Abstract

   The space industry has made significant strides, leading to an era of
   commercial spaceflight. Meanwhile, understanding molecular responses to
   spaceflight is crucial for astronauts’ safety. To this end, we examined
   transcriptomic and epigenetic changes in two astronauts’ blood samples
   at three timepoints: two weeks before spaceflight (T0), 24 hours after
   spaceflight (T2), and three months after spaceflight (T3).
   Transcriptomic analysis identified two gene clusters with opposing
   transient expression trends post-flight (T2), normalized at T3: one
   upregulated and the other downregulated. Mapped immune cell types
   through the CIBERSORT coupled with the pathway analysis suggested
   monocytes’ role in coordinated cellular response. Epigenetic analysis
   identified four methylation patterns with transient and persistent
   changes post-flight, enriched in nervous system development and cell
   apoptosis pathways. Methylation changes implicated genes associated
   with bone disorders, including FBLIM1, IHH, and SCAMP2. eQTM analysis
   suggested a link between RNA transcriptional level and DNA methylation
   through transcriptional regulator ZNF684. In conclusion, our study
   revealed significant short-term transcriptional and methylation changes
   as well as long-term methylation changes.

Supplementary Information

   The online version contains supplementary material available at
   10.1038/s41598-025-13383-8.

   Keywords: Microgravity, Space radiation, Multi-omics, Immune response

   Subject terms: DNA methylation, Transcriptomics

Introduction

   Space exploration has been a fascinating subject to humanity for
   thousands years, from ancient myths to the ambitious Mars Exploration
   Program^[40]1. The pace of space exploration has been accelerating and
   the recent advancements made in space technology are remarkable. Since
   Yuri Gagarin completed the first human spaceflight in 1961, we have
   come a long way, and now we can even send space tourists to low Earth
   orbit (LEO). Statistics show that over 500 professional astronauts and
   40 space tourists have visited LEO. The space tourism industry is
   gaining momentum and attracting unprecedented attention. However, the
   harsh space environment and its hazards cannot be neglected thus
   require intensive study.

   The space environment presents five main hazards that can harm human
   health, including distance, confinement, hostile and closed
   environment, radiation, and microgravity^[41]2,[42]3. These hazards
   have been found to cause cognitive impairment, muscle and bone
   deterioration, immune dysfunction, and cardiovascular health
   risks^[43]2,[44]4,[45]5, among other detrimental consequences. It is
   crucial to understand the underlying cellular and molecular factors
   responsible for these health risks in order to develop effective
   countermeasures for astronauts’ safety and guide future space
   activities. To this end, we conducted a multi-omics study and explored
   the transcriptomic and epigenetic influences of spaceflight.

   In this study, we aim to explore the changes in transcriptomics and DNA
   methylation patterns that occur in astronauts on the first all-private
   astronaut mission to the International Space Station, Axiom Space’s
   AX-1. We investigated the transcriptomic and epigenetic changes in
   blood samples from two astronauts, before and after their spaceflight.
   We profiled the dynamic molecular patterns in response to spaceflight.
   Our findings add to the growing body of literature on molecular changes
   in astronauts’ blood immediately post-flight and in the longer term.

Results

Whole blood transcriptomics

   Two participants provided three blood samples each (Fig. [46]1A). The
   blood samples were collected at the pre-flight mark (T0, within two
   weeks before departure), at post-flight (T2, within 24 hours after
   landing), and at the three months post-flight (T3, within two months
   after landing). In-flight (T1) blood draw was not performed for
   logistical reasons.

Fig. 1.

