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BackgroundDigestive processes in the rumen lead to the release of methyl-compounds, mainly methanol and methylamines, which are used by methyltrophic methanogens to form methane, an important agricultural greenhouse gas. Methylamines are produced from plant phosphatidylcholine degradation, by choline trimethylamine lyase, while methanol comes from demethoxylation of dietary pectins via pectin methylesterase activity. We have screened rumen metagenomic and metatranscriptomic datasets, metagenome assembled genomes, and the Hungate1000 genomes to identify organisms capable of producing methyl-compounds. We also describe the enrichment of pectin-degrading and methane-forming microbes from sheep rumen contents and the analysis of their genomes via metagenomic assembly.ResultsScreens of metagenomic data using the protein domains of choline trimethylamine lyase (CutC), and activator protein (CutD) found good matches only to Olsenella umbonata and to Caecibacter, while the Hungate1000 genomes and metagenome assembled genomes from the cattle rumen found bacteria within the phyla Actinobacteria, Firmicutes and Proteobacteria. The cutC and cutD genes clustered with genes that encode structural components of bacterial microcompartment proteins. Prevotella was the dominant genus encoding pectin methyl esterases, with smaller numbers of sequences identified from other fibre-degrading rumen bacteria. Some large pectin methyl esterases (> 2100 aa) were found to be encoded in Butyrivibrio genomes. The pectin-utilising, methane-producing consortium was composed of (i) a putative pectin-degrading bacterium (phylum Tenericutes, class Mollicutes), (ii) a galacturonate-using Sphaerochaeta sp. predicted to produce acetate, lactate, and ethanol, and (iii) a methylotrophic methanogen, Methanosphaera sp., with the ability to form methane via a primary ethanol-dependent, hydrogen-independent, methanogenesis pathway.ConclusionsThe main bacteria that produce methyl-compounds have been identified in ruminants. Their enzymatic activities can now be targeted with the aim of finding ways to reduce the supply of methyl-compound substrates to methanogens, and thereby limit methylotrophic methanogenesis in the rumen.

Productivity of ruminant livestock depends on the rumen microbiota, which ferment indigestible plant polysaccharides into nutrients used for growth. Understanding the functions carried out by the rumen microbiota is important for reducing greenhouse gas production by ruminants and for developing biofuels from lignocellulose. We present 410 cultured bacteria and archaea, together with their reference genomes, representing every cultivated rumen-associated archaeal and bacterial family. We evaluate polysaccharide degradation, short-chain fatty acid production and methanogenesis pathways, and assign specific taxa to functions. A total of 336 organisms were present in available rumen metagenomic data sets, and 134 were present in human gut microbiome data sets. Comparison with the human microbiome revealed rumen-specific enrichment for genes encoding de novo synthesis of vitamin B12, ongoing evolution by gene loss and potential vertical inheritance of the rumen microbiome based on underrepresentation of markers of environmental stress. We estimate that our Hungate genome resource represents ∼75% of the genus-level bacterial and archaeal taxa present in the rumen.

Ruminant animals contribute significantly to the global value of agriculture and rely on a complex microbial community for efficient digestion. However, little is known of how this microbial-host relationship develops and is maintained. To begin to address this, we have determined the ability of three Bifidobacterium species isolated from the faeces of newborn calves to grow on carbohydrates typical of a newborn ruminant diet. Genome sequences have been determined for these bacteria with analysis of the genomes providing insights into the host association and identification of several genes that may mediate interactions with the ruminant gastrointestinal tract. The present study provides a starting point from which we can define the role of potential beneficial microbes in the nutrition of young ruminants and begin to influence the interactions between the microbiota and the host. The differences observed in genomic content hint at niche partitioning among the bifidobacterial species analysed and the different strategies they employ to successfully adapt to this habitat.

Mathematical models play an important role in biochemical engineering. For example, the models developed in the field of systems biology have been used to identify drug targets to treat pathogens such as Pseudomonas aeruginosa in biofilms. In addition, competitive binding models for chromatography processes have been developed to predict expanded bed breakthrough of a recombinant protein expressed in a high cell density fermentation. Numerical computing tools such as MATLAB can facilitate the development of these models in biochemical engineering. Compared to other computational tools and programming languages, such as C++ and Java, MATLAB is easier for students to learn since it has a large library of functions and toolboxes, including some developed in systems biology. Basic MATLAB modeling techniques have been introduced in advanced engineering courses to enhance student learning experiences. However, a nationwide survey conducted by Villanova University showed that only 28.5% of schools in United States provide training in MATLAB in graduate-level courses in biochemical engineering. Instructors in this survey also indicated they would have a strong interest in using MATLAB modules, if the modules were readily available, in courses such as systems biology. To address the need for MATLAB education in biochemical engineering, Villanova University has developed MATLAB modules and integrated them in the course CHE8663 Systems Biology (Fall semester 2012 & 2013). The teaching modules as well as the teaching strategies for MATLAB are provided here for a course that was offered to in class students as well as distance-learning (i.e., e-learning) students.

Simple Summary: T cell-based therapies can be costly, inconsistent, and differentially effective for patients. The limiting reagent for T cell-based immunotherapies is the T cell itself. T cell growth has been linked to endogenously delivered cytokines and messenger proteins that indicate the status of inflammation of the local system in the body. IL-2, IL-7, and IL-15 are the specific cytokines that have been identified in combination to deliver accelerated growth to different T cell subsets. This study focuses on the growth of T cell subsets subject to different combinations of IL-2, IL-7, and IL-15 in the experiment. Based on growth data, mathematical models were developed to project the optimal growth conditions for CD4+ and CD8+ Naïve and Memory T Cells. RNA sequence data is finally analyzed to study the gene networks that influence growth pathways, specifically for CD4+ and CD8+ naïve and memory subsets. The growth of T cells ex vivo for the purpose of T cell therapies is a rate-limiting step in the overall process for cancer patients to achieve remission. Growing T cells is a fiscally-, time-, and resource-intensive process. Cytokines have been shown to accelerate the growth of T cells, specifically IL-2, IL-7, and IL-15. Here a design of experiments was conducted to optimize the growth rate of different naïve and memory T cell subsets using combinations of cytokines. Mathematical models were developed to study the impact of IL-2, IL-7, and IL-15 on the growth of T cells. The results show that CD4+ and CD8+ naïve T cells grew effectively using moderate IL-2 and IL-7 in combination, and IL-7, respectively. CD4+ and CD8+ memory cells favored moderate IL-2 and IL-15 in combination and moderate IL-7 and IL-15 in combination, respectively. A statistically significant interaction was observed between IL-2 and IL-7 in the growth data of CD4+ naïve T cells, while the interaction between IL-7 and IL-15 was found for CD8+ naïve T cells. The important genes and related signaling pathways and metabolic reactions were identified from the RNA sequencing data for each of the four subsets stimulated by each of the three cytokines. This systematic investigation lays the groundwork for studying other T cell subsets. [ABSTRACT FROM AUTHOR]