Evaluation of bacterial diversity in the rumen and feces of cattle fed different levels of dried distillers grains plus solubles using bacterial tag-encoded FLX amplicon pyrosequencing
ABSTRACT Dietary components and changes cause shifts in the gastrointestinal microbial ecology that can play a role in animal health and productivity. However, most information about the microbial populations in the gut of livestock species has not been quantitative. In the present study, we utilized a new molecular method, bacterial tag-encoded FLX amplicon pyrosequencing (bTEFAP) that can perform diversity analyses of gastrointestinal bacterial populations. In the present study, cattle (n = 6) were fed a basal feedlot diet and were subsequently randomly assigned to 1 of 3 diets (n = 2 cows per diet). In each diet, 0, 25, or 50% of the concentrate portion of the ration was replaced with dried distillers grain (DDGS). Ruminal and fecal bacterial populations were different when animals were fed DDGS compared with controls; ruminal and fecal Firmicute:Bacteroidetes ratios were smaller (P = 0.07) in the 25 and 50% DDG diets compared with controls. Ruminal pH was decreased (P < 0.05) in ruminal fluid from cattle fed diets containing 50% compared with 0% DDGS. Using bTEFAP, the normal microbiota of cattle were examined using modern molecular methods to understand how diets affect gastrointestinal ecology and the gastrointestinal contribution of the microbiome to animal health and production.
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ABSTRACT: High-grain (HG) feeding used in intensive goat production can affect the physiology of the rumen wall, but the changes induced in the epimural bacterial community and host Toll-like receptors (TLRs) are not well understood. In this study, 10 male goats were randomly allocated to two groups and fed either a hay diet (0% grain; n = 5) or an HG diet (65% grain; n = 5). The changes in the ruminal epithelial bacterial community and expression of TLRs during long-term (7 weeks) HG feeding were determined using pyrosequencing and quantitative real-time polymerase chain reaction. Principal coordinate analysis and analysis of molecular variance (AMOVA) results showed that HG feeding caused a strong shift in bacterial composition and structure. At the genus level, our data revealed that it increased the relative abundance of taxa Butyrivibrio, unclassified Clostridiales, Mogibacterium, unclassified Anaerolineaceae, and Succiniclasticum, and decreased the proportion of unclassified Ruminococcaceae, unclassified Rikenellaceae, unclassified Erysipelotrichaceae, Howardella, and unclassified Neisseriaceae. The HG-fed goats also exhibited upregulation of the relative mRNA expression of TLR2, TLR3, and TLR5 in the rumen epithelium (P < 0.05). Correlation analysis revealed that the increase in TLR expression was associated with changes in the relative abundance of ruminal epithelial bacteria. This study provides a first insight into the adaptive response of ruminal epithelial bacterial populations to HG feeding in goats and shows that these changes were associated with alterations in TLR expression. These findings provide new insight into understanding of host-microbial relationships in ruminants.Frontiers in Microbiology 03/2015; 6:167. DOI:10.3389/fmicb.2015.00167 · 3.94 Impact Factor
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ABSTRACT: The rumen is a complex ecosystem enriched for microorganisms able to degrade biomass during the animal's digestion process. The recovery of new enzymes from naturally evolved biomass-degrading microbial communities is a promising strategy to overcome the inefficient enzymatic plant destruction in industrial production of biofuels. In this context, this study aimed to describe the bacterial composition and functions in the sheep rumen microbiome, focusing on carbohydrate-active enzymes (CAE). Here, we used phylogenetic profiling analysis (inventory of 16S rRNA genes) combined with metagenomics to access the rumen microbiome of four sheep and explore its potential to identify fibrolytic enzymes. The bacterial community was dominated by Bacteroidetes and Firmicutes, followed by Proteobacteria. As observed for other ruminants, Prevotella was the dominant genus in the microbiome, comprising more than 30 % of the total bacterial community. Multivariate analysis of the phylogenetic profiling data and chemical parameters showed a positive correlation between the abundance of Prevotellaceae (Bacteroidetes phylum) and organic matter degradability. A negative correlation was observed between Succinivibrionaceae (Proteobacteria phylum) and methane production. An average of 2 % of the shotgun metagenomic reads was assigned to putative CAE when considering nine protein databases. In addition, assembled contigs allowed recognition of 67 putative partial CAE (NCBI-Refseq) representing 12 glycosyl hydrolase families (Pfam database). Overall, we identified a total of 28 lignocellulases, 22 amylases and 9 other putative CAE, showing the sheep rumen microbiome as a promising source of new fibrolytic enzymes.Antonie van Leeuwenhoek 04/2015; DOI:10.1007/s10482-015-0459-6 · 2.14 Impact Factor
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ABSTRACT: In addition to efforts aimed at reducing anthropogenic production of greenhouse gases, geological storage of CO<sub>2</sub> is being explored as a strategy to reduce atmospheric greenhouse gas emission and mitigate climate change. Previous studies of the deep subsurface in North America have not fully considered the potential negative effects of CO<sub>2</sub> leakage into shallow drinking water aquifers, especially from a microbiological perspective. A test well in the Newark Rift Basin was utilized in two field experiments to investigate patterns of microbial succession following injection of CO<sub>2</sub>-saturated water into an isolated aquifer interval, simulating a CO<sub>2</sub> leakage scenario. A decrease in pH following injection of CO<sub>2</sub> saturated aquifer water was accompanied by mobilization of trace elements (e.g. Fe and Mn), and increased bacterial cell concentrations in the recovered water. 16S ribosomal RNA gene sequence libraries from samples collected before and after thePLoS ONE 01/2015; 10(1-1):e0117812. DOI:10.1371/journal.pone.0117812 · 3.53 Impact Factor