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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Data for Miscanthus giganteus Biolistic Transformation Using the Visible RUBY Red Marker Gene to Monitor Transformation Efficiency

Miscanthus × giganteus ( M × g ) is a high- yielding perennial C4 bioenergy crop, but genetic improvement by breeding is constrained by triploid sterility and clonal propagation. Improving genetic transformation methods for M × g would provide opportunities for advantageous trait introgression. Use of an easy to phenotype reporter gene is a promising strategy to improve transformation processes and efficiency. This study presents an efficient novel method for biolistic transformation of inflorescence- derived callus in M × g and demonstrates its efficacy using RUBY, a betalain-based noninvasive reporter that is visible throughout the transformation process. RUBY expression ( Zea mays codon optimized) was visible from callus stage through plantlet development into maturity. RUBY expressing independently transformed plants were confirmed by hygromycin phosphotransferase ELISA and by genomic PCR demonstrating that the RUBY phenotype is sufficient for screening transformants. The Zea mays codon optimized hygromycin selection marker was driven by previously established promoters for Miscanthus, ZmUBI and 2×35S, while the RUBY gene expression was controlled by a known Zea mays C4 promoter, Brachypodium UBI10, newly employed in Miscanthus. The construct containing the 2×35S promoter for hygromycin had a 15.1% transformation efficiency while the ZmUBI promoter had a 20.5% transformation efficiency. This study provides a novel, highly efficient protocol for successful biolistic transformation of M × g for stable expression. This study also demonstrates that RUBY expression can be used as a convenient and powerful monitor of transformation in ongoing and future work to engineer M × g into an improved bioproduct feedstock. **NOTE: in "TableS2_ProtocolComparison.csv", the data from row 665 to 971 should be removed.

Gene Editing↗
DOE OSTI2018

CRISPR-Cas9/Cas12a biotechnology and application in bacteria

CRISPR-Cas technologies have greatly reshaped the biology field. In this review, we discuss the CRISPR-Cas with a particular focus on the associated technologies and applications of CRISPR-Cas9 and CRISPR-Cas12a, which have been most widely studied and used. We discuss the biological mechanisms of CRISPR-Cas as immune defense systems, recently-discovered anti-CRISPR-Cas systems, and the emerging Cas variants (such as xCas9 and Cas13) with unique characteristics. Then, we highlight various CRISPR-Cas biotechnologies, including nucleasedependent genome editing, CRISPR gene regulation (including CRISPR interference/activation), DNA/RNA base editing, and nucleic acid detection. Last, we summarize up-to-date applications of the biotechnologies for synthetic biology and metabolic engineering in various bacterial species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Biosystems Design by Machine Learning

Biosystems such as enzymes, pathways, and whole cells have been increasingly explored for biotechnological applications. Yet, the intricate connectivity and complexity of biosystems pose a major hurdle in designing biosystems with desired features. As -omics and other high throughput technologies have been rapidly developed, the promise of applying machine learning (ML) techniques in biosystems design has started to become a reality. ML models enable the identification of patterns within complicated biological data across multiple scales of analysis and can augment biosystems design applications by predicting new candidates for optimized performance. ML is being used at every stage of biosystems design to help find non-obvious engineering solutions with fewer design iterations. In this review, we first describe commonly used models and modeling paradigms within ML. We then discuss some applications of these models that have already shown success in biotechnological applications. Moreover, we discuss successful applications at all scales of biosystems design, including nucleic acids, genetic circuits, proteins, pathways, genomes, and bioprocess. Lastly, we discuss some limitations of these methods and potential solutions as well as prospects of the combination of ML and biosystems design.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Compilation and utilization of a sorghum transcriptome compendium for gene regulatory network analysis and crop trait engineering

