The Role of the Gut Microbiome in Cattle Production and Health: Driver or Passenger? DOI Open Access
Eóin O’Hara, A. L. A. Neves, Yang Song

и другие.

Annual Review of Animal Biosciences, Год журнала: 2020, Номер 8(1), С. 199 - 220

Опубликована: Фев. 15, 2020

Ruminant production systems face significant challenges currently, driven by heightened awareness of their negative environmental impact and the rapidly rising global population. Recent findings have underscored how composition function rumen microbiome are associated with economically valuable traits, including feed efficiency methane emission. Although omics-based technological advances in last decade revolutionized our understanding host-associated microbial communities, there remains incongruence over correct approach for analysis large omic data sets. A that examines host/microbiome interactions both lower digestive tract is required to harness full potential gastrointestinal sustainable ruminant production. This review highlights animal community may identify exploit causal relationships between gut host traits interest a practical application health

Язык: Английский

Microbiome Datasets Are Compositional: And This Is Not Optional DOI Creative Commons
Gregory B. Gloor,

Jean M. Macklaim,

Vera Pawlowsky‐Glahn

и другие.

Frontiers in Microbiology, Год журнала: 2017, Номер 8

Опубликована: Ноя. 15, 2017

Datasets collected by high-throughput sequencing (HTS) of 16S rRNA gene amplimers, metagenomes or metatranscriptomes are commonplace and being used to study human disease states, ecological differences between sites, the built environment. There is increasing awareness that microbiome datasets generated HTS compositional because they have an arbitrary total imposed instrument. However, many investigators either unaware this assume specific properties data. The purpose review alert dangers inherent in ignoring nature data, point out derived from studies can should be treated as compositions at all stages analysis. We briefly introduce illustrate pathologies occur when data analyzed inappropriately, finally give guidance resources examples for analysis using

Язык: Английский

Процитировано

2268

Normalization and microbial differential abundance strategies depend upon data characteristics DOI Creative Commons

Sophie Weiss,

Zhenjiang Zech Xu, Shyamal D. Peddada

и другие.

Microbiome, Год журнала: 2017, Номер 5(1)

Опубликована: Март 3, 2017

Data from 16S ribosomal RNA (rRNA) amplicon sequencing present challenges to ecological and statistical interpretation. In particular, library sizes often vary over several ranges of magnitude, the data contains many zeros. Although we are typically interested in comparing relative abundance taxa ecosystem two or more groups, can only measure taxon specimens obtained ecosystems. Because comparison specimen is not equivalent ecosystems, this presents a special challenge. Second, because (as well as ecosystem) sum 1, these compositional data. constrained by simplex (sum 1) unconstrained Euclidean space, standard methods analysis applicable. Here, evaluate how impact performance existing normalization differential analyses. Effects on normalization: Most enable successful clustering samples according biological origin when groups differ substantially their overall microbial composition. Rarefying clearly clusters than other techniques do for ordination metrics based presence absence. Alternate measures potentially vulnerable artifacts due size. testing: We build previous work seven proposed using rarefied raw Our simulation studies suggest that false discovery rates abundance-testing increased rarefying itself, although course results loss sensitivity elimination portion available For with large (~10×) differences average size, lowers rate. DESeq2, without addition constant, smaller datasets (<20 per group) but tends towards higher rate samples, very uneven sizes, and/or effects. drawing inferences regarding ecosystem, composition microbiomes (ANCOM) sensitive (for >20 also critically method tested has good control These findings guide which use characteristics given study.

Язык: Английский

Процитировано

1748

Establishing microbial composition measurement standards with reference frames DOI Creative Commons
James T. Morton, Clarisse Marotz, Alex Washburne

и другие.

Nature Communications, Год журнала: 2019, Номер 10(1)

Опубликована: Июнь 20, 2019

Abstract Differential abundance analysis is controversial throughout microbiome research. Gold standard approaches require laborious measurements of total microbial load, or absolute number microorganisms, to accurately determine taxonomic shifts. Therefore, most studies rely on relative data. Here, we demonstrate common pitfalls in comparing across samples and identify two solutions that reveal changes without the need estimate load. We define notion “reference frames”, which provide deep intuition about compositional nature In an oral time series experiment, reference frames alleviate false positives produce consistent results both raw cell-count normalized Furthermore, consistent, differentially abundant microbes previously undetected independent published datasets from subjects with atopic dermatitis. These methods allow reassessment data reproducible sequencing output for new assays.

