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NASA NTRS2017

Cardiovascular Disease Risk in NASA Astronauts Across the Lifespan: Historical Cohort Studies

Acute effects of spaceflight on the cardiovascular system have been studied extensively, but the combined chronic effects of spaceflight and aging are not well understood. Preparation for and participation in space flight activities are potentially associated with cardiovascular disease risk factors (e.g., altered dietary and exercise habits, physical and emotional stress, circadian shifts, radiation). Further, astronauts who travel into space multiple times may be at an increased risk across their lifespan. However, comparing the risk of cardiovascular disease in astronauts to other large cohorts is difficult. For example, comparisons between astronauts and large national cohorts, such as the National Health and Nutrition Examination Survey and the National Health Information Survey, are hampered by significant differences in health status between astronauts and the general population, and most of these national studies fail to provide longitudinal data on population health. To address those limitations, NASA's Longitudinal Study of Astronaut Health previously sought to compare the astronauts to a cohort of civil servants employed at the Johnson Space Center. However, differences between the astronauts and civil servants at the beginning of the study, as well as differential follow up, limited the ability to interpret the results. To resolve some of these limitations, two unique cohorts of healthy workers, U.S. Air Force aviators and Cooper Center Longitudinal Study participants, have been identified as potential comparison populations for the astronaut corps. The Air Force cohort was chosen due to similarities in health at selection, screening, and some occupational exposures that Air Force aviators endure, many of which mirror that of the astronaut corps. The Cooper Clinic cohort, a generally healthy prevention cohort, was chosen for the vast array of clinical cardiovascular measures collected in a longitudinal manner complementary to those collected on astronauts, for a large number of subjects since 1971. The purpose of this study is to understand the incidence of cardiovascular disease outcomes and risk factors in the astronaut corps and determine whether the rates of disease are different than these two cohorts. The research questions are: 1. Are there differences in the incidence of CVD outcomes (MI, revascularization, and stroke) between each cohort and the NASA Astronaut cohort? 2. Are there differences in the incidence of CVD risks (hypertension, hyperlipidemia, arrhythmias, and diabetes) between each cohort and the NASA Astronaut cohort? 3. Are there differences between each cohort and the NASA Astronaut cohort in how CVD risk factors (e.g., lipids, behaviors) change across time? Collectively, results from these studies will enhance our understanding of how cardiovascular disease outcomes and risk factors change across time in astronauts compared to other longitudinally-studied healthy cohorts and determine if there are interactions between or additive effects of the occupational health effects of spaceflight exposure and normal aging.

Charvat, Jacqueline M.↗
NASA NTRS2017

Abiotic Synthesis of Nucleic Acids: Hypochromicity and Future Research

The earliest forms of life would likely have a protocellular form, with a membrane encapsulating some form of linear charged polymer. These polymers could have enzymatic as well as genetic properties. We can simulate plausible prebiotic conditions in the laboratory to test hypotheses related to this concept. In earlier work we have shown that mononucleotides organized within a multilamellar lipid matrix can produce oligomers in the anhydrous phase of dehydration- rehydration cycles (Rajamani, 2008). If mononucleotides are in solution at millimolar concentrations, then oligomers resembling RNA are synthesized and exist in a steady state with their monomers DeGuzman, 2014). We have used conventional and novel techniques to demonstrate that secondary structures stabilized by hydrogen bonds may be present in the condensation products produced in dehydration- rehydration cycles that simulate hydrothermal fields that were present on the early Earth. Gel electrophoresis data corroborates the presence of up to 200-base pair length RNA fragments in products of Hydration-Dehydration experiments. Furthermore, hypochromicity measurements demonstrate a degree of hypochromicity found in single RNA strand of known sequence, as well as results that indicate this is true also for a sample of complementary strands of RNA. Analysis of ionic current signatures of known RNA hairpin molecule as measured using a nanopore detector indicate a significant variability in pattern, different from the signatures produced by DNA hairpin molecules. This informs how we may interpret nanopore data gathered from prebiotic simulations.

