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At least 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Genome-wide approaches delineate the additive, epistatic, and pleiotropic nature of variants controlling fatty acid composition in peanut ( Arachis hypogaea L.)

Abstract The fatty acid composition of seed oil is a major determinant of the flavor, shelf-life, and nutritional quality of peanuts. Major QTLs controlling high oil content, high oleic content, and low linoleic content have been characterized in several seed oil crop species. Here, we employ genome-wide association approaches on a recently genotyped collection of 787 plant introduction accessions in the USDA peanut core collection, plus selected improved cultivars, to discover markers associated with the natural variation in fatty acid composition, and to explain the genetic control of fatty acid composition in seed oils. Overall, 251 single nucleotide polymorphisms (SNPs) had significant trait associations with the measured fatty acid components. Twelve SNPs were associated with two or three different traits. Of these loci with apparent pleiotropic effects, 10 were associated with both oleic (C18:1) and linoleic acid (C18:2) content at different positions in the genome. In all 10 cases, the favorable allele had an opposite effect—increasing and lowering the concentration, respectively, of oleic and linoleic acid. The other traits with pleiotropic variant control were palmitic (C16:0), behenic (C22:0), lignoceric (C24:0), gadoleic (C20:1), total saturated, and total unsaturated fatty acid content. One hundred (100) of the significantly associated SNPs were located within 1000 kbp of 55 genes with fatty acid biosynthesis functional annotations. These genes encoded, among others: ACCase carboxyl transferase subunits, and several fatty acid synthase II enzymes. With the exception of gadoleic (C20:1) and lignoceric (C24:0) acid content, which occur at relatively low abundance in cultivated peanuts, all traits had significant SNP interactions exceeding a stringent Bonferroni threshold (α = 1%). We detected 7682 pairwise SNP interactions affecting the relative abundance of fatty acid components in the seed oil. Of these, 627 SNP pairs had at least one SNP within 1000 kbp of a gene with fatty acid biosynthesis functional annotation. We evaluated 168 candidate genes underlying these SNP interactions. Functional enrichment and protein-to-protein interactions supported significant interactions (P-value < 1.0E−16) among the genes evaluated. These results show the complex nature of the biology and genes underlying the variation in seed oil fatty acid composition and contribute to an improved genotype-to-phenotype map for fatty acid variation in peanut seed oil.

Otyama, Paul I.↗
DOE OSTI2023

Acute stress reduces population-level metabolic and proteomic variation

Abstract Background Variation in omics data due to intrinsic biological stochasticity is often viewed as a challenging and undesirable feature of complex systems analyses. In fact, numerous statistical methods are utilized to minimize the variation among biological replicates. Results We demonstrate that the common statistics relative standard deviation (RSD) and coefficient of variation (CV), which are often used for quality control or part of a larger pipeline in omics analyses, can also be used as a metric of a physiological stress response. Using an approach we term Replicate Variation Analysis (RVA), we demonstrate that acute physiological stress leads to feature-wide canalization of CV profiles of metabolomes and proteomes across biological replicates. Canalization is the repression of variation between replicates, which increases phenotypic similarity. Multiple in-house mass spectrometry omics datasets in addition to publicly available data were analyzed to assess changes in CV profiles in plants, animals, and microorganisms. In addition, proteomics data sets were evaluated utilizing RVA to identify functionality of reduced CV proteins. Conclusions RVA provides a foundation for understanding omics level shifts that occur in response to cellular stress. This approach to data analysis helps characterize stress response and recovery, and could be deployed to detect populations under stress, monitor health status, and conduct environmental monitoring.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Using Machine Learning to Improve Thermostability of MHETase

Protein engineering is a field which utilizes proteins as tools, which has many useful applications in medicine, industry, biofuels and more.1 One such protein is MHETase, which is a protein that plays an important function in the degradation of polyethylene terephthalate (PET) plastics, which are commonly used in water and soda bottles.2 However, these proteins are adapted to work in specific conditions, and may not satisfy the desired properties that a new application would desire, or could be improved. For instance, a more thermostable MHETase would be more effective in the plastic degradation conditions.3 To make these desired changes, the primary structure of the protein is mutated, but there are many possible mutations and positions to mutate to make with the 20 canonical amino acids. Therefore, to narrow down the possibilities and to make the process of finding a thermostable MHETase variant, we used sequence design tools that are grounded in machine learning to find mutations that would improve thermostability of MHETase.4 In particular, we used the tools Protein MPNN and Fireprot to design a more thermostable MHETase enzyme. We then compiled these mutations into a library and grew these proteins using bacteria colonies, and measured their effectiveness using a fluorescent protein marker. Thermostable proteins and their marker would fold correctly and fluorescence would be seen, but if neither folded correctly then there would be no marker detected. We grew these proteins in bacteria and then intend to use these methods to evaluate their thermostability.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Using Machine Learning to Improve Thermostability of MHETase

