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Spaceflight has several detrimental effects on human and rodent health. For example, liver dysfunction is a common phenotype observed in space-flown rodents, and this dysfunction is partially reflected in transcriptomic changes. Studies linking transcriptomics with liver dysfunction rely on tools which exploit correlation, but these tools make no attempt to disambiguate true correlations from spurious ones. In this work, we use a machine learning ensemble of causal inference methods called the Causal Research and Inference Search Platform (CRISP) which was developed to predict causal features of a binary response variable from high-dimensional input. We used CRISP to identify genes robustly correlated with a lipid density phenotype using transcriptomic and histological data from the NASA Open Science Data Repository (OSDR). Our approach identified genes and molecular targets not predicted by previous traditional differential gene expression analyses. These genes are likely to play a pivotal role in the liver dysfunction observed in space-flown rodents, and this work opens the door to identifying novel countermeasures for space travel.

The spaceflight environment affects the structure and function of the cardiovascular system, including fluid redistribution, alterations in blood pressure, and changes in cardiac output. The goal of this project is to quantitatively synthesize the data on the effects of actual or simulated microgravity on the cardiovascular system. In collaboration with the Ames Life Science Data Archive (ALSDA) Analysis Working Group we developed a list of relevant cardiovascular search terms, based on which medical librarians generated the search strategy in Medline, CINAHL, Embase, and NASA repositories, yielding 18,837 articles. In parallel, we recruited ~100 young professionals through space industry-affiliated organizations. These individuals completed a virtual training course on the nature and methodologies of the project. They initially screened at a rate of 5,000 articles/month; however, individualized training from project supervisors increased this rate to 60,000 articles/month. Following title/abstract screening, 3,539 included articles were labelled and grouped according to population (human, rodent) and experimental methods (simulated microgravity, actual spaceflight) for full-text screening. Full-text screening of ~900 articles in a humans and actual microgravity subgroup is currently underway, with 200 articles now completed. We anticipate that teams will be extracting and curating and submitting data into the new ALSDA submission portal and repository. Our approach reduces the time to complete screening from years to several months, and will enrich publicly accessible datasets for reuse, modeling, and machine learning. Our project provides a unique, open-access educational experience to space research and training in knowledge synthesis tools.

As part of the risk management plan for human system risks at the US National Aeronautics and Space Administration (NASA), the NASA Human Systems Risk Board uses causal diagrams (in the form of directed, acyclic graphs, or DAGs) to communicate the complex web of events that leads from exposure to the spaceflight environment to performance and health outcomes. However, the use of DAGs in this way is relatively new at NASA, and thus far, no method has been articulated for testing their veracity using empirical data. In this paper, we demonstrate a set of procedures for doing so, using (a) a DAG related to the risk of bone fracture after exposure to spaceflight; and (b) four datasets originally generated to investigate this phenomenon in rodents. Tests of expected marginal correlation and conditional independencies derived from the DAG indicate that the rodent data largely agree with the structure of the diagram. Incongruencies between tests and the expected relationships in one of the datasets are likely explained by inadequate representation of a key DAG variable in the dataset. Future directions include greater tie-in with human data sources, including multiomics data, which may allow for more robust characterization and measurement of DAG variables.

There has long been an interest in understanding how the hazards from spaceflight may trigger or exacerbate human diseases. With the goal of advancing our knowledge on physiological changes during space travel, NASA GeneLab provides an open-source repository of multi-omics data from real and simulated spaceflight studies. Alone, this data enables identification of biological changes during spaceflight, but cannot infer how that may impact an astronaut at the phenotypic level. To bridge this gap, SPOKE, a heterogeneous knowledge graph connecting biological and clinical data from over 30 databases, was used in combination with GeneLab transcriptomic data from six studies. This integration identified critical symptoms and physiological changes incurred during spaceflight.

The adverse effects of microgravity exposure on mammalian physiology during spaceflight necessitate a deep understanding of the underlying mechanisms to develop effective countermeasures. One such concern is muscle atrophy, which is partly attributed to the dysregulation of calcium levels due to abnormalities in SERCA pump functioning. To identify potential biomarkers for this condition, multi-omics data and physiological data available on the NASA Open Science Data Repository (osdr.nasa.gov) were used, and machine learning methods were employed. Specifically, we used multi-omics (transcriptomic, proteomic, and DNA methylation) data and calcium reuptake data collected from C57BL/6 J mouse soleus and tibialis anterior tissues during several 30+ day-long missions on the international space station. The QLattice symbolic regression algorithm was introduced to generate highly explainable models that predict either experimental conditions or calcium reuptake levels based on multi-omics features. The list of candidate models established by QLattice was used to identify key features contributing to the predictive capability of these models, with Acyp1 and Rps7 proteins found to be the most predictive biomarkers related to the resilience of the tibialis anterior muscle in space. These findings could serve as targets for future interventions aiming to reduce the extent of muscle atrophy during space travel.

