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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
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Probing Light-Dependent Regulation of the Calvin Cycle Using a Multi-Omics Approach

Photoautotrophic microorganisms are increasingly explored for the conversion of atmospheric carbon dioxide into biomass and valuable products. The Calvin-Benson-Bassham (CBB) cycle is the primary metabolic pathway for net CO 2 fixation within oxygenic photosynthetic organisms. The cyanobacteria, Synechocystissp. PCC 6803, is a model organism for the study of photosynthesis and a platform for many metabolic engineering efforts. The CBB cycle is regulated by complex mechanisms including enzymatic abundance, intracellular metabolite concentrations, energetic cofactors and post-translational enzymatic modifications that depend on the external conditions such as the intensity and quality of light. However, the extent to which each of these mechanisms play a role under different light intensities remains unclear. In this work, we conducted non-targeted proteomics in tandem with isotopically non-stationary metabolic flux analysis (INST-MFA) at four different light intensities to determine the extent to which fluxes within the CBB cycle are controlled by enzymatic abundance. The correlation between specific enzyme abundances and their corresponding reaction fluxes is examined, revealing several enzymes with uncorrelated enzyme abundance and their corresponding flux, suggesting flux regulation by mechanisms other than enzyme abundance. Additionally, the kinetics of 13 C labeling of CBB cycle intermediates and estimated inactive pool sizes varied significantly as a function of light intensity suggesting the presence of metabolite channeling, an additional method of flux regulation. These results highlight the importance of the diverse methods of regulation of CBB enzyme activity as a function of light intensity, and highlights the importance of considering these effects in future kinetic models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Considering Strain Variation and Non-Type Strains for Yeast Metabolic Engineering Applications

A variety of yeast species have been considered ideal hosts for metabolic engineering to produce value-added chemicals, including the model organism Saccharomyces cerevisiae, as well as non-conventional yeasts including Yarrowia lipolytica, Kluyveromyces marxianus, and Pichia pastoris. However, the metabolic capacity of these microbes is not simply dictated or implied by genus or species alone. Within the same species, yeast strains can display distinct variations in their phenotypes and metabolism, which affect the performance of introduced pathways and the production of interesting compounds. Moreover, it is unclear how this metabolic potential corresponds to function upon rewiring these organisms. These reports thus point out a new consideration for successful metabolic engineering, specifically: what are the best strains to utilize and how does one achieve effective metabolic engineering? Understanding such questions will accelerate the host selection and optimization process for generating yeast cell factories. In this review, we survey recent advances in studying yeast strain variations and utilizing non-type strains in pathway production and metabolic engineering applications. Additionally, we highlight the importance of employing portable methods for metabolic rewiring to best access this metabolic diversity. Finally, we conclude by highlighting the importance of considering strain diversity in metabolic engineering applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Central transcriptional regulator controls photosynthetic growth and carbon storage in response to high light

Carbon capture and biochemical storage are some of the primary drivers of photosynthetic yield and productivity. To elucidate the mechanisms governing carbon allocation, we designed a photosynthetic light response test system for genetic and metabolic carbon assimilation tracking, using microalgae as simplified plant models. The systems biology mapping of high light-responsive photophysiology and carbon utilization dynamics between two variants of the same Picochlorum celeri species, TG1 and TG2 elucidated metabolic bottlenecks and transport rates of intermediates using instationary 13 C-fluxomics. Simultaneous global gene expression dynamics showed 73% of the annotated genes responding within one hour, elucidating a singular, diel-responsive transcription factor, closely related to the CCA1/LHY clock genes in plants, with significantly altered expression in TG2. Transgenic P. celeri TG1 cells expressing the TG2 CCA1/LHY gene, showed 15% increase in growth rates and 25% increase in storage carbohydrate content, supporting a coordinating regulatory function for a single transcription factor.

