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DOE OSTI2020

Single Amino Acid Mutations Affect Zika Virus Replication In Vitro and Virulence In Vivo

The 2014–2016 Zika virus (ZIKV) epidemic in the Americas resulted in large deposits of next-generation sequencing data from clinical samples. This resource was mined to identify emerging mutations and trends in mutations as the outbreak progressed over time. Information on transmission dynamics, prevalence, and persistence of intra-host mutants, and the position of a mutation on a protein were then used to prioritize 544 reported mutations based on their ability to impact ZIKV phenotype. Using this criteria, six mutants (representing naturally occurring mutations) were generated as synthetic infectious clones using a 2015 Puerto Rican epidemic strain PRVABC59 as the parental backbone. The phenotypes of these naturally occurring variants were examined using both cell culture and murine model systems. Mutants had distinct phenotypes, including changes in replication rate, embryo death, and decreased head size. In particular, a NS2B mutant previously detected during in vivo studies in rhesus macaques was found to cause lethal infections in adult mice, abortions in pregnant females, and increased viral genome copies in both brain tissue and blood of female mice. Additionally, mutants with changes in the region of NS3 that interfaces with NS5 during replication displayed reduced replication in the blood of adult mice. This analytical pathway, integrating both bioinformatic and wet lab experiments, provides a foundation for understanding how naturally occurring single mutations affect disease outcome and can be used to predict the of severity of future ZIKV outbreaks. To determine if naturally occurring individual mutations in the Zika virus epidemic genotype affect viral virulence or replication rate in vitro or in vivo, we generated an infectious clone representing the epidemic genotype of stain Puerto Rico, 2015. Using this clone, six mutants were created by changing nucleotides in the genome to cause one to two amino acid substitutions in the encoded proteins. The six mutants we generated represent mutations that differentiated the early epidemic genotype from genotypes that were either ancestral or that occurred later in the epidemic. We assayed each mutant for changes in growth rate, and for virulence in adult mice and pregnant mice. Three of the mutants caused catastrophic embryo effects including increased embryonic death or significant decrease in head diameter. Three other mutants that had mutations in a genome region associated with replication resulted in changes in in vitro and in vivo replication rates. These results illustrate the potential impact of individual mutations in viral phenotype.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Predicting the Identities of su(met-2) and met-3 in Neurospora crassa by Genome Resequencing

A significant number of classical genetic Neurospora crassa biochemical mutants remain anonymous, unassociated with a physical genome locus. By utilizing short read next-generation sequencing methods, it is possible to sequence the genomes of mutant strains rapidly and economically for the purpose of identifying genes associated with mutant phenotypes. We have taken this approach to connect genes and mutations to “methionineless” phenotypes in N. crassa.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

DISCo-microbe: design of an identifiable synthetic community of microbes

Microbiomes are extremely important for their host organisms, providing many vital functions and extending their hosts’ phenotypes. Natural studies of host-associated microbiomes can be difficult to interpret due to the high complexity of microbial communities, which hinders our ability to track and identify individual members along with the many factors that structure or perturb those communities. For this reason, researchers have turned to synthetic or constructed communities in which the identities of all members are known. However, due to the lack of tracking methods and the difficulty of creating a more diverse and identifiable community that can be distinguished through next-generation sequencing, most such in vivo studies have used only a few strains. To address this issue, we developed DISCo-microbe, a program for the design of an identifiable synthetic community of microbes for use in in vivo experimentation. The program is composed of two modules; (1) create, which allows the user to generate a highly diverse community list from an input DNA sequence alignment using a custom nucleotide distance algorithm, and (2) subsample, which subsamples the community list to either represent a number of grouping variables, including taxonomic proportions, or to reach a user-specified maximum number of community members. As an example, we demonstrate the generation of a synthetic microbial community that can be distinguished through amplicon sequencing. The synthetic microbial community in this example consisted of 2,122 members from a starting DNA sequence alignment of 10,000 16S rRNA sequences from the Ribosomal Database Project. We generated simulated Illumina sequencing data from the constructed community and demonstrate that DISCo-microbe is capable of designing diverse communities with members distinguishable by amplicon sequencing. Using the simulated data we were able to recover sequences from between 97–100% of community members using two different post-processing workflows. Furthermore, 97–99% of sequences were assigned to a community member with zero sequences being misidentified. We then subsampled the community list using taxonomic proportions to mimic a natural plant host–associated microbiome, ultimately yielding a diverse community of 784 members. DISCo-microbe can create a highly diverse community list of microbes that can be distinguished through 16S rRNA gene sequencing, and has the ability to subsample (i.e., design) the community for the desired number of members and taxonomic proportions. Although developed for bacteria, the program allows for any alignment input from any taxonomic group, making it broadly applicable. The software and data are freely available from GitHub and Python Package Index (PYPI).

