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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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18 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Skeletal Micro-RNA Responses to Simulated Weightlessness

Astronauts lose bone structure during long-duration spaceflight. These changes are due, in part, to insufficient bone formation by the osteoblast cells. Little is known about the role that small (approximately 22 nucleotides), non-coding micro-RNAs (miRNAs) play in the osteoblast response to microgravity. We hypothesize that osteoblast-lineage cells alter their miRNA status during microgravity exposure, contributing to impaired bone formation during weightlessness. To simulate weightlessness, female mice (C57BL/6, Charles River, 10 weeks of age, n = 7) were hindlimb unloaded up to 12 days. Age-matched and normally ambulating mice served as controls (n=7). To assess the expression of miRNAs in skeletal tissue, the tibia was collected ex vivo and cleaned of soft-tissue and marrow. Total RNA was collected from tibial bone and relative abundance was measured for miRNAs of interest using quantitative real time PCR array looking at 372 unique and well-characterized mature miRNAs using the delta-delta Ct method. Transcripts of interest were normalized to an average of 6 reference RNAs. Preliminary results show that hindlimb unloading decreased the expression of 14 miRNAs to less than 0.5 times that of the control levels and increased the expression of 5 miRNAs relative to the control mice between 1.2-1.5-fold (p less than 0.05, respectively). Using the miRSystem we assessed overlapping target genes predicted to be regulated by multiple members of the 19 differentially expressed miRNAs as well as in silico predicted targets of our individual miRNAs. Our miRsystem results indicated that a number of our differentially expressed miRNAs were regulators of genes related to the Wnt-Beta Catenin pathway-a known regulator of bone health-and, interestingly, the estrogen-mediated cell-cycle regulation pathway, which may indicate that simulated weightlessness modulated systemic hormonal levels or hormonal transduction that additionally contributed to bone loss. We plan to follow up these findings by measuring gene expression of miRNA-regulated genes within these two pathways with the aim of furthering our understanding of the function of miRNAs in the skeletal response to spaceflight.

musculoskeletal system↗
NASA NTRS2016

Regulation of Bone Formation During Disuse by miRNA

Astronauts lose bone structure during long-duration spaceflight. These changes are due, in part, to insufficient bone formation by the osteoblast cells. Little is known about the role that small (approximately 22 nucleotide), non-coding micro-RNAs (miRNAs) play in the osteoblast response to microgravity. We hypothesize that osteoblast-lineage cells alter their miRNA status during microgravity exposure, contributing to impaired bone formation during weightlessness. To simulate weightlessness, female mice (C57BL/6, Charles River, 10 weeks of age, n = 6) were hindlimb unloaded for 12 days. Age-matched and normally ambulating mice served as controls (n=6). To assess the expression of miRNAs in skeletal tissue, the right and left tibia of the mice were collected ex vivo and cleaned of soft-tissue and marrow. Total RNA was collected from tibial bone and relative abundance was measured for miRNAs of interest using quantitative real time PCR array looking at 372 unique and well-characterized mature miRNAs using the delta-delta Ct method. Transcripts of interest were normalized to an average of 6 reference RNAs. Preliminary results show that hindlimb unloading decreased the expression of 14 miRNAs to less than 1.4-2.9X control levels and increased the expression of 5 miRNAs relative to the control mice greater than 1-2-1.5X (p less than 0.05, respectively). Using the miRSystem we assessed overlapping target genes predicted to be regulated by multiple members of the 19 differentially expressed miRNAs as well as in silico predicted targets of our individual miRNAs. Our miRSystem results indicated that a number of our differentially expressed miRNAs were regulators of genes related to the Wnt-Beta Catenin pathway-a known regulator of bone health-and, interestingly, the estrogen-mediated cell-cycle regulation pathway, which may indicate that simulated weightlessness induced systemic hormonal changes that contributed to bone loss. We plan to follow up these findings by measuring gene expression of miRNA-regulated genes within these two pathways with the aim of furthering our understanding of the function of miRNAs in the skeletal response to spaceflight.

bone↗
NASA NTRS1996

Sense and antisense transcripts of the developmentally regulated murine hsp70.2 gene are expressed in distinct and only partially overlapping areas in the adult brain