Protein engineering is a field which utilizes proteins as tools, which has many useful applications in medicine, industry, biofuels and more. One such protein is MHETase, which is a protein that plays an important function in the degradation of polyethylene terephthalate (PET) plastics, which are commonly used in water and soda bottles.2 However, these proteins are adapted to work in specific conditions, and may not satisfy the desired properties that a new application would desire, or could be improved. For instance, a more thermostable MHETase would be more effective in the plastic degradation conditions.3 To make these desired changes, the primary structure of the protein is mutated, but there are many possible mutations and positions to mutate to make with the 20 canonical amino acids. Therefore, to narrow down the possibilities and to make the process of finding a thermostable MHETase variant, we used sequence design tools that are grounded in machine learning to find mutations that would improve thermostability of MHETase.4 In particular, we used the tools Protein MPNN and FireProt to design a more thermostable MHETase enzyme. We then compiled these mutations into a library and grew these proteins using bacteria colonies, and measured their effectiveness using a fluorescent protein marker. Thermostable proteins and their marker would fold correctly and fluorescence would be seen, but if neither folded correctly then there would be no marker detected. We grew these proteins in bacteria and then intend to use these methods to evaluate their thermostability.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Leveraging High-resolution Molecular Composition of Soil Organic Matter to Enhance Carbon Cycling Modeling

Soils store more carbon than the atmosphere and vegetation combined, yet Earth system models still struggle to predict how this vast reservoir will respond to environmental change. A central limitation is that most soil biogeochemical models represent organic matter using bulk conceptual pools or chemically homogeneous fractions, preventing direct use of rapidly expanding molecular-scale datasets. Here we develop and test a new soil decomposition framework that explicitly integrates high-resolution information on organic matter composition. First, we construct a molecularly informed litter decomposition module in which plant inputs are partitioned into five functional compound classes—carbohydrates, proteins, lignin-like aromatics, lipids, and carbonyls—using a molecular mixing model calibrated to solid-state 13 C Nuclear Magnetic Resonance (NMR) spectra. Class-specific kinetics, lignin-dependent physical protection, and substrate-driven microbial carbon use efficiency allow the module to capture metabolic tradeoffs associated with enzyme production and nutrient limitation. We then embed this litter module within a microbially explicit whole-soil model that tracks the transformation of these compound classes through particulate organic matter, dissolved organic matter, mineral-associated organic matter, and microbial biomass. High-resolution Fourier Transform Ion Cyclotron Resonance mass spectrometry (FTICR-MS) data are used to link internal pools to measurable soil organic matter fractions and to constrain key process parameters. Applications at soil-core and ecosystem scales demonstrate that the new model reproduces observed soil respiration dynamics while providing mechanistic attribution of CO 2 fluxes to specific chemical classes and pools. Compared to existing frameworks such as the Community Land Model soil biogeochemistry module and the Millennial model, our approach maintains competitive predictive skill while substantially improving interpretability and opportunities for data–model integration. This work illustrates a viable pathway for leveraging molecular-scale observations to reduce structural uncertainty in soil carbon–climate feedback projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Integrative Modeling and Analysis of Fungal Central Carbon Metabolism

