SEARCH · Search NASA

Results for “transcriptomics”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records

All sources NASA NTRS DOE OSTI

NASA NTRSDate not supplied

Transcriptomics Processing Pipelines for Space Biology: An Open Source and Consensus-Driven Approach

Transcriptomics holds significant value in elucidating the relationship between gene expression, experimental factors, biological factors, and various types of omics data. Enhancing our understanding of these connections is paramount for foundational biology, which plays a pivotal role in devising solutions for challenges pertinent to both space travel and terrestrial life. The NASA GeneLab project, part of the Open Science Data Repository (OSDR.nasa.gov), seeks to accelerate space biology research through cataloging and democratizing ‘omics data, including transcriptomics. Since raw omics data are largely inaccessible to non-bioinformaticians, GeneLab works with the scientific community via the Open Science Analysis Working Groups (AWGs) to develop standard processing pipelines to generate and publish processed data. Unlike raw data, processed data have greater immediate value to diverse users with varying technical backgrounds and computational capabilities. Standardizing processing workflows is essential to match the pace of raw data generation, ensure reproducibility, and enable standardized processed data for comparison across datasets. As of June 2023, transcriptomics studies comprise over half of GeneLab datasets hosted on the OSDR, including data from bulk RNA-seq and Affymetrix or Agilent 1-Channel DNA microarray assays. In collaboration with the AWGs, GeneLab developed consensus processing pipelines for these transcriptomics data types that includes quality control, background correction (microarray only), data normalization and quantification, culminating in the detection and annotation of differentially expressed genes. The work presented here describes Nextflow implementations of GeneLab’s consensus transcriptomics pipelines that automates and accelerates processing of these datasets. In addition to the core data processing, these workflows also include raw data staging and a robust verification and validation program to identify errors in real-time, stop additional downstream computation, and preserve computational resources. These workflows are used to generate GeneLab processed data hosted on the OSDR, and are publicly available as open source software for others to use at: https://github.com/nasa/GeneLab_Data_Processing.

Jonathan Oribello↗

NASA NTRS2018

Transcriptomics and Proteomics Discussion

This presentation will cover the the basic pipelines for transcriptomics and proteomics that the GeneLab Analysis Working Groups (AWGs) have so far determined to be optimal. Basic transcriptomic pipelines will first be presented from primary analysis to higher-order systems analysis. Examples of how the data has been analyzed will be presented. Proteomics pipelines will also be presented compiled from various AWG members. Discussion will be generated from the AWG members to reach a consensus for each omic type.

Transcriptomics↗

NASA NTRSDate not supplied

Bionutrients-1: Utilizing Genomics and Transcriptomics to Assess the Reliability of Microorganisms for In Situ Nutrient Production on Long Duration Missions

The resupply of current long-duration crewed missions to the ISS relies on ground-launched supplies. As NASA looks toward Mars, ground-based resupply will no longer be an option. Critical nutrients, including vitamin C, vitamin K, folate, and thiamin, degrade during long-term storage, and regular consumption of these nutrients is essential for astronaut health. Another challenge of current food systems is the difficulty of consuming sufficient calories when subsisting on the limited flavors of freeze-dried food, which can lead to weight loss. The inclusion of microorganism-based food systems could alleviate both concerns. For example, the fermentation of rehydrated milk into yogurt with microorganisms genetically incorporating genes to produce critical vitamins would allow for both in situ production of nutrients and a fresh food product with additional flavor profiles. In comparison to plant food production, microorganisms require less flight infrastructure. The BioNutrients-1 mission is demonstrating viability of microbial fermentation food production in microgravity and testing the reliability of this approach for long-duration missions lacking resupply. While the BioNutrients-1 mission includes the collection of multiple phenotypic measurements, this status update will focus on the processing of samples for genomics and transcriptomics analyses as well as the planned analysis pipelines. First, the BioNutrients-1 mission seeks to identify microorganisms capable of surviving long-duration storage at ambient temperatures while maintaining genetic fidelity. To achieve this, nine commonly employed microbial species were stored at ambient temperatures in Stasis Packs on the ISS for five years. The viability and mutation rates will be measured at multiple time points for both flown and ground control samples. From an omics perspective, the changes in the bulk rates of point mutations and genetic rearrangements across the Stasis Pack species during the five years of storage will be determined, providing valuable insights into the potential of these microorganisms for long-duration space missions. Second, the BioNutrients-1 mission is characterizing the impact of microgravity on fermentation. Two strains of the yeast Saccharomyces cerevisiae, each encoding antioxidants (β-carotene or zeaxanthin) were flown to ISS for storage and fermentation within simplified bioreactors (Production Packs). The impact of microgravity on the expression of the antioxidant production genes and general metabolic genes will be determined using RNA sequencing. Ultimately, the transcriptome data will be compared to phenotypic measurements, such as the antioxidant yield, end-state biomass, and the production of EtOH, to determine the impacts of microgravity and long-term storage on microbial fermentation. The findings from this research will be instrumental in understanding the challenges and opportunities of microorganism-based food systems in space missions.

