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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 379 records · Page 21

Systematic engineering for production of anti-aging sunscreen compound in Pseudomonas putida

Sunscreen has been used for thousands of years to protect skin from ultraviolet radiation. However, the use of modern commercial sunscreen containing oxybenzone, ZnO, and TiO 2 has raised concerns due to their negative effects on human health and the environment. In this study, we aim to establish an efficient microbial platform for production of shinorine, a UV light absorbing compound with anti-aging properties. First, we methodically selected an appropriate host for shinorine production by analyzing central carbon flux distribution data from prior studies alongside predictions from genome-scale metabolic models (GEMs). We enhanced shinorine productivity through CRISPRi-mediated downregulation and utilized shotgun proteomics to pinpoint potential competing pathways. Simultaneously, we improved the shinorine biosynthetic pathway by refining its design, optimizing promoter usage, and altering the strength of ribosome binding sites. Finally, we conducted amino acid feeding experiments under various conditions to identify the key limiting factors in shinorine production. The study combines meta-analysis of 13 C-metabolic flux analysis, GEMs, synthetic biology, CRISPRi-mediated gene downregulation, and omics analysis to improve shinorine production, demonstrating the potential of Pseudomonas putida KT2440 as platform for shinorine production.

59 BASIC BIOLOGICAL SCIENCES↗

Diel dynamics of multi-omics in elkhorn fern provide new insights into weak CAM photosynthesis

Crassulacean acid metabolism (CAM) has high water-use efficiency (WUE) and is widely recognized to have evolved from C 3 photosynthesis. Different plant lineages have convergently evolved CAM, but the molecular mechanism that underlies C 3 -to-CAM evolution remains to be clarified. Platycerium bifurcatum (elkhorn fern) provides an opportunity to study the molecular changes underlying the transition from C 3 to CAM photosynthesis because both modes of photosynthesis occur in this species, with sporotrophophyll leaves (SLs) and cover leaves (CLs) performing C 3 and weak CAM photosynthesis, respectively. Here, we report that the physiological and biochemical attributes of CAM in weak CAM-performing CLs differed from those in strong CAM species. We investigated the diel dynamics of the metabolome, proteome, and transcriptome in these dimorphic leaves within the same genetic background and under identical environmental conditions. We found that multi-omic diel dynamics in P. bifurcatum exhibit both tissue and diel effects. Our analysis revealed temporal rewiring of biochemistry relevant to the energy-producing pathway (TCA cycle), CAM pathway, and stomatal movement in CLs compared with SLs. We also confirmed that PHOSPHOENOLPYRUVATE CARBOXYLASE KINASE (PPCK) exhibits convergence in gene expression among highly divergent CAM lineages. Gene regulatory network analysis identified candidate transcription factors regulating the CAM pathway and stomatal movement. Taken together, our results provide new insights into weak CAM photosynthesis and new avenues for CAM bioengineering.

59 BASIC BIOLOGICAL SCIENCES↗

Aggregation Methods for Quantifying PTM and Structural Changes in Bottom-Up Proteomics

Bottom-up proteomic workflows rely on sequential preprocessing steps, commonly including peptide-to-protein aggregation (“roll-up”), to enhance data reliability and interpretability. While roll-up is effective for protein-centered analyses, it may be suboptimal for applications focused on post-translational modifications (PTMs) or protein structural changes, such as limited proteolysis–mass spectrometry (LiP-MS). Here, we investigate how different roll-up strategies influence site-level quantification in PTM differential analysis. Moreover, we introduce a novel site-centric roll-up approach tailored for LiP-MS, which quantifies proteolytic fragments rather than solely tryptic peptides. We benchmark these methods through simulation studies, comparing their sensitivity and specificity in detecting structural and PTM-driven changes. We found that the median and mean roll-up methods outperform the sum method in both PTM and LiP proteomics, and site-level quantification in LiP outperforms peptide-level quantification. Our findings offer the first systematic, data-driven guidance for selecting roll-up techniques in site-level proteomic analyses, with implications for both PTM-focused and structural proteomics studies.

aggregation↗

Mass Spectrometry–Based Proteogenomics: New Therapeutic Opportunities for Precision Medicine

