Evidence map›Paper›PMID 35172905›Full record

ArticleMicrobiome2022

Integrated meta-omics reveals new ruminal microbial features associated with feed efficiency in dairy cattle.

Ming-Yuan Xue, Yun-Yi Xie, Yifan Zhong, Xiao-Jiao Ma, Hui-Zeng Sun, Jian-Xin Liu

Open access · goldAbstract readVideo-Audio Media
In one paragraph

Article in Microbiome, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 119 papers.

0numbers the graph read from it
0cells of the map it votes in
119citing papers in PubMed
44.5field-weighted citation impact, top 1% 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

119 citing papers in PubMed, 186 citations in OpenAlex.

  1. Review
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  3. Effects ofAnimals : an open access journal from MDPI · 2026
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59 more citing papers are in PubMed but not listed here.

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

6 authors at 3 institutions in 2 countries.

Ming-Yuan XueInstitute of Dairy Science, College of Animal Sciences, Zhejiang University, Hangzhou, 310058, China.
Yun-Yi XieInstitute of Dairy Science, College of Animal Sciences, Zhejiang University, Hangzhou, 310058, China.
Yifan ZhongInstitute of Dairy Science, College of Animal Sciences, Zhejiang University, Hangzhou, 310058, China.
Xiao-Jiao MaInstitute of Dairy Science, College of Animal Sciences, Zhejiang University, Hangzhou, 310058, China.
Hui-Zeng SunInstitute of Dairy Science, College of Animal Sciences, Zhejiang University, Hangzhou, 310058, China. huizeng@zju.edu.cn.
Jian-Xin LiuInstitute of Dairy Science, College of Animal Sciences, Zhejiang University, Hangzhou, 310058, China. liujx@zju.edu.cn.ORCID 0000-0002-5812-5186
Zhejiang University · CNMinistry of Education · NZInnovation Team (China) · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAs the global population continues to grow, competition for resources between humans and livestock has been intensifying. Increasing milk protein production and improving feed efficiency are becoming increasingly important to meet the demand for high-quality dairy protein. In a previous study, we found that milk protein yield in dairy cows was associated with the rumen microbiome. The objective of this study was to elucidate the potential microbial features that underpins feed efficiency in dairy cows using metagenomics, metatranscriptomics, and metabolomics.

resultsComparison of metagenomic and metatranscriptomic data revealed that the latter was a better approach to uncover the associations between rumen microbial functions and host performance. Co-occurrence network analysis of the rumen microbiome revealed differential microbial interaction patterns between the animals with different feed efficiency, with high-efficiency animals having more and stronger associations than low-efficiency animals. In the rumen of high-efficiency animals, Selenomonas and members of the Succinivibrionaceae family positively interacted with each other, functioning as keystone members due to their essential ecological functions and active carbohydrate metabolic functions. At the metabolic level, analysis using random forest machine learning suggested that six ruminal metabolites (all derived from carbohydrates) could be used as metabolic markers that can potentially differentiate efficient and inefficient microbiomes, with an accuracy of prediction of 95.06%.

conclusionsThe results of the current study provided new insights into the new ruminal microbial features associated with feed efficiency in dairy cows, which may improve the ability to select animals for better performance in the dairy industry. The fundamental knowledge will also inform future interventions to improve feed efficiency in dairy cows. Video Abstract.

Indexed as

Animal FeedRumenAnimalsCattleDietFemaleFermentationLactationDairy cattleFeed efficiencyMetabolomicsMetagenomicsMetatranscriptomicsRumen microbiome

Identifiers

PMID35172905
PMCPMC8849036
OpenAlexW4213147845

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.