The Glycobiome of the Rumen Bacterium Butyrivibrio proteoclasticus B316T Highlights Adaptation to a Polysaccharide-Rich Environment

Determining the role of rumen microbes and their enzymes in plant polysaccharide breakdown is fundamental to understanding digestion and maximising productivity in ruminant animals. Butyrivibrio proteoclasticus B316T is a Gram-positive, butyrate-forming rumen bacterium with a key role in plant polysaccharide degradation. The 4.4Mb genome consists of 4 replicons; a chromosome, a chromid and two megaplasmids. The chromid is the smallest reported for all bacteria, and the first identified from the phylum Firmicutes. B316 devotes a large proportion of its genome to the breakdown and reassembly of complex polysaccharides and has a highly developed glycobiome when compared to other sequenced bacteria. The secretion of a range of polysaccharide-degrading enzymes which initiate the breakdown of pectin, starch and xylan, a subtilisin family protease active against plant proteins, and diverse intracellular enzymes to break down oligosaccharides constitute the degradative capability of this organism. A prominent feature of the genome is the presence of multiple gene clusters predicted to be involved in polysaccharide biosynthesis. Metabolic reconstruction reveals the absence of an identifiable gene for enolase, a conserved enzyme of the glycolytic pathway. To our knowledge this is the first report of an organism lacking an enolase. Our analysis of the B316 genome shows how one organism can contribute to the multi-organism complex that rapidly breaks down plant material in the rumen. It can be concluded that B316, and similar organisms with broad polysaccharide-degrading capability, are well suited to being early colonizers and degraders of plant polysaccharides in the rumen environment

The Genome Sequence of the Rumen Methanogen Methanobrevibacter ruminantium Reveals New Possibilities for Controlling Ruminant Methane Emissions

BACKGROUND: Methane (CH(4)) is a potent greenhouse gas (GHG), having a global warming potential 21 times that of carbon dioxide (CO(2)). Methane emissions from agriculture represent around 40% of the emissions produced by human-related activities, the single largest source being enteric fermentation, mainly in ruminant livestock. Technologies to reduce these emissions are lacking. Ruminant methane is formed by the action of methanogenic archaea typified by Methanobrevibacter ruminantium, which is present in ruminants fed a wide variety of diets worldwide. To gain more insight into the lifestyle of a rumen methanogen, and to identify genes and proteins that can be targeted to reduce methane production, we have sequenced the 2.93 Mb genome of M. ruminantium M1, the first rumen methanogen genome to be completed. METHODOLOGY/PRINCIPAL FINDINGS: The M1 genome was sequenced, annotated and subjected to comparative genomic and metabolic pathway analyses. Conserved and methanogen-specific gene sets suitable as targets for vaccine development or chemogenomic-based inhibition of rumen methanogens were identified. The feasibility of using a synthetic peptide-directed vaccinology approach to target epitopes of methanogen surface proteins was demonstrated. A prophage genome was described and its lytic enzyme, endoisopeptidase PeiR, was shown to lyse M1 cells in pure culture. A predicted stimulation of M1 growth by alcohols was demonstrated and microarray analyses indicated up-regulation of methanogenesis genes during co-culture with a hydrogen (H(2)) producing rumen bacterium. We also report the discovery of non-ribosomal peptide synthetases in M. ruminantium M1, the first reported in archaeal species. CONCLUSIONS/SIGNIFICANCE: The M1 genome sequence provides new insights into the lifestyle and cellular processes of this important rumen methanogen. It also defines vaccine and chemogenomic targets for broad inhibition of rumen methanogens and represents a significant contribution to worldwide efforts to mitigate ruminant methane emissions and reduce production of anthropogenic greenhouse gases

Multi-Vehicle Trajectory Tracking towards Digital Twin Intersections for Internet of Vehicles

Digital Twin (DT) provides a novel idea for Intelligent Transportation Systems (ITS), while Internet of Vehicles (IoV) provides numerous positioning data of vehicles. However, complex interactions between vehicles as well as offset and loss of measurements can lead to tracking errors of DT trajectories. In this paper, we propose a multi-vehicle trajectory tracking framework towards DT intersections (MVT2DTI). Firstly, the positioning data is unified to the same coordinate system and associated with the tracked trajectories via matching. Secondly, a spatial–temporal tracker (STT) utilizes long short-term memory network (LSTM) and graph attention network (GAT) to extract spatial–temporal features for state prediction. Then, the distance matrix is computed as a proposed tracking loss that feeds tracking errors back to the tracker. Through the iteration of association and prediction, the unlabeled coordinates are connected into the DT trajectories. Finally, four datasets are generated to validate the effectiveness and efficiency of the framework