More than a Feeling: Dermatological Changes Impacted by Spaceflight

Spaceflight poses a unique set of challenges to humans and the hostile Spaceflight environment can induce a wide range of increased health risks, including dermatological issues. The biology driving the frequency of skin issues in astronauts is currently not well understood. To address this issue, we used a systems biology approach utilizing NASA’s Open Science Data Repository (OSDR) on spaceflown murine transcriptomic datasets focused on the skin, biomedical profiles from fifty NASA astronauts, and confirmation via transcriptomic data from JAXA astronauts, the NASA Twins Study, and the first civilian commercial mission, Inspiration4. Key biological changes related to skin health, DNA damage & repair, and mitochondrial dysregulation were determined to be involved with skin health risks during Spaceflight. Additionally, a machine learning model was utilized to determine key genes driving Spaceflight response in the skin. These results can be used for determining potential countermeasures to mitigate Spaceflight damage to the skin.


Introduction
Throughout the course of a space ight mission, the astronaut exposome includes altered gravity, elevated radiation, and con nement within a closed environment with limited hygiene procedures and ventilation 1 . These stressors perturb biological systems, inducing gene regulatory changes, mitochondrial dysregulation, microbiome shifts, and DNA damage 1 . Dermatological issues are not typically regarded as a key risk to astronaut health and mission success, yet they are amongst the most common in-ight health issues reported by astronauts. During regular ISS missions averaging 6-months, skin rashes have been identi ed as the most frequently reported in-ight clinical symptom, with 1.1 cases per ight year, accounting for 40% of all notable medical events and a 25-fold increase compared to the US general population 2 . An additional 0.3 cases per ight year include skin manifestations accompanied by symptoms of infection; skin lesions associated with viral reactivation were also reported and have been studied in-ight 2,3 . Notably, an in-ight skin rash was reported to have occurred during a 6-month ISS mission, worsening immediately after extravehicular activity (EVA) 4 , and a post-ight skin rash was reported following the 1-year long NASA Twins space ight study 5 . These events arouse particular concern for future Moon and Mars missions, which will be longer, include high-levels of EVA, and also include risk of exposure to irritant dust, reported previously to induce skin issues in Apollo astronauts 6 .
Elucidating the biological response of skin in space could aid development of new countermeasures, to manage dermatological issues and optimize astronaut performance during these future missions.
As the body's largest organ, skin serves a myriad of important health functions, including uid diffusion, wound healing, thermoregulation and tactile sense. Importantly for space ight, skin is also the rst line of defense against pathogens and radiation. Studies of the astronaut skin microbiome have identi ed microbial interchange between the skin and the ISS interior 7 , and have also hypothesized that abnormal proliferation of certain types of opportunistic microorganisms on astronaut skin may stem from the unusual hygiene procedures on the ISS, where wipes are used as opposed to showering 8 . While these microbiome shifts and their associated health effects require further investigation, investigations into the molecular response to skin in space are lacking. In murine skin, the 13-day STS-135 study and the 91-day Mouse Drawer System (MDS) study reported signi cant space ight-induced modulation of extracellular matrix (ECM) genes 9,10 ; the set of genes did not overlap, which could be due to signi cant differences in study design (e.g. duration). The MDS study also reported a 42% increase in synthesized procollagen, a 15% reduction in dermal thickness, and an increase in hair follicles growing in the anagen stage accompanied by dysregulation of hair follicle genes 10 . Corroborating gene regulatory changes associated with hair cycle were also reported in an analysis of hair follicle samples from 10 astronauts in a JAXA study 11 , yet reports of skin physiological changes in astronauts, including dermal atrophy, have been mixed 12,13 . In a recent analysis of astronaut skin punch biopsies taken before and after the rst 3-day commercial Inspiration4 (i4) mission, gene expression changes within different layers of the skin were explored alongside microbiome changes, revealing immunological changes in the inner regions of the skin (i.e., vasculature, outer dermis) and DNA damage and repair changes in the outer epidermal layer of the skin 14 .
In this study, we performed an analysis of ve previously unreported murine skin RNA-Seq datasets from the JAXA Mouse Habitat Unit 2 (MHU-2), and NASA Rodent Research 5 (RR-5) and 7 (RR-7) experiments, to identify global signatures in space ight-exposed skin, and to investigate the effect of study design on biological signatures relating to skin health and common space ight themes including DNA damage and repair, and mitochondrial dysregulation 1 . In addition to examining gene regulatory patterns, we used an explainable arti cial intelligence (AI) modeling approach to construct interpretable machine learning models to identify synergistic effects between pairs of genes, which we interpret as possible biological interactions that reveal a putative disequilibria of dependent processes. We then compared gene regulatory changes in the rodent data to astronaut data, including JAXA hair follicle data 11 and data from the NASA Twins 15 , JAXA Cell-Free Epigenome (CFE), and i4 studies. We conclude by suggesting how these molecular signatures may eventually lead to follow-up studies and pharmaceutical interventions.

