# Interplay between genetic, epigenetic, and gene expression variability: Considering complexity in evolvability

Jean-Pascal Capp

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Jean-Pascal Capp. Interplay between genetic, epigenetic, and gene expression variability: Considering complexity in evolvability. Evolutionary Applications, 2021, 10.1111/eva.13204. hal-03170207

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# Interplay between genetic, epigenetic, and gene expression variability: Considering complexity in evolvability

Jean-Pascal Capp ID

Toulouse Biotechnology Institute, INSA, CNRS, INRAE, University of Toulouse, Toulouse, France

**Correspondence**

Jean-Pascal Capp, Toulouse Biotechnology Institute, University of Toulouse, INSA, CNRS, INRAE, Toulouse, France.
Email: capp@insa-toulouse.fr

**Funding information**

Toulouse White Biotechnology; INRAE

**Abstract**

Genetic variability, epigenetic variability, and gene expression variability (noise) are generally considered independently in their relationship with phenotypic variation. However, they appear to be intrinsically interconnected and influence it in combination. The study of the interplay between genetic and epigenetic variability has the longest history. This article rather considers the introduction of gene expression variability in its relationships with the two others and reviews for the first time experimental evidences over the four relationships connected to gene expression noise. They show how introducing this third source of variability complicates the way of thinking evolvability and the emergence of biological novelty. Finally, cancer cells are proposed to be an ideal model to decipher the dynamic interplay between genetic, epigenetic, and gene expression variability when one of them is either experimentally increased or therapeutically targeted. This interplay is also discussed in an evolutionary perspective in the context of cancer cell drug resistance.

**KEYWORDS**

cancer evolution, cell-to-cell heterogeneity, chromatin, DNA repair, stochastic gene expression

## 1 | INTRODUCTION

The major technological breakthroughs made during the last 20 years allowed the analysis of many biological processes at the single-cell level and revolutionized molecular biology. Molecular and cellular processes do not occur deterministically. A precise molecular process can exhibit a great variability in the sequence of its steps and in its final products that generates a very large degree of intercellular and intermolecular heterogeneity.

Especially, results obtained on individual cells and molecules have invalidated the deterministic view of gene expression. As the reactions governing gene expression involve a small number of molecules, it was previously assumed that they may exhibit random fluctuations, a phenomenon often called gene expression noise

(McAdams & Arkin, 1999). These stochastic fluctuations in gene expression started to be finely quantified in the early 2000s when the rate of protein synthesis was measured in genetically identical cells placed in a homogeneous environment (Blake et al., 2003; Elowitz et al., 2002; Ozbudak et al., 2002; Raser & O'Shea, 2004). The role of molecular interactions within chromatin has been emphasized in the generation of this variability. In particular, regulatory proteins associate probabilistically with chromatin and produce cell-to-cell variability in gene expression (Voss et al., 2009). Also, evolution due to selection arises through variability and heritability of fitness-related features, and feedback regulations were shown to be important modulators for both variability and phenotypic heritability. Positive and negative feedback regulation can affect the level of variability, and positive feedback can preserve phenotypic states over many

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

© 2021 The Authors. Evolutionary Applications published by John Wiley & Sons Ltd
cell generations (Becskei et al., 2001; Becskei & Serrano, 2000; Nevozhay et al., 2009, 2012).

Different sources of gene expression variability have been distinguished: The intrinsic (allele-specific) variability is actually related to the random protein binding events on the gene or mRNA regulatory regions, while the extrinsic variability refers to fluctuations that originate from sources affecting multiple alleles, thus related to the underlying cell state and metabolism of each cell (Elowitz et al., 2002) (e.g., different number of ribosomes or RNA polymerases, different cell size or cell cycle stage, spatial (micro-)environmental variability...) [see (Foreman & Wollman, 2020) for a recent demonstration of the major role of the extrinsic component in mammalian cells]. Thus, the expression level of a protein in a cell population reflects the probability of gene expression in each cell (Kaern et al., 2005). Changes in the mean expression level in the population are the consequence of changes in the probability of its expression in each cell.

All the major fields of molecular and cellular biology are now aware of the necessity to take into account this source of nongenetic heterogeneity: This important contributor to phenotypic diversification can have important consequences from developmental to evolutionary processes (Ackermann, 2015; Eling et al., 2019; Raj & van Oudenaarden, 2008). However, its relationships with other processes that produce phenotypic variation by acting at the genetic or epigenetic level are only partly deciphered.

The interplay between genetic, epigenetic, and gene expression variability is summarized in Figure 1. Comprehensive reviews on the relationships between genetic and epigenetic variability have been provided elsewhere, especially in the onset of cancer hallmarks (Shen & Laird, 2013). For instance, they influence each other in cancer cells through genetic modifications of genes coding for chromatin modifiers on the one hand and epigenetic modifications of promoters of genes involved in genetic stability on the other

hand. The present article rather considers the introduction in this interplay of the third category—that is, gene expression variability. Each one of the four arrows connected to gene expression variability (Figure 1) is examined.

Genetic and epigenetic influences on gene expression variability begin to be well characterized (Eling et al., 2019; Sanchez & Golding, 2013), while the reverse relationships are poorly explored. This article aims for the first time at both giving up-to-date description of the formers and highlighting recent works investigating the possible consequences of the latters, especially the influence of gene expression variability over epigenetic variability that just starts to be investigated.

## 2 | INFLUENCE OF GENETIC VARIABILITY OVER GENE EXPRESSION VARIABILITY

The influence of genetic variability over gene expression variability is by far the most studied relationship among the four considered here. Since the seminal works in the early 2000s on noise in bacteria (Elowitz et al., 2002) and yeast (Blake et al., 2003; Raser & O'Shea, 2004), the genetic determinants and cellular constraints on noisy gene expression were well described (Sanchez & Golding, 2013). Especially, the influence of genetic variations in gene promoters and other cis-elements over the level of noise has been widely studied, especially in eukaryotic cells. Studies on the effect of promoter architecture identified the main genetic determinants that modulate transcriptional burst frequency and size (the average number of mRNAs synthesized during a burst of transcription) and thus intrinsic noise [for a recent update on the concept of transcriptional bursting, see (Tunnacliffe & Chubb, 2020)].

In bacterial cells, it was soon shown that expression noise is affected at both the transcriptional and translational levels,

<figure><img src="image_3.png" /><figcaption>**FIGURE 1** Interplay between genetic, epigenetic, and gene expression variability. The three sources of biological variability studied here are interconnected. Question marks indicate largely unexplored relationships. For each relationship, few examples are given (see the text for details and references)</figcaption></figure>
translational bursts having the largest effect on cell-to-cell variability (Ozbudak et al., 2002; Thattai & van Oudenaarden, 2001). On the contrary, transcriptional bursting was assumed to be the major determinant in eukaryotic cells (Bar-Even et al., 2006; Blake et al., 2003; Newman et al., 2006). High expression variability in prokaryotes results predominantly from low transcription and efficient translation (Ozbudak et al., 2002; Thattai & van Oudenaarden, 2001), with the consequence that noise can be reduced by less efficient ribosomal binding sites or mutations in the start codon (Ozbudak et al., 2002). Nevertheless, later works on *Escherichia coli* suggested that transcription and translation contribute to expression variability at approximately the same level in prokaryotes (Guimaraes et al., 2014). This was corroborated by a genome-wide study showing that half of the *E. coli* promoters has significant promoter-specific levels of noise (Silander et al., 2012). This level can be tuned in these cells by changing promoter-specific features such as repressor-binding site sequences (Jones et al., 2014).

