Suchergebnisse

Increasing feed efficiency is a key target in ruminant science which requires a better understanding of rumen microbiota. This study investigated the effect of a shift from a non-grazing to a grazing diet on the rumen bacterial, methanogenic archaea, fungal, and protozoal communities. A systems biology approach based on a description of the community structure, core microbiota, network analysis, and taxon abundance linked to the rumen fermentation was used to explore the benefits of increasing depth of the community analysis. A total of 24 sheep were fed ryegrass hay supplemented with concentrate (CON) and subsequently ryegrass pasture (PAS) following a straight through experimental design. Results showed that concentrate supplementation in CON-fed animals (mainly starch) promoted a simplified rumen microbiota in terms of network density and bacterial, methanogen and fungal species richness which favored the proliferation of amylolytic microbes and VFA production (+48%), but led to a lower (ca. 4-fold) ammonia concentration making the N availability a limiting factor certain microbes. The adaptation process from the CON to the PAS diet consisted on an increase in the microbial concentration (biomass of bacteria, methanogens, and protozoa), diversity (+221, +3, and +21 OTUs for bacteria, methanogens, and fungi, respectively), microbial network complexity (+18 nodes and +86 edges) and in the abundance of key microbes involved in cellulolysis (Ruminococcus, Butyrivibrio, and Orpinomyces), proteolysis (Prevotella and Entodiniinae), lactate production (Streptococcus and Selenomonas), as well as methylotrophic archaea (Methanomassiliicoccaceae). This microbial adaptation indicated that pasture degradation is a complex process which requires a diverse consortium of microbes working together. The correlations between the abundance of microbial taxa and rumen fermentation parameters were not consistent across diets suggesting a metabolic plasticity which allowed microbes to adapt to different substrates and to shift their fermentation products. The core microbiota was composed of 34, 9, and 13 genera for bacteria, methanogens, and fungi, respectively, which were shared by all sheep, independent of diet. This systems biology approach adds a new dimension to our understanding of the rumen microbial interactions and may provide new clues to describe the mode of action of future nutritional interventions. ; This work has been supported by the European Regional Development Fund Program through the Welsh Government (WISE Network) and by the BBRSC (BBS/E/W/10964A01). The authors thank Eleanor Jones, Pauline Rees Stevens, Andrew P. Detheridge, and Wanchang Lin for their contribution to the sample analysis. ; Peer Reviewed

The rumen is a complex ecosystem composed of anaerobic bacteria, protozoa, fungi, methanogenic archaea and phages. These microbes interact closely to breakdown plant material that cannot be digested by humans, whilst providing metabolic energy to the host and, in the case of archaea, producing methane. Consequently, ruminants produce meat and milk, which are rich in high-quality protein, vitamins and minerals, and therefore contribute to food security. As the world population is predicted to reach approximately 9.7 billion by 2050, an increase in ruminant production to satisfy global protein demand is necessary, despite limited land availability, and whilst ensuring environmental impact is minimized. Although challenging, these goals can be met, but depend on our understanding of the rumen microbiome. Attempts to manipulate the rumen microbiome to benefit global agricultural challenges have been ongoing for decades with limited success, mostly due to the lack of a detailed understanding of this microbiome and our limited ability to culture most of these microbes outside the rumen. The potential to manipulate the rumen microbiome and meet global livestock challenges through animal breeding and introduction of dietary interventions during early life have recently emerged as promising new technologies. Our inability to phenotype ruminants in a high-throughput manner has also hampered progress, although the recent increase in >omic> data may allow further development of mathematical models and rumen microbial gene biomarkers as proxies. Advances in computational tools, high-throughput sequencing technologies and cultivation-independent >omics> approaches continue to revolutionize our understanding of the rumen microbiome. This will ultimately provide the knowledge framework needed to solve current and future ruminant livestock challenges. ; SH, DM, MP, RM-T, SW, IT, HS, JE, SK, GA, and CC acknowledge the support of ERA-net gas co-fund for funding (Project name: RumenPredict). SH, HM and CC acknowledge support from BBSRC (BBL/L026716/1 and BBL/L026716/2) and a British Council Newton Institutional Links funding (Grant 172629373). IM acknowledges funding from the European Research Council under the European Union's Horizon 2020 research and innovation program (Grant 640384). JE acknowledges funding from an EU H2020 Marie Curie Fellowship (706899). CC, AK-S, and EH were supported by the Biotechnology and Biological Sciences Research Council (Grants BBS/OS/GC/000011B and BBS/E/W/0012843D). CN and OM acknowledge the support of the British Council Newton Institutional Links funding (Grant 216425215). SRUC receives financial support from the Scottish Government's Rural and Environment Science and Analytical Services Division (RESAS). RD and RR acknowledge financial support from the Biotechnology and Biological Sciences Research Council (BBSRC BB/N01720X/1). DY-R and AB acknowledge funding from MINECO, Spain (Grant AGL2017-86938-R). GS acknowledges funding from the U.S. Department of Agriculture National Institute of Food and Agriculture foundational (Grant 2015-67015-23246). EP acknowledges funding from CNPq (Grant 401590/2014-3). All authors are also members of the Global Research Alliance Rumen Microbial Genomics network. ; Peer Reviewed