We have examined the spatial pattern of expression of a member of the hsp70 gene family, hsp70.2, in the mouse central nervous system. Surprisingly, RNA blot analysis and in situ hybridization revealed abundant expression of an 'antisense' hsp70.2 transcript in several areas of adult mouse brain. Two different transcripts recognized by sense and antisense riboprobes for the hsp70.2 gene were expressed in distinct and only partially overlapping neuronal populations. RNA blot analysis revealed low levels of the 2.7 kb transcript of hsp70.2 in several areas of the brain, with highest signal in the hippocampus. Abundant expression of a slightly larger (approximately 2.8 kb) 'antisense' transcript was detected in several brain regions, notably in the brainstem, cerebellum, mesencephalic tectum, thalamus, cortex, and hippocampus. In situ hybridization revealed that the sense and antisense transcripts were both predominantly neuronal and localized to the same cell types in the granular layer of the cerebellum, trapezoid nucleus of the superior olivary complex, locus coeruleus and hippocampus. The hsp70.2 antisense transcripts were particularly abundant in the frontal cortex, dentate gyrus, subthalamic nucleus, zona incerta, superior and inferior colliculi, central gray, brainstem, and cerebellar Purkinje cells. Our findings have revealed a distinct cellular and spatial localization of both sense and antisense transcripts, demonstrating a new level of complexity in the function of the heat shock genes.

NASA Discipline Developmental Biology↗
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

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↗
NASA NTRS2001

Analysis and expression of the alpha-expansin and beta-expansin gene families in maize

Expansins comprise a multigene family of proteins in maize (Zea mays). We isolated and characterized 13 different maize expansin cDNAs, five of which are alpha-expansins and eight of which are beta-expansins. This paper presents an analysis of these 13 expansins, as well as an expression analysis by northern blotting with materials from young and mature maize plants. Some expansins were expressed in restricted regions, such as the beta-expansins ExpB1 (specifically expressed in maize pollen) and ExpB4 (expressed principally in young husks). Other expansins such as alpha-expansin Exp1 and beta-expansin ExpB2 were expressed in several organs. The expression of yet a third group was not detected in the selected organs and tissues. An analysis of expansin sequences from the maize expressed sequence tag collection is also presented. Our results indicate that expansin genes may have general, overlapping expression in some instances, whereas in other cases the expression may be highly specific and limited to a single organ or cell type. In contrast to the situation in Arabidopsis, beta-expansins in maize seem to be more numerous and more highly expressed than are alpha-expansins. The results support the concept that beta-expansins multiplied and evolved special functions in the grasses.

NASA Program Fundamental Space Biology↗
NASA NTRS2003

Differential mesodermal expression of two amphioxus MyoD family members (AmphiMRF1 and AmphiMRF2)

To explore the evolution of myogenic regulatory factors in chordates, we isolated two MyoD family genes (AmphiMRF1 and AmphiMRF2) from amphioxus. AmphiMRF1 is first expressed at the late gastrula in the paraxial mesoderm. As the first somites form, expression is restricted to their myotomal region. In the early larva, expression is strongest in the most anterior and most posterior somites. AmphiMRF2 transcription begins at mid/late gastrula in the paraxial mesoderm, but never spreads into its most anterior region. Through much of the neurula stage, AmphiMRF2 expression is strong in the myotomal region of all somites except the most anterior pair; by late neurula expression is downregulated except in the most posterior somites forming just rostral to the tail bud. These two MRF genes of amphioxus have partly overlapping patterns of mesodermal expression and evidently duplicated independent of the diversification of the vertebrate MRF family.

NASA Discipline Evolutionary Biology↗
NASA NTRS2017

Systemic Microgravity Response: Utilizing GeneLab to Develop Hypotheses for Spaceflight Risks

Biological risks associated with microgravity are a major concern for long-term space travel. Although determination of risk has been a focus for NASA research, data examining systemic (i.e., multi- or pan-tissue) responses to space flight are sparse. To perform our analysis, we utilized the NASA GeneLab database which is a publicly available repository containing a wide array of omics results from experiments conducted with: i) with different flight conditions (space shuttle (STS) missions vs. International Space Station (ISS); ii) a variety of tissues; and 3) assays that measure epigenetic, transcriptional, and protein expression changes. Meta-analysis of the transcriptomic data from 7 different murine and rat data sets, examining tissues such as liver, kidney, adrenal gland, thymus, mammary gland, skin, and skeletal muscle (soleus, extensor digitorum longus, tibialis anterior, quadriceps, and gastrocnemius) revealed for the first time, the existence of potential master regulators coordinating systemic responses to microgravity in rodents. We identified p53, TGF1 and immune related pathways as the highly prevalent pan-tissue signaling pathways that are affected by microgravity. Some variability in the degree of change in their expression across species, strain and time of flight was also observed. Interestingly, while certain skeletal muscle (gastrocnemius and soleus) exhibited an overall down-regulation of these genes, some other muscle types such as the extensor digitorum longus, tibialis anterior and quadriceps, showed an up-regulated expression, indicative of potential compensatory mechanisms to prevent microgravity-induced atrophy. Key genes isolated by unbiased systems analyses displayed a major overlap between tissue types and flight conditions and established TGF1 to be the most connected gene across all data sets. Finally, a set of microgravity responsive miRNA signature was identified and based on their predicted functional state and subsequent impact on health, a theoretical health risk score was calculated. The genes and miRNAs identified from our analyses can be targeted for future research involving efficient countermeasure design. Our study thus exemplifies the utility of GeneLab data repository to aid in the process of performing novel hypothesis based spaceflight research aimed at elucidating the global impact of environmental stressors at multiple biological scales.