Glass, K.↗
NASA NTRS2018

Is Water Necessary for Life?

The universality of water as the solvent for life is usually justified by its role in supporting the rich organic chemistry. It has been pointed out, however, that even richer synthetic chemistry is possible in other organic solvents. Does it mean that water is not necessary for life? Here, other, essential criteria for solvent for life that have not been sufficiently considered are discussed. In biological systems, complex molecules are not only constantly synthesized but also degraded. Solvent-mediated degradation is essential for regulating cell content, preventing overcrowding and allowing for recycling organic material. Achieving a balance between synthetic and degradative processes is facile in water, but not in many other organic liquids. Thus, the so-called “water paradox” according to which water is both necessary to life and toxic to biopolymer synthesis might not be paradoxical at all. The machinery of life is based on non-covalent interactions that do not involve making or breaking chemical bonds. Their strength needs to be properly tuned. If they are too weak, there might be undesired response to natural fluctuations of physical or chemical parameters. If they are too strong, the kinetics and energetics of cellular processes could adversely influenced. The solvent must allow for balancing these interactions, which provides strong, universal constraints on the medium for life. Water influences non-covalent interactions mainly by two mechanisms. First, it reduces strong, electrostatic interactions between molecules, chemical groups or atoms carrying electric charge or dipole. Second, it induces the hydrophobic effect, the tendency to remove non-polar (hydrophobic) molecules and groups from direct contact with aqueous solution and, instead, interact with each other. In living systems, the hydrophobic effect is largely responsible for self-organization of molecules to more complex structures, such as aggregation of lipid molecules to form biological membranes and protein folding. Water exists as stable liquid in a large temperature range, and the hydrophobic effects are a consequence of the temperature insensitivity of essential properties of its liquid state. In summary, water accomplishes an amazing feat – it reduces strong interactions between dissolved species and simultaneously increases the strength of weak interactions, bringing all of them to the right range. Once we consider not only synthetic capabilities but also other required traits of the solvent for life, no viable alternative to water is currently known.

Life↗
NASA NTRS2019

Constraints on the Metabolic Activity of Microorganisms in Atacama Surface Soils Inferred from Refractory Biomarkers: Implications for Martian Habitability and Biomarker Detection

Dryness is one of the main environmental challenges to microbial survival. Understanding the threshold of microbial tolerance to extreme dryness is relevant to better constrain the environmental limits of life on Earth and critically evaluate long-term habitability models of Mars. Biomolecular proxies for microbial adaptation and growth were measured in Mars-like hyper-arid surface soils in the Atacama Desert that experience only a few millimeters of precipitation per decade, and in biologically active soils a few hundred kilometers away that experience two- to fivefold more precipitation. Diversity and abundance of lipids and other biomolecules decreased with increasing dryness. Cyclopropane fatty acids (CFAs), which are indicative of adaptive response to environmental stress and growth in bacteria, were only detected in the wetter surface soils. The ratio of trans to cis isomers of an unsaturated fatty acid, another bacterial stress indicator, decreased with increasingly dry conditions. Aspartic acid racemization ratios increased from 0.01 in the wetter soils to 0.1 in the driest soils, which is indicative of racemization rates comparable to de novo biosynthesis over long timescales (10,000 years). The content and integrity of stress proteins profiled by immunoassays were additional indicators that biomass in the driest soils is sin situ microbial growth in the driest surface soils of the Atacama, and any metabolic activity is likely to be basal for cellular repair and maintenance only. Our data add to a growing body of evidence that the driest Atacama surface soils represent a threshold for long-term habitability (i.e., growth and reproduction). These results place constraints on the potential for extant life on the surface of Mars, which is 100–1000 times drier than the driest regions in the Atacama.

