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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Risk of Mortality in Family Members of Men Seeking Fertility Assessment

Objective: To assess mortality in family members of men seeking fertility assessment. Subfertility serves as a biomarker for overall somatic health, and poor semen quality is associated with increased risk of hospitalization and mortality from chronic conditions. However, it is unclear if these risks extend to family members of men with low sperm count. Design: Retrospective cohort study. Subjects: Family members, up to third-degree relatives, of men in the Subfertility, Health and Assisted Reproduction and the Environment cohort who underwent a semen analysis as part of a fertility assessment 1996–2017. Relatives of men with a recorded total sperm count who lived in Utah for ≥1 year 1904–2017 were included in the analysis (N = 22,280 families). Exposure: Individuals were classified by family membership. Families were classified as relatives of azoospermic (0M), oligozoospermic (<39M), or normozoospermic (≥39M) men. The average total sperm count of the proband (male relative) with fertility assessment was also included as a continuous exposure measure. Main Outcome Measures: The main outcomes were all-cause and cause-specific mortality risk by sex, age, and degree of relation: first-, second-, and third-degree. Cox proportional hazard models were used to test the association between fertility classification and mortality, controlling for sex, race/ethnicity, and birth year. Results: A total of 666,437 relatives of men with fertility assessment (N deaths = 183,974) were included in the analysis. Relative to normozoospermia families, all-cause mortality risk increased in oligozoospermia families (hazard ratio [HR] oligozoospermia , 1.03; 95% confidence interval [CI], 1.01–1.05). Close relatives, first- (HR oligozoospermia , 1.17; 95% CI, 1.07–1.28) and second-degree relatives (HR azoospermia , 1.11; 95% CI, 1.04–1.20; HR oligozoospermia , 1.05; 95% CI,1.01–1.09), of azoospermic and oligozoospermic men had the highest all-cause and cause-specific mortality risk, including death attributed to cardiovascular disease or congenital birth conditions. Conclusion: Our results suggest that familial all-cause and cause-specific mortality risk differ by fertility phenotype. Families of azoospermic and oligozoospermic men showed significantly increased risk, particularly for close relatives. This study provides further evidence that shared genetic and/or environmental factors could influence both fertility and somatic health.

Male fertility↗
NASA NTRSDate not supplied

RadBREAD: Radiation Biology Research at an Elevated Altitude through Dosimetry – A student-designed payload

NASA uses extreme environment platforms (ground testing facilities, high-altitude balloons and aircraft, and CubeSats) to provide greater understanding of the conditions and limitations of extra-terrestrial environments. As part of a two-week flight planned for summer 2021, RadBREAD (Radiation Biology Research at an Elevated Altitude through Dosimetry) will fly as a secondary payload consisting of a M-42C (German Aerospace Center, DLR) ionizing radiation dosimeter, UV micro-logger, and multiple desiccated yeast samples. The platform is a novel high-altitude solar-powered aircraft: the Swift Engineering High-Altitude samples. The platform is a novel high-altitude solar-powered aircraft: the Swift Engineering High-Altitude Long-Endurance Unmanned Aircraft System (HALE UAS), which offers significantly longer flight durations than other high-altitude platforms. The yeast Saccharomyces cerevisiae will provide meaningful biological correlation for the sensor readings, due to its resistance to extremely low temperature and pressure when desiccated, ease of genetic manipulation, and homology to human genes. The RadBREAD team comprises the 2020 cohort of NASA’s Space Life Sciences Training Program (SLSTP) research associates as well as NASA scientists, engineers and radiation experts from NASA and the DLR. Yeast survival, metabolic, and transcriptomic changes will be correlated with environmental data collected during long-term exposure to the upper atmosphere. Additionally, the team will evaluate the upper atmospheric environment (radiation, pressure, and temperature) provided by the HALE UAS platform as a Mars surface analog for biological payloads. We hypothesize that exposure to upper atmospheric conditions during the HALE UAS flight will alter the survival, metabolism, and transcriptome of desiccated wild-type S. cerevisiae upon rehydration compared to sensitive and tolerant yeast strains exposed to the same conditions, and between the flight samples compared to asynchronous ground controls.

radiation exposure↗
NASA NTRS2021

Generation-based Evolutionary Tool for the Optimization of Constellations (GenETOC)