Proteogenomics refers to the integration of comprehensive genomic, transcriptomic, and proteomic measurements from the same samples with the goal of fully understanding the regulatory processes converting genotypes to phenotypes, often with an emphasis on gaining a deeper understanding of disease processes. Although specific genetic mutations have long been known to drive the development of multiple cancers, gene mutations alone do not always predict prognosis or response to targeted therapy. The benefit of proteogenomics research is that information obtained from proteins and their corresponding pathways provides insight into therapeutic targets that can complement genomic information by providing an additional dimension regarding the underlying mechanisms and pathophysiology of tumors. This review describes the novel insights into tumor biology and drug resistance derived from proteogenomic analysis while highlighting the clinical potential of proteogenomic observations and advances in technique and analysis tools.

60 APPLIED LIFE SCIENCES↗

Bioinformatic and experimental evidence for suicidal and catalytic plant THI4s

Like fungi and some prokaryotes, plants use a thiazole synthase (THI4) to make the thiazole precursor of thiamin. Fungal THI4s are suicide enzymes that destroy an essential active-site Cys residue to obtain the sulfur atom needed for thiazole formation. In contrast, certain prokaryotic THI4s have no active-site Cys, use sulfide as sulfur donor, and are truly catalytic. The presence of a conserved active-site Cys in plant THI4s and other indirect evidence implies that they are suicidal. To confirm this, we complemented the Arabidopsistz-1 mutant, which lacks THI4 activity, with a His-tagged Arabidopsis THI4 construct. LC–MS analysis of tryptic peptides of the THI4 extracted from leaves showed that the active-site Cys was predominantly in desulfurated form, consistent with THI4 having a suicide mechanism in planta. Unexpectedly, transcriptome data mining and deep proteome profiling showed that barley, wheat, and oat have both a widely expressed canonical THI4 with an active-site Cys, and a THI4-like paralog (non-Cys THI4) that has no active-site Cys and is the major type of THI4 in developing grains. Transcriptomic evidence also indicated that barley, wheat, and oat grains synthesize thiamin de novo, implying that their non-Cys THI4s synthesize thiazole. Structure modeling supported this inference, as did demonstration that non-Cys THI4s have significant capacity to complement thiazole auxotrophy in Escherichia coli. There is thus a prima facie case that non-Cys cereal THI4s, like their prokaryotic counterparts, are catalytic thiazole synthases. Bioenergetic calculations show that, relative to suicide THI4s, such enzymes could save substantial energy during the grain-filling period.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

GeneLab: A Systems Biology Platform for Omics Analysis

NASA GeneLab is an open-access repository for omics datasets generated by biological experiments conducted in space or experiments relevant to spaceflight (e.g. simulated cosmic radiation, simulated microgravity, bed rest studies). The GeneLab Data Systems (GLDS) version 4.0 will be available on October 1st 2019, and will provide the latest in terms of professional state-of-the-art bioinformatics platform for the space biology and radiation community to upload their data into an omics data commons, to process their data with vetted standard workflows and to compare to existing analyses. Started in 2015 as a repository designed to archive omics data from space experiments, GeneLab has expanded its scope to all ionizing radiation omics experiments conducted on the ground and has put considerable effort in providing carefully characterized radiation metadata on all dataset. GeneLab is also providing processed data derived from the raw data covering a large spectrum of omics (genome, epigenome, transcriptome, epitranscriptome, proteome, metabolome) to help users explore important questions: 1) Which genes or proteins are expressed differently in space for various living organisms? 2) What specific DNA mutations or epigenetic changes happen in space or after exposure to ionizing radiation? and 3) How does genetics affect these responses? Processed data available on GeneLab are derived by standard data analysis workflows vetted by hundreds of scientists who volunteered to join one of the four GeneLab Analysis Working Groups (Animal AWG, Plant AWG, Microbe AWG, Multi-Omics AWG). In this presentation, we will discuss how to bridge the gap between irradiation studies performed on earth and biological experiments conducted in space since the early 1990's. We will discuss how radiation dosimetry was estimated for datasets derived from samples collected during the Space Shuttle era or on the International Space Station. Finally, we will address future strategies regarding dose monitoring in future missions into space, inter-agency efforts to unify data under one umbrella, and knowledge dissemination across the radiation research community and the space biology community.