In *Saccharomyces cerevisiae*, genes with TATA box-containing promoters show higher variability than the other genes (Newman et al., 2006; Zaugg & Luscombe, 2012; Zhang et al., 2009) and mutations in TATA box decrease promoter-mediated expression noise (Blake et al., 2006; Hornung et al., 2012; Raser & O'Shea, 2004). This relationship has also been observed in mammalian genomes (Faure et al., 2017; Larsson et al., 2019; Ochiai et al., 2020; Tantale et al., 2016; Zoller et al., 2015) where genes with TATA elements have larger burst sizes and intrinsic variability compared to genes without such elements (Larsson et al., 2019; Ochiai et al., 2020), even if other works found negligible function of TATA box in regulating expression noise in human embryonic cells (Wu et al., 2017).

Transcription factor (TF)-binding sites strength, number, and position also have an influence (Sanchez et al., 2013; Sanchez & Golding, 2013): For instance, promoters with more TF-binding sites exhibit higher expression variability (Sharon et al., 2014; To & Maheshri, 2010), which might due to the stochasticity of TF binding and falling off (Sanchez et al., 2013). Moreover, noise increases when a repressor-binding site is moved closer to the TATA box within a promoter in yeast (Murphy et al., 2007). Recent works showed that the presence of several transcription regulators in promoters is positively correlated with burst size, while those bound to enhancers are poorly correlated (Ochiai et al., 2020), suggesting that burst size is primarily controlled at the core promoter level. On the contrary, distal enhancers mainly control burst frequency and thus noise (Bartman et al., 2016; Fukaya et al., 2016; Larson et al., 2013; Larsson et al., 2019; Ochiai et al., 2020).

Finally, other effects of local sequence architecture should be highlighted. Especially, transcriptional variability decreases with the number of transcriptional start sites (TSSs) and the presence of CpG islands in gene promoter, TSS, and gene body (Faure et al., 2017). Other works showed that CpG island size is negatively correlated with expression noise (Morgan & Marioni, 2018). Similarly, polynucleosome-disfavoring sequences in promoters confer lower transcriptional variability (Sharon et al., 2014). Indeed, according to the simplest model of promoter activation with two alternative—OFF

(silenced) and ON (initiated)—states (Tunnacliffe & Chubb, 2020), switching between ON and OFF states should reflect chromatin remodeling (Golding & Cox, 2006), with nucleosome-binding sites generating transient open states leading to transcriptional bursting, which generates more variability in gene expression (Sanchez & Golding, 2013) (see below).

Given these multiple influences of cis-elements, genetic variations are expected to modulate transcriptional variability. For instance, the mutational effects on mean and noise of a large number of natural yeast promoter variants were revealed (Liu et al., 2015; Metzger et al., 2015). Especially, promoter mutations increasing noise appear to be under purifying selection (Metzger et al., 2015). As suggested by previous works on synthetic promoters (Blake et al., 2006), promoter engineering toward such enhancement can be highly beneficial in stressful conditions (Liu et al., 2018). Later works with mutant alleles of the *TDH3* promoter in yeast indeed demonstrated the fitness effects of modifications of gene expression variability through genetic variation (Duveau et al., 2018). The fitness effect of mutations in 33 yeast promoters was also used to more globally study the fitness landscape of mean-noise expression space (Schmiedel et al., 2019). It revealed that enhanced variability is detrimental for many genes in normal conditions (while this would not be necessarily the case in stressful conditions). It also showed the importance of considering the interplay between genetic and gene expression variability in the understanding of the mechanisms that shape variation in cis-regulatory sequences.

## 3 | INFLUENCE OF EPIGENETIC VARIABILITY OVER GENE EXPRESSION VARIABILITY

Epigenetic mechanisms have also strong effects on gene expression variability. Mutations in chromatin remodelers clearly affect this phenomenon in yeast (Weinberger et al., 2012). Among the cis-elements mentioned above, several impact chromatin properties, especially nucleosome occupancy. Promoters with nucleosome-binding sites alternate between silenced or open states. Their transient opening and reclosing leads to transcriptional bursting events of variable duration and frequency, generating cell-to-cell variability in gene expression (Sanchez & Golding, 2013). Especially, nucleosome density around TSSs influences burst frequency (Brown et al., 2013; Dey et al., 2015) and expression noise (Small et al., 2014; Tirosh & Barkai, 2008). This is why promoters with polynucleosome-disfavoring sequences exhibit less noise: Their presence produces higher burst frequencies and thus leads to lower cell-to-cell heterogeneity (Sharon et al., 2014).

First, as high nucleosome occupancies are associated with TATA elements and highly variable genes (Tirosh & Barkai, 2008), the presence of a TATA box is expected to impact expression through the nucleosome architecture favored by this element. Indeed, a qualitative model for nucleosome positioning in yeast showed that nucleosome positioning in TATA-containing promoters increases transcriptional variability (Zaugg & Luscombe, 2012). Similarly, promoters containing
both TATA boxes and nucleosome-occupied TF-binding sites exhibit such high expression noise (Field et al., 2008). On the contrary, in human embryonic cells, TSS-proximal nucleosome occupancy is only weakly correlated with expression variability (Wu et al., 2017).

Epigenetic modifications can also influence cell-to-cell expres-
sion variability. In gene bodies, DNA methylation at CpG dinucle-
otides suppresses transcriptional noise (Huh et al., 2013). Histone
modifications also impact this phenomenon through chromatin ac-
cessibility (Wu et al., 2017), with promoter-proximal modifications
being associated with increased noise (H3K27me3, H3K4me1, and
H3K9me3) and modifications in gene bodies being associated with
decreased noise (H3K36me3, H3K4me3, and H3K9ac) (Faure et al.,
2017). Intriguingly, gene body histone marks determine more gene
expression variability than promoter sequence features (Faure et al.,
2017). Mechanistically, the promoter histone-acetylation level di-
rectly influences burst frequency for many genes, while burst size
is less impacted (Nicolas et al., 2018). H3K27ac and H3K9ac are es-
pecially highly correlated with burst frequency (Nicolas et al., 2018).

