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DOE OSTI2024

Transcriptomic Analysis of Arachidonic Acid Pathway Genes Provides Mechanistic Insight into Multi-Organ Inflammatory and Vascular Diseases

Arachidonic acid (AA) metabolites have been associated with several diseases across various organ systems, including the cardiovascular, pulmonary, and renal systems. Lipid mediators generated from AA oxidation have been studied to control macrophages, T-cells, cytokines, and fibroblasts, and regulate inflammatory mediators that induce vascular remodeling and dysfunction. AA is metabolized by cyclooxygenase (COX), lipoxygenase (LOX), and cytochrome P450 (CYP) to generate anti-inflammatory, pro-inflammatory, and pro-resolutory oxidized lipids. As comorbid states such as diabetes, hypertension, and obesity become more prevalent in cardiovascular disease, studying the expression of AA pathway genes and their association with these diseases can provide unique pathophysiological insights. In addition, the AA pathway of oxidized lipids exhibits diverse functions across different organ systems, where a lipid can be both anti-inflammatory and pro-inflammatory depending on the location of metabolic activity. Therefore, we aimed to characterize the gene expression of these lipid enzymes and receptors throughout multi-organ diseases via a transcriptomic meta-analysis using the Gene Expression Omnibus (GEO) Database. In our study, we found that distinct AA pathways were expressed in various comorbid conditions, especially those with prominent inflammatory risk factors. Comorbidities, such as hypertension, diabetes, and obesity appeared to contribute to elevated expression of pro-inflammatory lipid mediator genes. Our results demonstrate that expression of inflammatory AA pathway genes may potentiate and attenuate disease; therefore, we suggest further exploration of these pathways as therapeutic targets to improve outcomes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Populus VariantDB v3.2 facilitates CRISPR and functional genomics research

The success of CRISPR genome editing studies depends critically on the precision of guide RNA (gRNA) design. Sequence polymorphisms in outcrossing tree species pose design hazards that can render CRISPR genome editing ineffective. Despite recent advances in tree genome sequencing with haplotype resolution, sequence polymorphism information remains largely inaccessible to various functional genomics research efforts. The Populus VariantDB v3.2 addresses these challenges by providing a user-friendly search engine to query sequence polymorphisms of heterozygous genomes. The database accepts short sequences, such as gRNAs and primers, as input for searching against multiple poplar genomes, including hybrids, with customizable parameters. We provide examples to showcase the utilities of VariantDB in improving the precision of gRNA or primer design. The platform-agnostic nature of the probe search design makes Populus VariantDB v3.2 a versatile tool for the rapidly evolving CRISPR field and other sequence-sensitive functional genomics applications. The database schema is expandable and can accommodate additional tree genomes to broaden its user base.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

