Dataset Information

Microbial Co-Occurrence Patterns and Keystone Species in the Gut Microbial Community of Mice in Response to Stress and Chondroitin Sulfate Disaccharide.

ABSTRACT: Detecting microbial interactions is essential to the understanding of the structure and function of the gut microbiome. In this study, microbial co-occurrence patterns were inferred using a random matrix theory based approach in the gut microbiome of mice in response to chondroitin sulfate disaccharide (CSD) under healthy and stressed conditions. The exercise stress disrupted the network composition and microbial co-occurrence patterns. Thirty-four Operational Taxonomic Units (OTU) were identified as module hubs and connectors, likely acting as generalists in the microbial community. Mucispirillum schaedleri acted as a connector in the stressed network in response to CSD supplement and may play a key role in bridging intimate interactions between the host and its microbiome. Several modules correlated with physiological parameters were detected. For example, Modules M02 (under stress) and S05 (stress + CSD) were strongly correlated with blood urea nitrogen levels (r = 0.90 and -0.75, respectively). A positive correlation between node connectivity of the OTUs assigned to Proteobacteria with superoxide dismutase activities under stress (r = 0.57, p < 0.05) provided further evidence that Proteobacteria can be developed as a potential pathological marker. Our findings provided novel insights into gut microbial interactions and may facilitate future endeavor in microbial community engineering.

SUBMITTER: Liu F

PROVIDER: S-EPMC6539173 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Microbial Co-Occurrence Patterns and Keystone Species in the Gut Microbial Community of Mice in Response to Stress and Chondroitin Sulfate Disaccharide.

Liu Fang F Li Zhaojie Z Wang Xiong X Xue Changhu C Tang Qingjuan Q Li Robert W RW

International journal of molecular sciences 20190430 9

Detecting microbial interactions is essential to the understanding of the structure and function of the gut microbiome. In this study, microbial co-occurrence patterns were inferred using a random matrix theory based approach in the gut microbiome of mice in response to chondroitin sulfate disaccharide (CSD) under healthy and stressed conditions. The exercise stress disrupted the network composition and microbial co-occurrence patterns. Thirty-four Operational Taxonomic Units (OTU) were identifi ...[more]

PMID: 31052157

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Project description:BackgroundThe extent to which deterministic rather than stochastic processes guide gut bacteria co-existence and ultimately their assembling into a community remains largely unknown. Co-occurrence networks of bacterial associations offer a powerful approach to begin exploring gut microbial community structure, maintenance and dynamics, beyond compositional aspects alone. Here we used an iconic model system, the cichlid fishes, with their multiple lake assemblages and extraordinary ecological diversity, to investigate a) patterns of microbial associations that were robust to major phylogeographical variables, and b) changes in microbial network structure along dietary shifts. We tackled these objectives using the large gut microbiota sequencing dataset available (nine lakes from Africa and America), building geographical and diet-specific networks and performing comparative network analyses.ResultsMajor findings indicated that lake and continental microbial networks were highly resembling in global topology and node taxonomic composition, despite the heterogeneity of the samples. A small fraction of the observed co-occurrences among operational taxonomic units (OTUs) was conserved across all lake assemblages. These were all positive associations and involved OTUs within the genera Cetobacterium and Turicibacter and several OTUs belonging to the families of Peptostreptococcaceae and Clostridiaceae (order Clostridiales). Mapping of diet contribution on the African Lake Tanganyika network (therefore excluding the geographic variable) revealed a clear community change from carnivores (C) to omnivores (O) to herbivores (H). Node abundances and effect size for pairwise comparisons between diets supported a strong contrasting pattern between C and H. Moreover, diet-associated nodes in H formed complex modules of positive interactions among taxonomically diverse bacteria (mostly Verrucomicrobia and Proteobacteria).ConclusionsConservation of microbial network topologies and specific bacterial associations across distinct lake assemblages point to a major host-associated effect and potential deterministic processes shaping the cichlid gut microbiota. While the origin and biological relevance of these common associations remain unclear, their persistence suggests an important functional role in the cichlid gut. Among the very diverse cichlids of L. Tanganyika, diet nonetheless represents a major driver of microbial community changes. By intersecting results from predictive network inferences and experimental trials, future studies will be directed to explore the strength of these associations, predict the outcome of community alterations driven by diet and ultimately help understanding the role of gut microbiota in cichlid trophic diversification.

Project description:Drawing on a long history in macroecology, correlation analysis of microbiome datasets is becoming a common practice for identifying relationships or shared ecological niches among bacterial taxa. However, many of the statistical issues that plague such analyses in macroscale communities remain unresolved for microbial communities. Here, we discuss problems in the analysis of microbial species correlations based on presence-absence data. We focus on presence-absence data because this information is more readily obtainable from sequencing studies, especially for whole-genome sequencing, where abundance estimation is still in its infancy. First, we show how Pearson's correlation coefficient (r) and Jaccard's index (J)-two of the most common metrics for correlation analysis of presence-absence data-can contradict each other when applied to a typical microbiome dataset. In our dataset, for example, 14% of species-pairs predicted to be significantly correlated by r were not predicted to be significantly correlated using J, while 37.4% of species-pairs predicted to be significantly correlated by J were not predicted to be significantly correlated using r. Mismatch was particularly common among species-pairs with at least one rare species (<10% prevalence), explaining why r and J might differ more strongly in microbiome datasets, where there are large numbers of rare taxa. Indeed 74% of all species-pairs in our study had at least one rare species. Next, we show how Pearson's correlation coefficient can result in artificial inflation of positive taxon relationships and how this is a particular problem for microbiome studies. We then illustrate how Jaccard's index of similarity (J) can yield improvements over Pearson's correlation coefficient. However, the standard null model for Jaccard's index is flawed, and thus introduces its own set of spurious conclusions. We thus identify a better null model based on a hypergeometric distribution, which appropriately corrects for species prevalence. This model is available from recent statistics literature, and can be used for evaluating the significance of any value of an empirically observed Jaccard's index. The resulting simple, yet effective method for handling correlation analysis of microbial presence-absence datasets provides a robust means of testing and finding relationships and/or shared environmental responses among microbial taxa.

Dataset Information

Microbial Co-Occurrence Patterns and Keystone Species in the Gut Microbial Community of Mice in Response to Stress and Chondroitin Sulfate Disaccharide.

Publications

Microbial Co-Occurrence Patterns and Keystone Species in the Gut Microbial Community of Mice in Response to Stress and Chondroitin Sulfate Disaccharide.

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