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Fibrobacter succinogenesis a cellulolytic predominant bacterium that plays an essential role in the degradation of plant fibers in the rumen ecosystem. It converts cellulose polymers into intracellular glycogen and the fermentation metabolites succinate, acetate, and formate. We developed dynamic models ofF. succinogenesS85 metabolism on glucose, cellobiose, and cellulose on the basis of a network reconstruction done with the Automatic Reconstruction of metabolic models (AuReMe) workspace. The reconstruction was based on genome annotation, 5 templates-based orthology methods, gap-filling and manual curation. The metabolic network ofF. succinogenesS85 comprises 1565 reactions with 77% linked to 1317 genes, 1586 unique metabolites and 931 pathways. The network was reduced using the NetRed algorithm and analyzed for computation of Elementary Flux Modes (EFMs). A yield analysis was further performed to select a minimal set of macroscopic reactions for each substrate. The accuracy of the models was acceptable in simulatingF. succinogenescarbohydrate metabolism with an average coefficient of variation of the Root mean squared error of 19%. Resulting models are useful resources for investigating the metabolic capabilities ofF. succinogenesS85, including the dynamics of metabolite production. Such an approach is a key step towards the integration of omics microbial information into predictive models of the rumen metabolism.

Simple Summary The equine hindgut is colonized by microorganisms, some of which are involved in fiber digestion and are crucial for the horse’s nutrition and health. These key microorganisms are very sensitive to dietary changes, which have been identified as a risk factor for colics. This study assessed the stressful effect of two consecutive abrupt dietary changes on the diversity, the composition, and the activity of fecal microorganisms focusing on fibrolytic bacteria. Twelve horses were subjected to an abrupt change from forage to a concentrate-rich diet, followed by a second change from a concentrate to forage-rich diet 5 days later. Half of the horses were given a supplement of living bacteria as a probiotic. Two days after the sudden change from forage to concentrate diet, the proportions and types of microorganisms were altered drastically, as was their capacity to degrade fibers. After this dietary stress, it took 3–4 weeks of a high-fiber diet to recover the basal state. Supplementation with probiotics promoted an earlier recovery of fibrolytic bacteria after the dietary stress. Abrupt dietary changes should be limited in horse management to protect the hindgut microorganisms and their capacity to use forage fibers, and consequently to limit the development of colic. Abstract In horses, abrupt changes from high-fiber (HF) to high-starch (HS) diets can affect the cecal and colonic microbiota. This study investigated modifications and recovery of fecal microbiota after two consecutive abrupt dietary changes. Twelve horses fed HF for 2 weeks were changed to HS for 5 days then returned to HF for 7 weeks. Six received lactic acid bacteria supplementation. Bacterial population diversity, structure, and activity, especially fibrolysis, were assessed to obtain an overview of alteration in hindgut microbiota. Two days after the abrupt change from HF to HS, the findings in feces were consistent with those previously reported in the cecum and colon, with a decrease in fibrolytic activity and an increase in amylolytic activity. Fecal parameters stabilized at their basal level 3–4 weeks after the return to HF. A bloom of cellulolytic bacteria and lower pH were observed after 1.5 weeks, suggesting a higher level of fiber degradation. In supplemented horses the relative abundance of potentially fibrolytic genera was enhanced 2 days after HS and 2 days to 2–3 weeks after the return to HF. Fecal analysis could be a promising technique for monitoring hindgut microbial variations accompanying dietary changes.

CD81 plays a role in a variety of physiological and pathological processes. Recent structural analysis of CD81 indicates that it contains an intramembrane cholesterol-binding pocket and that interaction with cholesterol may regulate a conformational switch in the extracellular domain of CD81. Therefore, CD81 possesses a potential cholesterol sensing mechanism; however, its relevance for protein function is thus far unknown. In this study we investigate CD81 cholesterol sensing in the context of its activity as a receptor for hepatitis C virus. Structure-led mutagenesis of the cholesterol-binding pocket reduced CD81-cholesterol association, but had disparate effects on HCV, both reducing and enhancing CD81 receptor activity. We reasoned that this could be explained by alterations in the consequences of cholesterol binding. To investigate this further we performed molecular dynamic simulations of CD81 with and without cholesterol; this identified an allosteric mechanism by which cholesterol binding regulates the conformation of CD81. To test this, we designed further mutations to force CD81 into either the open (cholesterol unbound) or closed (cholesterol bound) conformation. The open mutant of CD81 exhibited reduced receptor activity whereas the closed mutant was enhanced. These data are consistent with cholesterol switching CD81 between a receptor active and inactive state. CD81 interactome analysis also suggests that conformational switching may modulate the assembly of CD81-partner networks. This work furthers our understanding of the molecular mechanism of CD81 cholesterol sensing, how this relates to HCV entry and CD81's function as a molecular scaffold; these insights are relevant to CD81's varied roles in health and disease.

