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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Applying MALDI-TOF MS to resolve morphologic and genetic similarities between two Dermacentor tick species of public health importance

Abstract Hard ticks (Acari: Ixodidae) have been historically identified by morphological methods which require highly specialized expertise and more recently by DNA-based molecular assays that involve high costs. Although both approaches provide complementary data for tick identification, each method has limitations which restrict their use on large-scale settings such as regional or national tick surveillance programs. To overcome those obstacles, the matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has been introduced as a cost-efficient method for the identification of various organisms, as it balances performance, speed, and high data output. Here we describe the use of this technology to validate the distinction of two closely relatedDermacentortick species based on the development of the first nationwide MALDI-TOF MS reference database described to date. The dataset obtained from this protein-based approach confirms that tick specimens collected from United States regions west of the Rocky Mountains and identified previously asDermacentor variabilisare the recently described species,Dermacentor similis. Therefore, we propose that this integrative taxonomic tool can facilitate vector and vector-borne pathogen surveillance programs in the United States and elsewhere.

Science & Technology - Other Topics↗
DOE OSTI2022

Harmonizing model organism data in the Alliance of Genome Resources

The Alliance of Genome Resources (the Alliance) is a combined effort of 7 knowledgebase projects: Saccharomyces Genome Database, WormBase, FlyBase, Mouse Genome Database, the Zebrafish Information Network, Rat Genome Database, and the Gene Ontology Resource. The Alliance seeks to provide several benefits: better service to the various communities served by these projects; a harmonized view of data for all biomedical researchers, bioinformaticians, clinicians, and students; and a more sustainable infrastructure. The Alliance has harmonized cross-organism data to provide useful comparative views of gene function, gene expression, and human disease relevance. The basis of the comparative views is shared calls of orthology relationships and the use of common ontologies. The key types of data are alleles and variants, gene function based on gene ontology annotations, phenotypes, association to human disease, gene expression, protein–protein and genetic interactions, and participation in pathways. The information is presented on uniform gene pages that allow facile summarization of information about each gene in each of the 7 organisms covered (budding yeast, roundworm Caenorhabditis elegans, fruit fly, house mouse, zebrafish, brown rat, and human). The harmonized knowledge is freely available on the alliancegenome.org portal, as downloadable files, and by APIs. We expect other existing and emerging knowledge bases to join in the effort to provide the union of useful data and features that each knowledge base currently provides.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Genomic profiles of four novel cyanobacteria MAGs from Lake Vanda, Antarctica: insights into photosynthesis, cold tolerance, and the circadian clock

Cyanobacteria in polar environments face environmental challenges, including cold temperatures and extreme light seasonality with small diurnal variation, which has implications for polar circadian clocks. However, polar cyanobacteria remain underrepresented in available genomic data, and there are limited opportunities to study their genetic adaptations to these challenges. This paper presents four new Antarctic cyanobacteria metagenome-assembled genomes (MAGs) from microbial mats in Lake Vanda in the McMurdo Dry Valleys in Antarctica. The four MAGs were classified as Leptolyngbya sp. BulkMat.35, Pseudanabaenaceae cyanobacterium MP8IB2.15, Microcoleus sp. MP8IB2.171, and Leptolyngbyaceae cyanobacterium MP9P1.79. The MAGs contain 2.76 Mbp – 6.07 Mbp, and the bin completion ranges from 74.2–92.57%. Furthermore, the four cyanobacteria MAGs have average nucleotide identities (ANIs) under 90% with each other and under 77% with six existing polar cyanobacteria MAGs and genomes. This suggests that they are novel cyanobacteria and demonstrates that polar cyanobacteria genomes are underrepresented in reference databases and there is continued need for genome sequencing of polar cyanobacteria. Analyses of the four novel and six existing polar cyanobacteria MAGs and genomes demonstrate they have genes coding for various cold tolerance mechanisms and most standard circadian rhythm genes with the Leptolyngbya sp. BulkMat.35 and Leptolyngbyaceae cyanobacterium MP9P1.79 contained kaiB3, a divergent homolog of kaiB.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Metagenome-assembled genomes from Slate River floodplain sediments near Crested Butte, CO, USA (September 2019)