BioNutrients↗

NASA NTRSDate not supplied

Space Biofilms - Phenotypic and Transcriptomic Behaviour of Pseudomonas Aeruginosa Biofilms on Board the International Space Station

Bacterial biofilms in space can have a positive or negative impact on the success of a mission. For example, in some instances, biofilms can improve plant growth, facilitate synthesis/recovery of metals from regolith, or bioremediate wastewater. On the other hand, biofilms can deteriorate or cause malfunctions of spaceflight hardware. Biofilms have been found on the wastewater tank of the Environmental Control and Life Support System (ECLSS), which poses a risk to the system. Even more alarming, some biofilms cause infections that may threaten astronauts’ health, like urinary tract infections that if left unclear could cause permanent damage to the kidneys. Given that biofilms can contribute to or hinder the efforts of space exploration, it is necessary to understand the effects of microgravity on biofilm behaviour. The Space Biofilms experiment intends to contribute to such understanding by analysing the morphology and transcriptomic profiles of Pseudomonas aeruginosa PA14 biofilms grown in spaceflight compared to matched ground controls. P. aeruginosa biofilms were grown onboard the International Space Station for 1, 2, or 3 days at 37°C over six surface materials: Stainless Steel 316 (SS316), passivated SS316, and a novel Lubricant Impregnated Surface (LIS) were grown in rich media supplemented with potassium nitrate (LBK) to simulate wastewater. While cellulose membrane, catheter grade silicone, and silicone with special nanotopography (DLIP) were grown in modified Artificial Urine Media supplemented with glucose and high phosphate (mAUMg-hi Pi) to simulate urine. Asynchronous ground controls replicated spaceflight procedures. Morphology analysis revealed that flight samples had a significant decrease in mass, thickness and surface area coverage in LBK. Additionally, biofilm surface coverage on LIS was only 11% of the equivalent samples on SS316 (p<0.001). Associated preliminary transcriptomic data will also be addressed.

Pamela Flores↗

NASA NTRSDate not supplied

Transcriptomic Changes in Seedlings from Seeds Exposed to Simulated Space Radiation

Outside the protection of Earth’s magnetic field, living organisms are constantly exposed to space radiation that consists of energetic protons and other heavier charged particles. With the goal of manned Mars exploration, the production of fresh crop during long duration space missions can be beneficial for meeting astronauts’ nutritional and psychological needs. In our study, we not only evaluated plant/fruit morphometrics and edible fresh mass, but also analyzed transcriptomic changes in seedlings from seeds of three plant species (Arabidopsis, mizuna, and tomato) exposed to simulated Galactic Cosmic Rays(GCR) and solar particle events(SPE). The radiation experiments were performed in the NASA Space Radiation Laboratory (NSRL) facility at Brookhaven National Lab (BNL). 10-day Arabidopsis seedlings were exposed acutely (~240 cGy/hr) to simulated GCR scenarios of combined ions including protons, helium, oxygen, titanium, and/or iron ions at 40 or 80 cGy. Seeds of Arabidopsis, mizuna, and tomato were exposed to 40 or 80 cGy simulated GCR (dry seeds) or SPE (imbibed seeds) at lower dose rates(20-26 cGy/hr). Seedlings from control and irradiated seeds were then collected in RNAlater at similar growth stages with true leaves emerged. Total RNA was isolated and analyzed via Illumina whole transcriptome sequencing technology. Plant species-specific bioinformatics revealed transcriptional biomarkers and signaling pathways induced by simulated space radiation that were found to be dose, dose-rate, and species dependent. DNA damage response, stress signaling, and metabolic pathways are among the most significant changes. These data highlight some critical insights on the mechanisms of how plants respond and adapt to the space radiation environment and provide a molecular basis for crop selection and refinement in deep space exploration.