GeneLab↗
NASA NTRS2017

Systemic Response to Microgravity: Utilizing GeneLab Datasets to Identify Molecular Targets for Future Hypotheses-Driven Spaceflight Studies

Biological risks associated with microgravity are a major concern for long-term space travel. Although determination of risk has been a focus for NASA research, data examining systemic (i.e., multi- or pan-tissue) responses to space flight are sparse. To perform our analysis, we utilized the NASA GeneLab database which is a publicly available repository containing a wide array of omics results from experiments conducted with: i) with different flight conditions (space shuttle (STS) missions vs. International Space Station (ISS); ii) a variety of tissues; and 3) assays that measure epigenetic, transcriptional, and protein expression changes. Meta-analysis of the transcriptomic data from 7 different murine and rat data sets, examining tissues such as liver, kidney, adrenal gland, thymus, mammary gland, skin, and skeletal muscle (soleus, extensor digitorum longus, tibialis anterior, quadriceps, and gastrocnemius) revealed for the first time, the existence of potential master regulators coordinating systemic responses to microgravity in rodents. We identified p53, TGF(beta)1 and immune related pathways as the highly prevalent pan-tissue signaling pathways that are affected by microgravity. Some variability in the degree of change in their expression across species, strain and time of flight was also observed. Interestingly, while certain skeletal muscle (gastrocnemius and soleus) exhibited an overall down-regulation of these genes, some other muscle types such as the extensor digitorum longus, tibialis anterior and quadriceps, showed an up-regulated expression, indicative of potential compensatory mechanisms to prevent microgravity-induced atrophy. Key genes isolated by unbiased systems analyses displayed a major overlap between tissue types and flight conditions and established TGF(beta)1 to be the most connected gene across all data sets. Finally, a set of microgravity responsive miRNA signature was identified and based on their predicted functional state and subsequent impact on health, a theoretical health risk score was calculated. The genes and miRNAs identified from our analyses can be targeted for future research involving efficient countermeasure design. Our study thus exemplifies the utility of GeneLab data repository to aid in the process of performing novel hypothesis based spaceflight research aimed at elucidating the global impact of environmental stressors at multiple biological scales.

GeneLab↗
NASA NTRS2001

Differential expression of members of the annexin multigene family in Arabidopsis

Although in most plant species no more than two annexin genes have been reported to date, seven annexin homologs have been identified in Arabidopsis, Annexin Arabidopsis 1-7 (AnnAt1--AnnAt7). This establishes that annexins can be a diverse, multigene protein family in a single plant species. Here we compare and analyze these seven annexin gene sequences and present the in situ RNA localization patterns of two of these genes, AnnAt1 and AnnAt2, during different stages of Arabidopsis development. Sequence analysis of AnnAt1--AnnAt7 reveals that they contain the characteristic four structural repeats including the more highly conserved 17-amino acid endonexin fold region found in vertebrate annexins. Alignment comparisons show that there are differences within the repeat regions that may have functional importance. To assess the relative level of expression in various tissues, reverse transcription-PCR was carried out using gene-specific primers for each of the Arabidopsis annexin genes. In addition, northern blot analysis using gene-specific probes indicates differences in AnnAt1 and AnnAt2 expression levels in different tissues. AnnAt1 is expressed in all tissues examined and is most abundant in stems, whereas AnnAt2 is expressed mainly in root tissue and to a lesser extent in stems and flowers. In situ RNA localization demonstrates that these two annexin genes display developmentally regulated tissue-specific and cell-specific expression patterns. These patterns are both distinct and overlapping. The developmental expression patterns for both annexins provide further support for the hypothesis that annexins are involved in the Golgi-mediated secretion of polysaccharides.

