Results for “genomic databases” — Page 7

DOE OSTI2023

A successful start for mLife

mLife made its debut with the publication of the inaugurating issue on March 30, 2022. We are pleased to have published 42 high-quality articles, including reviews, research articles, and application notes, which cover microbial genetics, biochemistry and physiology, ecology and evolution, pathogenic and public health microbiology, and so forth. Nine of the articles concern technical innovations and applications in areas including genome editing, single-cell analysis, and database. To pay tribute to the great microbiologist Carl Woese on the 10th anniversary of his death, the Journal published a special section containing three thought-provoking articles in the last issue of the year. mLife and its collaborating publisher Wiley have made great efforts to increase the Journal's national and continental representation. As a result, the Journal received submissions from over 20 countries in five continents last year. The authors of the published articles are from China, United States, Great Britain, France, Canada, Ger many, Japan, Switzerland, and Austria.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Multi-genome Phage Annotation Toolkit and Evaluator

Summary: To address the need for improved tools for annotation and comparative genomics of bacteriophage genomes, we developed multiPhATE2. As an extension of the multiPhATE code, multiPhATE2 includes comparative genomics codes for gene matching among sets of input bacteriophage genomes, and scales well to large input data sets due to incorporation of multiprocessing in the functional annotation and comparative genomics subsystems. Furthermore, additional search algorithms and databases have been added to the functional annotation subsystem. MultiPhATE2 was implemented in Python 3.7, and runs as a command-line code under Linux or MAC-OS.

Kimbrel, JeffreyA.↗

DOE OSTI2023

TranSyT , an innovative framework for identifying transport systems

The importance and rate of development of genome-scale metabolic models have been growing for the last few years, increasing the demand for software solutions that automate several steps of this process. However, since TRIAGE’s release, software development for the automatic integration of transport reactions into models has stalled. Here, in this paper, we present the Transport Systems Tracker (TranSyT). Unlike other transport systems annotation software, TranSyT does not rely on manual curation to expand its internal database, which is derived from highly curated records retrieved from the Transporters Classification Database and complemented with information from other data sources. TranSyT compiles information regarding transporter families and proteins, and derives reactions into its internal database, making it available for rapid annotation of complete genomes. All transport reactions have GPR associations and can be exported with identifiers from four different metabolite databases. TranSyT is currently available as a plugin for merlin v4.0 and an app for KBase.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Signatures of Mollicutes-related endobacteria in publicly available Mucoromycota genomes

ABSTRACT Mucoromycota fungi and their Mollicutes-related endobacteria (MRE) are an ideal system for studying bacterial–fungal interactions and evolution due to the long-term and intimate nature of their interactions. However, methods for detecting MRE face specific challenges due to the poor representation of MRE in sequencing databases coupled with the high sequence divergence of their genomes, making traditional similarity searches unreliable. This has precluded estimations on the diversity of MRE associated with Mucoromycota. To determine the prevalence of previously undetected MRE in fungal genome sequences, we scanned 389 Mucoromycota genome assemblies available from the National Center for Biotechnology Information for the presence of MRE sequences using publicly available tools to map contigs from fungal assemblies to publicly available MRE genomes. We demonstrate a higher diversity of MRE genomes than previously described in Mucoromycota and a lack of cophylogeny between MRE and the majority of their fungal hosts. This supports the late invasion hypothesis regarding MRE acquisition across most of the examined fungal families. In contrast with other Mucoromycota lineages, MRE from the Gigasporaceae displayed some degree of cophylogeny with their hosts, which may indicate that horizontal transmission is restricted between members of this family or that transmission is strictly vertical. These results underscore the need for a refined process to capture sequencing data from potential fungal endosymbionts to discern their evolution and transmission. Screens of fungal genomes for MRE can help improve the quality of fungal genome assemblies while identifying new MRE lineages to further test hypotheses on their origin and evolution. IMPORTANCE Mollicutes-related endobacteria (MRE) are obligate intracellular bacteria found within Mucoromycota fungi. Despite their frequent detection, MRE roles in host functioning are still unknown. Comparative genomic investigations can improve our understanding of the impact of MRE on their fungal hosts by identifying similarities and differences in MRE genome evolution. However, MRE genomes have only been assembled from a small fraction of Mucoromycota hosts. Here, we demonstrate that MRE can be present yet undetected in publicly available Mucoromycota genome assemblies. We use these newfound sequences to assess the broader diversity of MRE and their phylogenetic relationships with respect to their hosts. We demonstrate that publicly available tools can be used to extract novel MRE sequences from assembled fungal genomes leading to insights on MRE evolution. This work contributes to a greater understanding of the fungal microbiome, which is crucial to improving knowledge on the dynamics and impacts of fungi in microbial ecosystems.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

