Gut microbiota ecology: Biodiversity estimated from hybrid neutral-niche model increases with health status and aging

RESEARCH ARTICLE Gut microbiota ecology: Biodiversity estimated from hybrid neutral-niche model increases with health status and aging Claudia SalaID1☯*, Enrico Giampieri2☯, Silvia Vitali3, Paolo Garagnani2, Daniel Remondini1, Armando Bazzani1, Claudio Franceschi4, Gastone C. Castellani2,5 1 Department of Physics and Astronomy, University of Bologna, Bologna, Italy, 2 Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy, 3 BCAM–Basque Center for Applied Mathematics, Bilbao, Bizkaia, Spain, 4 Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia, 5 Galvani Interdepartmental Center, Bologna, Italy ☯ These authors contributed equally to this work. * claudia.sala3@unibo.it Abstract In this work we propose an index to estimate the gut microbiota biodiversity using a model- ing approach with the aim of describing its relationship with health and aging. The gut micro- biota, a complex ecosystem that links nutrition and metabolism, has a pervasive effect on all body organs and systems, undergoes profound changes with age and life-style, and sub- stantially contributes to the pathogenesis of age-related diseases. For these reasons, the gut microbiota is a suitable candidate for assessing and quantifying healthy aging, i.e. the capability of individuals to reach an advanced age, avoiding or postponing major age-related diseases. The importance of the gut microbiota in health and aging has been proven to be related not only to its taxonomic composition, but also to its ecological properties, namely its

biodiversity. Following an ecological approach, here we intended to characterize the rela- tionship between the gut microbiota biodiversity and healthy aging through the development a parsimonious model of gut microbiota from which biodiversity can be estimated. We ana- lysed publicly available metagenomic data relative to subjects of different ages, countries, nutritional habits and health status and we showed that a hybrid niche-neutral model well describes the observed patterns of bacterial relative abundance. Moreover, starting from such ecological modeling, we derived an estimate of the gut microbiota biodiversity that is consistent with classical indices, while having a higher statistical power. This allowed us to unveil an increase of the gut microbiota biodiversity during aging and to provide a good pre- dictor of health status in old age, dependent on life-style and aging disorders. Introduction The Gut Microbiota (GM) is a complex ecological system composed of a large number of interacting microorganisms with diversified trophic relationships [1]. This inherent complex- ity has limited the development of predictive models of interaction between the GM and the PLOS ONE PLOS ONE | https://doi.org/10.1371/journal.pone.0237207 October 30, 2020 1 / 23 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Sala C, Giampieri E, Vitali S, Garagnani P, Remondini D, Bazzani A, et al. (2020) Gut microbiota ecology: Biodiversity estimated from hybrid neutral-niche model increases with health status and aging. PLoS ONE 15(10): e0237207.

https://doi.org/10.1371/journal.pone.0237207 Editor: Samir Suweis, Uniiversity of Padova, ITALY Received: July 20, 2020 Accepted: October 19, 2020 Published: October 30, 2020 Copyright: © 2020 Sala et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: The ELDERMET and Kong data sets are available from the Sequence Read Archive database (accession numbers PRJNA283106, SRP076167). The Biagi & Schnorr and the Biagi data sets are available from the MG- RAST database (accession numbers mgp10557, mgp7058, 17761). The Odamaki data set is available from the DDBJ database (accession number DRA004160). The Bian data set is available from the Supplementary Information of the original paper (Bian et al, 2017).