Wilhelm, Mary Beth↗
NASA NTRS2014

De Novo Synthesis and Functional Study of Primitive Polypeptides in the Prebiotic Protein World

DNA, RNA and proteins within a lipid-bound membrane are the core components of life, but the order of their appearance during the origin of life is still under debate. The widely accepted RNA World hypothesis states that RNA likely emerged prior to proteins and DNA since RNA can serve both replicative and catalytic roles. While biochemists have reproduced the synthesis, polymerization, and replication of nucleotides and RNA under controlled prebiotic conditions, it is clear that such complex organic molecules were are not present in significant amounts in the the starting prebiotic material on Earth either from endogenous production or meteoritic input. In contrast, amino acids are naturally abundant in various prebiotic contexts such as carbonaceous chondrites and Urey-Miller type experiments, and many studies have demonstrated that under plausible prebiotic conditions amino acids could condense or polymerize to give rise to short peptides. These findings support the basis of a Protein World hypothesis for life, however little has been done to study the functions of such primitive peptides. Here we present our novel synthetic biology-based approach to the de novo synthesis of billions of primitive peptidesproteins derived from a limited set of naturally abundant proteinogenic amino acids such as glycine, alanine, aspartic acid, glutamic acid, valine and serine. Of these peptides, the ones with divalent metal-binding capability are of particular interest and will be screened and identified. Certain divalent metals were likely present in prebiotic environments. Not only do they coordinate well with amino acids, but they also catalyze reactions, which are difficult to achieve in organic chemistry. Since D-chiral and non-proteinogenic amino acids are also abundant in the universe and may provide insight into the pathway by which life developed, we will also discuss methods to analyze primitive peptides consisting of these amino acids and D-chiral and non-proteinogenic amino acids. By understanding this naturalistic pathway, we will be able to better understand how life developed here on Earth. Since these amino acids are abundant in universe, this work provide insight into pathways by which life developed on Earth and, by extension, the probability of life arising elsewhere.

Fujishima, Kosuke↗
NASA NTRS2014

Oxidative Stress Responses to Simulated Spaceflight in Mineralized and Marrow Compartments of Bone and Associated Vasculature

Long-term spaceflight causes profound changes to the musculoskeletal system attributable to unloading and fluid shifts in microgravity. Future space explorations beyond the earth’s magnetosphere will expose astronauts to space radiation, which may cause additional skeletal deficits that are not yet fully understood. Our long-term goals are twofold: to define the mechanisms and risk of bone loss in the spaceflight environment and to facilitate the development of effective countermeasures if necessary. Our central hypothesis is that oxidative stress plays a key role in progressive bone loss and vascular dysfunction caused by spaceflight. In animal’s models, overproduction of free radicals is associated with increased bone resorption, lower bone formation, and decrements in bone mineral density and structure which can ultimately lead to skeletal fragility. Evidence in support of a possible causative role for oxidative stress in spaceflight-induced bone loss derive from knockout and transgenic mouse studies and the use of pharmacological interventions with known anti-oxidant properties. In our studies to simulate spaceflight, 16-wk old, male C56Bl/6J mice were assigned to one of four groups: hind limb unloading to simulate weightlessness (HU), normally loaded Controls (‘NL’) (sham irradiated, no hind limb unloading), irradiated at NASA Space Radiation Laboratory ‘IR’ with 1-2Gy of (600MeV/n) alone, or in combination with protons (0.5Gy Protons/0.5Gy 56Fe), (IR) or both hind limb unloaded and irradiated, ‘HU+IR’. Mice were exposed to radiation 3 days after initiating HU and tissues harvested were 1-14 days after initiating treatments for analyses. Results from our laboratories, which employ various biochemical, gene expression, functional, and transgenic animal model methods, implicate dynamic regulation of redox-related pathways by spaceflight-related environmental factors. As one example, we found that combined HU and radiation exposure caused oxidative damage in skeletal tissues (lipid peroxidation) of wildtype mice, whereas bone from transgenic mice that overexpress human catalase in mitochondria were protected. Interestingly, marrow cells grown under culture conditions that select for endothelial progenitor cells (EPC), showed that HU but not IR reduced EPC cell migration; in contrast HU and IR each inhibited growth of marrow-derived osteoblast progenitors. Taken together, these results indicate that unloading and ionizing elicit distinct effects on progenitor and mature cells of vascular and skeletal tissue, and that oxidative damage may contribute to skeletal and vascular deficits that may emerge during extended space travel.

