NASA GeneLab: interfaces for the exploration of space omics data

Satellite-enabled enviromics to enhance crop improvement

Enviromics refers to the characterization of micro- and macroenvironments based on large-scale environmental datasets. By providing genotypic recommendations with predictive extrapolation at a site-specific level, enviromics could inform plant breeding decisions across varying conditions and anticipate productivity in a changing climate. Enviromics-based integration of statistics, envirotyping (i.e., determining environmental factors), and remote sensing could help unravel the complex interplay of genetics, environment, and management. To support this goal, exhaustive envirotyping to generate precise environmental profiles would significantly improve predictions of genotype performance and genetic gain in crops. Already, informatics management platforms aggregate diverse environmental datasets obtained using optical, thermal, radar, and light detection and ranging (LiDAR)sensors that capture detailed information about vegetation, surface structure, and terrain. This wealth of information, coupled with freely available climate data, fuels innovative enviromics research. While enviromics holds immense potential for breeding, a few obstacles remain, such as the need for (1) integrative methodologies to systematically collect field data to scale and expand observations across the landscape with satellite data; (2) state-of-the-art AI models for data integration, simulation, and prediction; (3) cyberinfrastructure for processing big data across scales and providing seamless interfaces to deliver forecasts to stakeholders; and (4) collaboration and data sharing among farmers, breeders, physiologists, geoinformatics experts, and programmers across research institutions. Overcoming these challenges is essential for leveraging the full potential of big data captured by satellites to transform 21st century agriculture and crop improvement through enviromics.

Microbial adaptation to spaceflight is correlated with bacteriophage-encoded functions

Evidence from the International Space Station suggests microbial populations are rapidly adapting to the spacecraft environment; however, the mechanism of this adaptation is not understood. Bacteriophages are prolific mediators of bacterial adaptation on Earth. Here we survey 245 genomes sequenced from bacterial strains isolated on the International Space Station for dormant (lysogenic) bacteriophages. Our analysis indicates phage-associated genes are significantly different between spaceflight strains and their terrestrial counterparts. In addition, we identify 283 complete prophages, those that could initiate bacterial lysis and infect additional hosts, of which 21% are novel. These prophage regions encode functions that correlate with increased persistence in extreme environments, such as spaceflight, to include antimicrobial resistance and virulence, DNA damage repair, and dormancy. Our results correlate microbial adaptation in spaceflight to bacteriophage-encoded functions that may impact human health in spaceflight.

Inspiration4 data access through the NASA Open Science Data Repository

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.

NASA GeneLab derived microarray studies of Mus musculus and Homo sapiens organisms in altered gravitational conditions

One of the greatest challenges of humanity for deep space exploration is to fully understand how altered gravitational conditions affect human physiology. It is evident that the spaceflight environment causes multiple alterations to musculoskeletal, cardiovascular, immune and central nervous systems, to name a few known effects. To better characterize these biological effects, we compare gene expression datasets from microarray studies found in NASA GeneLab, part of the NASA Open Science Data Repository. In this review, we summarize these archived results for various tissues, emphasizing key genes which are highly reproducible in different mice or human experiments. Such exhaustive mining shows the potential of NASA Open Science data to identify and validate mechanisms taking place when mammalian organisms are exposed to microgravity or other spaceflight conditions. Our comparative meta-analysis findings highlight certain degrees of overlap and reproducibility in genes identified as differentially expressed within musculoskeletal tissues in each species across a variety of altered gravity conditions. However, the level of overlap between species was found to be significantly limited, partly attributed to the limited availability of human samples.

Using single-sample networks to identify the contrasting patterns of gene interactions and reveal the radiation dose-dependent effects in multiple tissues of spaceflight mice

Transcriptome profiles are sensitive to space stressors and serve as valuable indicators of the biological effects during spaceflight. Herein, we transformed the expression profiles into gene interaction patterns by single-sample networks (SSNs) and performed the integrated analysis on the 301 spaceflight and 290 ground control samples, which were obtained from the GeneLab platform. Specifically, an individual SSN was established for each sample. Based on the topological structures of 591 SSNs, the differentially interacted genes (DIGs) were identified between spaceflights and ground controls. The results showed that spaceflight disrupted the gene interaction patterns in mice and resulted in significant enrichment of biological processes such as protein/amino acid metabolism and nucleic acid (DNA/RNA) metabolism (P-value < 0.05). We observed that the mice exposed to radiation doses within the three intervals (4.66-7.14, 7.592-8.295, 8.49-22.099 mGy) exhibited similar gene interaction patterns. Low and medium doses resulted in changes to the circadian rhythm, while the damaging effects on genetic material became more pronounced in higher doses. The gene interaction patterns in response to space stressors varied among different tissues, with the spleen, lung, and skin being the most responsive to space radiation (P-value < 0.01). The changes observed in gene networks during spaceflight conditions might contribute to the development of various diseases, such as mental disorders, depression, and metabolic disorders, among others. Additionally, organisms activated specific gene networks in response to virus reactivation. We identified several hub genes that were associated with circadian rhythms, suggesting that spaceflight could lead to substantial circadian rhythm dysregulation.

