Numerous studies have documented the rapid evolution of mammalian transcriptional regulation, especially enhancers, and yet gene expression patterns are often conserved between species. How stable gene expression is maintained by rapidly evolving regulatory landscapes is a fundamental question in evolutionary genetics.We tested the contributions of both complexity and conservation of regulatory elements on gene expression evolution, using an integrated dataset of active promoters, enhancers and gene expression output from the same liver samples across twenty mammalian species. Our methodology captures regulatory activities that range from essentialto dispensable, and from highly-conserved across mammals to present in only one species. This analysis revealed that gene expression levels and stability are reflected by the complexity of their regulatory landscape, both within a single species and across mammals. Regulatory activities conserved across placental mammals exert a powerful stabilizing effect, associating with gene expression levels that are simultaneously high and evolutionarily stable. In contrast,recently-evolved enhancers contribute weakly to gene expression and transcriptional stability, consistent with a model whereby a sizable fraction…

Cancer is a disease that is mostly driven by somatic mutations accumulating in the genome during an individual’s lifetime. Recent advances in DNA sequencing technology have enabled genome-wide measurements of these mutations in large cohorts of cancer patients. A major challenge in analyzing these data is to distinguish functional "driver" mutations responsible for cancer progression from “passenger”, random mutations not related to the disease. Recent cancer sequencing studies have shown that somatic mutations are distributed over a large number of genes. This mutational heterogeneity is due in part to the fact that somatic mutations target pathways, or groups of genes, and that a mutation in any of dozens possible genes might be sufficient to perturb a pathway. While some of the cancer driver pathways are well characterized, many others are only approximately known. I will describe algorithms for discovering cancer driver pathways using DNA mutation data from large cohorts of cancer samples.…

In the last ten years or so it has become clear that transcription occurs largely outside of annotated regions, which produces a wealth of non-coding RNAs, some of which are rapidly degraded. The pervasive occurrence of transcription in most genomes underscores the notion that DNA is a crowded environment where many different machineries coordinate to read, replicate and repair the information stored. Interferences between transcription and other DNA associated processes, or between concomitant transcription events are potentially disruptive. Transcription termination is also one of the main strategies that the cell employs to control pervasive/hidden transcription. I will present our work on the mechanisms of transcription termination and on the function of the latter in the maintenance of the stability of the transcriptome.

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Inferring the directionality of interactions between cellular processes is a major challenge in systems biology. Time-lagged correlations allow to discriminate between alternative models, but they still rely on assumed underlying interactions. Here, we show that an information-theoretic quantity, the transfer entropy (TE), quantifies the directional influence between fluctuating variables in a model-free way. We present a theoretical approach to compute the transfer entropy, even when the noise has an extrinsic component or in the presence of feedback. We re-analyze the experimental data from Kiviet et al. (2014) [1], where fluctuations in gene expression of metabolic enzymes and growth rate have been measured in single cells of Escherichia coli. We confirm the formerly detected modes between growth and gene expression, while prescribing more stringent conditions on the structure of noise sources [2]. [1] Stochasticity of metabolism and growth at the single-cell level, D. J. Kiviet et al., Nature, 514, 376 (2014).[2] Information theoretic…

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Among the signals that control the epigenetic landscape, non-coding (nc)RNAs have been identified to play a major role but the generalization and the mechanisms of their activity are still poorly apprehended. Recent data using high-throughput technologies have profoundly changed our view on how the genomes are organized. Indeed transcriptome analyses have shown that large genomic regions are pervasively transcribed producing a broad variety of lncRNA which functions on the epigenome remain to be deciphered. Recent works showed that S. cerevisiae’s genome also supports production of non-coding protein large transcripts, which are strongly controlled by RNA decay pathways. Of specific interest are the lncRNA XUT that we described recently, mostly antisense to class II genes and associated with a consistent regulatory potential through epigenetic modifications. Here, I will present data providing insights on their regulatory activity which is conserved also in S. pombe containing RNAi pathway. The transcription and post-transcription-mediated regulatory activities of antisense…