** Difference between two promoters in T-test (P<0.01)

Summary

The increase in phenotypic variability through gene expression noise is proposed to be an evolutionary strategy in selective environments. Differences in promoter-mediated noise between Saccharomyces cerevisiae strains could have been selected for thanks to the benefit conferred by gene expression heterogeneity in the stressful conditions, for instance, those experienced by industrial strains. In the first part of this thesis, we used a genome-wide approach to identify promoters conferring high noise levels in the industrial wine strain EC1118. Many promoters of genes related to environmental factors were identified, some of them containing genetic variations compared to their counterpart in the laboratory strain S288c. Each variant of eight promoters has been fused to yEGFP and integrated in the genome of both strains. Some industrial variants conferred higher expression associated, as expected, to lower noise, but other variants either increased or decreased expression without modifying variability, so that they might exhibit different levels of transcriptional-mediated noise at equal mean. At different induction conditions giving similar expression for both variants of the CUP1 promoter (pCUP1), we indeed observed higher noise with the industrial variant. Nevertheless, this difference was only observed in the industrial strain, revealing epistasis in the generation of promoter-mediated noise. Moreover, the increased expression variability conferred by this natural yeast promoter variant provided a clear benefit in the face of an environmental stress. Thus modulation of gene expression noise by a combination of promoter modifications and trans-influences might be a possible adaptation mechanism in yeast.

During the screening of high noise promoters, we noticed that some fragments conferred bimodal expression profiles. This expression pattern is generally considered as a bet-hedging strategy to generate distinct subpopulations in the population to cope with unpredictable environments. In the second part, we tried to find the genetic basis of these expression patterns. Unfortunately, genomic integration of the fragments generally eliminated this expression pattern so as expression from centromeric plasmid was the main factor leading to bimodal expression. Among the eight chosen promoters, only pCUP1 in BY4720 (an auxotrophic derivative of S288c) exhibited bimodal expression at the genomic level when it was induced by copper. But the YFP-fusion at the original position of CUP1 showed unimodal expression whether induced or not. Thus bimodal expression patterns are controlled at several levels and further studies are needed to understand the underling mechanisms.

The third part of this thesis was focused on the phenotypic effects of expression noise in DNA-repair or maintenance genes. The main hypothesis underlying this work is that expression noise of these genes might produce sub-populations which exhibit higher genome instability and therefore can more rapidly adapt to new environments through genome modification. Thus high noise in these genes might increase the global fitness in varying environments. To test this hypothesis, RAD52 and RAD27 were fused to YFP and expressed under the dependence of promoters conferring similar mean expression but different noise levels. These genes were chosen because their deletion affects the homologous recombination (HR) frequency in opposite ways. Systems to measure the HR frequency and perform experiments in fluctuating environments have been constructed. Some promoter variants described in the literature were tested to find pairs conferring similar expression levels but different noise with the YFP-fused genes. But additional experiments are needed to get a system allowing testing the initial hypothesis.

Résumé

Les variations des niveaux d'expression des gènes entre les cellules individuelles existent dans les populations isogéniques même dans des environnements constants et peuvent avoir des conséquences phénotypiques profondes [START_REF] Raser | Control of stochasticity in eukaryotic gene expression[END_REF][START_REF] Raj | Nature, nurture, or chance: stochastic gene expression and its consequences[END_REF]Balazsi et al., 2011). Ce phénomène qui s'appelé le bruit d'expression des gènes permettrait les cellules génétiquement identiques ayant des phénotypes hétérogènes et des comportements différents. Ainsi, il serait favorable à la prolifération des cellules pré-adapté lorsqu'une contrainte apparaît [START_REF] Blake | Phenotypic consequences of promoter-mediated transcriptional noise[END_REF][START_REF] Acar | Stochastic switching as a survival strategy in fluctuating environments[END_REF]. Fraser & Kaern, 2009;[START_REF] Ito | How selection affects phenotypic fluctuation[END_REF][START_REF] Ackermann | Microbial individuality in the natural environment[END_REF].

Les gènes répondant aux changements environnementaux sont plus bruyants que ceux qui sont impliqués dans les processus d'entretien ménager (Bar-Même et al. 2006;[START_REF] Newman | Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise[END_REF]. Les niveaux de bruit semblent avoir été sélectionnés selon les coûts et les avantages potentiels de cette variation. Le bruit est également minimisée pour des gènes essentielles [START_REF] Fraser | Noise minimization in eukaryotic gene expression[END_REF][START_REF] Lehner | Selection to minimise noise in living systems and its implications for the evolution of gene expression[END_REF]. Une étude chez Escherichia coli montre que les mutants avec le bruit d'expression plus élevé ainsi que les mutants avec le moyen d'expression plus élevé sont sélectionné sous une pression forte [START_REF] Ito | How selection affects phenotypic fluctuation[END_REF]. L'augmentation de l'hétérogénéité phénotypique est aussi observée chez Saccharomyces cerevisiae dans les expériences évolutionnaires [START_REF] New | Different Levels of Catabolite Repression Optimize Growth in Stable and Variable Environments[END_REF][START_REF] Holland | Phenotypic heterogeneity is a selected trait in natural yeast populations subject to environmental stress[END_REF], qui a probablement eu lieu par le bruit de la transcription médiée par l'expression du gène.

Partie I Connexion le bruit d'expression et l'adaptation du stress

Comme le bruit d'expression est évolutif et héréditaire [START_REF] Ito | How selection affects phenotypic fluctuation[END_REF] et le bruit des gènes liés au stress est plus élevés [START_REF] Newman | Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise[END_REF], les souches industrielles pourraient avoir évolué vers des niveaux de bruit plus élevés pour les gènes impliqués l'adaptation des environnements stressants.

Notre objectif dans cette partie était de déterminer si les différences génétiques observées entre la souche industrielle (EC1118) et la souche laboratoire (S288c) génèrent des différences de bruit entre elles et si ces différences sont suffisamment importantes pour générer une différence de croissance dans des environnements sélectifs spécifiques.

L'approche génomique utilisée pour identifier les promoteurs qui confèrent un haut niveau de stimulation du promoteur dans la souche EC1118 [START_REF] Novo | Eukaryote-to-eukaryote gene transfer events revealed by the genome sequence of the wine yeast Saccharomyces cerevisiae EC1118[END_REF] était basée sur le procédé développé initialement avec Salmonella typhimurium [START_REF] Freed | A simple screen to identify promoters conferring high levels of phenotypic noise[END_REF]. Des fragments d'ADN génomique de l'haploïde 59A (dérivé de la souche EC1118 diploïde) ont été insérés avant le yEGFP dans des vecteurs centromériques sans promoteur et ensuite transformée dans la souche de laboratoire CEN.PK. La méthode de la sélection fluctuante décrite par [START_REF] Freed | A simple screen to identify promoters conferring high levels of phenotypic noise[END_REF] permettant l'enrichissement des fragments produisant une expression de yEGFP plus variable a ensuite été appliquée. Sept cycles de tri ont été effectués alternativement avec les 5% plus fluorescent cellules ou les 5% moins fluorescent cellules. On a mesuré l'expression de yEGFP des cellules individuelles dans la banque après sélection. Les fragments conduisant l'expression de yEGFP plus bruyant ont été séquencés avec succès dans 97 clones. Cinquante promoteurs distincts ont été identifiés. On a choisi et étudié 8 paires de variantes de promoteur au niveau génomique dans BY4720 et 59A par rapport leurs fonctionnalités. Au contraire dans la littérature, la variante avec une moyenne plus forte n'a pas toujours été associée à un bruit plus faible. Néanmoins, cela dépend souvent du contexte génétique parce que certaines variantes donnent différentes moyennes en 59A mais pas dans BY4720. Ainsi, les éléments cis-(variations dans la séquence du promoteur) et les facteurs trans (facteurs cellulaires impliqués dans l'expression des gènes) sont associés pour permettre un profil d'expression spécifique, révélant l'épitasis dans la génération du bruit d'expression.

Nous avons confirmé la différence de bruit entre les variantes de pCUP1 en les induisant avec différentes concentrations de sulfate de cuivre qui confèrent la même moyenne d'expression. Mais le bruit plus élevé est une caractéristique de la variante industrielle seulement révélée dans la souche 59A. Nous montrons ici l'influence combinée et corrélée des facteurs cis et trans qui contribuent à augmenter le bruit d'expression d'un gène. Par contre, les conséquences des SNPs et du Indel entre les variantes de pCUP1 ne sont pas dramatiques et peuvent révéler que les systèmes naturels évoluent par des modifications de promoteur générant de petits effets sur le bruit mais n'influant pas fortement la variabilité entre les cellules.

En fusionnant les variantes naturelles de pCUP1 avec ZeoR, nous montrons ici que leurs différences de bruit à une moyenne d'expression égale offrent des capacités distinctes pour survivre dans un environnement sélectif, même si les différences sont faibles. Mais ce bénéfice conféré par pCUP1EC1118 applique seulement aux concentrations intermédiaires de phléomycine. L'identification d'un pCUP1 plus fort dans une souche de vin industrielle est logique car les levures de vins sont fréquemment exposées aux concentrations fortes de cuivre pendants la fermentation. Certains gènes impliqués dans la réponse au stress, comme CUP1, ont été corrélés positivement avec la durée de la fermentation [START_REF] Ambroset | Deciphering the molecular basis of wine yeast fermentation traits using a combined genetic and genomic approach[END_REF]. Ainsi, un bruit d'expression plus élevé serait avantageux en ce sens qu'il rendrait la population optimale entre l'adaptabilité aux environnements riches en cuivre et la capacité de fermentation.

Partie II Etude du profil d'expression bimodal

Au cours du ciblage des clones avec un bruit d'expression plus élevé dans la banque après la sélection FACS, nous avons identifié des clones intéressants conférant des profils d'expression bimodaux, ce qui signifie qu'ils avaient deux pics dans la distribution alors que les autres clones n'avaient qu'un seul pic. L'un des avantages est qu'il confère deux sous-populations très différentes qui peuvent être adaptées à des situations contraintes. Mais les promoteurs correspondants fusionnés à GFP intégrés dans le locus LEU2 ont tous présenté un profil d'expression uni-modal dans YPD. En échangeant le promoteur de CAN1 et le fragment correspondant entre les plasmides intégratifs et centromériques, nous avons montré que les deux ont conféré un profile bimodal lorsqu'ils sont situés dans le plasmide centromérique alors qu'ils présentent un profile uni-modal après l'intégration génomique. Ainsi, le profil d'expression bimodal est une caractéristique de l'expression des plasmides centromériques. Ces effets sont supposés être dus aux différentes caractéristiques structurelles entre les plasmides et les chromosomes. Les chromosomes sont fortement structurés par des histones et d'autres protéines associées à la chromatine et leurs arrangements sont précisément contrôlé dans le noyau alors que les plasmides sont moins régulés par tels événements.

Le profil bimodal a également été observé avec des variantes de CUP1 dans la souche BY4720 lorsqu'ils sont induits par CuSO4, mais pas dans 59A. Ce phénomène pourrait être lié au différent nombre de copie de CUP1 entre BY4720 et 59A. On a aussi étudié la cinétique de la bimodalité de

Partie III Connexion le bruit d'expression et l'instabilité du génome

Dans cette partie, nous avons essayé de tester notre hypothèse : le bruit d'expression plus élevé dans certains gènes de maintenance du génome peut donner une meilleure adaptabilité globale aux différents stress en augmentant le taux de génération de variantes génétiques (RGVG, Capp, 2010).

Ainsi, nous avons fusionné des gènes de réparation et de maintenance de l'ADN avec deux promoteurs modifiés de PHO5, qui confèrent des moyenne d'expression similaires mais différents niveaux de bruit (Raser & O'Shea, 2004,. pPHO5TATA-A1T6 et pPHO5UASm1). pPHO5TATA-A1T6 a un niveau de bruit inférieur que pPHO5UASm1. Ces constructions ont été transformées dans la souches JA0200 qui a URA3 inséré dans les répétitions en tandem (TR) qui présentent dans le gène FLO1 [START_REF] Verstrepen | Intragenic tandem repeats generate functional variability[END_REF]. La recombinaison homologue (HR) entre les TRs peut supprimer le gène URA3 qui permet la survie dans les boites contenant 5-FOA. Par conséquent, nous pouvons utiliser JA0200 pour mesurer la fréquence de HR.

En variant les conditions d'induction, nous avons réussi à obtenir des moyennes d'expression similaire, mais des bruits différents pour les deux variantes de pPHO5 (pPHO5UASm1 et pPHO5TATA-A1T6) fusionnées avec RAD52-YFP ou RAD27-YFP. Nous avons testé si des bruits d'expression différents des gènes de maintenance du génome peuvent conduire à différents niveaux d'instabilité du génome en mesurant la fréquence de HR. Mais malheureusement, nous n'avons pas observé de différence significative. Étant donné que les cellules ne se sont poussées que 1,5 fois pendant la période d'induction dans le milieu sans phosphate, la fréquence était principalement contrôlée par la saltus initiale de la population. Par conséquent, la différence phénotypique potentielle générée par les variantes de pPHO5 induites n'était pas détectable.

Introduction 1 Introduction

Variability of gene expression 1.Expression of protein-coding genes

How genetic information is stored in the DNA was elucidated after the discovery of its structure [START_REF] Watson | Molecular Structure of Nucleic Acids: A Structure for Deoxyribose Nucleic Acid[END_REF]. Gene expression is the process by which information from DNA is used in the synthesis of a functional gene product. The products could be proteins or functional RNAs (such as ribosomal RNA (rRNA) or transfer RNA (tRNA)).

For protein-coding genes, which are the purpose of this dissertation, gene expression includes two main steps: transcription and translation (Figure 1). Transcription is the process by which genetic information is transferred into RNA. This process produces messager RNAs (mRNA) used to produce proteins by ribosomes, in the process called translation.

Transcription

Figure 1: Expression of protein-coding genes (modified from [START_REF] Kervestin | NMD: a multifaceted response to premature translational termination[END_REF]).

Transcription

The transcription process is performed by RNA polymerases [START_REF] Hurwitz | The discovery of RNA polymerase[END_REF]. RNA polymerases use DNA as a template to synthesize single strand RNA from 5' to 3' end. In eukaryotic cells, there are three RNA polymerases (I, II, III). Here we will focus on RNA polymerase II (RNAPII) which is involved in the transcription of protein-coding genes [START_REF] Roeder | Multiple Forms of DNA-dependent RNA Polymerase in Eukaryotic Organisms[END_REF].

The RNAPII-dependent transcription process generally includes 4 steps: pre-initiation, initiation, elongation and termination [START_REF] Hirose | RNA polymerase II and the integration of nuclear events[END_REF]. The whole process is finely controlled in the cell by transcription factors and cis-regulatory elements. Transcription factors (trans-regulatory factors) are proteins that can recognize and interact with specific sequences in gene promoters (through ciselements) to regulate transcription. They can be General Transcription Factors (GTF), such as the ones involved in all RNAPII-dependent transcription initiation events (TFIIA, TFIIB, TFIID, TFIIE, TFIIF and TFIIH etc.) or transcription regulators (activator or repressor). Co-regulators can also be involved to connect transcription factors to RNAPII and to modify the chromatin structure that plays an important role in the process of transcription [START_REF] Shandilya | The transcription cycle in eukaryotes: from productive initiation to RNA polymerase II recycling[END_REF]. Before describing in details the different trans-acting factors, cis-elements important in transcription are described.

Cis-elements

A gene is usually defined as the DNA region which is transcribed from the transcription starting site (TSS) to the terminator into a pre-RNA. The region upstream of a gene is the promoter: it is used to initiate the transcription, while the terminator is the region where the transcription is terminated. In yeast, the cis-regulatory elements are usually located in the promoter region.

The length of promoters varies from 100 to 1000bp. It contains the indispensable elements for transcription initiation (core promoter) and some regulatory elements. The core promoter region contains sequences around the TSS that are recognized by GTF, while regulatory elements are gene specific and vary in terms of number and position. Sometimes, especially in higher eukaryotes, regulatory elements, such as enhancers, can be several kilobases away from the TSS.

i) Core promoter

The structure of the core promoter is shown in Figure 2 [START_REF] Butler | The RNA polymerase II core promoter: a key component in the regulation of gene expression[END_REF]. The TSS is numbered as the +1 position, and the position of the cis-elements are shown by the distance between the element and the TSS. Around the TSS, a motif called initiator (Inr) is present in metazoans (e.g. human, Drosophila) [START_REF] Singh | Coupling Between Noise and Plasticity in Escherichia coli. G3 (Bethesda).: g3.113.008540[END_REF][START_REF] Juven-Gershon | The RNA polymerase II core promoterthe gateway to transcription[END_REF], whereas Inr-like sequences are found in some yeast core promoters [START_REF] Yang | Prevalence of the initiator over the TATA box in human and yeast genes and identification of DNA motifs enriched in human TATA-less core promoters[END_REF]). Recently, [START_REF] Lubliner | Sequence features of yeast and human core promoters that are predictive of maximal promoter activity[END_REF] found that the sequences around TSS are predictive of the maximal promoter activity in yeast. There are T-rich regions and A-rich regions located upstream and downstream of the TSS, respectively, that are indicative of high promoter activity. Upstream of the Inr, the first identified eukaryotic core promoter motif can be observed: the TATA box [START_REF] Breathnach | Organization and expression of eucaryotic split genes coding for proteins[END_REF]. The consensus sequence for TATA box is TATAWAWR. TATA box is not a very common motif. In yeast, only about 20% of genes contain a TATA box [START_REF] Shandilya | The transcription cycle in eukaryotes: from productive initiation to RNA polymerase II recycling[END_REF] and its position varies from about -40 to -100 [START_REF] Butler | The RNA polymerase II core promoter: a key component in the regulation of gene expression[END_REF]. But if sequence elements with up to two mismatches with the TATA box consensus are searched, almost all yeast promoters contain "TATA-like" sequences [START_REF] Rhee | Noise decomposition of intracellular biochemical signaling networks using nonequivalent reporters[END_REF]. TATA box-containing genes are associated with stress-response and are stringently regulated whereas TATA-less promoters are generally engaged in basic housekeeping functions [START_REF] Basehoar | Identification and distinct regulation of yeast TATA boxcontaining genes[END_REF].

The TFIIB recognition element (BRE) can be upstream or downstream of the TATA box [START_REF] Deng | TFIIB and the regulation of transcription by RNA polymerase II[END_REF]. Depending on the promoter context, it can act in either a positive or negative manner.

The downstream promoter element (DPE) is a downstream TFIID recognition sequence first found in Drosophila [START_REF] Burke | Drosophila TFIID binds to a conserved downstream basal promoter element that is present in many TATA-box-deficient promoters[END_REF]. In yeast, BRE and DPE are very rare [START_REF] Yang | Prevalence of the initiator over the TATA box in human and yeast genes and identification of DNA motifs enriched in human TATA-less core promoters[END_REF].

ii) Regulatory elements

The core promoter is found upstream of all protein-coding genes, while regulatory elements are gene-specific, such as enhancers that are cis-acting regulatory sequences that markedly increase expression level and can be either upstream or downstream of the gene, and silencers, cis-acting regulatory sequences that decrease expression level [START_REF] Gaszner | Insulators: exploiting transcriptional and epigenetic mechanisms[END_REF].

In yeast, a well-studied cis-acting regulatory sequence is the upstream activating sequence (UAS) which is located upstream of promoters and increases expression level [START_REF] Gaszner | Insulators: exploiting transcriptional and epigenetic mechanisms[END_REF].

Many different UAS exist such as the Gal4 binding site [START_REF] Brand | Targeted gene expression as a means of altering cell fates and generating dominant phenotypes[END_REF] and the Gcn4 binding site [START_REF] Hope | GCN4, a eukaryotic transcriptional activator protein, binds as a dimer to target DNA[END_REF] and several UAS can be found in the same promoter. Transcriptional activators are able to bind UAS and help recruitment of the core transcriptional machinery. Detailed examples will be given in the next section.

iii) Nucleosome-influencing sequences

Genomic DNA is wrapped into nucleosomes in the nucleus. These nucleosomes are an obstacle for the recruiting of the Pre-Initiation Complex (PIC). In vivo, nucleosome positioning takes place at preferred sequences which have periodic AA/TT/AT dinucleotide sequences every ~10 bp [START_REF] Segal | A genomic code for nucleosome positioning[END_REF], while poly(dT:dA) acts as nucleosome disfavoring sequence [START_REF] Sharon | Inferring gene regulatory logic from high-throughput measurements of thousands of systematically designed promoters[END_REF]. When these sequences are present on promoters, they affect gene expression through their influence on nucleosome positioning [START_REF] Carey | Promoter sequence determines the relationship between expression level and noise[END_REF][START_REF] Choi | Intrinsic variability of gene expression encoded in nucleosome positioning sequences[END_REF][START_REF] Sharon | Inferring gene regulatory logic from high-throughput measurements of thousands of systematically designed promoters[END_REF].

Trans-acting factors

Eukaryotic transcription is a precisely timed event and, at every step, there are different transacting factors used to regulate the process (Figure 3). It starts with the recruitment of GTFs and RNAPII to the target gene promoters. They form the PIC. Then the transcription is initiated and elongated along the gene. Finally, RNAPII reaches the termination sites and is released [START_REF] Thomas | The General Transcription Machinery and General Cofactors[END_REF][START_REF] Shandilya | The transcription cycle in eukaryotes: from productive initiation to RNA polymerase II recycling[END_REF]. The recognition of the TATA box sequence by the TATA Binding Protein (TBP) is the beginning of the assembly of the PIC. TBP along with the TBP-associated factors (TAFs) forms the TFIID complex. The TAFs can recognize the Inr and DPE sites. Thus TFIID is a core promoter-binding factor [START_REF] Thomas | The General Transcription Machinery and General Cofactors[END_REF]. Meanwhile, the TFIIB binding to the BRE can stabilize the interaction between TFIID and the core promoter. Then the RNAPII-TFIIF complex binding to the TSS, followed by TFIIE, and TFIIH, forming the PIC [START_REF] Butler | The RNA polymerase II core promoter: a key component in the regulation of gene expression[END_REF]. The recruitment of TBP is a critical step for the PIC formation and can be regulated both positively and negatively. In yeast, transcription activators such as Hsf1 and Msn2 can stimulate the interaction between TBP and the TATA-box [START_REF] Kuras | Binding of TBP to promoters in vivo is stimulated by activators and requires Pol II holoenzyme[END_REF] while the repressors such as Mot1 and Taf1 suppress this binding [START_REF] Cang | A new regulatory domain on the TATA-binding protein[END_REF]. Another TAF containing co-activator is the SAGA (Spt-Ada-Gcn5 acetyltransferase) complex that can contact with the TBP and activate transcription. About half of the TATA-containing promoters, which are stressinducible, appear to be SAGA-dependent. The TBP on these promoters preferably contacts SAGA rather than forms the TFIID complex [START_REF] Basehoar | Identification and distinct regulation of yeast TATA boxcontaining genes[END_REF]. Zanton et Pugh (2004) also showed that, during heat stress, yeast can induce transcription of several genes through the recruitment of both TFIID and SAGA.

b) Sequence specific regulators

Sequence specific regulators and components affect the transcription efficiency by promoting or preventing the formation of the PIC [START_REF] Venters | A canonical promoter organization of the transcription machinery and its regulators in the Saccharomyces genome[END_REF][START_REF] Venters | A comprehensive genomic binding map of gene and chromatin regulatory proteins in Saccharomyces[END_REF]. The signals from these factors are transmitted to the general transcription machinery by Mediator. Mediator can enhance RNAPII entry to the PIC as well as the TBP binding to the TATA-box. Mediator can also facilitate the reinitiation of transcription from the same promoter [START_REF] Thomas | The General Transcription Machinery and General Cofactors[END_REF].

Two examples of sequence specific regulators can be cited here: Gal4 and Pho4. Gal4 has two domains: the DNA-binding domain (DB) and the activation domain (AD). The BD recognizes the UAS sequence, and the AD binds to mediator and activates the transcription of Gal1 by facilitating the formation of PIC (Figure 4, [START_REF] Ang | Mediator acts upstream of the transcriptional activator Gal4[END_REF].

Figure 4: The interaction between Gal4 and UAS activate GAL1 transcription [START_REF] Ang | Mediator acts upstream of the transcriptional activator Gal4[END_REF] Pho4 activates the expression of PHO5 [START_REF] Nakao | Saccharomyces cerevisiae PHO5 promoter region: location and function of the upstream activation site[END_REF][START_REF] Venter | A nucleosome precludes binding of the transcription factor Pho4 in vivo to a critical target site in the PHO5 promoter[END_REF][START_REF] Barbaric | Cooperative Pho2-Pho4 interactions at the PHO5 promoter are critical for binding of Pho4 to UASp1 and for efficient transactivation by Pho4 at UASp2[END_REF]. ii) Initiation and elongation

The formation of a functional and stable PIC is indispensable for transcription initiation. The DNA-GTF interactions in the PIC link RNAPII to the promoter, whereas TFIIH promotes ATP-dependent unwinding of approximately 10 base pairs in the promoter, leading to the open complex state [START_REF] Grünberg | Structural insights into transcription initiation by RNA polymerase II[END_REF]. This progress is favored by the presence of a TATA box because of its high AT proportion. Otherwise, the negative supercoiling introduced by histones can also facilitate the unwinding step [START_REF] Thomas | The General Transcription Machinery and General Cofactors[END_REF]. RNAPII then starts mRNA synthesis from the TSS. Transcripts of less than 10nt are unstable, resulting in a high frequency of abortive initiation because the RNA molecule is too short to form a stable DNA:RNA duplex. At about 10 nt, this duplex is stabilized and promoter escape is favored over abortive initiation. At transcript length of around 25 nt, productive initiation is achieved and transcription elongation begins [START_REF] Saunders | Breaking barriers to transcription elongation[END_REF].

Then RNAPII is dissociated from the initiation complex and starts moving along the gene. The synthesis occurs at a varying speed, especially because secondary structures in the mRNA can slow down or even stop RNAPII. The duration of pausing depends on the rate of recruitment of factors that trigger pause release. Universally conserved transcription elongation factors are the Spt5/NusG factors, which reduce the frequency of transcription pausing or arrest in cooperation with Spt4. TFIIS is also beneficial to the elongation: it can help RNAPII to bypass specific blocks [START_REF] Wind | Transcription elongation factor SII[END_REF].

Another elongation factor is elongin: it activates elongation by suppressing transient pausing of RNAPII [START_REF] Aso | Elongin (SIII): a multisubunit regulator of elongation by RNA polymerase II[END_REF]. Otherwise, the maintenance of the RNA:DNA hybrid is also a critical determinant of elongation [START_REF] Shandilya | The transcription cycle in eukaryotes: from productive initiation to RNA polymerase II recycling[END_REF].

The mRNA maturation events such as 5'-capping and intron splicing are performed during transcription elongation. The 5'-cap is a guanine nucleotide connected to the mRNA via an unusual 5' to 5'-triphosphate linkage. This guanosine is methylated on the 7 position (m7G) [START_REF] Shatkin | Capping of eucaryotic mRNAs[END_REF]. The cap is formed shortly after transcription initiation, when nascent RNA chains are about 25-30 nucleotides in length [START_REF] Coppola | Promoter-proximal pausing by RNA polymerase II in vitro: transcripts shorter than 20 nucleotides are not capped[END_REF]. The formation of the cap needs the RNA methyltransferase, RNA guanylyltransferase and RNA triphosphatase. The RNAPII subunit IIO and its Carboxy-Terminal Domain (CTD) are involved in this process too [START_REF] Hirose | RNA polymerase II and the integration of nuclear events[END_REF]. The most important role of 5'cap is protecting mRNA from degradation, but is also involved in all the transcription, translation, even degradation processes [START_REF] Cowing | Regulation of mRNA cap methylation[END_REF].

Moreover when introns are present in genes, they are spliced to form the mature mRNA. In S.

cerevisiae, only 5% of genes have introns and more than 95% of them contain only one single intron [START_REF] Nash | Expanded protein information at SGD: new pages and proteome browser[END_REF]. Splicing of mRNA precursors takes place in a large macromolecular complex called spliceosome, which is composed of small nuclear ribonucleoprotein particles (snRNPs) and non-snRNP proteins [START_REF] Hirose | RNA polymerase II and the integration of nuclear events[END_REF]. The CTD of RNAPII and the IIO subunit also play an important role during splicing [START_REF] Hirose | Phosphorylated RNA polymerase II stimulates pre-mRNA splicing[END_REF]. Thus the transcription and splicing processes are coupled [START_REF] Hirose | RNA polymerase II and the integration of nuclear events[END_REF]. An interesting aspect is the alternative splicing process which can produce variable mature mRNAs [START_REF] Kalsotra | Functional consequences of developmentally regulated alternative splicing[END_REF] but it is very rare in S. cerevisiae. For example, in S.

cerevisiae, PTC7 can be alternatively spliced to create two mRNAs that code for distinct proteins. The protein translated from the spliced mRNA localizes to the mitochondria, while the protein translated from the unspliced mRNA localizes to the nuclear envelope [START_REF] Juneau | Alternative splicing of PTC7 in Saccharomyces cerevisiae determines protein localization[END_REF].

iii) Termination

Termination is the last step of transcription, but may also serve as a junction for recycling RNAPII at the promoter for subsequent rounds of transcription [START_REF] Hirose | RNA polymerase II and the integration of nuclear events[END_REF]. There are two wellstudied pathways of transcription termination, the poly (A)-dependent pathway and the Nrd1-Nab3-Sen1-dependent pathway [START_REF] Shandilya | The transcription cycle in eukaryotes: from productive initiation to RNA polymerase II recycling[END_REF]. The Nrd1-Nab3-Sen1-dependent pathway is found in the termination of non-coding RNA transcription by RNAPII in yeast [START_REF] Vasiljeva | Nrd1 interacts with the nuclear exosome for 3' processing of RNA polymerase II transcripts[END_REF]. For protein-coding genes, the poly (A)-dependent pathway is used to terminate transcription.

In eukaryotes, protein-coding genes have a highly conserved poly(A) signal (AATAAA) which is followed by a GC-rich sequence. This signal leads to the polyadenylation of precursors and the release of RNAPII. There are several protein complexes that involved in transcription termination. In yeast, the most important complexes is the cleavage and polyadenylation factor (CPF) and cleavage factor IA (CFIA) [START_REF] Kuehner | Unravelling the means to an end: RNA polymerase II transcription termination[END_REF]. They cut the pre-RNA at the signal site what is followed by poly(A) addition to the 3'end of the upstream cleavage product by the poly(A) polymerase [START_REF] Shandilya | The transcription cycle in eukaryotes: from productive initiation to RNA polymerase II recycling[END_REF], and by the RNAPII release.

As for capping and splicing, the CTD of RNAPII and its subunit IIO play an important role during polyadenylation [START_REF] Shatkin | The ends of the affair: capping and polyadenylation[END_REF]. The 3'-tail can stabilize mRNA and facilitate translation. But recent researches have shown that lots of genes contain more than one polyadenylation site, a phenomenon called alternative polyadenylation (APA). APA contributes to the complexity of the transcriptome [START_REF] Elkon | Alternative cleavage and polyadenylation: extent, regulation and function[END_REF]. [START_REF] Ozsolak | Comprehensive polyadenylation site maps in yeast and human reveal pervasive alternative polyadenylation[END_REF] found that almost seventy percent of the yeast genes had more than one polyadenylation site.

Chromatin architecture

The organization of eukaryotic DNA into the chromatin is a significant obstacle for the binding of transcription factors to their cognate sequences and hence in most scenarios negatively influences all steps of transcription [START_REF] Shandilya | The transcription cycle in eukaryotes: from productive initiation to RNA polymerase II recycling[END_REF]. The basic unit of chromatin is the nucleosome ( Figure 6). In a nucleosome, the DNA is wrapped twice around a core histone octamer (a structure that consists of two copies each of the core histones, H2A, H2B, H3, and H4) in a left-handed super helix. Then a linker histone H1 binds to the nucleosome to stabilize it. The diameter of nucleosome is around 10 nm. The 10 nm fibre can be further compacted to form the 30 nm fibre (secondary structure, Figure 6). The 30 nm structure can be finally compacted to form higher-order chromatin fibre as shown in Figure 6 [START_REF] Luger | New insights into nucleosome and chromatin structure: an ordered state or a disordered affair?[END_REF]. repressed state to a transcriptionally competent state [START_REF] Paranjape | Role of chromatin structure in the regulation of transcription by RNA polymerase II[END_REF]. During the induction of PHO5, nucleosome repositioning is indispensable. Mutations in the PHO5 promoter which promote expression under non-permissive conditions lead to shifts of positioned nucleosomes similar to the ones observed during the induction of the PHO5 promoter. By contrast, mutations that reduce gene expression upon induction stabilize nucleosomes on the promoter region [START_REF] Small | Single-cell nucleosome mapping reveals the molecular basis of gene expression heterogeneity[END_REF].

ii) Histone modifications

Every histone has several acetylation and methylation sites [START_REF] Paranjape | Role of chromatin structure in the regulation of transcription by RNA polymerase II[END_REF]. The reversible modifications of these sites have important effect on the regulation of transcription. The most common example in yeast is the gene silencing of the silent mating-type cassettes and telomeres. The silenced gene have hypoacetylated histones on the chromatin, compared to active genes [START_REF] Braunstein | Transcriptional silencing in yeast is associated with reduced nucleosome acetylation[END_REF]. More generally, histone acetylation creates an open chromatin that favors transcription whereas deacetylation is correlated to compacted and repressive chromatin. The effects of histone methylation on the chromatin compaction level depend on the modified histone and amino acid.

