Multiple frailty related biomarkers are differentially expressed in rodent muscles during spaceflight

To determine the impact of frailty during spaceflight, we constructed, based on previous literature19,20,21,22, a list of putative frailty biomarker genes for humans and mice (Supplementary Data 1). Mouse (OSD-21, 99, 101, 103, 104, 105) datasets from OSDR were analyzed to identify differentially expressed genes (DEGs) in flight versus control condition with a statistical cut-off of adjusted p-value<0.5. In mice, altered expression of frailty-related genes in the following tissues were identified: gastrocnemius (34 genes in OSD-21 and 8 genes in OSD-101); extensor digitorum longus (EDL) (45 genes in OSD-99); quadriceps (26 genes in OSD-101); soleus (36 genes in OSD-104); tibialis anterior (32 genes in OSD-105) (Fig.2A). A maximum number of four frailty-related genes was also found to be unique to each tissue type and a maximum number of 4 was common between the different datasets (Supplementary Data 2). Hierarchical clustering of the overlapping gene expression across muscle types revealed a bias towards the up-regulation of frailty-related genes (Fig.2B). As an example, the extensor digitorum longus had several upregulated genes (EGLN3, PTGS2, VDR, FREM2, KRT18, BCL2L1, LGALS3, CXCL10, CX3CL1, FNDC5, TGFB1, CAN, and PPARGC1A). Whereas the soleus (OSD-104) had relatively few downregulated genes (GDF15, PTGS2, BDNF, PAX5, CX3CL1, FNDC5, VCAN, CALU, and SESN2).

Frailty-related biomarkers are differentially expressed in rodent muscles during spaceflight. Putative frailty linked genes from NASA Open Science Data Repository (former GeneLab). The transcriptomic signature of spaceflight is investigated with differential expression analysis in multiple tissues. (A) Upset plots of overlapping differentially expressed frailty genes in rodent and human samples. (B) Heatmap of differential expression analysis for the frailty gene in human and rodent samples. Rodent samples comprise spaceflight skeletal muscle. Heatmap considers only DEG with adjustedp-value<0.5. Black color indicates no value.

To determine overall frailty impact of spaceflight on tissues Gene Set Enrichment Analysis (GSEA)23 analysis was performed on specific aging-related pathways (selected from the Molecular Signatures Database (MSigDB)23 (Supplementary Data 3). Rodent datasets showed a general enrichment of the pathways with an overall upregulation in EDL and tibialis anterior, downregulation in quadriceps, and a mixed regulation in gastrocnemius soleus (Fig. 3A,B). Summary themes of each functional cluster are displayed by the external color panel at the right side of sub-figure B and C. Despite a mixed direction of regulation, a clear enrichment of these pathways in the spaceflight group when compared to the control was evident across the datasets. The soleus muscle revealed an increase in the innate immune response inflammatory signature and concomitant downregulation of the IGF-1 pathway (Fig.3B). Previous literature showed that the soleus muscle is the first to be impacted by spaceflight and also known to experience a significant dysregulation of mitochondrial and immune functions in space24. Immune response can downregulate IGF-1 anabolic activity, promoting muscle wasting16. Of note, this muscle shows the largest decline in mass in the RR1 mission and IGF-1 pathway might be involved25. Several putative aging-related pathways were enriched in human datasets (Fig.3C), showing up-regulation in the majority of cases. Of note, interferon alpha and gamma response pathways are upregulated in all the datasets investigated. The increase in immune and inflammatory signatures we identified is consistent with various reports that associate chronic inflammation with frailty, although causality has yet to be established8,26. Nonetheless, our results could be useful for biomarkers related to spaceflight risk and consistent with clinical correlations of increased low-grade inflammation and muscle wasting16.

