WO2025038925A1

WO2025038925A1 - Methods for simulating inflammatory aging, cardiac dysfunction, neural dysfunction, and changes associated with spaceflight in cells and organoids, and methods for identifying and using compounds useful for treatment of cellular changes associated with inflammatory aging, cardiac dysfunction, neural dysfunction, and spaceflight

Info

Publication number: WO2025038925A1
Application number: PCT/US2024/042658
Authority: WO
Inventors: Daniel Winer; David Furman; Fei Wu; Jordan BAECHLE; Huixun DU
Original assignee: Buck Institute for Research on Aging
Current assignee: Buck Institute for Research on Aging
Priority date: 2023-08-17
Filing date: 2024-08-16
Publication date: 2025-02-20
Anticipated expiration: 2026-02-17

Abstract

Methods for simulating inflammatory aging, simulating changes in cellular physiology due to spaceflight, and/or causing changes in gene expression associated with cellular senescence, neural dysfunction, and cardiac dysfunction by exposing one or more cells or organoids to simulated reduced gravity below 1G are provided. Additionally, methods of identifying cellular transformations associated with aging, aging hallmarks, cardiac dysfunction, neural dysfunction, and/or spaceflight are provided. Methods for identifying compounds useful for treatment, normalization, or reversal of cellular transformations and/or differential gene expression associated with aging, cardiac dysfunction, neural dysfunction, and/or spaceflight are also provided. Finally, methods for treating, normalizing, and/or reversing cellular transformations of one or more cells, tissues, or organoids exposed to reduced gravity under 1G or cellular transformations associated with an aging hallmark, cardiac dysfunction, neural dysfunction, and/or an age-related disease are provided.

Description

TITLE

[0001] METHODS FOR SIMULATING INFLAMMATORY AGING, CARDIAC DYSFUNCTION, NEURAL DYSFUNCTION, AND CHANGES ASSOCIATED WITH SPACEFLIGHT IN CELLS AND ORGANOIDS, AND METHODS FOR IDENTIFYING AND USING COMPOUNDS USEFUL FOR TREATMENT OF CELLULAR CHANGES ASSOCIATED WITH INFLAMMATORY AGING, CARDIAC DYSFUNCTION, NEURAL DYSFUNCTION, AND SPACEFLIGHT

BACKGROUND

[0002] Changes in the physical environment modulate cell responses and may lead to the impairment or even failure of tissue function through altered mechanotransduction processes. Evidence suggests this phenomenon occurs in some age-related diseases and pathological conditions observed in space, such as inflammatory aging, neural dysfunction, and cardiovascular deconditioning.

[0003] Exposure to reduced gravity, including microgravity (uG), is associated with immunological dysfunction, though the underlying mechanisms have generally been poorly understood. In particular, astronauts in low earth orbit (LEO), such as on the international space station (IS S), experience immune dysfunction associated with the microgravity environment. Multiple studies have described immune dysregulation in short or long term simulated or actual microgravity. For the most part, such studies have described impaired T-cell responses, coupled to some form of heightened innate immunity, though some innate immune cells, like natural killer (NK) cells, also show impaired function. Consistent with altered adaptive immunity, potentially due to impaired cytotoxic and Thl T-cell function, and reduced NK cell function, astronauts develop increased reactivation of latent viruses, including herpesviruses (EBV, CMV, VZV). In one study, viral shedding after 9-14 days of spaceflight was linked to changes in serum cytokines, including a preferential large increase in IL-4 compared to interferon (IFN)y, indicating a possible shift away from Thl immunity towards Th2 immunity. Consistently, some astronauts report heightened hypersensitivity reactions, such as increased allergic and Th2-like responses in space.

[0004] Multiple studies using higher throughput approaches have started to add insight into pathways impacted by spaceflight. In the Twins study, a one-year ISS mission altered innate, adaptive, and NK cell-mediated immunity across bulk RNA sequencing analysis. In T-cells, increases in DNA methylation were seen in the promoters of notch3 for CD4+ T-cells, linked to T-cell differentiation, and in scllci5lasct2, linked to activation, for CD8+ T-cells. A total of 50 of 62 assayed cytokines were also altered by spaceflight or landing. During a recent multi -omic analysis, including bulk RNA and DNA methylation sequencing, of astronauts and mice in space, mouse organs such as the liver and kidney demonstrated reduced IFN signatures, coupled to altered methylation patterns of these gene sets, while muscles had increased IFNy, IL-1, and TNF10. Serum inflammatory markers from 59 astronauts in this study (and in a similar companion study) showed increased VEGF-1, IGF-1, and IL-1 during spaceflight, that resolved upon returning to Earth. This same study also identified mitochondrial dysfunction as a major response of different non- hematolymphoid tissues to spaceflight. Another study utilized a 41 -parameter mass cytometry approach to show that short term (18-22 hours) of simulated microgravity can dampen NK cell, CD4+, and CD8+ T-cell responses to Concanavalin A/anti-CD28 stimulation, but potentiates STAT5 signaling to boost Tregs. Despite these important advances, the core fundamental mechanisms, genes, and pathways which are directly altered by exposure to reduced gravity or microgravity, thereby adversely impacting immunity (including at single cell resolution), are largely unknown.

[0005] Immune dysfunction during spaceflight is an important health risk, and manifests primarily as increased vulnerability to opportunistic infections, including latent viral reactivation. Latent viruses can reactivate on both short- and long-term spaceflights, and commonly involve herpes viruses (HSV1, EBV, CMV, VZV). Astronauts also experience heightened skin sensitivity reactions, and this mechanism was thought to be related to a possible Type 2 immune bias in space. Recent work in simulated microgravity has also shown reduced JAK/STAT signaling in CD8+ T cells, coupled to increased pSTAT5 signaling in Tregs. Despite these important advances, major mechanisms explaining these phenotypes of immune dysfunction, in simulated microgravity have remained unclear.

[0006] Decades of studies have also shown that short and long-term spaceflight results in a constellation of cardiovascular alterations characteristic of cardiovascular deconditioning including decreased heart rate, increased atrial and ventricle compliance, decreased left ventricular mass, reduced contractility, and decreased ejection fraction. Many acute changes appear reversible with postflight rehabilitation but may ultimately contribute to the increased incidence of cardiovascular disease compared with non-astronaut controls.

[0007] Although technological advances have enabled clinical and in vitro experimentation during short and long-term spaceflight, given the challenges of conducting cardiovascular research in space, numerous analog models have been developed to investigate the mechanisms underpinning the cardiovascular effects of microgravity. These include in vivo ground-based analog models developed to simulate microgravity including head-down tilt bed rest in humans and hindlimb unloading for mouse models, as well as ground-based microgravity simulators, such as 2- dimensional clinostats, random positioning machines (RPMs), and rotating wall vessels (RWV) for in vitro models. In recent years, the technological convergence of microgravity experimentation (spaceflight and simulated microgravity, smG) and cardiovascular stem cells (human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) and cardiac progenitor cells (CPCs)) have emerged as valuable tools for studying the impact of extracellular mechanical forces on the molecular and cellular mechanisms of cardiomyogenesis and disease.

[0008] Studies have shown RPM smG can improve the functional and structural properties of CPCs resulting in enhanced proliferation and survival compared to 1G controls. On the other hand, other studies comparing space-flown differentiated hiPSC-CMs monolayers to ground controls found no significant difference in beat rate or contraction-relaxation velocities. More recently, a study directly compared the impact of microgravity on hiPSC-derived cardiac progenitors cultured to cardiomyocyte spheres aboard the International Space Station (ISS) by comparing the function and transcriptomics of space uG to simulated 1G abroad the ISS, and ground controls. Cardiomyocytes aboard the ISS exposed to uG demonstrated upregulated mitochondria- and hypertrophy-related transcriptomics. Altogether, these findings suggest space and smG may have a beneficial effect on the structure and function of cardiomyocytes.

[0009] However, these studies have been limited to pure cardiomyocyte and cardiomyocyte progenitor cells, and emerging evidence suggests that other cell types, particularly endothelial cells, pericytes, and fibroblasts to play pivotal roles in mechanosensing and orchestrating responses to altered mechanical forces in cardiovascular diseases. The difference between pure cardiomyocyte organoids and cardiomyocytes co-cultured with endothelial cells were investigated and evaluated for their functional and transcriptional responses to RWV uG exposure herein.

[0010] The studies disclosed herein in which cells and organoids were exposed to reduced gravity also serve as models for the study of and treatment for aging/inflammatory aging maladies, aging-related neural dysfunction, and cardiovascular dysfunction/aging, given the overlap between the pathways affected by reduced gravity and pathways known to coincide with, for example, aging hallmarks, inflammatory aging processes, immune dysfunction, neural dysfunction, and cardiovascular deconditioning.

J SUMMARY

[0011] The present disclosure generally relates to methods for simulating inflammatory aging, neural dysfunction, and cardiovascular dysfunction, simulating changes in cellular physiology due to spaceflight, and/or causing changes in gene expression; methods of identifying cellular transformations associated with aging, aging hallmarks, and/or spaceflight; and methods for identifying compounds useful for treatment, normalization, or reversal of dysfunction, cellular transformations and/or differential gene expression associated with aging and/or spaceflight.

BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The following detailed description of embodiments of the methods, will be better understood when read in conjunction with the appended drawings and tables of exemplary embodiments. It should be understood, however, that the invention is not limited to the precise arrangements and instrumentalities shown.

[0013] In the drawings:

[0014] Fig. 1 A is a UMAP plot of unstimulated peripheral blood mononuclear cell/PBMC single-cell transcriptomes (10X Genomics), pooled together from a male (36 years old) and a female (25 years old) donor, that underwent either 1G or simulated microgravity (uG) for 25 hours total, in accordance with an exemplary embodiment of the present invention.

[0015] Fig. IB displays graphs quantifying relative abundance of each cluster of single PBMCs by percentage, or log2Fold Change (FC) between simulated uG and 1G conditions in accordance with an exemplary embodiment of the present invention.

[0016] Fig. 1C is a volcano plot of differentially expressed genes (DEGs) across all immune cell types between uG and 1G (adj. p cutoff is 0.05, and log2FC cutoff is 0.25) in accordance with an exemplary embodiment of the present invention.

[0017] Fig. ID depicts volcano plots of differentially expressed genes (DEGs) between simulated microgravity (uG) and 1G (25 hours exposure) for 10 of the most abundant immune cell types (Adj.p cutoff is 0.05, and log2Fold Change (log2FC) cutoff is 0.25) in accordance with an exemplary embodiment of the present invention.

[0018] Fig. IE depicts two dot plots showing the top DEGs from Fig. 1C (one depicting upregulation and one depicting downregulation) and their expression levels across 22 immune cell populations (spot intensity reflects Log2FC of uG vs 1G, while spot size shows -loglO (adj. p)) in accordance with an exemplary embodiment of the present invention. [0019] Fig. IF depicts UMAP trajectory analyses of 1G and simulated uG unstimulated PBMCs; in accordance with an exemplary embodiment of the present invention. White circles represent the root nodes of the trajectory. Black circles indicate branch nodes, where cells can travel to a variety of outcomes. Light gray circles designate different trajectory outcomes.

[0020] Fig. 1G depicts canonical pathway enrichment analysis obtained from Ingenuity Pathway Analysis (IP A) across 19 immune cell clusters. Spot intensity reflects IP A z-score enrichment of simulated uG vs 1G, with the first plot displaying predicted activation or upregulation of the pathway in uG and the second ploy displaying downregulation or repression of the pathway in uG. Spot size shows the level of significance via -loglO (adj. p).

[0021] Fig. 1H is a table depicting z-score enrichment of simulated uG vs. 1G of pathways in different PBMC cell types.

[0022] Fig. II displays plots showing differences in iAge index between all cell types (top) and across 22 individual immune cell types (bottom) at 1G or simulated uG (****p < 0.0001, ***p < 0.001, **p < 0.01, *p < 0.05).

[0023] Fig. 1J displays a plot depicting differences in cellular senescence secretory product score, calculated from the SenMayo gene set, between all cell types at 1G or simulated uG (****p < 0.0001).

[0024] Fig. IK depicts smooth density distribution of SenMayo scores in 1G and uG conditions (25 hours exposure) of total PBMCs (top) and individual cell type (bottom).

[0025] Fig. IL depicts plots displaying metatranscriptome detection of mycobacteria, retrovirus, and total virus abundance in 1G and uG conditions (*p < 0.05, ****p < 0.0001).

[0026] Fig. IM depicts plots displaying microbial gene expression validation analysis. Singlecell RNA-seq data of PBMCs from two donors (1 male, 1 female) were re-analyzed using alignment tool Magic-Blast to quantify the reads amount for gammaretrovirus, and

Mycobacterium canettii. The microbial quantity was represented by the fraction of the microbial reads to the total read counts in the sample.

[0027] Fig. IN depicts NicheNet predicted significant ligand-receptor interaction between total T cells (Receiver) and the antigen-presenting cells (Sender) as B cells, DCs, and Monocytes in uG vs 1G conditions (z.e., induced in uG over 1G).

[0028] Fig. 10 depicts a differential NicheNet ligand-receptors analysis between uG vs. 1G conditions in unstimulated PBMCs. The sender cell type is the B cell, which provides ligands, whereas the receiver cell type is the T cell. The minimum log2FC of ligand gene expression in sender cells as compared to all the sender cell types of the other niche is calculated, and the top 30 ligands are prioritized. Then the top 2 receptors were selected by the highest minimum log2FC of the receptor gene in receiver cells.

[0029] Fig. IP depicts a differential NicheNet ligand-receptors analysis between uGvs. 1 G conditions in unstimulated PBMCs. The sender cell type is the B cell, which provides ligands, whereas the receiver cell type is the T cell. From left to right are the ligand gene expression level, ligand activity, and its target genes in the receiver cells. The target gene expression levels in the receiver cell type are used to define the ligand activity.

[0030] Fig. IQ depicts a differential NicheNet ligand-receptors analysis between uG vs. 1G conditions in unstimulated PBMCs. The sender cell type is the Dendritic Cell, which provides ligands, whereas the receiver cell type is the T cell. The minimum log2FC of ligand gene expression in sender cells as compared to all the sender cell types of the other niche is calculated, and the top 30 ligands are prioritized. Then the top 2 receptors are selected by the highest minimum log2FC of the receptor gene in receiver cells.

[0031] Fig. 1R depicts a differential NicheNet ligand-receptors analysis between uG vs. 1G conditions in unstimulated PBMCs. The sender cell type is the Dendritic Cell, which provides ligands, whereas the receiver cell type is the T cell. From left to right are the ligand gene expression level, ligand activity, and its target genes in the receiver cells. The target gene expression levels in the receiver cell type are used to define the ligand activity.

[0032] Fig. IS depicts a differential NicheNet ligand-receptors analysis between uG vs. 1G conditions in unstimulated PBMCs. The sender cell type is the monocyte, which provides ligands, whereas the receiver cell type is the T cell. The minimum log2FC of ligand gene expression in sender cells as compared to all the sender cell types of the other niche is calculated, and the top 30 ligands are prioritized. Then the top 2 receptors are selected by the highest minimum log2FC of the receptor gene in receiver cells.

[0033] Fig. IT depicts a differential NicheNet ligand-receptors analysis between uG vs. 1G conditions in unstimulated PBMCs. The sender cell type is the monocyte, which provides ligands, whereas the receiver cell type is the T cell. From left to right are the ligand gene expression level, ligand activity, and its target genes in the receiver cells. The target gene expression levels in the receiver cell type are used to define the ligand activity.

[0034] Fig. 2A depicts a UMAP plot of TLR7/8 agonist-stimulated (9 hours stimulation + 16 hours conditioning prior to stimulation = 25 hours total culture) PBMCs single-cell transcriptomics, pooled from a male (36 years) and a female (25 years) donor, that underwent either 1G or simulated uG. Cells were resolved into 23 distinct clusters. [0035] Fig. 2B shows two plots quantifying the relative abundance of each cluster of single PBMCs by percentage, or cumulative frequency (FC) between TLR7/8 agonist-stimulated uG and 1G conditions.

[0036] Fig. 2C depicts a volcano plot of DEGs across all immune cell types between TLR7/8 agonist stimulated uG and 1G PBMCs (adj. p cutoff is 0.05, and log2FC cutoff is 0.25).

[0037] Fig. 2D depicts two dot plots (one displaying upregulation and one displaying downregulation) showing the top DEGs from Fig. 2C and their expression levels across 19 immune cell populations. Spot intensity reflects Log2FC of TLR7/8 agonist simulated uG vs 1G, while spot size shows -loglO(adj. p).

[0038] Fig. 2E depicts volcano plots of differentially expressed genes (DEGs) between TLR7/8 stimulated (9 hours stimulation, 25 hours total culture) uG and 1G for the 8 most abundant immune cell types (adj.p cutoff is 0.05, and log2FC cutoff is 0.25).

[0039] Fig. 2F depicts UMAP trajectory analyses of 1G (top) and simulated uG (bottom) TLR7/8 agonist stimulated PBMCs. White circles represent the root nodes of the trajectory. Black circles indicate branch nodes, where cells can travel to a variety of outcomes. Light gray circles designate different trajectory outcomes.

[0040] Fig. 2G depicts canonical pathway enrichment analyses obtained from IPA across 19 immune cell clusters. Spot intensity reflects IPA z-score enrichment of TLR7/8 agonist activated simulated uG vs 1G, with the first plot displaying predicted upregulation or activation of the pathway in simulated uG and the second plot displaying downregulation or repression of the pathway in simulated uG. Spot size shows the level of significance via -loglO (adj. p).

[0041] Fig. 2H displays graphs demonstrating differences in iAge index between all cell types (top) and across 19 individual immune cell types (bottom) after TLR7/8 agonist activated 1G or simulated uG (****p < 0.0001, ***p < 0.001, **p < 0.01, *p < 0.05).

[0042] Fig. 21 displays a graph demonstrating differences in cellular senescence secretory product score, calculated from the SenMayo gene set, between all cell types with TLR7/8 agonist activated 1G or simulated uG (****p < 0.0001).

[0043] Fig. 2J depicts smooth density distribution for SenMayo scores in TLR7/8 stimulated 1G and uG conditions (9 hours stimulation, 25 hours total culture) for general PBMCs (top) and individual cell types (bottom).

[0044] Fig. 2K depicts NicheNet predicted significant ligand-receptor interaction between total T cells (Receiver) and the antigen-presenting cells (Sender) as B cells, DCs, and Monocytes in TLR7/8 agonist activated simulated uG vs 1G condition (z.e., induced in uG over 1G). [0045] Fig. 2L depicts two dot plots showing the top 25 most upregulated (first plot) and top 25 most downregulated (second plot) DEGs and their expression levels across 18 immune cell populations. Spot intensity reflects log2FC of stimulated vs unstimulated PBMCs under 1 G, while spot size shows -loglO (adj. p). The stimulation alters the single-cell transcriptional landscape of human PBMCs in 1G.

[0046] Fig. 2M depicts canonical pathway enrichment analyses obtained from Ingenuity Pathway Analysis (IP A) across 19 immune cell clusters. Spot intensity reflects IPA z-score enrichment of stimulated vs unstimulated PBMCs under 1G. The left plot shows predicted upregulation or activation of the pathway in stimulated PBMCs, and the right plot shows downregulation or repression of the pathway in stimulated PBMCs. Spot size shows the level of significance via -loglO (adj. p).

[0047] Fig. 2N depicts two plots quantifying the relative abundance of each cluster of PBMCs by percentage, or log2FC between unstimulated and TLR7/8 agonist- stimulated conditions under uG.

[0048] Fig. 20 depicts two dot plots showing the top 25 most upregulated and top 25 most downregulated DEGs and their expression levels across 17 immune cell populations. Spot intensity reflects log2FC of TLR7/8 agonist-stimulated PBMCs vs unstimulated PBMCs under uG, while spot size shows -loglO (adj. p). Fig. 20 also depicts two plots of canonical pathway enrichment analysis obtained from IPA across 19 immune cell clusters. Spot intensity reflects IPA z-score enrichment of stimulated vs unstimulated PBMCs under uG, with the left lower plot indicating predicted upregulation or activation of the pathway in stimulated PBMCs and the right lower plot indication downregulation or repression of the pathway in stimulated PBMCs. Spot size shows the level of significance via -loglO (adj. p).

[0049] Fig. 2P displays a comparison of log2FC in DEGs between stimulated and unstimulated PBMCs under uG and 1G conditions, presented as the difference in log2FC values (uG - 1G). The top 50 most upregulated DEGs between stimulated and unstimulated PBMCs under 1G were selected for comparison.

[0050] Fig. 2Q depicts two dot plots showing the top 50 conserved DEGs specifically sensitive to uG, ranked by the absolute sum of log2FC values, derived separately from the sum of positive log2FC values and the sum of negative log2FC values, under both stimulated and unstimulated conditions in the "Overall" group, and with expression patterns displayed for all cell types. The first plot displays the upregulated conserved genes, and the second plot displays the downregulated conserved genes. [0051] Fig. 2R displays a Rank-Rank Hypergeometric Overlap (RRHO) analysis showing the overlap in gene expression data between 1G and uG of stimulated vs unstimulated PBMCs. The x- axis and y-axis represent the ranks of the genes in the two gene lists, which were determined by calculating -loglO(adj.p)*log2FC. The intensity represents the -loglO transform of the P-value, which was calculated using the hypergeometric test for each pair of ranks from the two ranked gene lists. Genes significantly changing in the same direction in both experiments are in the upper-right quadrant (both down) and bottom-left (both up) and in opposite directions in the upper-left and bottom-right. Fig. 2R also depicts Venn diagrams summarizing the overlapping of down-regulated (upper Venn diagram) and up-regulated (lower Venn diagram) genes of stimulated vs unstimulated PBMCs between 1G and uG conditions.

[0052] Fig. 2S depicts sex differences in the number of up-regulated and down-regulated DEGs in uG vs 1G of unstimulated PBMCs. The comparison was presented by log2 fold change of the number of DEGs in female divided by the number of DEGs in male. The DEGs were determined by adjusted P value <0.05 and the absolute log2FC>=0.1.

[0053] Fig. 2T depicts sex differences in the number of up-regulated and down-regulated DEGs in uG vs 1G of stimulated PBMCs. The comparison was presented by log2 fold change of the number of DEGs in female divided by the number of DEGs in male. The DEGs were determined by adjusted P value <0.05 and the absolute log2FC>=0.1.

[0054] Fig. 2U shows volcano plots of DEGs in uG vs 1G in TLR7/8 agonist- stimulated and unstimulated PBMC from a female and male donor. Results for unstimulated male donor PBMCs, unstimulated female donor PBMCs, stimulated male donor PBMCs, and stimulated female donor PBMCs are shown (adj.p cutoff is 0.05, and log2FC cutoff is 0.25).

[0055] Fig. 2V shows a heatmap comparison of IP A canonical pathways between the male and female samples. The pathways that are significantly (adj.p<0.05) enriched in both sexes in each condition are shown on the heatmap.

[0056] Fig. 3A depicts two plots demonstrating cell type frequency changes within PBMCs as predicted by CIBERSORTx using bulk RNA-sequencing. The top plot shows the bulk RNA- sequencing data of PBMCs from 6 donors (3 male, 3 female) analyzed using CIBERSORTx to predict cell type frequency in the sample. The single-cell RNA-seq data from PBMCs was used to build the Signature Matrix File as the reference to predict the cell proportion in the bulk RNA-seq data. The bar height represents the average proportion of the cell type in the group. The error bar shows the standard error. The lower plot displays quantification of cell proportion alteration between uG and 1G in PBMC by log2Fold change. The comparison was made by the Student’s t- test (*p < 0.05).

[0057] Fig. 3B depicts a volcano plot of DEGs from simulated uG vs.lG (25 hours), with both . Bulk RNA-sequencing genes that are consistently up-regulated (upper right quadrant) across singlecell and bulk sequencing and genes that are consistently down-regulated (upper left quadrant) across the two datasets shown. Data were obtained from PBMCs from 3 male (ages 37, 22, 32 years old) and 3 female (age 27, 26, 40 years old) donors.

[0058] Fig. 3C displays Spearman correlations of normalized counts between single-cell and bulk RNA-seq from simulated uG (R=0.82, p < 0.0001) and 1G (R=0.8, p < 0.0001) conditions. [0059] Fig. 3D displays a Venn diagram summarizing the overlapping DEGs between single cell (SC; adj . p < 0.05, log2FC > |0.11) and bulk RNA-seq (Bulk; p < 0.05) simulated uG vs. 1 G. 269 up- regulated DEGs were identified and are shown on the left side of the Venn diagram, while 2043 down-regulated DEGs were identified and are shown on the right side of the Venn diagram. DEGs that are up-regulated in both datasets and DEGs that are down-regulated in both datasets are listed and identified.

[0060] Fig. 3E depicts a volcano plot of DEGs from flight (ISS 33 days) vs. ground mouse spleen Bulk RNA-sequencing (GLDS-420). Genes that are consistently up-regulated across singlecell human PBMCs and bulk mouse spleen RNA-seq (upper right quadrant) and genes that are consistently down-regulated across the two sets (upper left quadrant) are shown.

[0061] Fig. 3F depicts a Venn diagram summarizing the overlapping DEGs between human PBMCs single cell (SC; adj. p <0.05, log2FC > |0.1|) simulated uG vs. 1G and the mouse orthologous DEGs from flight vs. ground spleen bulk RNA-seq (GLDS420; p < 0.05). 325 up- regulated DEGs were identified and are shown on the left side of the Venn diagram, while 1398 down-regulated DEGs were identified and are shown on the right side of the Venn diagram. DEGs that are up-regulated in both datasets and DEGs that are down-regulated in both datasets are listed and identified.

[0062] Fig. 3G depicts a heatmap of overlapping DEGs between human PBMCs simulated uG vs 1G and the 14 mission post-flight (R+l) vs pre-flight (L-44) dataset. Both datasets are single-cell RNA-seq with DEGs defined by adj. p-value < 0.05 and log2FC > |0.1 |. Genes that are consistently up-regulated across single cell human PBMCs and 14 datasets are labeled with + symbols and high- intensity blocks. Genes that are consistently down-regulated across datasets lack + and are also high- intensity blocks. [0063] Fig. 3H depicts a heatmap of IP A canonical pathways enriched from DEGs between human PBMCs SC (single-cell RNA-seq uG vs 1G) and 14 (R+l vs L-44). Enriched pathways have adjusted p values < 0.05 (-logl O(adj p)>l .3). A + symbol indicates a predicted activation in pathways, whereas blocks lacking a + symbol indicate a predicted inhibition in pathways.

[0064] Fig. 31 depicts single-cell transcriptomic signature validation with 14 and JAXA6 datasets, displaying a pathway enrichment analysis of overlapped DEGs between human PBMCs(SC) and 14 data. The shared DEGs between SC uG vs 1G and 14 post-flight (R+l) vs preflight (L-44) were further filtered by their directionality, and 122 altered genes with the same directions between SC and 14 were used for IP A analysis.

[0065] Fig. 3 J depicts a volcano plot of overlapping DEGs from uG vs. 1G between human

PBMCs (single cell core 375 gene list) and JAXA6 dataset (cell-free RNA 30 days in-flight vs preflight). Genes that are consistently up-regulated across single-cell and bulk sequencing (upper right quadrant) and genes that are consistently down-regulated across single-cell and bulk RNA-seq (upper left quadrant) are shown. One gene of interest, Cdc42, the most significantly upregulated gene after 30 days in-flight is particularly noted in the upper right quadrant. Genes that are not consistent across the two datasets are not labeled.

[0066] Fig. 3K depicts single-cell transcriptomic signature validation employing data from the NASA Twins study, displaying overlapping DEGs from uG vs. 1G between human PBMCs and the Twins datasets for CD4, CD8, and CD 19 cell types. Both datasets are RNA-seq with DEGs defined by adjusted P-value <0.05 and log2FC>|0.11. P value for overlapping genes is calculated by Fisher Exact Test for gene overlap.

[0067] Fig. 3L depicts single-cell transcriptomic signature validation employing data from the NASA Twins study, displaying overlapping DEGs from uG vs. 1G between human PBMCs and the Twins datasets for lymphocyte-depleted (LD) cell type. Both datasets are RNA-seq with DEGs defined by adjusted P-value <0.05 and log2FC>|0.11. P value for overlapping genes is calculated by Fisher Exact Test for gene overlap.

[0068] Fig. 3M displays graphical results of super-resolution microscopy analysis of two- dimensional actin maximum intensity projection analysis of mean cell area (left), mean actin intensity (middle), and actin Punctate Diffuse Index (PDI, variance/mean, right) between 25 hours of 1G or simulated uG. Dots represent parameters of individual PBMCs from 4 donors. One outlier for actin intensity and actin PDI from each unstimulated group is removed based on Grubbs’ test (****p < 0.0001, ***p <0.001). Donors were male (25 years old) and females (35, 38 and 46 years old).

Fig. 3M also displays representative super-resolution microscopy images (2D left, 3D right) of PBMCs from 1G and simulated uG (25 hours) from two of the donors. 3D images better highlight changes to overall cell shape and actin protrusions in simulated uG. Scale bar = 2pm and 1 m respectively.

[0069] Fig. 3N depicts a sixteen channel granularity spectrum measurement of PBMCs stimulated with TLR7/8 agonist (9 hours stimulation, 16 hours conditioning prior to stimulation) from 1G and uG minus the corresponding unstimulated cells (25 hours total culture). The difference in unstimulated granularity spectrum is also plotted (n=3 donors tested from Fig. 3M; the 35-year- old female sample was not used; **p < 0.01, *p < 0.05).

[0070] Fig. 30 depicts a super-resolution microscopy analysis of three-dimensional actin surface area (left) and actin spike length (right) between 25 hours of 1G or simulated uG. Dots represent parameters of individual PBMCs from 4 donors. *p <0.05, **p < 0.01. Donors were male (25 years old), and females (35, 38 and 46 years old).

[0071] Fig. 3P depicts a cytoskeleton and mitochondrial assessment of PBMCs in simulated uG and 1G, showing representative super-resolution microscopy images of PBMCs from 1G and uG from all 4 donors assessed pursuant to Fig. 30. White arrows point to actin projections. Note border irregularity in some uG cells (black arrow with white outline). Scale bar=2um.

[0072] Fig. 3Q depicts two-dimensional mitochondrial maximum intensity projection analysis of mean MitoTracker Red intensity, mitochondrial Punctate Diffuse Index (PDI) (variance/mean), mitochondrial fiber length, mean mitochondrial area, and mitochondrial cell volume fraction between 25 hours of 1G or simulated uG. Dots represent parameters of individual PBMCs from the 4 donors from Fig. 30. **P < 0.01, *P < 0.05.

[0073] Fig. 3R depicts G-LISA levels of active GTP -bound Cdc42 in PBMCs either unstimulated (25 hours) or treated with TLR7/8 agonist (9 hours + 16 hours conditioning) from 1G vs. simulated uG (n=7, *p < 0.05, ***p <0.001). Donors were male (25 years old) and females (38, 46, 25, 27, 26 and 40 years old).

[0074] Fig. 3S depicts G-LISA levels of active GTP -bound Rael and RhoA in PBMCs from 1G and uG (total culture, 25 hours; n=7), and G-LISA levels of active GTP-bound Rael and RhoA in PBMCs treated with TLR7/8 agonist (9 hours) from 1G and uG (total culture, 25 hours; n=7). One outlier from 1G Stim is removed individually from Rael and RhoA based on Grubb’s (alpha=0.01) test.

