Systemic epigenetic response to recombinant lentiviral vectors independent of proviral integration

Background: Lentiviral vectors (LV) are widely used for various gene transfer or gene therapy applications. The effects of LV on target cells are expected to be limited to gene delivery. Yet, human hematopoietic CD34+ cells respond to functional LVs as well as several types of non-integrating LVs by genome-wide DNA methylation changes. Results: A new algorithm for the analysis of 450K Illumina data showed that these changes were marked by de novo methylation. The same 4126 cytosines located in islands corresponding to 1059 genes were systematically methylated. This effect required cellular entry of the viral particle in the cells but not the genomic integration of the vector cassette. Some LV preparations induced only mild sporadic changes while others had strong effects suggesting that LV batch heterogeneity may be related to the extent of the epigenetic response. Conclusion: These findings identify a previously uncharacterized but consistent cellular response to viral components and provide a novel example of environmentally modified epigenome. © 2016 The Author(s)

Modélisation des systèmes biologiques, analyse de données multiomiques et visualisation à large échelle

The sheer complexity of biological systems makes their representation in simplified models a crucial step in their study. In the case of the cellular system, the rapid evolution of measurement methods has created a change of scale in the amount of data available for modeling. Omics data generation is now widespread, and biologists are faced with the challenge of processing an ever-increasing amount of data. While large-scale modeling is becoming possible, visualization of these models remains a crucial challenge for structuring knowledge. Thus, this thesis investigates the challenge of large-scale visualization of omics data. We explored the use of 3D models of genomes generated from Hi-C data as a support for the visualization and integration of omics data. To this end, we assembled a workflow from raw Hi-C data to fully annotated 3D models and reanalyzed publicly available omics datasets for three fungal model species: S. cerevisiae, S. pombe, N. crassa.L'ampleur de la complexité des systèmes biologiques fait de leur représentation en modèles simplifiés une étape cruciale de leur étude. Dans le cas du système cellulaire, l'évolution rapide des méthodes de mesure a créé un changement d'échelle dans la quantité de données disponibles pour la modélisation. La génération de données omiques est maintenant courante et les biologistes sont confrontés au défi de traiter toujours plus de données. Alors que la construction de modèles à grande échelle devient possible, la visualisation de ces modèles reste un défi crucial pour la structuration des connaissances. Ainsi, cette thèse étudie l'enjeu de la visualisation à grande échelle des données omiques. Dans cette optique, nous avons exploré l'utilisation des modèles 3D des génomes générés à partir des données Hi-C comme support à la visualisation et à l'intégration de données omiques. Pour cela nous avons assemblé un workflow allant des données brutes Hi-C aux modèles 3D entièrement annotés et nous avons réanalysé des ensembles de données omiques publiques disponibles pour trois espèces modèles de champignons : S. cerevisiae, S. pombe, N. crassa

Working with omics data, an interdisciplinary challenge at the crossroads of biology and computer science

International audienceNowadays, generating omics data is a common activity for laboratories in biology. Experimental protocols to prepare biological samples are well described, and technical platforms to generate omics data from these samples are available in most research institutes. Furthermore, manufacturers constantly propose technical improvements, simultaneously decreasing the cost of experiments and increasing the amount of omics data obtained in a single experiment. In this context, biologists are facing the challenge of dealing with large omics datasets, also called "big data" or "data deluge". Working with omics data raises issues usually handled by computer scientists and thus cooperation between biologists and computer scientists has become essential to efficiently study cellular mechanisms in their entirety, as omics data promise. In this chapter, we define omics data, explain how they are produced, and finally, present some of their applications in fundamental and medical research

Working with omics data, an interdisciplinary challenge at the crossroads of biology and computer science

International audienceNowadays, generating omics data is a common activity for laboratories in biology. Experimental protocols to prepare biological samples are well described, and technical platforms to generate omics data from these samples are available in most research institutes. Furthermore, manufacturers constantly propose technical improvements, simultaneously decreasing the cost of experiments and increasing the amount of omics data obtained in a single experiment. In this context, biologists are facing the challenge of dealing with large omics datasets, also called "big data" or "data deluge". Working with omics data raises issues usually handled by computer scientists and thus cooperation between biologists and computer scientists has become essential to efficiently study cellular mechanisms in their entirety, as omics data promise. In this chapter, we define omics data, explain how they are produced, and finally, present some of their applications in fundamental and medical research