Globus, R. K.↗
NASA NTRS2018

MPLA as an Innovative Immune Countermeasure

Spaceflight perturbs the human immune system. Among other manifestations, crewmembers may experience latent herpes viruses reactivation due to impaired lymphocyte function, as well as allergic/hypersensitivity reactions. Considering future space travel will be of longer duration (thereby increasing stress, exposure to radiation, etc…) with no rapid return option, it is of paramount importance to develop a countermeasure(s) to immune dysregulation. Monophosphoryl lipid A (MPLA) is a derivative of lipopolysaccharide (LPS), a potent inflammatory agent that can cause septic shock. MPLA possesses the immune-stimulatory effects of LPS without the adverse inflammatory effects. We hypothesize that treating immune cells with MPLA will boost their function enough to overcome the inhibitory effects of microgravity. While MPLA has been tested as an adjuvant extensively in mice and preliminarily for human vaccines, it has never been assessed for efficacy in microgravity.

Makedonas, George↗
NASA NTRSDate not supplied

ELM: Europa Luminescence Microscope

The Europa Luminescence Microscope (ELM) is an automated fluorescence and bright-field microscope designed to meet key objectives defined in the 2016 NASA Europa Lander Study Report, including the identification and characterization of morphological biosignatures. ELM’s heritage stems from a 2U cubesat fluorescence microscope, the Fluorescence Analysis for In situ Research imager, designed and built at NASA Ames Research Center, for the autonomous study of microbial biology in low Earth orbit. For ELM implementation, a sample is autonomously manipulated with a microfluidic system using in-line 10, 1.0, and 0.2 μm pore-size filters to capture successively smaller particles for imaging. For bright-field imaging, ELM uses deep-ultraviolet, ultraviolet and visible light to image organic and inorganic structures with submicron resolution. The ability to detect biosignatures as small as 0.2 μm in size is achieved by imaging native fluorescence and using fluorescence microscopy stains to identify key structural and functional indicators of microbial life (proteins, lipids, nucleic acids). For fluorescence imaging, ELM uses 265, 370, 470, and 530 nm LEDs with five emission bands. The use of multiple excitation and emission wavelengths for native fluorescence imaging enables the detection of a wide range of molecular species and their rough classification. Excitation at 265 nm allows for the detection of smaller polycyclic aromatic hydrocarbons (PAH), aromatic amino acids, and proteins with little to no interference from mineral fluorescence, given proper emission band selection. 370 and 470 nm light excites increasingly larger PAH structures and larger aromatic biomolecules that may be present (e.g., protective pigments). Similarly, inorganic fluorescence can be characterized and separated from organic fluorescence, allowing the recognition and in some cases classification, of minerals and other abiotic particles. ELM is based upon work supported by the NASA COLDTech and ICEE-2 programs.