[0075] Fig. 3T depicts ELISA levels of secreted IFNs by PBMCs treated with TLR7/8 agonist (9 hours + 16 hours conditioning) from 1G vs. simulated uG (n=9, *p < 0.05). Donors were male (36 years old), and females (33, 25, 38, 46, 27, 25, 26, and 40 years old). [0076] Fig. 3U depicts functional validation of the impact of simulated microgravity on overall immune cell cytokine production, showing ELISA validation results of cytokines IFN-a and IFN-y in supernatants from unstimulated PBMCs after 25 hours 1G or uG treatment (n=9, 1 male (36 yrs) and 8 females (33, 25, 38, 46, 27, 25, 26, and 40 yrs)).

[0077] Fig. 3V depicts Luminex assay on cytokines secreted by unstimulated and R848 stimulated (9 hours) PBMCs after 25 hours 1G or uG treatment. n=12, 3 males (36, 33, 26 yrs) and 9 females (32, 25, 38, 46, 25, 27, 26, 40, 33 yrs). ***P < 0.001 **P < 0.01, *P < 0.05.

[0078] Fig. 3W depicts ELISA validation results of cytokines IL-ip in unstimulated PBMCs after 25 hours 1G or uG treatment. n=9, 1 male (26 yrs) and 8 females (32, 25, 36, 46, 25, 27, 26, 40 yrs).

[0079] Fig. 3X depicts ELISA validation results of cytokines IL-ip in IL-ip, and IL-8, IL-6 in stimulated PBMCs (9 hours) after 25 hours 1G or uG treatment. n=9, 1 male (26 yrs) and 8 females (32, 25, 36, 46, 25, 27, 26, 40 yrs).

[0080] Fig. 3Y depicts gating strategy for flow cytometry immunophenotyping on PBMCs of 25-hour simulated microgravity treated with 9-hour R848 (luM) in the presence of 2.5ug/mL Brefeldin A (NK, Natural killer; CM, Central memory; EM, Effector memory, SCM, Stem cell memory; TEMRA, CD45RA+ T effector memory).

[0081] Fig. 3Z depicts flow cytometry analysis from PBMCs subjected to 1G (light bar) and simulated uG (dark bar) for 16 hours acclimation + 9 hours stimulation with R848 (luM) in the presence of 2.5ug/mL Brefeldin A (n=6, males (40, 42, 43 yrs), females (38, 43, 35 yrs)). The relative proportion of different monocytes were compared, as well as comparison in the relative expression of IL-1 P, IL-6, and HLA-DR in different monocyte subsets.

[0082] Fig. 3AA depicts a comparison of the relative expression of CD69, IFNy, and LAMP1 in CD56 dim CD 16 bright NK cells subjected to 1G (light bar) and simulated uG (dark bar) for 16 hours acclimation + 9 hours stimulation with R848 (luM) in the presence of 2.5ug/mL Brefeldin A (n=6, males (40, 42, 43 yrs), females (38, 43, 35 yrs)).

[0083] Fig. 3AB depicts the relative number of subsets of CD4+ and CD8+ T cells were compared, as well as the relative expression of CD69, HLA-DR, and Ki67 (*P < 0.05; PBMCs subjected to 1G (light bar) and simulated uG (dark bar)). P-values were generated from pair wise one-tailed t test comparisons.

[0084] Fig. 4A depicts a pipeline of microgravity and gene interacting compounds from discovery to validation. [0085] Fig. 4B depicts a heatmap of top 50 simulated uG altered gene to compound interaction candidates. Compounds are listed on the right, and the predicted interacting genes are listed at the bottom. The color indicates the STITCH confidence score for compound-gene interaction.

[0086] Fig. 4C demonstrates that quercetin reverses the core gene expression signatures in simulated uG. Log2FC levels of 106 core DEGs from simulated uG vs. 1G are plotted side-by-side to quercetin-treated uG vs. 1G in the heatmap. Positive symbols (+) indicates positive Log2FC, and lack of positive symbols indicates negative Log2FC. 70% of the genes are reversed after quercetin treatment. The scatter plot below shows a negative association (R = -0.35, p < 0.001) between the log2FC levels of the 106 core genes from simulated uG vs. 1G and quercetin-treated uG vs. 1G.

[0087] Fig. 4D depicts gene set enrichment analysis showing the reversal effect of quercetin on the 106 core DEGs plotted in the Fig. 4C heatmap. Quercetin treatment inverts the enrichment score (ES) in the up-regulated core genes (from 0.8 to -0.64) and increases the ES of the down-regulated core genes (from -0.75 to -0.55). All the p-values are < 0.0001.

[0088] Fig. 4E demonstrates that quercetin reduces senescence and age-associated inflammatory gene outputs. Both SenMayo scores and iAge index are reduced in the quercetin-treated group.

Compared with the untreated group, quercetin downregulates more senescence-related and age- associated inflammatory genes. n=6, *p < 0.05. Donors were 3 males (ages 37, 22, 32 years old) and 3 females (age 27, 26, 40 years old).

[0089] Fig. 4F demonstrates that quercetin (25 hours treatment) reduces ROS levels measured by 2’, 7’ -dichlorofluorescin diacetate (DCFDA) assay (n=6 for 1G vs uG, donors were males (32, 37 and 38 years old) and females (34, 32, 37 years old)). 34yrs and 32yrs female samples were not treated with quercetin, resulting in n=4 for comparisons between 1G vs IG+Quercetin and uG vs uG+ Quercetin. *p < 0.05.

[0090] Fig. 5A depicts a schematic for developing cardiomyocyte-only organoids (CM organoids) and organoids including co-cultured cardiomyocytes and endothelial cells (CMEC organoids) from healthy human donor pluripotent fibroblasts.

[0091] Fig. 5B depicts an experimental model for determining functional properties of and changes in functional characteristics of CM and CMEC organoids exposed to simulated microgravity in comparison to 1G control groups (video analysis).

[0092] Fig. 5C depicts graphs demonstrating visible physical changes (roundness, circularity, solidity, area, maximum diameter, and minimum diameter) from video light microscopy in CM and CMEC organoids exposed to simulated microgravity in comparison to 1G control groups. [0093] Fig. 5D displays contraction-relaxation cycles for CM organoids as measured by pixel intensity changes from video light microscopy comparing before (baseline) and after exposure to microgravity simulations (uG 24hr) or 1 G (1 G 24hr).

[0094] Fig. 5E displays contraction-relaxation cycles for CMEC organoids as measured by pixel intensity changes from video light microscopy comparing before (baseline) and after exposure to microgravity simulations (uG 24hr) or 1G (1G 24hr).

[0095] Fig. 5F depicts a chart comparing beats per minute (bpm) between before (baseline) and after exposure to 24hr microgravity simulation (uG 24hr) or 1G (1G 24hr) for both CMs and CMECs.

[0096] Fig. 5G depicts a chart comparing time from peak relaxation to peak contraction (seconds) between before (baseline) and after exposure to 24hr microgravity simulation (uG 24hr) or 1G (1G 24hr) for both CMs and CMECs.

[0097] Fig. 5H depicts a chart comparing time from peak contraction to peak relaxation (seconds) between before (baseline) and after exposure to 24hr microgravity simulation (uG 24hr) or 1G (1G 24hr) for both CMs and CMECs.

[0098] Fig. 51 depicts an experimental model for determining electrophysiological properties of CM and CMEC organoids exposed to simulated microgravity in comparison to 1G control groups (microelectrode assay analysis).

[0099] Fig. 5J depicts an electrophysiological analysis protocol carried out on both CM and CMEC organoids at baseline and after exposure to to 24hr microgravity simulation (uG 24hr) or 1G (lG 24hr).

[00100] Fig. 5K depicts charts measuring impedence to determine the contractility of CMEC organoids at baseline and the CMEC organoids 24 hr post-treatment (uG or 1G), and 48 hr 1G and 24 hr 1G following 24hr uG.

[00101] Fig. 5L depicts the action potential spike frequency for CMEC organoids as measured by pixel intensity changes from MEA plates comparing before (baseline) and after exposure (24hrs microgravity simulations (uG 24hr) or 1G (1G 24hr) and after exposure (24hrs microgravity simulations (uG 24hr) or 1G (1G 24hr) and then 24hrs following 24hr 1G (1G 48hr) or uG (uG 24hr + lG 24hr).

[00102] Fig. 6A depicts a chart comparing transcriptomics of both CM and CMEC organoids to human donor tissue derived from Gtex.

[00103] Fig. 6B depicts a volcano plot of differential expression for both CM and CMEC organoids exposed to 24hr simulated microgravity vs 1G controls. [00104] Fig. 6C shows an IPA comparison of differential expression between CMEC organoids exposed to 24hr simulated microgravity vs CMEC 1G controls, mapping the differential expression to various cardiac dysfunction signaling pathways.

[00105] Fig. 6D displays a heart transcriptomic age score chart developed using human donor tissues (405 sample donors, ranging from 20 to 70 years of age) to capture the transcriptomic changes to cardiac tissue associating with aging.

[00106] Fig. 6E depicts a chart of heart transcriptomic age scores for both CM and CMEC organoids exposed to 24 hours of simulated microgravity.

[00107] Fig. 6F depicts the results of a drug repurposing analysis identifying compounds and drugs for reversing (negative score) and mimicking (positive score) the transcriptomic signature associated with microgravity simulation in CMEC organoids.

[00108] Fig. 7A depicts a schematic for developing wild-type organoids including both cardiomyocytes and endothelial cells (CMEC organoids) and LMNA-mutant CMEC organoids from human donor pluripotent fibroblasts.

[00109] Fig. 7B displays the overlapping differentially expressed genes of both wild-type CMEC organoids exposed to 24hr simulated microgravity versus CMEC organoid 1G controls and LMNA- mutant CMEC organoids versus wild-type CMEC organoids.

[00110] Fig. 7C displays a chart demonstrating that the overlapping differentially expressed genes of both wild-type CMEC organoids exposed to 24hr simulated microgravity LMNA-mutant CMEC organoids map to cardiomyopathy and dilated cardiomyopathy human disease.

[00111] Fig. 8A displays charts comparing the effects on rhythmicity for both CM and CMEC organoids exposed to 24 hours of simulated microgravity against CM and CMEC organoids subjected to normal 1G gravity for 24 hours.

[00112] Fig. 8B displays graphs comparing beats per minute and contraction time for both CM and CMEC organoids exposed to 24 hours of simulated microgravity against CM and CMEC organoids subjected to normal 1G gravity for 24 hours.

[00113] Fig. 9A displays a chart showing the IPA z-score enrichment of simulated microgravity in cardiac organoids. Spot intensity reflects IPA z-score enrichment of stimulated vs unstimulated cardiac organoids under uG.

[00114] Fig. 9B depicts graphs demonstrating the increase in transcriptomic age of cardiac organoids subjected to simulated microgravity.

[00115] Fig. 10A depicts the effects of compounds proposed for reversal of microgravity-induced adverse effects on CMEC organoids with respect to rhythmicity. [00116] Fig. 10B depicts the effects of compounds proposed for reversal of microgravity-induced adverse effects on CMEC organoids with respect to beats per minute.

[00117] Fig. 1 1 A depicts an IPA over-representation analysis comparing differential expression between neural organoids exposed to 24hr simulated microgravity vs neural organoid 1G controls, mapping the differential expression to various gene signaling pathways. DE cutoff: padj < 0.05 and FC >= |1.2|.

[00118] Fig. 1 IB depicts an IPA comparison of differential expression between neural organoids exposed to 24hr simulated microgravity vs neural organoid 1G controls, mapping the differential expression to various gene signaling pathways.

[00119] Fig. 11C shows an IPA over-representation analysis comparing differential expression between neural organoids exposed to 24hr simulated microgravity vs neural organoid 1G controls, mapping the differential expression to various neural dysfunction signaling pathways.

[00120] Fig. 1 ID shows an IPA comparison of differential expression between neural organoids exposed to 24hr simulated microgravity vs neural organoid 1G controls, mapping the differential expression to various neural dysfunction signaling pathways.

[00121] Fig. 1 IE shows an IPA over-representation analysis comparing differential expression between neural organoids exposed to 24hr simulated microgravity vs neural organoid 1G controls, mapping the differential expression to various neural dysfunction signaling pathways.

[00122] Fig. 1 IF shows an IPA gene set enrichment analysis of differential expression between neural organoids exposed to 24hr simulated microgravity vs neural organoid 1G controls, mapping the differential expression to various neural dysfunction signaling pathways.

[00123] Fig. 12 displays a neural tissue transcriptomic age score chart developed using human donor tissues to capture the transcriptomic changes to neural tissue associating with aging.

DETAILED DESCRIPTION

[00124] The present disclosure demonstrates in part that mechanical forces are orchestrators of cellular function (e.g., immune cell function, neural function, cardiovascular function); that is, mechanotransduction affects cellular function including tuning of immune cell responsiveness to danger signals, brain morphology, and cardiovascular conditioning. Some of these effects occur through environmental modulation of mechanosensing pathways that alter ion currents in cells, metabolism, or directly act on the cytoskeleton. Changes in the physical environment modulate cell responses and may lead to the impairment or even failure of tissue function through altered mechanotransduction processes. Evidence suggests this phenomenon occurs in some age-related diseases and pathological conditions observed in space, such as immune dysfunction, neurological dysfunction, and cardiovascular deconditioning. For example, a spaceflight environment, which alters forces such as gravity, associated hydrostatic pressure, and shear forces in relation to immune cells, is shown to contribute to immune system dysfunction. In the present disclosure, exposure to short-term (e.g., 25 hours) low-shear modeled microgravity is shown to impact the human immune system in detail at single cell resolution. Combining this data with validation experiments from mice and crewmembers in LEO, as well as with machine learning algorithms, the inventors identified numerous core genes and pathways in immune cells that are altered by reduced gravity/simulated microgravity or spaceflight, and identified numerous potential compounds that directly map onto immune cell transcriptional signatures in reduced gravity/simulated microgravity.

[00125] The present disclosure relates to using single cell analysis of human cells (e.g., peripheral blood mononuclear cells/PBMCs) exposed to short term (e.g., 25 hours) simulated reduced gravity or microgravity to characterize altered genes and pathways across cells under basal and stimulated states with a Toll like Receptor-7/8 agonist. At basal state, simulated reduced gravity/microgravity was shown to have altered the transcriptional landscape across the exposed cells, with particular subsets of exposed cells showing the most pathway changes. Under stimulation in simulated reduced gravity/microgravity, nearly all exposed cells demonstrated differences in functional pathways. Results from single cell analysis were validated against additional cellular samples, including by RNA sequencing and super-resolution microscopy, and against data from the Inspiration-4 (14) mission, JAXA (Cell-Free Epigenome study) mission, Twins study, and spleens from mice housed on the international space station. The combined results show significant impacts of reduced gravity/microgravity on pathways essential for optimal immunity, including the cytoskeleton, interferon signaling, pyroptosis, temperature-shock, innate inflammation (e.g. Coronavirus pathogenesis pathway and IL-6 signaling), nuclear receptors, and sirtuin signaling pathways. The present disclosure further relates to the use of machine learning to identify numerous compounds linking exposure to reduced gravity/microgravity to cellular transcription, and further demonstrates that particular compounds (e.g., flavonoids such as quercetin) can reverse most abnormal pathways, thereby identifying countermeasures that can be used to maintain normal immunity in space and/or combat cellular aging, including inflammatory aging. The present disclosure also relates to administration of compounds to treat, normalize, or reverse cellular transformations or differential gene expression changes associated with aging (e.g., inflammatory aging, age-related diseases) and/or exposure to reduced gravity/microgravity (e.g., spaceflight). [00126] The present disclosure also investigates the role of endothelial cells in response to 24- hour rotating wall vessel simulated microgravity by comparing the function and transcriptome of iPSC-derived cardiomyocyte (CM) organoids with and without co-cultured endothelial cells (CMEC organoids). Simulated microgravity (smG) was shown to have altered the functional properties of CMECs more so than CMs. Results from RNA sequencing analysis were validated against random positioning machine smG and spaceflight smG.

[00127] The present disclosure also investigates the response of neural cells after exposure to simulated microgravity by comparing the function and transcriptome of the neural cells at baseline and under uG exposure (with 1G control).

[00128] The term “about” or “approximately” is used herein to provide literal support for the exact number that it precedes, as well as a number that is near to or approximately the number that the term precedes. In determining whether a number is near to or approximately a specifically recited number, the near or approximating unrecited number may be a number, which, in the context in which it is presented, provides the substantial equivalent of the specifically recited number. It should be appreciated that all numerical values and ranges disclosed herein are approximate values and ranges, whether “about” is used in conjunction therewith. It should also be appreciated that the term “about,” as used herein, in conjunction with a numeral refers to a value that may be ±0.01% (inclusive), ±0.1% (inclusive), ±0.5% (inclusive), ±1% (inclusive) of that numeral, ±2% (inclusive) of that numeral, ±3% (inclusive) of that numeral, ±5% (inclusive) of that numeral, ±10% (inclusive) of that numeral, or ±15% (inclusive) of that numeral. It should further be appreciated that when a numerical range is disclosed herein, any numerical value falling within the range is also specifically disclosed.

[00129] It will be appreciated by those skilled in the art that changes could be made to the exemplary embodiments shown and described above without departing from the broad inventive concepts thereof. It is to be understood that the embodiments and claims disclosed herein are not limited in their application to the details of construction and arrangement of the components set forth in the description and illustrated in the drawings. Rather, the description and the drawings provide examples of the embodiments envisioned. The embodiments and claims disclosed herein are further capable of other embodiments and of being practiced and carried out in various ways. [00130] Specific features of the exemplary embodiments may or may not be part of the claimed invention and various features of the disclosed embodiments may be combined. Unless specifically set forth herein, the terms “a”, “an” and “the” are not limited to one element but instead should be read as meaning “at least one”. Finally, unless specifically set forth herein, a disclosed or claimed method should not be limited to the performance of their steps in the order written, and one skilled in the art can readily appreciate that the steps may be performed in any practical order.

[00131] In one aspect, the present disclosure provides a method for simulating hallmarks of cellular aging, simulating changes in cellular physiology due to spaceflight, and/or causing changes in gene expression associated with cellular aging. In another aspect, the present disclosure provides a method for simulating cardiovascular aging, deconditioning, and/or dysfunction, and/or causing changes in gene expression associated therewith. In yet another aspect, the present disclosure provides a method for simulating neural aging and or dysfunction, and/or causing changes in gene expression associated therewith. The method includes exposing one or more cells, tissues, or organoids to simulated reduced gravity below 1G (e.g., microgravity).

[00132] In some embodiments, the changes in gene expression associated with cellular aging result in one or more of fibrosis, increase in cellular inflammation, increase in cytokine production, immunosenescence, cytoskeleton changes, increase in oxidative stress, and onset of mitochondrial dysfunction.

[00133] In some embodiments, the simulated reduced gravity below 1G is produced by a low- shear modeled microgravity rotating wall vessel apparatus. In some embodiments, the simulated reduced gravity below 1G is produced by a random positioning machine. In some embodiments, the simulated reduced gravity below 1G is produced by a 2D clinostat. In some embodiments, the simulated reduced gravity below 1G is produced by a 3D clinostat. In some embodiments, the simulated reduced gravity below 1G is produced by a magnetic levitation apparatus. In some embodiments, the simulated reduced gravity below 1G is produced by parabolic flight.

[00134] In some embodiments, the simulated reduced gravity is between 0G and 0.9999G. In some embodiments, the simulated reduced gravity is between 0G and 0.38G.

[00135] In some embodiments, the one or more cells, tissues, or organoids are exposed to simulated reduced gravity for at least 10 minutes. In some embodiments, the one or more cells, tissues, or organoids are exposed to simulated reduced gravity for at least 30 minutes. In some embodiments, the one or more cells, tissues, or organoids are exposed to simulated reduced gravity for at least 1 hour. In some embodiments, the one or more cells, tissues, or organoids are exposed to simulated reduced gravity for at least 5 hours. In some embodiments, the one or more cells, tissues, or organoids are exposed to simulated reduced gravity for at least 10 hours. In some embodiments, the one or more cells, tissues, or organoids are exposed to simulated reduced gravity for at least 15 hours. In some embodiments, the one or more cells, tissues, or organoids are exposed to simulated reduced gravity for at least 20 hours. In some embodiments, the one or more cells, tissues, or organoids are exposed to simulated reduced gravity for at least 24 hours. In some embodiments, the one or more cells, tissues, or organoids are exposed to simulated reduced gravity between 10 minutes and 30 hours. In some embodiments, the one or more cells, tissues, or organoids are exposed to simulated reduced gravity between 30 minutes and 25 hours. In some embodiments, the one or more cells, tissues, or organoids are exposed to simulated reduced gravity between 1 hour and 20 hours.

[00136] In some embodiments, the changes in cellular physiology due to spaceflight and/or the hallmarks of cellular aging include one or more of cellular senescence, onset of fibrosis, increases in inflammatory aging processes, increases in cytokine production, onset of immunosenescence, cytoskeletal changes, increase in oxidative stress, and mitochondrial dysfunctions.

[00137] In some embodiments, the one or more cells are immune cells.

[00138] In some embodiments, the step of exposing the one or more cells to simulated reduced gravity induces physiological changes in the one or more cells.

[00139] In some embodiments, the physiological changes include one or more changes to cellular function pathways. In some embodiments, the one or more changes to cellular function pathways include changes to the cytoskeleton of the one or more cells, changes in interferon signaling pathways within the one or more cells, changes in pyroptosis pathways, changes in temperatureshock response pathways, changes in innate inflammation pathways, changes in nuclear receptor functionality, changes in proteostasis, and changes in sirtuin signaling. In further embodiments, the changes in inflammation pathways include one or both of changes to IL-6 signaling and changes to Coronavirus pathogenesis pathways.

[00140] In some embodiments, the physiological changes may include one or more of changes in cellular function, changes in cellular structure, and changes in molecular content of the one or more cells. In some embodiments, the changes in cellular function include immune dysfunction. In some embodiments, the physiological changes comprise differential expressions of genes and/or pathways. In some embodiments, the differential expressions of genes and/or pathways include induction of genetic expression in one or more of: acute immune response genes, heat shock genes, chemokine genes, iron storage genes, matrix metalloproteinase genes, cytokine genes, proteostasis genes, and hypoxia genes. In some embodiments, the differential expressions of genes and/or pathways include reduction of genetic expression in one or more of: interferon response genes, guanylate binding protein genes, cold shock genes, and nuclear receptor genes. In some embodiments, the differential expressions of genes and/or pathways include one or more of: reduction in operation of oxidative phosphorylation pathway, reduction in interferon signaling pathways, reduction in nuclear receptor signaling pathways, reduction in pyroptosis signaling pathways, increase in heat shock protein signaling pathways, increase in fibrosis signaling pathways, increase in actin-based motility pathways, increase in RAC and/or CDC42 GTPase protein signaling pathways, increase in focal adhesion kinase (FAK) signaling pathways, increase in HIF1 signaling pathways, increase in acute immune phase response pathways, increase in oxidative stress signaling pathways, increase in sirtuin signaling pathways, increase in unfolded protein response signaling pathways, and increase in EIF2 signaling pathways.

[00141] In another aspect of the invention, the present disclosure provides a method of identifying cellular transformations associated with aging, aging hallmarks and/or spaceflight. The method includes: analyzing, with one or more omics, a first set of one or more cells of a cellular population, wherein the first set of one or more cells was subjected to simulated reduced gravity of less than 1G, to obtain a first set of data for a reduced gravity omics profile; and analyzing, with the one or more omics, a second set of one or more cells of the same cellular population, wherein the second set of one or more cells was subjected to normal gravity (1G), to obtain a second set of data for a normal gravity omics profile. The method further includes comparing the first set of data with the second set of data to identify differences in omics profiles, gene expression, and cellular pathway expression between the first set of one or more cells subjected to simulated reduced gravity and the second set of one or more cells subjected to normal gravity (1 G). In some embodiments, the cellular population is immune cells.

[00142] In some embodiments, the steps of analyzing the first set of one or more cells and the second set of one or more cells includes analysis with transcriptomics.

[00143] In some embodiments, the identified differences in gene expression and cellular pathway expression between the first set of one or more cells subjected to simulated reduced gravity and the second set of one or more cells subjected to normal gravity (1G) include one or more of: differences in cellular function, differences in cellular structure, and differences in cellular molecular content. [00144] In some embodiments, the method further includes identifying immune dysfunction in the first set of one or more cells as compared to the second set of one or more cells.

[00145] In some embodiments, the method further includes the step of linking the differences in cellular function, differences in cellular structure, and/or differences in cellular molecular content with genes responsible for the differences by applying cross-validated machine learning (ML) to the first set of data and the second set of data.

[00146] In some embodiments, the method further includes the step of identifying differential expressions of genes and/or pathways between the first set of one or more cells and the second set of one or more cells. In further embodiments, the method includes the step of identifying induction of genetic expression in the first set of one or more cells as compared to the second set of one or more cells, wherein the induction of genetic expression is in one or more of: acute immune response genes, heat shock genes, chemokine genes, iron storage genes, matrix metalloproteinases, and cytokine genes. In further embodiments, the method includes the step of identifying reduction of genetic expression in the first set of one or more cells as compared to the second set of one or more cells, wherein the reduction of genetic expression is in one or more of: interferon response genes, guanylate binding protein genes, and cold shock genes. In further embodiments, the method comprises identifying one or more of: reduction in operation of oxidative phosphorylation pathways in the first set of one or more cells in comparison to the second set of one or more cells; reduction in interferon signaling pathways in the first set of one or more cells in comparison to the second set of one or more cells; reduction in nuclear receptor signaling pathways in the first set of one or more cells in comparison to the second set of one or more cells; reduction in RHOA GTPase protein signaling pathways in the first set of one or more cells in comparison to the second set of one or more cells; reduction in pyroptosis signaling pathways in the first set of one or more cells in comparison to the second set of one or more cells; increase in heat shock protein signaling pathways in the first set of one or more cells in comparison to the second set of one or more cells; increase in fibrosis signaling pathways in the first set of one or more cells in comparison to the second set of one or more cells; increase in actin-based motility pathways in the first set of one or more cells in comparison to the second set of one or more cells; increase in RAC and/or CDC42 GTPase protein signaling pathways in the first set of one or more cells in comparison to the second set of one or more cells; increase in focal adhesion kinase (FAK) signaling pathways in the first set of one or more cells in comparison to the second set of one or more cells; increase in HIF1 signaling pathways in the first set of one or more cells in comparison to the second set of one or more cells; increase in acute immune phase response pathways in the first set of one or more cells in comparison to the second set of one or more cells; increase in oxidative stress signaling pathways in the first set of one or more cells in comparison to the second set of one or more cells; and increase in sirtuin signaling pathways in the first set of one or more cells in comparison to the second set of one or more cells. [00147] In some embodiments, the method further includes the steps of: stimulating the first set of one or more cells subjected to simulated reduced gravity with an immunogen prior to analyzing the first set of one or more cells with the one or more omics; and stimulating the second set of one or more cells subjected to normal gravity (1G) with the immunogen prior to analyzing the second set of one or more cells with the one or more omics. In some embodiments, the immunogen is a toll-like receptor (TLR) agonist. In further embodiments, the TLR agonist is a TLR 7/8 agonist.

[00148] In another inventive aspect, the present disclosure provides a method for identifying a compound useful for treatment, normalization, or reversal of cellular transformations and/or differential gene expression associated with aging, inflammatory aging, aging hallmarks, spaceflight, inflammation, fibrosis, cytokine production, immunosenescence, cytoskeletal abnormalities, oxidative stress, mitochondrial dysfunction, and/or cellular senescence processes or with physiological changes induced by spaceflight. The method includes the steps of assessing interactions between genes altered by simulated reduced gravity and compounds using compoundgene interactome machine learning (ML), and identifying at least one compound that interacts with one or more of the genes altered by simulated reduced gravity using the compound-gene interactome machine learning (ML).

[00149] In some embodiments, the at least one compound is a bioactive molecule derived from food. In some embodiments, the at least one compound is an active pharmaceutical ingredient. [00150] In some embodiments, the at least one compound is a flavonoid. In further embodiments, the at least one compound is a flavonol. In further embodiments, the flavonoid is quercetin.

[00151] In some embodiments, the differential gene expression and the cellular transformations associated with aging, inflammatory aging, aging hallmarks, and/or cellular senescence processes are correlated with at least one of a factor in causing an age-related disease and a biomarker of an age-related disease. In further embodiments, the age-related disease is one or more of cardiovascular disease, neurodegenerative disease, inflammation, stroke/ischemia, sarcopenia, and autoimmune disease. In further embodiments, the age-related disease is a fibrotic disease. In still further embodiments, the fibrotic disease is one or more of cirrhosis, non-alcoholic steatohepatitis, and pulmonary fibrosis.

[00152] In another inventive aspect, the present disclosure provides a method for treating, normalizing, and/or reversing cellular transformations of one or more cells exposed to reduced gravity under 1G. The method includes identifying least one compound that interacts with genes altered by cellular exposure to reduced gravity under 1G, and administering the at least one compound to a patient in need thereof.

[00153] In some embodiments, the compound is a flavonoid. In further embodiments, the flavonoid is quercetin. [00154] In some embodiments, the genes altered by cellular exposure to reduced gravity include one or more of RBM3, CIRBP, HNRNPH1, and MMP9.

[00155] In another inventive aspect, the present disclosure provides a method for treating, normalizing, and/or reversing cellular transformations associated with an aging hallmark and/or an age-related disease. The method includes identifying least one compound that interacts with genes altered by cellular exposure to reduced gravity under 1G, and administering the at least one compound to a patient in need thereof.

[00156] In some embodiments, the compound is a flavonoid. In further embodiments, the flavonoid is quercetin.

[00157] In some embodiments, the genes altered by cellular exposure to reduced gravity include one or more of RBM3, CIRBP, HNRNPH1, and MMP9.

[00158] In some embodiments, the age-related disease is one or more of cardiovascular disease, neurodegenerative disease, inflammation, stroke/ischemia, sarcopenia, and autoimmune disease. [00159] In another inventive aspect, the present disclosure provides a method for treating, normalizing, or reversing cellular transformations correlated with gene expression change associated with aging hallmarks, age-related disease, and/or exposure to reduced gravity under 1G. The method includes identifying least one compound that interacts with genes altered by exposure to reduced gravity under 1G, and administering the at least one compound to a patient in need thereof.

[00160] In some embodiments, the compound is a flavonoid. In further embodiments, the flavonoid is quercetin.

[00161] In some embodiments, the genes altered by cellular exposure to reduced gravity include one or more of RBM3, CIRBP, HNRNPH1, and MMP9

[00162] Numerous core pathways and genes altered across human immune cells in simulated microgravity have been described herein, with validation against datasets of humans in LEO, as well as spleens from mice flow on the ISS. Overall, changes consistent with basal innate immune cell inflammatory changes in simulated microgravity have been noted, coupled to distinct pathways of dysfunction in multiple immune cells. Specifically, the most consistently reproduced pathways impacted by simulated microgravity across immune cells in both single cell and validation cohorts included changes to pathways and signaling linked to acute phase response signaling, Coronavirus pathogenesis, IL-6 signaling, the cytoskeleton, interferon response, pyroptosis, heat-shock, nuclear receptors, and sirtuin biology.

