Section: New Results

Assembly, annotation and comparison of bacterial Omics data

Participants : Elisabeth Bon [correspondant] , Laetitia Bourgeade, Pascal Durrens, Aurélie Goulielmakis, Tiphaine Martin, David James Sherman.

Oenococcus oeni is part of the natural microflora of wine and related environments, and is the main agent of the malolactic fermentation (MLF), a step of wine making that generally follows alcoholic fermentation (AF) and contributes to wine deacidification, improvement of sensorial properties and microbial stability. The start, duration and achievement of MLF are unpredictable since they depend both on the wine characteristics and on the properties of the O. oeni strains. In collaboration with Patrick Lucas’s lab of the ISVV Bordeaux that is currently proceeding with genome sequencing, explorative and, and comparative genomics, Elisabeth Bon coordinates our efforts into the OENIKITA project (since 2009), a scale switching challenge including highthrouput exploratory and comparative genomics for oenological bacterial starters, and the development of an online web-collaborative multigenomic comparative platform based on the the Génolevures database architecture and MAGUS / YAGA systems.

OENI-Genomics: In comparative genomics, we investigated gene repertoire and genomic organization conservation through intra- and inter-species genomic comparisons, which clearly show that the O. oeni genome is highly plastic and fast-evolving. Results reveal that the optimal adaptation to wine of a strain mostly depends on the presence of key adaptive loops and polymorphic genes. They also point up the role of horizontal gene transfer and mobile genetic elements in O. oeni genome plasticity, and give the first clues of the genetic origin of its oenological aptitudes[3] , [14] , [29] , [33] , [35] , [36] . As a result of the scaling out challenge, we participated to the assembly and annotation of 19 fully sequenced O. oeni genome variants.

KITA-Genomics (E. Bon, D. Sherman): This project that is focused on the sequencing, assembly, exploration and comparison of the O. kitaharae genome, has benefited to an international collaboration involving Dr V. Makeev. MAGNOME was involved into the pilot assembly, exploration and comparison of the O. kitaharae genome.

Transcriptomic axis (E. Bon, A. Goulielmakis, P. Durrens): Under the supervision of E. Bon, Aurélie Goulielmakis has completed for the ANR DIVOENI a detailed manual annotation of a new reference strain of O. oeni and performed comparative transcriptome analysis to identify genes differentially expressed under different culture conditions. We explored and compared how the expression system is solicited when O. oeni strains adapted to grow in some niches are placed under stress-exposure conditions. The monitoring of gene expression status between strains, through the definition of a global expression pattern proper to each gene, partially lift the veil on how O. oeni genome adapts function to its environment. The weight of genetic background and ecological niche pressure on gene expression flexibility was evaluated, and the O. oeni pan-transcriptome architecture characterized. The first guidelines revealed a supra-spatial organization of stress response into activated and repressed larger macro-domains defining functional landmarks and intra-chromosomal territories. Decryption of stress-sensitive gene repertoires promises to be an efficient tool in the conquest of O. oeni “domestication” through the identification of molecular markers responsible for different physiological capabilities, and the selection of the best adapted strains [21] , [43] .

Gene plasticity modelisation (E. Bon, L. Bourgeade): A novel axis of research recently emerged under the initiative of E. Bon (pseudOE project) around the detection, characterization and conservation of pseudogenes populations in Oenococcus bacteria. Such topic presents a double interest: phylogenetic at first because it should allow to better estimate the degree of genic/genomic plasticity of these bacteria, and algorithmic then because the pseudogenes are a source of confusion for the automatic prediction of genes. Through a transversal collaboration and a cooperative supervision with the Algorithms for Analysis of Biological Structures Group (P. Ferraro, J. Allali) at LaBRI, Laetitia Bourgeade (PhD, Univ. Bordeaux1) was recruited to develop dedicated methods to improve pseudogenes automatic detection, and therefore gene predictions, and to reconstruct fossil and modern genes evolutionary history [20] , [23] .