Evidence map›Paper›PMID 31238883›Full record

ArticleBMC genomics2019

Prediction of hub genes associated with intramuscular fat content in Nelore cattle.

Danielly Beraldo Dos Santos Silva, Larissa Fernanda Simielli Fonseca, Daniel Guariz Pinheiro, Maria Malane Magalhães Muniz, Ana Fabrícia Braga Magalhães, Fernando Baldi, Jesus Aparecido Ferro, Luis Artur Loyola Chardulo, Lucia Galvão de Albuquerque

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 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
3.0field-weighted citation impact, top 9% 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

10 citing papers in PubMed, 28 citations in OpenAlex.

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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

9 authors at 2 institutions in 1 country.

Danielly Beraldo Dos Santos SilvaSchool of Agricultural and Veterinarian Sciences, São Paulo State University (UNESP), Jaboticabal, SP, Brazil. daniellyberaldo@gmail.com.ORCID http://orcid.org/0000-0002-3144-7476
Larissa Fernanda Simielli FonsecaSchool of Agricultural and Veterinarian Sciences, São Paulo State University (UNESP), Jaboticabal, SP, Brazil.
Daniel Guariz PinheiroSchool of Agricultural and Veterinarian Sciences, São Paulo State University (UNESP), Jaboticabal, SP, Brazil.
Maria Malane Magalhães MunizSchool of Agricultural and Veterinarian Sciences, São Paulo State University (UNESP), Jaboticabal, SP, Brazil.
Ana Fabrícia Braga MagalhãesSchool of Agricultural and Veterinarian Sciences, São Paulo State University (UNESP), Jaboticabal, SP, Brazil.
Fernando BaldiSchool of Agricultural and Veterinarian Sciences, São Paulo State University (UNESP), Jaboticabal, SP, Brazil.
Jesus Aparecido FerroSchool of Agricultural and Veterinarian Sciences, São Paulo State University (UNESP), Jaboticabal, SP, Brazil.
Luis Artur Loyola CharduloSchool of Veterinary and Animal Science, São Paulo State University (UNESP), Botucatu, SP, Brazil.
Lucia Galvão de AlbuquerqueSchool of Agricultural and Veterinarian Sciences, São Paulo State University (UNESP), Jaboticabal, SP, Brazil.
National Council for Scientific and Technological Development · BRUniversidade Estadual Paulista (Unesp) · BR

Funding

Fundação de Amparo à Pesquisa do Estado de São Paulo #2009/16118-5Fundação de Amparo à Pesquisa do Estado de São Paulo #2015/16850-9
6 · The paper itself

Abstract

backgroundThe aim of this study was to use transcriptome RNA-Seq data from longissimus thoracis muscle of uncastrated Nelore males to identify hub genes based on co-expression network obtained from differentially expressed genes (DEGs) associated with intramuscular fat content.

resultsA total of 30 transcriptomics datasets (RNA-Seq) obtained from longissimus thoracis muscle were selected based on the phenotypic value of divergent intramuscular fat content: 15 with the highest intramuscular fat content (HIF) and 15 with the lowest intramuscular fat content (LIF). The transcriptomics datasets were aligned with a reference genome and 65 differentially expressed genes (DEGs) were identified, including 21 upregulated and 44 downregulated genes in HIF animals. The normalized count data from DEGs was then used for co-expression network construction. From the co-expression network, four modules were identified. The topological properties of the network were analyzed; those genes engaging in the most interactions (maximal clique centrality method) with other DEGs were predicted to be hub genes (PDE4D, KLHL30 and IL1RAP), which consequently may play a role in cellular and/or systemic lipid biology in Nelore cattle. Top modules screened from the gene co-expression network were identify. The two candidate modules had clear associated biological pathways related to fat development, cell adhesion, and muscle differentiation, immune system, among others. The hub genes belonged in top modules and were downregulated in HIF animals. PDE4D and IL1RAP have known effects on lipid metabolism and the immune system through the regulation of cAMP signaling. Given that cAMP is known to play a role in lipid systems, PDE4D and IL1RAP downregulation may contribute to increased levels of intracellular cAMP and thus may have effects on IF content differences in Nelore cattle. KLHL30 may have effects on muscle metabolism. Klhl protein families play a role in protein degradation. However, the downregulation of this gene and its role in lipid metabolism has not yet been clarified.

conclusionsThe results reported in this study indicate candidate genes and molecular mechanisms involved in IF content difference in Nelore cattle.

Indexed as

Gene Expression ProfilingAdipose TissueAnimalsCattleGene Regulatory NetworksMuscle, SkeletalRNA-SeqBovineCo-expressionGenesLipidsTranscriptome

Identifiers

PMID31238883
PMCPMC6591902
OpenAlexW2953605123

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.