Results
Space ight transcriptome global changes in rodent skin re ect common biological hallmarks of space ight To explore whether transcriptomic changes in the skin would occur during space ight we analyzed ve RNA-Seq datasets from the NASA Open Science Data Repository (OSDR) 16 , all representing skin tissue from space own mice and matching ground control replicates. These datasets are derived from three different space ight missions (Fig. 1A). The most variable genes across the datasets cluster into functional groups related to established physiological risks of space ight (Fig. 1B). For example, space ight is known to induce immune dysfunction 1 and Cluster 1 involves highly-correlated genes associated with immune response, including genes linked to modulation of Immunoglobulin G (IgG) levels. Microgravity is also well established to cause degradation of muscle 1 and Cluster 9 contains a signi cant number of genes related to muscular morphology and muscle disorders.
Upon subsetting each dataset based on experimental groups (i.e. diet, strain, skin site) and performing differential gene expression (DGE) analysis for space ight samples versus ground control samples, we end up with 10 data subsets ( Fig. 2A). In total 476 unique genes are signi cant (FDR ≤ 0.1) in at least 2 out of the 10 data subsets. One signi cant gene uniquely shared across data subsets from the RR-7 study is GLYCAM1 which is shared between both data subsets for the C57BL/6J strain. GLYCAM1, which supports lymphocytes in transfer from the bloodstream into lymphoid tissues 17 , is strongly upregulated (LFC ≈ 18.81) at the 25 day time point and strongly downregulated (LFC ≈ -15.66) at the 75 day time point, which could indicate an immune response within the rst half of the RR-7 mission for the C57BL/6J mice. Over representation analysis (ORA) reveals that the 247 signi cant genes uniquely shared between MHU-2 data subsets are associated with the organization of collagen and the ECM (Fig. 2B), with the 225 signi cant genes uniquely shared across missions primarily associated with cellcycle processes, including cell division (Fig. 2C). Space ight is well-documented to perturb the cell-cycle 1 and signi cant modulation of genes associated with ECM homeostasis were previously reported in analyses of the skin of space own mice 9,10 . The MHU-2 subset from the dorsal skin of mice fed a standard JAXA chow diet with supplemental prebiotic fructooligosaccharides (FOS) contains the highest quantity of signi cant (FDR ≤ 0.1) differentially expressed genes by a substantial margin ( Fig. 2A). In ground-based rodent studies, FOS has been shown to improve gut microbiome balance, increase bone density, and affect the immune system through increased short chain fatty acid (SCFA) production (modifying interleukin production and natural killer cell activity), and modi cation of the immune system via gut-associated lymphoid tissue 18, 19 . Thus, we may expect FOS ingestion to increase ECM activity, due to restructuring of the collagenous ECM of bone. Due to this disparity between the number of signi cant DEGs in the data subsets, and the variety of conditions between data subsets, care must be taken when combining these data subsets to infer shared responses of rodent skin to space ight.
Key genes involved in rodent skin space ight response are associated with cell cycle regulation and lipogenesis To characterize the shared response of murine skin to space ight across the datasets, a manageable set of key genes were identi ed. In an effort to overcome the aforementioned bias of the MHU-2 dorsal skin with FOS-supplemented diet, this set of 102 key genes was derived by performing Gene Set Enrichment Analysis (GSEA) on all genes, and taking the genes involved in signi cant biological pathways common across the majority (i.e., 8/10) of the datasets, and then further ltering to only include genes that were signi cant across multiple missions (Fig. 3B). All 102 key genes overlap with the aforementioned 472 signi cant (FDR ≤ 0.1) genes intersecting between combinations of 2 or more data subsets. For the majority of the genes, genes are signi cant with a trend of downregulation in the MHU-2 dorsal skin dataset with the FOS-supplemented diet, and in the RR-7 mission C57BL/6J mice after 75 days of ight ( Fig. 3B). A smaller collection of genes (LAMA1, HMGCS2, TXNIP, IGFBP3, ADGRF5, CAR4, HSD17B11, C1QTNF9) are also signi cant in those same data subsets, but are signi cantly upregulated (Fig. 3B).
For the most signi cant genes, convincing correlations exist between space ight status and the up-or down-regulation of a particular gene. For example, in the case of the gene with the lowest p-value, LAMA1 (Laminin Subunit Alpha 1), the mean gene expression of space ight mice is 2.2 times the mean expression of ground mice, indicating signi cant up-regulation in mice that went to space. To go beyond single-gene signi cance, we used QLattice modeling 20 to derive a quantitative measure of synergy between all possible 2-gene combinations in the set of key genes. For every gene, our method picks out the ve best partner genes and we interpret these ve partner genes as the ones most likely to have a meaningful interaction with the target gene. In this way, the strengths of the connections become proxies for the interaction strength/likelihood in the actual data, and remains agnostic to interactions known from literature. To visualize the results, we display a network of key genes and their top 5 partners drawn as node connections (Fig. 3C). The more central a gene is, the more synergistic partners the gene has.
Subsequently, we can then use external evidence to annotate our ndings, which identi ed three functionally distinct clusters in the network via Protein Protein Interaction (PPI) analysis 21 . In this PPI network, the rst cluster primarily contains genes involved with pathways associated with lipogenesis.
This family of pathways might be crucial in space ight, as evidenced by previous studies showing the capacity of space ight to alter liver metabolism, resulting in loss of liver function from lipid accumulation and changes to lipid related proteins in murine liver samples 22,23 .