YEASTRACT+: a portal for the exploitation of global transcription regulation and metabolic model data in yeast biotechnology and pathogenesis

Abstract YEASTRACT+ (http://yeastract-plus.org/) is a tool for the analysis, prediction and modelling of transcription regulatory data at the gene and genomic levels in yeasts. It incorporates three integrated databases: YEASTRACT (http://yeastract-plus.org/yeastract/), PathoYeastract (http://yeastract-plus.org/pathoyeastract/) and NCYeastract (http://yeastract-plus.org/ncyeastract/), focused on Saccharomyces cerevisiae, pathogenic yeasts of the Candida genus, and non-conventional yeasts of biotechnological relevance. In this release, YEASTRACT+ offers upgraded information on transcription regulation for the ten previously incorporated yeast species, while extending the database to another pathogenic yeast, Candida auris. Since the last release of YEASTRACT+ (January 2020), a fourth database has been integrated. CommunityYeastract (http://yeastract-plus.org/community/) offers a platform for the creation, use, and future update of YEASTRACT-like databases for any yeast of the users’ choice. CommunityYeastract currently provides information for two Saccharomyces boulardii strains, Rhodotorula toruloides NP11 oleaginous yeast, and Schizosaccharomyces pombe 972h-. In addition, YEASTRACT+ portal currently gathers 304 547 documented regulatory associations between transcription factors (TF) and target genes and 480 DNA binding sites, considering 2771 TFs from 11 yeast species. A new set of tools, currently implemented for S. cerevisiae and C. albicans, is further offered, combining regulatory information with genome-scale metabolic models to provide predictions on the most promising transcription factors to be exploited in cell factory optimisation or to be used as novel drug targets. The expansion of these new tools to the remaining YEASTRACT+ species is ongoing.

Teixeira, Miguel Cacho (ORCID:0000000256766174)↗

DOE OSTI2020

Survey of Early-Diverging Lineages of Fungi Reveals Abundant and Diverse Mycoviruses

ABSTRACT Mycoviruses are widespread and purportedly common throughout the fungal kingdom, although most are known from hosts in the two most recently diverged phyla, Ascomycota and Basidiomycota, together called Dikarya. To augment our knowledge of mycovirus prevalence and diversity in underexplored fungi, we conducted a large-scale survey of fungi in the earlier-diverging lineages, using both culture-based and transcriptome-mining approaches to search for RNA viruses. In total, 21.6% of 333 isolates were positive for RNA mycoviruses. This is a greater proportion than expected based on previous taxonomically broad mycovirus surveys and is suggestive of a strong phylogenetic component to mycoviral infection. Our newly found viral sequences are diverse, composed of double-stranded RNA, positive-sense single-stranded RNA (ssRNA), and negative-sense ssRNA genomes and include novel lineages lacking representation in the public databases. These identified viruses could be classified into 2 orders, 5 families, and 5 genera; however, half of the viruses remain taxonomically unassigned. Further, we identified a lineage of virus-like sequences in the genomes of members of Phycomycetaceae and Mortierellales that appear to be novel genes derived from integration of a viral RNA-dependent RNA polymerase gene. The two screening methods largely agreed in their detection of viruses; thus, we suggest that the culture-based assay is a cost-effective means to quickly assess whether a laboratory culture is virally infected. This study used culture collections and publicly available transcriptomes to demonstrate that mycoviruses are abundant in laboratory cultures of early-diverging fungal lineages. The function and diversity of mycoviruses found here will help guide future studies into mycovirus origins and ecological functions. IMPORTANCE Viruses are key drivers of evolution and ecosystem function and are increasingly recognized as symbionts of fungi. Fungi in early-diverging lineages are widespread, ecologically important, and comprise the majority of the phylogenetic diversity of the kingdom. Viruses infecting early-diverging lineages of fungi have been almost entirely unstudied. In this study, we screened fungi for viruses by two alternative approaches: a classic culture-based method and by transcriptome-mining. The results of our large-scale survey demonstrate that early-diverging lineages have higher infection rates than have been previously reported in other fungal taxa and that laboratory strains worldwide are host to infections, the implications of which are unknown. The function and diversity of mycoviruses found in these basal fungal lineages will help guide future studies into mycovirus origins and their evolutionary ramifications and ecological impacts.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Chicken Production and Human Clinical Escherichia coli Isolates Differ in Their Carriage of Antimicrobial Resistance and Virulence Factors