09 BIOMASS FUELS↗
DOE OSTI2021

Diversifying Isoprenoid Platforms via Atypical Carbon Substrates and Non-model Microorganisms

Isoprenoid compounds are biologically ubiquitous, and their characteristic modularity has afforded products ranging from pharmaceuticals to biofuels. Isoprenoid production has been largely successful in Escherichia coli and Saccharomyces cerevisiae with metabolic engineering of the mevalonate (MVA) and methylerythritol phosphate (MEP) pathways coupled with the expression of heterologous terpene synthases. Yet conventional microbial chassis pose several major obstacles to successful commercialization including the affordability of sugar substrates at scale, precursor flux limitations, and intermediate feedback-inhibition. Now, recent studies have challenged typical isoprenoid paradigms by expanding the boundaries of terpene biosynthesis and using non-model organisms including those capable of metabolizing atypical C1 substrates. Conversely, investigations of non-model organisms have historically informed optimization in conventional microbes by tuning heterologous gene expression. Here, we review advances in isoprenoid biosynthesis with specific focus on the synergy between model and non-model organisms that may elevate the commercial viability of isoprenoid platforms by addressing the dichotomy between high titer production and inexpensive substrates.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Temperature dependence of nitrate-reducing Fe(II) oxidation by Acidovorax strain BoFeN1 – evaluating the role of enzymatic vs. abiotic Fe(II) oxidation by nitrite

ABSTRACT Fe(II) oxidation coupled to nitrate reduction is a widely observed metabolism. However, to what extent the observed Fe(II) oxidation is driven enzymatically or abiotically by metabolically produced nitrite remains puzzling. To distinguish between biotic and abiotic reactions, we cultivated the mixotrophic nitrate-reducing Fe(II)-oxidizing Acidovorax strain BoFeN1 over a wide range of temperatures and compared it to abiotic Fe(II) oxidation by nitrite at temperatures up to 60°C. The collected experimental data were subsequently analyzed through biogeochemical modeling. At 5°C, BoFeN1 cultures consumed acetate and reduced nitrate but did not significantly oxidize Fe(II). Abiotic Fe(II) oxidation by nitrite at different temperatures showed an Arrhenius-type behavior with an activation energy of 80±7 kJ/mol. Above 40°C, the kinetics of Fe(II) oxidation were abiotically driven, whereas at 30°C, where BoFeN1 can actively metabolize, the model-based interpretation strongly suggested that an enzymatic pathway was responsible for a large fraction (ca. 62%) of the oxidation. This result was reproduced even when no additional carbon source was present. Our results show that at below 30°C, i.e. at temperatures representing most natural environments, biological Fe(II) oxidation was largely responsible for overall Fe(II) oxidation, while abiotic Fe(II) oxidation by nitrite played a less important role.

Microbiology↗
DOE OSTI2025

Smart culture medium optimization for recombinant protein production: Experimental, modeling, and AI/ML-driven strategies

Recombinant protein production (RPP) is central to biotechnology, where recombinant proteins are used as either end products or catalysts in the synthesis of chemicals, fuels, and materials. Among the major cost drivers, culture medium plays a pivotal role in determining protein yield and quality. This review presents a comprehensive perspective on the critical stages of “smart” culture medium optimization: planning, screening, modeling, optimization, and validation. In the planning stage, we examine the nutritional and energetic roles of medium components, including carbon, nitrogen, amino acids, salts, and trace metals, and their impacts on culture parameters such as pH, oxidative state, and osmolality. We highlight the variability in trace metal content due to water sources, culture vessels, and raw materials, which can substantially influence RPP. The screening stage covers Design of Experiments (DoE) approaches, assessing their theoretical basis, implementation, and limitations. For modeling, we describe methods that integrate experimental data to develop predictive models for smart medium formulation. Model-based optimization strategies can then be employed to select optimal media compositions for a given application. The validation stage aims to evaluate model predictions and provide feedback for model training and refinement. Finally, we survey mechanistic and artificial intelligence/machine learning (AI/ML)-driven models as integrated, transformational tools for predictive modeling of bioprocess conditions, nutrient availability, cellular metabolism, and protein quality, with the goal of optimizing culture media to enhance protein yields while reducing costs and environmental impact. We conclude by addressing the challenges of translating laboratory-scale medium optimization to industrial-scale settings and exploring future AI/ML-driven approaches that may overcome current bottlenecks and accelerate medium design for RPP. Overall, this review provides a unified framework for advancing smart medium design in RPP.