[00163] The link between simulated microgravity and the cytoskeleton (and other pathways) demonstrated herein may be especially relevant in immune dysfunction and inflammatory aging. Cytoskeleton dynamics are controlled by a number of factors, but small GTPases, including Ras homology (RHO) GTPases, are major orchestrators with critical impact on immune cell function, migration, gene expression, trafficking, phagocytosis, proliferation, and antigen recognition. Of note, RHO GTPases have been implicated in response to simulated microgravity in other cell types, but this connection is understudied in immune cells. Across most datasets described herein, changes to RHO GTPase signaling were seen, including individually in RAC, RHOA, or CDC42 signaling, or combined in a global “regulation of actin-based motility by Rho” pathway in IPA. While some variability between initial unstimulated vs stimulated single cell data in these pathways was observed, these pathways tended to show reduced RHOA signaling without stimulation, coupled to increased RAC signaling, analogous to what was observed from the 14 crew members upon landing. The JAXA dataset also demonstrated cdc42 to be the most significantly induced cell free transcript in astronauts after 30 days in space. Pathways strongly linked to cytoskeletal remodeling, such as leukocyte extravasation, were also typically induced in most of the datasets described herein. There were also changes in some active RHO GTPases observed by G-LISA, as well as in F-actin granularity, variance, 3D surface area, sphericity, actin protrusion length, and dynamic change to TLR stimulation observed by super-resolution microscopy, providing further evidence for changes in actin, including possibly immune cytoskeleton alteration or dysfunction, in simulated microgravity.

[00164J Importantly, changes to the actin cytoskeleton are now being linked to the ability of an immune cell to mount an interferon response. Indeed, danger sensing molecules like TLRs utilize Rho GTPases to facilitate IFN responses, or antiviral sensors can directly modulate actin rearrangement. One example is the PKR antiviral response, which was consistently downregulated by simulated microgravity in the datasets described herein. In this system, PKR binds gelsolin to enforce basal innate immune defense, though upon viral sensing, PKR dissociates from gelsolin, leading to severing of actin, and activation of RIG-I-like receptor (RLR)s signaling and interferon response. Other antiviral sensors like RIG-I directly bind F-actin in resting cells, and then relocalize to the mitochondria via actin rearrangements on viral infection, to induce type 1 IFN. In single cell data, reduced interferon signaling without stimulation was seen mainly in monocytes, linking it to innate immunity, though with TLR7/8 stimulation, reduced interferon signaling was seen across many cells, including most T cell subsets, and NK cells, displaying the broad importance of this pathway across most immune cells to microgravity. In simulated microgravity, reduced IFNa production by ELISA with stimulation was observed, and so the possibility cannot be ruled out that the reduced IFN signaling seen in simulated microgravity starts with reduced capacity for IFN production in some conditions, in addition to potential defects in downstream signaling itself. While a focus was placed on type 1 IFN signaling herein, some reduced interferon responses are also linked to reduced signaling from the IFN gamma receptor (IFNGR). Consistently, reduced IFNy production in simulated microgravity upon TLR7/8 stimulation was also noted. Whether the cytoskeleton is needed for IFNGR clustering and signaling remains to be seen.

[00165] Consistent with reduced interferon signaling in simulated microgravity, a reduction in some IFN-inducible GTPase superfamily genes, namely guanylate binding proteins (GBPs) across the datasets described herein was noted. Various GBPs (e.g. gbp5) were reduced as well in the Twins study. Interestingly, GBPs, which are heavily induced by fFNy signaling, have been shown to be critical in maintaining responses to mycobacterium tuberculosis, and reactivation of similar bacteria (in addition to some retroviruses) in simulated microgravity was noticed after as little as 25 hours of exposure. GBPs and associated IFN responses also help direct inflammasome activation and pyroptosis (an inflammatory form of cell death) linked to antimicrobial defense that was consistently down in monocytes and B cells in simulated microgravity, and in nearly all immune cells in response to TLR7/8 stimulation in simulated microgravity. Interestingly, pyroptosis and inflammasome activation can also be directly controlled by Rho-GTPases and the cytoskeleton. [00166] Another pathway found consistently down across datasets included LXR signaling. Interestingly, LXR signaling also can promote antimicrobial defense mechanisms. Macrophage LXR has been shown to reduce bacterial infection by reducing intracellular NAD+ in a CD38 manner, with mechanistic impacts on the cytoskeleton. Whether NAD+ levels fall in microgravity remains to be seen, though an interesting increase in sirtuin signaling across datasets, including in the 14 mission, was observed. Sirtuins may be functioning to counter acute oxidative stress in microgravity. Reduced oxidative phosphorylation transcriptional signatures across all unstimulated immune cells in simulated microgravity was also seen. Altered metabolite levels (and possibly ROS) from impaired oxidative phosphorylation might also contribute to HIF-la stabilization as observed in some of the simulated microgravity and spaceflight datasets set forth herein. Reduced oxidative phosphorylation may also skew immune cells to glycolysis, fueling “Ml-like” pro-inflammatory changes in macrophages, potentiating NF-Kb signaling, acute responses and IL-6 release, another cytokine frequently induced in microgravity. Consistently, a preferential enrichment of predicted “macrophage classical activation” signatures across the gene sets in the Twins’ study was noted. [00167] Interestingly, frequent increases in heat shock genes, coupled to increased associated BAG signaling pathways across antigen presenting cells (monocytes, B cells, and DCs) were observed, as well as in double negative T cells. Heat shock expression may be reflective of altered proteostasis in simulated microgravity, and may be required for adaptation to mechanical unloading in some cells, though this may also be linked to higher temperatures. Across all gene sets, a reduction in the cold shock gene, rbm3 was observed, which was reduced in nearly all immune cells in the single cell data disclosed herein. Increased heat shock coupled with reduced cold shock genes raises the possibility of higher intracellular temperatures directly induced by microgravity, but whether microgravity, or associated increase in cytokines or binding partners such as IL-lra, directly induce the observed “space fever” in astronauts requires further insight. Interestingly, a number of significant IL-1 ligands in innate cell to T cell interactions was noticed in the microgravity Interactome disclosed herein, highlighting the possible importance of this cytokine family and downstream interacting molecules.

[00168] Pertaining to the aforementioned skin lesions in astronauts, it has been postulated that persistent skin hypersensitivity reactions in some crew members may be linked to allergic responses. While analysis of the datasets disclosed herein cannot rule out this possibility, no increased Th2 signatures were observed across the simulated microgravity systems, or with the specific gene-sets validated across the 14 mission. There were also inconsistent changes to IL-23 and IL-17 signaling across these datasets, though these cytokines are known contributors to skin disease. Interestingly, reduced aryl hydrocarbon receptor (AHR) signaling was observed in most of the datasets disclosed herein, especially in CD14 monocytes and conventional type 2 dendritic cells, raising the idea of reduced AHR signaling in space to contribute to skin lesions. However, AHR signaling was enriched in the Twins’ study gene set result and so more experimentation is needed to tease out a possible role for the AHR in astronaut skin lesions.

[00169] Using machine learning algorithms, numerous compounds mapping to microgravity’s transcriptional response to the immune system were identified as disclosed herein. This algorithm focuses on the strength of interaction and does not specify direction. However, one of the most significantly interacting compounds, the flavonol quercetin, was tested for its ability to reverse transcriptional changes to simulated microgravity on the immune system, and it was found that quercetin could reverse approximately 70% of altered core genes. Of note, quercetin reversed numerous pathways, including core pathways such as reduced nuclear receptor activation, sirtuin signaling, Coronavirus pathogenesis pathway, associated acute phase responses and IL-6 signaling. Quercetin also showed impact on the cytoskeleton, favoring a freezing of pathways linked to its mobility in microgravity, by reducing genes associated with Rho GTPase signaling (e.g. reducing RAC, RHOA and CDC42 signaling), and boosting RHOGDI signaling. Despite these changes, quercetin was unable to revert the core immunosuppression pathway of reduced interferon responses. However, since actin skeleton mobility is needed to induce an IFN response in many instances, it is believed too much interference could contribute to a persistent lack of IFN signaling here, and might represent a novel mechanism of immune suppression mediated by quercetin that requires more study. Interestingly, after 25 hours in simulated microgravity, variable results on the induction of senescence pathways were observed, though quercetin markedly reduced senescence associated transcripts in this data. Thus, while quercetin may also be acting in part through its senolytic mechanisms, the enormous breadth of additional other pathways suggests multiple beneficial modes of activity for immune modulation in microgravity.

[00170] The data disclosed herein supports a model where microgravity alters forces sensed by immune cells, leading to changes in the actin cytoskeleton, and nuclear receptor signaling, coupled to changes in core pathways in space such as mitochondrial dysfunction and oxidative stress. Recent work in other cells, such as endothelial cells, has identified cytoskeletal abnormalities as a key feature of simulated microgravity that drives autophagy and a reduction in mitochondrial mass after 72 hours of exposure. The datasets disclosed herein would support some of these findings.

Combined, these pathways would contribute to reduced oxidative phosphorylation and associated basal inflammatory processes, as well as reduced viral sensing pathways, associated reduced interferon responses and altered pyroptosis capability. Reduced interferon responses and signaling, impact both innate cells like monocytes and NK cells, as well as adaptive cells like T cells. Such changes could cumulate in viral or mycobacterial reactivation in microgravity. These processes would also be complemented by the psychological and physiological stresses of spaceflight, which also may independently associate with viral reactivation.

[00171] In an aspect of the invention herein, the present disclosure provides a method for simulating hallmarks of cardiovascular aging, simulating changes in cardiac cellular physiology due to spaceflight, and/or modeling cardiomyopathy. The method includes exposing one or more cardiac cells to simulated reduced gravity below 1G. The one or more cardiac cells may be part of cardiac tissues and/or organoids.

[00172] In some embodiments of the method, the one or more cardiac cells include one or both of cardiomyocytes and endothelial cells. In further embodiments, the one or more cardiac cells are present in one or more organoids. In particular embodiments, the one or more organoids include cardiomyocytes. In still further embodiments, the one or more organoids including cardiomyocytes also include co-cultured endothelial cells (e.g., CMEC organoids). In some embodiments of the method, the one or more organoids is a CMEC organoid and/or a CM organoid.

[00173] In some embodiments of the method, the cardiomyopathy is dilated cardiomyopathy. [00174] In some embodiments, the hallmarks of cardiovascular aging, the changes in cardiac cellular physiology due to spaceflight, and/or the cardiomyopathy modeling result in one or more changes in gene expression. In particular, the one or more changes in gene expression result in one or more of fibrosis, changes in expression of hypertrophy -related genes, increase in cytokine production, cytoskeleton changes, increase in oxidative stress, and onset of mitochondrial dysfunction.

[00175] In some embodiments of the methods set forth herein, the simulated reduced gravity below 1G is produced by a low-shear modeled microgravity rotating wall vessel apparatus, a random positioning machine, a 2D clinostat, a 3D clinostat, parabolic flight, and/or a magnetic levitation apparatus.

[00176] In some embodiments, the simulated reduced gravity is between 0G and 0.9999G. In further embodiments, the simulated reduced gravity is between 0G and 0.38G.

[00177] In some embodiments, the one or more cardiac cells, tissues, or organoids are exposed to simulated reduced gravity for at least 10 minutes. In some embodiments, the one or more cells, tissues, or organoids are exposed to simulated reduced gravity for at least 30 minutes. In some embodiments, the one or more cardiac cells, tissues, or organoids are exposed to simulated reduced gravity for at least 1 hour. In some embodiments, the one or more cardiac cells, tissues, or organoids are exposed to simulated reduced gravity for at least 5 hours. In some embodiments, the one or more cardiac cells, tissues, or organoids are exposed to simulated reduced gravity for at least 10 hours. In some embodiments, the one or more cardiac cells, tissues, or organoids are exposed to simulated reduced gravity for at least 15 hours. In some embodiments, the one or more cardiac cells, tissues, or organoids are exposed to simulated reduced gravity for at least 20 hours. In some embodiments, the one or more cardiac cells, tissues, or organoids are exposed to simulated reduced gravity for at least 24 hours. In some embodiments, the one or more cardiac cells, tissues, or organoids are exposed to simulated reduced gravity between 10 minutes and 30 hours. In some embodiments, the one or more cardiac cells, tissues, or organoids are exposed to simulated reduced gravity between 30 minutes and 25 hours. In some embodiments, the one or more cardiac cells, tissues, or organoids are exposed to simulated reduced gravity between 1 hour and 20 hours.

[00178] In some embodiments, the step of exposing the one or more cardiac cells to simulated reduced gravity induces physiological changes in the one or more cardiac cells. In particular embodiments, the physiological changes comprise one or more of: changes in cardiac cellular function, changes in cardiac cellular structure, and changes in molecular content of the one or more cardiac cells. In particular embodiments, the changes in cardiac cellular function include: changes to contraction-relaxation cycles, changes to beats per minute, changes to contraction time, changes to relaxation time, changes in rhythmicity, changes in action potential transduction, changes in cardiac development, changes in ion flux and handling, changes in ejection fraction, changes in contraction force, changes in beat rate variability, and/or changes in cardiac response to stress. In further embodiments, the changes in ion flux and handling include one or more of changes to calcium ion flux and/or handling, potassium ion flux and/or handling, and sodium ion flux and/or handling. [00179] In particular embodiments, the physiological changes induced by exposure of the one or more cardiac cells to simulated reduced gravity comprise differential expressions of genes and/or pathways. In some embodiments, the differential expressions in genes and/or pathways can include upregulation or induction of genetic expression in one or more of: telomerase RNA localization genes (e.g, CCT2, CCT3, CCT5, CCT7, NOPIO, RUVBL1, TCP1); chaperone-mediated protein folding and assembly genes (e.g, CCT2, CCT3, CCT5, CCT7, CHORDCI, CLU, FKBP4, HSPA8, HSPA9, HSPE1, HSPH1, PPID, ST I 3, TCP1, UNC45B); genes involved in regulation of protein and RNA localization to the Cajal body (e.g, CCT2, CCT3, CCT5, CCT7, NOPIO, RUVBL1, TCP1); genes involved in the regulation of response to DNA damage (e.g, BAZ1B, BRD7, CCDC1 17, CD44, CLU, DHX9, DTX3L, EYA3, HMGA2, MAP3K20, MSX1, NSD2, PARP1, PIAS4, PPP1R10, PPP4R3B, RAD52, RUVBL1, SF3B3, SNAI2, SPRED2, TIGAR, TIMELESS, TRIP12, TTI1); genes involved in regulation of intrinsic apoptosis (e.g, AEN, AIFM1, CD44, CLU, CYP1B1, DAB2IP, DNAJA1, EDA2R, FLCN, HINT1, HM0X1, MSX1, PARP1, PIAS4, PPIF, PPM1F, PTGS2, RRN3, SNAI2, SOD1, UBB, USP28); and/or genes involved in telomere maintenance via telomere lengthening (e.g, CCT2, CCT3, CCT5, CCT7, GNL3L, HSP90AA1, HSP90AB1, NOPIO, PARP1, TCP1). In some embodiments, the differential expressions in genes and/or pathways can include downregulation or reduction of genetic expression in one or more of: genes involved in extracellular matrix organization (e.g, ADAMTS13, ADAMTS14, ADAMTS15, ADAMTS17, COL11A1, COL11A2, COL12A1, COL15A1, COL16A1, COL18A1, COL1A1, COL1A2, COL22A1, COL23A1, COL27A1, COL2A1, COL3A1, COL4A1, COL4A2, COL4A5, COL4A6, COL5A1, COL6A6, COL9A1, COL9A2, COL9A3, COLGALT2, MMP11, MMP14, MMP15, MMP16, MMP2, MYH11); heart contraction (e.g, ACE, ACE2, ACTC1, ADM, ADM2, ADORA1, ADRB1, AGT, ATP1A2, ATP1B2, ATP2A1, ATP2A2, ATP2A3, ATP2B2, ATP2B4, BINI, CACNA1C, CACNA1D, CASQ2, DES, DMD, DMPK, DRD2, MYBPC3, MYH6, MYH7, MYL3); genes involved with cell-junction assembly (e.g, ABL1, ADAMTS13, ADAMTS14, ADAMTS15, ADAMTS17, ADAMTS2, ADAMTS3, ADAMTS4, ADAMTS7, ADAMTS8, ADAMTS9, ADAMTSL1, ADAMTSL2, ADAMTSL3, ADAMTSL4, AEBP1, AGT, ANTXR1, ATXN1L, B4GALT1, BCL3, BMP2, CCDC80, C0L11A1, COL11A2, COL12A1, COL15A1, COL16A1, COL18A1, COL1A1, COL1A2, COL22A1, COL23A1, COL27A1, COL2A1, COL3A1, COL4A1, COL4A2, COL4A5, COL4A6, COL5A1 , COL6A6, COL9A1, COL9A2, COL9A3, COLGALT2, LAMC1, LOX, LOXL1, LOXL2, LOXL3, LRP1, LTBP3, MELTF, MFAP4, MMP1 1, MMP14, MMP15, MMP16, MMP2, MYH11, NIDI, NID2); genes associated with actin filament organization (e.g., AB 12, ABL1, ACTA1, ACTC1, ACTG1, ACTN1, ADD1, ADD2, AIF1L, AR AP I , ARHGAP17, ARHGAP25, ARHGAP35, ARHGAP6, ARHGEF10L, ARHGEF18, ARPIN, ARRB1, MTSS1, MY ADM, MY01C, MYOID, MY05B, MY05C, MY07B); genes associated with cardiac chamber development and/or morphogenesis (e.g., ACVR1, APLNR, BMP2, BMP4, BMP7, BMPR2, COL11A1, DAND5, DHRS3, EDNRA, ENG, FGFRL1, FOXCI, FZD1, FZD2, GATA4, GATA6, HEG1, HEY2, HEYL, IGF1R, MAML1, MYBPC3, MYH6, MYH7, MYL3, NDST1, NKX25, NOTCH2, NPRL3, NRP1, NRP2, NSD2, PARVA, PLXND1, POU4F1, PPP1R13L, PTK7, ROBO1, RYR2, SCN5A, SFRP2, SHOX2, SLIT2, SLIT3, SMAD6, SMAD7, SMO, SNX17, SOX4, SRF, SUFU, TAB1, TBX20, TBX5, TGFB1, TGFBR2, TGFBR3, TNNT2, TP53, WNT5A, ZFPM1); and/or cardiac conduction genes (e.g., ACE2, AGT, ATP1A2, ATP1B2, ATP2A1, ATP2A2, ATP2A3, ATP2B2, ATP2B4, BINI, CACNA1C, CACNA1D, CASQ2, EHD3, GJC1, GJD3, HCN1, HCN2, HCN4, JUP, KCND3, KCNH2, KCNJ5, KCNN2, KCNQ1, NKX25, PRKACA, RNF207, RYR2, SCN1B, SCN5A, SLC4A3, SLC9A1, SPTBN4, TBX5, TRPM4).

[00180] In some embodiments, the differential expressions of genes and/or pathways include changes in expression of one or more of telomerase RNA localization genes (e.g., CCT2, CCT3, CCT5, CCT7, NOPIO, RUVBL1 , TCP1); chaperone-mediated protein folding and assembly genes (e.g., CCT2, CCT3, CCT5, CCT7, CHORDCI, CLU, FKBP4, HSPA8, HSPA9, HSPE1, HSPH1, PPID, STB, TCP1, UNC45B); genes involved in regulation of protein and RNA localization to the Cajal body (e.g., CCT2, CCT3, CCT5, CCT7, NOPIO, RUVBL1, TCP1); genes involved in the regulation of response to DNA damage (e.g., BAZ1B, BRD7, CCDC117, CD44, CLU, DHX9, DTX3L, EYA3, HMGA2, MAP3K20, MSX1, NSD2, PARP1, PIAS4, PPP1R10, PPP4R3B, RAD52, RUVBL1, SF3B3, SNAI2, SPRED2, TIGAR, TIMELESS, TRIP12, TTI1); genes involved in regulation of intrinsic apoptosis (e g., AEN, AIFM1, CD44, CLU, CYP1B1, DAB2IP, DNAJA1, EDA2R, FLCN, HINT1, HM0X1, MSX1, PARP1, PIAS4, PPIF, PPM1F, PTGS2, RRN3, SNAI2, SOD1, UBB, USP28); genes involved in telomere maintenance via telomere lengthening (e.g., CCT2, CCT3, CCT5, CCT7, GNL3L, HSP90AA1, HSP90AB1, NOPIO, PARP1, TCP1); genes involved in extracellular matrix organization (e.g., ADAMTS13, ADAMTS14, ADAMTS15, ADAMTS17, C0L11A1, COL11A2, COL12A1, COL15A1, COL16A1, COL18A1, COL1A1, COL1A2, COL22A1, COL23A1, COL27A1, COL2A1, COL3A1, COL4A1, COL4A2, COL4A5, COL4A6, COL5A1, COL6A6, COL9A1, COL9A2, COL9A3, COLGALT2, MMP1 1, MMP14, MMP15, MMP16, MMP2, MYH11); heart contraction (e.g., ACE, ACE2, ACTC1, ADM, ADM2, AD0RA1, ADRB1, AGT, ATP1A2, ATP1B2, ATP2A1, ATP2A2, ATP2A3, ATP2B2, ATP2B4, BINI, CACNA1C, CACNA1D, CASQ2, DES, DMD, DMPK, DRD2, MYBPC3, MYH6, MYH7, MYL3); genes involved with cell-junction assembly (e.g., ABL1, ADAMTS13, ADAMTS14, ADAMTS15, ADAMTS17, ADAMTS2, ADAMTS3, ADAMTS4, ADAMTS7, ADAMTS8, ADAMTS9, ADAMTSL1, ADAMTSL2, ADAMTSL3, ADAMTSL4, AEBP1, AGT, ANTXR1, ATXN1L, B4GALT1, BCL3, BMP2, CCDC80, COL11A1, COL11A2, COL12A1, COL15A1, COL16A1, COL18A1, COL1A1, COL1A2, COL22A1, COL23A1, COL27A1, COL2A1, COL3A1, COL4A1, COL4A2, COL4A5, COL4A6, COL5A1, COL6A6, COL9A1, COL9A2, COL9A3, COLGALT2, LAMC1, LOX, LOXL1, LOXL2, LOXL3, LRP1, LTBP3, MELTF, MFAP4, MMP1 1, MMP14, MMP15, MMP16, MMP2, MYH11, NIDI, NID2); genes associated with actin fdament organization (e.g., ABE, ABL1, ACTA1, ACTC1, ACTG1, ACTN1, ADD1, ADD2, AIF1L, AR AP I, ARHGAP17, ARHGAP25, ARHGAP35, ARHGAP6, ARHGEF10L, ARHGEF18, ARPIN, ARRB1, MTSS1, MY ADM, MY01C, MYOID, MY05B, MY05C, MY07B); genes associated with cardiac chamber development and/or morphogenesis (e.g., ACVR1, APLNR, BMP2, BMP4, BMP7, BMPR2, COL11A1, DAND5, DHRS3, EDNRA, ENG, FGFRL1, FOXCI, FZD1, FZD2, GATA4, GATA6, HEG1, HEY2, HEYL, IGF1R, MAML1, MYBPC3, MYH6, MYH7, MYL3, NDST1, NKX25, NOTCH2, NPRL3, NRP1, NRP2, NSD2, PARVA, PLXND1, POU4F1, PPP1R13L, PTK7, ROBO1, RYR2, SCN5A, SFRP2, SHOX2, SLIT2, SLIT3, SMAD6, SMAD7, SMO, SNX17, SOX4, SRF, SUFU, TAB1, TBX20, TBX5, TGFB1, TGFBR2, TGFBR3, TNNT2, TP53, WNT5A, ZFPM1); and/or cardiac conduction genes (e.g., ACE2, AGT, ATP1A2, ATP1B2, ATP2A1, ATP2A2, ATP2A3, ATP2B2, ATP2B4, BINI, CACNA1C, CACNA1D, CASQ2, EHD3, GJC1, GJD3, HCN1, HCN2, HCN4, JUP, KCND3, KCNH2, KCNJ5, KCNN2, KCNQ1, NKX25, PRKACA, RNF207, RYR2, SCN1B, SCN5A, SLC4A3, SLC9A1, SPTBN4, TBX5, TRPM4).

[00181] In some embodiments, the physiological changes include one or more changes to cardiac cellular function pathways. In particular embodiments, the one or more changes to cardiac cellular function pathways include changes to the extracellular matrix, changes to the mechanism of contraction and/or conduction, and/or changes to cytoskeleton regulation of the one or more cardiac cells. [00182] In a further aspect of the invention disclosed herein, a method of identifying cardiac cellular transformations associated with aging, aging hallmarks, age-related cardiac dysfunction, and/or spaceflight is provided. The method includes: analyzing with one or more omics a first set of one or more cells of a cardiac cellular population, wherein the first set of one or more cells was subjected to simulated reduced gravity of less than 1G, to obtain a first set of data for a reduced gravity omics profile; analyzing with the one or more omics a second set of one or more cells of the same cardiac cellular population, wherein the second set of one or more cells was subjected to normal gravity (1 G), to obtain a second set of data for a normal gravity omics profile; and comparing the first set of data with the second set of data to identify differences in omics profiles, gene expression, and cellular pathway expression between the first set of one or more cells subjected to simulated reduced gravity and the second set of one or more cells subjected to normal gravity (1G). In some embodiments, the method further comprises identifying cardiac dysfunction in the first set of one or more cells as compared to the second set of one or more cells. In further embodiments, the method includes the steps of linking the differences in cellular function, differences in cellular structure, and/or differences in cellular molecular content with genes responsible for the differences by applying cross-validated machine learning (ML) to the first set of data and the second set of data.

[00183] In some embodiments, the steps of analyzing the first set of one or more cells and the second set of one or more cells includes analysis with transcriptomics.

[00184] In some embodiments, the identified differences in gene expression and cellular pathway expression between the first set of one or more cells subjected to simulated reduced gravity and the second set of one or more cells subjected to normal gravity (1G) include one or more of: differences in cellular function, differences in cellular structure, and differences in cellular molecular content. [00185] In some embodiments, the method further includes the step of identifying differential expressions of genes and/or pathways between the first set of one or more cells and the second set of one or more cells. In further embodiments, the method includes the step of identifying induction of genetic expression in the first set of one or more cells as compared to the second set of one or more cells, wherein the induction of genetic expression is in one or more of: CCT2, CCT3, CCT5, CCT7, NOP10, RUVBL1, TCP1, HSP90AA1, HSP90AB, HSPA8, HSPA9, HSPE1, HSPH1, PPID, ST13, TCP1, UNC45B, BAZ1B, BRD7, CCDC117, CD44, CLU, DHX9, DTX3L, EYA3, HMGA2, MAP3K20, MSX1, NSD2, PARP1, PIAS4, PPP1R10, PPP4R3B, RAD52, RUVBL1, SF3B3, SNAI2, SPRED2, TIGAR, TIMELESS, TRIP12, and TTI1 genes. In further embodiments, the method includes identifying reduction of genetic expression in the first set of one or more cells as compared to the second set of one or more cells, wherein the reduction of genetic expression is in one or more of: ABL1, ADAMTS13, ADAMTS14, ADAMTS15, ADAMTS17, ADAMTS2, ADAMTS3, ADAMTS4, ADAMTS7, ADAMTS8, ADAMTS9, ADAMTSL1 , ADAMTSL2, ADAMTSL3, ADAMTSL4, AEBP1, AGT, ANTXR1, ATXN1L, B4GALT1, BCL3, BMP2, CCDC80, C0L11A1, COL11A2, C0L12A1, COL15A1, C0L16A1, COL18A1, C0L1A1, COL1A2, COL22A1, COL23A1, COL27A1, COL2A1, COL3A1, COL4A1, COL4A2, COL4A5, COL4A6, COL5A1, COL6A6, COL9A1, COL9A2, COL9A3, COLGALT2, CREB3L1, CYP1B1, DAG1, DDR1, DDR2, EFEMP2, ELN, EMILIN1, ENG, ERCC2, EXT1, FAP, LAMC1, LOX, L0XL1, L0XL2, L0XL3, MFAP4, MMP11, MMP14, MMP15, MMP16, MMP2, MYH11 N0TCH2, NPRL3, NRP1, NRP2, NSD2, PARVA, PLXND1, POU4F1, PPP1R13L, PTK7, R0B01, RYR2, SCN5A, SFRP2, SHOX2, SLIT2, SLIT3, SMAD6, SMAD7, SMO, SNX17, SOX4, SRF, SUFU, TAB1, TBX20, TBX5, TGFB1, TGFBR2, TGFBR3, TNNT2, TP53, WNT5A, ZFPM1, ACE2, AGT, ATP1A2, ATP1B2, ATP2A1, ATP2A2, ATP2A3, ATP2B2, ATP2B4, BINI, CACNA1C, CACNA1D, CASQ2, EHD3, GJC1, GJD3, HCN1, HCN2, HCN4, JUP, KCND3, KCNH2, KCNJ5, KCNN2, KCNQ1, NKX25, PRKACA, RNF207, RYR2, SCN1B, SCN5A, SLC4A3, SLC9A1, SPTBN4, TBX5, and TRPM4 genes.

[00186] In some embodiments, the step of identifying differential expressions of genes and/or pathways further comprises identifying changes in expression of one or more of: CCT2, CCT3, CCT5, CCT7, NOPIO, RUVBL1, TCP1, HSP90AA1, HSP90AB, HSPA8, HSPA9, HSPE1, HSPH1, PPID, ST I 3, TCP1, UNC45B, BAZ1B, BRD7, CCDC117, CD44, CLU, DHX9, DTX3L, EYA3, HMGA2, MAP3K20, MSX1, NSD2, PARP1, PIAS4, PPP1R10, PPP4R3B, RAD52, RUVBL1 , SF3B3, SNAI2, SPRED2, TIGAR, TIMELESS, TRIP12, TTI1, ABL1, ADAMTS13, ADAMTS14, ADAMTS15, ADAMTS17, ADAMTS2, ADAMTS3. ADAMTS4, ADAMTS7, ADAMTS8, ADAMTS9, ADAMTSL1, ADAMTSL2, ADAMTSL3, ADAMTSL4, AEBP1, AGT, ANTXR1, ATXN1L, B4GALT1, BCL3, BMP2, CCDC80, COL11A1, COL11 A2, COL12A1, COL15A1, COL16A1, COL18A1, COL1A1, COL1A2, COL22A1, COL23A1, COL27A1, COL2A1, COL3A1, COL4A1, COL4A2, COL4A5, COL4A6, COL5A1, COL6A6, COL9A1, COL9A2, COL9A3, COLGALT2, CREB3L1, CYP1B1, DAG1, DDR1, DDR2, EFEMP2, ELN, EMILIN1, ENG, ERCC2, EXT1, FAP, LAMC1, LOX, LOXL1, LOXL2, LOXL3, MFAP4, MMP11, MMP14, MMP15, MMP16, MMP2, MYH11 NOTCH2, NPRL3, NRP1, NRP2, NSD2, PARVA, PLXND1, POU4F1, PPP1R13L, PTK7, ROBO1, RYR2, SCN5A, SFRP2, SHOX2, SLIT2, SLIT3, SMAD6, SMAD7, SMO, SNX17, SOX4, SRF, SUFU, TAB1, TBX20, TBX5, TGFB1, TGFBR2, TGFBR3, TNNT2, TP53, WNT5A, ZFPM1, ACE2, AGT, ATP1A2, ATP1B2, ATP2A1, ATP2A2, ATP2A3, ATP2B2, ATP2B4, BINI, CACNA1C, CACNA1D, CASQ2, EHD3, GJC1, GJD3, HCN1, HCN2, HCN4, JUP, KCND3, KCNH2, KCNJ5, KCNN2, KCNQ1, NKX25, PRKACA, RNF207, RYR2, SCN1B, SCN5A, SLC4A3, SLC9A1, SPTBN4, TBX5, and TRPM4 genes.

