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All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Topical: Stem Cell-Based Tissue Regenerative Health in Space

The maintenance of healthy adult tissues in mammals requires a complex homeostasis of molecular, cellular, tissue, and metabolic processes which are fundamentally different from the development and aging processes that bookend life. Cellular homeostasis in the adult requires molecular maintenance and repair of non-dividing cells such as cardiomyocytes and neurons, but also stem cell-based tissue regeneration via direct replacement of cell loss, such as in the blood, immune system, bone, skin, liver, intestine, and other tissues. Because stem cell-based tissue regenerative health requires constant proliferation and differentiation of stem cell progenitors in the bone marrow, and other adult stem cell niches, it is uniquely sensitive to the stresses of spaceflight including exposure to space radiation and mechanical unloading in microgravity. A key central hypothesis in this field is that those spaceflight stress factors can have profound negative effects on long-term tissue regenerative health mediated by adult stem cells, and that unmitigated, they may lead to premature tissue aging and functional failure. Specifically, it is thought that mechanical unloading due to lack of weight-bearing in space reduces mitogenic mechanotransduction necessary to promote adult stem cell proliferation and differentiation, and that space radiation can also lead to activation of cell cycle arrest mechanisms, further reducing adult stem cell proliferation. These hypotheses are being tested in low earth orbit(LEO)using a variety of cellular and whole organism tissue model systems, suggesting that spaceflight consistently interferes with stem cell tissue regenerative processes such as in mammalian embryoid bodies, regenerating newt tails, and mouse bone marrow hematopoietic and osteoprogenitor cells. Furthermore, potential molecular mechanisms integrating both space radiation and mechanical disuse via oxidative stress and the cell cycle inhibitor Cdkn1aare now under study using single cell (scRNAseq) expressome analysis of bone marrow osteoprogenitors, both under stretch loading, and spaceflight conditions including in various mouse transgenic null backgrounds relevant to these mechanisms. Future work in the area of tissue regenerative health in space for the coming decade should seek to understand the responses the various tissue regenerative stem cell niches in humans and relevant model organisms, and how they respond to long-term exposure to the space environment. Special emphases of future work should be on how regenerative deficits in whole-organism stem cell niches may lead to tissue degeneration and premature aging, and on the long-term proliferation and differentiation of stem cell derived tissue organoid models in the deep space environment outside of LEO.

Eduardo A C Almeida↗
DOE OSTI2023

Shortwave‐Infrared‐Emitting Nanoprobes for CD8 Targeting and In Vivo Imaging of Cytotoxic T Cells in Breast Cancer

Checkpoint immunotherapy has made great strides in the treatment of solid tumors, but many patients do not respond to immune checkpoint inhibitors. Identification of tumor‐infiltrating cytotoxic T cells (CTLs) has the potential to stratify patients and monitor immunotherapy responses. In this study, the design of cluster of differentiation (CD8 + ) T cell‐targeted nanoprobes that emit shortwave infrared (SWIR) light in the second tissue‐transparent window for noninvasive, real‐time imaging of CTLs in murine models of breast cancer is presented. SWIR‐emitting rare‐earth nanoparticles encapsulated in human serum albumin are conjugated with anti‐CD8α to target CTLs with high specificity. CTL targeting is validated in vitro through binding of nanoprobes to primary mouse CTLs. The potential for the use of SWIR fluorescence intensity to determine CTL presence is validated in two syngeneic mammary fat pad tumor models, EMT6 and 4T1, which differ in immune infiltration. SWIR imaging using CD8‐targeted nanoprobes successfully identifies the presence of CTLs in the more immunogenic EMT6 model, while imaging confirms the lack of substantial immune infiltration in the nonimmunogenic 4T1 model. In this work, the opportunity for SWIR imaging using CD8‐targeted nanoprobes to assess CTL infiltration in tumors for the stratification and monitoring of responders to checkpoint immunotherapy is highlighted.

