Engineering topics

Brown, Joseph M.

Publications and source records attributed to Brown, Joseph M..

8 records

DOE OSTI2021

New Insights Into Lignification via Network and Multi-Omics Analyses of Arogenate Dehydratase Knock-Out Mutants in Arabidopsis thaliana

Multiple Arabidopsis arogenate dehydratase (ADT) knock-out (KO) mutants, with phenotypes having variable lignin levels (up to circa 70% reduction), were studied to investigate how differential reductions in ADTs perturb its overall plant systems biology. Integrated "omics" analyses (metabolome, transcriptome, and proteome) of wild type (WT), single and multiple ADT KO lines were conducted. Transcriptome and proteome data were collapsed into gene ortholog (GO) data, with this allowing for enzymatic reaction and metabolome cross-comparisons to uncover dominant or likely metabolic biosynthesis reactions affected. Network analysis of enzymes-highly correlated to stem lignin levels-deduced the involvement of novel putative lignin related proteins or processes. These included those associated with ribosomes, the spliceosome, mRNA transport, aminoacyl tRNA biosynthesis, and phosphorylation. While prior work helped explain lignin biosynthesis regulation at the transcriptional level, our data here provide support for a new hypothesis that there are additional post-transcriptional and translational level processes that need to be considered. These findings are anticipated to lead to development of more accurate depictions of lignin/phenylpropanoid biosynthesis models in situ, with new protein targets identified for further biochemical analysis and/or plant bioengineering. Additionally, using KEGG defined functional categorization of proteomics and transcriptomics analyses, we detected significant changes to glucosinolate, a-linolenic acid, nitrogen, carotenoid, aromatic amino acid, phenylpropanoid, and photosynthesis-related metabolic pathways in ADT KO mutants. Metabolomics results also revealed that putative carotenoid and galactolipid levels were generally increased in amount, whereas many glucosinolates and phenylpropanoids (including flavonoids and lignans) were decreased in the KO mutants.

Arabidopsis, arogenate dehydratases, lignin, multi↗

DOE OSTI2019

Metaphenomic Responses of a Native Prairie Soil Microbiome to Moisture Perturbations

Climate change is causing shifts in precipitation patterns in the central grasslands of the United States, with largely unknown consequences on the collective physiological responses of the soil microbial community, i.e., the metaphenome. Here, we used an untargeted omics approach to determine the soil microbial community’s metaphenomic response to soil moisture and to define specific metabolic signatures of the response. Specifically, we aimed to develop the technical approaches and metabolic mapping framework necessary for future systematic ecological studies. We collected soil from three locations at the Konza Long-Term Ecological Research (LTER) field station in Kansas, and the soils were incubated for 15 days under dry or wet conditions and compared to field-moist controls. The microbiome response to wetting or drying was determined by 16S rRNA amplicon sequencing, metatranscriptomics, and metabolomics, and the resulting shifts in taxa, gene expression, and metabolites were assessed. Soil drying resulted in significant shifts in both the composition and function of the soil microbiome. In contrast, there were few changes following wetting. The combined metabolic and metatranscriptomic data were used to generate reaction networks to determine the metaphenomic response to soil moisture transitions. Site location was a strong determinant of the response of the soil microbiome to moisture perturbations. However, some specific metabolic pathways changed consistently across sites, including an increase in pathways and metabolites for production of sugars and other osmolytes as a response to drying. Using this approach, we demonstrate that despite the high complexity of the soil habitat, it is possible to generate insight into the effect of environmental change on the soil microbiome and its physiology and functions, thus laying the groundwork for future, targeted studies. Climate change is predicted to result in increased drought extent and intensity in the highly productive, former tallgrass prairie region of the continental United States. These soils store large reserves of carbon. The decrease in soil moisture due to drought has largely unknown consequences on soil carbon cycling and other key biogeochemical cycles carried out by soil microbiomes. In this study, we found that soil drying had a significant impact on the structure and function of soil microbial communities, including shifts in expression of specific metabolic pathways, such as those leading toward production of osmoprotectant compounds. This study demonstrates the application of an untargeted multi-omics approach to decipher details of the soil microbial community’s metaphenotypic response to environmental perturbations and should be applicable to studies of other complex microbial systems as well.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Bioinformatics Resource Manager: A systems biology web tool for microRNA and omics data integration

