Aging and putative frailty biomarkers are altered by spaceflight

doi:10.21203/rs.3.rs-2512123/v1

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Aging and putative frailty biomarkers are altered by spaceflight

https://doi.org/10.21203/rs.3.rs-2512123/v1

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published 11 Jun, 2024

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Human space exploration is hazardous, causing molecular changes that can alter astronauts' health. This can include genomic instability, mitochondrial dysfunction, increased inflammation, homeostatic dysregulation, and epigenomic changes. These alterations are similar to changes during aging on Earth. However, little is known about the link between these changes and disease development in space. Frailty syndrome is a robust predictor associated with biological aging, however its existence during spaceflight has not been examined. We used murine data from NASA’s GeneLab and astronaut data from JAXA and Inspiration4 missions to evaluate the presence of biological markers and pathways related to frailty, aging and sarcopenia. We identified changes in gene expression that could be related to the development of a frailty-like condition. These results suggest that the parallels between spaceflight and aging may extend to frailty as well. Future studies examining the utility of a frailty index in monitoring astronaut health appear warranted.

Biological sciences/Systems biology/Systems analysis

Health sciences/Medical research/Genetics research

Health sciences/Risk factors

Missions beyond low Earth orbit are the new frontier of crewed space exploration. The future mission to Mars as well as long-duration missions to the Moon, will be significantly longer than any previous deep space mission. During transit, the space environment presents a key challenge for astronauts’ safety¹. Previous literature describes microgravity and radiation exposure as major stressors that are able to induce pathophysiological changes in the heart², skeletal muscle, and immune system³, as well as bone loss, cognitive impairment, and increased cancer risk. Thus, there is an increasing interest to identify the molecular mechanisms driving those health risks, including changes in mitochondrial function, genetic and epigenetic regulation, telomere-length dynamics, DNA damage, and oxidative stress^4,5 (Fig. 1). Interestingly, the molecular mechanisms of spaceflight-related stressors are similar to the hallmarks of aging: mitochondrial dysfunction, genomic instability, epigenetic alterations, telomere attrition, cellular senescence, and dysbiosis among others⁶. As a defined set of aging-related molecular changes, the hallmarks of aging may be specifically applicable to assessing spaceflight risk, which may induce premature or pathological aging⁵.

Aging is a state of depleted biological resilience resulting in an increased vulnerability to stressors, leading to a systemic loss of our capacity to maintain homeostasis and health⁷. The aging phenotype is manifested in metabolic syndrome, cardiovascular disease, diabetes, neurological deterioration, and cancer⁸. Old age is also marked by immune dysregulation, which can result in a chronic low-grade inflammation state called inflammaging, a status associated with increased levels of pro-inflammatory markers in blood and tissues⁹. The deterioration in muscle quality and quantity is another result of aging¹⁰, which is characterized by altered myofiber metabolism and impaired satellite cell activity. Loss of muscle mass increases the risk of suffering from other comorbidities in a vicious cycle that leads to unhealthy aging^11,12. These features are often associated with a multifactorial geriatric syndrome also known as frailty¹³.

Frailty, a recent concept in aging science, can be defined as a syndrome caused by the combination of numerous age-related alterations that lead to a depletion of physiological reserve and/or deterioration of cognitive functions, and thus increases the risk of morbidity and mortality. It can be clinically diagnosed by the presence of three or more of the following components: unintentional weight loss over the last year, weakness, exhaustion, slow gait and low physical activity level¹⁴. Other criteria for diagnosing and assessing frailty are functional factors such as grip strength and gait speed^15,16. More holistic definitions of frailty also consider the presence of comorbidities, cognitive impairment, psychosocial risk factors and other common geriatric syndromes¹⁷. The use of clinical scoring has also attempted to quantify frailty, but limitations in terms of ethnicity, risk prediction, diagnosis and prognosis have been encountered. However, advancements in the understanding of the biological processes related to frailty and aging, and the development of high-throughput analyses, have allowed novel assessment models based on “omics” biomarkers¹⁸.

Sarcopenia is generally defined as a progressive and age-related condition characterized by loss of skeletal muscle quality, performance, strength and mass¹⁹. When this condition is driven by aging, it is defined as primary sarcopenia¹⁰. Muscle loss in astronauts shares similarities with sarcopenia in the elderly and could be related to a sarcopenia-like syndrome, especially during long duration spaceflight. Yet, despite the reported similarities between the physiological effects of frailty and spaceflight, it remains unclear whether the space environment can influence the onset of age-related dysfunction in astronauts. Getting insights about the signature of sarcopenia in clinical patients and investigating similarities with spaceflight data is a key step towards the development of countermeasures to achieve safer crewed space missions and translate therapy to patients on Earth. Here, we use multi-omics and systems-informatic approaches to evaluate the emergence of a frailty, aging and sarcopenia-related transcriptomic signature during and after spaceflight. We analyzed transcriptomic data from rodent research missions flown to the International Space Station (ISS) (available from NASA’s Open Science Data Repository Search (OSDR), previously known as GeneLab²⁰), astronaut data from a recent JAXA study, and data from the first civilian commercial spaceflight mission, Inspiration4 (i4). We identified genes related to frailty and muscle loss, that may lead to an early frailty phenotype. Moreover, our results raise the possibility that exposure to the space environment leads to changes consistent with frailty, including inflammation, muscle wasting and other age-related features. Our findings provide a way to study the development of frailty-related health risks that astronauts may develop during spaceflight, in the perspective of developing adequate preventative measures.

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 literature^21–24, a list of putative frailty biomarker genes for humans (88) and mice (82). Human (OSD-52 and 195) and mouse (OSD-21, 99, 101, 103, 104, 105, 202) 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); and brain (15 genes in OSD-202) (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. In humans, vastus lateralis muscle (OSD-52) and endothelial cells (OSD-195) showed 22 and 4 frailty-related genes, respectively (Fig. 2B).