GLBRC Soil Yearlong Incubation 13C-SIP-Lipidomics

Data package for Lipids represent a dynamic, yet stable pool of microbially-derived soil carbon This data is published under a CC0 license. The authors encourage data reuse and request attribution by referencing the below citations for the data packages and associated manuscript. Please cite as: Rempfert KR, Bell SL, Kasanke CP, Kyle JE, Hofmockel KS. 2025. GLBRC Soil Yearlong Incubation 13C-SIP-Lipidomics. [Data Set] PNNL DataHub. doi: Rempfert KR, Bell SL, Kasanke CP, Kyle JE, Hofmockel KS. 2025. MSV000097435: GLBRC soil yearlong incubation 13C-SIP-Lipidomics [Data Set] MassIVE. doi:10.25345/C57659T3K Rempfert KR, Bell SL, Kasanke CP, Kyle JE, Hofmockel KS. 2025. Lipids represent a dynamic, yet stable pool of microbially-derived soil carbon. In Prep This data package consists of compound-specific 13C SIP-lipidomics data from a yearlong tracer incubation experiment designed to investigate microbial lipid persistence in switchgrass bioenergy crop soils. In order to explore how lipid structure may modulate the persistence of C in soil lipids, we leveraged soils from two sites (Michigan - sandy texture, Wisconsin - silty texture) operated by the U.S. Department of Energy-funded Great Lakes Bioenergy Research Center (GLBRC). These sites had comparable climates, identical management practices, but contrasting soil textures, allowing us to assess the variability of lipid accrual or degradation in soils as well as provide insight regarding the degree to which edaphic properties may regulate the retention of soil lipids. Untargeted lipidomics analyses were performed to identify 13C-labeled lipids in the soil microbiome after long-term incubation. Soils were supplemented with 100 micrograms glucose per gram dry soil (99 atom % 13C or natural abundance for paired control) and incubated; samples were collected two months and one year after glucose addition. Lipid extracts (MPLEx) were analyzed by LC-MS/MS and identified using LIQUID. Calculation of isotopic enrichment of lipids was performed by targeted approach using TarMet to quantify lipid isotopologues and IsoCorrectoR to correct for natural abundance isotopes. Contents: Data package contents reported here are the first version and contain downstream analysis files for the raw LC-MS mass spectrometry files (.mzXML) deposited at the MassIVE database repository under accession MSV000097435 (80 experimental runs; 5.85 GB) | MassIVE DOI: 10.25345/C57659T3K. Support files include the additional data download 'Read Me' file containing data descriptor information. Reported data download contents are structured for compliance with project data sharing guidelines, community standards initiatives, and sponsor stakeholder policies supporting FAIR data principles. Data processing software, analysis tools, and data workflows are listed below corresponding to the host repository long-term location. Available Data Downloads (0.3 GB): "GLBRC soil yearlong incubation 13C-SIP-Lipidomics_readme.txt" - 'Read Me' data package content file (txt) "GLBRC_DataPackage_analysis files" - Data processing files (Rmd) and saved intermediate data processing outputs (rds, csv, xlsx) "GLBRC_13C_lipidomics_dataset.xlsx" - processed data in tabular format (xlsx) Linked Software: LIQUID LC-MS Analysis Software | 10.5281/zenodo.6459462 Lipid Mini-On Software Tools | 10.5281/zenodo.1492803 pmartR Omics Statistical Software | 10.5281/zenodo.6108667 xcms (v4.3.3) TarMet (v1.1.1) IsoCorrectoR (1.24.0) Funding Acknowledgments: This research was supported by an Early Career Research Program award funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research (OBER) Genomic Science program under FWP 68292, FWP 07880 and EMSL Exploratory Research Project 51095. A portion of this work was performed in the William R. Wiley Environmental Molecular Sciences Laboratory, a national scientific user facility sponsored by OBER and located at Pacific Northwest National Laboratory (PNNL). PNNL is a multi-program national laboratory operated by Battelle for the DOE under Contract DE-AC05-76RLO1830.

Rempfert, Kaitlin R [Pacific Northwest National La↗
DOE OSTI2025

The Global Spectra-Trait Initiative: A database of paired leaf spectroscopy and functional traits associated with leaf photosynthetic capacity (v1.0.0)

The Global Spectra-Trait Initiative (GSTI) aims to generate generalizable spectra trait models using reflectance data to predict leaf traits associated with the photosynthesis capacity of leaves. It comprises a synthesized dataset of leaf trait data, input datasets and code. Leaf traits include the maximum carboxylation rate of rubisco (Vcmax), the maximum electron transport rate (Jmax), the dark respiration, as well as the prediction of leaf nitrogen, leaf mass per area (LMA), and leaf water content (LWC). The dataset comprises >7500 paired observations from around 400 species from a broad range of biomes. This dataset comprises a zip file of the GSTI GitHub repository (https://github.com/plantphys/gsti), the synthesized database (.csv) and database metadata files. This dataset was updated on 2025-12-12 with minor edits to mirror the accepted manuscript version and GitHub release (Version 1.0.0 (ESSD accepted version)). Edits included minor changes to the project documentation on GitHub and removal of 12 duplicate entries from the database.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