Background: The Bioinformatics Resource Manager (BRM) is a web-based tool developed to facilitate identifier conversion and data integration for Homo sapiens (human), Mus musculus (mouse), Rattus norvegicus (rat), Danio rerio (zebrafish), and Macaca mulatta (macaque), as well as perform orthologous conversions among the supported species. In addition to providing a robust means of identifier conversion, BRM also incorporates a suite of miRNA-target databases upon which to query target genes or to perform reverse target lookups using gene identifiers. Results: BRM has the capability to perform cross-species identifier lookups across common identifier types, directly integrate datasets across platform or species by performing identifier retrievals in the background, and retrieve miRNA targets from multiple databases simultaneously and integrate the resulting gene targets with experimental mRNA data. Here we use workflows provided in BRM to integrate RNAseq data across species to identify common biomarkers of exposure after treatment of human lung cells and zebrafish to benzo[a]pyrene (BAP). We further use the miRNA Target workflow to experimentally determine the role of miRNAs as regulators of BAP toxicity and identify the predicted functional consequences of miRNA-target regulation in our system. The output from BRM can easily and directly be uploaded to freely available visualization tools for further analysis. From these examples, we were able to identify an important role for several miRNAs as potential regulators of BAP toxicity in human lung cells associated with cell migration, cell communication, cell junction assembly and regulation of cell death. Conclusions: Overall, BRM provides bioinformatics tools to assist biologists having minimal programming skills with analysis and integration of high-content omics’ data from various transcriptomic and proteomic platforms. BRM workflows were developed in Java and other open-source technologies and are served publicly using Apache Tomcat at https://cbb.pnnl.gov/brm/.

bioinformatics, MicroRNA, systems biology, Genomic↗

DOE OSTI2019

ISiCLE: A Quantum Chemistry Pipeline for Establishing in Silico Collision Cross Section Libraries

High throughput, comprehensive, and confident identifications of metabolites and other chemicals in biological and envi-ronmental samples will revolutionize our understanding of the role of these intriguing and chemically diverse molecules in biological systems. Despite recent technological advances, a metabolomics study still results in the detection of a dispropor-tionate number of features than cannot be confidently assigned to a chemical structure. This inadequacy is driven by the sin-gle most significant limitation in metabolomics: the reliance on reference libraries constructed through the analysis of au-thentic reference chemicals and the limited commercial availability of these compounds. To this end, we have developed the in silico chemical library engine (ISiCLE), a high-performance computing-friendly cheminformatics workflow for generating chemical properties. In the instantiation described here, we predict probable three-dimensional molecular conformers (i.e. conformational isomers) using chemical identifiers as input, from which collision cross sections (CCS) are derived. The ap-proach employs state-of-the-art first-principles simulation, distinguished by use of molecular dynamics, quantum chemistry, and ion mobility calculations to generate predictions, all without training data. Importantly, optimization of ISiCLE includ-ed a refactoring of the popular MOBCAL code for trajectory-based mobility calculations, improving its computational effi-ciency by over two orders of magnitude. Calculated CCS values were validated against 1,983 experimental CCS values and compared to previously reported CCS calculation approaches. Average CCS calculation absolute error is 3.2% with standard deviation 3.1%. An online database is introduced for sharing both calculated and experimental CCS values (metabolom-ics.pnnl.gov), initially including a CCS library with over 0.8M entries. Finally, three successful applications of molecule characterization using calculated CCS are described, including the identification of an environmental degradation product, the separation of molecular isomers, and the decoding of complex blinded mixtures of exposure chemicals. This work represents a promising method to address the limitations of small molecule identification, and offers alternatives to standards-based chemical identification amenable to high-performance computation.