Microorganisms play a key role in cycling nutrients and contaminants in the terrestrial environment depending on their genetic potential. Here we present metagenome-assembled genomes (MAGs) for the bacterial and archaeal community in floodplain sediment samples taken September 2019 at one locations (OBJ1) near the confluence of the Oh-Be-Joyful Creek and Slate River. The site is one of the field sites in focus for the SLAC National Accelerator Laboratory Groundwater Quality Science Focus Area (SFA) program. Sediment samples from a deep soil pit were collected from 50 to 150 cm depth below surface at discrete depths every 20 cm for microbial analyses. A total of 6 metagenomes were sequenced through the Joint Genome Institute (JGI) and can be found under Genomes Online Database (GOLD) sequencing project: Gs0142591. Metagenomes were assembled, binned, and refined using metawrap to generate MAGs (>50% complete and < 10% contamination based on checkM scores). This dataset includes a zip file of 2562 MAG fasta files and a csv file with quality, taxonomic classification (Genome Taxonomy Database Release RS220), and metagenome accessions for MAGs. This dataset also includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type.Part of this work was performed at SLAC Accelerator Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-76SF00515.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1995

PCR Based Microbial Monitor for Analysis of Recycled Water Aboard the ISSA: Issues and Prospects

The monitoring of spacecraft life support systems for the presence of health threatening microorganisms is paramount for crew well being and successful completion of missions. Development of technology to monitor spacecraft recycled water based on detection and identification of the genetic material of contaminating microorganisms and viruses would be a substantial improvement over current NASA plans to monitor recycled water samples that call for the use of conventional microbiology techniques which are slow, insensitive, and labor intensive. The union of the molecular biology techniques of DNA probe hybridization and polymerase chain reaction (PCR) offers a powerful method for the detection, identification, and quantification of microorganisms and viruses. This technology is theoretically capable of assaying samples in as little as two hours with specificity and sensitivity unmatched by any other method. A major advance in probe-hybridization/PCR has come about in a technology called TaqMan(TM), which was invented by Perkin Elmer. Instrumentation using TaqMan concepts is evolving towards devices that could meet NASA's needs of size, low power use, and simplicity of operation. The chemistry and molecular biology needed to utilize these probe-hybridization/PCR instruments must evolve in parallel with the hardware. The following issues of chemistry and biology must be addressed in developing a monitor: Early in the development of a PCR-based microbial monitor it will be necessary to decide how many and which organisms does the system need the capacity to detect. We propose a set of 17 different tests that would detect groups of bacteria and fungus, as well as specific eukaryotic parasites and viruses; In order to use the great sensitivity of PCR it will be necessary to concentrate water samples using filtration. If a lower limit of detection of 1 microorganism per 100 ml is required then the microbes in a 100 ml sample must be concentrated into a volume that can be added to a PCR assay; There are not likely to be contaminants in ISSA recycled water that would inhibit PCR resulting in false-negative results; The TaqMan PCR product detection system is the most promising method for developing a rapid, highly automated gene-based microbial monitoring system. The method is inherently quantitative. NASA and other government agencies have invested in other technologies that, although potentially could lead to revolutionary advances, are not likely to mature in the next 5 years into working systems; PCR-based methods cannot distinguish between DNA or RNA of a viable microorganism and that of a non-viable organism. This may or may not be an important issue with reclaimed water on the ISSA. The recycling system probably damages the capacity of the genetic material of any bacteria or viruses killed during processing to serve as a template in a PCR desinged to amplify a large segment of DNA (less than 650 base pairs). If necessary, vital dye staining could be used in addition to PCR, to enumerate the viable cells in a water sample; The quality control methods have been developed to insure that PCR's are working properly, and that reactions are not contaminated with PCR carryover products which could lead to the generation of false-positive results; and The sequences of the small rRNA subunit gene for a large number of microorganisms are known, and they consititue the best database for rational development of the oligonucleotide reagents that give PCR its great specificity. From those gene sequences, sets of oligonucleotide primers for PCR and Taqman detection that could be used in a NASA microbial monitor were constructed using computer based methods. In addition to space utilization, a microbial monitior will have tremendous terrestrial applications. Analysis of patient samples for microbial pathogens, testing industrial effluent for biofouling bacteria, and detection biological warfare agents on the battlefield are but a few of the diverse potential uses for this technology. Once fully developed, gene-based microbial monitors will become the fundamental tool in every lab that tests for microbial contaminants, and serve as a powerful weapon in mankind's war with the germ world.

