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Prachi Kothiyal

Publications and source records attributed to Prachi Kothiyal.

16 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

A Multi-omics Longitudinal Study of the Murine Retinal Response to Chronic Low-dose Irradiation and Simulated Microgravity

The space environment includes unique hazards like radiation and microgravity which can adversely affect biological systems. We assessed a multi-omics NASA GeneLab dataset where mice were hindlimb unloaded and/or gamma irradiated for 21 days followed by retinal analysis at 7 days, 1 month or 4 months post-exposure. We compared time-matched epigenomic and transcriptomic retinal profiles resulting in a total of 4,178 differentially methylated loci or regions, and 457 differentially expressed genes. Highest correlation in methylation difference was seen across different conditions at the same time point. Nucleotide metabolism biological processes were enriched in all groups with activation at 1 month and suppression at 7 days and 4 months. Genes and processes related to Notch and Wnt signaling showed alterations 4 months post-exposure. A total of 23 genes showed significant changes in methylation and expression compared to unexposed controls, including genes involved in retinal function and inflammatory response. This multi-omics analysis interrogates the epigenomic and transcriptomic impacts of radiation and hindlimb unloading on the retina in isolation and in combination and highlights important molecular mechanisms at different post-exposure stages.

Prachi Kothiyal↗
NASA NTRSDate not supplied

Overview of the Translational Radiation Research and Countermeasures (TRRaC) Project in Space Radiation

The Translational Radiation Research and Countermeasures (TRRaC) Project was initiated by the Space Radiation (SR) Element within NASA’s Human Research Program (HRP) to support the SR mission to understand and characterize the space radiation environment, understand and quantify radiation-associated risks, and mitigate the impacts of radiation-induced adverse health outcomes to enable human space exploration. TRRaC’s mission is to translate radiation research results from experimental studies and epidemiological data to humans and astronauts using bioinformatics and computational modeling. TRRaC will leverage existing datasets such as those available from NASA’s GeneLab repository and human medical radiation exposure registries, along with relevant data generated from ground-based research at molecular, cellular, tissue, and system levels, to: (1) refine the radiation dose rate effectiveness factor, (2) characterize radiation quality effects to improve estimates of the risk of exposure-induced death (REID) from space-relevant radiation exposures, and (3) identify pathways and biomarkers for cancer, cardiovascular disease, and central nervous system changes that are impacted by space radiation exposure.

Parastou Eslami↗
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

Multi-Omics Analysis of Mouse Retina Following Low Dose Radiation and/or Hindlimb Unloading

Rodent models have been used as analogs for studying the effects of spaceflight. NASA’s GeneLab provides access to omics datasets generated from spaceflight and ground-based experiments allowing for additional retrospective analysis. 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. The purpose of this study was to understand the effect of gamma radiation and/or hindlimb unloading on mice retinas through a multi-omics analysis. In the experiment that generated the omics data, mice were irradiated with gamma-ray and/or subjected to hindlimb unloading for 21 days and multi-omics analysis was performed at 7 days, 1 month, or 4 months post exposure. In the current study, for each of the three timepoints, we compared epigenomic profiles for retinas from exposed mice against timepoint-matched controls. We identified a total of 5,271 differentially methylated loci (DML) and 321 differentially methylated regions (DMR; using a sliding window and step size of 500 bp) with methylation difference > 10% and q-value < 0.05 (sliding linear model corrected p-value) across the nine exposure groups. Highest correlation in methylation difference was seen for significant DMLs (q-value < 0.05) across different conditions at same post exposure timepoint (Figure 1).The location of DMLs and DMRs were characterized with respect to CpG islands and shores, putative promoters, gene body, and intergenic regions (Table 1). We analyzed RNA-seq counts and performed gene set enrichment analysis using differential expression results from comparing each exposure group to its timepoint-matched control group. Significant pathways (adjusted p-value <0.05) enriched in all three microgravity-only groups were related to morphogenesis of a branching epithelium, skeletal muscle cell differentiation, and response to fibroblast growth factor. Common processes across all timepoints in the radiation-only groups were retina homeostasis, synaptic vesicle exocytosis-endocytosis, and chemotaxis. In the combination groups, regulation of trans-synaptic signaling, and Rho protein signal transduction were enriched at all three timepoints. Processes related to purine nucleotide metabolism were enriched in all nine exposure groups, with activation at 1 month, and suppression at 7 days and 4 months. A total of 14 genes contained at least one DML and were differentially expressed at adjusted p-value < 0.05, including genes implicated in cataract development (Sipa1l3, Crybb3) and those involved in cytoskeletal organization (Plec, Flnb, Eef1a1). This analysis is part of a larger effort to understand the molecular mechanisms following spaceflight exposures that can help translate effects observed in animal models to human impacts.

