Using Systems Approaches to Understand the Mechanism of Disease

EVENT : C3BI Seminars


Main speaker : Nevan Krogan, from Quantitative Biosciences Institute , UC San Francisco, USA Date : 11-04-2019 at 02:00 pm Location : Auditorium Francois Jacob – BIME (26) ,Institut Pasteur, Paris


There is a wide gap between the generation of large-scale biological data sets and more-detailed, structural and mechanistic studies. However, recent work that explicitly combine data from systems and structural biological approaches is having a profound effect on our ability to predict how mutations and small molecules affect atomic-level mechanisms, disrupt systems-level networks and ultimately lead to changes in organismal fitness. Our group aims to create a stronger bridge between these areas primarily using three types of data: genetic interactions, protein-protein interactions and post-translational modifications.  Protein structural information helps to prioritize and functionally understand these large-scale datasets; conversely global, unbiasedly collected datasets helps inform the more mechanistic studies. Our efforts in this respect have been focused on three disease areas: cancer, infectious diseases and neuropsychiatric disorders. Our work has found remarkable similarities between these and other disease areas which are leading to novel therapeutic strategies.


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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.

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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. https://github.com/jaak-s/macau/tree/master/python/macau.


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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 (scope.aertslab.org). 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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Hands-on microbiome data analysis: tools for understanding microbial communities in health and disease

EVENT : C3BI Training


Main speaker : Gregorio Iraola, from Institut Pasteur de Montevideo Date : 03-12-2018 at 09:00 am Location : Institut Pasteur de Montevideo


This course aims to provide the theoretical and practical concepts for standard bioinformatic analysis in the field of microbiome research. The course will focus on the application of state-of-the-art software tools for the analysis of environmental and host-associated microbiomes, with particular emphasis on understanding how they change or constitute a risk for human health. The course will have expert lectures and theoretical/practical data analysis sessions with real datasets.

 

STUDENT’S PRE-REQUISITES • Directed to post-graduation (M.Sc. or Ph.D.) students. • Basic concepts of high-throughput sequencing technologies. • Basic understanding of metagenomics and microbial ecology. • Basic skills in the Linux terminal.

 

TEACHERS

Institut Pasteur Montevideo

  • Chair: Gregorio Iraola
  • Pablo Fresia
  • Daniela Costa
  • Cecilia Salazar
  • Verónica Antelo
  • Ignacio Ferrés
  • Matias Giménez
Institut Pasteur Paris
  • Marie Lopez
  • Amine Ghozlane
  • Angèle Benard
    • INVITED SPEAKERS
      • Gianfranco Grompone, Discovery Microbiome, Nutrition & Health Science Lead, Lesaffre, France.
      • David Danko, Director of Bioinformatics, MetaSUB International Consortium, Weill Cornell Medicine, US
       

      DEADLINE APPLICATIONS October 19, 2018. Send your CV (one page) and letter of motivation to: antonio.borderia@pasteur.fr

      Flyer_Microbiome_health-course_Montevideo_2018  

Integrated and spatial-temporal multiscale modeling of liver guide in vivo experiments in healthy & chronic disease states: a blue print for systems medicine?

EVENT : C3BI Seminars


Main speaker : Dirk Drasdo, from INRIA / IZBI Joint Research Group Date : 20-09-2018 at 02:00 pm Location : Salle Retrovirus – Bâtiment LWOFF ,Institut Pasteur, Paris


Background and Aims:  Hyperammonemia after drug-induced peri-central liver lobule damage, as from overdosing acetaminophen (paracetamol), and can lead to encephalopathy and dead of the patient. Guided by mathematical models, the consensus set of chemical reactions for detoxification of liver from ammonia has recently been shown to fail in explaining ammonia-detoxification after drug-induced peri-central damage (Schliess et. al., 2014). Our aim is to demonstrate how integrated and spatial-temporal models mimicking detoxification of the blood from ammonia in virtual tissue samples can assist in guiding identification of missing molecular mechanisms, or predicting the impact of micro-architectural alterations due to acute or chronic damage on ammonia detoxification. Our modeling methodology is very general.     Method:The consensus and alternative detoxification mechanisms have been implemented within mathematical integrated and spatial-temporal multi-scale models to test various hypotheses on potentially missing mechanisms in ammonia detoxification during liver regeneration after drug-induced pericentral damage in silicoin a virtual liver lobule (Drasdo et. al., J. Hepat. 2014). The multi-scale model simulates blood flow and molecular transport in the spatial lobule micro-architecture and displays each individual hepatocyte in space and time. Detoxification reactions are executed in each virtual hepatocyte. This makes in silicotesting of hypothesized mechanisms feasible from the molecular up to the tissue scale. The results are directly compared to experiments in mouse. Finally, fibrotic streets have been added to the model to predict the possible impact of architectural distortions and micro-shunts.     Results:We demonstrate how multiscale and multilevel models guided experiments towards identification of a previously unrecognized ammonia detoxification mechanism, that has the potential of improving treatment in hyperammonemia (Ghallab et. al., J. Hepat. 2016). The same model predicts for CCl4-induced fibrosis a reduced detoxification capacity for ammonia. Finally we outline how the whole body scale can be included to arrive at a model spanning molecular up to whole body scale permitting to study the relation of molecular changes and micro-architecture on whole body blood circulation, and briefly summarize results of integration of APAP toxic pathway as HGF signaling.    