PAVC Gridded 20m Alaska NGEE Tier3 PFTs v1.0

These 20-meter spatial resolution gridded products provide per-pixel fractional cover (%) of Next Generation Ecosystem Experiments (NGEE) Arctic Plant Functional Types (PFTs) Tier 3 across Alaska, north of the boreal treeline. The products were developed for the NGEE Arctic project, which is improving Arctic vegetation representation and parameterization of the E3SM Land Model. This dataset includes 8 files containing fractional cover for NGEE Tier 3 PFTs (https://data.ess-dive.lbl.gov/view/doi:10.15485/2529470): (1) bryophytes; (2) lichens; (3) non-vascular plants, i.e., the sum of lichens and bryophytes; (4) deciduous shrubs, (5) evergreen shrubs, (6) forbs, (7) graminoids, and a non-PFT class, (8) litter. Each pixel contains the percent cover (expressed as a fraction of total ground cover) that was predicted by random-forest regression models. The random-forest models were trained on cover data collected at 978 plots from 2010 to 2021, of which are archived in the Pan-Arctic Vegetation Cover (PAVC) database (https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2483557). The plot cover was linked to 20-meter spatial resolution, satellite-derived predictor variables: Sentinel-2 spectra and Sentinel-1 polarizations averaged over the 2019 growing season, as well as topographical features derived from ArcticDEM. Then, spatio-temporally anomalous plot data that introduced large variability to the regression outcomes were dropped using the Cook’s distance outlier detection method, and the models were re-created using high-quality plots and their associated satellite derived explanatory variables per each PFT. The correlations between plot-observed and satellite-derived fractional cover for all PFTs were well correlated (R2 = 0.69–0.95 and 0.5 for litter) and had low RMSE bias (0.02–0.11). This research was performed as a part of the NGEE Arctic project. The NGEE Arctic project was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Genomes OnLine Database (GOLD) v.10: new features and updates

The Genomes OnLine Database (GOLD; https://gold.jgi.doe.gov/) at the Department of Energy Joint Genome Institute is a comprehensive online metadata repository designed to catalog and manage information related to (meta)genomic sequence projects. GOLD provides a centralized platform where researchers can access a wide array of metadata from its four organization levels namely Study, Organism/Biosample, Sequencing Project and Analysis Project. GOLD continues to serve as a valuable resource and has seen significant growth and expansion since its inception in 1997. With its expanded role as a collaborative platform, it not only actively imports data from other primary repositories like National Center for Biotechnology Information but also supports contributions from researchers worldwide. This collaborative approach has enriched the database with diverse datasets, creating a more integrated resource to enhance scientific insights. As genomic research becomes increasingly integral to various scientific disciplines, more researchers and institutions are turning to GOLD for their metadata needs. To meet this growing demand, GOLD has expanded by adding diverse metadata fields, intuitive features, advanced search capabilities and enhanced data visualization tools, making it easier for users to find and interpret relevant information. This manuscript provides an update and highlights the new features introduced over the last 2 years.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Data from TropiRoot 1.0 database: tropical root characteristics across environments

TropiRoot 1.0 is a new tropical root database with root characteristics across environment gradients. It has data extracted from 104 new sources, resulting in more than 8000 rows of data (either species or community data). Most of the data in TropiRoot 1.0 includes root characteristics such as root biomass, morphology, root dynamics, mass fraction, architecture, anatomy, physiology and root chemistry. This initiative represents an approximately 30% increase in the currently available data for tropical roots in the Fine Root Ecology Database (FRED). TropiRoot 1.0, contains root characteristics from 25 different countries where seven are located in Asia, six in South America, five in Central America and the Caribbean, four in Africa, two in North America, and 1 in Oceania. Due to the volume of data, when ancillary data was available, including soil data, these data was either extracted and included in the database or their availability was recorded in an additional column. Multiple contributors checked the entries for outliers during the collation process to ensure data quality. For text-based observations, we examined all cells to ensure that their content relates to their specific categories. For numerical observations, we ordered each numerical value from least to greatest and plotted the values, checking apparent outliers against the data in their respective sources and correcting or removing incorrect or impossible values. Some data (soil and aboveground) have different columns for the same variable presented in different units, including originally published units, but root characteristics data had units converted to match the ones reported in FRED. By filling a gap from global databases, TropiRoot 1.0 expands our knowledge of otherwise so far underrepresented regions, and our ability to assess global trends. This advancement can be used to improve tropical forest representation in vegetation models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Spatial top-down proteomics for the functional characterization of human kidney

