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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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NASA NTRS1997

[Ribosomal RNA Evolution]

It is generally believed that an RNA World existed at an early stage in the history of life. During this early period, RNA molecules are seen to be potentially involved in both catalysis and the storage of genetic information. Translation presents several interrelated themes of inquiry for exobiology. First, it is essential, for understanding the very origin of life, how peptides and eventually proteins might have come to be made on the early Earth in a template directed manner. Second, it is necessary to understand how a machinery of similar complexity to that found in the ribosomes of modern organisms came to exist by the time of the last common ancestor (as detected by 16S rRNA sequence studies). Third, the ribosomal RNAs themselves likely had a very early origin and studies of their history may be very informative about the nature of the RNA World. Moreover, studies of these RNAs will contribute to a better understanding of the potential roles of RNA in early evolution.During the past year we have ave conducted a comparative study of four completely sequenced bacterial genoames. We have focused initially on conservation of gene order. The second component of the project continues to build on the model system for studying the validity of variant 5S rRNA sequences in the vicinity of the modern Vibrio proteolyticus 5S rRNA that we established earlier. This system has made it possible to conduct a detailed and extensive analysis of a local portion of the sequence space. These core methods have been used to construct numerous mutants during the last several years. Although it has been a secondary focus, this work has continued over the last year such that we now have in excess of 125 V. proteolyticus derived constructs which have been made and characterized. We have also continued high resolution NMR work on RNA oligomers originally initiated by G. Kenneth Smith who was funded by a NASA Graduate Student Researcher's Fellowship Award until May of 1996. Mr. Smith developed synthesis & purification protocols in order to obtain large quantities of RNA oligomers for NMR studies. He succeeded in obtaining preliminary one dimensional spectra for an analog of the helix II/loop B/helix III domain of 5S rRNAs well as a 29mer which includes helix III and the highly characteristic 13 nucleotide loop C. Significant progress in laying the required groundwork for this work has been accomplished during the past year. A plasmid, pLeuS-1, carrying the E. coli leucine tRNA synthetase gene (LeuRS) has been obtained and used to overexpressed the leucine synthetase. A T7 runoff transcription has been established in our laboratory and successfully used to make both minihelix RNAs and tRNAs. In addition, encapsulation studies have been conducted and we have successfully encapsulated tRNA in lipid vesicles. Initial efforts to obtain peptide bond synthesis will be underway over the next several moths.

Source record↗
NASA NTRS1999

Polyphasic taxonomy of the genus Shewanella and description of Shewanella oneidensis sp. nov

The genus Shewanella has been studied since 1931 with regard to a variety of topics of relevance to both applied and environmental microbiology. Recent years have seen the introduction of a large number of new Shewanella-like isolates, necessitating a coordinated review of the genus. In this work, the phylogenetic relationships among known shewanellae were examined using a battery of morphological, physiological, molecular and chemotaxonomic characterizations. This polyphasic taxonomy takes into account all available phenotypic and genotypic data and integrates them into a consensus classification. Based on information generated from this study and obtained from the literature, a scheme for the identification of Shewanella species has been compiled. Key phenotypic characteristics were sulfur reduction and halophilicity. Fatty acid and quinone profiling were used to impart an additional layer of information. Molecular characterizations employing small-subunit 16S rDNA sequences were at the limits of resolution for the differentiation of species in some cases. As a result, DNA-DNA hybridization and sequence analyses of a more rapidly evolving molecule (gyrB gene) were performed. Species-specific PCR probes were designed for the gyrB gene and used for the rapid screening of closely related strains. With this polyphasic approach, in addition to the ten described Shewanella species, two new species, Shewanella oneidensis and 'Shewanella pealeana', were recognized; Shewanella oneidensis sp. nov. is described here for the first time.

NASA Discipline Exobiology↗
NASA NTRS1999

Syntrophus aciditrophicus sp. nov., a new anaerobic bacterium that degrades fatty acids and benzoate in syntrophic association with hydrogen-using microorganisms