The second cluster primarily consists of genes predictive of cell cycle pathways, with several of the genes (LAMA1, HMGCS2, TXNIP, C1QTNF9) clustering with distinct average trends of upregulation across the datasets (Figs. 3C and 3B). These genes tend also to be involved in metabolic pathways, particularly those associated with diabetes; HMGCS2 has been shown to regulate mitochondrial fatty acid oxidation 24 , while LAMA1 variations have been shown to be risk factors in type 2 diabetes 25 . Aside from being a protector against oxidative stress, TXNIP is similarly implicated in metabolic diseases, and is typically upregulated in diabetic and prediabetic muscle tissue 26 . C1QTNF9 (also known as CTRP9) has also emerged as a potentially important component of pathways involving lipid metabolism and adipose tissue, exempli ed by the fact that C1QTNF9 transgenic mice have been shown to resist weight gain and metabolic dysfunction 27 . It is interesting to note the signi cantly lower upregulation of these metabolic pathway genes for female mice. This might be linked to the same mechanisms offering higher protection for females against diet-induced obesity compared to male mice, as has been noted in other studies 28 .
The third cluster (Fig. 3C) relates to cell cycle pathways linked to cell division and microtubules. This also becomes apparent when clustering the key genes with a larger set of 1060 genes (Cluster 6 in Fig. 3D), that contains genes related to cell cycle, cell anatomy/morphology and cell function, including DNA functions. This cluster contains around 80% of the key genes, which indicates that their inclusion in the key genes arise from the fact that cell and DNA functions can be regarded as key functions in space ight. These pathways are often considered to be important in space ight, with studies showing that space ight and altered gravity affects microtubules and mitochondria as well as altering apoptosis in several organisms in vivo and in vitro 29,30 . In the synergy network (Fig. 3C), BIRC5 (Baculoviral IAP Repeat Containing 5) is the gene with the highest node degree, i.e. it is the gene with the most synergistic partners. This does not in itself guarantee that the best performing synergistic gene combination involves BIRC5, though in this case it did (Fig. 3E). BIRC5, which encodes Survivin, is involved with negative regulation of apoptosis or programmed cell death, when downregulated in cancer cells it has been shown to induce apoptosis and suppress tumor growth 31 . While the BIRC5-NEIL3 model attains the highest performance gain when going from one to two model genes, it is not the best-performing model overall. It has an AUC of around 0.73, whereas the best 2-gene model that can be extracted from the key genes has an AUC of 0.85 and an accuracy of 0.82. Thus, the model is correct in predicting mouse space ight status correctly in more than 4 out of 5 cases based on these two genes alone. The model consists of a combination of LAMA1 and PPP1R3B (Protein phosphatase 1 regulatory subunit 3B) (Fig. 3F).
Astronaut data correlates with key biological changes occurring in the murine skin models Having established a set of 102 key genes regulated by space ight in rodent skin, we then decided to investigate the expression of these genes in humans. We compared data from the blood of astronauts from the NASA Twins and JAXA CFE studies, astronaut hair follicles from a JAXA mission, and blood and skin from i4 astronauts (Figs. 4A and 4B). In the NASA Twins Study, the most noteworthy results are in CD4 cell type in-ight vs pre-ight samples with signi cant downregulation in a group of genes correlating positively with murine skin samples, e.g. CA4 and PTGS2. These genes display no signi cant changes in CD8 cells, which can indicate that downregulation is a response to immune system stressors affecting the helper function and signaling of CD4 cells. Overall, the effects seen in the NASA Twins Study are mostly constrained to one type of sample, and only two genes have noteworthy changes across all biospecimens (KIF4A, SKA3).
In hair follicles extracted from JAXA astronauts the most common pattern is genes experiencing in-ight vs pre-ight upregulation, with subsequent post-ight vs in-ight downregulation (e.g. PPP1R3B, CCNB1, HSD17B11, KRT2, GINS1). In the CFE study, blood samples from JAXA astronauts had the strongest inight upregulation in RAD54B, FLG, CA4 and in particular in CASP14, where upregulation continues postight. FLG, RAD54B and CASP14 were all genes with general downregulation across the murine data, whereas CA4 saw general upregulation. This indicates that some genes have similar responses in skin and blood, whereas others see counteractive circulating responses. Another gene of interest to our analysis is BIRC5, where in-ight vs pre-ight downregulation persists and neither return to baseline or further downregulation is seen post-ight.
We observed an interesting vascular response for the genes from the i4 skin samples (Fig. 4C); genes respond oppositely when comparing the outer epidermis to the vascular samples. Genes MCOLN3 through ERO1A (Fig. 4C) are e.g. strongly downregulated in vascular samples, with mild to moderate upregulation in dermal tissues. This group might have decreased circulating expression as counteracting effects to skin damage, which we elaborate on in the discussion. For skin-speci c genes, we saw a moderate upregulation of the gene controlling the production of the protein Filaggrin (FLG), which is important for maintaining epidermis structure and consequently plays a role in allergic skin diseases 32 .