open-science↗

Computational tools and algorithms for ion mobility spectrometry-mass spectrometry

Ion mobility spectrometry-mass spectrometry (IMS-MS or IM-MS) is a powerful analytical technique that combines the gas-phase separation capabilities of IM with the identification and quantification capabilities of MS. IM-MS can differentiate molecules with indistinguishable masses but different structures (e.g., isomers, isobars, molecular classes, and contaminant ions). The importance of this analytical technique is reflected by a staged increase in the number of applications for molecular characterization across a variety of fields, from different MS-based omics (proteomics, metabolomics, lipidomics, etc.) to the structural characterization of glycans, organic matter, proteins, and macromolecular complexes. With the increasing application of IM-MS there is a pressing need for effective and accessible computational tools. This article presents an overview of the most recent free and open-source software tools specifically tailored for the analysis and interpretation of data derived from IM-MS instrumentation. This review enumerates these tools and outlines their main algorithmic approaches, while highlighting representative applications across different fields. Finally, a discussion of current limitations and expectable improvements is presented.

59 BASIC BIOLOGICAL SCIENCES↗

Ampk alpha2 T172 activation dictates exercise performance and energy transduction in skeletal muscle

Adenosine 5′-monophosphate–activated protein kinase (AMPK) is an energetic sensor for metabolic regulation and integration. Here, we used CRISPR-Cas9 to generate nonactivatable Ampkα knock-in (KI) mice with mutation of threonine-172 phosphorylation site to alanine (T172A), circumventing the limitations of previous genetic interventions that disrupt the protein stoichiometry. KI mice of Ampkα2, but not Ampkα1, demonstrated phenotypic changes with increased fat-to-lean mass, impaired endurance exercise capacity, and diminished mitochondrial maximal respiration and conductance in skeletal muscle. Integrated temporal multiomics analysis (proteomics/phosphoproteomics/metabolomics) in skeletal muscle at rest and during exercise establishes a pleiotropic yet imperative role of Ampkα2 T172 activation for glycolytic and oxidative metabolism, mitochondrial respiration, and contractile function. There is a substantial overlap of skeletal muscle proteomic changes in Ampkα2 T172A KI mice with that of patients with type 2 diabetes. Our findings suggest that Ampkα2 T172 activation is critical for exercise performance and energy transduction in skeletal muscle and may serve as a therapeutic target for type 2 diabetes.

Bioenergetics↗

New insights into the natural history of bronchopulmonary dysplasia from proteomics and multiplexed immunohistochemistry

Bronchopulmonary dysplasia (BPD) is a disease of prematurity related to the arrest of normal lung development. The objective of this study was to better understand how proteome modulation and cell-type shifts are noted in BPD pathology. Pediatric human donors aged 1–3 yr were classified based on history of prematurity and histopathology consistent with “healed” BPD (hBPD, n = 3) and “established” BPD (eBPD, n = 3) compared with respective full-term born (n = 6) age-matched term controls. Proteins were quantified by tandem mass spectroscopy with selected Western blot validations. Multiplexed immunofluorescence (MxIF) microscopy was performed on lung sections to enumerate cell types. Protein abundances and MxIF cell frequencies were compared among groups using ANOVA. Cell type and ontology enrichment were performed using an in-house tool and/or EnrichR. Proteomics detected 5,746 unique proteins, 186 upregulated and 534 downregulated, in eBPD versus control with fewer proteins differentially abundant in hBPD as compared with age-matched term controls. Cell-type enrichment suggested a loss of alveolar type I, alveolar type II, endothelial/capillary, and lymphatics, and an increase in smooth muscle and fibroblasts consistent with MxIF. Histochemistry and Western analysis also supported predictions of upregulated ferroptosis in eBPD versus control. Finally, several extracellular matrix components mapping to angiogenesis signaling pathways were altered in eBPD. Despite clear parsing by protein abundance, comparative MxIF analysis confirms phenotypic variability in BPD. This work provides the first demonstration of tandem mass spectrometry and multiplexed molecular analysis of human lung tissue for critical elucidation of BPD trajectory-defining factors into early childhood.