Shah, Jay V.↗
DOE OSTI2023

Intermediate Molecular Phenotypes to Identify Genetic Markers of Anthracycline-Induced Cardiotoxicity Risk

Cardiotoxicity due to anthracyclines (CDA) affects cancer patients, but we cannot predict who may suffer from this complication. CDA is a complex trait with a polygenic component that is mainly unidentified. We propose that levels of intermediate molecular phenotypes (IMPs) in the myocardium associated with histopathological damage could explain CDA susceptibility, so variants of genes encoding these IMPs could identify patients susceptible to this complication. Thus, a genetically heterogeneous cohort of mice (n = 165) generated by backcrossing were treated with doxorubicin and docetaxel. We quantified heart fibrosis using an Ariol slide scanner and intramyocardial levels of IMPs using multiplex bead arrays and QPCR. We identified quantitative trait loci linked to IMPs (ipQTLs) and cdaQTLs via linkage analysis. In three cancer patient cohorts, CDA was quantified using echocardiography or Cardiac Magnetic Resonance. CDA behaves as a complex trait in the mouse cohort. IMP levels in the myocardium were associated with CDA. ipQTLs integrated into genetic models with cdaQTLs account for more CDA phenotypic variation than that explained by cda-QTLs alone. Allelic forms of genes encoding IMPs associated with CDA in mice, including AKT1, MAPK14, MAPK8, STAT3, CAS3, and TP53, are genetic determinants of CDA in patients. Two genetic risk scores for pediatric patients (n = 71) and women with breast cancer (n = 420) were generated using machine-learning Least Absolute Shrinkage and Selection Operator (LASSO) regression. Thus, IMPs associated with heart damage identify genetic markers of CDA risk, thereby allowing more personalized patient management.

60 APPLIED LIFE SCIENCES↗
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↗
DOE OSTI2020

Dual-Purpose Canister Filling Demonstration Project Progress Report

This report discusses the initial progress made at the Oak Ridge National Laboratory to support direct disposal of dual-purpose canisters (DPCs) using filler materials to demonstrate that the probability of criticality in DPCs during disposal to be below the probability for inclusion in a repository performance assessment. In the initial phase of a multi-phase effort that will result in a full-scale demonstration, a computational fluid dynamics (CFD) model was developed to gauge the filling process and to uncover any unforeseen issues. The initial filling simulations of the lower region (mouse holes) of a prototypic DPC show successful removal of the inner space voids and smooth, even progression of the liquid level. In the initial phase, flow through a pipe that is similar to the drain pipe in a DPC will be investigated separately to gain valuable insight of flow regime inside a pipe. The initial experimental setups for validating the computational filling model have been designed, and the various assembly parts are being procured. The experience gained from the initial experiments will be applied to the next steps toward a full-scale demonstration and to the validation of multiphysics filling simulation models.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Adult dental epithelial stem cell-derived organoids deposit hydroxylapatite biomineral

Ameloblasts are specialized cells derived from the dental epithelium that produce enamel, a hierarchically structured tissue comprised of highly elongated hydroxylapatite (OHAp) crystallites. The unique function of the epithelial cells synthesizing crystallites and assembling them in a mechanically robust structure is not fully elucidated yet, partly due to limitations with in vitro experimental models. Herein, we demonstrate the ability to generate mineralizing dental epithelial organoids (DEOs) from adult dental epithelial stem cells (aDESCs) isolated from mouse incisor tissues. DEOs expressed ameloblast markers, could be maintained for more than five months (11 passages) in vitro in media containing modulators of Wnt, Egf, Bmp, Fgf and Notch signaling pathways, and were amenable to cryostorage. When transplanted underneath murine kidney capsules, organoids produced OHAp crystallites similar in composition, size, and shape to mineralized dental tissues, including some enamel-like elongated crystals. DEOs are thus a powerful in vitro model to study mineralization process by dental epithelium, which can pave the way to understanding amelogenesis and developing regenerative therapy of enamel.