Previous works showed that some bivalent promoters with
both the repressive H3K27me3 mark (catalyzed by the Polycomb
Repressive Complex 2 [PRC-2]) and the activation-associated
H3K4me3 mark especially display high variability in mouse embry-
onic stem cells that might be caused by switching between the re-
pressed and active states (Kar et al., 2017). In fact, Polycomb could
modulate bursting and enhance switching between ON and OFF
states in this subset of genes (Kar et al., 2017). Finally, more recent
works showed that promoter binding of PRC-2-related factors influ-
ence bursting and have effects on specific genes (Ochiai et al., 2020).
On the other hand, the effects of PRC2-related factors bound on
promoter are opposite from those on gene body (Ochiai et al., 2020).

Single-cell nucleosome mapping revealed cell-to-cell variation in nucleosome occupancy during induction of the *PHO5* gene in yeast, and a small fraction of cells that exhibit nucleosome-free regions at the promoter even in nonstressed environments (Small et al., 2014), suggesting that nucleosome positioning heterogeneity contributes to gene expression variability independently of the promoter sequence. Indeed, this nucleosome promoter variation was proposed to arise stochastically and to be a source of gene expression variability (Brown & Boeger, 2014). Also, single-cell chromatin accessibility studies showed a link between accessibility and expression heterogeneity (Buenrostro et al., 2015). Especially, improved accessibility of the promoter for the transcription machinery during initiation thanks to increased eviction or sliding of the nucleosomes probably reduces expression noise during induction (Rawal et al., 2018).

Finally, the chromatin influence on gene expression variability is re-
vealed by various works showing that changing the location of a gene
changes its level of noise in S. cerevisiae (Becskei et al., 2005), Candida
albicans (Anderson et al., 2014), or chicken cells (Vinuelas et al., 2013).
Expression mean and variability are uncorrelated across genomic lo-
cations in mammalian cells (Dey et al., 2015). In these cells, genomic
locations displaying higher expression noise are associated with more
repressed chromatin, thereby indicating that the level of noise is influ-
enced by the chromatin environment (Dey et al., 2015).

4 | INFLUENCE OF GENE EXPRESSION VARIABILITY OVER GENETIC VARIABILITY

The impact of gene expression variability on cellular response to DNA damage and mutagenesis has been more recently studied, mainly in bacterial cells. These investigations were possible thanks to the development of single-molecule imaging methods to analyze bacterial DNA-repair processes (Ghodke et al., 2018; Robert et al., 2019; Uphoff & Sherratt, 2017). A pioneering work monitored how expression variation of the Ada protein impacts the response to DNA alkylation damage in *E. coli* (Uphoff et al., 2016). The initial expression level of Ada determines the induction times of the damage response at the single-cell level, producing cell-to-cell heterogeneity in DNA repair in the population, especially with cells that do not respond for generations because no Ada proteins are initially expressed (Uphoff et al., 2016). Thus, this subpopulation accumulates foci of the DNA mismatch recognition protein MutS which was used as a marker for labeling nascent mutations (Uphoff et al., 2016). This observation led the authors to conclude that heterogeneity in mutation rate exists at the single-cell level in *E. coli* and that nongenetic variation in protein abundances can lead to genetic heterogeneity. These results were next confirmed in a study showing that the different rates at which the mismatches stochastically arise in single cells depend upon both the preexisting levels of DNA damage-processing proteins and the cell's ability to up-regulate its own repair responses (Uphoff, 2018).

Concomitant works finely quantified mutation rates (Robert
et al., 2018) and spontaneous DNA replication errors (Woo et al.,
2018) in single *E. coli* cells, while not connecting it to expression
variability of genes involved in DNA transactions. Several molecular
processes can be evoked to explain cell-to-cell variation in mutagen-
esis: stochastic events in DNA damage and repair, heterogeneous
expression of DNA repair genes or variation in other cellular pro-
cesses influencing DNA repair such as cell cycle or cell growth rate
[for a comprehensive review, see (Vincent & Uphoff, 2020)]. Thus,
gene expression variability is only one of the parameters that can
generate intercellular variation in mutation rate.

Besides DNA repair activity in bacteria, heterogeneity in homol-
ogous recombination (HR) rate in relationship with gene expression
variability was also studied with another experimental approach
using cell sorting in S. cerevisiae (Liu et al., 2019). Here, genes im-
pacting HR either directly through their involvement in the pathway
(RAD52) or indirectly through their involvement in DNA replication
(RAD27) were fused to a fluorescent marker that allowed to sort cells
with extreme expression levels in the population. By using a strain
harboring an intrachromosomal HR substrate, it was possible to
demonstrate the existence of a high heterogeneity in HR rate among
yeast cells, with low Rad52 or Rad27 levels being associated with
lower HR rate (Liu et al., 2019). Effects of cell cycle heterogeneity
and heterogeneity in DNA damage between the subpopulations
were excluded so as to conclude that HR rate heterogeneity is di-
rectly caused by gene expression noise. By sorting more subpop-
ulations than the two extreme ones, Rad27 levels were shown to
nonlinearly scale with HR activity. This observation has important
consequences because it means that the total amount of HR does not depend only on the averaged Rad27 expression: Doubling the mean Rad27 expression would not lead to a doubling of HR rate, and slight modifications of its mean expression level could generate high variation in HR activity.

Thus, subpopulations with increased mutation or HR rates are expected to improve whole population adaptability in changing environments because they constitute a reservoir of increased genetic variability, with important evolutionary consequences (Matic, 2019; Woo et al., 2018). The presence of subpopulations of cells with enhanced mutagenesis could be particularly important when several mutations are required for adaptation to new environments (Alexander et al., 2017). Gene expression variability has a clear role in the appearance of this heterogeneity and modifying noise in the expression of genes involved in the DNA transaction processes could allow tuning the mutation and/or recombination rates and have consequences in terms of evolvability in fluctuating environments (Capp, 2010).

## 5 | INFLUENCE OF GENE EXPRESSION VARIABILITY OVER EPIGENETIC VARIABILITY

Almost nothing is known about the influence of gene expression variability over epigenetic variability. The possible influence of noise in the expression of epigenetic regulators on chromatin modification activity in *S. cerevisiae* was recently explored by analyzing the well-known histone modifier of the Sirtuin family *Sir2* and epigenetic silencing at subtelomeres (Liu et al., 2020). Like for *RAD52* and *RAD27* in the HR context, *SIR2* was fused to a fluorescent marker allowing to sort cells with extreme expression levels in a strain containing a subtelomeric *URA3* silencing substrate. No difference in silencing activity was observed, probably because *Sir2* is not limiting for silencing subtelomeric *URA3* (Liu et al., 2020). Nevertheless, this might not be true for other reporter genes and other genomic locations. Finally, other chromatin remodelers and epigenetic events should be tested.