Nitrosative Stress in Astronaut Skeletal Muscle in Spaceflight

Long-duration mission (LDM) astronauts from the International Space Station (ISS) (>180 ISS days) revealed a close-to-normal sarcolemmal nitric oxide synthase type-1 (NOS1) immunoexpression in myofibers together with biochemical and quantitative qPCR changes in deep calf soleus muscle. Nitro-DIGE analyses identified functional proteins (structural, metabolic, mitochondrial) that were over-nitrosylated post- vs. preflight. In a short-duration mission (SDM) astronaut (9 ISS days), s-nitrosylation of a nodal protein of the glycolytic flux, specific proteins in tricarboxylic acid (TCA) cycle, respiratory chain, and over-nitrosylation of creatine kinase M-types as signs of impaired ATP production and muscle contraction proteins were seen. S-nitrosylation of serotransferrin (TF) or carbonic anhydrase 3 (CA3b and 3c) represented signs of acute response microgravity muscle maladaptation. LDM nitrosoprofiles reflected recovery of mitochondrial activity, contraction proteins, and iron transporter TF as signs of muscle adaptation to microgravity. Nitrosated antioxidant proteins, alcohol dehydrogenase 5/S-nitrosoglutathione reductase (ADH5/GSNOR), and selenoprotein thioredoxin reductase 1 (TXNRD1) levels indicated signs of altered redox homeostasis and reduced protection from nitrosative stress in spaceflight. This work presents a novel spaceflight-generated dataset on s-nitrosylated muscle protein signatures from astronauts that helps both to better understand the structural and molecular networks associated to muscular nitrosative stress and to design countermeasures to dysfunction and impaired performance control in human spaceflight missions.

Spaceflight effects on human vascular smooth muscle cell phenotype and function

The cardiovascular system is strongly impacted by the hazards of spaceflight. Astronauts spending steadily increasing lengths of time in microgravity are subject to cardiovascular deconditioning resulting in loss of vascular tone, reduced total blood volume, and diminished cardiac output. Appreciating the mechanisms by which the cells of the vasculature are altered during spaceflight will be integral to understanding and combating these deleterious effects as the human presence in space advances. In this study, we performed RNA-Seq analysis coupled with review by QIAGEN Ingenuity Pathway Analysis software on human aortic smooth muscle cells (HASMCs) cultured for 3 days in microgravity and aboard the International Space Station to assess the transcriptomic changes that occur during spaceflight. The results of our RNA-Seq analysis show that SMCs undergo a wide range of transcriptional alteration while in space, significantly affecting 4422 genes. SMCs largely down-regulate markers of the contractile, synthetic, and osteogenic phenotypes including smooth muscle alpha actin (αSMA), matrix metalloproteinases (MMPs), and bone morphogenic proteins (BMPs). Additionally, components of several cellular signaling pathways were strongly impacted including the STAT3, NFκB, PI3K/AKT, HIF1α, and Endothelin pathways. This study highlights the significant changes in transcriptional behavior SMCs exhibit during spaceflight and puts these changes in context to better understand vascular function in space.

Construction of dose prediction model and identification of sensitive genes for space radiation based on single-sample networks under spaceflight conditions

PURPOSE

To identify sensitive genes for space radiation, we integrated the transcriptomic samples of spaceflight mice from GeneLab and predicted the radiation doses absorbed by individuals in space.

METHODS AND MATERIALS

A single-sample network (SSN) for each individual sample was constructed. Then, using machine learning and genetic algorithms, we built the regression models to predict the absorbed dose equivalent based on the topological structure of SSNs. Moreover, we analyzed the SSNs from each tissue and compared the similarities and differences among them.

RESULTS

Our model exhibited excellent performance with the following metrics: R 2 = 0.980 , MSE = 6.74 e - 04 , and the Pearson correlation coefficient of 0.990 (p value <.0001) between predicted and actual values. We identified 20 key genes, the majority of which had been proven to be associated with radiation. However, we uniquely established them as space radiation sensitive genes for the first time. Through further analysis of the SSNs, we discovered that the different tissues exhibited distinct mechanisms in response to space stressors.