Sorghum bicolor (Sorghum) is a drought and heat tolerant C4 grass crop used to produce grain, forage, biofuels, and other bioproducts. Genetic improvement of sorghum hybrid crops is aided by a large and diverse germplasm, sorghum's diploid inbreeding genetics, and a relatively small genome that has facilitated genomic research. Over the past 20 years, the sorghum research community characterized the cytogenetic and recombinant landscapes of sorghum's 10 chromosomes, sequenced and annotated the sorghum genome, and used that information to identify genes/alleles that modulate flowering time, plant height, seed shattering, and other important traits. More recently, >1000 RNA-seq transcriptome profiles were collected from 15 sorghum genotypes to help understand the genetic basis of variation in growth and development of sorghum stems, tillers, roots, and leaves, and the regulation of biosynthetic pathways that produce epicuticular wax, dhurrin, and RFOs, compounds that contribute to sorghum's resilience. Transcriptome studies were designed to identify differentially expressed genes that are co-expressed during development or in response to a treatment to enable construction of gene regulatory networks. Co-expression and network analysis identified transcription factors and their cognate binding sites in target gene promoters and signaling pathways that modulate gene regulatory networks providing gene editing targets for further trait optimization. RNA-seq data from >20 experiments targeting sorghum organs, tissues, cell types, developmental stages, and responses to environmental conditions (i.e., diel, day-length, shading, water-deficit, temperature) has been compiled in a sorghum transcriptome compendium. The goal of this resource paper is to describe compendium content, accessibility, and a compendium data analysis pipeline and to illustrate the types of information that can be derived from the compendium with a focus on the elucidation of gene regulatory networks useful for guiding the improvement of sorghum traits through gene editing.

RNA-seq↗
DOE OSTI2022

Integration of physiologically relevant photosynthetic energy flows into whole genome models of light‐driven metabolism

SUMMARY Characterizing photosynthetic productivity is necessary to understand the ecological contributions and biotechnology potential of plants, algae, and cyanobacteria. Light capture efficiency and photophysiology have long been characterized by measurements of chlorophyll fluorescence dynamics. However, these investigations typically do not consider the metabolic network downstream of light harvesting. By contrast, genome‐scale metabolic models capture species‐specific metabolic capabilities but have yet to incorporate the rapid regulation of the light harvesting apparatus. Here, we combine chlorophyll fluorescence parameters defining photosynthetic and non‐photosynthetic yield of absorbed light energy with a metabolic model of the pennate diatom Phaeodactylum tricornutum. This integration increases the model predictive accuracy regarding growth rate, intracellular oxygen production and consumption, and metabolic pathway usage. Through the quantification of excess electron transport, we uncover the sequential activation of non‐radiative energy dissipation processes, cross‐compartment electron shuttling, and non‐photochemical quenching as the rapid photoacclimation strategy in P. tricornutum. Interestingly, the photon absorption thresholds that trigger the transition between these mechanisms were consistent at low and high incident photon fluxes. We use this understanding to explore engineering strategies for rerouting cellular resources and excess light energy towards bioproducts in silico . Overall, we present a methodology for incorporating a common, informative data type into computational models of light‐driven metabolism and show its utilization within the design–build–test–learn cycle for engineering of photosynthetic organisms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Is Genetic Engineering a Route to Enhance Microalgae-Mediated Bioremediation of Heavy Metal-Containing Effluents?

Contamination of the biosphere by heavy metals has been rising, due to accelerated anthropogenic activities, and is nowadays, a matter of serious global concern. Removal of such inorganic pollutants from aquatic environments via biological processes has earned great popularity, for its cost-effectiveness and high efficiency, compared to conventional physicochemical methods. Among candidate organisms, microalgae offer several competitive advantages; phycoremediation has even been claimed as the next generation of wastewater treatment technologies. Furthermore, integration of microalgae-mediated wastewater treatment and bioenergy production adds favorably to the economic feasibility of the former process—with energy security coming along with environmental sustainability. However, poor biomass productivity under abiotic stress conditions has hindered the large-scale deployment of microalgae. Recent advances encompassing molecular tools for genome editing, together with the advent of multiomics technologies and computational approaches, have permitted the design of tailor-made microalgal cell factories, which encompass multiple beneficial traits, while circumventing those associated with the bioaccumulation of unfavorable chemicals. Previous studies unfolded several routes through which genetic engineering-mediated improvements appear feasible (encompassing sequestration/uptake capacity and specificity for heavy metals); they can be categorized as metal transportation, chelation, or biotransformation, with regulation of metal- and oxidative stress response, as well as cell surface engineering playing a crucial role therein. This review covers the state-of-the-art metal stress mitigation mechanisms prevalent in microalgae, and discusses putative and tested metabolic engineering approaches, aimed at further improvement of those biological processes. Finally, current research gaps and future prospects arising from use of transgenic microalgae for heavy metal phycoremediation are reviewed.