[00187] In some embodiments of the method, the one or more cells of a cardiac cellular population includes one or both of cardiomyocytes and endothelial cells. In some embodiments, the one or more cells of a cardiac cellular population are present in one or more organoids. In further embodiments, the one or more organoids include cardiomyocytes. In still further embodiments, the one or more organoids including cardiomyocytes also include co-cultured endothelial cells. In particular embodiments, the one or more organoids include CM organoids and/or CMEC organoids. In certain embodiment, the one or more organoids is a CMEC organoid.

[00188] In some embodiments of the method, the age-related cardiac dysfunction is cardiomyopathy. In particular embodiments, the cardiomyopathy is dilated cardiomyopathy. [00189] In another aspect of the invention set forth herein, a method for identifying a compound useful for treatment, normalization, or reversal of cellular transformations and/or differential gene expression associated with cardiac aging, cardiac aging hallmarks, cardiac dysfunction, spaceflight- induced cardiac deconditioning, and/or cardiac physiological changes induced by spaceflight is provided. The method includes: assessing interactions between genes altered by simulated reduced gravity and compounds using compound-gene interactome machine learning (ML), and identifying at least one compound that interacts with one or more of the genes altered by simulated reduced gravity using the compound-gene interactome machine learning (ML).

[00190] In some embodiments, the at least one compound is a bioactive molecule derived from food. In some embodiments, the at least one compound is an active pharmaceutical ingredient. In particular embodiments, the at least one compound is selected from mebendazole, resveratrol, trichostatin A, thioridazine, and rapamycin.

[00191] In some embodiments, the genes altered by cellular exposure to reduced gravity include one or more of the following: CCT2, CCT3, CCT5, CCT7, NOP10, RUVBL1, TCP1, HSP90AA1, HSP90AB, HSPA8, HSPA9, HSPE1, HSPH1, PPID, STB, TCP1, UNC45B, BAZ1B, BRD7, CCDC1 17, CD44, CLU, DHX9, DTX3L, EYA3, HMGA2, MAP3K20, MSX1, NSD2, PARP1, PIAS4, PPP1R10, PPP4R3B, RAD52, RUVBL1, SF3B3, SNAI2, SPRED2, TIGAR, TIMELESS, TRIP12, TTI1, ABL1, ADAMTS13, ADAMTS14, ADAMTS15, ADAMTS17, ADAMTS2, ADAMTS3, ADAMTS4, ADAMTS7, ADAMTS8, ADAMTS9, ADAMTSL1, ADAMTSL2, ADAMTSL3, ADAMTSL4, ANTXR1, ATXN1L, BCL3, BMP2, CCDC80, COL11A1, COL11A2, C0L12A1, C0L15A1, C0L16A1, C0L18A1, C0L1A1, COL1A2, COL22A1, COL23A1, COL27A1, COL2A1, COL3A1, COL4A1, COL4A2, COL4A5, COL4A6, COL5A1, COL6A6, COL9A1, COL9A2, COL9A3, COLGALT2, CREB3L1, CRISPLD2, CRTAP, CYP1B1, LAMC1, LOX, LOXL1, LOXL2, LOXL3, LRP1, LTBP3, MELTF, MFAP4, MMP11, MMP14, MMP15, MMP16, MMP2, MYH11, NIDI, NID2, NPHS1, NTNG2, OLFML2A, P3H4, PAPLN, PHLDB1, POMT1, POSTN, PXDN, QSOX1, RAMP2, SCX, SFRP2, SH3PXD2B, SLC2A10, SLC39A8, BMP2, BMP4, BMP7, BMPR2, MYBPC3, MYH6, MYH7, MYL3, NDST1, NKX25, NOTCH2, NPRL3, NRP1, NRP2, NSD2, PARVA, PLXND1, POU4F1, PPP1R13L, PTK7, ROBO1, RYR2, SCN5A, SFRP2, SHOX2, SLIT2, SLIT3, SMAD6, SMAD7, SMO, SNX17, SOX4, SRF, SUFU, TAB1, TBX20, TBX5, TGFB1, TGFBR2, TGFBR3, TNNT2, TP53, WNT5A, ZFPM1, ACE2, AGT, ATP1A2, ATP1B2, ATP2A1, ATP2A2, ATP2A3, ATP2B2, ATP2B4, BINI, CACNA1C, CACNA1D, CASQ2, HCN1, HCN2, HCN4, KCND3, KCNH2, KCNJ5, KCNN2, KCNQ1, RYR2, SCN1B, SCN5A, SLC4A3, SLC9A1, SPTBN4, TBX5, and TRPM4

[00192] In some embodiments, the cardiac dysfunction is one or more of cardiovascular disease, cardiomyopathy, and dilated cardiomyopathy.

[00193] In a further aspect of the invention disclosed herein, a method for treating, normalizing, or reversing cardiac cellular transformations correlated with gene expression change associated with cellular transformations and/or differential gene expression associated with cardiac aging, cardiac aging hallmarks, cardiac dysfunction, spaceflight-induced cardiac deconditioning, cardiac physiological changes induced by spaceflight, and/or exposure to reduced gravity under 1G is provided. The method includes: identifying least one compound that interacts with genes altered in cardiac cells by exposure to reduced gravity under 1G, and administering the at least one compound to a patient in need thereof.

[00194] In some embodiments, the at least one compound is a bioactive molecule derived from food. In some embodiments, the at least one compound is an active pharmaceutical ingredient. In particular embodiments, the at least one compound is selected from mebendazole, resveratrol, trichostatin A, thioridazine, and rapamycin.

[00195] In an aspect of the invention herein, the present disclosure provides a method for simulating hallmarks of neural aging, simulating changes in cardiac cellular physiology (e.g., cell cycle changes, metabolic changes, protein folding changes) due to spaceflight, and/or modeling Parkinson’s Disease. The method includes exposing one or more neural cells to simulated reduced gravity below 1G. The one or more neural cells may be part of neural tissues and/or organoids. [00196] In some embodiments of the method, the one or more neural cells include Ast,, astrocytes; ExDpl, excitatory deep layer 1; ExDp2, excitatory deep layer 2; ExM, maturing excitatory; ExM-U, maturing excitatory upper enriched; ExN, newborn excitatory; Glia, unspecified glia/non-neuronal cells; InCGE, interneurons caudal ganglionic eminence; InMGE, interneurons medial ganglionic eminence; IP, intermediate progenitors; OPC, oligodendrocyte precursor cells; oRG, outer radial glia; PgG2M, cycling progenitors (G2/M phase); PgS, cycling progenitors (S phase); UN, unspecified neurons; vRG, ventricular radial glia. In further embodiments, the one or more neural cells are present in one or more organoids. In some embodiments of the method, the one or more organoids is an organoid with microglia or an organoid without microglia..

[00197] In some embodiments, the hallmarks of neural aging, the changes in neural cellular physiology due to spaceflight, and/or the Parkinson’s Disease modeling result in one or more changes in gene expression. In particular, the one or more changes in gene expression result in one or more of fibrosis, changes in expression of hypertrophy -related genes, increase in cytokine production, changes in protein-folding, cell cycle changes, metabolic changes, cytoskeleton changes, increase in oxidative stress, and onset of mitochondrial dysfunction.

[00198] In some embodiments of the methods set forth herein, the simulated reduced gravity below 1G is produced by a low-shear modeled microgravity rotating wall vessel apparatus, a random positioning machine, a 2D clinostat, a 3D clinostat, parabolic flight, and/or a magnetic levitation apparatus.

[00199] In some embodiments, the simulated reduced gravity is between 0G and 0.9999G. In further embodiments, the simulated reduced gravity is between 0G and 0.38G.

[00200] In some embodiments, the one or more neural cells, tissues, or organoids are exposed to simulated reduced gravity for at least 10 minutes. In some embodiments, the one or more cells, tissues, or organoids are exposed to simulated reduced gravity for at least 30 minutes. In some embodiments, the one or more neural cells, tissues, or organoids are exposed to simulated reduced gravity for at least 1 hour. In some embodiments, the one or more neural cells, tissues, or organoids are exposed to simulated reduced gravity for at least 5 hours. In some embodiments, the one or more neural cells, tissues, or organoids are exposed to simulated reduced gravity for at least 10 hours. In some embodiments, the one or more neural cells, tissues, or organoids are exposed to simulated reduced gravity for at least 15 hours. In some embodiments, the one or more neural cells, tissues, or organoids are exposed to simulated reduced gravity for at least 20 hours. In some embodiments, the one or more neural cells, tissues, or organoids are exposed to simulated reduced gravity for at least 24 hours. In some embodiments, the one or more neural cells, tissues, or organoids are exposed to simulated reduced gravity between 10 minutes and 30 hours. In some embodiments, the one or more neural cells, tissues, or organoids are exposed to simulated reduced gravity between 30 minutes and 25 hours. In some embodiments, the one or more neural cells, tissues, or organoids are exposed to simulated reduced gravity between 1 hour and 20 hours.

[00201] In some embodiments, the step of exposing the one or more neural cells to simulated reduced gravity induces physiological changes in the one or more neural cells. In particular embodiments, the physiological changes comprise one or more of: changes in neural cellular function, changes in neural cellular structure, and changes in molecular content of the one or more neural cells. In particular embodiments, the changes in neural cellular function include: one or more of fibrosis, changes in expression of hypertrophy-related genes, increase in cytokine production, changes in protein-folding, cell cycle changes, metabolic changes, cytoskeleton changes, increase in oxidative stress, and onset of mitochondrial dysfunction.

[00202] In particular embodiments, the physiological changes induced by exposure of the one or more neural cells to simulated reduced gravity comprise differential expressions of genes and/or pathways. In some embodiments, the differential expressions in genes and/or pathways can include upregulation or induction of genetic expression in one or more of: telomerase RNA localization genes (e.g, CCT2, CCT3, CCT5, CCT7, NOP10, RUVBL1, TCP1); chaperone-mediated protein folding and assembly genes (e.g, CCT2, CCT3, CCT5, CCT7, CHORDCI, CLU, FKBP4, HSPA8, HSPA9, HSPE1, HSPH1, PPID, ST13, TCP1, UNC45B); genes involved in regulation of protein and RNA localization to the Cajal body (e.g, CCT2, CCT3, CCT5, CCT7, NOP10, RUVBL1, TCP1); genes involved in the regulation of response to DNA damage (e.g, BAZ1B, BRD7, CCDC1 17, CD44, CLU, DHX9, DTX3L, EYA3, HMGA2, MAP3K20, MSX1, NSD2, PARP1 , PIAS4, PPP1R10, PPP4R3B, RAD52, RUVBL1, SF3B3, SNAI2, SPRED2, TIGAR, TIMELESS, TRIP12, TTI1); genes involved in regulation of intrinsic apoptosis (e.g, AEN, AIFM1, CD44, CLU, CYP1B1, DAB2IP, DNAJA1, EDA2R, FLCN, HINT1, HM0X1, MSX1, PARP1, PIAS4, PPIF, PPM1F, PTGS2, RRN3, SNAI2, SOD1, UBB, USP28); and/or genes involved in telomere maintenance via telomere lengthening (e.g, CCT2, CCT3, CCT5, CCT7, GNL3L, HSP90AA1, HSP90AB1, NOP10, PARP1, TCP1). In some embodiments, the differential expressions in genes and/or pathways can include downregulation or reduction of genetic expression in one or more of: genes involved in extracellular matrix organization (e.g, ADAMTS13, ADAMTS14, ADAMTS15, ADAMTS17, COL11A1, COL11A2, COL12A1, COL15A1, COL16A1, COL18A1, COL1A1, COL1A2, COL22A1, COL23A1, COL27A1, COL2A1, COL3A1, COL4A1, COL4A2, COL4A5, COL4A6, COL5A1, COL6A6, COL9A1, COL9A2, COL9A3, COLGALT2, MMP11, MMP14, MMP15, MMP16, MMP2, MYH11); genes involved with cell-junction assembly (e.g., ABL1, AD AMTS 13, AD AMTS 14, ADAMTS15, ADAMTS17, ADAMTS2, ADAMTS3, ADAMTS4, ADAMTS7, ADAMTS8, ADAMTS9, ADAMTSL1, ADAMTSL2, ADAMTSL3, ADAMTSL4, AEBP1, AGT, ANTXR1, ATXN1L, B4GALT1, BCL3, BMP2, CCDC80, COL11A1, COL11A2, COL12A1, COL15A1, COL16A1, COL18A1, COL1A1, COL1A2, COL22A1, COL23A1, COL27A1, COL2A1, COL3A1, COL4A1, COL4A2, COL4A5, COL4A6, COL5A1, COL6A6, COL9A1, COL9A2, COL9A3, COLGALT2, LAMC1, LOX, LOXL1, LOXL2, LOXL3, LRP1, LTBP3, MELTF, MFAP4, MMP11, MMP14, MMP15, MMP16, MMP2, MYH11, NIDI, NID2); and/or genes associated with actin filament organization (e.g., ABE, ABL1, ACTA1, ACTC1, ACTG1, ACTN1, ADD1, ADD2, AIF1L, ARAP1, ARHGAP17, ARHGAP25, ARHGAP35, ARHGAP6, ARHGEF10L, ARHGEF18, ARPIN, ARRB1, MTSS1, MYADM, MY01C, MYOID, MY05B, MY05C, MY07B);.

[00203] In some embodiments, the differential expressions of genes and/or pathways include changes in expression of one or more of telomerase RNA localization genes (e.g., CCT2, CCT3, CCT5, CCT7, NOPIO, RUVBL1, TCP1); chaperone-mediated protein folding and assembly genes (e.g., CCT2, CCT3, CCT5, CCT7, CHORDCI, CLU, FKBP4, HSPA8, HSPA9, HSPE1, HSPH1, PPID, ST13, TCP1, UNC45B); genes involved in regulation of protein and RNA localization to the Cajal body (e.g., CCT2, CCT3, CCT5, CCT7, NOPIO, RUVBL1, TCP1); genes involved in the regulation of response to DNA damage (e.g., BAZ1B, BRD7, CCDC117, CD44, CLU, DHX9, DTX3L, EYA3, HMGA2, MAP3K20, MSX1, NSD2, PARP1, PIAS4, PPP1R10, PPP4R3B, RAD52, RUVBL1, SF3B3, SNAI2, SPRED2, TIGAR, TIMELESS, TRIP12, TTI1); genes involved in regulation of intrinsic apoptosis (e g., AEN, AIFM1, CD44, CLU, CYP1B1, DAB2IP, DNAJA1 , EDA2R, FLCN, HINT1, HM0X1, MSX1, PARP1, PIAS4, PPIF, PPM1F, PTGS2, RRN3, SNAI2, SOD1, UBB, USP28); genes involved in telomere maintenance via telomere lengthening (e.g., CCT2, CCT3, CCT5, CCT7, GNL3L, HSP90AA1, HSP90AB1, NOP 10, PARP1, TCP1); genes involved in extracellular matrix organization (e.g., ADAMTS13, ADAMTS14, ADAMTS15, ADAMTS17, COL11A1, COL11A2, COL12A1, COL15A1, COL16A1, COL18A1, COL1A1, COL1A2, COL22A1, COL23A1, COL27A1, COL2A1, COL3A1, COL4A1, COL4A2, COL4A5, COL4A6, COL5A1, COL6A6, COL9A1, COL9A2, COL9A3, COLGALT2, MMP11, MMP14, MMP15, MMP16, MMP2, MYH11); genes involved with cell-junction assembly (e.g., ABL1, ADAMTS13, ADAMTS14, ADAMTS15, ADAMTS17, ADAMTS2, ADAMTS3, ADAMTS4, ADAMTS7, ADAMTS8, ADAMTS9, ADAMTSL1, ADAMTSL2, ADAMTSL3, ADAMTSL4, AEBP1, AGT, ANTXR1, ATXN1L, B4GALT1, BCL3, BMP2, CCDC80, COL11A1, COL11A2, C0L12A1, C0L15A1, C0L16A1, C0L18A1, C0L1A1, COL1A2, COL22A1, COL23A1, COL27A1, COL2A1, COL3A1, COL4A1, COL4A2, COL4A5, COL4A6, COL5A1, COL6A6, COL9A1, COL9A2, COL9A3, COLGALT2, LAMC1, LOX, LOXL1 , LOXL2, LOXL3, LRP1 , LTBP3, MELTF. MFAP4, MMP1 I, MMP14, MMP15, MMP16, MMP2, MYH I I . NIDI, NID2); and/or genes associated with actin filament organization (e.g., AB 12, ABL1, ACTA1, ACTC1, ACTG1, ACTN1, ADD1, ADD2, AIF1L, ARAP1, ARHGAP I 7, ARHGAP25, ARHGAP35, ARHGAP6, ARHGEF10L, ARHGEF18, ARPIN, ARRB1, MTSS1, MYADM, MY01C, MYOID, MY05B, MY05C, MY07B).

[00204] In a further aspect of the invention disclosed herein, a method of identifying neural cellular transformations associated with aging, aging hallmarks, age-related neural dysfunction, and/or spaceflight is provided. The method includes: analyzing with one or more omics a first set of one or more cells of a neural cellular population, wherein the first set of one or more cells was subjected to simulated reduced gravity of less than 1G, to obtain a first set of data for a reduced gravity omics profile; analyzing with the one or more omics a second set of one or more cells of the same neural cellular population, wherein the second set of one or more cells was subjected to normal gravity (1 G), to obtain a second set of data for a normal gravity omics profile; and comparing the first set of data with the second set of data to identify differences in omics profiles, gene expression, and cellular pathway expression between the first set of one or more cells subjected to simulated reduced gravity and the second set of one or more cells subjected to normal gravity (1G). In some embodiments, the method further comprises identifying neural dysfunction in the first set of one or more cells as compared to the second set of one or more cells. In further embodiments, the method includes the steps of linking the differences in cellular function, differences in cellular structure, and/or differences in cellular molecular content with genes responsible for the differences by applying cross-validated machine learning (ML) to the first set of data and the second set of data.

[00205] In some embodiments, the steps of analyzing the first set of one or more neural cells and the second set of one or more neural cells includes analysis with transcriptomics.

[00206] In some embodiments, the identified differences in gene expression and cellular pathway expression between the first set of one or more neural cells subjected to simulated reduced gravity and the second set of one or more neural cells subjected to normal gravity (1G) include one or more of: differences in protein-folding, differences in cell cycles, and differences in metabolic processes. [00207] In some embodiments, the method further includes the step of identifying differential expressions of genes and/or pathways between the first set of one or more neural cells and the second set of one or more neural cells. In further embodiments, the method includes the step of identifying induction of genetic expression in the first set of one or more cells as compared to the second set of one or more cells, wherein the induction of genetic expression is in one or more of: CCT2, CCT3, CCT5, CCT7, NOPIO, RUVBL1 , TCP1 , HSP90AA1, HSP90AB, HSPA8, HSPA9, HSPE1, HSPH1, PPID, ST13, TCP1, UNC45B, BAZ1B, BRD7, CCDC117, CD44, CLU, DHX9, DTX3L, EYA3, HMGA2, MAP3K20, MSX1, NSD2, PARP1, PIAS4, PPP1R10, PPP4R3B, RAD52, RUVBL1, SF3B3, SNAI2, SPRED2, TIGAR, TIMELESS, TRIP12, and TTI1 genes. In further embodiments, the method includes identifying reduction of genetic expression in the first set of one or more cells as compared to the second set of one or more cells, wherein the reduction of genetic expression is in one or more of: ABL1, ADAMTS13, ADAMTS14, ADAMTS15, ADAMTS I 7, ADAMTS2, ADAMTS3, ADAMTS4, ADAMTS7, ADAMTS8, ADAMTS9, ADAMTSL1, ADAMTSL2, ADAMTSL3, ADAMTSL4, AEBP1, AGT, ANTXR1, ATXN1L, B4GALT1, BCL3, BMP2, CCDC80, COL11A1, COL11A2, COL12A1, COL15A1, COL16A1, COL18A1, COL1A1, COL1A2, COL22A1, COL23A1, COL27A1, COL2A1, COL3A1, COL4A1, COL4A2, COL4A5, COL4A6, COL5A1, COL6A6, COL9A1, COL9A2, COL9A3, COLGALT2, CREB3L1, CYP1B1, DAG1, DDR1, DDR2, EFEMP2, ELN, EMILIN1, ENG, ERCC2, EXT1, FAP, LAMC1, LOX, L0XL1, L0XL2, L0XL3, MFAP4, MMP11, MMP14, MMP15, MMP16, MMP2, MYH11 NOTCH2, NPRL3, NRP1, NRP2, NSD2, PARVA, PLXND1, POU4F1, PPP1R13L, PTK7, R0B01, RYR2, SCN5A, SFRP2, SHOX2, SLIT2, SLIT3, SMAD6, SMAD7, SMO, SNX17, SOX4, SRF, SUFU, TAB1, TBX20, TBX5, TGFB1, TGFBR2, TGFBR3, TNNT2, TP53, WNT5A, ZFPM1, ACE2, AGT, ATP1A2, ATP1B2, ATP2A1, ATP2A2, ATP2A3, ATP2B2, ATP2B4, BINI, CACNA1C, CACNA1D, CASQ2, EHD3, GJC1, GJD3, HCN1, HCN2, HCN4, JUP, KCND3, KCNH2, KCNJ5, KCNN2, KCNQ1 , NKX25, PRKACA, RNF207, RYR2, SCN1B, SCN5A, SLC4A3, SLC9A1, SPTBN4, TBX5, and TRPM4 genes.

[00208] In some embodiments, the step of identifying differential expressions of genes and/or pathways further comprises identifying changes in expression of one or more of: CCT2, CCT3, CCT5, CCT7, NOPIO, RUVBL1, TCP1, HSP90AA1, HSP90AB, HSPA8, HSPA9, HSPE1, HSPH1, PPID, ST13, TCP1, UNC45B, BAZ1B, BRD7, CCDC117, CD44, CLU, DHX9, DTX3L, EYA3, HMGA2, MAP3K20, MSX1, NSD2, PARP1, PIAS4, PPP1R10, PPP4R3B, RAD52, RUVBL1, SF3B3, SNAI2, SPRED2, TIGAR, TIMELESS, TRIP12, TTI1, ABL1, ADAMTS13, AD AMTS 14, ADAMTS15, ADAMTS17, ADAMTS2, ADAMTS3, ADAMTS4, ADAMTS7, ADAMTS8, ADAMTS9, ADAMTSL1, ADAMTSL2, ADAMTSL3, ADAMTSL4, AEBP1, AGT, ANTXR1, ATXN1L, B4GALT1, BCL3, BMP2, CCDC80, COL11A1, COL11A2, COL12A1, COL15A1, COL16A1, COL18A1, COL1A1, COL1A2, COL22A1, COL23A1, COL27A1, C0L2A1, C0L3A1, C0L4A1, COL4A2, COL4A5, COL4A6, C0L5A1, COL6A6, C0L9A1, COL9A2, COL9A3, C0LGALT2, CREB3L1, CYP1B1, DAG1, DDR1, DDR2, EFEMP2, ELN, EMILIN1, ENG, ERCC2, EXT1, FAP, LAMC1, LOX, LOXL1, LOXL2, LOXL3, MFAP4, MMP I 1, MMPI 4, MMP15, MMPI 6, MMP2, MYH11 NOTCH2, NPRL3, NRP1, NRP2, NSD2, PARVA, PLXND1, POU4F1, PPP1R13L, PTK7, ROBO1, RYR2, SCN5A, SFRP2, SHOX2, SLIT2, SLIT3, SMAD6, SMAD7, SMO, SNX17, SOX4, SRF, SUFU, TAB1, TBX20, TBX5, TGFB1, TGFBR2, TGFBR3, TNNT2, TP53, WNT5A, ZFPM1, ACE2, AGT, ATP1A2, ATP1B2, ATP2A1, ATP2A2, ATP2A3, ATP2B2, ATP2B4, BINI, CACNA1C, CACNA1D, CASQ2, EHD3, GJC1, GJD3, HCN1, HCN2, HCN4, JUP, KCND3, KCNH2, KCNJ5, KCNN2, KCNQ1, NKX25, PRKACA, RNF207, RYR2, SCN1B, SCN5A, SLC4A3, SLC9A1, SPTBN4, TBX5, and TRPM4 genes.

[00209] In some embodiments of the method, the one or more cells of a neural cellular population includes Ast, astrocytes; ExDpl, excitatory deep layer 1; ExDp2, excitatory deep layer 2; ExM, maturing excitatory; ExM-U, maturing excitatory upper enriched; ExN, newborn excitatory; Glia, unspecified glia/non-neuronal cells; InCGE, interneurons caudal ganglionic eminence; InMGE, interneurons medial ganglionic eminence; IP, intermediate progenitors; OPC, oligodendrocyte precursor cells; oRG, outer radial glia; PgG2M, cycling progenitors (G2/M phase); PgS, cycling progenitors (S phase); UN, unspecified neurons; vRG, ventricular radial glia. In some embodiments, the one or more cells of a neural cellular population are present in one or more organoids. In further embodiments, the one or more organoids include organoids with microglia and organoids without microglia.

[00210] In some embodiments of the method, the age-related cardiac dysfunction is Parkinson’s Disease.

[00211] In another aspect of the invention set forth herein, a method for identifying a compound useful for treatment, normalization, or reversal of cellular transformations and/or differential gene expression associated with neural aging, neural aging hallmarks, neural dysfunction, spaceflight- induced neural dysfunction, and/or neural physiological changes induced by spaceflight is provided. The method includes: assessing interactions between genes altered by simulated reduced gravity and compounds using compound-gene interactome machine learning (ML), and identifying at least one compound that interacts with one or more of the genes altered by simulated reduced gravity using the compound-gene interactome machine learning (ML).

[00212] In some embodiments, the at least one compound is a bioactive molecule derived from food. In some embodiments, the at least one compound is an active pharmaceutical ingredient. In particular embodiments, the at least one compound is selected from mebendazole, resveratrol, trichostatin A, thioridazine, and rapamycin.

[00213] In some embodiments, the genes altered by cellular exposure to reduced gravity include one or more of the following: CCT2, CCT3, CCT5, CCT7, NOPIO, RUVBL1, TCP1, HSP90AA1, HSP90AB, HSPA8, HSPA9, HSPE1, HSPH1, PPID, ST13, TCP1, UNC45B, BAZ1B, BRD7, CCDC1 17, CD44, CLU, DHX9, DTX3L, EYA3, HMGA2, MAP3K20, MSX1, NSD2, PARP1, PIAS4, PPP1R10, PPP4R3B, RAD52, RUVBL1, SF3B3, SNAI2, SPRED2, TIGAR, TIMELESS, TRIP12, TTI1, ABL1, ADAMTS13, ADAMTS14, ADAMTS15, ADAMTS17, ADAMTS2, ADAMTS3, ADAMTS4, ADAMTS7, ADAMTS8, ADAMTS9, ADAMTSL1, ADAMTSL2, ADAMTSL3, ADAMTSL4, ANTXR1, ATXN1L, BCL3, BMP2, CCDC80, COL11A1, COL11A2, COL12A1, COL15A1, COL16A1, COL18A1, COL1A1, COL1A2, COL22A1, COL23A1, COL27A1, COL2A1, COL3A1, COL4A1, COL4A2, COL4A5, COL4A6, COL5A1, COL6A6, COL9A1, COL9A2, COL9A3, COLGALT2, CREB3L1, CRISPLD2, CRTAP, CYP1B1, LAMC1, LOX, LOXL1, LOXL2, LOXL3, LRP1, LTBP3, MELTF, MFAP4, MMP11, MMP14, MMP15, MMP16, MMP2, MYH11, NIDI, NID2, NPHS1, NTNG2, OLFML2A, P3H4, PAPLN, PHLDB1, POMT1, POSTN, PXDN, QSOX1, RAMP2, SCX, SFRP2, SH3PXD2B, SLC2A10, SLC39A8, BMP2, BMP4, BMP7, BMPR2, MYBPC3, MYH6, MYH7, MYL3, NDST1, NKX25, NOTCH2, NPRL3, NRP1, NRP2, NSD2, PARVA, PLXND1, POU4F1, PPP1R13L, PTK7, ROBO1, RYR2, SCN5A, SFRP2, SHOX2, SLIT2, SL1T3, SMAD6, SMAD7, SMO, SNX17, SOX4, SRF, SUFU, TAB1, TBX20, TBX5, TGFB1, TGFBR2, TGFBR3, TNNT2, TP53, WNT5A, ZFPM1, ACE2, AGT, ATP1A2, ATP1B2, ATP2A1, ATP2A2, ATP2A3, ATP2B2, ATP2B4, BINI, CACNA1C, CACNA1D, CASQ2, HCN1, HCN2, HCN4, KCND3, KCNH2, KCNJ5, KCNN2, KCNQ1, RYR2, SCN1B, SCN5A, SLC4A3, SLC9A1, SPTBN4, TBX5, and TRPM4.

[00214] In some embodiments, the neural dysfunction is one or more of neuropathy, ischemia, movement disease, Parkinsonism, and Parkinson’s Disease.

[00215] In a further aspect of the invention disclosed herein, a method for treating, normalizing, or reversing neural cellular transformations correlated with gene expression change associated with cellular transformations and/or differential gene expression associated with neural aging, neural aging hallmarks, neural dysfunction, spaceflight-induced neural dysfunction, neural physiological changes induced by spaceflight, and/or exposure to reduced gravity under 1G is provided. The method includes: identifying least one compound that interacts with genes altered in neural cells by exposure to reduced gravity under 1G, and administering the at least one compound to a patient in need thereof. [00216] In some embodiments, the at least one compound is a bioactive molecule derived from food. In some embodiments, the at least one compound is an active pharmaceutical ingredient. In particular embodiments, the at least one compound is selected from mebendazole, resveratrol, trichostatin A, thioridazine, and rapamycin.

[00217] Overall, the present disclosure discloses methods for understanding and addressing microgravity’s effects on “astroimmunology”, in particular how and why the immune system changes in smG and spaceflight; methods for modeling, understanding, and addressing cardiac deconditioning and dysfunction (e.g., cardiomyopathy); and methods for modeling, understanding, and addressing neural dysfunction in smG and spaceflight. These results also provide opportunities to develop countermeasures that will help normalize cell, tissue, and organ function in microgravity and spaceflight (e.g., immune cell function). Additionally, immunological and cellular changes induced by simulated microgravity provide a useful model for understanding aging and dysfunctional processes, including age-related diseases, cardiac and neural disorders, and inflammatory aging.

Materials and Methods

[00218] The following materials and methods were used in the examples below.

[00219] Human blood sample and cell culture

[00220] De-identified peripheral blood buffy coat samples were obtained from 29 healthy human donors between the ages of 20 and 46 from the Stanford University Blood Center. PBMCs were isolated using a Ficoll gradient method. PBMCs were counted and re-suspended in complete media at 1x106 cells/ml (RPMI 1640, 10% Fetal Bovine Serum, 2mM L-Glutamine, 1% penicillin/streptomycin, O.lmM non-essential Amino acid,lmM sodium pyruvate, 50uM 2- mercaptoethanol,10mM HEPES). To generate simulated microgravity, the cell suspension was loaded into 10 ml disposable high aspect ratio vessels (Synthecon, Houston, TX) and rotated at 15 rpm for 25 hours. For the 1G control, the cell suspension was plated in standard 6-well culture plates, as standard static culture plates or culture flasks have been shown to be comparable to static high aspect ratio vessels by others in major immunological assays, and it has been observed that there are no significant differences in major immunological markers (e.g. live/dead, MHC and activation, and exhaustion markers) in tested resting and TLR7/8 stimulated immune cells (e.g. B cells) by flow cytometry (data not shown). Additionally, the goal was to compare differences in simulated microgravity to standard widely used immune cell cultures. 1G and simulated microgravity cultures were simultaneously placed in the same 37C, 5% CO2 incubator. To stimulate PBMCs, samples were mixed with luM R848 (TLR7/8 agonist, Invivogen, San Diego, CA) after 16 hours, for 9 hours of stimulation. At the end of each experiment, the cell suspension was quickly collected, spun down at 500x g, washed with phosphate-buffered saline, and used for downstream analysis. For super-resolution imaging cells were fixed before spinning.

