Linking Associations of Rare Low-Abundance Species to Their Environments by Association Networks.

Tatiana V Karpinets,Vancheswaran Gopalakrishnan,Christopher W Schadt,Andrew P Futreal,Jennifer Wargo,Jianhua Zhang

doi:10.3389/fmicb.2018.00297

Abstract

Studies of microbial communities by targeted sequencing of rRNA genes lead to recovering numerous rare low-abundance taxa with unknown biological roles. We propose to study associations of such rare organisms with their environments by a computational framework based on transformation of the data into qualitative variables. Namely, we analyze the sparse table of putative species or OTUs (operational taxonomic units) and samples generated in such studies, also known as an OTU table, by collecting statistics on co-occurrences of the species and on shared species richness across samples. Based on the statistics we built two association networks, of the rare putative species and of the samples respectively, using a known computational technique, Association networks (Anets) developed for analysis of qualitative data. Clusters of samples and clusters of OTUs are then integrated and combined with metadata of the study to produce a map of associated putative species in their environments. We tested and validated the framework on two types of microbiomes, of human body sites and that of the Populus tree root systems. We show that in both studies the associations of OTUs can separate samples according to environmental or physiological characteristics of the studied systems.

Highlights

IntroductionThe rare low-abundance microbial species, which have been referred to as the “rare biosphere” (Sogin et al, 2006), have attracted increasing attention in the recent literature because of their unknown ecology and potential evolutionary and ecological importance (Youssef et al, 2010; Pedros-Alio, 2012; Coveley et al, 2015; Lynch and Neufeld, 2015; Sharon et al, 2015; Jousset et al, 2017)
Based on the results we conclude that the Association networks (Anets) algorithm recover similar groupings of samples from OTU tables produced by two commonly used 16S rRNA amplicon data processing pipelines regardless of the observed batch effects and type of sequencing (v13 or v35) as well as from an OTU table comprised of different samples from the same environments. In this proof of concept study we aimed to demonstrate the use of the Anets-based computational framework for linking associations of rare OTUs to their environment
Results of the study demonstrate that a combination of the AnetsOTUs and Anets-Samples has a potential to serve as a powerful unsupervised methods for discovering relationships and associations of rare species from phylogenetic marker gene datasets used in microbiome studies

Summary

Introduction

The rare low-abundance microbial species, which have been referred to as the “rare biosphere” (Sogin et al, 2006), have attracted increasing attention in the recent literature because of their unknown ecology and potential evolutionary and ecological importance (Youssef et al, 2010; Pedros-Alio, 2012; Coveley et al, 2015; Lynch and Neufeld, 2015; Sharon et al, 2015; Jousset et al, 2017). The numerous rare OTUs are a typical output of 16S rRNA amplicon sequencing studies, especially those with many and diverse samples. The species-like groups are typically inferred by a conventional aggregation of sequences into OTUs based on a sequence identity threshold or, in more recent work, by amplicon sequence variants (ASVs) (Callahan et al, 2016; Callahan, 2017). In both cases, most species-like groups could be representative of species-specialists; they are low in abundance in a given sample, but are rare across samples and environments. It is not clear how extensive this loss might be

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