Human gut resistome

EVENT : C3BI Seminars

Main speaker : Amine Ghozlane, from HUB, C3BI Pasteur Date : 04-04-2019 at 02:00 pm Location : Auditorium Francois Jacob – BIME (26) ,Institut Pasteur, Paris

The intestinal microbiota is considered to be a major reservoir of antibiotic resistance determinants (ARDs) that could potentially be transferred to bacterial pathogens via mobile genetic elements. Yet, this assumption is poorly supported by empirical evidence due to the distant homologies between known ARDs (mostly from culturable bacteria) and ARDs from the intestinal microbiota. Consequently, an accurate census of intestinal ARDs (that is, the intestinal resistome) has not yet been fully determined. For this purpose, we developed and validated an annotation method (called pairwise comparative modelling) on the basis of a three-dimensional structure (homology comparative modelling), leading to the prediction of 6,095 ARDs in a catalogue of 3.9 million proteins from the human intestinal microbiota. We found that the majority of predicted ARDs (pdARDs) were distantly related to known ARDs (mean amino acid identity 29.8%) and found little evidence supporting their transfer between species. According to the composition of their resistome, we were able to cluster subjects from the MetaHIT cohort (n = 663) into six resistotypes that were connected to the previously described enterotypes. Finally, we found that the relative abundance of pdARDs was positively associated with gene richness, but not when subjects were exposed to antibiotics. Altogether, our results indicate that the majority of intestinal microbiota ARDs can be considered intrinsic to the dominant commensal microbiota and that these genes are rarely shared with bacterial pathogens.

Due to security policy in Institut Pasteur, please register before if you plan to come to this meeting

Bayesian matrix factorization for drug discovery and precision medicine

EVENT : C3BI Seminars

Main speaker : Yves Moreau, from Center for Computational Systems Biology, KU Leuven Date : 31-01-2019 at 02:00 pm Location : Auditorium Francois Jacob – BIME (26) ,Institut Pasteur, Paris

Matrix factorization/completion methods provide an attractive framework to handle sparsely observed data, also called “scarce” data. A typical setting for scarce data are is clinical diagnosis in a real-world setting. Not all possible symptoms (phenotype/biomarker/etc.) will have been checked for every patient. Deciding which symptom to check based on the already available information is at the heart of the diagnostic process. If genetic information about the patient is also available, it can serve as side information (covariates) to predict symptoms (phenotypes) for this patient. While a classification/regression setting is appropriate for this problem, it will typically ignore the dependencies between different tasks (i.e., symptoms). We have recently focused on a problem sharing many similarities with the diagnostic task: the prediction of biological activity of chemical compounds against drug targets, where only 0.1% to 1% of all compound-target pairs are measured. Matrix factorization searches for latent representations of compounds and targets that allow an optimal reconstruction of the observed measurements. These methods can be further combined with linear regression models to create multitask prediction models. In our case, fingerprints of chemical compounds are used as “side information” to predict target activity. By contrast with classical Quantitative Structure-Activity Relationship (QSAR) models, matrix factorization with side information naturally accommodates the multitask character of compound-target activity prediction. This methodology can be further extended to a fully Bayesian setting to handle uncertainty optimally, and our reformulation allows scaling up this MCMC scheme to millions of compounds, thousands of targets, and tens of millions of measurements, as demonstrated on a large industrial data set from a pharmaceutical company. We also show applications of this methodology to the prioritization of candidate disease genes and to the modeling of longitudinal patient trajectories. We have implemented our method as an open source Python/C++ library, called Macau, which can be applied to many modeling tasks, well beyond our original pharmaceutical setting.

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Linking gene and function, comparative genomics tools for biologists

EVENT : C3BI Training

Main speaker : Valerie de Crecy-Lagard, from University of Florida · Department of Microbiology and Cell Science
Date : 17-06-2019 at 08:00 am
Location : Yersin Training room (24) ,Institut Pasteur, Paris

Students will need to bring their laptop.

More than twenty years after the first bacterial genome has been sequenced, microbiologists are faced with an avalanche of genomic data. However, the quality of the functional annotations of the sequenced proteome is very poor with more than half of the sequenced proteins remaining of unknown function.

With nearly 80,000 whole genomes sequences available and increasing amount of post-genomics experimental data available, it is possible to gather different types of information that lead to better functional annotations and can guide the experimental process. The workshop will guide the attendees through practical examples and show them an array of tools and databases that they can apply directly to their research problem.