   [47]Fig. 1
   [48]Open in a new tab

   Blood transcriptomics changes in astronauts. (A) Study design. The
   study was to investigate the impacts of short-duration spaceflight,
   where blood samples were drawn at pre-flight (T0), at post-flight (T2),
   and at the three months post-flight (T3). Blood was collected from two
   study participants and sent for deep-sequencing and DNA methylation
   assay. (B) Monocytes fractions. The fractions of monocytes, estimated
   with CIBERSORT from bulk RNA-Seq, were tracked with time in two
   anonymized participants: Axiom1 (upward-pointing orange triangles) and
   Axiom2 (downward-pointing blue triangles). (C,D) Gene expression
   patterns organized in clusters identified by Clust^[49]9. The X axes
   tracked time and the Y axes the normalized gene expression. (C) The “V”
   cluster, the gene expression in this cluster are down-regulated during
   the flight. (D) The “A” cluster, the gene expression in this cluster
   are upregulated during the flight. (E-F) Top summarized pathways
   significant at the 10% FDR level enriched for genes in the “V” cluster
   (E) or in the “A” cluster (F).

   We first mapped how immune cell types varied in population size with
   time. To do so, we used CIBERSORT^[50]6, a tool to infer blood cell
   type fractions from bulk RNA-Seq after quality control and gene
   expression quantification(Supplementary Fig. [51]S1A and S1B,
   Supplementary Table [52]S1). CIBERSORT estimates were obtained for each
   participant at each timepoint (Supplementary Table [53]S2). Next, we
   used an unequal variance ANOVA test for each immune cell type to
   identify cell types for which proportions varied in time (Supplementary
   Table S3). The tests identified only one cell type, the monocytes (FDR
   17%), with fractions exhibiting a V-shaped time evolution in both study
   participants (Fig. [54]1B). The monocytes accounted for about 20% of
   blood cells pre-flight (T0), dropped to about 15% post-flight (T2),
   then were restored to pre-flight levels by the three-month post-flight
   mark (T3). Our results thus point to a substantial reduction of
   circulating monocytes during the flight, potentially in response to
   some physical or metabolic challenges.

   We next aimed to determine which gene(s) underwent differential
   regulation between the three timepoints. To do so, we employed the
   limma-voom pipeline^[55]7,[56]8, able to combine the three pair-wise
   time comparisons (T2 vs. T0, T3 vs. T0, and T3 vs. T2) into a single
   F-test (Supplementary Fig. [57]S1C, Supplementary Table S4). After
   correction for multiple testing at the genome-wide level, no genes were
   found significantly differentially expressed, which was understandable
   given the small number of participants.

   To identify the patterns of expression levels trajectories between the
   three timepoints, we utilized the cluster approach^[58]9, which
   extracted clusters of genes that were co-regulated based on the gene
   expression data (Supplementary Fig. [59]S1D). Interestingly, we
   identified only two co-regulated clusters each with about 300 genes
   (Supplementary Table S5): one with a time expression profile shaped
   like the letter “V” (Fig. [60]1C), the other one like “^” or the letter
   “A” (Fig. [61]1D). They both highlighted a change in gene expression,
   either up- or down-regulation at the immediate post-flight timepoint
   (T2), compared with matched levels at pre- (T0) and three months
   post-flight (T3). In other words, the gene expression patterns “V” and
   “A” -shaped seemed to indicate a disturbance flanked by homeostatic
   levels measured pre- and 3-months post-flight. The “V” pattern also
   matched the time evolution of monocyte fractions (Fig. [62]1B). These
   results suggest that although many genes change their expression during
   spaceflight, the absolute majority of these changes were transient.

   The top three genes most differentially expressed part of the “V”
   cluster encoded proteins: (1) Integrin Subunit Alpha V (ITGAV, ANOVA
   P = 2.4E-3, FDR > 10%), involved in cell adhesion and signaling. (2)
   Microtubule Associated Protein 9 (MAP9, P = 2.4E-3, FDR > 10%),
   required for bipolar spindle assembly, mitosis progression and
   cytokinesis. Down-regulation of MAP9 during the flight can help promote
   the proper monocyte response to skin damage caused by
   radiation^[63]10,[64]11. (3) Integrin Subunit Alpha 4 (ITGA4,
   P = 2.6E-3, FDR > 10%), a key component of cell adhesion and migration.
   The top three genes most differentially expressed part of the “A”
   cluster encoded proteins: (1) Inositol 1,4,5-Trisphosphate Receptor
   Type 2 (ITPR2, ANOVA P = 2.8E-3, FDR > 10%), involved in various
   cellular processes, including cell migration, cell division, smooth
   muscle contraction, and neuronal signaling. (2) Dimethylarginine
   Dimethylaminohydrolase 2 (DDAH2, P = 3.5E-3, FDR > 10%), an enzyme that
   functions in nitric oxide generation. The protein is secreted by
   monocytes after hypoxia^[65]12, which can be a result of skin
   irradiation^[66]13. (3) ST3 Beta-Galactoside
   Alpha-2,3-Sialyltransferase 4 (ST3GAL4, P = 5.1E-3, FDR > 10%)
   (Supplementary Table S4 and S5), involved in sialylation during
   hemostasis, immune responses, and viral infection. The gene is crucial
   for leukocyte rolling and adhesion during extravasation^[67]14.