Additional insights into the organization of transcriptional regulatory modules based on a 3D model of the Saccharomyces cerevisiae genome

International audienceAbstract Objectives Transcriptional regulatory modules are usually modelled via a network, in which nodes correspond to genes and edges correspond to regulatory associations between them. In the model yeast Saccharomyces cerevisiae , the topological properties of such a network are well-described (distribution of degrees, hierarchical levels, organization in network motifs, etc.). To go further on this, our aim was to search for additional information resulting from the new combination of classical representations of transcriptional regulatory networks with more realistic models of the spatial organization of S. cerevisiae genome in the nucleus. Results Taking advantage of independent studies with high-quality datasets, i.e. lists of target genes for specific transcription factors and chromosome positions in a three dimensional space representing the nucleus, particular spatial co-localizations of genes that shared common regulatory mechanisms were searched. All transcriptional modules of S. cerevisiae , as described in the latest release of the YEASTRACT database were analyzed and significant biases toward co-localization for a few sets of target genes were observed. To help other researchers to reproduce such analysis with any list of genes of their interest, an interactive web tool called 3D-Scere ( https://3d-scere.ijm.fr/ ) is provided

3D modeling of Hi-C contacts: seeing the spatial organization of fungal chromosomes

International audienceIn recent years, significant technical advances have been made to better observe the structure and the organization of chromatin. Many Hi-C data are now available in public databases, at different accuracy and resolution and for various species. Generally, the final results of Hi-C data analyses are summarized with the representation of “contact maps”. Using a color scale, these maps (heat-maps) show the frequency of contacts observed between different portions of a genome, both at short-rangeand long-range distances.Even if they are widely used, our observation is that the biological interpretation of contact maps is not trivial. It requires training and significant experience to be useful to validate or invalidate hypotheses concerning the overall organization of the studied genome. In this context, alternative tools have been developed. Some of them produce 3D models ofcontact networks, as a substitute for visualization of Hi-C outputs. By 3D models, we mean representations in a 3D space of a genome, in which euclidean distances are derived from the contact frequency matrices. The information contained in the Hi-C data is thus directly translated into distances in a 3D space. Although care must be taken in interpreting these models (they are not “pictures” of the interior of a cell), we observed they are very helpful to highlight specific structural patterns in chromatin organization, originally hidden in contact maps.Developing new strategies to visualize and integrate large scale multi-omics data is the major objective of Thibault Poinsignon’s thesis (ANR MinOmics). These led us to start building 3D models of yeasts andfilamentous fungi genomes, from Hi-C raw sequencing data. We present here our evaluations of the biological significance and the interest to use these models for projecting heterogeneous epigenomics data and hence better support their biological interpretations

Dynamics of the compartmentalized Streptomyces chromosome during metabolic differentiation

Publisher: Cold Spring Harbor Laboratory Section: New ResultsInternational audienceStreptomyces are among the most prolific bacterial producers of specialized metabolites, including antibiotics. The linear genome is partitioned into a central region harboring core genes and two extremities enriched in specialized metabolite biosynthetic gene clusters (SMBGCs). The molecular mechanisms governing structure and function of these compartmentalized genomes remain mostly unknown. Here we show that in exponential phase, chromosome structure correlates with genetic compartmentalization: conserved, large and highly transcribed genes form boundaries that segment the central part of the genome into domains, whereas the terminal ends are transcriptionally, largely quiescent compartments with different structural features. Onset of metabolic differentiation is accompanied by remodeling of chromosome architecture from an open to a rather closed conformation, in which the SMBGCs are expressed forming new boundaries. Altogether, our results reveal that S. ambofaciens linear chromosome is partitioned into structurally distinct entities, indicating a link between chromosome folding, gene expression and genome evolution