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RNA-Seq Meta-analysis identifies genes in skeletal muscle associated with gain and intake across a multi-season study of crossbred beef steers

Overview of attention for article published in BMC Genomics, June 2018
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Title
RNA-Seq Meta-analysis identifies genes in skeletal muscle associated with gain and intake across a multi-season study of crossbred beef steers
Published in
BMC Genomics, June 2018
DOI 10.1186/s12864-018-4769-8
Pubmed ID
Authors

Brittney N. Keel, Christina M. Zarek, John W. Keele, Larry A. Kuehn, Warren M. Snelling, William T. Oliver, Harvey C. Freetly, Amanda K. Lindholm-Perry

Abstract

Feed intake and body weight gain are economically important inputs and outputs of beef production systems. The purpose of this study was to discover differentially expressed genes that will be robust for feed intake and gain across a large segment of the cattle industry. Transcriptomic studies often suffer from issues with reproducibility and cross-validation. One way to improve reproducibility is by integrating multiple datasets via meta-analysis. RNA sequencing (RNA-Seq) was performed on longissimus dorsi muscle from 80 steers (5 cohorts, each with 16 animals) selected from the outside fringe of a bivariate gain and feed intake distribution to understand the genes and pathways involved in feed efficiency. In each cohort, 16 steers were selected from one of four gain and feed intake phenotypes (n = 4 per phenotype) in a 2 × 2 factorial arrangement with gain and feed intake as main effect variables. Each cohort was analyzed as a single experiment using a generalized linear model and results from the 5 cohort analyses were combined in a meta-analysis to identify differentially expressed genes (DEG) across the cohorts. A total of 51 genes were differentially expressed for the main effect of gain, 109 genes for the intake main effect, and 11 genes for the gain x intake interaction (Pcorrected < 0.05). A jackknife sensitivity analysis showed that, in general, the meta-analysis produced robust DEGs for the two main effects and their interaction. Pathways identified from over-represented genes included mitochondrial energy production and oxidative stress pathways for the main effect of gain due to DEG including GPD1, NDUFA6, UQCRQ, ACTC1, and MGST3. For intake, metabolic pathways including amino acid biosynthesis and degradation were identified, and for the interaction analysis the pathways identified included GADD45, pyridoxal 5'phosphate salvage, and caveolar mediated endocytosis signaling. Variation among DEG identified by cohort suggests that environment and breed may play large roles in the expression of genes associated with feed efficiency in the muscle of beef cattle. Meta-analyses of transcriptome data from groups of animals over multiple cohorts may be necessary to elucidate the genetics contributing these types of biological phenotypes.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 56 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 29%
Researcher 12 21%
Student > Master 10 18%
Student > Bachelor 3 5%
Other 2 4%
Other 3 5%
Unknown 10 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 19 34%
Biochemistry, Genetics and Molecular Biology 10 18%
Medicine and Dentistry 4 7%
Engineering 2 4%
Nursing and Health Professions 1 2%
Other 6 11%
Unknown 14 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 07 June 2018.
All research outputs
#14,563,539
of 23,323,574 outputs
Outputs from BMC Genomics
#5,768
of 10,743 outputs
Outputs of similar age
#187,625
of 330,585 outputs
Outputs of similar age from BMC Genomics
#125
of 252 outputs
Altmetric has tracked 23,323,574 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,743 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 42nd percentile – i.e., 42% of its peers scored the same or lower than it.
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We're also able to compare this research output to 252 others from the same source and published within six weeks on either side of this one. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.