Strain SBT is a new, strictly anaerobic, gram-negative, nonmotile, non-sporeforming, rod-shaped bacterium that degrades benzoate and certain fatty acids in syntrophic association with hydrogen/formate-using microorganisms. Strain SBT produced approximately 3 mol of acetate and 0.6 mol of methane per mol of benzoate in coculture with Methanospirillum hungatei strain JF1. Saturated fatty acids, some unsaturated fatty acids, and methyl esters of butyrate and hexanoate also supported growth of strain SBT in coculture with Desulfovibrio strain G11. Strain SBT grew in pure culture with crotonate, producing acetate, butyrate, caproate, and hydrogen. The molar growth yield was 17 +/- 1 g cell dry mass per mol of crotonate. Strain SBT did not grow with fumarate, iron(III), polysulfide, or oxyanions of sulfur or nitrogen as electron acceptors with benzoate as the electron donor. The DNA base composition of strain SBT was 43.1 mol% G+C. Analysis of the 16 S rRNA gene sequence placed strain SBT in the delta-subdivision of the Proteobacteria, with sulfate-reducing bacteria. Strain SBT was most closely related to members of the genus Syntrophus. The clear phenotypic and genotypic differences between strain SBT and the two described species in the genus Syntrophus justify the formation of a new species, Syntrophus aciditrophicus.

Non-NASA Center↗
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 NTRS1992

Cloning of the cDNA for U1 small nuclear ribonucleoprotein particle 70K protein from Arabidopsis thaliana

We cloned and sequenced a plant cDNA that encodes U1 small nuclear ribonucleoprotein (snRNP) 70K protein. The plant U1 snRNP 70K protein cDNA is not full length and lacks the coding region for 68 amino acids in the amino-terminal region as compared to human U1 snRNP 70K protein. Comparison of the deduced amino acid sequence of the plant U1 snRNP 70K protein with the amino acid sequence of animal and yeast U1 snRNP 70K protein showed a high degree of homology. The plant U1 snRNP 70K protein is more closely related to the human counter part than to the yeast 70K protein. The carboxy-terminal half is less well conserved but, like the vertebrate 70K proteins, is rich in charged amino acids. Northern analysis with the RNA isolated from different parts of the plant indicates that the snRNP 70K gene is expressed in all of the parts tested. Southern blotting of genomic DNA using the cDNA indicates that the U1 snRNP 70K protein is coded by a single gene.

NASA Discipline Plant Biology↗
NASA NTRS1999

A molecular description of the evolution of resistance

BACKGROUND: In vitro evolution has been used to obtain nucleic acid molecules with interesting functional properties. The evolution process usually is carried out in a stepwise manner, involving successive rounds of selection, amplification and mutation. Recently, a continuous in vitro evolution system was devised for RNAs that catalyze the ligation of oligonucleotide substrates, allowing the evolution of catalytic function to be studied in real time. RESULTS: Continuous in vitro evolution of an RNA ligase ribozyme was carried out in the presence of a DNA enzyme that was capable of cleaving, and thereby inactivating, the ribozyme. The DNA concentration was increased steadily over 33.5 hours of evolution, reaching a final concentration that would have been sufficient to inactivate the starting population in one second. The evolved population of ribozymes developed resistance to the DNA enzyme, reducing their vulnerability to cleavage by 2000-fold but retaining their own catalytic function. Based on sequencing and kinetic analysis of the ribozymes, two mechanisms are proposed for this resistance. One involves three nucleotide substitutions, together with two compensatory mutations, that alter the site at which the DNA enzyme binds the ribozyme. The other involves enhancement of the ribozyme's ability to bind its own substrate in a way that protects it from cleavage by the DNA enzyme. CONCLUSIONS: The ability to direct the evolution of an enzyme's biochemical properties in response to the behavior of another macromolecule provides insight into the evolution of resistance and may be useful in developing enzymes with novel or enhanced function.

