Evidence map›Paper›PMID 35413801›Full record

ArticleBMC genomics2022

Whole blood transcriptome profiling identifies candidate genes associated with alopecia in male giant pandas (Ailuropoda melanoleuca).

Haibo Shen, Caiwu Li, Ming He, Yan Huang, Jing Wang, Jing Luo, Minglei Wang, Bisong Yue, Xiuyue Zhang

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

Article in BMC genomics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.2field-weighted citation impact, top 50% 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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
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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

9 authors at 3 institutions in 1 country.

Haibo Shen *Key Laboratory of Bio-Resources and Eco-Environment, Ministry of Education, College of Life Science, Sichuan University, Chengdu, 610064, PR China.
Caiwu Li *Key Laboratory of State Forestry and Grassland Administration On Conservation Biology of Rare Animals in The Giant Panda National Park, China Conservation and Research Center for the Giant Panda, Dujiangyan, 611830, Sichuan, PR China.
Ming HeKey Laboratory of State Forestry and Grassland Administration On Conservation Biology of Rare Animals in The Giant Panda National Park, China Conservation and Research Center for the Giant Panda, Dujiangyan, 611830, Sichuan, PR China.
Yan HuangKey Laboratory of State Forestry and Grassland Administration On Conservation Biology of Rare Animals in The Giant Panda National Park, China Conservation and Research Center for the Giant Panda, Dujiangyan, 611830, Sichuan, PR China.
Jing WangKey Laboratory of State Forestry and Grassland Administration On Conservation Biology of Rare Animals in The Giant Panda National Park, China Conservation and Research Center for the Giant Panda, Dujiangyan, 611830, Sichuan, PR China.
Jing LuoKey Laboratory of Bio-Resources and Eco-Environment, Ministry of Education, College of Life Science, Sichuan University, Chengdu, 610064, PR China.
Minglei WangKey Laboratory of State Forestry and Grassland Administration On Conservation Biology of Rare Animals in The Giant Panda National Park, China Conservation and Research Center for the Giant Panda, Dujiangyan, 611830, Sichuan, PR China.
Bisong YueSichuan Key Laboratory of Conservation Biology On Endangered Wildlife, College of Life Sciences, Sichuan University, Chengdu, 610064, PR China.
Xiuyue ZhangKey Laboratory of Bio-Resources and Eco-Environment, Ministry of Education, College of Life Science, Sichuan University, Chengdu, 610064, PR China. zhangxy317@126.com.
Chengdu Research Base of Giant Panda Breeding · CNSichuan University · CNState Forestry and Grassland Administration · CN

Funding

Open Project of Key Laboratory of State Forestry and grassland administration KLSFGAGP2020.010Project of Key Laboratory of State Forestry and grassland administration CCRCGP181913
6 · The paper itself

Abstract

backgroundThe giant panda (Ailuropoda melanoleuca) is a threatened species endemic to China. Alopecia, characterized by thinning and broken hair, mostly occurs in breeding males. Alopecia significantly affects the health and public image of the giant panda and the cause of alopecia is unclear.

resultsHere, we researched gene expression profiles of four alopecia giant pandas and seven healthy giant pandas. All pandas were approximately ten years old and their blood samples collected during the breeding season. A total of 458 up-regulated DEGs and 211 down-regulated DEGs were identified. KEGG pathway enrichment identified that upregulated genes were enriched in the Notch signaling pathway and downregulated genes were enriched in ribosome, oxidative phosphorylation, and thermogenesis pathways. We obtained 28 hair growth-related DEGs, and identified three hub genes NOTCH1, SMAD3, and TGFB1 in PPI analysis. Five hair growth-related signaling pathways were identified with abnormal expression, these were Notch, Wnt, TGF-β, Mapk, and PI3K-Akt. The overexpression of NOTCH1 delays inner root sheath differentiation and results in hair shaft abnormalities. The delayed hair regression was associated with a significant decrease in the expression levels of TGFB1.

conclusionsOur data confirmed the abnormal expression of several hair-related genes and pathways and identified alopecia candidate genes in the giant panda. Results of this study provide theoretical basis for the establishment of prevention and treatment strategies for giant pandas with alopecia.

Indexed as

AlopeciaUrsidaeAnimalsGene Expression ProfilingMalePhosphatidylinositol 3-KinasesTranscriptomeAlopeciaDifferential gene expressionGiant pandaPeripheral bloodTranscriptome profiling

Identifiers

PMID35413801
PMCPMC9004003
OpenAlexW4223589524

What OpenQuestion holds

Textmetadata
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.