Evidence map›Paper›PMID 39402126›Full record

ArticleScientific reports2024

Effect of different genetic backgrounds on rumen microbiota and serum metabolic phenotypes in beef cattle.

Yaxing Zhao, Hao Chen, Pengfei Zhao, Chunhua Zhang, Yi Wu, Xiaorui Li, Mingke Huangfu, Zhimeng Chen, Chunjie Wang, Bo Liu and 3 more

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 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.

Yaxing Zhao *Institute of Animal Nutrition and Feed Research, Inner Mongolia Academy of Agricultural and Animal Husbandry Science, Inner Mongolia Key Laboratory of Herbivores Nutrition, Key Laboratory of Grass-Feeding Livestock Healthy Breeding and Livestock Product Quality Control, 010031, Hohhot, People's Republic of China.
Hao Chen *College of Animal Science, Inner Mongolia Agricultural University, Hohhot, 010018, People's Republic of China.
Pengfei Zhao *College of Animal Science, Inner Mongolia Agricultural University, Hohhot, 010018, People's Republic of China.
Chunhua ZhangInstitute of Animal Nutrition and Feed Research, Inner Mongolia Academy of Agricultural and Animal Husbandry Science, Inner Mongolia Key Laboratory of Herbivores Nutrition, Key Laboratory of Grass-Feeding Livestock Healthy Breeding and Livestock Product Quality Control, 010031, Hohhot, People's Republic of China.
Yi WuCollege of Animal Science, Inner Mongolia Agricultural University, Hohhot, 010018, People's Republic of China.
Xiaorui LiCollege of Animal Science, Inner Mongolia Agricultural University, Hohhot, 010018, People's Republic of China.
Mingke HuangfuCollege of Animal Science, Inner Mongolia Agricultural University, Hohhot, 010018, People's Republic of China.
Zhimeng ChenCollege of Animal Science, Inner Mongolia Agricultural University, Hohhot, 010018, People's Republic of China.
Chunjie WangCollege of Veterinary Medicine, Inner Mongolia Agricultural University, Hohhot, 010018, People's Republic of China.
Bo LiuCollege of Animal Science, Inner Mongolia Agricultural University, Hohhot, 010018, People's Republic of China.
Huasai SimujideCollege of Animal Science, Inner Mongolia Agricultural University, Hohhot, 010018, People's Republic of China.
Aorigele ChenCollege of Animal Science, Inner Mongolia Agricultural University, Hohhot, 010018, People's Republic of China. aori6009@163.com.
Haizhou SunInstitute of Animal Nutrition and Feed Research, Inner Mongolia Academy of Agricultural and Animal Husbandry Science, Inner Mongolia Key Laboratory of Herbivores Nutrition, Key Laboratory of Grass-Feeding Livestock Healthy Breeding and Livestock Product Quality Control, 010031, Hohhot, People's Republic of China. sunhaizhou@china.com.

Funding

High level Achievement Cultivation Special Project of the College of Animal Science, Inner Mongolia Agricultural University CG202401High-level Talent Start-up Research Project No. NDYB 2022-2Inner Mongolia Autonomous Region Natural Science Foundation Project 2024QN03083
6 · The paper itself

Abstract

Species with different genetic backgrounds exhibit distinct metabolic traits. Nine beef cattle were selected for the experiment to study changes in serum metabolic phenotypes, rumen microbiota diversity, and composition in beef cattle from different genetic backgrounds. Three groups were Chinese Simmental (S group), Simmental×Chinese Holstein (SH group), and Simmental × Mongolian (SM group) cattle. We used ELISA to detect serum biochemical indicators. The Short-chain fatty acids (SCFAs) in the rumen were examined, and a significant difference was observed in the acetic acid content of the three experimental groups (p < 0.01). The propionic acid content in the rumen of the S group was significantly higher than that of the SH and SM groups (p < 0.05). The A/P ratios of both the S and SM groups were significantly higher than that of the SH group (p < 0.05). We analyzed rumen microbiota composition and diversity in each group of cattle using 16 S rRNA sequencing and found that their composition was generally similar in the three groups of crossbred fattening cattle; however, the f_Bacteroidales_RF16_group and g_norank_f_Bacteroidales_RF16_group were significantly enriched in the SH group, whereas Treponema and Spirochaetia were significantly enriched in the SM group. Spirochaetia was significantly enriched in the SM group. Differences in rumen bacterial enrichment indicated that starch, protein, and cellulolytic abilities differed among the S, SH, and SM groups. The results of Spearman correlation analysis confirmed the correlation between rumen genera and serum biochemical indices. Overall, differences in rumen microflora play an important role in influencing the serum metabolic phenotype.

Indexed as

PhenotypeRumenAnimalsBacteriaCattleFatty Acids, VolatileGastrointestinal MicrobiomeGenetic BackgroundMicrobiotaRNA, Ribosomal, 16SFatty Acids, VolatileRNA, Ribosomal, 16S16S rRNA sequencingRumen microbiotaRumen SCFAsSerum biochemical indicatorsSimmental cattle

Identifiers

PMID39402126
PMCPMC11473713

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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.