With the rapid growth in the capabilities of smaller satellites, satellite architectures that replace a single, extremely capable spacecraft with multiple, cheaper ones are gaining in popularity. Unfortunately, the orbit design process for constellations can be significantly more involved, especiallywhen the relative placement of the individual spacecraft within the constellation is not constrained by mission and/or science objectives. Optimizing a satellite constellation in the presence of multiple, competing objectives is a highly complex problem to which many traditional mathematical optimization methods cannot be applied and few tools exist to help mission designers search for promising candidate mission designs. The Generation-based Evolutionary Tool for the Optimization of Constellations (GenETOC) has been created to search for near-optimal constellation design options. GenETOC combines a modified version of the Non-dominated Sorting Genetic Algorithm II (NSGA II) with STK Components libraries (a 3rdparty .NET package created by Analytical Graphics Inc.) to create a framework that enables a mission designer to generate a simulation that models the design problem and obtain a family of potential, near-optimal solutions that can be investigated more in detail. GenETOC was developed in C# using the .NET framework with Windows Presentation Foundation (WPF) serving as the framework from which to create the graphical user interface (GUI). GenETOC user inputs can be categorized into three major data components: definition of the problem (areas of interest, satellite decision parameters, and sensor configurations), definition of performance objectives, and specification of the genetic algorithm (GA) parameters. In the problem definition component, the user is prompted to define the areas of interest against which the performance metrics will be computed, define the sensor parameters and attach them to specific spacecraft, select which satellite orbital parameters will be added to the decision space of the GA, and specify the range of desired values for each optimization parameter. For performance objectives, the user is presented with a list of available coverage and revisit performance based calculation options from which two metrics are chosen to serve as the objective functions that the GA will use to evaluate solutions during the optimization process. Finally, the definition of the GA parameters provides user control over the number of generations (number of optimization iterations), the population size (number of candidate constellations created in each generation), and the adaptive mutation and crossover threshold values (control parameters for how frequently each process occurs during the optimization). GenETOC has been extensively tested to verify the individual components of the optimization process. The GA has been tested against a suite of GA test problems to confirm convergence to the known two and three-dimensional Pareto fronts. The coverage and revisit performance metrics obtained in GenETOC are compared with STK desktop scenarios, confirming the constellations are being appropriately modeled within GenETOC simulations. A walkthrough of a simple, example problem is provided to illustrate the workings of GenETOC and to demonstrate the output available to the mission designer.

mission design↗
DOE OSTI2025

Leveraging CRISPR Cas9 RNPs and Cre- loxP in Picochlorum celeri for generation of field deployable strains and selection marker recycling

As new highly productive strains of algae are discovered and developed to meet the energy, chemical, and food requirements of the future, genetic engineering of those strains in a manner that yields deployable transformants is paramount. This study introduces the novel CRoxP ($\underline{\textrm{C}}$$\textrm{as9}$ $\underline{\textrm{R}}$$\textrm{NPs}$ coupled with an inducible $\underline{\textrm{CR}}$$\textrm{e}$-$\textrm{l}\underline{\textrm{oxP}}$) system for rapid generation of marker- and transgene-free strains of Picochlorum celeri. The CRoxP system allows reuse of selection markers without Cas9 expression in vivo, eliminating many of the bottlenecks associated with conventional CRISPR Cas9 use for precise genome editing. In P. celeri, transformants were generated with a turnaround time as short as 21 days between transformation and being ready for another round of transformation with the same selection marker by using the CRoxP system. As a use-case for CRoxP, depigmented strains of P. celeri were generated by multiplexed Cas9 disruption of major LHCII genes followed by either a second round of LHCII targeting, or knockout of an LHCI gene. One transformant tested in flask culture (R6) exhibited similar biomass production to the wild type with 46% less Chl a + b on a biomass basis. In photobioreactors and under diel light simulating a solar day, a transformant (LhcBM31) exhibited 34 g AFDW m –2 d –1 with 54% less Chl a + b on a biomass basis vs. wild type.