59 BASIC BIOLOGICAL SCIENCES↗
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

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↗
DOE OSTI2022

Transcriptional pathways linked to fetal and maternal hepatic dysfunction caused by gestational exposure to perfluorooctanoic acid (PFOA) or hexafluoropropylene oxide-dimer acid (HFPO-DA or GenX) in CD-1 mice

Per- and polyfluoroalkyl substances (PFAS) comprise a diverse class of chemicals used in industrial processes, consumer products, and fire-fighting foams which have become environmental pollutants of concern due to their persistence, ubiquity, and associations with adverse human health outcomes, including in pregnant persons and their offspring. Multiple PFAS are associated with adverse liver outcomes in adult humans and toxicological models, but effects on the developing liver are not fully described. Here we performed transcriptomic analyses in the mouse to investigate the molecular mechanisms of hepatic toxicity in the dam and its fetus after exposure to two different PFAS, perfluorooctanoic acid (PFOA) and its replacement, hexafluoropropylene oxide-dimer acid (HFPO-DA, known as GenX). Pregnant CD-1 mice were exposed via oral gavage from embryonic day (E) 1.5-17.5 to PFOA (0, 1, or 5 mg/kg-d) or GenX (0, 2, or 10 mg/kg-d). Maternal and fetal liver RNA was isolated (N = 5 per dose/group) and the transcriptome analyzed by Affymetrix Array. Differentially expressed genes (DEG) and differentially enriched pathways (DEP) were obtained. DEG patterns were similar in maternal liver for 5 mg/kg PFOA, 2 mg/kg GenX, and 10 mg/kg GenX (R2: 0.46-0.66). DEG patterns were similar across all 4 dose groups in fetal liver (R2: 0.59-0.81). There were more DEGs in fetal liver compared to maternal liver at the low doses for both PFOA (fetal = 69, maternal = 8) and GenX (fetal = 154, maternal = 93). Upregulated DEPs identified across all groups included Fatty Acid Metabolism, Peroxisome, Oxidative Phosphorylation, Adipogenesis, and Bile Acid Metabolism. Transcriptome-phenotype correlation analyses demonstrated > 1000 maternal liver DEGs were significantly correlated with maternal relative liver weight (R 2 >0.92). These findings show shared biological pathways of liver toxicity for PFOA and GenX in maternal and fetal livers in CD-1 mice. The limited overlap in specific DEGs between the dam and fetus suggests the developing liver responds differently than the adult liver to these chemical stressors. This work helps define mechanisms of hepatic toxicity of two structurally unique PFAS and may help predict latent consequences of developmental exposure.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2016

Analysis of Adult Female Mouse (Mus musculus) Group Behavior on the International Space Station (ISS)

As interest in long duration effects of space habitation increases, understanding the behavior of model organisms living within the habitats engineered to fly them is vital for designing, validating, and interpreting future spaceflight studies. A handful of papers have previously reported behavior of mice and rats in the weightless environment of space. The Rodent Research Hardware and Operations Validation (Rodent Research-1; RR1) utilized the Rodent Habitat (RH) developed at NASA Ames Research Center to fly mice on the ISS (International Space Station). Ten adult (16-week-old) female C57BL/6 mice were launched on September 21st, 2014 in an unmanned Dragon Capsule, and spent 37 days in microgravity. Here we report group behavioral phenotypes of the RR1 Flight (FLT) and environment-matched Ground Control (GC) mice in the Rodent Habitat (RH) during this long-duration flight. Video was recorded for 33 days on the ISS, permitting daily assessments of overall health and well-being of the mice, and providing a valuable repository for detailed behavioral analysis. We previously reported that, as compared to GC mice, RR1 FLT mice exhibited the same range of behaviors, including eating, drinking, exploration, self- and allo-grooming, and social interactions at similar or greater levels of occurrence. Overall activity was greater in FLT as compared to GC mice, with spontaneous ambulatory behavior, including organized 'circling' or 'race-tracking' behavior that emerged within the first few days of flight following a common developmental sequence, and comprised the primary dark cycle activity persisting throughout the remainder of the experiment. Participation by individual mice increased dramatically over the course of the flight. Here we present a detailed analysis of 'race-tracking' behavior in which we quantified: (1) Complete lap rotations by individual mice; (2) Numbers of collisions between circling mice; (3) Lap directionality; and (4) Recruitment of mice into a group phenotype. This analysis contributes to the first NASA long-duration study of rodent behavior, providing evidence for the emergence of a distinctive, organized group behavior unique to the weightless space environment.

