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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Data for Engineering and Evolution of Yarrowia lipolytica for Producing Lipids from Lignocellulosic Hydrolysates

Yarrowia lipolytica , an oleaginous yeast, shows promise for industrial fermentation due to its robust acetyl-CoA flux and well-developed genetic engineering tools. However, its lack of an active xylose metabolism restricts the conversion of cellulosic sugars to valuable products. To address this, metabolic engineering, and adaptive laboratory evolution (ALE) were applied to the Y. lipolytica PO1f strain, resulting in an efficient xylose-assimilating strain (XEV). Whole-genome sequencing (WGS) of the XEV followed by reverse engineering revealed that the amplification of the heterologous oxidoreductase pathway and a mutation in the GTPase-activating protein gene (YALI0B12100g) might be the primary reasons for improved xylose assimilation in the XEV strain. When a sorghum hydrolysate was used, the XEV strain showed superior xylose consumption and lipid production compared to its parental strain (X123). This study advances our understanding of xylose metabolism in Y. lipolytica and proposes effective metabolic engineering strategies for optimizing lignocellulosic hydrolysates.

Hydrolysate↗
DOE OSTI2026

Data for Promoter Deletion in the Soybean Compact Mutant Leads to Overexpression of a Gene with Homology to the C20-Gibberellin 2-Oxidase Family

Height is a critical component of plant architecture, significantly affecting crop yield. The genetic basis of this trait in soybean remains unclear. In this study, we report the characterization of the Compact mutant of soybean, which has short internodes. The candidate gene was mapped to chromosome 17, and the interval containing the causative mutation was further delineated using biparental mapping. Whole-genome sequencing of the mutant revealed an 8.7 kb deletion in the promoter of the Glyma.17g145200 gene, which encodes a member of the class III gibberellin (GA) 2-oxidases. The mutation has a dominant effect, likely via increased expression of the GA 2-oxidase transcript observed in green tissue, as a result of the deletion in the promoter of Glyma.17g145200. We further demonstrate that levels of GA precursors are altered in the Compact mutant, supporting a role in GA metabolism, and that the mutant phenotype can be rescued with exogenous GA3. We also determined that overexpression of Glyma.17g145200 in Arabidopsis results in dwarfed plants. Thus, gain of promoter activity in the Compact mutant leads to a short internode phenotype in soybean through altered metabolism of gibberellin precursors. These results provide an example of how structural variation can control an important crop trait and a role for Glyma.17g145200 in soybean architecture, with potential implications for increasing crop yield.

Biomass Analytics↗
NASA NTRSDate not supplied

Developing Open-Source Training Materials for AI/ML and Space Biological Sciences Using NASA Cloud-Based Data

Artificial Intelligence (AI) and Machine Learning (ML) has gained significant traction in the biological and biomedical research fields in the last two decades, in part thanks to an increasing culture of open data sharing and reuse. Due to its capability for identifying complex relationships and patterns, AI/ML methodology is particularly well suited to recognize and predict biological patterns from high-dimensional next-generation sequencing data (e.g. whole genome sequencing, transcriptomic sequencing), as well as from biological or medical imaging data (e.g. microscopy, computed tomography, ultrasound, magnetic resonance imaging, radiography). These methodologies hold particular promise for space biosciences research and automated space health monitoring systems. However, there are many key considerations for properly training, validating, and testing a machine learning model in biological research or clinical application. Even with the positive culture of Open Science and data sharing, inexperienced researchers working quickly without proper checks can produce models that perform poorly outside of the immediate training dataset. Lessons learned from biological AI/ML research indicate that Open Science principles such as data sharing and open-source code must go hand-in-hand with publicly available, high-quality training curricula in best practices, with modules centered on real-life scientific use cases and data so future AI/ML practitioners gain experience on real problems. Here we present the development of open-source training materials for AI/ML and space biosciences, as part of the NASA Transform to Open Science Training (TOPST) initiative. We develop 4 independent training programs, focused on the following topics: 1) Fundamentals of Machine Learning and Space Biosciences Domain, 2) Open Science, Artificial Intelligence, and Ethical Best Practices for Data Sharing and Analysis, 3) Using AI/ML Classification to Identify Gene Networks Affected By Space Exposure in Mouse Liver, and 4) Using Neural Networks to Find DNA Damage Patterns in Immune Cells after Radiation. All programs leverage cloud-based NASA biological datasets. The curriculum we present will enable worldwide access to training in AI/ML and scientific analysis.