Non-NASA Center↗
NASA NTRSDate not supplied

MULTI-OMICS ANALYSIS OF THE IMPACT OF CHRONIC LOW-DOSE RADIATION AND HINDLIMB SUSPENSION ON MURINE BRAIN AND RETINA

The space environment includes hazards like radiation and microgravity which can adversely affect biological systems. We assessed multi-omics multi-tissue NASA GeneLab datasets where 6-month-old female mice were gamma irradiated (IR) and/or hindlimb unloaded (HLU) for 21 days. Whole transcriptome shotgun sequencing (RNA-Seq) and reduced representation bisulfite sequencing (RRBS) of brain and retina samples collected at 4 months post-exposure was performed to better characterize the retinal and neurological responses to spaceflight. We compared epigenomic and transcriptomic profiles within each exposure group for both tissue types to identify correlation (Pearson’s correlation test; p-value < 0.05) between gene expression and DNA methylation levels that may be related to transcriptional regulation. We then obtained genes with methylation-expression correlation that also showed differences in mean expression or dispersion between exposed and control groups (adjusted p-value < 0.25; relaxed to denote ‘hypothesis’) in the brain (37 genes in HLU, 4 in IR, and 156 in HLU+IR) or retina (92 genes in HLU, 1 in IR, and 55 in HLU+IR). Enriched Gene Ontology (GO) terms for these genes are listed in Table 1 for HLU and HLU+IR for both tissue types. No enriched terms and only a few genes were detected with IR-only exposure in the brain (Chmp1a, Limd1, Rab40b, Ubc) and retina (retinoblastoma binding protein Rbbp7). Cellular components related to synapse were enriched in both tissue types. Previous analysis of differentially expressed genes in the retina after 1 month of HLU+IR showed enrichment in the somatodendritic compartment of the neuron, which was also observed in the brain 4 months post-exposure. Interestingly, genes related to ubiquitination showed correlation between methylation and expression and were differentially expressed or dispersed in different exposure groups indicating that this pathway may play an important role in multi-stressor response (Figure 1). The current multi-omics and multi-tissue analysis interrogates the epigenomic and transcriptomic impacts of radiation and hindlimb unloading, in isolation and in combination, on the retina and the brain. The results provide insight into the adaptive response to individual spaceflight hazard analogs and their interplay, as well as hypotheses to be further tested for understanding spaceflight-induced neurological and vision effects.

Prachi Kothiyal↗
NASA NTRS2004

Microbial monitoring of spacecraft and associated environments

Rapid microbial monitoring technologies are invaluable in assessing contamination of spacecraft and associated environments. Universal and widespread elements of microbial structure and chemistry are logical targets for assessing microbial burden. Several biomarkers such as ATP, LPS, and DNA (ribosomal or spore-specific), were targeted to quantify either total bioburden or specific types of microbial contamination. The findings of these assays were compared with conventional, culture-dependent methods. This review evaluates the applicability and efficacy of some of these methods in monitoring the microbial burden of spacecraft and associated environments. Samples were collected from the surfaces of spacecraft, from surfaces of assembly facilities, and from drinking water reservoirs aboard the International Space Station (ISS). Culture-dependent techniques found species of Bacillus to be dominant on these surfaces. In contrast, rapid, culture-independent techniques revealed the presence of many Gram-positive and Gram-negative microorganisms, as well as actinomycetes and fungi. These included both cultivable and noncultivable microbes, findings further confirmed by DNA-based microbial detection techniques. Although the ISS drinking water was devoid of cultivable microbes, molecular-based techniques retrieved DNA sequences of numerous opportunistic pathogens. Each of the methods tested in this study has its advantages, and by coupling two or more of these techniques even more reliable information as to microbial burden is rapidly obtained. Copyright 2004 Springer-Verlag.

Environmental Monitoring/methods↗
NASA NTRSDate not supplied

NASA GeneLab Multi-study Visualization Portal

NASA GeneLab has helped advance the field of Space Biology by providing a public repository where researchers can store, share, analyze and visualize the results of space flight related omics experiments. The GeneLab data visualization portal allows any user, regardless of bioinformatics knowledge or access to computational resources, to interact with the experimental data, draw their own conclusions, and gain insights about the effects of space on living systems. These tools help democratize scientific research and foster the NASA Open Science initiative. The new multi-study feature of the GeneLab visualization platform allows users to mine study metadata from RNA sequencing (RNA-seq) experiments to identify samples of interest by filtering datasets based on organism, tissue, assay technology type, and/or factor. Once samples are selected from multiple datasets, users can combine and normalize the sample data, then utilize the visualization displays, including Principal Component Analysis (PCA) plots, to assess sample distributions. Finally, users can perform differential gene expression analysis on the combined data and visualize the results through PCA plots, Volcano plots, Pair plots, Heatmap, Ideogram and Gene Set Enrichment Analysis. All user-generated results and visualizations will be available for download. Here, we present a biological study using samples from multiple GeneLab RNA-seq datasets and analyzed using the multi-study visualization platform to demonstrate inter- and intra-study variability, as well as commonly differentially expressed genes between spaceflight and ground control conditions across datasets. This new feature opens a wide range of possibilities and opportunities for further development including combining other assay technology types and integration with batch effect correction techniques and machine learning applications. Overall, this tool allows users to increase the statistical power of individual experiments, validate hypothesis, identify patterns, and opens the door to new and exciting research.