16S rRNA↗

NASA NTRS2016

Genomic and Phenotypic Characterization of Yeast Biosensor for Deep-space Radiation

The BioSentinel mission was selected to launch as a secondary payload onboard NASA Exploration Mission 1 (EM-1) in 2018. In BioSentinel, the budding yeast Saccharomyces cerevisiae will be used as a biosensor to measure the long-term impact of deep-space radiation to living organisms. In the 4U-payload, desiccated yeast cells from different strains will be stored inside microfluidic cards equipped with 3-color LED optical detection system to monitor cell growth and metabolic activity. At different times throughout the 12-month mission, these cards will be filled with liquid yeast growth media to rehydrate and grow the desiccated cells. The growth and metabolic rates of wild-type and radiation-sensitive strains in deep-space radiation environment will be compared to the rates measured in the ground- and microgravity-control units. These rates will also be correlated with measurements obtained from onboard physical dosimeters. In our preliminary long-term desiccation study, we found that air-drying yeast cells in 10% trehalose is the best method of cell preservation in order to survive the entire 18-month mission duration (6-month pre-launch plus 12-month full-mission periods). However, our study also revealed that desiccated yeast cells have decreasing viability over time when stored in payload-like environment. This suggests that the yeast biosensor will have different population of cells at different time points during the long-term mission. In this study, we are characterizing genomic and phenotypic changes in our yeast biosensor due to long-term storage and desiccation. For each yeast strain that will be part of the biosensor, several clones were reisolated after long-term storage by desiccation. These clones were compared to their respective original isolate in terms of genomic composition, desiccation tolerance and radiation sensitivity. Interestingly, clones from a radiation-sensitive mutant have better desiccation tolerance compared to their original isolate without losing radiation sensitivity. We employed Next-Generation Sequencing technology to better understand this phenotypic variation. Current effort is focusing on the analysis of high-throughput sequencing data to look for genomic changes in these reisolated clones compared to their original isolate.

yeast↗

NASA NTRS2019

Leafy Greens Grown on the International Space Station May Provide a Nutritious Supplement to Astronauts' Diet

Supplemental safe food production has been an essential goal of NASA to meet the nutritional needs of astronauts on the International Space Station (ISS) as well as for future long duration missions to the moon and beyond. Food crops grown in space experience different environmental conditions than plants grown on Earth (i.e. microgravity and spaceflight physical sciences impacts). To test the growth methods and effects of the space environment, red romaine lettuce Lactuca sativa cv. 'Outredgeous', was grown in Veggie plant growth chambers on the ISS. Microbiological food safety of the plants grown on the ISS was determined by heterotrophic plate counts to assess total microbial load for bacteria and fungi as well as screening for specific pathogens and isolate identification. Molecular characterization was completed using Next Generation Sequencing (NGS) to provide valuable information on the taxonomic composition and community structure of the plant microbiome. Chemical analyses of plant tissue were conducted to understand spaceflight-induced changes in key elements in the space diet, phenolics, anthocyanin levels, and Oxygen radical absorbance capacity (ORAC), a measure of antioxidant capacity. Three growth tests of red romaine lettuce were completed on ISS, VEG-01A, VEG-01B, and VEG-03A. Plants were harvested using two harvest methods, either a single terminal harvest (after 33 days) or cut-and-come-again repetitive harvesting (64 days total growth). Ground controls were grown simultaneously with a delay to accommodate condition monitoring and replication. A comparison of the plant tissue returned to Earth showed leaves from the second grow-out had significantly higher bacterial counts than the preceding or subsequent growth test or any of the ground controls. Fungal counts were significantly higher on the final cut-and-come-again harvest of the third grow out. None of the potential foodborne pathogens that were screened for were detected. Bacterial and fungal isolate identification and community characterization indicated similar diversity between VEG-01A and VEG-01B growth tests, however, there appeared to be subtle differences in diversity and distribution among the three growth tests. Chemical analysis of plant tissue revealed significant variation in a few elemental data, but variation in levels of phenolics, anthocyanins, and ORAC was not significantly different. This study indicated that leafy vegetable crops could safely provide an edible supplement to astronauts' diet, and our analysis provided baseline data for continual operation of the Veggie plant growth units on ISS. This research was funded by NASA's space biology program.

