Evidence map›Paper›PMID 30630417›Full record

ArticleBMC genomics2019

Gene co-expression networks associated with carcass traits reveal new pathways for muscle and fat deposition in Nelore cattle.

Bárbara Silva-Vignato, Luiz L Coutinho, Mirele D Poleti, Aline S M Cesar, Cristina T Moncau, Luciana C A Regitano, Júlio C C Balieiro

Open access · goldAbstract read
In one paragraph

Article in BMC genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed
9.0field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

34 citing papers in PubMed, 57 citations in OpenAlex.

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  18. The Effect of the Feeding System on Fat Deposition in Yak Subcutaneous Fat.International journal of molecular sciences · 2023
    Article
  19. BovineCells · 2023
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 3 institutions in 1 country.

Bárbara Silva-VignatoCollege of Agriculture "Luiz de Queiroz", University of São Paulo, Piracicaba, SP, 13418-900, Brazil. bsilvavignato@gmail.com.ORCID http://orcid.org/0000-0001-9796-8007
Luiz L CoutinhoCollege of Agriculture "Luiz de Queiroz", University of São Paulo, Piracicaba, SP, 13418-900, Brazil.
Mirele D PoletiCollege of Animal Science and Food Engineering, University of São Paulo, Pirassununga, SP, 13635-900, Brazil.
Aline S M CesarCollege of Agriculture "Luiz de Queiroz", University of São Paulo, Piracicaba, SP, 13418-900, Brazil.
Cristina T MoncauFederal University of Lavras, Lavras, MG, 37200-000, Brazil.
Luciana C A RegitanoEmbrapa Pecuária Sudeste, São Carlos, SP, 13560-970, Brazil.
Júlio C C BalieiroCollege of Veterinary Medicine and Animal Science, University of São Paulo, Pirassununga, SP, 13635-900, Brazil.
Universidade de São Paulo · BRBrazilian Agricultural Research Corporation · BRUniversidade Federal de Lavras · BR

Funding

Empresa Brasileira de Pesquisa Agropecuária Macroprograma 1, 01/2005Fundação de Amparo à Pesquisa do Estado de São Paulo 2012/23638-8
6 · The paper itself

Abstract

backgroundPositively correlated with carcass weight and animal growth, the ribeye area (REA) and the backfat thickness (BFT) are economic important carcass traits, which impact directly on producer's payment. The selection of these traits has not been satisfactory since they are expressed later in the animal's life and multigene regulated. So, next-generation technologies have been applied in this area to improve animal's selection and better understand the molecular mechanisms involved in the development of these traits. Correlation network analysis, performed by tools like WGCNA (Weighted Correlation Network Analysis), has been used to explore gene-gene interactions and gene-phenotype correlations. Thus, this study aimed to identify putative candidate genes and metabolic pathways that regulate REA and BFT by constructing a gene co-expression network using WGCNA and RNA sequencing data, to better understand genetic and molecular variations behind these complex traits in Nelore cattle.

resultsThe gene co-expression network analysis, using WGCNA, were built using RNA-sequencing data normalized by transcript per million (TPM) from 43 Nelore steers. Forty-six gene clusters were constructed, between them, three were positively correlated (p-value< 0.1) to the BFT (Green Yellow, Ivory, and Light Yellow modules) and, one cluster was negatively correlated (p-value< 0.1) with REA (Salmon module). The enrichment analysis performed by DAVID and WebGestalt (FDR 5%) identified eight Gene Ontology (GO) terms and three KEGG pathways in the Green Yellow module, mostly associated with immune response and inflammatory mechanisms. The enrichment of the Salmon module demonstrated 19 GO terms and 21 KEGG pathways, related to muscle energy metabolism, lipid metabolism, muscle degradation, and oxidative stress diseases. The Ivory and Light yellow modules have not shown significant results in the enrichment analysis.

conclusionWith this study, we verified that inflammation and immune response pathways modulate the BFT trait. Energy and lipid metabolism pathways, highlighting fatty acid metabolism, were the central pathways associated with REA. Some genes, as RSAD2, EIF2AK2, ACAT1, and ACSL1 were considered as putative candidate related to these traits. Altogether these results allow us to a better comprehension of the molecular mechanisms that lead to muscle and fat deposition in bovine.

Indexed as

AdiposityAnimalsCattleEnergy MetabolismGene ExpressionGene Regulatory NetworksGenetic Association StudiesLipid MetabolismMetabolic Networks and PathwaysMuscle DevelopmentSequence Analysis, RNABackfat thicknessFunctional enrichment analysisRibeye areaRNA-Seq dataWGCNA

Identifiers

PMID30630417
PMCPMC6329100
OpenAlexW2943434170

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.