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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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Raw data for publication: Moni Qiande and Fredy Altpeter. 2025. Comparing four heat-inducible promoters in stably transformed sugarcane regarding spatial and temporal control of transgene expression reveals candidates to drive stem preferred transgene expression. Frontiers in Plant Science (accepted for publication).
The author reviews current thought about life originating in hyperthermophilic microorganisms. Hyperthermophiles obtain food from chemosynthesis of sulfur and have an RNA nucleotide sequence different from bacteria and eucarya. It is postulated that a hyperthermophile may be the common ancestor of all life. Current research efforts focus on the synthesis of organic compounds in hydrothermal systems.
Localized wounding of one leaf in intact tomato (Lycopersicon esculentum Mill.) plants triggers rapid systemic transcriptional responses that might be involved in defense. To better understand the mechanism(s) of intercellular signal transmission in wounded tomatoes, and to identify the array of genes systemically up-regulated by wounding, a subtractive cDNA library for wounded tomato leaves was constructed. A novel cDNA clone (designated LebZIP1) encoding a DNA-binding protein was isolated and identified. This clone appears to be encoded by a single gene, and belongs to the family of basic leucine zipper domain (bZIP) transcription factors shown to be up-regulated by cold and dark treatments. Analysis of the mRNA levels suggests that the transcript for LebZIP1 is both organ-specific and up-regulated by wounding. In wounded wild-type tomatoes, the LebZIP1 mRNA levels in distant tissue were maximally up-regulated within only 5 min following localized wounding. Exogenous abscisic acid (ABA) prevented the rapid wound-induced increase in LebZIP1 mRNA levels, while the basal levels of LebZIP1 transcripts were higher in the ABA mutants notabilis (not), sitiens (sit), and flacca (flc), and wound-induced increases were greater in the ABA-deficient mutants. Together, these results suggest that ABA acts to curtail the wound-induced synthesis of LebZIP1 mRNA.
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.
The microbial burdens of 69 cabin air samples collected from commercial airliners were assessed via conventional culture-dependent, and molecular-based microbial enumeration assays. Cabin air samples from each of four separate flights aboard two different carriers were collected via air-impingement. Microbial enumeration techniques targeting DNA, ATP, and endotoxin were employed to estimate total microbial burden. The total viable microbial population ranged from 0 to 3.6 x10 4 cells per 100 liters of air, as assessed by the ATP-assay. When these same samples were plated on R2A minimal medium, anywhere from 2% to 80% of these viable populations were cultivable. Five of the 29 samples examined exhibited higher cultivable counts than ATP derived viable counts, perhaps a consequence of the dormant nature (and thus lower concentration of intracellular ATP) of cells inhabiting these air cabin samples. Ribosomal RNA gene sequence analysis showed these samples to consist of a moderately diverse group of bacteria, including human pathogens. Enumeration of ribosomal genes via quantitative-PCR indicated that population densities ranged from 5 x 10 1 ' to IO 7 cells per 100 liters of air. Each of the aforementioned strategies for assessing overall microbial burden has its strengths and weaknesses; this publication serves as a testament to the power of their use in concert.
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.
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.
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.
De novo assembly of the transcriptome is crucial for functional genomics studies in bioenergy research, since many of the organisms lack high quality reference genomes. In a previous study we successfully de novo assembled simple eukaryote transcriptomes exclusively from short Illumina RNA-Seq reads [1]. However, extensive alternative splicing, present in most of the higher eukaryotes, poses a significant challenge for current short read assembly processes. Furthermore, the size of next-generation datasets, often large for plant genomes, presents an informatics challenge. To tackle these challenges we present a combined experimental and informatics strategy for de novo assembly in higher eukaryotes. Using maize as a test case, preliminary results suggest our approach can resolve transcript variants and improve gene annotations.
