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DOE OSTI2021

The combined application of the anti- RAS1 and anti- RIM101 2'- O MethylRNA oligomers enhances Candida albicans filamentation control

Abstract Although antisense oligomers (ASOs) have been successfully utilized to control gene expression, they have been little exploited to control Candida albicans virulence's determinants. Filamentation is an important virulence factor of C. albicans, and RAS1 and RIM101 genes are involved in its regulation. Thus, the main goal of this work was to project ASOs, based on 2'-OMethyl chemical modification, to target RAS1 and RIM101 mRNA and to validate its application either alone or in combination, to reduce Candida filamentation in different human body fluids. It was verified that both, anti-RAS1 2'OMe and anti-RIM101 2'OMe oligomers, were able to reduce the levels of RAS1 and RIM101 genes’ expression and to significantly reduce C. albicans filamentation. Furthermore, the combined application of anti-RAS1 2'OMe oligomer and anti-RIM101 2'OMe oligomer enhances the control of C. albicans filamentation in artificial saliva and urine. Our work confirms that ASOs are useful tools for research and therapeutic development on the control of candidiasis.

Barbosa, Ana↗

DOE OSTI2022

Synthetic genetic circuits as a means of reprogramming plant roots

We report the shape of a plant’s root system influences its ability to reach essential nutrients in the soil and to acquire water during drought. Progress in engineering plant roots to optimize water and nutrient acquisition has been limited by our capacity to design and build genetic programs that alter root growth in a predictable manner. We developed a collection of synthetic transcriptional regulators for plants that can be compiled to create genetic circuits. These circuits control gene expression by performing Boolean logic operations and can be used to predictably alter root structure. This work demonstrates the potential of synthetic genetic circuits to control gene expression across tissues and reprogram plant growth.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

The influence of microbial colonization on inflammatory versus pro-healing trajectories in combat extremity wounds

A combination of improved body armor, medical transportation, and treatment has led to the increased survival of warfighters from combat extremity injuries predominantly caused by blasts in modern conflicts. Despite advances, a high rate of complications such as wound infections, wound failure, amputations, and a decreased quality of life exist. To study the molecular underpinnings of wound failure, wound tissue biopsies from combat extremity injuries had RNA extracted and sequenced. Wounds were classified by colonization (colonized vs. non-colonized) and outcome (healed vs. failed) status. Differences in gene expression were investigated between timepoints at a gene level, and longitudinally by multi-gene networks, inferred proportions of immune cells, and expression of healing-related functions. Differences between wound outcomes in colonized wounds were more apparent than in non-colonized wounds. Colonized/healed wounds appeared able to mount an adaptive immune response to infection and progress beyond the inflammatory stage of healing, while colonized/failed wounds did not. Although, both colonized and non-colonized failed wounds showed increasing inferred immune and inflammatory programs, non-colonized/failed wounds progressed beyond the inflammatory stage, suggesting different mechanisms of failure dependent on colonization status. Overall, these data reveal gene expression profile differences in healing wounds that may be utilized to improve clinical treatment paradigms.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Evaluating two live-attenuated vaccines against Salmonella enterica serovar Reading in turkeys: reduced tissue colonization and cecal tonsil transcriptome responses

Vaccines that cross-protect across serovars ofSalmonella enterica(Salmonella) would be a beneficial intervention against emerging and persistentSalmonellaisolates of concern for the turkey industry. The 2017–2019 foodborne outbreak ofSalmonella entericaserovar Reading (S. Reading) revealed the need for effective control of this serovar in turkey production. This study evaluated two live-attenuatedSalmonellavaccines, an internally developed cross-protective vaccine and a commercially available vaccine, against an outbreak-associated strain ofS. Reading in turkeys. At 1 day and 3 weeks of age, male turkey poults were either mock-vaccinated with phosphate buffered saline (PBS) or given one of the vaccines by oral gavage (primary and booster) or aerosol spray (primary) then drinking water (booster). At 7 weeks of age, poults were challenged with 10 9 colony forming units (CFU) ofS. Reading; a mock-vaccinated group was mock-challenged with PBS. Colonization of the cecal contents and cecal tonsil was 1.5–3 log 10 CFU/g lower in vaccinated birds than mock-vaccinated birds at 7 and/or 14 days post-inoculation (DPI).Salmonelladissemination to the spleen was significantly reduced by both vaccines. Gene expression of intestinal transporters (such asSCNN1BandSLC10A2) and tight junction proteins was significantly decreased in the turkey cecal tonsil transcriptome at 2 DPI withS. Reading. Vaccination with either vaccine mitigated most cecal tonsil gene expression responses toS. Reading challenge. Therefore, both the internally developed vaccine and commercial vaccine were cross-protective against colonization and dissemination, and both were able to limit transcriptional changes from challenge in intestinal health-related genes in the cecal tonsil, thereby providing vaccination efficacy and impact data againstS. Reading in turkeys.

