Evidence map›Paper›PMID 28759650›Full record

ArticlePloS one2017

MicroRNA expression profiles differ between primary myofiber of lean and obese pig breeds.

Dongting He, Tiande Zou, Xiangrong Gai, Jideng Ma, Mingzhou Li, Zhiqing Huang, Daiwen Chen

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
2.3field-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

16 citing papers in PubMed, 34 citations in OpenAlex.

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  7. microRNA, the Innate-Immune System and SARS-CoV-2.Frontiers in cellular and infection microbiology · 2022
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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.

Dongting HeInstitute of Animal Nutrition, Sichuan Agricultural University, Chendu, Sichuan, People's Republic of China.
Tiande ZouInstitute of Animal Nutrition, Sichuan Agricultural University, Chendu, Sichuan, People's Republic of China.
Xiangrong GaiState Key Laboratory of Animal Nutrition, Ministry of Agriculture Feed Industry Centre, China Agricultural University, Beijing, People's Republic of China.
Jideng MaInstitute of Animal Genetics and Breeding, Sichuan Agricultural University, Chendu, Sichuan, People's Republic of China.
Mingzhou LiInstitute of Animal Genetics and Breeding, Sichuan Agricultural University, Chendu, Sichuan, People's Republic of China.
Zhiqing HuangInstitute of Animal Nutrition, Sichuan Agricultural University, Chendu, Sichuan, People's Republic of China.
Daiwen ChenInstitute of Animal Nutrition, Sichuan Agricultural University, Chendu, Sichuan, People's Republic of China.ORCID http://orcid.org/0000-0002-8351-7421
Sichuan Agricultural University · CNFeed Research Institute · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

MicroRNAs (miRNAs) are non-coding small miRNAs ~22 nucleotides in length and play a vital role in muscle development by binding to messenger RNAs (mRNAs). Large White (LW, a lean type pig) and Meishan pigs (MS, a Chinese indigenous obese breed) have significant postnatal phenotype differences in growth rate, muscle mass and meat quality, and these differences are programmed during prenatal muscle development. Little research shed light directly on the miRNA transcriptome difference in prenatal muscles between these two distinct pig breeds. Myofiber phenotypes of LW and MS were measured at developmental stages of 35, 55 and 90 days post-conception (dpc), which revealed that the myogenesis process is more intense in MS than in LW at 35 dpc. To investigate the role of miRNAs involved in regulating muscle development at earlier stages of myogenesis and decipher the miRNAs transcriptome difference between LW and MS, here, the miRNAomes of longissimus dorsi muscle collected at 35 dpc from female LW and MS were analyzed by deep sequencing. Overall, 1147 unique miRNAs comprising 434 known miRNAs, 239 conserved miRNAs and 474 candidate miRNAs were identified. Expression analysis of the 10 most abundant miRNAs in every library indicated that functional miRNAome may be a small amount and tend to be greater expressed. These sets of miRNA may play house keeping roles that were involved in myogenesis. A total of 87 miRNAs were significantly differentially expressed between LW and MS (reads > 1000, P < 0.05). Gene ontology (GO) and KEGG pathway enrichment analysis revealed that the differentially expressed miRNAs (DE miRNAs) were associated mainly with muscle contraction, WNT, mTOR, and MAPK signaling pathways. Some myogenesis related miRNAs (miR-133, miR-1, miR-206 and miR-148a) are highly abundant in MS, while other miRNAs (let-7 family, miR-214, miR-181) highly expressed in LW. In addition, the expression patterns of miRNAs (miR-1, -133, -206) at three prenatal stages (35, 55 and 90 dpc) were determined using qRT-PCR. Notably, ssc-miR-133 was significantly more highly expressed in LW pigs skeletal muscle at all prenatal stages compared with its expression in LW pigs skeletal muscle. Taken together, the main functional miRNAs during muscle development are different between lean and obese pig breeds. The present study adds new information to existing data on porcine miRNAs and will be helpful to investigate the dominant (main functional) muscle-related miRNAs sets in different pig breeds.

Indexed as

Gene Expression Regulation, DevelopmentalTranscriptomeAnimalsBreedingCluster AnalysisDisease Models, AnimalFemaleGene Expression ProfilingHigh-Throughput Nucleotide SequencingMicroRNAsMuscle DevelopmentMuscle, SkeletalObesityPhenotypeSus scrofaMicroRNAs

Identifiers

PMID28759650
PMCPMC5536276
OpenAlexW2741183125

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.