Relationship between the Rumen Microbiome and Residual Feed Intake-Efficiency of Brahman Bulls Stocked on Bermudagrass Pastures

McCann, Joshua C; Wiley, Leanne; Forbes, T. D. A.; Rouquette, F. M.; Tedeschi, L. O.

doi:10.1371/journal.pone.0091864

Cited by 105 publications

(99 citation statements)

References 44 publications

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“…In other ecosystems, increased biodiversity is associated with greater success and resilience of the ecosystem 10,11 ; however, the data presented in this study suggest that α -diversity may not be a significant contributing factor to feed efficiency phenotypes in stable bacterial communities in growing beef steers. Previous studies have also observed the same relationships between rumen bacterial α -diversity and feed efficiency phenotypes in steers 12,13 . Other factors, such as divergences in individual taxa or functionality of the rumen microbiota, may play a greater role in dictation of host feed efficiency phenotypes.…”

Section: Discussionsupporting

confidence: 73%

Rumen Bacteria and Serum Metabolites Predictive of Feed Efficiency Phenotypes in Beef Cattle

Clemmons

Martino

Powers

et al. 2019

Preprint

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The rumen microbiome is critical in ruminant nutrition and contributes to nutrient utilization and feed efficiency in cattle. Therefore, the objective of this study was to interrogate microbial and biochemical factors affecting divergences in feed efficiency in Angus steers using 16S amplicon sequencing and untargeted metabolomics. Average residual feed intake (RFI) was calculated, and steers were divided into low-and high-RFI groups. Features were ranked in relation to RFI through supervised machine learning on microbial and metabolite compositions. Residual feed intake was associated with several attributes of the rumen bacterial community. Low-RFI steers were associated with decreased bacterial α -(P=0.03) and β -diversity (P<0.001). Several serum metabolites were associated with RFI. Based on fold change (high/low RFI), low-RFI steers had greater abundances of pantothenate (P=0.02). Machine learning on RFI was predictive of both serum metabolomic signature and rumen bacterial composition (AUC ≥ 0.7). Log-ratio proportions of the bacterial classes Flavobacteriia over Fusobacteriia were enriched in low-RFI steers (F=6.8, P=.01). Greater proportions of pantothenate-producing bacteria, such as Flavobacteriia, and/or reductions in Fusobacteriia may result in improved nutrient utilization in low-RFI steers. Pantothenate and Flavobacteriia may serve as potentially novel biomarkers to assess or predict feed efficiency in Angus steers.

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Section: Discussionsupporting

confidence: 73%

Rumen Bacteria and Serum Metabolites Predictive of Feed Efficiency Phenotypes in Beef Cattle

Clemmons

Martino

Powers

et al. 2019

Preprint

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“…Some studies suggested correlations between rumen microbial groups and bovine feed efficiency traits (Carberry, Kenny, Han, McCabe, & Waters, 2012;Guan, Nkrumah, Basarab, & Moore, 2008;Hernandez-Sanabria et al, 2012). The relative proportion of sequences that were assigned to Prevotella appeared to be positively associated with high residual feed intake (RFI) in bulls, whereas an unidentified group within the order Bacteroidales was positively associated with low RFI in bulls (McCann et al, 2014). Lima et al (2015) have characterized the rumen fluid microbiomes of prepartum and postpartum high-producing Holstein cows and revealed that some bacteria have strong correlations with milk production.…”

Section: Introductionmentioning

confidence: 99%

High‐production dairy cattle exhibit different rumen and fecal bacterial community and rumen metabolite profile than low‐production cattle

