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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

The contribution of BvgR, RisA, and RisS to global gene regulation, intracellular cyclic-di-GMP levels, motility, and biofilm formation in Bordetella bronchiseptica

Bordetella bronchiseptica is a highly contagious respiratory bacterial veterinary pathogen. In this study the contribution of the transcriptional regulators BvgR, RisA, RisS, and the phosphorylation of RisA to global gene regulation, intracellular cyclic-di-GMP levels, motility, and biofilm formation were evaluated. Next Generation Sequencing (RNASeq) was used to differentiate the global gene regulation of both virulence-activated and virulence-repressed genes by each of these factors. The BvgAS system, along with BvgR, RisA, and the phosphorylation of RisA served in cyclic-di-GMP degradation. BvgR and unphosphorylated RisA were found to temporally regulate motility. Additionally, BvgR, RisA, and RisS were found to be required for biofilm formation.

59 BASIC BIOLOGICAL SCIENCES↗
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↗
DOE OSTI2022

Transcriptome Mining Provides Insights into Cell Wall Metabolism and Fiber Lignification in Agave tequilana Weber

Resilience of growing in arid and semiarid regions and a high capacity of accumulating sugar-rich biomass with low lignin percentages have placed Agave species as an emerging bioenergy crop. Although transcriptome sequencing of fiber-producing agave species has been explored, molecular bases that control wall cell biogenesis and metabolism in agave species are still poorly understood. Here, through RNAseq data mining, we reconstructed the cellulose biosynthesis pathway and the phenylpropanoid route producing lignin monomers in A. tequilana, and evaluated their expression patterns in silico and experimentally. Most of the orthologs retrieved showed differential expression levels when they were analyzed in different tissues with contrasting cellulose and lignin accumulation. Phylogenetic and structural motif analyses of putative CESA and CAD proteins allowed to identify those potentially involved with secondary cell wall formation. RT-qPCR assays revealed enhanced expression levels of AtqCAD5 and AtqCESA7 in parenchyma cells associated with extraxylary fibers, suggesting a mechanism of formation of sclerenchyma fibers in Agave similar to that reported for xylem cells in model eudicots. Overall, our results provide a framework for understanding molecular bases underlying cell wall biogenesis in Agave species studying mechanisms involving in leaf fiber development in monocots.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Sorghum bicolor BTx623 Nitrogen Grown Conditions Gene Expression Profiling

Dhurrin, a cyanogenic glucoside, plays an important role in Sorghum bicolor physiology and defense. The concentration of dhurrin in sorghum is influenced by both nitrogen status and stage of plant organ development. While nitrogen resupply activates the expression of genes for dhurrin biosynthesis, the molecular mechanisms underlying this regulation remain unclear. In this study, we investigated the transcriptional response of sorghum to nitrogen resupply following growth under nitrogen-limiting conditions. Using a time-course design, we measured hydrogen cyanide potential (HCNp), growth, and nitrate content at 0-, 2-, 6-, 12-, 24-, 36-, 48-, and 60-h after resupply and collected tissue for RNAseq analysis in parallel for analysis of gene expression and construction of gene regulatory networks (GRNs). HCNp (mg g−1 DW) increased significantly in leaf and stem tissues following nitrogen resupply, with increases in the leaf partially driven by continued declines in controls under ongoing nitrogen stress. Expression of the dhurrin pathway genes was upregulated in leaves from 24 h after nitrogen resupply, with diel expression patterns observable over the remaining time points. No upregulation was observed in roots or stems, suggesting that developmental context overrides environmental cues. GRN analysis identified candidate transcription factors regulating dhurrin biosynthesis genes, including members of the MYB, bZIP, and GARP-type transcription factor families. Some of these candidate transcription factors may be involved in relieving senescence-associated suppression of dhurrin biosynthesis and link nitrogen signaling to pathway activation. These findings provide new insight into the nitrogen-responsive regulation of dhurrin in sorghum, highlighting candidate regulators for future functional characterization.