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. 2C). Out of 73 differentially expressed frailty-related genes, 21 (29%) were common in humans and mice (Fig. 2C). Forty-six (64%) were unique to mice and 4 (7%) were unique to only humans. In humans, 9 frailty genes were up-regulated and 13 down-regulated in the vastus lateralis muscle(Fig. 2D). Several down-regulated genes were associated with immunity-related pathways, while most up-regulated genes were associated with metabolism and Vitamin K or D pathways. In endothelial cells, two genes were down-regulated, and two were up-regulated. The down-regulated genes, TMEM245 and PPARGC1A, are associated with the cell-membrane and gluconeogenesis, while the up-regulated genes, MSTN and PTGS2, are associated with regulation of skeletal muscle growth and prostaglandin biosynthesis²⁵.

Murine muscles and brain tissue showed a higher number of up-regulated than down-regulated frailty-related genes (Fig. 2E). As an example, the extensor digitorum longus had several up-regulated genes (EGLN3, PTGS2, VDR, FREM2, KRT18, BCL2L1, LGALS3, CXCL10, CX3CL1, FNDC5, TGFB1, CAN, and PPARGC1A). Whereas the soleus (OSD-104) had relatively few down-regulated genes (GDF15, PTGS2, BDNF, PAX5, CX3CL1, FNDC5, VCAN, CALU, and SESN2).

Interferon inflammatory response and muscle tissue development pathways are enriched in rodent muscles during spaceflight

To determine overall frailty impact of spaceflight on tissues Gene Set Enrichment Analysis (GSEA)²⁶ analysis was performed on specific aging-related pathways (selected from the Molecular Signatures Database (MSigDB)²⁶. 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 (Figs. 3A and B). Despite a mixed direction of regulation, a clear enrichment of these pathways across the datasets was evident in the spaceflight group when compared to the control. 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 space²⁷. Immune response can downregulate IGF-1 anabolic activity, promoting muscle wasting¹⁹. Of note, this muscle shows the largest decline in mass in the RR1 mission²⁸ and IGF-1 pathway might be involved. Several putative aging-related pathways were enriched in human datasets (Fig. 3C), showing up-regulation in a majority of cases. Of note, interferon alpha and gamma response pathways are upregulated in all the datasets investigated. In the clinic, an increased low-grade inflammatory response is critical in muscle wasting¹⁹.

Multiple frailty related biomarkers are differentially expressed in astronauts

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 studies²⁹. First, using astronaut data from JAXA cell-free transcriptomic study, we examined the changes occurring in the frailty biomarker genes between pre-flight, in-flight and post-flight (i.e. return to Earth) (Fig. 4). Most of the genes investigated are subject to changes in the expression level when compared to pre-flight conditions, either during spaceflight or later after return to Earth. A large number of genes that were downregulated during spaceflight, showed upregulation after re-entry (e.g., AKT1, NOS2, FGF23, and HIF3A). Conversely, several genes show an opposite behavior. These tended to be upregulated during spaceflight, and underwent downregulation after re-entry (e.g., TGFB1, B2M, NOS1, AOC1, SOD2, SOD3, and OAZ1). Interestingly, several genes (e.g. FGF23, KRT18, AKT1, B2M, NOS1, AOC1, SOD2 and SOD3) did not return to the pre-flight baseline expression levels, even after 120 days. The data suggest that space conditions activate the HIF1 pathway which stimulates the expression of various 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 β production and prion stabilization^30,31. An interesting observation emerging from the data is the elevation of HIF1A and HIF3A post-flight. Hypoxia Inducible Factor (HIF) is a key regulator of immune cell function³², and its dysregulation could alter immune response. We also observe upregulation of 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 (NO2) and endothelial synthases (NOS3)³³. 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 brain³⁴. Chronic AKT activation intensified aging-induced cardiac hypertrophy in murine heart tissues³⁵. 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 processes^31,37.

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 signature³⁸. From the I4 mission, single-cell gene expression data from peripheral blood mononuclear cells (PBMCs) data were generated and compared across multiple timepoints (Fig. 5A). Frailty genes were increased in PBMCs and subpopulations, and the percentage of the increased genes were higher than the percentage of differentially expressed genes (DEGs) (Fig. 5B). The percentage of increased frailty genes was the highest in PBMCs, lowest in dendritic cells (DCs), and similar in the remaining subpopulations (Fig. 5B). 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. 5C). For example, several genes 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), cell-cell 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. 5C).

Novel sarcopenia-related genes are differentially expressed during spaceflight

Sarcopenia is a process in the clinic that is associated with frailty. As such, it is important to elucidate any shared pathways between these processes. Our analysis identified that the best predictors of sarcopenia were genes part of autophagic and protein degradation processes. After studying databases from 118 people with and without sarcopenia (GSE111006, GSE111010, and GSE111016)³⁹, 6,892 DEGs were identified by performing Mann–Whitney U tests⁴⁰ on gene expression data for every single gene (i.e., 65217 genes) in a pair-wise manner across samples from both sets of patients. A simple classifier (ie, k-nearest neighbors⁴¹), was then used to estimate individual predictive power for that condition. Next, via co-expression network analysis⁴² 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 sarcopenia.

Here, we found that the frailty biomarkers list was enriched in BP GO terms in a very similar manner to those found with sarcopenic biomarkers alone⁴¹ (Fig. 6A). In addition to biological processes (BP) GO, the same was true for molecular functions (MF) GO term enrichment (Fig. 6C). Interestingly, we found that eight of the biomarkers identified for frailty had an ability to predict sarcopenia with a Mean Accuracy Score (MAS) of > 0.65 (RP1L1, SH3GL3, HIF1A, FGF23, FASLG, MAS1, PAX5, and REV1) (Fig. 6B and D).

Having identified putative sarcopenia genes, we re-examined the existing datasets for alterations in these 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 proposed 21 genes (Fig. 6E). 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 CX30.3⁴³. HNRNPCL1 plays a role in consolidating the nucleosome and neutralizing core hnRNPs proteins⁴⁴. GOLGA2 encodes the GM130 protein necessary for the assembly of the Golgi apparatus. Interestingly, mutations in GOLGA2 lead to neuromuscular disorders and muscular dystrophy⁴⁵. POMC codes for the precursor protein proopiomelanocortin producing active peptides generating melanocyte stimulating hormones (MSHs), corticotropin (ACTH) and β-endorphin. POMC deficiency leads to adrenal failure and obesity⁴⁶. 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).

Metabolic Flux Simulations of Gene Expression changes reveal dysregulation of metabolic functions related to frailty

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. 7).

