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

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Abstract

Background

As 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.

Results

Comparison 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%.

Conclusions

The 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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40168-022-01228-9.

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Most cited references 60

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Yoav Benjamini, Yosef Hochberg (1995)

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Trimmomatic: a flexible trimmer for Illumina sequence data

Anthony M. Bolger, Marc Lohse, Bjoern Usadel (2014)

Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.

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Fast and accurate short read alignment with Burrows–Wheeler transform

Heng Li, Richard Durbin (2009)

Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk

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Author and article information

Contributors

Hui-Zeng Sun: huizeng@zju.edu.cn

Jian-Xin Liu:

ORCID: http://orcid.org/0000-0002-5812-5186

liujx@zju.edu.cn

Journal

Journal ID (nlm-ta): Microbiome

Journal ID (iso-abbrev): Microbiome

Title: Microbiome

Publisher: BioMed Central (London )

ISSN (Electronic): 2049-2618

Publication date (Electronic): 16 February 2022

Publication date PMC-release: 16 February 2022

Publication date Collection: 2022

Volume: 10

Electronic Location Identifier: 32

Affiliations

[1 ]GRID grid.13402.34, ISNI 0000 0004 1759 700X, Institute of Dairy Science, College of Animal Sciences, , Zhejiang University, ; Hangzhou, 310058 China

[2 ]GRID grid.13402.34, ISNI 0000 0004 1759 700X, Ministry of Education Key Laboratory of Molecular Animal Nutrition, , Zhejiang University, ; Hangzhou, 310058 China

[3 ]GRID grid.13402.34, ISNI 0000 0004 1759 700X, Ministry of Education Innovation Team of Development and Function of Animal Digestive System, , Zhejiang University, ; Hangzhou, 310058 China

Author information

Jian-Xin Liu http://orcid.org/0000-0002-5812-5186

Article

Publisher ID: 1228

DOI: 10.1186/s40168-022-01228-9

PMC ID: 8849036

PubMed ID: 35172905

SO-VID: df09da9a-59c2-4fff-8abd-2f1f4205d977

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 5 September 2021

Date accepted : 7 January 2022

Custom metadata

Keywords: dairy cattle,feed efficiency,metabolomics,metagenomics,metatranscriptomics,rumen microbiome

Data availability:

Keywords: dairy cattle, feed efficiency, metabolomics, metagenomics, metatranscriptomics, rumen microbiome

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

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Supplementary Information

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Microbial Genomics

Most cited references 60

Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

Trimmomatic: a flexible trimmer for Illumina sequence data

Fast and accurate short read alignment with Burrows–Wheeler transform

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