cyanogenic glucoside↗
DOE OSTI2024

Sorghum bicolor BTx623 Nitrogen Grown Conditions Set2 Gene Expression Profiling

Dhurrin, a cyanogenic glucoside, plays an important role in Sorghum bicolor physiology and defense. The concentration of dhurrin in sorghum is influenced by both nitrogen status and stage of plant organ development. While nitrogen resupply activates the expression of genes for dhurrin biosynthesis, the molecular mechanisms underlying this regulation remain unclear. In this study, we investigated the transcriptional response of sorghum to nitrogen resupply following growth under nitrogen-limiting conditions. Using a time-course design, we measured hydrogen cyanide potential (HCNp), growth, and nitrate content at 0-, 2-, 6-, 12-, 24-, 36-, 48-, and 60-h after resupply and collected tissue for RNAseq analysis in parallel for analysis of gene expression and construction of gene regulatory networks (GRNs). HCNp (mg g−1 DW) increased significantly in leaf and stem tissues following nitrogen resupply, with increases in the leaf partially driven by continued declines in controls under ongoing nitrogen stress. Expression of the dhurrin pathway genes was upregulated in leaves from 24 h after nitrogen resupply, with diel expression patterns observable over the remaining time points. No upregulation was observed in roots or stems, suggesting that developmental context overrides environmental cues. GRN analysis identified candidate transcription factors regulating dhurrin biosynthesis genes, including members of the MYB, bZIP, and GARP-type transcription factor families. Some of these candidate transcription factors may be involved in relieving senescence-associated suppression of dhurrin biosynthesis and link nitrogen signaling to pathway activation. These findings provide new insight into the nitrogen-responsive regulation of dhurrin in sorghum, highlighting candidate regulators for future functional characterization.

cyanogenic glucoside↗
NASA NTRS2019

DNA Damage Response to Low and High-LET in a Large Cohort of Mice and Humans and Latest Advancement in NASA Space Omics

This presentation will first focus on a thorough evaluation of the DNA damage response to both low and high-LET in a cohort of 76 mice primary skin fibroblast derived from 15 different strains or in human blood mononuclear cells derived from 550 healthy donors. In both the human and mice work, we have hypothesized that DNA repair capacity can be used as a marker to evaluate and differentiate individual radiation sensitivity. More specifically, this work is based on the concept that the combined time-dose dependence of radiation-induced foci (RIF) of p53-binding protein 1 (53BP1) following low-LET exposure contains sufficient information to infer sensitivity to any other LET. This work is one of the most extensive studies on the kinetics and possible genetic underpinnings of radiation-induced DNA damage and repair. Results on humans are still preliminary as we are still in the process of collecting and isolating primary blood mononuclear cells from 500 to 800 healthy subjects of European descent, 18-75 years of age, 50/50 male/female distribution. We have analyzed 53BP1+ RIF formation as well as oxidative stress and cell death in primary cells from 192 subjects in response to the same HZE particles as used in mice: 600 MeV/n Fe, 350 MeV/n Ar and 350 MeV/n Si, 1.1 and 3 particles/100m2, 4 and 24 hours after irradiation. The second part of the talk will focus on describing GeneLab: The NASA Systems Biology Platform for Space Omics Repository, Analysis and Visualization. NASA GeneLab is an open-access repository for omics datasets generated by biological experiments conducted in space or experiments relevant to spaceflight (e.g. simulated cosmic radiation, simulated microgravity, bed rest studies). Started as a repository designed to archive precious omics from space experiments, GeneLab has expanded its scope to maximize the intelligibility of the raw data (e.g. RNAseq, microarray, WGBS, metagenome), particularly for users with limited bioinformatics knowledge. As such GeneLab is now providing processed data derived from the raw data covering a large spectrum of omics (genome, epigenome, transcriptome, epitranscriptome, proteome, metabolome), to help users explore important questions: Which genes or proteins are expressed differently in space for various living organisms? What are the consequences arising from these changes? What specifics DNA mutations or epigenetic changes happen in space? What species or genetic features lead to better adaption to such a unique environment? In this presentation, we will report on the current and future objectives for GeneLab, and review recent published studies relating molecular changes observed in various animal models and tissue with microgravity, radiation, circadian rhythm, hydration and carbon dioxide conditions.

DNA repair kinetics↗
NASA NTRS2019

Simulated Weightlessness Alters Cardiomyocyte Structure and Transcriptional Regulation of Mediators Related to Immunity and Cardiovascular Disease

