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      Comparing Microbiome Sampling Methods in a Wild Mammal: Fecal and Intestinal Samples Record Different Signals of Host Ecology, Evolution

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          Abstract

          The gut microbiome is a community of host-associated symbiotic microbes that fulfills multiple key roles in host metabolism, immune function, and tissue development. Given the ability of the microbiome to impact host fitness, there is increasing interest in studying the microbiome of wild animals to better understand these communities in the context of host ecology and evolution. Human microbiome research protocols are well established, but wildlife microbiome research is still a developing field. Currently, there is no standardized set of best practices guiding the collection of microbiome samples from wildlife. Gut microflora are typically sampled either by fecal collection, rectal swabbing, or by destructively sampling the intestinal contents of the host animal. Studies rarely include more than one sampling technique and no comparison of these methods currently exists for a wild mammal. Although some studies have hypothesized that the fecal microbiome is a nested subset of the intestinal microbiome, this hypothesis has not been formally tested. To address these issues, we examined guano (feces) and distal intestinal mucosa from 19 species of free-ranging bats from Lamanai, Belize, using 16S rRNA amplicon sequencing to compare microbial communities across sample types. We found that the diversity and composition of intestine and guano samples differed substantially. In addition, we conclude that signatures of host evolution are retained by studying gut microbiomes based on mucosal tissue samples, but not fecal samples. Conversely, fecal samples retained more signal of host diet than intestinal samples. These results suggest that fecal and intestinal sampling methods are not interchangeable, and that these two microbiotas record different information about the host from which they are isolated.

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          Most cited references45

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          Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample.

          The ongoing revolution in high-throughput sequencing continues to democratize the ability of small groups of investigators to map the microbial component of the biosphere. In particular, the coevolution of new sequencing platforms and new software tools allows data acquisition and analysis on an unprecedented scale. Here we report the next stage in this coevolutionary arms race, using the Illumina GAIIx platform to sequence a diverse array of 25 environmental samples and three known "mock communities" at a depth averaging 3.1 million reads per sample. We demonstrate excellent consistency in taxonomic recovery and recapture diversity patterns that were previously reported on the basis of metaanalysis of many studies from the literature (notably, the saline/nonsaline split in environmental samples and the split between host-associated and free-living communities). We also demonstrate that 2,000 Illumina single-end reads are sufficient to recapture the same relationships among samples that we observe with the full dataset. The results thus open up the possibility of conducting large-scale studies analyzing thousands of samples simultaneously to survey microbial communities at an unprecedented spatial and temporal resolution.
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            Comparison of phylogenetic trees

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              Bias in template-to-product ratios in multitemplate PCR.

              Bias introduced by the simultaneous amplification of specific genes from complex mixtures of templates remains poorly understood. To explore potential causes and the extent of bias in PCR amplification of 16S ribosomal DNAs (rDNAs), genomic DNAs of two closely and one distantly related bacterial species were mixed and amplified with universal, degenerate primers. Quantification and comparison of template and product ratios showed that there was considerable and reproducible overamplification of specific templates. Variability between replicates also contributed to the observed bias but in a comparatively minor way. Based on these initial observations, template dosage and differences in binding energies of permutations of the degenerate, universal primers were tested as two likely causes of this template-specific bias by using 16S rDNA templates modified by site-directed mutagenesis. When mixtures of mutagenized templates containing AT- and GC-rich priming sites were used, templates containing the GC-rich permutation amplified with higher efficiency, indicating that different primer binding energies may to a large extent be responsible for overamplification. In contrast, gene copy number was found to be an unlikely cause of the observed bias. Similarly, amplification from DNA extracted from a natural community to which different amounts of genomic DNA of a single bacterial species were added did not affect relative product ratios. Bias was reduced considerably by using high template concentrations, by performing fewer cycles, and by mixing replicate reaction preparations.
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                Author and article information

                Contributors
                Journal
                Front Microbiol
                Front Microbiol
                Front. Microbiol.
                Frontiers in Microbiology
                Frontiers Media S.A.
                1664-302X
                01 May 2018
                2018
                : 9
                : 803
                Affiliations
                [1] 1The Richard Gilder Graduate School, American Museum of Natural History , New York, NY, United States
                [2] 2Department of Mammalogy, American Museum of Natural History , New York, NY, United States
                [3] 3Division of Invertebrate Zoology, American Museum of Natural History , New York, NY, United States
                [4] 4Sackler Institute for Comparative Genomics, American Museum of Natural History , New York, NY, United States
                [5] 5Department of Biological Sciences, Hunter College, City University of New York , New York, NY, United States
                [6] 6Center for Translational and Basic Research, Hunter College , New York, NY, United States
                [7] 7Institute of Computational Biomedicine, Weill Cornell Medical College , New York, NY, United States
                Author notes

                Edited by: Yasir Muhammad, King Abdulaziz University, Saudi Arabia

                Reviewed by: Jamie S. Foster, The University of Florida, United States; Luigi Orrù, Consiglio per la Ricerca in Agricoltura e l'Analisi Dell'economia Agraria (CREA), Italy

                *Correspondence: Melissa R. Ingala ingala.melissar@ 123456gmail.com

                This article was submitted to Evolutionary and Genomic Microbiology, a section of the journal Frontiers in Microbiology

                Article
                10.3389/fmicb.2018.00803
                5938605
                29765359
                9aba6d67-2290-4c89-b615-b6d085ac71ba
                Copyright © 2018 Ingala, Simmons, Wultsch, Krampis, Speer and Perkins.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 12 February 2018
                : 10 April 2018
                Page count
                Figures: 5, Tables: 2, Equations: 0, References: 83, Pages: 13, Words: 9335
                Funding
                Funded by: American Museum of Natural History 10.13039/100005835
                Categories
                Microbiology
                Original Research

                Microbiology & Virology
                16s rrna,microbiome,field sampling methods,metagenomics,chiroptera
                Microbiology & Virology
                16s rrna, microbiome, field sampling methods, metagenomics, chiroptera

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