Speci c pathways and genes related to skin health are altered in space ight To determine direct relevance of space ight to skin health we conducted targeted analysis with a curated list of key pathways involved in skin health from MSigDB and identi ed which of these pathways were signi cantly modulated in the murine skin datasets. A Collagen biosynthesis pathway and a set of ) were signi cantly suppressed/downregulated across all dataset subsets for the MHU-2 mission, while showing a trend of weak enrichment/upregulation across two other missions (Fig. 5A). Overall, the contrasting ndings suggest a study design difference in the MHU-2 mission, where the use of young single-housed male mice or dissection shortly after the hypergravity event of LAR may have suppressed collagen biosynthesis. We also nd an enrichment of pathways relating to thin skin and dermal atrophy in the RR-5 and RR-7 mice, with suppression occurring in the MHU-2 mice. Several SERPINB6 genes are signi cantly downregulated (FDR ≤ 0.1) in the MHU-2 dorsal skin data subset. The RR-5 mission generally lacks any signi cant results for genes relating to skin health, which could be due to the 30 days of recovery post-ight.
When comparing these results to the astronaut data, similar to FLG being downregulated in 9/10 of the murine data subsets, FLG is downregulated for in-ight vs pre-ight timepoints for the JAXA hair study, the JAXA Liquid Biopsy study and the CD4 blood cell type from the NASA Twins Study (Fig. 5B).
Conversely, FLG was upregulated in the outer and inner dermis of the i4 study post-ight vs pre-ight comparison, which could be due to the signi cantly shorter duration of space ight (Fig. 4C).
Radiosensitivity of mouse strains impacts DNA damage response following space ight As part of our targeted analysis, we also opted to investigate the modulation of DNA damage and repair pathways in rodent skin. DNA damage & repair is a well established response to space radiation 15,33 and little research is done on the consequences this will have on the skin during space ight. When ionizing radiation hits DNA molecules, single-strand breaks (SSBs) and double-strand breaks (DSBs) occur, with DNA Damage Response (DDR) mechanisms activated to repair these breaks 33 . For the majority of DNA damage and repair pathways, both the dorsal and femoral skin RR-5 mission data subsets and the 25 day time point from the C3H/HeJ mice in the RR-7 mission show an opposing pattern compared to the other data subsets. In these three data subsets pathways relating DNA damage and repair are generally enriched, while being suppressed in other data subsets. BALB/c and C3H/HeJ mice, as used in these data subsets, have been shown to be more radiosensitive compared to C57BL/6J mice 34 , so repair mechanisms may be activated to mitigate increased radiation-induced DNA damage. There is a trend of decreasing activity for the DNA DDR pathways from the 25-day to 75-day timepoints which can indicate either adaptation of the DNA repair pathways over time in space or dysregulation following extendedduration space ight. The enrichment of DNA damage and repair pathways in RR-5 follows 30-days of space ight and a post-ight recovery period of 30-days. However, DNA repair genes were reported to still be dysregulated compared to pre-ight levels when evaluated at 6-months post-ight during the NASA Twins study 15 , so persistent DNA damage and repair activation during post-ight recovery is expected.

Mitochondrial dysregulation increased in the skin during space ight
Mitochondrial stress has been identi ed as a key hub for space ight response in multi-tissue analysis, yet skin was not included 30 . Skin is a tissue with high turnover and energy requirements 35 , so we decided to investigate space ight changes relating to mitochondrial stress in murine skin. We found that space ight signi cantly alters mitochondrial pathways in the skin (Fig. 7), as previously observed in other tissues and across species 30 . Enrichment of an integrated stress response (ISR) pathway in the majority of the datasets (Fig. 7A) is consistent with a previous report of potentially activated ISR due to mitochondrial dysfunction in space own mice 30 . Interestingly, mice that were sacri ced on the ISS without any recovery period on Earth, had an overall suppression of the majority of the OXPHOS complexes which will also be associated with the increased ISR pathways 36 . Additionally, we ran the QLattice machine learning model on the full gene set to obtain a set of models that were unbiased by feature selection due to e.g. variance ltering and the selection of key genes. One of these models demonstrated how the upregulation of D2HGDH (D-2-hydroxyglutarate dehydrogenase) synergizes with the downregulation of RPLP0-PS1 (a gene coding for the ribosomal protein RPLP0) (Fig. 7B) 37 . The upregulation of D2HGDH generally indicates an increased ability to break down the toxic D-2-hydroxyglutarate (D2H) compound in the mitochondria. In our case, this could be a compensatory mechanism due to larger build-up of the compound, and the model indicates that this is further dependent on the abundance of the RPLP0 protein.
Circulating physiological markers from astronauts indicate that exercise countermeasures may have improved skin health We investigated standard physiological biomarkers collected from astronaut urine and blood 38,39 to see whether average trends in these biomarkers connect to space ight gene regulatory changes occurring in the skin (Fig. 8). In-ight increases in IGF-1, leptin and white blood cell levels may indicate altered stress response due to space ight. Following a hypothesis that improved countermeasures may improve skin physiological parameters in astronauts on the ISS 12,13 , we split the data into astronauts that used the older Interim Resistive Exercise Device (iRED), and astronauts that used the newer Advanced Resistive Exercise Device (ARED). Notably overlapping data to that shown in Fig. 8 have been used as evidence of improvements in parameters relating to bone mineral density 38 . Indeed, while vitamin D decreased inight and normalized upon return to Earth for both exercise devices, ARED appeared to reduce this drop.
