is the collection of all known literature on this topic. This means that these datasets contain both strongly and weakly supported binding site, hence data-cleaning is required. Since no objective criterion exists to clean the datasets, one published method 360 was used as well as 2 other ones that seemed relevant to the topic. These results showed that most periods could not be found again in other datasets. However, the 40,5 kb SoxS period and the 8,6 kb Fur period were found again, giving them some extra weight.

Harmonic analysis further strengthened this point by finding a connection between the ChIP-exo periods of OxyR-SoxS-Fur regulons. Interestingly, these common periodic organizations reflected observed in vivo crosstalk between these stress response systems 350 .

In conclusion, the results presented in this work suggest more caution is needed when interpreting periodic signals and provides a blueprint for future work in the field.

The relation between gene position, chromosome structure and gene expression

In order to further support the periodicity data from the previous section, a transcription factor was displaced to observe the effect on gene expression of its targets. The OxyR-SoxS-Fur regulon was considered for further analysis based on the periodicity results. The OxyR regulon was chosen as test regulon, because (i) OxyR has the ability to oligomerize, which is a necessary condition for colocalization 346 (ii) OxyR mutants are hypersensitive to oxidative stress 351 , maximally impacting oxidative stress response under experimental conditions and (iii) OxyR controls a relatively small regulon dedicated to oxidative stress response and will minimally perturb cellular metabolism under non-experimental conditions 323,350 .

Next, oxyRS was moved to six locations in the chromosome in order to assess the effect of gene position on the stress response. As an initial experiment, three locations near the origin were chosen mammalian cells. PLOS Genet. 15, e1008389 (2019).

Supplementary material annex I