Food Production↗

NASA NTRS2019

Plant Microbiomes May Provide Vital Information to Plant Success

Plant associated microbiomes, the rhizosphere and phyllosphere, are composed of communities of bacteria and fungi that may be mutualistic or pathogenic. These communities have the potential to influence plant health and development and can affect plant growth. Crop plants are being investigated as a fresh and safe supplement to astronauts’ diet and it is critical to understand and characterize these microbial communities. Multi-species crops, Mizuna mustard (Brassica rapa var japonica), ‘Outredgeous’ red romaine lettuce (Lactuca sativa), and Waldman’s Green lettuce (Lactuca sativa) were grown in two Veggie units on the International Space Station (ISS) for three grow outs in various combinations of plant types. Upon harvest, plant and pillow samples were frozen and returned to Earth for analysis. Bacterial and fungal community analyses for plant leaf and root, as well as pillow components, wick and media, were completed using next generation sequencing with the goal of surveying the composition of the entire community and identifying any potential pathogens. Bacteria were identified using the 16S rRNA gene whereas, fungi were identified with the internal transcribed spacer (ITS). The community composition for these three crops was compared between crop types and between plant tissue types. It is vital to mission success for the short term and long term to add nutritious, safe to eat vegetables providing a supplement to the crew members’ dietary requirements as well as to develop planning for deep space missions as we reach for the moon and on to Mars. Veggie technology validation tests were supported by NASA’s Space Biology Program.

Khodadad, Christina L.↗

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

Insights From Routine Microbiological Monitoring of Air in Astromaterials Curation Cleanrooms

NASA maintains nine separate cleanrooms at the Johnson Space Center to curate and preserve astromaterials samples. Routine microbial monitoring of the surfaces in these cleanrooms began in 20181. Until recently, materials compatibility requirements prevented monitoring airborne biological particles in all but one of these cleanrooms. New astromaterials collections from carbon rich asteroids are more susceptible to biological degradation than previous collections. Therefore, it is important to monitor the bioburden in the air and on surfaces in these labs. Establishing a comprehensive microbial monitoring program will help inform the monitoring and curation plans for Mars sample return which will include samples that are extremely biologically sensitive. In April of 2022 we began routinely collecting air samples in seven of the nine curation cleanrooms (Meteorite ISO 7 equivalent, Lunar ISO 6 equivalent, Stardust, Hayabusa2, OSIRIS-REx ISO 5 equivalent, and Genesis ISO 4 equivalent) using a sampling device that collects airborne biological particles on an electret filter instead conventional sampler that collect cells in a liquid media or onto organic rich Petri dishes. Electret is a generic term for electrostatically charged media. The charge on these materials increases particle trapping when compared to non-charged filters of similar thickness. N95 respirators also use electret filters. This dry sample collection method allows us to meet materials requirements for all the curation labs and reduces the risk of inadvertently introducing contamination as part of our monitoring effort. This method also allows us to preserve a portion of each sample for DNA extraction and next generation sequencing. DNA sequencing helps us to characterize the portion of the cleanroom microbiome that we cannot culture. We will present the results of our first 8 months of monitoring, compare these results to particle counts in the labs and to measurements made directly onto Petri dishes when possible. We will also make recommendations for modifications to the sampling method to improve sampling efficiency and preserve diversity.