Over a thousand fungal genomes have been sequenced, yet manually curated genome-scale metabolic models (GEMs) are available for only a limited number of species. Moreover, these models have often been developed independently, leading to inconsistencies in namespaces, compartment definitions, and pathway representations that hinder comparative analysis, the systematic reuse of prior curation efforts, and the integration of consolidated metabolic knowledge. Here, we present the Consolidated Fungal Core Metabolism Model (CFCMM), constructed by integrating thirteen published fungal models spanning Ascomycota, Mucoromycota, and both Crabtree-positive and Crabtree-negative yeasts. We harmonized metabolites and reactions into a non-redundant shared ModelSEED ontological space, standardized compartmentalization, and refined gene–protein–reaction (GPR) rules. Using pathway-level visualization and systematic gap detection, we further improved the integrated network through literature-guided curation to correct stoichiometry, stereospecificity, and pathway architecture. Orthologous protein family reconstruction and functional annotation workflows were used to validate and inform GPR associations, with particular emphasis on ambiguous enzyme superfamilies and membrane-associated components. Using the resulting CFCMM, we built high-quality central carbon core models for each fungus and performed flux balance analysis to quantify ATP-yield variation under aerobic and anaerobic conditions, explicitly evaluating scenarios driven by differences in electron transport chain (ETC) composition. Simulations reproduced the expected fermentative yield of approximately 2 mmol ATP per mmol glucose under anaerobic conditions and separated the thirteen fungi into two bioenergetic groups under aerobic respiration based on Complex I status, with predicted yields of approximately 30 versus 22 mmol ATP per mmol glucose. Forcing flux through the alternative oxidase bypass further reduced ATP yields to approximately 12 and 4 mmol ATP per mmol glucose in Complex I-containing and Complex I-lacking fungi, respectively. Collectively, this work provides a manually curated, ModelSEED-consistent, and extensible fungal core metabolic template, deployed in DOE KBase as a resource for automated reconstruction of central carbon core models from any sequenced fungal genome. In addition, the CFCMM provides modular components for developing GEMs with more accurate energy predictions and enables robust comparative analyses of fungal bioenergetics and core metabolic diversity

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Proteomic insights into the physiology and metabolism of oleaginous yeasts and filamentous fungi

Fungi are vital to the bioeconomy, serving as key producers of food, beverages, biofuels, and medicines, while also acting as essential resource recyclers in ecosystem management. For nearly a century, oleaginous yeast and filamentous fungi have been explored for their proficiency in oleochemicals production and carbon storage. Lipogenesis is one of the most well-studied fungal processes, with substantial progress having been made through reductionist biochemical approaches; however, the physiology and metabolism of fungal systems operating under different conditions arise from the functions of thousands of proteins, for which very little is known outside of model yeast. In this review, we discuss how proteomics provides a valuable analytical approach to contextualize lipogenesis within a complex biological system, where lipid accumulation is fundamentally governed by changes in proteins of multiple pathways. In the past two decades, proteomics has been applied to study stress response to nutrient limitations, metabolism of various carbon and nitrogen sources, the lipid droplet hub of carbon storage, protein post-translational modifications and signaling pathways, as well as oleochemical biosynthesis, thereby advancing our understanding of the oleaginous phenotype. Over 40 studies are reviewed herein to evaluate the impact, critically assess the utility, and propose future applications of proteomics. In the coming years, large systems-level proteomics studies will lay a foundation for marrying modeling and metabolic engineering strategies to optimize oleochemicals production in oleaginous fungi.

Lipid production↗
DOE OSTI2023

Expression and characterization of spore coat Cot$\mathrm{H}$ kinases from the cellulosomes of anaerobic fungi ( Neocallimastigomycetes )

Anaerobic fungi (Neocallimastigomycetes) found in the guts of herbivores are biomass deconstruction specialists with a remarkable ability to extract sugars from recalcitrant plant material. Anaerobic fungi, as well as many species of anaerobic bacteria, deploy multi-enzyme complexes called cellulosomes, which modularly tether together hydrolytic enzymes, to accelerate biomass hydrolysis. While the majority of genomically encoded cellulosomal genes in Neocallimastigomycetes are biomass degrading enzymes, the second largest family of cellulosomal genes encode spore coat CotH domains, whose contribution to fungal cellulosome and/or cellular function is unknown. Structural bioinformatics of CotH proteins from the anaerobic fungus Piromyces finnis shows anaerobic fungal CotH domains conserve key ATP and Mg 2+ binding motifs from bacterial Bacillus CotH proteins known to act as protein kinases. Experimental characterization further demonstrates ATP hydrolysis activity in the presence and absence of substrate from two cellulosomal P. finnis CotH proteins when recombinantly produced in E. coli. These results present foundational evidence for CotH activity in anaerobic fungi and provide a path towards elucidating the functional contribution of this protein family to fungal cellulosome assembly and activity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Using diverse potentials and scoring functions for the development of improved machine-learned models for protein–ligand affinity and docking pose prediction