Spaceflight and the ensuing fluid shifts, together with an overall reduction in physical activity, lead to acute and latent effects on the cardiovascular system. This current study makes use of the rodent hindlimb unloading (HU) model to determine how factors such as sex, age, and duration of exposure impact cardiac responses to weightlessness. We hypothesize that extended exposure to simulated weightlessness and the ensuing recovery alters cardiac structure and expression of select genes, including those involved in redox signaling which together, negatively impact long-term cardiac tissue health. To begin to test this hypothesis, male and female rats underwent HU at various durations up to 90 days, with a subset reambulated after 90 days of HU. Physiological stress or contractility changes lead to alterations in ventricular cardiomyocyte size and ventricular wall thickness to adapt to greater functional demand and mitigate mechanical stress to ventricular tissue; under certain conditions, these changes also may mark progression to cardiac failure. Hence, left ventricular cardiomyocyte size (cardiomyocyte cross sectional area, CSA) was quantified to determine if HU leads to structural adaptation responses in cardiac tissue and if age and sex had any impact on this outcome. Cardiomyocyte CSA of older males (9 months) were altered by HU in a time-dependent manner, where HU led to decreases in CSA at 14 days and increases at 90 days. In contrast, younger males (3 months) did not show any changes at day 14 of HU. CSA of females (3 months) was increased in response to short-term HU (14 days) suggesting sex-dependence of structural changes. In older HU males, cardiomyocyte CSA was comparable to controls after 90 days of re-ambulation. Levels of the DNA oxidative damage marker, 8-hydroxydeoxyguanosine (8-OHdG) were greater in left ventricular tissue of females that underwent HU compared to sex-matched controls, while there were no such differences in older or younger males. To gain insight into the signals that drive cardiac adaptations to HU, global transcriptomic analysis (RNAseq) was performed on left ventricular tissue of older males that underwent 14 days of HU. Short-term simulated weightlessness led to differential expression of genes involved in immune and pro-inflammatory signaling. A subset of these genes play a role in autoimmune and cardiovascular disease and are targets of current drugs used to treat bradycardia, hypertension, atherosclerosis and rheumatoid arthritis, amongst others. Oxidative damage/redox signaling pathways were not enriched at the timepoint tested in older males. Since young females displayed greater oxidative damage to DNA, activation of oxidative stress responses at earlier or later time points cannot be ruled out. In summary, simulated weightlessness in adult rats caused changes in cardiomyocyte structure in a sex and age-dependent manner, and the transcriptional regulation of key mediators of immunity and cardiovascular disease, meriting further study to define cardiac risks for interplanetary travel of human crew. Our findings also confirm the value of the rat HU model for cardiac health and countermeasure research.

Tahimic, Candice↗
NASA NTRS2019

Results of the Micro-12 Flight Experiment: Effects of Microgravity on Shewanella oneidensis MR-1

The Micro-12 flight experiment was launched on SpaceX-15 and completed during berthing on the International Space Station. The goal of this experiment was to understand the effects of spaceflight and microgravity on the physiology of the model exoelectrogen Shewanella oneidensis MR-1. BioServe Fluid Processing Apparatus (FPA) and Group Activation Pack (GAP) hardware systems were used for both flight and ground control tests. Under spaceflight conditions, extracellular electron transfer (EET) rates were found to be significantly increased on insoluble substrates, while biofilm development appeared to be unchanged under the conditions tested; these processes are critical for microbial-assisted bioelectrochemical systems. Additionally, RNAseq analysis, proteomic profiling, and competitive mutant fitness profiling were performed to gain further understanding of microbial physiology under EET-respiring conditions during spaceflight. Overall, the results of the Micro-12 project support the idea that Shewanella oneidensis MR-1, in particular, and exoelectrogens in general could be useful chassis organisms for synthetic biology applications using microbial bioelectrochemical systems. These findings will assist bioengineering and synthetic biology development efforts harnessing the unique capabilities of exoelectrogens for life support and in situ resource utilization.

Dougherty, Michael↗
NASA NTRSDate not supplied

Evaluation of Correction Methods for NASA GeneLab Transcriptomic Datasets

Conducting space biology experiments aboard the International Space Station, particularly those utilizing complex model organisms like mice, is expensive and difficult due to limited crew availability, hardware, and space. As a result, sample numbers from these studies are low, reducing the statistical power of any one experiment. Aggregating spaceflight datasets serves as a method to increase sample numbers, allowing for novel insights through bioinformatic analysis of ‘omics data from merged datasets. However, aggregating datasets can introduce unwanted variation including 1) differences in sample handling, processing, and sequencing platforms between datasets (technical variation) as well as 2) differences in experimental design between datasets such as sex or age of the model organism used. In the present study, NASA GeneLab-hosted RNAseq datasets from rodent liver tissues were used to evaluate several statistical methods to correct for this unwanted variation through two approaches, reference-based and standard. The following correction algorithms were applied with (reference-based) and/or without (standard) considering Universal Mouse RNA Reference samples: ComBat and ComBat_seq from the SVA package, median polish, empirical Bayes, and ANOVA-based algorithms from the MBatch package, and negative binomial regression normalization in the DESeq2 package. For each approach, after the correction algorithm was applied, differential gene expression (DGE) analysis of flight and ground control samples was performed with the combined data. The robustness of each tool was evaluated using BatchQC, to determine statistical differences between datasets before and after correction, Principal Component Analysis, to evaluate global gene expression in samples before and after correction, and by comparing DGE analysis of individual datasets and combined datasets before and after correction. The results showed that the reference-based approach introduced several additional (and likely artificial) DEGs when compared with the standard approach. Thus, the most robust standard correction will be implemented in the GeneLab Visualization 2.0 platform when datasets are combined.