CONCLUSIONS

The topology structures of SSNs effectively predicted radiation doses under spaceflight conditions, and the SSNs revealed the gene regulatory patterns within the organisms under space stressors.

Omics Studies of Tumor Cells under Microgravity Conditions

Cancer is defined as a group of diseases characterized by abnormal cell growth, expansion, and progression with metastasis. Various signaling pathways are involved in its development. Malignant tumors exhibit a high morbidity and mortality. Cancer research increased our knowledge about some of the underlying mechanisms, but to this day, our understanding of this disease is unclear. High throughput omics technology and bioinformatics were successful in detecting some of the unknown cancer mechanisms. However, novel groundbreaking research and ideas are necessary. A stay in orbit causes biochemical and molecular biological changes in human cancer cells which are first, and above all, due to microgravity (µg). The µg-environment provides conditions that are not reachable on Earth, which allow researchers to focus on signaling pathways controlling cell growth and metastasis. Cancer research in space already demonstrated how cancer cell-exposure to µg influenced several biological processes being involved in cancer. This novel approach has the potential to fight cancer and to develop future cancer strategies. Space research has been shown to impact biological processes in cancer cells like proliferation, apoptosis, cell survival, adhesion, migration, the cytoskeleton, the extracellular matrix, focal adhesion, and growth factors, among others. This concise review focuses on publications related to genetic, transcriptional, epigenetic, proteomic, and metabolomic studies on tumor cells exposed to real space conditions or to simulated µg using simulation devices. We discuss all omics studies investigating different tumor cell types from the brain and hematological system, sarcomas, as well as thyroid, prostate, breast, gynecologic, gastrointestinal, and lung cancers, in order to gain new and innovative ideas for understanding the basic biology of cancer.

Plant Reactome Knowledgebase: empowering plant pathway exploration and OMICS data analysis

Plant Reactome (https://plantreactome.gramene.org) is a freely accessible, comprehensive plant pathway knowledgebase. It provides curated reference pathways from rice (Oryza sativa) and gene-orthology-based pathway projections to 129 additional species, spanning single-cell photoautotrophs, non-vascular plants, and higher plants, thus encompassing a wide-ranging taxonomic diversity. Currently, Plant Reactome houses a collection of 339 reference pathways, covering metabolic and transport pathways, hormone signaling, genetic regulations of developmental processes, and intricate transcriptional networks that orchestrate a plant's response to abiotic and biotic stimuli. Beyond being a mere repository, Plant Reactome serves as a dynamic data discovery platform. Users can analyze and visualize omics data, such as gene expression, gene-gene interaction, proteome, and metabolome data, all within the rich context of plant pathways. Plant Reactome is dedicated to fostering data interoperability, upholding global data standards, and embracing the tenets of the Findable, Accessible, Interoperable and Re-usable (FAIR) data policy.

A multi-omics data analysis workflow packaged as a FAIR Digital Object

BACKGROUND

Applying good data management and FAIR (Findable, Accessible, Interoperable, and Reusable) data principles in research projects can help disentangle knowledge discovery, study result reproducibility, and data reuse in future studies. Based on the concepts of the original FAIR principles for research data, FAIR principles for research software were recently proposed. FAIR Digital Objects enable discovery and reuse of Research Objects, including computational workflows for both humans and machines. Practical examples can help promote the adoption of FAIR practices for computational workflows in the research community. We developed a multi-omics data analysis workflow implementing FAIR practices to share it as a FAIR Digital Object.

FINDINGS

We conducted a case study investigating shared patterns between multi-omics data and childhood externalizing behavior. The analysis workflow was implemented as a modular pipeline in the workflow manager Nextflow, including containers with software dependencies. We adhered to software development practices like version control, documentation, and licensing. Finally, the workflow was described with rich semantic metadata, packaged as a Research Object Crate, and shared via WorkflowHub.

CONCLUSIONS

Along with the packaged multi-omics data analysis workflow, we share our experiences adopting various FAIR practices and creating a FAIR Digital Object. We hope our experiences can help other researchers who develop omics data analysis workflows to turn FAIR principles into practice.

Assembling spatial clustering framework for heterogeneous spatial transcriptomics data with GRAPHDeep

MOTIVATION

Spatial clustering is essential and challenging for spatial transcriptomics' data analysis to unravel tissue microenvironment and biological function. Graph neural networks are promising to address gene expression profiles and spatial location information in spatial transcriptomics to generate latent representations. However, choosing an appropriate graph deep learning module and graph neural network necessitates further exploration and investigation.