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 pathways⁴⁷. However, the flux simulation analysis showed that other pathways associated with lipid metabolism, fatty acid oxidation, fatty acid synthesis, and bile acid synthesis, are down-regulated 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. 7A and 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. 7B and 4D). The cells also exhibited increased galactose metabolism, nucleotide interconversion, 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 microgravity⁴⁸.

It has been shown that the space environment accelerates the aging process and thus observations from the aging field can be relevant to understand the biological changes driven by spaceflights⁵. Studies in the genetic/genomic aging model C. elegans and clinical evidence have demonstrated that the control of lifespan and health span are not identical^48,49; therefore, there is an increasing need to identify markers able to determine the biological age of an individual rather than just the chronological (e.g., hand grip strength). One such marker is the frailty index, which can serve as a better health indicator than chronological age, translating what is suggested by previous biological and clinical literature⁷. Frailty is a complex syndrome characterized by a reduced ability to recover from pathological or iatrogenic stresses caused by aging-related impairments⁴⁹. Here, we examined if aging and putative frailty biomarkers change during spaceflight. Our results from both rodent and human samples suggest that alterations indeed occurred in aging-related pathways to a different extent depending on the tissue. Importantly, we also found changes in expression of putative frailty genes, raising the possibility that these could be used as indicators of physiological changes during spaceflight much as they are being examined for utility in clinical assessment and treatment of physiologic changes with age on Earth.

The NASA Twins study along with the JAXA Cell-Free Epigenome and Inspiration4 data reported here and elsewhere (Figs. 4 and 5) highlight the emerging ability to conduct -omic analyses on astronaut samples^29,38,50. As previously suggested, this represents an opportunity to apply these cutting-edge clinical research tools to monitoring and researching astronaut health. Indeed, genome sequencing onboard the ISS is currently used in the microbiological monitoring program⁵¹, paving the way for use in astronaut studies on the ISS. The results from our present study suggest that assessment of aging and putative frailty biomarkers could be useful avenues of future investigation. Previous studies of astronaut health have largely been conducted using standard clinical markers such as blood panels. While there is an emerging blood panel for assessment of frailty⁵² this would need to be processed on Earth. By contrast, assessment of a future clinically validated transcriptomic panel assessment of frailty could be conducted on ISS or other spacecrafts. Regardless of which panel or where such a panel is assessed, our findings suggest that future studies aimed at establishing such a validated model for astronauts could be fruitful areas of investigation for monitoring and improving astronaut health in- and post-flight. The establishment and validation of such a panel could either be done by correlating with physiologic measures already captured by NASA’s standard measures program or correlating with physiologic measures adapted from the emerging frailty indexes.

Beyond health assessment, the ability to conduct -omic studies on astronauts allows for combining astronaut and model organism datasets in order to conduct truly translational research in space. Indeed, our present study, along with others, demonstrates this is now feasible. Such studies can begin to shed light or expand our understanding of molecular control of (patho)physiologic changes in response to spaceflight. Having found that aging and putative frailty biomarkers change in response to spaceflight, our present study provides an example of how we can begin to shed light on why spaceflight appears to induce frailty.

One of the most pronounced findings in both rodent and human datasets is induction of interferon alpha and gamma responsive pathways. Interferons are cytokines associated with modulation of the immune response and modulation of the immune response is a known consequence of spaceflight³. Our observation of the activation of interferon pathways (Fig. 3) suggests that activation of the innate immune system is a conserved mammalian response to spaceflight. Interestingly, alteration of the immune system is a conserved observation for spaceflight and aging. In the aging field, the concept of inflammaging suggests that the chronic low-grade dysregulation of the immune system that happens during aging underpins the development of diseases such as atherosclerosis, type II diabetes, and sarcopenia⁸. Indeed, at least ten clinical biomarkers of inflammation correlate well with frailty index⁵³. Making the inflammaging parallel to spaceflight, it could be that an increased low-grade inflammatory response during spaceflight drives and/or worsens frailty and/or related processes (for example muscle wasting). However, inflammation does not appear to explain other, perhaps frailty independent, changes in astronauts. Low grade inflammation contributes to the development of endothelial dysfunction and atherosclerosis on Earth^8,54. In addition, radiation exposure can lead to the development of cardiovascular disease in cancer patients, when the heart is in or close to the field of exposure⁵⁵ and in occupational exposures^56,57. Atherosclerosis in astronauts might be promoted by an increased inflammatory status in the space environment promoted by different stressors (Fig. 1) and radiation exposure can play a primary role. A significant difference between the Moon and Mars missions compared with the ISS is the radiation environment, as astronauts traveling beyond Low Earth Orbit are potentially exposed to higher doses of galactic cosmic rays and solar particle event radiation⁵⁸. Radiation exposure could promote multiple dysregulation, causing cellular senescence in the endothelium, mitochondrial dysfunction and inflammation, driving endothelial dysfunction⁵⁹. Exposure to higher doses of space radiation beyond low Earth orbit could have a different, and perhaps increased, impact on frailty-related hallmarks and genes.

Across species, altered metabolism is one of the most consistently reported responses to spaceflight. Unsurprisingly, these changes vary from organism to organism among microbes and tissue to tissue in animals (Fig. 7). A recent rodent transcriptomic study confirmed that the typical mammalian coordination of metabolism between the liver and muscles on Earth continues to function similarly in flight⁶⁰. This metabolic axis is key to health with age where disruption is associated with obesity, diabetes, and frailty. Emerging techniques such as metabolomics are beginning to address the role and mechanisms of metabolic alteration in human muscle with age⁶¹. Here we have employed another emerging analytical approach, metabolic flux modeling from transcriptomic data (Fig. 7). Our results confirm that metabolic flux analysis successfully demonstrated metabolic alterations as expected dependent on tissue type. Importantly, while our results confirm past observations that mitochondrial and metabolic changes are conserved features of space flown tissues and organisms⁵, they also demonstrate that the functional changes via metabolic flux are more important than the simple global up versus down changes often employed with reductionist approaches. Thus, using data from multiple tissues and time points can begin to provide a more comprehensive understanding of the mechanisms underlying the development of frailty in response to spaceflight. Importantly, our results demonstrate that these approaches can be employed directly in astronauts and/or in combination with model systems.