[00221] Single-cell RNA sequencing

[00222] 1x104 PBMCs from each condition were counted and loaded on the 10X Genomics

Chromium Controller and the libraries were prepared using Chromium Next GEM Single Cell 5’ Reagent Kit v2 according to the manufacturer’s protocol (10X Genomics, Pleasanton, CA). The quality of libraries was assessed using Agilent TapeStation 4200, and test-sequenced on Illumina NextSeq 550. The full sequencing was performed on an Illumina NovaSeq 6000 by SeqMatic (Fremont, CA).

[00223] Processing of Single-cell RNA-seq data

[00224] Data processing was performed using lOx Genomics Cell Ranger v6.1.2 and MTD26 pipelines. The "cellranger count" was used to perform transcriptome alignment, fdtering, and UMI counting from the FASTQ (raw data) files. Alignment was done against the human genome GRCh38-2020- A. Cell numbers after processing were: 1G unstimulated 13,304 cells, uG unstimulated 21,709 cells, 1G stimulated 16,397 cells, and uG stimulated 14,913 cells. The MTD pipeline was used to generate the single-cell microbiome count matrix from the FASTQ files.

[00225] Downstream analyses were performed in R (version 4.2.0), primarily using the Seurat R package (version 4.1.1)65,66 and custom analysis scripts. First, a quality control step was executed that removed the cells containing >10% mitochondrial RNA and <=250 genes/features. The doublet cells were identified and removed from the downstream analysis by using the DoubletFinder R package (version 2.0.3)67 with parameters PCs=l:30, pN=0.25, and nExp=7.5%. To avoid the influence of hemoglobin transcripts on the analysis, the putative red blood cells were filtered out (defined by the method below) before the following process. Raw RNA counts were first normalized and stabilized with the SCTransform v2 function (SCT), then followed by the CCA integration workflow for joint analysis of single-cell datasets. In doing so, the top 3,000 highly variable genes/features among the datasets were used to run SCT; and then 3,000 highly variable genes/features and the 30 top principal components (PCs) with k.anchor=5 were used to find "anchors" for integration. The clustering step was executed by using the 30 top PCs summarizing the RNA expression of each cell with a resolution parameter of 0.8.

[00226] To identify putative cell types, Azimuth (version 0.3.2) pipeline was used with the reference dataset of Human - PBMC cell type. Cell type annotation results from Azimuth were validated by checking the markers of each cell type. Gene differential expression analyses were done by Seurat PrepSCTFindMarkers then FindAllMarkers/FindMarkers functions with MAST68 algorithm. The pseudo-bulk analysis was conducted to find overall DEGs of uG against 1G in either unstimulated or stimulated PBMCs, using the FindMarker function with parameter min.pct=0.005 and logFC=0.1. To compare the stimulated and unstimulated PBMCs under uG and 1G conditions, log2FC values of their DEGs were subtracted (uG - 1G). The top 50 most upregulated DEGs between stimulated and unstimulated PBMCs under 1G were used for comparison.

FindConservedMarkers function was used to find DEGs that are conserved between the groups with the same parameter settings as FinderMarkers. The top 50 conserved DEGs specifically sensitive to uG were selected based on the rank of the absolute sum of log2FC values, derived separately from the sum of positive log2FC values and the sum of negative log2FC values. Rank-Rank Hypergeometric Overlap (RRHO) analysis was performed by using RRH02 R package (version 1.0) to compare the differential expression patterns between 1G and uG of stimulated vs unstimulated PBMCs. The ranks of the genes in the two gene lists were determined by calculating - log 10(adj pvalue)*log2F C .

[00227] Pathway Analysis

[00228] Following differential expression, Ingenuity Pathway Analysis (IP A, Qiagen) was used to discover changes in enriched pathways in each comparison. DEGs with p-values < 0.05 and |Log2FC|> 0.1 were incorporated into the IPA canonical pathway analysis.

[00229] Trajectory Analysis

[00230] To study the inferred trajectory of PBMC differentiation, cell trajectory analysis was performed by using the Monocle 3 R package (version 1 .2.9). Seurat data was first subsetted to uG and 1G groups, then the functions were run as.cell_data_set(), cluster_cells(), and leam_graph(). Then, order cell s() was run with the selection of cell types representing early development stages (CD4 naive, B naive, plasmablast, and HSPC) as the roots of the trajectory.

[00231] Calculating cell scores of inflammatory aging and cellular senescence

[00232] The inflammatory aging (iAge) index was calculated by the sum of the cell scores that count by multiplying normalized and scaled gene expression with the corresponding coefficient of the gene in the iAge gene set. Cellular senescence was scored using Seurat AddModule Score function on the SenMayo gene set.

[00233] Viral and microbial abundance analysis

[00234] The output reads counts from MTD pipeline were then combined with the host reads and analyzed in R with Seurat package and other customized scripts. The relative abundance (frequency) of a virus or microbe was determined by dividing its reads count by the total reads count (host and non-host) in that sample. The classification results were further validated using a different method (Magic-BLAST).

[00235] APCs to T cell intercellular communication

[00236] To study the difference in intercellular communication from APCs to T cells between uG and 1G, nichenet R packages (version 1.1.0) were used to analyze cells in the dataset belonging to APCs (B cells, DCs, or monocytes) and T cell types. The "Differential NicheNet" workflow was implemented. The expressed genes in sender cells - APCs were selected if they were expressed in at least 10% of that APC cell population. The gene set of interest in receiver cells - T cells was defined by adjusted p-value <=0.05 and Log2FC >= 0.25 in the DEGs. Top 30 ligands that were further used to predict activated target genes and construct an activated ligand-receptor network. Default settings were used for all other parameters.

[00237] Bulk RNA sequencing

[00238] Total RNA was extracted using RNeasy Plus Mini Kit (Cat# 74134, Qiagen) as per the manufacturer's instructions. RNA quantity check, preparation of RNA library, and mRNA sequencing were conducted by Novogene Co., LTD (CA, US). About 20 million paired-end 150 bp reads per sample were generated from Illumina NovaSeq 6000 Sequencing System. FASTQ raw reads were analyzed using the MTD pipeline. Differential gene expression analysis between groups was done by DESeq2 R package (version 1.36.0) with control for the subject effect. Genes with adjusted p-value < 0.05 and |log2(FoldChange)| > 0 were considered as differentially expressed. DEGs with p-values < 0.05 and |Log2FC|>0.5 were used for the IPA canonical pathway analysis. Different from single-cell (SC), to calculate the i Age index for bulk RNA-seq, normalized and scaled gene expression was multiplied with the gene's coefficient in the iAge gene set, then summed for each sample. Cellular senescence was scored using the ssGSEA method on the SenMayo gene set. Cell Type Frequency Changes within PBMCs were predicted by CIBERSORTx Docker image - Fractions Mode version 1.0. The single-cell RNA-seq data from PBMCs was used to build the Signature Matrix File as the reference to predict the cell proportion in the bulk RNA-seq data.

[00239] Mouse spleen Bulk RNA-seq

[00240] Mouse spleen Bulk RNA-seq raw data was acquired from NASA GeneLab Data Repository with the accession ID GLDS-420. The detailed study description and experiment protocols are on the data repository https://genelab-data.ndc.nasa.gov/genelab/accession/GLDS-420. MTD pipeline was used to process the FASTQ raw data, generate the count matrix, and then analyze differentially expressed genes between Flight and Ground groups. [00241] Gene set overlapping analysis

[00242] The p-value of gene overlapping between two datasets was calculated by Fisher's Exact Test in GeneOverlap R package (version 1 ,32.0)[Shen L, Sinai ISoMaM (2022)]. GeneOverlap: Test and visualize gene overlaps. R package version 1.32.0, http://shenlab-sinai.github.io/shenlab-sinai/). The 375 DEGs in uG vs. 1G from unstimulated PBMCs single-cell RNA-seq results were used to match with the genes from PBMC bulk RNA-seq, 14, Twins, or JAXA studies. For the mouse genes in GLDS-420, they were first converted to the human orthologous before the analysis. In the matched genes, those expressions that were in the same log2FC direction as 375 DEGs as well as with p-value < 0.05, were considered overlapping (except for 14, where either direction was considered overlapping). Complete linkage hierarchical clustering was used to analyze dissimilarities in genes or pathways between datasets, and the results were visualized by the ComplexHeatmap R package (version 2.12.0). Moreover, the IPA canonical pathway analysis was performed on the matched genes of 14 and Twins studies. The 106 core gene set was constituted by DEGs that consistently change their log2FC directions in both SC and bulk data of PBMCs. The alteration of the core gene set by the compound was measured by Gene Set Enrichment Analysis (GSEA) and Pearson correlation test.

[00243] Compound Analysis

[00244] FDA-approved drugs (n = 1692) were selected from the DrugBank database and food compounds (n = 7962) were selected from the FoodDB database as previously described. LINCS compounds (n = 5414) were obtained from the LINCS L1000 project. ‘Compound’ is used as a general term for ‘drug’, ‘food compound’ and ‘LINCS compound’ throughout the present disclosure.

[00245] Compound-protein interactions are extracted from the STITCH database v5.079 by matching the InChi keys of drugs/food/LINCS compounds. STITCH collects information from multiple sources and individual scores from each source are combined into an overall confidence score. After processing, three data sets are obtained: i) drug-gene interaction dataset containing 1890 drugs and 16,654 genes with 542,577 interactions ii) food compound-gene interaction dataset containing 7654 compounds and 116,375 genes and 818,737 interactions iii) LINCS compoundgene interaction dataset containing 5414 compounds and 16,794 genes and 692,152 interactions. [00246] Statistical significance for the overlap between compound genes and the DEGs from the uG vs 1G of the unstimulated PBMCs single-cell RNA-seq is calculated using Fisher’s exact test. The universal gene set contains all genes that interact with at least one compound. The compound with a low p-value interacts with a higher proportion of the DEGs than that expected by chance. Statistically significant compounds were then obtained after the Bonferroni adjustment of p-values. The pipeline for this compound analysis is implemented in the R script GCEA.

[00247] Cell Staining and Imaging (Super-resolution microscopy)

[00248] Live PBMCs were stained with 60 nM MitoTracker Red-CMX-Ros (ThermoFisher, Waltham, MA) either in 6-well plates or in the microgravity chambers for the last 2 hr of the microgravity simulation. At the end of the microgravity simulation cells were immediately fixed by 1 : 1 mixing the cell suspensions with 2x concentrated fixative (10 % Sucrose (w/v) 120 mM KC1, 1% (w/v) glutaraldehyde, 8% (w/v) PFA pH 7.4) and incubated for 15 minutes at room temperature followed by 15 minutes on ice. Fixed cells were washed and stored in PBS until further staining for up to a week at 4 C. 1 million fixed cells were resuspended in 1 mL of permeabilization solution (0.1% TritonX-100 in PBS) for 5 minutes. After twice washing in PBS, pellets were resuspended in 0.5 mL 1% BSA PBS containing Phalloidin-iFluor-488 (cat# abl76753, Abeam pic., Cambridge, UK) at the manufacturer’s recommended dilution, and were incubated for 90 minutes with gentle agitation. After washing in PBS, cells were stained with Hoechst 33342 (1 pg/mL in PBS) for 10 minutes. The fixed-stained cells were immobilized at 3x105 cells per well density in glass-bottom 96 well microplates (Greiner Bio-One, Monroe, NC), which were pre-coated with polyethyleneimine (1 : 15,000 (w/v)) for 16 hours in a 37°C incubator, and washed twice with PBS. Microplates with the cell suspensions were centrifuged in a swing plate rotor centrifuge (Eppendorf 5810 R) at 400 x g and for 10 min and then fixed on the surface by adding an equal volume of 8% (w/v) PFA for 5 min. Finally, the fixative was replaced with 100 pL of antifade reagent (Vector Prolong Gold (ThermoFisher). Samples were imaged immediately after this procedure.

[00249] Image acquisition

[00250] Immobilized fixed-stained PBMCs were imaged on a Zeiss LSM980 Airyscan2 laser scanning confocal microscope (Carl Zeiss Microscopy, White Plains, NY). Single PBMCs were manually selected for recording based on low-resolution preview scans showing only nuclei. All singlet cells were selected in a small neighborhood to avoid biases. In each microscopy session, 24- 40 cells were selected for recording in one well for each condition. This was performed in an interleaved manner, capturing 6-8 cells at a time, and then moving to the next well and then repeating this multiple times using the Experiment Designer module for automation. Super resolution volumes of (358^358x70 pixels, 0.035x0.035x0.13 pm/pixels resolution) were recorded in the above determined positions using Definite Focus autofocusing. A Plan-Apochromat 63x 1.40 Oil lens, Airyscan2 SR (super resolution) mode with optimal sampling and frame switching between 3 fluorescence channels to minimize spectral cross-bleed were used. MitoTracker Red, iFluor488, and Hoechs33342 were excited with 561, 488, and 405 nm solid-state lasers, respectively, using the optimal emission filter for each channel. 3D Airyscan2 processing was performed with standard filtering settings. With PBMCs from four donors, in six staining and microscopy sessions total of 930 valid volumes have been recorded.

[00251] 2D 2-dimensional image analysis

[00252] Staining intensities, mitochondrial size, and punctate over diffuse index (defined as variance over mean) were determined in Image Analyst MKII 4.1.14 (Image Analyst Software, Novato, CA) in maximum intensity projection images using a custom pipeline. Cellpose 2.0 with the “cyto2” neural network was used for finding cells in the images based on nuclear and actin staining. Protruding actin bundles were analyzed by first binarizing projection images of actin using the trainable LAB KIT segmentation, and this was followed by separation of protrusions and measurement of their maximal distance from the bulk of the cell using morphological erosion and distance image functions in Image Analyst MKII. Rescaled projection images were saved and further analyzed in CellProfiler 4.2.4, where images were segmented for nuclei and these segments were extended to the cell boundaries based on the phalloidin staining. These profiles were used for measuring shape, granularity spectrum, and texture in actin, mitochondria, and nuclei. For actin granularity spectrum measurement the following parameters were used in CellProfiler MeasureGranularity function: “Subsampling factor for granularity measurements’^ 1, “Subsampling factor for background reduction=0.125”, “Radius of structuring element”=12, “Range of granular spectrum” =16. Similar results were obtained using a set of discrete Fourier transformation-based Butterworth band pass filters in Image Analyst MKII for analysis of actin granularity spectrum changes in simulated microgravity and TLR stimulation. Here a series of 16 adjacent 4-pixels wide (in Fourier space of a 512x512 pixels image), 300-order band pass filters with “Corrected Integral” normalization and absolute value calculation were used starting at 1 pixel, and mean pixel intensities over whole cells in the filtered images were normalized to the unfiltered image. We have previously shown that this technique is primarily sensitive to sub-resolution changes in thickness of underlying filamentous structures, such as actin bundles in this case. We found no changes in granularity spectra measured by CellProfiler or Image Analyst MKII when analyzing mitochondria or nuclei of the same cells, excluding optical biases.

[00253] 3 -dimensional image analysis

[00254] Mitochondria: cell volume fraction was measured using a modification of the “Mitochondria:cell volume fractionator (basic)” pipeline in Image Analyst MKII, using the hole- filled actin image as cell marker and MitoTracker Red as mitochondrial marker, and all image planes to measure areas of mitochondrial and cell profiles. Cell and nucleus volumes and surface areas were measured using Imaris 9.9 (Oxford Instruments, Concord, MA) using the Cell and Batch modules.

[00255] For 2D and 3D image analyses, tabular data generated by Image Analyst MKII, CellProfiler and Imaris were matched to conditions in Microsoft Excel and in Mathematica 13 (Wolfram Research, Champaign, IL) and visualized in Prism 9 (GraphPad, La lolla, CA) for statistical analysis.

[00256] Cdc42, Rael, and RhoA G-LISA Activation Assay

[00257] PBMCs from different conditions were collected and 7x106 cells were lysed and snap- frozen immediately in liquid nitrogen. Cell lysate protein concentrations were measured using Precision Red Advanced Protein Assay Reagent (cat# ADV02, Cytoskeleton Inc., Denver, CO) and equalized. The GTP -bound Cdc42, Rael, and RhoA levels were performed according to the manufacturer’s protocol (cat# BK127-S, BK128-S, and BK124-S respectively, Cytoskeleton Inc.) and measured with a spectrophotometer at 490 nm.

[00258] ROS detection

[00259] The abundance of ROS was measured via 2',7'-dichlorodihydrofluorescein diacetate (DCFDA). Collected cells (100,000 cells per well) from each condition were incubated with 10 uM DCFDA Staining Buffer in dark at 37C for 30 minutes as per the manufacturer's suggestions (cat# 601520; Cayman Chemical, Ann Arbor, Ml). The fluorescence was measured with a Pherastar FSx (BMG Labtech Inc., Cary, NC) microplate reader with the excitation wavelength at 495 nm, and emission at 530 nm.

[00260] Multiple cytokine analysis

[00261] Luminex Bead Array

[00262] Cell culture media (supernatant) from all experimental conditions were separately collected and snap-frozen. Samples were sent to the Stanford Human Immune Monitoring Center and MILLIPLEX® 48 Plex Premixed Magnetic Bead Panel (MilliporeSigma, Burlington, MA) was performed per the manufacturers’ instructions.

[00263] IFN ELISA level measurement

[00264] Cell culture media (supernatant) from microgravity and 1G +/- R848 were separately collected at each experiment and snap-frozen. The samples were then thawed and used to detect the levels of IFN-y (cat# 430104; Biolegend Inc., San Diego, CA), IFN-a all subtypes (cat# 41135; Pestka Biomedical Laboratories, Inc., Piscataway, NJ) per the manufacturers’ instructions.

[00265] IL ELISA level measurement [00266] Cell culture media (supernatant) from microgravity and 1G were separately collected at each experiment and snap-frozen. The samples were then thawed and used to detect the levels of IL- 8 (cat# 431504; Biolegend Inc., San Diego, CA), IL-6 (cat# 430504; Biolegend Inc., San Diego, CA) per the manufacturers’ instructions.

[00267] Flow cytometry

[00268] Single cell suspensions from different donors and conditions were stained with LIVE/DEAD™ Fixable Blue Dead Cell Stain kit(cat# L34962; Invitrogen) for viability followed by Fc-blocking with human igG (cat# AG714, Sigma-Aldrich) at room temperature for 10 mins. For staining of intracellular cytokines, single cell suspension was either stimulated with luM R848 or not in the presence of 2.5ug/ml Brefeldin A (cat#420601; Biolegend) for 9 hours prior to surface staining. The cells were further stained with fluorophore-conjugated surface antibodies for 20 min at 4C and intracellular antibodies for 30 min at room temperature following fixation and permeabilization using Foxp3 staining buffer set (eBioscience). Cell phenotyping was analyzed on a Cytek Aurora™ instrument and analyzed using FlowJo™.

[00269] JAXA transcriptomic data

[00270] JAXA cell-free RNA differential expression data was shared by Dr. Masafumi Muratani at the University of Tsukuba. Briefly, blood samples were collected from 6 astronauts before, during, and after the spaceflight on the ISS. Data from the samples of the 6 astronauts was pooled into a single count, at day 5 and also at day 30, post-launch (i.e. in-flight), and compared to prelaunch. In this study, human blood from astronauts was collected using Vacutainer EDTA-plasma separate gel collection tubes and centrifuged for 30 min at 3,800 rpm (1,239 g, ISS) or 1,600 g (ground) before freezing at -95°C (ISS) or -80°C (ground). Cell-free RNA was purified from plasma samples through a TRIzol/chloroform method, sequenced (SMART-seq Stranded Kit, Takara Bio) and analyzed by the team leading the JAXA collaboration. Data was provided in csv format as normalized mean counts and normalized SEM of each gene at preflight and inflight time points. DEGs between 30 days in-flight and pre-flight time points were calculated by log2FC with p-value < 0.05.

[00271] Inspiration4 mission data

[00272] Four astronauts’ transcriptomic data from the Inspiration4 (14) mission was shared by Dr. Christopher E. Mason and his team at Cornell University. Blood samples were collected before (preflight L-92, L-44, and L-3), during, and after (R+l) the 3 -day spaceflight in the SpaceX Dragon capsule. For this mission, data from the samples of the four astronauts was used to compare postflight (R+l) vs pre-flight (L-44) DEGs. Referring to the analysis workflow used by the 14 Cornell team, a list of fold change and p values based on post flight vs preflight findings was generated. The Seurat FindMarker parameters used to calculate 14 DEGs were the same as those used for the identified “core” 375 DEGs. Next, the DEGs and pathway overlap from 14 single-cell analysis were calculated by using the methods described above in the section on Gene set overlapping analysis.

[00273] NASA Twins study data

[00274] Gene expression data from the NASA Twins study was provided by Cem Meydan in the Mason Lab in csv format and organized by Dr. Afshin Beheshti as normalized mean counts for 4 immune cell types. In brief, one astronaut was monitored before, during, and after a 1-year mission onboard the ISS, and his identical twin sibling was also monitored at the same time serving as a genetically matched ground control for this study. The NASA Twins study team matched the “core’ list of 375 genes against their Twins DEGs in their 4 immune cell types, and provided a list of fold change and p values based on inflight vs pre-flight findings. Next, the DEGs and pathway overlap from the Twins analysis were calculated by using the methods described above in the section on Gene set overlapping analysis.

[00275] ROS reduction compound

[00276] Quercetin (Sigma Aldrich, St Louis, MO) stock solution was prepared with DMSO at lOOOx. In the cell culture experiments utilizing quercetin, the concentration of quercetin was decided based on existing literature. After incubation with quercetin, cells were counted with a Cellometer Auto 2000 Cell Viability Counter (Nexcelom, San Diego, CA), which utilizes Acridine Orange and Propidium iodide dual-staining systems to accurately distinguish live vs dead cells. After 25 hours of 50uM quercetin treatment, the cell viability across PBMCs in both 1G and simulated microgravity conditions were at least 93%. There were no statistical differences in viability observed between the groups with and without quercetin treatment.

[00277] Statistical Analyses

[00278] In addition to the methods described above, the Wilcoxon Rank Sum Test was used to assess whether the distributions of data from cell score or microbiome abundance were significantly different between the 1G and uG cell populations from single-cell data. The association between single-cell and bulk RNA-seq in gene expressions was tested by Spearman's correlation. Mann- Whitney test was performed on ROS reduction by quercetin. Unpaired parametric two-tailed t-tests were performed on single-cell iAge, SenMayo and microbial abundance and imaging analyses for statistics. G-LISA, ELISA, Luminex and DCFDA results were assessed by paired parametric two- tailed t-test. However, given the existing transcriptomic and cytokine data showed decreased interferon coupled to increased IL-ip, IL-6, and IL-8 production in microgravity, for validation the flow cytometry results were assessed by one-tail paired parametric t-test. In PBMCs bulk RNA-seq results, the difference in iAge and SenMayo scores of samples with or without compound treatment was evaluated by two-tailed paired parametric t-test. R (version 4.2.0) and GraphPad Prism 9 were used to conduct the statistical analyses. Significance was set at 0.05. Outliers in datasets were assessed using Grubbs’ test (alpha =0.01) and specified in figure legends if any were removed for all data.

[00279] Data and code availability

[00280] Raw and processed lOx Genomics and bulk RNA-seq data can be found at Gene Expression Omnibus (GEO) using accession number GSE218937. The code used for analysis of sequencing data is available at GitHub repository (https://github.com/FEI38750/Immune_Dysfunction_in_Microgravity).

Examples

[00281] Hereinafter, the present application will be described in more detail with reference to Examples. However, the following Examples are merely preferred embodiments for illustrating the present application, and therefore, the scope of the present application is not intended to be limited thereto. Meanwhile, technical matters not described in this specification can be sufficiently understood and easily implemented by those skilled in the art of the present application or similar technical fields. With reference to the appended figures, these exemplary embodiments of the present disclosure will be described in detail below.

Simulated Microgravity Alters the Transcriptional Landscape of Individual Immune Cells [00282] Human peripheral blood mononuclear cells (PBMC) samples from two young healthy CMV+ donors, one male and one female, were taken, with cells from both donors either loaded into a rotating wall vessel (RWV) to simulate reduced gravity, or exposed to normal gravity (1G) as a static control, for 16 hours of conditioning. Both groups of PBMCs were then either stimulated for an additional 9 hours with R848, a standard TLR7/8 agonist, or left unstimulated, for a total of 25 hours. TLR7/8 was chosen as a putative target because stimulation thereof mimics viral infection, and it is expressed on most human immune cells, including T-cells. Using this methodology, a single cell atlas of 66,323 human PBMCs exposed to these conditions was developed.

[00283] Results

[00284] After 25 hours of exposure to both the control (1G) and simulated microgravity testing conditions, 28 clusters of immune cells visualized by UMAP (Uniform Manifold Approximation and Projection) were identified and quantitatively compared for unstimulated cells of both gravity exposure groups, including cell types such as mucosal associated invariant T cells (MAIT cells), double negative T-cells, y8 T-cells, innate lymphoid cells, and plasmacytoid dendritic cells (Fig. 1A). Cells were resolved into 28 distinct clusters. Simulated microgravity altered proportions of immune cell clusters to a mild extent, with B intermediate cells, and MAIT cell proportions being most negatively impacted, and CD14+ monocytes, and CD4+ T effector memory cells being most increased based on percent change (Fig. IB). Across all immune populations, simulated microgravity altered expression of over 4500 genes with adj P cutoff of <0.05. This list was refined to a core list of approximately 375 differentially expressed genes (DEGs) with an additional cutoff of |log2FC| > 0.1. This list was further condensed to visualize on a Volcano plot with |log2FC| > 0.25 (Fig. 1C), showing only the very top positively and negatively altered genes. Volcano plots of DEGs for individual unstimulated immune cell clusters under simulated microgravity are shown in Fig. ID. Across all immune cells, some of the most induced genes in simulated microgravity included acute response genes such as sl00a8, sl00a9, sl00al2, thbsl, heat shock genes such hsp90abl, chemokines like ccl2, ccl4, iron storage genes (fthl, ftl), and matrix metalloproteinases (mmp9). The most reduced genes in simulated microgravity included interferon response (statl) and associated guanylate binding proteins (gbpl), heterogeneous nuclear ribonucleoprotein H (HNRNPH1), and cold shock genes (rbm3, cirbp). Expression of the top DEGs (with mitochondrial encoded genes excluded for visual simplicity) across 22 populations of immune cells are shown in Fig. IE. CD14+ classical monocytes, CD16+ nonclassical monocytes, and natural killer (NK) cells exhibited the most pronounced changes across major gene sets, consistent with short term simulated microgravity’s direct effect at reprogramming transcriptional changes most prominently in innate immunity. Consistently, using single-cell trajectory analysis, numerous trajectories mainly in the innate immune cell clusters were identified, especially the monocyte cluster, in response to simulated microgravity. Trajectory analysis is used to construct a path that describes how cells move through different states, and the numerous states seen in the monocyte cluster in simulated microgravity may reflect an increased capacity to generate distinct transcriptional states to simulated microgravity (Fig. IF).

[00285] Ingenuity pathway analysis (IP A) (Fig. 1G and Fig. 1H) generated using the core list of 375 genes from the overall populations, as well as the DEGs in major immune cell types revealed that monocytes, conventional dendritic cells type 2 (cDC2)s, double negative (dn)T cells and NK cells show the most notable pathway alterations. Major pathways altered by simulated microgravity across immune cells included reductions in oxidative phosphorylation, interferon signaling like protein kinase R (PKR) in interferon response, nuclear receptor signaling (LXR/RXR, PPAR, AHR), RHOA and pyroptosis signaling, as well as increases in BAG2 (heat shock protein 70 interactor) signaling, fibrosis signaling, actin-based motility, RAC, HIF1 signaling, acute phase response, oxidative stress and sirtuin signaling, amongst others.

[00286] Given that the multiple pathways detected were associated with inflammatory processes linked to aging (i.e. increased innate immunity coupled to reduced adaptive immunity), next it was determined whether acute exposure to simulated microgravity mimicked inflammatory aging processes in immune cells. The gene expression signatures of individual immune cells and overall immune signatures were mapped against two recently developed inflammatory signatures of aging, the inflammatory age (iAge) clock, and the SenMayo list of senescence associated secretory inflammatory products. Simulated microgravity induced a significant enrichment in inflammatory aging related genes, consistent with the notion that short term simulated microgravity can induce aging-like inflammatory changes in unstimulated immune cells (Fig. II, Fig. 1J, and Fig. IK).

[00287] Next, because both spaceflight and aging are associated with reactivation of latent viruses, the meta-transcriptome of the single cell analysis was mined with meta-transcriptome detector (MTD) pipeline. Surprisingly, it was shown that as little as 25 hours of simulated microgravity could induce the transcription of latent retroviruses and mycobacteria within human immune cells (Fig. IL, Fig. IM), directly implicating microgravity itself as a contributing trigger for latent pathogen activation. The meta-transcriptome results were confirmed with a different alignment tool, and increases in gammaretrovirus and mycobacterium canettii transcripts seen with MTD pipeline were still detected.

[00288] Finally, as strong changes in gene expression pathways linked to innate cells were identified, including those with the capacity to present antigen, this knowledge was leveraged to utilize NicheNet algorithms to generate a comprehensive predicted ligand:receptor interactome map of human antigen presenting cells (APCs, plus plasmacytoid dendritic cells) and T cells in simulated microgravity vs 1G (Fig. IN). Across APC donors and recipient T cells, numerous significantly predicted ligand receptor interactions were identified as elevated in simulated microgravity vs 1G. For instance, monocytes and dendritic cells induced IL-1 proteins while some B cells provided IL- 23 A, and IL-7. All APCs provided unique chemokine signals to T cells. Mmp9, ccl2 and thbsl were amongst the most significantly induced genes in simulated microgravity, and the products of these genes show differential predicted receptor expression (e.g. CD44, CD47, ITGB1, CCR4, CCR5) in T cells (Figs. 10, IP, IQ, 1R, IS, and IT]), but all show predicted enhanced target gene expression in T cells. Thus, while simulated microgravity itself likely induces direct transcriptional changes in immune cells, it cannot be excluded that local paracrine effects of secreted products from one immune cell to another also contribute to the overall gene expression and pathway changes.

[00289] After stimulating PBMCs with a TLR7/8 agonist in 1G and simulated microgravity, 23 clusters of immune cells were characterized by UMAP (Fig. 2A). In contrast to the unstimulated conditions, stimulation with a TLR7/8 agonist induced massive preferential expansion of CD4+ central memory (TCM) cells (Fig. 2B). The microgravity itself impacted differential response to stimulation. Consistent with previous reports, simulated microgravity dampened expansi on/responses of NK cells, and, to a lesser extent in the donors examined, CD8 T effector memory (TEM) cells. MAIT cells, a cell type with previously unknown responses to microgravity, were also shown to be reduced in number. Simulated microgravity drove a preferential increase in CD14+ monocytes over 1G controls, indicating that this cell type is especially sensitive to the combination of simulated microgravity and TLR7/8 activation.