Veterinary Sciences↗

NASA NTRS2014

MicroRNA Expression Profiles in Cultured Human Fibroblasts in Space

Microgravity, or an altered gravity environment from the static 1g, has been shown to influence global gene expression patterns and protein levels in living organisms. However, it is unclear how these changes in gene and protein expressions are related to each other or are related to other factors regulating such changes. A different class of RNA, the small non-coding microRNA (miRNA), can have a broad effect on gene expression networks by mainly inhibiting the translation process. Previously, we investigated changes in the expression of miRNA and related genes under simulated microgravity conditions on the ground using the NASA invented bioreactor. In comparison to static 1 g, simulated microgravity altered a number of miRNAs in human lymphoblastoid cells. Pathway analysis with the altered miRNAs and RNA expressions revealed differential involvement of cell communication and catalytic activity, as well as immune response signaling and NGF activation of NF-kB pathways under simulated microgravity condition. The network analysis also identified several projected networks with c- Rel, ETS1 and Ubiquitin C as key factors. In a flight experiment on the International Space Station (ISS), we will investigate the effects of actual spaceflight on miRNA expressions in nondividing human fibroblast cells in mostly G1 phase of the cell cycle. A fibroblast is a type of cell that synthesizes the extracellular matrix and collagen, the structural framework for tissues, and plays a critical role in wound healing and other functions. In addition to miRNA expressions, we will investigate the effects of spaceflight on the cellular response to DNA damages from bleomycin treatment.

Wu, Honglu↗

DOE OSTI2026

Multi-omics reveals nitrogen dynamics associated with soil microbial blooms during snowmelt

Snowmelt triggers a soil microbial bloom and crash that affects nitrogen (N) export in high-elevation watersheds. The mechanisms underlying these microbial dynamics are uncertain, making soil nitrogen processes difficult to predict as snowpack declines globally. Here, integration of genome-resolved metagenomics, metatranscriptomics and metabolomics in a high-elevation watershed revealed ecologically distinct soil microorganisms linked across the snowmelt time-period by their unique nitrogen cycling capacities. The molecular properties and transformations of dissolved organic N suggested that degradation or recycling of microbial biomass provided N for biosynthesis during the microbial bloom. Winter-adapted Bradyrhizobia spp. oxidized amino acids anaerobically and had the highest gene expression for denitrification during the microbial bloom. A pulse of nitrate was driven by spring-adapted Nitrososphaerales after snowmelt, but dissimilatory nitrate reduction to ammonia (DNRA) gene expression indicated significant nitrate retention potential. These findings inform our understanding of nitrogen cycling in environments sensitive to snowpack decline due to global change.

Sorensen, Patrick O↗

DOE OSTI2025

Testing for the Genomic Footprint of Conflict Between Life Stages in an Angiosperm and Moss Species

Abstract The maintenance of genetic variation by balancing selection is of considerable interest to evolutionary biologists. An important but understudied potential driver of balancing selection is antagonistic pleiotropy between diploid and haploid stages of the plant life cycle. Despite sharing a common genome, sporophytes (2n) and gametophytes (n) may undergo differential or even opposing selection. Theoretical work suggests antagonistic pleiotropy between life stages can generate balancing selection and maintain genetic variation. Despite the potential for far-reaching consequences of gametophytic selection, empirical tests of its pleiotropic effects (neutral, synergistic, or antagonistic) on sporophytes are generally lacking. Here, we examined the population genomic signals of selection across life stages in the angiosperm Rumex hastatulus and the moss Ceratodon purpureus. We compared gene expression between life stages and sexes, combined with neutral diversity statistics and the analysis of the distribution of fitness effects. In contrast to what would be predicted under balancing selection due to antagonistic pleiotropy, we found that unbiased genes between life stages were under stronger purifying selection, likely explained by a predominance of synergistic pleiotropy between life stages and strong purifying selection on broadly expressed genes. In addition, we found that 30% of candidate genes under balancing selection in R. hastatulus were located within inversion polymorphisms. Our findings provide novel insights into the genome-wide characteristics and consequences of plant gametophytic selection.

