Adipose tissue in altered lipid metabolism of rats exposed to centrifugation stress.
Adipose tissues in altered lipid metabolism of rats exposed to centrifugal stress
Feller, D. D.↗
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The Effects of Space Flight on Some Liver Enzymes Concerned with Carbohydrate and Lipid Metabolism in Rats
The activities of about 30 enzymes concerned with carbohydrate and lipid metabolism and the levels of glycogen and of individual fatty acids were measured in livers of rats ex- posed to prolonged space flight (18.5 days) aboard COSMOS 986 Biosatellite. When flight stationary, (FS) and flight centrifuged (FC) rats were compared at recovery (R(sub 0)), decrceases in the activities of glycogen phosphorylase, alpha glycerphosphate, acyl transferase, diglyceride acyl transferase, acconitase and Epsilon-phosphogluconate dehydrogenase were noted in the weightless group (FS). The significance of these findings was strengthened since all activities, showing alterations at R(sub 0), returned to normal 25 days post-flight. Differences were also seen in levels of two liver constituents. When glycogen and total fatty acids of the two groups of flight animals were determined, differences that could be attributed to reduced gravity were observed, the FS group at R(sub 0) contained, on the average, more than twice the amount of glycogen than did controls ad a remarkable shift in the ratio of palmitate to palmitoleate were noted. These metabolic alterations appear to be unique to the weightless condition. Our data justify the conclusion that centrifugation during space flight is equivalent to terrestrial gravity.
Abraham, S.↗
The effects of space flight on some rat liver enzymes regulating carbohydrate and lipid metabolism
The effects of space flight conditions on the activities of certain enzymes regulating carbohydrate and lipid metabolism in rat liver are investigated in an attempt to account for the losses in body weight observed during space flight despite preflight caloric consumption. Liver samples were analyzed for the activities of 32 cytosolic and microsomal enzymes as well as hepatic glycogen and individual fatty acid levels for ground control rats and rats flown on board the Cosmos 936 biosatellite under normal space flight conditions and in centrifuges which were sacrificed upon recovery or 25 days after recovery. Significant decreases in the activities of glycogen phosphorylase, alpha-glycerol phosphate acyl transferase, diglyceride acyl transferase, aconitase and 6-phosphogluconate dehydrogenase and an increase in palmitoyl CoA desaturase are found in the flight stationary relative to the flight contrifuged rats upon recovery, with all enzymes showing alterations returning to normal values 25 days postflight. The flight stationary group is also observed to be characterized by more than twice the amount of liver glycogen of the flight centrifuged group as well as a significant increase in the ratio of palmitic to palmitoleic acid. Results thus indicate metabolic changes which may be involved in the mechanism of weight loss during weightlessness, and demonstrate the equivalence of centrifugation during space flight to terrestrial gravity.
Abraham, S.↗
T-Cell Shenanigans: The Impact of MHC Pathway & Lipid Metabolism Genes on T-Cell Differentiation in the Thymus
Astronaut health and proper immune function are key to the success and sustainability of long term missions in space. While previous studies have observed adaptive immune dysfunction such as diminished WBC counts and cytokine production during spaceflight, the underlying mechanisms behind why immune function worsens are poorly understood. Using transcriptomic data analyzed from mouse thymus tissues available from the GLDS-421 experiment on the Rodent Research-9 mission, we used the GeneLab standardized RNA-Seq pipeline and identified four genes that were largely dysregulated (p<0.05): lipid metabolic genes Hpgd and Pgr and cell cycle regulator genes Cenpe and Kif11. We analyzed how these genes regulate the MHC-TCR interaction, and used this understanding to propose a novel mechanism for alterations to T-cell differentiation. In our experimental methodology, we propose to use 36 transgenic CD4-CD8 mice and 12 wild-type mice from the Jackson Lab to create a microgravity-simulating hindlimb unloading model and test our hypothesis. By silencing the aforementioned genes using custom siRNA based primers, we will use flow cytometry and RNA-Seq to quantify Treg counts in comparison to naive CD4+ cells, Alamar Blue assay for observing the metabolic activity in T-cells, and Enzyme-Linked Immunosorbent Assay (ELISA) to observe the general immune response in terms of T-cell differentiation. We hope this methodology will improve our knowledge of MHC-TCR interaction, contribute to a better understanding of the mechanisms of autoimmune diseases such as lupus, and improve the success rate of newer cancer treatments such as CAR-T cell therapy.
