Evaluation of 16S rDNA-Based Community Profiling for Human Microbiome Research

Ward, Doyle V.; Gevers, Dirk; Giannoukos, Georgia; Earl, Ashlee M.; Methé, Barbara A.; Sodergren, Erica; Feldgarden, Michael; Ciulla, Dawn; Tabbaa, Diana; Arze, Cesar; Appelbaum, Elizabeth L.; Aird, Leigh; Anderson, Scott; Ayvaz, Tulin; Belter, Edward A.; Bihan, Monika; Bloom, Toby; Crabtree, Jonathan; Courtney, Laura; Carmichael, Lynn K.; Dooling, David J.; Erlich, Rachel; Farmer, Candace N.; Fulton, Lucinda L.; Fulton, Robert S.; Gao, Hongyu; Gill, John A.; Haas, Brian J.; Hemphill, Lisa; Hall, Otis; Hamilton, Susanna; Hepburn, Theresa A.; Lennon, Niall J.; Joshi, Vandita; Kells, Cristyn; Kovar, Christie; Kalra, Dipak; Li, Kelvin; Lewis, Lora; Leonard, Shawn; Muzny, Donna M.; Mardis, Elaine R.; Mihindukulasuriya, Kathie A.; Magrini, Vincent; O’Laughlin, Michelle; Pohl, Craig; Qin, Xiang; Ross, Keenan; Ross, Matthew C.; Rogers, Yu Hui; Singh, Navjeet; Shang, Yue; Wilczek-Boney, Katarzyna; Wortman, Jennifer; Worley, Kim C.; Youmans, Bonnie P.; Yooseph, Shibu; Zhou, Yanjiao; Schloss, Patrick D.; Wilson, Richard K.; Gibbs, Richard A.; Nelson, Karen E.; Weinstock, George M.; DeSantis, Todd Z.; Petrosino, Joseph F.; Highlander, Sarah K.; Birren, Bruce W.

doi:10.1371/journal.pone.0039315

Cited by 234 publications

(148 citation statements)

References 38 publications

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“…For example, V1V3 amplicons may underestimate Acinetobacter and Escherichia genera, but V3V5 provides both breadth and depth of communities dominated by these genera. Furthermore, V6V9 may underestimate Bacteroides but provides good coverage for Pseudomonas and Escherichia (Human Microbiome Project 2012b). …”

Section: Metagenomic Science: Methods Applications and Challenges Imentioning

confidence: 99%

The Perinatal Microbiome and Pregnancy: Moving Beyond the Vaginal Microbiome

Prince

Chu

Seferovic

et al. 2015

Cold Spring Harbor Perspectives in Medicine

110

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The human microbiome, the collective genome of the microbial community that is on and within us, has recently been mapped. The initial characterization of healthy subjects has provided investigators with a reference population for interrogating the microbiome in metabolic, intestinal, and reproductive health and disease states. Although it is known that bacteria can colonize the vagina, recent metagenomic studies have shown that the vaginal microbiome varies among reproductive age women. Similarly, the richness and diversity of intestinal microbiota also naturally fluctuate among gravidae in both human and nonhuman primates, as well as mice. Moreover, recent evidence suggests that microbiome niches in pregnancy are not limited to maternal body sites, as the placenta appears to harbor a low biomass microbiome that is presumptively established in early pregnancy and varies in association with a remote history of maternal antenatal infection as well as preterm birth. In this article, we will provide a brief overview on metagenomics science as a means to investigate the microbiome, observations pertaining to both variation and the presumptive potential role of a varied microbiome during pregnancy, and how future studies of the microbiome in pregnancy may lend to a better understanding of human biology, reproductive health, and parturition.

show abstract

Section: Metagenomic Science: Methods Applications and Challenges Imentioning

confidence: 99%

The Perinatal Microbiome and Pregnancy: Moving Beyond the Vaginal Microbiome

Prince

Chu

Seferovic

et al. 2015

Cold Spring Harbor Perspectives in Medicine

110

View full text Add to dashboard Cite

show abstract

“…Forward primers contained the B adapter, and the reverse primers contained the A adapter. PCR products were then sequenced using the Roche-454 FLX Titanium platform according to the “HMP 16S Protocol” (http://www.hmpdacc.org/doc/HMP_MDG_454_16S_Protocol.pdf), and Ward et al (2012). After adoption of the 16S rRNA protocol, including removal of multiple sources of potential artifacts or bias generated by 16S rRNA sequencing using pyrosequencing (Huse et al, 2007), phylogenetic analysis and taxonomic assignments of the V3-V5 regions within the 16S rRNA gene were performed using the mothur and QIIME (Schloss et al, 2009; Caporaso et al, 2010).…”

Section: Methodsmentioning

confidence: 99%

Development of aminoglycoside and Î²-lactamase resistance among intestinal microbiota of swine treated with lincomycin, chlortetracycline, and amoxicillin

Sun

Liu

et al. 2014

Front. Microbiol.

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Lincomycin, chlortetracycline, and amoxicillin are commonly used antimicrobials for growth promotion and infectious disease prophylaxis in swine production. In this study, we investigated the shifts and resistance development among intestinal microbiota in pregnant sows before and after lincomycin, chlortetracycline, and amoxicillin treatment by using phylogenetic analysis, bacterial enumeration, and PCR. After the antimicrobial treatment, shifts in microbial community, an increased proportion of resistant bacteria, and genes related to antimicrobial resistance as compared to the day before antimicrobial administration (day 0) were observed. Importantly, a positive correlation between antimicrobial resistance gene expression in different categories, especially those encoding aminoglycoside and β-lactamase and antimicrobial resistance, was observed. These findings demonstrate an important role of antimicrobial usage in animals in the development of antimicrobial resistance, and support the notion that prudent use of antimicrobials in swine is needed to reduce the risk of the emergence of multi-drug resistant zoonotic pathogens.

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“…They are anked by conserved sequences that enable PCR ampli cation of targeted sequences (amplicons) using universal or speci cally designed primers. However, depending on the chosen variable regions and primers, the resulting OTUs clusters and estimations of diversity may not be the same (see for example [21], [22]). Moreover, amplicon sequencing excludes unknown species or highly diverged microbes from the analysis.…”

Section: Choice Of Datamentioning

confidence: 99%

Machine learning for metagenomics: methods and tools

Soueidan¹,

Nikolski²

2017

Metagenomics

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Owing to the complexity and variability of metagenomic studies, modern machine learning approaches have seen increased usage to answer a variety of question encompassing the full range of metagenomic NGS data analysis. We review here the contribution of machine learning techniques for the eld of metagenomics, by presenting known successful approaches in a uni ed framework. This review focuses on ve important metagenomic problems: OTU-clustering, binning, taxonomic pro ing and assignment, comparative metagenomics and gene prediction. For each of these problems, we identify the most prominent methods, summarize the machine learning approaches used and put them into perspective of similar methods. We conclude our review looking further ahead at the challenge posed by the analysis of interactions within microbial communities and di erent environments, in a eld one could call "integrative metagenomics".

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Evaluation of 16S rDNA-Based Community Profiling for Human Microbiome Research

Cited by 234 publications

References 38 publications

The Perinatal Microbiome and Pregnancy: Moving Beyond the Vaginal Microbiome

The Perinatal Microbiome and Pregnancy: Moving Beyond the Vaginal Microbiome

Development of aminoglycoside and Î²-lactamase resistance among intestinal microbiota of swine treated with lincomycin, chlortetracycline, and amoxicillin

Machine learning for metagenomics: methods and tools

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