Evidence map›Paper›PMID 23915301›Full record

ArticleBMC genomics2013

Correlated mRNAs and miRNAs from co-expression and regulatory networks affect porcine muscle and finally meat properties.

Siriluck Ponsuksili, Yang Du, Frieder Hadlich, Puntita Siengdee, Eduard Murani, Manfred Schwerin, Klaus Wimmers

Open access · goldAbstract read
In one paragraph

Article in BMC genomics, 2013. 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
2.2field-weighted citation impact, top 12% 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, 47 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

7 authors at 2 institutions in 1 country.

Siriluck PonsuksiliResearch Group Functional Genome Analyses, Leibniz Institute for Farm Animal Biology, FBN, Wilhelm-Stahl-Allee 2, D-18196 Dummerstorf, Germany.
Yang Du
Frieder Hadlich
Puntita Siengdee
Eduard Murani
Manfred Schwerin
Klaus Wimmers
Leibniz Association · DEResearch Institute for Farm Animal Biology (FBN) · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPhysiological processes aiding the conversion of muscle to meat involve many genes associated with muscle structure and metabolic processes. MicroRNAs regulate networks of genes to orchestrate cellular functions, in turn regulating phenotypes.

resultsWe applied weighted gene co-expression network analysis to identify co-expression modules that correlated to meat quality phenotypes and were highly enriched for genes involved in glucose metabolism, response to wounding, mitochondrial ribosome, mitochondrion, and extracellular matrix. Negative correlation of miRNA with mRNA and target prediction were used to select transcripts out of the modules of trait-associated mRNAs to further identify those genes that are correlated with post mortem traits.

conclusionsPorcine muscle co-expression transcript networks that correlated to post mortem traits were identified. The integration of miRNA and mRNA expression analyses, as well as network analysis, enabled us to interpret the differentially-regulated genes from a systems perspective. Linking co-expression networks of transcripts and hierarchically organized pairs of miRNAs and mRNAs to meat properties yields new insight into several biological pathways underlying phenotype differences. These pathways may also be diagnostic for many myopathies, which are accompanied by deficient nutrient and oxygen supply of muscle fibers.

Indexed as

Gene Regulatory NetworksMeatTranscriptomeAnimalsMicroRNAsMusclesPhenotypeRNA, MessengerSwineMicroRNAsRNA, Messenger

Identifiers

PMID23915301
PMCPMC3750351
OpenAlexW2087048695

What OpenQuestion 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 OpenQuestion graph.