A. B. Regberg↗

NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Machine Learning↗

NASA NTRSDate not supplied

A Dominant Arabidopsis Thaliana ACTIN7 Mutant for Studies of Cytoskeletal-Mediated Root Gravity and Spaceflight Stress Responses

A forward genetic screen for Arabidopsis thaliana mutants that exhibited differential sensitivity to the actin-disrupting compound, latrunculin B (LatB), was conducted to uncover new players involved in actin-mediated root gravity responses. This to the isolation of a mutant that exhibited robust primary root growth at 100 nM LatB, which is a concentration that severely inhibits wild-type root elongation. Phenotypic analysis revealed that hypocotyl elongation in the dark and root hair tip growth in the mutant could tolerate LatB concentrations that impaired these processes in wild type. A cross between the mutant and wild type resulted in progeny resistance to LatB, which indicated that the mutant is dominant (hereafter referred to as LBR1 for LatB Resistant1). Filamentous-actin (F-actin) organization in LBR1 primary roots remained intact at 100 nM LatB, while that of wild type exhibited fragmented F-actin. Next generation sequencing revealed that LBR1 had a single nucleotide polymorphism (SNP) in the AT5G09810 gene that changed cytosine to a thiamine at the first exon. AT5G09810 encodes ACTIN7 (ACT7), which is one of three vegetative actin isoforms in A. thaliana. The SNP in the ACT7 gene led to a change in a single amino acid from proline at position 32 to a serine. Transgenic complementation of LBR1 plants with wild-type ACT7 under the control of the ACT7 promoter (pACT7:ACT7) and wild-type plants with LBR1, which contained the proline to serine mutation, also under the ACT7 promoter (pACT7:LBR1) confirmed that LBR1 is ACT7. The pACT7:LBR1 construct was also able to confer LatB resistance to the act7-5 and act2-3 vegetative ACT mutants. Moreover, LBR1 exhibited partial tolerance to salt and low phosphate, and enhanced root skewing on a clinostat, suggesting that site-directed engineering of vegetative ACT presents a strategy for generating stress-tolerant plants for spaceflight applications and studies of actin-mediated gravity responses.

Plant Space Biology↗

NASA NTRSDate not supplied

Developing Open-Source Training Materials for AI/ML and Space Biological Sciences Using NASA Cloud-Based Data

Artificial Intelligence (AI) and Machine Learning (ML) has gained significant traction in the biological and biomedical research fields in the last two decades, in part thanks to an increasing culture of open data sharing and reuse. Due to its capability for identifying complex relationships and patterns, AI/ML methodology is particularly well suited to recognize and predict biological patterns from high-dimensional next-generation sequencing data (e.g. whole genome sequencing, transcriptomic sequencing), as well as from biological or medical imaging data (e.g. microscopy, computed tomography, ultrasound, magnetic resonance imaging, radiography). These methodologies hold particular promise for space biosciences research and automated space health monitoring systems. However, there are many key considerations for properly training, validating, and testing a machine learning model in biological research or clinical application. Even with the positive culture of Open Science and data sharing, inexperienced researchers working quickly without proper checks can produce models that perform poorly outside of the immediate training dataset. Lessons learned from biological AI/ML research indicate that Open Science principles such as data sharing and open-source code must go hand-in-hand with publicly available, high-quality training curricula in best practices, with modules centered on real-life scientific use cases and data so future AI/ML practitioners gain experience on real problems. Here we present the development of open-source training materials for AI/ML and space biosciences, as part of the NASA Transform to Open Science Training (TOPST) initiative. We develop 4 independent training programs, focused on the following topics: 1) Fundamentals of Machine Learning and Space Biosciences Domain, 2) Open Science, Artificial Intelligence, and Ethical Best Practices for Data Sharing and Analysis, 3) Using AI/ML Classification to Identify Gene Networks Affected By Space Exposure in Mouse Liver, and 4) Using Neural Networks to Find DNA Damage Patterns in Immune Cells after Radiation. All programs leverage cloud-based NASA biological datasets. The curriculum we present will enable worldwide access to training in AI/ML and scientific analysis.