RESULTS

In this article, we present GRAPHDeep to assemble a spatial clustering framework for heterogeneous spatial transcriptomics data. Through integrating 2 graph deep learning modules and 20 graph neural networks, the most appropriate combination is decided for each dataset. The constructed spatial clustering method is compared with state-of-the-art algorithms to demonstrate its effectiveness and superiority. The significant new findings include: (i) the number of genes or proteins of spatial omics data is quite crucial in spatial clustering algorithms; (ii) the variational graph autoencoder is more suitable for spatial clustering tasks than deep graph infomax module; (iii) UniMP, SAGE, SuperGAT, GATv2, GCN, and TAG are the recommended graph neural networks for spatial clustering tasks; and (iv) the used graph neural network in the existent spatial clustering frameworks is not the best candidate. This study could be regarded as desirable guidance for choosing an appropriate graph neural network for spatial clustering.

AVAILABILITY AND IMPLEMENTATION

The source code of GRAPHDeep is available at https://github.com/narutoten520/GRAPHDeep. The studied spatial omics data are available at https://zenodo.org/record/8141084.

Explainable machine learning identifies multi-omics signatures of muscle response to spaceflight in mice

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.

Database of space life investigations and information on spaceflight plant biology

Extensive spaceflight life investigations (SLIs) have revealed observable space effects on plants, particularly their growth, nutrition yield, and secondary metabolite production. Knowledge of these effects not only facilitates space agricultural and biopharmaceutical technology development but also provides unique perspectives to ground-based investigations. SLIs are specialized experimental protocols and notable biological phenomena. These require specialized databases, leading to the development of the NASA Science Data Archive, Erasmus Experiment Archive, and NASA GeneLab. The increasing interests of SLIs across diverse fields demand resources with comprehensive content, convenient search facilities, and friendly information presentation. A new database SpaceLID (Space Life Investigation Database http://bidd.group/spacelid/ ) was developed with detailed menu search tools and categorized contents about the phenomena, protocols, and outcomes of 459 SLIs (including 106 plant investigations) of 92 species, where 236 SLIs and 57 plant investigations are uncovered by the existing databases. The usefulness of SpaceLID as an SLI information source is illustrated by the literature-reported analysis of metabolite, nutrition, and symbiosis variations of spaceflight plants. In conclusion, this study extensively investigated the impact of the space environment on plant biology, utilizing SpaceLID as an information source and examining various plant species, including Arabidopsis thaliana, Brassica rapa L., and Glycyrrhiza uralensis Fisch. The findings provide valuable insights into the effects of space conditions on plant physiology and metabolism.

Big Data for a Small World: A Review on Databases and Resources for Studying Microbiomes

Microorganisms are ubiquitous in nature and form complex community networks to survive in various environments. This community structure depends on numerous factors like nutrient availability, abiotic factors like temperature and pH as well as microbial composition. Categorising accessible biomes according to their habitats would help in understanding the complexity of the environment-specific communities. Owing to the recent improvements in sequencing facilities, researchers have started to explore diverse microbiomes rapidly and attempts have been made to study microbial crosstalk. However, different metagenomics sampling, preprocessing, and annotation methods make it difficult to compare multiple studies and hinder the recycling of data. Huge datasets originating from these experiments demand systematic computational methods to extract biological information beyond microbial compositions. Further exploration of microbial co-occurring patterns across the biomes could help us in designing cross-biome experiments. In this review, we catalogue databases with system-specific microbiomes, discussing publicly available common databases as well as specialised databases for a range of microbiomes. If the new datasets generated in the future could maintain at least biome-specific annotation, then researchers could use those contemporary tools for relevant and bias-free analysis of complex metagenomics data.

Current Knowledge about the Impact of Microgravity on Gene Regulation

Microgravity (µg) has a massive impact on the health of space explorers. Microgravity changes the proliferation, differentiation, and growth of cells. As crewed spaceflights into deep space are being planned along with the commercialization of space travelling, researchers have focused on gene regulation in cells and organisms exposed to real (r-) and simulated (s-) µg. In particular, cancer and metastasis research benefits from the findings obtained under µg conditions. Gene regulation is a key factor in a cell or an organism’s ability to sustain life and respond to environmental changes. It is a universal process to control the amount, location, and timing in which genes are expressed. In this review, we provide an overview of µg-induced changes in the numerous mechanisms involved in gene regulation, including regulatory proteins, microRNAs, and the chemical modification of DNA. In particular, we discuss the current knowledge about the impact of microgravity on gene regulation in different types of bacteria, protists, fungi, animals, humans, and cells with a focus on the brain, eye, endothelium, immune system, cartilage, muscle, bone, and various cancers as well as recent findings in plants. Importantly, the obtained data clearly imply that µg experiments can support translational medicine on Earth.