Richard C Quinn↗
NASA NTRS2020

Maternal provisioning gives young-of-the-year Hammerheads a head start in early life

For species that do not provide parental care after birth, excess maternal provisioning during development, beyond what is required for embryogenesis, provides offspring with resources to increase their chances of survival. Maternally derived resources are expected to be important for buffering offspring against limited food resources at birth or time needed to learn how to properly feed. Young-of-the-year (YOY) cryptic Scalloped Hammerheads (Sphyrna lewini) and Carolina Hammerheads (Sphyrna gilberti) were sampled from nurseries along the US Atlantic Coast and compared for a number of biological condition metrics across three developmental stages. Large declines in liver lipid content and hepatosomatic indices were found in neonatal sharks, using umbilical scar healing as a proxy for time since birth. Feeding commenced quickly as 96% of sharks had prey remnants in their stomachs. The combination of rapid exhaustion of maternally provided resources and high occurrence of stomachs with prey contents indicate that nursery quality, with respect to prey availability, may be important for YOY hammerhead survivorship. While externally the two species are morphologically similar, longer length-at-birth in S. lewini and higher hepatic condition in neonatal S. gilberti suggest that aspects of reproductive biology, including physiology, may differ between species. While more information is needed to distinguish life history differences between these two species, data collected from YOY may serve as a useful proxy to inform management when adult samples of cryptic species are difficult to collect.

none↗
NASA NTRSDate not supplied

Mitochondria Dysfunction Central in Driving Health Risks Associated with Spaceflight

Background: Determining the biological impact of spaceflight through novel approaches is essential to reduce the health risks to astronauts for long-term space missions. The current established health risks due to spaceflight are only reflecting known symptomatic and physiologic responses and do not reflect early onset of other potential diseases. There are many unknown variables which still need to be identified to fully understand the health impacts due to the environmental factors in space. Materials and Methods: One method to uncover potential novel biological mechanisms responsible for health risks in astronauts is by utilizing NASA’s GeneLab platform (genelab.nasa.gov). GeneLab is public repository that hosts multiple omics datasets generated from space biology experiments that include experiments flown in space, simulated cosmic radiation experiments, and simulated microgravity experiments. This presentation will provide an example of analysis and novel hypothesis generation that is being produced with GeneLab datasets. A comprehensive multi-omics approach was implemented correlating transcriptomics, proteomics, metabolomics, and methylation analysis. Results: We found that cells have stronger overall biological response than the tissues to spaceflight, with mitochondrial activity and innate immunity pathways being heavily impacted. NASA Twin Study results are consistent with a specific alteration in mitochondrial ATP production. Our results indicate that the space environment can directly induce mitochondrial damage, with mitochondrial dysfunctions being a cause for chronic inflammation and both being involved in the development of metabolic disorders that cause changes in lipid metabolism. We also found biological changes occurring during spaceflight with cell cycle, circadian rhythm and olfactory activity pathways can also influence and be influenced by alterations on mitochondrial activity

Afshin Beheshti↗
NASA NTRSDate not supplied

Europa Luminescence Microscope

The Europa Luminescence Microscope (ELM) is an automated fluorescence and dark-field mi-croscope designed to meet key objectives defined in the NASA Europa Lander Study Report, includ-ing the identification and characterization of morphological biosignatures. ELM’s heritage stems from a 2U cubesat fluorescence microscope, the Fluorescence Analysis for In-situ Research (FLAIR) im-ager, designed and built at NASA Ames Research Center, for the autonomous study of microbial bi-ology in low Earth orbit. For the ELM implementation, a sample is autonomously manipulated with a microfluidic system using in-line filter sets to capture successively smaller particles on 10, 1.0, and 0.1 µm pore-size filters for imaging. For darkfield imaging, ELM uses ultraviolet and visible light to image organic and inorganic structures with submicron resolution. The ability to detect structural and chemical biosignatures as small as 0.2 µm in size is achieved by imaging native fluorescence and us-ing fluorescence microscopy stains to identify key molecular and structural indicators of microbial life (proteins, lipids, nucleic acids). To excite fluorescence, ELM uses LEDs with wavelengths centered near 265, 370, 470, and 530 nm and five emission bands. The use of multiple excitation and emission wavelengths for native fluorescence imaging not only enables the detection of different molecular species, but also their rough classification. Excitation at 265 nm allows for the detection of smaller polyaromatic hydrocarbons (PAHs; 1-5 rings), aromatic amino acids, and proteins with little to no interference from mineral fluorescence, given proper emission band selection. 370 and 470 nm light excites increasingly larger PAH structures (e.g., coronene) and larger aromatic biomolecules that may be present (e.g., protective pigments). Similarly, inorganic fluorescence can be characterized and sep-arated from organic fluorescence, allowing the recognition and in some cases classification, of miner-als and other abiotic particles. ELM is based upon work supported by the NASA COLDTech and ICEE-2 programs.

