Evidence map›Paper›PMID 38115081›Full record

ArticleAnimal microbiome2023

Integrated rumen microbiome and serum metabolome analysis responses to feed type that contribution to meat quality in lambs.

Shuai Du, Zhenkun Bu, Sihan You, Zipeng Jiang, Weifa Su, Tenghao Wang, Yushan Jia

Open access · goldAbstract read
In one paragraph

Article in Animal microbiome, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
6.2field-weighted citation impact, top 3% 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

15 citing papers in PubMed, 18 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

7 authors at 3 institutions in 1 country.

Shuai DuKey Laboratory of Forage Cultivation, Processing and High Efficient Utilization, Ministry of Agriculture, Key Laboratory of Grassland Resources, Ministry of Education, College of Grassland, Resources and Environment, Inner Mongolia Agricultural University, Huhhot, 010019, Inner Mongolia, China.
Zhenkun BuGuangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agriculture Genomics Institute, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China.
Sihan YouKey Laboratory of Forage Cultivation, Processing and High Efficient Utilization, Ministry of Agriculture, Key Laboratory of Grassland Resources, Ministry of Education, College of Grassland, Resources and Environment, Inner Mongolia Agricultural University, Huhhot, 010019, Inner Mongolia, China.
Zipeng JiangNational Engineering Laboratory of Biological Feed Safety and Pollution Prevention and Control, Key Laboratory of Molecular Nutrition, Ministry of Education, Key Laboratory of Animal Nutrition and Feed, Ministry of Agriculture and Rural Affairs, Key Laboratory of Animal Nutrition and Feed Science of Zhejiang Province, Institute of Feed Science, Zhejiang University, Hangzhou, 310058, Zhejiang, China.
Weifa SuNational Engineering Laboratory of Biological Feed Safety and Pollution Prevention and Control, Key Laboratory of Molecular Nutrition, Ministry of Education, Key Laboratory of Animal Nutrition and Feed, Ministry of Agriculture and Rural Affairs, Key Laboratory of Animal Nutrition and Feed Science of Zhejiang Province, Institute of Feed Science, Zhejiang University, Hangzhou, 310058, Zhejiang, China.
Tenghao WangKey Laboratory of Forage Cultivation, Processing and High Efficient Utilization, Ministry of Agriculture, Key Laboratory of Grassland Resources, Ministry of Education, College of Grassland, Resources and Environment, Inner Mongolia Agricultural University, Huhhot, 010019, Inner Mongolia, China. 0619731@zju.edu.cn.
Yushan JiaKey Laboratory of Forage Cultivation, Processing and High Efficient Utilization, Ministry of Agriculture, Key Laboratory of Grassland Resources, Ministry of Education, College of Grassland, Resources and Environment, Inner Mongolia Agricultural University, Huhhot, 010019, Inner Mongolia, China. jys_nm@sina.com.
Inner Mongolia Agricultural University · CNMinistry of Agriculture and Rural Affairs · CNAgricultural Genomics Institute at Shenzhen · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLifestyle factors, such as diet, are known to be a driver on the meat quality, rumen microbiome and serum metabolites. Rumen microbiome metabolites may be important for host health, the correlation between rumen microbiome and production of rumen metabolites are reported, while the impact of rumen microbiome on the serum metabolome and fatty acid of meat are still unclear. This study was designed to explore the rumen microbiome, serum metabolome and fatty acid of meat in response to the grass diet and concentrate diet to lambs, and the relationship of which also investigated.

methodsIn the present study, 12 lambs were randomly divided into two groups: a grass diet (G) and a concentrate diet (C). Here, multiple physicochemical analyses combined with 16S rRNA gene sequences and metabolome analysis was performed to reveal the changes that in response to feed types.

resultsThe concentrate diet could improve the growth performance of lambs compared to that fed with the grass diet. The microbiome composition was highly individual, compared to the concentrate group, the abundance of Rikenellaceae_RC9_gut_group, F082_unclassified, Muribaculaceae_unclassified, Ruminococcaceae_NK4A214_group, Bacteroidetes_unclassified, and Bacteroidales_UCG-001_unclassified were significantly (P < 0.05) lower in the grass group, while, the abundance of Succinivibrio, Succinivibrionaceae_UCG-002, Fibrobacter and Christensenellaceae_R-7_group were significantly (P < 0.05) higher in the grass group. Serum metabolomics analysis combined with enrichment analysis revealed that serum metabolites were influenced by feed type as well as the metabolic pathway, and significantly affected serum metabolites involved in amino acids, peptides, and analogues, bile acids, alcohols and derivatives, linoleic acids derivatives, fatty acids and conjugates. Most of the amino acids, peptides, and analogues metabolites were positively associated with the fatty acid contents. Among the bile acids, alcohols and derivatives metabolites, glycocholic was positively associated with all fatty acid contents, except C18:0, while 25-Hydroxycholesterol and lithocholic acid metabolites were negatively associated with most of the fatty acid contents.

conclusionCorrelation analysis of the association of microbiome with metabolite features, metabolite features with fatty acid provides us with comprehensive understanding of the composition and function of microbial communities. Associations between utilization or production were widely identified among affected microbiome, metabolites and fatty acid, and these findings will contribute to the direction of future research in lamb.

Indexed as

Concentrate dietFatty acid profileGrass dietMetabolomeMicrobiomeRumen

Identifiers

PMID38115081
PMCPMC10729572
OpenAlexW4389942398

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.