## 6 | APPLIED QUESTIONS IN THE CONTEXT OF ONCOGENESIS

Neoplasms are characterized by a global increase in cellular stochasticity and intra-tumoral heterogeneity (ITH), with higher epigenetic and gene expression variability compared to normal cells (Jenkinson et al., 2017). Together with their well-known genetic instability, these features of cancer cells constitute an ideal model to decipher their interplay. The level of genetic ITH was found to be positively correlated with the level of epigenetic ITH in leukemia (Landau et al., 2014; Oakes et al., 2014) or aggressive prostate cancer (Brocks et al., 2014), among others (Mazor et al., 2016). In addition to this correlation, promoters with high methylation ITH in leukemia were also associated with high cell-to-cell expression heterogeneity of the

corresponding gene (Landau et al., 2014), suggesting that genetic, epigenetic, and gene expression ITHs are mostly correlated. They probably contribute concomitantly to cancer cell evolvability and to the increase of phenotypic diversity in cancer cell populations during progression. Their interplay in oncogenesis deserves to be investigated in an evolutionary perspective, especially how they dynamically interact and change when the rate of appearance of genetic, epigenetic, or gene expression diversity is modified.

Therefore, it should be interesting to see how the two other types of variability dynamically evolve when the third is experimentally enhanced. The approach that consists in studying cancerous phenotypes arising upon artificially imposed gene expression noise, using noise-controlling genetic devices, methods, or chemicals (Guinn et al., 2020), is certainly relevant in this context for studying the influence of gene expression variability over the others. Would cell populations follow distinct evolutionary trajectories mainly based on the one that has been increased, or would they converge in all cases toward similar states relying on the same mixture of genetic, epigenetic, and gene expression variability? On the contrary, would targeting one of them be somehow compensated by an increase in the other types of variability, and which targeting would be more efficient in decreasing cellular stochasticity and acquiring phenotypic stability? Answering these questions might open new therapeutic perspectives on rational combinations of genetic-, epigenetic-, and/or gene expression-based strategies able to counteract the still understood power of cellular evolvability (Payne & Wagner, 2019).

Finally, studying this interplay in oncogenesis is especially interesting in the context of drug resistance. The interplay between genetic and nongenetic phenomena has already been highlighted in the appearance of resistant cells (Bell & Gilan, 2020; Salgia & Kulkarni, 2018). A recent work explicitly studied the complex interplay among genetic, epigenetic, and stochastic sources of ITH in the context of drug resistance and showed that in almost all clonal sublines, drug-response variability is due to epigenetic rather than genetic differences (Hayford et al., 2020). Nongenetic heterogeneity in diverse epigenetic and gene expression processes now appears to be a major contributor in the appearance of persistent cancer cells, which constitute a rare subpopulation that can survive cancer drug treatment and constitute a major cause of treatment failure (Shen et al., 2020). For instance, the rare and transient transcription of a number of resistance markers at high levels in a very small percentage of single melanoma cells is at the origin of a resistance phenomenon due to transcriptional variability, which is followed by epigenetic "reprogramming" in these cells converting this transient transcriptional state to a stably resistant state (Shaffer et al., 2017). These groups of genes co-fluctuate in "coordinated rare-cell expression programs" and are heritable for several generations but ultimately transient (Shaffer et al., 2020). A prominent role of gene expression variability in the emergence of resistant cells was also found among estrogen receptor-positive breast cancers (Hinohara et al., 2018). When gene expression stochasticity and transcriptomic and phenotypic heterogeneity were decreased through the inhibition of the activity
of members of the KDM5 demethylase family, resistance to endo-
crine therapies was reduced because less cells acquire resistance
(Hinohara et al., 2018).

Cells surviving through nongenetic mechanisms can then give rise
to genetic resistance upon continuous anticancer treatment (Hata
et al., 2016; Ramirez et al., 2016; S. Shen et al., 2019). Thus, tuning
these nongenetic mechanisms and the level of diversity that they gen-
erate could impact the evolution of drug resistance in mammalian cells.
Such a modulation of gene expression variability was recently used
to study whether high or low expression noise of specific resistance
proteins and network circuits can generate stable resistance by ac-
quisition of mutations within the circuits (Farquhar et al., 2019). Only
cells with the low noise gene circuit mutated to stably adapt, showing
the relevance of studying this interplay and its implications in cancer
treatments with known regulatory mechanisms of resistance (Farquhar
et al., 2019). The works mentioned above showing that higher noise
can promote survival for cell populations via mutagenesis could appear
contradictory with the Farquhar et al's study, but the low versus high
noise circuit in the latter drives the expression of the gene that confers
resistance, not directly genes involved in DNA maintenance as in the
formers. Thus, adaptation by mutations would be favored when noise
in the expression of genes involved in DNA maintenance is higher and
when noise in the expression of genes involved in therapeutic resis-
tance is lower. Studies on the interplay between genetic, epigenetic,
and expression variability, and the role of noise in evolutionary tra-
jectories, could be particularly challenging in cancer cell populations
because of the many disrupted regulatory levels and the high levels of
instability that characterize cancer cells, but the importance of these
phenomena in this context also shows their high applicability.

7 | CONCLUSION

The interplay between genetic, epigenetic, and gene expression
variability adds layers of complexity in the generation of phenotypic
variation. These three sources of biological variability are gener-
ally considered independently in their relationship with phenotypic
variation while they appear to be intrinsically interconnected and
influence it in combination. Future works will be needed to decipher
still largely unexplored influences, especially how epigenetic mecha-
nisms might be impacted by expression variability of chromatin re-
modelers, and to apply these concepts in the context of complex and
fluctuating environments such as the ones found in oncogenesis.

ACKNOWLEDGEMENT

This work was supported by the Toulouse White Biotechnology
(TWB) consortium (precompetitive financial support "NoHighRec")
and INRAE, MICA department ("LimitNoise" project). I thank Mohit
Kumar Jolly, Ivan Matic, Andràs Paldi, and Abhyudai Singh for their
critical reading and advices.

CONFLICT OF INTEREST

None declared.

DATA AVAILABILITY STATEMENT

Data sharing is not applicable to this article as no new data were cre-
ated or analyzed in this study.

ORCID

Jean-Pascal Capp https://orcid.org/0000-0002-6470-079X

REFERENCES

Ackermann, M. (2015). A functional perspective on phenotypic heterogeneity in microorganisms. *Nature Reviews Microbiology*, *13*(8), 497-508. https://doi.org/10.1038/nrmicro3491

Alexander, H. K., Mayer, S. I., & Bonhoeffer, S. (2017). Population heterogeneity in mutation rate increases the frequency of higher-order mutants and reduces long-term mutational load. *Molecular Biology and Evolution*, *34*(2), 419–436. https://doi.org/10.1093/molbev/msw244

Anderson, M. Z., Gerstein, A. C., Wigen, L., Baller, J. A., & Berman, J. (2014). Silencing is noisy: Population and cell level noise in telomere-adjacent genes is dependent on telomere position and sir2. *PLoS Genetics*, *10*(7), e1004436. https://doi.org/10.1371/journal.pgen.1004436

Bar-Even, A., Paulsson, J., Maheshri, N., Carmi, M., O'Shea, E., Pilpel, Y., & Barkai, N. (2006). Noise in protein expression scales with natural protein abundance. *Nature Genetics*, *38*(6), 636-643. https://doi.org/10.1038/ng1807