Ranjbar, Saeed (ORCID:0000000256518605)↗
DOE OSTI2015

The auxin-inducible degradation (AID) system enables versatile conditional protein depletion in C. elegans

Experimental manipulation of protein abundance in living cells or organisms is an essential strategy for investigation of biological regulatory mechanisms. Whereas powerful techniques for protein expression have been developed in Caenorhabditis elegans, existing tools for conditional disruption of protein function are far more limited. To address this, we have adapted the auxin-inducible degradation (AID) system discovered in plants to enable conditional protein depletion in C. elegans. We report that expression of a modified Arabidopsis TIR1 F-box protein mediates robust auxin-dependent depletion of degron-tagged targets. We document the effectiveness of this system for depletion of nuclear and cytoplasmic proteins in diverse somatic and germline tissues throughout development. Target proteins were depleted in as little as 20-30 min, and their expression could be re-established upon auxin removal. We have engineered strains expressing TIR1 under the control of various promoter and 3' UTR sequences to drive tissue-specific or temporally regulated expression. The degron tag can be efficiently introduced by CRISPR/Cas9-based genome editing. We have harnessed this system to explore the roles of dynamically expressed nuclear hormone receptors in molting, and to analyze meiosis-specific roles for proteins required for germ line proliferation. Together, our results demonstrate that the AID system provides a powerful new tool for spatiotemporal regulation and analysis of protein function in a metazoan model organism.

Auxin-inducible degradation↗
DOE OSTI2026

Finding the missing pieces: filling gaps that impede the translation of omics data into models

High-throughput omics technologies such as DNA sequencing have made the sequencing and computational assembly of microbial genomes recovered from the environment relatively routine. Computational inference of the protein products encoded by these genomes, and the associated biochemical functions, should enable the accurate prediction and modeling of microbial metabolism, organismal interactions, and ecosystem processes. However, a lack of scalable, probabilistic protein annotation tools limits the full potential of modeling for understanding the metabolism and biogeochemical cycles of microbial communities. Our approach to improve inference of protein annotations and metabolic models relied on learning from and emulating expert manual curation, leveraging software engineering and data science best practices to scale up the throughput and accuracy of annotations and metabolic model construction, building software to objectively evaluate different annotation strategies, and more closely linking the protein annotation and metabolic model inference process. Outcomes of this research include several improved or new computational tools, including DRAM (Distilled and Refined Annotation of Metabolism) for annotating microbial genomes with protein function and metabolic traits, CAMPER (Curated Annotations for Microbial Polyphenol Enzymes and Reactions) for annotating key polyphenol metabolisms, EC-Bench for comprehensive and unbiased benchmarking of annotation tools, and several apps available via the DOE Systems Biology Knowledgebase (KBase) for building genome-scale metabolic models. We demonstrate that these tools allow us to scalably annotate and understand thousands of genomes for microbial communities from a variety of systems and test cases, including rivers, thawing permafrost, and gut microbiomes. All of these computational tools are available as open-source software, with most broadly and easily accessible to the scientific community via KBase apps.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Towards engineering ectomycorrhization into switchgrass bioenergy crops via a lectin receptor‐like kinase