   We next asked if, among genes that were co-regulated and grouped into
   clusters, there were an overrepresentation of these genes in specific
   biological processes (Supplementary Fig. [68]S1E). To do so, we relied
   on what’s termed gene set enrichment analysis (GSEA) (Supplementary
   Table S6 and S7), using the cluster to provide the lists of
   co-regulated genes, and analyzed each gene cluster separately. The
   genes in cluster “V”, where genes were down-regulated during the
   flight, were found mainly enriched for the protein modification by
   small protein conjugation or removal pathway (GO:0070647; FDR < 1%)
   (Fig. [69]1E), followed by regulation of macromolecule metabolic
   process (GO:0060255; FDR < 1%). Pathways for cell cycle (GO:0007049;
   FDR 8%), growth (GO:0016049; FDR 8%), and division (GO:0051301; FDR 8%)
   were also found in the “V” pattern. The genes in cluster “A”, where
   genes were up-regulated during the flight, were found mainly enriched
   for the regulation of metabolic process pathway (GO:0019222; FDR < 1%)
   (Fig. [70]1F). Pathways for response to hormone (GO:0009725; FDR < 1%)
   as well as regulation of autophagy (GO:0010506; FDR < 1%) were also
   up-regulated post-flight.

Genome-wide DNA methylation profile

   We next aimed to discover whether transient, short-lasting, or
   long-lasting changes were imprinted at the DNA molecular level
   following a spaceflight. Epigenetic memory has been reported to
   regulate stable shifts in gene expression^[71]15, whereas mRNA
   transcripts have a median ½-life of about ten hours^[72]16. Here, we
   conducted genome-wide DNA methylation assays and explored the
   methylation profiles of the two astronauts at the three timepoints.

   The first question we addressed was to determine whether the
   spaceflight environment had any impact on populations of immune cells
   using methylomics data. To answer this question, we estimated the
   fractions of different blood cell types in both samples at three
   timepoints (T0, T2, and T3) via DNA methylation analysis (Supplementary
   Table S8). We then compared the fractions between any pair of time
   points for each blood cell type using repeated measures ANOVA
   (Supplementary Fig. S4). Our methylomic results did not indicate any
   blood cell types exhibiting significant change over time (FDR > 20%)
   (Supplementary Table S8). However, we observed a similar trend for
   monocytes from the expression data: their fraction decreased at T2 from
   T0 and recovered at T3 (P = 0.20).

   We next aimed to assess how the DNA methylation responded to the
   spaceflight environments. Therefore, we compared the methylation levels
   between each pair of time points to identify any significant changes.
   We identified 924 differentially methylated probes (DMPs) at the FDR
   20% level (Supplementary Table S9), corresponding to 919 genes. Three
   most significant DMPs were located in the RUN And SH3 Domain Containing
   1 (RUSC1, which is involved in neuronal differentiation and signaling
   pathways), Doublesex And Mab-3 Related Transcription Factor 3 (DMRT3,
   which plays an important role in the development of the neuronal
   architecture controlling limb movement and locomotion) loci, and an
   intergenic region on chromosome 7. Functional enrichment analysis
   revealed that these genes were involved in 945 significant pathways
   (Supplementary Table S10). The top 10 pathways were presented in
   Supplementary Fig. S5, with the most critical specific pathways related
   to macromolecular metabolic processes and nervous system development.
   These findings suggest that the spaceflight environment can affect
   multiple human body systems, leading to comprehensive epigenetic
   adaptations, such as those in the immune and nervous systems.

