Microbial co-occurrence networks of gut microbiota reveal community conservation and diet-associated shifts in cichlid fishes

Animal Microbiome - Tập 2 Số 1 - 2020
Joan Lluís Riera1, Laura Baldo1
1Department of Evolutionary Biology, Ecology and Environmental Sciences, University of Barcelona, Barcelona, Spain

Tóm tắt

Abstract Background The 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. Results Major 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). Conclusions Conservation 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.

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Tài liệu tham khảo

Rawls JF, Mahowald MA, Ley RE, Gordon JI. Reciprocal gut microbiota transplants from Zebrafish and mice to germ-free recipients reveal host habitat selection. Cell. 2006;127:423–33.

Yan Q, Li J, Yu Y, Wang J, He Z, Van Nostrand JD, et al. Environmental filtering decreases with fish development for the assembly of gut microbiota. Environ Microbiol. 2016;18:4739–54.

Burns AR, Stephens WZ, Stagaman K, Wong S, Rawls JF, Guillemin K, et al. Contribution of neutral processes to the assembly of gut microbial communities in the zebrafish over host development. ISME J. 2016;10:655–64.

Baldo L, Pretus JL, Riera JL, Musilova Z, Bitja Nyom AR, Salzburger W. Convergence of gut microbiotas in the adaptive radiations of African cichlid fishes. ISME J. 2017;11:1975–87.

Ley RE, Hamady M, Lozupone C, Turnbaugh PJ, Ramey RR, Bircher JS, et al. Evolution of mammals and their gut microbes. Science. 2008;322:1188.

Foster KR, Schluter J, Coyte KZ, Rakoff-Nahoum S. The evolution of the host microbiome as an ecosystem on a leash. Nature. 2017;48:43–51.

Sieber M, Pita L, Weiland-Bräuer N, Dirksen P, Wang J, Mortzfeld B, et al. Neutrality in the metaorganism. PLoS Biol. 2019;17:1–21.

Röttjers L, Faust K. From hairballs to hypotheses–biological insights from microbial networks. FEMS Microbiol Rev. 2018;42:761–80.

Faust K, Raes J. Microbial interactions: from networks to models. Nat Rev Microbiol. 2012;10:538–50.

Williams RJ, Howe A, Hofmockel KS. Demonstrating microbial co-occurrence pattern analyses within and between ecosystems. Front Microbiol. 2014;5:1–10.

Faust K, Lima-Mendez G, Lerat JS, Sathirapongsasuti JF, Knight R, Huttenhower C, et al. Cross-biome comparison of microbial association networks. Front Microbiol. 2015;6:1–13.

Jackson MA, Bonder MJ, Kuncheva Z, Zierer J, Fu J, Kurilshikov A, et al. Detection of stable community structures within gut microbiota co-occurrence networks from different human populations. PeerJ. 2018;6:e4303.

Mandakovic D, Rojas C, Maldonado J, Latorre M, Travisany D, Delage E, et al. Structure and co-occurrence patterns in microbial communities under acute environmental stress reveal ecological factors fostering resilience. Sci Rep. 2018;8:1–12.

Hegde S, Khanipov K, Albayrak L, Golovko G, Pimenova M, Saldaña MA, et al. Microbiome interaction networks and community structure from laboratory-reared and field-collected Aedes aegypti, Aedes albopictus, and Culex quinquefasciatus mosquito vectors. Front Microbiol. 2018;9:1–16.

Li G, Li J, Kohl KD, Yin B, Wei W, Wan X, et al. Dietary shifts influenced by livestock grazing shape the gut microbiota composition and co-occurrence networks in a local rodent species. J Anim Ecol. 2019;88:302–14.

Salzburger W. The interaction of sexually and naturally selected traits in the adaptive radiations of cichlid fishes. Mol Ecol. 2009;18:169–85.

Salzburger W. Understanding explosive diversification through cichlid fish genomics. Nat Rev Genet. 2018;19:705–17.

Seehausen O. African cichlid fish: a model system in adaptive radiation research. Proc R Soc B Biol Sci. 2006;273:1987–98.

Muschick M, Indermaur A, Salzburger W. Convergent evolution within an adaptive radiation of cichlid fishes. Curr Biol. 2012;22:2362–8.

Barluenga M, Stölting KN, Salzburger W, Muschick M, Meyer A. Sympatric speciation in Nicaraguan crater lake cichlid fish. Nature. 2006;439:719–23.