James Andrew Casaletto↗

NASA NTRSDate not supplied

Somatic Mutation in Mice on the International Space Station (ISS): Guanine Substitution Suggests Link to Cancer Risk

We conducted comprehensive analysis of single nucleotide somatic mutations in mice exposed to microgravity and other factors aboard the International Space Station (ISS), using data archived in GeneLab. Animals in the experimental cohort consisted of mice that spent 37 days on the ISS within the Rodent Habitat. Ground control animals consisted of mice of identical age, sex, strain, in a terrestrial Rodent Habitat controlled for temperature, humidity and carbon dioxide levels, to match ISS conditions as closely as possible. RNA extracted from eye, liver, skeletal muscle, and kidney tissue specimens was subjected to next-generation sequencing to acquire primary data. Our analysis employed cutting-edge software developed at NASA Ames Research Center, executed on the NASA Ames Supercomputer and on another high-performance computer, for accurate variant calling of single point mutations. ISS-flown mice exhibited a notably heightened level of somatic mutation compared to control mice. The degree of somatic mutation correlated with the degree of gene expression across the four tissue types, i.e., the greatest rate of mutation accumulation was seen in highly expressed genes. We discovered that guanine substitutions were the most common type of somatic mutation. This observation is consistent with the hypothesis that DNA mutation events stem from reactive oxygen/nitrogen/chlorine species-mediated guanine oxidation induced by the spaceflight environment. Since guanine oxidation is a prominent feature of the DNA mutation landscape that accompanies malignant transformation, our findings suggest a possible link between the spaceflight environment and cancer risk that is independent of radiation carcinogenesis.

ISS↗

NASA NTRSDate not supplied

A Dominant Arabidopsis Thaliana ACTIN7 Mutant for Studies of Cytoskeletal-Mediated Root Gravity and Spaceflight Stress Responses

A forward genetic screen for Arabidopsis thaliana mutants that exhibited differential sensitivity to the actin-disrupting compound, latrunculin B (LatB), was conducted to uncover new players involved in actin-mediated root gravity responses. This led to the isolation of a mutant that exhibited robust primary root growth at 100 nM LatB, which is a concentration that severely inhibits wild-type root elongation. Phenotypic analysis revealed that hypocotyl elongation in the dark and root hair tip growth in the mutant could tolerate LatB concentrations that impaired these processes in wild type. A cross between the mutant and wild type resulted in progeny resistance to LatB, which indicated that the mutant is dominant (hereafter referred to as LBR1 for LatB Resistant1). Filamentous-actin (F-actin) organization in LBR1 primary roots remained intact at 100 nM LatB, while that of wild type exhibited fragmented F-actin. Next generation sequencing revealed that LBR1 had a single nucleotide polymorphism (SNP) in the AT5G09810 gene that changed cytosine to a thiamine at the first exon. AT5G09810 encodes ACTIN7 (ACT7), which is one of three vegetative actin isoforms in A. thaliana. The SNP in the ACT7 gene led to a change in a single amino acid from proline at position 32 to a serine. Transgenic complementation of LBR1 plants with wild-type ACT7 under the control of the ACT7 promoter (pACT7:ACT7) and wild-type plants with LBR1, which contained the proline to serine mutation, also under the ACT7 promoter (pACT7:LBR1) confirmed that LBR1 is ACT7. The pACT7:LBR1 construct was also able to confer LatB resistance to the act7-5 and act2-3 vegetative ACT mutants. Moreover, LBR1 exhibited partial tolerance to salt and low phosphate, and enhanced root skewing on a clinostat, suggesting that site-directed engineering of vegetative ACT presents a strategy for generating stress-tolerant plants for spaceflight applications and studies of actin-mediated gravity responses.

Plant Space Biology↗

NASA NTRSDate not supplied

Developing Open-Source Training Materials for AI/ML and Space Biological Sciences Using NASA Cloud-Based Data