Contamination of food animal products by Escherichia coli is a leading cause of foodborne disease outbreaks, hospitalizations, and deaths in humans. Chicken is the most consumed meat both in the United States and across the globe according to the U.S. Department of Agriculture. Although E. coli is a ubiquitous commensal bacterium of the guts of humans and animals, its ability to acquire antimicrobial resistance (AMR) genes and virulence factors (VFs) can lead to the emergence of pathogenic strains that are resistant to critically important antibiotics. Thus, it is important to identify the genetic factors that contribute to the virulence and AMR of E. coli. In this study, we performed in-depth genomic evaluation of AMR genes and VFs of E. coli genomes available through the National Antimicrobial Resistance Monitoring System GenomeTrackr database. Our objective was to determine the genetic relatedness of chicken production isolates and human clinical isolates. To achieve this aim, we first developed a massively parallel analytical pipeline (Reads2Resistome) to accurately characterize the resistome of each E. coli genome, including the AMR genes and VFs harbored. We used random forests and hierarchical clustering to show that AMR genes and VFs are sufficient to classify isolates into different pathogenic phylogroups and host origin. We found that the presence of key type III secretion system and AMR genes differentiated human clinical isolates from chicken production isolates. These results further improve our understanding of the interconnected role AMR genes and VFs play in shaping the evolution of pathogenic E. coli strains.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Gaia: An AI-enabled genomic context–aware platform for protein sequence annotation

Protein sequence similarity search is fundamental to biology research, but current methods are typically not able to consider crucial genomic context information indicative of protein function, especially in microbial systems. Here, we present Gaia (Genomic AI Annotator), a sequence annotation platform that enables rapid, context-aware protein sequence search across genomic datasets. Gaia leverages gLM2, a mixed-modality genomic language model trained on both amino acid sequences and their genomic neighborhoods to generate embeddings that integrate sequence-structure-context information. This approach allows for the identification of functionally and/or evolutionarily related genes that are found in conserved genomic contexts, which may be missed by traditional sequence- or structure-based search alone. Gaia enables real-time search of a curated database comprising more than 85 million protein clusters from 131,744 microbial genomes. We compare the homolog retrieval performance of Gaia search against other embedding and alignment-based approaches. We provide Gaia as a web-based, freely available tool.