Summary Soil‐borne microbes can establish compatible relationships with host plants, providing a large variety of nutritive and protective compounds in exchange for photosynthesized sugars. However, the molecular mechanisms mediating the establishment of these beneficial relationships remain unclear. Our previous genetic mapping and whole‐genome resequencing studies identified a gene deletion event of a Populus trichocarpa lectin receptor‐like kinase gene PtLecRLK1 in Populus deltoides that was associated with poor‐root colonization by the ectomycorrhizal fungus Laccaria bicolor . By introducing PtLecRLK1 into a perennial grass known to be a non‐host of L. bicolor , switchgrass ( Panicum virgatum L.), we found that L. bicolor colonizes ZmUbipro‐PtLecRLK1 transgenic switchgrass roots, which illustrates that the introduction of PtLecRLK1 has the potential to convert a non‐host to a host of L. bicolor . Furthermore, transcriptomic and proteomic analyses on inoculated‐transgenic switchgrass roots revealed genes/proteins overrepresented in the compatible interaction and underrepresented in the pathogenic defence pathway, consistent with the view that pathogenic defence response is down‐regulated during compatible interaction. Metabolomic profiling revealed that root colonization in the transgenic switchgrass was associated with an increase in N‐containing metabolites and a decrease in organic acids, sugars, and aromatic hydroxycinnamate conjugates, which are often seen in the early steps of establishing compatible interactions. These studies illustrate that PtLecRLK1 is able to render a plant susceptible to colonization by the ectomycorrhizal fungus L. bicolor and shed light on engineering mycorrhizal symbiosis into a non‐host to enhance plant productivity and fitness on marginal lands.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Predictive models of the genetic bases underlying budding yeast fitness in multiple environments

Abstract The ability of organisms to adapt and survive depends on the effects of genes and the environment on fitness. However, the multigenic nature of fitness and genotype-by-environment interactions hinder our understanding of the genetic basis of fitness. Here, we established fitness prediction models for 35 environments using machine learning and existing fitness data and different genetic variant types for a Saccharomyces cerevisiae population. Models revealed that the predictive ability of genetic variants varied across environments, with copy number variants explaining the majority of fitness variation in most cases. Model interpretation showed that different variant types identified distinct gene sets associated with predictive variants. These gene sets were significantly enriched in experimentally validated genes affecting fitness in only a subset of environments, indicating that many genes influencing fitness remain unexplored. Notably, non-experimentally validated genes were more important than validated ones for fitness predictions. Gene contributions to predictions were both isolate- and environment-dependent, pointing to gene-by-gene and gene-by-environment interactions. Furthermore, models uncovered experimentally validated and novel candidate genetic interactions for a well-characterized stress, the fungicide benomyl. These findings highlight the feasibility of identifying the genetic basis of fitness by using different genetic variant types and offer novel targets for future functional analysis.

DNA copy number variations↗
DOE OSTI2023

Engineering Rhodosporidium toruloides for production of 3-hydroxypropionic acid from lignocellulosic hydrolysate

Microbial production of valuable bioproducts is a promising route towards green and sustainable manufacturing. The oleaginous yeast, Rhodosporidium toruloides, has emerged as an attractive host for the production of biofuels and bioproducts from lignocellulosic hydrolysates. 3-hydroxypropionic acid (3HP) is an attractive platform molecule that can be used to produce a wide range of commodity chemicals. This study focuses on establishing and optimizing the production of 3HP in R. toruloides. As R. toruloides naturally has a high metabolic flux towards malonyl-CoA, we exploited this pathway to produce 3HP. Upon finding the yeast capable of catabolizing 3HP, we then implemented functional genomics and metabolomic analysis to identify the catabolic pathways. Deletion of a putative malonate semialdehyde dehydrogenase gene encoding an oxidative 3HP pathway was found to significantly reduce 3HP degradation. We further explored monocarboxylate transporters to promote 3HP transport and identified a novel 3HP transporter in Aspergillus pseudoterreus by RNA-seq and proteomics. Combining these engineering efforts with media optimization in a fed-batch fermentation resulted in 45.4 g/L 3HP production. This represents one of the highest 3HP titers reported, and the highest titer produced from lignocellulosic hydrolysate to the best of our knowledge. This work demonstrates microbial production of 3HP in R. toruloides from lignocellulosic hydrolysate at high titers, and it represents a significant step toward enabling industrial production of 3HP in the future.