The advent of computational drug discovery holds the promise of significantly reducing the effort of experimentalists, along with monetary cost. More generally, predicting the binding of small organic molecules to biological macromolecules has far-reaching implications for a range of problems, including metabolomics. However, problems such as predicting the bound structure of a protein–ligand complex along with its affinity have proven to be an enormous challenge. In recent years, machine learning-based methods have proven to be more accurate than older methods, many based on simple linear regression. Nonetheless, there remains room for improvement, as these methods are often trained on a small set of features, with a single functional form for any given physical effect, and often with little mention of the rationale behind choosing one functional form over another. Moreover, it is not entirely clear why one machine learning method is favored over another. Here, we endeavor to undertake a comprehensive effort towards developing high-accuracy, machine-learned scoring functions, systematically investigating the effects of machine learning method and choice of features, and, when possible, providing insights into the relevant physics using methods that assess feature importance. Here, we show synergism among disparate features, yielding adjusted R 2 with experimental binding affinities of up to 0.871 on an independent test set and enrichment for native bound structures of up to 0.913. When purely physical terms that model enthalpic and entropic effects are used in the training, we use feature importance assessments to probe the relevant physics and hopefully guide future investigators working on this and other computational chemistry problems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

High throughput, accurate gene annotation through AI and HPC-enabled structural analysis

With the advances in next generation sequencing technologies, the number of sequenced genomes is growing exponentially, resulting in a technology bottleneck for the translation of sequence information into usable hypotheses about the function of each gene. We have proposed leveraging our leadership high-performance computing (HPC) resources to help break this annotation bottleneck. Here we design an HPC-based framework to infer gene function from gene sequence by incorporating information about protein structure and interactions predicted by deep learning approaches. Accurate functional prediction and gene annotation using computational methods will facilitate breakthroughs in the genomic sciences essential to understanding and harnessing life processes in bacteria, fungi and plants. The development and applications of the state-of-the-art deep neural networks to protein structural modeling, interaction prediction, sequence comparison, and quality assessment of protein structural models will be made possible by leadership computational resources. These HPC-enabled bioinformatics and molecular modeling tools will provide powerful insights into molecular functions of genes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

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↗
DOE OSTI2023

High-Throughput Directed Evolution of Marine Microalgae and Phototrophic Consortia for Improved Biomass Yields (Final Report)

Primary project achievements include using selective pressures (O 2 , light, temperature) and developing culturing regimes for the diatom Nitzschia inconspicua str. hildebrandi to attain enrichments with an ~90% increase in areal biomass productivity relative to the parental strain under pond-mimicking conditions with high O 2 stress in laboratory bioreactors. The resulting strain (GAI-337) was tested further for dilution time, culture density, CO 2 supplementation, pH, temperature, and dissolved O 2 concentration under outdoor pond-mimicking conditions to improve areal productivities. These experiments yielded an optimum harvest and dilution time just after sunset, ~0.45 g AFDW L -1 initial culture density for maximal productivities, no requirement for CO 2 supplementation or pH control, maximal performance under a diel temperature curve going from 24 °C at night to 36 °C during the day, and benefits from some O 2 removal from the culture by bubbling with air. Using pond-mimicking laboratory bioreactors, N. inconspicua GAI-337 achieved ~42 g AFDW m -2 d -1 . Nutrient limitation experiments resulted in a biomass composition that equated to ~160 Gallons of Gasoline Equivalent energy per ton AFDW, highlighting the potential of GAI-337 as a promising renewable fuel feedstock strain. Genome resequencing has revealed genome alterations potentially contributing to the improved growth of GAI-337 in the laboratory. Based on the comparative analyses of the GAI-337 and GAI-229 (reference) strains, we identified 144 single nucleotide substitutions that resulted in amino acid change, 7 single nucleotide substitutions that resulted in protein truncation; 5 deletions; and 1 frameshift mutation. From the mutations that potentially affect expression of functionally annotated genes, particular interest was noted for an interferon-induced 6-16 family protein that may be involved in the host immune response against microbe invasion; the chaperone protein DnaK, which may function to protect the folding of proteins within the cell; and SPRY domain protein that is found in many eukaryotic proteins important in cell signaling pathways. Transcriptome analysis revealed over 1000 genes with increased transcript levels. Many of these and many of the genes with mutations are not yet functionally annotated and an increased bioinformatics effort is necessary to more completely analyze the Nitzschia inconspicua genome. Adaptive laboratory evolution (ALE) was performed for over 300 days using consecutive 0.5°C temperature increases in a constant temperature incubator to attain greater thermal tolerance in Nitzschia inconspicua. The adapted strain was able to grow at a constant temperature of 37.5°C; whereas this constant temperature was lethal to the parental control, which had an upper temperature boundary of 35.5°C prior to adaptive evolution. Several high-temperature clonal isolates were obtained from the evolved population following ALE, and increased temperature tolerance was observed in clonal adapted cultures. The final temperature adaptation was maintained through cryopreservation and was observed in multiple clonal isolates, including multiple clonal isolates with significantly increased cell size, indicating the potential occurrence of a sexual cycle during the clonal isolation process. A survey of Nannochloropsis strains was conducted for tolerances to high pH and high bicarbonate media. Nannochloropsis granulata showed promising growth in diel bioreactors and was successfully grown at the GAI Kauai farm site in long-term growth campaigns. Co-culturing using Nitzschia inconspicua, Nannochloropsis and a cyanobacterium were assembled in the laboratory to determine if productivity synergies could be attained. Although all strains grew well in the laboratory high-bicarbonate media individually, the cyanobacterium quickly outgrew the other strains in the laboratory consortium pushing the co-culture away from a diverse (and potentially synergistic assemblage) phototroph culture towards a monoculture dominated by the cyanobacterium. Several outdoor growth campaigns were conducted, with productivities ranging between 10-20 g/m 2 /d of biomass. The best performing strain in the laboratory (GAI-337) did not outperform reference strains at the Kauai farm under the conditions used. Addition growth campaigns are necessary under conditions that result in higher biomass (>20 g/m 2 /d) and that attain higher O 2 levels are likely necessary. Initial data indicate that the thermally adapted strain did slightly better than the control strain at higher temperatures; however, additional campaigns are necessary to establish statistical significance. In summary, Nitzschia inconspicua is able to attain exemplary biomass and lipid yields in the laboratory bioreactors. Strain evolution to both O 2 and temperature resulted in targeted strain improvements. Additional outdoor campaigns are necessary to determine if laboratory improvements translate to the field.