Jha, Nishant↗

DOE OSTI2023

Reclassification of ASFV into 7 Biotypes Using Unsupervised Machine Learning

In 2007, an outbreak of African swine fever (ASF), a deadly disease of domestic swine and wild boar caused by the African swine fever virus (ASFV), occurred in Georgia and has since spread globally. Historically, ASFV was classified into 25 different genotypes. However, a newly proposed system recategorized all ASFV isolates into 6 genotypes exclusively using the predicted protein sequences of p72. However, ASFV has a large genome that encodes between 150–200 genes, and classifications using a single gene are insufficient and misleading, as strains encoding an identical p72 often have significant mutations in other areas of the genome. We present here a new classification of ASFV based on comparisons performed considering the entire encoded proteome. A curated database consisting of the protein sequences predicted to be encoded by 220 reannotated ASFV genomes was analyzed for similarity between homologous protein sequences. Weights were applied to the protein identity matrices and averaged to generate a genome-genome identity matrix that was then analyzed by an unsupervised machine learning algorithm, DBSCAN, to separate the genomes into distinct clusters. We conclude that all available ASFV genomes can be classified into 7 distinct biotypes.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Unveiling the microbial realm with VEBA 2.0: a modular bioinformatics suite for end-to-end genome-resolved prokaryotic, (micro)eukaryotic and viral multi-omics from either short- or long-read sequencing

Abstract The microbiome is a complex community of microorganisms, encompassing prokaryotic (bacterial and archaeal), eukaryotic, and viral entities. This microbial ensemble plays a pivotal role in influencing the health and productivity of diverse ecosystems while shaping the web of life. However, many software suites developed to study microbiomes analyze only the prokaryotic community and provide limited to no support for viruses and microeukaryotes. Previously, we introduced the Viral Eukaryotic Bacterial Archaeal (VEBA) open-source software suite to address this critical gap in microbiome research by extending genome-resolved analysis beyond prokaryotes to encompass the understudied realms of eukaryotes and viruses. Here we present VEBA 2.0 with key updates including a comprehensive clustered microeukaryotic protein database, rapid genome/protein-level clustering, bioprospecting, non-coding/organelle gene modeling, genome-resolved taxonomic/pathway profiling, long-read support, and containerization. We demonstrate VEBA’s versatile application through the analysis of diverse case studies including marine water, Siberian permafrost, and white-tailed deer lung tissues with the latter showcasing how to identify integrated viruses. VEBA represents a crucial advancement in microbiome research, offering a powerful and accessible software suite that bridges the gap between genomics and biotechnological solutions.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

The parallelism motifs of genomic data analysis

Genomic datasets are growing dramatically as the cost of sequencing continues to decline and small sequencing devices become available. Enormous community databases store and share these data with the research community, but some of these genomic data analysis problems require large-scale computational platforms to meet both the memory and computational requirements. These applications differ from scientific simulations that dominate the workload on high-end parallel systems today and place different requirements on programming support, software libraries and parallel architectural design. For example, they involve irregular communication patterns such as asynchronous updates to shared data structures. We consider several problems in high-performance genomics analysis, including alignment, profiling, clustering and assembly for both single genomes and metagenomes. We identify some of the common computational patterns or ‘motifs’ that help inform parallelization strategies and compare our motifs to some of the established lists, arguing that at least two key patterns, sorting and hashing, are missing. This article is part of a discussion meeting issue ‘Numerical algorithms for high-performance computational science’.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Novel Gloeobacterales spp. from Diverse Environments across the Globe

Photosynthetic Cyanobacteria and their descendants are the only known organisms capable of oxygenic photosynthesis. Their metabolism permanently changed the Earth’s surface and the evolutionary trajectory of life, but little is known about their evolutionary history. Genomes of the Gloeobacterales, an order of deeply divergent photosynthetic Cyanobacteria, may hold clues about the evolutionary process. However, there are only three published genomes within this order, and it is difficult to make broad inferences based on such little data. Here, I describe five species within the Gloeobacterales retrieved from publicly available databases and examine their photosynthetic gene content and the environments in which Gloeobacterales genomes and 16S rRNA gene sequences are found. The Gloeobacterales contain reduced photosystems and inhabit cold, wet-rock, and low-light environments. They are likely present in low abundances due to their low growth rate. Future searches for Gloeobacterales should target these environments, and samples should be deeply sequenced to capture the low-abundance taxa. Publicly available databases contain undescribed taxa within the Gloeobacterales. However, searching through all available data with current methods is computationally expensive. Therefore, new methods must be developed to search for these and other evolutionarily important taxa. Once identified, these novel photosynthetic Cyanobacteria will help illuminate the origin and evolution of oxygenic photosynthesis. Early branching photosynthetic Cyanobacteria such as the Gloeobacterales may provide clues into the evolutionary history of oxygenic photosynthesis, but there are few genomes or cultured taxa from this order. Five new metagenome-assembled genomes suggest that members of the Gloeobacterales all contain reduced photosystems and lack genes associated with thylakoids and circadian rhythms. Their distribution suggests that they may thrive in environments that are marginal for other species, including wet-rock and cold environments. These traits may aid in the discovery and cultivation of novel species in this clade.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Plant sulfate transporter protein sequences for phylogenetic analysis