Inflammatory response pathways are enriched in rodent muscles during spaceflight. The transcriptomic signature of spaceflight is investigated with gene set enrichment analysis (GSEA) for putative aging-related pathways in multiple tissues. (A) Percentage of the differentially expressed genes which are stable, increased or decreased in rodent samples. (B) and (C) Heatmap of the normalized enrichment score for the enriched aging-related pathways in rodents and human samples. The dark gray locations in the heatmap indicate missing values for the NES, resulting from off-range adjusted p-values (padj) of the analysis. The assumed range is padj<0.3.

Sarcopenia is a condition associated with frailty. In our analysis,the best predictors of sarcopenia were genesthat are part of autophagic and protein degradation processes. After studying databases from 118 people with and without sarcopenia (GSE111006, GSE111010, and GSE111016)27, 6,892 DEGs were identified by performing MannWhitney U tests28 on gene expression data for every single gene (i.e., 65,217 genes) in a pair-wise manner across samples from both sets of patients (Supplementary Data 4). A simple classifier (i.e., k-nearest neighbors) was then used to estimate individual predictive power for that condition29. Next, via co-expression network analysis upon these DEGs, the most highly correlated module (i.e., BROWN=0.93) to sarcopenia was found. We used a pathway and gene ontology analysis upon BROWN to curate a list of 21 genes that were significantly enriched in biological processes related to sarcopenia30.

Here, we found that the frailty biomarkers list was enriched in Biological Processes Gene Ontology (BP GO) terms in a very similar manner to those found with sarcopenic biomarkers alone (Fig.4A)29. In addition to BP GO, the same was true for molecular functions (MF) GO term enrichment (Fig.4C). Interestingly, we found that eight of the biomarkers identified for frailty had the ability to predict sarcopenia in GSE111006, GSE111010, and GSE111016 with a Mean Accuracy Score (MAS) of>0.65 (RP1L1, SH3GL3, HIF1A, FGF23, FASLG, MAS1, PAX5, and REV1) (Fig.4B,D).

Evidence of shared catabolic pathways between sarcopenia and frailty markers and their differential expression in space-flown mice. (A) Significantly enriched Biological Processes using a curated biomarker gene list obtained by the overlap of three gene sets studying sarcopenia (superseries GSE111017: GSE111006, GSE111010, and GSE111016) defined through a Mann- Whitney analysis. (B) The frailty biomarkers found to be part of ten GO Biological Processes terms, from which R1PL1 had the highest Mean Accuracy Score (MAS) score. (C) Significantly enriched Molecular Functions using a curated biomarker gene list. (D) Similarly, three GO Molecular Function terms were found to be a shared pathway with the defined frailty biomarkers from which SH3GL3 had the highest MAS score. (E) Schematic of the data utilized for the heatmap showing the four genes out of the 21 sarcopenia frailty genes that were present in the murine data sets. Heatmap considers only DEG with p<0.05.

Using the sarcopenia gene expression classifier we hadestablished above, we re-examined the existing datasets for alterations in the 21 genes. To do so, we took the expression data from the murine datasets (EDL (ODS-99), left gastrocnemius (ODS-101), quadriceps (ODS-103), soleus (ODS-104), and tibialis anterior (ODS-105)) and evaluated the expression of our sarcopenia classifier (Fig.4E). We found that only GJB4, HNRNPCL1, GOLGA2 and POMC were DEGs in at least one of the datasets. GJB4 is a connexin (Cx) gene encoding the gap junction protein CX30.331. HNRNPCL1 plays a role in consolidating the nucleosome and neutralizing core hnRNPs proteins32. GOLGA2 encodes the GM130 protein necessary for the assembly of the Golgi apparatus. Interestingly, mutations in GOLGA2 lead to neuromuscular disorders and muscular dystrophy33. POMC codes for the precursor protein proopiomelanocortin producing active peptides generating melanocyte stimulating hormones (MSHs), corticotropin (ACTH) and -endorphin. POMC deficiency leads to adrenal failure and obesity34. Of note, the dataset from the soleus muscle in mice (OSD-104), demonstrated to have a significant overexpression of GJB4, POMC and significant downregulation of HNRNPC (p<0.05).

