ArticleBMC bioinformatics2016
Stochastic neutral modelling of the Gut Microbiota's relative species abundance from next generation sequencing data.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Linking complex microbial interactions and dysbiosis through a disordered Lotka-Volterra model.eLife · 2026Article
- Flowtigs: Safety in flow decompositions for assembly graphs.iScience · 2024Article
- Emergent ecological patterns and modelling of gut microbiomes in health and in disease.PLoS computational biology · 2024Article
- Island biogeography theory provides a plausible explanation for why larger vertebrates and taller humans have more diverse gut microbiomes.The ISME journal · 2024Article
- Clinical Parasitology and Parasitome Maps as Old and New Tools to Improve Clinical Microbiomics.Pathogens (Basel, Switzerland) · 2021Review
- Comparison between 16S rRNA and shotgun sequencing data for the taxonomic characterization of the gut microbiota.Scientific reports · 2021Article
- Stochastic colonization of hosts with a finite lifespan can drive individual host microbes out of equilibrium.PLoS computational biology · 2020Article
- Gut microbiota ecology: Biodiversity estimated from hybrid neutral-niche model increases with health status and aging.PloS one · 2020Article
- Quantifying spatiotemporal variability and noise in absolute microbiota abundances using replicate sampling.Nature methods · 2019Article
- Neutrality in the Metaorganism.PLoS biology · 2019Article
- Microbial nutrient niches in the gut.Environmental microbiology · 2017Review
- Escherichia coli, cattle and the propagation of disease.FEMS microbiology letters · 2017Review
- Article
- Statistical analysis of co-occurrence patterns in microbial presence-absence datasets.PloS one · 2017Article
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10 authors.
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Abstract
backgroundInterest in understanding the mechanisms that lead to a particular composition of the Gut Microbiota is highly increasing, due to the relationship between this ecosystem and the host health state. Particularly relevant is the study of the Relative Species Abundance (RSA) distribution, that is a component of biodiversity and measures the number of species having a given number of individuals. It is the universal behaviour of RSA that induced many ecologists to look for theoretical explanations. In particular, a simple stochastic neutral model was proposed by Volkov et al. relying on population dynamics and was proved to fit the coral-reefs and rain forests RSA. Our aim is to ascertain if this model also describes the Microbiota RSA and if it can help in explaining the Microbiota plasticity.
resultsWe analyzed 16S rRNA sequencing data sampled from the Microbiota of three different animal species by Jeraldo et al. Through a clustering procedure (UCLUST), we built the Operational Taxonomic Units. These correspond to bacterial species considered at a given phylogenetic level defined by the similarity threshold used in the clustering procedure. The RSAs, plotted in the form of Preston plot, were fitted with Volkov's model. The model fits well the Microbiota RSA, except in the tail region, that shows a deviation from the neutrality assumption. Looking at the model parameters we were able to discriminate between different animal species, giving also a biological explanation. Moreover, the biodiversity estimator obtained by Volkov's model also differentiates the animal species and is in good agreement with the first and second order Hill's numbers, that are common evenness indexes simply based on the fraction of individuals per species.
conclusionsWe conclude that the neutrality assumption is a good approximation for the Microbiota dynamics and the observation that Volkov's model works for this ecosystem is a further proof of the RSA universality. Moreover, the ability to separate different animals with the model parameters and biodiversity number are promising results if we think about future applications on human data, in which the Microbiota composition and biodiversity are in close relationships with a variety of diseases and life-styles.
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