GeneLab, RNA-seq, Batch Correction↗
NASA NTRSDate not supplied

Inception of a Spaceflight-specific Mouse to Human Expression Profiling Translation Model

Rodents are foundational model organisms often utilized due to their seemingly analogous morphologies and biological responses to humans. However, recent studies have demonstrated that murine model data are limited in their applicability, particularly in inflammatory disease. In space studies, accurately predicting human response from mouse data is critical due to extreme limiting factors in both rodent and human spaceflight research. With successful prediction, spaceflight ailments can be predicted and prevented while respecting the constraints of the spaceflight industry and minimizing danger to humans. To do so, novel methodologies must be developed that predict human response from murine data after considering biological differences between rodents and humans in spaceflight. After considering terrestrial models, we determined that a spaceflight-based expression profiting translation tool should be created to accurately capture predictions of human gene expression in spaceflight from mouse data. To prepare to build this model, we organized known human spaceflight risks, chose analog human diseases as training data categories, then identified existing RNASeq disease datasets from GEO as potential training data. In addition, we classified existing Genelab mouse differential gene expression datasets for use as experimental data.

Translation↗
NASA NTRSDate not supplied

Characterizing Antibiotic Resistance in Microgravity Environments

The gram-positive bacterium Staphylococcus aureus and the gram-negative Pseudomonas aeruginosa are two of NASA’s designated “medically important microorganisms” commonly co-isolated from spaceflight environments like the International Space Station (ISS). Prior studies demonstrate evidence of increased virulence and antibiotic resistance of each bacterium when grown as single species in space. “Characterizing Antibiotic Resistance in Microgravity Environments” (CARMEn) is an autonomous 30-day ISS payload experiment probing the phenotypic changes of S. aureus and P. aeruginosa biofilms grown together in microgravity. Phenotypic changes are probed through three parallel experiments measuring Extracellular Matrix (ECM) production, antibiotic tolerance, and RNA expression respectively. The ECM experiment uses time-lapse imaging to collect observational morphological data from biofilms stained with Congo Red and Coomassie Blue, the antibiotic tolerance experiment utilizes E-strip testing once samples return to Earth, and RNA expression is measured through RNAseq of samples treated with RNAProtect® during spaceflight.

Antibiotic resistance↗
NASA NTRSDate not supplied

Looking into Ocular Risks of Spaceflight through the Mouse Retina

Ocular alterations have been observed at anatomical levels in astronauts on long duration spaceflight missions, such as what would be required for missions to Mars. These alterations cause an array of signs which together constitute the Spaceflight-Associated Neuro-ocular Syndrome (SANS), one of the top risk priorities of the NASA Human Research Program. Not much is known about SANS at the cellular and molecular level, but studies in mice and rats have recently begun to yield observations on how the spaceflight environment might affect the eye’s biology. Preliminary data from shuttle mouse experiments, and more recently experiments on ISS, have shown changes in retinal physiology via histology and gene expression analysis. This study utilizes samples from the CASIS sponsored Rodent Research 8 Experiment (RRRM-1) tissue sharing opportunity, delivered to the ISS by SpaceX CRS-16 on 12/08/2018. Female BALB/cAnNTac mice were on the ISS for 45 days, while ground controls consisted ofa standard vivarium group and spaceflight habitat group. Here we investigate the molecular response of the mouse retina to identify genes and pathways affected by spaceflight conditions using histology and transcriptomic RNAseq data. This Differentially Expressed Gene (DEG) data was used for pathway analysis with Galaxy (Genelab) and Ingenuity Pathway Analysis (IPA). We identified pathways related to neuronal differentiation, cellular transport/movement, and wound healing. Some of the top DEGs have known relation to ophthalmic diseases. Though there were DEGs throughout the comparisons we tested, there was no clear effect of spaceflight. This could be due to sample processing, which required mice to be returned to Earth about a day before they were sacrificed, possibly allowing for readaptation affecting the retinal transcriptome. However, there was a clear effect of age, between the young (10-12 weeks) and old (32 weeks) groups, and between the baseline and end of experiment, about 46 days.