60 APPLIED LIFE SCIENCES↗

GeneLab

GeneLab collects and enables analysis of spaceflight and ground-based spaceflight simulation genomic data, RNA and protein expression, and metabolic profiles. It interfaces with other existing databases containing spaceflight omic data. The 2011 National Research Council (NRC) Decadal Survey on NASA Life and Physical Sciences called for increased opportunities for multi-investigator spaceflight opportunities and greater use of genomic approaches to meet the needs of NASA researchers. To address these recommendations of the NRC Decadal Survey, the Space Life and Physical Sciences Research and Applications Division of NASA's Human Exploration and Operations Mission Directorate has initiated a transition to an Open Science architecture to increase research opportunities, and has developed the GeneLab Platform based on highly leveraged and integrated bioinformatics analytics. GeneLab is an interactive, open-access resource where scientists can upload, download, store, search, share, transfer, and analyze omics data from spaceflight and corresponding analogue experiments. Users can explore GeneLab datasets in the Data Repository, analyze data using the Analysis Platform, visualize high-order data and create collaborative projects using the Collaborative Workspace. Our primary goal is to maximize the utilization of the valuable biological research conducted aboard the International Space Station (ISS) by collecting genomic, transcriptomic, proteomic, and metabolomics data known as “omics”. By providing a portal linking processed data to flight parameters, GeneLab enables exploration of the molecular network responses of terrestrial biology to the space environment. This allows researchers to understand the complex responses of biological systems to the space environment. This technology development activity was transferred from the Human Exploration and Operations Mission Directorate to the Science Mission Directorate Division of Biological and Physical Sciences (BPS) in October 2020.

GeneLab↗

Symbiosis Under Stress: How Arbuscular Mycorrhizal Fungi and Sorghum Metabolism Shift Under Drought

As drought becomes more prevalent across the globe, causing billions of dollars in agricultural loss, the need to maintain crop health and productivity grows increasingly important. Out of the most important cereal crops, sorghum shows the greatest drought tolerance, and plant-microbiome interactions at the root region play a crucial role in this. A key microbial player is arbuscular mycorrhizal fungi (AMF), which deliver water and nutrients to plants in exchange for nutrients they cannot produce. Using sorghum as a model plant, we combine mass-spectrometry based proteomics and metabolomics to examine metabolic interactions between sorghum roots and AMF under drought stress. With AMF, sorghum downregulated lipid-related biological processes involving fatty acid biosynthesis and metabolism. Similarly, amino acid biosynthesis pathways were also suppressed; however, metabolite analysis revealed increased amino acid abundance related to the phenylpropanoid pathway. AMF also enhanced the upregulation of drought-protective osmolytes, such as mannitol and sorbitol, suggesting their key role in mediating sorghum’s response to drought stress. AMF hyphal biomass also had an increased abundance of key osmoprotectant amino acids, indicating similar mechanisms of drought tolerance between sorghum and AMF. Metabolomic evidence also suggested that carbohydrate exchange between sorghum and AMF shifted under stress, indicating an altered exudation pattern likely driven by drought response. Our results demonstrate the molecular mechanisms through which AMF modulate sorghum metabolism under drought conditions, highlighting their promising role in improving crop resilience. By identifying the molecular targets that can improve drought tolerance, we can begin engineering drought resistant agricultural biosystems.

60 APPLIED LIFE SCIENCES↗

The Thiamine-Pyrophosphate-Motif

Thiamin pyrophosphate (TPP), a derivative of vitamin B1, is a cofactor for enzymes performing catalysis in pathways of energy production including the well known decarboxylation of a-keto acid dehydrogenases followed by transketolation. TPP-dependent enzymes constitute a structurally and functionally diverse group exhibiting multimeric subunit organization, multiple domains and two chemically equivalent catalytic centers. Annotation of functional TPP-dependcnt enzymes, therefore, has not been trivial due to low sequence similarity related to this complex organization. Our approach to analysis of structures of known TPP-dependent enzymes reveals for the first time features common to this group, which we have termed the TPP-motif. The TPP-motif consists of specific spatial arrangements of structural elements and their specific contacts to provide for a flip-flop, or alternate site, enzymatic mechanism of action. Analysis of structural elements entrained in the flip-flop action displayed by TPP-dependent enzymes reveals a novel definition of the common amino acid sequences. These sequences allow for annotation of TPP-dependent enzymes, thus advancing functional proteomics. Further details of three-dimensional structures of TPP-dependent enzymes will be discussed.