Histone modifications during the transcription process play an important role. Gene-specific activators mediate the recruitment of histone modifying enzymes. Indeed, TFs recruit coactivators that can remove nucleosomes from the promoter or modify histones. Then these modified histones constitute an open and permissive chromatin environment competent for transcription [START_REF] Ansari | MLL histone methylases in gene expression, hormone signaling and cell cycle[END_REF].

For example, the binding of activated Pho4 (unphosphorylated) to the UASp1 site on the PHO5 promoter, when yeast cells encounter phosphate (Pi) starvation, triggers histone hyperacetylation on the promoter which leads to nucleosome disassociation. Thus TFs can more easily bind the promoter and finally activate PHO5 transcription [START_REF] Fuda | Defining mechanisms that regulate RNA polymerase II transcription in vivo[END_REF].

iii) Gene and chromosome looping

Gene looping is a phenomenon by which the terminal and promoter regions of active genes interact by chromatin bending (Figure 7). The formation of a gene loop needs CPSF (cleavage and polyadenylation specific factor) and CstF (cleavage stimulatory factor), the initiation factors TFIIB and TFIIH, as well as the 3'-end processing complex [START_REF] Singh | A transcription-independent role for TFIIB in gene looping[END_REF]. Gene loops have been found in yeast, in HIV and human cancer cells. Gene loops facilitate transcriptional memory, both for activation and repression [START_REF] Lainé | A physiological role for gene loops in yeast[END_REF]. Gene loops also enforces transcriptional directionality on bidirectional promoters to produce mRNA rather than noncoding RNA (Tan-Wong et al, 2012).

Figure 7: RNAPII recycling via gene looping [START_REF] Shandilya | The transcription cycle in eukaryotes: from productive initiation to RNA polymerase II recycling[END_REF] Active alleles of genes that are separated by several megabases, or even located on different chromosomes, are found to co-localize in the nucleus under certain conditions [START_REF] Deng | Transcription factories, chromatin loops, and the dysregulation of gene expression in malignancy[END_REF].

These transcriptional foci are called "transcription factories", which are discrete entities composed of active RNAPII and several transcription associated factors. It was found that multiple genes might share the same transcription factory [START_REF] Shandilya | The transcription cycle in eukaryotes: from productive initiation to RNA polymerase II recycling[END_REF].

Transcriptional diversity

In yeast, non-coding RNAs (ncRNAs) which also have a 5'-cap and a 3'-poly(A) tail are produced: they are called cryptic unstable transcripts (CUTs) and the stable uncharacterized transcripts (SUTs).

They are transcripts, which do not overlap with existing annotations, found in RNA-arrays or RNA-seq experiments. These ncRNAs mainly come from the anti-sense transcription of bidirectional promoters [START_REF] Xiong | The human splicing code reveals new insights into the genetic determinants of disease[END_REF]. It was already known that pervasive transcription exists in yeast: 85% of the genome can be transcribed [START_REF] David | A high-resolution map of transcription in the yeast genome[END_REF]. [START_REF] Pelechano | Extensive transcriptional heterogeneity revealed by isoform profiling[END_REF] Moreover the mean RNA molecule number per gene in yeast is one copie per cell [START_REF] Miura | Absolute quantification of the budding yeast transcriptome by means of competitive PCR between genomic and complementary DNAs[END_REF]. Thus, if 26 isoforms are possible and if only one copie is present per cell in mean, one can expect a high cell-to-cell variability in mRNA structure. Finally, isoform diversity generates diversity in RNA stability because of variations in binding of RNA binding proteins [START_REF] Gupta | Alternative polyadenylation diversifies posttranscriptional regulation by selective RNA-protein interactions[END_REF].

mRNA degradation

Indeed the stability of a particular mRNA is controlled by specific interactions between its structural elements and RNA-binding proteins that can be general or mRNA-specific. The sequence elements important for stability are throughout the whole mRNA [START_REF] Tourrière | mRNA degradation machines in eukaryotic cells[END_REF]. The most common elements are the 5'-cap and the 3'-poly(A) tail. The process of 3'-poly(A) tails shortening which is performed by deadenylases can trigger mRNA decay in eukaryotic cells. After deadenylation, the mRNAs can be degraded by 3' → 5' exonucleases or through the decapping pathway [START_REF] Chen | Mechanisms of deadenylation-dependent decay[END_REF]. The cap structure can be removed by a complex consisting of two proteins, Dcp1 and Dcp2 [START_REF] Dunckley | The DCP2 protein is required for mRNA decapping in Saccharomyces cerevisiae and contains a functional MutT motif[END_REF], then the mRNA body will be degraded by 5' → 3' exonucleases. Moreover, eukaryotic mRNAs that have abnormalities in translation will be degraded through specific pathways such as Nonsense-Mediated Decay (NMD) for mRNAs with premature translation termination codons, Nonstop Decay (NSD) for mRNAs lacking translation termination codons and No-Go decay (NGD) for mRNAs having strong pauses in elongation [START_REF] Doma | RNA Quality Control in Eukaryotes[END_REF].

Translation and post-translational processes

In eukaryotes, mature mRNAs are exported from the nucleus to the cytoplasm, and then ribosomes are assembled on mRNAs to translate them into proteins. Like for transcription, there are lots of translation factors used to ensure productive translation.

Translation factors i) Ribosome

The ribosome consists of two subunits in all species. In eukayotes, the subunits are designated as 40S and 60S, and together make up the 80S ribosome. In S. cerevisiae, the small 40S subunit contains a 18s rRNA and 33 ribosomal proteins whereas the large 60S subunit contains the 5.8S, 25S and 5S rRNAs and 46 ribosomal proteins [START_REF] Melnikov | One core, two shells: bacterial and eukaryotic ribosomes[END_REF]. There are three binding sites for tRNA, designated the A (aminoacyl) site, which accepts the incoming aminoacylated tRNA, the P (peptidyl) site, which holds the tRNA with the nascent peptide chain, and E (exit) site, which holds the deacylated tRNA before it leaves the ribosome. The small subunit binds the mRNA and the anticodon stem-loops of tRNAs, and contributes to the fidelity of translation by monitoring base pairing between codon and anticodon in the decoding process. The large subunit binds the acceptor arms of tRNA and catalyzes peptide bond formation between the incoming amino acid and the nascent peptide chain [START_REF] Ramakrishnan | Ribosome Structure and the Mechanism of Translation[END_REF].

ii) Initiation

In eukaryote, the 40S subunit carrying the eIF2-GTP-Met-tRNAi ternary complex first interacts with the 5'-cap of a mRNA (Figure 9), and then goes through the 5'-Untranslated Region (5'UTR) until the start codon where it is assembled with the 60S subunit. Many eukaryotic initiation factors are involved translation initiation and can simulate translation (Figure 9). [START_REF] Jackson | The mechanism of eukaryotic translation initiation and principles of its regulation[END_REF] iii) Elongation

Figure 9: Translation initiation cycle

The elongation cycle is shown in Figure 10 [START_REF] Schneider-Poetsch | Garbled messages and corrupted translations[END_REF]. An aminoacylated tRNA bound to GTP and the eukaryotic elongation factor 1A (eEF1A) enters the A site. If its anticodon matches the next codon in the mRNA (with full complementarity or only partial complementarity thanks to base wobble), the ribosome catalyzes the peptide bond formation. After ribosome translocation, the released deacetylated tRNA enters the E site before ejection, and the tRNA with the nascent amino acid chain is in the P site awaiting the next cycle.

iv) Termination

The presence of a stop codon in the A site stimulates the termination process. Because no tRNA is able to recognize the stop codons, the eRF1-eRF3-GTP ternary complex can enter the A site and recognize the stop codon. After GTP hydrolysis, eRF3 is released [START_REF] Dever | The elongation, termination, and recycling phases of translation in eukaryotes[END_REF]. Then the binding of ABCE1/Rli1 to eRF1 facilitate hydrolysis of the peptidyl-tRNA linkage. Thus the polypeptide and the mRNA are released and the ribosome disassembles through several steps [START_REF] Kapp | The molecular mechanics of eukaryotic translation[END_REF].

Post-translational process i) Protein folding

Polypeptides resulting from translation have to fold into a proper 3-dimensional structure which is indispensable for their functions. In the cell, there are alternative pathways to help and control polypeptides folding [START_REF] Wickner | Posttranslational Quality Control: Folding, Refolding, and Degrading Proteins[END_REF]. Chaperones are proteins that catalyze protein folding. By binding exposed hydrophobic patches on proteins, chaperones prevent proteins from aggregating into insoluble, nonfunctional aggregates and help them to reach their stable native state [START_REF] Goloubinoff | Review: mechanisms of disaggregation and refolding of stable protein aggregates by molecular chaperones[END_REF]. Some proteins are co-translationally folded whereas others are released in an unfolded form. Then they spontaneously fold or are submitted to chaperone-mediated folding. The nascent chains first bind to the nascent-chain-associated complex (NAC), Hsp70 proteins and Hsp40

proteins. Then with the help of nucleotide exchange factors (NEFs), these chaperons mediate co-and posttranslational folding. Hsp70s can also transmit their substrate to Hsp90 proteins to mediate folding with additional cofactors. Partially folded substrates are transferred to the tailless complex polypeptide-1 (TCP-1) ring complex (TRiC)/chaperonin-containing TCP-1 (CCT) to be further folded (Figure 11).

Figure 11: Eukaryotic chaperone pathways [START_REF] Kim | Molecular chaperone functions in protein folding and proteostasis[END_REF] ii) Protein degradation

When polypeptides are irreversible misfolded or when proteins are damaged because of severe environmental stress, they have to be degraded. The ubiquitin-dependent proteasome pathway plays an important role in the misfolding protein degradation. An ubiquitin chain (ubiquitin is a 76 aminoacid peptide) is added onto lateral lysine of damaged proteins and serves as a signal to bring these proteins to the proteasome (Kravtsova-Ivantsiv & Ciechanover, 2012). The proteasome recognizes ubiquitin tags in substrates through its receptors and then initiates degradation at an unstructured region in the substrate. The substrate is then pulled into the degradation channel, the ubiquitin chain is cleaved off and the substrate unfolded and finally cleaved into peptides (Figure 12).

Figure 12:

Steps of proteasomal degradation [START_REF] Bhattacharyya | Regulated protein turnover: snapshots of the proteasome in action[END_REF] This pathway also needs cooperation of chaperons such as Hsp70 and Hsp90 as well as other cofactors. The proteasome pathway can only degrade proteins in a non-aggregated state. Aggregated proteins are removed by autophagy and lysosomal/vacuolar degradation [START_REF] Kim | Molecular chaperone functions in protein folding and proteostasis[END_REF].

The gene expression process is well controlled at every step so that mean protein levels in the population are stable under constant environment. Nevertheless the protein level varies at the single cell level even in isogenic population and in fixed environment [START_REF] Raser | Noise in gene expression: origins, consequences, and control[END_REF]. This gene expression variability from cell-to-cell, also called "gene expression noise", confers phenotypic heterogeneity in the population, which might play a role in adaptation to changing environments and cell fate decisions [START_REF] Raj | Nature, nurture, or chance: stochastic gene expression and its consequences[END_REF]). Next we discuss origins and consequences of gene expression noise.

Gene expression noise

In an isogenic population, the expression level of every gene is variable from cell to cell even under constant environments. The term 'noise' is used to indicate this heterogeneity of gene expression levels. The stochastic phenomena underlying many cellular processes have attracted interests for many years because of their implications for cellular regulation and non-genetic heterogeneity, but the limits of the technologies allowing single cell analysis were the main obstacle for researchers to study them. Thanks to advances in techniques which enable precise and quantitative measurements of gene expression levels in single cells, the basic mechanisms underlying these phenomena and their consequences can be deciphered.

Gene expression noise refers to the gene expression variability in an isogenic population. To quantify the level of noise, it is important to take into account the effect of the mean expression value.

Several values are used to quantify gene expression noise among a population [START_REF] Chalancon | Interplay between gene expression noise and regulatory network architecture[END_REF]: a) Coefficient of variation (CV): standard deviation (σ) divided by mean value (µ); b) CV 2 : variance (σ 2 ) divided by the squared mean (µ 2 ); c) Noise strength (Fano factor): variance (σ 2 ) divided by mean (µ) [START_REF] Thattai | Intrinsic noise in gene regulatory networks[END_REF].

The relationships between noise and mean value will be discussed later. In this dissertation, we will use CV or CV 2 to quantify gene expression noise. CV is the most direct and unambiguous measurement of noise [START_REF] Kaern | Stochasticity in gene expression: from theories to phenotypes[END_REF], while CV 2 better reflects than the CV how large the standard deviation is compared to the mean expression level [START_REF] Chalancon | Interplay between gene expression noise and regulatory network architecture[END_REF]. The noise strength could reveal some trends obscured by the dependence of noise to mean level [START_REF] Kaern | Stochasticity in gene expression: from theories to phenotypes[END_REF]. For example, from the simulation of [START_REF] Kaern | Stochasticity in gene expression: from theories to phenotypes[END_REF], noise decreases when mean level is increased by varying either transcription rate or translation efficiency, but noise strength increases when mean level is increased by varying translation efficiency.

Sources of noise

Gene expression noise can be divided into two components: intrinsic noise which is due to stochastic events during the gene expression process and affect the expression of every gene independently and differently, and extrinsic noise which is due to any existing cell-to-cell heterogeneity in cellular components involved in the global gene expression process in the cell and affect the expression of all genes in a given cell [START_REF] Swain | Intrinsic and extrinsic contributions to stochasticity in gene expression[END_REF]. Details about the sources of these two types of noise are given in the next section.

Using double fluorescent reporter systems, the contribution of each noise component can be deciphered [START_REF] Elowitz | Stochastic gene expression in a single cell[END_REF]. In yeast, [START_REF] Raser | Control of stochasticity in eukaryotic gene expression[END_REF] constructed diploid yeast strains that express both Cyan and Yellow Fluorescent Proteins (CFP and YFP) from identical promoters, integrated at the same locus on homologous chromosomes, to distinguish intrinsic noise and extrinsic noise (Figure 13). In S. cerevisiae, the contribution of intrinsic noise and extrinsic noise to the total noise is variable and depends on the gene. The GAL1 and PHO84 promoters have small and constant intrinsic noise, while the PHO5 promoter has larger basal intrinsic noise that decreases when gene expression level increases [START_REF] Raser | Control of stochasticity in eukaryotic gene expression[END_REF]). [START_REF] Colman-Lerner | Regulated cell-to-cell variation in a cell-fate decision system[END_REF] showed that the expression variability of the α-factor response gene PRM1 mainly comes from differences among cells in the signal transition pathway which affect most genes (extrinsic noise). Moreover extrinsic noise is the main component for abundant protein while extrinsic and intrinsic noise are equal for proteins of intermediate abundance [START_REF] Bar-Even | Noise in protein expression scales with natural protein abundance[END_REF]). Finally, Zhang et al. (2013) showed that intrinsic noise is dominant for the HO promoter. Thus, the level of intrinsic noise is gene-specific or promoter-specific and it is a heritable trait.

Recently, [START_REF] Rhee | Noise decomposition of intracellular biochemical signaling networks using nonequivalent reporters[END_REF] proposed a method to decompose noise by nonequivalent reporters.

They used two different reporters in the same signaling pathway. The signaling pathway can be simplified as: signal → common upstream L → different reporters (X and Y). Then the total noise can be decomposed as trunk noise and branch noise. Trunk noise is the noise introduced to L, whereas branch noise is the noise introduced to X and Y respectively. When equivalent reporters are used in this method, the trunk noise is the same as extrinsic noise, and the average of two branch noise is the same as intrinsic noise.

Origins of intrinsic noise

Intrinsic noise is due to the inherent randomness of biochemical processes such as transcription and translation. These processes involve proteins that can be at low cellular concentrations, therefore exhibiting stochastic variations from cell-to-cell. More generally, stochastic fluctuations in production and degradation of mRNA and proteins generate intrinsic noise. Nevertheless transcription is by far the most studied process for its contribution to intrinsic noise.

i) Promoter sequence

The transcription machinery assembly at a promoter site can be perturbed in many ways, therefore causing intermittent and random bursts in mRNA production, especially for highly expressed genes. These successive bursts of mRNA production can largely influence gene expression noise [START_REF] Hebenstreit | Are gene loops the cause of transcriptional noise?[END_REF][START_REF] Chalancon | Interplay between gene expression noise and regulatory network architecture[END_REF]. The statistics of mRNA expression can be described by two parameters: burst frequency (the frequency at which bursts occur) and burst size (the average number of mRNA produced within each burst). In theory, increasing burst size would increase mean expression without changing noise, whereas increasing burst frequency would increase mean expression and decrease noise [START_REF] Hornung | Noise -mean relationship in mutated promoters[END_REF]. Promoter structure affects transcriptional bursting and noise.

Recently, researches in E. coli showed that bacterial promoter structure dictates intrinsic transcriptional noise [START_REF] Jones | Promoter architecture dictates cell-to-cell variability in gene expression[END_REF]. The authors have systematically varied promoter strength, transcription factor binding strength, and transcription factor copy numbers in a set of promoters to observe how these changes affected variability in gene expression. In bacteria, it also appears that transcriptional bursting is strongly linked to the DNA supercoiled state [START_REF] Chong | Mechanism of Transcriptional Bursting in Bacteria[END_REF]. Positive supercoiling buildup on a DNA segment by transcription slows down transcription elongation and eventually stops transcription initiation. Nevertheless many studies on transcriptional-mediated noise also exist on eukaryotic cells, especially on yeast promoters.

a) TATA box

Systemic analysis of the yeast GFP-tagged library showed that TATA-box containing genes are noisier than the others [START_REF] Newman | Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise[END_REF]. Also, mutations in the TATA-box of the PHO5 promoter that weaken its strength decrease the noise level [START_REF] Raser | Control of stochasticity in eukaryotic gene expression[END_REF]. Same results are observed with the GAL1 promoter [START_REF] Blake | Phenotypic consequences of promoter-mediated transcriptional noise[END_REF]. It is suggested that the relationships between TATA box strength and noise (stronger TATA box confers higher noise) is due to its effects on the stability of the pre-initiation complex (PIC) that influences the burst size (Sanchez et al, 2013) (Sanchez et al, 2013). The deletion of any component of chromatin remodeling complexes and mutations in UAS sites that affect the recruitment of these complexes both increase the intrinsic noise of the PHO5 promoter, because they prevent the removal of nucleosomes in the promoter region during activation [START_REF] Raser | Control of stochasticity in eukaryotic gene expression[END_REF].

Using electron microscopy, [START_REF] Brown | Linking Stochastic Fluctuations in Chromatin Structure and Gene Expression[END_REF] have studied the different structural nucleosome configurations of these two states of the PHO5 promoter, indicating that gene expression fluctuations are related to promoter nucleosome dynamics. Furthermore, they have observed the nucleosome configuration of two conjugated PHO5 promoters, showing that this configuration is intrinsically variable and can be a cause of expression noise [START_REF] Brown | Linking Stochastic Fluctuations in Chromatin Structure and Gene Expression[END_REF]. Single-cell nucleosome mapping found significant cell-to-cell variation in nucleosome positions during the induction of PHO5 [START_REF] Small | Single-cell nucleosome mapping reveals the molecular basis of gene expression heterogeneity[END_REF]), suggesting that an underlying complexity of nucleosome positioning may contribute to the flexibility and heterogeneity of gene expression.

Recently, [START_REF] Sharon | Inferring gene regulatory logic from high-throughput measurements of thousands of systematically designed promoters[END_REF], using thousands of designed promoters, showed that poly nucleosome-disfavouring sequences (specifically, poly(dA:dT) tracts that are highly prevalent in eukaryotic promoters) result in lower noise through affecting the transcription burst frequency, what is consistent with the results of [START_REF] Dadiani | Two DNA-encoded strategies for increasing expression with opposing effects on promoter dynamics and transcriptional noise[END_REF]. Also, computational analyses of genomic nucleosome occupation patterns found that promoters containing nucleosome binding sites close to the TSS have large cell-to-cell variability in gene expression [START_REF] Tirosh | Promoter architecture and the evolvability of gene expression[END_REF]. Otherwise, essential genes which should be less noisy tend to be clustered in nucleosome-depleted open-chromatin regions on the chromosome [START_REF] Batada | Evolution of chromosome organization driven by selection for reduced gene expression noise[END_REF]. Thus genes in open chromatin regions where nucleosomes are depleted have lower noise whereas genes covered by nucleosomes have higher noise (Sanchez et al, 2013).

Moreover, the nucleosome remodeling dynamics can affect the transition from TF signal input to expression output, with effects on expression noise. [START_REF] Hansen | Promoter decoding of transcription factor dynamics involves a trade-off between noise and control of gene expression[END_REF]

c) Transcription factor binding sites

Transcription factor (TF) binding site strength, number and position also have an influence on promoter-mediated noise. [START_REF] To | Noise can induce bimodality in positive transcriptional feedback loops without bistability[END_REF] inserted different number (1 or 7) of the tettranscriptional activator (tTA) binding site (tetO) into a synthetic yeast promoter and found that the 7XtetO promoter exhibited higher noise than the 1XtetO promoter. Recently, same results were obtained by [START_REF] Sharon | Inferring gene regulatory logic from high-throughput measurements of thousands of systematically designed promoters[END_REF]. They tested thousands of artificial promoters with different numbers of nucleosome disfavoring sequences and different numbers of Gcn4 binding sites and found that, for a given expression level, promoters that contained more Gcn4 binding sites were noisier. This phenomenon might be due to stochasticity of TF binding and falling off the promoter (Sanchez et al, 2013).

Moreover, TF binding site position can also affect gene expression noise. [START_REF] Octavio | Epigenetic and conventional regulation is distributed among activators of FLO11 allowing tuning of population-level heterogeneity in its expression[END_REF] put tetO at different positions within the FLO11 promoter and showed that promoters with tetO close to the TATA box have lower noise than the others which is consistent with results on the GAL1 promoter [START_REF] Murphy | Combinatorial promoter design for engineering noisy gene expression[END_REF].

One promoter can also have different types of binding sites for the same TF. [START_REF] Carey | Promoter sequence determines the relationship between expression level and noise[END_REF] showed an interesting expression profile of ZRT2 expression which is regulated by Zap1. As Zap1 expression level increases, ZRT2 expression level first increases and then decreases, while its noise level first decreases and then remains constant. Thus at the same mean expression level, the distribution at high induction is less noisy than at low induction. It can be explained by the fact that the ZRT2 promoter has two different Zap1 binding sites, an activating binding site and a repressive binding site.

At low induction, the activation site increases the expression level by increasing burst frequency and thus decreases noise whereas at high induction, the repression site near the TATA box decreases ZRT2 expression through burst size reduction that does not change noise level and leads to lower expression with lower noise.

ii) Chromosomal structure

a) Chromatin epigenetics

Chromatin epigenetics which includes histone modifications and DNA methylation that can be removed and added to change the state of a promoter (on/off) contributes to gene expression noise [START_REF] Chalancon | Interplay between gene expression noise and regulatory network architecture[END_REF].

In diploid yeast cells, the two copies of FLO11 have random epigenetic states that change independently and lead to expression heterogeneity in the population [START_REF] Octavio | Epigenetic and conventional regulation is distributed among activators of FLO11 allowing tuning of population-level heterogeneity in its expression[END_REF]. Finally, expression heterogeneity linked to chromatin epigenetics can be inherited during several cell cycles (a phenomenon called memory) and then become stochastic in the progeny cells [START_REF] Kaufmann | Heritable stochastic switching revealed by single-cell genealogy[END_REF]. Zhang et al. (2013) showed that HO promoter states in a mutated yeast strain (swi5) where its expression profile is bimodal are inherited during one cell generation. By mutation analysis, they suggested that this memory is related to histone acetylation in the promoter region. Recently, researches on human cells showed that this memory also depends on mRNA and protein stability [START_REF] Corre | Stochastic fluctuations and distributed control of gene expression impact cellular memory[END_REF].

b) Chromosomal position

Gene position on chromosomes also has important effects on noise. The position can affect gene expression activation rate of low abundance mRNA and produce remarkable variability in the population [START_REF] Becskei | Contributions of low molecule number and chromosomal positioning to stochastic gene expression[END_REF]. [START_REF] Anderson | Silencing Is Noisy: Population and Cell Level Noise in Telomere-Adjacent Genes Is Dependent on Telomere Position and Sir2[END_REF] studied the expression profile of TLO genes at different chromosome positions in Candida albicans. They found that TLO genes in sub-telomeric regions where genes are highly silenced by chromatin modifications exhibit higher noise levels than in chromosome internal regions and that the deletion of the key silencing gene SIR2 can reduce the noise level from sub-telomeric regions.

c) Gene loop

Gene loop makes its promoter and terminator spatially close to each other and form a 'bridge' for the polymerase to join the PIC immediately after transcription termination. This phenomena can cause transcription bursting which is a major source of expression noise in eukaryotes [START_REF] Hebenstreit | Are gene loops the cause of transcriptional noise?[END_REF].

iii) Others

In theory, all the steps of the gene expression process can have effects on intrinsic noise, such as nuclear architecture, translation rates, mRNA degradation rates and protein degradation rates [START_REF] Chalancon | Interplay between gene expression noise and regulatory network architecture[END_REF]. [START_REF] Blake | Noise in eukaryotic gene expression[END_REF] have shown that, under full transcription efficiency, translation efficiency has little effects on noise while with partial transcription, high translation efficiency leads to high noise. Noise arising from transcription contributes more than noise generated at the translational level in eukaryotes, while translational level is the dominant source of expression noise in prokaryote because of the translational burst from individual transcripts [START_REF] Ozbudak | Regulation of noise in the expression of a single gene[END_REF].

Origins of extrinsic noise i) Variable availability of machineries

The gene expression process depends on the abundance of expression machineries in the cell, such as RNA polymerases or ribosomes, and this abundance varies a lot in the population, having great effects on global noise [START_REF] Raj | Nature, nurture, or chance: stochastic gene expression and its consequences[END_REF]. The number of ribosomes can affect translation efficiency, and some researchers showed that translation efficiency has remarkable influences on gene expression noise [START_REF] Guimaraes | Transcript level and sequence determinants of protein abundance and noise in Escherichia coli[END_REF][START_REF] Salari | Teasing apart translational and transcriptional components of stochastic variations in eukaryotic gene expression[END_REF]. [START_REF] Yang | Contribution of RNA polymerase concentration variation to protein expression noise[END_REF] constructed an interesting system in E. coli where they can follow the abundance of T7 RNAP by fusing YFP to the protein and also can follow gene expression controlled by T7 RNAP through fusion of CFP and mCherry to T7 promoters.

The results showed that extrinsic noise is correlated to the variation of RNAP abundance whereas intrinsic noise is independent to it. Moreover [START_REF] Johnston | Mitochondrial variability as a source of extrinsic cellular noise[END_REF] proposed a model explaining that differences of volume and functionality of mitochondria among cells strongly affect expression variability.

ii) Noise propagation

The intrinsic noise of upstream regulators can propagate to downstream targets as a source of extrinsic noise [START_REF] Chalancon | Interplay between gene expression noise and regulatory network architecture[END_REF]. Increasing expression noise of a TF raises expression variability of its target genes in yeast [START_REF] Blake | Noise in eukaryotic gene expression[END_REF]. [START_REF] Hooshangi | Ultrasensitivity and noise propagation in a synthetic transcriptional cascade[END_REF] [START_REF] To | Noise can induce bimodality in positive transcriptional feedback loops without bistability[END_REF]. [START_REF] Stewart-Ornstein | Cellular noise regulons underlie fluctuations in Saccharomyces cerevisiae[END_REF] performed systematic pair-wise correlation analysis demonstrating noise transmission from upstream factors to several downstream genes along their regulation pathway. But researches on HeLa cells showed that propagation of noise from TFs to their targets is blocked by formation of heterodimers and chromatin compaction [START_REF] Shah | Barriers to transmission of transcriptional noise in a c-fos c-jun pathway[END_REF].

Otherwise, fluctuations of metabolism genes can lead to growth fluctuations in the population, and furthermore can cause fluctuations of other genes which have no functional interactions between them [START_REF] Kiviet | Stochasticity of metabolism and growth at the single-cell level. Nature Kravtsova-Ivantsiv Y & Ciechanover A[END_REF]. The authors studied fluctuations of the lac operon genes induced by different concentrations of IPTG in the medium using lactulose as carbon source in E. coli which was metabolized by lac genes but didn't induce this operon. They found that fluctuations of the lac genes can cause growth fluctuations which leads to increased expression variability of unrelated genes.

iii) Asymmetric partitioning during cell division

During cell division, segregation of mRNAs and proteins is not equal (asymmetric partitioning), and thus causes heterogeneity between mother and daughter cells. Few studies focused on this aspect and most of stochastic gene expression models consider cell division with symmetric partitioning, while [START_REF] Huh | Non-genetic heterogeneity from stochastic partitioning at cell division[END_REF] proposed a stochastic model including asymmetric partitioning during cell division. Their in silico simulation shows that random segregation is a great source of extrinsic noise in gene expression.

iv) Cell cycle

In a population, cells are in different cell cycle phases in which global transcription and translation activities are different, thus cell cycle is an important extrinsic source of expression noise. [START_REF] Colman-Lerner | Regulated cell-to-cell variation in a cell-fate decision system[END_REF] constructed a yeast strain that can be arrested at the G2/M transition and showed that arrested cells reduced total noise by 45%. Moreover, a synthetic noisy promoter (7XtetO) in yeast showed that expression noise is driven by differences in transcription rates between G1 and S/G2/M [START_REF] Zopf | Cell-Cycle Dependence of Transcription Dominates Noise in Gene Expression[END_REF]).

v) Fluctuations micro-environment

The micro-environment encountered by microorganisms in their culture medium is not totally homogeneous and might lead to different global expression noises [START_REF] Chalancon | Interplay between gene expression noise and regulatory network architecture[END_REF].

Interestingly, in multicellular organisms, the micro-environment can be changed via paracrine signal, which further regulates gene expression noise. [START_REF] Shalek | Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells[END_REF] used single-cell RNA-seq to show that paracrine signaling can affect cellular expression heterogeneity through cell-to-cell communication in mouse bone-marrow-derived dendritic cells.

vi) Cell volume

Recently, [START_REF] Kempe | The volumes and transcript counts of single cells reveal concentration homeostasis and capture biological noise[END_REF] took into account the effects of cell volume to expression noise in human cells. They found that the variability of mRNA concentration (mRNA number divided by cell volume) is due to different gene expression activities among cells, while the noise of mRNA number is much larger because of the variance in cell volume.

Expression noise is a quantitative trait

As for the mean expression level of genes, quantitative trait studies can be used to identify the genetic determinants of noise between different yeast strains. Quantitative trait studies combine the genetic information and phenotypic information from different strains to identify the regions or loci that might contribute to a quantitative strait (referred as Quantitative Trait Loci (QTL)) through statistic tools. [START_REF] Ansel | Cell-to-cell stochastic variation in gene expression is a complex genetic trait[END_REF] first mapped 3 QTLs affecting pMET17-GFP expression noise in yeast using BY×RM F1 segregants and introgression. They found that the defective uracil metabolism increases expression noise of pMET17-GFP by impairing transcription elongation. Furthermore, successive backcrosses have been performed by selecting clones with higher noise and unaffected mean value [START_REF] Fehrmann | Natural sequence variants of yeast environmental sensors confer cell-to-cell expression variability[END_REF]. After 7 generations, 3 high noise segregants were generated. By genome sequencing, four loci which contribute to high noise without changing mean expression level were identified. The authors defined these loci as expression Probabilistic Trait Loci (ePTL) that can change the probability that an individual displays a given expression level without necessarily changing the mean expression value of all individuals carrying the same genotype. Finally, they finely mapped 2 loci:

one with a shifted mutation in the Erc1p transmembrane transporter in BY which reduces expression variability of pMET17-GFP and one with cis-regulatory polymorphisms in the MUP1 methionine permease gene in RM which increase expression of MUP1 in cis and expression variability of pMET17-GFP in trans.

