Evidence map›Paper›PMID 25938964›Full record

ArticleInternational journal of molecular sciences2015

The miRNA Transcriptome Directly Reflects the Physiological and Biochemical Differences between Red, White, and Intermediate Muscle Fiber Types.

Jideng Ma, Hongmei Wang, Rui Liu, Long Jin, Qianzi Tang, Xun Wang, Anan Jiang, Yaodong Hu, Zongwen Li, Li Zhu and 3 more

Open access · goldAbstract read
In one paragraph

Article in International journal of molecular sciences, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
0.7field-weighted citation impact, top 31% 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

11 citing papers in PubMed, 21 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

13 authors at 2 institutions in 1 country.

Jideng MaInstitute of Animal Genetics & Breeding, College of Animal Science & Technology, Sichuan Agricultural University, Ya'an 625014, Sichuan, China. jideng_ma@sina.com.
Hongmei WangInstitute of Animal Genetics & Breeding, College of Animal Science & Technology, Sichuan Agricultural University, Ya'an 625014, Sichuan, China. hongmei_wang0826@163.com.
Rui LiuInstitute of Animal Genetics & Breeding, College of Animal Science & Technology, Sichuan Agricultural University, Ya'an 625014, Sichuan, China. rui_liu777@163.com.
Long JinInstitute of Animal Genetics & Breeding, College of Animal Science & Technology, Sichuan Agricultural University, Ya'an 625014, Sichuan, China. longjin8806@163.com.
Qianzi TangInstitute of Animal Genetics & Breeding, College of Animal Science & Technology, Sichuan Agricultural University, Ya'an 625014, Sichuan, China. wupie@163.com.
Xun WangInstitute of Animal Genetics & Breeding, College of Animal Science & Technology, Sichuan Agricultural University, Ya'an 625014, Sichuan, China. xun_wang007@163.com.
Anan JiangInstitute of Animal Genetics & Breeding, College of Animal Science & Technology, Sichuan Agricultural University, Ya'an 625014, Sichuan, China. lingdang317@163.com.
Yaodong HuInstitute of Animal Genetics & Breeding, College of Animal Science & Technology, Sichuan Agricultural University, Ya'an 625014, Sichuan, China. yaodong_hu@163.com.
Zongwen LiNovogene Bioinformatics Institute, Beijing 100083, China. lizongwen@novogene.cn.
Li ZhuInstitute of Animal Genetics & Breeding, College of Animal Science & Technology, Sichuan Agricultural University, Ya'an 625014, Sichuan, China. zhuli7508@163.com.
Ruiqiang LiNovogene Bioinformatics Institute, Beijing 100083, China. lirq@novogene.cn.
Mingzhou LiInstitute of Animal Genetics & Breeding, College of Animal Science & Technology, Sichuan Agricultural University, Ya'an 625014, Sichuan, China. mingzhou.li@163.com.
Xuewei LiInstitute of Animal Genetics & Breeding, College of Animal Science & Technology, Sichuan Agricultural University, Ya'an 625014, Sichuan, China. xuewei.li@sicau.edu.cn.
Sichuan Agricultural University · CNNovogene Bioinformatics Institute · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

MicroRNAs (miRNAs) are small non-coding RNAs that can regulate their target genes at the post-transcriptional level. Skeletal muscle comprises different fiber types that can be broadly classified as red, intermediate, and white. Recently, a set of miRNAs was found expressed in a fiber type-specific manner in red and white fiber types. However, an in-depth analysis of the miRNA transcriptome differences between all three fiber types has not been undertaken. Herein, we collected 15 porcine skeletal muscles from different anatomical locations, which were then clearly divided into red, white, and intermediate fiber type based on the ratios of myosin heavy chain isoforms. We further illustrated that three muscles, which typically represented each muscle fiber type (i.e., red: peroneal longus (PL), intermediate: psoas major muscle (PMM), white: longissimus dorsi muscle (LDM)), have distinct metabolic patterns of mitochondrial and glycolytic enzyme levels. Furthermore, we constructed small RNA libraries for PL, PMM, and LDM using a deep sequencing approach. Results showed that the differentially expressed miRNAs were mainly enriched in PL and played a vital role in myogenesis and energy metabolism. Overall, this comprehensive analysis will contribute to a better understanding of the miRNA regulatory mechanism that achieves the phenotypic diversity of skeletal muscles.

Indexed as

AnimalsEnergy MetabolismFemaleHigh-Throughput Nucleotide SequencingMetabolic Networks and PathwaysMicroRNAsMuscle DevelopmentMuscle Fibers, SkeletalNeovascularization, PhysiologicOrgan SpecificitySus scrofaTranscriptomeMicroRNAs

Identifiers

PMID25938964
PMCPMC4463610
OpenAlexW1979977517

What OpenQuestion holds

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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.