Evidence map›Paper›PMID 36139132›Full record

ArticleBiomolecules2022

Comparative Transcriptomic Analysis of mRNAs, miRNAs and lncRNAs in the

Jian Zhang, Jiying Wang, Cai Ma, Wenlei Wang, Heng Wang, Yunliang Jiang

Open access · goldAbstract read
In one paragraph

Article in Biomolecules, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
1.7field-weighted citation impact, top 15% 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

12 citing papers in PubMed, 19 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Non-Coding RNAs in Regulating Fat Deposition in Farm Animals.Animals : an open access journal from MDPI · 2025
    Review
  10. Article
  11. Review
  12. Transcriptome analysis of adipose tissue and muscle of Laiwu and Duroc pigs.Animal nutrition (Zhongguo xu mu shou yi xue hui) · 2024
    Article
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

6 authors at 2 institutions in 1 country.

Jian ZhangShandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, Shandong Agricultural University, No. 61 Daizong Street, Taian 271018, China.
Jiying WangShandong Key Laboratory of Animal Disease Control and Breeding, Institute of Animal Science and Veterinary Medicine, Shandong Academy of Agricultural Sciences, Jinan 250100, China.
Cai MaShandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, Shandong Agricultural University, No. 61 Daizong Street, Taian 271018, China.
Wenlei WangShandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, Shandong Agricultural University, No. 61 Daizong Street, Taian 271018, China.
Heng WangShandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, Shandong Agricultural University, No. 61 Daizong Street, Taian 271018, China.
Yunliang JiangShandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, Shandong Agricultural University, No. 61 Daizong Street, Taian 271018, China.
Shandong Agricultural University · CNShandong Academy of Agricultural Sciences · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In pigs, meat quality and production are two important traits affecting the pig industry and human health. Compared to lean-type pigs, fat-type pigs contain higher intramuscular fat (IMF) contents, better taste and nutritional value. To uncover genetic factors controlling differences related to IMF in pig muscle, we performed RNA-seq analysis on the transcriptomes of the Longissimus dorsi (LD) muscle of Laiwu pigs (LW, fat-type pigs) and commercial Duroc × Landrace × Yorkshire pigs (DLY, lean-type pigs) at 150 d to compare the expression profiles of mRNA, miRNA and lncRNA. A total of 225 mRNAs, 12 miRNAs and 57 lncRNAs were found to be differentially expressed at the criteria of |log2(foldchange)| > 1 and q < 0.05. The mRNA expression of LDHB was significantly higher in the LD muscle of LW compared to DLY pigs with log2(foldchange) being 9.66. Using protein interaction prediction method, we identified more interactions of estrogen-related receptor alpha (ESRRA) associated with upregulated mRNAs, whereas versican (VCAN) and proenkephalin (PENK) were associated with downregulated mRNAs in LW pigs. Integrated analysis on differentially expressed (DE) mRNAs and miRNAs in the LD muscle between LW and DLY pigs revealed two network modules: between five upregulated mRNA genes (GALNT15, FKBP5, PPARGC1A, LOC110258214 and LOC110258215) and six downregulated miRNA genes (ssc-let-7a, ssc-miR190-3p, ssc-miR356-5p, ssc-miR573-5p, ssc-miR204-5p and ssc-miR-10383), and between three downregulated DE mRNA genes (IFRD1, LOC110258600 and LOC102158401) and six upregulated DE miRNA genes (ssc-miR1379-3p, ssc-miR1379-5p, ssc-miR397-5p, ssc-miR1358-5p, ssc-miR299-5p and ssc-miR1156-5p) in LW pigs. Based on the mRNA and ncRNA binding site targeting database, we constructed a regulatory network with miRNA as the center and mRNA and lncRNA as the target genes, including GALNT15/ssc-let-7a/LOC100523888, IFRD1/ssc-miR1379-5p/CD99, etc., forming a ceRNA network in the LD muscles that are differentially expressed between LW and DLY pigs. Collectively, these data may provide resources for further investigation of molecular mechanisms underlying differences in meat traits between lean- and fat-type pigs.

Indexed as

MicroRNAsRNA, Long NoncodingAnimalsHumansMusclesRNA, MessengerSwineTranscriptomeVersicansMicroRNAsMIRN204 microRNA, humanMIRN573 microRNA, humanRNA, Long NoncodingRNA, MessengerVersicansintramuscular fatlncRNALongissimus dorsi musclemiRNAmRNApigtranscriptome

Identifiers

PMID36139132
PMCPMC9496231
OpenAlexW4295814160

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.