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Understanding how an animal organism and its gut microbes form an integrated biological organization, known as a holobiont, is becoming a central issue in biological studies. Such an organization inevitably involves a complex web of transmission processes that occur on different scales in time and space, across microbes and hosts. Network-based models are introduced in this chapter to tackle aspects of this complexity and to better take into account vertical and horizontal dimensions of transmission. Two types of network-based models are presented, sequence similarity networks and bipartite graphs. One interest of these networks is that they can consider a rich diversity of important players in microbial evolution that are usually excluded from evolutionary studies, like plasmids and viruses. These methods bring forward the notion of “gene externalization,” which is defined as the presence of redundant copies of prokaryotic genes on mobile genetic elements (MGEs), and therefore emphasizes a related although distinct process from lateral gene transfer between microbial cells. This chapter introduces guidelines to the construction of these networks, reviews their analysis, and illustrates their possible biological interpretations and uses. The application to human gut microbiomes shows that sequences present in a higher diversity of MGEs have both biased functions and a broader microbial and human host range. These results suggest that an “externalized gut metagenome” is partly common to humans and benefits the gut microbial community. We conclude that testing relationships between microbial genes, microbes, and their animal hosts, using network-based methods, could help to unravel additional mechanisms of transmission in holobionts.

International audience; DNA sequencing technology is becoming more accessible to a variety of researchers as costs continue to decline. As researchers begin to sequence novel transcriptomes, most of these data sets lack a reference genome and will have to rely on de novo assemblers. Making comparisons across assemblies can be difficult: each program has its strengths and weaknesses, and no tool exists to comparatively evaluate these data sets. We developed software in R, called Sequence Comparative Analysis using Networks (SCAN), to perform statistical comparisons between distinct assemblies. SCAN uses a reference data set to identify the most accurate de novo assembly and the ``good'' transcripts in the user's data. We tested SCAN on three publicly available transcriptomes, each assembled using three assembly programs. Moreover, we sequenced the transcriptome of the oomycete Achlya hypogyna and compared de novo assemblies from Velvet, ABySS, and the CLC Genomics Workbench assembly algorithms. One thousand one hundred twenty-eight of the CLC transcripts were statistically similar to the reference, compared with 49 of the Velvet transcripts and 937 of the ABySS transcripts. SCAN's strength is providing statistical support for transcript assemblies in a biological context. However, SCAN is designed to compare distinct node sets in networks, therefore it can also easily be extended to perform statistical comparisons on any network graph regardless of what the nodes represent.

International audience; Clin Microbiol Infect 2012; 18 (Suppl. 4): 4043 Abstract In order to study complex microbial communities and their associated mobile genetic elements, such as the human gut microbiome, evolutionists could explore their genetic diversity with shared sequence networks. In particular, the detection of remarkable structures in gene networks of the gut microbiome could serve to identify important functions within the community, and would ease comparison of data sets from microbiomes of various sources (human, ape, mouse etc.) in a single analysis.

International audience; Analysis of microbial biodiversity is hampered by a lack of reference genomes from most bacteria, viruses, and algae. This necessitates either the cultivation of a restricted number of species for standard sequencing projects or the analysis of highly complex environmental DNA metagenome data. Single-cell genomics (SCG) offers a solution to this problem by constraining the studied DNA sample to an individual cell and its associated symbionts, prey, and pathogens. We used SCG to study marine heterotrophic amoebae related to Paulinella ovalis (A. Wulff) P.W. Johnson, P.E. Hargraves & J.M. Sieburth (Rhizaria). The genus Paulinella is best known for its photosynthetic members such as P.chromatophora Lauterborn that is the only case of plastid primary endosymbiosis known outside of algae and plants. Here, we studied the phagotrophic sister taxa of P.chromatophora that are related to P.ovalis and found one SCG assembly to contain -cyanobacterial DNA. These cyanobacterial contigs are presumably derived from prey. We also uncovered an associated cyanophage lineage (provisionally named phage PoL_MC2). Phylogenomic analysis of the fragmented genome assembly suggested a minimum genome size of 200Kbp for phage PoL_MC2 that encodes 179 proteins and is most closely related to Synechococcus phage S-SM2. For this phage, gene network analysis demonstrates a highly modular genome structure typical of other cyanophages. Our work demonstrates that SCG is a powerful approach for discovering algal and protist biodiversity and for elucidating biotic interactions in natural samples.

Background A central goal in Huntington's disease (HD) research is to identify and prioritize candidate targets for neuroprotective intervention, which requires genome-scale information on the modifiers of early-stage neuron injury in HD. Results Here, we performed a large-scale RNA interference screen in C. elegans strains that express N-terminal huntingtin (htt) in touch receptor neurons. These neurons control the response to light touch. Their function is strongly impaired by expanded polyglutamines (128Q) as shown by the nearly complete loss of touch response in adult animals, providing an in vivo model in which to manipulate the early phases of expanded-polyQ neurotoxicity. In total, 6034 genes were examined, revealing 662 gene inactivations that either reduce or aggravate defective touch response in 128Q animals. Several genes were previously implicated in HD or neurodegenerative disease, suggesting that this screen has effectively identified candidate targets for HD. Network-based analysis emphasized a subset of high-confidence modifier genes in pathways of interest in HD including metabolic, neurodevelopmental and pro-survival pathways. Finally, 49 modifiers of 128Q-neuron dysfunction that are dysregulated in the striatum of either R/2 or CHL2 HD mice, or both, were identified. Conclusions Collectively, these results highlight the relevance to HD pathogenesis, providing novel information on the potential therapeutic targets for neuroprotection in HD.

Background We introduce several forest-based and network-based methods for exploring microbial evolution, and apply them to the study of thousands of genes from 30 strains of E. coli. This case study illustrates how additional analyses could offer fast heuristic alternatives to standard tree of life (TOL) approaches. Results We use gene networks to identify genes with atypical modes of evolution, and genome networks to characterize the evolution of genetic partnerships between E. coli and mobile genetic elements. We develop a novel polychromatic quartet method to capture patterns of recombination within E. coli, to update the clanistic toolkit, and to search for the impact of lateral gene transfer and of pathogenicity on gene evolution in two large forests of trees bearing E. coli. We unravel high rates of lateral gene transfer involving E. coli (about 40% of the trees under study), and show that both core genes and shell genes of E. coli are affected by non-tree-like evolutionary processes. We show that pathogenic lifestyle impacted the structure of 30% of the gene trees, and that pathogenic strains are more likely to transfer genes with one another than with non-pathogenic strains. In addition, we propose five groups of genes as candidate mobile modules of pathogenicity. We also present strong evidence for recent lateral gene transfer between E. coli and mobile genetic elements. Conclusions Depending on which evolutionary questions biologists want to address (i.e. the identification of modules, genetic partnerships, recombination, lateral gene transfer, or genes with atypical evolutionary modes, etc.), forest-based and network-based methods are preferable to the reconstruction of a single tree, because they provide insights and produce hypotheses about the dynamics of genome evolution, rather than the relative branching order of species and lineages. Such a methodological pluralism - the use of woods and webs - is to be encouraged to analyse the evolutionary processes at play in microbial evolution. This manuscript was reviewed by: Ford Doolittle, Tal Pupko, Richard Burian, James McInerney, Didier Raoult, and Yan Boucher