Язык: Английский

Процитировано

550

Microbiome differential abundance methods produce different results across 38 datasets DOI Creative Commons
Jacob T. Nearing, Gavin M. Douglas,

Molly G. Hayes

и другие.

Nature Communications, Год журнала: 2022, Номер 13(1)

Опубликована: Янв. 17, 2022

Identifying differentially abundant microbes is a common goal of microbiome studies. Multiple methods are used interchangeably for this purpose in the literature. Yet, there few large-scale studies systematically exploring appropriateness using these tools interchangeably, and scale significance differences between them. Here, we compare performance 14 differential abundance testing on 38 16S rRNA gene datasets with two sample groups. We test amplicon sequence variants operational taxonomic units (ASVs) Our findings confirm that identified drastically different numbers sets significant ASVs, results depend data pre-processing. For many number features correlate aspects data, such as size, sequencing depth, effect size community differences. ALDEx2 ANCOM-II produce most consistent across agree best intersect from approaches. Nevertheless, recommend researchers should use consensus approach based multiple to help ensure robust biological interpretations. Many available, but it lacks systematic comparison among authors groups, show results.

Язык: Английский

Процитировано

550

Intercellular wiring enables electron transfer between methanotrophic archaea and bacteria DOI
Gunter Wegener, Viola Krukenberg, Dietmar Riedel

и другие.

Nature, Год журнала: 2015, Номер 526(7574), С. 587 - 590

Опубликована: Окт. 20, 2015

Язык: Английский

Процитировано

520

The Microbiota of Breast Tissue and Its Association with Breast Cancer DOI Creative Commons
Camilla Urbaniak, Gregory B. Gloor, Muriel Brackstone

и другие.

Applied and Environmental Microbiology, Год журнала: 2016, Номер 82(16), С. 5039 - 5048

Опубликована: Июнь 26, 2016

ABSTRACT In the United States, 1 in 8 women will be diagnosed with breast cancer her lifetime. Along genetics, environment contributes to disease development, but what these exact environmental factors are remains unknown. We have previously shown that tissue is not sterile contains a diverse population of bacteria. thus believe host's local microbiome could modulating risk development. Using 16S rRNA amplicon sequencing, we show bacterial profiles differ between normal adjacent from and healthy controls. Women had higher relative abundances Bacillus , Enterobacteriaceae Staphylococcus . Escherichia coli (a member family) epidermidis isolated patients, were induce DNA double-stranded breaks HeLa cells using histone-2AX (H2AX) phosphorylation (γ-H2AX) assay. also found microbial similar sampled directly tumor. This study raises important questions as role plays development or progression how can manipulate this for possible therapeutics prevention. IMPORTANCE shows different exist those cancer. Higher bacteria ability cause damage vitro detected was decrease some lactic acid bacteria, known their beneficial health effects, including anticarcinogenic properties. mammary

Язык: Английский

Процитировано

498

Human milk microbiota profiles in relation to birthing method, gestation and infant gender DOI Creative Commons
Camilla Urbaniak,

Michelle Angelini,

Gregory B. Gloor

и другие.

Microbiome, Год журнала: 2016, Номер 4(1)

Опубликована: Янв. 6, 2016

Human milk is an important source of bacteria for the developing infant and has been shown to influence bacterial composition neonate, which in turn can affect disease risk later life. Very little known about what factors shape human microbiome. The goal present study was examine microbiota from a range women who delivered vaginally or by caesarean (C) section, gave birth males females, at term preterm.Milk collected 39 Caucasian Canadian women, microbial profiles were analyzed 16S ribosomal RNA (rRNA) sequencing using Illumina platform.A diverse community found with most dominant phyla being Proteobacteria Firmicutes genus level, Staphylococcus, Pseudomonas, Streptococcus Lactobacillus. Comparison between preterm births, C section (elective non-elective) vaginal deliveries, male female infants showed no statistically significant differences.The revealed types transferred newborns. We postulate that there may be fail-safe mechanism whereby mother "ready" pass along her imprint irrespective when how baby born.

