A pinboard by
Andre Neves

PhD Student, University of Alberta


We intend to use the predictable individualized rumen microbiota to improve cattle feed efficiency

Current understanding regarding the impact of interactions between diet and host on the rumen microbiota is limited. In this study, we aimed to identify the rumen microbial population dynamics in beef cattle fed grain (high starch content) and/or forage-based diets (rich in neutral detergent fiber, NDF) over two 80-day feeding periods. Increased starch intake decreased abundance of bacteria and fungi while increased NDF (fiber) intake increased bacterial abundance. In terms of methane (CH4) emissions, the ratio of methanogens to bacteria declined as CH4 (kg/day) increased, and the bacterial abundance increased with increased CH4 production per kg of dry matter intake (food consumed). To assess the rumen microbial dynamics, we grouped bulls based on the magnitude of the log2-fold change (log2fc) in bacteria and fungi populations between each feeding period (log2fc < -1 = low; -1 < log2fc < 1 = stable; and log2fc > 1 = high). Bulls classified as low produced more CH4 as bacteria density increased, whereas bacterial abundance and CH4 remained constant in the stable and high groups. Bulls in the stable group were more efficient when linked to a higher bacterial abundance while bulls categorized in the low and high groups utilized the diet more efficiently when associated with the lowest bacteria densities. These findings suggest the potential use of the predictable individualized rumen microbiota in host-tailored precision feeding systems, specially designed to improve feed efficiency in cattle.


Changes in the rumen microbiome and metabolites reveal the effect of host genetics on hybrid crosses.

Abstract: The rumen microbiota plays important roles in nutrient metabolism and absorption of the host. However, it is poorly understood how host genetic variation shapes the community structure of the rumen microbiota and its metabolic phenotype. Here, we used sika deer (Cervus nippon) and elk (Cervus elaphus) to produce the following two types of hybrid offspring: sika deer ♀ × elk ♂ (SEH) and elk ♀ × sika deer ♂ (ESH). Then, we examined the rumen microbiome and metabolites in the parents and their hybrid offspring. The rumen microbiota in the hybrids differed from that in their parents, suggesting a significant effect of host genetics on the rumen microbiome that may have resulted from vertical transmission. The rumen metabolites displayed patterns similar to the structure of the rumen microbiome, with changes in the amounts of volatile fatty acids and metabolites of amino acids. The alanine, arginine, proline, and phenylalanine pathways were enriched in the rumen of hybrid animals. The enriched metabolites in the above pathways were positively correlated with the bacteria Prevotella spp., Acetitomaculum spp., Quinella spp., Succinivibrio spp. and Ruminobacter spp. These results suggest that host genetics has a major impact on the rumen microbiome and metabolites in hybrid animals. This article is protected by copyright. All rights reserved.

Pub.: 08 Oct '16, Pinned: 28 Jun '17

Integrated Metagenomic Analyses of the Rumen Microbiome of Cattles Reveals Key Biological Mechanisms Associated with Methane Traits.

Abstract: Methane is one of major contributors to global warming. The rumen microbiota is directly involved in methane production in cattle. The link between variations in rumen microbial communities and host genetics has important applications and implications in bioscience. Having the potential to reveal the full extent of microbial gene diversity and complex microbial interactions, integrated metagenomics and network analysis holds great promises in this endeavour. This study investigates the rumen microbial community in cattle through the integration of metagenomic and network-based approaches. Based on the relative abundance of 1570 microbial genes identified in a metagenomics analysis, the co-abundance network was constructed and functional modules of microbial genes were identified. One of the main contributions is to develop a random matrix theory-based approach to automatically determining the correlation threshold used to construct the co-abundance network. The resulting network, consisting of 549 microbial genes and 3349 connections, exhibits a clear modular structure with certain trait-specific genes highly over-represented in modules. More specifically, all the 20 genes previously identified to be associated with methane emissions are found in a module (hypergeometric test, p < 10(-11)). One third of genes are involved in methane metabolism pathways. The further examination of abundance profiles across 8 samples of genes highlights that the revealed pattern of metagenomics abundance has a strong association with methane emissions. Furthermore, the module is significantly enriched with microbial genes encoding enzymes that are directly involved in methanogenesis (hypergeometric test, p < 10(-9)).

Pub.: 13 Jun '17, Pinned: 28 Jun '17