09 BIOMASS FUELS↗
DOE OSTI2025

MINE: maximally informative next experiment—toward a new GWAS experimental design and methodology

Abstract The computational methodology of Genome Wide Association Studies (GWAS) currently has several limitations: (i) the number of observations (rows) on a quantitative trait tends to be smaller than the number of single nucleotide polymorphisms (SNPs) (columns) in the design matrix; (ii) each SNP is usually modeled separately, failing to acknowledge interaction between each other (ie epistasis); (iii) there is implicit linkage disequilibrium (LD) between neighboring SNPs due to their linkage. To overcome these issues, we developed a tool that uses ensemble methods to fit mixed linear models to GWAS data, and these ensemble methods include the development of a new experimental design approach in GWAS, which uses the resultant models and data to select the next informative experiment over time. This new adaptive and staged approach for GWAS experimental design was developed and tested in a 3 yr adaptive model-guided discovery experiment against a fixed classical design. In Sorghum bicolor a total of 79, 86, and 78 accessions were tested in years 1, 2, and 3, respectively out of 343 accessions available in the Bioenergy Association Panel (BAP) each identified for 232,303 SNPs, 1 every 2–3 kb in the genomes. We demonstrated the feasibility of MINE enacted with 8 people in the field per year over 3 yr vs in 1 large classical design enacted with 20 people in 1 yr. The MINE results for chromosomal regions identified controlling dry weight were confirmed against results from previous sorghum GWAS experiments and 1 large classical design for the BAP panel.

Genetics & Heredity↗
NASA NTRS2002

Molnets: An Artificial Chemistry Based on Neural Networks

The fundamental problem in the evolution of matter is to understand how structure-function relationships are formed and increase in complexity from the molecular level all the way to a genetic system. We have created a system where structure-function relationships arise naturally and without the need of ad hoc function assignments to given structures. The idea was inspired by neural networks, where the structure of the net embodies specific computational properties. In this system networks interact with other networks to create connections between the inputs of one net and the outputs of another. The newly created net then recomputes its own synaptic weights, based on anti-hebbian rules. As a result some connections may be cut, and multiple nets can emerge as products of a 'reaction'. The idea is to study emergent reaction behaviors, based on simple rules that constitute a pseudophysics of the system. These simple rules are parameterized to produce behaviors that emulate chemical reactions. We find that these simple rules show a gradual increase in the size and complexity of molecules. We have been building a virtual artificial chemistry laboratory for discovering interesting reactions and for testing further ideas on the evolution of primitive molecules. Some of these ideas include the potential effect of membranes and selective diffusion according to molecular size.

Colombano, Silvano↗
DOE OSTI2020

Chlorophyll fluorescence imaging captures photochemical efficiency of grain sorghum (Sorghum bicolor) in a field setting

Abstract Background Photosynthesis is one of the most important biological reactions and forms the basis of crop productivity and yield on which a growing global population relies. However, to develop improved plant cultivars that are capable of increased productivity, methods that can accurately and quickly quantify photosynthetic efficiency in large numbers of genotypes under field conditions are needed. Chlorophyll fluorescence imaging is a rapid, non-destructive measurement that can provide insight into the efficiency of the light-dependent reactions of photosynthesis. Results To test and validate a field-deployed fluorescence imaging system on the TERRA-REF field scanalyzer, leaves of potted sorghum plants were treated with a photosystem II inhibitor, DCMU, to reduce photochemical efficiency (F V /F M ). The ability of the fluorescence imaging system to detect changes in fluorescence was determined by comparing the image-derived values with a handheld fluorometer. This study demonstrated that the imaging system was able to accurately measure photochemical efficiency (F V /F M ) and was highly correlated ( r = 0.92) with the handheld fluorometer values. Additionally, the fluorescence imaging system was able to track the decrease in photochemical efficiency due to treatment of DCMU over a 7 day period. Conclusions The system’s ability to capture the temporal dynamics of the plants’ response to this induced stress, which has comparable dynamics to abiotic and biotic stressors found in field environments, indicates the system is operating correctly. With the validation of the fluorescence imaging system, physiological and genetic studies can be undertaken that leverage the fluorescence imaging capabilities and throughput of the field scanalyzer.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRSDate not supplied

PowerCell aboard the EuCROPIS satellite - Results from the first synthetic biology experiment in space