Cassell, Gail H.↗
DOE OSTI2025

Metagenome-assembled genomes from Slate River floodplain sediments near Crested Butte, CO, USA (June 2018)

Microorganisms play a key role in cycling nutrients and contaminants in the terrestrial environment depending on their genetic potential. Here, we present metagenome-assembled genomes (MAGs) for the bacterial and archaeal community in floodplain sediment samples taken June 2018 at two locations (OBJ1 and OBJ2) near the confluence of the Oh-Be-Joyful Creek and Slate River. The site is one of the field sites in focus for the SLAC National Accelerator Laboratory Groundwater Quality Science Focus Area (SFA) program. Sediment samples from a deep soil pit were collected from 50 to 150 cm depth below surface at discrete depths every 20 cm for microbial analyses. A total of 12 metagenomes were sequenced through the Joint Genome Institute (JGI) and can be found under Genomes Online Database (GOLD) sequencing project: Gs0142591. Metagenomes were assembled, binned, and refined using metawrap to generate MAGs (>50% complete and < 10% contamination based on checkM scores). This dataset includes a zip file of 1233 MAG fasta files and a csv file with quality, taxonomic classification (Genome Taxonomy Database Release RS220), and metagenome accessions for MAGs. This dataset also includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type.Part of this work was performed at SLAC Accelerator Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-76SF00515.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

FleetREDI Dashboard Fleet DNA Data Summaries

Developing daily duty cycle summaries for every vehicle-day within NLR’s Fleet DNA database was a key output of the FleetREDI project. This project captured second-by-second GPS and controller area network (CAN) data on in-use medium- and heavy-duty fleet vehicles and then summarized the data to provide an overview of vehicle operation throughout the United States. These data summaries were then displayed in aggregated formats on the FleetREDI dashboard, where users can explore the data within Fleet DNA. Fleet DNA’s clearinghouse of commercial fleet vehicle operating data helps vehicle manufacturers and developers optimize vehicle designs and helps fleet managers choose advanced technologies for their fleets. This online tool, which provides data summaries and visualizations similar to real-world "genetics" for medium- and heavy-duty fleet vehicles, helps users understand the broad operational range of commercial vehicles across vocations and weight classes.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1998

Phylogenetic mapping of bacterial morphology

The availability of a meaningful molecular phylogeny for bacteria provides a context for examining the historical significance of various developments in bacterial evolution. Herein, the classical morphological descriptions of selected members of the domain Bacteria are mapped upon the genealogical ancestry deduced from comparison of small-subunit rRNA sequences. For the species examined in this study, a distinct pattern emerges which indicates that the coccus shape has arisen and accumulated independently multiple times in separate lineages and typically survived as a persistent end-state morphology. At least two other morphologies persist but have evolved only once. This study demonstrates that although bacterial morphology is not useful in defining bacterial phylogeny, it is remarkably consistent with that phylogeny once it is known. An examination of the experimental evidence available for morphogenesis as well as microbial fossil evidence corroborates these findings. It is proposed that the accumulation of persistent morphologies is a result of the biophysical properties of peptidoglycan and their genetic control, and that an evolved body-plan strategy based on peptidoglycan may have been a fate-sealing step in the evolution of Bacteria. More generally, this study illustrates that significant evolutionary insights can be obtained by examining biological and biochemical data in the context of a reliable phylogenetic structure.

