Macrogenomics-based analysis of rumen microbial composition and their metabolic pathways in yaks under different dietary concentrate-to-forage ratios
Description
This study aimed to investigate the regulatory mechanisms underlying feed efficiency(FE) in yaks by analyzing the composition of rumen microorganisms and their major metabolic pathways using metagenomic analysis under different dietary concentrate-to-forage ratios. A total of 40 Qinghai Plateau yaks (8–9months old) with similar body weights (68.725±18.973kg)were randomly assigned to four treatment groups (n=10per group).The experimental groups were fed diets with concentrate-to-forage ratios of 80:20 (C80), 65:35 (C65), 50:50 (C50), and 35:65 (C35), respectively. The study included a 15-day pre-feeding period followed by a 105-day experimental feeding period.The results indicated that the total weight gain in the C65 group was significantly higher than in the C50 and C35 groups by 29.91% and 28.97%, respectively (P<0.05). Additionally, the rumen pH in the C80 group was significantly higher than in the C65, C50, and C35 groups (P<0.05). Metagenomic analysis revealed significant differences (P<0.05) in bacterial and archaeal community compositions across groups. Bacteroidota, Bacillota, Prevotella, Bacteroides, and Ruminococcus were identified as the dominant bacterial taxa at the phylum and genus levels. Functional analysis of rumen microbial metabolism showed that in the C35 group, pathways related to starch and sucrose metabolism, as well as fructose and mannose metabolism, were significantly different from those in other groups. The C35 group exhibited higher activity in functional pathways related to starch and sucrose metabolism, fructose and mannose metabolism, cellulose degradation, and methanolysis. In contrast, the C80 group showed greater activity in cellulose degradation and methane metabolism. Notably, the C65 group exhibited the highest activity in sugar metabolism pathways (ko00500), facilitating starch and soluble sugar degradation and the rapid conversion of pyruvic acid into acetic acid and propionate. This enhanced energy utilization efficiency, suggesting a superior capacity for sugar metabolism.In conclusion, the dietary composition of the C65 group demonstrated the most favorable effects on growth performance, rumen fermentation optimization, and microbial balance maintenance.
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DNA extraction, library construction, and metagenomic sequencing Total genomic DNA was extracted from rumen fluid samples using the Mag-Bind® DNA Kit (Omega Bio-tek, Norcross, GA, U.S.) according to manufacturer’s instructions. Concentration and purity of extracted DNA was determined with TBS-380 and NanoDrop2000, respectively. DNA extract quality was checked on 1% agarose gel. DNA extract was fragmented to an average size of about 350 bp using Covaris M220 (Gene Company Limited, China) for paired-end library construction. Paired-end library was constructed using NEXTFLEX Rapid DNA-Seq (Bioo Scientific, Austin, TX, USA). Adapters containing the full complement of sequencing primer hybridization sites were ligated to the blunt-end of fragments. Paired-end sequencing was performed on Illumina NovaSeq(Illumina Inc., San Diego, CA, USA) at Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China) using NovaSeq 6000 S4 Reagent Kit v1.5 (300 cycles) according to the manufacturer’s instructions (www.illumina.com). 2.3.5 Sequence quality control and genome assembly The data were analyzed on the free online platform of Majorbio Cloud Platform (www.majorbio.com). Briefly, the paired-end Illumina reads were trimmed of adaptors, and low-quality reads (length<50 bp or with a quality value <20 ) were removed by fastp (Chen S, 2018) (https://github.com/OpenGene/fastp, version 0.23.0). Metagenomics data were assembled using MEGAHIT (Li D, 2015) (https://github.com/voutcn/megahit, version 1.1.2), Contigs with with a length ≥ 300 bp were selected as the final assembling result, and then the contigs were used for further gene prediction and annotation. 2.3.6 Gene prediction, taxonomy, and functional annotation Open reading frames (ORFs) from each assembled contig were predicted using Prodigal/MetaGene (Hyatt D 2010; Noguchi H, 2006) (http://metagene.cb.k.u-tokyo.ac.jp/). The predicted ORFs with a length ≥ 100 bp were retrieved and translated into amino acid sequences using the NCBI translation table (http://www.ncbi.nlm.nih.gov/Taxonomy/taxonomyhome.html/index.cgi?chapter=tgencodes#SG1. A non-redundant gene catalog was constructed using CD-HIT (Fu L, 2012) (http://www.bioinformatics.org/cd-hit/, version 4.6.1) with 90% sequence identity and 90% coverage. High-quality reads were aligned to the non-redundant gene catalogs to calculate gene abundance with 95% identity using SOAPaligner (Li R, 2008) (http://soap.genomics.org.cn/, version 2.21).
Institutions
- Qinghai University
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Funders
- Kunlun Talent High-end Innovation and Entrepreneurship TalentsGrant ID: N/A