Anirudha Dixit↗

NASA NTRSDate not supplied

Transcriptomic Analysis of ISS Crewmembers’ Peripheral Blood Mononuclear Cells Reveals Homeostatic Regulations in Space

The impact of spaceflight on the immune system has been investigated for decades. Studies conducted in cell models, animals and humans suggest that the spaceflight environment affects the innate and acquired immune systems, as the ability to recognize antigens, defend against foreign invaders, and orchestrate repair is significantly hindered. However, the molecular mechanisms behind spaceflight-induced immune dysregulations are still unclear. In this study, blood from eleven (11) International Space Station (ISS) crewmembers was collected before, during and after long duration space missions, as well as from 11 matched ground control subjects. Transcriptomic analysis was performed in isolated peripheral blood mononuclear cells (PBMCs) using the RNA-sequencing technique. In comparison to the blood samples collected from the crewmembers pre-flight, a total of ~1000 genes were found to be upregulated and ~1000 genes downregulated in PBMC collected between 4 and 6 months after they were in space. The most significantly DEGs (differentially expressed genes) include activation of RUBCNL which is an autophagy enhancer and inhibition of GRASP which regulates cell trafficking. Genes involved in cell adhesion, cell cycle progression and other functions were also dysregulated. Pathway analysis of the DEGs indicates mitochondria dysfunction, particularly reduced ATP production in the electron transport chain. Other pathways impacted by spaceflight include glycolysis, autophagy and inflammatory response. Our results suggest that, in space, blood cells may have also experienced energy depletion and reduced metabolism. Consequently, the cells may become autophagic, which is a known homeostatic mechanism for blood cells to become quiescent, but to stay alive. Further analysis of the data shows recovery of the crewmembers after mission and potential differential responses between genders to the space environment. Our data potentially explains some of the physiological changes that have been observed in space such as mitochondria dysfunction, inhibition of T cell activation and telomere lengthening. Comparison of our results with other transcriptomics studies of ISS crewmembers’ blood cells will also be presented.

Maria Moreno Villanueva↗

NASA NTRS2021

Knowledge Network Embedding of Transcriptomic Data From Spaceflown Mice Uncovers Signs and Symptoms Associated With Terrestrial Diseases

There has long been an interest in understanding how the hazards from spaceflight may trigger or exacerbate human diseases. With the goal of advancing our knowledge on physiological changes during space travel, NASA GeneLab provides an open-source repository of multi-omics data from real and simulated spaceflight studies. Alone, this data enables identification of biological changes during spaceflight, but cannot infer how that may impact an astronaut at the phenotypic level. To bridge this gap, SPOKE, a heterogeneous knowledge graph connecting biological and clinical data from over 30 databases, was used in combination with GeneLab transcriptomic data from six studies. This integration identified critical symptoms and physiological changes incurred during spaceflight.

spaceflight↗

NASA NTRS2014

Radiation Quality Effects on Transcriptome Profiles in 3-D Cultures After Charged Particle Irradiation