We applied the same list of putative frailty biomarker genes (Supplementary Data 1) to investigate differentially expressed genes in Open Science Datasets human samples as in Fig.2. OSD-52 and 195 were analyzed to identify differentially expressed genes (DEGs) in flight, on random positioning machine or in bed rest versus control condition with a statistical cut-off of adjusted p-value<0.5. Vastus lateralis muscle (OSD-52), cardiac progenitors (OSD-127) and endothelial cells (OSD-195) showed 22, 2 and 4 frailty-related genes, respectively (Fig.5A).

Frailty-related biomarkers are differentially expressed in humans during spaceflight and ground-based spaceflight simulated conditions. Putative frailty linked genes from NASA Open Science Data Repository (former GeneLab). The transcriptomic signature of spaceflight is investigated with differential expression analysis in multiple tissues. (A) Upset plot of overlapping differentially expressed frailty genes in human samples. (B) Venn diagram of differentially expressed frailty genes in rodent and human samples shows the common differentially expressed genes between the two species. (C) Heatmap of differential expression analysis for the frailty gene in human samples. Human samples comprise spaceflight human umbilical vein endothelial cells, bed rest skeletal muscle cells and cardiac progenitors differentiated from human pluripotent stem cells in 3D culture under simulated microgravity. Heatmap considers only DEG with adjustedp-value<0.5. Black color indicates no value.

We compared the differential expression profiles between mice and human dataset. Approximately a third of the frailty genes were conserved between humans and mice, which suggests that the murine models can provide good translation to human biology (Fig.5B). Out of 73 differentially expressed frailty-related genes, 22 (32%) were common in humans and mice (Fig.5B). Forty-three (62%) were unique to mice and 4 (6%) were unique to only humans. In humans, 9 frailty genes were upregulated and 13were downregulated in the vastus lateralis muscle (Fig.5C and Supplementary Data 2).

Several downregulated genes were associated with immunity-related pathways, while most upregulated genes were associated with metabolism and Vitamin K or D pathways. In endothelial cells, two genes were downregulated, and two were upregulated. The downregulated genes, TMEM245 and PPARGC1A, are associated with the cell-membrane and gluconeogenesis, while the upregulated genes, MSTN and PTGS2, are associated with regulation of skeletal muscle growth and prostaglandin biosynthesis (Supplementary Data 5)35. While there is no direct link between gluconeogenesis and frailty, both are related to the body's response to stress and maintaining homeostasis. Diabetes, a condition that affects glucose metabolism, has been linked to frailty36,37. In diabetes, the body's ability to regulate blood glucose levels is impaired, potentially impacting gluconeogenesis. Frail individuals, who have a diminished ability to resist stressors, may be more susceptible to the effects of these metabolic imbalances38.

Having confirmed altered aging and frailty signatures in largely rodent transcriptomic data, we wanted to test if frailty biomarkers were also altered in astronauts. To enable this analysis, we used two recent studies39. First, using astronaut data from JAXA plasma cell-free RNA profiling study, we examined the changes occurring in RNAs from the frailty biomarker genes between pre-flight, in-flight, and post-flight (i.e., afterreturn to Earth) (Fig.6). Our RNA analysis reveals a global response of frailty-related gene expression to the space environment, which is characterized by in-flight and post-flight expression changes. Most of the genes investigated were subject to changes when compared to pre-flight conditions, either during spaceflight or later after return to Earth. A large number of genes that were reduced during spaceflight showed an increase after re-entry (e.g., AKT1, NOS2, FGF23, and HIF3A). Conversely, several genes show an opposite behavior and tended to be reduced during spaceflight, and underwent reduction after re-entry (e.g., TGFB1, B2M, NOS1, AOC1, SOD2, SOD3, and OAZ1).