Thanks to the advances in high throughput next-generation technologies, QTL analysis provides a powerful tool to study the effects of genetic variants on expression noise. But these studies can only identify effects in trans which come from upstream elements in pathways related to the expression of the gene studied. They cannot identify cis-effects coming from promoter sequences.

Relationship between noise and mean expression levels

In general, high expression level is associated with low noise. This correlation has been verified

with different promoters and in several conditions in yeast. Figure 14 gives some examples of noise versus mean plots. The first graph (A) shows the yeast GFP-tagged library [START_REF] Newman | Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise[END_REF]. It contains thousands of proteins fused to GFP in C-terminal and under the control of their native promoter. The second graph (B) represents the natural ZRT1 promoter fused to GFP [START_REF] Carey | Promoter sequence determines the relationship between expression level and noise[END_REF] induced by different concentration of Zn 2+ . The third graph (C) shows mutated ERG6 promoters fused to GFP [START_REF] Hornung | Noise -mean relationship in mutated promoters[END_REF]. Finally, the fourth graph (D) represents the synthetic promoter p4XUPRE

(the CYC1 promoter with four UPRE (unfolded protein response element)) motifs fused to GFP [START_REF] Zuleta | Dynamic characterization of growth and gene expression using high-throughput automated flow cytometry[END_REF] and induced by different concentration of tunicamycin. The noise / mean relationship can be defined as , where is the noise, the mean expression value, the bust size, the extrinsic noise [START_REF] Hornung | Noise -mean relationship in mutated promoters[END_REF]. But this formula is only correct when the expression level is low or medium. For high expression levels, extrinsic noise is dominant and the total noise is more stable when the mean increases [START_REF] Zuleta | Dynamic characterization of growth and gene expression using high-throughput automated flow cytometry[END_REF], as shown in graph A and D. This is the global tendency between expression noise and mean expression level. We can decouple these two elements through genetic modifications in the promoter. The modifications that only increase burst frequency increase expression level and decrease noise level whereas the modifications that only increase burst size will increase expression level without changing noise level.

Expression noise versus expression plasticity

Expression noise is the expression variability of one gene among different cells in an isogenic population, while expression plasticity is the expression variability of one gene among different conditions. These two phenomena have common correlations as the ones with strong TATA box containing promoters and high nucleosome occupancy promoters. Thus it is proposed that they might be highly coupled [START_REF] Tirosh | Promoter architecture and the evolvability of gene expression[END_REF]. Some genes have been shown to exhibit coupling between expression noise and plasticity [START_REF] Blake | Phenotypic consequences of promoter-mediated transcriptional noise[END_REF][START_REF] Anderson | Silencing Is Noisy: Population and Cell Level Noise in Telomere-Adjacent Genes Is Dependent on Telomere Position and Sir2[END_REF].

In contrast, large analyses of yeast expression data showed that the coupling of noise and plasticity is not a general rule [START_REF] Lehner | Conflict between noise and plasticity in yeast[END_REF][START_REF] Bajić | Balancing noise and plasticity in eukaryotic gene expression[END_REF]. It is linked to the property of particular promoter architectures, and also under evolutionary constraints. Noise and plasticity are uncoupled for essential genes and gene duplication favours the coupling [START_REF] Lehner | Conflict between noise and plasticity in yeast[END_REF]. The relationship between noise and plasticity is more complicated than expected, depending on the regulation strategy of the genes. As shown in Figure 15, TATA box containing or SAGA regulated promoters together with nucleosome-covered promoters have high noise and plasticity, whereas poor translational regulated genes with low translational efficiency have low noise and plasticity. Short genes with high translational efficiency exhibit high noise but low plasticity. Some growth-related genes that are dependent on TAF1 exhibit low noise but high plasticity [START_REF] Bajić | Balancing noise and plasticity in eukaryotic gene expression[END_REF]. Recently, similar results have obtained in E.coli [START_REF] Singh | Coupling Between Noise and Plasticity in Escherichia coli. G3 (Bethesda).: g3.113.008540[END_REF], showing that the evolution and selection for coupling of noise and plasticity are common in different organisms.

Bimodal expression

In general the distribution of gene expression levels in a population has only one peak (Figure 16, A). It means that the majority of the population is around the mean value. But some genes can have a bimodal expression profile (Figure 16B) where the distribution has two peaks. Thus bimodal expression confers high noise. This expression pattern makes the population having two highly different subpopulations that can have very different behaviours. This phenomenon has been found in bacteria (Silva-Rocha & de Lorenzo, 2012), yeast [START_REF] Pelet | Transient activation of the HOG MAPK pathway regulates bimodal gene expression[END_REF] and mammalian [START_REF] Shalek | Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells[END_REF],

showing that it might be a common evolutionary strategy.

Figure 16: Unimodal and bimodal expression profiles in GAL1 expression in yeast [START_REF] Kar | Stochastic galactokinase expression underlies GAL gene induction in a GAL3 mutant of Saccharomyces cerevisiae[END_REF] The underlying mechanism of this pattern is still not clear. First, high noise in an upstream regulator can lead to bimodal expression of downstream outputs. Using an artificial regulation system in yeast, [START_REF] Blake | Noise in eukaryotic gene expression[END_REF] first observed bimodal expression in early induction stages, but the population returned to unimodal in later stages. Then by increasing the expression noise of the input regulator, they observed stable bimodal expression during long-term induction. Similarly, [START_REF] To | Noise can induce bimodality in positive transcriptional feedback loops without bistability[END_REF] achieved bimodal output by increasing input noise through adding more TF sites in the promoter of a regulator. Recently, it was found that GAL1 has a bimodal expression pattern in the yeast gal3Δ strain when induced by galactose [START_REF] Kar | Stochastic galactokinase expression underlies GAL gene induction in a GAL3 mutant of Saccharomyces cerevisiae[END_REF] (Figure 16). This pattern is caused by the lognormal distribution of Gal4p. Second, intermediate stimulus can induce bimodal expression in yeast.

Under the stimulus of 0.1 M NaCl, the STL1 promoter exhibits bimodal pattern expression in yeast [START_REF] Pelet | Transient activation of the HOG MAPK pathway regulates bimodal gene expression[END_REF]. Moreover, being pretreated with 8 µM ATc, an artificial regulation network exhibits bimodal output when the carbon source was changed from glucose to galactose, while without pretreatment or with 250 µM ATc, it exhibits unimodal output [START_REF] Wu | Identification of rad27 Mutations That Confer Differential Defects in Mutation Avoidance, Repeat Tract Instability, and Flap Cleavage[END_REF]. In mammalian cells, key immune genes are already expressed in bimodal pattern under normal stress [START_REF] Shalek | Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells[END_REF].

Other bimodal expression patterns are found in specific conditions. For example, Silva-Rocha and de Lorenzo (2012) showed that mixed carbon sources medium (succinate and m-xylene) induces the bimodal expression of Pu and Pm (promoters induced by m-xylene) in Pseudomonas putida mt-2. Also, antisense transcription of PHO84 can lead to bimodal expression of its sense transcription in yeast [START_REF] Castelnuovo | Bimodal expression of PHO84 is modulated by early termination of antisense transcription[END_REF].

Evolution of gene expression noise

Gene expression noise is a gene special traits under natural selection and evolution. [START_REF] Newman | Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise[END_REF] systemically measured the expression noise of the yeast GFP tagged genes library and showed that house-keeping genes have lower noise while stress related genes have higher noise.

Further analysis of their results showed that genes which is essential for the proliferation tend to keep low expression noise [START_REF] Lehner | Selection to minimise noise in living systems and its implications for the evolution of gene expression[END_REF]. Recently Metzger et al.( 2015) compared the expression profile of 85 nature variants of TDH3 promoter and 236 point mutations in the same promoter and showed that the TDH3 promoter is undergoing natural selection to minimize noise. These researches suggested that essential genes are subjected to purifying selection to maintain low expression noise.

In contrast, positive selection for high noise are also observed for some genes. [START_REF] Zhang | Stochastic expression and epigenetic memory at the yeast HO promoter[END_REF] reanalyzed the data of [START_REF] Newman | Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise[END_REF] and showed that plasma-membrane transporter genes exhibit elevated expression noise which might confer some advantages under certain conditions such as fluctuant and unpredictable environments where higher noise can confer better growth with less cost. Artificial system in E. coli showed the evolution towards higher noise under stressful conditions while more evidence should be found in yeast.

Measurement of noise

The measurement of noise strongly relies on advances in single-cell technologies allowing simple and accurate expression measurement in individual cells. Gene expression noise studies need tools to detect the amount of molecules (mRNA or protein) in single cells. But considering the limited number of these molecules and the necessary sampling volume to get accurate statistical results, these methods must have capacities of high-resolution and high-throughput [START_REF] Ohno | Protein Expression Analyses at the Single Cell Level[END_REF]. Recent advances have made possible the detection of single molecules in single cells, and the analysis of thousands of individual cells during one experiment [START_REF] Golding | Real-time kinetics of gene activity in individual bacteria[END_REF][START_REF] Huang | Counting low-copy number proteins in a single cell[END_REF][START_REF] Castelnuovo | Bimodal expression of PHO84 is modulated by early termination of antisense transcription[END_REF]. Besides high sensitivity, reproducibility among biological and technical repeats is also critical [START_REF] Lidstrom | The role of physiological heterogeneity in microbial population behavior[END_REF]. Combining requirements of high-throughput and minimization of experimental handling errors, automated manipulation systems are preferred [START_REF] Newman | Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise[END_REF][START_REF] Little | Precise Developmental Gene Expression Arises from Globally Stochastic Transcriptional Activity[END_REF][START_REF] Zuleta | Dynamic characterization of growth and gene expression using high-throughput automated flow cytometry[END_REF]. Ultimately, single cell methods are preferably non-destructive so that they can follow the dynamics of living cell to dissect the temporal property of gene expression profiles [START_REF] Rosenfeld | Gene regulation at the single-cell level[END_REF][START_REF] Huang | Counting low-copy number proteins in a single cell[END_REF]. Besides, simultaneous multiparameter analysis is the trend of single cell methods [START_REF] Lidstrom | The role of physiological heterogeneity in microbial population behavior[END_REF]: it can be used to understand the regulation network of expression heterogeneity or be used to combine phenotypic variability analysis with gene expression noise measurement.

Single-cell manipulation

Flow cytometry and microscopy

In the research field on gene expression noise, flow cytometry and microscopy that can detect fluorescence signals in individual cells play a very important role that is hard to overstate. Both of them need to target specific molecules by fluorescence-tagging. Also, they allow multi-parameter analysis through tagging of several targets with different fluorescence markers [START_REF] Spiller | Measurement of single-cell dynamics[END_REF].

i) Flow cytometry

The main advantage of flow cytometry is its high-throughput capacity. Moreover, sample preparation is generally easy. The traditional flow cytometry consists of 3 key systems: the fluidic system, the optic system and the electronic system. The fluidic system drives cells to the narrow "core" of a capillary, where the fluorophores can be excited, by hydrodynamic forces produced by flowing sheath fluid surrounding the injected samples. The optical system allows excitement of the fluorophores by lasers at precise wavelengths and collects emission fluorescence (or scatter lights). The electronic system converts optical signals into electric signals that can be treated by the computer [START_REF] Shapiro | Practical Flow Cytometry[END_REF]. Thus flow cytometry can be used to measure the level of target fluorescences (reflecting the amount of target molecules), the forward scatter (reflecting cell size) and the side scatter (reflecting cell complexity). New technologies have been applied to improve the capacity, sensitivity and stability of flow cytometry, such as the Attune® acoustic focusing cytometer from Life Technologies which uses ultrasonic waves to position cells into a single, focused line along the central axis of the capillary and thus reduces the variability due to cell positioning in the capillary [START_REF] Picot | Flow cytometry: retrospective, fundamentals and recent instrumentation[END_REF]. Other new systems have been developed by combining flow cytometry with other technologies. For instance, the Amnis® Corporation developed the imaging flow cytometry [START_REF] George | Distinguishing modes of cell death using the ImageStream multispectral imaging flow cytometer[END_REF] combining highspeed multi-spectral imaging with flow cytometry. This system can detect the fluorescence level and take a picture of the cell at the same time, thus combining physiological and morphological data.

ii) Microscopy

Using fluorescent microscopy, the abundance of the labeled target molecules can be measured by the optical intensity of fluorescence spots [START_REF] Elowitz | Stochastic gene expression in a single cell[END_REF][START_REF] Ozbudak | Regulation of noise in the expression of a single gene[END_REF]. The advantage of microscopy is that it can provide the spatial distribution of the target molecule in the cell. Also, it can provide cell shape information as well as the cell cycle state (in yeast) [START_REF] Zopf | Cell-Cycle Dependence of Transcription Dominates Noise in Gene Expression[END_REF]. An important application is the time-lapse microscopy which regularly records pictures of selected clones along the experimental procedure, thus providing the temporal dynamics of gene expression [START_REF] Sanchez | Genetic Determinants and Cellular Constraints in Noisy Gene Expression[END_REF]. The recent advances in microscopy technologies are mainly in the improvement of their resolution, for instance, spatial resolution has been beyond the optical diffraction limit [START_REF] Davis | The 'super-resolution' revolution[END_REF]. It is especially important to study expression noise at the mRNA level (see below).

Microfluidic system

Microfluidic systems have attracted more and more attention in the study of expression noise because they can combine multi-parameter measurement and single cell manipulation (de Vargas

Roditi & Claassen, 2015). Figure 17 gives two examples of microfluidic systems. On the left is a microfluidic system for imaging [START_REF] Cai | Frequency-modulated nuclear localization bursts coordinate gene regulation[END_REF]. Cells were kept in chambers to be recorded by microscopy for the fluorescence. On the right is the C1 TM Single Cell Auto Prep chip from Fluidigm that can capture only one cell in each chamber of the chip [START_REF] Shalek | Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells[END_REF]. Then the chip can be used for DNA-Seq or RNA seq for instance (see below for these methods).

A B

Figure 17: Examples of microfluidic systems

Microfluidic systems are not only used to capture single cell, they can also be used to perform long-term live cell imaging [START_REF] Raj | Single-molecule approaches to stochastic gene expression[END_REF]) that allow studying the response dynamics of the target by adding different stimuli in the system. An important advantage of microfluidic systems to study expression noise is that they can reduce environmental fluctuations [START_REF] Spiller | Measurement of single-cell dynamics[END_REF], because the chambers are very small and each chamber carries out the same stimulation at the nanoliter or even picoliter scale. [START_REF] Hansen | Promoter decoding of transcription factor dynamics involves a trade-off between noise and control of gene expression[END_REF] used a microfluidic system to study the transition from environmental signals to expression outputs and found that slowly activated promoters exhibit high noise response expression.

RNA level

In eukaryotic cells, expression heterogeneity mainly comes from the transcription process [START_REF] Blake | Noise in eukaryotic gene expression[END_REF][START_REF] Ochiai | Stochastic promoter activation affects Nanog expression variability in mouse embryonic stem cells[END_REF]. Thus, it is interesting to carry out research at the mRNA level. mRNA expression noise analysis uses methods for studying mRNA at the single cell level. Here two types of methods are discussed: visualizing methods (based on fluorescence tagging and microscopy) and nonvisualizing methods (based on single cell cDNA synthesis and sequencing).

Visualizing methods

Visualizing methods are indirect methods where the number of target mRNA molecules is measured by fluorescent reporter molecules binding to the target. There are two major tagging systems to visualize mRNA: the MS2 system and the fluorescent in situ hybridization system (FISH).

i) MS2 system

MS2 is a bacteriophage whose coat proteins strongly bind to a particular RNA hairpin [START_REF] Raj | Single-molecule approaches to stochastic gene expression[END_REF]. This system needs two steps of recombination. First, the coat protein gene fused to the GFP gene is integrated into the genome and expressed under a constitutive promoter. Second, several repeated RNA hairpins are integrated into the 5'-untranslated region of the target gene. Finally, the target mRNA produced by RNAPII is bound by the GFP-tagged protein through the hairpin structures, and thereby can be visualized by fluorescence microscopy. [START_REF] Golding | Real-time kinetics of gene activity in individual bacteria[END_REF] using this method in E. coli demonstrated that the artificial promoter Plac/ara (which is induced by IPTG and arabinose) stochastically switched between an active and an inactive state. Recently, Ochiai et al.

(2014) constructed this system in mouse embryonic stem cells to study the expression profile of the important transcription factor Nanog. They found that the Nanog gene is stochastically activated to be transcribed and that this 'intrinsic noise' contributed to 45% of the total expression variability.

ii) RNA-FISH

This method visualizes target mRNA molecules through fluorescent labeled probes that specifically hybridize with the target in situ [START_REF] Junker | When noisy neighbors are a blessing: analysis of gene expression noise identifies coregulated genes[END_REF]. This method is widely applied to study expression noise. In yeast, RNA-FISH has been used to reveal the expression profile of several genes [START_REF] Zenklusen | Single-RNA counting reveals alternative modes of gene expression in yeast[END_REF]. Constitutively expressed genes exhibit smaller than expected noise, whereas a SAGA-regulated gene, PDR5, has larger expression variability through transcription bursting. More recently, [START_REF] Zopf | Cell-Cycle Dependence of Transcription Dominates Noise in Gene Expression[END_REF] studied expression from an artificial promoter 7texO in yeast at different cell cycle stages. They found that the different transcription rates among the different cell cycle stages largely contribute to expression variability.

Non-visualizing method

Non-visualizing methods are methods where absolute mRNA concentrations in the cell can be measured. Here we discuss two major methods: mRNA-sequencing and RT-qPCR.

i) mRNA-sequencing

Thanks to advances in high-throughput next generation sequencing, single cell mRNA-seq can be carried out to study gene expression noise in a population. The first challenge of single cell RNA-seq is how to capture single cells. [START_REF] Shalek | Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells[END_REF] first proposed fluorescence activated cell sorting (FACS) by flow cytometry. Single cells were sorted into 96 well-plates, and the plates were then subjected to regular mRNA extraction, cDNA synthesis and sequencing. This method was employed to study the response of mice bone-marrow-derived dendritic cells (BMDCs) to lipopolysaccharides. They found that immune system related genes exhibit bimodal expression after stimulation. Recently, the same group [START_REF] Shalek | Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells[END_REF] proposed a new method using a microfluidic system to capture single cells.

They studied how mice BMDCs respond to several conditions, showing that cell-to-cell communication contributes to expression variability. Obviously, the main advantage of single cell mRNA-seq is that it can simultaneously measure the concentration of all mRNA in the same experiment.

ii) RT-qPCR

Even if the cost of next generation sequencing is much cheaper than few years ago, considering the minimum sampling volume to get statistical reliable results of single cell RNA-seq, it is still very expensive. Thus, if only few genes have to be measured, single cell RT-qPCR is more appropriate. For instance, [START_REF] Bengtsson | Gene expression profiling in single cells from the pancreatic islets of Langerhans reveals lognormal distribution of mRNA levels[END_REF] used a micromanipulator to isolate single mice islet cells and carry out RT-qPCR on 5 genes for each cell. They found that gene expression levels in the population are lognormally distributed. [START_REF] Warren | Transcription factor profiling in individual hematopoietic progenitors by digital RT-PCR[END_REF] combined single cell sorting by FACS and digital RT-qPCR that uses a microfluidic device to achieve high throughput RT-qPCR analysis to study the expression profile of PU.1 and GAPDH in five hematopoietic precursors, showing different expression patterns in different cell types.

Protein level

Fluorescent proteins

The use of fluorescent proteins in the researches on gene expression noise is quite common. For instance, to study the effects of different promoter architectures on expression noise in yeast, a gene coding for a fluorescent protein can be directly fused to the promoter sequence [START_REF] Raser | Noise in gene expression: origins, consequences, and control[END_REF][START_REF] Sharon | Inferring gene regulatory logic from high-throughput measurements of thousands of systematically designed promoters[END_REF]. Of course, the fluorescence protein can be fused to the studied protein too [START_REF] Newman | Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise[END_REF][START_REF] Anderson | Silencing Is Noisy: Population and Cell Level Noise in Telomere-Adjacent Genes Is Dependent on Telomere Position and Sir2[END_REF]. One advantage of this method is that the protein amount can be followed in real-time experiments. New fluorescence proteins are still developed to enlarge the spectrum to achieve multi-parameter analysis or to increase detection capacity and sensitivity [START_REF] Ohno | Protein Expression Analyses at the Single Cell Level[END_REF].

β-galactosidase

β-galactosidase can hydrolyze synthetic fluorogenic substrates to produce various fluorescent products. A microfluidic system has been developed to determine the β-galactosidase expression pattern in E. coli, yeast and mouse cells showing its potential applications to study gene expression noise [START_REF] Cai | Stochastic protein expression in individual cells at the single molecule level[END_REF]. The fluorescent molecules produced by β-galactosidase are rapidly expelled from the cell and the fluorescence is measured inside a microfluidic chamber where single cells are captured. Thus, this method can reduce the risks of phototoxicity and autofluorescence, and be used to study low protein numbers.

Antibodies

The expression level of a protein can also be measured through a fluorescent labeled antibody.

This method does not need any gene recombination but it is highly affected by the recognition ability of the antibody. Moreover, cells have to be permeabilized to give the antibody access to the protein if it is not on the cell surface, thus this method cannot be used to perform real-time analysis. [START_REF] Huang | Counting low-copy number proteins in a single cell[END_REF] proposed this method to study the distribution of β2 adrenergic receptors expressed in insect cells (SF9), showing diverging expression levels in the population. [START_REF] Shi | Single-cell proteomic chip for profiling intracellular signaling pathways in single tumor cells[END_REF] also used protein antibody chips to study the expression pattern of genes involved in the PI3K signaling pathway in tumor cells showing notable cell-cell heterogeneity. [START_REF] Chang | Transcriptome-wide noise controls lineage choice in mammalian progenitor cells[END_REF] used antibodies to label the stem cell marker Sca-1 in mouse haematopoietic progenitor cells. They found marketable differences of Sca-1 level in the population that lead to different cell states that have different differentiation tendencies.

Cell free system

A cell free system is a cell-sized container where are placed all the necessary elements to achieve the studied biological process. Recently, two articles have been published using cell-free systems to study gene expression noise [START_REF] Karig | Probing cell-free gene expression noise in femtoliter volumes[END_REF][START_REF] Nishimura | Stochasticity in gene expression in a cell-sized compartment[END_REF]. They both showed that expression noise is an inherent property of the expression process which is determined by the structure of the genes. The advantage of cell-free systems is that they can eliminate extrinsic noise and study different aspects of intrinsic noise.

Consequences of noise

Expression variability (noise) enables an isogenic population to contain non-genetic phenotypic heterogeneity [START_REF] Raser | Noise in gene expression: origins, consequences, and control[END_REF] that can be important in stressful environments and for cell fate decisions. On one hand, non-genetic heterogeneity can make the population have different responses to extrinsic signals and further have different fates. On the other hand, this heterogeneity can give the population more chances of adaptation to variable conditions thanks to subpopulations that might be already adapted to certain stressful conditions (a phenomenon also called bed-hedging [START_REF] Beaumont | Experimental evolution of bet hedging[END_REF]). Moreover, noise dependent heterogeneity could be tunable under selection and has effects on long timescale evolutionary transition because it represents an evolutionarily accessible phenotypic parameter [START_REF] Eldar | Functional roles for noise in genetic circuits[END_REF]. The relationships between noise and adaptation will be discussed in depth in section 1.2, here we only focus on cell fate decisions.

Cell state of unicellular organisms

Unicellular organisms can exhibit different cell states or types depending on the environmental conditions. Moreover microorganism populations can contain heterogeneous subpopulations with different behaviours and properties, and the transition from one cell type to another can be a stochastic process. Noise in the expression of key genes can play an important role during these cell fate transitions. An example is the transition to the competent state in Bacillus subtilis, which gives the cells the ability (competence) to uptake exogenous extracellular DNA in stationary phase. Only around 15% of cells in the population become competent. This transition depends on the expression level of ComK. When the number of ComK proteins exceeds a threshold, the competent state is acquired. By increasing the rate of transcription and decreasing the rate of translation, [START_REF] Maamar | Noise in gene expression determines cell fate in Bacillus subtilis[END_REF] decreased the expression noise of ComK with similar mean expression level compared to wild type cells. They found that low ComK noise highly decreased the transition rate to competence. Another example is yeast sporulation. [START_REF] Nachman | Dissecting timing variability in yeast meiosis[END_REF] followed the sporulation dynamics in yeast (S.

cerevisiae) population during nutritional starvation. They found that the entry in sporulation and the duration of sporulation are both variable among identical cells and this variability is determined by the variable expression level of Ime1 which is an early regulator of sporulation.

Cell differentiation in multicellular organisms

Multicellular organisms consist of different types of differentiated cells with numerous differences in size, components, structure, functions, etc. Cell differentiation is the basic developmental process in multicellular organisms. Moreover the differentiation process occurs continuously in some tissues from adult stem cells during the whole lifetime. But whether one stem cell in the population will further develop to a differentiated progeny type and which one can be stochastic [START_REF] Eldar | Functional roles for noise in genetic circuits[END_REF]. Some genes are responsible for switches in the differentiation process. Their expression pattern in space and time influences the direction of differentiation and their expression noise might largely affect the differentiation decision [START_REF] Chalancon | Interplay between gene expression noise and regulatory network architecture[END_REF]). An example is the transcription factor Nanog in mouse, which is a key regulator of the pluripotent state of embryonic stem cells (ESCs). The expression level of Nanog is highly variable in the population [START_REF] Kalmar | Regulated fluctuations in nanog expression mediate cell fate decisions in embryonic stem cells[END_REF][START_REF] Ochiai | Stochastic promoter activation affects Nanog expression variability in mouse embryonic stem cells[END_REF].

Furthermore, cells with a low Nanog level are more susceptible to enter differentiation whereas a high Nanog level stabilizes the pluripotent state [START_REF] Abranches | Stochastic NANOG fluctuations allow mouse embryonic stem cells to explore pluripotency[END_REF]. Another example is provided by [START_REF] Raj | Variability in gene expression underlies incomplete penetrance[END_REF] who found that embryonic cells of Caenorhabditis elegans skn-1 mutants exhibit variable expression of end-1 whose expression level determines the differentiation decision. Thus only cells where its expression level exceeds a precise threshold undergo the differentiation process whereas the others keep their original state, resulting in heterogeneous cell fates in the population. In this network, the feedback loops might act to decrease expression noise and ensure reproducible cell fate decisions.

Diseases

Cancer

Cancer cells can show large heterogeneity in their response to drugs. Some of them can rapidly develop drug resistance, whereas others die. Non-genetic heterogeneity, especially due to gene expression noise, can be responsible for these differences. Indeed noise in the expression of key genes plays an important role because it can produce a subpopulation pre-resistant to some drugs [START_REF] Miller-Jensen | Varying virulence: epigenetic control of expression noise and disease processes[END_REF]. [START_REF] Cohen | Dynamic proteomics of individual cancer cells in response to a drug[END_REF] showed that, treated with chemotherapy drugs such as camptothecin, individual human H1299 lung carcinoma cells with high expression level of certain proteins such as DDX5 and RFC1 can survive when cells with low levels die. Similar results are also observed during the process of apoptosis induced by TRAIL (tumor necrosis factor (TNF)-related apoptosis-inducing ligand) in Hela cells [START_REF] Spencer | Non-genetic origins of cell-to-cell variability in TRAIL-induced apoptosis[END_REF]. Moreover, the heterogeneity in signaling pathways among the population is also well correlated to drug resistance [START_REF] Singh | Patterns of basal signaling heterogeneity can distinguish cellular populations with different drug sensitivities[END_REF]. [START_REF] Levin | Stochastic receptor expression determines cell fate upon interferon treatment[END_REF] showed that the response to type-I interferons of cancer cells is heterogeneous and depends on the expression level of its receptors. Only cells with a high number of receptors carry out the antiproliferative response. Finally, [START_REF] Sharma | A chromatin-mediated reversible drug-tolerant state in cancer cell subpopulations[END_REF] showed that the de novo drug resistance of lung cancer cells is reversible. The appearance of resistance is due to altered chromatin configurations arising trough noise in the expression of chromatin modifying genes.

Noise-mediated heterogeneity is also observed during cancer cell proliferation. For instance, a small proportion of melanoma cells harboring high expression level of H3K4 demethylase JARID1B have long cycling time but highly proliferating progeny [START_REF] Roesch | A temporarily distinct subpopulation of slow-cycling melanoma cells is required for continuous tumor growth[END_REF]. JARID1B is important for continuous growth but is not a prerequisite for the tumor initiation in vivo. The sorted subpopulations with different levels of JARID1B recover the heterogeneous distribution after several days of growth in conventional medium, indicating that switches between alternative states of cancer cells might be a basic procedure for tumor maintenance.

HIV latency

The human immunodeficiency virus (HIV) has a latent period after infection. During this period, transcription of the viral genes is silenced and there is no viremia in patients. But these proviruses are also unaffected by normal antiretroviral therapies which only target active viruses. Latent viruses can be reactivated in individual cells what leads to viremia rebound. Thus, the latent state of these viruses forms the main barrier for HIV cure [START_REF] Miller-Jensen | Varying virulence: epigenetic control of expression noise and disease processes[END_REF]. During HIV infection, the transcriptional activator Tat is essential for the decision between productive infection or latency. Tat exhibits a very noisy expression pattern that leads to bifurcation phenotypes in a clonal population. These heterogeneous subpopulations might enter different stages [START_REF] Weinberger | Stochastic gene expression in a lentiviral positive-feedback loop: HIV-1 Tat fluctuations drive phenotypic diversity[END_REF][START_REF] Weinberger | Transient-mediated fate determination in a transcriptional circuit of HIV[END_REF]. Moreover, [START_REF] Dar | Screening for noise in gene expression identifies drug synergies[END_REF] discovered chemicals that affect the expression noise of HIV genes by screening thousands of compounds and proposed a new effective therapy approach that combines conventional drugs and drugs acting on noise.

Gene expression noise and adaptation

In this thesis, we mainly focus on the relationships between gene expression noise and stress adaptation. Moreover, a Saccharomyces cerevisiae industrial strain was chosen as a model to study some of our hypotheses. Thus in this section, we will first discuss the general mechanisms of stress adaptation in industrial yeasts, then we will review the known or possible relationships between gene expression noise and adaptation in yeast, and finally we will give our working hypothesis.

Stress adaptation in industrial yeasts

Industrial S. cerevisiae strains provide a good model to study molecular adaptation to changing environments. They have been selected for rapid fermentations and are specifically adapted to the stressful conditions of fermentation, characterized by high sugar content, high alcohol content, low pH, presence of sulfites, limiting amount of nitrogen, lipid and vitamins, anaerobiosis, and other environmental stresses. While they are genetically highly related to their laboratory counterpart, the genetic basis of their technological properties as compared to laboratory yeast strains that are inefficient under these fermentation conditions are still largely unknown. Genome-wide approaches have received a strong interest in the recent years to address the question of the adaptation of industrial yeasts to these specific conditions [START_REF] Dunn | Analysis of the Saccharomyces cerevisiae pan-genome reveals a pool of copy number variants distributed in diverse yeast strains from differing industrial environments[END_REF][START_REF] Dunn | Microarray karyotyping of commercial wine yeast strains reveals shared, as well as unique, genomic signatures[END_REF][START_REF] Novo | Eukaryote-to-eukaryote gene transfer events revealed by the genome sequence of the wine yeast Saccharomyces cerevisiae EC1118[END_REF][START_REF] Ambroset | Deciphering the molecular basis of wine yeast fermentation traits using a combined genetic and genomic approach[END_REF][START_REF] Dequin | The genomes of fermentative Saccharomyces[END_REF][START_REF] Salinas | The genetic basis of natural variation in oenological traits in Saccharomyces cerevisiae[END_REF].

