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Understanding the response to endurance exercise using a systems biology approach: combining blood metabolomics, transcriptomics and miRNomics in horses

Overview of attention for article published in BMC Genomics, February 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

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13 X users

Citations

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35 Dimensions

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110 Mendeley
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Title
Understanding the response to endurance exercise using a systems biology approach: combining blood metabolomics, transcriptomics and miRNomics in horses
Published in
BMC Genomics, February 2017
DOI 10.1186/s12864-017-3571-3
Pubmed ID
Authors

Núria Mach, Yuliaxis Ramayo-Caldas, Allison Clark, Marco Moroldo, Céline Robert, Eric Barrey, Jesús Maria López, Laurence Le Moyec

Abstract

Endurance exercise in horses requires adaptive processes involving physiological, biochemical, and cognitive-behavioral responses in an attempt to regain homeostasis. We hypothesized that the identification of the relationships between blood metabolome, transcriptome, and miRNome during endurance exercise in horses could provide significant insights into the molecular response to endurance exercise. For this reason, the serum metabolome and whole-blood transcriptome and miRNome data were obtained from ten horses before and after a 160 km endurance competition. We obtained a global regulatory network based on 11 unique metabolites, 263 metabolic genes and 5 miRNAs whose expression was significantly altered at T1 (post- endurance competition) relative to T0 (baseline, pre-endurance competition). This network provided new insights into the cross talk between the distinct molecular pathways (e.g. energy and oxygen sensing, oxidative stress, and inflammation) that were not detectable when analyzing single metabolites or transcripts alone. Single metabolites and transcripts were carrying out multiple roles and thus sharing several biochemical pathways. Using a regulatory impact factor metric analysis, this regulatory network was further confirmed at the transcription factor and miRNA levels. In an extended cohort of 31 independent animals, multiple factor analysis confirmed the strong associations between lactate, methylene derivatives, miR-21-5p, miR-16-5p, let-7 family and genes that coded proteins involved in metabolic reactions primarily related to energy, ubiquitin proteasome and lipopolysaccharide immune responses after the endurance competition. Multiple factor analysis also identified potential biomarkers at T0 for an increased likelihood for failure to finish an endurance competition. To the best of our knowledge, the present study is the first to provide a comprehensive and integrated overview of the metabolome, transcriptome, and miRNome co-regulatory networks that may have a key role in regulating the metabolic and immune response to endurance exercise in horses.

X Demographics

X Demographics

The data shown below were collected from the profiles of 13 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 110 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 <1%
Unknown 109 99%

Demographic breakdown

Readers by professional status Count As %
Researcher 28 25%
Student > Ph. D. Student 13 12%
Student > Bachelor 11 10%
Student > Master 11 10%
Other 8 7%
Other 12 11%
Unknown 27 25%
Readers by discipline Count As %
Agricultural and Biological Sciences 23 21%
Veterinary Science and Veterinary Medicine 17 15%
Medicine and Dentistry 13 12%
Biochemistry, Genetics and Molecular Biology 11 10%
Sports and Recreations 10 9%
Other 4 4%
Unknown 32 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 26 December 2017.
All research outputs
#4,541,029
of 22,953,506 outputs
Outputs from BMC Genomics
#1,880
of 10,686 outputs
Outputs of similar age
#81,866
of 309,434 outputs
Outputs of similar age from BMC Genomics
#58
of 236 outputs
Altmetric has tracked 22,953,506 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 10,686 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done well, scoring higher than 82% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 309,434 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.
We're also able to compare this research output to 236 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.