Ciszak, Ewa↗

Genelab: Scientific Partnerships and an Open-Access Database to Maximize Usage of Omics Data from Space Biology Experiments

NASA's mission includes expanding our understanding of biological systems to improve life on Earth and to enable long-duration human exploration of space. The GeneLab Data System (GLDS) is NASA's premier open-access omics data platform for biological experiments. GLDS houses standards-compliant, high-throughput sequencing and other omics data from spaceflight-relevant experiments. The GeneLab project at NASA-Ames Research Center is developing the database, and also partnering with spaceflight projects through sharing or augmentation of experiment samples to expand omics analyses on precious spaceflight samples. The partnerships ensure that the maximum amount of data is garnered from spaceflight experiments and made publically available as rapidly as possible via the GLDS. GLDS Version 1.0, went online in April 2015. Software updates and new data releases occur at least quarterly. As of October 2016, the GLDS contains 80 datasets and has search and download capabilities. Version 2.0 is slated for release in September of 2017 and will have expanded, integrated search capabilities leveraging other public omics databases (NCBI GEO, PRIDE, MG-RAST). Future versions in this multi-phase project will provide a collaborative platform for omics data analysis. Data from experiments that explore the biological effects of the spaceflight environment on a wide variety of model organisms are housed in the GLDS including data from rodents, invertebrates, plants and microbes. Human datasets are currently limited to those with anonymized data (e.g., from cultured cell lines). GeneLab ensures prompt release and open access to high-throughput genomics, transcriptomics, proteomics, and metabolomics data from spaceflight and ground-based simulations of microgravity, radiation or other space environment factors. The data are meticulously curated to assure that accurate experimental and sample processing metadata are included with each data set. GLDS download volumes indicate strong interest of the scientific community in these data. To date GeneLab has partnered with multiple experiments including two plant (Arabidopsis thaliana) experiments, two mice experiments, and several microbe experiments. GeneLab optimized protocols in the rodent partnerships for maximum yield of RNA, DNA and protein from tissues harvested and preserved during the SpaceX-4 mission, as well as from tissues from mice that were frozen intact during spaceflight and later dissected on the ground. Analysis of GeneLab data will contribute fundamental knowledge of how the space environment affects biological systems, and as well as yield terrestrial benefits resulting from mitigation strategies to prevent effects observed during exposure to space environments.

bioinformatics↗

GeneLab: Scientific Partnerships and an Open-Access Database to Maximize Usage of Omics Data from Space Biology Experiments

NASA's mission includes expanding our understanding of biological systems to improve life on Earth and to enable long-duration human exploration of space. The GeneLab Data System (GLDS) is NASAs premier open-access omics data platform for biological experiments. GLDS houses standards-compliant, high-throughput sequencing and other omics data from spaceflight-relevant experiments. The GeneLab project at NASA-Ames Research Center is developing the database, and also partnering with spaceflight projects through sharing or augmentation of experiment samples to expand omics analyses on precious spaceflight samples. The partnerships ensure that the maximum amount of data is garnered from spaceflight experiments and made publically available as rapidly as possible via the GLDS. GLDS Version 1.0, went online in April 2015. Software updates and new data releases occur at least quarterly. As of October 2016, the GLDS contains 80 datasets and has search and download capabilities. Version 2.0 is slated for release in September of 2017 and will have expanded, integrated search capabilities leveraging other public omics databases (NCBI GEO, PRIDE, MG-RAST). Future versions in this multi-phase project will provide a collaborative platform for omics data analysis. Data from experiments that explore the biological effects of the spaceflight environment on a wide variety of model organisms are housed in the GLDS including data from rodents, invertebrates, plants and microbes. Human datasets are currently limited to those with anonymized data (e.g., from cultured cell lines). GeneLab ensures prompt release and open access to high-throughput genomics, transcriptomics, proteomics, and metabolomics data from spaceflight and ground-based simulations of microgravity, radiation or other space environment factors. The data are meticulously curated to assure that accurate experimental and sample processing metadata are included with each data set. GLDS download volumes indicate strong interest of the scientific community in these data. To date GeneLab has partnered with multiple experiments including two plant (Arabidopsis thaliana) experiments, two mice experiments, and several microbe experiments. GeneLab optimized protocols in the rodent partnerships for maximum yield of RNA, DNA and protein from tissues harvested and preserved during the SpaceX-4 mission, as well as from tissues from mice that were frozen intact during spaceflight and later dissected on the ground. Analysis of GeneLab data will contribute fundamental knowledge of how the space environment affects biological systems, and as well as yield terrestrial benefits resulting from mitigation strategies to prevent effects observed during exposure to space environments.