GL4HS↗
Blood glucose and corticosterone changes accompanying altered lipid metabolism induced by exposure to acceleration stress.
Acceleration stress-induced changes in fat metabolism, level of circulating glucose and corticosterone level in rats
CORTICOSTEROID↗
Chemical and metabolic changes of hepatic lipids from rats exposed to chronic radial acceleration.
Changes in liver lipid metabolism of rats subjected to high-g environment obtained by centrifuging over long time periods
LIPID METABOLISM↗
A Multi-Omics Approach Demonstrates that Spaceflight Leads to Lipid Accumulation in Mouse Livers
Spaceflight has several detrimental effects on the physiology of astronauts, many of which are recapitulated in rodent models. We analyzed liver transcriptomic and proteomic data from three mouse spaceflight experiments flown aboard the International Space Station (Rodent Research-1 NASA (RR-1 NASA), Rodent Research-1 CASIS (RR-1 CASIS), Rodent Research-3 (RR-3)), and one mouse experiment flown on the Space Shuttle (Commercial Biomedical Testing Module-3 (CBTM-3) aboard STS-135). Despite the differences in genetic background and time of exposure to microgravity it was shown through Oil Red staining and histology that increased lipid accumulation was occurring in the liver of all mice flown in space compared to the ground controls. This led to further pursue the existing GeneLab datasets related to liver omics data from these mice. We were able to discover key conserved pathways across all the mice independent of the flight conditions that were related to increased lipid metabolism, fatty acid metabolism, both lipid and fatty acid processing, lipid catabolic processing, and lipid localization. In addition, key upstream regulators were predicted to be commonly regulated across all conditions which include ESR1, GCG, and NR1I2 being inhibited and INS being activated. Interestingly, estrogen receptor alpha (ESR1) expression has been known to be heavily involved with lipoprotein metabolism. In addition, insulin (INS) is the primary driver for fat metabolism and increased INS has been associated with increased fatty acids in the liver. Through additional proteomic analysis we were able to identify the majority of the key proteins related to lipids for both the RR-1 and RR-3 rodents were being up-regulated in the livers when comparing flight to ground controls. This additional confirmation of the lipid associated activity also showed that the lipid related proteins are heavily involved with lipid metabolism, cholesterol binding, and cholesterol metabolism. Lastly, the analysis also revealed that the circadian clock related pathways in the liver are commonly being increased across all space flight conditions which has also been reported in the literature to potentially cause increased liver damage. The combination of the very strong lipid uptake in the liver and the transcriptomic/proteomic signatures (including the circadian clock pathways) following spaceflight are consistent with early onset of liver disease. Taken together, these data indicate that, activation of lipotoxic pathways could persist during longer duration spaceflight which might result in the development of liver disease
Lipids↗
Metabolic changes in rats exposed to an oxygen- enriched environment.
Acetylative capacity and lipid metabolic changes and readjustment to normality in rats in oxygen- rich environment
LIPID METABOLISM↗
Mitochondria Dysfunction Central in Driving Health Risks Associated with Spaceflight
Background: Determining the biological impact of spaceflight through novel approaches is essential to reduce the health risks to astronauts for long-term space missions. The current established health risks due to spaceflight are only reflecting known symptomatic and physiologic responses and do not reflect early onset of other potential diseases. There are many unknown variables which still need to be identified to fully understand the health impacts due to the environmental factors in space. Materials and Methods: One method to uncover potential novel biological mechanisms responsible for health risks in astronauts is by utilizing NASA’s GeneLab platform (genelab.nasa.gov). GeneLab is public repository that hosts multiple omics datasets generated from space biology experiments that include experiments flown in space, simulated cosmic radiation experiments, and simulated microgravity experiments. This presentation will provide an example of analysis and novel hypothesis generation that is being produced with GeneLab datasets. A comprehensive multi-omics approach was implemented correlating transcriptomics, proteomics, metabolomics, and methylation analysis. Results: We found that cells have stronger overall biological response than the tissues to spaceflight, with mitochondrial activity and innate immunity pathways being heavily impacted. NASA Twin Study results are consistent with a specific alteration in mitochondrial ATP production. Our results indicate that the space environment can directly induce mitochondrial damage, with mitochondrial dysfunctions being a cause for chronic inflammation and both being involved in the development of metabolic disorders that cause changes in lipid metabolism. We also found biological changes occurring during spaceflight with cell cycle, circadian rhythm and olfactory activity pathways can also influence and be influenced by alterations on mitochondrial activity
