Stochastic neutral drifts seem prevalent in driving human virome assembly: Neutral, near-neutral and non-neutral theoretic analyses.

Comput Struct Biotechnol J

Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, China.

Published: March 2022

It is estimated that human body is inhabited by approximately 380 trillions of viruses, which exist in the form of viral communities and are collectively termed as human virome. How virome is assembled and what kind of forces maintain the composition and diversity of viral communities is still an open question. The question is of obvious importance because of its implications to human health and diseases. Here we address the question by harnessing the power of Hubbell's unified neutral theory of biodiversity (UNTB) in terms of three neutral models including standard Hubbell's neutral model (HNM), Sloan's near-neutral model (SNM) and Harris et al. (2017) multi-site neutral model (MSN), further augmented by Ning et al. (2019) normalized stochasticity ratio (NSR) and Hammal et al. (2015) power analysis for the neutral test (PNT). With the five models applied to 179 virome samples, we aim to obtain robust findings given both Type-I and Type-II errors are addressed and possible alternative, non-neutral processes are detected. It was found that stochastic neutral drifts seem prevalent: approximately 65-92% at metacommunity/landscape scales and 67-80% at virus species scale. The non-neutral selection is approximately 26-28% at community scale and 23% at species scale. The false negative rate is about 2-3%, which suggested rather limited confounding effects of non-neutral process on neutrality tests. We postulate that prevalence of neutrality in human virome is likely due to extremely simple structure of viruses (stands of DNA/RNA) and their inter-species homogeneities, forming the foundation of species equivalence-the hallmark of neutral theory.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9065738PMC
http://dx.doi.org/10.1016/j.csbj.2022.03.027DOI Listing

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