Artificial Intelligence (AI) and Machine Learning (ML) has gained significant traction in the biological and biomedical research fields, in part due to a culture of open data sharing and reuse. AI/ML methodology is well-suited to recognize and predict biological patterns from high-dimensional next-generation sequencing data (e.g. whole genome sequencing, transcriptomic sequencing), as well as from biological or medical imaging data (e.g. microscopy, computed tomography, ultrasound, magnetic resonance imaging, radiography). These methodologies hold particular promise for space biosciences research and automated space health monitoring systems. However, there are key considerations for properly training, validating, and testing a machine learning model in biological research or clinical application. Inexperienced researchers can produce models that perform poorly outside of the training dataset. Open Science principles such as data sharing and open-source code must go hand-in-hand with publicly available, high-quality training curricula in best practices, with modules centered on real-life scientific use cases and data so future AI/ML practitioners gain experience on real problems. Here we present the development of open-source training materials for AI/ML and space biosciences, as part of the NASA Transform to Open Science Training (TOPST) initiative. We develop 4 independent training programs, focused on the following topics: 1) Fundamentals of Machine Learning and Space Biosciences Domain, 2) Open Science, Artificial Intelligence, and Ethical Best Practices for Data Sharing and Analysis, 3) Using AI/ML Classification to Identify Gene Networks Affected By Space Exposure in Mouse Liver, and 4) Using Neural Networks to Find DNA Damage Patterns in Immune Cells after Radiation. All programs leverage cloud-based NASA biological datasets. The curriculum we present will enable worldwide access to training in AI/ML and scientific analysis.

James Casaletto↗

NASA NTRSDate not supplied

High School Citizen Scientists Use AI/ML to Predict Intra-Ocular Pressure From Gene Expression Data for Spaceflown Mice

Artificial Intelligence (AI) and Machine Learning (ML) have increasingly become pivotal in biological and biomedical research, largely due to the culture of open data sharing and its associated benefits. The methodologies inherent in AI/ML are particularly adept at identifying and forecasting biological phenotypes from the vast amounts of data generated by next-generation sequencing technologies. These techniques offer substantial promise for advancing research in space biosciences and for the development of automated systems for monitoring space health. Nevertheless, there are crucial aspects to consider when training, validating, and testing machine learning models in both biological research and clinical contexts. It is essential that Open Science principles, including data sharing and the availability of open-source code, are complemented by high-quality, publicly accessible training resources. These resources should focus on best practices and include modules based on real-world scientific cases and data to ensure that future AI/ML practitioners gain practical experience with genuine problems. Addressing this knowledge gap, we have designed, developed, and delivered both interactive and self-paced training programs for citizen scientists worldwide, enabling them to utilize AI/ML for space biology research. This initiative was made possible through generous funding from a Transformation to Open Science Training grant. The interactive training sessions, conducted this summer, utilized AI/ML techniques to analyze data from the Open Science Data Repository, specifically targeting the effects of spaceflight on ocular structure and function. The dataset OSD-583, from the Rodent Research 9 mission, provides experimental data detailing the ocular responses of mice subjected to a 35-day spaceflight, compared with ground control counterparts. Using OSD-583 as observational data, our summer training participants applied AI/ML methods to predict intraocular pressure from RNA-seq data and identify the genes most predictive of the observed responses. Further analysis through pathway enrichment and gene set enrichment revealed that these genes are involved in molecular and cellular processes contributing to retinal degeneration.

James Casaletto↗

DOE OSTI2022

evSeq: Cost-Effective Amplicon Sequencing of Every Variant in a Protein Library

Widespread availability of protein sequence-fitness data would revolutionize both our biochemical understanding of proteins and our ability to engineer them. Unfortunately, even though thousands of protein variants are generated and evaluated for fitness during a typical protein engineering campaign, most are never sequenced, leaving a wealth of potential sequence-fitness information untapped. Primarily, this is because sequencing is unnecessary for many protein engineering strategies; the added cost and effort of sequencing is thus unjustified. It also results from the fact that, even though many lower cost sequencing strategies have been developed, they often require at least some sequencing or computational resources, both of which can be barriers to access. In this work, we present every variant sequencing (evSeq), a method and collection of tools/standardized components for sequencing a variable region within every variant gene produced during a protein engineering campaign at a cost of cents per variant. evSeq was designed to democratize low-cost sequencing for protein engineers and, indeed, anyone interested in engineering biological systems. Execution of its wet-lab component is simple, requires no sequencing experience to perform, relies only on resources and services typically available to biology labs, and slots neatly into existing protein engineering workflows. Analysis of evSeq data is likewise made simple by its accompanying software (found at github.com/fhalab/evSeq, documentation at fhalab.github.io/evSeq), which can be run on a personal laptop and was designed to be accessible to users with no computational experience. Here, low-cost and easy to use, evSeq makes collection of extensive protein variant sequence-fitness data practical.

59 BASIC BIOLOGICAL SCIENCES↗