Characterization of microbial associations with methanotrophic archaea and sulfate-reducing bacteria through statistical comparison of nested Magneto-FISH enrichments.

Victoria J Orphan,Elizabeth Trembath-Reichert,David H Case

doi:10.7717/peerj.1913

Victoria J Orphan, Elizabeth Trembath-Reichert + Show 1 more

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https://doi.org/10.7717/peerj.1913

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Journal: PeerJ	Publication Date: Apr 18, 2016
Citations: 40	License type: CC BY 4.0

Affiliation: California Institute of Technology

Abstract

Methane seep systems along continental margins host diverse and dynamic microbial assemblages, sustained in large part through the microbially mediated process of sulfate-coupled Anaerobic Oxidation of Methane (AOM). This methanotrophic metabolism has been linked to consortia of anaerobic methane-oxidizing archaea (ANME) and sulfate-reducing bacteria (SRB). These two groups are the focus of numerous studies; however, less is known about the wide diversity of other seep associated microorganisms. We selected a hierarchical set of FISH probes targeting a range of Deltaproteobacteria diversity. Using the Magneto-FISH enrichment technique, we then magnetically captured CARD-FISH hybridized cells and their physically associated microorganisms from a methane seep sediment incubation. DNA from nested Magneto-FISH experiments was analyzed using Illumina tag 16S rRNA gene sequencing (iTag). Enrichment success and potential bias with iTag was evaluated in the context of full-length 16S rRNA gene clone libraries, CARD-FISH, functional gene clone libraries, and iTag mock communities. We determined commonly used Earth Microbiome Project (EMP) iTAG primers introduced bias in some common methane seep microbial taxa that reduced the ability to directly compare OTU relative abundances within a sample, but comparison of relative abundances between samples (in nearly all cases) and whole community-based analyses were robust. The iTag dataset was subjected to statistical co-occurrence measures of the most abundant OTUs to determine which taxa in this dataset were most correlated across all samples. Many non-canonical microbial partnerships were statistically significant in our co-occurrence network analysis, most of which were not recovered with conventional clone library sequencing, demonstrating the utility of combining Magneto-FISH and iTag sequencing methods for hypothesis generation of associations within complex microbial communities. Network analysis pointed to many co-occurrences containing putatively heterotrophic, candidate phyla such as OD1, Atribacteria, MBG-B, and Hyd24-12 and the potential for complex sulfur cycling involving Epsilon-, Delta-, and Gammaproteobacteria in methane seep ecosystems.

Highlights

A central goal in microbial ecology is identifying and understanding microbial interactions in the environment
Relative sequence abundances of the methane seep microbiome characteristic taxa, Anaerobic methane-oxidizing (ANME) archaea, Deltaproteobacteria, Hyd24-12, and Atribacteria (Ruff et al 2015), were compared two ways: 1) between Illumina tag 16S rRNA gene sequencing (iTag) and gene library 16S rRNA gene samples to determine how relative sequence abundances differed between sequencing methodologies, and 2) between Magneto-FISH enrichment and bulk sediment to determine taxa-specific relative sequence abundance for each probe (Table 1)
Mock community analysis showed that ANME-2 were always underrepresented in iTag data (0.320.81 fold of what was expected), whereas the Deltaproteobacteria and ANME-1b were more faithfully represented (Sup Table 2)

Summary

Introduction

A central goal in microbial ecology is identifying and understanding microbial interactions in the environment. This goal can be addressed at many scales from statistical analyses of entire ecosystems (Barberán et al 2012; Malfatti & Azam 2010; Ruff et al 2015; Steele et al 2011; Sunagawa et al 2015) to high resolution image analysis of specific symbioses (Malfatti & Azam 2010; McGlynn et al 2015; Orphan 2009; Orphan et al 2001b; Wegener et al 2015). Many physical separation methods have been developed to partition complex microbial assemblages before analysis, including fluorescence-activated flow sorting (Amann et al 1990; Yilmaz et al 2010), optical trapping (Ashkin 1997), microfluidics (Melin & Quake 2007), and immunomagnetic beads (Pernthaler et al 2008; Šafařík & Šafaříková 1999) that use characteristics of interest such as phylogenetic identity (Fluorescence In-Situ Hybridization; FISH) or activity (Berry et al 2015; Hatzenpichler & Orphan 2015; Hatzenpichler et al 2014; Kalyuzhnaya et al 2008; Wegener et al 2012)

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