Muscle decline is another conserved feature between spaceflight and aging. It is also thought to contribute to frailty. Recently, functional measures of muscle decline with age have been well correlated with evolutionarily conserved gene expression changes with age^39,62. In humans, changes in metabolic gene expression appear to occur prior to changes in inflammatory gene expression, suggesting that aging human muscle undergoes metabolic remodeling prior to becoming inflamed and sarcopenic; also highlighting the importance of temporal/dynamic data. Similarly, in humans subjected to bedrest, an analogue for spaceflight, changes in metabolic gene expression and metabolic activity are an early event and one tied to altered intramuscular calcium levels⁶³. The recent study of muscle-liver metabolic cross talk in mice⁶⁰ used the same mouse muscle datasets we used (Fig. 7) and found altered glucose and lipid metabolism in response to spaceflight, consistent with past literature on space flown mammalian muscle. Combining this past analysis with our new analysis of sarcopenic gene expression changes, it is noteworthy that few sarcopenic gene expression changes are noted in the space flown muscles with more observed in the soleus, which was the muscle displaying the largest decline in mass²⁸. Together, these results raise the possibility that the metabolic changes may indeed precede the loss of muscle mass in spaceflight, as observed with age and bedrest on Earth. It remains an open question if, like sarcopenia, the metabolic alterations occur earlier than the inflammatory alterations in flight and whether the inflammation or altered metabolism is the key driver of frailty. Lastly, given the similarities between transcriptomic changes with age and spaceflight, the facts that estrogen receptor has recently been proposed to be a key regulator of muscle gene expression changes with age^39,62 and that estrogen receptor pathways have recently been shown to display differences in flight, raise the possibility that estrogen receptor pathways are key modulators of muscle health in space⁶⁴. Future studies will be conducted to observe global and systemic impact on how the frailty index will change throughout the body during spaceflight.

Methods of Analyses of Open Science (Genelab) Datasets: Differential Gene Expression and GSEA Pathway Analysis

The genelab datasets (GLDS) that were analyzed for this work are: OSD-21, OSD-52, OSD-99, OSD-101, OSD-103, OSD-104, OSD-105, OSD-195, and OSD-202. Information on the experimental investigations leading to the datasets is provided in Table 1.

Table 1. Genelab datasets analyzed in the manuscript

Identifier	Title	Authors, title, publisher, version, DOI	Analyzed Tissue
OSD-21	Effects of spaceflight on murine skeletal muscle gene expression	Barth J. "Effects of spaceflight on murine skeletal muscle gene expression", NASA Open Science Data Repository, Version 3, http://doi.org/10.25966/c36b-3g68	Gastrocnemius muscle
OSD-52	Expression data from SPHINX (SPaceflight of Huvec: an INtegrated eXperiment)	Bradamante S, Versari S, Longinotti G, Barenghi L, Maier J. "Expression data from SPHINX (SPaceflight of Huvec: an INtegrated eXperiment)", NASA Open Science Data Repository, Version 4, http://doi.org/10.26030/nt3p-p547	Endothelial
OSD-99	Rodent Research-1 (RR1) NASA Validation Flight: Mouse extensor digitorum longus muscle transcriptomic and epigenomic data	Galazka J, Globus R "Rodent Research 1", GeneLab, Version 4, http://doi.org/10.26030/1h3m-3q49	Extensor digitorum longus muscle
OSD-101	Rodent Research-1 (RR1) NASA Validation Flight: Mouse left gastrocnemius muscle transcriptomic, proteomic, and epigenomic data	Galazka J, Globus R "Rodent Research 1", GeneLab, Version 5, http://doi.org/10.26030/sdmt-ae51	Gastrocnemius muscle
OSD-103	Rodent Research-1 (RR1) NASA Validation Flight: Mouse quadriceps muscle transcriptomic, proteomic, and epigenomic data	Galazka J, Globus R "Rodent Research 1", GeneLab, Version 4, http://doi.org/10.26030/9vzk-b116	Quadriceps muscle
OSD-104	Rodent Research-1 (RR1) NASA Validation Flight: Mouse soleus muscle transcriptomic and epigenomic data	Galazka J, Globus R "Rodent Research 1", GeneLab, Version 4, http://doi.org/10.26030/em9r-w619	Soleus muscle
OSD-105	Rodent Research-1 (RR1) NASA Validation Flight: Mouse tibialis anterior muscle transcriptomic, proteomic, and epigenomic data	Galazka J, Globus R "Rodent Research 1", GeneLab, Version 4, http://doi.org/10.26030/xgw6-6t64	Tibialis anterior muscle
OSD-195	Effects of 21 days of bedrest on human skeletal muscle gene expression	Rullman E. "Effects of 21 days of bedrest on human skeletal muscle gene expression", NASA Open Science Data Repository, Version 1, http://doi.org/10.26030/r6bv-rk07	Vastus lateralis muscle
OSD-202	Low dose (0.04 Gy) irradiation (LDR) and hindlimb unloading (HLU) microgravity in mice: brain transcriptomic and epigenomic data	Mao X. "Low dose (0.04 Gy) irradiation (LDR) and hindlimb unloading (HLU) microgravity in mice: brain transcriptomic and epigenomic data", NASA Open Science Data Repository, Version 8, http://doi.org/10.26030/ewfb-7g23	Brain

The Genelab datasets are re-analyzed to produce the t-score. The Differential Expression (DE) analysis of RNA-Seq datasets (OSD-99, OSD-101, OSD-103, OSD-104, OSD-105, OSD-202) is performed using DESeq2 version 1.26.0⁶⁵, in R software version 4.1.2. Expected counts from the RSEM step were extracted and rounded up to the next integer and used as input for DE analysis. The Differential Expression (DE) analysis of microarray datasets (OSD-21, OSD-52, OSD-195) is performed using the package limma⁶⁶, in R version 4.1.2. The ensembl IDs of the genes in the DE datasets were annotated to their corresponding gene symbols using the R package biomaRt^67,68.