Vitamin D and L-asparaginase emerged as potential countermeasures for targeting space ight skin dysfunction Having established a key list of genes that changed in the rodent skin, we investigated potential drug targets for these genes via Ingenuity Pathway Analysis (IPA) (Fig. 9). While no consistent potential drug candidates were identi ed for all of the data subsets, Calcitriol and L-asparaginase exhibited signi cant activation scores for the majority of the datasets, including all of the data subsets with minimal postight recovery period. Calcitriol is the active form of Vitamin D, of which oral supplementation is typically used to prevent low calcium levels and bone disease and topical usage is used to treat plaque psoriasis by inhibiting skin cell buildup, and by decreasing the activity of immune cells in the skin 40 . While Vitamin D supplementation is already used on the ISS 38 , L-asparaginase is a more unexpected target for space ight dysfunction. It is injected to treat acute lymphoblastic leukemia and lymphoblastic lymphoma by depriving leukemic cells of circulating asparagine, leading to cell death 41 .

Discussion
Skin is well-established as an essential organ for health on Earth, yet despite the frequency of dermatological issues in astronauts 2 , the molecular response of skin to space ight is understudied (see Table S1 for the up-to-date space ight associated skin literature). Here, we have performed a comprehensive study on the impact of space ight on skin, with the aim of addressing gaps in the understanding of space ight associated skin health risks. Our analysis indicated intriguing biological changes occurring in the skin from different mouse strains that provide similar changes occurring in astronauts.
The downregulation of collagen genes in MHU-2 (Figs. 3 and 5) mirrors previous studies wherein a decrease in collagen synthesis was found as a result of hypergravity in cultured human broblasts 42 . In contrast, RR-5 (30-day recovery) and RR-7 (frozen in space) mice had upregulated collagen expression, which is in line with analysis from the same cultured human broblast study reporting a 143% increase in collagen synthesis during microgravity 42 . The recovery period of RR-5 mice may re ect terrestrial recovery of collagen expression, but we note that the BALB/c strain is radiosensitive, while the C56BL/6 strain is radioresistant 34 , so although the BALB/c mice in RR-5 received a low duration-dependent dose of radiation, it is likely that radiation-induced changes will be more severe. Increased collagen gene expression may also represent compensatory increases in the production of procollagen, as noted in analysis of the space ight MDS study where an increase in 2 matricellular proteins known to stimulate collagen synthesis in mice skin were found; CTGF and CCN2 10 . The dermal atrophy observed in the MDS study was hypothesized to be connected to early degradation of newly formed, perhaps defective, procollagen molecules 10 .
The difference between missions may also be confounded by sex; all MHU-2 mice are male, while RR-5 and RR-7 mice are female. Mouse studies have shown that the male dermis is thicker than the female, whereas the epidermis (top layer) and hypodermis (subcutaneous fatty layer) are thicker in the female, resulting in skin that is altogether 40% thicker in the male 43 . Skin also generally reacts to circumstantial changes, and studies have found that dermal broblasts in vitro sense and react to changes in their mechanical environment 44 by altering their metabolic activity 45 . Skin samples from male space own mice have previously shown thinning of the dermis due to reductions in dermal thickness and collagen 10 .
There were no signi cant skin marker changes in RR5 (Fig. 5), but it should be noted that overall no statistically signi cant skin health markers changed in these missions, which might be due to the 30 days of post-ight recovery. The genes that do not return to basal levels are naturally those of gravest concern in this case, of which DCUN1D3, a cell cycle/survival gene typically expressed in tumor tissues, was the only one. This terrestrial return to baseline indicates that in-ight changes are acute responses to the changing environment, which is in line with the acute stress response and cortisol surge previously observed in rodents exposed to hypergravity events 46 and astronauts exposed to space ight 47 . Such responses are also seen elsewhere in skin; dermal broblasts that are exposed to cotricotropin-releasing (CRH) hormone increase proopiomelanocortin (POMC) gene expression and ACTH production which stimulate the production of corticosterone 48 . This ts well with our observations, since the hypothesized hypergravity-induced surge in cortisol in MHU-2 may then be linked to the downregulation of collagen genes.
In the human physiological data (Fig. 8), we also noted changes in stress-responsive markers, e.g. leptin, which is normally downregulated in response to stress 49 , which contrasts the leptin upregulation seen upon re-entry to Earth. Similarly, white blood cell levels are higher on re-entry, which may be due to leukocytosis -a response that can occur as a result of physical and emotional stress 50 , as seen in overexertion, seizures, anxiety, anesthesia and epinephrine administration. IGF-1 increases are another well-known stress response, but we noticed higher levels during ight than pre-ight. IGF-1 is, however, known to increase with exercise. Accordingly, the results may indicate that the astronaut exercise regimen is effective. Additionally, we noted the higher levels of Vitamin D in astronauts using the ARED device ( Fig. 8A). Exercise has been shown to mobilize Vitamin D from fat stores 51 via upregulation of VDR expression in muscles and to increase circulating vitamin D. Other studies have shown that exercise increases the serum's 25(OH)D 3 concentration in young trained boys, and our results support the hypothesis that muscles may both store and release 25(OH)D 3 52 . This mechanism is likely activated by the stimulating exercise afforded by the > 600lbs loading in the ARED device, and may also be the reason for overall better urinary markers observed in astronauts using this device (Fig. 8C).