Bartman, C. R., Hsu, S. C., Hsiung, C. C., Raj, A., & Blobel, G. A. (2016). Enhancer regulation of transcriptional bursting parameters revealed by forced chromatin looping. *Molecular Cell*, *62*(2), 237-247. https://doi.org/10.1016/j.molcel.2016.03.007

Becskei, A., Kaufmann, B. B., & van Oudenaarden, A. (2005). Contributions of low molecule number and chromosomal positioning to stochastic gene expression. *Nature Genetics*, *37*(9), 937-944. https://doi.org/10.1038/ng1616

Becskei, A., Seraphin, B., & Serrano, L. (2001). Positive feedback in eukaryotic gene networks: Cell differentiation by graded to binary response conversion. *EMBO Journal*, *20*(10), 2528-2535. https://doi.org/10.1093/emboj/20.10.2528

Becskei, A., & Serrano, L. (2000). Engineering stability in gene networks by autoregulation. *Nature*, *405*(6786), 590-593. https://doi.org/10.1038/35014651

Bell, C. C., & Gilan, O. (2020). Principles and mechanisms of non-genetic resistance in cancer. *British Journal of Cancer*, *122*(4), 465-472. https://doi.org/10.1038/s41416-019-0648-6

Blake, W. J., Balazsi, G., Kohanski, M. A., Isaacs, F. J., Murphy, K. F., Kuang, Y., Cantor, C. R., Walt, D. R. & Collins, J. J. (2006). Phenotypic consequences of promoter-mediated transcriptional noise. *Molecular Cell*, *24*(6), 853-865. https://doi.org/10.1016/j.molcel.2006.11.003

Blake, W. J., Kærn, M., Cantor, C. R., & Collins, J. J. (2003). Noise in eukaryotic gene expression. *Nature*, *422*(6932), 633-637. https://doi.org/10.1038/nature01546

Brocks, D., Assenov, Y., Minner, S., Bogatyrova, O., Simon, R., Koop, C., Oakes, C., Zucknick, M., Lipka, D. B., Weischenfeldt, J., Feuerbach, L., Cowper-Sal Lari, R., Lupien, M., Brors, B., Korbel, J., Schlomm, T., Tanay, A., Sauter, G., Gerhäuser, C., ... ICGC Early Onset Prostate Cancer Project (2014). Intratumor DNA methylation heterogeneity reflects clonal evolution in aggressive prostate cancer. *Cell Reports*, *8*(3), 798-806. https://doi.org/10.1016/j.celrep.2014.06.053

Brown, C. R., & Boeger, H. (2014). Nucleosomal promoter variation generates gene expression noise. *Proceedings of the National Academy of Sciences of the United States of America*, *111*(50), 17893-17898. https://doi.org/10.1073/pnas.1417527111

Brown, C. R., Mao, C., Falkovskaia, E., Jurica, M. S., & Boeger, H. (2013). Linking stochastic fluctuations in chromatin structure and gene expression. *PLoS Biology*, *11*(8), e1001621. https://doi.org/10.1371/journal.pbio.1001621
Buenrostro, J. D., Wu, B., Litzenburger, U. M., Ruff, D., Gonzales, M. L., Snyder, M. P., Chang, H. Y., & Greenleaf, W. J. (2015). Single-cell chromatin accessibility reveals principles of regulatory variation. *Nature*, 523(7561), 486–490. https://doi.org/10.1038/nature14590

Capp, J. P. (2010). Noise-driven heterogeneity in the rate of genetic-variant generation as a basis for evolvability. *Genetics*, 185(2), 395–404. https://doi.org/10.1534/genetics.110.118190

Dey, S. S., Foley, J. E., Limsirichai, P., Schaffer, D. V., & Arkin, A. P. (2015). Orthogonal control of expression mean and variance by epigenetic features at different genomic loci. *Molecular Systems Biology*, 11(5), 806. https://doi.org/10.15252/msb.20145704

Duveau, F., Hodgins-Davis, A., Metzger, B. P., Yang, B., Tryban, S., Walker, E. A., Lybrook, T., & Wittkopp, P. J. (2018). Fitness effects of altering gene expression noise in *Saccharomyces cerevisiae*. *Elife*, 7, e37272. https://doi.org/10.7554/eLife.37272

Eling, N., Morgan, M. D., & Marioni, J. C. (2019). Challenges in measuring and understanding biological noise. *Nature Reviews Genetics*, 20(9), 536–548. https://doi.org/10.1038/s41576-019-0130-6

Elowitz, M. B., Levine, A. J., Siggia, E. D., & Swain, P. S. (2002). Stochastic gene expression in a single cell. *Science*, 297(5584), 1183–1186. https://doi.org/10.1126/science.1070919

Farquhar, K. S., Charlebois, D. A., Szenk, M., Cohen, J., Nevozhay, D., & Balazsi, G. (2019). Role of network-mediated stochasticity in mammalian drug resistance. *Nature Communications*, 10(1), 2766. https://doi.org/10.1038/s41467-019-10330-w

Faure, A. J., Schmiedel, J. M., & Lehner, B. (2017). Systematic analysis of the determinants of gene expression noise in embryonic stem cells. *Cell Syst*, 5(5), 471–484 e474. https://doi.org/10.1016/j.cels.2017.10.003

Field, Y., Kaplan, N., Fondufe-Mittendorf, Y., Moore, I. K., Sharon, E., Lubling, Y., Widom, J., & Segal, E. (2008). Distinct modes of regulation by chromatin encoded through nucleosome positioning signals. *PLoS Computational Biology*, 4(11), e1000216. https://doi.org/10.1371/journal.pcbi.1000216

Foreman, R., & Wollman, R. (2020). Mammalian gene expression variability is explained by underlying cell state. *Molecular Systems Biology*, 16(2), e9146. https://doi.org/10.15252/msb.20199146

Fukaya, T., Lim, B., & Levine, M. (2016). Enhancer control of transcriptional bursting. *Cell*, 166(2), 358–368. https://doi.org/10.1016/j.cell.2016.05.025

Ghodke, H., Ho, H., & van Oijen, A. M. (2018). Single-molecule live-cell imaging of bacterial DNA repair and damage tolerance. *Biochemical Society Transactions*, 46(1), 23–35. https://doi.org/10.1042/BST20170055

Golding, I., & Cox, E. C. (2006). Eukaryotic transcription: What does it mean for a gene to be 'on'? *Current Biology*, 16(10), R371–373. https://doi.org/10.1016/j.cub.2006.04.014

Guimaraes, J. C., Rocha, M., & Arkin, A. P. (2014). Transcript level and sequence determinants of protein abundance and noise in *Escherichia coli*. *Nucleic Acids Research*, 42(8), 4791–4799. https://doi.org/10.1093/nar/glu126

