Evidence map›Paper›PMID 35396568›Full record

ArticleScientific reports2022

Integrated analysis of expression profiles with meat quality traits in cattle.

Yunxiao Li, Miaosen Yang, Angang Lou, Jinyan Yun, Chunyu Ren, Xiangchun Li, Guangjun Xia, Kichang Nam, Duhak Yoon, Haiguo Jin and 2 more

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.1field-weighted citation impact, top 24% 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

8 citing papers in PubMed, 13 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

12 authors at 7 institutions in 2 countries.

Yunxiao LiCollege of Life Science, Shandong University, Qingdao, China.
Miaosen YangDepartment of Chemistry, Northeast Electric Power University, Jilin, China.
Angang LouDepartment of Veterinary Medicine, College of Agriculture, Yanbian University, Yanji, China.
Jinyan YunCollege of Animal Science and Technology, Jilin Agricultural Science and Technology University, Jilin, China.
Chunyu RenAnimal Husbandry Bureau of Yanbian Autonomous Prefecture, Yanji, China.
Xiangchun LiDepartment of Veterinary Medicine, College of Agriculture, Yanbian University, Yanji, China.
Guangjun XiaDepartment of Veterinary Medicine, College of Agriculture, Yanbian University, Yanji, China.
Kichang NamDepartment of Animal Science and Technology, College of Life Science and Natural Resources, Sunchon National University, Sunchon, South Korea.
Duhak YoonDepartment of Animal Science, Kyungpook National University, Taegu, South Korea.
Haiguo JinBranch of Animal Husbandry, Jilin Academy of Agricultural Sciences, Changchun, China.
Kangseok SeoDepartment of Animal Science and Technology, College of Life Science and Natural Resources, Sunchon National University, Sunchon, South Korea. sks@sunchon.ac.kr.
Xin JinEngineering Research Center of North-East Cold Region Beef Cattle Science and Technology Innovation, Ministry of Education, Yanbian University, Yanji, China. jinxin@ybu.edu.cn.
Yanbian University · CNSunchon National University · KRJilin Academy of Agricultural Sciences · CNJilin Agricultural Science and Technology University · CNKyungpook National University · KRNortheast Electric Power University · CNShandong University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

MicroRNAs (miRNAs) play a vital role in improving meat quality by binding to messenger RNAs (mRNAs). We performed an integrated analysis of miRNA and mRNA expression profiling between bulls and steers based on the differences in meat quality traits. Fat and fatty acids are the major phenotypic indices of meat quality traits to estimate between-group variance. In the present study, 90 differentially expressed mRNAs (DEGs) and 18 differentially expressed miRNAs (DEMs) were identified. Eighty-three potential DEG targets and 18 DEMs were used to structure a negative interaction network, and 75 matching target genes were shown in this network. Twenty-six target genes were designated as intersection genes, screened from 18 DEMs, and overlapped with the DEGs. Seventeen of these genes enriched to 19 terms involved in lipid metabolism. Subsequently, 13 DEGs and nine DEMs were validated using quantitative real-time PCR, and seven critical genes were selected to explore the influence of fat and fatty acids through hub genes and predict functional association. A dual-luciferase reporter and Western blot assays confirmed a predicted miRNA target (bta-miR-409a and PLIN5). These findings provide substantial evidence for molecular genetic controls and interaction among genes in cattle.

Indexed as

Gene Regulatory NetworksMicroRNAsAnimalsCattleFatty AcidsGene Expression ProfilingMaleMeatRNA, MessengerFatty AcidsMicroRNAsRNA, Messenger

Identifiers

PMID35396568
PMCPMC8993808
OpenAlexW4223477980

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.