James Andrew Casaletto↗
NASA NTRSDate not supplied

Developing Open-Source Training Materials for AI/ML and Space Biological Sciences Using NASA Cloud-Based Data

Artificial Intelligence (AI) and Machine Learning (ML) has gained significant traction in the biological and biomedical research fields, in part due to a culture of open data sharing and reuse. AI/ML methodology is well-suited to recognize and predict biological patterns from high-dimensional next-generation sequencing data (e.g. whole genome sequencing, transcriptomic sequencing), as well as from biological or medical imaging data (e.g. microscopy, computed tomography, ultrasound, magnetic resonance imaging, radiography). These methodologies hold particular promise for space biosciences research and automated space health monitoring systems. However, there are key considerations for properly training, validating, and testing a machine learning model in biological research or clinical application. Inexperienced researchers can produce models that perform poorly outside of the training dataset. Open Science principles such as data sharing and open-source code must go hand-in-hand with publicly available, high-quality training curricula in best practices, with modules centered on real-life scientific use cases and data so future AI/ML practitioners gain experience on real problems. Here we present the development of open-source training materials for AI/ML and space biosciences, as part of the NASA Transform to Open Science Training (TOPST) initiative. We develop 4 independent training programs, focused on the following topics: 1) Fundamentals of Machine Learning and Space Biosciences Domain, 2) Open Science, Artificial Intelligence, and Ethical Best Practices for Data Sharing and Analysis, 3) Using AI/ML Classification to Identify Gene Networks Affected By Space Exposure in Mouse Liver, and 4) Using Neural Networks to Find DNA Damage Patterns in Immune Cells after Radiation. All programs leverage cloud-based NASA biological datasets. The curriculum we present will enable worldwide access to training in AI/ML and scientific analysis.

James Casaletto↗
DOE OSTI2024

Expanded genome and proteome reallocation in a novel, robust Bacillus coagulans strain capable of utilizing pentose and hexose sugars

Bacillus coagulans, a Gram-positive thermophilic bacterium, is recognized for its probiotic properties and recent development as a microbial cell factory. Despite its importance for biotechnological applications, the current understanding of B. coagulans’ robustness is limited, especially for undomesticated strains. To fill this knowledge gap, we characterized the metabolic capability and performed functional genomics and systems analysis of a novel, robust strain, B. coagulans B-768. Genome sequencing revealed that B-768 has the largest B. coagulans genome known to date (3.94 Mbp), about 0.63 Mbp larger than the average genome of sequenced B. coagulans strains, with expanded carbohydrate metabolism and mobilome. Functional genomics identified a well-equipped genetic portfolio for utilizing a wide range of C5 (xylose, arabinose), C6 (glucose, mannose, galactose), and C12 (cellobiose) sugars present in biomass hydrolysates, which was validated experimentally. For growth on individual xylose and glucose, the dominant sugars in biomass hydrolysates, B-768 exhibited distinct phenotypes and proteome profiles. Faster growth and glucose uptake rates resulted in lactate overflow metabolism, which makes B. coagulans a lactate overproducer; however, slower growth and xylose uptake diminished overflow metabolism due to the high energy demand for sugar assimilation. Carbohydrate Transport and Metabolism (COG-G), Translation (COG-J), and Energy Conversion and Production (COG-C) made up 60%–65% of the measured proteomes but were allocated differently when growing on xylose and glucose. The trade-off in proteome reallocation, with high investment in COG-C over COG-G, explains the xylose growth phenotype with significant upregulation of xylose metabolism, pyruvate metabolism, and tricarboxylic acid (TCA) cycle. Strain B-768 tolerates and effectively utilizes inhibitory biomass hydrolysates containing mixed sugars and exhibits hierarchical sugar utilization with glucose as the preferential substrate.

