Literature DB >> 29274958

Symposium review: Mining metagenomic and metatranscriptomic data for clues about microbial metabolic functions in ruminants.

Fuyong Li1, Andre L A Neves1, Bibaswan Ghoshal1, Le Luo Guan2.   

Abstract

Metagenomics and metatranscriptomics can capture the whole genome and transcriptome repertoire of microorganisms through sequencing total DNA/RNA from various environmental samples, providing both taxonomic and functional information with high resolution. The unique and complex rumen microbial ecosystem is receiving great research attention because the rumen microbiota coevolves with the host and equips ruminants with the ability to convert cellulosic plant materials to high-protein products for human consumption. To date, hundreds to thousands of microbial phylotypes have been identified in the rumen using culture-independent molecular-based approaches, and genomic information of rumen microorganisms is rapidly accumulating through the single genome sequencing. However, functional characteristics of the rumen microbiome have not been well described because there are numerous uncultivable microorganisms in the rumen. The advent of metagenomics and metatranscriptomics along with advanced bioinformatics methods can help us better understand mechanisms of the rumen fermentation, which is vital for improving nutrient utilization and animal productivity. Therefore, in this review, we summarize a general workflow to conduct rumen metagenomics and metatranscriptomics and discuss how the data can be interpreted to be useful information. Moreover, we review recent literatures studying associations between the rumen microbiome and host phenotypes (e.g., feed efficiency and methane emissions) using these approaches, aiming to provide a useful guide to include studying the rumen microbiome as one of the research objectives using these 2 approaches.
Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  metagenomics; metatranscriptomics; microbiome; microbiota; rumen

Mesh:

Substances:

Year:  2017        PMID: 29274958     DOI: 10.3168/jds.2017-13356

Source DB:  PubMed          Journal:  J Dairy Sci        ISSN: 0022-0302            Impact factor:   4.034


  8 in total

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Authors:  Tansol Park; Laura M Cersosimo; Wendy Radloff; Geoffrey I Zanton; Wenli Li
Journal:  Anim Microbiome       Date:  2022-01-05

3.  Microbiome in Eosinophilic Esophagitis-Metagenomic, Metatranscriptomic, and Metabolomic Changes: A Systematic Review.

Authors:  Jordan D Busing; Matthew Buendia; Yash Choksi; Girish Hiremath; Suman R Das
Journal:  Front Physiol       Date:  2021-09-10       Impact factor: 4.755

4.  Bayesian modeling reveals host genetics associated with rumen microbiota jointly influence methane emission in dairy cows.

Authors:  Qianqian Zhang; Gareth Difford; Goutam Sahana; Peter Løvendahl; Jan Lassen; Mogens Sandø Lund; Bernt Guldbrandtsen; Luc Janss
Journal:  ISME J       Date:  2020-05-04       Impact factor: 10.302

5.  Host genetics influence the rumen microbiota and heritable rumen microbial features associate with feed efficiency in cattle.

Authors:  Fuyong Li; Changxi Li; Yanhong Chen; Junhong Liu; Chunyan Zhang; Barry Irving; Carolyn Fitzsimmons; Graham Plastow; Le Luo Guan
Journal:  Microbiome       Date:  2019-06-13       Impact factor: 14.650

6.  Effects of Lactobacillus rhamnosus and Enterococcus faecalis Supplementation as Direct-Fed Microbials on Rumen Microbiota of Boer and Speckled Goat Breeds.

Authors:  Takalani Whitney Maake; Olayinka Ayobami Aiyegoro; Matthew Adekunle Adeleke
Journal:  Vet Sci       Date:  2021-06-07

7.  Comparative metagenomic and metatranscriptomic analyses reveal the breed effect on the rumen microbiome and its associations with feed efficiency in beef cattle.

Authors:  Fuyong Li; Thomas C A Hitch; Yanhong Chen; Christopher J Creevey; Le Luo Guan
Journal:  Microbiome       Date:  2019-01-14       Impact factor: 14.650

8.  Metagenomics for taxonomy profiling: tools and approaches.

Authors:  Mukesh Kumar Awasthi; B Ravindran; Surendra Sarsaiya; Hongyu Chen; Steven Wainaina; Ekta Singh; Tao Liu; Sunil Kumar; Ashok Pandey; Lal Singh; Zengqiang Zhang
Journal:  Bioengineered       Date:  2020-12       Impact factor: 6.832

  8 in total

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