Non-NASA Center↗
NASA NTRS1991

A deficiency of the homeotic complex of the beetle Tribolium

In Drosophila, the establishment of regional commitments along most of the anterior/posterior axis of the developing embryo depends on two clusters of homeotic genes: the Antennapedia complex (ANT-C) and the bithorax complex (BX-C). The red flour beetle has a single complex (HOM-C) representing the homologues of the ANT-C and BX-C in juxtaposition. Beetles trans-heterozygous for two particular HOM-C mutations spontaneously generate a large deficiency, presumably by an exchange within the common region of two overlapping inversions. Genetic and molecular results indicate that this deficiency spans at least the interval between the Deformed and abdominal-A homologues. In deficiency homozygous embryos, all gnathal, thoracic and abdominal segments develop antennal appendages, suggesting that a gene(s) has been deleted that acts to distinguish trunk from head. There is no evidence that beetles have a homologue of the segmentation gene fushi tarazu of similar genomic location and function. On the basis of the genetic tractability, convenient genome size and organization of Tribolium, and its relatively long phylogenetic divergence from Drosophila (>300 million years), we have integrated developmental genetic and molecular analyses of the HOM-C. We isolated about 70 mutations in the complex representing at least six complementation groups. The homeotic phenotypes of adults and lethal embryos lead us to believe that these beetle genes are homologous with the Drosophila genes indicated in Fig. 1 (see text).

NASA Program NSCORT↗
NASA NTRS2001

Leiomodins: larger members of the tropomodulin (Tmod) gene family

The 64-kDa autoantigen D1 or 1D, first identified as a potential autoantigen in Graves' disease, is similar to the tropomodulin (Tmod) family of actin filament pointed end-capping proteins. A novel gene with significant similarity to the 64-kDa human autoantigen D1 has been cloned from both humans and mice, and the genomic sequences of both genes have been identified. These genes form a subfamily closely related to the Tmods and are here named the Leiomodins (Lmods). Both Lmod genes display a conserved intron-exon structure, as do three Tmod genes, but the intron-exon structure of the Lmods and the Tmods is divergent. mRNA expression analysis indicates that the gene formerly known as the 64-kDa autoantigen D1 is most highly expressed in a variety of human tissues that contain smooth muscle, earning it the name smooth muscle Leiomodin (SM-Lmod; HGMW-approved symbol LMOD1). Transcripts encoding the novel Lmod gene are present exclusively in fetal and adult heart and adult skeletal muscle, and it is here named cardiac Leiomodin (C-Lmod; HGMW-approved symbol LMOD2). Human C-Lmod is located near the hypertrophic cardiomyopathy locus CMH6 on human chromosome 7q3, potentially implicating it in this disease. Our data demonstrate that the Lmods are evolutionarily related and display tissue-specific patterns of expression distinct from, but overlapping with, the expression of Tmod isoforms. Copyright 2001 Academic Press.

Carrier Proteins/biosynthesis/genetics↗
NASA NTRS2001

A novel reagentless sensing system for measuring glucose based on the galactose/glucose-binding protein

The galactose/glucose-binding protein (GBP) is synthesized in the cytoplasm of Escherichia coli in a precursor form and exported into the periplasmic space upon cleavage of a 23-amino-acid leader sequence. GBP binds galactose and glucose in a highly specific manner. The ligand induces a hinge motion in GBP and the resultant protein conformational change constitutes the basis of the sensing system. The mglB gene, which codes for GBP, was isolated from the chromosome of E. coli using the polymerase chain reaction (PCR). Since wild-type GBP lacks cysteines in its structure, introducing this amino acid by site-directed mutagenesis ensures single-label attachment at specific sites with a sulfhydro-specific fluorescent probe. Site-directed mutagenesis by overlap extension PCR was performed to prepare three different mutants to introduce a single cysteine residue at positions 148, 152, and 182. Since these residues are not involved in ligand binding and since they are located at the edge of the binding cleft, they experience a significant change in environment upon binding of galactose or glucose. The sensing system strategy is based on the fluorescence changes of the probe as the protein undergoes a structural change on binding. In this work a reagentless sensing system has been rationally designed that can detect submicromolar concentrations of glucose. The calibration plots have a linear working range of three orders of magnitude. Although the system can sense galactose as well, this epimer is not a potential interfering substance since its concentration in blood is negligible. Copyright 2001 Academic Press.