carbohydrate metabolism↗
DOE OSTI2024

Poplar

SAND2025-00683O Poplar is a software tool that generates a phylogenetic tree from input gene and genome sequences. It integrates established tools to identify genes within genomes, group sequences, construct gene trees, and infer a species tree. Poplar processes nucleotide sequences, identifies similar sequences using Nucleotide BLAST, groups them with DBSCAN, aligns sequences with MAFFT, constructs gene trees with RAxML-NG, and infers a species tree using ASTRAL-Pro3. This pipeline provides a structured approach to phylogenetic analysis, facilitating the study of evolutionary relationships among species. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Krishnakumar, Raga↗
DOE OSTI2024

Unveiling the Arsenal of Apple Bitter Rot Fungi: Comparative Genomics Identifies Candidate Effectors, CAZymes, and Biosynthetic Gene Clusters in Colletotrichum Species

The bitter rot of apple is caused by Colletotrichum spp. and is a serious pre-harvest disease that can manifest in postharvest losses on harvested fruit. In this study, we obtained genome sequences from four different species, C. chrysophilum, C. noveboracense, C. nupharicola, and C. fioriniae, that infect apple and cause diseases on other fruits, vegetables, and flowers. Our genomic data were obtained from isolates/species that have not yet been sequenced and represent geographic-specific regions. Genome sequencing allowed for the construction of phylogenetic trees, which corroborated the overall concordance observed in prior MLST studies. Bioinformatic pipelines were used to discover CAZyme, effector, and secondary metabolic (SM) gene clusters in all nine Colletotrichum isolates. We found redundancy and a high level of similarity across species regarding CAZyme classes and predicted cytoplastic and apoplastic effectors. SM gene clusters displayed the most diversity in type and the most common cluster was one that encodes genes involved in the production of alternapyrone. Our study provides a solid platform to identify targets for functional studies that underpin pathogenicity, virulence, and/or quiescence that can be targeted for the development of new control strategies. With these new genomics resources, exploration via omics-based technologies using these isolates will help ascertain the biological underpinnings of their widespread success and observed geographic dominance in specific areas throughout the country.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Methods for safely sharing dual-use genetic data

Background: Some genetic data has dual-use potential. Sharing pathogen data has shown tremendous value. For example therapeutic development and lineage tracking during the COVID pandemic. This data sharing is complicated by the fact that these data have the potential to be used for harm. The genome sequence of a pathogen can be used to enable malicious genetic engineering approaches or to recreate the pathogen from synthetic DNA. Standard data security methods can be applied to genetic data, but when data is shared between institutions, ensuring appropriate security can be difficult. Sensitive data that is shared internationally among a wide array of institutions can be especially difficult to control. Methods for securely storing and sharing genetic data with potential for dual-use are needed to mitigate this potential harm.Results: Here we propose new methods that allow genetic data to be shared in a data format that prevents a nefarious actor from accessing sensitive aspects of the data. Our methods obfuscate raw sequence data by pooling reads from different samples. This approach can ensure that data is secure while stored and during electronic transfer. We demonstrate that by pooling raw sequence data from multiple samples of the same organism, the ability to fully reconstruct any individual sample is prevented. In the pooled data, most genomic information remains, but reads or mutations cannot be directly attributed to any individual sample. To further restrict access to information, regions of a genome can be removed from the reads.Conclusion: Our methods obscure genomic information within raw sequence reads. This method can allow genetic data to be stored and shared while preventing a nefarious actor from being able to perfectly reconstruct an organism. Broad-scale sequence information remains, while fine scale details about specific samples are difficult or impossible to reconstruct. Our software is available at https://github.com/Geneinfosec-Inc/ReadMixer.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1999

