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Genomic characterisation of novel extremophile lineages from the thalassohaline lake Dziani Dzaha expands the metabolic repertoire of the PVC superphylum
No full textInternational audienceAbstract Background Extreme environments are useful systems to investigate limits of life, microbial biogeography and ecology, and the adaptation and evolution of microbial lineages. Many novel microbial lineages have been discovered in extreme environments, especially from the Planctomycetota–Verrucomicrobiota–Chlamydiota (PVC) superphyla. However, their evolutionary history and roles in ecosystem functioning and microbiome assemblage are poorly understood. Results Applying a genome-centric approach on an 8-year metagenomic timeseries produced from the hypersaline and hyperalkaline waters of Lake Dziani Dzaha (Mayotte), we recovered 5 novel PVC extremophilic candidate lineages from the biosphere of the lake. Sibling to Elusimicrobia and Omnitrophota, these lineages represented novel halophilic clades, with global distributions bounded to soda lakes and hypersaline hydrosystems. Genome mining of these newly defined clades revealed contrasted, but ecologically relevant, catabolic capabilities involved in the carbon, hydrogen and iron/electron cycles of the Dziani Dzaha ecosystem. This also includes extracellular electron transfer for two of them, suggesting metal reduction or potential electron exchanges with other members of the lake community. By contrast, a putative extracellular giant protein with multiple carbohydrate binding domains and toxin-like structures, as observed in virulence factors, was identified in the genome of another of these clades, suggesting predatory capabilities. Conclusions Our results provided genomic evidences for original metabolism in novel extremophile lineages of the PVC superphyla, revealing unforeseen implications for members of this widespread and diverse bacterial radiation in aquatic saline ecosystems. Finally, monitoring the in-situ distribution of these lineages through the timeseries reveals the drastic effects of environmental perturbations on extreme ecosystem biodiversity
From approximation of dissipative systems to semi-passive vibration damping and control - an illustration
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Multilayer Louvain: a modularity-based community detection algorithm for multilayer networks
No full textInternational audienceIn recent times, most of the real-world systems are heterogeneous, containing multiple kinds of nodes and edges, which can be naturally conceptualized as multilayer networks. In this paper, we develop a methodology for detecting communities in such multilayer networks. In order to do so, first, we propose a multilayer modularity index QM and then develop the multilayer Louvain (ML) algorithm leveraging it (QM). The proposed algorithm can simultaneously detect communities consisting of only single type, as well as multiple types of nodes (and edges). Furthermore, it is scalable and easily adaptable to complex network structures. For evaluating the performance of the proposed multilayer Louvain (ML) algorithm, we leverage synthetic networks with preplanted multilayer community structures as well as three real-world multilayer networks (Yelp, Meetup and Digg). Results show the significance of our proposed methods in discovering homogeneous as well as heterogeneous communities over multiple layers, and also highlight its ability in producing better community structures compared to competing state-of-the-art approaches
