Evidence map›Paper›PMID 31149403›Full record

ArticlePeerJ2019

Comprehensive transcriptional profiling of aging porcine liver.

Jianning Chen, Qin Zou, Daojun Lv, Muhammad Ali Raza, Xue Wang, Yan Chen, Xiaoyu Xi, Peilin Li, Anxiang Wen, Li Zhu and 4 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PeerJ, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.4field-weighted citation impact, top 45% 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

6 citing papers in PubMed, 7 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

14 authors at 1 institution in 1 country.

Jianning Chen *Department of Zoology, College of Life Science, Sichuan Agricultural University, Ya'an, Sichuan, China.
Qin Zou *Department of Zoology, College of Life Science, Sichuan Agricultural University, Ya'an, Sichuan, China.
Daojun LvSichuan Weimu Modern Agricultural Science and Technology Co., Ltd, Chengdu, Sichuan, China.
Muhammad Ali RazaDepartment of Crop Cultivation and Farming System, College of Agronomy, Sichuan Agricultural University, Chengdu, Sichuan, China.
Xue WangDepartment of Zoology, College of Life Science, Sichuan Agricultural University, Ya'an, Sichuan, China.
Yan ChenDepartment of Zoology, College of Life Science, Sichuan Agricultural University, Ya'an, Sichuan, China.
Xiaoyu XiDepartment of Zoology, College of Life Science, Sichuan Agricultural University, Ya'an, Sichuan, China.
Peilin LiSichuan Weimu Modern Agricultural Science and Technology Co., Ltd, Chengdu, Sichuan, China.
Anxiang WenDepartment of Zoology, College of Life Science, Sichuan Agricultural University, Ya'an, Sichuan, China.
Li ZhuFarm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, Sichuan, China.
Guoqing TangFarm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, Sichuan, China.
Mingzhou LiFarm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, Sichuan, China.
Xuewei LiFarm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, Sichuan, China.
Yanzhi JiangDepartment of Zoology, College of Life Science, Sichuan Agricultural University, Ya'an, Sichuan, China.
Sichuan Agricultural University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAging is a major risk factor for the development of many diseases, and the liver, as the most important metabolic organ, is significantly affected by aging. It has been shown that the liver weight tends to increase in rodents and decrease in humans with age. Pigs have a genomic structure, with physiological as well as biochemical features that are similar to those of humans, and have therefore been used as a valuable model for studying human diseases. The molecular mechanisms of the liver aging of large mammals on a comprehensive transcriptional level remain poorly understood. The pig is an ideal model animal to clearly and fully understand the molecular mechanism underlying human liver aging.

methodsIn this study, four healthy female Yana pigs (an indigenous Chinese breed) were investigated: two young sows (180-days-old) and two old sows (8-years-old). High throughput RNA sequencing was performed to evaluate the expression profiles of messenger RNA, long non-coding RNAs, micro RNAs, and circular RNAs during the porcine liver aging process. Gene Ontology (GO) analysis was performed to investigate the biological functions of age-related genes.

resultsA number of age-related genes were identified in the porcine liver. GO annotation showed that up-regulated genes were mainly related to immune response, while the down-regulated genes were mainly related to metabolism. Moreover, several lncRNAs and their target genes were also found to be differentially expressed during liver aging. In addition, the multi-group cooperative control relationships and constructed circRNA-miRNA co-expression networks were assessed during liver aging.

conclusionsNumerous age-related genes were identified and circRNA-miRNA co-expression networks that are active during porcine liver aging were constructed. These findings contribute to the understanding of the transcriptional foundations of liver aging and also provide further references that clarify human liver aging at the molecular level.

Indexed as

AgingLiverPigTranscriptome

Identifiers

PMID31149403
PMCPMC6526898
OpenAlexW2945755026

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.