   We further performed a temporal analysis of methylation changes in
   response to spaceflight environments. We compared the methylation
   levels at T2 and T3 with the baseline level at T0 and identified four
   distinct patterns of methylation changes over time, each involving at
   least 80 probes (Fig. [73]2, Supplementary Table S9 and S11). The
   pattern I (N = 817 probes, Fig. [74]2A; pattern I), the largest cluster
   by far, showed a transient increase in methylation at T2 followed by a
   return to baseline at T3. Pattern II (N = 144 probes, Fig. [75]2A;
   pattern II) showed a long-lasting increase in methylation at both T2
   and T3. Pattern III (N = 140 probes, Fig. [76]2A; pattern III) showed a
   constant decrease in methylation at both T2 and T3. Finally, pattern IV
   (N = 83 probes, Fig. [77]2A; pattern IV) showed an immediate increase
   in methylation at T2 that was then stable at T3. These results suggest
   that the majority of the genes were transiently silenced during
   spaceflight, but more persistent changes were also common.

Fig. 2.

   [78]Fig. 2
   [79]Open in a new tab

   Blood methylation epigenetics changes in astronauts. (A) Four common
   methylation patterns (I, II, III, IV) were detected from total of
   776,047 CpG sites across three visits (T0, T2, T3). Methylation levels
   were normalized to baseline ones. N is the number of CpG sites. (B) Top
   10 pathways enriched in the four patterns. X axis shows negative
   logarithm of false positive rate to base 10. Abbreviations: NR:
   Negative Regulation; PR: Positive Regulation; KD: Kidney Development;
   PMAM: Plasma Membrane Adhesion Molecules; MMAT: Mitotic
   Metaphase/Anaphase Transition; MAPK: Mitogen-activated protein kinase;
   CPA: Cell Projection Assembly; SO: Structure Organization.

   We next performed pathway enrichment analysis to explore further the
   implications of these biological patterns (Supplementary Table S12-15).
   The top ten enriched pathways for each pattern were shown in
   Fig. [80]2B. We observed that Pattern I pathways were enriched in
   processes related to the regulation of biosynthesis (GO:0009890,
   GO:0010558, GO:0031327) and nervous system development (GO:0007399).
   Genes in this pattern were predominantly linked to nervous system
   development, which appeared to be reversible. Pathways enriched in
   Pattern II were associated with processes that participate in mitosis.
   Pattern III pathways were enriched in protein metabolic processes
   (GO:0030162, GO:0051246, GO:0031399). In contrast, Pattern IV pathways
   were related to regulating membrane potential (GO:0042391) and
   apoptotic signalling pathway (GO:0097190). These patterns, which
   correspond to persistent methylation changes, suggest a sustained
   impact of spaceflight on the human body, potentially extending to the
   systemic level.