Thermal adaptation analyzed by comparison of protein sequences from mesophilic and extremely thermophilic Methanococcus species

The genome sequence of the extremely thermophilic archaeon Methanococcus jannaschii provides a wealth of data on proteins from a thermophile. In this paper, sequences of 115 proteins from M. jannaschii are compared with their homologs from mesophilic Methanococcus species. Although the growth temperatures of the mesophiles are about 50 degrees C below that of M. jannaschii, their genomic G+C contents are nearly identical. The properties most correlated with the proteins of the thermophile include higher residue volume, higher residue hydrophobicity, more charged amino acids (especially Glu, Arg, and Lys), and fewer uncharged polar residues (Ser, Thr, Asn, and Gln). These are recurring themes, with all trends applying to 83-92% of the proteins for which complete sequences were available. Nearly all of the amino acid replacements most significantly correlated with the temperature change are the same relatively conservative changes observed in all proteins, but in the case of the mesophile/thermophile comparison there is a directional bias. We identify 26 specific pairs of amino acids with a statistically significant (P < 0.01) preferred direction of replacement.

NASA Discipline Exobiology↗
DOE OSTI2025

Laminarin stimulates single cell rates of sulfate reduction whereas oxygen inhibits transcriptomic activity in coastal marine sediment

Abstract The chemical cycles carried out by bacteria and archaea living in coastal sediments are vital aspects of benthic ecology. These ecosystems are subject to physical disruption, which may allow for increased respiration and complex carbon consumption—impacting chemical cycling in this environment often thought to be a terminal place of deposition. We use the redox-enzyme sensitive probe RedoxSensor Green to measure rates of electron transfer physiology in individual sulfate reducer cells residing in anoxic sediment, subjected to transient exposure of oxygen and laminarin. We use index fluorescence activated cell sorting and single cell genomics sequencing to link those measurements to genomes of respiring cells. We measure per-cell sulfate reduction rates in marine sediments (0.01–4.7 fmol SO42− cell−1 h−1) and determine that cells within the Chloroflexota phylum are the most active in respiration. Chloroflexota respiration activity is also stimulated with the addition of laminarin, even in marine sediments already rich in organic matter. Evaluating metatranscriptomic data alongside this respiration-based technique, Chloroflexota genomes encode laminarinases indicating a likely ability to degrade laminarin. We also provide evidence that abundant Patescibacteria cells do not use electron transport pathways for energy, and instead likely carry out fermentation of polysaccharides. There is a decoupling of respiration-related activity rates from transcription, as respiration rates increase while transcription decreases with oxygen exposure. Overall, we reveal an active community of respiring Chloroflexota that cycles sulfate at potential rates of 23–40 nmol h−1 per cm3 sediment in incubation settings, and non-respiratory Patescibacteria that can cycle complex polysaccharides.