Behavior in Space↗
NASA NTRS1992

Effect of microgravity and mechanical stimulation on the in vitro mineralization and resorption of fetal mouse long bones (7-IML-1)

Mechanical forces play an important role in the differentiation, growth, and remodeling of skeletal tissues. An increase in the normal loading pattern of the skeleton leads to an increase in bone mass. An overall decrease in the functional load exerted on the skeleton produces mineral loss and osteoporosis. However, the responses of the skeletal tissue cells to various loading conditions are still largely unresolved, as is the mechanism of the cellular response to changed mechanical environment. Using an in vitro approach, we hope to avoid some problems encountered in the use of in vivo animal and man models, which have been extensively used in the past. In a number of experiments we have demonstrated that 16 and 17 day old fetal mouse long bone rudiments (metatarsalia), cultured in a liquid culture medium, are very suitable to study mineralization and resorption, respectively. We have also demonstrated that under hydrostatic compression, mineralization is increased while resorption is decreased. Culture of long bone rudiments under noncompressed control conditions can be regarded as a situation of partial unloading, showing some phenomena of a disuse situation. Under microgravity conditions, responses of osteoblasts and chondrocytes (involved in mineralization) and osteoclasts (involved in mineral resorption), to culture with and without compression, may be much more outspoken. This will have advantages for the study and the interpretation of the role of cellular events in the process of mineralization and resorption of developing skeletal tissues under various loading conditions. The BONES Experiment is carried out in four type I/O and four type I/E containers. Various aspects of the investigation are discussed.

Veldhuijzen, J. Paul↗
NASA NTRS2005

Development and Function of the Mouse Vestibular System in the Absence of Gravity Perception

The hypothesis that was tested in this research was that the absence of gravity perception, such as would occur in space, would affect the development and function of the vestibular and central nervous systems. Further, we postulated that these effects would be more significant at specific stages of post-natal development of the animal. We also proposed the use of molecular genetic approaches that would provide important information as to the hierarchy of gene function during the development and subsequent function of the vestibular system. The tilted (tlt) mutant mouse has been characterized as lacking the ability to provide sensory input to the gravity receptors. The tlt/tlt mutant mice were a particularly attractive model for the study of vestibular function since the primary defect was limited to the receptor part of the vestibular system, and there were no detectable abnormal phenotypes in other organ systems. The goal of the proposed studies was to assess immediate and delayed effects of the lack of gravity perception on the vestibular system. Particular attention was paid to characterizing primarily affected periods of vestibular morphogenesis, and to identifying downstream genetic pathways that are altered in the CNS of the tlt/tlt mutant mouse. The specific aims were: (1) to characterize the postnatal morphogenesis of the CNS in the tlt mutant mouse, using detailed morphometric analysis of isolated vestibular ganglia and brain tissue at different stages of postnatal development and assessment of apoptotic cell death; (2) to examine the expression of selected genes implicated by mutational analysis to be important in vestibular development or function by in situ hybridization or immunohistochemistry in the mutant mice; and (3) to identify other genes involved in vestibular development and function, using differential cloning strategies to isolate genes whose expression is changed in the mutant versus normal vestibular system.

Wolgemuth, Debra J.↗
NASA NTRS2002

Size distribution of retrovirally marked lineages matches prediction from population measurements of cell cycle behavior

