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…

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…

Mutations have been investigated for more than a century but never witnessed in action in single cells, thus preventing direct characterization of their dynamics and reliable estimation of the distribution of their fitness effects. Such estimates have wide application and are critical for most evolutionary models/studies. We accomplished this in Escherichia coli by employing microfluidics, time-lapse imaging, and by visualizing mutations in single cells by using a fluorescent tag of the Mismatch Repair System. I will present our results obtained for strains having a wide range of mutation rates and growing under controlled condition in the absence of exogenous stress.