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Poster De Conférence Année : 2019

Comparative microbial pangenomics to explore mobilome dynamics

Résumé

For the last decade, pangenomics has provided new tools for researchers to estimate genomic diversity by partitioning gene content in terms of core and accessory genome [1]. The core genome consists in ubiquitous genes within the taxonomic group being studied and the accessory genome are the genes present in one or some individuals but not all. However, the content of the core genome is highly dependent on the number of genomes included in a study limiting the relevance of comparisons between studies. Moreover, the concept of accessory genome lacks subtlety as it gathers genes with a large range of frequencies. Recently, a new tool named PPanGGOLiN (Gautreau et al., in preparation) [2] was developed to exploit gene neighborhood topology, gene frequency and population structure to classify gene families using a graph-based approach into a 3-class partitioning of persistent, which is a relaxed definition of the core genome, shell which corresponds to genes belonging to some individuals of the population and potentially associated to environmental adaptations, and cloud genome which are genes present at very low frequencies in the pangenome. Using this new approach, we can compute the pangenome of any microbial clade with a sufficient number of genomes. Thus the variable regions of the pangenome which are defined as areas with mostly non-persistent genes in a genome can be detected. Using the graph structure provided by PPanGGOLiN, we can detect gene modules within each pangenome by integrating both co-occurrences in variable regions and co-localization information thus grouping non-persistent genes with a potential functional linkage [3]. Once modules are defined, we can study module conservation between species by searching for common groups of colocalized genes using subgraph mining methods on the different pangenome graphs. Then, we can explore mobilome dynamics in a comparative pangenomics approach by studying module conservation between clades. The developed method can be applied to any clades with a sufficient number of genomes. This methodology applied on different collections of pangenomes may help to associate functional modules that are shared between individuals of the same taxonomic group and/or between individuals of different clades with common environmental factors or phenotypic traits. References [1] Tettelin, H., Masignani, V., Cieslewicz, M. J., Donati, C., Medini, D., Ward, N. L., ... & DeBoy, R. T. Genome analysis of multiple pathogenic isolates of Streptococcus agalactiae: implications for the microbial “pan-genome”. Proceedings of the National Academy of Sciences, 102(39)13950-13955. 2005. [2] PPanGGOLiN: depicting microbial species diversity via a pangenome graph, https://github.com/ggautreau/PPanGGOLiN [3] Overbeek, R., Fonstein, M., D’souza, M., Pusch, G. D., & Maltsev, N. The use of gene clusters to infer functional coupling. Proceedings of the National Academy of Sciences, 96(6), 2896-2901. 1999.
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hal-04358338 , version 1 (21-12-2023)

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Adelme Bazin, Guillaume Gautreau, Claudine Médigue, Alexandra Calteau, David Vallenet. Comparative microbial pangenomics to explore mobilome dynamics. JOBIM 2019 Journées Ouvertes Biologie, Informatique et Mathématiques, Jul 2019, Nantes, France. ⟨hal-04358338⟩
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