Mechanisms that regulate neuron production in the developing mouse neocortex were examined by using a retroviral lineage marking method to determine the sizes of the lineages remaining in the proliferating population of the ventricular zone during the period of neuron production. The distribution of clade sizes obtained experimentally in four different injection-survival paradigms (E11-E13, E11-E14, E11-E15, and E12-E15) from a total of over 500 labeled lineages was compared with that obtained from three models in which the average behavior of the proliferating population [i.e., the proportion of cells remaining in the proliferative population (P) vs. that exiting the proliferative population (Q)] was quantitatively related to lineage size distribution. In model 1, different proportions of asymmetric, symmetric terminal, and symmetric nonterminal cell divisions coexisted during the entire developmental period. In model 2, the developmental period was divided into two epochs: During the first, asymmetric and symmetric nonterminal cell divisions occurred, but, during the second, asymmetric and symmetric terminal cell divisions occurred. In model 3, the shifts in P and Q are accounted for by changes in the proportions of the two types of symmetric cell divisions without the inclusion of any asymmetric cell divisions. The results obtained from the retroviral experiments were well accounted for by model 1 but not by model 2 or 3. These findings demonstrate that: 1) asymmetric and both types of symmetric cell divisions coexist during the entire period of neurogenesis in the mouse, 2) neuron production is regulated in the proliferative population by the independent decisions of the two daughter cells to reenter S phase, and 3) neurons are produced by both asymmetric and symmetric terminal cell divisions. In addition, the findings mean that cell death and/or tangential movements of cells in the proliferative population occur at only a low rate and that there are no proliferating lineages "reserved" to make particular laminae or cell types. Copyright 2002 Wiley-Liss, Inc.

NASA Discipline Developmental Biology↗
NASA NTRS2023

Applicability of the Nasa Galactic Cosmic Ray Simulator for Mice, Rats, and Minipigs

Space radiation poses multiple health risks to astronauts including cancer, cardiovascular disease, and damage to the central nervous system. Radiation from galactic cosmic rays (GCR) is the main source of these risks for missions beyond low Earth orbit to the moon and beyond. A galactic cosmic ray simulator (GCRsim) was developed at the NASA Space Radiation Laboratory to better understand and mitigate these risks by simulating the complex mixed field of the GCR environment at a ground-based facility. The GCRsim delivers a radiation field to cell and animal models in a laboratory setting that is comparable to the shielded radiation environment within internal organs of astronauts in deep space missions. Previous verification studies using Monte Carlo simulations with mouse (Digimouse) and rat (Digirat) digital phantoms showed that the GCRsim yields acceptable dose homogeneity within the internal organs of these animals. Spectral characteristics of the intended space radiation environment are also accurately reproduced at radiosensitive sites in both phantoms. In this work, similar Monte Carlo simulations were performed in a minipig model (Digipig) to show the applicability and limitations of the current GCRsim for larger animal model systems. The results showed dose homogeneity in internal organs of the three animal models with GCRsim irradiation. While slightly lower average doses were seen in the minipig compared to the external beam dose, the individual voxel doses in slices of the phantoms were found to be within 6%–8% of the average voxel dose, establishing that the GCRsim can still provide a relatively homogeneous irradiation within larger animals. Furthermore, simulated dose and fluence spectral results across the relevant linear energy transfer (LET) range agreed well with the reference field in the most relevant regions of the spectra, further verifying that the GCRsim beam represents the reference field in larger animals.

Galactic cosmic rays↗
NASA NTRS1999

NSBRI Radiation Effects: Carcinogenesis in Sprague-Dawley Rats Irradiated with Iron Ions, Protons, or Photons

Our ability to confidently develop appropriate countermeasures for radiations in space in terms of shielding and design of a spacecraft, the mission scenario, or chemoprevention is severely limited by the uncertainties in both the risk itself and the change in that risk with intervention. Despite the fact that the risk of carcinogenesis from exposures of personnel to radiations on long-term missions is considered one of the worst hazards in space, only a limited amount of in-vivo data exist for tumor induction from exposures to protons or energetic heavy ions (HZEs) at lower doses. The most extensive work remains the landmark study. for tumor development in the harderian gland of the mouse. The objective of this study is to characterize the level of risk for tumor induction in another relevant animal model. Subsequent experiments are designed to test the hypothesis that the level of risk can be reduced by pharmaceutical intervention in the promoting and progressing stages of the disease rather than in the initiating stage. The work presented here results from a cooperative effort on the part of investigators from two projects of the Radiation-Effects Team of the National Space Biomedical Research Institute (NSBRI). The collaborating projects are the Core Project which is investigating the risk of carcinogenesis in Sprague-Dawley rats and the Chemoprevention Project which is investigating the ability of Tamoxifen to reduce the number of malignant tumors in the irradiated animals. Research at the cellular and subcellular levels is being conducted in two other projects of the Radiation-Effects Team, Cytogenetics with J. R. Williams as Principal Investigator and Mutations from Repeated DNA Sequences. Results for these other projects also are being presented at this Workshop.