metabolomics, standards-free, collision cross sect↗

DOE OSTI2019

Spatial distribution of prokaryotic communities in hypersaline soils

Increasing salinization in wetland systems is a major threat to ecosystem services carried out by microbial communities. Therefore, it is paramount to understand how salinity drives both microbial community structures and their diversity. Here we selected a range of highly saline soils (EC 1:5 from 5.96 to 61.02 dS/m) from the Odiel Saltmarshes (SW Spain) and evaluated how the prokaryotic community structures and diversity were correlated to soil salinity and other soil physicochemical features. We found that these soils harbored unique communities mainly comprised of halophiles from the phyla Euryarchaeota, Proteobacteria and Bacteroidetes . Among the most abundant genera we found groups known for thriving in other hypersaline environments (Halorubrum, Natronomonas and Salinibacter) as well as others from which limited environmental information is available (Salinimicrobium, Fodinibius). In the studied soils, several site-specific properties, but not salinity, were correlated to the microbial community structure and individual abundances of individual genera. Therefore, the dominant halophiles residing in these soils are already well-adapted to a wide range of salinities.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2018

Identification of metabolite and protein explanatory variables governing microbiome establishment and re-establishment within a cellulose-degrading anaerobic bioreactor

Proteins, metabolites, and 16S rRNA measurements were used to examine the community structure and functional relationships within a cellulose degrading anaerobic bioreactor. The bioreactor was seeded with bovine rumen fluid and operated with a 4 days hydraulic retention time on cellulose (avicel) as sole carbon and energy source. The reactor performance and microbial community structure was monitored during the establishment of the cellulose-degrading community. After stable operation was established in the bioreactor, the mixing intensity was increased in order to investigate the effect of a physical disruption of the microbial community structure. Lastly, the original conditions were re-established to understand the stability of the microbial community after a perturbation. All factors measured were found to be inter-correlated during these three distinct phases of operation (establishment, perturbation and re-establishment). In particular, the return of community structure and function to pre-perturbed conditions suggests that propionate fermentation and acetate utilization were the explanatory factors for community establishment and re-establishment.

09 BIOMASS FUELS↗

DOE OSTI2018

Dispersal limitation and thermodynamic constraints govern spatial structure of permafrost microbial communities

Understanding drivers of permafrost microbial community composition is critical for understanding permafrost microbiology and predicting ecosystem responses to thaw, however studies describing ecological controls on these communities are lacking. We hypothesize that permafrost communities are uniquely shaped by constraints imposed by prolonged freezing, and decoupled from factors that influence non-permafrost soil communities. To test this hypothesis, we characterized patterns of environmental variation and microbial community composition in permafrost across an Alaskan boreal forest landscape. We used null modeling to estimate the relative importance of selective and neutral assembly processes on community composition, and identified environmental factors influencing ecological selection through regression and structural equation modeling (SEM). Proportionally, the strongest process influencing community composition was dispersal limitation (0.36), exceeding the influence of homogenous selection (0.21), variable selection (0.16), and homogenizing dispersal (0.05). Fe(II) content was the most important factor explaining variable selection, and was significantly associated with total selection by univariate regression (R2=0.14, 41 p=0.003). SEM supported a model in which Fe(II) content mediated influences of the Gibbs free energy of the organic matter pool and organic acid concentration on total selection. These findings reveal that the processes shaping microbial communities in permafrost are distinct from those in non-permafrost soils, as the stability of the permafrost environment imposes dispersal and thermodynamic constraints on permafrost communities. Models of permafrost community composition will need to account for these unique drivers in order to predict community characteristics across permafrost landscapes, and in efforts to understand how pre-thaw conditions will influence post-thaw ecological and biogeochemical processes.

permafrost↗

DOE OSTI2017

ATLAS (Automatic Tool for Local Assembly Structures) - A Comprehensive Infrastructure for Assembly, Annotation, and Genomic Binning of Metagenomic and Metaranscripomic Data

ATLAS (Automatic Tool for Local Assembly Structures) is a comprehensive multiomics data analysis pipeline that is massively parallel and scalable. ATLAS contains a modular analysis pipeline for assembly, annotation, quantification and genome binning of metagenomics and metatranscriptomics data and a framework for reference metaproteomic database construction. ATLAS transforms raw sequence data into functional and taxonomic data at the microbial population level and provides genome-centric resolution through genome binning. ATLAS provides robust taxonomy based on majority voting of protein coding open reading frames rolled-up at the contig level using modified lowest common ancestor (LCA) analysis. ATLAS provides robust taxonomy based on majority voting of protein coding open reading frames rolled-up at the contig level using modified lowest common ancestor (LCA) analysis. ATLAS is user-friendly, easy install through bioconda maintained as open-source on GitHub, and is implemented in Snakemake for modular customizable workflows.

genome binning↗