Frailty-related biomarkers are differentially expressed in astronauts exposed to 120-days of Low Earth Orbit Spaceflight. Putative frailty linked genes from JAXA Cell-Free Epigenome (CFE). Heatmap of the normalized plasma cell-free RNA expression values for the frailty genes over time for the six astronauts over 120days in space from JAXA study. The values shown are the averaged normalized expression values for all six astronauts for each time point during flight and post-flight. The three pre-flight time points were averaged together, since the changes for genes in the time leading up to flight are considered to be the same and part of the baseline values. For the time, L=Launch (i.e., meaning time after launch from Earth and the number indicates length in space) and R=Return to Earth.

Interestingly, cell-free RNAs from several genes (e.g,. FGF23, KRT18, AKT1, B2M, NOS1, AOC1, SOD2 and SOD3) did not return to the pre-flight baseline levels, even after 120days. The data suggest that space conditions alter the HIF1 pathway which stimulates the various molecular or cellular processes related to hypoxia-responsive genes such as HIF1A, HIF1AN, ARNT, ARNT2, NOS1, NOS2, NOTCH1 and RBX1, that are known to regulate a wide variety of cellular physiology including metabolic reprogramming, anti-apoptosis, migration, proliferation, amyloid production and prion stabilization40,41. An interesting observation emerging from the data is the increased cell-free RNA signature of HIF1A and HIF3A post-flight. Hypoxia Inducible Factor (HIF) is a key regulator of immune cell function42, and its dysregulation could alter immune response. We also observe an increase of RNAs derived from several nitric oxide (NO) related genes, which are biologic mediators in multiple processes, such as in neurotransmission and microbial and antitumoral activities. It is understood that nitric oxide (NO) is a key vasodilator in the cardiovascular system and its synthesis is catalyzed by the enzyme family nitric oxide synthases (NOS), neuronal (NOS1), inducible (NOS2) and endothelial synthases (NOS3)43. NOS1 and NOS2 are constitutively expressed by tubules of the human kidney, while NOS3 is expressed by endothelial cells and is implicated with the formation and maintenance of vascularized tissues. Furthermore, AKT1 plays a role in the signal transduction of growth factors, as well as in cell survival, cellular senescence, and aging. There is evidence that AKT signaling is associated with an imbalance of phosphatidylinositide 3-kinases that is altering the aged brain44. Chronic AKT activation intensified aging-induced cardiac hypertrophy in murine heart tissues45. In connection to phosphate intake, FGF23 is known to be secreted from the skeletal system and influence the kidneys through the klotho gene receptor36. The upregulation of HIF-related genes could be interpreted through findings from earlier studies which have implicated the HIF pathway with the impairment of energy-dependent cellular processes, and mutations in mitochondrial DNA which accelerate aging processes41,46.

Next, we used data from the first civilian commercial 3-day space mission (referred to as Inspiration4 (I4), to examine the impact of short-duration spaceflight on putative frailty biomarker transcriptomic signature47. From the I4 mission, single-cell gene expression data from peripheral blood mononuclear cells (PBMCs) were generated and compared across multiple timepoints (Fig.7A). Frailty genes were increased in PBMCs and subpopulations post-flight compared to pre-flight timepoints, and the percentage of the increased genes were higher than the percentage of differentially expressed genes (DEGs) (Fig.7B). The percentage of increased frailty genes was the highest in PBMCs, lowest in dendritic cells (DCs), and similar in the remaining subpopulations (Fig.7B). Generally, the average expression and percentage of expression of the increased genes were increased at R+1 compared to pre-flights (L-92, L-44, L-3) and returned to baseline over time (Fig.7C). For example, severalgenes were upregulated in various pathways at R+1 compared to pre-flight and reverted to baseline over time. Implicated pathways include: immunity (ARG2, PPARD), EGFR trafficking (ATXN2), regulators of apoptosis (BCL2L1, FAS), survival factor for neuronal cell types (CNTF), cellcell signaling (JAG1), metabolism (PPARD), DNA repair (REV1), neuronal excitability and synaptic transmission (SNX14), structural component of sarcomeric Z-line (TMEM245) and cell cycle regulation (TP53) (Fig.7C).

