Biology subjects

Henry, C. S.

Publications and source records attributed to Henry, C. S..

23 records · Page 2Linked to original sources

bioRxiv2021

ORT: A workflow linking genome-scale metabolic models with reactive transport codes

MotivationNutrient and contaminant behavior in the subsurface are governed by multiple coupled hydrobiogeochemical processes which occur across different temporal and spatial scales. Accurate description of macroscopic system behavior requires accounting for the effects of microscopic and especially microbial processes. Microbial processes mediate precipitation and dissolution and change aqueous geochemistry, all of which impacts macroscopic system behavior. As omics data describing microbial processes is increasingly affordable and available, novel methods for using this data quickly and effectively for improved ecosystem models are needed. ResultsWe propose a workflow ( Omics to Reactive Transport - ORT) for utilizing metagenomic and environmental data to describe the effect of microbiological processes in macroscopic reactive transport models. This workflow utilizes and couples two open-source software packages: KBase (a software platform for systems biology) and PFLOTRAN (a reactive transport modeling code). We describe the architecture of ORT and demonstrate an implementation using metagenomic and geochemical data from a river system. Our demonstration uses microbiological drivers of nitrification and denitrification to predict nitrogen cycling patterns which agree with those provided with generalized stoichiometries. While our example uses data from a single measurement, our workflow can be applied to spatiotemporal metagenomic datasets to allow for iterative coupling between KBASE and PFLOTRAN. Availability and ImplementationInteractive models available at https://pflotranmodeling.paf.subsurfaceinsights.com/pflotran-simple-model/. Microbiological data available at NCBI via BioProject ID PRJNA576070. ORT Python code available at https://github.com/subsurfaceinsights/ort-kbase-to-pflotran. KBase narrative available at https://narrative.kbase.us/narrative/71260 or static narrative (no login required) at https://kbase.us/n/71260/258 Contactrebecca.rubinstein@subsurfaceinsights.com or roelof.versteeg@subsurfaceinsights.com Supplementary informationSupplementary data are available online.

bioinformatics↗

bioRxiv2020

AGORA2: Large scale reconstruction of the microbiome highlights wide-spread drug-metabolising capacities.

The human microbiome influences the efficacy and safety of a wide variety of commonly prescribed drugs, yet comprehensive systems-level approaches to interrogate drug-microbiome interactions are lacking. Here, we present a computational resource of human microbial genome-scale reconstructions, deemed AGORA2, which accounts for 7,206 strains, includes microbial drug degradation and biotransformation, and was extensively curated based on comparative genomics and literature searches. AGORA2 serves as a knowledge base for the human microbiome and as a metabolic modelling resource. We demonstrate the latter by mechanistically modelling microbial drug metabolism capabilities in single strains and pairwise models. Moreover, we predict the individual-specific drug conversion potential in a cohort of 616 colorectal cancer patients and controls. This analysis reveals that some drug activation capabilities are present in only a subset of individuals, moreover, drug conversion potential correlate with clinical parameters. Thus, AGORA2 paves the way towards personalised, predictive analysis of host-drug-microbiome interactions.

systems biology↗

bioRxiv2020

The ModelSEED Database for the integration of metabolic annotations and the reconstruction, comparison, and analysis of metabolic models for plants, fungi, and microbes

For over ten years, ModelSEED has been a primary resource for the construction of draft genome-scale metabolic models based on annotated microbial or plant genomes. Now being released, the biochemistry database serves as the foundation of biochemical data underlying ModelSEED and KBase. The biochemistry database embodies several properties that, taken together, distinguish it from other published biochemistry resources by: (i) including compartmentalization, transport reactions, charged molecules and proton balancing on reactions;; (ii) being extensible by the user community, with all data stored in GitHub; and (iii) design as a biochemical "Rosetta Stone" to facilitate comparison and integration of annotations from many different tools and databases. The database was constructed by combining chemical data from many resources, applying standard transformations, identifying redundancies, and computing thermodynamic properties. The ModelSEED biochemistry is continually tested using flux balance analysis to ensure the biochemical network is modeling-ready and capable of simulating diverse phenotypes. Ontologies can be designed to aid in comparing and reconciling metabolic reconstructions that differ in how they represent various metabolic pathways. ModelSEED now includes 33,978 compounds and 36,645 reactions, available as a set of extensible files on GitHub, and available to search at https://modelseed.org and KBase.

systems biology↗

bioRxiv2020

Metabolic multi-stability and hysteresis in a model aerobe-anaerobe microbiome community

Changes in the composition of the human microbiome are associated with health and disease. Some microbiome states persist in seemingly unfavorable conditions, e.g., the proliferation of aerobe-anaerobe communities in oxygen-exposed environments in wounds or small intestinal bacterial overgrowth. However, it remains unclear how different stable microbiome states can exist under the same conditions, or why some states persist under seemingly unfavorable conditions. Here, using two microbes relevant to the human microbiome, we combine genome-scale mathematical modeling, bioreactor experiments, transcriptomics, and dynamical systems theory, to show that multi-stability and hysteresis (MSH) is a mechanism that can describe the shift from an aerobe-dominated state to a resilient, paradoxically persistent aerobe-anaerobe state. We examine the impact of changing oxygen and nutrient regimes and identify factors, including changes in metabolism and gene expression, that lead to MSH. When analyzing the transitions between the two states in this system, the familiar conceptual connection between causation and correlation is broken and MSH must be used to interpret the dynamics. Using MSH to analyze microbiome dynamics will improve our conceptual understanding of the stability of microbiome states and the transitions among microbiome states. One sentence summaryMulti-stability and hysteresis (MSH) is a potential mechanism to describe shifts to and persistence of aerobe-anaerobe communities in the microbiome.

ecology↗

bioRxiv2016

The DOE Systems Biology Knowledgebase (KBase)

The U.S. Department of Energy Systems Biology Knowledgebase (KBase) is an open-source software and data platform designed to meet the grand challenge of systems biology -- predicting and designing biological function from the biomolecular (small scale) to the ecological (large scale). KBase is available for anyone to use, and enables researchers to collaboratively generate, test, compare, and share hypotheses about biological functions; perform large-scale analyses on scalable computing infrastructure; and combine experimental evidence and conclusions that lead to accurate models of plant and microbial physiology and community dynamics. The KBase platform has (1) extensible analytical capabilities that currently include genome assembly, annotation, ontology assignment, comparative genomics, transcriptomics, and metabolic modeling; (2) a web-browser-based user interface that supports building, sharing, and publishing reproducible and well-annotated analyses with integrated data; (3) access to extensive computational resources; and (4) a software development kit allowing the community to add functionality to the system.

bioinformatics↗