Evolutionary Biology↗

DOE OSTI2022

A Pilot Study of Whole-Blood Transcriptomic Analysis to Identify Genes Associated with Repetitive Low-Level Blast Exposure in Career Breachers

Repetitive low-level blast exposure is one of the major occupational health concerns among US military service members and law enforcement. This study seeks to identify gene expression using microRNA and RNA sequencing in whole-blood samples from experienced breachers and unexposed controls. We performed experimental RNA sequencing using Illumina’s HiSeq 2500 Sequencing System, and microRNA analysis using NanoString Technology nCounter miRNA expression panel in whole-blood total RNA samples from 15 experienced breachers and 14 age-, sex-, and race-matched unexposed controls. We identified 10 significantly dysregulated genes between experienced breachers and unexposed controls, with FDR corrected <0.05: One upregulated gene, LINC00996 (long intergenic non-protein coding RNA 996); and nine downregulated genes, IGLV3-16 (immunoglobulin lambda variable 3-16), CD200 (CD200 molecule), LILRB5 (leukocyte immunoglobulin-like receptor B5), ZNF667-AS1 (ZNF667 antisense RNA 1), LMOD1 (leiomodin 1), CNTNAP2 (contactin-associated protein 2), EVPL (envoplakin), DPF3 (double PHD fingers 3), and IGHV4-34 (immunoglobulin heavy variable 4-34). The dysregulated gene expressions reported here have been associated with chronic inflammation and immune response, suggesting that these pathways may relate to the risk of lasting neurological symptoms following high exposures to blast over a career.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Chromosome compartmentalization alterations in prostate cancer cell lines model disease progression

Prostate cancer aggressiveness and metastatic potential are influenced by gene expression and genomic aberrations, features that can be influenced by the 3D structure of chromosomes inside the nucleus. Using chromosome conformation capture (Hi-C), we conducted a systematic genome architecture comparison on a cohort of cell lines that model prostate cancer progression, from normal epithelium to bone metastasis. We describe spatial compartment identity (A-open versus B-closed) changes with progression in these cell lines and their relation to gene expression changes in both cell lines and patient samples. In particular, 48 gene clusters switch from the B to the A compartment, including androgen receptor, WNT5A, and CDK14. These switches are accompanied by changes in the structure, size, and boundaries of topologically associating domains (TADs). Further, compartment changes in chromosome 21 are exacerbated with progression and may explain, in part, the genesis of the TMPRSS2-ERG translocation. These results suggest that discrete 3D genome structure changes play a deleterious role in prostate cancer progression.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Expanding the genetic engineering toolbox for the metabolically flexible acetogen Eubacterium limosum

Abstract Acetogenic bacteria are an increasingly popular choice for producing fuels and chemicals from single carbon (C1) substrates. Eubacterium limosum is a promising acetogen with several native advantages, including the ability to catabolize a wide repertoire of C1 feedstocks and the ability to grow well on agar plates. However, despite its promise as a strain for synthetic biology and metabolic engineering, there are insufficient engineering tools and molecular biology knowledge to leverage its native strengths for these applications. To capitalize on the natural advantages of this organism, here we extended its limited engineering toolbox. We evaluated the copy number of three common plasmid origins of replication and devised a method of controlling copy number and heterologous gene expression level by modulating antibiotic concentration. We further quantitatively assessed the strength and regulatory tightness of a panel of promoters, developing a series of well-characterized vectors for gene expression at varying levels. In addition, we developed a black/white colorimetric genetic reporter assay and leveraged the high oxygen tolerance of E. limosum to develop a simple and rapid transformation protocol that enables benchtop transformation. Finally, we developed two new antibiotic selection markers—doubling the number available for this organism. These developments will enable enhanced metabolic engineering and synthetic biology work with E. limosum.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Endothelium-specific endothelin-1 expression promotes pro-inflammatory macrophage activation by regulating miR-33/NR4A axis