Lindsay, Melody R.↗
NASA NTRS2002

Genes encoding calmodulin-binding proteins in the Arabidopsis genome

Analysis of the recently completed Arabidopsis genome sequence indicates that approximately 31% of the predicted genes could not be assigned to functional categories, as they do not show any sequence similarity with proteins of known function from other organisms. Calmodulin (CaM), a ubiquitous and multifunctional Ca(2+) sensor, interacts with a wide variety of cellular proteins and modulates their activity/function in regulating diverse cellular processes. However, the primary amino acid sequence of the CaM-binding domain in different CaM-binding proteins (CBPs) is not conserved. One way to identify most of the CBPs in the Arabidopsis genome is by protein-protein interaction-based screening of expression libraries with CaM. Here, using a mixture of radiolabeled CaM isoforms from Arabidopsis, we screened several expression libraries prepared from flower meristem, seedlings, or tissues treated with hormones, an elicitor, or a pathogen. Sequence analysis of 77 positive clones that interact with CaM in a Ca(2+)-dependent manner revealed 20 CBPs, including 14 previously unknown CBPs. In addition, by searching the Arabidopsis genome sequence with the newly identified and known plant or animal CBPs, we identified a total of 27 CBPs. Among these, 16 CBPs are represented by families with 2-20 members in each family. Gene expression analysis revealed that CBPs and CBP paralogs are expressed differentially. Our data suggest that Arabidopsis has a large number of CBPs including several plant-specific ones. Although CaM is highly conserved between plants and animals, only a few CBPs are common to both plants and animals. Analysis of Arabidopsis CBPs revealed the presence of a variety of interesting domains. Our analyses identified several hypothetical proteins in the Arabidopsis genome as CaM targets, suggesting their involvement in Ca(2+)-mediated signaling networks.

NASA Discipline Plant Biology↗
NASA NTRS2003

An in silico assessment of gene function and organization of the phenylpropanoid pathway metabolic networks in Arabidopsis thaliana and limitations thereof

The Arabidopsis genome sequencing in 2000 gave to science the first blueprint of a vascular plant. Its successful completion also prompted the US National Science Foundation to launch the Arabidopsis 2010 initiative, the goal of which is to identify the function of each gene by 2010. In this study, an exhaustive analysis of The Institute for Genomic Research (TIGR) and The Arabidopsis Information Resource (TAIR) databases, together with all currently compiled EST sequence data, was carried out in order to determine to what extent the various metabolic networks from phenylalanine ammonia lyase (PAL) to the monolignols were organized and/or could be predicted. In these databases, there are some 65 genes which have been annotated as encoding putative enzymatic steps in monolignol biosynthesis, although many of them have only very low homology to monolignol pathway genes of known function in other plant systems. Our detailed analysis revealed that presently only 13 genes (two PALs, a cinnamate-4-hydroxylase, a p-coumarate-3-hydroxylase, a ferulate-5-hydroxylase, three 4-coumarate-CoA ligases, a cinnamic acid O-methyl transferase, two cinnamoyl-CoA reductases) and two cinnamyl alcohol dehydrogenases can be classified as having a bona fide (definitive) function; the remaining 52 genes currently have undetermined physiological roles. The EST database entries for this particular set of genes also provided little new insight into how the monolignol pathway was organized in the different tissues and organs, this being perhaps a consequence of both limitations in how tissue samples were collected and in the incomplete nature of the EST collections. This analysis thus underscores the fact that even with genomic sequencing, presumed to provide the entire suite of putative genes in the monolignol-forming pathway, a very large effort needs to be conducted to establish actual catalytic roles (including enzyme versatility), as well as the physiological function(s) for each member of the (multi)gene families present and the metabolic networks that are operative. Additionally, one key to identifying physiological functions for many of these (and other) unknown genes, and their corresponding metabolic networks, awaits the development of technologies to comprehensively study molecular processes at the single cell level in particular tissues and organs, in order to establish the actual metabolic context.

NASA Program Fundamental Space Biology↗
DOE OSTI2026

Elucidation of odd-chain dicarboxylate metabolism in Acinetobacter baylyi and application to polyethylene upcycling

Polyethylene (PE) is a versatile polymer, but its end-of-life management is challenging due to its recalcitrant structure. We present a promising approach combining chemical degradation and bio-upcycling to convert postconsumer PE waste into a value-added bioproduct. Specifically, PE was degraded into acetic acid and C 4 –C 7 dicarboxylic acids by nitric acid. We then elucidated the catabolic pathways for glutarate (C 5 ) and pimelate (C 7 ) in the nonmodel bacterium Acinetobacter baylyi ADP1 through RNA sequencing, phenotyping, and enzymatic assays. Whole-genome sequencing of evolved isolates also identified a crucial IclR family transcriptional regulator, DcaS, which acts as a repressor of dicarboxylate metabolism. The reverse-engineered strain exhibited enhanced substrate utilization compared to the wild-type strain. Using rational metabolic engineering, the PE deconstruction products were bioconverted into the valuable chemical lycopene, highlighting the potential of this microbial chassis to produce value-added bioproducts from postconsumer PE waste, thus promoting a circular economy for plastics.