Afshin Beheshti↗
Experiment K304: Studies of specific hepatic enzymes and liver constituents involved in the conversion of carbohydrates to lipids in rats exposed to prolonged space flight
The effects of space flight on the activities of 26 enzymes concerned with carbohydrate and lipid metabolism in hepatic tissue taken from male Wistar rats are investigated. These activities were measured in the various hepatic cell compartments, i.e., cytosol, mitochondria and microsomes. In addition, the levels of glycogen, total lipids, phospholipids, triglycerides, cholesterol, cholesterol esters, and the fatty acid composition of the rat livers were also examined and quantified. A similar group of ground-based rats treated in an identical manner served as controls. Both flight and synchronous control rats were sacrificed at three time intervals: R+0, 7-11 hours after recovery; R+6, after 6 days; R+6(S), after 6 days (having undergone 2-5 hour periods of fixed stress in a "backupward" position on days 0, 3, 4, 5 and 6) and R+29, after 29 days post-flight. Although most of the enzyme activities and the amounts of liver constituents studied were unaffected by the period of weightlessness, some significant differences were observed.
Abraham, S.↗
The NASA Twins Study: The Effect of One Year in Space on Long-Chain Fatty Acid Desaturases and Elongases
Background: To date, there is no clear understanding of the effect of long-duration spaceflight on the major enzymes that govern the metabolism of omega-6 and omega-3 fatty acids. To address this gap in knowledge, we used data from the NASA Twins Study, which includes a multi-scale omic investigation of the changes that occurred during a year-long (340 days) human spaceflight. Embedded within the NASA Twins data are specific analytes associated with fatty acid metabolism. Objectives: To examine the long-chain fatty acid desaturases and elongases in a single human during one year in space. Method: One male twin was on board the International Space Station (ISS) for one year, while his monozygotic twin served as a genetically matched ground control. Longitudinal assessments included the genome, epigenome, transcriptome, proteome, metabolome, microbiome, and immunome during the mission, as well as six months before and after. The gene-specific fatty acid desaturase and elongase transcriptome data (FADS1, FADS2, ELOVL2 and ELOVL5) were extracted from untargeted RNA-seq measurements derived from white blood cell fractions. Results: Most data from the elongases and desaturases exhibited relatively similar expression profiles (R2>0.6) over time for the CD8, CD19, and LD cell fractions, indicating overall conservation of function within and between the subjects. Both cell-type and temporal specificity was observed in some cases, and some differences were also apparent between the poly-adenylated fraction (polyA) of processed RNAs vs. the ribo-depleted (ribo-) fraction. The flight subject showed a stronger enrichment of the Fatty Acid Metabolic processes pathway across almost all cell types (columns, CD4, CD8, CPT, LD), most especially in the ribodepleted fraction of RNA, but also with the polyA+ fraction of RNA. GSEA enrichment measures across three related Fatty Acid Metabolism pathways showed a differential between the ground and flight subject. Conclusions: There appears to be no persistent alteration of desaturase and elongase gene expression associated with one year in space. However, these data provide evidence that cellular lipid metabolism can be responsive and dynamic to spaceflight, even though it appears cell-type- and context-specific, most notably in terms of the fraction of RNA measured and the collection protocols. These results also provide new evidence of mid-flight spikes in expression of selected genes, which may indicate transient responses to specific insults during spaceflight.
Elongase↗
Content of lipids in blood and tissues of animals during hypodynamia
Experiments on 97 rats and 50 rabbits were undertaken to study the influence of hypodynamia on the lipid content in the blood, liver, heart, and in the aorta. Reduction of muscular activity contributed to the increase of cholesterol and beta lipoprotein levels in the blood and to accumulation of cholesterol in the liver and the heart. The total lipid content in these tissues decreased. In the aorta the total lipid content increased, while lecithin and cephalin figures went down. The character of biochemical changes in hypodynamia resembles in many ways the lipid metabolism changes in atherosclerosis.
