Cited by 380Metabolomics and Mass Spectrometry StudiesGenetic and phenotypic traits in livestockAnimal Nutrition and Physiology

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Livestock metabolomics and the livestock metabolome: A systematic review

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Abstract

Metabolomics uses advanced analytical chemistry techniques to comprehensively measure large numbers of small molecule metabolites in cells, tissues and biofluids. The ability to rapidly detect and quantify hundreds or even thousands of metabolites within a single sample is helping scientists paint a far more complete picture of system-wide metabolism and biology. Metabolomics is also allowing researchers to focus on measuring the end-products of complex, hard-to-decipher genetic, epigenetic and environmental interactions. As a result, metabolomics has become an increasingly popular "omics" approach to assist with the robust phenotypic characterization of humans, crop plants and model organisms. Indeed, metabolomics is now routinely used in biomedical, nutritional and crop research. It is also being increasingly used in livestock research and livestock monitoring. The purpose of this systematic review is to quantitatively and objectively summarize the current status of livestock metabolomics and to identify emerging trends, preferred technologies and important gaps in the field. In conducting this review we also critically assessed the applications of livestock metabolomics in key areas such as animal health assessment, disease diagnosis, bioproduct characterization and biomarker discovery for highly desirable economic traits (i.e., feed efficiency, growth potential and milk production). A secondary goal of this critical review was to compile data on the known composition of the livestock metabolome (for 5 of the most common livestock species namely cattle, sheep, goats, horses and pigs). These data have been made available through an open access, comprehensive livestock metabolome database (LMDB, available at http://www.lmdb.ca). The LMDB should enable livestock researchers and producers to conduct more targeted metabolomic studies and to identify where further metabolome coverage is needed.

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Crossref · 10.1371/journal.pone.0177675

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Fig 1 PRISMA diagram. The preferred reporting items for systematic reviews and meta-analysis (PRISMA) flow diagram identifies the total number of articles initially surveyed, the number of articles included and excluded for this systematic review. From: Moher D, Liberati A, Tetzlaff J, Altman DG, The PRISMA Group (2009). Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PloS Med 6(7): e1000097. doi:10.1371/journal.pmed1000097. For more information, visit www.prisma-statement.org.
Fig 1 PRISMA diagram. The preferred reporting items for systematic reviews and meta-analysis (PRISMA) flow diagram identifies the total number of articles initially surveyed, the number of articles included and excluded for this systematic review. From: Moher D, Liberati A, Tetzlaff J, Altman DG, The PRISMA Group (2009). Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PloS Med 6(7): e1000097. doi:10.1371/journal.pmed1000097. For more information, visit www.prisma-statement.org.CC BY · PubMed Central
Fig 2 Literature mining. Total number of livestock metabolomics articles considering only articles that reported ≥8 metabolites resulted in selection of 149 manuscripts for this review.
Fig 2 Literature mining. Total number of livestock metabolomics articles considering only articles that reported ≥8 metabolites resulted in selection of 149 manuscripts for this review.CC BY · PubMed Central
Fig 3 Sample types. Different varieties of samples and animal products have been analyzed in livestock metabolomics studies.
Fig 3 Sample types. Different varieties of samples and animal products have been analyzed in livestock metabolomics studies.CC BY · PubMed Central
Fig 4 Relative sensitivity of metabolomics platforms. Nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC-MS), and liquid chromatography (LC)-MS are the commonly used metabolomics platforms with varying detection limits.
Fig 4 Relative sensitivity of metabolomics platforms. Nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC-MS), and liquid chromatography (LC)-MS are the commonly used metabolomics platforms with varying detection limits.CC BY · PubMed Central

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Metadata

Authors
Goldansaz, S. A., Guo, A. C., Sajed, T., Steele, M. A., Plastow, G. S., & Wishart, D. S.
Affiliations
University of Alberta (CA)
Corresponding author
Wishart, David S.
Venue
PLoS ONE
Published
2017-05-22
Volume
12
Issue
5
Pages
e0177675
ISSN
1932-6203 (ISSN-L)
OpenAlex
W2619865607
Cited by
379
References
100
Citations by year
2026: 42 · 2025: 49 · 2024: 36 · 2023: 46 · 2022: 51 · 2021: 60 · 2020: 50 · 2019: 25 · 2018: 19 · 2017: 1
FWCI
2.48
Type
journal article
Language
en
Open access
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Genome Canada
  • Genome Alberta
  • Alberta Livestock and Meat Agency
  • Agriculture Funding Consortium
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
  • Life Sciences › Agricultural and Biological Sciences › Animal Science and Zoology › Animal Nutrition and Physiology
Keywords
animal health assessment, disease diagnosis, biomarker discovery, feed efficiency, milk production, growth potential, cattle, sheep, goats, horses, pigs, metabolite quantification
MeSH terms
Animals, Cattle, Goats, Horses, Sheep, Swine, Internet, Quantitative Trait Loci, Metabolomics, Metabolome, Livestock, Databases, Chemical

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