Conclusion:Refined multi-scale models increasingly permit realistic prediction of liver function as well as of toxic injury in acute and chronic damage states. Those models can integrate data from various sources, in vitro, different animal models or human data. The direct representation of liver micro-architecture in those models will open up the future perspective to feed these models with patient-specific data, hence generating a virtual twin of a patients’ liver to guide personalized diagnosis and therapy planning.


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Signatures of ecological processes in microbial community time series

EVENT : C3BI Seminars


Main speaker : Karoline Faust, from KU Leuven Date : 04-10-2018 at 02:00 pm Location : Auditorium Francois Jacob – BIME (26) ,Institut Pasteur, Paris


Nowadays, a number of densely sampled microbial community time series is available, where the abundance of community members is tracked over several months through sequencing. These data allow exploring community dynamics by investigating signatures of underlying ecological processes that are present in the community time series. In this seminar, I will present our work on the exploitation of time series properties to distinguish between different ecological processes behind the observed dynamics

  http://psbweb05.psb.ugent.be/conet/karoline/

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Nucleotide-level analysis of genetic variation in the bacterial pan-genome

EVENT : C3BI Seminars


Main speaker : Zamin Iqbal, from Royal Society/Wellcome Trust Sir Henry Dale Fellow, EMBL-EBI Date : 07-03-2019 at 02:00 pm Location : Auditorium Francois Jacob – BIME (26) ,Institut Pasteur, Paris


When we study evolution of a species, we use different models, depending on what we want to achieve or infer. We might restrict to SNP variation in the “core genome”  (presumably inherited vertically) to study phylogeography or to study an outbreak. In reducing the problem to the analysis of SNPs (and invariant sites), it has been possible for researchers to build a range of sophisticated phylogenetic models. However once we try to incorporate genome organisation, chromosomal rearrangements, movement of plasmids, transposons or phage, then the modelling problem is far harder. The question of how to  properly model bacterial genetic variation is wide open and extremely challenging.
A prerequisite for any solution to this, is a decision on how to describe the variation in the first place – you cannot model variation until you represent it. Note that this is true even if you have perfect genome assemblies: even if it were possible to multiple sequence align them, this would not really help with how to notice that a SNP at one position in one genome is “the same” as a SNP somewhere else in another.
In this talk,  I want to discuss a solution we have been developing to this representation problem. We show how it is possible to represent the pan genome of a species as a network of “floating” graphs, representing the ensemble of known variation in  pathology blocks (we use genes and intergenic regions, but this could be done for mobile elements also). In doing so it becomes possible to discover and describe genetic variation at fine (SNP/indel) and coarse (gene order) level.
This is a major research theme for my group and I describe progress to date, including results on both illumina and nanopore data.

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C3BI Courses: Introduction to Molecular Phylogenetics – Hong Kong 2018

General Information:

This introductory course aims to give the basic theoretical and practical concepts, best practices, and software necessary to start working on molecular phylogenetics and its applications to epidemiology. The course will have theoretical morning sessions followed by small groups practice for a few selected students with their own data. Flyer for the course: CLICK ME

Topics:

  • Introduction to phylogeny: General principles for the inference, interpretation of trees, and application to infectious diseases
  • Introduction to the math behind the trees and evolutionary models
  • Distance and parsimony methods
  • Maximum likelihood methods
  • Bayesian methods, phylodynamics
  • Branch supports, bootstrapping
  • How to select the best method and evolutionary model
  • Tree dating, reconstructing and using character evolution
  • Molecular epidemiology

Teachers:

Chair: Olivier Gascuel, C3BI, Institut Pasteur (France)   Anna Zhukova, C3BI, Institut Pasteur (France) Frédéric Lemoine, C3BI, Institut Pasteur (France) Hein Min Tun, School of Public Health, The University of Hong Kong Julien Guglielmini, C3BI, Institut Pasteur (France) Sebastian Duchene, University of Melbourne (Australia) Tim Vaughan, ETH Zürich (Switzerland) Tommy Lam, School of Public Health, The University of Hong Kong Veronika Boskova, ETH Zürich (Switzerland)

Course dates:

Monday, October 22nd to Saturday, October 27th

Pre-requisites:

  • Basic knowledge on how to use sequence databanks
  • Basic knowledge using Blast and multiple alignments software
  • Basic knowledge on statistics (tests, distributions, parameter estimation)

Applications:

Open to postgraduate students, MD, DVM, postdoctoral fellows and young scientists from Hong Kong and overseas. The course fees are 500HK for the theory sessions and 1000HK for the full course. Students coming from the Institut Pasteur International Network will have the fees waived. Please fill in the following application form before August 14th Midnight (HK time). Use the link if you can’t see the embedded form: https://goo.gl/forms/rgYrUNrEz6rqgELP2)