Sulfur is an essential macronutrient that supports plant growth, development, and responses to environmental stress. Sulfate is the predominant inorganic form of sulfur in soils, and its uptake by roots and translocation to shoots are facilitated by the sulfate transporter (SULTR) family of proteins. Although the first plant SULTR gene was identified nearly three decades ago, several subfamily members, particularly those in the expansive and angiosperm-specific SULTR3 group, remain poorly characterized. To support comprehensive phylogenetic and sequence-based analyses, we compiled a curated dataset of 262 SULTR protein sequences from 22 plant species spanning the evolutionary breadth of land plants. This collection includes representatives from two basal lineages, two early-divergent angiosperms, six monocots, and ten dicots. All sequences were extracted from genome assemblies available in Phytozome v13 (Joint Genome Institute) and manually curated, with cross-referencing to additional databases such as NCBI when needed. This dataset provides a valuable resource for reconstructing the evolutionary history of the SULTR family, with particular emphasis on the diversification of SULTR3 transporters in flowering plants. This resource may also support functional annotation, comparative genomics, and structural modeling of sulfate transport proteins.

CBI↗

DOE OSTI2024

Genomic fingerprints of the world’s soil ecosystems

Despite the explosion of soil metagenomic data, we lack a synthesized understanding of patterns in the distribution and functions of soil microorganisms. These patterns are critical to predictions of soil microbiome responses to climate change and resulting feedbacks that regulate greenhouse gas release from soils. To address this gap, we assay 1,512 manually curated soil metagenomes using complementary annotation databases, read-based taxonomy, and machine learning to extract multidimensional genomic fingerprints of global soil microbiomes. Our objective is to uncover novel biogeographical patterns of soil microbiomes across environmental factors and ecological biomes with high molecular resolution. We reveal shifts in the potential for (i) microbial nutrient acquisition across pH gradients; (ii) stress-, transport-, and redox-based processes across changes in soil bulk density; and (iii) greenhouse gas emissions across biomes. We also use an unsupervised approach to reveal a collection of soils with distinct genomic signatures, characterized by coordinated changes in soil organic carbon, nitrogen, and cation exchange capacity and in bulk density and clay content that may ultimately reflect soil environments with high microbial activity. Genomic fingerprints for these soils highlight the importance of resource scavenging, plant-microbe interactions, fungi, and heterotrophic metabolisms. Across all analyses, we observed phylogenetic coherence in soil microbiomes—more closely related microorganisms tended to move congruently in response to soil factors. Collectively, the genomic fingerprints uncovered here present a basis for global patterns in the microbial mechanisms underlying soil biogeochemistry and help beget tractable microbial reaction networks for incorporation into process-based models of soil carbon and nutrient cycling.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design

The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using density functional theory (DFT), classical force-fields (FF), and machine learning (ML) techniques. JARVIS is motivated by the Materials Genome Initiative (MGI) principles of developing open-access databases and tools to reduce the cost and development time of materials discovery, optimization, and deployment. The major features of JARVIS are: JARVIS-DFT, JARVIS-FF, JARVIS-ML, and JARVIS-tools. To date, JARVIS consists of ≈40,000 materials and ≈1 million calculated properties in JARVIS-DFT, ≈500 materials and ≈110 force-fields in JARVIS-FF, and ≈25 ML models for material-property predictions in JARVIS-ML, all of which are continuously expanding. JARVIS-tools provides scripts and workflows for running and analyzing various simulations. We compare our computational data to experiments or high-fidelity computational methods wherever applicable to evaluate error/uncertainty in predictions. In addition to the existing workflows, the infrastructure can support a wide variety of other technologically important applications as part of the data-driven materials design paradigm. The JARVIS datasets and tools are publicly available at the website: https://jarvis.nist.gov .