Background: The Human Proteome Project has credibly detected nearly 93% of the roughly 20,000 proteins which are predicted by the human genome. However, the proteome is enigmatic, where alterations in amino acid sequences from polymorphisms and alternative splicing, errors in translation, and post-translational modifications result in a proteome depth estimated at several million unique proteoforms. Recently mass spectrometry has been demonstrated in several landmark efforts mapping the human proteoform landscape in bulk analyses. Herein, we developed an integrated workflow for characterizing proteoforms from human tissue in a spatially resolved manner by coupling laser capture microdissection, nanoliter-scale sample preparation, and mass spectrometry imaging. Results: Using healthy human kidney sections as the case study, we focused our analyses on the major functional tissue units including glomeruli, tubules, and medullary rays. After laser capture microdissection, these isolated functional tissue units were processed with microPOTS (microdroplet processing in one-pot for trace samples) for sensitive top-down proteomics measurement. This provided a quantitative database of 616 proteoforms that was further leveraged as a library for mass spectrometry imaging with near-cellular spatial resolution over the entire section. Notably, several mitochondrial proteoforms were found to be differentially abundant between glomeruli and convoluted tubules, and further spatial contextualization was provided by mass spectrometry imaging confirming unique differences identified by microPOTS, and further expanding the field-of-view for unique distributions such as enhanced abundance of a truncated form (1-74) of ubiquitin within cortical regions. Conclusions: We developed an integrated workflow to directly identify proteoforms and reveal their spatial distributions. Where of the 20 differentially abundant proteoforms identified as discriminate between tubules and glomeruli by microPOTS, the vast majority of tubular proteoforms were of mitochondrial origin (8 of 10) where discriminate proteoforms in glomeruli were primarily hemoglobin subunits (9 of 10). These trends were also identified within ion images demonstrating spatially resolved characterization of proteoforms that has the potential to reshape discovery-based proteomics because the proteoforms are the ultimate effector of cellular functions. Applications of this technology have the potential to unravel etiology and pathophysiology of disease states, informing on biologically active proteoforms, which remodel the proteomic landscape in chronic and acute disorders.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

BRAKER3: Fully automated genome annotation using RNA-seq and protein evidence with GeneMark-ETP, AUGUSTUS, and TSEBRA

Gene prediction has remained an active area of bioinformatics research for a long time. Still, gene prediction in large eukaryotic genomes presents a challenge that must be addressed by new algorithms. The amount and significance of the evidence available from transcriptomes and proteomes vary across genomes, between genes, and even along a single gene. User-friendly and accurate annotation pipelines that can cope with such data heterogeneity are needed. The previously developed annotation pipelines BRAKER1 and BRAKER2 use RNA-seq or protein data, respectively, but not both. A further significant performance improvement integrating all three data types was made by the recently released GeneMark-ETP. We here present the BRAKER3 pipeline that builds on GeneMark-ETP and AUGUSTUS, and further improves accuracy using the TSEBRA combiner. BRAKER3 annotates protein-coding genes in eukaryotic genomes using both short-read RNA-seq and a large protein database, along with statistical models learned iteratively and specifically for the target genome. We benchmarked the new pipeline on genomes of 11 species under an assumed level of relatedness of the target species proteome to available proteomes. BRAKER3 outperforms BRAKER1 and BRAKER2. The average transcript-level F1-score is increased by about 20 percentage points on average, whereas the difference is most pronounced for species with large and complex genomes. BRAKER3 also outperforms other existing tools, MAKER2, Funannotate, and FINDER. The code of BRAKER3 is available on GitHub and as a ready-to-run Docker container for execution with Docker or Singularity. Overall, BRAKER3 is an accurate, easy-to-use tool for eukaryotic genome annotation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