space biology↗
NASA NTRS1996

Light-modulated abundance of an mRNA encoding a calmodulin-regulated, chromatin-associated NTPase in pea

A CDNA encoding a 47 kDa nucleoside triphosphatase (NTPase) that is associated with the chromatin of pea nuclei has been cloned and sequenced. The translated sequence of the cDNA includes several domains predicted by known biochemical properties of the enzyme, including five motifs characteristic of the ATP-binding domain of many proteins, several potential casein kinase II phosphorylation sites, a helix-turn-helix region characteristic of DNA-binding proteins, and a potential calmodulin-binding domain. The deduced primary structure also includes an N-terminal sequence that is a predicted signal peptide and an internal sequence that could serve as a bipartite-type nuclear localization signal. Both in situ immunocytochemistry of pea plumules and immunoblots of purified cell fractions indicate that most of the immunodetectable NTPase is within the nucleus, a compartment proteins typically reach through nuclear pores rather than through the endoplasmic reticulum pathway. The translated sequence has some similarity to that of human lamin C, but not high enough to account for the earlier observation that IgG against human lamin C binds to the NTPase in immunoblots. Northern blot analysis shows that the NTPase MRNA is strongly expressed in etiolated plumules, but only poorly or not at all in the leaf and stem tissues of light-grown plants. Accumulation of NTPase mRNA in etiolated seedlings is stimulated by brief treatments with both red and far-red light, as is characteristic of very low-fluence phytochrome responses. Southern blotting with pea genomic DNA indicates the NTPase is likely to be encoded by a single gene.

NASA Discipline Number 40-50↗
NASA NTRS2017

Novel End-to-End Molecular Biology Approach for Direct Nanopore 1D cDNA Sequencing of Reverse Transcribed mRNAs Purified from Cell Cultures by the NASA ISS WetLab2 SPM

Continued space bioscience research onboard the International Space Station (ISS) and future long-duration flight missions to the Moon or Mars will require the ability to conduct on-orbit molecular analysis of biological samples independently from Earth. In the last year two new molecular analytic technologies have been installed and the technologies demonstrated onboard the ISS: The Sample Prep Module (SPM) WetLab-2 (WL2) qRT-PCR toolbox and the Oxford Nanopore MinIon Biomolecule Sequencer. Here we describe protocol development and integration into existing ISS technology for end-to-end on-orbit biological sample processing and molecular analysis with real time results generated utilizing only field offline analytic software. For this experiment we isolated primary cells from bone marrow flushes of wild type B6129SF2 mice (Jackson Labs) long bones. The cell isolate was then processed using the SPM to produce total 147nanograms of RNA. The total RNA was purified to only messenger RNA (mRNA) and transferred to Smartcycler Thermocycle ISS kit consumable tube using Eppendorf gel loading pipette tips for further processing. Complementary first strand cDNA was synthesized using OLIGO dT priming followed by addition of SuperScript II Reverse Transcriptase and thermal cycling as per manufacturers instruction. All thermal cycling was conducted using the ISS WetLab-2 Cephid Smarcycler real time thermal cycler. Our protocol takes advantage of mRNAs native poly(A) tail, synthesized in vivo to protect the mRNA from degradation by endonucleases, to eliminate end-prep for adapter ligation. The adapted library is purified using MyOne C1 Streptavidin beads before elution in buffer. The pre-sequencing library is diluted in the loading buffer and injected into the MinIon sample port, drawn into the nanopore window by capillary action, and sequenced using the MinKnown software with local basecalling. The sequencing read produced 34.5 million events and local basecalling produced 117,301 successful reads. NCBI Blast of the data for the mouse genome resulted in 2,462 successful nucleotide collection matches (gene sequences) exceeding 70 homology. These results demonstrate the viability of this novel flight ready end-to-end sample analytic methodology and provide a real time homolog for flight experimentation utilizing supply kits and technologies that have already been demonstrated on ISS.

MinIon↗
NASA NTRS1991

The sequence of Methanospirillum hungatei 23S rRNA confirms the specific relationship between the extreme halophiles and the Methanomicrobiales

We have determined the sequence of the 23S rRNA from the methanogenic archaeon Methanospirillum hungatei. This is the first such sequence from a member of the Methanomicrobiales. Moreover, it brings additional evidence to bear on the possible specific relationship between this particular group of methanogens and the extreme halophiles. Such evidence is critical in that several new (and relatively untested) methods of phylogenetic inference have lead to the controversial conclusion that the extreme halophiles are either not related to the archaea, or are only peripherally so. Analysis of the Methanospirillum hungatei 23S rRNA sequence shows the Methanomicrobiales are indeed a sister group of the extreme halophiles, further strengthening the conclusions reached from analysis of 16S rRNA sequences.