NASA Discipline Exobiology↗
NASA NTRS2011

Improve Data Mining and Knowledge Discovery Through the Use of MatLab

Data mining is widely used to mine business, engineering, and scientific data. Data mining uses pattern based queries, searches, or other analyses of one or more electronic databases/datasets in order to discover or locate a predictive pattern or anomaly indicative of system failure, criminal or terrorist activity, etc. There are various algorithms, techniques and methods used to mine data; including neural networks, genetic algorithms, decision trees, nearest neighbor method, rule induction association analysis, slice and dice, segmentation, and clustering. These algorithms, techniques and methods used to detect patterns in a dataset, have been used in the development of numerous open source and commercially available products and technology for data mining. Data mining is best realized when latent information in a large quantity of data stored is discovered. No one technique solves all data mining problems; challenges are to select algorithms or methods appropriate to strengthen data/text mining and trending within given datasets. In recent years, throughout industry, academia and government agencies, thousands of data systems have been designed and tailored to serve specific engineering and business needs. Many of these systems use databases with relational algebra and structured query language to categorize and retrieve data. In these systems, data analyses are limited and require prior explicit knowledge of metadata and database relations; lacking exploratory data mining and discoveries of latent information. This presentation introduces MatLab(R) (MATrix LABoratory), an engineering and scientific data analyses tool to perform data mining. MatLab was originally intended to perform purely numerical calculations (a glorified calculator). Now, in addition to having hundreds of mathematical functions, it is a programming language with hundreds built in standard functions and numerous available toolboxes. MatLab's ease of data processing, visualization and its enormous availability of built in functionalities and toolboxes make it suitable to perform numerical computations and simulations as well as a data mining tool. Engineers and scientists can take advantage of the readily available functions/toolboxes to gain wider insight in their perspective data mining experiments.

Shaykhian, Gholam Ali↗
NASA NTRS2011

Improve Data Mining and Knowledge Discovery through the use of MatLab

Data mining is widely used to mine business, engineering, and scientific data. Data mining uses pattern based queries, searches, or other analyses of one or more electronic databases/datasets in order to discover or locate a predictive pattern or anomaly indicative of system failure, criminal or terrorist activity, etc. There are various algorithms, techniques and methods used to mine data; including neural networks, genetic algorithms, decision trees, nearest neighbor method, rule induction association analysis, slice and dice, segmentation, and clustering. These algorithms, techniques and methods used to detect patterns in a dataset, have been used in the development of numerous open source and commercially available products and technology for data mining. Data mining is best realized when latent information in a large quantity of data stored is discovered. No one technique solves all data mining problems; challenges are to select algorithms or methods appropriate to strengthen data/text mining and trending within given datasets. In recent years, throughout industry, academia and government agencies, thousands of data systems have been designed and tailored to serve specific engineering and business needs. Many of these systems use databases with relational algebra and structured query language to categorize and retrieve data. In these systems, data analyses are limited and require prior explicit knowledge of metadata and database relations; lacking exploratory data mining and discoveries of latent information. This presentation introduces MatLab(TradeMark)(MATrix LABoratory), an engineering and scientific data analyses tool to perform data mining. MatLab was originally intended to perform purely numerical calculations (a glorified calculator). Now, in addition to having hundreds of mathematical functions, it is a programming language with hundreds built in standard functions and numerous available toolboxes. MatLab's ease of data processing, visualization and its enormous availability of built in functionalities and toolboxes make it suitable to perform numerical computations and simulations as well as a data mining tool. Engineers and scientists can take advantage of the readily available functions/toolboxes to gain wider insight in their perspective data mining experiments.