3-Hydroxypropionic acid↗
DOE OSTI2026

Data for Resourceful and Economical Designing of Fermentation Medium for Lab and Commercial Strains of Yeast from Alternative Feedstock: Transgenic Oilcane

Sugarcane plant engineered to accumulate lipids in its vegetative tissue is being developed as a new bioenergy crop. The new crop would be a source of juice, oil, and cellulosic sugars. However, limited tolerance of industrially recognized yeasts towards inhibitors generated during the processing of lignocellulosic biomass to produce fermentable sugars is a major challenge in developing scalable processes for second-generation drop-in fuel production. To this end, hydrolysates generated from engineered sugarcane—‘oilcane’ bagasse contain added phenolics and fatty acids that further restrict the growth of fermenting microorganisms and necessitate nutrient supplementation and/or detoxification of hydrolysate which makes the fermentation process expensive. Herein, we propose a resourceful and economical approach for growing lab and commercial strains of S. cerevisiae on unrefined cellulosic sugars aerobically and fermentatively. An equal ratio of hydrolysate and juice was found optimum for growth and fermentation by lab and commercial strains of Saccharomyces cerevisiae engineered for xylose fermentation. The industrial strain grew and fermented efficiently under low aeration conditions having an ethanol titer, yield, specific and volumetric productivities of 46.96 ± 0.19 g/l, 0.51 ± 0.00 g/g, 0.27 ± 0.02 g/g.h and 1.95 ± 0.01 g/l.h, respectively, while the lab strain grew better under higher aeration conditions having the ethanol titer, yield, specific and volumetric productivities of 24.93 ± 0.09, 0.27 ± 0.00 g/g, 0.17 ± 0.00 g/g.h and 1.04 ± 0.00 g/l.h, respectively. Acclimation of cultures in a blended medium significantly improved the performance of the yeast strains. The addition of transgenic oilcane juice, which is inedible and rich in amino acids, to the hydrolysate averted the need for expensive nutrient supplementation and detoxification steps of hydrolysate. The approach provides an economical solution to reduce the cost of fermentation at an industrial scale for second-generation drop-in fuel production.

Biomass Analytics↗
DOE OSTI2016

Engineering microbial consortia for controllable outputs

Much research has been invested into engineering microorganisms to perform desired biotransformations; nonetheless, these efforts frequently fall short of expected results due to the unforeseen effects of biofeedback regulation and functional incompatibility. In nature, metabolic function is compartmentalized into diverse organisms assembled into resilient consortia, in which the division of labor is thought to lead to increased community efficiency and productivity. Here, we consider whether and how consortia can be designed to perform bioprocesses of interest beyond the metabolic flexibility limitations of a single organism. Advances in post-genomic analysis of microbial consortia and application of high-resolution global measurements now offer the promise of systems-level understanding of how microbial consortia adapt to changes in environmental variables and inputs of carbon and energy. We argue that when combined with appropriate modeling framework that predictive knowledge generates testable hypotheses and orthogonal synthetic biology tools, such understanding can dramatically improve our ability to control the fate and functioning of consortia. In this article, we articulate our collective perspective on the current and future state of microbial community engineering and control while placing specific emphasis on ecological principles that promote control over community function and emergent properties.

controllable outputs↗
DOE OSTI2019

Genomewide and Enzymatic Analysis Reveals Efficient D-Galacturonic Acid Metabolism in the Basidiomycete Yeast Rhodosporidium toruloides

