Operator Sequence Alters Gene Expression Independently of Transcription Factor Occupancy in Bacteria

A canonical quantitative view of transcriptional regulation holds that the only role of operator sequence is to set the probability of transcription factor binding, with operator occupancy determining the level of gene expression. In this work, we test this idea by characterizing repression in vivo and the binding of RNA polymerase in vitro in experiments where operators of various sequences were placed either upstream or downstream from the promoter in Escherichia coli. Surprisingly, we find that operators with a weaker binding affinity can yield higher repression levels than stronger operators. Repressor bound to upstream operators modulates promoter escape, and the magnitude of this modulation is not correlated with the repressor-operator binding affinity. This suggests that operator sequences may modulate transcription by altering the nature of the interaction of the bound transcription factor with the transcriptional machinery, implying a new layer of sequence dependence that must be confronted in the quantitative understanding of gene expression

Ribosome traffic on mRNAs maps to gene ontology : genome-wide quantification of translation initiation rates and polysome size regulation

Peer reviewedPublisher PD

A stochastic and dynamical view of pluripotency in mouse embryonic stem cells

Pluripotent embryonic stem cells are of paramount importance for biomedical research thanks to their innate ability for self-renewal and differentiation into all major cell lines. The fateful decision to exit or remain in the pluripotent state is regulated by complex genetic regulatory network. Latest advances in transcriptomics have made it possible to infer basic topologies of pluripotency governing networks. The inferred network topologies, however, only encode boolean information while remaining silent about the roles of dynamics and molecular noise in gene expression. These features are widely considered essential for functional decision making. Herein we developed a framework for extending the boolean level networks into models accounting for individual genetic switches and promoter architecture which allows mechanistic interrogation of the roles of molecular noise, external signaling, and network topology. We demonstrate the pluripotent state of the network to be a broad attractor which is robust to variations of gene expression. Dynamics of exiting the pluripotent state, on the other hand, is significantly influenced by the molecular noise originating from genetic switching events which makes cells more responsive to extracellular signals. Lastly we show that steady state probability landscape can be significantly remodeled by global gene switching rates alone which can be taken as a proxy for how global epigenetic modifications exert control over stability of pluripotent states.Comment: 11 pages, 7 figure

Optimal signal processing in small stochastic biochemical networks

We quantify the influence of the topology of a transcriptional regulatory network on its ability to process environmental signals. By posing the problem in terms of information theory, we may do this without specifying the function performed by the network. Specifically, we study the maximum mutual information between the input (chemical) signal and the output (genetic) response attainable by the network in the context of an analytic model of particle number fluctuations. We perform this analysis for all biochemical circuits, including various feedback loops, that can be built out of 3 chemical species, each under the control of one regulator. We find that a generic network, constrained to low molecule numbers and reasonable response times, can transduce more information than a simple binary switch and, in fact, manages to achieve close to the optimal information transmission fidelity. These high-information solutions are robust to tenfold changes in most of the networks' biochemical parameters; moreover they are easier to achieve in networks containing cycles with an odd number of negative regulators (overall negative feedback) due to their decreased molecular noise (a result which we derive analytically). Finally, we demonstrate that a single circuit can support multiple high-information solutions. These findings suggest a potential resolution of the "cross-talk" dilemma as well as the previously unexplained observation that transcription factors which undergo proteolysis are more likely to be auto-repressive.Comment: 41 pages 7 figures, 5 table

Crossovers between epigenesis and epigenetics. A multicenter approach to the history of epigenetics (1901-1975)

The origin of epigenetics has been traditionally traced back to Conrad Hal Waddington's foundational work in 1940s. The aim of the present paper is to reveal a hidden history of epigenetics, by means of a multicenter approach. Our analysis shows that genetics and embryology in early XX century--far from being non-communicating vessels--shared similar questions, as epitomized by Thomas Hunt Morgan's works. Such questions were rooted in the theory of epigenesis and set the scene for the development of epigenetics. Since the 1950s, the contribution of key scientists (Mary Lyon and Eduardo Scarano), as well as the discussions at the international conference of Gif-sur-Yvette (1957) paved the way for three fundamental shifts of focus: 1. From the whole embryo to the gene; 2. From the gene to the complex extranuclear processes of development; 3. From cytoplasmic inheritance to the epigenetics mechanisms