BGC Atlas: a web resource for exploring the global chemical diversity encoded in bacterial genomes

Secondary metabolites are compounds not essential for an organism’s development, but provide significant ecological and physiological benefits. These compounds have applications in medicine, biotechnology and agriculture. Their production is encoded in biosynthetic gene clusters (BGCs), groups of genes collectively directing their biosynthesis. The advent of metagenomics has allowed researchers to study BGCs directly from environmental samples, identifying numerous previously unknown BGCs encoding unprecedented chemistry. Here, we present the BGC Atlas (https://bgc-atlas.cs.uni-tuebingen.de), a web resource that facilitates the exploration and analysis of BGC diversity in metagenomes. The BGC Atlas identifies and clusters BGCs from publicly available datasets, offering a centralized database and a web interface for metadata-aware exploration of BGCs and gene cluster families (GCFs). We analyzed over 35 000 datasets from MGnify, identifying nearly 1.8 million BGCs, which were clustered into GCFs. The analysis showed that ribosomally synthesized and post-translationally modified peptides are the most abundant compound class, with most GCFs exhibiting high environmental specificity. We believe that our tool will enable researchers to easily explore and analyze the BGC diversity in environmental samples, significantly enhancing our understanding of bacterial secondary metabolites, and promote the identification of ecological and evolutionary factors shaping the biosynthetic potential of microbial communities.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Sensitive and error-tolerant annotation of protein-coding DNA with BATH

We present BATH, a tool for highly sensitive annotation of protein-coding DNA based on direct alignment of that DNA to a database of protein sequences or profile hidden Markov models (pHMMs). BATH is built on top of the HMMER3 code base, and simplifies the annotation workflow for pHMM-based translated sequence annotation by providing a straightforward input interface and easy-to-interpret output. BATH also introduces novel frameshift-aware algorithms to detect frameshift-inducing nucleotide insertions and deletions (indels). BATH matches the accuracy of HMMER3 for annotation of sequences containing no errors, and produces superior accuracy to all tested tools for annotation of sequences containing nucleotide indels. These results suggest that BATH should be used when high annotation sensitivity is required, particularly when frameshift errors are expected to interrupt protein-coding regions, as is true with long-read sequencing data and in the context of pseudogenes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Shifts in evolutionary lability underlie independent gains and losses of root-nodule symbiosis in a single clade of plants

Abstract Root nodule symbiosis (RNS) is a complex trait that enables plants to access atmospheric nitrogen converted into usable forms through a mutualistic relationship with soil bacteria. Pinpointing the evolutionary origins of RNS is critical for understanding its genetic basis, but building this evolutionary context is complicated by data limitations and the intermittent presence of RNS in a single clade of ca. 30,000 species of flowering plants, i.e., the nitrogen-fixing clade (NFC). We developed the most extensive de novo phylogeny for the NFC and an RNS trait database to reconstruct the evolution of RNS. Our analysis identifies evolutionary rate heterogeneity associated with a two-step process: An ancestral precursor state transitioned to a more labile state from which RNS was rapidly gained at multiple points in the NFC. We illustrate how a two-step process could explain multiple independent gains and losses of RNS, contrary to recent hypotheses suggesting one gain and numerous losses, and suggest a broader phylogenetic and genetic scope may be required for genome-phenome mapping.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Multiplex detection and identification of viral, bacterial, and protozoan pathogens in human blood and plasma using an expanded high-density resequencing microarray platform

