The Microbiome Stress Project: Toward a Global Meta-Analysis of Environmental Stressors and Their Effects on Microbial Communities.

Jennifer D Rocca,Alex D Washburne,Joanna R Blaszczak,Sean M Gibbons,Marie Simonin,Firas S Midani,Jessica G Ernakovich

doi:10.3389/fmicb.2018.03272

Abstract

Microbial community structure is highly sensitive to natural (e.g., drought, temperature, fire) and anthropogenic (e.g., heavy metal exposure, land-use change) stressors. However, despite an immense amount of data generated, systematic, cross-environment analyses of microbiome responses to multiple disturbances are lacking. Here, we present the Microbiome Stress Project, an open-access database of environmental and host-associated 16S rRNA amplicon sequencing studies collected to facilitate cross-study analyses of microbiome responses to stressors. This database will comprise published and unpublished datasets re-processed from the raw sequences into exact sequence variants using our standardized computational pipeline. Our database will provide insight into general response patterns of microbiome diversity, structure, and stability to environmental stressors. It will also enable the identification of cross-study associations between single or multiple stressors and specific microbial clades. Here, we present a proof-of-concept meta-analysis of 606 microbiomes (from nine studies) to assess microbial community responses to: (1) one stressor in one environment: soil warming across a variety of soil types, (2) a range of stressors in one environment: soil microbiome responses to a comprehensive set of stressors (incl. temperature, diesel, antibiotics, land use change, drought, and heavy metals), (3) one stressor across a range of environments: copper exposure effects on soil, sediment, activated-sludge reactors, and gut environments, and (4) the general trends of microbiome stressor responses. Overall, we found that stressor exposure significantly decreases microbiome alpha diversity and increases beta diversity (community dispersion) across a range of environments and stressor types. We observed a hump-shaped relationship between microbial community resistance to stressors (i.e., the average pairwise similarity score between the control and stressed communities) and alpha diversity. We used Phylofactor to identify microbial clades and individual taxa as potential bioindicators of copper contamination across different environments. Using standardized computational and statistical methods, the Microbiome Stress Project will leverage thousands of existing datasets to build a general framework for how microbial communities respond to environmental stress.

Highlights

In the past decade, the advent of high-throughput sequencing technologies has enabled microbial ecologists to characterize microbial community responses to environmental change at an unprecedented pace
We found that warming treatments altered alpha diversity through decreased observed exact sequence variant (ESV) richness (−21%, P < 0.0001) and Shannon-Weiner index
Species often live in environmental optima, suggesting that moving in either direction from that optimum will result in a decrease in that species abundance (Holt, 2009). These unimodal patterns in niche space likely give rise to unimodal diversity-disturbance relationships, if a large enough range of an environmental gradient is explored. This might explain the contrasted response of permafrost soil microbiomes to elevated temperature (+2C; Ernakovich et al, 2017), with increased alpha diversity across all indices (Observed ESVs: +34.5%, ShannonWeiner: +21%, Pielou’s Evenness: +16.2%, n = 7) (Figure 4B)

Summary

Introduction

The advent of high-throughput sequencing technologies has enabled microbial ecologists to characterize microbial community responses to environmental change at an unprecedented pace. Like the Earth and Human Microbiome Projects, revealed fundamental biogeographic patterns of microbial diversity under “baseline” or “steady state” conditions (Human Microbiome Project Consortium, 2012; Gilbert et al, 2014; Lloyd-Price et al, 2017; Thompson et al, 2017). While this baseline knowledge is crucial, similar large-scale initiatives are necessary for clarifying how microbiomes respond to fluctuating environmental conditions. We use the term stressor to refer to any factor that alters steady-state environmental conditions (biotic or abiotic) and influences the growth or mortality of organisms in a community, resulting in either deterministic or stochastic shifts in stationary relative abundance profiles of microbiomes

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