Shaykahian, Gholan Ali↗
DOE OSTI2025

Metagenome-assembled genomes from Slate River floodplain sediments near Crested Butte, CO, USA (June to October 2020)

Microorganisms play a key role in cycling nutrients and contaminants in the terrestrial environment depending on their genetic potential. Here we present metagenome-assembled genomes (MAGs) for the bacterial and archaeal community in floodplain sediment samples taken June to October 2020 at two locations (OBJ1 and OBJ2) near the confluence of the Oh-Be-Joyful Creek and Slate River. The site is one of the field sites in focus for the SLAC National Accelerator Laboratory Groundwater Quality Science Focus Area (SFA) program. Sediment samples from a deep soil pit were collected from 30 cm depth below surface to just above the cobble layer (~190-250 cm depth) at discrete depths every 40 cm for microbial analyses. A total of 35 metagenomes were sequenced through the Joint Genome Institute (JGI) and can be found under Genomes Online Database (GOLD) sequencing project: Gs0142591. Metagenomes were assembled, binned, and refined using metawrap to generate MAGs (>50% complete and < 10% contamination based on checkM scores). This dataset includes a zip file of 2848 MAG fasta files and a csv file with quality, taxonomic classification (Genome Taxonomy Database Release RS220), and metagenome accessions for MAGs. This dataset also includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type.Part of this work was performed at SLAC Accelerator Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-76SF00515.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Metagenome-assembled genomes from East River floodplain sediments near Crested Butte, CO, USA (June to September 2019)

Microorganisms play a key role in cycling nutrients and contaminants in the terrestrial environment depending on their genetic potential. Here, we present metagenome-assembled genomes (MAGs) for the bacterial and archaeal community in floodplain sediment samples taken in 2019 in June (flooded conditions) and September (drained conditions) at two locations (MCB1 and MCB3) near the Meander C/Pumphouse floodplain sites of the East River. Sediment cores were collected from 2 depths, a near-surface, generally unsaturated depth (30-40 centimeter (cm) depth below surface) and a deeper depth influenced by flooding with redoximorphic features (70-80 cm depth below surface). Sediments were homogenized from the 10 cm core for microbial analyses. A total of 24 metagenomes were sequenced through the Joint genome institute (JGI) corresponding to 8 samples sequenced in triplicate. These metagenomes can be found under Genomes Online Database (GOLD) sequencing project: Gs0141020. Metagenomes were assembled, binned, and refined using metawrap to generate MAGs (>50% complete and < 10% contamination based on checkM scores). This dataset includes a zip file of 436 MAG fasta files and a csv file with quality, taxonomic classification (Genome Taxonomy Database Release RS220), and metagenome accessions for MAGs. This dataset also includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type.This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231. Part of this work was performed at SLAC Accelerator Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-76SF00515.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Metagenome-assembled genomes from East River floodplain sediments near Crested Butte, CO, USA (June to September 2017)

Microorganisms play a key role in cycling nutrients and contaminants in the terrestrial environment depending on their genetic potential. Here, we present metagenome-assembled genomes (MAGs) for the bacterial and archaeal community in floodplain sediment samples taken in 2017 in June (flooded conditions) and September (drained conditions) at two locations (MCB1 and MCB3) in an active meander (Meander C) of the East River. Sediment cores were collected from 2 depths, a near-surface, generally unsaturated depth (15-40 centimeter (cm) depth below surface) and a deeper depth influenced by flooding with redoximorphic features (50-88 cm depth below surface). Sediments were homogenized from the ~10 cm cores for microbial analyses. A total of 24 metagenomes were sequenced through the Joint genome institute (JGI) corresponding to 8 samples sequenced in triplicate. These metagenomes can be found under Genomes Online Database (GOLD) sequencing project: Gs0151851. Metagenomes were assembled, binned, and refined using metawrap to generate MAGs (>50% complete and < 10% contamination based on checkM scores). This dataset includes a zip file of 405 MAG fasta files and a csv file with quality, taxonomic classification (Genome Taxonomy Database Release RS220), and metagenome accessions for MAGs. This dataset also includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type.This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231. Part of this work was performed at SLAC Accelerator Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-76SF00515.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Metagenome-assembled genomes from East River floodplain sediments near Crested Butte, CO, USA (May to September 2018)