The cellulosome is an extracellular multi-enzyme complex that is considered one of the most efficient plant cell wall-degrading strategies devised by nature. Its unique modular architecture, achieved by high affinity and specific interaction between protein modules (cohesins and dockerins) enables formation of various enzyme combinations. Extensive research has been dedicated to the mechanistic nature of the cellulosome complex. Nevertheless, little is known regarding its distribution and abundance among microbes in natural plant fiber-rich environments. Here, we explored these questions in bovine rumen microbial communities, specialized in efficient degradation of lignocellulosic plant material. We bioinformatically screened for cellulosomal modules in this complex environment using a previously published ultra-deep fiber-adherent rumen metagenome. Intriguingly, a large portion of the functions of the dockerin-containing proteins were related to alternative biological processes, and not necessarily to the classic fiber degradation function. Our analysis was experimentally validated by characterizing specific interactions between selected cohesins and dockerins and revealed that cellulosome is a more generalized strategy employed by diverse bacteria, some of which were not previously associated with cellulosome production. Remarkably, our results provide additional proof of similarity among rumen microbial communities worldwide. This study suggests a broader and widespread role for the cellulosomal machinery in nature. This article is protected by copyright. All rights reserved.

Whole Genome Shotgun (WGS) metagenomics is increasingly used to study the structure and functions of complex microbial ecosystems, both from the taxonomic and functional point of view. Gene inventories of otherwise uncultured microbial communities make the direct functional profiling of microbial communities possible. The concept of community aggregated trait has been adapted from environmental and plant functional ecology to the framework of microbial ecology. Community aggregated traits are quantified from WGS data by computing the abundance of relevant marker genes. They can be used to study key processes at the ecosystem level and correlate environmental factors and ecosystem functions. In this paper we propose a novel model based approach to infer combinations of aggregated traits characterizing specific ecosystemic metabolic processes. We formulate a model of these Combined Aggregated Functional Traits (CAFTs) accounting for a hierarchical structure of genes, which are associated on microbial genomes, further linked at the ecosystem level by complex co-occurrences or interactions. The model is completed with constraints specifically designed to exploit available genomic information, in order to favor biologically relevant CAFTs. The CAFTs structure, as well as their intensity in the ecosystem, is obtained by solving a constrained Non-negative Matrix Factorization (NMF) problem. We developed a multicriteria selection procedure for the number of CAFTs. We illustrated our method on the modelling of ecosystemic functional traits of fiber degradation by the human gut microbiota. We used 1408 samples of gene abundances from several high-throughput sequencing projects and found that four CAFTs only were needed to represent the fiber degradation potential. This data reduction highlighted biologically consistent functional patterns while providing a high quality preservation of the original data. Our method is generic and can be applied to other metabolic processes in the gut or in other ecosystems., Author Summary Microbial communities are highly complex ecosystems, harboring a high taxonomic diversity, rapidly varying in time. Focusing on the functional traits of microorganisms instead of species offers an interesting and complementary insight since quite often, owing to environmental selection pressure, traits are less variable in time and shared by many species. By providing gene inventories of otherwise uncultured microbial communities, Whole Genome Shotgun (WGS) metagenomics made the functional profiling of microbial communities possible, since community aggregated traits can be quantified by the abundance of relevant marker genes. Considering a global metabolic process in an ecosystem, we propose a method that exploits gene abundance data to determine combinations of functional traits characterizing this process. When applied to fiber degradation in the human gut microbiota, the method shows that only four complex functional traits are needed to characterize this process. The approach is generic and could be applied to other processes or ecosystems. It allows summarizing complex datasets by a limited number of biologically interpretable functional patterns.