Stability of the Fecal and Oral Microbiome over 2 Years at -80°C for Multiple Collection Methods.

BACKGROUND: In prospective cohorts, biological samples are generally stored over long periods before an adequate number of cases have accrued. We investigated the impact of sample storage at -80°C for 2 years on the stability of the V4 region of the 16S rRNA gene across seven different collection methods (i.e., no additive, 95% ethanol, RNAlater stabilization solution, fecal occult blood test cards, and fecal immunochemical test tubes for feces; OMNIgene ORAL tubes and Scope mouthwash for saliva) among 51 healthy volunteers. METHODS: Intraclass correlation coefficients (ICC) were calculated for the relative abundance of the top three phyla, the 20 most abundant genera, three alpha-diversity metrics, and the first principal coordinates of three beta-diversity matrices. RESULTS: The subject variability was much higher than the variability introduced by the sample collection type, and storage time. For fecal samples, microbial stability over 2 years was high across collection methods (range, ICCs = 0.70-0.99), except for the samples collected with no additive (range, ICCs = 0.23-0.83). For oral samples, most microbiome diversity measures were stable over time with ICCs above 0.74; however, ICCs for the samples collected with Scope mouthwash were lower for two alpha-diversity measures, Faith's phylogenetic diversity (0.23) and the observed number of operational taxonomic units (0.23). CONCLUSIONS: Fecal and oral samples in most used collection methods are stable for microbiome analyses after 2 years at -80°C, except for fecal samples with no additive. IMPACT: This study provides evidence that samples stored for an extended period from prospective studies are useful for microbiome analyses.

The sponge microbiome project.

Marine sponges (phylum Porifera) are a diverse, phylogenetically deep-branching clade known for forming intimate partnerships with complex communities of microorganisms. To date, 16S rRNA gene sequencing studies have largely utilised different extraction and amplification methodologies to target the microbial communities of a limited number of sponge species, severely limiting comparative analyses of sponge microbial diversity and structure. Here, we provide an extensive and standardised dataset that will facilitate sponge microbiome comparisons across large spatial, temporal, and environmental scales. Samples from marine sponges (n = 3569 specimens), seawater (n = 370), marine sediments (n = 65) and other environments (n = 29) were collected from different locations across the globe. This dataset incorporates at least 268 different sponge species, including several yet unidentified taxa. The V4 region of the 16S rRNA gene was amplified and sequenced from extracted DNA using standardised procedures. Raw sequences (total of 1.1 billion sequences) were processed and clustered with (i) a standard protocol using QIIME closed-reference picking resulting in 39 543 operational taxonomic units (OTU) at 97% sequence identity, (ii) a de novo clustering using Mothur resulting in 518 246 OTUs, and (iii) a new high-resolution Deblur protocol resulting in 83 908 unique bacterial sequences. Abundance tables, representative sequences, taxonomic classifications, and metadata are provided. This dataset represents a comprehensive resource of sponge-associated microbial communities based on 16S rRNA gene sequences that can be used to address overarching hypotheses regarding host-associated prokaryotes, including host specificity, convergent evolution, environmental drivers of microbiome structure, and the sponge-associated rare biosphere.

Advancing our understanding of the human microbiome using QIIME.

High-throughput DNA sequencing technologies, coupled with advanced bioinformatics tools, have enabled rapid advances in microbial ecology and our understanding of the human microbiome. QIIME (Quantitative Insights Into Microbial Ecology) is an open-source bioinformatics software package designed for microbial community analysis based on DNA sequence data, which provides a single analysis framework for analysis of raw sequence data through publication-quality statistical analyses and interactive visualizations. In this chapter, we demonstrate the use of the QIIME pipeline to analyze microbial communities obtained from several sites on the bodies of transgenic and wild-type mice, as assessed using 16S rRNA gene sequences generated on the Illumina MiSeq platform. We present our recommended pipeline for performing microbial community analysis and provide guidelines for making critical choices in the process. We present examples of some of the types of analyses that are enabled by QIIME and discuss how other tools, such as phyloseq and R, can be applied to expand upon these analyses.