Synthetic biology is the design and construction of new biological parts and systems not found in nature, a technology that we believe will be a game-changing technology for space exploration. The PowerCell concept is an ecology based on photosynthetic microbes taking advantage of in situ materials and energy to generate, on-demand, useful products (food, fuel, cloths, drugs, etc.) that satisfy specific needs of long-term human presence away from Earth. This first PowerCell experiment is testing this concept as well as several key mechanisms of engineering biology in a 48-well plate microfluidics system. NASA's PowerCell experiment was loaded in the fluidics system at NASA Ames in May 2016, and subsequently integrated on the DLR's compact satellite Eu:CROPIS (Euglena Combined Regenerative Organic food Production In Space), as a secondary payload. Launched into Earth’s orbit from the Vandenberg Air Force Base, California, USA, in December 3, 2019, the Eu:CROPIS platform has provided an artificial gravity through rotational acceleration, simulating gravity ranges compatible to those found on Mars and on the Moon, as well as microgravity in the absence of acceleration. Each set of biological experiments performed at Earth's orbit was compared to ground controls. Over 400 days' worth of mission data showed that all hardware and software worked as planned, and microbial growth was observed in all four gravity regimes, including growth on cyanobacterial cell extract in three out of four gravity regimes. The genetic transformation experiment was successful in microgravity and provided valuable long-term reagent stability data, including antibiotic efficacy. The demonstrated ability of hardy bacterial spores to recover from a long-term stasis period will be fundamental for deep human space missions. Lessons learned from hardware design to experimental operations are leading to improved designs and capabilities for future missions.

Ivan Glaucio Paulino Lima↗
DOE OSTI2025

Sustainable recovery of critical metals from spent lithium-ion batteries through gluconic acid-based bioleaching: Techno-economic analysis, life cycle assessment and process optimization

Recycling spent lithium-ion batteries (LIB) could potentially bridge the ever increasing supply and demand gap for critical metals and simultaneously facilitate the management of hazardous battery waste. This study investigated the optimization of gluconic acid-based bioleaching technology through design of experiments (DOE), combined with techno-economic analysis (TEA), and life cycle assessment (LCA) with the aim of maximizing the net present value (NPV) and minimizing global warming impacts of the process. Biolixiviant containing predominantly gluconic acid produced by the genetically engineered (ΔpstS, P 112 :mgdh) Gluconobacter oxydans B58 through fermentation using non-recyclable paper as a growth substrate was used for the LIB leaching. At optimal bioleaching conditions of gluconic acid (160 mM), leaching time (2.5 h), reducing agent FeSO 4 to metal, i.e., cobalt (Co), nickel (Ni) and manganese (Mn), mole ratio (0.88), temperature (55 °C) and pulp density (2.5 %), the leaching efficiency was 87 % 72 %, 94 %, and 88 % for Co, Ni, Mn and lithium (Li), respectively. TEA analysis confirmed that bioleaching plant with an annual black mass processing capacity of 10,000 metric tons and plant life of 30 years would be economically viable with an NPV and profit margin of $136 million and 11 %, respectively. The predicted carbon footprint of gluconic acid-based bioleaching for recovering 1 kg of Co (13.2 kg of CO 2 eq.) is lower compared to that of most state-of-the-art leaching technologies. Moreover, gluconic acid-based bioleaching effectively recovered target metals when tested for different black mass chemistries.

Bioleaching↗
DOE OSTI2026

An improved dataset for predicting mammal infecting viruses from genetic sequence information

There have been several attempts to develop machine learning (ML) models to identify human infecting viruses from their genomic sequences, with varying degrees of success. Direct comparison between models is problematic, because these models are typically trained and evaluated on different datasets with alternative data splitting schemes, features, and model performance metrics. In this paper we present a standardized dataset of mammal infecting and non-infecting viral pathogens, refined from the previous work of Mollentze et al. to include the latest literature evidence, roughly doubling the number of curated host-virus records available to the community, and new host target labels, primate and mammal. The new host labels were included for several reasons, including previous reports that classification performance is better at broader taxonomic ranks and the idea that there may be more data for primate infection that might serve as a suitable proxy for zoonotic potential and avoidance of false positives for human infection due to absence of evidence. On this dataset, we report the performance of eight machine learning models for predicting mammal-infecting viruses from their genomic sequences. We find that randomly assigning cases in our improved dataset to training/testing sets, when compared to the original assignments into training/testing in Mollentze et al., increases the overall average ROC AUC of prediction of human infection from 0.663 ± 0.070 to 0.784 ± 0.013, consistent with the reduction in phylogenetic distance between train and test sets (relative entropy change from 3.00 to 0.08). The broadest host category of mammal infection can be predicted most reliably at 0.850 ± 0.020. We share our improved dataset and code to enable standardized comparisons of machine learning methods to predict human host infections. Overall, we have presented preliminary evidence that classification of virus host infection is more tractable at higher taxonomic ranks, that unsurprisingly reducing the phylogenetic distance between training and test sets can improve predictive performance, that peptide kmer features appear to be harmful to out of sample model performance, and we are left with the question of whether models for virus host prediction can reasonably be expected to perform well in out of sample scenarios given the likelihood that viruses do not share a common ancestor. Consistent with this concern, when the data is resampled such that there is no overlap between viral families in training and test sets (relative entropy > 24), models perform no better than random chance at prediction of human infection regardless of whether kmers are included (ROC AUC 0.50 ± 0.08) or not (ROC AUC 0.50 ± 0.04).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