Non-NASA Center↗
NASA NTRS1978

Origins of prokaryotes, eukaryotes, mitochondria, and chloroplasts

A computer branching model is used to analyze cellular evolution. Attention is given to certain key amino acids and nucleotide residues (ferredoxin, 5s ribosomal RNA, and c-type cytochromes) because of their commonality over a wide variety of cell types. Each amino acid or nucleotide residue is a sequence in an inherited biological trait; and the branching method is employed to align sequences so that changes reflect substitution of one residue for another. Based on the computer analysis, the symbiotic theory of cellular evolution is considered the most probable. This theory holds that organelles, e.g., mitochondria and chloroplasts invaded larger bodies, e.g., bacteria, and combined functions to form eucaryotic cells.

Schwartz, R. M.↗
NASA NTRSDate not supplied

GL4U: Using Space Biology Omics Data to Provide Bioinformatics Training for Students and Educators

NASA’s GeneLab project provides researchers open access to space-relevant multi-omics data via the Open Science Data Repository (OSDR) that can be mined to understand the effects of spaceflight on biological systems. To maximize the number of scientists who understand and utilize GeneLab data and data processing pipelines, GeneLab created GeneLab for Colleges and Universities (GL4U). GL4U provides space biology-relevant training in bioinformatics to the next generation of scientists through direct (training students) and indirect (training educators) approaches. The GL4U pilot programs were conducted in June 2021 (direct training) and 2022 (indirect training). During the pilots, students and educators at Historically Black Colleges and Universities (HBCUs) and Minority Serving Institutions (MSIs) participated in a week-long (direct training) or two-week-long (indirect training) bootcamp consisting of space biology-specific lectures and hands-on instruction using Jupyter Notebooks to analyze space biology RNA sequencing data from OSDR. During the educator pilot, participants received materials, training, and the necessary compute resources to enable them to run the bootcamp at their home institutions, thereby extending the reach of this initiative. In July 2023, GeneLab is partnering with JPL to expand GL4U to include amplicon sequencing (Amp-Seq) analysis training. During the GL4U Amp-Seq bootcamp, student and educator participants will receive training on how to analyze and interpret Amp-Seq data using the NASA GeneLab data processing pipeline. All bootcamp material, including instructions for requesting compute resources, will be made publicly available on GitHub for educators to teach the GL4U content in subsequent semesters. GL4U provides undergraduate students from underrepresented groups the opportunity to learn about NASA and Space Biology, and to enhance their career prospects by gaining hands-on experience analyzing omics data, a skillset that is highly applicable and marketable in the life sciences. We present results from pre- and post-training surveys completed by all participants of the Amp-Seq bootcamp.

Amanda M Saravia-Butler↗
NASA NTRS1993

Gibberellin (GA3) enhances cell wall invertase activity and mRNA levels in elongating dwarf pea (Pisum sativum) shoots

The invertase (EC 3.2.1.26) purified from cell walls of dwarf pea stems to homogeneity has a molecular mass of 64 kilodaltons (kD). Poly(A)+RNA was isolated from shoots of dwarf pea plants, and a cDNA library was constructed using lambda gt11 as an expression vector. The expression cDNA library was screened with polyclonal antibodies against pea cell wall invertase. One invertase cDNA clone was characterized as a full-length cDNA with 1,863 base pairs. Compared with other known invertases, one homologous region in the amino acid sequence was found. The conserved motif, Asn-Asp-Pro-Asn-Gly, is located near the N-terminal end of invertase. Northern blot analysis showed that the amount of invertase mRNA (1.86 kb) was rapidly induced to a maximal level 4 h after GA3 treatment, then gradually decreased to the control level. The mRNA level at 4 h in GA3-treated peas was fivefold higher than that of the control group. The maximal increase in activity of pea cell wall invertase elicited by GA3 occcured at 8 h after GA3 treatment. This invertase isoform was shown immunocytochemically to be localized in the cell walls, where a 10-fold higher accumulation occurred in GA3-treated tissue compared with control tissue. This study indicates that the expression of the pea shoot cell-wall invertase gene could be regulated by GA3 at transcriptional and/or translational levels.

NASA Discipline Number 40-50↗