Stochastic neutral modelling of the Gut Microbiota's relative species abundance from next generation sequencing data

Abstract

Background: 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. 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.

Results: We 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.

Conclusions: We 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.

Fig. 1

Preston plot of one cattle rumen sample. Empirical RSAs have been built considering four different similarity threshold: 90 %, 93 %, 95 % and 97 %. The RSA tends to become more Log-Series like for higher similarity thresholds, that correspond to finer phylogenetic levels

Fig. 2

Fit of one swine’s RSA. Empirical RSA (gray histogram) and fit with Eq. 10 (black line and dots) relative to one swine sample for the four similarity thresholds considered

Fig. 3

S/b versus similarity. Plot of the parameter S/b versus the similarity threshold used in computing the OTUs for all five samples. Note that S/b differentiate the three animal species considered and that it tends to diminish for high similarity thresholds, indicating that the RSA becomes more similar to a Log-Series, as attended from Fig. 1

Fig. 4

θ versus similarity. Hubbell biodiversity number θ [18, 22] computed with Eq. 11 for the five animals GM at the four similarity thresholds considered. Biodiversity increases with similarity and clusters animals according to their belonging species

Fig. 5

H ₁ and H ₂ versus similarity. First (left) and second (right) order Hill’s numbers for the five animals GM at the four similarity thresholds considered. The result is consistent with the biodiversity number θ

Fig. 6

Human GM RSA and fit with Eq. 10. RSA of one human Gut Microbiota from [12] and fit with Eq. 10. OTUs were built with UCLUST with 95 % similarity threshold