FUN-PROSE: A deep learning approach to predict condition-specific gene expression in fungi

mRNA levels of all genes in a genome is a critical piece of information defining the overall state of the cell in a given environmental condition. Being able to reconstruct such condition-specific expression in fungal genomes is particularly important to metabolically engineer these organisms to produce desired chemicals in industrially scalable conditions. Most previous deep learning approaches focused on predicting the average expression levels of a gene based on its promoter sequence, ignoring its variation across different conditions. Here we present FUN-PROSE—a deep learning model trained to predict differential expression of individual genes across various conditions using their promoter sequences and expression levels of all transcription factors. We train and test our model on three fungal species and get the correlation between predicted and observed condition-specific gene expression as high as 0.85. We then interpret our model to extract promoter sequence motifs responsible for variable expression of individual genes. We also carried out input feature importance analysis to connect individual transcription factors to their gene targets. A sizeable fraction of both sequence motifs and TF-gene interactions learned by our model agree with previously known biological information, while the rest corresponds to either novel biological facts or indirect correlations.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1993

Orthopyroxenes as recorders of diogenite petrogenesis: Trace element systematics

Eucrite, howardite, and diogenite members of the achondrites are considered, by many, to be genetically related. Therefore, each provide a piece of the puzzle for reconstructing magmatic processes on the eucrite parent body (EPB). The relationship between eucrites and diogenites can be viewed within the context of two distinctly different models: (1) fractional crystallization; and (2) partial melting. In fractional crystallization models, eucrites and diogenites represent a complementary continuum of planetary fractional crystallization products in which the diogenites represent crystal accumulations during the crystallization of eucritic magmas at shallow to deep levels in the EPB. Alternatively, experimental studies may be interpreted as indicating eucrites represent peritectic partial melts of a primitive, chondritic EPB mantle. Within this type of model, the diogenites are also generally considered to be cumulates; however, their petrogenetic relationship to the eucrites is less clear. Sack et al. proposed that the olivine diogenites represent residua from the partial melting events that produced eucritic liquids. Initial trace element studies of orthopyroxene (OPX) are consistent with this model. However, this trace element modeling of the olivine diogenites is nonunique. As a further test of these models, we did the following three things: (1) analyzed OPX from cumulate diogenites to compare with the olibine diogenite data; (2) improved ion microprobe analytical techniques for the analysis of elements critical to our interpretations; and (3) selected more relevant Kd's for OPX-eucritic melt.

Shearer, C. K.↗
DOE OSTI2023

The minimal SUF system is not required for Fe–S cluster biogenesis in the methanogenic archaeon Methanosarcina acetivorans

Abstract Iron–sulfur (Fe–S) proteins are essential for the ability of methanogens to carry out methanogenesis and biological nitrogen fixation (diazotrophy). Nonetheless, the factors involved in Fe–S cluster biogenesis in methanogens remain largely unknown. The minimal SUF Fe–S cluster biogenesis system (i.e., SufBC) is postulated to serve as the primary system in methanogens. Here, the role of SufBC in Methanosarcina acetivorans , which contains two sufCB gene clusters, was investigated. The CRISPRi-dCas9 and CRISPR-Cas9 systems were utilized to repress or delete sufC1B1 and sufC2B2 , respectively. Neither the dual repression of sufC1B1 and sufC2B2 nor the deletion of both sufC1B1 and sufC2B2 affected the growth of M. acetivorans under any conditions tested, including diazotrophy. Interestingly, deletion of only sufC1B1 led to a delayed-growth phenotype under all growth conditions, suggesting that the deletion of sufC2B2 acts as a suppressor mutation in the absence of sufC1B1 . In addition, the deletion of sufC1B1 and/or sufC2B2 did not affect the total Fe–S cluster content in M. acetivorans cells. Overall, these results reveal that the minimal SUF system is not required for Fe–S cluster biogenesis in M. acetivorans and challenge the universal role of SufBC in Fe–S cluster biogenesis in methanogens.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2005