Introduction: Nucleic acid tests for blood donor screening have improved the safety of the blood supply; however, increasing numbers of emerging pathogen tests are burdensome. Multiplex testing platforms are a potential solution. Methods: The Blood Borne Pathogen Resequencing Microarray Expanded (BBP-RMAv.2) can perform multiplex detection and identification of 80 viruses, bacteria and parasites. This study evaluated pathogen detection in human blood or plasma. Samples spiked with selected pathogens, each with one of 6 viruses, 2 bacteria and 5 protozoans were tested on this platform. The nucleic acids were extracted, amplified using multiplexed sets of primers, and hybridized to a microarray. The reported sequences were aligned to a database to identify the pathogen. To directly compare the microarray to an emerging molecular approach, the amplified nucleic acids were also submitted to nanopore next generation sequencing (NGS). Results: The BBP-RMAv.2 detected viral pathogens at a concentration as low as 100 copies/ml and a range of concentrations from 1,000 to 100,000 copies/ml for all the spiked pathogens. Coded specimens were identified correctly demonstrating the effectiveness of the platform. The nanopore sequencing correctly identified most samples and the results of the two platforms were compared. Discussion: These results indicated that the BBP-RMAv.2 could be employed for multiplex detection with potential for use in blood safety or disease diagnosis. The NGS was nearly as effective at identifying pathogens in blood and performed better than BBP-RMAv.2 at identifying pathogen-negative samples.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

HIV Molecular Immunology 2025

HIV Molecular Immunology is a companion volume to HIV Sequence Compendium. This publication, the 2025 edition, is the PDF version of Los Alamos Na tional Laboratory’s web-based HIV Molecular Immunology Database (https://www.hiv.lanl.gov/content/ immunology/). The web interface for this relational database has many search interfaces for HIV immunological in formation, as well as interactive tools to help immunologists design reagents and interpret their results.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Untargeted GC-MS Metabolic Profiling of Anaerobic Gut Fungi Reveals Putative Terpenoids and Strain-Specific Metabolites

Background/Objectives: Anaerobic gut fungi (Neocallimastigomycota) are biotechnologically relevant, lignocellulose-degrading microbes with under-explored biosynthetic potential for secondary metabolites. Untargeted metabolomic profiling with gas chromatography–mass spectrometry (GC-MS) was applied to two gut fungal strains, Anaeromyces robustus and Caecomyces churrovis, to establish a foundational metabolomic dataset to identify metabolites and provide insights into gut fungal metabolic capabilities. Methods: Gut fungi were cultured anaerobically in rumen-fluid-based media with a soluble substrate (cellobiose), and metabolites were extracted using the Metabolite, Protein, and Lipid Extraction (MPLEx) method, enabling metabolomic and proteomic analysis from the same cell samples. Samples were derivatized and analyzed via GC-MS, followed by compound identification by spectral matching to reference databases, molecular networking, and statistical analyses. Results: Distinct metabolites were identified between A. robustus and C. churrovis, including 2,3-dihydroxyisovaleric acid produced by A. robustus and maltotriitol, maltotriose, and melibiose produced by C. churrovis. C. churrovis may polymerize maltotriose to form an extracellular polysaccharide, like pullulan. GC-MS profiling potentially captured sufficiently volatile products of proteomically detected, putative non-ribosomal peptide synthetases and polyketide synthases of A. robustus and C. churrovis. The triterpene squalene and triterpenoid tetrahymanol were putatively identified in A. robustus and C. churrovis. Their conserved, predicted biosynthetic genes—squalene synthase and squalene tetrahymanol cyclase—were identified in A. robustus, C. churrovis, and other anaerobic gut fungal genera. Conclusions: This study provides a foundational, untargeted metabolomic dataset to unmask gut fungal metabolic pathways and biosynthetic potential and to prioritize future efforts for compound isolation and identification.

Biochemistry & Molecular Biology↗
DOE OSTI2024

An FPGA-based hardware accelerator supporting sensitive sequence homology filtering with profile hidden Markov models