The hallmark of atherogenesis is characterized as endothelial dysfunction and subsequent macrophage activation. Although our previous study has demonstrated that endothelin-1 (ET-1) plays an important role in atherogenesis, the underlying mechanism remains deeply investigation. Enhanced atherosclerotic plaques were observed in endothelium-specific ET-1 overexpression ApoE{sup −/-} mice (eET-1/ApoE{sup −/-}) concomitant with increased secretion of pro-inflammatory adhesion molecules and cytokines. The conditional media used for culturing human umbilical vein endothelial cells (HUVECs) with AdET-1 infection and subjected to OX-LDL stimulation, was collected and utilized for bone marrow-derived macrophages (BMDMs) culturing. RT-PCR analysis showed increased genes expression related to classical M1 macrophages but decreased alternative activated M2 macrophages genes expression in macrophage culturing with the conditional media. Furthermore, consistent regulations of macrophage polarization were observed using isolated exosomes from the conditional media. More importantly, we noticed that miR-33 was enriched in the exosomes derived by HUVECs with AdET-1 infection, while bioinformatics analysis further indicated that miR-33 directly targeted NR4A and miR-33/NR4A axis was required for the effect of endothelial-specific ET-1 overexpression on pro-inflammatory macrophage activation. By contrast, such effects could be reversed by ET-1 knockdown. Taken together, our study indicated that the exosomes derived by HUVECs with AdET-1 infection can transfer miR-33 to macrophages and subsequently promote pro-inflammatory macrophage activation by directly targeting to NR4A. These evidences clearly revealed that miR-33/NR4A axis was the important mechanism underlying the effect of ET-1 on macrophage activation and indicated that ET-1 may act as a promising target for atherosclerosis management.

60 APPLIED LIFE SCIENCES↗

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↗

DOE OSTI2021

The Landscape of RNA-Protein Interactions in Plants: Approaches and Current Status

RNAs transmit information from DNA to encode proteins that perform all cellular processes and regulate gene expression in multiple ways. From the time of synthesis to degradation, RNA molecules are associated with proteins called RNA-binding proteins (RBPs). The RBPs play diverse roles in many aspects of gene expression including pre-mRNA processing and post-transcriptional and translational regulation. In the last decade, the application of modern techniques to identify RNA–protein interactions with individual proteins, RNAs, and the whole transcriptome has led to the discovery of a hidden landscape of these interactions in plants. Global approaches such as RNA interactome capture (RIC) to identify proteins that bind protein-coding transcripts have led to the identification of close to 2000 putative RBPs in plants. Interestingly, many of these were found to be metabolic enzymes with no known canonical RNA-binding domains. Here, we review the methods used to analyze RNA–protein interactions in plants thus far and highlight the understanding of plant RNA–protein interactions these techniques have provided us. We also review some recent protein-centric, RNA-centric, and global approaches developed with non-plant systems and discuss their potential application to plants. We also provide an overview of results from classical studies of RNA–protein interaction in plants and discuss the significance of the increasingly evident ubiquity of RNA–protein interactions for the study of gene regulation and RNA biology in plants.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRSDate not supplied

Looking into Ocular Risks of Spaceflight through the Mouse Retina

Ocular alterations have been observed at anatomical levels in astronauts on long duration spaceflight missions, such as what would be required for missions to Mars. These alterations cause an array of signs which together constitute the Spaceflight-Associated Neuro-ocular Syndrome (SANS), one of the top risk priorities of the NASA Human Research Program. Not much is known about SANS at the cellular and molecular level, but studies in mice and rats have recently begun to yield observations on how the spaceflight environment might affect the eye’s biology. Preliminary data from shuttle mouse experiments, and more recently experiments on ISS, have shown changes in retinal physiology via histology and gene expression analysis. This study utilizes samples from the CASIS sponsored Rodent Research 8 Experiment (RRRM-1) tissue sharing opportunity, delivered to the ISS by SpaceX CRS-16 on 12/08/2018. Female BALB/cAnNTac mice were on the ISS for 45 days, while ground controls consisted ofa standard vivarium group and spaceflight habitat group. Here we investigate the molecular response of the mouse retina to identify genes and pathways affected by spaceflight conditions using histology and transcriptomic RNAseq data. This Differentially Expressed Gene (DEG) data was used for pathway analysis with Galaxy (Genelab) and Ingenuity Pathway Analysis (IPA). We identified pathways related to neuronal differentiation, cellular transport/movement, and wound healing. Some of the top DEGs have known relation to ophthalmic diseases. Though there were DEGs throughout the comparisons we tested, there was no clear effect of spaceflight. This could be due to sample processing, which required mice to be returned to Earth about a day before they were sacrificed, possibly allowing for readaptation affecting the retinal transcriptome. However, there was a clear effect of age, between the young (10-12 weeks) and old (32 weeks) groups, and between the baseline and end of experiment, about 46 days.

SANS↗

NASA NTRS2015

Oxidative Stress and Autophagy Responses of Osteocytes Exposed to Spaceflight-like Radiation.