   To explore the effects of space travel on methylation at the regional
   level rather than at the level of individual CpG, we performed pairwise
   comparisons of methylation profiles between different time points (T0,
   T2, and T3). Specifically, we compared methylation levels in regions of
   probes between T0 and T2, T2 and T3, and T0 and T3. After applying FDR
   criteria, we identified five areas that showed significant changes in
   methylation levels (Supplementary Fig. S6, Supplementary Table S16).
   The first region was chr1:16085237–16,085,984, mapped to gene Filamin
   Binding LIM Protein 1 (FBLIM1). It was slightly hypermethylated at T2
   compared to baseline and significantly hypomethylated at T3 compared to
   methylation level at T2 (Supplementary Fig. S6A). FBLIM1 is involved in
   cell adhesion, cell shape modulation, and cell motility, and its
   deficiency has been linked to increased osteoclast activation and
   severe osteopenia in rodent models^[81]17. The second region was
   chr2:20219924408-219925251, overlapped with gene Indian Hedgehog
   Signaling Molecule (IHH). The methylation level was significantly
   upregulated at T3 compared to methylation at T0 and T2 (Supplementary
   Fig. S6B). IHH regulates growth processes. Mutations in this gene have
   been associated with Acrocapitofemoral Dysplasia^[82]18,[83]19. The
   third region was chr9:135754117–135,754,163 from gene Adenylate Kinase
   8 (AK8). It was significantly hypermethylated at T2 and restored at T3
   compared to baseline (Supplementary Fig. S6C). AK8 is involved in
   adenosine monophosphate binding and nucleobase-containing compound
   kinase activity. Its regional methylation level was hypermethylated at
   T2 and returned to baseline at T3, compared to T0. AK8 is known to
   inhibit epithelial cell migration^[84]20. The last region was chr15:
   75,165,338–75,165,821 from gene Secretory Carrier Membrane Protein 2
   (SCAMP2, which contributes to the processes of membrane fusion and
   exocytosis, particularly in neurons). The methylation level was mildly
   hypermethylated at T2 compared to baseline and significantly
   hypomethylated at T3 compared to T2 (Supplementary Fig. S6D). SCAMP2
   acts as a secretory carrier to the cell surface. SCAMP2 is associated
   with Chromosome 15q24 Deletion Syndrome^[85]21, characterized by
   hypotonia. Notably, three of the differentially methylated genes
   (FBLIM1, IHH, and SCAMP2) are linked to muscular diseases, and one
   (AK8) affects cell migration. This aligns with the observation that
   microgravity contributes to bone and skeletal muscle loss in
   astronauts^[86]22.

Expression quantitative trait methylation (eQTM) sites analysis

   We further aimed to explore the relationship between genes from the two
   expression clusters and CpG sites from the four methylation patterns to
   see whether gene expression patterns were affected by methylation
   patterns. We first collected all genes presented in the expression
   patterns and all CpG sites presented in the methylation patterns. We
   then tested the pairwise association between local genes and CpG sites.
   In total, we tested 1122 gene-CpG pairs (Supplementary Table S17) and
   found one significant result (FDR = 0.049) from gene Zinc Finger
   Protein 684 (ZNF684) and CpG cg21011110 (Fig. [87]3). We found that
   ZNF684 expression was reversely associated with cg21011110 methylation
   status. Furthermore, expression of ZNF684 gene represented a ‘V’
   pattern over time, thus, its expression is inhibited during the flight
   but normalized after 3 months. The methylation pattern of CpG
   cg21011110 represented an “A” pattern over time, thus, it is
   over-methylated during the spaceflight but normalized in 3 months.
   ZNF684 is a protein-coding gene predicted to play a role in the
   negative regulation of transcription by RNA polymerase II. it is
   ubiquitously expressed in all human tissues^[88]23 and its function is
   related to the innate immune response^[89]24–[90]26.

Fig. 3.

   Fig. 3
   [91]Open in a new tab

   Association between gene ZNF684 expression and CpG cg21011110
   methylation across time. Top panel: Expression of ZNF684 (blue lines,
   tpM level showed on the left Y axis) and methylation of cg2101110 (red
   lines, beta level showed on the right Y axis) across time. Participants
   are distinguished by line types. Timepoints are presented in the X
   axis. Bottom panel: Genomic position(hg19) of ZNF684 and cg21011110.

Discussion

   It is well-known that spaceflight experiences can have a severe impact
   on astronauts’ immune system, given that immune cells are highly
   sensitive to microgravity and other space hazards^[92]5,[93]27,[94]28.
   Several transcriptional, epigenetic, and other multi-omics studies have
   been conducted in rodents ([95]https://genelab.nasa.gov/newrnaseqdata)
   and humans exposed to a spaceflight environment^[96]29–[97]31. A
   multi-omics study on both mouse and human data revealed the systemic
   shifts in mitochondrial function in most tissues^[98]32. A recent
   spatial transcriptomics study in female mice brains reported
   significant alterations in essential brain processes^[99]33. Previous
   research has indicated that epigenetic changes occur in response to
   spaceflight as well^[100]5,[101]29,[102]32. All these previous studies
   identified plenty of genes and pathways affected by spaceflight. In
   this study, we conducted a longitudinal omics study to explore the
   molecular changes in astronauts’ peripheral blood in response to
   short-term spaceflight at the epigenetic and transcriptional levels.
   Our approach was to identify the co-regulation of gene expression. Our
   findings revealed the sensitivity and resilience of immune cells,
   underscoring their significance in spaceflight. More importantly, we
   identified robust patterns of co-regulated genes. We found two
   transient patterns from transcriptomic data and one transient pattern
   from DNA methylation data. We further found three long-lasting patterns
   from DNA methylation data. On the level of blood cell type, our
   transcriptomics and methylomics results converged on the monocyte
   dynamics identifying its reduction right after the spaceflight and then
   coming back to the baseline at 3 months. In brief, our study
   demonstrated significant shifts in the immune system caused by the
   unique stressors of spaceflight, which are mostly transient.

