title: Correcting for Microbial Blooms in Fecal Samples during Room-Temperature Shipping.
abstract: The use of sterile swabs is a convenient and common way to collect microbiome samples, and many studies have shown that the effects of room-temperature storage are smaller than physiologically relevant differences between subjects. However, several bacterial taxa, notably members of the classGammaproteobacteria, grow at room temperature, sometimes confusing microbiome results, particularly when stability is assumed. Although comparative benchmarking has shown that several preservation methods, including the use of 95% ethanol, fecal occult blood test (FOBT) and FTA cards, and Omnigene-GUT kits, reduce changes in taxon abundance during room-temperature storage, these techniques all have drawbacks and cannot be applied retrospectively to samples that have already been collected. Here we performed a meta-analysis using several different microbiome sample storage condition studies, showing consistent trends in which specific bacteria grew (i.e., "bloomed") at room temperature, and introduce a procedure for removing the sequences that most distort analyses. In contrast to similarity-based clustering using operational taxonomic units (OTUs), we use a new technique called "Deblur" to identify the exact sequences corresponding to blooming taxa, greatly reducing false positives and also dramatically decreasing runtime. We show that applying this technique to samples collected for the American Gut Project (AGP), for which participants simply mail samples back without the use of ice packs or other preservatives, yields results consistent with published microbiome studies performed with frozen or otherwise preserved samples.IMPORTANCEIn many microbiome studies, the necessity to store samples at room temperature (i.e., remote fieldwork) and the ability to ship samples without hazardous materials that require special handling training, such as ethanol (i.e., citizen science efforts), is paramount. However, although room-temperature storage for a few days has been shown not to obscure physiologically relevant microbiome differences between comparison groups, there are still changes in specific bacterial taxa, notably, in members of the classGammaproteobacteria, that can make microbiome profiles difficult to interpret. Here we identify the most problematic taxa and show that removing sequences from just a few fast-growing taxa is sufficient to correct microbiome profiles.
authors.affiliation: Department of Pediatrics, University of California San Diego, La Jolla, California, USA.
authors.forename: Amnon
authors.initials: A
authors.lastname: Amir
authors.affiliation: Department of Pediatrics, University of California San Diego, La Jolla, California, USA.
authors.forename: Daniel
authors.initials: D
authors.lastname: McDonald
authors.affiliation: Department of Pediatrics, University of California San Diego, La Jolla, California, USA.
authors.forename: Jose A
authors.initials: JA
authors.lastname: Navas-Molina
authors.affiliation: Department of Pediatrics, University of California San Diego, La Jolla, California, USA.
authors.forename: Justine
authors.initials: J
authors.lastname: Debelius
authors.affiliation: Department of Pediatrics, University of California San Diego, La Jolla, California, USA.
authors.forename: James T
authors.initials: JT
authors.lastname: Morton
authors.affiliation: Department of Pediatrics, University of California San Diego, La Jolla, California, USA.
authors.forename: Embriette
authors.initials: E
authors.lastname: Hyde
authors.affiliation: Department of Computer Science, University of Colorado Boulder, Boulder, Colorado, USA.
authors.forename: Adam
authors.initials: A
authors.lastname: Robbins-Pianka
authors.affiliation: Department of Pediatrics, University of California San Diego, La Jolla, California, USA; Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, USA; Center for Microbiome Innovation, University of California San Diego, San Diego, California, USA.
authors.forename: Rob
authors.initials: R
authors.lastname: Knight
keywords: 16S rRNA; DNA sequencing; bioinformatics