Artificial Intelligence/Machine Learning (AI/ML)↗
DOE OSTI2026

Hypophosphite Is a Naturally Occurring Selective Inhibitor of Syntrophic Methanogenesis

Microbial methanogenesis is a major contributor to global warming, and methane fluxes represent a loss of energy and electrons from industrial ecosystems. The chemical space of methane control strategies is still underexplored. Most known methanogenesis inhibitors target methanogenic archaeal enzymes. However, interference with the exchange of syntrophic electron carriers (H 2 or formate) in methanogenic systems presents an additional target for methane control. Here, we show that hypophosphite (H 2 PO 2 − ), an inorganic formate analogue, is a potent and selective inhibitor of syntrophic methanogenesis versus primary fermentation in rice field sediments and cattle rumens. Hypophosphite is also generally recognized as safe and relatively nontoxic to plants and animals. Genetic screens and physiological assays in the model methanogen Methanococcus maripaludis S2 implicate formate metabolism as the target of hypophosphite inhibition. Currently, there is no known biological pathway for anaerobic hypophosphite oxidation, and hypophosphite is stable in anoxic sediments for weeks to months. Given its widespread natural occurrence, we propose that hypophosphite may modulate the carbon cycle in natural environments. Taken together, our results suggest that hypophosphite could be used as a safe, inexpensive strategy for methane control in syntrophic methanogenic ecosystems.

chemical inhibitor↗
DOE OSTI2020

Mapping O 2 concentration in ex-vivo tissue samples on a fast PLIM macro-imager

O 2 PLIM microscopy was employed in various studies, however current platforms have limitations in sensitivity, image acquisition speed, accuracy and general usability. We describe a new PLIM imager based on the Timepix3 camera (Tpx3cam) and its application for imaging of O 2 concentration in various tissue samples stained with a nanoparticle based probe, NanO 2 -IR. Upon passive staining of mouse brain, lung or intestinal tissue surface with minute quantities of NanO 2 -IR or by microinjecting the probe into the lumen of small or large intestine fragments, robust phosphorescence intensity and lifetime signals were produced, which allow mapping of O 2 in the tissue within 20 s. Inhibition of tissue respiration or limitation of O 2 diffusion to tissue produced the anticipated increases or decreases in O 2 levels, respectively. The difference in O 2 concentration between the colonic lumen and air-exposed serosal surface was around 140 µM. Furthermore, subcutaneous injection of 5 µg of the probe in intact organs (a paw or tail of sacrificed mice) enabled efficient O 2 imaging at tissue depths of up to 0.5 mm. Overall, the PLIM imager holds promise for metabolic imaging studies with various ex vivo models of animal tissue, and also for use in live animals.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Stress Drivers of Glucose Dynamics during Ozone Exposure Measured Using Radiotelemetry in Rats