spaceflight↗

Metatranscriptomics analysis reveals a novel transcriptional and translational landscape during Middle East respiratory syndrome coronavirus infection

Among all RNA viruses, coronavirus RNA transcription is the most complex and involves a process termed “discontinuous transcription” that results in the production of a set of 3'-nested, co-terminal genomic and subgenomic RNAs during infection. While the expression of the classic canonical set of subgenomic RNAs depends on the recognition of a 6- to 7-nt transcription regulatory core sequence (TRS), here, we use deep sequence and metagenomics analysis strategies and show that the coronavirus transcriptome is even more vast and more complex than previously appreciated and involves the production of leader-containing transcripts that have canonical and noncanonical leader-body junctions. Moreover, by ribosome protection and proteomics analyses, we show that both positive- and negative-sense transcripts are translationally active. The data support the hypothesis that the coronavirus proteome is much vaster than previously noted in the literature.

59 BASIC BIOLOGICAL SCIENCES↗

The cardiac molecular setting of metabolic syndrome in pigs reveals disease susceptibility and suggests mechanisms that exacerbate COVID-19 outcomes in patients

Abstract Although metabolic syndrome (MetS) is linked to an elevated risk of cardiovascular disease (CVD), the cardiac-specific risk mechanism is unknown. Obesity, hypertension, and diabetes (all MetS components) are the most common form of CVD and represent risk factors for worse COVID-19 outcomes compared to their non MetS peers. Here, we use obese Yorkshire pigs as a highly relevant animal model of human MetS, where pigs develop the hallmarks of human MetS and reproducibly mimics the myocardial pathophysiology in patients. Myocardium-specific mass spectroscopy-derived metabolomics, proteomics, and transcriptomics enabled the identity and quality of proteins and metabolites to be investigated in the myocardium to greater depth. Myocardium-specific deregulation of pro-inflammatory markers, propensity for arterial thrombosis, and platelet aggregation was revealed by computational analysis of differentially enriched pathways between MetS and control animals. While key components of the complement pathway and the immune response to viruses are under expressed, key N6-methyladenosin RNA methylation enzymes are largely overexpressed in MetS. Blood tests do not capture the entirety of metabolic changes that the myocardium undergoes, making this analysis of greater value than blood component analysis alone. Our findings create data associations to further characterize the MetS myocardium and disease vulnerability, emphasize the need for a multimodal therapeutic approach, and suggests a mechanism for observed worse outcomes in MetS patients with COVID-19 comorbidity.

60 APPLIED LIFE SCIENCES↗

Myalgic encephalomyelitis/chronic fatigue syndrome and fibromyalgia are indistinguishable by their cerebrospinal fluid proteomes

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) and fibromyalgia have overlapping neurologic symptoms particularly disabling fatigue. This has given rise to the question whether they are distinct central nervous system (CNS) entities or is one an extension of the other. To investigate this, we used unbiased quantitative mass spectrometry-based proteomics to examine the most proximal fluid to the brain, cerebrospinal fluid (CSF). This was to ascertain if the proteome profile of one was the same or different from the other. We examined two separate groups of ME/CFS, one with (n = 15) and one without (n = 15) fibromyalgia. We quantified a total of 2083 proteins using immunoaffinity depletion, tandem mass tag isobaric labelling and offline two-dimensional liquid chromatography coupled to tandem mass spectrometry, including 1789 that were quantified in all the CSF samples. ANOVA analysis did not yield any proteins with an adjusted p value <.05. This supports the notion that ME/CFS and fibromyalgia as currently defined are not distinct entities.