Biorefining of renewable feedstocks is one of the most promising routes to replace fossil-based products. Since many common fermentation hosts, such as Saccharomyces cerevisiae , are naturally unable to convert many component plant cell wall polysaccharides, the identification of organisms with broad catabolism capabilities represents an opportunity to expand the range of substrates used in fermentation biorefinery approaches. The red basidiomycete yeast Rhodosporidium toruloides is a promising and robust host for lipid- and terpene-derived chemicals. Previous studies demonstrated assimilation of a range of substrates, from C 5 /C 6 sugars to aromatic molecules similar to lignin monomers. In the current study, we analyzed the potential of R. toruloides to assimilate d -galacturonic acid, a major sugar in many pectin-rich agricultural waste streams, including sugar beet pulp and citrus peels. d -Galacturonic acid is not a preferred substrate for many fungi, but its metabolism was found to be on par with those of d -glucose and d -xylose in R. toruloides . A genomewide analysis by combined transcriptome sequencing (RNA-seq) and RB-TDNA-seq revealed those genes with high relevance for fitness on d -galacturonic acid. While R. toruloides was found to utilize the nonphosphorylative catabolic pathway known from ascomycetes, the maximal velocities of several enzymes exceeded those previously reported. In addition, an efficient downstream glycerol catabolism and a novel transcription factor were found to be important for d -galacturonic acid utilization. These results set the basis for use of R. toruloides as a potential host for pectin-rich waste conversions and demonstrate its suitability as a model for metabolic studies with basidiomycetes. The switch from the traditional fossil-based industry to a green and sustainable bioeconomy demands the complete utilization of renewable feedstocks. Many currently used bioconversion hosts are unable to utilize major components of plant biomass, warranting the identification of microorganisms with broader catabolic capacity and characterization of their unique biochemical pathways. d -Galacturonic acid is a plant component of bioconversion interest and is the major backbone sugar of pectin, a plant cell wall polysaccharide abundant in soft and young plant tissues. The red basidiomycete and oleaginous yeast Rhodosporidium toruloides has been previously shown to utilize a range of sugars and aromatic molecules. Using state-of-the-art functional genomic methods and physiological and biochemical assays, we elucidated the molecular basis underlying the efficient metabolism of d -galacturonic acid. This study identified an efficient pathway for uronic acid conversion to guide future engineering efforts and represents the first detailed metabolic analysis of pectin metabolism in a basidiomycete fungus.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Performance Analysis Tool for HPC and Big Data Applications on Scientific Clusters

Big data is prevalent in HPC computing. Many HPC projects rely on complex workflows to analyze terabytes or petabytes of data. These workflows often require running over thousands of CPU cores and performing simultaneous data accesses, data movements, and computation. It is challenging to analyze the performance involving terabytes or petabytes of workflow data or measurement data of the executions, from complex workflows over a large number of nodes and multiple parallel task executions. To help identify performance bottlenecks or debug the performance issues in large-scale scientific applications and scientific clusters, we have developed a performance analysis framework, using state-ofthe- art open-source big data processing tools. Our tool can ingest system logs and application performance measurements to extract key performance features, and apply the most sophisticated statistical tools and data mining methods on the performance data. It utilizes an efficient data processing engine to allow users to interactively analyze a large amount of different types of logs and measurements. To illustrate the functionality of the big data analysis framework, we conduct case studies on the workflows from an astronomy project known as the Palomar Transient Factory (PTF) and the job logs from the genome analysis scientific cluster. Our study processed many terabytes of system logs and application performance measurements collected on the HPC systems at NERSC. The implementation of our tool is generic enough to be used for analyzing the performance of other HPC systems and Big Data workows.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

An Integrated Computer-Aided Design and Manufacturing Workflow for Synthetic Biology

Biological computer-aided design and manufacturing (bioCAD/CAM) tools facilitate the design and build processes of engineering biological systems using iterative design-build-test-learn (DBTL) cycles. In this book chapter, we highlight some of the bioCAD/CAM tools developed and used at the US Department of Energy (DOE) Joint Genome Institute (JGI), Joint BioEnergy Institute (JBEI), and Agile BioFoundry (ABF). We demonstrate the use of these bioCAD/CAM tools on a common workflow for designing and building a multigene pathway in a hierarchical fashion. Additionally, each tool presented in this book chapter is specifically tailored to support one or more specific steps in a workflow, can be integrated with the others into design and build workflows, and can be deployed at academic, government, or commercial entities.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2012

BRIC-17 Mapping Spaceflight-Induced Hypoxic Signaling and Response in Plants

Goals of this work are: (1) Define global changes in gene expression patterns in Arabidopsis plants grown in microgravity using whole genome microarrays (2) Compare to mutants resistant to low oxygen challenge using whole genome microarrays Also measuring root and shoot size Outcomes from this research are: (1) Provide fundamental information on plant responses to the stresses inherent in spaceflight (2) Potential for informing on genetic strategies to engineer plants for optimal growth in space

Gilroy, Simon↗