Frailty-related biomarkers are differentially expressed in astronauts exposed to 3-days of Low Earth Orbit Spaceflight. Frailty linked genes from Inspiration4 (i4) human peripheral blood mononuclear cells (PBMCs). (A) Schematic of the i4 experiments and the samples utilized for this analysis. (B) The overall percentage of up (i.e., increased), down (i.e., decreased), and no change (i.e., stable) expressed frailty genes in the i4 data (top plot) compared the overall gene distribution (bottom plot). (C) Dot plot of the single cell RNA expression for the frailty genes over time for the 4 astronauts over 3days in space from the i4 civilian crew mission. The image shows the differential expression values for each cell type in analysis. The values are based on expression for each time point before-flight and post-flight. However, data from samples collected just after reentry (R+1) is considered spaceflight condition. For the time, L=Launch, R=Return to Earth, the number+n is the time (in days) after L or R.

Having found alterations in gene expression associated with aging and frailty and knowing that biologic systems are dynamic, we used a subset of the gene expression to examine dynamic changes in metabolism. We applied our updated, context-specific, metabolic models that performed custom-made flux balance analysis (FBA) simulations. Here, we used two different transcriptional changes (RNA-seq) between flight and ground (OSD-91 (GSE65943) for cultured human TK6 lymphoblastoid cells; and OSD-127 (E-GEOD-84582) for cardiomyocytes from human pluripotent stem cells) (Fig.8).

Metabolic flux simulation analysis on OSD-91 and OSD-127. (A) and (B) Overview of carbohydrate metabolism illustrated by custom-made Escher [81] for OSD-91 and OSD-127, respectively. The associated pathways (i.e., TCA Cycle, Glycolysis, Pentose phosphate pathway, Pyruvate metabolism) whose metabolic reactions with relative activations are demonstrated. The red color presents the upregulated metabolic fluxes in flight and the blue color represents the downregulated fluxes. (C) and (D) Heatmaps showing relative metabolic flux rates (rows) versus human samples (columns) for OSD-91 and OSD-127, respectively. Only particular pathways demonstrating significant alteration of metabolic flux rates are listed, where the blue to yellow heatmap color scales indicate row-wise Z-scores for those flux rates. The leftmost bar represents differential testing results between Flight and Ground in p values<0.05 (black) or p values between 0.05 and 0.1 (gray) through the Van Der Waerden test. Genes in the boxes are enzymes showing significantly different expressions for their corresponding reactions.

In TK6 lymphoblastoid cells, microgravity led to transcriptional changes through altered methylation patterns. These transcriptional changes, in turn, altered the oxidative stress and carbohydrate metabolism pathways48. However, the flux simulation analysis showed that other pathways associated with lipid metabolism, fatty acid oxidation, fatty acid synthesis, and bile acid synthesis, are downregulated during flight. While chondroitin sulfate degradation, nucleotide interconversion, and peroxisomal transport are upregulated. Considering the carbohydrate metabolism aspect of the flux simulation analysis, only pyruvate metabolism (end product of glycolysis) showed significantly altered expression in microgravity (Figs. 8A,C).

By contrast, the other metabolic flux simulation displayed marked up-regulation during flight in lipid metabolism associated pathways: fatty acid oxidation, fatty acid synthesis, and glycerophospholipid metabolism (Figs. 8B,D). The cells also exhibited increased galactose metabolism, nucleotide interconversion, Coenzyme A (CoA) synthesis, glutathione metabolism, as well as pentose phosphate pathway in carbohydrate metabolism. The only significant downregulation in microgravity was detected in folate metabolism. This cardiomyocyte study (using 3D tissue engineering of cardiac progenitors from human pluripotent stem cells) found increased gene expression levels associated with growth, development, and survival for cardiac progenitors in microgravity49.

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Aging and putative frailty biomarkers are altered by spaceflight | Scientific Reports - Nature.com