Space flight exposes astronauts to stressors that alter the immune response, rendering them vulnerable to infections and diseases. In this study, we aimed to determine the levels of inflammasome activation in the brains of mice that were housed in the International Space Station (ISS) for 37 days. C57BL/6 mice were launched to the ISS as part of NASA’s Rodent Research 1 Mission on SpaceX-4 CRS-4 Dragon cargo spacecraft from 21 September 2014 to 25 October 2014. Dissected mouse brains from that mission were analyzed by immunoblotting of inflammasome signaling proteins and Electrochemiluminescence Immunoassay (ECLIA) for inflammatory cytokine levels. Our data indicate decreased inflammasome activation in the brains of mice that were housed in the ISS for 37 days when compared to the brains of mice that were maintained on the ground, and in mice corresponding to the baseline group that were sacrificed at the time of launching of SpaceX-4. Moreover, we did not detect any significant changes in the expression levels of the pro-inflammatory cytokines TNF-α, IL-2, IFN-γ, IL-5, IL-6, IL-12p70 and IL-10 between the ground control and the flight groups. Together, these studies suggest that spaceflight results in a decrease in the levels of innate immune signaling molecules that govern inflammasome signaling in the brain of mice.

The increasing accessibility of commercial and private space travel necessitates a profound understanding of its impact on human health. The NASA Open Science Data Repository (OSDR) provides transparent and FAIR access to biological studies, notably the SpaceX Inspiration4 (I4) mission, which amassed extensive data from civilian astronauts. This dataset encompasses omics and clinical assays, facilitating comprehensive research on space-induced biological responses. These data allow for multi-modal, longitudinal assessments, bridging the gap between human and model organism studies. Crucially, community-driven data standards established by NASA’s OSDR Analysis Working Groups empower artificial intelligence and machine learning to glean invaluable insights, guiding future mission planning and health risk mitigation. This article presents a concise guide to access and analyze I4 data in OSDR, including programmatic access through GLOpenAPI. This pioneering effort establishes a precedent for post-mission health monitoring programs within space agencies, propelling research in the burgeoning field of commercial space travel’s impact on human physiology.

In the era of renewed space exploration, comprehending the effects of the space environment on human health, particularly for deep space missions, is crucial. While extensive research exists on the impacts of spaceflight, there is a gap regarding female reproductive risks. We hypothesize that space stressors could have enduring effects on female health, potentially increasing risks for future pregnancies upon return to Earth, particularly related to small-for-gestational-age (SGA) fetuses. To address this, we identify a shared microRNA (miRNA) signature between SGA and the space environment, conserved across humans and mice. These miRNAs target genes and pathways relevant to diseases and development. Employing a machine learning approach, we identify potential FDA-approved drugs to mitigate these risks, including estrogen and progesterone receptor antagonists, vitamin D receptor antagonists, and DNA polymerase inhibitors. This study underscores potential pregnancy-related health risks for female astronauts and proposes pharmaceutical interventions to counteract the impact of space travel on female health. A circulating miRNA signature linked to birth defect risk, (i.e. as small-for-gestational-age (SGA) fetuses), was identified for females exposed to the space environment. AI/ML tools were used to predict potential countermeasures to mitigate this risk. @AfshinBeheshti @PittTweet

SummaryEvidence regarding the risk factors and characteristics of those with foot fragility fractures compared to non-foot fragility fractures is limited. Foot fragility fracture patients are more likely to be younger female with a higher BMI. A foot fragility fracture is strongly predictive of a subsequent foot fragility fracture.PurposeOsteoporosis can clinically result in fragility fractures. Evidence regarding the risk factors and characteristics of foot fragility fractures compared to non-foot fragility fractures is limited. The American Orthopaedic Association’s Own the Bone (OTB) is a bone health initiative with a substantial dataset. The purpose of this study was to examine and compare characteristics of patients presenting with isolated foot fragility fracture to those with a non-foot fragility fracture.MethodsBetween January 2009 and March of 2022, 58,001 fragility fractures occurred that were included in this cohort. A total of 750 patients had foot fragility fracture(s) and 57,251 patients had a non-foot fragility fracture that included shoulder, arm, elbow, forearm, wrist, spine, ribs, pelvis, hip, thigh, knee, tibia/fibula, and ankle. Demographics, fracture history, bone health factors, medication history, and medication use for each patient were reported in the OTB database. This data was utilized in our secondary cohort comparative analysis of characteristics and the risk of future fractures between foot fragility fracture and non-foot fragility fracture groups.ResultsFoot fragility fracture patients have a significantly higher probability to be younger (66.9 years old), female (91.5%), and have a higher BMI (28.3 kg/m2) compared to non-foot fragility fracture (p < 0.0001) patients. Patients with a foot fragility fracture are nine times (OR = 9.119, CI = 7.44–11.18, p < 0.001) more likely to have had a prior foot fragility fracture. Young, female patients with a prior foot fragility fracture are at higher risk of a future foot fragility fracture, and this risk increased as BMI increased.ConclusionsFoot fragility fracture patients are more likely to be female and younger compared to patients with a non-foot fragility fracture. A foot fragility fracture is a sentinel event considering that a prior foot fragility fracture is strongly predictive of a subsequent foot fragility fracture.Level of evidence: 3 (retrospective cohort).