Язык: Английский

Процитировано

422

PICRUSt2: An improved and customizable approach for metagenome inference DOI Creative Commons
Gavin M. Douglas, Vincent J. Maffei, Jesse Zaneveld

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2019, Номер unknown

Опубликована: Июнь 15, 2019

One major limitation of microbial community marker gene sequencing is that it does not provide direct information on the functional composition sampled communities. Here, we present PICRUSt2 ( https://github.com/picrust/picrust2 ), which expands capabilities original PICRUSt method 1 to predict potential a based profiles. This updated and implementation includes several improvements over previous algorithm: an expanded database families reference genomes, new approach now compatible with any OTU-picking or denoising algorithm, novel phenotype predictions. Upon evaluation, was more accurate than PICRUSt1 other current approaches overall. also flexible allows addition custom databases. We highlight these important caveats regarding use predicted metagenomes, are related inherent challenges analyzing metagenome data in general.

Язык: Английский

Процитировано

370

QIIME 2 Enables Comprehensive End‐to‐End Analysis of Diverse Microbiome Data and Comparative Studies with Publicly Available Data DOI Creative Commons
Mehrbod Estaki, Lingjing Jiang, Nicholas A. Bokulich

и другие.

Current Protocols in Bioinformatics, Год журнала: 2020, Номер 70(1)

Опубликована: Апрель 28, 2020

QIIME 2 is a completely re-engineered microbiome bioinformatics platform based on the popular platform, which it has replaced. facilitates comprehensive and fully reproducible data science, improving accessibility to diverse users by adding multiple user interfaces. can be combined with Qiita, an open-source web-based re-use available for meta-analysis. The following basic protocol describes how install single computer analyze sequence data, from processing of raw DNA reads through generating publishable interactive figures. These figures allow readers study interact same ease as its authors, advancing science transparency reproducibility. We also show plug-ins developed community add analysis capabilities installed used 2, enhancing various aspects analyses-e.g., taxonomic classification accuracy. Finally, we illustrate perform meta-analyses combining different datasets using readily public Qiita. In this tutorial, subset Early Childhood Antibiotics Microbiome (ECAM) study, tracked composition development 43 infants in United States birth years age, identifying associations antibiotic exposure, delivery mode, diet. For more information about see https://qiime2.org. To troubleshoot or ask questions analysis, join active at https://forum.qiime2.org. © 2020 Authors. Basic Protocol: Using Support Further analyses.

Язык: Английский

Процитировано

362

Oral Microbiome Composition Reflects Prospective Risk for Esophageal Cancers DOI Open Access
Brandilyn A. Peters, Jing Wu, Zhiheng Pei

и другие.

Cancer Research, Год журнала: 2017, Номер 77(23), С. 6777 - 6787

Опубликована: Ноя. 30, 2017

Abstract Bacteria may play a role in esophageal adenocarcinoma (EAC) and squamous cell carcinoma (ESCC), although evidence is limited to cross-sectional studies. In this study, we examined the relationship of oral microbiota with EAC ESCC risk prospective study nested two cohorts. Oral bacteria were assessed using 16S rRNA gene sequencing prediagnostic mouthwash samples from n = 81/160 25/50 cases/matched controls. Findings largely consistent across both Metagenome content was predicted PiCRUST. We associations between centered log-ratio transformed taxon or functional pathway abundances conditional logistic regression adjusting for BMI, smoking, alcohol. found periodontal pathogen Tannerella forsythia be associated higher EAC. Furthermore, that depletion commensal genus Neisseria species Streptococcus pneumoniae lower risk. Bacterial biosynthesis carotenoids also protection against Finally, abundance Porphyromonas gingivalis trended ESCC. Overall, our findings have potential implications early detection prevention Cancer Res; 77(23); 6777–87. ©2017 AACR.

Язык: Английский

Процитировано

348