Kautt AF, Machado-Schiaffino G, Meyer A. Lessons from a natural experiment: allopatric morphological divergence and sympatric diversification in the Midas cichlid species complex are largely influenced by ecology in a deterministic way. Evol Lett. 2018;2:323–40.

Curry-Lindahl K, Fryer G, Iles TD. The cichlid fishes of the Great Lakes of Africa: their biology and evolution. J Wildl Manag. 1976;40:1.

Brawand D, Wagner CE, Li YI, Malinsky M, Keller I, Fan S, et al. The genomic substrate for adaptive radiation in African cichlid fish. Nature. 2015;513:375–81.

Baldo L, Riera JL, Salzburger W, Barluenga M. Phylogeography and ecological niche shape the cichlid fish gut microbiota in central American and African Lakes. Front Microbiol. 2019;10:2372.

Faust K, Raes J. CoNet app: inference of biological association networks using Cytoscape. F1000Research. 2016;5:1519.

Csardi G, Nepusz T. The igraph software package for complex network research. Inter J Complex Syst. 2006;1695.

Yan Q, van der Gast CJ, Yu Y. Bacterial community assembly and turnover within the intestines of developing zebrafish. PLoS One. 2012;7:e30603.

Tsuchiya C, Sakata T, Sugita H. Novel ecological niche of Cetobacterium somerae, an anaerobic bacterium in the intestinal tracts of freshwater fish. Lett Appl Microbiol. 2008;46:43–8.

Sokolovskaya OM, Shelton AN, Taga ME. Sharing vitamins: Cobamides unveil microbial interactions. Science (80- ). 2020;369:eaba0165.

Barberán A, Bates ST, Casamayor EO, Fierer N. Using network analysis to explore co-occurrence patterns in soil microbial communities. ISME J. 2012;6:343–51.

Goodrich JK, Davenport ER, Waters JL, Clark AG, Ley RE. Cross-species comparisons of host genetic associations with the microbiome. Science. 2016;352:532–5.

Dearing MD, Kohl KD. Beyond fermentation: other important services provided to endothermic herbivores by their gut microbiota. Integr Comp Biol. 2017;57:723–31.

Flint HJ, Scott KP, Duncan SH, Louis P, Forano E. Microbial degradation of complex carbohydrates in the gut. Gut Microbes. 2012;3:289–306.

Martin M, Barbeyron T, Martin R, Portetelle D, Michel G, Vandenbol M. The cultivable surface microbiota of the brown alga Ascophyllum nodosum is enriched in macroalgal-polysaccharide-degrading bacteria. Front Microbiol. 2015;6:1487.

Baldo L, Riera JL, Tooming-Klunderud A, Albà MM, Salzburger W. Gut microbiota dynamics during dietary shift in eastern African cichlid fishes. PLoS One. 2015;10:e0127462.

Klindworth A, Pruesse E, Schweer T, Peplies J, Quast C, Horn M, et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 2013;41:e1.

Weiss S, Van Treuren W, Lozupone C, Faust K, Friedman J, Deng Y, et al. Correlation detection strategies in microbial data sets vary widely in sensitivity and precision. ISME J. 2016;10:1669–81.

Blondel VD, Guillaume JL, Lambiotte R, Lefebvre E. Fast unfolding of communities in large networks. J Stat Mech Theory Exp. 2008;arXiv:0803.0476.

Gloor GB, Macklaim JM, Pawlowsky-Glahn V, Egozcue JJ. Microbiome datasets are compositional: and this is not optional. Front Microbiol. 2017;8:2224.

Palarea-Albaladejo J, Martín-Fernández JA. ZCompositions - R package for multivariate imputation of left-censored data under a compositional approach. Chemom Intell Lab Syst. 2015;143:85–96.

Aitchison J, Barceló-Vidal C, Martín-Fernández JA, Pawlowsky-Glahn V. Logratio analysis and compositional distance. Math Geol. 2000;32:271–5.

Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, et al. Vegan: Community Ecology Package. R package version 2.5–2. Cran R. 2019.

Fernandes AD, Reid JNS, Macklaim JM, McMurrough TA, Edgell DR, Gloor GB. Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis. Microbiome. 2014;2:15.

Fruchterman TMJ, Reingold EM. Graph drawing by force-directed placement. Softw Pract Exp. 1991;21:1129–64.

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Animal Microbiome

Tập 2 Số 1

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