In this work, we evaluated the differential effects of low- and high-LET radiation on 3-D organotypic cultures in order to investigate radiation quality impacts on gene expression and cellular responses. Current risk models for assessment of space radiation-induced cancer have large uncertainties because the models for adverse health effects following radiation exposure are founded on epidemiological analyses of human populations exposed to low-LET radiation. Reducing these uncertainties requires new knowledge on the fundamental differences in biological responses (the so-called radiation quality effects) triggered by heavy ion particle radiation versus low-LET radiation associated with Earth-based exposures. In order to better quantify these radiation quality effects in biological systems, we are utilizing novel 3-D organotypic human tissue models for space radiation research. These models hold promise for risk assessment as they provide a format for study of human cells within a realistic tissue framework, thereby bridging the gap between 2-D monolayer culture and animal models for risk extrapolation to humans. To identify biological pathway signatures unique to heavy ion particle exposure, functional gene set enrichment analysis (GSEA) was used with whole transcriptome profiling. GSEA has been used extensively as a method to garner biological information in a variety of model systems but has not been commonly used to analyze radiation effects. It is a powerful approach for assessing the functional significance of radiation quality-dependent changes from datasets where the changes are subtle but broad, and where single gene based analysis using rankings of fold-change may not reveal important biological information.

Patel, Zarana S.↗

NASA NTRS2014

Radiation Quality Effects on Transcriptome Profiles in 3-d Cultures After Particle Irradiation

In this work, we evaluate the differential effects of low- and high-LET radiation on 3-D organotypic cultures in order to investigate radiation quality impacts on gene expression and cellular responses. Reducing uncertainties in current risk models requires new knowledge on the fundamental differences in biological responses (the so-called radiation quality effects) triggered by heavy ion particle radiation versus low-LET radiation associated with Earth-based exposures. We are utilizing novel 3-D organotypic human tissue models that provide a format for study of human cells within a realistic tissue framework, thereby bridging the gap between 2-D monolayer culture and animal models for risk extrapolation to humans. To identify biological pathway signatures unique to heavy ion particle exposure, functional gene set enrichment analysis (GSEA) was used with whole transcriptome profiling. GSEA has been used extensively as a method to garner biological information in a variety of model systems but has not been commonly used to analyze radiation effects. It is a powerful approach for assessing the functional significance of radiation quality-dependent changes from datasets where the changes are subtle but broad, and where single gene based analysis using rankings of fold-change may not reveal important biological information. We identified 45 statistically significant gene sets at 0.05 q-value cutoff, including 14 gene sets common to gamma and titanium irradiation, 19 gene sets specific to gamma irradiation, and 12 titanium-specific gene sets. Common gene sets largely align with DNA damage, cell cycle, early immune response, and inflammatory cytokine pathway activation. The top gene set enriched for the gamma- and titanium-irradiated samples involved KRAS pathway activation and genes activated in TNF-treated cells, respectively. Another difference noted for the high-LET samples was an apparent enrichment in gene sets involved in cycle cycle/mitotic control. It is plausible that the enrichment in these particular pathways results from the complex DNA damage resulting from high-LET exposure where repair processes are not completed during the same time scale as the less complex damage resulting from low-LET radiation.

Patel, Z. S.↗

NASA NTRS2016

Transcriptomic Response of Drosophila Melanogaster Pupae Developed in Hypergravity

The metamorphosis of Drosophila is evolutionarily adapted to Earth's gravity, and is a tightly regulated process. Deviation from 1g to microgravity or hypergravity can influence metamorphosis, and alter associated gene expression. Understanding the relationship between an altered gravity environment and developmental processes is important for NASA's space travel goals. In the present study, 20 female and 20 male synchronized (Canton S, 2 to 3day old) flies were allowed to lay eggs while being maintained in a hypergravity environment (3g). Centrifugation was briefly stopped to discard the parent flies after 24hrs of egg laying, and then immediately continued until the eggs developed into P6-staged pupae (25 - 43 hours after pupation initiation). Post hypergravity exposure, P6-staged pupae were collected, total RNA was extracted using Qiagen RNeasy mini kits. We used RNA-Seq and qRT-PCR techniques to profile global transcriptomic changes in early pupae exposed to chronic hypergravity. During the pupal stage, Drosophila relies upon gravitational cues for proper development. Assessing gene expression changes in the pupa under altered gravity conditions helps highlight gravity dependent genetic pathways. A robust transcriptional response was observed in hypergravity-exposed pupae compared to controls, with 1,513 genes showing a significant (q < 0.05) difference in gene expression. Five major biological processes were affected: ion transport, redox homeostasis, immune response, proteolysis, and cuticle development. This outlines the underlying molecular changes occurring in Drosophila pupae in response to hypergravity.