No prior programming experience is required, all the tools available can be used through graphic user interfaces.

For background read (

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Deciphering gene expression programs at single-cell resolution

EVENT : JOINT Seminar C3BI –  DPT DE Biologie du développement et cellules souches

Main speaker : Stein Aerts, from Laboratory of Computational Biology. KU Leuven Center for Human Genetics. VIB Center for Brain and Disease Research. Date : 15-02-2019 at 11:00 am Location : Jules Bordet room – METCHNIKOFF (67) ,Institut Pasteur, Paris

Single-cell technologies are revolutionising biology and provide new opportunities to trace genomic regulatory programs underlying cell fate. In this talk I will present several computational strategies for the analysis of single-cell RNA-seq and single-cell ATAC-seq data that exploit the genomic regulatory code, to guide the identification of transcription factors and cell states. I will illustrate these methods on several model systems, including the Drosophila brain. Finally I will discuss how single-cell analyses can contribute to cross-species comparisons of regulatory programs.

Prof. Stein Aerts has a multidisciplinary background in both bio-engineering and computer science. During his PhD he was trained in bioinformatics, and during his Postdoc he worked on the genomics of gene regulation in Drosophila. Stein now heads the Laboratory of Computational Biology at the VIB Center for Brain & Disease Research and the KU Leuven Department of Human Genetics. His lab focuses on deciphering the genomic regulatory code, using a combination of single-cell and machine-learning approaches. His most recent scientific contributions include new bioinformatics methods for the analysis of single-cell gene regulatory networks, namely SCENIC and cisTopic. Aerts co-founded the Fly Cell Atlas consortium and generated a single-cell atlas of the ageing Drosophila brain ( Stein holds an ERC Consolidator Grant and was awarded the 2017 Prize for Bioinformatics and Computational Science from the Biotech Fund and the 2016 Astrazeneca Foundation Award Bioinformatics.

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A Polymer Physics View on Universal and Sequence-Specific Aspects of Chromosome Folding

EVENT : C3BI Seminars

Main speaker : Ralf Everaers, from Laboratoire Physique ENS Lyon (UMR CNRS 5672) Date : 17-01-2019 at 02:00 pm Location : Auditorium Francois Jacob – BIME (26) ,Institut Pasteur, Paris

Recent advances in genome-wide mapping and imaging techniques have strikingly improved the resolution at which nuclear genome folding can be analyzed and revealed numerous conserved features organizing the one-dimensional chromatin fiber into tridimensional nuclear domains. Understanding the underlying mechanisms and the link to gene regulation requires a crossdisciplinary approach that combines the new high-resolution techniques with computational modeling of chromatin and chromosomes. In the presentation I will discuss our current understanding of generic aspects of chromosome behavior during interphase. In collaboration with the Cavalli lab in Montpellier for the HiC experiments, we are using simulation techniques to explore their ability to explain the large scale chromosome folding in Drosophila nuclei during the course of development. We find that territory formation is fully described by the idea of topologically constrained relaxation of decondensing metaphase chromosomes. The characteristic signature of Rabl territories due to the memory of quasi-nematic chromosome alignment is visible during early stages of development, but disappears in late embryo nuclei. Compartimentalization of centromeric heterochromatin is well accounted for by co-polymer models with like-like attraction between hetero- and eu-chromatin. The additional distinction of a small number of epigenetic states allows to reasonably well predict the formation of (and interaction between) TADs.

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An evolutionary perspective on meiotic recombination in vertebrates

EVENT : C3BI Seminars

Main speaker : Molly Przeworksi, from College de France – Columbia University Date : 20-12-2018 at 02:00 pm Location : Auditorium Francois Jacob – BIME (26) ,Institut Pasteur, Paris

Meiotic recombination is a fundamental genetic process that generates new combinations of alleles on which natural selection can act and ensures the proper alignment and segregation of chromosomes. Recombination events are initiated by double strand breaks deliberately inflicted on the genome during meiosis. As I will discuss, in vertebrates, there appear to be two main mechanisms by which the locations of these double strand breaks are specified: through binding of the gene PRDM9 or by localization to promoter-like features of the genome. I will present our recent work linking these two mechanisms to dramatic differences in the evolutionary dynamics of recombination hotspots, and draw out potential implications for hybridization between closely related species.

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Frequently Asked Questions (in data analysis)