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.
Highlights
Interest 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
A similar trend of the Relative Species Abundance (RSA) have been observed in [22] for the coral-reef ecosystem, in which the authors firstly considered many small semi-isolated local communities and assembled them into bigger and bigger metacommunities. It seems that considering the Gut Microbiota ecosystem at higher phylogenetic levels somehow corresponds, from a dynamical point of view, to assembling semi-isolated local communities into metacommunities
In the past the RSA shape has been described in terms of Log-Series or Log-Normal distribution [18], but the Log-Series is a special case of the Negative Binomial, that is obtained when S/b → 0 [22], and, for what concerns the Log-Normal, it has been proved not to be an appropriate null model for the RSA [35], and its bell-shaped cases can be well described by the Negative Binomial distribution, due to the fact that, dealing with experimental data, these two distributions can take on similar shapes and are often hard to distinguish in practice [36]
Summary
Interest 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. As a result of millions of years of coevolution, the microbial genome stands in dynamical relationship with the host organism and helps it in crucial functions. These include metabolic processes like food absorption and short chain fatty Acid (SCFA) and vitamins production [4], and the shaping, control and protection of the immune system development [5]. One of the main characteristics of Metagenome, that is crucial in the case of unhealthy people, is the molecular composition of the intersection between the host and the Microbiome This interface is the way by which the host and the Microbiota communicate. Such interaction is bidirectional and history-dependent and can be characterized as a function of the exchanged metabolic, genetic and immunological bio-molecules [15]
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