The higher glucose levels seen upon re-entry to Earth (Fig. 8C) may be linked to the hyperglycemic state induced by the body under stress in an effort to mobilize energy stores 53 . Conversely, in-ight blood glucose levels were lower, which might be related to the best performing 2-gene model for the key genes LAMA1 and PPP1R3B (Fig. 3F). LAMA1 is important in the formation of laminins which are involved in metabolic tissue signaling 54 , and a 2018 study showed that high blood glucose concentrations downregulates the expression of LAMA1 55 . The PPP1R3B gene is the regulatory subunit for the activity of protein phosphatase 1, which activates glycogen synthase and limits glycogenolysis 56 . The expression level of PPP1R3B thus correlates blood glucose levels, which ts well with the observation that LAMA1 upregulation is less signi cant for space own mice with high PPP1R3B expression. Furthermore, it has been reported that transgenic overexpression of LAMA1 can mitigate muscle wasting and paralysis in mouse models of congenital muscular dystrophy type 1A 57 , which is a plausible counter-active role for LAMA1 in muscular dystrophy due to microgravity. This is in line with an observed pattern of counteractive mechanisms observed in space ight, where the upregulation of D2HGDH (Fig. 7B) indicates a larger need for breakdown of toxic D-2-hydroxyglutarate (D2H) molecules in mitochondria. In this model, the partner gene RPLP0-PS1 codes for the ribosomal protein RPLP0 and is down-regulated in space mice. RPLP0 has previously been implicated in the top signi cant network for the mitochondria-mediated cycle of Alzheimer's disease 58 . Low or absent levels of RPLP0 seem to limit the compensatory mechanism in the model (Fig. 7B). If this occurs, build-up of D2H and suppression of certain enzyme functions can result, causing DNA and histones to enter hypermethylated states and activate oncogenes and suppress tumor suppressors 59 . Since many key genes are linked to effects on tumor progression and metastasis, the build-up of D2H in the mitochondria may provide an explanation for cell changes demonstrated by the model involving BIRC5 and NEIL3 (Fig. 3E). We note that NEIL3 is important for cell proliferation, with increased expression seen in highly replicative tissues such as bone marrow and cancerous tissues 60 . BIRC5 is a well-studied apoptosis inhibitor 61,62 , and from these functions our data indicated (Fig. 3E) a perturbed apoptosis-proliferation balance for space ight mice, which we speculate to be due to cell and DNA damage from space radiation.
Overall, the themes of DNA repair and cellular health in the Twin Study corroborate the centrality of cell cycle genes in the murine skin samples, where BIRC5 was also the single best synergistic partner for a signi cant number of genes (i.e. CENPH, KIF14, CDKN3, PRC1, NUSAP1 and RAD51, and NEIL3) (cluster 6 in Fig. 3D). These genes are also in the identi ed lipogenesis cluster (Fig. 3C), and the centrality of BIRC5 may be connected to its role in maintaining adipocytes in response to in ammatory stress 63 , which is likely connected to the protective effect of BIRC5 upregulation in human adipocyte-derived stem cells in obese patients 64 . BIRC5 is also downregulated in the ight JAXA liquid biopsy data, whereas CASP14, another important apoptosis inhibitor, is strongly upregulated (Fig. 4A).
CASP14 was one of only few genes with downregulation across all murine skin subsets (Fig. 4b), as well as in the JAXA hair follicles. This is interesting due to the role played by CASP14 in formation of the skin barrier as well as in skin diseases such as ichthyosis, psoriasis and melanoma 65 . The strong upregulation seen in JAXA liquid biopsies could be hypothesized to counter downregulation in hair follicles and skin by increasing circulation of CASP14. This might also be the case for FLG, where downregulation was seen in-ight for both the liquid biopsies and hair follicles (Fig. 4A). A subtle difference exists between the two specimen types, however, since the liquid biopsies have FLG upregulation in both post-ight vs pre-ight and vs in-ight, whereas upregulation in the hair follicles was only seen in post-ight vs in-ight. The link between laggrin de ciency and atopic dermatitis is wellknown 32 . Since FLG downregulation was also seen in the skin samples from space ight mice, we hypothesize that the increased circulation of FLG may be a response to repair skin cell damage, where laggrin production is impaired. The i4 data also contained a group with strongly downregulated genes in the vascular samples where a similar mechanism could be at play for e.g. SERPINE1 ( Fig. 4B and 4C).
Downregulation of this gene has been shown to accelerate skin wound healing 66 while overexpression of SKA3 and KIF20A (which was also observed in this group) has been shown to predict poor outcomes in melanoma patients 67,68 .
In conclusion, we have provided a comprehensive study on the impact of space ight on skin health.
Currently there is a gap in knowledge for how space radiation and microgravity affect skin biology, yet many of the common themes of space ight dysfunction emerged in our analysis, suggesting that skin could be an easily-accessible candidate for studying the biological impact of space ight. Our unbiased systems biology analysis revealed some genes of interest and potential countermeasures that can be targeted and utilized in future studies. We believe with our study we have started to address the current gaps and provided some clues on how to potentially mitigate the adverse effects of the space environment to the skin.

Limitations Of Study
The rodent datasets used come from three different space ight experiments with a variety of confounding variables associated with differences in study design. This means that direct comparison between the experiments is challenging, but indeed this also presents an opportunity to hypothesize how these factors may in uence biology, as done in this manuscript. A lack of physiological data, such as dermal thickness, from the rodent datasets means that generated hypotheses relating to mouse physiology cannot be con rmed without follow up investigations, but the use of astronaut data helps translate the results to human relevance.