Polyploidy and aneuploidy

In general, yeast have a stable karyotype, either haploid or diploid, under optimal conditions [START_REF] Nishant | The baker's yeast diploid genome is remarkably stable in vegetative growth and meiosis[END_REF]. During the fermentation process, yeast strains are predominantly diploid [START_REF] Carreto | Comparative genomics of wild type yeast strains unveils important genome diversity[END_REF], while polyploid or aneuploid isolates are also common [START_REF] Storchova | Ploidy changes and genome stability in yeast[END_REF]. [START_REF] Legras | Bread, beer and wine: Saccharomyces cerevisiae diversity reflects human history[END_REF] have shown the remarkable existence of tetraploidy and aneuploidy in industrial strains by exploring the biodiversity of 12 microsatellite marker sites in a collection of 651 strains from 56 different geographical origins, worldwide. Tetraploid strains can arise by duplication of identical genomes or by fusion of two or more cells of same or closely related species [START_REF] Albertin | Evidence for autotetraploidy associated with reproductive isolation in Saccharomyces cerevisiae: towards a new domesticated species[END_REF].

Aneuploidy can improve adaptation. [START_REF] Selmecki | Mismatch Correction Acts as a Barrier to Homeologous Recombination in Saccharomyces cerevisiae[END_REF] showed that polyploid cells exhibit high rate of beneficial mutations, and therefore can rapidly adapt to new environments. These cells also exhibit high level of chromosomal instability (CIN) that can rapidly lead to aneuploidy by losing certain chromosomes. Aneuploid cells can also arise from unequal segregation during cell division [START_REF] Storchova | Ploidy changes and genome stability in yeast[END_REF].

Aneuploid cells exhibit higher growth rate than euploid cells with the same genetic background [START_REF] Pavelka | Aneuploidy confers quantitative proteome changes and phenotypic variation in budding yeast[END_REF]. [START_REF] Chen | Hsp90 stress potentiates rapid cellular adaptation through induction of aneuploidy[END_REF] found that some S. cerevisiae clones surviving when Hsp90 is inhibited have gained additional copies of chromosome XV. Nevertheless, [START_REF] Yona | Chromosomal duplication is a transient evolutionary solution to stress[END_REF] showed that aneuploidy is just a transient step during adaptation in long evolutionary experiments: the gained chromosomes were further eliminated because some de novo mutations with the same adaptive effects were then fixed. The adaptive advantages of aneuploidy is probably due to the increased gene expression levels with gained chromosomes or the decreased expression levels upon chromosome loss [START_REF] Storchova | Ploidy changes and genome stability in yeast[END_REF].

Chromosomal rearrangement

Yeast chromosomes can be rearranged through translocations, deletions and amplifications, the so called gross chromosomal rearrangements (GCR), that can lead to high level of chromosomal length polymorphism [START_REF] Dequin | The genomes of fermentative Saccharomyces[END_REF]. The GCR events are mainly mediated by ectopic recombination between repeated Ty sequences (retrotransposons) [START_REF] Carreto | Comparative genomics of wild type yeast strains unveils important genome diversity[END_REF], duplicated genes or repeated telomeric sequences [START_REF] Aksenova | Genome rearrangements caused by interstitial telomeric sequences in yeast[END_REF]. Translocations and insertions of chromosomal regions are found in many yeast strains, such as the wine strain EC1118 [START_REF] Novo | Eukaryote-to-eukaryote gene transfer events revealed by the genome sequence of the wine yeast Saccharomyces cerevisiae EC1118[END_REF], the clinical isolate YJM789 [START_REF] Wei | Genome sequencing and comparative analysis of Saccharomyces cerevisiae strain YJM789[END_REF] and flor yeasts [START_REF] Infante | Genome-wide amplifications caused by chromosomal rearrangements play a major role in the adaptive evolution of natural yeast[END_REF], showing the potential advantages of GCR in natural adaptation while the underlying mechanisms are still unclear.

One possible assumption is that GCR can modulate the expression pattern or the number of genes on the modified regions and thereby confer certain adaptive advantages. For example, the translocation between chromosomes VIII and XV in wine yeasts, which increases the expression of SSU1, can confer sulfite resistance [START_REF] Pérez-Ortín | Molecular characterization of a chromosomal rearrangement involved in the adaptive evolution of yeast strains[END_REF]. Also, the segmental duplication on chromosome VII and VIII, which increases the number of CUP1 and CUP2 copies, can increase copper resistance in EC strains [START_REF] Chang | Dynamic large-scale chromosomal rearrangements fuel rapid adaptation in yeast populations[END_REF]. Otherwise chromosomal rearrangements play an important role during the evolution process of interspecific hybrid cells to gain adaptive advantages [START_REF] Zheng | Genomic structural variations contribute to trait improvement during wholegenome shuffling of yeast[END_REF]) (see below).

Copy number variations

Copy number variations (CNV) generated by gene amplification or gene deletion also play an important role during the yeast adaptation process to stressful environments [START_REF] Dequin | The genomes of fermentative Saccharomyces[END_REF]. Studies on the genetic biodiversity of large yeast collections by different methods have shown the widespread existence of CNV in industrial yeasts. Most variations are found in transporter genes or genes located in telomeric chromosomal regions [START_REF] Legras | Bread, beer and wine: Saccharomyces cerevisiae diversity reflects human history[END_REF][START_REF] Carreto | Comparative genomics of wild type yeast strains unveils important genome diversity[END_REF][START_REF] Dunn | Microarray karyotyping of commercial wine yeast strains reveals shared, as well as unique, genomic signatures[END_REF][START_REF] Dunn | Analysis of the Saccharomyces cerevisiae pan-genome reveals a pool of copy number variants distributed in diverse yeast strains from differing industrial environments[END_REF]. Recent evolutionary experiments demonstrated the advantages of CNV. Zhang et al. (2013a) showed that CNV of SFA1 or CUP1 mediated by ectopic recombination between Ty elements on chromosome V confer notable resistance to formaldehyde or copper, respectively. Moreover, CNV of nitrogen transporters including PUT4, DUR3 and DAL4 are the predominant evolutionary solution during the long-term nitrogen limitation [START_REF] Hong | Molecular specificity, convergence and constraint shape adaptive evolution in nutrient-poor environments[END_REF]). A well-documented example is that different numbers of CUP1 copies that vary from 1 to 18 in different strains confer different copper resistance levels [START_REF] Zhao | Structures of Naturally Evolved CUP1 Tandem Arrays in Yeast Indicate That These Arrays Are Generated by Unequal Nonhomologous Recombination[END_REF]. This number can be increased under copper treatment [START_REF] Adamo | Amplification of the CUP1 gene is associated with evolution of copper tolerance in Saccharomyces cerevisiae[END_REF]. Finally, CNV can arise from chromosome rearrangement or unequal crossover between tandem repeats during cell division [START_REF] Zhao | Structures of Naturally Evolved CUP1 Tandem Arrays in Yeast Indicate That These Arrays Are Generated by Unequal Nonhomologous Recombination[END_REF][START_REF] Chang | Dynamic large-scale chromosomal rearrangements fuel rapid adaptation in yeast populations[END_REF].

Gene introgression

Gene introgression is the phenomenon by which an organism gains exogenous DNA from other species by horizontal gene transfer (HGT) [START_REF] Dequin | The genomes of fermentative Saccharomyces[END_REF]. Many researchers have shown the existence of gene introgression in various yeast strains, indicating its probable selective advantages during natural and domestic evolution [START_REF] Novo | Eukaryote-to-eukaryote gene transfer events revealed by the genome sequence of the wine yeast Saccharomyces cerevisiae EC1118[END_REF][START_REF] Zhang | Saccharomyces paradoxus and Saccharomyces cerevisiae reside on oak trees in New Zealand: evidence for migration from Europe and interspecies hybrids[END_REF][START_REF] Muller | A multispecies-based taxonomic microarray reveals interspecies hybridization and introgression in Saccharomyces cerevisiae[END_REF][START_REF] Dunn | Analysis of the Saccharomyces cerevisiae pan-genome reveals a pool of copy number variants distributed in diverse yeast strains from differing industrial environments[END_REF]. For example, the sequencing of the industrial wine yeast EC1118 [START_REF] Novo | Eukaryote-to-eukaryote gene transfer events revealed by the genome sequence of the wine yeast Saccharomyces cerevisiae EC1118[END_REF] revealed large chromosomal insertions containing novel genes related to nitrogen and carbon metabolism coming from different species, even non-Saccharomyces species. The function of some of these genes has been documented, such as FSY1, encoding a high-affinity fructose symporter [START_REF] Galeote | FSY1, a horizontally transferred gene in the Saccharomyces cerevisiae EC1118 wine yeast strain, encodes a high-affinity fructose/H+ symporter[END_REF], and the fungal oligopeptide transporters (FOT) genes [START_REF] Damon | A novel fungal family of oligopeptide transporters identified by functional metatranscriptomics of soil eukaryotes[END_REF]. Thus these genes could enlarge the range of potential carbon and nitrogen resources for wine yeast and thereby ensure adaptive advantages during fermentative process, where nutrient resources are limited. Finally, interspecific hybrid might contribute to the gene introgression [START_REF] Dunn | Recurrent rearrangement during adaptive evolution in an interspecific yeast hybrid suggests a model for rapid introgression[END_REF][START_REF] Zheng | Genomic structural variations contribute to trait improvement during wholegenome shuffling of yeast[END_REF].

Sequence polymorphism

Large number of sequence polymorphisms (Single-Nucleotide Polymorphisms (SNPs) or small Insertions/deletions (Indels)) have been observed among different yeast collections [START_REF] Liti | Population genomics of domestic and wild yeasts[END_REF][START_REF] Schacherer | Comprehensive polymorphism survey elucidates population structure of Saccharomyces cerevisiae[END_REF]. The consequences of some polymorphisms have also been characterized. For example, the two mutations observed in FLO11 in flor yeast strains located in the promoter and the ORF can increase the expression level and the adhesion ability among cells respectively [START_REF] Fidalgo | Adaptive evolution by mutations in the FLO11 gene[END_REF]. Otherwise, QTL analyses that use statistical methods to link phenotypic information (trait measurements) and genetic information (molecular markers like SNPs) have been applied to study the effects of sequence polymorphisms on fermentation ability for instance. Large numbers of QTLs and eQTLs have been identified in different positions through several combination of different strains [START_REF] Ambroset | Deciphering the molecular basis of wine yeast fermentation traits using a combined genetic and genomic approach[END_REF][START_REF] Salinas | The genetic basis of natural variation in oenological traits in Saccharomyces cerevisiae[END_REF][START_REF] Brion | Differential adaptation to multistressed conditions of wine fermentation revealed by variations in yeast regulatory networks[END_REF]. For example, the ABZ1 allele of some wine yeasts can increase the fermentation rate by facilitating nitrogen utilization.

In this work, we used the oenological strain Lalvin EC1118 as a model to study our hypotheses.

This strain, also known as ''Prise de mousse'', is a S. cerevisiae wine strain isolated in Champagne (France) and manufactured by Lallemand Inc. This strain has been sequenced by [START_REF] Novo | Eukaryote-to-eukaryote gene transfer events revealed by the genome sequence of the wine yeast Saccharomyces cerevisiae EC1118[END_REF].

The map is 11.7 Mb long with 31 scaffolds, corresponding to 96.7% of the S288c genome with 46,825 SNPs. 5,728 ORFs have been predicted, of which 5,685 were mapped to S288c. Three introgression regions have been identified with 34 novel genes which are not present in the S288C genome. They are involved in key wine fermentation functions. EC1118 is a diploid wine strain, whereas the haploid derivatives are named as 59A or 59α. The sequencing has been performed on the derivative 59A.

Interspecific hybrid

As shown in Figure 18, interspecific hybridization is an common breeding process during the domestication of industrial Saccharomyces yeasts [START_REF] Dequin | The genomes of fermentative Saccharomyces[END_REF]. Hybridization can highly increase the genome complexity and by this way, cells can rapidly acquire new genes with essential functions, thus conferring notable advantages under selective pressure [START_REF] Morales | Evolutionary role of interspecies hybridization and genetic exchanges in yeasts[END_REF]. Moreover, the heterosis, which is the improved or increased function of any biological feature in a hybrid offspring gained from the combination of heterogeneous chromosome sets, may also provide certain advantages [START_REF] Comai | The advantages and disadvantages of being polyploid[END_REF]. At the same time, hybridization can serve as an important source of chromosomal rearrangements and gene introgression [START_REF] Storchova | Ploidy changes and genome stability in yeast[END_REF]. Recently, the feasibility of yeast breeding by artificial interspecific hybridization has been demonstrated to improve the fermentation ability of some yeast strains [START_REF] Dunn | Recurrent rearrangement during adaptive evolution in an interspecific yeast hybrid suggests a model for rapid introgression[END_REF][START_REF] Zheng | Genomic structural variations contribute to trait improvement during wholegenome shuffling of yeast[END_REF].

Noise and stress adaptation

The consequences of noise depend on the gene function. Some researchers showed that housekeeping genes exhibit less variability (noise minimization) [START_REF] Lehner | Selection to minimise noise in living systems and its implications for the evolution of gene expression[END_REF][START_REF] Fraser | Noise minimization in eukaryotic gene expression[END_REF][START_REF] Monteoliva | Noise Minimisation in Gene Expression Switches[END_REF][START_REF] Newman | Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise[END_REF] whereas stress-related genes exhibit higher noise in yeast [START_REF] Newman | Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise[END_REF][START_REF] Zhang | Stochastic expression and epigenetic memory at the yeast HO promoter[END_REF][START_REF] Venancio | Robustness and evolvability in natural chemical resistance: identification of novel systems properties, biochemical mechanisms and regulatory interactions[END_REF]. Thus expression noise seems to be a special property of a gene and might be an evolvable trait, especially in stressful environments.

Constant stress

Under constant stress, cell survival mainly depends on the mean expression level of some key genes. For example, cells expressing enough antibiotic resistance proteins are selected in the presence of this antibiotic. Thus the selective advantage of expression noise in constant stress depends on the position of the expression threshold allowing survival. As shown in Figure 19, if the threshold is lower than the mean value, low-noise genotypes are favoured, and vice versa, high noise is favourable when the threshold is higher than the mean value [START_REF] Eldar | Functional roles for noise in genetic circuits[END_REF]. This phenomenon has been clarified and experimentally tested with an artificial yeast regulation network using the GAL1 promoter and zeoR (conferring zeocin resistance) as an indicator gene expressed with different levels of noise but the same mean in different strains [START_REF] Blake | Phenotypic consequences of promoter-mediated transcriptional noise[END_REF]. In weak zeocin concentrations, the clone exhibiting low noise grows better, wheheas the clone exhibiting high noise is the only one surviving in high zeocin concentrations (Figure 20).

High noise Low noise

Figure 20: Viability (percentage of growth with zeocin compared to growth without zeocin) of clones with similar mean expression level but different noise of zeoR [START_REF] Blake | Phenotypic consequences of promoter-mediated transcriptional noise[END_REF].

Fluctuating environments

On one hand, increasing the expression level of key genes is beneficial in specific constant stressful conditions, but it can be harmful when the environmental conditions vary. On the other hand,

when key genes are noisy, an isogenic population can contain cells with heterogeneous phenotypes and some 'persister' cells (representing a small part of the population that resist to certain stress levels) can emerge, thereby increasing the global adaptation to fluctuating environments [START_REF] Chalancon | Interplay between gene expression noise and regulatory network architecture[END_REF]. Several researchers have demonstrated the fact that variable expression of certain genes can confer resistance and can be an advantage in fluctuating conditions. For example, the heterogeneous expression of GTS1 is responsible for the oscillations of glutathione (GSH) content in yeast and confers heterogeneous resistance to cadmium (Cd) and H2O2 [START_REF] Smith | Glutathione and Gts1p drive beneficial variability in the cadmium resistances of individual yeast cells[END_REF]. Furthermore, the clone with higher GTS1 expression noise showed a significant selective advantage over the more stable one at same mean expression level during competition in fluctuating experiments (medium successively changed between YPD and YPD + Cd).

Phenotypic heterogeneity has been shown to predominantly exist in wild yeast isolates, indicating the large selection pressure acting on it [START_REF] Holland | Phenotypic heterogeneity is a selected trait in natural yeast populations subject to environmental stress[END_REF]. Strategies increasing heterogeneity are also called bet-hedging strategies where geometric mean fitness is maximized at the expense of arithmetic mean fitness. These strategies increase the potential adaptation to certain environments in subpopulations whereas decreasing the mean adaptive effects in the current environment [START_REF] Viney | Adaptive noise[END_REF]

Instability switches

As we mentioned before, bimodal expression is an extreme example of high noise expression and is widely observed in different organisms [START_REF] Acar | Enhancement of cellular memory by reducing stochastic transitions[END_REF][START_REF] Blake | Phenotypic consequences of promoter-mediated transcriptional noise[END_REF][START_REF] Shalek | Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells[END_REF][START_REF] Silva-Rocha | Stochasticity of TOL plasmid catabolic promoters sets a bimodal expression regime in Pseudomonas putida mt-2 exposed to m-xylene[END_REF]. This expression pattern often results in bistable cell states in the population, each of them conferring advantages in certain conditions. The switching rate between different states can affect stress adaptation. [START_REF] Acar | Stochastic switching as a survival strategy in fluctuating environments[END_REF] created an artificial gene network with the GAL1 promoter whose expression pattern (its switching rate between on and off states) can be modified by different inducing conditions. Using URA3 as indicator gene, they measured the growth rate of clones with different switching rates under different fluctuating environments (successively changed between medium without uracil and medium with uracil and 5-FOA). They found that the fast switching clone is favoured in rapidly fluctuating environments whereas the slow switching clone grows better in environments that rarely change. Moreover, an evolutionary experiment with Pseudomonas fluorescens selecting for clones switching their morphology identified isolates with rapid phenotypic switching [START_REF] Beaumont | Experimental evolution of bet hedging[END_REF], indicating that modification of the switching rate between bistable or even multistable states is an important evolutionary strategy for stress adaptation.

Our hypothesis

Our hypothesis is based on two fundamental assumptions. First, industrial yeast strains are challenged by various inconstant stresses and at the same time they are well adapted to these fluctuating conditions with good global fitness compared to laboratory yeasts. This adaptation is gained through various molecular mechanisms during domestication. Second, gene expression noise of stressrelated genes leads to phenotypic heterogeneity that increases the fitness of the population and is evolvable. Thus, we assume that fermentative strains might exhibit higher level of noise in key genes related to stress adaptation than laboratory strains.

The identification of a panel of genes harboring different noise levels in different S. cerevisiae strains would constitute an original result and represent an innovative strategy for the study of genetic determinants of stress adaptation and tolerance. More generally, our main aim is to determine the role of stochastic variation of gene expression in yeast adaptation through its impact on genetic and non-genetic variability. It will improve our knowledge of domesticated yeasts strains, along with our basic knowledge of eukaryotic cell adaptation to fluctuating environments.

Gene expression noise and genome stability

As we mentioned in the previous chapter on the adaptation of industrial yeasts (section 1.2.1), their genomes display high plasticity, especially in stressful fermentation conditions. Some works showed that acquisition of chromosome rearrangements in yeast genomes can be influenced by exposure to environmental stress. Cells exposed to stress conditions like fermentation in high specific gravity wort or at higher than normal temperatures, undergo gross chromosomal rearrangements, small deletions and regional amplifications whereas 'standard' fermentation conditions did not generate significant changes [START_REF] James | Lager yeasts possess dynamic genomes that undergo rearrangements and gene amplification in response to stress[END_REF]. High genetic instability and loss of heterozygosity (LOH) were often observed in natural wine yeasts [START_REF] Ambrona | Rapid asymmetrical evolution of Saccharomyces cerevisiae wine yeasts[END_REF][START_REF] Carreto | Comparative genomics of wild type yeast strains unveils important genome diversity[END_REF][START_REF] Dunn | Microarray karyotyping of commercial wine yeast strains reveals shared, as well as unique, genomic signatures[END_REF][START_REF] Dunn | Analysis of the Saccharomyces cerevisiae pan-genome reveals a pool of copy number variants distributed in diverse yeast strains from differing industrial environments[END_REF][START_REF] Dunn | Recurrent rearrangement during adaptive evolution in an interspecific yeast hybrid suggests a model for rapid introgression[END_REF], indicating that the maintenance of an unbalanced chromosome set is advantageous in such environments. Their complex genomes allow loss, duplication, inversion or translocation of genetic material which form at a faster rate than other types of mutations [START_REF] Hastings | Mechanisms of change in gene copy number[END_REF]. Here we will describe our assumption that expression noise of genes related to genome stability could favour rapid genome modifications and therefore could increase the fitness to varying environments.

The genome can be modified through different mechanisms. For example, chromosome rearrangements can arise from homologous recombination (HR) or non-homologous end joining (NHEJ) induced by double-strand breaks (DSB) repair after DNA damage. Otherwise, mutations and Indels can arise from DNA replication errors. These modifications can be repaired by the mismatch repair pathway. In this work, we mainly focused on the homologous recombination pathway.

Homologous recombination

HR pathways

There are several possible pathways in HR. Here we will focus on the Rad51-dependent pathway.

The HR process begins from a DSB occasionally occurring in a cell (Figure 21A). Several replication proteins A (RPA) bind to the damaged DNA ends which is resected as single-stranded DNA (ssDNA)

(Figure 21B-C) [START_REF] Holthausen | Regulation of DNA strand exchange in homologous recombination[END_REF]. RPA can protect the unstable ssDNA from further damage.

Then, with the help of Rad52, several recombinase proteins Rad51 replace RPA on the ssDNA [START_REF] Sung | Function of yeast Rad52 protein as a mediator between replication protein A and the Rad51 recombinase[END_REF]. Rad51 helps the search for a homologous fragment and facilitate the invasion of the ssDNA overhang into the homologous double-stranded DNA (dsDNA) sequence (Figure 21E). The specific DNA structure after invasion is referred to as a displacement-loop (D-loop) [START_REF] Arai | Vital roles of the second DNA-binding site of Rad52 protein in yeast homologous recombination[END_REF]. Once the formation of D-loop is formed, a DNA polymerase can extend the ssDNA end using the homologous DNA as a template, thereby repairing the DSB [START_REF] Karpenshif | From yeast to mammals: recent advances in genetic control of homologous recombination[END_REF].

Figure 21: Rad51-dependent homologous recombination [START_REF] Karpenshif | From yeast to mammals: recent advances in genetic control of homologous recombination[END_REF] After synthesis, there are two different pathways to create the final repaired products: synthesis dependent strand annealing (SDSA, Figure 22A) and double-Holliday junction (dHJ, Figure 22B) [START_REF] Karpenshif | From yeast to mammals: recent advances in genetic control of homologous recombination[END_REF]. SDSA creates non-crossover products whereas dHJs can result in crossover or non-crossover products (Figure 22). Non-crossover products repair the DSB without modifying the chromosome structure. Otherwise crossover products repair the DSB and introduce chromosome exchanges at the same time and create genome rearrangements.

A B

Figure 22: Synthesis dependent strand annealing and double-Holliday junction [START_REF] Karpenshif | From yeast to mammals: recent advances in genetic control of homologous recombination[END_REF]) A: Synthesis dependent strand annealing (SDSA) is a form of non-crossover homologous recombination, in that it uses a homologous sequence as a template for DNA repair. However, this process does not involve second end capture of the DNA end. After end processing, one strand is synthesized using a homologous template and then is re-ligated to the broken end. The newly synthesized strand is then used as a template and base pairs to the complimentary sequence, consequently resolving the break. B: Homologous recombination uses a homologous template for repair. After the DNA ends are resected, the ssDNA 3' overhang invades a homologous sequence and restores any missing information at the break site.

The second end of the DNA is captured resulting in a double-Holliday junction that can be resolved into a crossover or non-crossover product depending upon where the junctions are cut. Legend: Red-Blue doublehelix is the broken molecule of DNA. Grey DNA is the homologous sequence. Blue highlighted segments of DNA are newly synthesized regions.

Rad52 and HR

Rad52 has two major functions during the HR process: it harbors a recombination mediator activity that helps Rad51 to displace RPA, and binds to the ssDNA ends to use its ssDNA annealing activity that helps the annealing process either in the SDSA pathway or in the dHJ pathway [START_REF] Xiong | The human splicing code reveals new insights into the genetic determinants of disease[END_REF]. The rad52 deletion in S.cerevisiae partially stabilizes the karyotype by suppressing recombination at telomeric and subtelomeric regions and reducing the rate of changes in chromosomal size by 30% [START_REF] Carro | Karyotype Rearrangements in a Wine Yeast Strain by rad52-Dependent and rad52-Independent Mechanisms[END_REF]. It is found that defect of RAD52 can also induce deletion rate in the genome [START_REF] Fritsch | Influence of genetic background on the occurrence of chromosomal rearrangements in Saccharomyces cerevisiae[END_REF]. By contrast, overexpression of RAD52 do not increase the HR frequency comparing to wild type [START_REF] Dornfeld | Effects of controlled RAD52 expression on repair and recombination in Saccharomyces cerevisiae[END_REF][START_REF] Paffett | Overexpression of Rad51 inhibits doublestrand break-induced homologous recombination but does not affect gene conversion tract lengths[END_REF].

Rad27 and HR

RAD27 encodes a flap endonuclease acting with Dna2 to cleave flap structures between Okazaki fragments during DNA replication [START_REF] Xiong | The human splicing code reveals new insights into the genetic determinants of disease[END_REF]. The RAD27 deletion causes the formation of double-stranded breaks during replication resulting in enhanced spontaneous recombination, and repetitive DNA instability to bypass the replication defect [START_REF] Debrauwère | Links between replication and recombination in Saccharomyces cerevisiae: a hypersensitive requirement for homologous recombination in the absence of Rad27 activity[END_REF]. It is shown that the rad27 null strains exhibit high recombination rate [START_REF] Sun | Complementary functions of the Saccharomyces cerevisiae Rad2 family nucleases in Okazaki fragment maturation, mutation avoidance, and chromosome stability[END_REF][START_REF] Ayyagari | Okazaki Fragment Maturation in Yeast: I. DISTRIBUTION OF FUNCTIONS BETWEEN FEN1 AND DNA2[END_REF][START_REF] Wu | Identification of rad27 Mutations That Confer Differential Defects in Mutation Avoidance, Repeat Tract Instability, and Flap Cleavage[END_REF].

Especially the RAD27 deletion favours the recombination between di-and trinucleotide repeats [START_REF] Wu | Identification of rad27 Mutations That Confer Differential Defects in Mutation Avoidance, Repeat Tract Instability, and Flap Cleavage[END_REF].

Measurement of HR frequency

There are several methods to measure the HR frequency. For example we can use the recombination reporter plasmid pBYA819 [START_REF] Dornfeld | Effects of controlled RAD52 expression on repair and recombination in Saccharomyces cerevisiae[END_REF] Another strain we will use takes advantage of the TRs in the promoter region of SDT1 (Figure 23) [START_REF] Vinces | Unstable tandem repeats in promoters confer transcriptional evolvability[END_REF]. The number of TRs in the promoter can affect the expression level of URA3 [START_REF] Vinces | Unstable tandem repeats in promoters confer transcriptional evolvability[END_REF]) (the promoter with 13 repeats exhibited highest expression level). Moreover, the number of TRs in this promoter evolves during evolutionary experiments towards the number giving the maximal expression level in medium without uracil [START_REF] Vinces | Unstable tandem repeats in promoters confer transcriptional evolvability[END_REF]. Considering that the expression level can be modified by changing TR numbers through HR in the promoter while the intact ORF was kept in the genome, we infer that it can be subjected to fluctuating experiments. In our work, we replaced SDT1 by URA3 (see Methods) because we expect an increase of the URA3 expression in URA -medium and a decrease of its expression level in 5-FOA containing medium by modification of the number of TRs in the promoter.

Our hypothesis

The expression level of DNA repair and maintenance genes (DRMGs) affects the genome stability by changing the rate of genetic variant generation (RGVG). Thus we can infer that high variability in the expression of DRMGs could favour the acquisition of genetic modifications favourable to population survival and enable fine tuning of the RGVG [START_REF] Capp | Noise-driven heterogeneity in the rate of genetic-variant generation as a basis for evolvability[END_REF]. No study has explored the expression variability of these genes at the single cell level and its potential consequences on the RGVG.

Moreover, industrial yeast populations are exposed to stressful and fluctuating environments and can probably take advantage of a high tunability of this rate. This tuning could be easier with a higher noise level in the expression of DNA repair and maintenance genes. A boarder range of their expression levels could optimize the emergence of cells harboring the 'just right' level of genetic variability. So we assume that these strains might have evolved to exhibit higher variability in the expression of DRMGs than laboratory strains do, because they are exposed to more variable growth conditions. Higher heterogeneity in the expression of these genes in industrial yeasts could explain their ability to rapidly acquire genetic modifications in stressful environments and their genomic instability in non-selective conditions.

Connecting Noise to Stress Adaptation

As we mentioned in the introduction, gene expression noise can increase the phenotypic heterogeneity in an isogenic population, and therefore can increase fitness in selective and fluctuating environments. Moreover stress-related genes have been shown to exhibit higher noise than housekeeping genes. Thus, considering that industrial strains are selected through complex fermentation conditions, we assume that the evolutionary strategy that modifies the expression noise of certain genes to increase the global fitness of the population might have occurred in the domestication of these strains. To study this hypothesis, we screened for promoters conferring high expression variability in the sequenced industrial wine S. cerevisiae strain EC1118, to compare their sequence with the ones of their counterpart of the laboratory strain S288c. Our aim was to determine if the observed genetic differences generate noise differences between the variants and if these differences are important enough to generate a difference of growth in specific selective environments.

Results

Screening for high noise promoters

Construction of the EC1118 genomic library

The genomic approach used to screen promoters conferring high promoter-mediated noise in the sequenced S. cerevisiae strain EC1118 [START_REF] Novo | Eukaryote-to-eukaryote gene transfer events revealed by the genome sequence of the wine yeast Saccharomyces cerevisiae EC1118[END_REF] was based on the method developed initially with Salmonella typhimurium [START_REF] Freed | A simple screen to identify promoters conferring high levels of phenotypic noise[END_REF]. In this modified protocol (see Methods), genomic DNA fragments from the haploid 59A strain (derivative of the winemaking diploid EC1118 strain) were inserted before the start codon of yEGFP in a series of three distinct promoterless yEGFP-coding vectors (the different plasmids contain zero, one or two additional base(s) between the insertion site and the yEGFP start codon). These centromeric plasmids minimized problems of copy number that would contribute to variations in fluorescence levels. The resulting library was transformed in the laboratory strain CEN.PK as we were looking for cis-effects on noise in yEGFP expression.

While no fluorescence was detected without genomic library, around 4% of the cells transformed with the library were fluorescent before any cell sorting (Figure 24). Fluorescence levels above the auto-fluorescence threshold were spread on at least two logs, showing strong promoter activity of some fragments. During the selection process, we chose for convenience to characterize cell-cell variability by the coefficient of variation (CV) (standard deviation divided by the mean). As expected, the selected population exhibited higher CV: it approximately doubled after the selection procedure (Figure 26). The mean expression level was also increased, showing that the library was enriched in fragments with

Isolation of high noise clones

In order to avoid sequencing of "non-noisy" clones, we screened for individual clones with high CV in the enriched library. By setting threshold values on mean and noise, only clones exhibiting highly variable yEGFP expression in the population were selected (Figure 28). Expression profiles among these single clones were highly heterogeneous, but they all possessed CV among the highest CV values that are observed in the yeast genome [START_REF] Newman | Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise[END_REF], confirming the efficiency of the screening.

Figure 28: Screening for clones with high CV among the selected population

To confirm that phenotypic noise is a stable property of a clone, we re-isolated on plates and analyzed 2 sub-clones from each selected clone. Ninety-nine clones with high CV have been chosen for further investigations because mean and CV values of their sub-clones were highly reproducible compared to the initial clone (R>0.97 for mean values and R>0.8 for noise values, Appendix I). We also wanted to verify that the clones were mainly dominated by intrinsic noise originating from transcription. The contribution of extrinsic noise in total noise can be decreased by reducing the FSC and SSC gates [START_REF] Newman | Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise[END_REF]. Indeed, whereas ungated populations are dominated by extrinsic noise, analysis on a more homogeneous part of the population decreases extrinsic noise either to levels comparable to intrinsic noise or to a level below that of intrinsic noise because the coefficient of variation in GFP expression is calculated from a subset of cells similar in size, shape, and cellular complexity. Thus, to reduce in our CV measurements the extrinsic noise linked to cell-to-cell variations in global physiological factors, we extracted a subset of cells that were very homogeneous in size and granularity. By measuring fluorescence only on a more homogeneous part of the population (around 50% of the cells), CV were reduced by around 28.5% (Table 1). We concluded that promoter-mediated noise was the main contributor to the elevated CV observed in these clones.