Prachi Kothiyal↗
NASA NTRSDate not supplied

Translational Line of Sight: a multi-omics longitudinal study of the murine retinal response to spaceflight hazard analogs

The space environment includes unique hazards like radiation and microgravity which adversely affect physiology and behavior of humans and rodent models. To better characterize the retinal response to spaceflight, we assessed a multi-omics NASA GeneLab dataset where 6-month-old female mice were gamma irradiated and/or hindlimb unloaded for 21 days followed by whole transcriptome shotgun sequencing (RNA-Seq) and reduced representation bisulfite sequencing (RRBS) of retina samples collected at 7 days, 1 month or 4 months post-exposure. We compared time-matched epigenomic and transcriptomic retinal profiles revealing a total of 4,178 differentially methylated loci or regions, and 457 differentially expressed genes. Highest correlation in methylation differences was seen across different conditions at the same time point (e.g., between radiation exposure and hindlimb unloaded at 7 days). Biological processes related to nucleotide metabolism were enriched in all groups with activation at 1 month and suppression at 7 days and 4 months. Genes and processes related to Notch and Wnt signaling showed alterations 4 months post-exposure. Interestingly, Notch3 and Lrg1 showed differential patterns in the NASA Twins Study in-flight samples and in response to stressors in the murine retina in the current study. A total of 23 genes were both differentially methylated and expressed, including genes involved in retinal disease or cataract development (Crybb3, Fgfr1, Pitpnm3, Sipa1l3, Sox9) and inflammatory response (B4galt6, Ppm1a, Sphk1). To our knowledge, the current multi-omics analysis is the first multi-omics study to interrogate the epigenomic and transcriptomic impacts of radiation and hindlimb unloading on the retina in isolation and in combination. The results provide an insight into the retinal response to individual spaceflight hazard analogs and their interplay at different post-exposure stages and contributes towards a mechanistic understanding of spaceflight-induced vision impairment using ground-based models.

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

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

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

Prachi Kothiyal↗
NASA 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

A Multi-omics Longitudinal Study of the Murine Retinal Response to Chronic Low-dose Irradiation and/or Simulated Microgravity

The space environment includes unique hazards like radiation and microgravity which adversely affect physiology and behavior of humans and rodent models. To better characterize the retinal response to spaceflight, we assessed a multi-omics NASA GeneLab dataset where 6-month-old female mice were gamma irradiated and/or hindlimb unloaded for 21 days followed by whole transcriptome shotgun sequencing (RNA-Seq) and reduced representation bisulfite sequencing (RRBS) of retina samples collected at 7 days, 1 month or 4 months post-exposure. We compared time-matched epigenomic and transcriptomic retinal profiles revealing a total of 4,178 differentially methylated loci or regions, and 457 differentially expressed genes. Highest correlation in methylation differences was seen across different conditions at the same time point (e.g., between radiation exposure and hindlimb unloaded at 7 days). Biological processes related to nucleotide metabolism were enriched in all groups with activation at 1 month and suppression at 7 days and 4 months. Genes and processes related to Notch and Wnt signaling showed alterations 4 months post-exposure. Interestingly, Notch3 and Lrg1 showed differential patterns in the NASA Twins Study in-flight samples and in response to stressors in the murine retina in the current study. A total of 23 genes were both differentially methylated and expressed, including genes involved in retinal disease or cataract development (Crybb3, Fgfr1, Pitpnm3, Sipa1l3, Sox9) and inflammatory response (B4galt6, Ppm1a, Sphk1). To our knowledge, the current multi-omics analysis is the first multi-omics study to interrogate the epigenomic and transcriptomic impacts of radiation and hindlimb unloading on the retina in isolation and in combination. The results provide an insight into the retinal response to individual spaceflight hazard analogs and their interplay at different post-exposure stages and contributes towards a mechanistic understanding of spaceflight-induced vision impairment using ground-based models.

Prachi Kothiyal↗
NASA NTRSDate not supplied

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

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

Machine Learning↗
NASA NTRSDate not supplied

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

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

Machine Learning↗
NASA 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↗