[00290] Across all cell types, the combination of simulated microgravity and TLR7/8 stimulation altered the expression of over 9000 differentially expressed genes (DEGs) with adj P cutoff of <0.05. As with the unstimulated data, this list was refined to a core gene list of approximately 317 DEGs based on |log2FC| of > 0.2. This list was further reduced to visualize on a Volcano plot with |log2FC | >0.25 (Fig. 2C), showing only the most positively and negatively altered genes. Some of the most induced genes by simulated microgravity over 1G in response to TLR7/8 included cytokines and chemokines, such as ccl8, ccl4, ccl7, cxcl8, and il lb, and acute response proteins like sl00a8, sl00a9, slOOal 1, and thbsl . Additional genes induced in simulated microgravity were linked to tryptophan breakdown (idol), mitochondrial antioxidant defense (sod2), the cytoskeleton (rhoq), and iron storage genes like fthl , ftl. The most downregulated genes when comparing simulated microgravity to 1G during TLR7/8 stimulation, included genes belonging to guanylate binding proteins (gbpl, gbp2, gbp4, gbp5), which were the most reduced set of genes by fold change and adj P, as well as interferon pathway genes, like irfl, statl, isg20, ifi 16, cold shock genes (rbm3, cirbp), heterogeneous nuclear ribonucleoprotein H (HNRNPH1), cell killing genes (prfl, gzmb), and T/NK cell activation markers like cd69. Many of these genes were consistently altered by simulated microgravity alone without stimulation, indicating a conserved response, even in the setting of additional exogenous stimulation with a TLR ligand. Expression of top DEGs across 19 populations of immune cells is shown in Fig. 2D. Volcano plots of DEGs for individual immune cell clusters are shown in FIG. 2E. CD14+ monocytes, NK cells, CD8+ TEM, and CD4+ TCM cells showed the most significant changes in the most-altered genes induced by TLR7/8 agonist stimulation in simulated microgravity. Interestingly, using single-cell trajectory analysis (Fig. 2F), fewer trajectories in simulated microgravity stimulated with TLR7/8 were identified compared to the 1G control. These findings suggest that under simulated microgravity, cells display reduced differentiation states in response to stimulation.

[00291] IPA results (Fig. 2G) generated using the core list of approximately 317 genes most affected by reduced gravity/microgravity from the overall populations, as well as the DEGs in major immune cell types demonstrated that nearly all immune cells show changes across numerous pathways during microgravity and TLR7/8 induction. Major pathways reduced across most immune cells in simulated microgravity included PKR in interferon response (and associated eif2 signaling), interferon signaling, JAK/STAT signaling, pyroptosis signaling, cytotoxic T cell mediated killing of target cells and death receptor signaling. Major pathways induced by short term simulated microgravity included sirtuin signaling, fibrosis signaling, signaling by Rho GTPases, BAG2 (heat shock protein 70 interactor) signaling, HIFla signaling, acute phase response and associated HMGB1 signaling, amongst others. These pathways are consistent with microgravity facilitating innate like inflammation at the expense of interferon driven adaptive immunity and adaptive immune effector function (e.g. CD8+ T cell killing). Despite some similarities in pathways altered to simulated microgravity alone (Fig. 1G), a lower iAge score was actually detected globally across all immune populations in simulated microgravity plus TLR7/8 compared to 1G controls (Fig. 2H). While the reason for this finding is unclear, and without being bound by theory, it appears to have been driven by a highly significant reduction in score by naive B cells, naive CD4+ T cells, and reductions in lesser studied PBMC populations, hematopoietic stem and progenitor cells (HSPC)s and double negative T cells. Lower iAge also could be reflective of altered immune activation in simulated microgravity, such as seen in CD16+ monocytes (Fig. 2H, right panel). Despite a reduction in iAge, an increased SenMayo score was still observed in simulated PBMCs (Fig. 21, Fig. 2J), illustrating different compositions of genes in these two gene sets. NicheNet analysis across major APC types to T cells post TLR7/8 agonist in simulated microgravity vs 1G (Fig. 2K) illustrated some of the significant cytokines, chemokines, surface molecule ligands and receptors used in simulated microgravity upon TLR7/8 stimulation. Compared to the unstimulated interactome (Fig. IN), increased production and diversity of inflammatory cytokines and chemokines were observed. IL-1 was also produced in APCs, but it was noted that increased TNF superfamily products like TNF, TNFSF12 (TNF-related weak inducer of apoptosis, TWEAK) and TNFSF15 (vascular endothelial growth inhibitor, VEGI), and lymphotoxin (LTA) were preferentially produced to modulate T cell function. [00292] Next, the differential responsiveness of immune cells to TLR7/8 stimulation was assessed. Under 1G conditions, stimulation led to a marked induction of CD4+ TCM, at the expense of CD14+ monocytes and CD4+ naive T cells, coupled to an expected pronounced inflammatory gene signature, including marked induction of interferon inducible genes, gbp transcripts, and chemokines across most immune cell populations (Figs. 2L and 2M). Under simulated microgravity, TLR7/8 stimulation also induced CD4+ TCM, at the expense of naive CD4+ T cells, though proportions of CD14+ monocyte populations did not reduce as seen in 1G (Fig. 2N). In simulated microgravity, TLR7/8 stimulation also induced a robust expression of inflammatory genes, including interferon inducing genes across most cell types (Fig. 20). Next, to determine the sensitivity of individual immune cell populations to TLR7/8 agonist in 1G versus simulated microgravity, the differences in responsiveness to stimulation were compared. The fold change induction in 1G from induction in simulated microgravity was subtracted to determine sensitivity to stimulation. Remarkably, across overall immune cells, a pattern of reduced responsiveness to TLR7/8 simulation was seen in simulated microgravity to most of the highest genes induced at 1G (Fig. 2P), with T cells and NK cells showing the most reduced inflammatory gene induction. T cells, NK cells, and overall across all cells exhibited blunting of induction of numerous genes in interferon signaling, and gbp genes in simulated microgravity in response to TLR7/8 agonist. Interestingly, monocytes tended to maintain such responses better in simulated microgravity, consistent with their predisposition to some inflammatory pathways in simulated microgravity. Some chemokines, such as ccl3, ccl4, ccl8, and cxcllO appeared to be induced better in simulated uG across overall immune cell populations, though monocytes actually showed reduced induction of some of these chemokines, likely due to their capacity to produce them in simulated microgravity without stimulation (Fig. IE). Nonetheless, the overall effects in sensitivity to stimulation in the “overall” category of immune cells largely followed the same pattern seen in the total magnitude of response of stimulated microgravity vs stimulated lG (Fig. 2D).

[00293] Next, genes uniquely altered by simulated microgravity irrespective of stimulation were identified, as well as genes unique to microgravity under stimulation. First, to identify unique genes altered by simulated microgravity regardless of stimulation, the most significant DEGs by the absolute sum of fold change under both stimulated and unstimulated conditions were plotted (Fig. 2Q). Across overall immune cells, regardless of stimulation, conserved increases in chemokines and acute response factors were still identified coupled to reduced gbp expression and other interferon genes (e.g. irfl, statl) imparted by simulated microgravity. Next, the overall overlap of gene signatures and common genes between post stimulation in 1G vs simulated microgravity was mapped out (Fig. 2R), and found that microgravity imparts a number of unique genes to TLR ligation that are not seen in 1G. Overall, these findings identify core conserved DEGs specifically sensitive to simulated microgravity as well as unique signatures to simulated microgravity.

[00294] Finally, it was assessed whether sex plays a role in the magnitude of response to simulated microgravity. In the unstimulated state, female cells showed only slightly more DEGs induced, while the male cells had slightly more genes reduced (Fig. 2S). Male NK cells and monocytes were more sensitive to microgravity while female B cells showed more sensitivity than male B cells. Upon stimulation, male cells overall were more sensitive to simulated microgravity, especially in having more downregulated DEGs (Fig. 2T). Volcano plots of DEGs across all cell types between the female and male are shown in Fig. 2U. In both male and female cells, acute phase response and inflammatory genes like mmp9, ccl2, sl00a8, and thbsl were among the most induced genes, while heterogeneous nuclear ribonucleoprotein H (HNRNPH1), interferon regulators like statl, and cold shock genes like rbm3 and cirbp were consistently downregulated in both sexes in simulated microgravity. Upon stimulation, both sexes again show increases in the total magnitude of acute inflammatory, reactive oxygen species related, and acute phase genes like chemokines, thbsl, mmp9, ncfl and sod2 in simulated microgravity coupled to reduced interferon, gbps, cold shock, and some ribosomal protein genes in simulated microgravity. Many of these changes are reflected in IPA pathway analysis by sex (Fig. 2V) and many of these core features were also conserved when data from sexes were pooled (Figs. 1G, 2G)

Single Cell Validation Identifies Core Features of Immune Dysfunction in Microgravity and Space [00295] Next, to better validate conserved genes and pathways from the single cell signatures without TLR7/8 agonist in simulated microgravity, the core signatures of 375 DEGs were compared against additional datasets. First, experiments were repeated in a validation cohort of freshly isolated PBMCs from young donors (n=6, age range 20-46), and these PBMCs were spun for 25 hours prior to performing bulk RNA-seq analysis. Using CIBERSORTx28, predicted changes in population frequency were first mapped and increased CD 14+ monocyte frequencies were confirmed in simulated microgravity (consistent with the single cell data) (Fig. 3A). Between validation samples spun in simulated microgravity vs 1G controls, 2149 genes were identified as differentially expressed (Fig. 3B). Despite the variability of data inherent to bulk RNA-seq of different populations of cells between donors, a highly significant correlation in normalized gene counts per specific gene was observed between datasets both at 1G and in simulated microgravity (Fig. 3C). Moreover, overlap was identified in over 28% of the core signature genes (same directionality) across all immune cells from the single cell analysis (106/375= 28.3%) (Fig. 3D). Many of the overlapping genes induced were consistent with the most robustly altered core pathways from the single cell data. For instance, shared overlapping genes induced in acute immune responses were seen (such as sl00a8, sl00al2, thbsl, il lb), chemokines (like cxcl8), heat shock proteins (hsp90aal, hspala, hspbl), autophagy (atg7) and the actin cytoskeleton (rhou). Overlapping reduced genes in simulated microgravity, like in the single cell data sets, included interferon response (statl, irfl) and associated guanylate binding proteins (gbpl, gbp2, gbp4, gbp5), and cold shock genes (rbm3), amongst others. Overall, there was a highly significant enrichment and over representation of the core single cell DEGs across the bulk validation cohort by Fisher’s Exact Test for gene overlap (Fig. 3D).

[00296] Next, validation of overlapping genes against mice and people flown in LEO was carried out. While multiple stressors exist in LEO, the proximity to Earth and the presence of the Earth’s magnetic field negates some effects of galactic cosmic rays, especially at the altitude of the International Space Station (ISS). Thus, microgravity plays an important role in driving phenotypic changes in LEO. To accomplish this goal, data was first mined from NASA’s GeneLab database for its largest study looking at a major immune organ, the spleen, in mice flown on the ISS. The GLDS- 420 study provides data from the spleens of ten mice housed on the ISS for 33 days compared to ten ground controls. Though this cohort represents longer exposure to microgravity than the single cell data’s more acute exposure, any overlapping genes could represent persistent microgravity sensitive immune cell genes across longer duration exposure. From the GLDS-420 dataset, 1448 significant DEGs were identified (Fig. 3E), of which 50/375 (13.3%) overlapped in the same direction as the single cell core list (Fig. 3F). Interestingly, many of the overlapping genes were represented as part of altered core pathways from the single cell data. For instance, shared induced overlapping genes were seen in acute immune responses or complement (such as c3), autophagy (atg7), heat-shock responses (hsp90abl, hsp90aal, hspala, hspal), and the cytoskeleton (dynlll). Overlapping reduced genes included interferon response (statl), and again, cold shock genes (cirbp, rbm3), amongst others (e.g., HNRNPH1). Overall, a significant enrichment was seen in the core single cell DEGs across mouse spleens flown in space by Fisher’s Exact Test for gene overlap (Fig. 3F). Pathway analysis with IPA was next performed to identify major canonical pathways altered across all four complete data sets (single cell unstimulated, single cell stimulated with TLR7/8 agonist, Bulk RNA- seq validation unstimulated, and GLDS-420), including overlapping pathways shared across all datasets. [00297] To better translate the usefulness of the single cell atlas to human spaceflight, the core list of 375 DEGs was compared across single cells in simulated microgravity to changes across all single cells from the Inspiration Four (14) crew members. The 14 mission provides a compelling comparison since crew members spent up to three days in LEO, a timeline not too different from the 25-hour time point used in the single cell analysis. Moreover, 14 gene lists were also generated by single cell sequencing, making it a comparable technology for this analysis. However, it is important to note that the 14 datasets contain a few important caveats. First, the altitude flown by 14 crew (585km / 364miles) predispose the astronauts to higher radiation exposures than what would typically be experienced on the ISS (408km / 254miles altitude). Additionally, the 14 datasets were derived from PBMC gene expression comparisons between post-flight (1 day after Retum/R+1 in this case) vs pre-flight (44 days before launch/L-44). Since the changes from the 14 single cell data represent changes encompassing effects of spaceflight, plus return to the ground, including short term exposures to hyper-gravity, and one day of return to 1G gravity (all of which manifest as increased gravity exposure to inflight conditions), overlapping immune cell genes in either direction on return were considered to be gravity sensitive genes. Remarkably, despite these caveats, a very robust overlap of nearly 60% of DEGs in PBMCs in simulated microgravity (210/375= 56%) was found to be also significantly altered across all immune cells in the 14 mission (Fig. 3G). See Table 1 as well for a list of DEGs in PBMCs in simulated microgravity and immune cells in the 14 mission (represented in z-scores). Of these significantly altered genes across 14 data, 122 were altered in the same direction as 14 data, and 88 in the opposite direction.

[00298] Table 1: DEGs in PBMCs in simulated microgravity in comparison to immune cells in the 14 mission

[00299] To gain a better understanding of the pathways and mechanisms impacted by gravity and spaceflight in the immune system, pathways between simulated microgravity and the entire 14 dataset were compared (Fig. 3H and Fig. 31, and Table 2 below). While all of these pathways are considered to be potentially gravity sensitive, those pathways altered in the opposite directions were considered to be potentially acutely sensitive to gravity, while those pathways altered in the same direction were considered likely take longer to normalize from a microgravity environment upon return.

[00300] Table 2: Comparison of changes in genetic pathways between simulated microgravity and 14 dataset.

[00301] Both simulated microgravity and the 14 mission pathway results indicated reduced T cell effector subset development, reduced oxidative phosphorylation, and increased pathways associated with innate immunity (e.g. Coronavirus pathogenesis, FcR phagocytosis in monocytes, cytokine storm, chemokine signaling, reactive oxygen species production in macrophages), as well as hypoxia and glycolytic metabolism (FUFla. signaling) and cell stress (e.g. sirtuin signaling). Interestingly, the return to gravity seen in the 14 mission reversed reductions in natural killer cell signaling and reversed pathways linked to poor adaptive immunity like IL- 15 signaling, suggesting these pathways may be sensitive to acute changes in gravity (Fig. 3H). From the 14 dataset, a consistent reduction in ribosomal subunit genes in the 14 data was also noticed (Fig. 3G), which might be reflective of a stress response and reduced protein translation upon return to earth), and only some of these genes were reduced in simulated microgravity. Consistently, there was a marked reduction in EIF2 signaling in the provided 14 DEGs. Across all pathways, regardless of direction, many pathways pertaining to the cytoskeleton or to a mechanical extracellular environment (e.g. fibrosis, RAC, CDC42, Rho family GTPases, RHOA, integrin signaling, leukocyte extravasation signaling, healing signaling etc.) were noticed to be altered by simulated microgravity or by spaceflight on immune cells.

[00302] Next, to look for overlapping genes relevant to longer exposures to human spaceflight, the core simulated microgravity signature of immune cells was compared against available data from the JAXA Cell-Free Epigenome study in LEO (GLDS-530) and the Twins study. From the JAXA mission, data pertaining to cell free RNA, which can sometimes give insight into changes to PBMCs amongst other cells, was used. During this mission, blood was sampled from 6 astronauts, with data pooled into a single count, at days 5, 30, 60, 120 post launch. Given that cell free RNA is not a fully ideal comparison to RNA-seq from isolated PBMCs, a focus was placed only on the two early time points, 5 days and 30 days post launch, because significant overlap was observed between the single cell data in both the three-day 14 mission and in the 33-day GLDS-420 dataset. Thus, 5 days and 30 days in-flight vs pre-flight differentially altered cell free RNA signatures were compared for any possible overlap with the single cell data in simulated microgravity. While not much overlap was observed in the core 375 immune gene signature at 5 days (less than 10 genes, not shown), significant overlap by 30 days was seen (42/375= 11.2% overlap in the same direction) (Fig. 3 J). Interestingly, it was observed that cell-free RNA levels generally decreased across most genes in flight. Consequently, it was hypothesized that identifying genes exhibiting increased expression could be particularly important for identifying over-represented processes. Remarkably, it was noted that the most significantly elevated gene at 30 days in-flight vs pre-flight was cdc42, a key modulator of the cytoskeleton, as well as dynlll a dynein gene that was also upregulated in the single-cell analysis.

[00303] The single cell data was then reclustered to compare the DEGs in the equivalent singlecell populations with those obtained from sorted CD4+ T cells, CD8+ T cells, B cells, and lymphocyte depleted immune cells from the NASA Twins study, which compares in-flight vs ground twin control. The Twins study provides intriguing data on the impact of LEO on the immune system, but has caveats in that exposure to LEO was calculated in only a single individual through bulk RNA-seq, and at multiple time points across one full year in space, a different duration than the present shorter gene-sets. Nonetheless, compared to the reclustered CD4+, CD8+, and CD 19+ gene sets, significant overlap was found in some genes comparing the effects of simulated microgravity to spaceflight (Fig. 3K). Across multiple cell types, there were changes in genes involved in redox regulation (e.g. reduced txnip), and in genes involved in interferon responses (e.g. reduced statl and gbp5). Interestingly, there was significantly reduced cold shock gene, cirbp, a similar functioning gene to rbm3, in B cells in space. In the lymphocyte depleted (i.e., myeloid) recluster, there was a large and highly significant overlap of about 163 genes with the Twins lymphocyte depleted bulk RNA seq data. Many of the overlapping genes induced by simulated microgravity or spaceflight including genes involved in innate immunity and inflammation (e.g. il lb, sl00al2, thbsl etc.), the cytoskeleton (rhoq, rhou), and hypoxia signaling (e.g. hifla). Some interesting downregulated genes in both data sets in myeloid cells included again gbp5, cirbp, txnip like seen in T and B cells from the Twins study. A number of overlapping downregulated genes in antigen presentation (e.g. tapl, tap2, hla-e, hla-dpla, etc) were also noted. IPA analysis on this data largely captured the increases in innate immune inflammatory pathways, including increases in fibrosis signaling, IL-6 signaling, acute phase response, cytokine storm, and HIFla signaling seen across some of the previous datasets (Fig. 3L). Overall, these data enforce the idea of classically activated basal myeloid inflammatory changes in microgravity and spaceflight.

[00304] Given that many of these altered pathways in simulated microgravity involved predicted mitochondria dysfunction and/or the cytoskeleton, Airyscan super-resolution confocal microscopy was used to characterize immune cell mitochondrial and actin morphological networks to look for altered parameters in simulated microgravity compared to 1G controls. Interestingly, while 25 hours of simulated microgravity did not alter mean cell area across PBMCs, it did alter actin granularity parameters, as well as intensity and variance, consistent with cytoskeletal changes in acute simulated microgravity (Fig. 3M and Fig. 3N), though these differences are mostly subtle to the naked eye. Using three dimensional (3D) super-resolution imaging, 25 hours of simulated microgravity did not alter cell or nucleus volume, or nucleus shape, but it increased mean cell surface area and actin spike length, and decreased sphericity of the cells across PBMCs (Figs. 30 and 3P). Remarkably, 1G immune cells and simulated microgravity immune cells demonstrate unique spectral changes to actin rearrangement post TLR stimulation, such that TLR stimulation resulted in a different pattern of actin granularity spectral change in 1G compared to stimulation in simulated microgravity (Fig. 3N). The effect of microgravity on the cytoskeleton in unstimulated immune cells was similar to the effect of TLR activation in 1G. During TLR stimulation in simulated microgravity, immune cells followed a unique dynamic actin rearrangement pattern, potentially even reversing the pattern observed in 1G with TLR stimulation. These results suggest that simulated microgravity itself may induce immune cytoskeleton alterations, which may mimic aspects of TLR ligation on the cytoskeleton. Short term exposure to simulated microgravity showed some increases in mitochondrial MitoTracker Red staining intensity and variance in the unstimulated conditions, without changes to fiber length, size, or volume (Fig. 3Q).

[00305] Since there were detected morphological changes to the actin network, as well as changes in multiple altered cytoskeleton related pathways across multiple datasets, including in the pathways “Signaling by Rho Family GTPases” or “regulation of actin-based motility by Rho”, r active GTP bound Rho GTPases, RAC1, RhoA, and Cdc42 were screened for using G-LISA technology across further batches of isolated paired PBMCs. After 25 hours of simulated microgravity, regardless of stimulation conditions, there were elevated levels of active GTP bound CDC42, consistent with cytoskeleton mobilization and the increase in actin spikes (indicative of fdopodia) observed due to simulated microgravity (Fig 3R). Active GTP -RAC was not altered at baseline in simulated microgravity, though showed a trend to induction with TLR7/8 stimulation). Levels of active GTP- RhoA were low in our samples, but trended lower in simulated microgravity without stimulation, and higher under stimulation, analogous to our single cell data predictions (Fig. 3S). Overall, these data suggest that simulated microgravity changes some Rho GTPase activity consistent with the transcriptional data, though ultimate impacts on cytoskeleton shape, variance, and dynamics likely involve additional contributing factors, including possibly other Rho GTPase family members not assessed.

[00306J Next, the core signature of reduced IFN signaling elicited in microgravity across immune cells was evaluated (Fig. 3T and Fig. 3U). Specifically, it was assessed whether reduced interferon signaling was due to reduced local production of interferons. Supernatants from 25 hours unstimulated or 9 hours R848 simulated (25 hours total culture) PBMCs were assessed by ELISA for total IFNa (detecting 12 IFNa subtypes), and IFNy. Simulated microgravity significantly reduced both IFNa and IFNy secretion with TLR7/8 stimulation. At baseline, the levels of these cytokines were low, and variable, and thus not significantly different between 1G and simulated microgravity. These findings point to reduced production of IFNs in simulated microgravity, at least under TLR stimulation, as measured by ELISA, potentially as one contributing mechanism to reduced interferon signaling observed at the transcriptional level.

[00307] Finally, to functionally validate how simulated microgravity impacts overall immune cell cytokine production, with and without TLR7/8 stimulation, across many cytokines simultaneously, a 48-plex Luminex assay on was performed on cytokines secreted by PBMCs from 12 donors (Fig. 3V). Consistent with the single cell and bulk RNA sequencing data, simulated microgravity was associated with increased or trending increases in mainly innate/monocyte immune cell derived inflammatory cytokines and chemokines (e.g. IL-6, IL-8, IL-12p40, CCL4), coupled to a reduction in cytokines that associate with T cell activation or proliferation (e.g. IL-2, IL-7, IL-15).

Concurrently, the Luminex results showed a significant IFNy and a trending IFNa2 reduction upon TLR7/8 agonist stimulation in simulated microgravity, consistent with the above ELISA data (Fig. 3T). IL-1, commonly induced in the sequencing data, also appeared elevated in simulated microgravity, though it exhibited high variability, precluding significance in the unstimulated state. In the stimulated state, IL- 1 P was significantly increased in simulated microgravity by Luminex analysis. Given the overlapping similarities between cytokines in the Luminex data and sequencing data for IL-ip, IL-6, and IL-8, these cytokines were assessed by ELISA validation. Both IL-6 and IL-8 showed significant or near significant increases by ELISA in simulated microgravity, while IL- ip demonstrated a trending increase (Fig. 3U and Fig. 3W). Upon stimulation, simulated microgravity further facilitated near significant increases in IL-i and IL-8 as validated by ELISA (Fig. 3X).To better understand how certain cell populations respond to TLR7/8 stimulation in simulated microgravity, key cytokines, IL-ip, IL-6 and IFNy, were further validated by intracellular flow cytometry in monocyte, NK, and T cell subsets exposed to simulated microgravity compared to 1G conditions (Fig. 3Y). Consistent with Luminex and ELISA data, there was increased IL-i production across all characterized monocyte populations (Fig. 3Z). Interestingly, despite no overall differences in IL-6 by Luminex or ELISA in simulated microgravity to TLR7/8 stimulation, significant increases were detected in a subset of monocytes only, as well as in NK cells. Despite increased cytokine production, there were no detected increases in the activation marker, HLA-DR, in monocyte populations. NK cells also showed a reduction in the proportion producing fFNy, as well as reduced proportions of expression in the activation marker, CD69, and degranulation marker, LAMP-1, consistent with reduced functionality and response to stimulation in simulated microgravity (Fig. 3AA). T cell subsets were less altered, though near significant or significant reductions were detected in the proportions of CD4+ and CD8+ central memory T cells expressing activation marker, CD69, and in effector memory CD4+ T cells expressing proliferation marker, Ki67 (Fig. 3AB). Taken together with the ELISA and Luminex data, these findings demonstrate that simulated microgravity, alone or in the presence of TLR7/8 agonist, can functionally alter cytokine production across immune cells. In general, consistent with sequencing data, the features demonstrate monocyte inflammatory function coupled to impaired T cell and NK cell functionality in simulated microgravity. Thus, changes in cytokine signaling observed in simulated microgravity may occur at least in part to changes in upstream cytokine production.

Reversing Simulated Microgravity Effects on the Immune System [00308] Multiple genes and pathways altered by simulated microgravity were characterized in the immune system; however, whether there are specific drugs or supplements that can directly target microgravity effects on immune cells is poorly characterized. Thus, a novel compound-gene interactome machine learning technology (Gene Compound Enrichment Analysis, GCEA) was used, building on the HyperFoods model, for the identification of drugs and food supplements that significantly map to altered genes in a dataset. Overall, this GCEA pipeline assesses >2 million interactions between genes, drugs, and foods, across DrugBank, LINCS and FoodDB32 (Fig. 4A). Using these algorithms across the core signature of 375 DEGs altered by simulated microgravity across the immune system, 115 compounds with adj p<0.05, and 474 compounds with p<0.05 were identified that significantly map to the signature altered genes (Fig. 4B). See also Table 3 with a list of 156 compounds with adjusted p-values of less than 0.1. Figure 4B shows the top 50 most significantly overlapping compounds to enriched DEGs. One compound, quercetin, was selected based on its widespread availability for future travelers to space, and for its prominence as an antiaging supplement, to validate whether it can reverse transcriptional insults of microgravity on the immune system. PBMCs (donors from the Fig. 3B cohort) were subjected to 25 hours in simulated microgravity, with or without quercetin, for bulk RNA seq analysis.

[00309] Table 3: List of Compounds identified as significantly mapping to signature altered genes

[00310] Table 4: List of Compounds identified as significantly mapping to signature altered gene

RBM3

[00311] Table 5: List of Compounds identified as significantly mapping to signature altered gene

HNRNPHI

[00312] Table 6: List of Compounds identified as significantly mapping to signature altered genes RBM3 and HNRNPH1

[00313] Remarkably, at the gene level, quercetin reversed the direction of expression of 70% (74/106) of the 106 genes (Fig. 3D) identified as significantly overlapping genes between single cell and bulk RNA seq validation (Fig. 4C). Reversal of gene expression was significant by correlation analysis (Fig. 4C) and demonstrated by GCEA enrichment plot (Fig. 4D). IPA pathway analysis was then performed to characterize pathways altered by quercetin, and compared against non-treated bulk RNA-seq controls, as well as pathways altered across the other 3 major datasets, including single-cell sequencing and GLDS-420 spleens in space. Overall, pathway analysis across all datasets showed consistent impacts of simulated and actual microgravity on pathways essential for optimal immunity. Some of the most consistently induced pathways in simulated microgravity and/or space included “coronavirus pathogenesis pathway” (linked to innate immune activation), acute phase responses, leukocyte extravasation signaling, IL-6 signaling, BAG2 signaling (linked to heat shock proteins and proteostasis), sirtuin signaling, and to a lesser extent “regulation of actin based motility by Rho”, RAC signaling, PKA signaling and oxidative stress response. Major pathways attenuated by microgravity were linked to immunity, including anti-microbial immunity, pyroptosis signaling, as well as “interferon signaling” (including PKR in IFN induction). Other reduced pathways across most datasets included reduced nuclear receptor activation (including LXR/RXR, PPAR, AHR) and reduced T cell NUR77 (activation) signaling. Interestingly, it was noted some genes were consistently reduced across all datasets, though were not properly represented in pathway analysis. The most striking of these genes is rbm3, a cold-shock protein, which was significantly reduced in all four of the microgravity datasets, as well as in the 14 and JAXA mission (30-day timepoint). Rbm3 was also reduced in the Twin’s study inflight data across all sorted immune cells, though not reaching significance.

[00314] Administration of quercetin in simulated microgravity could reverse many of the altered transcriptional signatures elicited by simulated microgravity on the immune system. Some of the major pathways it could reverse include “regulation of actin-based motility by Rho”, leukocyte extravasation signaling, RAC signaling, LXR/RXR, PPAR signaling, NUR77 signaling in T cells, “coronavirus pathogenesis” (innate immunity), acute phase response, fibrosis, IL-6 signaling, amongst others. Though quercetin has gained prominence for its senolytic properties, these results show that reducing senescence pathways was only one of many (approximately 174) pathway effects this compound has on immune cells in simulated microgravity. Nonetheless, in simulated microgravity, quercetin could reduce senescence and age associated inflammatory gene outputs, as demonstrated by reductions in both the SenMayo and iAge index scores (Fig. 4E). These changes occurred for the most part by downregulating inflammatory genes. Despite the marked transcriptional reversal in simulated microgravity observed with one compound, quercetin failed to reverse reductions in interferon signaling, a major hallmark of microgravity on immune system dysfunction from our data. Other studies have also linked microgravity and spaceflight to mitochondrial dysfunction and reactive oxygen species (ROS) production. In this regard, quercetin also showed an outstanding capacity to reduce ROS levels after 25 hours of simulated microgravity (Fig. 4F), though ROS was only marginally increased as a trend by simulated microgravity itself after 25 hours, likely due to the expression of endogenous anti-oxidant systems at this timepoint. Consistently, increased oxidative stress responses, such as NRF2-mediated or sirtuin signaling in many of the transcriptomic datasets, was also observed by IPA analysis.

Methods and Processes for Modeling Cardiovascular Deconditioning, Cardiovascular Aging, and Cardiomyopathy in Stem Cell-Derived Organoids

[00315] First, to assess the effect that simulated microgravity has on the functional properties of CM and CMEC organoids, donor-derived pluripotent fibroblasts were obtained from healthy human donors. These fibroblasts were then developed into cardiac organoids; some organoids included cardiomyocytes only (CM) while the remainder included co-cultured cardiomyocytes and endothelial cells (CMEC) (Fig. 5 A). CM-only and CMEC organoids were then exposed to 24 hrs of simulated microgravity using a rotating wall vessel (experimental groups) and compared to CM-only and CMEC organoids at normal 1G (control groups) (Fig. 5B).