Genetic differences between noisy promoters from EC1118 and their counterpart in S288c

The fragments driving yEGFP expression were successfully sequenced in 97 clones (Table 2) and 95 were localized by mapping reads to the S288c reference genome (Appendix II). First, around 33% of the inserted fragments were found at least 2 times, sometimes with different end points. These fragments with different ends were independently selected and reinforced the validation of the fluctuating selection. Second, the mean length of the fragments was around 650bp. Most of them were fully sequenced by a single round of Sanger sequencing starting from 75 bp downstream the start codon of yEGFP. This mean length seemed low compared to the range of size selected to construct the genomic library (500 to 3000bp) and probably reflected the preference for smaller fragments in the cloning process. (Nevertheless this mean was slightly under-estimated because a small minority of longer fragments has not been fully sequenced.) Third, the majority of fragments corresponded to promoter sequences (Appendix II). A total of 50 distinct promoters were found (Table 2) (a fragment is considered as a promoter if its last base is at less than 350bp from the start codon of the downstream ORF). Among these fragments with known promoter sequences, 27 contained the promoter only, with 4 to 350 bp lacking before the start codon of the corresponding ORF, and 23 contained the promoter and a part of the corresponding ORF fused to yEGFP (Appendix II). GO processes analysis on these 50 promoters revealed over-representation of genes involved in nitrogen compound transport and anion transport (Table 3). Plasma-membrane transporters are known to show significantly elevated expression noise [START_REF] Zhang | Stochastic expression and epigenetic memory at the yeast HO promoter[END_REF]. Moreover ion transport and nitrogen compound metabolic processes are among the few GOs identified to have greater-thanexpected expression noise in yeast [START_REF] Zhang | Stochastic expression and epigenetic memory at the yeast HO promoter[END_REF]. We compared these EC1118 genomic fragments with their counterpart in S288c. A total of 170 genetic variations were detected in 37 of the 50 fragments containing known promoters (Table 2, details in Appendix III). The remaining 13 fragments did not show any difference between the strains.

The variations were mostly SNPs and small Indels ranging from 1 to 7 bp. Only one longer insertion of 21pb was present in a promoter from EC1118. The number of variations greatly varied from promoter to promoter, ranging from one SNP to 15 different variations including SNPs and Indels. Interestingly promoters driving expression of genes involved in stress response (e.g. HAC1, CUP1) or in diverse transports (e.g. CAN1, GNP1, AGP2) were present in this list. We hypothesized that some of these natural genetic variations could generate differences in noise level in the expression of these genes, and thus could confer an adaptive advantage in specific challenging environments. Eight promoters were particularly interesting because they are related to environmental factors (Table 4). We choose these genes on the basis of their function and not depending on either the nature or the localization of the genetic variations because even the effects of mutations in regions well-known to modify noise strongly depends on promoter context [START_REF] Hornung | Noise -mean relationship in mutated promoters[END_REF] and because of the high number of potential binding sites for transcription factors in these promoters.

Independent loci 65 Fragments with known promoters 50 Known promoters with SNPs or INDELs compared to S288c 37 Total SNPs and INDELs in known promoters compared to S288c 170

Table 4 Genes whose promoter variants are studied at the genomic level Gene Function BMH1/YER177W 14-3-3 protein, major isoform, regulates many processes BMH2/YDR099W 14-3-3 protein, minor isoform, regulates many processes CUP1/YHR055C

Binds copper, mediates resistance to high concentrations of copper CAN1/YEL063C

Plasma membrane arginine permease YCK2/YNL154C Palmitoylated plasma membrane-bound casein kinase I isoform HAC1/YFL031W

Transcription factor, regulates the unfolded protein response GNP1/YDR508C

High-affinity glutamine permease; also transports Leu,Ser,Thr,Cys,Met,Asn AGP2/YBR132C

Plasma membrane regulator of polyamine and carnitine transport

Noise levels conferred by each variant of selected promoters at the genomic level

We decided to finely compare at the genomic level the expression and noise levels conferred by both variants of these promoters of interest. Indeed, plasmids do not fully recapitulate chromosomal organization and might generate experimental bias. It was also necessary to compare expression profiles from each variant in both strains (BY4720, an auxotrophic derivative of S228c, and 59A, the haploid derivative of EC1118) to distinguish cis-effects (promoter variations) on noise from trans-effects linked to the genetic background. Indeed, strain effects on noise are known [START_REF] Ansel | Cell-to-cell stochastic variation in gene expression is a complex genetic trait[END_REF] and require determining if epistasis is observed in the generation of promoter-mediated noise. Comparing promoter pairs in both strains also required insertion of the variants in strictly the same chromosomal context. We took advantage from an insertion plasmid (pJRL2) previously used to compare expression variability from mutated PHO5 promoters inserted in the LEU2 locus [START_REF] Raser | Control of stochasticity in eukaryotic gene expression[END_REF]. By replacing the PHO5 promoter by our 16 variants, we were able to insert them in the same locus. We chose to clone 1000bp before the start codon, and not only the fragment inserted in the library plasmids. These longer loci sometimes contained more genetic variations between S228c and EC1118 compared to the cloned fragments but did not contain additional gene or promoter.

To be as accurate as possible, we wanted to reduce extrinsic noise even if both variants of the same promoter were always compared in a given genetic background. Thus fluorescence levels were measured in small gates where cells are homogenous in terms of size and granularity (strictly the same gate was used for the different variants of a promoter in a given background). Also, noise was calculated in the next steps by dividing the variance by the squared mean because this value reflects better than the CV how large the standard deviation is compared to the mean expression level [START_REF] Chalancon | Interplay between gene expression noise and regulatory network architecture[END_REF]. Mean expression and noise levels have been measured in optimal growth conditions and exponential phase at the same OD. Finally, the 59A strain possesses a high aggregation tendency making flow cytometry analysis difficult. Therefore the amn1Δ 59A strain was used because Amn1p plays the leading role in this cell aggregation [START_REF] Li | Polygenic molecular architecture underlying non-sexual cell aggregation in budding yeast[END_REF]. The results obtained were first plot on a graph (Figure 29) with mean expression as x-axis and noise as y-axis. In both background noise decreases as mean increases, which is overserved in many researches. Comparing the two stains, we found that with low expression level (below 1000) 59A

shows higher noise level than BY4720 but with high expression level 59A shows lower noise than BY4720. These results indicate the complexity of background effects on noise.

Then the results for the same promoter pair in both strains are presented on the same histogram in Figure 30 but it is worth noting that comparing results for a given variant in the different backgrounds is not possible because the gates where fluorescence levels were measured were not strictly identical for both strains (59A had higher cell size and granularity). Nevertheless, as we compared the consequences on noise of promoter sequences variations and their dependence on the trans-background, we chose to show them on the same plot. On one hand, both variants of pBMH1, pGNP1 and pBMH2 gave the same mean expression and noise levels in each strain (Figure 30A-C). The genetic differences between these variants did not generate any effect on fluorescence profiles. On the other hand, the five other promoter pairs exhibited differences in mean and/or noise in at least one backgrounds. pHAC1EC1118 conferred higher mean expression compared to the lab variant in both backgrounds (p=0.02 and p=0.018) (Figure 30D) while pYCK2EC1118, pCUP1EC1118 and pCAN1EC1118 gave increased expression only in 59A (p=10 -4 , p=10 -4 and p=0.02 respectively) (Figure 30E, G and H), indicating a strain-effect that contributed to reveal the consequences of the genetic variations between the variants. This was also observed with pAGP2 but here expression was lower with the industrial variant only in 59A (p=0.005) (Figure 30F). A higher expression for one version was associated to lower noise only in 2/6 cases among these promoter pairs while higher mean expression is generally accompanied by decreased expression variability [START_REF] Bar-Even | Noise in protein expression scales with natural protein abundance[END_REF][START_REF] Newman | Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise[END_REF][START_REF] Hornung | Noise -mean relationship in mutated promoters[END_REF][START_REF] Carey | Promoter sequence determines the relationship between expression level and noise[END_REF]. Thus laboratory and industrial variants of these promoters differed by their mean expression in at least one background, but enhanced expression did not necessarily produce lower noise so that the genetic variations might generate a different level of noise at similar mean expression level. This hypothesis has been tested by inducing the pCUP1 variants with different copper concentrations.

Noise levels conferred by the pCUP1 variants during copper induction

CUP1 is involved in copper detoxification. The CUP1 copy number is highly correlated to copper resistance [START_REF] Zhao | Structures of Naturally Evolved CUP1 Tandem Arrays in Yeast Indicate That These Arrays Are Generated by Unequal Nonhomologous Recombination[END_REF] and strains evolving in copper-rich environments amplify CUP1 and contain many copies [START_REF] Adamo | Amplification of the CUP1 gene is associated with evolution of copper tolerance in Saccharomyces cerevisiae[END_REF][START_REF] Chang | Dynamic large-scale chromosomal rearrangements fuel rapid adaptation in yeast populations[END_REF]. Moreover its promoter has been studied in details for instance in terms of transcription factors binding kinetics [START_REF] Karpova | Concurrent fast and slow cycling of a transcriptional activator at an endogenous promoter[END_REF] or nucleosome repositioning [START_REF] Shen | Remodeling of yeast CUP1 chromatin involves activator-dependent repositioning of nucleosomes over the entire gene and flanking sequences[END_REF] during copper induction. We induced the pCUP1 variants by copper to determine in which conditions mean expression levels were similar with both variants, and if increased noise was conferred by pCUP1EC1118 in this case.

Induction of pCUP1

Induction of pCUP1 in YPD medium

We first measured mean expression levels after 1h induction when cells were exposed to different copper sulfate (CuSO4) concentrations in YPD medium (Figure 31A-B). In both strains and with both variants, induction increased with copper sulfate concentration to reach a plateau in concentrations higher than 20µM CuSO4 in BY4720, and 6µM in 59A. While the variants behaved very similarly in BY4720 (Figure 31A), pCUP1EC1118 was more strongly induced than pCUP1S288c in 59A at each concentration (Figure 31B). We chose 5 µM CuSO4 for both variants in BY4720, and 20 µM and 1.5 µM CuSO4 for pCUP1S288c and pCUP1EC1118 respectively in 59A to compare noise at similar mean levels and thus independently of the mean. These concentrations avoided experimental bias linked to heterogeneous copper concentrations in the basis medium. We found no differences in BY4720 (p=0.25) (Figure 31C) but pCUP1EC1118 was clearly noisier than pCUP1S288c in 59A (p=0.015) (Figure 31D).

Induction of pCUP1 in YNB medium

To confirm the differences observed in YPD, one hour induction of the two pCUP1 variants in 59A was performed in YNB medium. We observed similar induction curve and significant difference in expression noise between the two variants at similar mean expression level (10 μM and 5 μM for pCUP1S288c and pCUP1EC1118 respectively, p=0.045, Figure 32A andC). Of note, this difference was also observed after overnight induction (p=0.016, Figure 32B andD). Finally, the difference in noise was still observed in the same CuSO4 concentration (10µM) in YNB medium after 1h induction (p=0.02, Figure 32E) (mean values are not significantly different in this case while pCUP1EC1118 conferred a slightly higher mean (p=0.12, Figure 32E)). Therefore this natural variant of pCUP1 exhibited higher promotermediated noise in gene expression.

Induction dynamics of pCUP1 in YPD medium

We also searched for induction times giving the same mean expression for both variants in each strain when cells were exposed to 20µM CuSO4 in YPD medium. On one hand, induction curves were similar for both variants in BY4720 (Figure 33A). It increased in the first hour of induction and then decreased in the next 1.5h. One the other hand, the promoters' behaviour is different in 59A: induction was clearly stronger with pCUP1EC1118 (Figure 33B). At similar mean expression levels (t=30 min for pCUP1EC1118 and t=90 min for pCUP1S288c), pCUP1EC1118 was clearly noisier than pCUP1S288c (Figure 33C) (p=0.014). In spite of the slightly lower mean expression from the industrial variant that could favour the observed difference in noise in Figure 32D and Figure 33C, these results were in the same tendency as results in Figure 32D and Figure 32C giving a statistically significant difference in noise with very similar mean levels. Also, increased noise is still observed with pCUP1EC1118 at the same copper concentration in YNB medium (Figure 32E), even if its mean value is slightly higher (while mean values are not significantly different), so that pCUP1EC1118 appears to be clearly noisier in all conditions. Finally, it is worth noting that the induction factor is far higher in 59A than in BY4720 for both variants (about 5 to 7 and 1.5 respectively in 20µM CuSO4).

Genetic differences between pCUP1EC1118 and pCUP1S288c

Various promoter elements contribute to noise mainly by modulating mRNA production burst size and thus noise (Sanchez et al, 2013;[START_REF] Sanchez | Genetic Determinants and Cellular Constraints in Noisy Gene Expression[END_REF]. Here three SNPs and one 4-bases deletion exist in pCUP1EC1118 compared pCUP1S288c (Appendix IV). Of note, these variations are common in 10 other wine strains while they are generally not present in laboratory strains (except in sigma1278b, Appendix IV). Other SNPs and Indels are also commonly found in pCUP1 in wine strains while the coding sequence is always identical either in lab or in wine strains (the SNP in T73 is synonymous), showing that the CUP1 transcription kinetics might be subject to many changes due to cis-modifications of the CUP1 promoter. The SNPs between pCUP1EC1118 and pCUP1S288c are upstream the transcription starting site, and the deletion is in the 5'-UTR. Several transcription factor binding sites are suppressed in pCUP1EC1118, but only in the reverse orientation (Appendix V). The first SNP is in an HSF1p binding site, but modify a position where any base can be found.

Directed mutagenesis of pCUP1S288c

We performed directed mutagenesis on each SNP or INDEL position in pCUP1S288c and measured mean and noise in 59A with induction at 5 µM CuSO4. Only the second SNP conferred significantly higher mean expression compared to pCUP1S288c (Figure 34). Nevertheless no significant change in noise level was observed so that this SNP might contribute to increase cell-cell-variability at equal mean expression levels.

Selective advantage conferred by the noisiest pCUP1 promoter variant

It remained to consider whether this higher noise observed with pCUP1EC1118 might confer a benefit and increase population survival upon exposure to constant selective conditions. To test for selective advantages, each variant was fused to the She ble gene (ZeoR) (conferring resistance to phleomycin) in the pJRL2 plasmid and integrated in the LEU2 locus in 59A. The main problem with using copper as a selective agent in these growth experiments would have been the need to use the same copper concentration for both variants. Indeed, as they are not induced in the same manner by copper (less copper is needed to get the same mean expression with the industrial variant), we would have had differences of mean expression levels between the variants by using copper as a selective agent, making interpretations about the impact of noise impossible. This led us to choose ZeoR and phleomycin as an adequate system to test our hypothesis. Experiments were performed at steady state induction levels after overnight induction (see Methods and [START_REF] Blake | Phenotypic consequences of promoter-mediated transcriptional noise[END_REF]). Briefly, each strain was induced in adequate copper concentrations (10 µM for pCUP1S288c and 5 µM CuSO4 for pCUP1EC1118) to obtain similar mean expression levels and differences of noise in YNB medium. The mean expression levels of ZeoR have been verified by RT-qPCR ( Figure 35).

Figure 35: RT-qPCR of ZeoR expression

Then in exponential phase, each strain was or was not exposed to different concentrations of phleomycin to determine the residual growth at each concentration (Figure 36). While the 59A control without ZeoR was highly sensitive, induced strains had the highest residual growth and non-induced strains had intermediate phenotypes. pCUP1EC1118 conferred a slightly higher residual growth in 30 µg/mL phleomycin without copper, probably linked to the higher basal mean expression (Figure 30G).

In inducing conditions, pCUP1EC1118 significantly improved growth in 50 µg/mL phleomycin compared to pCUP1S288c (Figure 36). In lower and higher concentrations, both strains exhibited the same behaviour.

Thus the effects of the increased cell-cell variability conferred by this variant on growth in selective environment are observed only in a specific range of phleomycin concentrations.

Figure 36: Residual growth after 24h treatment with various phleomycin concentrations

To finely determine growth kinetics, we followed the growth curves in 40, 50 and 60 µg/mL phleomycin in proper copper concentrations for both strains to have identical mean expression levels, as well as the growth curves with copper only (Figure 37). While growth did not show any difference in 5 µM and 10 µM CuSO4 without phleomycin, pCUP1EC1118-ZeoR induced in 5µM CuSO4 gave a better growth than pCUP1S288c-ZeoR induced in 10µM CuSO4 at all measurement points in 50 µg/mL phleomycin (Figure 37B). We also observed a significant difference in 40 and 60 µg/mL phleomycin (Figure 37A and C) but it was less important, as one could have expected by looking at the residual growth curves (Figure 36). Taken together, this result confirmed that the difference of promotermediated noise between the natural promoter variants pCUP1EC1118 and pCUP1S288c is sufficient to confer different abilities to survive, but only in a given range of selective pressure.

Discussion

The genetic determinants of transcriptional-mediated noise have been characterized by rationally manipulating or randomly mutating gene promoters [START_REF] Sanchez | Genetic Determinants and Cellular Constraints in Noisy Gene Expression[END_REF]. All these studies unravel the origins that underlie gene-specific expression variability by showing that intrinsic noise is at least in part generated by cis-acting regulatory elements embedded within the DNA sequence of each promoter. As far as we are aware, there is no studies focused on natural promoter variants that may confer different noise levels and consequently different benefits in stressful environments, so that the relevance to adaptation in natural systems remains to be determined [START_REF] Ackermann | Microbial individuality in the natural environment[END_REF][START_REF] Viney | Adaptive noise[END_REF][START_REF] Holland | Phenotypic heterogeneity is a selected trait in natural yeast populations subject to environmental stress[END_REF].

Here we screened for promoters conferring high noise in the genome of the haploid 59A yeast strain derived from the industrial wine EC1118 strain to search for natural variants conferring adaptive advantage in stressful conditions through enhanced cell-cell variability. As expected, sequenced EC1118 genomic fragments conferring noisy expression were enriched in GO categories possessing significantly greater-than-expected expression noise [START_REF] Zhang | Stochastic expression and epigenetic memory at the yeast HO promoter[END_REF]. Especially, promoters of genes involved in nitrogen compounds transport were over-represented. Genes implicated in nitrogen metabolism are also among the genes showing significant variation in expression among natural isolates of S. cerevisiae [START_REF] Carreto | Expression variability of co-regulated genes differentiates Saccharomyces cerevisiae strains[END_REF]. These data should make sense because a positive correlation is known between gene expression noise and gene expression divergence in yeast [START_REF] Lehner | Selection to minimise noise in living systems and its implications for the evolution of gene expression[END_REF][START_REF] Zhang | Stochastic expression and epigenetic memory at the yeast HO promoter[END_REF].

Moreover, nitrogen assimilation is highly variable among wine strains and correlates to fermentation efficiency [START_REF] Treu | Oxidative stress response and nitrogen utilization are strongly variable in Saccharomyces cerevisiae wine strains with different fermentation performances[END_REF].

Our study of 8 promoter variant pairs at the genomic level reveals that higher mean expression with one variant was not always associated to lower noise while this correlation has been reported many times [START_REF] Newman | Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise[END_REF][START_REF] Hornung | Noise -mean relationship in mutated promoters[END_REF][START_REF] Carey | Promoter sequence determines the relationship between expression level and noise[END_REF]. When differences in mean expression exist, higher expression is generally observed with promoters from EC1118 (except for pAGP2). Nevertheless it often depends on the genetic background because the variants of some promoters give different mean expression levels in 59A while they do not show any difference in BY4720. Therefore cis-(promoter sequence variations) and trans-(cellular factors involved in gene expression) factors are associated to enable this enhanced expression in the industrial strain.

Interestingly, the difference of noise observed between variants of several promoter pairs also depends on the genetic background, revealing epitasis in the generation of promoter-mediated noise.

We confirmed the difference in terms of noise between the pCUP1 promoter variants by inducing them in different copper sulfate concentrations conferring the same mean expression level. One problem could be that changes in gene expression level could be from gene expression burst frequency or/and burst size and that adding more copper into a media might increase burst frequency and thus produce less gene expression noise for the laboratory variant. Nevertheless the difference in noise observed between the two variants in these induction conditions seems not to be due to changes in burst frequency generated by different copper concentrations because at the basal level, we already observed a non-expected result between the two variants of the CUP1 promoter in 59A (while it is not observed in S288c): the higher mean conferred by the industrial variant was not associated with lower noise. Moreover, pCUP1EC1118 is still noisier than pCUP1S288c in 59A at the same copper concentration, even if the mean level conferred by pCUP1EC1118 is higher (while mean values are not significantly different in Figure 32E). Thus, increased noise is a feature of the industrial variant only revealed in the 59A background, independently of copper concentration. This result indicates that the genetic variations probably increase burst size and decrease burst frequency at the same copper concentration in 59A, explaining the increased mean and noise levels.

Differences in global constraints on noise have already been reported between yeast strains [START_REF] Ansel | Cell-to-cell stochastic variation in gene expression is a complex genetic trait[END_REF][START_REF] Fehrmann | Natural sequence variants of yeast environmental sensors confer cell-to-cell expression variability[END_REF] but most studies showed that stochastic transcriptional kinetics in yeast is mainly determined by gene-specific effects (Sanchez et al, 2013). Here we show the combined and correlated influence of cis-and trans-acting factors which contribute to enhance expression noise for a given gene. Thus, as suggested recently, single-cell transcriptional kinetics is affected by both promoter architecture and genome-wide processes in yeast [START_REF] Sanchez | Genetic Determinants and Cellular Constraints in Noisy Gene Expression[END_REF]. The hypothesis of noisier expression of regulatory factors (noise propagates in regulatory pathways [START_REF] Blake | Noise in eukaryotic gene expression[END_REF]) is not relevant because both versions would be identically affected. As pCUP1EC1118 enhances noise only in 59A, the fact that proteins involved in CUP1 transcription or in the global transcriptional process in this strain might be more sensitive to genetic variations in pCUP1 in terms of promoter binding. This hypothesis might provide an explanation for this epistatic interaction in the consequences of pCUP1 sequence modifications. In any case, the consequences of the SNP or the deletion between the pCUP1 variants are not dramatic and might reveal that natural systems evolve through promoter modifications generating small effects on noise, and not strongly affecting cell-cell variability such as the ones produced by rational manipulation of yeast promoters.

Phenotypic consequences of noise in gene expression in terms of survival in selective environments are little studied [START_REF] Viney | Adaptive noise[END_REF]. Only artificial systems using rationally modified promoters have been employed to test noise-mediated fitness differences [START_REF] Blake | Phenotypic consequences of promoter-mediated transcriptional noise[END_REF][START_REF] Smith | Glutathione and Gts1p drive beneficial variability in the cadmium resistances of individual yeast cells[END_REF]. For instance, by introducing mutations within the TATA region of an engineered S. cerevisiae GAL1 promoter, Blake et al have shown that increased cell-cell variability in the expression of ZeoR confer a clear benefit in Zeocin containing medium [START_REF] Blake | Phenotypic consequences of promoter-mediated transcriptional noise[END_REF]. Nevertheless differences of noise between the mutated promoters were very high and this proof of principle did not imply that such an adaptation mechanism through noise modulation may naturally occur. By fusing natural yeast variants of pCUP1 with ZeoR, we show here that their different noise levels at equal mean expression indeed provide distinct abilities to survive in a constant environmental stress, even if the noise difference is far less important than in Blake's study. Growth curves show that growth reduction is only observable in a specific range of phleomycin concentrations. This result is quite different from Blake's study where enhanced noise was always either disadvantageous or beneficial in all tested concentrations. Here a benefit is conferred by pCUP1EC1118 at intermediate concentrations of phleomycin and no growth difference with pCUP1S288c exists at lower or higher antibiotics concentrations. The slightly more heterogeneous expression distribution with pCUP1EC1118 might provide a possible explanation. Indeed the difference between the distributions might be too weak to reveal a selective advantage at high phleomycin concentrations because very few cells express more ZeoRp with pCUP1EC1118 in the extreme subpopulation. On the contrary, at intermediate concentrations a larger proportion of the population is above the expression threshold necessary to grow in these concentrations. Therefore the difference between the distributions might be sufficient in this case to generate a benefit because more cells express more ZeoRp with pCUP1EC1118.

Identification of a stronger pCUP1 in an industrial wine strain makes sense because wine yeasts are frequently exposed to high copper content in fermentation must, especially because the Bordeaux mixture containing copper sulfate is widely used as a fungicide in vineyards. CUP1 is one of the best examples of high correlation between evolution in a stressful environment, the expression level of the gene conferring resistance to this environment, and the resistance itself [START_REF] Adamo | Amplification of the CUP1 gene is associated with evolution of copper tolerance in Saccharomyces cerevisiae[END_REF][START_REF] Chang | Dynamic large-scale chromosomal rearrangements fuel rapid adaptation in yeast populations[END_REF]. One can also expect that the genome of wine strains would contain more CUP1 copies than laboratory strains. which is actually not the case as for EC1118 as well as for other wine strains [START_REF] Dunn | Microarray karyotyping of commercial wine yeast strains reveals shared, as well as unique, genomic signatures[END_REF]. This might be explained by a selective disadvantage of having lots of CUP1 copies counterbalancing the benefits of copper resistance. Interestingly, various genes involved in stress response, especially CUP1, have been positively correlated with the fermentation duration [START_REF] Ambroset | Deciphering the molecular basis of wine yeast fermentation traits using a combined genetic and genomic approach[END_REF]. Thus a high stress response was associated with a low fermentation capacity. Evolution of CUP1 towards higher noise in its expression is conceivable: high mean expression is both advantageous in high copper concentrations and disadvantageous for fermentation. These traits exert opposing selective pressures on CUP1 expression. Noisier expression would be advantageous in that it would make the population harbour an optimum between fermentation capacity and adaptability to copper rich environments when necessary. In bacteria, a constant selective pressure select for mutants harbouring either higher mean with no noise increase or higher noise with similar mean [START_REF] Ito | How selection affects phenotypic fluctuation[END_REF]. But opposing selective pressures would most likely select for increased expression variability only. Thus, pCUP1 might have evolved towards higher noise because it might increases fitness in fermentation-associated copper-rich environments. More direct evidence could be provided by evolving S. cerevisiae in controlled fermentative copper-rich environments, where high CUP1 expression is advantageous regarding copper, and disadvantageous regarding fermentation efficiency. It would likely confirm the selection for noisy CUP1 expression. It has recently been shown that environmental stress selects for organisms with increased phenotypic heterogeneity in yeast populations, but no links with increased variability in the expression of key genes have been established yet [START_REF] Holland | Phenotypic heterogeneity is a selected trait in natural yeast populations subject to environmental stress[END_REF]. Finally, numerous genomic studies are in progress and new yeast genomes will be available soon to determine if positive selection seems to have recently occurred on the pCUP1 of wine strains. Genetic studies would also determine if the observed genetic variations are under positive selection or if alternative explanations (neutral evolution, relaxed selective constrain, or fixation of slightly deleterious mutation) should be favoured.

Collectively, our results provide evidences that natural yeast promoter variants can exhibit different levels of transcriptional-mediated noise but also that epistasis exists in the generation of this noise: the combined influence of promoter sequence modifications and the trans-background acting contribute to modify it. Finally, we show that natural yeast promoter variants conferring distinct abilities to survive in a stressful environment through noise modulation can be found among S.

cerevisiae strains, showing that this possible adaptation mechanism has to be considered when studying yeast evolution and when exploring natural and artificial genetic diversity to improve industrial yeast strains [START_REF] Steensels | Improving industrial yeast strains: exploiting natural and artificial diversity[END_REF].

Perspectives

Further exploration of the library

Newman et al (2006) have shown that the yeast genome contains at least one thousand high noise genes. As the library we have constructed confidently covers the whole yeast genome, there should be much more "noisy clones" in the library after the FACS selection than the number that we have identified. Therefore, it should be possible to identify more high noise clones in the library by large scale screening using high performance flow cytometry and robotics. Single cells can be isolated into 96-well plates by a sorting system and their expression profile analyzed as described before. By this way, more high noise promoters would be identified after sequencing of the fragments conferring the most variable yEGFP expression. Moreover, analysis of these new data by bioinformatic tools could reveal interesting information such as particular sequence motifs or cis-elements in the high noise promoters in the industrial strain which might be more likely to evolve.

The study of 2 variants of 8 yeast promoters at the genomic level in different strains indicated that cis-elements and trans-factors both play an important role in the control of expression noise. Here we only focused on the effects of cis-elements (in promoters), but the effects of trans-factors are also worth of exploiting. QTL analysis is an useful tool to find how elements of the genetic background affects noise levels in trans [START_REF] Ansel | Cell-to-cell stochastic variation in gene expression is a complex genetic trait[END_REF][START_REF] Fehrmann | Natural sequence variants of yeast environmental sensors confer cell-to-cell expression variability[END_REF]. If we compare the same variant of a promoter in different strains in Figure 30, we can see for instance that pCUP1EC1118 exhibited higher mean level and higher noise in 59A compared with BY4720, whereas pCAN1S288c exhibited lower noise in 59A with the same mean expression level as in BY4720. Thus these 2 promoters in different strains could be used for independent QTL analysis to find the trans-factors in 59A which might increase or decrease the expression noise.

Moreover, we have demonstrated that the higher noise conferred by pCUP1 EC1118 in 59A can increase the phleomycin resistance at certain concentrations when driving ZeoR expression. But whether this difference can confer better resistance to copper is still to be tested. We have previously explained that these two promoters have different mean expression levels when the media where they are induced contain the same copper concentration, making impossible any interpretation of the effects of noise. An alternative method would be using promoters independent of the copper concentration, which confer similar mean expression levels and different noise levels, either when induced or at the basal expression level. By expressing the CUP1 gene under the control of these promoters, it would be possible to test if a promoter giving higher noise indeed gives better resistance to high concentrations of copper. Nevertheless, we need cup1Δ strains for this experiment but this deletion is very difficult to obtain because of gene duplication. Alternatively, its regulator CUP2 could be deleted (the cup2Δ strains are highly sensitive to copper) what would affect all the pCUP1 copies.

Promoters such as pAGP2 and pCAN1 are also interesting to be further studied. Like pCUP1EC1118, pCAN1EC1118 confers a higher expression level in 59A without decreasing expression noise, suggesting that it probably provides higher noise at equal mean. Unfortunately, pCAN1 is not inducible, thus we cannot directly compare the noise level from different variants to confirm this hypothesis because modulation of the mean expression level is not possible. Nevertheless, opposing selection pressures selecting for noisy CAN1 expression might be provided by its positive effect in nitrogen-poor musts (favouring enhanced CAN1 expression to transport arginine) and its negative consequence in musts where toxic agents transported by the permease Can1p are present (such as the arginine analogue canavanine which is synthesized by many plants). Again, it would be interesting to directly test this hypothesis by placing S.cerevisiae in fluctuating environments containing alternatively high canavanine or low arginine concentrations.

In contrast to pCAN1EC1118, pAGP2EC1118 exhibits lower expression level in 59A with similar expression noise as pAGP2S288c, indicating that its expression noise is more constrained in the industrial strain. pAGP2 cannot be induced neither but AGP2 is a sensor for environmental changes [START_REF] Schreve | Yeast Agp2p and Agp3p function as amino acid permeases in poor nutrient conditions[END_REF] and affects the regulation of many transporter genes [START_REF] Maraganore | High-resolution whole-genome association study of Parkinson disease[END_REF]. Further analysis of AGP2 to explore hypotheses explaining why its noise level is lower in 59A could give some insights on the different aspects of noise evolution in industrial strains.

Strain improvement through increasing expression noise

Increased expression noise of certain genes confers selective advantages in both fluctuating and constant environments through increasing the global phenotypic heterogeneity in the population. Thus it should be possible to improve the ability of industrial stains to resist to certain stresses through increasing the expression noise of the genes related to these stresses. For instance, we can consider the osmoadaptation pathway in yeast: the membrane proteins Sln1, Hkr1, or Msb2 act as osmosensors that react to environmental information and simulate the MAPK signaling pathway to allow resistance to osmotic stress [START_REF] Tanaka | Yeast osmosensors Hkr1 and Msb2 activate the Hog1 MAPK cascade by different mechanisms[END_REF]. The transmembrane transporter genes are shown to exhibit high expression noise in wild strains [START_REF] Fehrmann | Natural sequence variants of yeast environmental sensors confer cell-to-cell expression variability[END_REF], thus by increasing their expression noise, resistance to osmotic stress might be increased. Their expression noise could be modified through mutating their promoter so that only the genes we are interested in would be affected, and that the industrial properties of the strain we want to improve will remain the same. As it is difficult to expect precise effects on noise by targeting specific promoter elements, and because it should be interesting to find mutated promoters with increased noise but similar mean (what is improbable because increasing noise often decreases the mean), the promoter region of these genes could be randomly mutagenized by PCR and then cloned and fused to yEGFP into an integrative plasmid. These mutated promoters fused to yEGFP could then be integrated in an industrial strain and the mutated promoter library be subjected to the same procedure as described previously to select high noise promoters.