Guinn, M. T., Wan, Y., Levovitz, S., Yang, D., Rosner, M. R., & Balazsi, G. (2020). Observation and control of gene expression noise: barrier crossing analogies between drug resistance and metastasis. *Frontiers in Genetics*, 11, 586726. https://doi.org/10.3389/fgene.2020.586726

Hata, A. N., Niederst, M. J., Archibald, H. L., Gomez-Caraballo, M., Siddiqui, F. M., Mulvey, H. E., Maruvka, Y. E., Ji, F., Bhang, H. E., Krishnamurthy Radhakrishna, V., Siravegna, G., Hu, H., Raoof, S., Lockerman, E., Kalsy, A., Lee, D., Keating, C. L., Ruddy, D. A., Damon, L. J., ... Engelman, J. A. (2016). Tumor cells can follow distinct evolutionary paths to become resistant to epidermal growth factor receptor inhibition. *Nature Medicine*, 22(3), 262–269. https://doi.org/10.1038/nm.4040

Hayford, C. E., Tyson, D. R., Robbins, C. J., Frick, P. L., Quaranta, V., & Harris, L. A. (2020). A unifying framework disentangles genetic, epigenetic,

and stochastic sources of drug-response variability in an in vitro model of tumor heterogeneity. *bioRxiv*. 2020.06.05.136119. https://doi.org/10.1101/2020.06.05.136119

Hinohara, K., Wu, H. J., Vigneau, S., McDonald, T. O., Igarashi, K. J., Yamamoto, K. N., Madsen, T., Fassl, A., Egri, S. B., Papanastasiou, M., Ding, L., Peluffo, G., Cohen, O., Kales, S. C., Lal-Nag, M., Rai, G., Maloney, D. J., Jadhav, A., Simeonov, A., ... Polyak, K. (2018). KDM5 histone demethylase activity links cellular transcriptomic heterogeneity to therapeutic resistance. *Cancer Cell*, 34(6), 939–953 e939. https://doi.org/10.1016/j.ccell.2018.10.014

Hornung, G., Bar-Ziv, R., Rosin, D., Tokuriki, N., Tawfik, D. S., Oren, M., & Barkai, N. (2012). Noise-mean relationship in mutated promoters. *Genome Research*, 22(12), 2409–2417. https://doi.org/10.1101/gr.139378.112

Huh, I., Zeng, J., Park, T., & Yi, S. V. (2013). DNA methylation and transcriptional noise. *Epigenetics Chromatin*, 6(1), 9. https://doi.org/10.1186/1756-8935-6-9

Jenkinson, G., Pujadas, E., Goutsias, J., & Feinberg, A. P. (2017). Potential energy landscapes identify the information-theoretic nature of the epigenome. *Nature Genetics*, 49(5), 719–729. https://doi.org/10.1038/ng.3811

Jones, D. L., Brewster, R. C., & Phillips, R. (2014). Promoter architecture dictates cell-to-cell variability in gene expression. *Science*, 346(6216), 1533–1536. https://doi.org/10.1126/science.1255301

Kaern, M., Elston, T. C., Blake, W. J., & Collins, J. J. (2005). Stochasticity in gene expression: from theories to phenotypes. *Nature Reviews Genetics*, 6, 451–464.

Kar, G., Kim, J. K., Kolodziejczyk, A. A., Natarajan, K. N., Torlai Triglia, E., Mifsud, B., Elderkin, S., Marioni, J. C., Pombo, A., & Teichmann, S. A. (2017). Flipping between Polycomb repressed and active transcriptional states introduces noise in gene expression. *Nature Communications*, 8(1), 36. https://doi.org/10.1038/s41467-017-00052-2

Landau, D. A., Clement, K., Ziller, M. J., Boyle, P., Fan, J., Gu, H., Stevenson, K., Sougnez, C., Wang, L., Li, S., Kotliar, D., Zhang, W., Ghandi, M., Garraway, L., Fernandes, S. M., Livak, K. J., Gabriel, S., Gnirke, A., Lander, E. S., ... Wu, C. J. (2014). Locally disordered methylation forms the basis of intratumor methylome variation in chronic lymphocytic leukemia. *Cancer Cell*, 26(6), 813–825. https://doi.org/10.1016/j.ccell.2014.10.012

Larson, D. R., Fritzsch, C., Sun, L., Meng, X., Lawrence, D. S., & Singer, R. H. (2013). Direct observation of frequency modulated transcription in single cells using light activation. *Elife*, 2, e00750. https://doi.org/10.7554/eLife.00750

Larsson, A. J. M., Johnsson, P., Hagemann-Jensen, M., Hartmanis, L., Faridani, O. R., Reinius, B., Segerstolpe, Å., Rivera, C. M., Ren, B., & Sandberg, R. (2019). Genomic encoding of transcriptional burst kinetics. *Nature*, 565(7738), 251–254. https://doi.org/10.1038/s4158 6-018-0836-1

Liu, J., Francois, J. M., & Capp, J. P. (2019). Gene expression noise produces cell-to-cell heterogeneity in eukaryotic homologous recombination rate. *Frontiers in Genetics*, 10, 475. https://doi.org/10.3389/fgene.2019.00475

Liu, J., Lestrade, D., Arabaciyan, S., Cescut, J., Francois, J. M., & Capp, J. P. (2018). A GRX1 promoter variant confers constitutive noisy bimodal expression that increases oxidative stress resistance in yeast. *Frontiers in Microbiology*, 9, 2158. https://doi.org/10.3389/fmicb.2018.02158

Liu, J., Martin-Yken, H., Bigey, F., Dequin, S., Francois, J. M., & Capp, J. P. (2015). Natural yeast promoter variants reveal epistasis in the generation of transcriptional-mediated noise and its potential benefit in stressful conditions. *Genome Biology and Evolution*, 7(4), 969–984. https://doi.org/10.1093/gbe/evv047

Liu, J., Mosser, L., Botanch, C., Francois, J. M., & Capp, J. P. (2020). SIR2 expression noise can generate heterogeneity in viability but does
not affect cell-to-cell epigenetic silencing of subtelomeric URA3 in Yeast. G3 (Bethesda), 10(9), 3435-3443. https://doi.org/10.1534/g3.120.401589

Matic, I. (2019). Mutation rate heterogeneity increases odds of survival in unpredictable environments. *Molecular Cell*, 75(3), 421-425. https://doi.org/10.1016/j.molcel.2019.06.029

Mazor, T., Pankov, A., Song, J. S., & Costello, J. F. (2016). Intratumoral heterogeneity of the epigenome. *Cancer Cell*, 29(4), 440-451. https://doi.org/10.1016/j.ccell.2016.03.009

McAdams, H. H., & Arkin, A. (1999). It's a noisy business! Genetic regulation at the nanomolar scale. *Trends in Genetics*, 15(2), 65-69.

