Taxonomic Assessment of Rumen Microbiota Using Total RNA and Targeted Amplicon Sequencing Approaches

Abstract

Taxonomic characterization of active gastrointestinal microbiota is essential to detect shifts in microbial communities and functions under various conditions. This study aimed to identify and quantify potentially active rumen microbiota using total RNA sequencing and to compare the outcomes of this approach with the widely used targeted RNA/DNA amplicon sequencing technique. Total RNA isolated from rumen digesta samples from five beef steers was subjected to Illumina paired-end sequencing (RNA-seq), and bacterial and archaeal amplicons of partial 16S rRNA/rDNA were subjected to 454 pyrosequencing (RNA/DNA Amplicon-seq). Taxonomic assessments of the RNA-seq, RNA Amplicon-seq, and DNA Amplicon-seq datasets were performed using a pipeline developed in house. The detected major microbial phylotypes were common among the three datasets, with seven bacterial phyla, fifteen bacterial families, and five archaeal taxa commonly identified across all datasets. There were also unique microbial taxa detected in each dataset. Elusimicrobia and Verrucomicrobia phyla; Desulfovibrionaceae, Elusimicrobiaceae, and Sphaerochaetaceae families; and Methanobrevibacter woesei were only detected in the RNA-Seq and RNA Amplicon-seq datasets, whereas Streptococcaceae was only detected in the DNA Amplicon-seq dataset. In addition, the relative abundances of four bacterial phyla, eight bacterial families and one archaeal taxon were different among the three datasets. This is the first study to compare the outcomes of rumen microbiota profiling between RNA-seq and RNA/DNA Amplicon-seq datasets. Our results illustrate the differences between these methods in characterizing microbiota both qualitatively and quantitatively for the same sample, and so caution must be exercised when comparing data.

Metatranscriptomic Profiling Reveals Linkages between the Active Rumen Microbiome and Feed Efficiency in Beef Cattle

“…Total RNAs were extracted from rumen digesta samples using the procedures outlined by Li et al (60), which were modified based on the acid guanidinium-phenolchloroform method (61,62). For each sample, the RNA yield was determined using a Qubit 2.0 fluorimeter (Invitrogen, Carlsbad, CA, USA), and the RNA quality was examined with an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA).…”

“…Taxonomic assessment of the active rumen microbiota was conducted by using the pipeline described by Li et al (60). Briefly, after quality control and filtration of raw reads, bacterial and archaeal 16S rRNA sequences were selected and further aligned to the V1-V3 region-enriched Greengenes database (63) and to the V6-V8 regionenriched rumen-specific archaea database (64), respectively, using mothur (65).…”

Metatranscriptomic Profiling Reveals the Effect of Breed on Active Rumen Eukaryotic Composition in Beef Cattle With Varied Feed Efficiency

“…The RNA-seq dataset was preliminarily processed as described by Li et al (2016), and reads after quality control and filtration were used in this study. The 18S rRNA reads were extracted by mapping the filtered reads to the rRNA reference database SILVA_SSU (release 119) using SortMeRNA (version1.9) (Kopylova et al, 2012) and subjected to downstream analysis using Quantitative Insights Into Microbial Ecology (QIIME 2, version 2019.4) (Bolyen et al, 2019).…”

Inter- and intra-domain functional redundancy in the rumen microbiome during plant biomass degradation

“…We hypothesized that the microbiome exhibits a defined successional pattern, reflecting a cascade of hydrolytic, fermentative and methanogenic steps, accompanied by distinct VFA and gas emission patterns. Based on a previous metatranscriptomic study from our lab [24] and work of others ([27] and references therein), we hypothesized that the recently discovered Methanomassiliicoccales are substantial contributors to ruminant CH 4 emissions and will therefore show high activity after ruminant feed intake.…”