Then resistance to osmotic stress of the strain where the original promoter have been replaced by a selected mutated variant would be tested by parallel experiments or competition experiments.

Study of bimodal expression pattern

During the screening for high noise clones in the genomic library after FACS selection, we identified some interesting clones harbouring bimodal expression patterns, meaning that they had two peaks in the distribution whereas the other clones had only one peak. This expression pattern has been observed in different organisms from E. coli to humans (Figure 16) (Silva-Rocha & de Lorenzo, 2012; [START_REF] Pelet | Transient activation of the HOG MAPK pathway regulates bimodal gene expression[END_REF][START_REF] Shalek | Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells[END_REF]. One of the advantages of this pattern is that it confers two very different subpopulations that can be adapted to contrasted situations in an isogenic population (referred as bet-hedging, [START_REF] Viney | Adaptive noise[END_REF]. The underlying mechanisms of a bimodal expression pattern are not clear. In this part, we worked on several bimodal clones to further analyze their behavior at the genomic level.

Results

Bimodal expression clones after FACS selection

During the isolation of high noise clones (section 2.1.1.3), we found around 30% of the clones we selected exhibiting bimodal expression (Figure 38A). This expression pattern was very stable and reproducible. First, we isolated 2 single sub-clones from the clones we selected and both of them exhibited bimodal expression as the original ones (Appendix I). Second, we applied a very small gate to select the most homogenous cells in the population and it did not change the expression profile (Figure 38B). Third, we extracted the plasmids from 12 of these clones and re-transformed them to the CEN.PK and BY4720 strains. The de novo transformants all exhibited bimodal expression (Figure 38C). Thus we inferred that this expression pattern is determined by the genomic sequences inserted before yEGFP in the plasmids. Finally, the plasmids from the 31 bimodal clones were sequenced, and 19 different fragments were mapped to S288c genome. All of them corresponded to promoter sequences (see section 2.1.2), seventeen for protein coding genes and two for non-coding RNA genes has been identified (Table 5).

Bimodal expression of CUP1

When we induced the CUP1 promoter variants at genomic level in BY4720 in YPD medium, both variants showed bimodal expression (Figure 40). There was no differences and we did not observe this expression profile in 59A (Figure 40), indicating that this expression pattern was mainly controlled by trans-factors. We studied the induction dynamics of pCUP1S288c in BY4720 (Figure 41). For the induced bimodal profile, the first peak was at the same expression level as the basal non-induced expression and the second peak moved as time increased. After the first 60 min, the positive peak moved to the highest levels but the cell proportions in the 2 peaks didn't change, so the mean expression level increased. After 1h the second peak moved to lower levels, and the proportion still did not change. Finally, the 2 peak fused to one peak (t=120min). Moreover, we also tried different concentrations of copper sulfate to induce pCUP1S288c (Figure 42). Again, the first peak was at the same position as for the basal expression. The second peak moved to higher levels as copper increased, and the cell proportion in this peak also increased.

pCUP1 S288c

Figure 42 Expression profile of pCUP1S288c induced by different concentrations of copper sulfate for 1h in BY4720

Finally we fused YFP to CUP1 at the original locus in BY4720 and 59A, and then measured the expression profiles in different conditions. Unfortunately, these strains did not exhibit bimodal expression profile neither in normal conditions nor in inducing conditions (Figure 43).

Discussion

We identified 19 different fragments conferring a bimodal expression pattern in a centromeric plasmid. But their corresponding promoters fused to YFP integrated in the LEU2 locus all exhibited a unimodal expression pattern in an optimal environment (YPD medium). By exchanging the whole promoter of CAN1 and its corresponding fragment between the centromeric and the integrative plasmids, we showed that both conferred bimodal pattern when they are located in the centromeric plasmid whereas they both exhibited unimodal pattern after genomic integration. Thus, in this case the bimodal expression pattern is a feature of the expression from centromeric plasmids. Different expression profiles of the same gene located on chromosome and centromeric plasmid have been observed for long time. In general, promoters giving low and moderate expression levels are sensitive to this context effect [START_REF] Marczynski | A transcription map of a yeast centromere plasmid: unexpected transcripts and altered gene expression[END_REF]. We observed this effect for pCUP1 and pAGP2: they conferred low unimodal expression at the genomic level, while a higher expression level and a bimodal profile were observed in the centromeric plasmid. On the contrary, the expression level from promoters giving higher expression level such as pBMH1, pBMH2 and pGNP2 did not change a lot, but the expression profile changed. These effects are supposed to be due to the different structural features between plasmids and chromosomes. Chromosomes are highly structured by histones and other proteins in the chromatin and their arrangement is precisely controlled in the nucleus whereas plasmids are less regulated by such events.

Bimodal pattern was also observed from the CUP1 promoter variants in BY4720 when they are induced by copper sulfate, but not in 59A. One hypothesis to explain this phenomenon might be the different copy number of CUP1 in BY4720 and 59A. Recent researches have shown that the laboratory strain S288c can contain up to 14 copies of CUP1 [START_REF] Zhao | Structures of Naturally Evolved CUP1 Tandem Arrays in Yeast Indicate That These Arrays Are Generated by Unequal Nonhomologous Recombination[END_REF], while the sequencing of EC1118 indicated that it contains much less number of copies than S288c [START_REF] Novo | Eukaryote-to-eukaryote gene transfer events revealed by the genome sequence of the wine yeast Saccharomyces cerevisiae EC1118[END_REF]. Thus, to compensate its lower CUP1 copy number, 59A might exhibit higher induction efficiency of CUP1 than BY4720 what might lead to unimodal expression pattern. Otherwise, CUP1

is mainly activated by the transcription factor Cup2 [START_REF] Shen | Remodeling of yeast CUP1 chromatin involves activator-dependent repositioning of nucleosomes over the entire gene and flanking sequences[END_REF]. The Cup2 binding site in the CUP1 promoter is identical in BY4720 and 59A. Thus the different induction efficiencies in these strains might be due to different CUP2 expression levels.

In BY4720, pCUP1 exhibited different behaviors during induction, either depending on time or copper concentration. Under certain copper concentrations, a constant proportion of the population is induced and the induction level of this subpopulation first increased then decreased along induction time, whereas after the same induction time, higher copper sulfate concentrations induced more cells at higher expression levels. But the underlying mechanism still has to be explored. We think the induction dynamics of CUP2 might play an important role in these phenomena.

Finally, when CUP1 was fused to YFP at its own chromosome locus in 59A and BY4720, it showed higher expression level and unimodal expression pattern in both cases (Figure 43) with or without induction by copper sulfate, indicating that the chromosome locus and environment play an important role in the regulation of CUP1 and their effects are independent of copper induction.

The expression level from the original locus is much higher than from the LEU2 locus indicating that the transcriptional regulators might preferably bind to the original locus.

When we fused YFP to CUP1 by transformation, we obtained several clones. We tested their expression profile and found all of them exhibited the unimodal expression pattern. Considering that there are several copies of CUP1 in the genome, YFP might be fused to different copy of CUP1 in different clones. But they all expressed CUP1 similarly, indicating that every copy of CUP1 seems to be similarly controlled.

Perspectives

Our results showed that the regulation of bimodal expression is very complex. The main question is why the centromeric plasmid can confer bimodal expression pattern. The deciphering of regulation differences between plasmids and chromosomes can help us to better understand the control of gene expression in yeast. The differences between plasmid and chromosome that affect gene expression might be their topological structure and/or their nucleosome configuration.

On one hand, in the population transformed with the centromeric plasmid, cells could be divided to two parts according to their expression level. Then the plasmids from these two subpopulations could be isolated to see if there are differences between them in terms of nucleosome configuration. On the other hand, for the bimodality at LEU2 locus, using inhibitors of chromatin modifications such as trichostatin A (inhibits histones deacetylation) and 5-azacytidine (inhibit DNA methylation) could change the chromatin status and help us to understand the possible relationship between chromosome structure and bimodality.

As mentioned before, Cup2 plays an important role in the CUP1 regulation. Exploring the CUP2 expression dynamics could help us to understand the complex expression pattern of CUP1.

Thus we could fuse tdTtomato to CUP2 in its original position in both backgrounds. YFP and tdTtomato can be detected simultaneously and can be used to couple the studies of the CUP1 and CUP2 expression dynamics what might uncover the possible mechanism of bimodal expression in BY4720. We could also try to eliminate the copy number effects of CUP1. All CUP1 repeats could be deleted and replaced by a single copy of pCUP1-CUP1-YFP, what would help us to better understand the regulation of CUP1 without copy number variation in the genome.

Otherwise we can see that the CUP1-YFP fusion in 59A exhibits much higher expression levels than in BY4720, but its noise level is similar. Thus CUP1-YFP probably exhibit higher noise in 59A.

We could perform gene introgression analysis to find the possible background effects that affect expression noise. In BY4720, YFP was integrated with kanR whereas in 59A it was with natR. Thus we could just mix these two strains to create diploid cells and select the diploid cells on plates with both G418 and nourseothricin. Then these diploids would be put on KAc plates to promote sporulation and the haploid cells be tested for CUP1-YFP expression level and noise. One haploid strain conferring high expression noise and neocin resistant would be backcrossed with BY4720.

This procedure could be repeated for at least 10 times to finally sequence the last strain to find QTLs affecting the expression noise of CUP1-YFP.

Connecting Noise to Genome Instability

In this part, we have tried to test our hypothesis that higher noise in some genome maintenance genes can give better global adaptability to different stresses by increasing the rate of genetic-variant generation (RGVG) [START_REF] Capp | Noise-driven heterogeneity in the rate of genetic-variant generation as a basis for evolvability[END_REF]. Thus we fused DNA repair and maintenance genes with two different modified PHO5 promoters, which confer similar mean expression levels with different levels of noise [START_REF] Raser | Control of stochasticity in eukaryotic gene expression[END_REF]. One of them had mutations in the TATA box region (TATATA were converted to AATATT, referred as pPHO5TATA-A1T6), the other one had mutations in the UAS (upstream activating sequence) region (CACG were converted to AAGC, referred as pPHO5UASm1). pPHO5TATA-A1T6 has a lower noise level than pPHO5UASm1. These promoters both need a medium without phosphate to induce the expression of the genes of interest, and the expression level can be modified by adding phytic acid as source of phosphate. These constructions were transformed to two different strains: JA0200 which has URA3 inserted inside the tandem repeats (TRs) present in the FLO1 gene [START_REF] Verstrepen | Intragenic tandem repeats generate functional variability[END_REF] and JA0300 where URA3 is placed under the dependence of the SDT1 promoter [START_REF] Vinces | Unstable tandem repeats in promoters confer transcriptional evolvability[END_REF]. In the first strain, homologous recombination between the TRs in FLO1 can delete the URA3 gene which allows survival on 5-FOA plates. Therefore we can use JA0200 to measure the homologous recombination frequency (HRF).

In the second strain, the number of TRs in the promoter of SDT1 can affect the mean expression level of URA3 which therefore can evolve towards higher or lower expression. Thus JA0300 can be used to perform fluctuating selection experiments.

Results

Effects of noise on the homologous recombination frequency

Induction conditions of the PHO5 promoter variants conferring similar expression levels and different noise levels

We first considered the HR pathway for studying the impact of noise on genome stability.

RAD52 and RAD27 are proteins that can impact the HRF. The RAD52 deletion can decrease the HR frequency whereas the RAD27 deletion increases the HRF. pJRL2 plasmids containing the different PHO5 promoter variants fused to RAD52-YFP or RAD27-YFP were transformed to be integrated into the LEU2 locus in the JA0200 strain. This strain is a derivative of the original KV133 strain (where URA3 has been inserted in FLO1) where LEU2 was reinserted into the genome(Figure 23). Then the native RAD52 or RAD27 genes were deleted using LYS2. Under full induction (Pi-free medium without any phytic acid), pPHO5TATA-A1T6 exhibited lower noise level but higher mean expression level than pPHO5UASm1 (Figure 44, p<0.01 for both mean values and noise levels and both RAD52-YFP and RAD27-YFP). We tried to decrease the expression level of pPHO5TATA-A1T6 by adding phytic acid into the phosphate free medium [START_REF] Raser | Control of stochasticity in eukaryotic gene expression[END_REF]. Finally, we found that pPHO5TATA-A1T6 induced in Pi-free medium containing 60µM phytic acid exhibited the same mean expression level as pPHO5UASm1 under full induction (Figure 44, p=0.56 for RAD52-YFP and p=0.99 for RAD27-YFP), whereas they had different noise levels (Figure 44, p=0.03 for RAD52-YFP and p=0.02 for RAD27-YFP). We also measured the native expression level of RAD52 and RAD27 through fusing YFP to the N-terminal domain at their original chromosomal positions. We found that, under the induction conditions we have chosen, the mean expression levels from the PHO5 promoters are about the half of their native levels (Figure 44).

Measurement of the homologous recombination frequency

First, we measured the HRF of all the JA0200 derivative strains incubated in induction media for 7h (Figure 45). The addition of 60µM phytic acid in the medium did not affect the frequency in the control strain JA0200. Thus, phytic acid has no effects on homologous recombination. The The strains harbouring RAD52-YFP under the control of the PHO5 promoter variants exhibited the same HFR as JA0200, indicating that the cassette pPHO5-RAD52-YFP can compensate the RAD52 deletion. On the contrary, the HRF of the strains harboring RAD27-YFP under the control of the PHO5 promoter variants were between those of Δrad27 and JA0200, indicating that the cassette pPHO5-RAD27-YFP could only partially compensate the RAD27 deletion. Unfortunately, we did not detect statistically phenotypic differences between the promoters confer similar expression level but different noise level.

We also transformed the plasmids containing only YFP under the control of the PHO5

promoter variants into JA0200. We found that, without induction, pPHO5UASm1 already exhibited higher expression than pPHO5TATA-A1T6 (Figure 46). Even though this difference was undetectable when YFP was fused to RAD52 or RAD27, we cannot ignore this effect which might generate different HRF in these strains before induction by Pi-free medium. Thus to be more accurate, we should measure the HRF before induction and subtract it from the final HRF. But this makes the experiments very complex. Thus some other interesting promoters were tried to simply the system.

Searching for new promoters conferring similar expression levels with different noise

We selected some other interesting promoters or promoter pairs from the literature which confer the same mean expression level but very different noise levels. We replaced the PHO5 promoter variants by these new promoter pairs in the plasmids containing RAD27-YFP and RAD52-YFP. The first candidate was pZRT2 which exhibit different noise (but similar mean) when induced by different concentrations of zinc sulfate [START_REF] Carey | Promoter sequence determines the relationship between expression level and noise[END_REF]. We selected 4 wild-type promoters (pRNR2, pCDC19, pCCW12 and pTDH2) from the results of [START_REF] Newman | Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise[END_REF] because they constitute two promoter pairs with similar mean and different noise. Finally, we selected six promoters from the library of [START_REF] Sharon | Inferring gene regulatory logic from high-throughput measurements of thousands of systematically designed promoters[END_REF]. These promoters consist of the HIS3 core promoter and a variable sequence containing different combinations of Gcn4 binding sites and poly (A:T) sequences.

All of these plasmids we mentioned above were transformed into JA0200 to integrate the constructions into the LEU2 locus, and their expression level in YNB URA -were compared in exponential phase. Finally, we found two promoters that we named P1 and P4 from Sharon et al.

(2014) which confer similar expression levels but different noise when driving RAD27-YFP and RAD52-YFP expression (Table 6).

Variations of the tandem repeats number in the SDT1 promoter

The JA0300 strain was constructed by replacing the ORF of SDT1 by URA3 at its original locus (Figure 23). With this strain, we can perform opposite selection on the expression of pSDT1-URA.

Using URA -medium, high expression level of pSDT1-URA can be selected for through modifications of the tandem repeats (TRs) number in the SDT1 promoter [START_REF] Vinces | Unstable tandem repeats in promoters confer transcriptional evolvability[END_REF], whereas in the opposite way we expect selection for low expression level of pSDT1-URA in 5-FOA containing medium. Thus, this strain could be used to create fluctuating selection and see if higher noise in the expression of RAD52 or RAD27 increases adaptability in this fluctuating context through increasing the ability to change the number of TRs. Then we tested if the promoters with short TRs can increase again their length to recover high expression of URA3 under URA -condition. Thus we selected clones P2 and B5 and spread them on URA -plate. After 3 days, some clones appeared. We randomly selected 10 clones to sequence the TRs-containing region of the SDT1 promoter and found that all the 10 clones had exactly the same TRs number as the original one (22 TRs).

Discussion

Through varying the induction conditions, we succeeded in getting similar mean expression but different noise levels for the two promoter variants pPHO5UASm1 and pPHO5TATA-A1T6 fused to RAD52-YFP or RAD27-YFP. But the fluorescence levels given by these fusion proteins and the difference between them are much smaller than when the promoter are fused to YFP only, indicating important post-transcriptional effects on noise regulation. This phenomenon was also observed with the promoters chosen from [START_REF] Sharon | Inferring gene regulatory logic from high-throughput measurements of thousands of systematically designed promoters[END_REF]. Thus post-transcriptional regulation of genome maintenance genes seems to minimize their expression noise when they are produced from these promoters. These genes might be subjected to negative selection of phenotypic heterogeneity buffering molecular variations [START_REF] Richard | How does evolution tune biological noise?[END_REF].

We tried to test if different expression noises in the expression of genome maintenance genes can lead to different levels of genome instability by measuring the HRF. But unfortunately we did not observe any significant difference. Considering that the cells divided only 1.5 times during the induction period in Pi-free medium, the HRF we measured were mainly controlled by the initial frequency in the population. Therefore any potential phenotypic difference generated by the induced pPHO5 variants was not easily detectable.

We also observed different expression levels from pPHO5UASm1 and pPHO5TATA-A1T6 before induction. This makes the precise measurement of the effect of noise on HRF very difficult. Thus, we tried different other promoters or promoter pairs from other published researches to find suitable candidates. Only a pair of synthetic promoters created by [START_REF] Sharon | Inferring gene regulatory logic from high-throughput measurements of thousands of systematically designed promoters[END_REF] gave us noninduced expression levels with similar means but different noises.

We also constructed a strain (JA0300) for fluctuating experiments where the URA3 expression is driven by pSDT1. The expression level conferred by pSDT1 can be varied by changing the TRs number in the promoter through HR. The sequencing results of clones growing either on URA-or 5-FOA medium showed that the TRs number varied between these 2 environments, suggesting that we were able to select for higher URA3 expression levels in URA -medium and select for lower URA3 expression level in 5-FOA medium obtained by HR between the TRs in pSDT1. Thus this system could be applied to test our hypothesis that high noise in the expression of genomestability related genes (RAD52 or RAD27 in this case) could increase the population fitness in fluctuating environments (URA -and 5-FOA in our system) by increasing the tunability of the RGVG.

Perspective

Among the promoters conferring similar expression levels but different noise levels that several researches have identified [START_REF] Newman | Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise[END_REF][START_REF] Hornung | Noise -mean relationship in mutated promoters[END_REF][START_REF] Sharon | Inferring gene regulatory logic from high-throughput measurements of thousands of systematically designed promoters[END_REF][START_REF] Carey | Promoter sequence determines the relationship between expression level and noise[END_REF], we have already tested several pairs, but they often exhibited different behaviours comparing to the reference, mainly because we expressed proteins fused to YFP and not YFP only as most of these articles did. This shows the complexity of noise regulation. On one hand, we will continue to test some interesting promoters from literature. On the other hand, we can also perform artificial mutations on native promoters (like in section 2.3.2) to create suitable Gene expression heterogeneity, the so-called noise, in an isogenic population has been observed for decades, but finely and quantitatively measured since the beginning of the 2000s. It is an inherent property of the gene expression process. Recent researches have demonstrated that gene expression noise can be tunable according to the gene function and the cell state. It might be subjected to selection: it is shown that high expression noise is a favoured under tight selections.

Expression noise also increases the phenotypic heterogeneity which is an advantage under unstable environments.

Compared to lab strains, wild and industrial yeast strains face to more complicated and stressful environments. Thus one can suppose that these strains exhibit higher expression noise in stress-related genes. In the first part of this thesis, we try to verify this hypothesis in an oenological strain EC1118. We first constructed a genomic library driving the expression of yEGFP and adapted a fluctuating selection procedure by FACS to select the genomic fragments conferring higher expression noise of yEGFP. We found that this procedure indeed enriched for high noise fragments in the library but there were still large number of negative clones. Thus we isolated single clones in the library after selection and tested their expression profiles. Finally we identify 98 clones exhibiting high expression noise of yEGFP. So combined FACS selection and single clone verification can efficiently select for the high noise promoters in yeast.

The fragments in the 98 clones were sequenced and mapped to the reference genome S288c.

We found 50 potential promoters in these sequences. According to the function of the corresponding genes, we selected 8 promoters to compare the yEGFP expression profile driven by different variant in different background. The expression profile is different between different backgrounds as well as different variants, indicating the interactions between background and promoter sequence are very common and also play an important role in the generation of gene expression noise.

There is a general tendency between the mean expression level and expression noise: high expression level tends to be associated with low expression noise. But, in 59A, pCUP1EC1118 exhibited high expression level but similar noise level as pCUP1S288. Thus we assumed that if we can make their expression level identical, pCUP1EC1118 would have higher noise than pCUP1S288. So we induced the expression of pCUP1 by different concentration of copper sulfate in different media.

We showed that indeed pCUP1EC1118 exhibits higher noise level than pCUP1S288 in the 59A background while these two promoter variants did not show any difference in the BY4720 background. So we demonstrated that industrial strain harbour high expression noise in the expression of certain genes (CUP1), but this effect needs the interaction between the promoter sequence (cis-effects) and the genetic background (trans-effects), revealing epistasis in the generation of promoter-mediated noise.

As CUP1 confers copper resistance in yeast, one crucial question comes: does high noise expression of CUP1 indeed increase copper resistance? Unfortunately, we cannot answer it directly because the two promoter variants are not induced at the same level by a precise copper concentration. So, we used the zeoR gene conferring resistance to phleomycin to study the phenotypic consequences of different noises of pCUP1. We demonstrated that the slightly higher noise in the expression of zeoR conferred by pCUP1EC1118 compared with pCUP1S288 is sufficient to provide a better resistance in a range of phleomycin concentrations. Moreover there are often many CUP1 copies in the yeast genome. In general, increasing the CUP1 copy numbers can increase copper resistance, but large number of CUP1 might decrease the fermentation ability. Thus increasing copper resistance through higher noise expression would be a compromise between these two aspects.

During the selection of high noise clones in the library, we found some clones which exhibited a bimodal expression profile, thus the second part of this thesis we tried to understand the underlying mechanisms of bimodal expression. We found that centromeric plasmid allow bimodal expression whereas genomic integration in the LEU2 locus generally suppress this property. One possible explanation is the difference between the genome and centromeric plasmids in terms of nucleosome binding and modifications. We also found that pCUP1-YFP in BY4720 could harbour a bimodal expression profile at the genomic level when it is induced by copper sulfate. Because Cup2 is the major transcription factor that regulates the expression of CUP1, we suppose that CUP2 might play a role in this bimodality.

The third part of this thesis tried to verify the hypothesis that high noise in genome stability related genes can increase the rate of genetic variant generation, what would be an advantage in stressful and fluctuating environments, and thus beneficial for rapid adaptation. We first focused on the homologous recombination pathway and two genes which impact this pathway (RAD52 and RAD27). Unfortunately no consequences on genome stability of a difference of noise in the expression of these genes have been found yet.

and then deleted the original gene).

Plasmids

The pJRL2 plasmids from [START_REF] Raser | Noise in gene expression: origins, consequences, and control[END_REF] were used for genomic integration in the LEU2 locus. Nevertheless the selection cassette was modified: the his-URA3-kanR-his sequence was replaced by kanMX4 only isolated from the pFA6a-GFP-kanMX4 vector (using BglII and KpnI).

Moreover the YFP gene where a Kozak sequence had been introduced has been replaced by yEGFP without Kozak sequence after PCR amplification from the pUG35 vector and insertion within pJRL2 The plasmids pFA6a-yEGFP-kanMX6 and pFA6a-YFP-kanMX6 were used to create N-terminal fusion of RAD52, RAD27 or CUP1 with GFP or YFP at their original chromosomal locus. All plasmids are listed in Appendix VII.

Primers

The primers are listed in Appendix VIII.

Media and culture conditions

E. coli

E. coli were grown in LB medium. It contains: 1% tryptone, 1% yeast extract, 0,5% NaCl. 50 μg.ml - 1 ampicillin (150 mg.ml -1 as stock) was added to select for transformants with the ampR gene. Solid media contain 2% agar. Ampicillin was added when the media were below 60 °C after autoclaving.

S. cerevisiae

Yeast strains were grown in YPD or YNB medium. YPD medium contains: 2% glucose, 1% peptone, 1% yeast extract. 200 μg.ml -1 G418 (100 mg.ml -1 as stock) was added to select for transformants with the kanR gene. 100 μg.ml -1 nourseothricin (100 mg.ml -1 as stock) was added to select for transformants with the natR gene. YNB medium contains: 2% glucose, 0.17% yeast nitrogen base, 0.5% ammonium sulfate. 0.077% complete supplement mixture (CSM) minus URA was added to select for URA3 transformants. 0.069% CSM minus LEU was added to select for LEU2 transformants.

0.074% CSM minus LYS was added to select for LYS2 transformants. Solid media contain 2% agar. All the antibiotics were added when the media were below 60 °C after autoclaving.

Pi-free medium contains five parts: (1) carbon source: 2% glucose; (2) nitrogen source: 0.5% ammonium sulfate; (3) minerals: 0.055% potassium chloride, 0.05% magnesium sulfate, 0.01% sodium chloride and 0.1% calcium chloride; (4) vitamins: 2 µg.L -1 biotin, 400 µg.L -1 calcium pantothenate, 2 µg.L -1 folic acid, 2000 µg.L -1 inositol, 400 µg.L -1 niacin, 200 µg.L -1 p-Aminobenzoic acid, 400 µg.L -1

Pyridoxine hydrochloride, 200 µg.L -1 riboflavin and 400 µg.L -1 thiamine hydrochloride; (5) trace elements: 500 µg.L -1 boric acid, 40 µg.L -1 copper sulfate, 100 µg.L -1 potsssium Iodide, 200 µg.L -1 ferric chloride, 400 µg.L -1 manganese sulfate, 200 µg.L -1 sodium molybdate and 400 µg.L -1 zinc sulfate.

Vitamins and trace elements were prepared as 1000× stock and sterilized through 0.22 µm filtering.

The carbon source, nitrogen source and minerals were mixed and autoclaved. 0.077% CSM minus URA was added to select for the URA3 gene.

5 min.

DNA cloning

The plasmids and PCR products were digested by the enzymes we have choosen with their proper buffer at their optimal temperature. The target fragments were separated by electrophoresis and purified through the GeneJET Gel Extraction Kit (Thermo), following its standard protocol in the manual. The digested plasmids and PCR products were ligated by T4 ligase (NEB) at room temperature (RT) overnight. The final plasmids were transformed to E.coli and verified by sequencing. For the fragments containing several construction sites inside them, we used the In-Fusion® HD Cloning Kit (Clontech) instead, following its stand protocol in the manual.

Transformation

Bacteria

Chemical transformation i) Competent cells preparation

An overnight culture was diluted to OD 0.1. The fresh culture was incubated at RT with shaking for 6h until the OD reach 0.6 to 0.8. The final culture was placed on ice for 10 min and centrifuged at 3000 rpm for 10 min at 4 °C. The pullets were suspended with 1/8 volume ice-cold TB buffer (10 mM pipes, 55 mM MnCl2, 15 mM CaCl2, 250 mM KCl, pH = 6.7). The suspension were kept on ice for 10 min and centrifuged for 10 min. The pullets were re-suspended with 1/12 volume TB buffer containing 7% DMSO. 100 µl aliquots were taken and thrown in liquid nitrogen immediately. The aliquots were stored at -80 °C.

ii) Transformation

The competent cells were taken out from -80 °C and thawed on ice. 2µl plasmid DNA were added and mixed gently. The suspension was kept on ice for 5 min. Then it was put into 42 °C bath for 45 seconds. Then the suspension was put on ice for another 5 min. 1ml LB medium was added to the suspension and was incubated for 1 h at 37 °C with shaking before it was spread on plates with ampicillin. The plates were incubated at 37 °C for one night.

Electroporation transformation i) Competent cells preparation

An overnight culture was diluted 100 times. The fresh culture was incubated at RT with shaking for 8h until the OD reach 0.5 to 0.8. The final culture was placed on ice for 15 min and centrifuged at 3000 rpm for 15 min at 4 °C. The pullets were suspended with the same volume of water. The suspension was centrifuged for another 15 min. The pullets were re-suspended with half volume water and centrifuged as above. Then 1/50 volume 10% glycerol solution was used to suspend the pullets.

This suspension was also centrifuged as above. Finally, 1/500 volume 10% glycerol solution was used to suspend the pullets. 40 µl aliquots were taken and thrown in liquid nitrogen immediately. The aliquots were stored at -80 °C.

ii) Transformation

The competent cells were taken out from -80 °C and thawed on ice. The suspension was mixed with 2µl plasmid DNA and transferred to a cold 1.5mm cuvette. The electroporation was performed on a Bio-Rad Gene Pulser (25 µF, 2.5 kV and 200 W). 1 ml LB was added immediately with gentle mixing.

The suspension was incubated for 1 h at 37 °C with shaking before it was spread on plates with ampicillin. The plates were incubated at 37 °C for one night.

Yeast

An overnight culture was diluted to OD 0.5. The fresh culture was incubated at 30 °C with shaking for about 4h (≈2 divisions, OD≈2). The final culture was centrifuged at 5000 rpm for 5 min (5ml culture per transformation). The pullets were washed by water and centrifuged again as above. 1.0 ml 100 mM LiAc were used to re-suspend the pullets. The suspension was centrifuged at 13000 rpm for 30 sec. The following mixture was added to the pullets: 240 µl PEG (50% w/v); 36 µl 1.0 M LiAc; 50 µl ss-DNA (2.0 mg/ml, denatured at 95 °C for 5 min and kept on ice ); X µl Plasmid DNA or PCR products (0.1 -10 µg, the pJRL2 plasmids have to be digested by AscI before transformation) and 34-X µl sterile H2O

(respecting the order above). The cells had to be well mixed with these solutions. The suspension was first incubated at 30 °C for 30 min and then at 42 °C for another 30 min. Finally the suspension was centrifuged at 8000 rpm for 30 sec. If auxotrophic marker was applied, the pullets were re-suspended with 200 µl water and spread on corresponding plates. If an antibiotic agent was applied, the pullets were re-suspended with 500 µl YPD and incubated at RT for one night. They were spread on corresponding plates the next day. The plates were incubated at 30 °C for at least 2 days. The integration of integrative plasmids was verified by PCR.

Directed mutagenesis of the CUP1 promoter

Point mutations

We used directed mutagenesis to insert three SNPs to the pCUP1S288c variant. Primers with one of these SNPs (Appendix VIII) were used to amplify the plasmid with pCUP1S288c by PCR. The original plasmids were then digested by DpnI. The final plasmids with one point mutation were transformed to E. coli and verified by sequencing.

Deletions

The pJRL2 plasmids containing pCUP1S288c-yEGFP and pCUP1EC1118-yEGFP were digested with XbaI (cutting between SNP3 and the deletion) and SacI (cutting downstream yEGFP) and the deletion was introduced into pCUP1S288c by exchanging the fragments.

RT-qPCR

RNA extraction

5ml yeast culture at OD ≈ 1 was centrifuged at 3000 rpm for 5 min. The cells were washed by water and centrifuged as above. The pullets were thrown in liquid nitrogen immediately and stored at -80 °C. The total RNAs were extracted through SV Total RNA Isolation Kits (Promega), following its standard protocol in the manual. The concentrations were measured by Nano-drop (Thermo).

The quality of RNA was controlled by RNA 6000 Lab-on-Chip bioanalyzer (Agilent).