   Overall, our findings highlight the intricate relationships between
   spaceflight and the human immune system. The conclusions drawn from our
   transcriptional analyses are multifaceted: (1) Significant
   transcriptional changes were observed immediately post-flight (T2) that
   reverted to pre-flight levels by the three-month mark (T3). Such a
   dynamic transcriptomics recovery pattern was also reported in previous
   studies, for example, in the NASA Twins Study^[103]29 and the Space
   Omics and Medical Atlas (SOMA) study^[104]34. It was linked to stress
   responses of the immune system and its robust adaptive mechanisms to
   return to homeostasis after the stress of spaceflight. (2) The changes
   led to a V-shape recovery pattern in monocyte fractions in response to
   spaceflight, which also has been described previously^[105]35,[106]36.
   (3) The changes affected hundreds of co-regulated genes, both
   positively and negatively, indicating a coordinated cellular response
   to the harsh conditions of space, potentially to sustain
   radiation-induced wound healing processes. In addition, we conducted a
   comprehensive differential methylation probe analysis over time. The
   key epigenetic observations include: (1) Transient restorable
   methylation changes occurred in response to spaceflight, which was also
   reported in the NASA Twins Study^[107]29 and a long-term spaceflight
   study^[108]5. Genes involved in these transient methylation changes
   were predominantly enriched in the nervous system development pathway,
   suggesting the nervous system adapts to the unique stressors of space,
   such as microgravity and radiation. (2) Long-lasting methylation
   changes that persisted 3 months after the return were also identified.
   Genes that were affected by the long-lasting methylation changes were
   engaged in the cell cycle and apoptosis processes, reflecting the
   long-term effect of spaceflight on the austraunaphs’s immune system.
   The study that previously assessed genome-wide methylation patterns
   also observed immediate methylation changes that reverted to baseline
   during the 500 days of spaceflight and the long lasting methylation
   chages persisted right after return^[109]5. (3) Region-level
   methylation responses pointed to genes associated with bone and
   skeletal muscle loss; further investigation of these changes may help
   develop targeted countermeasures to mitigate bone and muscle loss in
   space. 4). Finally, our integrated analysis of transcriptional and
   methylation patterns identified the transcriptional regulator ZNF684
   revealing a significant correlation between the expression of ZNF684
   and the methylation of CpG site cg21011110 over time. A transient
   decrease in ZNF684 expression at T2 suggests transcriptional activation
   at this timepoint, hinting at potential molecular responses in line
   with transcriptional cluster “V” to the spaceflight environment via DNA
   methylation. Together, the implications of our findings offer exciting
   avenues for future research.

   This study, while insightful, has certain limitations. Firstly, the
   small sample size may have constrained the study’s power. Secondly, the
   absence of data resources beyond gene expression and DNA methylation,
   coupled with missing data during spaceflight, restricts a comprehensive
   study of space biology. Thirdly, the relatively short duration of the
   spaceflight in this study may limit the applicability of our findings
   to long-duration space missions. Despite these limitations, our results
   provide valuable insights into the largely transient nature of
   transcriptional and hypermethylation changes within the immune cells
   during spaceflight. They also underlie nevertheless that some
   substantial methylation changes are persistent at least three months
   after the flight and may have long-term effects on the health of the
   astronauts.