We performed gene set enrichment analysis (GSEA) on the differentially expressed datasets using fgsea⁶⁹ to determine to what extent aging and putative frailty pathways were impacted in spaceflight. Tentative aging and frailty gene pathways were downloaded from the Molecular Signatures Database (MSigDB)^26,70 – a joint project of UC San Diego and Broad Institute. MSigDB is a multicollection of databases among which are databases of curated gene sets, and databases of ontology gene sets that are used in the pathway analysis. The latest version MSigDB v2022.1 was used in the pathway search.

In search of pathways that are potentially related to frailty, in MSigDB, we entered keywords of processes that are common denominators to aging in different organisms, and are described in⁶. The processes of interest are: changes in genomic and genetic material; cellular processes; nutrient signaling; and responses at the tissue level with bone and muscle as the tissues of interest. Changes in genomic and genetic material that are deemed to potentially associate with frailty include genome instability, epigenetic alterations, histone modifications, dna methylation, dna damage, and changes in chromatin structure. Cellular processes that are of interest to frailty pathways include: 1) processes that are key to maintaining the cell cycle – cell senescence, stem-cell generation, and the pathways that involve p53, the protein tumor suppressor; 2) mechanisms involved in proteostasis (protein regulation); 3) mitochondrial processes involving dysfunction and disease; and 4) intercellular processes that involve inflammation and intercellular communication. The actions of the protein complexes – proteasome, lysosome, and chaperone – are considered in the network of pathways that regulate proteins. Putative pathways of nutrient sensing and signaling include pathways mediated by insulin signaling and mTor signaling, and the role of Ampk.

The downloaded pathways were identified as putative frailty pathways when at least two (2) putative frailty biomarker genes were present among the genes that are involved in the pathway. This is applied from the following reasoning: if multiple genes that can possibly interact to express a phenotype are present in a pathway, then that pathway can be associated with that phenotype.

The outcome of the fGSEA analysis is the normalized enrichment score (NES) of any given pathway, alongside valuable information on the analysis, such as the adjusted p-value, and the leading-edge genes of the pathway. The normalized enrichment score is the enrichment score normalized based on the number of genes in the gene set. It indicates the representation of the pathway genes in the dataset gene list, which is priorly ranked according to the values of the t-score of the genes. A positive NES indicates that the pathway genes are mostly represented at the top of the gene list, while a negative NES indicates that the pathway genes are mostly represented at the bottom of the gene list. The plots of normalized enrichment score (NES) of the different pathways for a given dataset are produced using ggplot2⁷¹, and the heatmaps across different datasets or different genes are obtained using a complex heatmap R package⁷².

Analysis on Frailty Biomarker Genes and Pathways

A list of Frailty Biomarkers for humans and mice were generated based on a literature survey. Subsequently, differentially expressed (log₂foldchange and adjPvalue <0.5) frailty biomarkers genes were identified from the following two human datasets (OSD-52 and 195) and seven mice datasets (OSD-21, 99, 101, 103, 104, 105, and 202). Subsequently, to generate the venn diagram, R language v4.1.0⁷³ was used, through the library ggplot2 v3.4.0⁷¹. It was extracted from the table of frailty genes, the genes unique to mice and humans and the genes in common for the two (the intersections). After generating the diagram, a list of genes was extracted and manually added to the diagram. To generate the two upset plot graphs (for mice genes and human genes), the R language⁷³ was also used, using the library ggplot2 v3.4.0⁷¹ and ComplexUpset v1.3.5⁷⁴. Finally, to generate the heatmap, the python 3 v3.10.0 language was used, through the Seaborn v0.12.1 and Clustermap v0.11.12 libraries. Two heatmaps were generated (one for the mice genes and the other for the human genes), relating the genes to the respective OSD datasets. To complement the heatmap analyses, a dendrogram has also been added for each graph.

Inspiration4 (i4) sample collection

Inspiration4 was the world’s first all-civilian mission to orbit Earth. Four civilians, two males and two females, spent three days in low-Earth orbit (LEO) at 585 km above Earth. The mission launched from NASA Kennedy Space Center on September 15th, 2021 and splashed down in the Atlantic Ocean near Cape Canaveral on September 18th, 2021. Several human related experiments were carried out to study the effects of spaceflight on human health and performance in collaboration with SpaceX, the Translational Research Institute for Space Health (TRISH) at Baylor College of Medicine (BCM), and Weill Cornell Medicine. The experiments carried out on the Inspiration4 crew-members were performed in accordance with the relevant guidelines at the principal investigators’ institutions. Moreover, the different study designs and the corresponding methods to collect and analyze the biological samples were approved by the corresponding institutional IRB. All biological data derived from the Inspiration4 mission were collected pre and post flights. For this study, only data from blood samples were used. Pre-flight samples were collected at L-92, L-44, and L-3 days prior to launch to space. Upon return, post-flight samples were collected at R+1, R+45, and R+82 days.

i4 PBMC Single cell sequencing and analysis

Blood samples were collected before (Pre-launch: L-92, L-44, and L-3) and after (Return; R+1, R+45, and R+82) the spaceflight. Chromium Next GEM Single Cell 5’ v2, 10x Genomics was used to generate single cell data from isolated PBMCs. Subpopulations were annotated based on Azimuth human PBMC reference⁷⁵. Dot plots are generated by the Seurat R package.

JAXA Cell-Free Epigenome (CFE) Study RNA quantification data

Aggregated RNA differential expression data and study protocols were shared through NASA’s Open Science Data Repository with accession number: OSD-530²⁹. Plasma cell-free RNA samples for RNA-seq analysis were derived from blood samples collected from 6 astronauts before, during, and after spaceflight on the ISS. Mean expression values were obtained from normalized read counts of 6 astronauts for each time point. Heatmaps were made for the frailty genes on the normalized values per time point using R package pheatmap version 1.0.12.