[00316] Video and Image Analysis

[00317] Video analysis was conducted for all groups before (baseline) and after exposure (24hrs microgravity simulations; uG 24hr) or 1G (1G 24hr). See Fig. 5B. CM and CMEC organoids were placed on CytoView MEA plates (Axion BioSystems, GA, USA) at baseline and 24 hr posttreatment (uG or 1G) to assess morphology changes. Morphologic characteristics were assessed at baseline and following treatment (24hr 1G or 24hr uG) using the first frame of the 15-second video (.AVI, 720p, 30fps, grayscale) depicting the relaxed state. Time Series Analyzer macro (Version 3.0) (citation: Balaji J, UCLA (2014)) was used to compare pixel intensity changes across all 440 frames to register contraction-relaxation cycles for both CM and CMEC organoids.

[00318] Organoid morphological features (roundness, circularity, solidity, area, maximum diameter, and minimum diameter) were all assessed from baseline and following exposure for 24 hours (Fig. 5C). While CMEC organoids in both 1G and uG exposure groups experienced little change in roundness from baseline, the CM organoids subjected to uG experienced a significant increase in roundness after 24 hours exposure. Similarly, CM organoids subjected to uG experienced a significant increase in circularity after 24 hours exposure, while changes to CMEC organoids were not significant.

[00319] In terms of solidity, CMEC organoids in both 1G and uG exposure groups experienced little change from baseline; however, CM organoids subjected to 24 hours uG demonstrated a small but significant increase in solidity. Neither CM nor CMEC organoids experienced significant change in either the 1G or uG exposure groups in terms of area (mm²), maximum diameter (in mm), or minimum diameter (mm).

[00320] Table 7: CM Organoid Features

[00321] Table 8: CMEC Organoid Features

[00322] Contraction-relaxation cycles for CM and CMEC organoids as measured by pixel intensity changes indicated that RWV microgravity simulation alters the gross function of CMEC organoids to a greater extent than CM-only organoids. Contraction-relaxation cycles for CM-only organoids demonstrated little difference between baseline measurements and CM organoids in either the uG 24hr or the 1G 24 hour groups (Fig. 5D). By contrast, contraction-relaxation cycles for the CMEC organoids from the uG 24hr group were substantially extended in comparison to both baseline and lG (Fig. 5E).

[00323] Beats per minute (bpm) were also compared between CM and CMEC organoids before and after exposure to either uG 24hr or 1G 24hr (Fig. 5F). For CM-only organoids, beats per minute appeared to increase slightly for organoids in the uG 24hr group as compared to both baseline and and 1G 24hr. By contrast, beats per minute significantly decreased for CMEC organoids in the uG 24hr group as compared to both baseline and the 1G 24hr group.

[00324] Time from peak relaxation to peak contraction (in seconds) was also analyzed and compared amongst the groups of organoids (Fig. 5G). For CM organoids, time from peak relaxation to peak contraction did not appreciably differ amongst the baseline, uG 24hr group, or 1G 24hr group. However, for the CMEC organoids, time from peak relaxation to peak contraction in the uG 24hr group was significantly longer than that of either the baseline or the 1G 24hr group.

[00325] Time from peak contraction to peak relaxation (in seconds) was also analyzed and compared amongst the groups of organoids (Fig. 5H). For CM organoids, time from peak relaxation to peak contraction appeared to increase slightly in the uG 24hr group from both baseline and the 1G 24hr group. However, for the CMEC organoids, time from peak relaxation to peak contraction in both the uG 24hr group and the 1G 24hr group appeared significantly longer than that of the baseline.

[00326] Microelectrode Assay

[00327] After CM and CMEC organoids were exposed to 24 hrs simulated microgravity using a rotating wall vessel (experimental groups), they were compared to CM and CMEC organoids at normal 1G (control groups) using Microelectrode Assay (MEA) analysis (Fig. 51). MEA analysis was conducted for all groups before (baseline) and after exposure (24hrs microgravity simulations (uG 24hr) or 1 G (1 G 24hr) and then 24hrs following 24hr 1 G (1 G 48hr) or uG (uG 24hr + 1 G 24hr) to investigate the recovery of function

[00328] CMEC organoids were placed on Microelectrode CytoView plates (Axion BioSystems, GA, USA) at baseline, 24 hr post-treatment (uG or 1G), and 48 hr 1G and 24 hr 1G following 24hr uG to assess the effects of uG on electrophysiological function (Fig. 5J). Impedence was recorded using the Maestro Pro system and analyzed using Cardiac Analysis Software v.3.1.8 and R programming package Signal Processing version 0.3-5) (citation: van Boxtel G, (2022). gsignal: Signal Processing, R package version 1.32.0, https://github.com/gjmvanboxtel/gsignal).

[00329] Change in contractility for CMEC organoids was analyzed via impedence measurements (Figs. 5K and 5L). Microgravity exposure for 24 hours significantly impacted spike frequency compared to 1G 24hr. Spike frequency for CMEC organoids at 1G demonstrated that contractility and waveforms for contractions changed little from baseline measurements to 24 hours and 48 hours post-lG — i.e., contractions were roughly the same strength as at baseline, with a slight reduction in pace of contractions. However, the CMEC organoids subjected to 24 hours of uG demonstrated significantly slowed pace (i.e., fewer spikes per minute as indicated in Fig. 5L) and erratic waveforms suggesting great differentials between strength of contractions when measured immediately after 24 hours of uG exposure. Interestingly, exposing these same uG-exposed CMEC organoids to normal 1G for 24 hours indicated little remediation back toward baseline. The CMEC organoids subjected to 24 hr 1G following 24hr uG demonstrated a slight reduction in the erratic strength of contractions (Fig. 5K), but the pace of contractions did not increase significantly after 24 hours 1G exposure (Fig. 5L).

[00330] Exposure to 24 hours of RWV simulated microgravity also disrupted the rhythm of CM and CMEC organoids (Fig. 8A). Interestingly, beats per minute for CM organoids subjected to microgravity for 24 hours appeared to increase in comparison to CM organoids subject to normal 1G gravity, while beats per minute for CMEC organoids subjected to microgravity for 24 hours appeared to substantially decrease in comparison to CMEC organoids subjected to normal 1G gravity (Fig. 8B). Additionally, contraction time for CMEC organoids subjected to 24 hours of simulated microgravity also significantly increased in comparison to baseline CMEC organoids and CMEC organoids exposed to 24 hours of normal 1G gravity (Fig. 8B). [00331] Exposure to 24 hours of RWV simulated microgravity demonstrably altered the functional properties of CMEC organoids in comparison to CM organoids, which retained more baseline functional characteristics.

[00332] Bulk RNA Processing for Cardiac Organoids

[00333] After determining that CMEC organoids undergo significant functional change after exposure to smG (in comparison to CM-only organoids), the extent to which CMEC organoids are representative of human tissue was determined. Total RNA was extracted from CM and CMEC organoids at baseline, using RNeasy Plus Mini Kit (Cat# 74134, Qiagen) as per the manufacturer's instruction. RNA quantity check, preparation of RNA library, and mRNA sequencing were conducted by Novogene Co., LTD (CA, US). About 20 million paired-end 150 bp reads per sample were generated from Illumina NovaSeq 6000 Sequencing System. FASTQ raw reads were analyzed using the MTD pipeline. Differential gene expression analysis between groups was done by DESeq2 R package (version 1.36.0) with controlling for the subject effect. Genes with adjusted p- value < 0.05 and |log2(FoldChange)| > 0 were considered differentially expressed genes (DEGs).

[00334] GTEx Bulk Sequencing Data

[00335] (PMID: 29022597) The gene read counts of the RNA-Seq GTEx data set were downloaded from the GTEx Portal (https://gtexportal.org/home/datasets), along with the deidentified sample and subject annotations. Ages of the GTEx subjects were acquired through dbGap with approval. Pertaining to human data, all methods were carried out in accordance with relevant guidelines and regulations. Transcriptomics from the RNA-Seq GTEx data set were compared to that of the CM and CMEC organoids, revealing that CMEC organoids are highly representative of human cardiac/heart tissue. CMEC organoids resemble human heart tissue more than CM organoids with respect to the transcriptomics data (Fig. 6A).

[00336] Transcriptomic Changes Induced by Microgravity Exposure as a Model for Age-Related Changes, Cardiomyopathy, and Spaceflight

[00337] Cardiomyocytes in Spaceflight, Rotating Wall Vessel (RWV), and Random Positioning Machine (RPM) Simulated Microgravity

After confirming that CMEC organoids are representative of human heart tissue, it was determined whether the functional changes exhibited by CM organoids exposed to smG are representative of changes to human CM cells during spaceflight and an alternative form of microgravity simulation using random positioning machine (RPM). To do this, RNA sequencing results from hiPSC-CMs that were sent to the ISS for 3 weeks (Rampoldi, 2022) and cardiomyocyte progenitor cells (CPCs) exposed to 72 hours of simulated microgravity using a random positioning machine (Jha, 2016) were compared to results from the RNA sequencing analysis performed on the CM organoids exposed to 24 hours of RWV smG. The results from this RNA sequencing analysis were validated against the impacts of smG on RPM smG and the ISS spaceflight uG. RWV smG was found to induce changes more similar to those aboard the ISS than RPM smG.

[00338] Pathway Analysis for Establishing a Microgravity-Induced Model for Cardiac Aging and Age-Related Dilated Cardiomyopathy

[00339] The differential expression in genes and pathways between CMEC organoids exposed to 24 hour smG versus 1G CMEC organoid controls was explored to determine whether the smG- exposed CMEC organoids could serve as models for cardiac aging or dysfunction (Tables 9 and 10 below).

[00340] Table 9: CMEC Organoid Up-regulated Pathways in uG (adjusted p-value < 0.0001)

[00341] Table 10: CMEC Organoid Down-regulated Pathways in uG (adjusted p-value < 0.0001)

[00342J Following RNA sequencing and determination of DEGs, Ingenuity Pathway Analysis (IP A; Qiagen, Redwood City, CA) was used to discover changes in enriched pathways in each comparison. DEGs (as described above and shown in the volcano plot of Fig. 6B, which displays a volcano plot of differentially expressed genes (DEGs) in both CM and CMEC organoids between uG and 1G) were incorporated into the IPA canonical pathway analysis. A dataset of Ensembl gene identifiers and fold changes was uploaded for Core Analysis.

[00343] Table 11 : CM Organoid IPA Cardiovascular Pathways Associated with 24hr uG

Exposure

[00344] Table 12: CMEC Organoid IPA Cardiovascular Pathways Associated with 24hr uG Exposure

[00345] IPA analysis revealed that CMEC organoids, when exposed to RWV smG for 24 hours, can accurately serve as a model for age-related changes and dilated cardiomyopathy based on the transcriptomic changes and differential expression observed (see Fig. 9 A). The differential expression between CMEC organoids exposed to 24hr simulated microgravity vs CMEC 1G controls mapped significantly to the dilated cardiomyopathy signaling pathway as indicated by the IPA software (Fig. 6C). Interestingly, only the transcriptomic changes in CMEC organoids exposed to 24 hours of smG significantly mapped to the changes associated with age-related dilated cardiomyopathy; transcriptional changes in smG-exposed CM-only organoids did not significantly map to the transcriptional changes associated with age-related dilated cardiomyopathy. Combined results show significant impacts of smG on pathways essential in the cytoskeleton and characteristic of dilated cardiomyopathy in CMEC but not CM organoids. These results offer insight into the role of endothelial-cardiomyocyte interactions in uG that may support the development of countermeasures for mitigating cardiovascular damage and risk in both uG and 1G.

[00346] A heart transcriptomic age score chart was developed from analyzing 212 genes from human donor tissue (405 samples, with donors ranging from 20 to 70 years old) that captures transcriptomic changes in the heart associated with aging (Fig. 6D and Fig. 9B). See Table 13 below.

[00347] Table 13: Genes Analyzed to Create a Heart Transcriptomic Age Score Chart

[00348] CM and CMEC organoids exposed to 24 hours of RWV simulated microgravity both had higher heart transcriptomic age scores than 1G controls when plotted against the heart transcriptomic age score chart with respect to the 212 analyzed genes (Fig. 6E). However, as demonstrated above with respect to Fig. 6B, CMEC organoids exposed to smG serve as a more representative model for the transcriptional changes associated with age-related dilated cardiomyopathy relative to pure CM organoids.

Reversing Simulated Microgravity Effects on the Human Cardiac Tissue

[00349] After establishing that CMEC organoids adequately represent human cardiac tissue and exhibit transcriptomic changes under uG exposure which resemble age-related changes and dilated cardiomyopathy transcriptomic changes, it was determined whether there are specific drugs or supplements that can directly target microgravity’s effects on cardiac tissue. GCEA was used for the identification of drugs and food supplements that significantly map to altered genes associated with microgravity simulation in CMEC organoids (Table 14). [00350] Table 14: Drugs and Compounds Identified as Mapping to Microgravity-Altered Genes in CMEC Organoids

Table 15: Compounds identified to reverse the gene signatures induced by microgravity.

[00351] This drug repurposing analysis identified 5 top hits for preventing/reversing (negative score) and mimicking (positive score) the transcriptomic signature associated with microgravity simulation in CMEC organoids (Fig. 6F). Resveratrol, trichostatin A, thioridazine, mebendazole, and rapamycin have been shown to be associated with longer lifespans in multiple non-human species. Three of these compounds, mebendazole, rapamycin, and resveratrol were tested for their ability to prevent/reverse microgravity-induced adverse effects on CMEC organoids. Rapamycin was found to have the strongest preventative/reversal effect on the rhythmicity of CMEC organoids subjected to 24 hours of microgravity, with resveratrol also demonstrating a modest normalizing influence (Fig. 10A). Rapamycin also demonstrated the strongest propensity toward preventing/reversing beats per minute changes, with resveratrol showing somewhat less efficacy in normalizing/reversal than rapamycin (Fig. 10B).

Validation of Microgravity -Exposed CMEC Organoids as Model for Cardiac Aging [00352] To validate findings that CMEC organoids exposed to 24 hours of smG are representative models of cardiac aging and dilated cardiomyopathy, the transcriptomic changes associated with these smG-exposed wild-type CMEC organoids were compared to the transcriptome of LMNA-mutant CMEC organoids. LMNA-mutant CMEC organoids are commonly used as models for accelerated cardiac aging and represent a known genetic form of dilated cardiomyopathy. [00353] Pluripotent fibroblasts were obtained from both a LMNA-DCM positive donor and a healthy control donor. These fibroblasts were then developed into LMNA-mutant CMEC organoids and wild-type CMEC organoids, respectively (Fig. 7A).

[00354] One group of the wild-type CMEC organoids were then exposed to 24 hours of RWV smG, with another group of the wild-type CMEC organoids exposed to 24 hours of 1G as controls. RNA sequencing analysis was then performed on the CMEC organoids exposed to 24 hours of RWV smG, the 1G control group wild-type CMEC organoids, and the LMNA-mutant CMEC organoids. After RNA sequencing and development of transcriptomic data for each group, IPA was carried out on the determined transcriptomes for each group. Differential expression between the groups was established, with the differential expression between wild-type CMEC organoids exposed to 24hr simulated microgravity vs CMEC 1G control organoids and LMNA-mutant CMEC organoids vs wild-type CMEC organoids showing a high degree of overlapping DEGs (both up- and down-regulated) (Fig. 7B). These overlapping genes were then analyzed for their association with human diseases using Online Mendelian Inheritance in Man (OMIM), demonstrating that cardiomyopathy and dilated cardiomyopathy were the top associations (Fig. 7C). Thus, CMEC organoids exposed to 24 hours of smG are representative models of cardiac aging and dilated cardiomyopathy further validating the model as representative of age-related and dilated cardiomyopathy.

Methods and Processes for Modeling Neural Dysfunction, Neural Aging, and Parkinson’s Disease in Stem Cell-Derived Organoids

[00355] First, to assess the effect that simulated microgravity has on the functional properties of neural organoids, donor-derived pluripotent cells were developed into neural organoids. Neural organoids were then exposed to 24 hrs of simulated microgravity using a rotating wall vessel (experimental groups) and compared to neural organoids at normal 1G (control groups).

[00356] Transcriptomic Changes Induced by Microgravity Exposure as a Model for Age-Related Changes, Parkinson’s Disease, and Spaceflight

[00357] Pathway Analysis for Establishing a Microgravity-Induced Model for Neural Aging and Parkinson’s Disease

[00358] The differential expression in genes and pathways between neural organoids exposed to 24 hour smG versus 1G neural organoid controls was explored to determine whether the smG- exposed neural organoids could serve as models for neural aging or dysfunction.

[00359] Following RNA sequencing and determination of DEGs, Ingenuity Pathway Analysis (IPA; Qiagen, Redwood City, CA) was used to discover changes in enriched pathways in each comparison. DEGs as described above were incorporated into the IPA canonical pathway analysis, and gene pathways and genes related to cell cycle, cell metabolism (e.g., mitochondrial function), and protein folding in neural cells of the organoids in particular were found to be differentially expressed (Fig. 11A and Fig. 1 IB, Tables 16 and 17 below). Table 16. Genes identified as significantly upregulated or downregulated.

Table 17. Genetic pathways identified as significantly affected.

[00360] IPA analysis revealed that neural organoids, when exposed to RWV smG for 24 hours, can accurately serve as a model for age-related changes and Parkinson’s Disease based on the transcriptomic changes and differential expression observed (Fig. 11C, Fig. 11D, Fig. HE, and Fig.

1 IF). The differential expression between neural organoids exposed to 24hr simulated microgravity vs neural organoid 1G controls mapped significantly to the Parkinson’s Disease signaling pathway as indicated by the IPA software.

A neural transcriptomic age score chart was developed for different neural tissues that captures transcriptomic changes in the neural system associated with aging (Fig. 12). Neural organoids exposed to 24 hours of RWV simulated microgravity had higher neural tissue transcriptomic age scores than 1G controls.

[00361] Organoid production

Organoids were generated at the NeuraCell core facility (Neural Stem Cell Institute, NY, USA) as previously described (Yoon et al., 2019) with minor modifications. When iPSC cultures reached 80% confluency, the medium was aspirated and wells rinsed twice with DMEM/F12. 2 mL of Accutase (StemCell Tech.) was added per 6-well and incubated for 10 minutes at 37C, 5% CO2 until cells detached from the dish. Using a 1000 mL pipette, gentle trituration was performed to achieve a single cell suspension, which was transferred to a 50 mL conical tube. Cells were washed with DMEM/F12 three times. Following, cells were counted using a hemocytometer and resuspended at approximately 3 million cells/mL in spheroid formation medium (SFM) consisting of E8 medium (Life Technologies) plus 10 mM ROCK inhibitor Y-27632 (Tocris).

To prepare the AggreWell800 plate (StemCell Tech.), wells were rinsed once with Anti -Adherence Rinsing Solution (StemCell Tech, 07010) and then centrifuged at 2,000 RCFs for 5 minutes in a swinging bucket rotor fitted with a plate holder to remove any air bubbles from the microwells, verified by checking under a microscope. The rinse solution was aspirated and replaced with 2 mL of DMEM/F12, centrifuged again and finally replaced with 0.5 mL of SFM per AggreWell. Next, 1 mL of cell suspension was added per well, with gentle pipetting to evenly distribute cells at 10,000 cells per microwell.

The AggreWell plate was centrifuged at 100 RCF for 3 minutes and then incubated at 37C and 5% CO2 overnight to generate spheroids. The next day (day 0 of differentiation), spheroids were transferred from the AggreWells into a conical tube containing 10 mL DMEM/F12 for each AggreWell of spheroids. Spheroids settled to the bottom of the tube, then the supernatant was gently aspirated and replaced with differentiation Medium A, which is comprised of E6 medium supplemented with 2.5 mM dorsomorphin (DM), 10 mM SB431542, and 2.5 mM XAV-939; 1 mL of medium was added for each AggreWell of spheroids. Spheroids were gently mixed and 1 mL of the suspension was added per ultra-low attachment 10 cm plate (Corning, 3262) containing 9 mL of Medium A and incubated at 37C and 5% CO2 for 48 hours. Plates were fed daily by gently aspirating medium from the plates and replacing with Medium A, achieving 65% medium exchange from day two until day five.

On the sixth day, the medium was changed to neural medium (NM), consisting of Neurobasal-A plus B-27 supplement without vitamin A, GlutaMax and Anti-A and supplemented with 20 ng/mL EGF plus 20 ng/mL FGF2. NM plus EGF/FGF2 (Medium B) was changed daily for 10 days then every other day for 9 days with 60% media exchanges. On day 25, the medium was replaced with NM supplemented with 20 ng/mL BDNF and 20 ng/mL NT3 (Medium C) with 65% media feeds every other day. From day 43 onward, the medium was changed every 3 to 4 days using NM without added growth factors with 15-20 mL per dish and 75% medium changes. Throughout the culture period, organoids that fused together were separated by cutting with a disposable scalpel (McKesson non-safety scalpels, 1626).

For experiments in Figures 6A, 7A-7H, 7J, 7K, and S7A-S7F, iPSCs were grown and patterned as described above with minor modifications, culturing in ultra-low attachment 96 well U-bottom plates (S-BIO, MS9096SZ).

Claims

CLAIMS What is claimed is:

1. A method for simulating hallmarks of cellular aging, simulating changes in cellular physiology due to spaceflight, and/or causing changes in gene expression associated with cellular senescence, the method comprising: exposing one or more cells to simulated reduced gravity below 1G.

2. The method of claim 1, wherein the changes in gene expression associated with cellular aging result in one or more of fibrosis, increase in cellular inflammation, increase in cytokine production, immunosenescence, cytoskeleton changes, increase in oxidative stress, and onset of mitochondrial dysfunction.

3. The method of claim 1 or claim 2, wherein the simulated reduced gravity below 1G is produced by a low-shear modeled microgravity rotating wall vessel apparatus, a random positioning machine, a 2D clinostat, a 3D clinostat, parabolic flight, and/or a magnetic levitation apparatus.

4. The method of any one of the preceding claims, wherein the simulated reduced gravity is between 0G and 0.9999G.

5. The method of any one of the preceding claims, wherein the simulated reduced gravity is between 0G and 0.38G.

6. The method of any one of the preceding claims, wherein the one or more cells are exposed to simulated reduced gravity for at least 10 minutes.

7. The method of any one of the preceding claims, wherein the changes in cellular physiology due to spaceflight and/or the hallmarks of cellular aging include one or more of cellular senescence, onset of fibrosis, increases in inflammatory aging processes, increases in cytokine production, onset of immunosenescence.

8. The method of any one of the preceding claims, wherein the one or more cells are immune cells.

9. The method of any one of the preceding claims, wherein the step of exposing the one or more cells to simulated reduced gravity induces physiological changes in the one or more cells.

10. The method of any one of the preceding claims, wherein the physiological changes comprise one or more of: a. changes in cellular function; b. changes in cellular structure; and c. changes in molecular content of the one or more cells.

11. The method of any one of the preceding claims, wherein the changes in cellular function include immune dysfunction.

12. The method of any one of the preceding claims, wherein the physiological changes comprise differential expressions of genes and/or pathways.

13. The method of any one of the preceding claims, wherein the differential expressions of genes and/or pathways include induction of genetic expression in one or more of: a. acute immune response genes; b. heat shock genes; c. chemokine genes; d. iron storage genes; e. matrix metalloproteinase genes; f. cytokine genes; g. proteostasis genes; and h. hypoxia genes.

14. The method of any one of the preceding claims, wherein the differential expressions of genes and/or pathways include reduction of genetic expression in one or more of: a. interferon response genes; b. guanylate binding protein genes; c. cold shock genes; and d. nuclear receptor genes.

15. The method of any one of the preceding claims, wherein the differential expressions of genes and/or pathways include one or more of: a. reduction in operation of oxidative phosphorylation pathway; b. reduction in interferon signaling pathways; c. reduction in nuclear receptor signaling pathways; d. reduction in pyroptosis signaling pathways; e. increase in heat shock protein signaling pathways; f. increase in fibrosis signaling pathways; g. increase in actin-based motility pathways; h. increase in RAC and/or CDC42 GTPase protein signaling pathways; i. increase in focal adhesion kinase (FAK) signaling pathways; j . increase in HIF 1 signaling pathways; k. increase in acute immune phase response pathways; l. increase in oxidative stress signaling pathways; m. increase in sirtuin signaling pathways; n. increase in unfolded protein response signaling pathways; and o. increase in EIF2 signaling pathways.

16. The method of any one of the preceding claims, wherein the physiological changes include one or more changes to cellular function pathways.

17. The method of any one of the preceding claims, wherein the one or more changes to cellular function pathways include changes to the cytoskeleton of the one or more cells, changes in interferon signaling pathways within the one or more cells, changes in pyroptosis pathways, changes in temperature-shock response pathways, changes in innate inflammation pathways, changes in nuclear receptor functionality, changes in proteostasis, and changes in sirtuin signaling.

18. The method of any one of the preceding claims, wherein the changes in inflammation pathways include one or both of changes to IL-6 signaling and changes to Coronavirus pathogenesis pathways.

19. A method of identifying cellular transformations associated with aging, aging hallmarks and/or spaceflight, the method comprising: analyzing with one or more omics a first set of one or more cells of a cellular population, wherein the first set of one or more cells was subjected to simulated reduced gravity of less than 1G, to obtain a first set of data for a reduced gravity omics profile; analyzing with the one or more omics a second set of one or more cells of the same cellular population, wherein the second set of one or more cells was subjected to normal gravity (1G), to obtain a second set of data for a normal gravity omics profde; and comparing the first set of data with the second set of data to identify differences in omics profiles, gene expression, and cellular pathway expression between the first set of one or more cells subjected to simulated reduced gravity and the second set of one or more cells subjected to normal gravity (1G).

20. The method of claim 19, wherein the steps of analyzing the first set of one or more cells and the second set of one or more cells includes analysis with transcriptomics.

21. The method of claim 19 or claim 20, wherein the identified differences in gene expression and cellular pathway expression between the first set of one or more cells subjected to simulated reduced gravity and the second set of one or more cells subjected to normal gravity (1G) include one or more of: a. differences in cellular function; b. differences in cellular structure; and c. differences in cellular molecular content.

22. The method of any one of the claims 19 to 21, further comprising identifying immune dysfunction in the first set of one or more cells as compared to the second set of one or more cells.

23. The method of any one of the claims 19 to 22, further comprising: linking the differences in cellular function, differences in cellular structure, and/or differences in cellular molecular content with genes responsible for the differences by applying cross-validated machine learning (ML) to the first set of data and the second set of data.

24. The method of any one of the claims 19 to 23, further comprising identifying differential expressions of genes and/or pathways between the first set of one or more cells and the second set of one or more cells.

25. The method of any one of the claims 19 to 24, further comprising identifying induction of genetic expression in the first set of one or more cells as compared to the second set of one or more cells, wherein the induction of genetic expression is in one or more of: a. acute immune response genes; b. heat shock genes; c. chemokine genes; d. iron storage genes; e. matrix metalloproteinases; and f. cytokine genes.

26. The method of any one of the claims 19 to 25, further comprising identifying reduction of genetic expression in the first set of one or more cells as compared to the second set of one or more cells, wherein the reduction of genetic expression is in one or more of: a. interferon response genes; b. guanylate binding protein genes; and c. cold shock genes.

27. The method of any one of the claims 19 to 26, further comprising identifying one or more of: a. reduction in operation of oxidative phosphorylation pathways in the first set of one or more cells in comparison to the second set of one or more cells; b. reduction in interferon signaling pathways in the first set of one or more cells in comparison to the second set of one or more cells; c. reduction in nuclear receptor signaling pathways in the first set of one or more cells in comparison to the second set of one or more cells; d. reduction in RHOA GTPase protein signaling pathways in the first set of one or more cells in comparison to the second set of one or more cells; e. reduction in pyroptosis signaling pathways in the first set of one or more cells in comparison to the second set of one or more cells; f. increase in heat shock protein signaling pathways in the first set of one or more cells in comparison to the second set of one or more cells; g. increase in fibrosis signaling pathways in the first set of one or more cells in comparison to the second set of one or more cells; h. increase in actin-based motility pathways in the first set of one or more cells in comparison to the second set of one or more cells; i. increase in RAC or CDC42 GTPase protein signaling pathways in the first set of one or more cells in comparison to the second set of one or more cells; j. increase in focal adhesion kinase (FAK) signaling pathways in the first set of one or more cells in comparison to the second set of one or more cells; k. increase in HIF1 signaling pathways in the first set of one or more cells in comparison to the second set of one or more cells; l. increase in acute immune phase response pathways in the first set of one or more cells in comparison to the second set of one or more cells; m. increase in oxidative stress signaling pathways in the first set of one or more cells in comparison to the second set of one or more cells; and n. increase in sirtuin signaling pathways in the first set of one or more cells in comparison to the second set of one or more cells.

28. The method according to any one of the claims 19 to 27, further comprising: stimulating the first set of one or more cells subjected to simulated reduced gravity with an immunogen prior to analyzing the first set of one or more cells with the one or more omics; and stimulating the second set of one or more cells subjected to normal gravity (1G) with the immunogen prior to analyzing the second set of one or more cells with the one or more omics.

29. The method according to any one of the claims 19 to 28, wherein the immunogen is a tolllike receptor (TLR) agonist.

30. The method according to any one of the claims 19 to 29, wherein the TLR agonist is a TLR 7/8 agonist.

31. The method according to any one of the claims 19 to 30, wherein the cellular population is immune cells.

32. A method for identifying a compound useful for treatment, normalization, or reversal of cellular transformations and/or differential gene expression associated with aging, inflammatory aging, aging hallmarks, spaceflight, inflammation, fibrosis, cytokine production, immunosenescence, cytoskeletal abnormalities, oxidative stress, mitochondrial dysfunction, and/or cellular senescence processes or with physiological changes induced by spaceflight, the method comprising: assessing interactions between genes altered by simulated reduced gravity and compounds using compound-gene interactome machine learning (ML), and identifying at least one compound that interacts with one or more of the genes altered by simulated reduced gravity using the compound-gene interactome machine learning (ML).

33. The method of claim 32, wherein the at least one compound is a bioactive molecule derived from food.

34. The method of claim 32 or claim 33, wherein the at least one compound is an active pharmaceutical ingredient.

35. The method of any one of the claims 32 to 34, wherein the at least one compound is a flavonoid.

36. The method of any one of the claims 32 to 35, wherein the flavonoid is quercetin.

37. The method of any one of the claims 32 to 36, wherein the differential gene expression and the cellular transformations associated with aging, inflammatory aging, aging hallmarks, and/or cellular senescence processes are correlated with at least one of a factor in causing an age-related disease and a biomarker of an age-related disease.

38. The method of any one of the claims 32 to 37, wherein the age-related disease is one or more of cardiovascular disease, neurodegenerative disease, inflammation, stroke/ischemia, sarcopenia, and autoimmune disease.

39. The method of any one of the claims 32 to 37, wherein the age-related disease is a fibrotic disease.

40. The method of claim 39, wherein the fibrotic disease is one or more of cirrhosis, nonalcoholic steatohepatitis, and pulmonary fibrosis.

41. A method for treating, normalizing, and/or reversing cellular transformations of one or more cells exposed to reduced gravity under 1G comprising: identifying least one compound that interacts with genes altered by cellular exposure to reduced gravity under 1 G, and administering the at least one compound to a patient in need thereof.

42. The method of claim 41, wherein the compound is a flavonoid.

43. The method of claim 41 or claim 42, wherein the flavonoid is quercetin.

44. The method of any one of the claims 41 to 43, wherein the genes altered by cellular exposure to reduced gravity include one or more of RBM3, CIRBP, HNRNPH1 , and MMP9.