Materials and methods

Experimental design

   This study investigates the influence of a short-duration spaceflight
   on astronauts at transcriptomic level and epigenetic level.
   Figure [110]1A describes the design of the study.

Population and consent

   This study was approved by the Institutional Review Board (IRB) of the
   McGill University Health Center (MUHC: 2022–7768), the IRB of the
   National Aeronautics and Space Administration (NASA STUDY 00000403) by
   the University of Texas - M.D. Anderson (Reliance Agreement M.D.
   Anderson - NASA 2021 − 1179). All study procedures were conducted
   according to the guidelines of the Declaration of Helsinki.

   Participants were astronauts of the Axiom Space’s AX-1, launched on
   April 8, 2022, at 11:17 AM ET and splashed down on April 25, 2022, at
   1:06 PM ET. The astronauts effectively remained in orbit for seventeen
   days and completed 240 orbits.

   Two crew members, healthy male astronauts (participant ID: Axiom1 and
   Axiom2), agreed to participate, and the final informed consent forms
   were signed in March 2022.

Specimen collection and processing

   Written consent was obtained from the two male astronauts to collect
   peripheral venous whole blood samples using PAXgene blood RNA tubes
   (Qiagen, Maryland, United States) and PAXgene blood DNA tubes within
   two weeks before departure (pre-flight T0, i.e. within two weeks before
   April 8, 2022), within 24 h after landing (post-flight T2, i.e. within
   24 h after April 25, 2022), and within three months after landing
   (post-flight T3, i.e. 3 months follow-up after April 25, 2022),
   centrifuged at 1300 G, room temperature for 10 min. Plasma was
   aliquoted into dedicated cryotubes and stored at − 80 °C until further
   processing.

Statistical analyses

Transcriptomics analyses

   Blood samples were collected at MD Anderson, Houston, TX, anonymized,
   then shipped to Canada at McGill University. RNA extraction was
   performed using the PAXgene Blood RNA kit and a corresponding protocol
   (Qiagen, Maryland, US). The isolated RNA was then stored in Eppendorf
   tubes (Eppendorf AG, Hamburg, Germany) at −80 C until they were sent to
   Genome Quebec Innovation Center (GQIC, Montreal, Canada) for quality
   control (QC) and deep-sequencing. RNA QC was done by Nanodrop (Thermo
   Fisher Scientific Inc, Waltham, US) and Bioanalyzer (Agilent
   Technologies Inc, Santa Clara, US).

   Total RNA then was deep-sequenced on an Illumina NovaSeq 6000 (Illumina
   Inc, San Diego, US), using reads of length 101 nucleotides in a
   paired-end fashion, for a total of 202 nucleotides per RNA fragment.
   The deep sequencing data were mapped on the human genome using
   STAR^[111]37 and quantified for gene expression with STAR and
   TPMcalculator (Supplementary Fig. [112]S1A)^[113]37,[114]38. The
   quality of the sequencing data was assessed three-fold: (1) via
   fragments mapping statistics, where 99.9% of the sequencing reads were
   mapped with specificity (≤ 10 different loci) (Supplementary Table
   [115]S1), (2) via an average mapped length of > 191 nucleotides out of
   the 202 sequenced (> 95% of the fragment’s length) (Supplementary Table
   [116]S1), and (3) via transcriptomes principal component analysis in
   which all samples were within two standard deviations of the means of
   the two largest transcriptomics principal components (Supplementary
   Fig. [117]S2).