Hardware platforms for MEMS gyroscope tuning based on evolutionary computation using open-loop and closed -loop frequency response

We propose a tuning method for MEMS gyroscopes based on evolutionary computation to efficiently increase the sensitivity of MEMS gyroscopes through tuning. The tuning method was tested for the second generation JPL/Boeing Post-resonator MEMS gyroscope using the measurement of the frequency response of the MEMS device in open-loop operation. We also report on the development of a hardware platform for integrated tuning and closed loop operation of MEMS gyroscopes. The control of this device is implemented through a digital design on a Field Programmable Gate Array (FPGA). The hardware platform easily transitions to an embedded solution that allows for the miniaturization of the system to a single chip.

evolvable hardware↗
DOE OSTI2022

Assessment of human nuclear and mitochondrial DNA qPCR assays for quantification accuracy utilizing NIST SRM 2372a

In forensic DNA casework, a highly accurate real-time quantitative polymerase chain reaction (qPCR) assay is recommended per the Scientific Working Group on DNA Analysis Methods (SWGDAM) (SWGDAM Validation Guidelines for DNA Analysis Methods [1]) to determine whether a DNA sample is of sufficient quantity and robust quality to move forward with downstream short tandem repeats (STR) or sequencing analyses. Most of these assays rely on a standard curve, referred to herein and traditionally as absolute qPCR, in which an unknown is compared, relative to that curve. However, one fundamental issue with absolute qPCR is the quantifiable concentration of commercial assay standards can vary depending on (1) origin, i.e., whether from a cell line or a human subject, (2) supplier, (3) lot number, (4) shipping method, etc. In 2018, the National Institute for Standards and Technology (NIST) released a human DNA standard reference material for evaluating qPCR quantification standards, Standard Reference Material (SRM) 2372a, Romsos et al. (2018) [2] which contains three well-characterized human genomic DNA samples: Component A) a single male1 donor, Component B) a single female 1 donor, and Component C) a 1:3 male 2 :female 2 donor, each with certification data for nDNA and informational mitochondrial DNA(mtDNA)/nuclear DNA (nDNA) ratio data. The SRM 2372a was used to assess four qPCR assays: (1) Quantifiler Trio (Thermo Fisher Scientific, Waltham, MA) for nDNA quantification, (2) NovaQUANT (EMD Millipore Corporation, San Diego, CA) for nDNA and mtDNA quantification, (3) a custom duplex mtDNA assay, and (4) a custom triplex mtDNA assay. Additionally, extracts from eighteen (18) skeletal remains were tested with the latter three assays for concordance of DNA concentration and with assays (2) and (3), for the degradation state. Our assessment revealed that an accurate, efficient, and reproducible qPCR assay is dependent on (1) the quality and reliability of the DNA standard, (2) the qPCR chemistry, and (3) the specific primers, and probes (if applicable), used in an assay. Finally, our findings indicate qPCR assays may not always quantify as expected and that performance of each lot should be verified using a well-characterized DNA standard such as the NIST SRM 2372a and adjusted if warranted.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Catalase protects against nonenzymatic decarboxylations during photorespiration in Arabidopsis thaliana