Abstract Background Sequence alignment lies at the heart of genome sequence annotation. While the BLAST suite of alignment tools has long held an important role in alignment-based sequence database search, greater sensitivity is achieved through the use of profile hidden Markov models (pHMMs). Here, we describe an FPGA hardware accelerator, called HAVAC, that targets a key bottleneck step (SSV) in the analysis pipeline of the popular pHMM alignment tool, HMMER. Results The HAVAC kernel calculates the SSV matrix at 1739 GCUPS on a $$\sim$$ ∼ $3000 Xilinx Alveo U50 FPGA accelerator card, $$\sim$$ ∼ 227× faster than the optimized SSV implementation in nhmmer . Accounting for PCI-e data transfer data processing, HAVAC is 65× faster than nhmmer’s SSV with one thread and 35× faster than nhmmer with four threads, and uses $$\sim$$ ∼ 31% the energy of a traditional high end Intel CPU. Conclusions HAVAC demonstrates the potential offered by FPGA hardware accelerators to produce dramatic speed gains in sequence annotation and related bioinformatics applications. Because these computations are performed on a co-processor, the host CPU remains free to simultaneously compute other aspects of the analysis pipeline.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

U.S. Pacific Coast Workshop Report on Preconstruction Research Recommendations (U.S. Offshore Wind Synthesis of Environmental Effects Research (SEER) Project)

In May 2022, the U.S. Offshore Wind Synthesis of Environmental Effects Research (SEER) project team hosted a stakeholder workshop focused on preconstruction (baseline) research needs for potential floating offshore wind (OSW) energy development on the U.S. Pacific Coast, including California, Oregon, and Washington. Prior to the workshop, the SEER team developed a set of initial synthesized research recommendations that were identified based on a review of relevant, publicly available resources and with advisory group input. The workshop covered three marine life breakout groups on subsequent days to discuss research recommendations related to 1) marine mammals and sea turtles, 2) fish and invertebrates, and 3) birds and bats. As part of the workshop, over a hundred participants from the public and private sectors provided feedback on various aspects of the initial research recommendations, including associated data and knowledge gaps, benefits/limitations of available methods and technologies, and technological advancements or infrastructure needed to address the recommendation. Approximately 1,000 total comments were received on the workshop MURAL boards and were synthesized in this report. Based on workshop feedback, SEER developed a final database of over 500 specific research recommendations based on more than 40 resources. In Fall 2022, the full database and a tool with updated synthesized research recommendations were disseminated on Tethys (https://tethys.pnnl.gov) to assist with informing future funding opportunities and research programming. There is a continued need to improve awareness of the potential environmental effects, monitoring technologies, and management strategies for floating OSW energy development on the U.S. Pacific Coast. Coordination of these activities will require the sustained involvement of multiple stakeholders from across sectors. Beyond the baseline considerations discussed in this workshop, future state-of-the-science activities should be planned to consider research needs across wind energy life cycle phases for all relevant wildlife taxa and associated habitat and ecosystem processes.

17 WIND ENERGY↗
DOE OSTI2025

Warming is Associated With More Encoded Antimicrobial Resistance Genes and Transcriptions Within Five Drug Classes in Soil Bacteria: A Case Study and Synthesis

ABSTRACT The effect of warming on anti‐microbial resistance (AMR) genes in the environment has critical implications for public health but is little studied. We collected published soil bacterial genomes from the BV‐BRC database and tested the correlation between reported optimal growth temperature and the number of encoded AMR genes. Furthermore, we tested the relationship between temperature and AMR gene transcription in a natural ecosystem by analysing soil transcriptomes from a warming manipulation experiment in an Alaskan boreal forest. We hypothesised that there is a positive relationship between warming and AMR prevalence in gene content in bacterial genomes and transcriptomic sequences, and that this effect would vary by drug class. Regarding the bacterial genomes, we found a positive relationship between the fraction of encoded AMR genes and the reported optimal temperature of soil bacteria. The drug classes tetracycline and lincosamide/macrolide/streptogramin had the strongest positive relationship with reported optimal temperature. For the case study in a natural ecosystem, we found 61 significantly upregulated AMR gene‐associated transcripts spanning eight drug classes in warmed plots. In the Alaskan soil samples, we found that warming elicited the strongest positive effect on transcripts targeting lincosamide/streptogramin, beta‐lactam and phenicol/quinolone antibiotics. Overall, higher temperatures were linked to AMR gene prevalence.

Hacopian, Melanie T. [Department of Ecology and Ev↗