45. A method for treating, normalizing, and/or reversing cellular transformations associated with an aging hallmark and/or an age-related disease, the method comprising: identifying least one compound that interacts with genes altered by cellular exposure to reduced gravity under 1G, and administering the at least one compound to a patient in need thereof.

46. The method of claim 45, wherein the compound is a flavonoid.

47. The method of claim 45 or claim 46, wherein the flavonoid is quercetin.

48. The method of any one of the claims 45 to 47, wherein the genes altered by cellular exposure to reduced gravity include one or more of RBM3, CIRBP, HNRNPH I , and MMP9.

49. The method of any one of the claims 45 to 48, wherein the age-related disease is one or more of cardiovascular disease, neurodegenerative disease, inflammation, stroke/ischemia, sarcopenia, and autoimmune disease.

50. A method for treating, normalizing, or reversing cellular transformations correlated with gene expression change associated with aging hallmarks, age-related disease, and/or exposure to reduced gravity under 1G, the method comprising: identifying least one compound that interacts with genes altered by exposure to reduced gravity under 1G, and administering the at least one compound to a patient in need thereof.

51. The method of claim 50, wherein the compound is a flavonoid.

52. The method of claim 50 or claim 51, wherein the flavonoid is quercetin.

53. The method of any one of the claims 50 to 52, wherein the genes altered by cellular exposure to reduced gravity include one or more of RBM3, CIRBP, HNRNPH I , and MMP9.

54. A method for simulating hallmarks of cardiovascular aging, simulating changes in cardiac cellular physiology due to spaceflight, and/or modeling cardiomyopathy, the method comprising: exposing one or more cardiac cells to simulated reduced gravity below 1G.

55. The method of claim 54, wherein the one or more cardiac cells include one or both of cardiomyocytes and endothelial cells.

56. The method of claim 54 or claim 55, wherein the one or more cardiac cells are present in one or more organoids.

57. The method of any one of the claims 54 to 56, wherein the one or more organoids include cardiomyocytes.

58. The method of any one of the claims 54 to 57, wherein the one or more organoids including cardiomyocytes also include endothelial cells.

59. The method of any one of the claims 54 to 58, wherein the one or more organoids is a CMEC organoid.

60. The method of any one of the claims 54 to 59, wherein the cardiomyopathy is dilated cardiomyopathy.

61. The method of any one of the claims 54 to 60, wherein the hallmarks of cardiovascular aging, the changes in cardiac cellular physiology due to spaceflight, and or the cardiomyopathy modeling result in one or more changes in gene expression

62. The method of any one of the claims 54 to 61, wherein the one or more changes in gene expression result in one or more of fibrosis, changes in expression of hypertrophy -related genes, increase in cytokine production, cytoskeleton changes, increase in oxidative stress, and onset of mitochondrial dysfunction.

63. The method of any one of the claims 54 to 62, wherein the simulated reduced gravity below 1G is produced by a low-shear modeled microgravity rotating wall vessel apparatus, a random positioning machine, a 2D clinostat, a 3D clinostat, parabolic flight, and/or a magnetic levitation apparatus.

64. The method of any one of the claims 54 to 63, wherein the simulated reduced gravity is between 0G and 0.9999G.

65. The method of any one of the claims 54 to 64, wherein the simulated reduced gravity is between 0G and 0.38G.

66. The method of any one of the claims 54 to 65, wherein the one or more cardiac cells are exposed to simulated reduced gravity for at least 10 minutes.

67. The method of any one of the claims 54 to 66, wherein the step of exposing the one or more cardiac cells to simulated reduced gravity induces physiological changes in the one or more cardiac cells.

68. The method of any one of the claims 54 to 67, wherein the physiological changes comprise one or more of: a. changes in cardiac cellular function; b. changes in cardiac cellular structure; and c. changes in molecular content of the one or more cardiac cells.

69. The method of any one of the claims 54 to 68, wherein the changes in cardiac cellular function include a. changes to contraction-relaxation cycles; b. changes to beats per minute; c. changes to contraction time; d. changes to relaxation time; e. changes in rhythmicity; f. changes in action potential transduction; g. changes in cardiac development; h. changes in ion flux and handling (including calcium, potassium, and sodium); i. changes in ejection fraction; j. changes in contraction force; k. changes in beat rate variability ; and l. changes in cardiac response to stress .

70. The method of any one of the claims 54 to 69, wherein the physiological changes comprise differential expressions of genes and/or pathways.

71. The method of any one of the claims 54 to 70, wherein the differential expression of genes and/or pathways include induction of genetic expression in one or more of: a. telomerase RNA localization genes including CCT2, CCT3, CCT5, CCT7, NOPIO, RUVBL1, and/or TCP1; b. chaperone-mediated protein folding and assembly genes including CCT2, CCT3, CCT5, CCT7, CHORDCI, CLU, FKBP4, HSPA8, HSPA9, HSPE1, HSPH1, PPID, STB, TCP1, and/or UNC45B; c. genes involved in regulation of protein and RNA localization to the Cajal body including CCT2, CCT3, CCT5, CCT7, NOP 10, RUVBL1, and/or TCP1; d. genes involved in the regulation of response to DNA damage including BAZ1B, BRD7, CCDC117, CD44, CLU, DHX9, DTX3L, EYA3, HMGA2, MAP3K20, MSX1, NSD2, PARP1, PIAS4, PPP1R10, PPP4R3B, RAD52, RUVBL1, SF3B3, SNAI2, SPRED2, TIGAR, TIMELESS, TRIP12, and/or TTI1 ; e. genes involved in regulation of intrinsic apoptosis including AEN, AIFM1, CD44, CLU, CYP1B1, DAB2IP, DNAJA1, EDA2R, FLCN, HINT1, HM0X1, MSX1, PARP1, PIAS4, PPIF, PPM1F, PTGS2, RRN3, SNAI2, SOD1, UBB, and/or USP28; and f. genes involved in telomere maintenance via telomere lengthening including CCT2, CCT3, CCT5, CCT7, GNL3L, HSP90AA1, HSP90AB1, NOPIO, PARP1, and/or TCP1.

72. The method of any one of the claims 54 to 70, wherein the differential expressions of genes and/or pathways include reduction of genetic expression in one or more of: a. Extracellular matrix organization genes inclduing AD AMTS 13, AD AMTS 14, ADAMTS15, ADAMTS17, COL11A1, COL11A2, COL12A1, COL15A1, COL16A1, COL18A1, COL1A1, COL1A2, COL22A1, COL23A1, COL27A1, COL2A1, COL3A1, COL4A1, COL4A2, COL4A5, COL4A6, COL5A1, COL6A6, COL9A1, COL9A2, COL9A3, COLGALT2, MMP11, MMP14, MMP15, MMP16, MMP2, and/or MYH11 b. Heart contraction genes including ACE, ACE2, ACTC1, ADM, ADM2, ADORA1, ADRB1, AGT, ATP1A2, ATP1B2, ATP2A1, ATP2A2, ATP2A3, ATP2B2, ATP2B4, BINI, CACNA1C, CACNA1D, CASQ2, DES, DMD, DMPK, DRD2, MYBPC3, MYH6, MYH7, and/or MYL3); c. Cell-junction assembly genes including ABL1, ADAMTS13, ADAMTS14, ADAMTS15, ADAMTS17, ADAMTS2, ADAMTS3, ADAMTS4, ADAMTS7, ADAMTS8, ADAMTS9, ADAMTSL1, ADAMTSL2, ADAMTSL3, ADAMTSL4, AEBP1, AGT, ANTXR1, ATXN1L, B4GALT1, BCL3, BMP2, CCDC80, C0L11A1, COL11A2, COL12A1, COL15A1, COL16A1, COL18A1, COL1A1, COL1A2, COL22A1, COL23A1, COL27A1, COL2A1 , COL3A1, COL4A1, COL4A2, COL4A5, COL4A6, COL5A1, COL6A6, COL9A1, COL9A2, COL9A3, COLGALT2, LAMC1, LOX, LOXL1, LOXL2, LOXL3, LRP1, LTBP3, MELTF, MFAP4, MMP11, MMP14, MMP15, MMP16, MMP2, MYH11, NIDI, and/or NID2; d. Actin filament organization genes including ABD, ABL1, ACTA1, ACTC1, ACTG1, ACTN1, ADD1, ADD2, AIF1L, ARAP1, ARHGAP17, ARHGAP25, ARHGAP35, ARHGAP6, ARHGEF10L. ARHGEF18. ARPIN, ARRB1. MTSS1, MY ADM, MY01C, MYOID, MY05B, MY05C, MY07B; e. Cardiac chamber development/morphogenesis genes including ACVR1, APLNR. BMP2. BMP4. BMP7, BMPR2, COL11 Al, DAND5, DHRS3, EDNRA, ENG, FGFRL1, FOXCI, FZD1, FZD2, GATA4, GATA6, HEG1, HEY2, HEYL, IGF1R, MAML1, MYBPC3, MYH6, MYH7, MYL3, NDST1, NKX25, NOTCH2, NPRL3, NRP1, NRP2, NSD2, PARVA, PLXND1, POU4F1, PPP1R13L, PTK7, ROBO1, RYR2, SCN5A, SFRP2, SHOX2, SLIT2, SLIT3, SMAD6. SMAD7, SMO, SNX17, SOX4. SRF, SUFU, TAB1, TBX20, TBX5, TGFB1. TGFBR2, TGFBR3, TNNT2, TP53, WNT5A, ZFPM1; and f. cardiac conduction genes includingACE2, AGT, ATP1A2, ATP1B2. ATP2A1. ATP2A2, ATP2A3, ATP2B2, ATP2B4, BINI, CACNA1C, CACNA1D, CASQ2, EHD3, GJC1, GJD3, HCN1, HCN2, HCN4, JUP, KCND3, KCNH2, KCNJ5, KCNN2, KCNQ1, NKX25, PRKACA, RNF207, RYR2, SCN1B, SCN5A, SLC4A3, SLC9A1, SPTBN4, TBX5, and/or TRPM4.

73. The method of claim 70, wherein the differential expressions of genes and/or pathways include changes to expression in one or more of:

CCT2, CCT3, CCT5, CCT7, NOP 10, RUVBL1, TCP1, HSP90AA1, HSP90AB, HSPA8, HSPA9, HSPE1, HSPH1, PPID, ST I 3, TCP1, UNC45B, BAZ1B, BRD7, CCDC117, CD44, CLU, DHX9, DTX3L, EYA3, HMGA2, MAP3K20, MSX1, NSD2, PARP1, PIAS4, PPP1R10, PPP4R3B, RAD52, RUVBL1, SF3B3, SNAI2, SPRED2, TIGAR, TIMELESS, TRIP12, TTI1, ABL1, ADAMTS13, ADAMTS14, ADAMTS15, ADAMTS17, ADAMTS2, ADAMTS3, ADAMTS4, ADAMTS7, ADAMTS8, ADAMTS9, ADAMTSL1, ADAMTSL2, ADAMTSL3, ADAMTSL4, ANTXR1, ATXN1L, BCL3, BMP2, CCDC80, COL11A1, COL11A2, COL12A1, COL15A1, COL16A1, COL18A1, COL1A1, COL1A2, COL22A1, COL23A1, COL27A1, COL2A1, COL3A1, COL4A1, COL4A2, COL4A5, COL4A6, COL5A1, COL6A6, COL9A1, COL9A2, COL9A3, COLGALT2, CREB3L1, CRISPLD2, CRTAP, CYP1B1, LAMC1, LOX, LOXL1, LOXL2, L0XL3, LRP1, LTBP3, MELTF, MFAP4, MMP I I , MMP14, MMP15, MMP16, MMP2, MYH11, NIDI, NID2, NPHS1, NTNG2, 0LFML2A, P3H4, PAPLN, PHLDB1, P0MT1, POSTN, PXDN, QSOX1, RAMP2, SCX, SFRP2, SH3PXD2B, SLC2A10, SLC39A8, BMP2, BMP4, BMP7, BMPR2, MYBPC3, MYH6, MYH7, MYL3, NDST1, NKX25, NOTCH2, NPRL3. NRP1, NRP2, NSD2, PARVA, PLXND1, POU4F1, PPP1R13L, PTK7, ROBO1, RYR2, SCN5A, SFRP2, SHOX2, SLIT2, SLIT3, SMAD6, SMAD7, SMO, SNX17, SOX4, SRF, SUFU, TAB1, TBX20, TBX5, TGFB1, TGFBR2, TGFBR3, TNNT2, TP53, WNT5A, ZFPM1, ACE2, AGT, ATP1A2, ATP1B2, ATP2A1, ATP2A2, ATP2A3, ATP2B2, ATP2B4, BINI, CACNA1C, CACNA1D, CASQ2, HCN1, HCN2, HCN4, KCND3, KCNH2, KCNJ5, KCNN2, KCNQ1, RYR2, SCN1B, SCN5A, SLC4A3, SLC9A1, SPTBN4, TBX5, and TRPM4 genes.

74. The method of any one of the claims 54 to 73, wherein the physiological changes include one or more changes to cardiac cellular function pathways.

75. The method of any one of the claims 54 to 74, wherein the one or more changes to cardiac cellular function pathways include changes to the extracellular matrix, mechanism of contraction and conduction and cytoskeleton regulation of the one or more cardiac cells.

76. A method of identifying cardiac cellular transformations associated with aging, aging hallmarks, age-related cardiac dysfunction, and/or spaceflight, the method comprising: analyzing with one or more omics a first set of one or more cells of a cardiac cellular population, wherein the first set of one or more cells was subjected to simulated reduced gravity of less than 1G, to obtain a first set of data for a reduced gravity omics profile; analyzing with the one or more omics a second set of one or more cells of the same cardiac cellular population, wherein the second set of one or more cells was subjected to normal gravity (1G), to obtain a second set of data for a normal gravity omics profile; and comparing the first set of data with the second set of data to identify differences in omics profiles, gene expression, and cellular pathway expression between the first set of one or more cells subjected to simulated reduced gravity and the second set of one or more cells subjected to normal gravity (1G).

77. The method of claim 76, wherein the steps of analyzing the first set of one or more cells and the second set of one or more cells includes analysis with transcriptomics.

78. The method of claim 76 or claim 77, wherein the identified differences in gene expression and cellular pathway expression between the first set of one or more cells subjected to simulated reduced gravity and the second set of one or more cells subjected to normal gravity (1G) include one or more of: a. differences in cellular function; b. differences in cellular structure; and c. differences in cellular molecular content.

79. The method of any one of the claims 76 to 78, further comprising identifying cardiac dysfunction in the first set of one or more cells as compared to the second set of one or more cells.

80. The method of any one of the claims 76 to 79, further comprising: linking the differences in cellular function, differences in cellular structure, and/or differences in cellular molecular content with genes responsible for the differences by applying cross-validated machine learning (ML) to the first set of data and the second set of data.

81. The method of any one of the claims 76 to 80, further comprising identifying differential expressions of genes and/or pathways between the first set of one or more cells and the second set of one or more cells.

82. The method of any one of the claims 76 to 81, further comprising identifying induction of genetic expression in the first set of one or more cells as compared to the second set of one or more cells, wherein the induction of genetic expression is in one or more of:

CCT2, CCT3, CCT5, CCT7, NOPIO, RUVBL1, TCP1, HSP90AA1, HSP90AB, HSPA8, HSPA9, HSPE1, HSPH1, PP1D. STB, TCP1. UNC45B, BAZ1B. BRD7. CCDC117. CD44, CLU, DHX9, DTX3L, EYA3. HMGA2, MAP3K20, MSX1, NSD2, PARP1, PIAS4, PPP1R10, PPP4R3B, RAD52, RUVBL1, SF3B3, SNAI2, SPRED2, TIGAR, TIMELESS, TRIP 12, and TTI1 genes.

83. The method of any one of the claims 76 to 82, further comprising identifying reduction of genetic expression in the first set of one or more cells as compared to the second set of one or more cells, wherein the reduction of genetic expression is in one or more of:

ABL1, ADAMTS13, ADAMTS14, ADAMTS15, ADAMTS17, ADAMTS2, ADAMTS3, ADAMTS4, ADAMTS7, ADAMTS8, ADAMTS9, ADAMTSL1, ADAMTSL2, ADAMTSL3, ADAMTSL4, AEBP1, AGT, ANTXR1, ATXN1L, B4GALT1, BCL3, BMP2, CCDC80, COL11A1, COL11A2, COL12A1, COL15A1, COL16A1, COL18A1, COL1A1, COL1A2, COL22A1, COL23A1, COL27A1, C0L2A1, C0L3A1, C0L4A1, COL4A2, COL4A5, COL4A6, C0L5A1, COL6A6, C0L9A1, COL9A2, COL9A3, C0LGALT2, CREB3L1, CYP1B1, DAG1, DDR1, DDR2, EFEMP2, ELN, EMILIN1, ENG, ERCC2, EXT1, FAP, LAMC1, LOX, LOXL1, LOXL2, LOXL3, MFAP4, MM P I 1, MMP14, MMP15, MMP16, MMP2, MYH11 NOTCH2, NPRL3, NRP1, NRP2, NSD2, PARVA, PLXND1, POU4F1, PPP1R13L, PTK7, ROBO1, RYR2, SCN5A, SFRP2, SHOX2, SLIT2, SLIT3, SMAD6, SMAD7, SMO, SNX17, SOX4, SRF, SUFU, TAB1, TBX20, TBX5, TGFB1, TGFBR2, TGFBR3, TNNT2, TP53, WNT5A, ZFPM1, ACE2, AGT, ATP1A2, ATP1B2, ATP2A1, ATP2A2, ATP2A3, ATP2B2, ATP2B4, BINI, CACNA1C, CACNA1D, CASQ2, EHD3, GJC1, GJD3, HCN1, HCN2, HCN4, JUP, KCND3, KCNH2, KCNJ5, KCNN2, KCNQ1, NKX25, PRKACA, RNF207, RYR2, SCN1B, SCN5A, SLC4A3, SLC9A1, SPTBN4, TBX5, and TRPM4 genes

84. The method of any one of the claims 76 to 83, wherein the step of identifying differential expressions of genes and/or pathways between the first set of one or more cells and the second set of one or more cells further comprises identifying one or more of:

CCT2, CCT3, CCT5, CCT7, NOP 10, RUVBL1, TCP1, HSP90AA1, HSP90AB, HSPA8, HSPA9, HSPE1, HSPH1, PPID, STB, TCP1, UNC45B, BAZ1B, BRD7, CCDC117, CD44, CLU, DHX9, DTX3L, EYA3, HMGA2, MAP3K20, MSX1, NSD2, PARP1, PIAS4, PPP1R10, PPP4R3B, RAD52, RUVBL1, SF3B3, SNAI2, SPRED2, TIGAR, TIMELESS, TRIP12, TTI1, ABL1, ADAMTS13, ADAMTS14, ADAMTS15, ADAMTS17, ADAMTS2, ADAMTS3, ADAMTS4, ADAMTS7, ADAMTS8, ADAMTS9, ADAMTSL1, ADAMTSL2, ADAMTSL3, ADAMTSL4, ANTXR1, ATXN1L, BCL3, BMP2, CCDC80, COL11 A1, COL11 A2, COL12A1, COL15A1, COL16A1, COL18A1, COL1A1, COL1A2, COL22A1, COL23A1, COL27A1, COL2A1, COL3A1, COL4A1, COL4A2, COL4A5, COL4A6, COL5A1, COL6A6, COL9A1, COL9A2, COL9A3, COLGALT2, CREB3L1, CRISPLD2, CRTAP, CYP1B1, LAMC1, LOX, LOXL1, LOXL2, LOXL3, LRP1, LTBP3, MELTF, MFAP4, MMP I I, MMP14, MMP15, MMP16, MMP2, MYH11, NIDI, NID2, NPHS1, NTNG2, OLFML2A, P3H4, PAPLN, PHLDB1, POMT1, POSTN, PXDN, QSOX1, RAMP2, SCX, SFRP2, SH3PXD2B, SLC2A10, SLC39A8, BMP2, BMP4, BMP7, BMPR2, MYBPC3, MYH6, MYH7, MYL3, NDST1, NKX25, NOTCH2, NPRL3, NRP1, NRP2, NSD2, PARVA, PLXND1, POU4F1, PPP1R13L, PTK7, ROBO1, RYR2, SCN5A, SFRP2, SHOX2, SLIT2, SLIT3, SMAD6, SMAD7, SMO, SNX17, SOX4, SRF, SUFU, TAB1, TBX20, TBX5, TGFB1, TGFBR2, TGFBR3, TNNT2, TP53, WNT5A, ZFPM1, ACE2, AGT, ATP1A2, ATP1B2, ATP2A1, ATP2A2, ATP2A3, ATP2B2, ATP2B4, BINI, CACNA1C, CACNA1D, CASQ2, HCN1, HCN2, HCN4, KCND3, KCNH2, KCNJ5, KCNN2, KCNQ1, RYR2, SCN1B, SCN5A, SLC4A3, SLC9A1, SPTBN4, TBX5, and TRPM4 genes.

85. The method according to any one of the claims 76 to 84, wherein the one or more cells of a cardiac cellular population includes one or both of cardiomyocytes and endothelial cells.

86. The method of any one of the claims 76 to 85, wherein the one or more cells of a cardiac cellular population are present in one or more organoids.

87. The method of any one of the claims 76 to 86, wherein the one or more organoids include cardiomyocytes.

88. The method of any one of the claims 76 to 87, wherein the one or more organoids including cardiomyocytes also include endothelial cells.

89. The method of any one of the claims 76 to 88, wherein the one or more organoids is a CMEC organoid.

90. The method of any one of the claims 76 to 89, wherein the age-related cardiac dysfunction is cardiomyopathy.

91. The method of any one of the claims 76 to 90, wherein the cardiomyopathy is dilated cardiomyopathy.

92. A method for identifying a compound useful for treatment, normalization, or reversal of cellular transformations and/or differential gene expression associated with cardiac aging, cardiac aging hallmarks, cardiac dysfunction, spaceflight-induced cardiac deconditioning, and/or cardiac physiological changes induced by spaceflight, the method comprising: assessing interactions between genes altered by simulated reduced gravity and compounds using compound-gene interactome machine learning (ML), and identifying at least one compound that interacts with one or more of the genes altered by simulated reduced gravity using the compound-gene interactome machine learning (ML).

93. The method of claim 92, wherein the at least one compound is a bioactive molecule derived from food.

94. The method of claim 92 or claim 93, wherein the at least one compound is an active pharmaceutical ingredient.

95. The method of any one of the claims 92 to 94, wherein the at least one compound is selected from mebendazole, resveratrol, trichostatin A, thioridazine, and rapamycin.

96. The method of any one of the claims 92 to 95, wherein the genes altered by cellular exposure to reduced gravity include one or more of the following: CCT2, CCT3, CCT5, CCT7, NOPIO, RUVBL1, TCP1, HSP90AA1, HSP90AB, HSPA8, HSPA9, HSPE1, HSPH1, PPID, ST13, TCP1, UNC45B, BAZ1B, BRD7, CCDC117, CD44, CLU, DHX9, DTX3L, EYA3, HMGA2, MAP3K20, MSX1, NSD2, PARP1, PIAS4, PPP1R10, PPP4R3B, RAD52, RUVBL1, SF3B3, SNAI2, SPRED2, TIGAR, TIMELESS, TRIP12, TTI1, ABL1, ADAMTS13, ADAMTS14, ADAMTS15, ADAMTS17, ADAMTS2, ADAMTS3, ADAMTS4, ADAMTS7, ADAMTS8, ADAMTS9, ADAMTSL1, ADAMTSL2, ADAMTSL3, ADAMTSL4, ANTXR1, ATXN1L, BCL3, BMP2, CCDC80, COL11A1, COL11A2, COL12A1, COL15A1, COL16A1, COL18A1, COL1A1, COL1A2, COL22A1, COL23A1, COL27A1, COL2A1, COL3A1, COL4A1, COL4A2, COL4A5, COL4A6, COL5A1, COL6A6, COL9A1, COL9A2, COL9A3, COLGALT2, CREB3L1, CRISPLD2, CRTAP, CYP1B1, LAMC1, LOX, LOXL1, LOXL2, LOXL3, LRP1, LTBP3, MELTF, MFAP4, MMP11, MMP14, MMP15, MMP16, MMP2, MYH11, NIDI, NID2, NPHS1, NTNG2, 0LFML2A, P3H4, PAPLN, PHLDB1, P0MT1, POSTN, PXDN, QSOX1, RAMP2, SCX, SFRP2, SH3PXD2B, SLC2A10, SLC39A8, BMP2, BMP4, BMP7, BMPR2, MYBPC3, MYH6, MYH7, MYL3, NDST1, NKX25, NOTCH2, NPRL3, NRP1, NRP2, NSD2, PARVA, PLXND1, POU4F1, PPP1R13L, PTK7, R0B01, RYR2, SCN5A, SFRP2, SHOX2, SLIT2, SLIT3, SMAD6, SMAD7, SMO, SNX17, SOX4, SRF, SUFU, TAB1, TBX20, TBX5, TGFB1, TGFBR2, TGFBR3, TNNT2, TP53, WNT5A, ZFPM1, ACE2, AGT, ATP1A2, ATP1B2, ATP2A1, ATP2A2, ATP2A3, ATP2B2, ATP2B4, BINI, CACNA1C, CACNA1D, CASQ2, HCN1, HCN2, HCN4, KCND3, KCNH2, KCNI5, KCNN2, KCNQ1, RYR2, SCN1B, SCN5A, SLC4A3, SLC9A1, SPTBN4, TBX5, TRPM4

97. The method of any one of the claims 92 to 96, wherein the cardiac dysfunction is one or more of cardiovascular disease, cardiomyopathy, and dilated cardiomyopathy.

98. A method for treating, normalizing, or reversing cardiac cellular transformations correlated with gene expression change associated with cellular transformations and/or differential gene expression associated with cardiac aging, cardiac aging hallmarks, cardiac dysfunction, spaceflight- induced cardiac deconditioning, cardiac physiological changes induced by spaceflight, and/or exposure to reduced gravity under 1G, the method comprising: identifying least one compound that interacts with genes altered in cardiac cells by exposure to reduced gravity under 1 G, and administering the at least one compound to a patient in need thereof.

99. The method of claim 98, wherein the at least one compound is a bioactive molecule derived from food.

100. The method of claim 98 or claim 99, wherein the at least one compound is an active pharmaceutical ingredient.

101. The method of any one of the claims 98 to 100, wherein the at least one compound is selected from mebendazole, resveratrol, trichostatin A, thioridazine, and rapamycin.

102. A method for simulating hallmarks of neural dysfunction, simulating changes in neural cellular physiology due to spaceflight, and/or modeling a neural disorder, the method comprising: exposing one or more neural cells to simulated reduced gravity below 1G.

103. The method of claim 102, wherein the one or more neural cells are present in one or more organoids.

104. The method of claim 102 or claim 103, wherein the neural disorder is Parkinson’s Disease.

105. The method of any one of claims 102-104, wherein the hallmarks of neural dysfunction, changes in neural cellular physiology due to spaceflight, and/or the neural disorder modeling result in one or more changes in gene expression.

106. The method of any one of claims 102-105, wherein the one or more changes in gene expression result in one or more of fibrosis, changes in expression of hypertrophy -related genes, changes in cell cycle, changes in cellular metabolism, changes in protein folding, increase in cytokine production, cytoskeleton changes, increase in oxidative stress, and onset of mitochondrial dysfunction.

107. The method of any one of claims 102-106, wherein the simulated reduced gravity below 1G is produced by a low-shear modeled microgravity rotating wall vessel apparatus, a random positioning machine, a 2D clinostat, a 3D clinostat, parabolic flight, and/or a magnetic levitation apparatus.

108. The method of any one of claims 102-107, wherein the simulated reduced gravity is between

0G and 0.9999G.

109. The method of any one of claims 102-108, wherein the simulated reduced gravity is between 0G and 0.38G.

110. The method of any one of claims 102-109, wherein the one or more neural cells are exposed to simulated reduced gravity for at least 10 minutes.

111. The method of any one of claims 102-110, wherein the step of exposing the one or more neural cells to simulated reduced gravity induces physiological changes in the one or more neural cells.

112. The method of any one of claims 102-111, wherein the physiological changes comprise differential expressions of genes and/or pathways.

113. The method of any one of claims 102-112, wherein the physiological changes include one or more changes to neural cellular function pathways.

114. A method of identifying neural cellular transformations associated with neural dysfunction and/or spaceflight, the method comprising: analyzing with one or more omics a first set of one or more cells of a neural cellular population, wherein the first set of one or more cells was subjected to simulated reduced gravity of less than 1G, to obtain a first set of data for a reduced gravity omics profile; analyzing with the one or more omics a second set of one or more cells of the same neural cellular population, wherein the second set of one or more cells was subjected to normal gravity (1G), to obtain a second set of data for a normal gravity omics profile; and comparing the first set of data with the second set of data to identify differences in omics profiles, gene expression, and cellular pathway expression between the first set of one or more cells subjected to simulated reduced gravity and the second set of one or more cells subjected to normal gravity (1G).

115. The method of claim 114, wherein the steps of analyzing the first set of one or more cells and the second set of one or more cells includes analysis with transcriptomics.

116. The method of claim 114 or claim 115, wherein the identified differences in gene expression and cellular pathway expression between the first set of one or more cells subjected to simulated reduced gravity and the second set of one or more cells subjected to normal gravity (1G) include one or more of: a. differences in cellular function; b. differences in cellular structure; and c. differences in cellular molecular content.

117. The method of any one of claims 114-116, further comprising identifying neural dysfunction in the first set of one or more cells as compared to the second set of one or more cells.

118. The method of any one of claims 114-117, further comprising: linking the differences in cellular function, differences in cellular structure, and/or differences in cellular molecular content with genes responsible for the differences by applying cross-validated machine learning (ML) to the first set of data and the second set of data.

119. The method of any one of claims 114-118, further comprising identifying differential expressions of genes and/or pathways between the first set of one or more cells and the second set of one or more cells.

120. The method of any one of claims 114-119, wherein the one or more cells of a neural cellular population are present in one or more organoids.

121. A method for identifying a compound useful for treatment, normalization, or reversal of cellular transformations and/or differential gene expression associated with neural dysfunction and/or neural physiological changes induced by spaceflight, the method comprising: assessing interactions between genes altered by simulated reduced gravity and compounds using compound-gene interactome machine learning (ML), and identifying at least one compound that interacts with one or more of the genes altered by simulated reduced gravity using the compound-gene interactome machine learning (ML).

122. The method of claim 121, wherein the at least one compound is a bioactive molecule derived from food.

123. The method of claim 121 or claim 122, wherein the at least one compound is an active pharmaceutical ingredient.

124. The method of any one of claims 121 to 123, wherein the at least one compound is selected from mebendazole, resveratrol, trichostatin A, thioridazine, and rapamycin.

125. A method for treating, normalizing, or reversing neural cellular transformations correlated with gene expression change associated with cellular transformations and/or differential gene expression associated with neural dysfunction, and/or neural physiological changes induced by spaceflight and/or exposure to reduced gravity under 1G, the method comprising: identifying least one compound that interacts with genes altered in neural cells by exposure to reduced gravity under 1 G, and administering the at least one compound to a patient in need thereof.

126. The method of claim 125, wherein the at least one compound is a bioactive molecule derived from food.

127. The method of claim 125 or claim 126, wherein the at least one compound is an active pharmaceutical ingredient.