Predicted Relative Metabolomic Turnover (PRMT): determining metabolic turnover from a coastal marine metagenomic dataset

We present an approach in which the semantics of an XML language is defined by means of a transformation from an XML document model (an XML schema) to an application specific model. The application specific model implements the intended behavior of documents written in the language. A transformation is specified in a model transformation language used in the Model Driven Architecture (MDA) approach for software development. Our approach provides a better separation of three concerns found in XML applications: syntax, syntax processing logic and intended meaning of the syntax. It frees the developer of low-level syntactical details and improves the adaptability and reusability of XML applications. Declarative transformation rules and the explicit application model provide a finer control over the application parts affected by adaptations. Transformation rules and the application model for an XML language may be composed with the corresponding rules and application models defined for other XML languages. In that way we achieve reuse and composition of XML applications

Satellite remote sensing data can be used to model marine microbial metabolite turnover

Sampling ecosystems, even at a local scale, at the temporal and spatial resolution necessary to capture natural variability in microbial communities are prohibitively expensive. We extrapolated marine surface microbial community structure and metabolic potential from 72 16S rRNA amplicon and 8 metagenomic observations using remotely sensed environmental parameters to create a system-scale model of marine microbial metabolism for 5904 grid cells (49 km2) in the Western English Chanel, across 3 years of weekly averages. Thirteen environmental variables predicted the relative abundance of 24 bacterial Orders and 1715 unique enzyme-encoding genes that encode turnover of 2893 metabolites. The genes’ predicted relative abundance was highly correlated (Pearson Correlation 0.72, P-value <10−6) with their observed relative abundance in sequenced metagenomes. Predictions of the relative turnover (synthesis or consumption) of CO2 were significantly correlated with observed surface CO2 fugacity. The spatial and temporal variation in the predicted relative abundances of genes coding for cyanase, carbon monoxide and malate dehydrogenase were investigated along with the predicted inter-annual variation in relative consumption or production of ~3000 metabolites forming six significant temporal clusters. These spatiotemporal distributions could possibly be explained by the co-occurrence of anaerobic and aerobic metabolisms associated with localized plankton blooms or sediment resuspension, which facilitate the presence of anaerobic micro-niches. This predictive model provides a general framework for focusing future sampling and experimental design to relate biogeochemical turnover to microbial ecology

Application of meta-omics techniques to understand greenhouse gas emissions originating from ruminal metabolism

The Rowett Institute of Nutrition and Health is funded by the Rural and Environment Science and Analytical Services Division (RESAS) of the Scottish Government. This study was financially supported by RuminOmics (Project No. 289319 of EC 7th Framework Programme: Food, Agriculture, Fisheries and Biotechnology). Erratum to: Application of meta-omics techniques to understand greenhouse gas emissions originating from ruminal metabolism Robert J. WallaceEmail author, Timothy J. Snelling, Christine A. McCartney, Ilma Tapio and Francesco Strozzi Genetics Selection Evolution 2017 49:27, DOI: 10.1186/s12711-017-0304-7 © The Author(s) 2017, Received: 22 February 2017, Accepted: 22 February 2017, Published: 28 February 2017Peer reviewedPublisher PD

Specialized Metabolites from the Microbiome in Health and Disease

The microbiota, and the genes that comprise its microbiome, play key roles in human health. Host-microbe interactions affect immunity, metabolism, development, and behavior, and dysbiosis of gut bacteria contributes to disease. Despite advances in correlating changes in the microbiota with various conditions, specific mechanisms of host-microbiota signaling remain largely elusive. We discuss the synthesis of microbial metabolites, their absorption, and potential physiological effects on the host. We propose that the effects of specialized metabolites may explain present knowledge gaps in linking the gut microbiota to biological host mechanisms during initial colonization, and in health and disease

Novel microbial guilds implicated in N2O reduction

N2O is a long-recognized greenhouse gas (GHG) with potential in global warming and ozone depletion. Terrestrial ecosystems are a major source of N2O due to imbalanced N2O production and consumption. Soil pH is a chief modulating factor controlling net N2O emissions, and N2O consumption has been considered negligible under acidic conditions (pH \u3c6). In this dissertation, we obtained solids-free cultures reducing N2O at pH 4.5. Furthermore, a co-culture (designated culture EV) comprising two interacting bacterial population was acquired via consecutive transfer in mineral salt medium. Integrated phenotypic, metagenomic and metabolomic analysis dictated that the Serratia population excreted certain amino acid to support Desulfosporosinus population growth. Characterization of co-culture EV demonstrated that organisms synthesizing functional NosZ exist in acidic soils. To further close the knowledge gap of low pH N2O reduction, we conducted enrichment experiments on two contrasting soils representing natural and agricultural soils, respectively. With varying combination of carbon sources and H2, N2O reduction activity was observed in a total of six solids-free cultures at pH 4.5. Comparative growth experiments documented that N2O was essential for N2O-reducing organisms’ growth. Reconstruction of the communities suggested the cultures were highly enriched following consecutive transfer efforts. Surprisingly, N2O-reducing organisms recovered from the cultures were all identified as clade II lineage or a novel clade lineage. These results together suggested that clade II and the putative novel clade N2O-reducing organisms are closely associated with low pH N2O reduction. The observation of low pH N2O reduction in current research was inconsistent to field observation, where the abiotic-biotic interaction occurs frequently. To simulate potential abiotic-biotic interaction of low pH N2O reduction, we tested the impact of geochemistry factors on N2O reduction by culture EV. The impeded N2O reduction of co-culture EV in the presence of trace amount (as low as 0.01 mM for nitrate and 0.001 mM for nitrite) of nitrogen oxyanions was unexpected, which may explain the weak N2O reduction activity under field conditions. These observations indicated N2O reduction in acidic soils may be limited by interacting abiotic factors, instead of pH itself