Inhaled irritant air pollutants may trigger stress-related metabolic dysfunction associated with altered circulating adrenal-derived hormones. We used implantable telemetry in rats to assess real-time changes in circulating glucose during and after exposure to ozone and mechanistically linked responses to neuroendocrine stress hormones. First, using a cross-over design, we monitored glucose during ozone exposures (0.0, 0.2, 0.4, and 0.8 ppm) and nonexposure periods in male Wistar Kyoto rats implanted with glucose telemeters. A second cohort of unimplanted rats was exposed to ozone (0.0, 0.4 or 0.8 ppm) for 30 min, 1 h, 2 h, or 4 h with hormones measured immediately post exposure. We assessed glucose metabolism in sham and adrenalectomized rats, with or without supplementation of adrenergic/glucocorticoid receptor agonists, and in a separate cohort, antagonists. Ozone (0.8 ppm) was associated with significantly higher blood glucose and lower core body temperature beginning 90 min into exposure, with reversal of effects 4–6 h post exposure. Glucose monitoring during four daily 4-h ozone exposures revealed duration of glucose increases, adaptation, and diurnal variations. Ozone-induced glucose changes were preceded by higher levels of adrenocorticotropic hormone, corticosterone, and epinephrine but lower levels of thyroid-stimulating hormone, prolactin, and luteinizing hormones. Higher glucose and glucose intolerance were inhibited in rats that were adrenalectomized or treated with adrenergic plus glucocorticoid receptor antagonists but exacerbated by agonists. We demonstrated the temporality of neuroendocrine-stress–mediated biological sequalae responsible for ozone-induced glucose metabolic dysfunction and mechanism in a rodent model.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2021

Illuminating gut microbiome signaling pathways that regulate host circadian rhythms

The gut microbiome affects host physiology by modulating nutrient acquisition and creating chemical signals that are received by host receptors. The molecular details of the orchestration of these microbial signaling processes remain largely unclear. We used a function-centric approach to interrogate the mechanistic links between gut microbiome metabolism and host circadian rhythms. We used rodent models, including germ-free mice, to determine how the gut microbiome influences host chronobiology by assaying metabolites in the gut and in circulation, and we used activity-based protein profiling to provide direct functional links to individual taxa. We specifically investigated how externally imposed behavioral schedules, such as feeding schedules, can desynchronize biological rhythms in metabolic organs (e.g., the gut, pancreas, and liver) from the central circadian rhythms regulated by the brain. We hypothesize that the gut microbiome creates chemical signals that can be entrained by feeding/fasting schedule to induce internal metabolic misalignment. Elaborating on the findings of this project will elucidate the chemical signals from the microbiome that modulate biological rhythms in the host’s metabolic organs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

RLMolLM: Reinforcement Learning-Enhanced Language Model Framework for Inverse Molecular Design

Inverse molecular design faces significant challenges due to vast chemical space and complex property requirements. While language models show promise for molecular generation, they struggle with validity, multi-property optimization, and structural constraints. This work presents RLMolLM, a reinforcement learning framework combining Proximal Policy Optimization (PPO) with genetic algorithms to address these limitations. Our approach optimizes multiple user-specified properties including quantitative estimates of drug-likeness (QED), synthetic accessibility (SA), and ADMET (absorption, distribution, metabolism, excretion, and toxicity) endpoints without requiring complete model retraining, while maintaining capability for scaffold-constrained generation where specific substructures must be preserved. We outperform state-of-the-art methods for molecular optimization, achieving best QED scores across GDB13, Moses, and Zinc datasets with up to 31% improvement over previous methods while maintaining excellent validity, uniqueness, and novelty metrics. For simultaneous multi-property optimization, our framework achieves substantial improvements in ADMET properties including 4.5-fold reduction in hERG toxicity and enhanced Caco-2 permeability compared to Moses dataset. Under structural constraints, the framework significantly improves molecular validity while preserving scaffolds and effectively optimizing properties. In conclusion, this versatile solution advances pharmaceutical and materials molecular design through effective integration of reinforcement learning and genetic algorithms with multi-property optimization and scaffold preservation.