Microscope↗
NASA NTRSDate not supplied

Advancing Launch Readiness of the Europa Light Microscope

The search for life in our solar system relies on our ability to measure key biomarkers in situ and identify them as likely of biotic origin. One class of biomarkers that would be indicative of life is cell-like features such as the colocalization of key biochemicals, spatial organization of native UV fluorescent compounds, and polarization across membrane compartments. The Europa Lander Science Definition Team has emphasized the need for a light microscope to search for these features on Europa, and the Europa Light Microscope (ELM) has been developed to address this need. This work presents the development towards high launch readiness of ELM by demonstrating its optical resolution under brightfield illumination, deep UV fluorescence imaging of unstained living samples, and multicolor imaging of samples stained with exogenous fluorophores targeting proteins, lipids, and nucleic acids. This work also seeks to extend the capabilities of ELM by incorporating fluorescence-based assays for membrane potential as a key biomarker for living cellular material.

Pavel Erich Zivanovic Klier↗
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), has typically limited machine learning (ML) in space studies and further study of 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 (RNAseq) data from 6 mouse liver GeneLab datasets (GLDS) with a total of 113 spaceflight and ground-control samples to determine top features relevant to spaceflight including the effect of radiation exposure. Data was normalized within each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. The top MRMR features were used to predict spaceflight vs. ground-control samples using a Random Forest (RF) classifier with 5-fold cross validation (CV). The ML-based gene sets were further compared against differential gene expression results from individual GLDS. CV training using the top 100 MRMR genes show averages of 86% accuracy and 0.95 AUC value on the validation set over 5 folds (Figure 1A). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 811 or 68 DEGs overlapping between at least 2 or 3 studies, respectively (Figure 1B). Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism. Set analysis between the MRMR features and the DEGs showed 60 or 8 genes overlapping with at least 1 or 2 studies, respectively. MRMR feature selection and ensemble ML methods (e.g. RF) improve performance relative to a Naïve Bayes classifier when NGS data sets are analyzed. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise ratio. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from RNASeq analysis. Non-intersecting sets introduce opportunity to explore spaceflight relevant genes and implementing ML methods across existing NGS datasets may overcome sample size limitations. ML coupled with existing analytical methods enhances understanding of disease by revealing common underlying pathways across datasets.

Machine Learning↗
NASA NTRSDate not supplied

The membrane bioreactor (MBR): A hybrid technology for bioregenerative wastewater treatment and resource recovery in space

