Quantifying the contribution of chromatin dynamics to stochastic gene expression reveals long, locus-dependent periods between transcriptional bursts.

BACKGROUND: A number of studies have established that stochasticity in gene expression may play an important role in many biological phenomena. This therefore calls for further investigations to identify the molecular mechanisms at stake, in order to understand and manipulate cell-to-cell variability. In this work, we explored the role played by chromatin dynamics in the regulation of stochastic gene expression in higher eukaryotic cells. RESULTS: For this purpose, we generated isogenic chicken-cell populations expressing a fluorescent reporter integrated in one copy per clone. Although the clones differed only in the genetic locus at which the reporter was inserted, they showed markedly different fluorescence distributions, revealing different levels of stochastic gene expression. Use of chromatin-modifying agents showed that direct manipulation of chromatin dynamics had a marked effect on the extent of stochastic gene expression. To better understand the molecular mechanism involved in these phenomena, we fitted these data to a two-state model describing the opening/closing process of the chromatin. We found that the differences between clones seemed to be due mainly to the duration of the closed state, and that the agents we used mainly seem to act on the opening probability. CONCLUSIONS: In this study, we report biological experiments combined with computational modeling, highlighting the importance of chromatin dynamics in stochastic gene expression. This work sheds a new light on the mechanisms of gene expression in higher eukaryotic cells, and argues in favor of relatively slow dynamics with long (hours to days) periods of quiet state.

Routine Whole-Genome Sequencing for Outbreak Investigations of Staphylococcus aureus in a National Reference Center.

The French National Reference Center for Staphylococci currently uses DNA arrays and spa typing for the initial epidemiological characterization of Staphylococcus aureus strains. We here describe the use of whole-genome sequencing (WGS) to investigate retrospectively four distinct and virulent S. aureus lineages [clonal complexes (CCs): CC1, CC5, CC8, CC30] involved in hospital and community outbreaks or sporadic infections in France. We used a WGS bioinformatics pipeline based on de novo assembly (reference-free approach), single nucleotide polymorphism analysis, and on the inclusion of epidemiological markers. We examined the phylogeographic diversity of the French dominant hospital-acquired CC8-MRSA (methicillin-resistant S. aureus) Lyon clone through WGS analysis which did not demonstrate evidence of large-scale geographic clustering. We analyzed sporadic cases along with two outbreaks of a CC1-MSSA (methicillin-susceptible S. aureus) clone containing the Panton-Valentine leukocidin (PVL) and results showed that two sporadic cases were closely related. We investigated an outbreak of PVL-positive CC30-MSSA in a school environment and were able to reconstruct the transmission history between eight families. We explored different outbreaks among newborns due to the CC5-MRSA Geraldine clone and we found evidence of an unsuspected link between two otherwise distinct outbreaks. Here, WGS provides the resolving power to disprove transmission events indicated by conventional methods (same sequence type, spa type, toxin profile, and antibiotic resistance profile) and, most importantly, WGS can reveal unsuspected transmission events. Therefore, WGS allows to better describe and understand outbreaks and (inter-)national dissemination of S. aureus lineages. Our findings underscore the importance of adding WGS for (inter-)national surveillance of infections caused by virulent clones of S. aureus but also substantiate the fact that technological optimization at the bioinformatics level is still urgently needed for routine use. However, the greatest limitation of WGS analysis is the completeness and the correctness of the reference database being used and the conversion of floods of data into actionable results. The WGS bioinformatics pipeline (EpiSeqTM) we used here can easily generate a uniform database and associated metadata for epidemiological applications.

Stochastic fluctuations and distributed control of gene expression impact cellular memory.

Despite the stochastic noise that characterizes all cellular processes the cells are able to maintain and transmit to their daughter cells the stable level of gene expression. In order to better understand this phenomenon, we investigated the temporal dynamics of gene expression variation using a double reporter gene model. We compared cell clones with transgenes coding for highly stable mRNA and fluorescent proteins with clones expressing destabilized mRNA-s and proteins. Both types of clones displayed strong heterogeneity of reporter gene expression levels. However, cells expressing stable gene products produced daughter cells with similar level of reporter proteins, while in cell clones with short mRNA and protein half-lives the epigenetic memory of the gene expression level was completely suppressed. Computer simulations also confirmed the role of mRNA and protein stability in the conservation of constant gene expression levels over several cell generations. These data indicate that the conservation of a stable phenotype in a cellular lineage may largely depend on the slow turnover of mRNA-s and proteins.

Towards experimental manipulation of stochasticity in gene expression.

For decades, most of molecular biology was driven by the "central dogma" in which the phenotype is defined by the genotype following a fully deterministic point of view. However, during the last 10 years, a wealth of studies has demonstrated that a given genotype can generate multiple phenotypes in identical environmental conditions, mainly because of the inherently probabilistic nature of the transcription process. It has also been shown that cells can tune this variability at the molecular level. Although previously described as a useless "noise", stochastic gene expression has now been shown by many authors to be an essential part of diverse biological processes. Chromatin dynamics having a central role in higher eukaryotes, we decided to investigate its involvement in the generation and control of stochasticity in gene expression (SGE). Our experiments reveal that the chromatin environment of a gene plays an important role in regulating SGE. Indeed, we find that histone acetylation and DNA methylation significantly affect SGE, suggesting that cells are able to adjust the variability of the expression of their genes through modification of chromatin marks. Given that the alteration of chromatin marks is itself subject to the expression of chromatin modifiers, our results shed light on a complex circular causality with on the one hand, the effect of gene expression on chromatin and on the other hand, the influence of the local chromatin environment of a gene on the dynamics of its expression.