Genetic algorithms↗
DOE OSTI2021

On the modeling paradigm of plant root nutrient acquisition

Plant root nutrient acquisition, and to a lesser extent foliar nutrient uptake, maintain plant metabolism and strongly regulate terrestrial biogeochemistry and carbon-climate feedbacks. However, terrestrial biogeochemical models differ in their representations of plant root nutrient acquisition, leading to significantly different, and uncertain, carbon cycle and future climate projections. Here we first review biogeochemical principles and observations relevant to three essential plant root nutrient acquisition mechanisms: activity of nutrient acquiring proteins, maintenance of nutrient stoichiometry, and energy expenditure for these processes. We next examine how these mechanisms are considered in three existing modeling paradigms, and conclude by recommending the capacity-based approach, the need for observations, and necessary modeling developments of plant root nutrient acquisition to improve carbon-climate feedback projections.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Gene network centrality analysis identifies key regulators coordinating day-night metabolic transitions in Synechococcus elongatus PCC 7942 despite limited accuracy in predicting direct regulator-gene interactions

Synechococcus elongatus PCC 7942 is a model organism for studying circadian regulation and bioproduction, where precise temporal control of metabolism significantly impacts photosynthetic efficiency and CO 2 -to-bioproduct conversion. Despite extensive research on core clock components, our understanding of the broader regulatory network orchestrating genome-wide metabolic transitions remains incomplete. We address this gap by applying machine learning tools and network analysis to investigate the transcriptional architecture governing circadian-controlled gene expression. While our approach showed moderate accuracy in predicting individual transcription factor-gene interactions - a common challenge with real expression data - network-level topological analysis successfully revealed the organizational principles of circadian regulation. Our analysis identified distinct regulatory modules coordinating day-night metabolic transitions, with photosynthesis and carbon/nitrogen metabolism controlled by day-phase regulators, while nighttime modules orchestrate glycogen mobilization and redox metabolism. Through network centrality analysis, we identified potentially significant but previously understudied transcriptional regulators: HimA as a putative DNA architecture regulator, and TetR and SrrB as potential coordinators of nighttime metabolism, working alongside established global regulators RpaA and RpaB. This work demonstrates how network-level analysis can extract biologically meaningful insights despite limitations in predicting direct regulatory interactions. The regulatory principles uncovered here advance our understanding of how cyanobacteria coordinate complex metabolic transitions and may inform metabolic engineering strategies for enhanced photosynthetic bioproduction from CO 2 .

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Genome-scale Design and Engineering of Non-model Yeast Organisms for Production of Biofuels and Bioproducts

The overall goal of this project was to develop genome-scale design and engineering tools for two non-model yeast organisms including Rhodotorula toruloides and Issatchenkia orientalis to produce high-levels of fatty acids-derived products and organic acids, respectively. The project was performed between 9/15/2017 and 9/14/2024 (the last two-years were no-cost extensions). The team consisted of Huimin Zhao (Lead PI) and Christopher Rao (Co-PI) from the University of Illinois at Urbana-Champaign (UIUC), Costas Maranas (Co-PI) from the Pennsylvania State University, Joshua Rabinowitz (Co-PI) and Martin Wuhr (Co-PI) from Princeton University, and Yasuo Yoshikuni (Co-PI) from the DOE Joint Genome Institute. The team has made great progress in both tool development and fundamental understanding of these two non-model yeasts. In total, there were 40 research publications (one of them is still under review) and one patent application as well as numerous oral presentations.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Data and scripts associated with "Coupled primary production and respiration in a large river contrasts with smaller rivers and streams."