   Reads were mapped on the human genome version GRCh38 with the STAR
   computer program^[118]37 using Ensembl gene definitions release
   103^[119]39. STAR provided both raw aligned fragment counts for each
   gene (option ‘‑‑quantMode GeneCounts’) as well as aligned reads in the
   BAM format (option ‘‑‑outSAMtype BAM Unsorted’)^[120]40. Raw counts
   have been analyzed for detection of differential gene expression with
   time, using the pipeline from R’s packages ‘limma’ and
   ‘voom’^[121]7,[122]8. Genes were sorted by decreasing F-statistic
   values, equivalent to a one-way ANOVA for each gene but moderated with
   knowledge of variance for all genes. The top 2,500 genes with the
   largest F-statistics were further analyzed with the Clust computer
   program^[123]9 to identify co-varying genes. Clusters of genes were
   analyzed for Gene Set Enrichment Analysis (GSEA) via a hypergeometric
   test for enriched category (in our case, pathways)^[124]41 with
   pathways featured in Gene Ontology (GO)^[125]42. Aligned reads were
   analyzed by the ‘TPMCalculator’ to yield gene expression levels for
   each gene in units of transcript-per-million (tpM)^[126]38, which
   corrects for library sample sizes and gene lengths. TPM gene levels
   were analyzed by the R package CIBERSORT^[127]6, which estimated white
   blood cell type fractions in each sample by the so-called bulk RNA-Seq
   deconvolution technique. Supplementary Figure [128]S1 details the
   bioinformatics pipelines used to derive the various results.

DNA methylation analyses

   The genomic DNA was extracted using the PAXgene Blood DNA Kit (Qiagen)
   following the manufacturer’s instructions. Then the DNA samples were
   quantified using Nanodrop 2000 Spectrophotometer (Thermo Fisher
   Scientific Inc, Waltham, US) by measuring absorbance at 260 nm. Next,
   the purity of the DNA was determined by calculating the absorbance
   ratio of A260/A280, which varied between 1.82 and 1.83. Finally, the
   DNA samples were aliquoted and stored at −20 °C, and one aliquot of
   each sample was sent to GQIC for methylation assay.

   Genome-wide DNA methylation assays were done using Infinium’s
   Methylation EPIC Beadchip (Illumina Inc). The samples were processed at
   GQIC following the manufacturer’s protocol. We used R (v 4.0.3) to
   import and analyze the raw data files. Cell fractions of six blood cell
   types (B-cells, CD4 + T-cells, CD8 + T-cells, granulocytes, monocytes,
   and Natural Killer (NK)-cells) were estimated by applying the Houseman
   method in Minfi^[129]43,[130]44. Quality control filters were applied
   to remove poor-quality probes: probes with low detection P value
   (> 0.01), mapped to sex chromosomes, affected by nearby
   single-nucleotide polymorphisms (SNPs), or found to be cross-reactive
   were all removed^[131]45. After filtering, 776,047 probes (CpG sites)
   were remained for further analysis. A quantile normalization was
   applied to methylation signals to correct batch effects and unwanted
   variation. The effect of space flight on blood cell fractions and
   methylation levels (probe-wise and region-wise) was evaluated by using
   repeated measures ANOVA and methods from both the limma package and the
   Minfi package in R^[132]7,[133]44. Gene pathway analysis was done using
   a hypergeometric test in GO^[134]42. Both significant blood cell
   fractions and gene probes were selected if their false discovery rate
   (FDR) was less than 20%. The significant regions were selected if they
   had at least three probes in the region; their family-wise error rate
   (FWER) was less than 20%, and their absolute difference in mean
   methylation beta value was larger than 0.05. Significant pathways were
   defined as pathways in that at least five genes were in the pathway,
   with positive enrichment scores and FDR less than 20%. Supplementary
   Figure S3 describes the methylation analysis pipeline.

eQTM analysis

   To explore the correlation between DNA methylation and gene expression
   over time, we conducted expression quantitative trait methylation
   (eQTM) sites analysis by using function Matrix_eQTL_main from R package
   MatrixEQTL (version 2.3)^[135]46 with default setting of parameters. We
   focused on cis-eQTM only, which defined as local gene-CpG pairs from
   the same chromosome within 1 M bps. We included participant ID and
   timepoint as factorial covariates. A Benjamini and Hochberg adjustment
   was applied to correct for multiple testing and a false discovery rate
   (FDR) < 5% was adopted to screen for significant association.

Supplementary Information

   Below is the link to the electronic supplementary material.
   [136]Supplementary Material 1^ (896.9KB, pdf)
   [137]Supplementary Material 2^ (3.8MB, xlsx)

Acknowledgements