Metagenomics analysis revealed the distinctive ruminal microbiome and resistive profiles in dairy buffaloes

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Abstract

Background

Antimicrobial resistance poses super challenges in both human health and livestock production. Rumen microbiota is a large reservoir of antibiotic resistance genes (ARGs), which show significant varations in different host species and lifestyles. To compare the microbiome and resistome between dairy cows and dairy buffaloes, the microbial composition, functions and harbored ARGs of rumen microbiota were explored between 16 dairy cows (3.93 ± 1.34 years old) and 15 dairy buffaloes (4.80 ± 3.49 years old) using metagenomics.

Results

Dairy buffaloes showed significantly different bacterial species (LDA > 3.5 & P < 0.01), enriched KEGG pathways and CAZymes encoded genes (FDR < 0.01 & Fold Change > 2) in the rumen compared with dairy cows. Distinct resistive profiles were identified between dairy cows and dairy buffaloes. Among the total 505 ARGs discovered in the resistome of dairy cows and dairy buffaloes, 18 ARGs conferring resistance to 16 antibiotic classes were uniquely detected in dairy buffaloes. Gene tcmA (resistance to tetracenomycin C) presented high prevalence and age effect in dairy buffaloes, and was also highly positively correlated with 93 co-expressed ARGs in the rumen ( R = 0.98 & P = 5E-11). In addition, 44 bacterial species under Lactobacillus genus were found to be associated with tcmA ( R > 0.95 & P < 0.001). L. amylovorus and L. acidophilus showed greatest potential of harboring tcmA based on co-occurrence analysis and tcmA-containing contigs taxonomic alignment.

Conclusions

The current study revealed distinctive microbiome and unique ARGs in dairy buffaloes compared to dairy cattle. Our results provide novel understanding on the microbiome and resistome of dairy buffaloes, the unique ARGs and associated bacteria will help develop strategies to prevent the transmission of ARGs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s42523-021-00103-6.

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Most cited references 72

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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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WGCNA: an R package for weighted correlation network analysis

Peter Langfelder, Steve Horvath (2008)

Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .

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fastp: an ultra-fast all-in-one FASTQ preprocessor

Shifu Chen, Yanqing Zhou, Yaru Chen … (2018)

Abstract Motivation Quality control and preprocessing of FASTQ files are essential to providing clean data for downstream analysis. Traditionally, a different tool is used for each operation, such as quality control, adapter trimming and quality filtering. These tools are often insufficiently fast as most are developed using high-level programming languages (e.g. Python and Java) and provide limited multi-threading support. Reading and loading data multiple times also renders preprocessing slow and I/O inefficient. Results We developed fastp as an ultra-fast FASTQ preprocessor with useful quality control and data-filtering features. It can perform quality control, adapter trimming, quality filtering, per-read quality pruning and many other operations with a single scan of the FASTQ data. This tool is developed in C++ and has multi-threading support. Based on our evaluation, fastp is 2–5 times faster than other FASTQ preprocessing tools such as Trimmomatic or Cutadapt despite performing far more operations than similar tools. Availability and implementation The open-source code and corresponding instructions are available at https://github.com/OpenGene/fastp.

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Author and article information

Contributors

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

Ke-Lan Peng: pengkelan@live.cn

Ming-Yuan Xue: myxue@zju.edu.cn

Jian-Xin Liu:

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

liujx@zju.edu.cn

Journal

Journal ID (nlm-ta): Anim Microbiome

Journal ID (iso-abbrev): Anim Microbiome

Title: Animal Microbiome

Publisher: BioMed Central (London )

ISSN (Electronic): 2524-4671

Publication date (Electronic): 1 July 2021

Publication date PMC-release: 1 July 2021

Publication date Collection: 2021

Volume: 3

Electronic Location Identifier: 44

Affiliations

GRID grid.13402.34, ISNI 0000 0004 1759 700X, Institute of Dairy Science, Ministry of Education Key Laboratory of Molecular Animal Nutrition, College of Animal Sciences, Zhejiang University, ; Hangzhou, 310058 China

Author information

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

Article

Publisher ID: 103

DOI: 10.1186/s42523-021-00103-6

PMC ID: 8247143

PubMed ID: 34210366

SO-VID: 17a936de-145d-44f0-9a11-1addac700fae

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/.

History

Date received : 22 November 2020

Date accepted : 31 May 2021

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;

Award ID: 31872380

Award ID: 32002207

Award Recipient : Hui-Zeng Sun

Funded by: “Hundred Talents Program” Research Professor Start-up Fund of Zhejiang University

Funded by: FundRef http://dx.doi.org/10.13039/501100012226, Fundamental Research Funds for the Central Universities;

Award ID: 2021-KYY-517103-0002

Award Recipient : Hui-Zeng Sun

Custom metadata

Keywords: dairy buffaloes,dairy cattle,rumen,antimicrobial resistant genes,microbiome

Data availability:

Keywords: dairy buffaloes, dairy cattle, rumen, antimicrobial resistant genes, microbiome

Metagenomics analysis revealed the distinctive ruminal microbiome and resistive profiles in dairy buffaloes

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Abstract

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Tick microbiome

Most cited references 72

Fast and accurate short read alignment with Burrows–Wheeler transform

WGCNA: an R package for weighted correlation network analysis

fastp: an ultra-fast all-in-one FASTQ preprocessor

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