This data package is associated with the publication "Coupled primary production and respiration in a large river contrasts with smaller rivers and streams." in review at Limnology and Oceanography (Roley et al. 2023). This study focuses on understanding ecosystem metabolism for the Hanford Reach of the Columbia River in Washington state, a free-flowing stretch with a substantial discharge. Large rivers have been overlooked compared to small and medium rivers due to the challenges associated with measurements. Our study presents novel ways to address these challenges and highlights that metabolism patterns in large rivers differ from those observed in small-medium rivers and requires the application of knowledge and tools beyond those implemented for smaller rivers.This data package includes the data and R scripts for the analyses described in Roley et al. 2023. It includes dissolved oxygen and temperature data from a dissolved oxygen HOBO sensor, light data collected from the National Solar Radiation Database (https://nsrdb.nrel.gov/) and hydrologic variables estimated from the MASS-1 model (Niehus et al.; 2014). It also includes metabolism estimates (gross primary production, ecosystem respiration, and net ecosystem production) estimated via streamMetabolizer (Appling et al.; 2018). All analyses in the paper can be replicated with these data and scripts.The data package is comprised of one main data folder. The folder includes (1) file-level metadata (flmd); (2) a data dictionary (dd) for each data file; (3) data files; and (4) R scripts for metabolism estimates and data analysis. All files are .R, .csv, or .pdf.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The metabolic origins of non-photorespiratory CO 2 release during photosynthesis: a metabolic flux analysis

Respiration in the light (R L ) releases CO 2 in photosynthesizing leaves and is a phenomenon that occurs independently from photorespiration. Since R L lowers net carbon fixation, understanding R L could help improve plant carbon-use efficiency and models of crop photosynthesis. Although R L was identified more than 75 years ago, its biochemical mechanisms remain unclear. To identify reactions contributing to R L , we mapped metabolic fluxes in photosynthesizing source leaves of the oilseed crop and model plant camelina (Camelina sativa). We performed a flux analysis using isotopic labeling patterns of central metabolites during 13 CO 2 labeling time course, gas exchange, and carbohydrate production rate experiments. To quantify the contributions of multiple potential CO 2 sources with statistical and biological confidence, we increased the number of metabolites measured and reduced biological and technical heterogeneity by using single mature source leaves and quickly quenching metabolism by directly injecting liquid N 2 ; we then compared the goodness-of-fit between these data and data from models with alternative metabolic network structures and constraints. Our analysis predicted that R L releases 5.2 μmol CO2 g –1 FW h –1 of CO 2 , which is relatively consistent with a value of 9.3 μmol CO 2 g –1 FW h –1 measured by CO 2 gas exchange. The results indicated that ≤10% of R L results from TCA cycle reactions, which are widely considered to dominate R L . Further analysis of the results indicated that oxidation of glucose-6-phosphate to pentose phosphate via 6-phosphogluconate (the G6P/OPP shunt) can account for >% of CO 2 released by R L .

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Robust Rapid Cellular Metabolite Sensing Using Benchtop NMR and SABRE-Hyperpolarized [1- 13 C]Pyruvate

Hyperpolarized NMR has emerged as a powerful analytical technique to significantly enhance targeted NMR signals, improving the sensitivity for investigations of unique chemical and biological dynamics. Here, we demonstrate the use of a hyperpolarization strategy based on Signal Amplification By Reversible Exchange (SABRE) to generate highly reproducible doses of a hyperpolarized [1- 13 C]pyruvate probe for benchtop characterization of yeast metabolism. This method allows rapid, scalable, and benchtop preparation of biocompatible hyperpolarized solutions suitable for live-cell experiments. We show that this production can be dove-tailed into a modular, compact workflow to characterize real-time metabolism in cell cultures, using Saccharomyces cerevisiae (Baker’s yeast) as a model organism. With high temporal resolution, we show that this method can resolve the conversion of hyperpolarized [1- 13 C]pyruvate into oxidative decarboxylation products CO 2 and bicarbonate. This conversion exhibits sustained and detectable metabolic activity for over 300 s after introduction of the agent to the cells. We model the metabolite kinetics to show decarboxylation activity and derive estimates of the pH over time from the CO 2 and bicarbonate (carbonic acid buffer system) equilibrium to probe changes in the cellular environment during active metabolism. These results highlight the utility of benchtop SABRE-hyperpolarized [1- 13 C]pyruvate as a scalable, specific probe for metabolic phenotyping of living cells using compact, low-cost instrumentation well-suited for future high-throughput applications across microbial engineering, drug response profiling, and dynamic metabolic screening.

fungi↗