Chromatin recruitment of activated AMPK drives fasting response genes co-controlled by GR and PPARα

Adaptation to fasting involves both Glucocorticoid Receptor (GRα) and Peroxisome Proliferator-Activated Receptor α (PPARα) activation. Given both receptors can physically interact we investigated the possibility of a genome-wide cross-talk between activated GR and PPARα, using ChIP- and RNA-seq in primary hepatocytes. Our data reveal extensive chromatin co-localization of both factors with cooperative induction of genes controlling lipid/glucose metabolism. Key GR/PPAR co-controlled genes switched from transcriptional antagonism to cooperativity when moving from short to prolonged hepatocyte fasting, a phenomenon coinciding with gene promoter recruitment of phosphorylated AMP-activated protein kinase (AMPK) and blocked by its pharmacological inhibition. In vitro interaction studies support trimeric complex formation between GR, PPARα and phospho-AMPK. Long-term fasting in mice showed enhanced phosphorylation of liver AMPK and GRα Ser211. Phospho-AMPK chromatin recruitment at liver target genes, observed upon prolonged fasting in mice, is dampened by refeeding. Taken together, our results identify phospho-AMPK as a molecular switch able to cooperate with nuclear receptors at the chromatin level and reveal a novel adaptation mechanism to prolonged fasting

Bioengineering models of cell signaling

Strategies for rationally manipulating cell behavior in cell-based technologies and molecular therapeutics and understanding effects of environmental agents on physiological systems may be derived from a mechanistic understanding of underlying signaling mechanisms that regulate cell functions. Three crucial attributes of signal transduction necessitate modeling approaches for analyzing these systems: an ever-expanding plethora of signaling molecules and interactions, a highly interconnected biochemical scheme, and concurrent biophysical regulation. Because signal flow is tightly regulated with positive and negative feedbacks and is bidirectional with commands traveling both from outside-in and inside-out, dynamic models that couple biophysical and biochemical elements are required to consider information processing both during transient and steady-state conditions. Unique mathematical frameworks will be needed to obtain an integrated perspective on these complex systems, which operate over wide length and time scales. These may involve a two-level hierarchical approach wherein the overall signaling network is modeled in terms of effective "circuit" or "algorithm" modules, and then each module is correspondingly modeled with more detailed incorporation of its actual underlying biochemical/biophysical molecular interactions

Defining a robust biological prior from Pathway Analysis to drive Network Inference

Inferring genetic networks from gene expression data is one of the most challenging work in the post-genomic era, partly due to the vast space of possible networks and the relatively small amount of data available. In this field, Gaussian Graphical Model (GGM) provides a convenient framework for the discovery of biological networks. In this paper, we propose an original approach for inferring gene regulation networks using a robust biological prior on their structure in order to limit the set of candidate networks. Pathways, that represent biological knowledge on the regulatory networks, will be used as an informative prior knowledge to drive Network Inference. This approach is based on the selection of a relevant set of genes, called the "molecular signature", associated with a condition of interest (for instance, the genes involved in disease development). In this context, differential expression analysis is a well established strategy. However outcome signatures are often not consistent and show little overlap between studies. Thus, we will dedicate the first part of our work to the improvement of the standard process of biomarker identification to guarantee the robustness and reproducibility of the molecular signature. Our approach enables to compare the networks inferred between two conditions of interest (for instance case and control networks) and help along the biological interpretation of results. Thus it allows to identify differential regulations that occur in these conditions. We illustrate the proposed approach by applying our method to a study of breast cancer's response to treatment