More than a Feeling: Dermatological Changes Impacted by Spaceflight

Abstract

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.

Figure 1. Global data overview.

A) Breakdown of the rodent datasets used in this study. B) Clustering of the most variable genes within the rodent datasets with functional annotation.

Figure 2. Categorizing overall key pathways and genes being regulated in the skin during Spaceflight.

A) An upset plot showing the number of significant (FDR ≤ 0.1) DEGs in Spaceflight versus ground data subsets and the number of overlapping significant DEGs between these data subsets. The colored annotation bar on the left of the plot shows how the original datasets divide into 10 data subsets with various conditions including diet, biological sex and strain. The bar plot on the right of the plot shows the number of significant DEGS in each of the 10 data subsets. The bar plot on the top shows the number of intersecting DEGs between combinations of the data subsets, as indicated by the connected dots within the body of the upset plot. Black connecting lines indicate combinations spanning across multiple missions, and other connecting lines are colored according to the annotation bar, based on their mission. B) A bar plot of the top 10 most significant GOBP pathways found in the union of cross-mission combinations (i.e., DEGs from the black bars in panel A). C) A bar plot of the top 10 most significant GOBP pathways found in the union of intra-MHU-2 combinations (i.e., DEGs from the blue bars in panel A).

Figure 3. Key genes involved in rodent skin Spaceflight response.

A) A graphical representation of the method for deriving the set of 102 key genes pathways, where highly significant (FDR ≤ 0.05) pathways in at least 8/10 data subsets were selected and then leading-edge genes that were significant (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 five top synergistic partners, as described in the main text and the methods section. We also show the three most significant 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.

Figure 5. Behavior of the specific genes associated with skin health in astronauts and rodents.

A) The orange (suppressed) and green (enriched) heatmap shows the normalized enrichment score (NES) of pathways that are significant (FDR ≤ 0.25) in at least 1. The red (upregulated) and blue (downregulated) heatmap shows the t-score for leading edge genes from the significant pathways that are significant (FDR ≤ 0.1) in at least 2 data subsets. B) The circular heatmap shows changes from the genes in A in human data.

Figure 6. DNA damage and repair pathways being regulated in rodents flown to space.

Heatmap of pathways relating to DNA damage response and repair mechanisms, highly significant (FDR ≤ 0.1) in at least 1 data subset.

Figure 7. Mitochondrial specific analysis on rodent Spaceflight skin tissue.

(A) Heatmap of pathways relating to the mitochondria, highly significant (FDR ≤ 0.05) in at least 1 data subset. (B) Decision boundary for the 2-gene model related to mitochondrial changes. The model indicates an increased removal of the toxic D2H compound in mitochondria through upregulation of D2HGDH, which is less pronounced when PPP1R3Bexpression is suppressed.

Figure 8. Astronaut physiological markers compiled from up to 50 astronauts.

A) Specific blood markers which contain data points for pre-launch (L−), flight (FD), and return to Earth (R+). The numbers on the x-axis axis indicate the number of days for each group. Interim Resistive Exercise Device (iRED) is shown in blue and Advanced Resistive Exercise Device (ARED) is shown in red. B) Specific blood markers which contain data points for pre-launch (L−) and return to Earth (R+). C) Specific urine markers which contain data points for pre-launch (L−), flight (FD), and return to Earth (R+). The statistics on the data are * p < 0.001 for significantly different from L-45 and ‡ p < 0.01 significantly different from ARED.

Figure 9. Predicted potential countermeasures for mitigating spaceflight 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 studies. A negative (blue) activation state implies the key gene expression changes are opposite to mRNA changes observed with the indicated drug.