Extraplanetary surface habitat life support systems (LSS) on the Moon and Mars, as well as long-duration space travel, will require novel capabilities to withstand anticipated unique, harsh conditions. In order to provide safe, habitable environments for the crew, water purification and waste processing systems will be required to treat all sources of water (condensate, Sabatier, urine, hygiene, fecal, food waste) in order to achieve the necessary levels of recovery needed to sustain life over the long-duration missions. The ability to recycle organic wastes creates an opportunity to recover critical elements (e.g., C, H, O, N, P) for subsequent food production, water purification, and atmospheric regeneration. Bioregenerative systems mimic functions of nature in engineered systems, or bioreactors, utilizing combination of prokaryotes, eukaryotes and archaea. While these systems are commonly used on Earth for wastewater treatment, bioreactors for space travel face additional challenges. Terrestrial bioreactors often rely on gravitational settling of dense flocs and granules for cell retention. For micro- or partial-gravity environments, density differential alone will not be adequate for cell retention; a gravity-independent means for cell retention is crucial. The membrane bioreactor represents the state of the art in wastewater treatment. This hybrid system combines biological processes with membrane filtration to achieve performance beyond what each can accomplish individually. The complete cell retention in an MBR allows for the decoupling of hydraulic retention time (HRT) and solids retention time (SRT), which result in a high-thruput, compact, treatment system. The Bioregenerative Water Technology Team at NASA Kennedy Space Center and the University of South Florida has developed a bioregenerative platform based on the hybrid MBR technology. The overall architecture is compact, modular, flexible, and adaptable to mission evolutions. The main subsystems of the bioregenerative architecture include: 1) Anaerobic membrane bioreactor (AnMBR): Also termed the Organic processor assembly (OPA), the function of the AnMBR is to treat organic wastes such as fecal and food wastes. These wastes are characterized by a concentration of suspended solids comprised of carbohydrates, proteins and lipids. The assigned function of the AnMBR is to break down and covert suspended solids to biogas (methane, hydrogen and carbon dioxide), reduce effluent chemical oxygen demand (COD), liberate organically-bound nutrients, and remove pathogenic organisms. 2) Phototrophic membrane bioreactor (PMBR): The PMBR is comprised of a co-culture of microalgae and bacteria. The assigned function of the PMBR is to polish the permeate of the AnMBR to further remove dissolved organic carbon, manage nutrients (nitrogen transformation, load dampening), and perform air revitalization. 3) Food processor assembly (FPA): The FPA is a food production platform (prokaryotic or eukaryotic), fueled by outputs from the AnMBR, or PMBR. For the presentation, we will describe each step of the bioregenerative architecture, and present performance data from extended trials treating analog and real metabolic wastes.

Bioreactor↗
NASA NTRSDate not supplied

Evaluation of the spaceflight cardiovascular risk using the clinical prediction model Astro-CHARM and the NASA Radiation Risk Model

- Exposure to space radiation may pose a risk for cardiovascular diseases (CVD) during and after spaceflight. - The CVD risk should be quantified to plan future long-duration space missions such as the manned flight to Mars. - Assessing CVD risk is complicated by its multifactorial nature, where an individual’s risk is strongly influenced by risk factors such as family history, blood pressure, and lipid profiles. - In the NASA Space Radiation Risk Model, the metrics of radiation exposure-induced cases (REIC) and radiation exposure-induced death (REID) are used to quantify the risk attributable to radiation over a lifetime after exposure.

Ianik Plante↗
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

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

Murine Host-gut Microbiota Interactions are Modulated During Spaceflight

The rodent habitat on the International Space Station has provided critical insight into the impact of spaceflight on mammalian physiology. These effects include dysfunction of carbohydrate, steroid and lipid metabolism, and immune response, as well as induction of symptoms characteristic of liver disease, insulin resistance, osteopenia and myopathy, which are anticipated to intensify over long-duration spaceflight. Although these physiological responses can involve the microbiome, the host-microorganism interactions during spaceflight are still largely unknown. NASA GeneLab curates a wide range of space research data and the current work harnesses GeneLab multi’omic data from recent Rodent Research studies to explore changes to gut microbiota during spaceflight and their associations with host physiology when compared to ground controls. Using a hybrid analysis of DNA barcoding and whole genome shotgun data, an array of bacteria, fungi and nematodes could be identified at species level, and significant differences in relative abundances associated with spaceflight. Functional prediction based on differential abundance of species and metagenome gene inventories as well as metatranscriptomic gene expression at the host-gut microbiome interface implicate microbiota interactions could contribute to spaceflight pathology. Harnessing carefully curated publicly available data, such as from Genelab, to generate multi‘omic space science discoveries can help decipher the complex host-microbiome interactions that influence both health on Earth and the feasibility of long-duration spaceflight.

Microbiome↗