60 APPLIED LIFE SCIENCES↗

Improved Biofuel Production through Discovery and Engineering of Terpene Metabolism in Switchgrass

Project Objectives - Of the myriad specialized metabolites that plants deploy to adapt to environmental challenges, terpenes form the largest group. In many major crops, unique terpene blends serve as key stress defenses that directly impact plant fitness and yield. In addition, terpenes, such as bisabolene and pinene, are used for producing renewable biofuels. Essential to advancing a broader use of terpenes for biofuel feedstock engineering is a system-wide knowledge of the diverse biosynthetic machinery and defensive potential of often species-specific terpene blends. The proposed project would merge genome-wide enzyme discovery with comparative –omics, protein structural and plant microbiome studies to define the biosynthesis and stress-defensive functions of the switchgrass (Panicum virgatum) terpene network. These insights would be combined with developing and applying non-transgenic genome editing tools to design plants with desirable terpene blends for higher productivity and biofuel production on marginal lands. As a dedicated lignocellulosic feedstock for U.S. biofuel production with high net energy yield, stress tolerance, and available genome resources, switchgrass is well-suited for devising new avenues for biofuel production. Project Description – The diversity of plant terpene defenses is governed by species-specific families of terpene synthase (TPS) and cytochrome P450 monooxygenase (P450) enzymes. Mining of the switchgrass genome (genotype Alamo) identified ~100 TPS and P450 candidate genes, and combinatorial biochemical analysis of synthesized TPSs and P450s revealed more than a dozen enzymes with common and novel activities. In addition, several identified terpene metabolites and the corresponding transcripts were up-regulated in response to abiotic stressors. These findings demonstrate a unique switchgrass terpene network with probable importance to abiotic stress tolerance, thus providing a large chemical portfolio for optimizing crop resistance, yield, and biofuel composition. Leveraging these preliminary data, we propose to generate a genome-wide map of the switchgrass terpene metabolic network through multi-gene co-expression analyses that allow the efficient cross-validation of TPS and P450 functions. Key enzymes would further be applied to structure-function studies via X-ray protein crystallography, homology modeling and site-directed mutagenesis to gain mechanistic insight into the catalytic specificity of switchgrass terpene metabolism and provide gene and amino acid targets for genome editing. In tandem with terpene pathway discovery, system-wide metabolomics, transcriptomics and proteomics studies in switchgrass accessions of contrasting drought tolerance would define the role of switchgrass terpene metabolism in conferring abiotic stress resilience. Metabolic changes would be assessed in a combined approach of targeted (terpenes) and untargeted metabolite profiling using a high-resolution LC-MS/MS approach, differential gene expression analyses through multiplexed Illumina RNA sequencing, and quantitative analysis of high-priority pathway enzymes using multiple reaction monitoring (MRM). Drawing on these insights, knock-down/out mutants of stress-associated pathway nodes would be generated by optimizing transient virus-induced gene silencing (VIGS) and CRISPR/Cas9 systems under control of the Tobacco Rattle Virus (TRV). The resulting mutant lines would then be analyzed for stress susceptibility and the impact on the root microbiome to define gene functions in planta. Knowledge of terpene pathways, enzyme mechanisms and bioactivities would be applied to enhance switchgrass stress resilience and to tailor-make terpene blends for biofuel production. Here, TRV-enabled CRISPR/Cas9 genome editing, including allele-specific knock-out of redundant genes, engineering of enzyme specificity via structure-guided point mutations, and overexpression of terpene genes relevant to stress-protection or biofuel production, would be used to increase metabolic flux toward desired pathways. Broader Impacts - Integrating the system-wide discovery, mechanistic analysis and non-transgenic genome engineering of the switchgrass terpene network aligns the required steps to unlock the chemical potential of this important metabolite class to generate crops that are more resistant to stress and provide advanced biofuel production in light of rising climate pressures as foreseeable challenges for bioenergy crop cultivation. The proposed project would further offer interdisciplinary student training through active involvement in the project and integration of research concepts and outcomes into newly-developed graduate and undergraduate courses on Plant Biotechnology.

09 BIOMASS FUELS↗