Microorganisms play a key role in cycling nutrients and contaminants in the terrestrial environment depending on their genetic potential. Here, we present metagenome-assembled genomes (MAGs) for the bacterial and archaeal community in floodplain sediment samples taken in 2018 in May (flooded conditions) and September (drained conditions) at two locations (MCB1 and MCB3) near the Meander C/Pumphouse floodplain sites of the East River. Sediment cores were collected from 2 depths, a near-surface, generally unsaturated depth (30-40 centimeter (cm) depth below surface) and a deeper depth influenced by flooding with redoximorphic features (70-80 cm depth below surface). Sediments were homogenized from the 10 cm core for microbial analyses. A total of 24 metagenomes were sequenced through the Joint genome institute (JGI) corresponding to 8 samples sequenced in triplicate. These metagenomes can be found under Genomes Online Database (GOLD) sequencing project: Gs0141020. Metagenomes were assembled, binned, and refined using metawrap to generate MAGs (>50% complete and < 10% contamination based on checkM scores). This dataset includes a zip file of 478 MAG fasta files and a csv file with quality, taxonomic classification (Genome Taxonomy Database Release RS220), and metagenome accessions for MAGs. This dataset also includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type.This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231. Part of this work was performed at SLAC Accelerator Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-76SF00515.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Antimicrobial resistance and genomic characterization of Salmonella Dublin isolates in cattle from the United States

Salmonella enterica subspecies enterica serotype Dublin is a host-adapted serotype in cattle, associated with enteritis and systemic disease. The primary clinical manifestation of Salmonella Dublin infection in cattle, especially calves, is respiratory disease. While rare in humans, it can cause severe illness, including bacteremia, with hospitalization and death. In the United States, S . Dublin has become one of the most multidrug-resistant serotypes. The objective of this study was to characterize S . Dublin isolates from sick cattle by analyzing phenotypic and genotypic antimicrobial resistance (AMR) profiles, the presence of plasmids, and phylogenetic relationships. S . Dublin isolates (n = 140) were selected from submissions to the NVSL for Salmonella serotyping (2014–2017) from 21 states. Isolates were tested for susceptibility against 14 class-representative antimicrobial drugs. Resistance profiles were determined using the ABRicate with Resfinder and NCBI databases, AMRFinder and PointFinder. Plasmids were detected using ABRicate with PlasmidFinder. Phylogeny was determined using vSNP. We found 98% of the isolates were resistant to more than 4 antimicrobials. Only 1 isolate was pan-susceptible and had no predicted AMR genes. All S . Dublin isolates were susceptible to azithromycin and meropenem. They showed 96% resistance to sulfonamides, 97% to tetracyclines, 95% to aminoglycosides and 85% to beta-lactams. The most common AMR genes were: sulf2 and tetA (98.6%), aph(6)-Id (97.9%), aph(3’’)-Ib, (97.1%), floR (94.3%), and blaCMY-2 (85.7%). All quinolone resistant isolates presented mutations in gyr A. Ten plasmid types were identified among all isolates with IncA/C2, IncX1, and IncFII(S) being the most frequent. The S . Dublin isolates show low genomic genetic diversity. This study provided antimicrobial susceptibility and genomic insight into S . Dublin clinical isolates from cattle in the U.S. Further sequence analysis integrating food and human origin S . Dublin isolates may provide valuable insight on increased virulence observed in humans.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Computationally evaluating high-yield metabolites for sustainable aviation fuel (SAF) using machine learning