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DNA and RNA molecules can be detected as they are driven through a nanopore by an applied electric field at rates ranging from several hundred microseconds to a few milliseconds per molecule. The nanopore can rapidly discriminate between pyrimidine and purine segments along a single-stranded nucleic acid molecule. Nanopore detection and characterization of single molecules represents a new method for directly reading information encoded in linear polymers. If single-nucleotide resolution can be achieved, it is possible that nucleic acid sequences can be determined at rates exceeding a thousand bases per second.
Abstract RNA-guided nucleases from CRISPR-Cas systems expand opportunities for precise, targeted genome modification. Endogenous CRISPR-Cas systems in many prokaryotes are attractive to circumvent expression, functionality, and unintended activity hurdles posed by heterologous CRISPR-Cas effectors. However, each CRISPR-Cas system recognizes a unique set of protospacer adjacent motifs (PAMs), which requires identification by extensive screening of randomized DNA libraries. This challenge hinders development of endogenous CRISPR-Cas systems, especially those based on multi-protein effectors and in organisms that are slow-growing or have transformation idiosyncrasies. To address this challenge, we present Spacer2PAM, an easy-to-use, easy-to-interpret R package built to predict and guide experimental determination of functional PAM sequences for any CRISPR-Cas system given its corresponding CRISPR array as input. Spacer2PAM can be used in a ‘Quick’ method to generate a single PAM prediction or in a ‘Comprehensive’ method to inform targeted PAM libraries small enough to screen in difficult to transform organisms. We demonstrate Spacer2PAM by predicting PAM sequences for industrially relevant organisms and experimentally identifying seven PAM sequences that mediate interference from the Spacer2PAM-informed PAM library for the type I-B CRISPR-Cas system from Clostridium autoethanogenum. We anticipate that Spacer2PAM will facilitate the use of endogenous CRISPR-Cas systems for industrial biotechnology and synthetic biology.
Double-stranded DNA (dsDNA) binding and cleavage by Cas9 is a hallmark of type II CRISPR-Cas bacterial adaptive immunity. All known Cas9 enzymes are thought to recognize DNA exclusively as a natural substrate, providing protection against DNA phage and plasmids. Here, we show that Cas9 enzymes from both subtypes II-A and II-C can recognize and cleave single-stranded RNA (ssRNA) by an RNA-guided mechanism that is independent of a protospacer-adjacent motif (PAM) sequence in the target RNA. RNA-guided RNA cleavage is programmable and site-specific, and we find that this activity can be exploited to reduce infection by single-stranded RNA phage in vivo. We also demonstrate that Cas9 can direct PAM-independent repression of gene expression in bacteria. These results indicate that a subset of Cas9 enzymes have the ability to act on both DNA and RNA target sequences, and suggest the potential for use in programmable RNA targeting applications.
Tolumonas auensis (Fischer-Romero et al. 1996) is currently the only validly named species of the genus Tolumonas in the family Aeromonadaceae. The strain is of interest because of its ability to produce toluene from phenylalanine and other phenyl precursors, as well as phenol from tyrosine. This is of interest because toluene is normally considered to be a tracer of anthropogenic pollution in lakes, but T. auensis represents a biogenic source of toluene. Other than Aeromonas hydrophila subsp. hydrophila, T. auensis strain TA 4T is the only other member in the family Aeromonadaceae with a completely sequenced type-strain genome. The 3,471,292-bp chromosome with a total of 3,288 protein-coding and 116 RNA genes was sequenced as part of the DOE Joint Genome Institute Program JBEI 2008.
