Evidence map›Paper›PMID 33024632›Full record

ArticlePeerJ2020

Analysis of dynamic and widespread lncRNA and miRNA expression in fetal sheep skeletal muscle.

Chao Yuan, Ke Zhang, Yaojing Yue, Tingting Guo, Jianbin Liu, Chune Niu, Xiaoping Sun, Ruilin Feng, Xiaolong Wang, Bohui Yang

Open access · goldAbstract read
In one paragraph

Article in PeerJ, 2020. 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
1.0field-weighted citation impact, top 29% 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, 19 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

10 authors at 3 institutions in 1 country.

Chao YuanSheep Breeding Engineering Technology Research Center, Lanzhou Institute of Husbandry and Pharmaceutical Sciences of Chinese Academy of Agricultural Sciences, Lanzhou, Gansu, China.
Ke ZhangCollege of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, China.
Yaojing YueSheep Breeding Engineering Technology Research Center, Lanzhou Institute of Husbandry and Pharmaceutical Sciences of Chinese Academy of Agricultural Sciences, Lanzhou, Gansu, China.
Tingting GuoSheep Breeding Engineering Technology Research Center, Lanzhou Institute of Husbandry and Pharmaceutical Sciences of Chinese Academy of Agricultural Sciences, Lanzhou, Gansu, China.
Jianbin LiuSheep Breeding Engineering Technology Research Center, Lanzhou Institute of Husbandry and Pharmaceutical Sciences of Chinese Academy of Agricultural Sciences, Lanzhou, Gansu, China.
Chune NiuSheep Breeding Engineering Technology Research Center, Lanzhou Institute of Husbandry and Pharmaceutical Sciences of Chinese Academy of Agricultural Sciences, Lanzhou, Gansu, China.
Xiaoping SunSheep Breeding Engineering Technology Research Center, Lanzhou Institute of Husbandry and Pharmaceutical Sciences of Chinese Academy of Agricultural Sciences, Lanzhou, Gansu, China.
Ruilin FengSheep Breeding Engineering Technology Research Center, Lanzhou Institute of Husbandry and Pharmaceutical Sciences of Chinese Academy of Agricultural Sciences, Lanzhou, Gansu, China.
Xiaolong WangCollege of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, China.
Bohui YangSheep Breeding Engineering Technology Research Center, Lanzhou Institute of Husbandry and Pharmaceutical Sciences of Chinese Academy of Agricultural Sciences, Lanzhou, Gansu, China.
Chinese Academy of Agricultural Sciences · CNLanzhou Institute of Husbandry and Pharmaceutical Sciences · CNNorthwest A&F University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The sheep is an economically important animal, and there is currently a major focus on improving its meat quality through breeding. There are variations in the growth regulation mechanisms of different sheep breeds, making fundamental research on skeletal muscle growth essential in understanding the regulation of (thus far) unknown genes. Skeletal muscle development is a complex biological process regulated by numerous genes and non-coding RNAs, including microRNAs (miRNAs) and long non-coding RNAs (lncRNAs). In this study, we used deep sequencing data from sheep longissimus dorsi (LD) muscles sampled at day 60, 90, and 120 of gestation, as well as at day 0 and 360 following birth, to identify and examine the lncRNA and miRNA temporal expression profiles that regulate sheep skeletal myogenesis. We stained LD muscles using histological sections to analyse the area and circumference of muscle fibers from the embryonic to postnatal development stages. Our results showed that embryonic skeletal muscle growth can be characterized by time. We obtained a total of 694 different lncRNAs and compared the differential expression between the E60 vs. E90, E90 vs. E120, E120 vs. D0, and D0 vs. D360 lncRNA and gene samples. Of the total 701 known sheep miRNAs we detected, the following showed a wide range of expression during the embryonic stage: miR-2387, miR-105, miR-767, miR-432, and miR-433. We propose that the detected lncRNA expression was time-specific during the gestational and postnatal stages. GO and KEGG analyses of the genes targeted by different miRNAs and lncRNAs revealed that these significantly enriched processes and pathways were consistent with skeletal muscle development over time across all sampled stages. We found four visual lncRNA-gene regulatory networks that can be used to explore the function of lncRNAs in sheep and may be valuable in helping improve muscle growth. This study also describes the function of several lncRNAs that interact with miRNAs to regulate myogenic differentiation.

Indexed as

Fetal sheeplncRNALongissimus dorsi musclemiRNATranscriptome

Identifiers

PMID33024632
PMCPMC7518186
OpenAlexW3087876097

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.