Quantification of genes associated with sarcopenia

The curated short gene list of predictors of sarcopenia was obtained by analyzing a population of age matched individuals with and without sarcopenia using the expression superseries GSE111017. This list contains 21 genes that can predict sarcopenia in patients with an accuracy >75%. The method for obtaining the gene list was performed as previously described⁴¹. Here, we used GOstat 2022-11-03 on R version 4.2 to find enriched Biological Process and Molecular Function terms (Fig A and C). Using the obtained GO terms we found that some of the Frailty linked genes from JAXA CFE were also part of some of the same processes (Fig B and D). The figure was created using the GOplot package 1.0.2 and modified to display the MAS for each gene. Finally, to determine the relevance of the shared 21 genes between all gene lists (Fig E), we assessed their expression in the mice datasets OSD-21, OSD-52, OSD-91, OSD-195, and OSD-202. Finally, the heatmap was generated as before using python 3 v3.10.0 language, through the Seaborn v0.12.1 and Clustermap v0.11.12 libraries and a dendrogram has also been added for each graph.

Metabolic Flux Simulation Analysis

We ran metabolic flux simulation on all available metabolic reactions for each RNA sequencing sample while applying our custom-made context-specific constraint-based metabolic modeling approach significantly updated from^27,76,77. This updated simulation model was constructed based on the RECON3D metabolic model⁷⁸ whose metabolic reactions were subsetted through CORDA⁷⁹ and Cobrapy⁸⁰ with gene-reaction-rule, where enzyme expression levels are linked to their corresponding metabolic reactions. For CORDA, flux confidence parameters ranging from 3 to 1 was decided as; i) 55%, 25%, and 20% for OSD-91; ii) 45%, 40%, and 15% for OSD-127, in order to minimize standard deviation of flux-levels across samples from each group for all groups. Also, several essential pathways such as ‘Glycolysis/Gluconeogenesis’, ‘CoA Synthesis’, ‘CoA Catabolism’, ‘NAD Metabolism’, ‘Fatty Acid Synthesis’, ‘Fatty Acid Oxidation’, and ‘Biomass and Maintenance Functions’ were manually activated for the simulation model stability. Note that ‘Oxidative Phosphorylation’ and ‘Citric Acid Cycle’ were not included in the essential pathways to take into account mitochondrial dysregulation. Our metabolic model optimized for NAD biosynthesis by Gurobi solver as LP (Gurobi Optimization, L. L. C., 2020) whose reference manual can be found in https://www.gurobi.com. NAD biosynthesis capacity through all pathways of human was computed by estimating optimized ‘NAD sink’ reaction flux level where the sink was defined as large pools of metabolites importing metabolites from/to the system. While gene expression levels were applied into the model by each sample, the other parameters were maintained identically for all simulation, where an environment i.e., Jupyter notebook 6.1.5 on Python 3.7.7 was utilized. The simulation reported outcomes as flux levels of all available reactions through the custom-made flux balance analysis (FBA) analyses and the levels were analyzed as grouped variables for comparison between ‘Flight’ and ‘Ground’ groups. Since it is impossible to presume variance or normality between ‘Flight’ and ‘Ground’, non-parametric Van der Waerden (VdW) test was applied to properly compare their flux levels using the R matrixTests package (v. 0.1.9). The comparison is illustrated as heatmaps with row-wise Z-scores on flux levels per each reaction in Figure 7.

Data Availability

The RNA-seq data for the RR1 missions and the JAXA CFE data are available via the NASA Open Science Data Repository’s (OSDR)’s Biological Data Management Environment (https://osdr.nasa.gov/bio/repo) with accession IDs: OSD-21, OSD-99, OSD-101, OSD-103, OSD-104, OSD-105, OSD-202, and OSD-530.

Acknowledgments

V.C. was supported by ANID-Subdirección de Capital Humano/Doctorado Nacional/2022- 21220897. N.J.S. was supported by grants from NASA [NSSC22K0250; NSSC22K0278]. A.B. was supported by NASA grant 16-ROSBFP_GL-0005: NNH16ZTT001N-FG Appendix G: Solicitation of Proposals for Flight and Ground Space Biology Research (Award Number: 80NSSC19K0883). M.S.K. was supported by the National Research Foundation of Korea (2022R1F1A1071814) and Kyung Hee University (20220780).

Author contributions

Conceptualization: A.B. and A.Camera.; Methodology: A.B., A.Camera, J.E.D., A.Caicedo, V.C., M.S.K., and F.K.; Formal Analysis: A.B., J.K., F.K., L.C.D.O., V.C., V.Z., J.E.D, M.T., and M.S.K.; Investigation: A.B., C.E.M., M.M., J.K., A.Camera., V.C., M.S.K., and M.T.; i4 data and omics: C.E.M., E.O., and J.K.; JAXA data: M.M.; Resource: A.B., and C.E.M..; Original Draft: A.Camera, V.C., M.T., N.J.S and A.B.; Review & Editing: all authors; Figures and Visualization: A.B., J.K., V.C., V.Z., F.K., A.S.G, J.J., A.Camera and M.T.; Funding Acquisition: A.B. and C.E.M. (for i4 study); Supervision: A.B.