Metzger, B. P., Yuan, D. C., Gruber, J. D., Duveau, F., & Wittkopp, P. J. (2015). Selection on noise constrains variation in a eukaryotic promoter. *Nature*, 521(7552), 344-347. https://doi.org/10.1038/natur e14244

Morgan, M. D., & Marioni, J. C. (2018). CpG island composition differences are a source of gene expression noise indicative of promoter responsiveness. *Genome Biology*, 19(1), 81. https://doi.org/10.1186/s13059-018-1461-x

Murphy, K. F., Balazsi, G., & Collins, J. J. (2007). Combinatorial promoter design for engineering noisy gene expression. *Proceedings of the National Academy of Sciences of the United States of America*, 104(31), 12726-12731. https://doi.org/10.1073/pnas.06084 51104

Nevozhay, D., Adams, R. M., Murphy, K. F., Josic, K., & Balazsi, G. (2009). Negative autoregulation linearizes the dose-response and suppresses the heterogeneity of gene expression. *Proceedings of the National Academy of Sciences of the United States of America*, 106(13), 5123-5128. https://doi.org/10.1073/pnas.0809901106

Nevozhay, D., Adams, R. M., Van Itallie, E., Bennett, M. R., & Balazsi, G. (2012). Mapping the environmental fitness landscape of a synthetic gene circuit. *PLoS Computational Biology*, 8(4), e1002480. https://doi.org/10.1371/journal.pcbi.1002480

Newman, J. R., Ghaemmaghami, S., Ihmels, J., Breslow, D. K., Noble, M., DeRisi, J. L., & Weissman, J. S. (2006). Single-cell proteomic analysis of *S. cerevisiae* reveals the architecture of biological noise. *Nature*, 441(7095), 840-846. https://doi.org/10.1038/nature04785

Nicolas, D., Zoller, B., Suter, D. M., & Naef, F. (2018). Modulation of transcriptional burst frequency by histone acetylation. *Proceedings of the National Academy of Sciences of the United States of America*, 115(27), 7153-7158. https://doi.org/10.1073/pnas.1722330115

Oakes, C. C., Claus, R., Gu, L., Assenov, Y., Hullein, J., Zucknick, M., Bieg, M., Brocks, D., Bogatyrova, O., Schmidt, C. R., Rassenti, L., Kipps, T. J., Mertens, D., Lichter, P., Döhner, H., Stilgenbauer, S., Byrd, J. C., Zenz, T., & Plass, C. (2014). Evolution of DNA methylation is linked to genetic aberrations in chronic lymphocytic leukemia. *Cancer Discovery*, 4(3), 348-361. https://doi.org/10.1158/2159-8290.CD-13-0349

Ochiai, H., Hayashi, T., Umeda, M., Yoshimura, M., Harada, A., Shimizu, Y., Nakano, K., Saitoh, N., Liu, Z., Yamamoto, T., Okamura, T., Ohkawa, Y., Kimura, H., & Nikaido, I. (2020). Genome-wide kinetic properties of transcriptional bursting in mouse embryonic stem cells. *Sci Adv*, 6(25), eaaz6699. https://doi.org/10.1126/sciadv.eaz6699

Ozbudak, E. M., Thattai, M., Kurtser, I., Grossman, A. D., & van Oudenaarden, A. (2002). Regulation of noise in the expression of a single gene. *Nature Genetics*, 31(1), 69-73. https://doi.org/10.1038/ng869

Payne, J. L., & Wagner, A. (2019). The causes of evolvability and their evolution. *Nature Reviews Genetics*, 20(1), 24-38. https://doi.org/10.1038/s41576-018-0069-z

Raj, A., & van Oudenaarden, A. (2008). Nature, nurture, or chance: stochastic gene expression and its consequences. *Cell*, 135(2), 216-226. https://doi.org/10.1016/j.cell.2008.09.050

Ramirez, M., Rajaram, S., Steininger, R. J., Osipchuk, D., Roth, M. A., Morinishi, L. S., Evans, L., Ji, W., Hsu, C. H., Thurley, K., Wei, S., Zhou, A., Koduru, P. R., Posner, B. A., Wu, L. F., & Altschuler, S. J. (2016).

Diverse drug-resistance mechanisms can emerge from drug-tolerant cancer persister cells. *Nature Communications*, 7, 10690. https://doi.org/10.1038/ncomms10690

Raser, J. M., & O'Shea, E. K. (2004). Control of stochasticity in eukaryotic gene expression. *Science*, 304(5678), 1811-1814. https://doi.org/10.1126/science.1098641

Rawal, Y., Chereji, R. V., Qiu, H., Ananthakrishnan, S., Govind, C. K., Clark, D. J., & Hinnebusch, A. G. (2018). SWI/SNF and RSC cooperate to reposition and evict promoter nucleosomes at highly expressed genes in yeast. *Genes and Development*, 32(9-10), 695-710. https://doi.org/10.1101/gad.312850.118

Robert, L., Ollion, J., & Elez, M. (2019). Real-time visualization of mutations and their fitness effects in single bacteria. *Nature Protocols*, 14(11), 3126-3143. https://doi.org/10.1038/s41596-019-0215-x

Robert, L., Ollion, J., Robert, J., Song, X., Matic, I., & Elez, M. (2018). Mutation dynamics and fitness effects followed in single cells. *Science*, 359(6381), 1283-1286. https://doi.org/10.1126/science.aan0797

Salgia, R., & Kulkarni, P. (2018). The genetic/non-genetic duality of drug 'resistance' in cancer. *Trends Cancer*, 4(2), 110-118. https://doi.org/10.1016/j.trecan.2018.01.001

Sanchez, A., Choubey, S., & Kondev, J. (2013). Regulation of noise in gene expression. *Annual Review of Biophysics*, 42, 469-491. https://doi.org/10.1146/annurev-biophys-083012-130401

Sanchez, A., & Golding, I. (2013). Genetic determinants and cellular constraints in noisy gene expression. *Science*, 342(6163), 1188-1193. https://doi.org/10.1126/science.1242975

Schmiedel, J. M., Carey, L. B., & Lehner, B. (2019). Empirical mean-noise fitness landscapes reveal the fitness impact of gene expression noise. *Nature Communications*, 10(1), 3180. https://doi.org/10.1038/s41467-019-11116-w

Shaffer, S. M., Dunagin, M. C., Torborg, S. R., Torre, E. A., Emert, B., Krepler, C., Beqiri, M., Sproesser, K., Brafford, P. A., Xiao, M., Eggan, E., Anastopoulos, I. N., Vargas-Garcia, C. A., Singh, A., Nathanson, K. L., Herlyn, M., & Raj, A. (2017). Rare cell variability and drug-induced reprogramming as a mode of cancer drug resistance. *Nature*, 546(7658), 431-435. https://doi.org/10.1038/nature22794

Shaffer, S. M., Emert, B. L., Reyes Hueros, R. A., Cote, C., Harmange, G., Schaff, D. L., Sizemore, A. E., Gupte, R., Torre, E., Singh, A., Bassett, D. S., & Raj, A. (2020). Memory sequencing reveals heritable single-cell gene expression programs associated with distinct cellular behaviors. *Cell*, 182(4), 947-959 e917. https://doi.org/10.1016/j.cell.2020.07.003