Lin

Wang

et al. 2018

MicrobiologyOpen

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Our aim was to simultaneously investigate the gut bacteria typical characteristic and conduct rumen metabolites profiling of high production dairy cows when compared to low‐production dairy cows. The bacterial differences in rumen fluid and feces were identified by 16S rDNA gene sequencing. The metabolite differences were identified by metabolomics profiling with liquid chromatography mass spectrometry ( LC ‐ MS ). The results indicated that the high‐production dairy cows presented a lower rumen bacterial richness and species evenness when compared to low‐production dairy cows. At the phylum level, the high‐production cows increased the abundance of Proteobacteria and decreased the abundance of Bacteroidetes, SR 1, Verrucomicrobia, Euryarchaeota, Planctomycetes, Synergistetes, and Chloroflexi significantly ( p < 0.05). At the genus level, the rumen fluid of the high‐production group was significantly enriched for Butyrivibrio , Lachnospira , and Dialister ( p < 0.05). Meanwhile, rumen fluid of high‐production group was depleted for Prevotella, Succiniclasticum, Ruminococcu, Coprococcus, YRC 22, CF 231, 02d06, Anaeroplasma, Selenomonas , and Ruminobacter significantly ( p < 0.05). A total of 92 discriminant metabolites were identified between high‐production cows and low‐production cows. Compared to rumen fluid of low‐production dairy cows, 10 differential metabolites were found up‐regulated in rumen fluid of high‐production dairy cows, including 6alpha‐Fluoropregn‐4‐ene‐3,20‐dione, 3‐Octaprenyl‐4‐hydroxybenzoate, disopyramide, compound III (S), 1,2‐Dimyristyl‐sn‐glycerol, 7,10,13,16‐Docosatetraenoic acid, ferrous lactate, 6‐Deoxyerythronolide B, vitamin D2, L‐Olivosyl‐oleandolide. The remaining differential metabolites were found down‐regulated obviously in high‐production cows. Metabolic pathway analyses indicated that most increased abundances of rumen fluid metabolites of high‐yield cows were related to metabolic pathways involving biosynthesis of unsaturated fatty acids, steroid biosynthesis, ubiquinone and other terpenoid‐quinone biosynthesis. Most down‐regulated metabolic pathways were relevant to nucleotide metabolism, energy metabolism, lipid metabolism and biosynthesis of some antibiotics.

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“…Recent research has focused on characterization of the rumen microbiome among animals differing in feed efficiency (Jami et al, 2014;McCann et al, 2014). These studies characterized the bacterial community by sequencing 16S rRNA regions V1-V3 or V1/V3 individually.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of 16S rRNA amplicon sequencing using two next-generation sequencing technologies for phylogenetic analysis of the rumen bacterial community in steers

Myer

Kim²,

Freetly³

et al. 2016

Journal of Microbiological Methods

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Next generation sequencing technologies have vastly changed the approach of sequencing of the 16S rRNA gene for studies in microbial ecology. Three distinct technologies are available for large-scale 16S sequencing. All three are subject to biases introduced by sequencing error rates, amplification primer selection, and read length, which can affect the apparent microbial community. In this study, we compared short read 16S rRNA variable regions, V1-V3, with that of near-full length 16S regions, V1-V8, using highly diverse steer rumen microbial communities, in order to examine the impact of technology selection on phylogenetic profiles. Short paired-end reads from the Illumina MiSeq platform were used to generate V1-V3 sequence, while long "circular consensus" reads from the Pacific Biosciences RSII instrument were used to generate V1-V8 data. The two platforms revealed similar microbial operational taxonomic units (OTUs), as well as similar species richness, Good's coverage, and Shannon diversity metrics. However, the V1-V8 amplified ruminal community resulted in significant increases in several orders of taxa, such as phyla Proteobacteria and Verrucomicrobia (P < 0.05). Taxonomic classification accuracy was also greater in the near full-length read. UniFrac distance matrices using jackknifed UPGMA clustering also noted differences between the communities. These data support the consensus that longer reads result in a finer phylogenetic resolution that may not be achieved by shorter 16S rRNA gene fragments. Our work on the cattle rumen bacterial community demonstrates that utilizing near full-length 16S reads may be useful in conducting a more thorough study, or for developing a niche-specific database to use in analyzing data from shorter read technologies when budgetary constraints preclude use of near-full length 16S sequencing.

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Relationship between the Rumen Microbiome and Residual Feed Intake-Efficiency of Brahman Bulls Stocked on Bermudagrass Pastures

Cited by 105 publications

References 44 publications

Rumen Bacteria and Serum Metabolites Predictive of Feed Efficiency Phenotypes in Beef Cattle

Rumen Bacteria and Serum Metabolites Predictive of Feed Efficiency Phenotypes in Beef Cattle

High‐production dairy cattle exhibit different rumen and fecal bacterial community and rumen metabolite profile than low‐production cattle

Evaluation of 16S rRNA amplicon sequencing using two next-generation sequencing technologies for phylogenetic analysis of the rumen bacterial community in steers

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