RT-qPCR

1µg total RNA was mixed with iScript™ Reverse Transcription Supermix (Bio-Rad) in 20 µl final. The reverse transcription was performed as describing in the manual. This system was diluted 10 times for qPCR analysis.

Real-time PCR were carried out on the iCycler iQ5 thermal cycler (Bio-Rad). cells were collected into a sterile Falcon tube. Cells were collected at medium flow rate and sorted on the basis of "single cell" and "purity". After sorting, cells were spun at 3000g for ten minutes and any FACS buffer was removed. Cells were re-suspended in 1ml YNB URA -medium and grown overnight. The following day the process was repeated but the gate included only the lowest 5% of cells expressing yEGFP. This process was repeated for a total of seven rounds of selection, with gates being drawn for selected populations in a fluctuating manner with alternatively the highest or the lowest 5% of yEGFP expression in the gate. After the 4th round of selection cells were placed at 4°C for 48 hours. After this time, selection was resumed as normal until the 7th round.

Flow cytometry analysis

Enrichment of high noise clones

The library after FACS selection was plated on YNB URA -agar plates, and single colonies were randomly selected to confirm the enrichment in clones with high noise in yEGFP expression.

Because not all the clones harbored noisy yEGFP expression, a screening was needed to sequence plasmids from the noisiest clones only. Single clones were randomly selected, grown in 96-well plates overnight in YNB URA -medium. 10 5 cells of each clone were analyzed for yEGFP expression on FACS Calibur (Becton Dickinson). Analysis of cytometry data was performed by the Cellquest™ software (Becton Dickinson). Calculation of variation in yEGFP expression was performed as followed to limit the influence of cellular aggregates, cell detritus, and undefined values: for each clone, a gate was created on the forward scatter (FSC) and side scatter (SSC) dot plot to exclude extreme or zero values from total counts and to include only a population of cells homogeneous in terms of size, shape, and cellular complexity. A single gate size was chosen for all analyses in order to maintain a conservative estimate of noise. The coefficient of variation (CV) was calculated for fluorescence in this gate. For some clones, a smaller gate was also applied on the densest subset of cells using the FSC/SSC density plot. This gating lowered average CV values because it minimizes "extrinsic" noise due to physiological differences between cells. This allowed verifying that CV was

Growth in phleomycin-containing medium

Individual colonies of ZeoR-expressing strains were used to inoculate YNB medium, and strains were grown overnight either with appropriate CuSO4 concentrations to get steady-state induction at the same mean level (10µM CuSO4 for pCUP1S288c-ZeoR and 5µM for pCUP1S288c-ZeoR)

or in the absence of CuSO4. The expression levels of ZeoR were verified by RT-qPCR described as above.

After dilution to OD=0.2, cultures were grown 5h in the same culture conditions as overnight (either with the same CuSO4 concentrations or in the absence of CuSO4) prior to phleomycin exposure. Then these cultures was diluted 100 times with the same media with the same CuSO4 concentrations and divided into 11 aliquots. Appropriate volume of phleomycin solution (Invivogen) was added to generate a series of cultures containing 0 to 100 μg.ml -1 phleomycin. This series was inoculated at 30°C with 200 rpm shaking and the OD of each tube was measured after 24h. The residual of growth was calculated as the percentage of OD with certain phleomycin comparing to OD without any phleomycin. For experimental growth time course at 40, 50 and 60 μg.ml-1 phleomycin or without phleomycin, OD were followed during 35h to draw the growth curve. All these experiments were repeated at least 3 times.

Measurement of the homologous recombination frequency 6.6.1 Induction of the PHO5 promoters

The induction of PHO5 promoters was modified from [START_REF] Raser | Control of stochasticity in eukaryotic gene expression[END_REF]. An overnight cultures (YNB URA -) was diluted to OD=0.5 by phosphate-free medium with different concentrations of phytic acid (cells were washed at least 4 times by water before inoculating).

These fresh cultures were incubated at 30 °C with 200 rpm shaking. The expression level of YFP at different time points were measured as described above. The conditions conferring similar expression level with different expression noises for the two PHO5 promoter variants were chosen for further analysis (60 µM phytic acid for TATA-A1T6 and without any phytic acid for UASm1, both at 7h induction).

Measurement of homologous recombination frequency

The cell density of the cultures was measured by flow cytometry. 100 cells were spread on YPD plate to measure the viability of each strain. 10 6 , 10 7 and 10 8 (Nt) cells were spread on 5-FOA plate (the plate which had around 100 clones on the plate was taken to calculate the homologous recombination frequency (HRF)). The number of clones appeared on the YPD plates (N1) and 5-FOA plates (N2) after 2 days were used to calculate the frequency of homologue recombination (FHR): f=(N2/Nt)*(N1/100). The HRF before and after induction were both measured.

List of abbreviations

List of figures

C T YIL143C SSL2 5 chr09 T C chr09 G A chr09 G GT chr09 T G chr09 TT T chr09 T C chr09 A C chr10 T C YJL184W GON7 1 chr10 G C chr10 G A chr10 G A chr10 131062 TT T YJL155C FBP26 1 chr10 131168 C T chr10 T C chr10 C T chr10 C T chr10 A G YJL117W PHO86 2 chr10 ATTGCA A chr10 T A chr10 AAA A chr10 A G YJR146W 1 chr10 C T chr10 T G chr10 AA A chr10 T A chr10 C A chr10 A G YJR162C 1 chr10 C A chr10 A C chr10 T G chr10 T A chr10 G T chr10 G GAG chr10 G GT chr10 A G chr10 T TA chr10 G A chr10 G C chr12 C A YLR390W-A CCW14 1 chr12 T A chr12 C T chr12 C T chr12 T C chr12 T TT chr12 T TT chr12 CT C chr13 T A YML008C ERG6 1 chr13 T C YMR091C NPL6 1 chr13 T C chr13 T C chr13 A G chr13 C T chr13 G A chr13 C T chr13 G A YMR145C NDE1 1 chr13 G A YMR147W 1 chr13 A G chr13 GA G chr13 T C chr13 A G chr13 AAA A chr13 A AA chr13 G T chr13 C A YMR203W chr14 A G YNL154C YCK2 1 chr14 T C YNL071W YNL071W LAT1 2 chr14 A C chr15 T TT SNR50/snR50 2 chr15 G A YOR099W KTR1 1 chr15 T A chr15 T C chr15 T C chr15 A AAA chr15 A C chr15 C T chr15 T TC chr15 C T chr15 G A YOR306C MCH5 1 chr15 C T chr15 G C chr15 G C chr15 G GG YOR335C ALA1 1 chr16 A G YPL226W NEW1 1 chr16 G A chr16 TT T chr16 C T YPL078C ATP4 1 chr16 G A chr16 T C YPR166C MRP2 2 chr16 A AA chr16 T C chr16 A G chr16 T C chr16 G A chr16 C CC chr16 T TT
Appendix V: Transcription factor binding sites in pCUP1 S288c and pCUP1 EC1118

pCUP1.

  Sous une concentration de cuivre précise, une proportion constante de la population est induite et le niveau d'induction de cette sous-population a d'abord augmenté puis diminué pendant le temps d'induction, alors qu'avec le même temps d'induction, des concentrations supérieures de CuSO4 ont induit plus de cellules à des niveaux d'expression plus élevés. Nous proposons que la dynamique d'induction de CUP2 puisse jouer un rôle important dans ces phénomènes. Enfin, lorsque CUP1 a été fusionné à YFP à son propre locus chromosomique dans 59A et BY4720, il a conféré un niveau d'expression plus élevé et un profil d'expression uni-modal avec ou sans induction par CuSO4, ce qui indique que le locus chromosomique joue un rôle important dans la régulation de CUP1 et ses effets sont indépendants de l'induction du cuivre. Le niveau d'expression au locus d'origine est beaucoup plus élevé qu'au locus LEU2 indiquant que les régulateurs de transcription pourraient de préférence se lier au locus d'origine.

Figure 2 :

 2 Figure 2: Structure of the core promoter of a RNAPII transcribed gene (modified from Fuda et al. (2009))

Figure 3 :

 3 Figure 3: RNAPII transcription cycle (modified from Shandilya & Roberts, 2012)

  There are two Pho4 binding sites on the PHO5 promoter: UASp1 and UASp2. During phosphate (Pi) starvation, Pho4 is accumulated in the nucleus and binds to the first site UASp1. This binding leads to the hyperacetylation of the nucleosomes located on the promoter. The hyperacetylated nucleosomes are then evicted. Thus the promoter becomes open and the UASp2 can be bound by Pho4 too. With the help of other factors, the binding of Pho4 to the UAS sites finally activates the transcription of PHO5 (Figure 5).

Figure 5 :

 5 Figure 5: Activation of the PHO5 promoter (modified from Fuda et al, 2009)

Figure 6 :

 6 Figure 6: Chromatin structure[START_REF] Luger | New insights into nucleosome and chromatin structure: an ordered state or a disordered affair?[END_REF]

  have characterized of the extensive diversity in the transcriptional output from the yeast genome. Through transcript isoform sequencing (TIF-seq), they have found large isoform diversity in yeast such as transcripts covering 2 open reading frames (ORFs) (bicistronic transcripts), overlapping 2 ORFs, overlapping 3' or 5' of one ORF, untranslated region (UTR) ends variations, etc.

Figure 8 :

 8 Figure 8: Major transcript isoforms in yeast[START_REF] Pelechano | Extensive transcriptional heterogeneity revealed by isoform profiling[END_REF]

Figure 10 :

 10 Figure 10: Translation elongation cycle (Schneider-Poetsch et al, 2010)

Figure 13 :

 13 Figure 13: Deciphering of intrinsic and extrinsic noises. Extrinsic noise is manifested as scatter along the diagonal and intrinsic noise as scatter perpendicular to the diagonal (Raser & O'Shea, 2004).

  Meanwhile, in theory,[START_REF] Miller-Jensen | Varying virulence: epigenetic control of expression noise and disease processes[END_REF] proposed a two-state model that explains how chromatin remodeling can affect noise level. More recently,[START_REF] Weinberger | Expression noise and acetylation profiles distinguish HDAC functions[END_REF] measured expression of ADH3-GFP in yeast strains with various deletion of chromatin-remodelling factors and showed that histone deacetylation complexes (HDACs) have different effects: Rpd3(L)C and Set3C both repress expression but Rpd3(L)C only also increases noise.[START_REF] Vinuelas | Quantifying the contribution of chromatin dynamics to stochastic gene expression reveals long, locus-dependent periods between transcriptional bursts[END_REF] used 5-azacytidine (inhibitor of DNA methyltransferase) and trichostatin A (inhibitor of histone deacetylase) to modify the chromatin state of chicken cells and showed that chromatin dynamics has remarkable effects on gene expression noise (but direction and magnitude are different among different cell lines).

Figure 14 :

 14 Figure 14: Relationship between noise and mean expression levels in various experiments in yeast.

Figure 15 :

 15 Figure 15: Relationship between expression noise and plasticity[START_REF] Bajić | Balancing noise and plasticity in eukaryotic gene expression[END_REF]

Ehrlich

  et al. (2011) used a parallel multi-channel microfluidic system with flow cytometry to largely increase the detection capacity of rare events.[START_REF] Bendall | Singlecell mass cytometry of differential immune and drug responses across a human hematopoietic continuum[END_REF] developed a new technology called mass cytometry which uses time-of-flight (TOF) mass spectrometry to detect and quantify target molecules labeled by antibodies with heavy metals (isotopes). This machine has lower sensitivity compare to fluorescence-based flow cytometry, but largely increases the multi-parameter analysis capacity. Finally,[START_REF] Zuleta | Dynamic characterization of growth and gene expression using high-throughput automated flow cytometry[END_REF] combined a robotic manipulating system with flow cytometry to enable a simultaneous high-throughput and time-resolved measurement of gene expression profiles, cellular growth rates and protein degradation rates.

Figure 18 :

 18 Figure 18: Schematic diagram of the phylogenetic relationships between the Saccharomyces species and their industrial specialization. The species involved in industrial processes and/or in hybrids are boxed in light grey. The products of industrial processes involving the hybrids and non-hybrids are boxed in dark grey. The arrows correspond to hybrids (Dequin & Casaregola, 2011).

Figure 19 :

 19 Figure19: Selection pressure on noise[START_REF] Eldar | Functional roles for noise in genetic circuits[END_REF] a: Directional selection for values of the trait above a threshold (black line and arrow) can lead to reduced noise when the threshold is low. Thus, the noisier distribution (blue line) has less area above the threshold (cross-hatch) than a less noisy distribution (green line, grey shading) with the same mean. b: By contrast, when selection is tighter, the noisier distribution is favoured, as shown by the larger above-threshold area under the blue distribution compared to the green distribution. c: Over evolutionary timescales, noise (σ, defined as the standard variation of the distribution) would thus be expected to increase under tight selection and decrease under weak selection.

  which contains two HIS3 gene with different mutations that can be recovered by HR. The rate of HIS3 restoration indicates the HR frequency. Another method take advantages of the strain FW1259(Selva et al, 1995). The defection of SPT15 in FW1259 creates LYS autotrophy. Two copy of SPT15 with different mutations are inserted to the genome, and then the recombination between them will generate intact SPT15 and thus LYS prototrophy. The rate of LYS prototrophy indicates the HR frequency.[START_REF] Verstrepen | Intragenic tandem repeats generate functional variability[END_REF] created a series of strains allowing measurement of the HR frequency. They took advantage of the tandem repeats (TRs) present inside the FLO1 gene encoding a cell-surface adhesin. In the wild type gene (in the laboratory strain S288c), there are 18 TRs of around 100 nt, separated by less conserved 45 nt sequences. A single copy of the URA3 gene was inserted among these repeats at several locations. These strains were first grown in URA -medium to avoid proliferation of cells lacking URA3, and then a precise number of cells were spread on 5-FOA plate. Finally, the number of clones appearing on the plate divided by the total number spread indicated the HR frequency. In this study we will use the strain KV133, where URA3 is in the middle of the repeats, which is the location conferring the highest HR frequency (Figure23). It has been shown that the deletion of RAD52 in this strain results in decreased HR frequency whereas the deletion of RAD27 increases the frequency.

Figure 23 :

 23 Figure 23: Structure of the genomic reporter used to measure HR frequency. FLO1 has tandem repeatsinside its coding sequence. In the strain KV133, the URA3 gene was inserted in the middle of the repeats (modified from[START_REF] Verstrepen | Intragenic tandem repeats generate functional variability[END_REF]). SDT1 has tandem repeats in its promoter. In the SDT1pr-URA strain, we inserted URA3 just downstream of the SDT1 promoter (modified from[START_REF] Vinces | Unstable tandem repeats in promoters confer transcriptional evolvability[END_REF]).

Figure 24 :

 24 Figure 24: Fluorescence distribution in the control population and the population transformed with the yEGFP-fused genomic library

Figure 25 :

 25 Figure 25: Procedure of fluctuating FACS selection

Figure 29 :

 29 Figure 29: Relationship between noise and mean expression. Blue dots show the data from BY4720 back ground and orange dots show the data from 59A background. Blue dash line is the noise-mean regression curve for BY4720. Orange dash line is the noise-mean regression curve for 59A. The formulae and coefficients of determination are shown beside the curves.

Figure 30 :

 30 Figure 30: Different behaviours of promoter variants at the genomic level. Dashed lines separate the results obtained in the different backgrounds for a given promoter pair (BY4720 on the left, 59A on the right).(A-H) Results for the pBMH1, pGNP1, pBMH2, pHAC1, pYCK2, pAGP2, pCUP1 and pCAN1 promoter variants respectively. Scale for the mean is on the right and scale for the noise is on the left of the histograms. The same nomenclature is used for each histogram: each variant is named by the promoter name with the name of the strain where it comes from in subscript (S288c or EC1118). Results are means of three independent cultures, and error bars are standard deviations. A significant statistical difference between mean or noise levels conferred by the promoter variants in a given genetic background is represented by (*) when p<0.05 in T test or (**) when p<0.01.

Figure 31 :

 31 Figure 31: One hour induction of pCUP1 in BY4720 and 59A in YPD medium. A and B: Induction curves of pCUP1S288c and pCUP1EC1118 by CuSO4. C and D: Different induction conditions of pCUP1S288c and pCUP1EC1118 giving similar expression levels. The asterisk mark indicates a difference in T-test (p < 0.05).

Figure 32 :

 32 Figure 32: One hour and overnight induction of pCUP1 in 59A in YNB medium. A and B: Induction curves of pCUP1S288c and pCUP1EC1118 by CuSO4. C , D and E: Different induction conditions of pCUP1S288c and pCUP1EC1118 giving similar expression levels. The asterisk mark indicates a difference in T-test (p < 0.05).

Figure 33 :

 33 Figure 33: Induction dynamics of pCUP1 in YPD with 20 µM CuSO4

Figure 34 :

 34 Figure 34: Measurement of mean and noise levels conferred by mutated pCUP1S288c

Figure 37 :

 37 Figure 37: The pCUP1EC1118 and pCUP1S288c promoter variants confer distinct growth abilities in various phleomycin concentrations.

Figure 38 :

 38 Figure 38: Fluorescence distribution in some selected clones showing bimodal expression

FluorescenceFigure 39 :

 39 Figure 39: Expression profile of pCAN1 and its partial fragment isolated from the enriched library in different plasmids in BY4720

Figure 40 :

 40 Figure 40: Expression profile of the CUP1 promoter variants in BY4720 and 59A. The figure shows the expression profile of pCUP1S288c and pCUP1EC1118 when they are induced by 20µM copper sulfate for 1h.

Figure 41 :

 41 Figure 41: Expression dynamics of pCUP1S288c at different time of induction by 20µM copper sulfate in BY4720

Figure 43 :

 43 Figure 43: Expression profile of YFP fused to CUP1 in BY4720 and 59A

Figure 44 :

 44 Figure 44: Induction of the pPHO5 promoter variants fused to RAD52-YFP or RAD27-YFP

RAD52Figure 45 :

 45 Figure 45: Homologous recombination frequency in the JA0200 derivative strains expressing different DNA recombination genes with different noise levels and similar mean expression

Figure 46 :

 46 Figure 46: Expression of pPHO5TATA-A1T6-YFP and pPHO5UASm1-YFP without induction

First

  , we tried to verify that the SDT1 promoter evolves in 5-FOA towards different numbers of TRs reducing URA3 expression. Cells grown in liquid YNB URA -medium were spread on YNB plates containing 0.5 g/L 5-FOA (which is half of the concentration used for the anti-selection of URA3 to avoid severe mutations under strong selection). After 3 days, some clones appeared, but there were two types of clones (Figure47): we called them big size clones (B) and small size clones (P).

Figure 47 :

 47 Figure 47: Two types of clones on 5-FOA plate (0.5 g/L) among the population harbouring the pSDT1-URA3 cassette

Figure 48 :

 48 Figure 48: Different TRs numbers in the SDT1 promoter driving URA3 expression in clones appearing on 5-FOA plate (0.5 g/L). C1 is the original strain which contains 22 TRs. The other clones are numbered randomly, while B means "big-size clone" and P means "small-size clone".

using

  EcoRI and NotI (Figure49). The 2 variants of all the 8 promoters studied at the genomic level in part 2 were PCR-amplified with SalI and EcoRI sites in the primers to replace the original promoter sequence driving GFP expression in the pJRL2 plasmid. ZeoR, RAD52-YFP and RAD27-YFP were PCR-amplified with EcoRI and NotI sites in the primers to replace the yEGFP gene in the pJRL2 plasmid. ZeoR was amplified from plasmid pZE2 (a gift from LBME (Laboratory of Eukaryotic Molecular Biology, Toulouse)). RAD52-YFP and RAD27-YFP was amplified from the genome DNA of their N-terminal YFP fusion strain (see below).

Figure 49 :

 49 Figure 49: Structure of the pJRL2 plasmids

  of the promoterless yEGFP-coding vectors Three different promoterless yEGFP-coding vectors (Figure 50) were constructed using the yEGFP-coding pUG35 centromeric plasmid as a backbone vector. The MET25 promoter and the multiple cloning site upstream yEGFP of this vector have been replaced by the kanMX4 gene and a unique SnaBI restriction site (generating blunt ends). The kanMX4 gene was PCR amplified from the pFA6 vector to recombine within pUG35. The forward primer contained homology to the beginning of the MET25 promoter of pUG35. The reverse primers contained homology to the end of the multiple cloning site of pUG35. Three different reverse primers have been used to construct three different plasmids containing 0, one or two additional base(s) between SnaBI and the start codon of yEGFP. The resulting plasmids were promoterless yEGFP-coding vectors containing a SnaBI restriction site before the start codon of yEGFP to fuse genomic fragments to yEGFP.

Figure 50 :

 50 Figure 50: Structure of the promoterless yEGFP-coding vectors

  of pCUP1-yEGFP by copperAn overnight culture was diluted to OD=0.3 and cells were grown in YPD or YNB medium until exponential phase (4~5 h) before adding CuSO4 (ProLabo). Time-dependent induction was measured in 20µM CuSO4 during up to 3h and concentration-dependent induction studies was measured after 1h in concentrations up to 50µM CUSO4. Strains are then analyzed by flow cytometry as described above. When induction conditions giving similar mean expression levels for the different CUP1 promoter variants have been determined, experiments were reproduced at fixed time and concentration for a given variant. Overnight induction was measured after dilution of a copper-induced overnight culture to OD=0.3 and growth in the same CuSO4 concentration during 5h.

Figure 14 :

 14 Figure 14: Relationship between noise and mean expression levels in various experiments in yeast ....................................................................................................................................................

Figure 15 :

 15 Figure 15: Relationship between expression noise and plasticity .....................................................

Figure 16 :

 16 Figure 16: Unimodal and bimodal expression profiles in GAL1 expression in yeast..........................

Figure 17 :

 17 Figure 17: Examples of microfluidic systems .....................................................................................

Figure 18 :

 18 Figure 18: Schematic diagram of the phylogenetic relationships between the Saccharomyces species and their industrial specialization .................................................................................

Figure 19 :

 19 Figure 19: Selection pressure on noise ..............................................................................................

Figure 20 :

 20 Figure 20: Viability of clones with similar mean expression level but different noise of zeoR ......... Figure 21: Rad51-dependent homologous recombination ................................................................

Figure 22 :

 22 Figure 22: Synthesis dependent strand annealing and double-Holliday junction. ............................

Figure 23 :

 23 Figure 23: Structure of the genomic reporter used to measure HR frequency ......................................

Figure 24 :

 24 Figure 24: Fluorescence distribution in the control population and the population transformed with the yEGFP-fused genomic library ...............................................................................................

Figure 25 :

 25 Figure 25: Procedure of fluctuating FACS selection ...........................................................................

Figure 26 :

 26 Figure 26: Distribution of the fluorescence levels in the library before and after selection .............

Figure 27 :

 27 Figure 27: CV of yEGFP expression in clones from selected and control populations .......................

Figure 28 :

 28 Figure 28: Screening for clones with high CV among the selected population .................................

Figure 29 :

 29 Figure 29: Relationship between noise and mean expression ..............................................................

Figure 30 :

 30 Figure 30: Different behaviours of promoter variants at the genomic level .....................................

Figure 31 :

 31 Figure 31: One hour induction of pCUP1 in BY4720 and 59A in YPD medium .......................................

Figure 32 :

 32 Figure 32: One hour and overnight induction of pCUP1 in 59A in YNB medium ................................... Figure 33: Induction dynamics of pCUP1 in YPD with 20 µM CuSO4 .....................................................

Figure 34 :

 34 Figure 34: Measurement of mean and noise levels conferred by mutated pCUP1S288c ..........................

Figure

  Figure 35: RT-qPCR of ZeoR expression ................................................................................................ 79

Figure 46 :Figure 47 :Figure 49 :Figure 50 :

 46474950 Figure 46: Expression of pPHO5TATA-A1T6-YFP and pPHO5UASm1-YFP without induction ..................... 102 Figure 47: Two types of clones on 5-FOA plate (0.5 g/L) among the population harbouring the pSDT1-URA3 cassette ............................................................................................................... 104 Figure 48: Different TRs numbers in the SDT1 promoter driving URA3 expression in clones appearing on 5-FOA plate (0.5 g/L) .......................................................................................... 105 Figure 49: Structure of the pJRL2 plasmids ..................................................................................... 116 Figure 50: Structure of the promoterless yEGFP-coding vectors ..................................................... 122

  After several rounds of constant selection, they found both clones with elevated mean expression level and clones with elevated expression noise but a similar mean. Recently, similar results were observed in yeast[START_REF] New | Different Levels of Catabolite Repression Optimize Growth in Stable and Variable Environments[END_REF]. They grow yeast cells in recurrent YPD and YPGal media with different interval time for a long period. The authors found two types of mutated clones during the evolutionary experiments: specialist clones with strict catabolite repression (low expression noise of genes related to alternative carbon sources), high growth rates in glucose but long lag phases when cells are placed in a different carbon source; and generalist

. Several researchers have shown that noisy or variable expression of certain genes play a role in bet-hedging strategies

[START_REF] Veening | Bet-hedging and epigenetic inheritance in bacterial cell development[END_REF][START_REF] Levy | Bet hedging in yeast by heterogeneous, age-correlated expression of a stress protectant[END_REF][START_REF] Silva-Rocha | Stochasticity of TOL plasmid catabolic promoters sets a bimodal expression regime in Pseudomonas putida mt-2 exposed to m-xylene[END_REF]

.

Moreover, evolutionary experiments can selected for clones with increased noise.

[START_REF] Ito | How selection affects phenotypic fluctuation[END_REF]

first confirmed the possibility to make noise evolve in E. coli.

clones with loose catabolite repression (high expression noise of genes related to alternative carbon sources), slow growth in glucose but short lag phases when cells are placed in a different carbon source. It is notable that selection with long interval periods between different carbon sources favours the specialist phenotype whereas selection with short interval periods favours the generalist phenotype.

Table 1 : Reduction of noise by sub-gating on a more homogeneous part of the population in selected clones

 1

	Clone	Number of cells	Mean	CV	Number of cells-small gate	Mean-small gate	CV-small gate	CV reduction (%)
	28-G4-01	99060	110,15 193,33	39475	120,2	128,64	0,335
	28-H3-02	98935	224,6	175,75	54986	151,47	108,87	0,381
	30-BE11-01	99036	1030,76 150,58	55414	718,37	117,55	0,219
	30-BE11-02	99238	1036,96 148,86	56247	739,07	118,23	0,206

Table 2 : Summary of sequencing results and mapping of genomic fragments from clones exhibiting noisy yEGFP expression Reads and genetic differences

 2

	Number

Table 3 : Results of GO analysis GO_ID GO_term Cluster frequency Background frequency p-value Gene(s) annotated to the term

 3

	6820	anion	7 out of 50	139 out of 7167	0.02	PHO88/AGP2/GNP1/CAN1/DNF1
		transport	genes,	background		/PHO86/MCH5/YOR306C
			14.0%	genes, 1.9%
	71705 nitrogen	8 out of 50	214 out of 7167	0.04	AGP2/GNP1/CAN1/VRG4/YSSL2/
		compound	genes,	background		/MCH5/NEW1/YPL226W
		transport	16.0%	genes, 3.0%

Table 6 : Expression and noise levels of RAD27-YFP or RAD52-YFP conferred by promoter variants chosen from Sharon et al. (2014)

 6

	Gene	Expression level (a.u.)	Noise
	P1-GFP*	13	0. 037
	P4-GFP*	9	0.0056
	P1-RAD52-YFP	1189±120	0.363±0.031**
	P4-RAD52-YFP	1073±143	0.493±0.032
	P1-RAD27-YFP	4872±573	0.254±0.021**
	P4-RAD27-YFP	4372±620	0.363±0.018

  °C to 90 °C by 0.5 °C per sec. The data were analyzed by CFX Manager™ software (Bio-Rad). TAF10, UBC6 and ALG9 were used as reference genes to normalize the gene expression level.

The qPCR system contains: 4 µl diluted RT system, 2 µl forward primer (2 µM), 2 µl reverse primer (2 µM), 2 µl water and 10 µl SsoAdvanced™ Universal Supermixes (Bio-Rad). The PCR cycles: 1, 98 °C 30 sec; 2, 98 °C 10 sec; 3, 54 °C 30 sec; steps 2-3 were repeated for 40 cycles; 4, 98 °C 5 min; 5, increasing from 55

  Figure 1: Expression of protein-coding genes. ........................................................................................Figure 2: Structure of the core promoter of a RNAPII transcribed gene ................................................Figure 3: RNAPII transcription cycle .....................................................................................................Figure 4: The interaction between Gal4 and UAS activate GAL1 transcription .....................................Figure 5: Activation of the PHO5 promoter .......................................................................................Figure 6: Chromatin structure ..............................................................................................................Figure 7: RNAPII recycling via gene looping ..........................................................................................Figure 8: Major transcript isoforms in yeast .........................................................................................Figure 9: Translation initiation cycle .....................................................................................................Figure 10: Translation elongation cycle .............................................................................................Figure 11: Eukaryotic chaperone pathways .......................................................................................Figure 12: Steps of proteasomal degradation ....................................................................................Figure 13: Deciphering of intrinsic and extrinsic noises ....................................................................