Federov, I. V.↗
Subcellular targeting of nine calcium-dependent protein kinase isoforms from Arabidopsis
Calcium-dependent protein kinases (CDPKs) are specific to plants and some protists. Their activation by calcium makes them important switches for the transduction of intracellular calcium signals. Here, we identify the subcellular targeting potentials for nine CDPK isoforms from Arabidopsis, as determined by expression of green fluorescent protein (GFP) fusions in transgenic plants. Subcellular locations were determined by fluorescence microscopy in cells near the root tip. Isoforms AtCPK3-GFP and AtCPK4-GFP showed a nuclear and cytosolic distribution similar to that of free GFP. Membrane fractionation experiments confirmed that these isoforms were primarily soluble. A membrane association was observed for AtCPKs 1, 7, 8, 9, 16, 21, and 28, based on imaging and membrane fractionation experiments. This correlates with the presence of potential N-terminal acylation sites, consistent with acylation as an important factor in membrane association. All but one of the membrane-associated isoforms targeted exclusively to the plasma membrane. The exception was AtCPK1-GFP, which targeted to peroxisomes, as determined by covisualization with a peroxisome marker. Peroxisome targeting of AtCPK1-GFP was disrupted by a deletion of two potential N-terminal acylation sites. The observation of a peroxisome-located CDPK suggests a mechanism for calcium regulation of peroxisomal functions involved in oxidative stress and lipid metabolism.
Non-NASA Center↗
Pyruvate ingestion for 7 days does not improve aerobic performance in well-trained individuals
The purposes of the present studies were to test the hypotheses that lower dosages of oral pyruvate ingestion would increase blood pyruvate concentration and that the ingestion of a commonly recommended dosage of pyruvate (7 g) for 7 days would enhance performance during intense aerobic exercise in well-trained individuals. Nine recreationally active subjects (8 women, 1 man) consumed 7, 15, and 25 g of pyruvate and were monitored for a 4-h period to determine whether blood metabolites were altered. Pyruvate consumption failed to significantly elevate blood pyruvate, and it had no effect on indexes of carbohydrate (blood glucose, lactate) or lipid metabolism (blood glycerol, plasma free fatty acids). As a follow-up, we administered 7 g/day of either placebo or pyruvate, for a 1-wk period to seven, well-trained male cyclists (maximal oxygen consumption, 62.3 +/- 3.0 ml. kg(-1). min(-1)) in a randomized, double-blind, crossover trial. Subjects cycled at 74-80% of their maximal oxygen consumption until exhaustion. There was no difference in performance times between the two trials (placebo, 91 +/- 9 min; pyruvate, 88 +/- 8 min). Measured blood parameters (insulin, peptide C, glucose, lactate, glycerol, free fatty acids) were also unaffected. Our results indicate that oral pyruvate supplementation does not increase blood pyruvate content and does not enhance performance during intense exercise in well-trained cyclists.
Randomized Controlled Trial↗
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↗
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↗
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↗
Murine Host-gut Microbiota Interactions are Modulated During Spaceflight
The rodent habitat on the International Space Station has provided critical insight into the impact of spaceflight on mammalian physiology. These effects include dysfunction of carbohydrate, steroid and lipid metabolism, and immune response, as well as induction of symptoms characteristic of liver disease, insulin resistance, osteopenia and myopathy, which are anticipated to intensify over long-duration spaceflight. Although these physiological responses can involve the microbiome, the host-microorganism interactions during spaceflight are still largely unknown. NASA GeneLab curates a wide range of space research data and the current work harnesses GeneLab multi’omic data from recent Rodent Research studies to explore changes to gut microbiota during spaceflight and their associations with host physiology when compared to ground controls. Using a hybrid analysis of DNA barcoding and whole genome shotgun data, an array of bacteria, fungi and nematodes could be identified at species level, and significant differences in relative abundances associated with spaceflight. Functional prediction based on differential abundance of species and metagenome gene inventories as well as metatranscriptomic gene expression at the host-gut microbiome interface implicate microbiota interactions could contribute to spaceflight pathology. Harnessing carefully curated publicly available data, such as from Genelab, to generate multi‘omic space science discoveries can help decipher the complex host-microbiome interactions that influence both health on Earth and the feasibility of long-duration spaceflight.
Microbiome↗