Evidence map›Paper›PMID 40414864›Full record

ArticleBMC veterinary research2025

The interaction between rumen microbiota and neurotransmitters plays an important role in the adaptation of phenological changes in Tibetan sheep.

Wei Huang, Yuzhu Sha, Qianling Chen, Xiaowei Chen, Min Gao, Xiu Liu, Yapeng He, Xu Gao, Jiang Hu, Jiqing Wang and 3 more

Abstract read
In one paragraph

Article in BMC veterinary research, 2025. 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
–field-weighted citation impact
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.

  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

13 authors.

Wei HuangCollege of Animal Science and Technology, Gansu Agricultural University, Lanzhou, 730070, China.
Yuzhu ShaCollege of Animal Science and Technology, Gansu Agricultural University, Lanzhou, 730070, China.
Qianling ChenCollege of Animal Science and Technology, Gansu Agricultural University, Lanzhou, 730070, China.
Xiaowei ChenCollege of Animal Science and Technology, Gansu Agricultural University, Lanzhou, 730070, China.
Min GaoCollege of Animal Science and Technology, Gansu Agricultural University, Lanzhou, 730070, China.
Xiu LiuCollege of Animal Science and Technology, Gansu Agricultural University, Lanzhou, 730070, China. liuxiu@gsau.edu.cn.
Yapeng HeCollege of Animal Science and Technology, Gansu Agricultural University, Lanzhou, 730070, China.
Xu GaoCollege of Animal Science and Technology, Gansu Agricultural University, Lanzhou, 730070, China.
Jiang HuCollege of Animal Science and Technology, Gansu Agricultural University, Lanzhou, 730070, China.
Jiqing WangCollege of Animal Science and Technology, Gansu Agricultural University, Lanzhou, 730070, China.
Shaobin LiCollege of Animal Science and Technology, Gansu Agricultural University, Lanzhou, 730070, China.
Zhiyun HaoCollege of Animal Science and Technology, Gansu Agricultural University, Lanzhou, 730070, China.
Yanyu HeSchool of Fundamental Sciences, Massey University, Palmerston North, 4410, New Zealand. y.he@massey.ac.nz.

Funding

Gansu Agricultural University Discipline Team Project GAU-XKTD-2022-21Gansu Agricultural University Young Tutor Support Fund Project GAU-QDFC-2022-06Gansu Provincial Higher Education Innovation Fund Project 2021A-441National Natural Science Foundation of China 32260820
6 · The paper itself

Abstract

The gut-brain axis is regarded as the "second brain" of the host. Gut microbiota and their metabolites affect intestinal homeostasis, function, and phenotype by regulating bidirectional communication between the gut and brain. This serves as a vital strategy for understanding how gut microbiota regulate nutrient metabolism and adaptability in animals. This study explored the metabolic mechanisms through which Tibetan sheep adapt to high-altitude environments via the rumen microbiota-gut-brain axis across different phenological periods (returning-green period, fresh grass period and withered grass period). By analyzing metabolic indicators, neurotransmitters, and gene and protein expression in serum, rumen, adipose, and hypothalamic tissues, we discovered that energy metabolism markers (creatine kinase, lactate dehydrogenase, glucose) and immunoglobulins (IgG, IgM) in the serum were significantly elevated during the fresh grass period (P < 0.05). In contrast, thyroid hormones T3 and T4 were at higher levels during the returning-green period (P < 0.05). The density of rumen fiber-degrading bacteria was higher during the returning-green period (P < 0.05). Meanwhile, the densities of Butyrivibrio fibrisolvens, Selenomonas ruminantium, and Treponema bryantii microbiota significantly during the fresh grass period and were positively correlated with isovaleric acid concentration (P < 0.05). Neurotransmitters (5-HT, DOPAC, 5-HIAA, and NE) were significantly elevated in both the rumen epithelium and hypothalamus during the fresh grass period (P < 0.05). The analysis of the cAMP-PKA-pCREB pathway showed that the genes and proteins of UCP1, PKA, and CREB1 were highly expressed in adipose tissue during the fresh grass and withered grass periods, and there significant negative correlations to specific microbiota (P < 0.05). In summary, Tibetan sheep adapt to high-altitude environments through the rumen microbiota-gut-brain axis, regulating metabolic and neurotransmitter changes to establish a unique metabolic adaptation mechanism.

Indexed as

Adaptation, PhysiologicalGastrointestinal MicrobiomeNeurotransmitter AgentsRumenAltitudeAnimalsEnergy MetabolismHypothalamusSheepTibetNeurotransmitter AgentsNeurotransmittersPhylogenyRumen microbiotaTibetan sheep

Identifiers

PMID40414864
PMCPMC12105162

What OpenQuestion holds

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LicenceCC BY-NC-ND
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.