Sharon, E., van Dijk, D., Kalma, Y., Keren, L., Manor, O., Yakhini, Z., & Segal, E. (2014). Probing the effect of promoters on noise in gene expression using thousands of designed sequences. *Genome Research*, 24(10), 1698-1706. https://doi.org/10.1101/gr.168773.113

Shen, H., & Laird, P. W. (2013). Interplay between the cancer genome and epigenome. *Cell*, 153(1), 38-55. https://doi.org/10.1016/j.cell.2013.03.008

Shen, S., Faouzi, S., Bastide, A., Martineau, S., Malka-Mahieu, H., Fu, Y., Sun, X., Mateus, C., Routier, E., Roy, S., Desaubry, L., André, F., Eggermont, A., David, A., Scoazec, J. Y., Vagner, S., & Robert, C. (2019). An epitranscriptomic mechanism underlies selective mRNA translation remodelling in melanoma persister cells. *Nature Communications*, 10(1), 5713. https://doi.org/10.1038/s41467-019-13360-6

Shen, S., Vagner, S., & Robert, C. (2020). Persistent cancer cells: The deadly survivors. *Cell*, 183(4), 860-874. https://doi.org/10.1016/j.cell.2020.10.027

Silander, O. K., Nikolic, N., Zaslaver, A., Bren, A., Kikoin, I., Alon, U., & Ackermann, M. (2012). A genome-wide analysis of promoter-mediated phenotypic noise in *Escherichia coli*. *PLoS Genetics*, 8(1), e1002443. https://doi.org/10.1371/journal.pgen.1002443

Small, E. C., Xi, L., Wang, J. P., Widom, J., & Licht, J. D. (2014). Single-cell nucleosome mapping reveals the molecular basis of gene expression
<figure><img src="image_4.png" /></figure>

heterogeneity. *Proceedings of the National Academy of Sciences of the United States of America*, **111**(24), E2462-E2471. https://doi.org/10.1073/pnas.1400517111

Tantale, K., Mueller, F., Kozulic-Pirher, A., Lesne, A., Victor, J. M., Robert, M. C., Capozi, S., Chouaib, R., Bäcker, V., Mateos-Langerak, J., Darzacq, X., Zimmer, C., Basyuk, E., & Bertrand, E. (2016). A single-molecule view of transcription reveals convoys of RNA polymerases and multi-scale bursting. *Nature Communications*, **7**, 12248. https://doi.org/10.1038/ncomms12248

Thattai, M., & van Oudenaarden, A. (2001). Intrinsic noise in gene regulatory networks. *Proceedings of the National Academy of Sciences of the United States of America*, **98**(15), 8614-8619. https://doi.org/10.1073/pnas.151588598

Tirosh, I., & Barkai, N. (2008). Two strategies for gene regulation by promoter nucleosomes. *Genome Research*, **18**(7), 1084-1091. https://doi.org/10.1101/gr.076059.108

To, T. L., & Maheshri, N. (2010). Noise can induce bimodality in positive transcriptional feedback loops without bistability. *Science*, **327**(5969), 1142-1145. https://doi.org/10.1126/science.1178962

Tunnacliffe, E., & Chubb, J. R. (2020). What is a transcriptional burst? *Trends in Genetics*, **36**(4), 288-297. https://doi.org/10.1016/j.tig.2020.01.003

Uphoff, S. (2018). Real-time dynamics of mutagenesis reveal the chronology of DNA repair and damage tolerance responses in single cells. *Proceedings of the National Academy of Sciences of the United States of America*, **115**(28), E6516-E6525. https://doi.org/10.1073/pnas.1801101115

Uphoff, S., Lord, N. D., Okumus, B., Potvin-Trottier, L., Sherratt, D. J., & Paulsson, J. (2016). Stochastic activation of a DNA damage response causes cell-to-cell mutation rate variation. *Science*, **351**(6277), 1094-1097. https://doi.org/10.1126/science.aac9786

Uphoff, S., & Sherratt, D. J. (2017). Single-molecule analysis of bacterial DNA repair and mutagenesis. *Annual Review of Biophysics*, **46**, 411-432. https://doi.org/10.1146/annurev-biophys-070816-034106

Vincent, M. S., & Uphoff, S. (2020). Bacterial phenotypic heterogeneity in DNA repair and mutagenesis. *Biochemical Society Transactions*, **48**(2), 451-462. https://doi.org/10.1042/BST20190364

Vinuelas, J., Kaneko, G., Coulon, A., Vallin, E., Morin, V., Mejia-Pous, C., Kupiec, J.-J., Beslon, G., & Gandrillon, O. (2013). Quantifying the

contribution of chromatin dynamics to stochastic gene expression reveals long, locus-dependent periods between transcriptional bursts. *BMC Biology*, **11**, 15. https://doi.org/10.1186/1741-7007-11-15

Voss, T. C., Schiltz, R. L., Sung, M. H., Johnson, T. A., John, S., & Hager, G. L. (2009). Combinatorial probabilistic chromatin interactions produce transcriptional heterogeneity. *Journal of Cell Science*, **122**(Pt 3), 345-356.

Weinberger, L., Voichek, Y., Tirosh, I., Hornung, G., Amit, I., & Barkai, N. (2012). Expression noise and acetylation profiles distinguish HDAC functions. *Molecular Cell*, **47**(2), 193-202. https://doi.org/10.1016/j.molcel.2012.05.008

Woo, A. C., Faure, L., Dapa, T., & Matic, I. (2018). Heterogeneity of spontaneous DNA replication errors in single isogenic *Escherichia coli* cells. *Science Advances*, **4**(6), eaat1608. https://doi.org/10.1126/sciadv.aat1608

Wu, S., Li, K., Li, Y., Zhao, T., Li, T., Yang, Y. F., & Qian, W. (2017). Independent regulation of gene expression level and noise by histone modifications. *PLoS Computational Biology*, **13**(6), e1005585. https://doi.org/10.1371/journal.pcbi.1005585

Zaugg, J. B., & Luscombe, N. M. (2012). A genomic model of condition-specific nucleosome behavior explains transcriptional activity in yeast. *Genome Research*, **22**(1), 84-94. https://doi.org/10.1101/gr.124099.111

Zhang, Z., Qian, W., & Zhang, J. (2009). Positive selection for elevated gene expression noise in yeast. *Molecular Systems Biology*, **5**, 299. https://doi.org/10.1038/msb.2009.58

Zoller, B., Nicolas, D., Molina, N., & Naef, F. (2015). Structure of silent transcription intervals and noise characteristics of mammalian genes. *Molecular Systems Biology*, **11**(7), 823. https://doi.org/10.15252/msb.20156257

**How to cite this article:** Capp J-P. Interplay between genetic, epigenetic, and gene expression variability: Considering complexity in evolvability. *Evol Appl.* 2021;00:1-9. https://doi.org/10.1111/eva.13204