GeneLab space ight murine datasets
Five RNA-Seq datasets (OSD-238, OSD-239, OSD-240, OSD-241, OSD-254) were downloaded from the NASA OSDR (https://osdr.nasa.gov/bio/repo) via the API, and full dataset descriptions can be found on the dataset pages. These datasets are derived from murine skin from the MHU-2, RR-5, and RR-7 space ight experiments. For MHU-2, singly-housed male C57BL/6J mice were 9 weeks of age when own on the ISS for 30 days; they were euthanized less than 1 day after return to Earth. Dorsal and femoral skin samples were extracted from MHU-2 mice, and 6 replicates in space ight microgravity and the matching 6 replicates in the 1G ground control were split into 2 sets of 3, with half fed a JAXA chow diet and the other half fed JAXA chow with supplemental FOS 18 . For RR-5, 30-week-old BALB/c mice in shared housing were own on the ISS for 30 days; following return to Earth, mice were given 30 days to recover before euthanasia and extraction of dorsal and femoral skin tissue. Finally, for RR-7, 11-week-old C57BL/6J mice and C3H/HeJ mice were own on the ISS for either 25 or 75 days before being euthanized on-orbit.

RNA-Seq analysis of rodent datasets
Raw counts data, derived via a previously reported pipeline 69 , were downloaded from the NASA OSDR.
ERCC genes were ltered out, as were genes with low counts across all samples (<10 and set sizes >15 were permitted. For the mitochondrial pathway analysis, human pathways from MitoPathways (v3.0) were used and set sizes >1 were permitted. To lter to pathways during derivations of the key genes, pathways that were highly signi cant (FDR ≤ 0.05) in at least 8/10 data subsets were selected (8/10 was chosen based on the experimental supplemented diet in the MHU-2 mission), and then leading-edge genes that were signi cant (FDR ≤ 0.1) in at least 2 datasets from different missions were accepted. All heatmaps were generated using the R package ComplexHeatmap (v2.9.4) JAXA Cell-Free Epigenome (CFE) Study RNA quanti cation data Aggregated RNA differential expression data and study protocols were shared through the NASA OSDR with accession number: OSD-530 72 . Plasma cell-free RNA samples for RNA-seq analysis were derived from blood samples collected from 6 astronauts before, during, and after the space ight on the ISS.

RNA-Seq data analysis on Twin Study samples
Mean expression values were obtained from normalized read counts of 6 astronauts for each time point.
Heatmaps were made for the 21 genes key genes on the normalized values per time point using R package pheatmap (v1.0.12).
Gene expression data from 10 JAXA astronauts' hair follicles 11 was downloaded from the NASA OSDR (OSD-174). Raw data for 60 total samples was processed using LIMMA with R/bioconductor 73 . Brie y, duplicate sample single-color Agilent microarray data was background corrected, ltered for low expression probes, and quantile normalized. Differential gene expression was measured between preight, in-ight, and post-ight data points using p-values adjusted for False Discovery Rates (FDR) with the Benjamini-Hochberg method.
Inspiration4 (i4) astronaut sample collection For skin spatial transcriptomics data, 4mm diameter skin biopsies were obtained from all Inspiration4 crew members, once before ight and as soon as possible after return (L-44 and R+1). These biopsies were ash frozen and processed with the NanoString GeoMx platform. Based on staining images using uorescent antibodies, a total of 95 freeform regions of interest (ROIs) were pro led across outer epidermal (OE), inner epidermal (IE), outer dermal (OD) and vascular (VA) regions. GeoMx WTA sequencing reads from NovaSeq6000 were compiled into FASTQ les corresponding to each ROI and converted to digital count conversion les using the NanoString GeoMx NGS DnD Pipeline. From the Q3 normalized count matrix that accounts for factors such as capture area, cellularity, and read quality, the DESeq2 method was used to perform DGE analysis.
Astronaut Physiological data We report here vitamins and metabolites, oxidative stress and damage markers, in ammatory markers and cytokines, liver enzymes and endocrine indices. These were analyzed using standard techniques as previously reported 76 .
As of this writing, data were available for 59 crewmembers (47 male, 12 female). Age at launch was 47.0 ± 5.6 y, body mass at launch was 79.

QLattice symbolic regression modeling
We used symbolic regression (QLattice v3.0.1 20 ) to construct both single-gene models and models involving combinations of synergistic genes which map from the gene expressions to space ight status.
For these models, we only distinguish between mice that went to space and mice that didn't. Conventional statistical methods typically allow for calculating the effect of a single gene at a time through metrics such as p-values and false discovery rates. In contrast, symbolic regression models can reveal combinations of genes and modules that best predict space ight status. These could be linear combinations involving two or more genes that have previously also been shown to be statistically signi cant, or it could be non-linear combinations that reveal features that on their own were nonsigni cant but in synergy with a second feature become highly predictive. In addition, known biological functions of genes included in models, as well as the resultant mathematical relationship between them, can potentially be interpreted to reveal regulations or interactions that are affected by space ight.