SANS↗
NASA NTRSDate not supplied

Space Flown Rodent Liver RNA Sequencing Data for Machine Learning in Space Biology Research

High-throughput nucleic acid sequencing (DNA-seq, RNA-seq) has become widespread in biomedical research due to the growing availability and affordability of these assays. Data analysis has been accelerated in recent years by the adoption of artificial intelligence (AI) and machine learning (ML) techniques by biomedical researchers. In space biology research, RNAseq datasets from space-flown experimental samples are critical for characterizing the gene expression aberrations associated with exposure to spaceflight stressors. However, space biological experiments tend to be very low sample size, so identifying proper AI/ML algorithms for sequencing data analysis is an ongoing challenge since these algorithms typically require large sample size. The NASA Science Mission Directorate (SMD) has started the “Benchmark Initiative for AI/ML”, focused on creating datasets meant for three main applications: 1) scientific benchmarking, which finds the best algorithm for a specific problem; 2) application benchmarking, which measures algorithm performance against a set of parameters; and 3) system benchmarking, which evaluates performance of hardware and software architecture. These scientific benchmarks consist of an AI-ready dataset and a reference implementation on a specific scientific question. In this work, we focused on generating standardized datasets to allow the scientific community to benchmark AI/ML algorithms in the domain of space biology. We present here a standardized, AI-ready, publicly available benchmark dataset for space biology RNA-seq data as a collaboration between the NASA AI4LS (Artificial Intelligence for Life Sciences) working group. and NASA’s SMD. This dataset consists of space-flown and ground control mouse liver found in the NASA GeneLab omics database. However, to amplify the small sample number (n=112 samples) for ML purposes, we employ Gaussian noise and a generative adversarial network to extend this dataset to 6,000 synthetic samples, matching the original gene expression characteristics.

James Casaletto↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗
NASA NTRSDate not supplied

Histological and Transcriptomic Analysis of Spaceflight-Induced Ocular Changes in the Mouse Retina

Anatomical changes have been observed in astronauts’ eyes after long duration spaceflight missions. These alterations can lead to visual impairment which in part constitutes the spaceflight-associated neuroocular syndrome (SANS), one of the top risk priorities for deep space missions. The HRP Systems Biology (SysBio) Translation Project will apply systems biology approaches utilizing current human physiological spaceflight data, molecular results from rodents, and future research with a multi-level, multi-system, and multi-species perspective to augment the existing research plan to resolve the SANS risk. Not much is known about SANS at the cellular and molecular level, but studies in mice and rats have recently begun to determine how spaceflight might affect the biology of the eye. Preliminary studies of mice that flew on the Space Shuttle, and more recently the International Space Station (ISS), have shown changes in retinal physiology as assessed by histology and gene expression analysis. The study presented here obtained samples from the CASIS sponsored Rodent Research 8 Experiment delivered to the ISS by SpaceX CRS-16 on 12/08/2018. Female BALB/cAnNTac mice flew on the ISS for 45 days, while ground controls were housed in a standard vivarium or animal enclosure module. Sacrifice and sample acquisition occurred once mice returned to Earth, possibly allowing for readaptation affecting retinal homeostasis. We applied standard transcriptomic (RNAseq) and histological approaches to characterize genes and pathways in the mouse retina affected by spaceflight or age. The differentially expressed gene (DEG) data was analyzed using Galaxy (GeneLab) and Ingenuity Pathway Analysis. Significant DEGs between flight and ground samples were relatively few but biologically meaningful. Pathways identified related to neuronal differentiation, cellular transport/movement, and wound healing. Age effects were detected between the young (10–12 weeks) and old (32 weeks) groups and between the baseline and end of experiment (~46 days). The biological relevance of specific DEGs were confirmed through immunohistochemical evaluation using fixed histological sections of the eye from four flight group mice and four habitat control mice. Staining was performed specific for synaptophysin, glial fibrillary acidic protein (GFAP), and neurofilament in the retinal periphery, equator, and peripapillary regions. For synaptophysin staining, the innerplexiform and outerplexiform layers were scored; for GFAP staining, Mueller cells and perivascular astrocytes were scored. Results show flight samples typically had more staining of GFAP and neurofilament while, conversely, the habitat control group had more staining of synaptophysin.

C. Perez↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning (ML) Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