The computational tool described in this report helps identify promising biological pathways that produce SAF platform molecules (either a drop-in SAF, or a precursor that can be easily converted to a drop-in SAF). The workflow the computational tool follows first identifies possible biological pathways from a user-defined metabolite. These pathways may, or may not lead to a SAF platform molecule, thus the second step involves insilico testing of the end product of each pathway to assess whether it is, or is not, a SAF platform molecule. The identification of biological pathways performed in the first step is facilitated by linking the metabolite to a biological reaction database. Pathways are found by identifying pathways in the reaction database that include the metabolite. The computational tool includes an alternative way to find pathways. The alternative way develops a Flux Balanced Analysis (FBA), and modifying the FBA to include reactions that transform the metabolite. These modifications serve as a basis for understanding, in a semi-quantitative way, if there is an increase in the flux to desirable products. The second step, in silico testing of the end-products, is accomplished by estimating key physical properties relevant to SAF. When good models are available, we have integrated those models into the computational tool. In a few instances, we have developed our own models. In all instances, we have validated the models against available measured data. Finally, we have evaluated the effectiveness of our computational tool by genetically engineering Rhodosporidium toruloides. Validation occurred without the use of a FBA, and further validation is required.

09 BIOMASS FUELS↗
DOE OSTI2023

Generating Co-expression Networks for Three Cyanobacteria: Synechococcus sp. PCC 7942, Synechococcus sp. PCC 7002, Synechocystis sp. PCC 6803

Cyanobacteria are photosynthetic organisms capable of high growth rate and represent a promising bioplatform for harnessing the sun’s energy to make biofuel. Additionally, the process of photosynthesis absorbs CO2 from the environment. Understanding the metabolic processes involved in photosynthesis could lead to solutions to the recent rise of CO2 concentration in Earth’s atmosphere and the associated climate change. More research on the transcriptional regulation of these cells is needed to learn how to harness the untapped potential of cyanobacteria for these applications. Transcriptional analysis via RNA-seq provides an understanding of how gene expression changes at the mRNA level under diverse growing conditions. I systematically collected and analyzed RNA-Seq data obtained under a variety of conditions and available on the NCBI database for three cyanobacteria model organisms: Synechococcus elongatus sp. PCC 7942, Synechococcus sp. PCC 7002, and Synechocystis sp. PCC 6803. For each organism, the data was mapped to a reference genome to characterize the RNA expression profile. Samples were checked for quality based on the number of reads and the correlation of the expression profile between labeled replicates. All samples were transformed into transcripts per million reads, followed by a log transformation to account for the wide range of sample sizes. Gene co-expression networks were generated and analyzed for each species using Cytoscape. These networks provide a base level of gene expression for each species. The network topology and high-betweeness nodes of these networks need to be analyzed further to provide insight on potential ways to harness cyanobacteria genetics. Additionally, these datasets can be used together to form a core genome network analysis- one that includes only the genes that are homologous between the three species. This project has prepared the way for a more in-depth study on photosynthetic microbes on a genetic level.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Metagenome-assembled genomes from Wind River Basin floodplain sediments Riverton, Wyoming site (May to September 2017)

Microorganisms play a key role in cycling nutrients and contaminants in the terrestrial environment depending on their genetic potential. Here we present metagenome-assembled genomes (MAGs) for the bacterial and archaeal community in floodplain sediment samples taken roughly every month in the period May 18 to September 13 in 2017 at a location (Pit2) close to DOE Legacy Management well 855 at the Riverton, Wyoming floodplain site in the Wind River Basin (WRB). The groundwater at this site exhibits persistent U, Mo, and sulfate plumes and is one of the field sites in focus for the SLAC Groundwater Quality SFA program. Cores were taken with a hand-auger and separated into 5-20 cm segments based on soil horizonation down to 150 cm depth below surface. Each segment was subsampled for microbial analyses. Corresponding 16S rRNA gene amplicon data is available at the NCBI Single Read Archive (SRA) Database BioProject ID PRJNA626616, and soil geochemistry data at doi:10.15485/1631972. 40 metagenomes were sequenced through JGI and can be found under Gold sequencing project: Gs0142591. Metagenomes were assembled, binned, and refined using metawrap to generate MAGs (>50% complete and < 10% contamination based on checkM scores). This dataset includes a zip file of 6993 MAG fasta files and a csv file with quality, taxonomic classification (GTDB RS220), and metagenome accessions for MAGs generated from the Wind River Basin (WRB). This dataset also includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type.

54 ENVIRONMENTAL SCIENCES↗