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A transcriptome multi-tissue analysis identifies biological pathways and genes associated with variations in feed efficiency of growing pigs

Overview of attention for article published in BMC Genomics, March 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (84th percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

Mentioned by

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1 news outlet
twitter
4 tweeters

Citations

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35 Dimensions

Readers on

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33 Mendeley
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Title
A transcriptome multi-tissue analysis identifies biological pathways and genes associated with variations in feed efficiency of growing pigs
Published in
BMC Genomics, March 2017
DOI 10.1186/s12864-017-3639-0
Pubmed ID
Authors

Florence Gondret, Annie Vincent, Magalie Houée-Bigot, Anne Siegel, Sandrine Lagarrigue, David Causeur, Hélène Gilbert, Isabelle Louveau

Abstract

Animal's efficiency in converting feed into lean gain is a critical issue for the profitability of meat industries. This study aimed to describe shared and specific molecular responses in different tissues of pigs divergently selected over eight generations for residual feed intake (RFI). Pigs from the low RFI line had an improved gain-to-feed ratio during the test period and displayed higher leanness but similar adiposity when compared with pigs from the high RFI line at 132 days of age. Transcriptomics data were generated from longissimus muscle, liver and two adipose tissues using a porcine microarray and analyzed for the line effect (n = 24 pigs per line). The most apparent effect of the line was seen in muscle, whereas subcutaneous adipose tissue was the less affected tissue. Molecular data were analyzed by bioinformatics and subjected to multidimensional statistics to identify common biological processes across tissues and key genes participating to differences in the genetics of feed efficiency. Immune response, response to oxidative stress and protein metabolism were the main biological pathways shared by the four tissues that distinguished pigs from the low or high RFI lines. Many immune genes were under-expressed in the four tissues of the most efficient pigs. The main genes contributing to difference between pigs from the low vs high RFI lines were CD40, CTSC and NTN1. Different genes associated with energy use were modulated in a tissue-specific manner between the two lines. The gene expression program related to glycogen utilization was specifically up-regulated in muscle of pigs from the low RFI line (more efficient). Genes involved in fatty acid oxidation were down-regulated in muscle but were promoted in adipose tissues of the same pigs when compared with pigs from the high RFI line (less efficient). This underlined opposite line-associated strategies for energy use in skeletal muscle and adipose tissue. Genes related to cholesterol synthesis and efflux in liver and perirenal fat were also differentially regulated in pigs from the low vs high RFI lines. Non-productive functions such as immunity, defense against pathogens and oxidative stress contribute likely to inter-individual variations in feed efficiency.

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
India 1 3%
Unknown 32 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 36%
Researcher 6 18%
Student > Master 5 15%
Student > Postgraduate 3 9%
Student > Bachelor 2 6%
Other 2 6%
Unknown 3 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 14 42%
Veterinary Science and Veterinary Medicine 3 9%
Biochemistry, Genetics and Molecular Biology 3 9%
Business, Management and Accounting 1 3%
Nursing and Health Professions 1 3%
Other 2 6%
Unknown 9 27%

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 15 November 2017.
All research outputs
#1,119,270
of 12,145,106 outputs
Outputs from BMC Genomics
#563
of 7,141 outputs
Outputs of similar age
#41,001
of 265,700 outputs
Outputs of similar age from BMC Genomics
#21
of 107 outputs
Altmetric has tracked 12,145,106 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,141 research outputs from this source. They receive a mean Attention Score of 4.3. This one has done particularly well, scoring higher than 91% 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 265,700 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 84% of its contemporaries.
We're also able to compare this research output to 107 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.