Abstract Photorespiration recovers carbon that would be otherwise lost following the oxygenation reaction of rubisco and production of glycolate. Photorespiration is essential in plants and recycles glycolate into usable metabolic products through reactions spanning the chloroplast, mitochondrion, and peroxisome. Catalase in peroxisomes plays an important role in this process by disproportionating H 2 O 2 resulting from glycolate oxidation into O 2 and water. We hypothesize that catalase in the peroxisome also protects against nonenzymatic decarboxylations between hydrogen peroxide and photorespiratory intermediates (glyoxylate and/or hydroxypyruvate). We test this hypothesis by detailed gas exchange and biochemical analysis of Arabidopsis thaliana mutants lacking peroxisomal catalase. Our results strongly support this hypothesis, with catalase mutants showing gas exchange evidence for an increased stoichiometry of CO 2 release from photorespiration, specifically an increase in the CO 2 compensation point, a photorespiratory‐dependent decrease in the quantum efficiency of CO 2 assimilation, increase in the 12 CO 2 released in a 13 CO 2 background, and an increase in the postillumination CO 2 burst. Further metabolic evidence suggests this excess CO 2 release occurred via the nonenzymatic decarboxylation of hydroxypyruvate. Specifically, the catalase mutant showed an accumulation of photorespiratory intermediates during a transient increase in rubisco oxygenation consistent with this hypothesis. Additionally, end products of alternative hypotheses explaining this excess release were similar between wild type and catalase mutants. Furthermore, the calculated rate of hydroxypyruvate decarboxylation in catalase mutant is much higher than that of glyoxylate decarboxylation. This work provides evidence that these nonenzymatic decarboxylation reactions, predominately hydroxypyruvate decarboxylation, can occur in vivo when photorespiratory metabolism is genetically disrupted.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

TULIP: An RNA-seq-based Primary Tumor Type Prediction Tool Using Convolutional Neural Networks

Background: With cancer as one of the leading causes of death worldwide, accurate primary tumor type prediction is critical in identifying genetic factors that can inhibit or slow tumor progression. There have been efforts to categorize primary tumor types with gene expression data using machine learning, and more recently with deep learning, in the last several years. Methods In this paper, we developed four 1-dimensional (1D) Convolutional Neural Network (CNN) models to classify RNA-seq count data as one of 17 highly represented primary tumor types or 32 primary tumor types regardless of imbalanced representation. Additionally, we adapted the models to take as input either all Ensembl genes (60,483) or protein coding genes only (19,758). Unlike previous work, we avoided selection bias by not filtering genes based on expression values. RNA-seq count data expressed as FPKM-UQ of 9,025 and 10,940 samples from The Cancer Genome Atlas (TCGA) were downloaded from the Genomic Data Commons (GDC) corresponding to 17 and 32 primary tumor types respectively for training and validating the models. Results: All 4 1D-CNN models had an overall accuracy of 94.7% to 97.6% on the test dataset. Further evaluation indicates that the models with protein coding genes only as features performed with better accuracy compared to the models with all Ensembl genes for both 17 and 32 primary tumor types. For all models, the accuracy by primary tumor type was above 80% for most primary tumor types. Conclusions: We packaged all 4 models as a Python-based deep learning classification tool called TULIP (TUmor CLassIfication Predictor) for performing quality control on primary tumor samples and characterizing cancer samples of unknown tumor type. Further optimization of the models is needed to improve the accuracy of certain primary tumor types.

Jones, Sara↗
DOE OSTI2024

Simulated Microgravity Alters Gene Regulation Linked to Immunity and Cardiovascular Disease

Microgravity exposure induces a cephalad fluid shift and an overall reduction in physical activity levels which can lead to cardiovascular deconditioning in the absence of countermeasures. Future spaceflight missions will expose crew to extended periods of microgravity among other stressors, the effects of which on cardiovascular health are not fully known. In this study, we determined cardiac responses to extended microgravity exposure using the rat hindlimb unloading (HU) model. We hypothesized that exposure to prolonged simulated microgravity and subsequent recovery would lead to increased oxidative damage and altered expression of genes involved in the oxidative response. To test this hypothesis, we examined hearts of male (three and nine months of age) and female (3 months of age) Long–Evans rats that underwent HU for various durations up to 90 days and reambulated up to 90 days post-HU. Results indicate sex-dependent changes in oxidative damage marker 8-hydroxydeoxyguanosine (8-OHdG) and antioxidant gene expression in left ventricular tissue. Three-month-old females displayed elevated 8-OHdG levels after 14 days of HU while age-matched males did not. In nine-month-old males, there were no differences in 8-OHdG levels between HU and normally loaded control males at any of the timepoints tested following HU. RNAseq analysis of left ventricular tissue from nine-month-old males after 14 days of HU revealed upregulation of pathways involved in pro-inflammatory signaling, immune cell activation and differential expression of genes associated with cardiovascular disease progression. Taken together, these findings provide a rationale for targeting antioxidant and immune pathways and that sex differences should be taken into account in the development of countermeasures to maintain cardiovascular health in space.

Genetics & Heredity↗