Biology subjects

Borton, M. A.

Publications and source records attributed to Borton, M. A..

23 records · Page 2Linked to original sources

bioRxiv2022

kb_DRAM: Annotating and functional profiling of genomes with DRAM in KBase

SummaryAnnotation is predicting the location of and assigning function to genes in a genome. DRAM is a tool developed to annotate bacterial, archaeal and viral genomes derived from pure cultures or metagenomes. DRAM distills multiple gene annotations to summaries of functional potential. Despite these benefits, a downside of DRAM is processing requires large computation resources, which limits its accessibility, and it did not integrate with downstream metabolic modelling tools. To alleviate these constraints, DRAM and the viral counterpart, DRAM-v, are now available and integrated in the freely accessible KBase cyberinfrastructure. With kb_DRAM users can generate DRAM annotations and functional summaries from microbial or viral genomes in a point and click interface, as well as generate genome scale metabolic models from these DRAM annotations. Availability and ImplementationThe kb_DRAM software is available at https://github.com/shafferm/kb_DRAM. The kb_DRAM apps on KBase can be found in the catalog at https://narrative.kbase.us/#catalog/modules/kb_DRAM. A narrative with examples of running all KBase apps is available at https://kbase.us/n/88325/84/. ContactMichael Shaffer, michael.t.shaffer@colostate.edu; Kelly Wrighton, kelly.wrighton@colostate.edu Supplementary InformationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2021

Variation in root exudate composition influences soil microbiome membership and function

Root exudation is one of the primary processes that mediate interactions between plant roots, microorganisms, and the soil matrix. Previous research has shown that plant root exudate profiles vary between species and genotypes which can likely support different microbial associations. Here, utilizing distinct sorghum genotypes as a model system, we characterized the chemical heterogeneity between root exudates and the effects of that variability on soil microbial membership and metabolisms. Distinct exudate chemical profiles were quantified and used to formulate synthetic root exudate treatments, a High Organic acid Treatment (HOT) and a High Sugar Treatment (HST). Root exudate treatments were added to laboratory soil reactors and 16S rRNA gene profiling illustrated distinct microbial membership in response to HST or HOT amendments. Alpha and beta diversity metrics were significantly different between treatments, (Shannons, p < 0.0001, mrpp = 0.01, respectively). Exometabolite production was highest in the HST, with increased production of key organic acids, non-proteinogenic amino acids, and three plant growth-promoting phytohormones (benzoic acid, salicylic acid, indole-3-acetic acid), suggesting plant-derived sugars fuel microbial carbon metabolism and contribute to phytohormone production. Linking the metabolic capacity of metagenome-assembled genomes in the HST to the exometabolite patterns, we identified potential plant growth-promoting microorganisms that could produce these phytohormones. Our findings emphasize the tractability of high-resolution multi-omics tools to investigate soil microbiomes, opening the possibility of manipulating native microbial communities to improve specific soil microbial functions and enhance crop production. ImportanceUnderstanding interactions between plant root exudates and the soil microbiome provides an avenue for a more comprehensive appreciation for how plant roots modulate their microbial counterparts to promote an environment favorable to plant fitness. Although these dynamics are appreciated as indispensable, mechanisms controlling specific rhizobiome membership and complexity are not fully understood. In this study, we investigate how variability in root exudation, modeled after differences observed between distinct sorghum genotypes, contributes to altered microbial membership and metabolisms. The results demonstrate how microbial diversity is influenced by root exudates of differing chemical composition and how changes in microbial membership correspond to modifications in carbon utilization and enhance production of plant-relevant metabolites. Our findings suggest carbon substrate preferences among bacteria in semi-arid climate soils and mechanisms for root exudate utilization. These findings provide new information on plant-soil environments useful for the development of efficient and precise microbiota management strategies in agricultural systems.

plant biology↗

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

DRAM for distilling microbial metabolism to automate the curation of microbiome function

Microbial and viral communities transform the chemistry of Earths ecosystems, yet the specific reactions catalyzed by these biological engines are hard to decode due to the absence of a scalable, metabolically resolved, annotation software. Here, we present DRAM (Distilled and Refined Annotation of Metabolism), a framework to translate the deluge of microbiome-based genomic information into a catalog of microbial traits. To demonstrate the applicability of DRAM across metabolically diverse genomes, we evaluated DRAM performance on a defined, in silico soil community and previously published human gut metagenomes. We show that DRAM accurately assigned microbial contributions to geochemical cycles, and automated the partitioning of gut microbial carbohydrate metabolism at substrate levels. DRAM-v, the viral mode of DRAM, established rules to identify virally-encoded auxiliary metabolic genes (AMGs), resulting in the metabolic categorization of thousands of putative AMGs from soils and guts. Together DRAM and DRAM-v provide critical metabolic profiling capabilities that decipher mechanisms underpinning microbiome function.

microbiology↗

bioRxiv2019

Clades of huge phage from across Earth’s ecosystems

Phage typically have small genomes and depend on their bacterial hosts for replication. DNA sequenced from many diverse ecosystems revealed hundreds of huge phage genomes, between 200 kbp and 716 kbp in length. Thirty-four genomes were manually curated to completion, including the largest phage genomes yet reported. Expanded genetic repertoires include diverse and new CRISPR-Cas systems, tRNAs, tRNA synthetases, tRNA modification enzymes, translation initiation and elongation factors, and ribosomal proteins. Phage CRISPR-Cas systems have the capacity to silence host transcription factors and translational genes, potentially as part of a larger interaction network that intercepts translation to redirect biosynthesis to phage-encoded functions. In addition, some phage may repurpose bacterial CRISPR-Cas systems to eliminate competing phage. We phylogenetically define major clades of huge phage from human and other animal microbiomes, oceans, lakes, sediments, soils and the built environment. We conclude that their large gene inventories reflect a conserved biological strategy, observed over a broad bacterial host range and across Earths ecosystems.

microbiology↗