(674f) Microbiome-Virome Interactions in Bovine Rumen: The Role of Auxiliary Metabolic Genes in Relaxing Metabolic Bottlenecks | AIChE

(674f) Microbiome-Virome Interactions in Bovine Rumen: The Role of Auxiliary Metabolic Genes in Relaxing Metabolic Bottlenecks


Islam, M. M. - Presenter, University of Nebraska-Lincoln
Schroeder, W. - Presenter, The Pennsylvania State University
Saha, R., University of Nebraska-Lincoln
Fernando, S. C., University of Nebraska-Lincoln
The complex microbial ecosystem of the rumen has a significant impact on the ruminant host’s health and productivity through the breakdown and fermentation of feed into volatile fatty acids. Although advancements in high-throughput sequencing are providing access to the vast diversity and functions of this complex microbial ecosystem, our understanding of factors shaping rumen microbial communities is in its infancy. In addition to the complex syntrophic, mutualistic, and sometimes competitive interactions within the rumen microbiome, viruses have been shown to impact microbial populations through a myriad of processes, including cell lysis and reprogramming of host metabolism via Auxiliary Metabolic genes (AMGs). However, little is known about the processes shaping the distribution of rumen viruses or modulation of microbe-driven processes in the rumen. It is well established that bovine dietary variations significantly alter the ruminal microbiome. However, a comprehensive and systematic computational study to decipher the effect of diet utilization and viral AMGs on the microbiome is still missing. With a gradual increase in computational capability and abundance of in silico genome-scale metabolic reconstruction tools, metabolic networks combined with constraint-based modeling provides opportunities to identify microbe-microbe and phage-microbe interactions. In this work, we investigated how rumen microbial and viral community structure and function respond in varied dietary treatments and investigated the distribution and ecological drivers of rumen viruses alongside their bacterial counterparts.

Utilizing four diets with varying degrees of total digestible nutrients (TDN), we investigated the effect of dietary perturbations of the rumen ecosystem by metagenome sequencing of the total microbiome and the viral fraction. For building a simplified rumen community metabolic model, we selected Prevotella ruminicola, Methanobrevibacter gottschalkii, and Ruminococcus flavefaciens as representative organisms that represent starch and protein digesters, methanogens and fiber digesters within the rumen, respectively. We have completed draft genome-scale models for P. ruminicola (467 genes, 1129 metabolites, 1043 reactions), M. gottschalkii (318 genes, 977 metabolites, 840 reactions), and R. flavefaciens (461 genes, 1100 metabolites, 981 reactions) from ModelSEED and are in the process of automatic and manual curation of the models. A microbiome community model will be developed by integrating the three models via interspecies constraints, using existing multi-level and multi-objective modeling frameworks[1-3]. The community model will be validated through experimental observations on metabolite secretion profiles and community compositions as a function of diet and host-specific variations. The identified functions of viral AMGs will be incorporated into the model as regulatory information. The metabolic hubs and bottlenecks in the community will be identified and the metabolite pools for important energy currencies in the community (i.e. ATP, NAD(P) etc.) will be predicted.

Our experimental results show rumen viruses have affected the structure of the previously identified core rumen microbiota and also the microbial metabolism through a vast array of AMGs. While viral communities displayed large shifts following dietary perturbations, 38 viral groups were shared across all host-diet combinations, suggesting the presence of a core rumen virome largely comprised of novel viruses. Analysis of viral AMGs shows glycosidic hydrolase activity to augment the breakdown of complex carbohydrates, redirecting carbon flux to pentose phosphate pathway, and boost viral replication. We showed that viral community dynamics is highly correlated with microbial density and dietary factors instead of host-specificity. This model serves to answer key ecological questions of ruminant nutrition through diet-virome-microbiome interactions, discover unidentified metabolite transactions, and promises to develop novel strategies for methane mitigation and increasing nutritional efficiency of domesticated bovine species.

1. Zhuang K, Ma E, Lovley DR, Mahadevan R. The design of long-term effective uranium bioremediation strategy using a community metabolic model. Biotechnology and bioengineering. 2012;109(10):2475-2483.
2. Zomorrodi AR, Maranas CD. OptCom: a multi-level optimization framework for the metabolic modeling and analysis of microbial communities. PLoS computational biology. Feb 2012;8(2):e1002363.
3. Zomorrodi AR, Islam MM, Maranas CD. d-OptCom: Dynamic Multi-level and Multi-objective Metabolic Modeling of Microbial Communities. ACS synthetic biology. Apr 18 2014;3(4):247-257.