Weightlessness and radiation, two of the unique elements of the space environment, causes a profound decrement in bone mass that mimics aging. This bone loss is thought to result from increased activity of bone-resorbing osteoclasts and functional changes in bone-forming osteoblasts, cells that give rise to mature osteocytes. Our current understanding of the signaling factors and mechanisms underlying bone loss is incomplete. However, it is known that oxidative stress, characterized by the excess production of free radicals, is elevated during radiation exposure. The goals of this study is to examine the response of osteocytes to spaceflight-like radiation and to identify signaling processes that may be targeted to mitigate bone loss in scenarios of space exploration, earth-based radiotherapy and accidental radiation exposure. We hypothesize that (1) oxidative stress, as induced by radiation, decreases osteocyte survival and increases pro-osteoclastogenic signals and that (2) autophagy is one of the key cellular defenses against oxidative stress. Autophagy is the process by which cellular components including organelles and proteins are broken down and recycled. To test our hypothesis, we exposed the osteocyte-like cell line, MLO-Y4, to 0.5, 1, and 2 Gy of simulated space radiation (Iron-56 radiation at 600 MeV/n) and assessed cell numbers, cell growth-associated molecules as well as markers of autophagy and oxidative stress at various time points post-irradiation. We observed a reduction in cell numbers in the groups exposed to 1 and 2 Gy of Iron-56 radiation. Collectively, flow cytometry and gene expression analysis revealed that radiation caused a shift in cell cycle distribution consistent with growth arrest. Compared to sham-treatment, 2 Gy of Iron-56 increased FoxO3, SOD1, and RANKL gene expression yet unexpectedly decreased LC3B-II protein levels at 4 and 24 hours post-IR. Taken together, these findings suggest that simulated space radiation invoke antioxidant, pro-osteoclastogenic, and growth arrest responses in osteocytes. The implications of reduced autophagy flux at the time points examined remain to be elucidated.

Biological Sciences↗

NASA NTRSDate not supplied

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

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗

NASA NTRSDate not supplied

Transcriptomics-based Machine Learning 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

Histological and Transcriptomic Analysis of Spaceflight-Induced Ocular Changes in the Mouse Retina

Anatomical changes have been observed in astronauts’ eyes after long duration spaceflight missions. These alterations can lead to visual impairment which in part constitutes the spaceflight-associated neuroocular syndrome (SANS), one of the top risk priorities for deep space missions. The HRP Systems Biology (SysBio) Translation Project will apply systems biology approaches utilizing current human physiological spaceflight data, molecular results from rodents, and future research with a multi-level, multi-system, and multi-species perspective to augment the existing research plan to resolve the SANS risk. Not much is known about SANS at the cellular and molecular level, but studies in mice and rats have recently begun to determine how spaceflight might affect the biology of the eye. Preliminary studies of mice that flew on the Space Shuttle, and more recently the International Space Station (ISS), have shown changes in retinal physiology as assessed by histology and gene expression analysis. The study presented here obtained samples from the CASIS sponsored Rodent Research 8 Experiment delivered to the ISS by SpaceX CRS-16 on 12/08/2018. Female BALB/cAnNTac mice flew on the ISS for 45 days, while ground controls were housed in a standard vivarium or animal enclosure module. Sacrifice and sample acquisition occurred once mice returned to Earth, possibly allowing for readaptation affecting retinal homeostasis. We applied standard transcriptomic (RNAseq) and histological approaches to characterize genes and pathways in the mouse retina affected by spaceflight or age. The differentially expressed gene (DEG) data was analyzed using Galaxy (GeneLab) and Ingenuity Pathway Analysis. Significant DEGs between flight and ground samples were relatively few but biologically meaningful. Pathways identified related to neuronal differentiation, cellular transport/movement, and wound healing. Age effects were detected between the young (10–12 weeks) and old (32 weeks) groups and between the baseline and end of experiment (~46 days). The biological relevance of specific DEGs were confirmed through immunohistochemical evaluation using fixed histological sections of the eye from four flight group mice and four habitat control mice. Staining was performed specific for synaptophysin, glial fibrillary acidic protein (GFAP), and neurofilament in the retinal periphery, equator, and peripapillary regions. For synaptophysin staining, the innerplexiform and outerplexiform layers were scored; for GFAP staining, Mueller cells and perivascular astrocytes were scored. Results show flight samples typically had more staining of GFAP and neurofilament while, conversely, the habitat control group had more staining of synaptophysin.

C. Perez↗