36 MATERIALS SCIENCE↗

DOE OSTI2023

Data for EMSL Project 60929 from August 2023: PI Goemann MONet Request

Just as humans rely on a healthy gut microbiome for resilience to illness, plants rely on a healthy root microbiome for resilience to environmental abiotic stress (heat, drought). To achieve a healthy root microbiome, plants release carbon (C)-rich compounds as root exudates to stimulate microbial activity and increase local nutrient mineralization. However, the enhanced performance comes at a cost: up to 44% of a plant’s C can be lost to root exudates, diverting C from plant growth and respiration. Critical knowledge gaps include how the ‘C cost’ is managed and how root exudates alter the microbiome under different environmental conditions. In addition, historical climate conditions, particularly mean annual precipitation, is known to shape local soil microbiomes and alter their sensitivity to drought. Therefore, studies that better characterize the plant-microbe responses to environmental stress will aid in efforts to harness the microbiome to improve crop resilience. However, current knowledge gaps make it challenging to engineer beneficial plant-microbe interactions to improve plant productivity in agricultural systems and to predict how increased climate variability will alter terrestrial C fluxes and climate feedbacks. To fill this knowledge gap our research group at Montana State University – Bozeman is currently studying blue grama (Bouteloua gracilis), a prairie grass native across the Northern Great Plains, as a model for drought tolerance. Our goal is to investigate the above- and belowground responses of blue grama to drought and heat stress to improve our understanding of stress-induced carbon allocation and plant-microbe interactions. Most recently, we investigated the influence of climate history on the blue grama drought response. We collected soil from three blue grama-dominated sites (those proposed to sample here) across a 150 mm mean annual precipitation gradient in SW Montana, USA, to use as inoculum for a greenhouse drought experiment. Preliminary results indicate that soil climate history has a strong influence on the blue grama physiological response to drought as well as on the chemical composition of root exudates and rhizosphere microbiome composition. Metabarcoding data from this experiment is scheduled to be submitted to public databases within the next year. Having in-depth analyses of the soil biogeochemistry and metagenomic composition through the MONet project at each of the field sites associated with this experiment will allow us to link underlying ecological processes with observed patterns of plant growth and productivity at each site. In addition, we plan to utilize the MONet database for future meta-analyses to compare the genomic and biogeochemical signatures of our field sites to others across a wider precipitation gradient throughout the native range of blue grama. This will further provide critical insights into the mechanisms that drive ecosystem functioning and resilience to drought stress.