RNASeq↗

NASA NTRS2017

Network Analysis of Rodent Transcriptomes in Spaceflight

Network analysis methods leverage prior knowledge of cellular systems and the statistical and conceptual relationships between analyte measurements to determine gene connectivity. Correlation and conditional metrics are used to infer a network topology and provide a systems-level context for cellular responses. Integration across multiple experimental conditions and omics domains can reveal the regulatory mechanisms that underlie gene expression. GeneLab has assembled rich multi-omic (transcriptomics, proteomics, epigenomics, and epitranscriptomics) datasets for multiple murine tissues from the Rodent Research 1 (RR-1) experiment. RR-1 assesses the impact of 37 days of spaceflight on gene expression across a variety of tissue types, such as adrenal glands, quadriceps, gastrocnemius, tibalius anterior, extensor digitorum longus, soleus, eye, and kidney. Network analysis is particularly useful for RR-1 -omics datasets because it reinforces subtle relationships that may be overlooked in isolated analyses and subdues confounding factors. Our objective is to use network analysis to determine potential target nodes for therapeutic intervention and identify similarities with existing disease models. Multiple network algorithms are used for a higher confidence consensus.

genomics↗

NASA NTRSDate not supplied

Transcriptomic Analysis of Irradiated Mouse Retina Following Readaptation

Rodent models are used as analogs for studying the effects of spaceflight. NASA GeneLab provides access to relevant omics datasets generated from spaceflight and ground-based experiments allowing for additional retrospective analysis. In this study, we used GeneLab’s GLDS-203, a dataset generated by researchers at Loma Linda University to study the impact of prolonged unloading and/or low-dose radiation on mouse retina. We analyzed transcriptomics data from retina of mice irradiated with gamma-rays for 21 days followed by 7 days, 1 month, or 4 months of readaptation. We obtained raw gene counts from GeneLab and performed differential gene expression analysis after data normalization. For each of the three timepoints, we performed differential expression analysis to compare transcriptional profiles for retina from irradiated vs. non-irradiated (controls) mice, all exposed to gravity. We observed the highest number of differentially expressed genes at 7 days, followed by 1 month and 4 months. Enrichment analysis showed top pathways (adjusted p-value < 0.05) were related to transport along microtubule and photoreceptor cell development in the 7-day readaptation group. Fewer significantly enriched pathways were observed for the 1-month group and included mRNA metabolic processes and neuron differentiation. No significantly enriched pathways were found in the 4-month group. The Gene Ontology biological processes common between the 7 days and 1-month groups include visual perception, synapse organization, and perception of light stimulus. This analysis is part of a larger effort to characterize the molecular mechanisms involved in retinal readaptation following radiation exposure. Future analyses will include other related retina datasets in GeneLab repository to assess whether gene expression patterns are consistent across different study cohorts.

Prachi Kothiyal↗

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

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↗

NASA NTRSDate not supplied

Transcriptomics-based Machine Learning (ML) Analysis Predicts Space-Exposed Murine Livers

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

Machine Learning↗

NASA NTRSDate not supplied

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

Machine Learning↗

NASA NTRSDate not supplied

Transcriptomics-based Machine Learning (ML) Analysis Predicts Space-Exposed Murine Livers

NASA has employed high-throughput molecular assays to identify sub-cellular changes impacting human physiology during spaceflight. Machine learning (ML) methods hold the promise to improve our ability to identify important signals within highly dimensional molecular data. However, the inherent limitation of study subject numbers within a spaceflight mission minimizes the utility of ML approaches. To overcome the sample power limitations, data from multiple spaceflight missions must be aggregated while appropriately addressing intra- and inter-study variabilities. Here we describe an approach to log transform, scale and normalize data from six heterogeneous, mouse liver derived transcriptomics datasets (ntotal=137) which enabled ML-methods to perform well (AUC ≥ 0.87) in classifying spaceflown vs ground control animals rather than mission-of-origin. Concordance was found between liver-specific biological processes identified from harmonized ML-based analysis and study-by-study classical omics analysis. This work demonstrates the feasibility of applying ML methods on integrated, heterogeneous datasets of small sample size.

Machine Learning↗