Non-NASA Center↗
NASA NTRS1997

Functional domains of plant chimeric calcium/calmodulin-dependent protein kinase: regulation by autoinhibitory and visinin-like domains

A novel calcium-binding calcium/calmodulin-dependent protein kinase (CCaMK) with a catalytic domain, calmodulin-binding domain, and a neural visinin-like domain was cloned and characterized from plants [Patil et al., (1995) Proc. Natl. Acad. Sci. USA 92, 4797-4801; Takezawa et al. (1996) J. Biol. Chem. 271, 8126-8132]. The mechanisms of CCaMK activation by calcium and calcium/calmodulin were investigated using various deletion mutants. The use of deletion mutants of CCaMK lacking either one, two, or all three calcium-binding EF hands indicated that all three calcium-binding sites in the visinin-like domain were crucial for the full calcium/calmodulin-dependent kinase activity. As each calcium-binding EF hand was deleted, there was a gradual reduction in calcium/calmodulin-dependent kinase activity from 100 to 4%. Another mutant (amino acids 1-322) which lacks both the visinin-like domain containing three EF hands and the calmodulin-binding domain was constitutively active, indicating the presence of an autoinhibitory domain around the calmodulin-binding domain. By using various synthetic peptides and the constitutively active mutant, we have shown that CCaMK contains an autoinhibitory domain within the residues 322-340 which overlaps its calmodulin-binding domain. Kinetic studies with both ATP and the GS peptide substrate suggest that the autoinhibitory domain of CCaMK interacts only with the peptide substrate binding motif of the catalytic domain, but not with the ATP-binding motif.

Non-NASA Center↗
NASA NTRS2018

Hawkeye Ocean Color Instrument – Performance Summary

Hawkeye is an ocean color instrument that is part of the SeaHawk satellite developed for SOCON, the Sustained Ocean Color Observations using Nanosatellites program funded by the Gordon and Betty Moore Foundation and managed by the University of North Carolina – Wilmington (UNC-W). HawkEye has spectral characteristics similar to SeaWiFS, but with 8 times finer resolution and a smaller field of view more appropriate for lakes, rivers, and near-shore terrestrial environments. With a volume of only 10 X 10 X 10 cm (a CubeSat 1U), it can produce 8 bands of image data in a single pass, each with 1800 x 6000 pixels, with a resolution of 120 meters per pixel. This paper will present a short summary of instrument design, the spacecraft interface, and “lessons learned” during this effort. Scientists considering using linear arrays in a pushbroom mode for remote sensing will find this useful. Much of the discussion will center on optical performance, such as flat field calibration, polarization effects, stray light, out-of-band response, and exposure linearity. Images from field tests will be shown.The Hawkeye instrument is an ocean color measuring instrument designed to fly on the SeaHawk satellite developed for SOCON, the Sustained Ocean Color Observations using Nanosatellites program funded by the Gordon and Betty Moore Foundation and managed by the University of North Carolina – Wilmington (UNC-W). The Hawkeye instrument measures ocean color in 8 spectral bands, similar to SeaWiFS, except Band 7, which is shifted to a slightly lower wavelength to avoid the oxygen absorption feature that a wider band overlapped on SeaWiFS. The instrument is approximately 1/3rd the volume of the entire satellite, which is a 3U Cubesat manufactured by Clydespace in Glasgow, Scotland. The purpose of this instrument is to ascertain the quality of ocean color data possible with such a small, inexpensive instrument and bus. The nominal orbit is 540 km, and the nominal pixel geometric instantaneous field of view (GIFOV) 120 meters on a side. Each band will produce an image 1800 x 6000 pixels in size, for a total field of view of 216 X 720 km.2) DESIGN CONCEPTThe Hawkeye instrument uses linear arrays in pushbroom mode to collect data over a two dimensional area. The instrument has 4 linear CCD arrays, the Onsemi KLI-4104, to collect the 8 bands of data. Figure 1 illustrates the optical design for two bands, sharing a single array.

Hawkeye↗
NASA NTRS2020

The Atacama Cosmology Telescope: A CMB lensing mass map over 2100 square degrees of sky and its cross-correlation with BOSS-CMASS galaxies

We construct cosmic microwave background lensing mass maps using data from the 2014 and 2015 seasons of observations with the Atacama Cosmology Telescope (ACT). These maps cover 2100 square degrees of sky and overlap with a wide variety of optical surveys. The maps are signal dominated on large scales and have fidelity such that their correlation with the cosmic infrared background is clearly visible by eye. We also create lensing maps with thermal Sunyaev-Zel’dovich contamination removed using a novel cleaning procedure that only slightly degrades the lensing signal-to-noise ratio. The cross-spectrum between the cleaned lensing map and the BOSS CMASS galaxy sample is detected at 10-σ significance, with an amplitude of A = 1.02±0.10 relative to the Planck best-fit LCDM cosmological model with fiducial linear galaxy bias. Our measurement lays the foundation for lensing cross-correlation science with current ACT data and beyond.

gravitational lensing↗