In biological pathway analysis, it is well-known that up-or down-regulation of one gene can have cascading effects such that the function of one gene becomes sensitive to that of another 77 . It has previously been demonstrated that parsimonious machine learning models are able to provide accurate outcome prediction in omics data, while preserving interpretability 78, 79  UMAP dimensional reduction and gene clustering For the clustering of genes shown in Fig. 2A, we performed a uniqueness ltering by rst dropping all genes where more than 1/4 of samples had identical expression levels. Subsequently, we ltered out all genes with a variance of less than 1.7, resulting in 2184 ltered genes with high variability. Similarly, prior to the clustering in Fig. 2D, we ltered out all genes with a variance of less than 2.5, to which we manually added all key genes that were not present in this set (all but 4), resulting in a nal set of 1060 genes for clustering analysis. The rationale behind the stricter variance ltering in Fig. 2D was that it increases the relative frequency of key genes in the clustering, and allows for a higher resolution in the nal plot. Thus, the 102 key genes comprise roughly a tenth of the genes included in Fig. 2D, allowing for a detailed analysis of the ontological themes present in the key gene set.
In both clustering gures, a dimensional reduction was performed subsequent to ltering by uniformly distributing the ltered data on a Riemannian manifold, using the Uniform Manifold Approximation and Projection for Dimension Reduction (UMAP). UMAP is a general purpose manifold learning and dimension reduction algorithm which is similar to t-SNE in that it predominantly preserves local structure. Yet, UMAP preserves more global structure, which makes it a more suited algorithm when the objective of the dimensional reduction is more than simple visualization (in this case the objective is clustering).
Genes were then clustered using the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) 80 . A primary advantage of HDBSCAN is that it is always deterministic for the same hyperparameters, and will thus always return the same clustering, all else being equal. In addition, comparable clustering algorithms such as k-means do not perform well unless clusters are of equal size and density with few outliers. With biological data such as gene expressions, we expect large variation in cluster size and density, making HDBSCAN the ideal choice.
Once genes were clustered, the gene sets belonging to each cluster were extracted and analyzed using three ontology databases in the Python implementation, GSEAPY, of the Gene Set Enrichment Analysis 81 tool Enrichr 82 . We used the Elsevier Pathway Collection, the 2021 WikiPathway Collection, and the 2021 MGI Mammalian Phenotype Level 4 Collection. These enrichment analyses were used to provide context to each cluster by appending an annotation if a notable amount of hits showed up for a particular association. Cluster 2 in Fig. 2B, for example, consists of 59 genes, of which 10 are found as hits for associations to decreased IgG1 level in the MGI Mammalian Phenotype Level 4 database from 2021, as well as more than 20 hits for B-cell receptor signaling in the Elsevier Pathway Collection, and thus these annotations are appended in Fig. 2B. In contrast, Cluster 0, consisting of 47 genes, has no more than one hit for any association in any database, and is thus deemed to have no signi cant interpretation.

QLattice synergy network
For the models involved in constructing the QLattice (https://pypi.org/project/feyn/) synergy network in Fig. 2C, we generally used the "Area Under Curve" (AUC) measure as our performance gain indicator. The AUC can in this case be interpreted as the probability that a classi er will be able to correctly distinguish a space ight mouse from a ground mouse for two randomly chosen data samples; one of each type. allowed us to identify three primary biological biological pathways. To visualize the correspondence of this synergy network to these established biological relations, we nally color each gene node according to which of the pathways they belong to.

Regulatory Network Analysis
We used the expression data from all key genes across each comparison to perform an upstream regulator analysis. We identi ed the biological or chemical drugs in the QIAGEN IPA library of regulators, de ned as having a P < 0.05 (Fisher's Exact Test) and an absolute z-score > 1. Drugs from the data set that met these criteria and were associated with similar gene expression changes in the QIAGEN   Key genes involved in rodent skin space ight response. A) A graphical representation of the method for deriving the set of 102 key genes pathways, where highly signi cant (FDR ≤ 0.05) pathways in at least 8/10 data subsets were selected and then leading-edge genes that were signi cant (FDR ≤ 0.1) in at least 2 datasets from different missions were accepted. B) A heatmap showing regulatory changes in the key genes within each rodent data subset. C) Shows the graph resulting from linking every gene (visualized as a node) to its ve top synergistic partners, as described in the main text and the methods section. We also show the three most signi cant functional clusters obtained through PPI analysis. D) Shows a functionally clustered set of 1060 genes, in which all key genes have been manually added to visualize the functional correlations present in the key gene sets and how they relate to other highly variable genes. E) Shows the decision boundary of the key gene model with the largest synergistic effect between two genes. F) Shows the decision boundary of the model with the highest predictive performance overall using only two genes. Both models have plausible biological interpretations, as outlined in the text.

Figure 4
The pro le of the key genes in astronauts. A) Heatmap investigating changes in the rodent skin data key genes in astronaut data derived from the NASA Twins, JAXA CFE, JAXA hair follicle data, and Inspiration4 studies.B) Heatmap showing average expression scaled in blood PBMCs data from the Inspiration4 mission for different timepoints. C) Heatmap showing average expression in skin data from the Inspiration4 mission for different skin layers.     Predicted potential countermeasures for mitigating space ight response to the skin. Predicted drug signatures using the key genes across each dataset represented by a hierarchically clustered heat map. A positive (orange) activation state implies key gene expression changes are consistent with mRNA expression changes observed with the indicated drug from curated causal gene expression relationship