09 BIOMASS FUELS↗
DOE OSTI2026

Characterization of the biofilm landscape of Bacillus subtilis by spatial microproteomics

Bulk proteomics has been demonstrated to differentiate subpopulations within bacterial colonies, yet advanced analyses by mass spectrometry imaging (MSI) hold even greater promise for the future. This technology can enable high-throughput spatial phenotyping that can reshape biological discovery by providing visualization of components of various biomolecular mechanisms. With high mass resolving power and high spatial resolution analyses being routine, we can confidently enable intact protein imaging directly from samples with minimal preparation. Pairing those analyses with bulk experimental libraries can provide high confidence in annotations of post-translational modifications (PTMs) and truncations. Revealing PTM localization within the samples unlocks a direct window into unknown biology at the microscale. However, top-down proteomics (TDP) is not commonplace for microbial species, largely due to challenges in identifying detected peptides and proteins; considering the theoretical proteome of even the well-studied model bacterium Bacillus subtilis was only partially mapped recently. With little still known about the form and function of many of these proteins – let alone proteoforms, where PTMs and truncations of the same protein may possess unique physiological roles – there is a wealth of work to be done. Here we jointly apply TDP and MSI to describe the microscale spatial proteomic landscape within B. subtilis and further demonstrate the feasibility of detecting differentiated subpopulations through proteoforms across the biofilm landscape.

bacterial biofilms↗
DOE OSTI2022

Protein ubiquitination in plant peroxisomes

Protein ubiquitination regulates diverse cellular processes in eukaryotic organisms, from growth and development to stress response. Proteins subjected to ubiquitination can be found in virtually all subcellular locations and organelles, including peroxisomes, single-membrane and highly dynamic organelles ubiquitous in eukaryotes. Peroxisomes contain metabolic functions essential to plants and animals such as lipid catabolism, detoxification of reactive oxygen species (ROS), biosynthesis of vital hormones and cofactors, and photorespiration. Plant peroxisomes possess a complex proteome with functions varying among different tissue types and developmental stages, and during plant response to distinct environmental cues. However, how these diverse functions are regulated at the post-translational level is poorly understood, especially in plants. Here, in this review, we summarized current knowledge of the involvement of protein ubiquitination in peroxisome protein import, remodeling, pexophagy, and metabolism, focusing on plants, and referencing discoveries from other eukaryotic systems when relevant. Based on previous ubiquitinomics studies, we compiled a list of 56 ubiquitinated Arabidopsis peroxisomal proteins whose functions are associated with all the major plant peroxisomal metabolic pathways. This discovery suggests a broad impact of protein ubiquitination on plant peroxisome functions, therefore substantiating the need to investigate this significant regulatory mechanism in peroxisomes at more depths.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Adipose-specific knockout of Protein Kinase D1 suppresses de novo lipogenesis in mice via SREBP1c-dependent signaling