Dicello, J. F.↗
NASA NTRS2011

Hurricane Data Analysis Tool

In order to facilitate Earth science data access, the NASA Goddard Earth Sciences Data Information Services Center (GES DISC) has developed a web prototype, the Hurricane Data Analysis Tool (HDAT; URL: http://disc.gsfc.nasa.gov/HDAT), to allow users to conduct online visualization and analysis of several remote sensing and model datasets for educational activities and studies of tropical cyclones and other weather phenomena. With a web browser and few mouse clicks, users can have a full access to terabytes of data and generate 2-D or time-series plots and animation without downloading any software and data. HDAT includes data from the NASA Tropical Rainfall Measuring Mission (TRMM), the NASA Quick Scatterometer(QuikSCAT) and NECP Reanalysis, and the NCEP/CPC half-hourly, 4-km Global (60 N - 60 S) IR Dataset. The GES DISC archives TRMM data. The daily global rainfall product derived from the 3-hourly multi-satellite precipitation product (3B42 V6) is available in HDAT. The TRMM Microwave Imager (TMI) sea surface temperature from the Remote Sensing Systems is in HDAT as well. The NASA QuikSCAT ocean surface wind and the NCEP Reanalysis provide ocean surface and atmospheric conditions, respectively. The global merged IR product, also known as, the NCEP/CPC half-hourly, 4-km Global (60 N -60 S) IR Dataset, is one of TRMM ancillary datasets. They are globally-merged pixel-resolution IR brightness temperature data (equivalent blackbody temperatures), merged from all available geostationary satellites (GOES-8/10, METEOSAT-7/5 & GMS). The GES DISC has collected over 10 years of the data beginning from February of 2000. This high temporal resolution (every 30 minutes) dataset not only provides additional background information to TRMM and other satellite missions, but also allows observing a wide range of meteorological phenomena from space, such as, hurricanes, typhoons, tropical cyclones, mesoscale convection system, etc. Basic functions include selection of area of interest and time, single imagery, overlay of two different products, animation,a time skip capability and different image size outputs. Users can save an animation as a file (animated gif) and import it in other presentation software, such as, Microsoft PowerPoint. Since the tool can directly access the real data, more features and functionality can be added in the future.

Liu, Zhong↗
DOE OSTI2022

Harmonizing model organism data in the Alliance of Genome Resources

The Alliance of Genome Resources (the Alliance) is a combined effort of 7 knowledgebase projects: Saccharomyces Genome Database, WormBase, FlyBase, Mouse Genome Database, the Zebrafish Information Network, Rat Genome Database, and the Gene Ontology Resource. The Alliance seeks to provide several benefits: better service to the various communities served by these projects; a harmonized view of data for all biomedical researchers, bioinformaticians, clinicians, and students; and a more sustainable infrastructure. The Alliance has harmonized cross-organism data to provide useful comparative views of gene function, gene expression, and human disease relevance. The basis of the comparative views is shared calls of orthology relationships and the use of common ontologies. The key types of data are alleles and variants, gene function based on gene ontology annotations, phenotypes, association to human disease, gene expression, protein–protein and genetic interactions, and participation in pathways. The information is presented on uniform gene pages that allow facile summarization of information about each gene in each of the 7 organisms covered (budding yeast, roundworm Caenorhabditis elegans, fruit fly, house mouse, zebrafish, brown rat, and human). The harmonized knowledge is freely available on the alliancegenome.org portal, as downloadable files, and by APIs. We expect other existing and emerging knowledge bases to join in the effort to provide the union of useful data and features that each knowledge base currently provides.

59 BASIC BIOLOGICAL SCIENCES↗