Peyton, Brent↗

DOE OSTI2023

Data for EMSL Project 60929 from August 2023: PI Goemann MONet Request

Just as humans rely on a healthy gut microbiome for resilience to illness, plants rely on a healthy root microbiome for resilience to environmental abiotic stress (heat, drought). To achieve a healthy root microbiome, plants release carbon (C)-rich compounds as root exudates to stimulate microbial activity and increase local nutrient mineralization. However, the enhanced performance comes at a cost: up to 44% of a plant’s C can be lost to root exudates, diverting C from plant growth and respiration. Critical knowledge gaps include how the ‘C cost’ is managed and how root exudates alter the microbiome under different environmental conditions. In addition, historical climate conditions, particularly mean annual precipitation, is known to shape local soil microbiomes and alter their sensitivity to drought. Therefore, studies that better characterize the plant-microbe responses to environmental stress will aid in efforts to harness the microbiome to improve crop resilience. However, current knowledge gaps make it challenging to engineer beneficial plant-microbe interactions to improve plant productivity in agricultural systems and to predict how increased climate variability will alter terrestrial C fluxes and climate feedbacks. To fill this knowledge gap our research group at Montana State University – Bozeman is currently studying blue grama (Bouteloua gracilis), a prairie grass native across the Northern Great Plains, as a model for drought tolerance. Our goal is to investigate the above- and belowground responses of blue grama to drought and heat stress to improve our understanding of stress-induced carbon allocation and plant-microbe interactions. Most recently, we investigated the influence of climate history on the blue grama drought response. We collected soil from three blue grama-dominated sites (those proposed to sample here) across a 150 mm mean annual precipitation gradient in SW Montana, USA, to use as inoculum for a greenhouse drought experiment. Preliminary results indicate that soil climate history has a strong influence on the blue grama physiological response to drought as well as on the chemical composition of root exudates and rhizosphere microbiome composition. Metabarcoding data from this experiment is scheduled to be submitted to public databases within the next year. Having in-depth analyses of the soil biogeochemistry and metagenomic composition through the MONet project at each of the field sites associated with this experiment will allow us to link underlying ecological processes with observed patterns of plant growth and productivity at each site. In addition, we plan to utilize the MONet database for future meta-analyses to compare the genomic and biogeochemical signatures of our field sites to others across a wider precipitation gradient throughout the native range of blue grama. This will further provide critical insights into the mechanisms that drive ecosystem functioning and resilience to drought stress.

Peyton, Brent↗

DOE OSTI2023

Data for EMSL Project 60929 from August 2023: PI Goemann MONet Request

Just as humans rely on a healthy gut microbiome for resilience to illness, plants rely on a healthy root microbiome for resilience to environmental abiotic stress (heat, drought). To achieve a healthy root microbiome, plants release carbon (C)-rich compounds as root exudates to stimulate microbial activity and increase local nutrient mineralization. However, the enhanced performance comes at a cost: up to 44% of a plant’s C can be lost to root exudates, diverting C from plant growth and respiration. Critical knowledge gaps include how the ‘C cost’ is managed and how root exudates alter the microbiome under different environmental conditions. In addition, historical climate conditions, particularly mean annual precipitation, is known to shape local soil microbiomes and alter their sensitivity to drought. Therefore, studies that better characterize the plant-microbe responses to environmental stress will aid in efforts to harness the microbiome to improve crop resilience. However, current knowledge gaps make it challenging to engineer beneficial plant-microbe interactions to improve plant productivity in agricultural systems and to predict how increased climate variability will alter terrestrial C fluxes and climate feedbacks. To fill this knowledge gap our research group at Montana State University – Bozeman is currently studying blue grama (Bouteloua gracilis), a prairie grass native across the Northern Great Plains, as a model for drought tolerance. Our goal is to investigate the above- and belowground responses of blue grama to drought and heat stress to improve our understanding of stress-induced carbon allocation and plant-microbe interactions. Most recently, we investigated the influence of climate history on the blue grama drought response. We collected soil from three blue grama-dominated sites (those proposed to sample here) across a 150 mm mean annual precipitation gradient in SW Montana, USA, to use as inoculum for a greenhouse drought experiment. Preliminary results indicate that soil climate history has a strong influence on the blue grama physiological response to drought as well as on the chemical composition of root exudates and rhizosphere microbiome composition. Metabarcoding data from this experiment is scheduled to be submitted to public databases within the next year. Having in-depth analyses of the soil biogeochemistry and metagenomic composition through the MONet project at each of the field sites associated with this experiment will allow us to link underlying ecological processes with observed patterns of plant growth and productivity at each site. In addition, we plan to utilize the MONet database for future meta-analyses to compare the genomic and biogeochemical signatures of our field sites to others across a wider precipitation gradient throughout the native range of blue grama. This will further provide critical insights into the mechanisms that drive ecosystem functioning and resilience to drought stress.

Peyton, Brent↗