Patel, Z. S. et al. Red risks for a journey to the red planet: The highest priority human health risks for a mission to Mars. npj Microgravity 6, 33 (2020).
Patel, S. The effects of microgravity and space radiation on cardiovascular health: From low-Earth orbit and beyond. IJC Heart & Vasculature 30, 100595 (2020).
Crucian, B. E. et al. Immune System Dysregulation During Spaceflight: Potential Countermeasures for Deep Space Exploration Missions. Front. Immunol. 9, 1437 (2018).
Deák, P., Udvarhelyi, P., Thiering, G. & Gali, A. The kinetics of carbon pair formation in silicon prohibits reaching thermal equilibrium. Nat Commun 14, 361 (2023).
Afshinnekoo, E. et al. Fundamental Biological Features of Spaceflight: Advancing the Field to Enable Deep-Space Exploration. Cell 183, 1162–1184 (2020).
López-Otín, C., Blasco, M. A., Partridge, L., Serrano, M. & Kroemer, G. The hallmarks of aging. Cell 153, 1194–1217 (2013).
Fried, L. P. et al. The physical frailty syndrome as a transition from homeostatic symphony to cacophony. Nat Aging 1, 36–46 (2021).
Ferrucci, L. & Fabbri, E. Inflammageing: chronic inflammation in ageing, cardiovascular disease, and frailty. Nat Rev Cardiol 15, 505–522 (2018).
Franceschi, C., Garagnani, P., Parini, P., Giuliani, C. & Santoro, A. Inflammaging: a new immune-metabolic viewpoint for age-related diseases. Nat Rev Endocrinol 14, 576–590 (2018).
Nascimento, C. M. et al. Sarcopenia, frailty and their prevention by exercise. Free Radical Biology and Medicine 132, 42–49 (2019).
Greco, E. A., Pietschmann, P. & Migliaccio, S. Osteoporosis and Sarcopenia Increase Frailty Syndrome in the Elderly. Front. Endocrinol. 10, 255 (2019).
Bowden Davies, K. A. et al. Reduced physical activity in young and older adults: metabolic and musculoskeletal implications. Therapeutic Advances in Endocrinology 10, 204201881988882 (2019).
Dodds, R. & Sayer, A. A. Sarcopenia and frailty: new challenges for clinical practice.
da Silva, V. D. et al. Association between frailty and the combination of physical activity level and sedentary behavior in older adults. BMC Public Health 19, 709 (2019).
Fried, L. P. et al. Frailty in Older Adults: Evidence for a Phenotype. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences 56, M146–M157 (2001).
Xue, Q.-L. The Frailty Syndrome: Definition and Natural History. Clinics in Geriatric Medicine 27, 1–15 (2011).
Ellwood, A., Quinn, C. & Mountain, G. Psychological and Social Factors Associated with Coexisting Frailty and Cognitive Impairment: A Systematic Review. Res Aging 44, 448–464 (2022).
Pradhananga, S. et al. Ethnic differences in the prevalence of frailty in the United Kingdom assessed using the electronic Frailty Index. Aging Med 2, 168–173 (2019).
Batsis, J. A. & Villareal, D. T. Sarcopenic obesity in older adults: aetiology, epidemiology and treatment strategies. Nat Rev Endocrinol 14, 513–537 (2018).
Berrios, D. C., Galazka, J., Grigorev, K., Gebre, S. & Costes, S. V. NASA GeneLab: interfaces for the exploration of space omics data. Nucleic Acids Research 49, D1515–D1522 (2021).
Cardoso, A. L. et al. Towards frailty biomarkers: Candidates from genes and pathways regulated in aging and age-related diseases. Ageing Research Reviews 47, 214–277 (2018).
Pan, Y., Ji, T., Li, Y. & Ma, L. Omics biomarkers for frailty in older adults. Clinica Chimica Acta 510, 363–372 (2020).
Lebrasseur, N. K. et al. Identifying Biomarkers for Biological Age: Geroscience and the ICFSR Task Force. J Frailty Aging 10, 196–201 (2021).
Marcos-Pérez, D. et al. Association of inflammatory mediators with frailty status in older adults: results from a systematic review and meta-analysis. GeroScience 42, 1451–1473 (2020).
Chen, M.-M., Zhao, Y.-P., Zhao, Y., Deng, S.-L. & Yu, K. Regulation of Myostatin on the Growth and Development of Skeletal Muscle. Front Cell Dev Biol 9, 785712 (2021).
Subramanian, A. et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. U.S.A. 102, 15545–15550 (2005).
da Silveira, W. A. et al. Comprehensive Multi-omics Analysis Reveals Mitochondrial Stress as a Central Biological Hub for Spaceflight Impact. Cell 183, 1185-1201.e20 (2020).
Choi, S. Y. et al. Validation of a New Rodent Experimental System to Investigate Consequences of Long Duration Space Habitation. Sci Rep 10, 2336 (2020).
Muratani M. Cell-free RNA analysis of plasma samples collected from six astronauts in JAXA Cell-Free Epigenome (CFE) Study - Version 1. 10.26030/r2xr-h714.
Wei, Y., Giunta, S. & Xia, S. Hypoxia in Aging and Aging-Related Diseases: Mechanism and Therapeutic Strategies. International Journal of Molecular Sciences 23, 8165 (2022).
Yeo, E.-J. Hypoxia and aging. Exp Mol Med 51, 1–15 (2019).
McGettrick, A. F. & O’Neill, L. A. J. The Role of HIF in Immunity and Inflammation. Cell Metabolism 32, 524–536 (2020).
Costa, E. D., Rezende, B. A., Cortes, S. F. & Lemos, V. S. Neuronal Nitric Oxide Synthase in Vascular Physiology and Diseases. Frontiers in Physiology 7, (2016).
Yang, S. et al. Reducing the Levels of Akt Activation by PDK1 Knock-in Mutation Protects Neuronal Cultures against Synthetic Amyloid-Beta Peptides. Front. Aging Neurosci. 9, 435 (2018).
Hua, Y. et al. Peripheral SMN restoration is essential for long-term rescue of a severe spinal muscular atrophy mouse model. Nature 478, 123–126 (2011).
Kuro-O, M. Aging and FGF23-klotho system. Vitam Horm 115, 317–332 (2021).
Yuan, Y. et al. Regulation of SIRT1 in aging: Roles in mitochondrial function and biogenesis. Mech Ageing Dev 155, 10–21 (2016).
Kim et al in progress.
Migliavacca, E. et al. Mitochondrial oxidative capacity and NAD+ biosynthesis are reduced in human sarcopenia across ethnicities. Nat Commun 10, 5808 (2019).
Mann, H. B. & Whitney, D. R. On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other. The Annals of Mathematical Statistics 18, 50–60 (1947).
Castañeda, V. under revision. (2023).
Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008).
Richard, G. et al. Genetic Heterogeneity in Erythrokeratodermia Variabilis: Novel Mutations in the Connexin Gene GJB4 (Cx30.3) and Genotype-Phenotype Correlations. Journal of Investigative Dermatology 120, 601–609 (2003).
Swaminathan, S. et al. Genomic Copy Number Analysis in Alzheimer’s Disease and Mild Cognitive Impairment: An ADNI Study. International Journal of Alzheimer’s Disease 2011, 1–10 (2011).