metabolic engineering↗
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 OSTI2024

Engineering and evolution of Yarrowia lipolytica for producing lipids from lignocellulosic hydrolysates

Yarrowia lipolytica, an oleaginous yeast, shows promise for industrial fermentation due to its robust acetyl-CoA flux and well-developed genetic engineering tools. However, its lack of an active xylose metabolism restricts the conversion of cellulosic sugars to valuable products. To address this, metabolic engineering, and adaptive laboratory evolution (ALE) were applied to the Y. lipolytica PO1f strain, resulting in an efficient xylose-assimilating strain (XEV). Whole-genome sequencing (WGS) of the XEV followed by reverse engineering revealed that the amplification of the heterologous oxidoreductase pathway and a mutation in the GTPase-activating protein gene (YALI0B12100g) might be the primary reasons for improved xylose assimilation in the XEV strain. When a sorghum hydrolysate was used, the XEV strain showed superior xylose consumption and lipid production compared to its parental strain (X123). This study advances our understanding of xylose metabolism in Y. lipolytica and proposes effective metabolic engineering strategies for optimizing lignocellulosic hydrolysates.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Ocelot: An Interactive, Efficient Distributed Compression-As-a-Service Platform With Optimized Data Compression Techniques

Large volumes of data generated by scientific simulations, genome sequencing, and other applications need to be moved among clusters for data collection/analysis. Data compression techniques have effectively reduced data storage and transfer costs. However, users' requirements on interactively controlling both data quality and compression ratios are non-trivial to fulfill. Here, we propose a novel Compression-as-a-Service (CaaS) platform called Ocelot with four important contributions: (1) It offers real-time visualization, interactive compression, and transfer of scientific datasets. (2) It incorporates new strategies for compressing diverse types of datasets more effectively than traditional methods. (3) It provides an effective method for estimating the compression ratio and execution time of compression tasks. (4) Experiments on multiple real-world datasets on geographically distributed computers show that Ocelot can significantly improve data transfer efficiency with a performance gain of more than 10x in computing clusters with relatively slow networks.

compression as a service (CaaS)↗
DOE OSTI2024

Fine-scale contemporary recombination variation and its fitness consequences in adaptively diverging stickleback fish

Despite deep evolutionary conservation, recombination rates vary greatly across the genome and among individuals, sexes and populations. Yet the impact of this variation on adaptively diverging populations is not well understood. Here we characterized fine-scale recombination landscapes in an adaptively divergent pair of marine and freshwater populations of threespine stickleback from River Tyne, Scotland. Through whole-genome sequencing of large nuclear families, we identified the genomic locations of almost 50,000 crossovers and built recombination maps for marine, freshwater and hybrid individuals at a resolution of 3.8 kb. We used these maps to quantify the factors driving variation in recombination rates. We found strong heterochiasmy between sexes but also differences in recombination rates among ecotypes. Hybrids showed evidence of significant recombination suppression in overall map length and in individual loci. Recombination rates were lower not only within individual marine–freshwater-adaptive loci, but also between loci on the same chromosome, suggesting selection on linked gene ‘cassettes’. Through temporal sampling along a natural hybrid zone, we found that recombinants showed traits associated with reduced fitness. Our results support predictions that divergence in cis-acting recombination modifiers, whose functions are disrupted in hybrids, may play an important role in maintaining differences among adaptively diverging populations.

59 BASIC BIOLOGICAL SCIENCES↗