Integration of time-series meta-omics data reveals how microbial ecosystems respond to disturbance.

The development of reliable, mixed-culture biotechnological processes hinges on understanding how microbial ecosystems respond to disturbances. Here we reveal extensive phenotypic plasticity and niche complementarity in oleaginous microbial populations from a biological wastewater treatment plant. We perform meta-omics analyses (metagenomics, metatranscriptomics, metaproteomics and metabolomics) on in situ samples over 14 months at weekly intervals. Based on 1,364 de novo metagenome-assembled genomes, we uncover four distinct fundamental niche types. Throughout the time-series, we observe a major, transient shift in community structure, coinciding with substrate availability changes. Functional omics data reveals extensive variation in gene expression and substrate usage amongst community members. Ex situ bioreactor experiments confirm that responses occur within five hours of a pulse disturbance, demonstrating rapid adaptation by specific populations. Our results show that community resistance and resilience are a function of phenotypic plasticity and niche complementarity, and set the foundation for future ecological engineering efforts

Investigating Eco-evolutionary Interactions between Hosts and Members of Their Gut Microbiota

Evolutionary and ecological interactions between hosts and their associated microbial communities, their microbiota, and between members of these communities are vital to understand. Microbial communities are widespread across diverse host taxa and hosts receive a variety of well-documented benefits from their microbial communities. Despite the importance of understanding eco-evolutionary dynamics for colonization outcomes and the benefits these communities provide to their hosts, our current knowledge in this area remains incomplete. For example, we do not know the full extent of coevolution and specific relationships between hosts and microbes, and between the microbes themselves, across host taxa. Questions remain about how host taxonomy, ecology and physiology, and other present microbes influence microbial community membership and function, host and microbe evolution, and specificity in colonization of hosts. I present several studies that aim to shed further light on these eco-evolutionary topics utilizing insect pollinators, with a particular focus on bumble bees, and their gut microbial communities

Self-reinoculation with fecal flora changes microbiota density and composition leading to an altered bile-acid profile in the mouse small intestine

Background: The upper gastrointestinal tract plays a prominent role in human physiology as the primary site for enzymatic digestion and nutrient absorption, immune sampling, and drug uptake. Alterations to the small intestine microbiome have been implicated in various human diseases, such as non-alcoholic steatohepatitis and inflammatory bowel conditions. Yet, the physiological and functional roles of the small intestine microbiota in humans remain poorly characterized because of the complexities associated with its sampling. Rodent models are used extensively in microbiome research and enable the spatial, temporal, compositional, and functional interrogation of the gastrointestinal microbiota and its effects on the host physiology and disease phenotype. Classical, culture-based studies have documented that fecal microbial self-reinoculation (via coprophagy) affects the composition and abundance of microbes in the murine proximal gastrointestinal tract. This pervasive self-reinoculation behavior could be a particularly relevant study factor when investigating small intestine microbiota. Modern microbiome studies either do not take self-reinoculation into account, or assume that approaches such as single housing mice or housing on wire mesh floors eliminate it. These assumptions have not been rigorously tested with modern tools. Here, we used quantitative 16S rRNA gene amplicon sequencing, quantitative microbial functional gene content inference, and metabolomic analyses of bile acids to evaluate the effects of self-reinoculation on microbial loads, composition, and function in the murine upper gastrointestinal tract. Results: In coprophagic mice, continuous self-exposure to the fecal flora had substantial quantitative and qualitative effects on the upper gastrointestinal microbiome. These differences in microbial abundance and community composition were associated with an altered profile of the small intestine bile acid pool, and, importantly, could not be inferred from analyzing large intestine or stool samples. Overall, the patterns observed in the small intestine of non-coprophagic mice (reduced total microbial load, low abundance of anaerobic microbiota, and bile acids predominantly in the conjugated form) resemble those typically seen in the human small intestine. Conclusions: Future studies need to take self-reinoculation into account when using mouse models to evaluate gastrointestinal microbial colonization and function in relation to xenobiotic transformation and pharmacokinetics or in the context of physiological states and diseases linked to small intestine microbiome and to small intestine dysbiosis