Shamseldin, H. E., Bennett, A. H., Alfadhel, M., Gupta, V. & Alkuraya, F. S. GOLGA2, encoding a master regulator of golgi apparatus, is mutated in a patient with a neuromuscular disorder. Hum Genet 135, 245–251 (2016).
Millington, G. W. The role of proopiomelanocortin (POMC) neurones in feeding behaviour. Nutr Metab (Lond) 4, 18 (2007).
Chowdhury, B. et al. A Study of Alterations in DNA Epigenetic Modifications (5mC and 5hmC) and Gene Expression Influenced by Simulated Microgravity in Human Lymphoblastoid Cells. PLoS ONE 11, e0147514 (2016).
Jha, R. et al. Simulated Microgravity and 3D Culture Enhance Induction, Viability, Proliferation and Differentiation of Cardiac Progenitors from Human Pluripotent Stem Cells. Sci Rep 6, 30956 (2016).
Koh, L. Y. & Hwang, N. C. Frailty in Cardiac Surgery. J Cardiothorac Vasc Anesth 33, 521–531 (2019).
Garrett-Bakelman, F. E. et al. The NASA Twins Study: A multidimensional analysis of a year-long human spaceflight. Science 364, eaau8650 (2019).
Castro-Wallace, S. L. et al. Nanopore DNA Sequencing and Genome Assembly on the International Space Station. Sci Rep 7, 18022 (2017).
Erusalimsky, J. D. et al. In Search of ’Omics’-Based Biomarkers to Predict Risk of Frailty and Its Consequences in Older Individuals: The FRAILOMIC Initiative. Gerontology 62, 182–190 (2016).
Mitnitski, A. et al. Age-related frailty and its association with biological markers of ageing. BMC Med 13, 161 (2015).
Kirkman, D. L., Robinson, A. T., Rossman, M. J., Seals, D. R. & Edwards, D. G. Mitochondrial contributions to vascular endothelial dysfunction, arterial stiffness, and cardiovascular diseases. Am J Physiol Heart Circ Physiol 320, H2080–H2100 (2021).
Quintero-Martinez, J. A., Cordova-Madera, S. N. & Villarraga, H. R. Radiation-Induced Heart Disease. JCM 11, 146 (2021).
Azizova, T. V. et al. An Assessment of Radiation-Associated Risks of Mortality from Circulatory Disease in the Cohorts of Mayak and Sellafield Nuclear Workers. Radiat Res 189, 371–388 (2018).
ICRP PUBLICATION 118: ICRP Statement on Tissue Reactions and Early and Late Effects of Radiation in Normal Tissues and Organs — Threshold Doses for Tissue Reactions in a Radiation Protection Context. https://journals.sagepub.com/doi/epdf/10.1016/j.icrp.2012.02.001 doi:10.1016/j.icrp.2012.02.001.
Benton, E. R. & Benton, E. V. Space radiation dosimetry in low-Earth orbit and beyond. Nucl Instrum Methods Phys Res B 184, 255–294 (2001).
Baselet, B., Rombouts, C., Benotmane, A. M., Baatout, S. & Aerts, A. Cardiovascular diseases related to ionizing radiation: The risk of low-dose exposure (Review). Int J Mol Med 38, 1623–1641 (2016).
Vitry, G. et al. Muscle atrophy phenotype gene expression during spaceflight is linked to a metabolic crosstalk in both the liver and the muscle in mice. iScience 25, 105213 (2022).
Alldritt, I., Greenhaff, P. L. & Wilkinson, D. J. Metabolomics as an Important Tool for Determining the Mechanisms of Human Skeletal Muscle Deconditioning. Int J Mol Sci 22, 13575 (2021).
Börsch, A. et al. Molecular and phenotypic analysis of rodent models reveals conserved and species-specific modulators of human sarcopenia. Commun Biol 4, 1–15 (2021).
Shur, N. F. et al. Human adaptation to immobilization: Novel insights of impacts on glucose disposal and fuel utilization. J Cachexia Sarcopenia Muscle 13, 2999–3013 (2022).
Hong, X. et al. Effects of spaceflight aboard the International Space Station on mouse estrous cycle and ovarian gene expression. npj Microgravity 7, 11 (2021).
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15, 550 (2014).
Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research 43, e47–e47 (2015).
Durinck, S., Spellman, P. T., Birney, E. & Huber, W. Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nat Protoc 4, 1184–1191 (2009).
Durinck, S. et al. BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis. Bioinformatics 21, 3439–3440 (2005).
Korotkevich, G. et al. Fast gene set enrichment analysis. http://biorxiv.org/lookup/doi/10.1101/060012 (2016) doi:10.1101/060012.
Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011).
Wickham, H. ggplot2: Elegant Graphics for Data Analysis. (Springer-Verlag New York, 2016).
Gu, Z., Eils, R. & Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32, 2847–2849 (2016).
R Core Team. A language and environment for statistical computing. R Foundation for Statistical Computing.
Krassowski, Michał. ComplexUpset. (2020).
Stuart, T. et al. Comprehensive Integration of Single-Cell Data. Cell 177, 1888-1902.e21 (2019).
Zhang, Y., Kim, M. S., Nguyen, E. & Taylor, D. M. Modeling metabolic variation with single-cell expression data. http://biorxiv.org/lookup/doi/10.1101/2020.01.28.923680 (2020) doi:10.1101/2020.01.28.923680.
Guarnieri, J. W. et al. Targeted Down Regulation Of Core Mitochondrial Genes During SARS-CoV-2 Infection. http://biorxiv.org/lookup/doi/10.1101/2022.02.19.481089 (2022) doi:10.1101/2022.02.19.481089.
Brunk, E. et al. Recon3D enables a three-dimensional view of gene variation in human metabolism. Nat Biotechnol 36, 272–281 (2018).
Schultz, A. & Qutub, A. A. Reconstruction of Tissue-Specific Metabolic Networks Using CORDA. PLoS Comput Biol 12, e1004808 (2016).
Ebrahim, A., Lerman, J. A., Palsson, B. O. & Hyduke, D. R. COBRApy: COnstraints-Based Reconstruction and Analysis for Python. BMC Syst Biol 7, 74 (2013).

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Aging and putative frailty biomarkers are altered by spaceflight

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Abstract

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Introduction

Results

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

Interferon inflammatory response and muscle tissue development pathways are enriched in rodent muscles during spaceflight

Multiple frailty related biomarkers are differentially expressed in astronauts

Novel sarcopenia-related genes are differentially expressed during spaceflight

Metabolic Flux Simulations of Gene Expression changes reveal dysregulation of metabolic functions related to frailty

Discussion

Methods

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References

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