Relative abundance profiles of bacterial populations measured by sequencing DNA or RNA of marker genes can widely differ. These differences, made apparent when calculating ribosomal RNA:DNA ratios, have been interpreted as variable activities of bacterial populations. However, inconsistent correlations between ribosomal RNA:DNA ratios and metabolic activity or growth rates have led to a more conservative interpretation of this metric as the cellular protein synthesis potential (PSP). Little is known, particularly in freshwater systems, about how PSP varies for specific taxa across temporal and spatial environmental gradients and how conserved PSP is across bacterial phylogeny. Here, we generated 16S rRNA gene sequencing data using simultaneously extracted DNA and RNA from fractionated (free-living and particulate) water samples taken seasonally along a eutrophic freshwater estuary to oligotrophic pelagic transect in Lake Michigan. In contrast to previous reports, we observed frequent clustering of DNA and RNA data from the same sample. Analysis of the overlap in taxa detected at the RNA and DNA level indicated that microbial dormancy may be more common in the estuary, the particulate fraction, and during the stratified period. Across spatiotemporal gradients, PSP was often conserved at the phylum and class levels. PSPs for specific taxa were more similar across habitats in spring than in summer and fall. This was most notable for PSPs of the same taxa when located in the free-living or particulate fractions, but also when contrasting surface to deep, and estuary to Lake Michigan communities. Our results show that community composition assessed by RNA and DNA measurements are more similar than previously assumed in freshwater systems. Furthermore, the similarity between RNA and DNA measurements and taxa-specific PSPs that drive community-level similarities are conditional on spatiotemporal factors.
Type ΙΙΙ CRISPR-Cas systems provide robust immunity against foreign RNA and DNA by sequence-specific RNase and target RNA-activated sequence-nonspecific DNase and RNase activities. We report on cryo-EM structures of Thermococcus onnurineus Csm crRNA binary, Csm crRNA -target RNA and Csm crRNA -target RNA anti-tag ternary complexes in the 3.1 Å range. The topological features of the crRNA 5'-repeat tag explains the 5'-ruler mechanism for defining target cleavage sites, with accessibility of positions -2 to -5 within the 5'-repeat serving as sensors for avoidance of autoimmunity. The Csm3 thumb elements introduce periodic kinks in the crRNA-target RNA duplex, facilitating cleavage of the target RNA with 6-nt periodicity. Key Glu residues within a Csm1 loop segment of Csm crRNA adopt a proposed autoinhibitory conformation suggestive of DNase activity regulation. Finally, these structural findings, complemented by mutational studies of key intermolecular contacts, provide insights into Csm crRNA complex assembly, mechanisms underlying RNA targeting and site-specific periodic cleavage, regulation of DNase cleavage activity, and autoimmunity suppression.
Hydrogels are supramolecular assemblies with both solute transport properties like liquids and mechanical properties like elastomers. To date, every type of biomolecules except ribonucleic acid (RNA), is capable of forming a hydrogel. Here, we report an RNA that forms a hydrogel by self-assembly. This RNA is originally identified by systematic evolution of ligands by exponential enrichment (SELEX) to enhance the activity of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors as a potential RNA drug for the treatment of cognitive disorders. The RNA hydrogel exhibits an elastic modulus plateau on the order of 102 Pa and shows dynamic RNA chain interactions with relaxation behaviors similar to living wormlike micellar solutions. Small-angle X-ray scattering and cryogenic electron microscopy characterization support the RNA network structures. Here, by sequence mutation and rheological measurements, we reveal two key sequence motifs in the RNA responsible for intermolecular recognition and the formation of a polymer network by self-assembly.
Studies of ribosomal RNA primary structure allow reconstruction of phylogenetic trees for prokaryotic organisms. Such studies reveal major dichotomy among the bacteria that separates them into eubacteria and archaebacteria. Both groupings are further segmented into several major divisions. The results obtained from 5S rRNA sequences are essentially the same as those obtained with the 16S rRNA data. In the case of Gram negative bacteria the ribosomal RNA sequencing results can also be directly compared with hybridization studies and cytochrome c sequencing studies. There is again excellent agreement among the several methods. It seems likely then that the overall picture of microbial phylogeny that is emerging from the RNA sequence studies is a good approximation of the true history of these organisms. The RNA data allow examination of the evolutionary process in a semi-quantitative way. The secondary structures of these RNAs are largely established. As a result it is possible to recognize examples of local structural evolution. Evolutionary pathways accounting for these events can be proposed and their probability can be assessed.