  35: RT-qPCR of ZeoR expression ................................................................................................ 79 Figure 36: Residual growth after 24h treatment with various phleomycin concentrations ................... 80 Figure 37: The pCUP1EC1118 and pCUP1S288c promoter variants confer distinct growth abilities in various phleomycin concentrations ......................................................................................................... 81 Figure 38: Fluorescence distribution in some selected clones showing bimodal expression ........... 90 Figure 39: Expression profile of pCAN1 and its partial fragment isolated from the enriched library in different plasmids in BY4720 ...................................................................................................... 92 Figure 40: Expression profile of the CUP1 promoter variants in BY4720 and 59A ............................ 93 Figure 41: Expression dynamics of pCUP1S288c at different time of induction by 20µM copper sulfate in BY4720 .................................................................................................................................... 94 Figure 42 Expression profile of pCUP1S288c induced by different concentrations of copper sulfate for 1h in BY4720 ............................................................................................................................... 94 Figure 43: Expression profile of YFP fused to CUP1 in BY4720 and 59A ............................................ 95 Figure 44: Induction of the pPHO5 promoter variants fused to RAD52-YFP or RAD27-YFP ............ 100 Figure 45: Homologous recombination frequency in the JA0200 derivative strains expressing different DNA recombination genes with different noise levels and similar mean expression .................................................................................................................................................. 101

Results of mapping of the sequenced fragments to S288c

	Number 1 2 3 4 5 6 7 8 9 Appendix II: #Read_name 1076,81 Origina clone Mean 197,56 131,73 CV 547,87 126,08 220,09 127,08 127,59 163,55 130,91 333,57 125,58 590,38 127,29 776,26 124,24 1219,14 124,18 737,52 129,9 251,22 124,81 424,14 124,81 238,95 137,21 232,69 130,66 390,62 127,36 230,11 132,05 574,99 128,5 667,87 126,27 855,96 129,06 211,03 127,27 248,13 126,06 34,4 121,22 77,42 117,45 27,88 113,75 30,32 120,76 21,53 122,67 67,01 116,44 28,47 112,87 54,48 118,65 62,3 119,07 37,01 116,95 55,03 122,16 32,42 123,41 47,18 119,94 82,44 114,89 82,25 114,34 46,11 120,2 55,97 114,06 85,53 122,54 40,36 113,98 32,26 117,49 28,35 115,45 54 118,11 45,84 120,3 43,12 123,91 67,87 112,2 26,95 122,82 44,39 123,09 33,52 124,04 41,58 113,72 41,84 114,55 56,04 119,29 33,31 117,31 63,67 119,6 33,99 116,36 40,36 111,99 32,05 122,05 30,48 115,3 81,34 118,32 32,42 115,57 81,58 122,46 30,19 119,35 40,55 122,55 43,12 115,73 51,25 119,4 37,58 117,03 41,65 117,89 39,32 114,55 chrom NewNoisyClones_A01-PrimSeqpUG35promless.ab1 chr04 NewNoisyClones_A02-PrimSeqpUG35promless.ab1 chr14 NewNoisyClones_A03-PrimSeqpUG35promless.ab1 chr05 NewNoisyClones_A04-PrimSeqpUG35promless.ab1 chr16 NewNoisyClones_A05-PrimSeqpUG35promless.ab1 chr12 NewNoisyClones_A06-PrimSeqpUG35promless.ab1 chr04 NewNoisyClones_A07-PrimSeqpUG35promless.ab1 chr04 NewNoisyClones_A08-PrimSeqpUG35promless.ab1 chr16 NewNoisyClones_A09-PrimSeqpUG35promless.ab1 chr15 NewNoisyClones_A10-PrimSeqpUG35promless.ab1 chr04 NewNoisyClones_A11-PrimSeqpUG35promless.ab1 chr04 NewNoisyClones_A12-PrimSeqpUG35promless.ab1 chr08 NewNoisyClones_B01-PrimSeqpUG35promless.ab1 chr05 NewNoisyClones_B02-PrimSeqpUG35promless.ab1 chr12 NewNoisyClones_B03-PrimSeqpUG35promless.ab1 chr04 NewNoisyClones_B04-PrimSeqpUG35promless.ab1 chr12 NewNoisyClones_B05-PrimSeqpUG35promless.ab1 chr06 NewNoisyClones_B06-PrimSeqpUG35promless.ab1 chr08 NewNoisyClones_B07-PrimSeqpUG35promless.ab1 chr04 NewNoisyClones_B08-PrimSeqpUG35promless.ab1 chr04 NewNoisyClones_B09-PrimSeqpUG35promless.ab1 chr07 NewNoisyClones_B10-PrimSeqpUG35promless.ab1 chr10 NewNoisyClones_B11-PrimSeqpUG35promless.ab1 chr02 NewNoisyClones_B12-PrimSeqpUG35promless.ab1 chr04 NewNoisyClones_C01-PrimSeqpUG35promless.ab1 chr15 NewNoisyClones_C02-PrimSeqpUG35promless.ab1 chr12 NewNoisyClones_C03-PrimSeqpUG35promless.ab1 chr05 NewNoisyClones_C04-PrimSeqpUG35promless.ab1 chr07 NewNoisyClones_C05-PrimSeqpUG35promless.ab1 chr05 NewNoisyClones_C06-PrimSeqpUG35promless.ab1 chr04 NewNoisyClones_C07-PrimSeqpUG35promless.ab1 chr12 NewNoisyClones_C08-PrimSeqpUG35promless.ab1 chr10 NewNoisyClones_C09-PrimSeqpUG35promless.ab1 chr16 NewNoisyClones_C10-PrimSeqpUG35promless.ab1 chr16 NewNoisyClones_C11-PrimSeqpUG35promless.ab2 NewNoisyClones_C12-PrimSeqpUG35promless.ab1 chr15 NewNoisyClones_D01-PrimSeqpUG35promless.ab1 chr04 NewNoisyClones_D02-PrimSeqpUG35promless.ab1 chr13 NewNoisyClones_D03-PrimSeqpUG35promless.ab2 NewNoisyClones_D04-PrimSeqpUG35promless.ab1 chr13 NewNoisyClones_D05-PrimSeqpUG35promless.ab1 chr08 NewNoisyClones_D06-PrimSeqpUG35promless.ab1 chr08 NewNoisyClones_D07-PrimSeqpUG35promless.ab1 chr02 NewNoisyClones_D08-chr08 PrimSeqpUG35promless.ab1 NewNoisyClones_D09-PrimSeqpUG35promless.ab1 chr02 NewNoisyClones_D10-PrimSeqpUG35promless.ab1 chr03 NewNoisyClones_D11-PrimSeqpUG35promless.ab1 chr02 NewNoisyClones_D12-PrimSeqpUG35promless.ab1 chr10 NewNoisyClones_E01-PrimSeqpUG35promless.ab1 chr15 NewNoisyClones_E02-PrimSeqpUG35promless.ab1 chr02 NewNoisyClones_F08-chr16 PrimSeqpUG35promless.ab1 chr05 511824 C T PrimSeqpUG35promless.ab1 NewNoisyClones_F09-PrimSeqpUG35promless.ab1 chr13 NewNoisyClones_F10-PrimSeqpUG35promless.ab1 chr02 NewNoisyClones_F11-PrimSeqpUG35promless.ab1 chr13 NewNoisyClones_F12-chr05 NewNoisyClones_H08-PrimSeqpUG35promless.ab1 chr13 NewNoisyClones_H09-PrimSeqpUG35promless.ab1 chr10 Appendix III: List of the polymorphisms 1st subclone Mean 136,77 133,04 CV 477,54 122,03 156,03 125,04 996,35 125,99 139,44 127,73 542,11 120,19 541,46 121,41 756 124,21 1028 124,55 861,7 128,4 242,59 118,79 404,44 123,59 216,06 140,75 345,12 128,58 254,92 122,69 224,6 131,36 557,57 121,12 606,06 120,9 794,33 126,42 209,08 126,08 243,94 120,08 34,63 123,85 76,48 116,24 27,91 112,51 31,74 128,46 24,4 124,6 65,99 116,94 27,93 114,38 57,42 115,05 67,32 120,76 32,36 119,72 58,31 124,42 28,47 129,5 35,17 120,61 85,63 113,94 84,95 118,8 42,5 115,78 53 104,77 89,8 129,66 38,8 115,57 29,97 112,52 25,68 116,84 55,88 114,97 38,15 120,08 45,28 125,38 69,64 113,8 23,42 124,38 43,07 115,64 35,37 124,98 44,38 119,81 42,52 112,5 51,22 115,3 31,6 115,46 58,66 118,44 38,07 116,38 42,63 117,35 31,98 123,34 38,44 117,08 87,92 111,06 35,32 119,16 80,43 128,27 33,39 115,78 35,7 124,29 45,64 119,69 56,65 116,69 30,43 119,23 39,75 117,18 35,37 114,8 begin end strand 341226 341926 1 345487 345916 1 140362 140965 1 121465 121750 -1 197902 198871 -1 652724 653587 -1 341226 341926 1 623162 623714 -1 258740 259351 -1 1468472 1469302 1 341226 341926 1 505222 506123 1 33421 34369 1 197950 198871 -1 556343 556846 1 198313 198808 -1 74506 75332 -1 212749 213681 1 1384840 1385156 -1 652605 653587 -1 76192 76880 -1 207378 207845 -1 501552 502527 1 341226 341926 1 258740 259351 -1 197895 198871 -1 544698 545638 -1 76192 76880 -1 544727 545638 -1 1468472 1469317 1 241765 242244 -1 745187 745396 1 794224 795035 -1 794314 795035 -1 101154 101566 1 974175 974596 -1 291059 291720 1 558857 559144 -1 198307 199084 1 198307 199084 1 429048 429658 -1 508244 509009 -1 451878 452748 -1 127501 128269 -1 590863 591660 -1 83124 83469 -1 891588 892152 1 451900 452748 -1 876197 877185 1 556608 557065 1 562929 563346 1 451404 452214 1 491978 492695 1 668119 668624 -1 130803 131553 1 chr05 545025 G T YER177W BMH1 chr05 545199 GGGG G NewNoisyClones_H10-PrimSeqpUG35promless.ab1 chr09 82948 83285 1 #Chr Pos Ref S228c Alt EC1118 Gene Name chr06 TT T chr02 A chr06 A AA T NewNoisyClones_H11-chr13 558857 559144 -1 chr02 C T chr06 C T PrimSeqpUG35promless.ab1 PrimSeqpUG35promless.ab1 NewNoisyClones_G01-PrimSeqpUG35promless.ab1 chr10 192138 192507 -1 NewNoisyClones_G02-PrimSeqpUG35promless.ab1 chr16 411695 412607 -1 NewNoisyClones_H12-chr02 G A chr06 G GG PrimSeqpUG35promless.ab1 AP9 chr14 51899 52725 1 AP22 chr15 778858 779788 -1 YBR132C AGP2 chr02 T chr06 A ATATATA C chr02 G A chr06 A AAA NewNoisyClones_E03-PrimSeqpUG35promless.ab1 chr07 208700 208920 -1 NewNoisyClones_E04-PrimSeqpUG35promless.ab1 chr02 562929 563345 1 NewNoisyClones_E05-PrimSeqpUG35promless.ab1 chr02 562929 563346 1 NewNoisyClones_E06-PrimSeqpUG35promless.ab1 chr05 511634 512449 -1 NewNoisyClones_G03-chr09 82948 83285 1 AP35 507925 509009 1 chr02 C T YFL031W HAC1 chr06 G C PrimSeqpUG35promless.ab1 NewNoisyClones_G04-PrimSeqpUG35promless.ab1 chr15 510811 511846 -1 chr02 A T chr06 A T chr02 G A chr06 G C NewNoisyClones_G05-PrimSeqpUG35promless.ab1 chr07 949385 950259 1 NewNoisyClones_G06-PrimSeqpUG35promless.ab1 chr04 1164914 1165625 -1 chr02 C chr06 AA A T YBR248C HIS7 chr02 G chr06 A G A NewNoisyClones_E07-PrimSeqpUG35promless.ab1 chr11 526259 526848 1 NewNoisyClones_E08-PrimSeqpUG35promless.ab1 chr09 282274 283107 1 NewNoisyClones_E09-PrimSeqpUG35promless.ab1 chr10 702391 703569 -1 NewNoisyClones_G07-chr05 490821 491359 1 chr03 T C YCR008W SAT4 chr06 A G PrimSeqpUG35promless.ab1 chr04 C CC chr06 A G NewNoisyClones_G08-PrimSeqpUG35promless.ab1 chr15 256031 256456 -1 chr04 T G chr07 A G NewNoisyClones_G09-PrimSeqpUG35promless.ab1 chr10 192138 192507 -1 YDR099W BMH2 chr04 T C chr07 A AA NewNoisyClones_E10-PrimSeqpUG35promless.ab1 chr04 1164914 1165625 -1 NewNoisyClones_E11-PrimSeqpUG35promless.ab1 chr15 948866 949834 1 NewNoisyClones_E12-PrimSeqpUG35promless.ab1 chr13 252855 253267 1 NewNoisyClones_G10-PrimSeqpUG35promless.ab1 chr15 101154 101566 1 NewNoisyClones_G11-PrimSeqpUG35promless.ab1 chr14 491233 491519 -1 chr04 C T chr04 T YGL225W VRG4 chr07 C T chr07 G A C chr04 1468519 A AA chr07 G A NewNoisyClones_G12-PrimSeqpUG35promless.ab1 chr14 491233 491519 -1 chr04 1468562 G A chr07 G C NewNoisyClones_F01-PrimSeqpUG35promless.ab1 chr09 282274 283123 1 NewNoisyClones_F02-PrimSeqpUG35promless.ab1 chr09 82948 83285 1 NewNoisyClones_F03-PrimSeqpUG35promless.ab1 chr12 837517 837938 -1 NewNoisyClones_F04-PrimSeqpUG35promless.ab1 chr08 150121 150556 -1 NewNoisyClones_H01-PrimSeqpUG35promless.ab1 chr09 82948 83285 1 NewNoisyClones_H02-chr02 716392 716854 1 chr04 1468662 AA A chr04 1468720 C chr07 208776 ATTTCTATAAA A TTCGGCCGAA YGL157W ARI1 A chr07 208835 G T PrimSeqpUG35promless.ab1 NewNoisyClones_H03-PrimSeqpUG35promless.ab1 chr05 511443 512449 -1 chr04 1468817 C A chr08 198394 G C NewNoisyClones_H04-PrimSeqpUG35promless.ab1 chr02 562929 563346 1 chr04 1468887 TT T chr04 1468900 C T YHR046C INM1 chr08 198485 TTTT T chr08 198549 T C NewNoisyClones_F05-PrimSeqpUG35promless.ab1 chr09 82948 83285 1 NewNoisyClones_F06-PrimSeqpUG35promless.ab1 chr16 876197 877017 1 NewNoisyClones_F07-PrimSeqpUG35promless.ab1 chr16 408770 409140 1 NewNoisyClones_H05-chr12 845812 846281 1 YDR508C GNP1 chr04 1468935 A G chr08 198648 A G PrimSeqpUG35promless.ab1 NewNoisyClones_H06-PrimSeqpUG35promless.ab1 chr04 872147 872910 -1 chr04 1469066 C T chr08 215155 C T NewNoisyClones_H07-chr12 902921 903799 -1 chr04 1469161 A G YHR055C CUP1 chr08 215281 C A chr04 1469246 G A chr08 215511 C T chr04 1469247 A AA chr08 508824 AAAA A YHR205W SCH9	length 700 429 603 285 969 863 700 552 611 830 700 901 948 921 503 495 826 932 316 982 688 467 975 700 611 976 940 688 911 845 479 209 811 721 412 421 661 287 777 777 700 765 870 768 797 345 564 848 988 457 417 810 717 505 750 337 287 369 912 826 930 220 416 417 815 337 1084 1035 874 711 589 833 1178 538 425 369 711 968 412 412 286 286 849 337 421 435 337 462 1006 417 337 820 370 469 763 878	2nd subclone Mean 157,28 136,89 CV 500,78 128,15 200,24 129,07 1000,54 128,14 150,25 124,05 500,41 119,41 551,24 122,2 761,2 121,96 1147,23 129,44 812,47 127,75 224,23 121,79 415,47 120,28 227,13 135,37 300,14 125,05 354,32 127,42 231,21 127,17 521,36 129,83 603,12 122,77 800,47 128,39 221,33 123,75 257,14 123,58 35,47 125,55 75,44 113,46 28,11 110,16 32,21 123,91 23,37 126,55 66,89 114,38 27,97 115,45 58,34 111,73 66,27 125,91 33,24 112,17 53,21 126,31 31,36 120,64 45,24 121,69 84,53 119,52 83,65 114,59 75,37 119,63 55,38 110,61 88,15 124,9 40,17 117,96 30,25 118,33 27,11 121,48 53,47 117,2 43,26 127,06 47 123,4 68,9 106,1 25,29 122,12 45,99 118,76 36,18 127,17 43,35 115,32 43,17 118,86 55,31 113,98 35,87 116,04 66,47 118,02 35,69 115,98 43,5 110,75 35,36 124 35,22 112,24 85,99 116,27 34,82 113,92 83,26 124,37 31,28 113,32 41,73 121,73 46,17 119,87 55,42 116,09 35,4 118,95 41,26 116,2 37,76 117,81 gene name YDL061C RPS29B YNL154C YCK2 YEL008C-A YPL226W NEW1 SNR30 YDR099W BMH2 YDL061C RPS29B YPR028W YOP1 SNR50 YDR508C GNP1 YDL061C RPS29B YHR203C RPS4B YEL063C CAN1 SNR30 YDR050C TPI1 SNR30 YFL031W HAC1 YHR055C CUP1-2 YDR461W MFA1 YDR099W BMH2 YGL225W VRG4 YJL111W CCT7 YBR132C AGP2 YDL061C RPS29B SNR50 SNR30 YER177W BMH1 YGL225W VRG4 YER177W BMH1 YDR508C GNP1 YLR048W RPS0B YJR162C YPR132W RPS23B YPR132W RPS23B YMR147W YHR046C INM1 YHR046C INM1 YHR205W SCH9 165 YBR106W PHO88 YCR008W SAT4 YJL184W GON7 YOR306C MCH5 YPR166C MRP2 YMR145C NDE1 YMR091C NPL6 YER159C BUR6 YMR203W TOM40 YJL155C FBP26 2 YIL143C SSL2 Number of fragments containing the promoter YMR147W YJL117W PHO86 YPL075W GCR1 YNL309W STB1 1 YGL157W ARI1 YER166W DNF1 YIL143C SSL2 1 YOR099W KTR1 1 YKR048C NAP1 YIL038C NOT3 YJR146W YER158C 1 YOL036W YJL117W PHO86 2 YOR335C ALA1 YML008C ERG6 YNL071W LAT1 2 YNL071W LAT1 YIL038C NOT3 YIL143C SSL2 YIL143C SSL2 YBR248C HIS7 1 YER166W DNF1 2 YIL143C SSL2 YPR166C MRP2 YPL078C ATP4 2 YLR390W-A CCW14 1 167 1	unimodal/ bimodal bi bi bi bi bi bi bi bi bi bi bi bi bi bi bi bi bi bi bi bi bi bi bi bi bi bi bi bi bi bi uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni uni	note 40 bp before 5'UTR intron 200bp before ATG ORF in the fragment 17bp before ATG promoter of snRNA contains 5'UTR intron 40 before ATG 5'UTR intron contains part of the ORF promoter of snRNA 30bp before ATG 40bp before ATG 5'UTR intron contains part of the ORF contains part of the ORF promoter of snRNA contains part of the ORF promoter of snRNA contains part of the ORF 30bp before ATG 20bp before ATG contain 5'UTR intron 14bp before ATG 32bp before ATG 110bp before ATG 40bp before ATG 5'UTR intron promoter of snRNA promoter of snRNA contains part of the ORF 14bp before ATG contains part of the ORF 30bp before ATG contains part of the ORF contains part of the ORF contains part of the ORF contains part of the ORF not sequenced no known promoter found no known promoter found no known promoter found not sequenced 55bp before ATG 22bp before ATG 22bp before ATG no known promoter found 350bp before ATG contains part of the ORF 201bp before ATG no known promoter found contains part of the ORF 156bp before ATG no known promoter found ORF in the fragment 133bp avant no known promoter found 39bp before ATG contains part of the ORF contains part of the ORF 150bp before ATG contains part of the ORF 55bp before ATG 24bp before ATG contains part of the ORF No alignment found contains part of the ORF no known promoter found 78bp before ATG no known promoter found no known promoter found 300bp before ATG contains part of the ORF no known promoter found contains part of the ORF no known promoter found no known promoter found contains part of the ORF contains part of the ORF 260bp before ATG 289bp before ATG 316bp before ATG 24bp before ATG no known promoter found 243bp before ATG contains part of the ORF no known promoter found 4 bp before ATG 4 bp before ATG contains part of the ORF contains part of the ORF no known promoter found no known promoter found contains part of the ORF contains part of the ORF 300bp before ATG no known promoter found contains part of the ORF ORF in the fragment 26bp before ATG no known promoter found no known promoter found contains part of the ORF
	chr04 chr09	790,47 67,4 33,52 1469271 A	132,04 116,82 115,87 G	1030,76 68,92 36,43	125,18 115,94 111,56		853,13 65,17 34,58	131,19 113,19 114,46
	chr04 chr04 chr05	191,43 40,49 50,01 1469289 947,8 95,31 42,94 1469311 33551 A T T	126,69 121,98 116,71 A 125,76 115,18 120,73 A G	197,84 43,84 55,59 886,84 90,65 37,04	126,1 122,44 113,03 123,85 117,53 122,73		199,78 44,71 52,47 900,85 93,31 41,82	121,81 118,14 121,01 126,91 113,24 128,47
	chr05	178,32 79,21 69,02 33621 A	127,18 119,4 112,05 AA	181,52 71,51 61,64	120,32 116,26 110,6		178,58 77,84 65,77	122,77 116,95 115,64
	chr05 chr05 chr05	710,52 96,32 55,45 33782 C 584,97 66,28 32,28 34040 A 34050 G	137,43 120,31 113,89 A 135,66 117,68 121,69 G A	705,61 90,21 53,02 498,29 66,13 27,62 YEL063C	133,33 121,52 115,64 135,09 118,93 115,35 CAN1		712,33 93,86 54,78 556,32 65,14 30,54	1	119,44 110,38 135,04 130,56 113,58 121,26
	chr05	143,72 28,35 56,73 34052	T	129,07 123,4 122,18 A	107,27 25,05 51,71	126,27 122,44 128,45		133,24 27,03 57,17	127,36 121,99 124,96
	chr05 chr05 chr05	507,99 69,02 33,99 34310 G 978,35 50,01 34320 G C	131,62 114,03 124,14 A 129,81 112,15 GG T	603,89 61,55 36,68 560,2 52,33 YEL009C	130,74 114,69 121,66 124,77 115,68 GCN4		557,36 68,61 34,32 887,12 53,14	1	113,5 127,36 137,62 129,37 115,8
	chr05	676,2 66,84	C	130,56 113,08 T	588,01 69,2 YER159C	122,89 115,47 BUR6		624,33 67	1	123,18 115,54
	chr05	55,45		T	111,27 C	60,12 YER166W	118,67 DNF1		57,15	2	115,04

General conclusion

We also measured the CV of individual clones randomly isolated from the library before or after selection. Among the selected population, more clones showed high CV (p = 0.047, Figure 27). Thus the fluctuating selection efficiently enriched the population in fragments giving high variability in yEGFP expression. Nevertheless, clones with noise levels similar to control clones were still present, as was previously observed with the original protocol [START_REF] Freed | A simple screen to identify promoters conferring high levels of phenotypic noise[END_REF] and might be explained by aggregation with non-fluorescent cells in the sorting process. Even if this method might not be the ideal way to strongly enrich for increased noise, selecting a smaller percentage of cells at each round could make the enrichment process more efficient.

Evolution of high noise promoter

In this work, we used the fluctuating selection method developed by [START_REF] Freed | A simple screen to identify promoters conferring high levels of phenotypic noise[END_REF] to enrich high noise clones in the library. But this method can also serve as a selection pressure and be used in evolutionary experiments. [START_REF] Wildenberg | Evolving a 24-hr oscillator in budding yeast[END_REF] subjected a strain harboring a high mutation rate and PFLO1-YFP in the genome to fluctuating selection for high and low fluorescence levels during 30 cycles (30 days). But they didn't apply any gate on cells homogeneous in terms of size and complexity during the experiment, so that the highest fluorescent part of the population they selected mostly was cell clumps, whereas the lowest part was individual cells. Finally, they selected for cells harboring a 24h oscillation between a single cell state and multicellular clumps. But what if we apply a gate to select for on the most homogeneous subpopulation containing only single cells with similar cell size and status?

We assume that the population would evolve towards higher noise expression if we subject a strain with YFP fused to a gene in its original locus, for example HOG1 which is a key kinase in the osmotic response pathway, to this long-term selection procedure. Small nuclear RNA SNR50/snR50

Small nuclear RNA All the eight genes that we have selected for further analysis at the genomic level in the first part of this work were included in these bimodal expression promoters (see section 2.1.2). Unfortunately, after integration into the locus LEU2, all these promoters exhibited a unimodal expression profile in both backgrounds (59A and BY4720). Considering that we used the full promoter region instead of the sequenced fragment for the integration, we exchanged the corresponding fragment and the promoter of CAN1 in the centromeric plasmid (pUG35, see methods) and the integrative plasmid (pJRL2, see methods). The new plasmids were transformed to BY4720. We found that only centromeric plasmids can exhibit bimodal expression (Figure 39).

variations.

Once we will have confirmed that we have suitable promoters, the HRF will be tested in the whole populations, but we could also sort subpopulations according to their fluorescence levels and measure the corresponding HRF, especially to test if the high noise clone has more cells exhibiting higher or lower genome instability. Moreover, these data could be used to establish precise relationships between the expression level of our genes of interest and the HRF. The E. coli strain DH 5-alpha (High efficiency, New England Biolabs) was used in this study to amplify plasmids by chemical transformation or electroporation transformation (see below).

Yeast

All the Saccharomyces cerevisiae strains we used and constructed in this study are listed in the Appendix VI.

Strains used in section 2 and 3

The genomic library was constructed in CEN.PK (MATa ura3-52, see below). The pJRL2 derivative plasmids (see below 5.1.2) containing selected promoters fused to yEGFP were integrated into the LEU2 locus of BY4720 (S288c auxotrophic derivative, MATα lys2∆0 trp1∆63 ura3∆0) and 59A MATa Δamn1-loxP. 59A is a haploid derivative of EC1118 which exhibits high level flocculation that do not allow analysis by flow cytometry. So we used the Δamn1 59A (provided by V. Galeote, INRA SupAgro) that no longer flocculates because of the deletion of AMN1 encoding a cell wall protein responsible for the flocculation.

Strains used in section 4

The JA0200 strain was created by adding the intact LEU2 gene into the KV133 strain (BY4742 MATα his3Δ1 leu2Δ0 lys2Δ0 ura3Δ0, FLO1::URA3, see [START_REF] Verstrepen | Intragenic tandem repeats generate functional variability[END_REF] at its original locus to then use the pJRL2 plasmids recombining in LEU2 [START_REF] Raser | Noise in gene expression: origins, consequences, and control[END_REF]. The JA0300 strain was created by replacing SDT1 by URA3 in BY4720. Then the pJRL2 derivative plasmids containing RAD52-YFP and RAD27-YFP under the control of two mutated PHO5 promoters conferring similar mean expression level with different noise were integrated into the LEU2 locus in JA0200 and JA0300. The original RAD52 and RAD27 genes were deleted afterwards by using the PCR-amplified LYS2 gene with short sequences of homology with RAD52 or RAD27 at the end of the primers (the RAD52 and RAD27 deletions affect the transformation efficiency, thus we first integrated plasmids

Plates containing 5-FOA were prepared as follows: Solutions I containing 4% glucose and 4% agar was autoclaved, solution II containing 0.34% yeast nitrogen base, 1% ammonium sulfate, 0.158% CSM and 0.2% 5-FOA was sterilized through 0.22 µm filter. Same volumes of these two solutions were mixed when solution I was below 60 °C.

Culture conditions

E. coli cells were grown in liquid media at 37 °C with shaking speed of 170 rpm/min, or on solid plate at 37 °C in an incubator. Yeast cells were grown in liquid medium at 30 °C with shaking speed of 200 rpm/min, or on solid plate at 30 °C in an incubator.

Basic molecular biology methods

DNA manipulation

DNA extraction

Yeast genomic DAN was extracted using the MasterPure™ Yeast DNA Purification Kit (Epicentre), following its standard protocol in the manual. Plasmids were extracted from E.coli using the GeneJET Plasmid Miniprep Kit (Thermo), following its standard protocol in the manual.

Extraction of plasmids from yeast was performed as follow: 1.5 ml overnight culture were centrifuged before the pullets were resuspended with 0.2 ml extraction mixture (2% Triton X-100, 1% SDS, 100 mM NaCl, 10 mM Tris-Cl (pH 8), 1 mM Na2-EDTA). Then 0.3g glass beads and 0.2 ml of phenol:chloroform:isoamyl alcohol (25:24:1) were added to the tube. The tube was vortex for 2 min and centrifuged for 5 min afterwards. The aqueous layer was collected for further usage.

PCR

PCR was carried out on the My cycler TM thermal cycler (Bio-Rad). PCR reaction tubes contained (50 µl): 23.5 µl H2O, 10 µl Phusion High-Fidelity (HF) buffer (NEB), 5 µl dNTP (2.5 mM), 5 µl forward primer (2.5 µM), 5 µl reverse primer (2.5 µM) and 0.5 µl Phusion HF DNA Polymerase (NEB).

The PCR cycles were: 1, initial denaturation at 98 °C for 30 seconds; 2, denaturation at 98 °C for 20 seconds; 3, annealing for 30 seconds; 4, extension at 72 °C for a certain time according to the length of the target (30 seconds per kb); steps 2 to 4 were repeated for 30 cycles; 5, final extension at 72 °C for

Construction of the genomic library

The 59A genomic DNA has been fragmented independently by the two 4-cutter restriction enzymes RsaI and AluI generating blunt ends compatible with the ends generated by SnaBI in the promoterless yEGFP-coding vectors. Reaction times and enzyme concentrations have been optimized to produce DNA fragments ranging from 500pb to 3kb. For RsaI, 1,5µg DNA has been digested during 15min by 1 U enzyme. For AluI, 3µg DNA have been digested during 30min by 0,5 U enzyme.

Fragments from 500pb to 3kb generated by each enzyme have been extracted from gel (QIAquick Gel Extraction Kit, Qiagen) and ligated independently by the DNA ligase T4 (overnight, 16°C, Quick Ligation Kit, New England Biolabs) with each of the three promoterless yEGFP-coding vectors previously digested by SnaBI, dephosphorylated (Antarctic Phosphatase, New England Biolabs), and purified. The ratio (vector:inserts) used for the 6 ligation reactions (2 enzymes, 3 vectors) was 1:2,6 because it gave the higher number of transformants. The number of transformants required for each (enzyme:vector) pair to give a 99% confidence level that all sequences of the genome are represented with a mean insert size of 2kb was 30000. This number has been multiplied by 2 because any fragment can be inserted in both senses. Thus 60000 transformants for each (enzyme:vector) pair have been independently obtained after ligation, transformation of competent E. coli cells using standard methodology, and growth on selective LB medium. Then cells were harvested and pooled to isolate the plasmids from the 6 bulk cultures (GenElute™ HP Plasmid Midiprep Kit, Sigma-Aldrich). Redigestion with SnaBI has been performed

to linearize empty promoterless yEGFP-coding plasmids. Plasmids from each (enzyme:vector) pair have then been retransformed in the laboratory yeast strain CEN-PK using classical lithium acetate method. Again 60000 transformants for each (enzyme:vector) pair have been independently obtained after growth in selective medium (YNB URA -). Finally the transformants originated from the 6 (enzyme:vector) pairs have been pooled together at similar OD and equal volume to form the final library used for fluctuating selection.

Fluctuating selection using cell sorting

The method described by [START_REF] Freed | A simple screen to identify promoters conferring high levels of phenotypic noise[END_REF] has been adapted to Saccharomyces cerevisiae.

An overnight culture of the population containing the genomic library was diluted to OD=0.5 and cells were grown for around 5 hours to reach exponential growth (OD=2) (YNB URA -). Cultures

were spun down at 3000g for five minutes at 4°C. Growth media was removed and cultures were mainly due to "intrinsic" noise. Clones with mean expression level greater than 30 or noise level (CV) greater than 120% were chosen. Two individual sub-clones were further re-isolated from each selected clone to analyze if the noise conferred by the genomic fragment was a stable property of the plasmid.

Measurement of fluorescence expression at genomic level

Analysis of mean and noise levels at the genomic level after integration to the LEU2 locus (all the pJRL2 derivatives) was performed on the Attune™ Acoustic Flow Cytomoter (Life Technologies).

Cells at exponential stage were collected and washed by PBS before analysis. For each strain, 10 5 cells were analyzed for yEGFP expression. Analysis of cytometry data was performed by the Attune™ software (Life Technologies). A gate containing at least 10 4 cells for robust analysis was applied on the densest subset of cells using the FSC/SSC density plot. The same gate has been used

to measure mean and noise levels conferred by the variants of a given promoter in a given strain in order to maintain a conservative estimate of noise.

Sequencing and bioinformatics analysis

Plasmids from 97 individual clones harboring noisy yEGFP expression have been extracted

using standard phenol-chloroform extraction method and inserted fragments have been sequenced using a primer hybridizing in the yEGFP gene 75 bp upstream the start codon.

Sequencing was performed using an Applied Biosystems 3730xl DNA Analyzer. Base calling was performed using TraceTuner 3.0.4 beta [START_REF] Denisov | System and method for improving the accuracy of DNA sequencing and error probability estimation through application of a mathematical model to the analysis of electropherograms[END_REF] to obtain fasta and quality values.

Vector and quality trimming were performed using Lucy 1.19p [START_REF] Chou | DNA sequence quality trimming and vector removal[END_REF]. Only 96 reads were retained after trimming. Mapping reads was performed using SMALT 0.7.3

(http://www.sanger.ac.uk/resources/software/smalt/) resulting in 95 reads correctly mapped to the S288c genome. Variants were obtained using the mpileup command of SAMtools 0.1.18 [START_REF] Li | The Sequence Alignment/Map format and SAMtools[END_REF] and further filtered to keep those found upstream of ORF using a custom perl script.

GO analysis has been performed using SGD Gene Ontology Term Finder (http://www.yeastgenome.org/cgi-bin/GO/goTermFinder.pl). Sequence alignments have been performed using the MultAlin on-line software (http://multalin.toulouse.inra.fr/multalin/). q-CUP1-for TGAAGGTCATGAGTGCCAATGC q-CUP1-rev GGGCATTTGTCGTCGCTGTT q-zeoR-for GCCAAGTTGACCAGTGCCGT q-zeoR-rev TGATGAACAGGGTCACGTCGTC 2/q-CUP1-for TCAGACTTGTTACCGCAGGGGC 2/q-CUP1-rev TGAGTGCCAATGCCAATGTGG 1/q-zeoR-for CGGAAGTTCGTGGACACGACCT 1/q-zeoR-rev TGTTCATCAGCGCGGTCCAG 2/q-zeoR-for GCCACGAAGTGCACGCAGTT 2/q-zeoR-rev TCGTGTCCACGAACTTCCGG 1/q-GFP-for CCATACCATGGGTAATACCAGCAGC 1/q-GFP-rev CCAATTGGTGATGGTCCAGTCTTGT 2/q-GFP-for TCTTGAACATAACCTTCTGGCATGG 2/q-GFP-rev CCAGTTCCATGGCCAACCTTAGT

Bibliography Lists

List of tables