Highlights: • Protein Kinase D1 is a functional regulator in adipocyte and white adipose tissue. • PKD1 drives de novo lipogenesis in adipocyte. • SREBP1c signaling regulates lipogenesis in adipocyte. • PKD1-mediated de novo lipogenesis relies on SREBP1c. Having healthy adipose tissue is essential for metabolic health, as excessive adipose tissue in the body can cause its dysregulation and driving chronic metabolic diseases. Protein kinase D1 (PKD1) is considered to be a key kinase in signal transduction, which regulates multiple cellular functions, but its physiological functions in adipose are still not fully understood. This study aimed at elucidating the function of adipocyte PKD1 on lipogenesis. From RNA-Sequencing data, we found that the fatty acid biosynthesis pathway in white adipose tissue lacking PKD1 was significantly affected. Critical rate-limiting enzymes for de novo lipogenesis in adipocytes, such as FASN, ACCα, and SCD1, were significantly repressed after deleting PKD1 in vivo and in vitro. Further studies revealed that blockade of PKD1 significantly increased phosphorylation of SREBP1c at serine 372 site. Co-immunoprecipitation analysis showed that PKD1 interacts with SREBP1c in vitro and in vivo. Importantly, overexpression of SREBP1c reversed the inhibition of FASN and ACCα expression caused by PKD1 silencing. Together, adipocyte PKD1 promotes de novo lipogenesis via SREBP1c-dependent manner in visceral white adipose tissue and might provide a new target for the development of anti-obesity therapies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

EC-Bench: A Benchmark for Enzyme Commission Number Prediction

Enzymes are proteins that catalyze specific biochemical reactions in cells. Enzyme Commission (EC) numbers are used to annotate enzymes in a four-level hierarchy that classifies enzymes based on the specific chemical reactions they catalyze. Accurate EC number prediction is essential for understanding enzyme functions. Despite the availability of numerous methods for predicting EC numbers from protein sequences, there is no unified framework for evaluating and studying such methods systematically. This gap limits the ability of the community to identify the most effective approaches for enzyme annotation. We introduce EC-Bench, a benchmark for EC number prediction, consisting of 1) an initial representative set of existing methods (including homology-based, deep learning, contrastive learning, and language model methods), 2) existing and novel accuracy and efficiency performance metrics, and 3) selected datasets to allow for comprehensive comparative study. EC-Bench is open-source and provides a framework for researchers to not only compare among existing methods objectively under uniform conditions, but also to introduce and effectively evaluate performance of new methods in a comparative framework. To demonstrate the utility of EC-Bench, we perform extensive experimentation to compare the existing EC number prediction methods and establish their advantages and disadvantages in a variety of prediction tasks, namely “exact EC number prediction”, “EC number completion” and (partial or additional) “EC number recommendation”. We find wide variation in the performance of different methods, but also subtle but potentially useful differences in the performance of different methods across tasks and for different parts of the EC hierarchy.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Induced protein expression in Leptospira spp. and its application to CRISPR/Cas9 mutant generation

Abstract Expanding the genetic toolkit for Leptospira spp. is a crucial step toward advancing our understanding of the biology and virulence of these atypical bacteria. Pathogenic Leptospiraare responsible for over 1 million human leptospirosis cases annually and significantly impact domestic animals. Bovine leptospirosis causes substantial financial losses due to abortion, stillbirths, and suboptimal reproductive performance. The advent of the CRISPR/Cas9 system has marked a turning point in genetic manipulation, with applications across multiple Leptospira species. However, incorporating controlled protein expression into existing genetic tools could further expand their utility. We developed and demonstrated the functionality of IPTG-inducible heterologous protein expression in Leptospira spp. This system was applied for regulated expression of dead Cas9 (dCas9) to generate knockdown mutants, and Cas9 to produce knockout mutants by inducing double-strand breaks (DSB) into desired targets. IPTG-induced dCas9 expression enabled validation of essential genes and non-coding RNAs. Additionally, IPTG-controlled Cas9 expression combined with a constitutive non-homologous end-joining (NHEJ) system allowed for successful recovery of knockout mutants, even in the absence of IPTG. These newly controlled protein expression systems will advance studies on the basic biology and virulence ofLeptospira, as well as facilitate knockout mutant generation for improved veterinary vaccines.

Science & Technology - Other Topics↗