Meta-analysis of the space flight and microgravity response of the Arabidopsis plant transcriptome

Spaceflight presents a multifaceted environment for plants, combining the effects on growth of many stressors and factors including altered gravity, the influence of experiment hardware, and increased radiation exposure. To help understand the plant response to this complex suite of factors this study compared transcriptomic analysis of 15 Arabidopsis thaliana spaceflight experiments deposited in the National Aeronautics and Space Administration's GeneLab data repository. These data were reanalyzed for genes showing significant differential expression in spaceflight versus ground controls using a single common computational pipeline for either the microarray or the RNA-seq datasets. Such a standardized approach to analysis should greatly increase the robustness of comparisons made between datasets. This analysis was coupled with extensive cross-referencing to a curated matrix of metadata associated with these experiments. Our study reveals that factors such as analysis type (i.e., microarray versus RNA-seq) or environmental and hardware conditions have important confounding effects on comparisons seeking to define plant reactions to spaceflight. The metadata matrix allows selection of studies with high similarity scores, i.e., that share multiple elements of experimental design, such as plant age or flight hardware. Comparisons between these studies then helps reduce the complexity in drawing conclusions arising from comparisons made between experiments with very different designs.

Machine intelligence for radiation science: summary of the Radiation Research Society 67th annual meeting symposium

The era of high-throughput techniques created big data in the medical field and research disciplines. Machine intelligence (MI) approaches can overcome critical limitations on how those large-scale data sets are processed, analyzed, and interpreted. The 67^th Annual Meeting of the Radiation Research Society featured a symposium on MI approaches to highlight recent advancements in the radiation sciences and their clinical applications. This article summarizes three of those presentations regarding recent developments for metadata processing and ontological formalization, data mining for radiation outcomes in pediatric oncology, and imaging in lung cancer.

Mouse genomic associations with in vitro sensitivity to simulated space radiation

Exposure to ionizing radiation is considered by NASA to be a major health hazard for deep space exploration missions. Ionizing radiation sensitivity is modulated by both genomic and environmental factors. Understanding their contributions is crucial for designing experiments in model organisms, evaluating the risk of deep space (i.e. high-linear energy transfer, or LET, particle) radiation exposure in astronauts, and also selecting therapeutic irradiation regimes for cancer patients. We identified single nucleotide polymorphisms in 15 strains of mice, including 10 collaborative cross model strains and 5 founder strains, associated with spontaneous and ionizing radiation-induced in vitro DNA damage quantified based on immunofluorescent tumor protein p53 binding protein (53BP1) positive nuclear foci. Statistical analysis suggested an association with pathways primarily related to cellular signaling, metabolism, tumorigenesis and nervous system damage. We observed different genomic associations in early (4 and 8 h) responses to different LET radiation, while later (24 hour) DNA damage responses showed a stronger overlap across all LETs. Furthermore, a subset of pathways was associated with spontaneous DNA damage, suggesting 53BP1 positive foci as a potential biomarker for DNA integrity in mouse models. Our results suggest several mouse strains as new models to further study the impact of ionizing radiation and validate the identified genetic loci. We also highlight the importance of future human in vitro studies to refine the association of genes and pathways with the DNA damage response to ionizing radiation and identify targets for space travel countermeasures.

Exploring microbial functional biodiversity at the protein family level-From metagenomic sequence reads to annotated protein clusters

Metagenomics has enabled accessing the genetic repertoire of natural microbial communities. Metagenome shotgun sequencing has become the method of choice for studying and classifying microorganisms from various environments. To this end, several methods have been developed to process and analyze the sequence data from raw reads to end-products such as predicted protein sequences or families. In this article, we provide a thorough review to simplify such processes and discuss the alternative methodologies that can be followed in order to explore biodiversity at the protein family level. We provide details for analysis tools and we comment on their scalability as well as their advantages and disadvantages. Finally, we report the available data repositories and recommend various approaches for protein family annotation related to phylogenetic distribution, structure prediction and metadata enrichment.

Challenges and considerations for single-cell and spatially resolved transcriptomics sample collection during spaceflight

Single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomics (SRT) have experienced rapid development in recent years. The findings of spaceflight-based scRNA-seq and SRT investigations are likely to improve our understanding of life in space and our comprehension of gene expression in various cell systems and tissue dynamics. However, compared to their Earth-based counterparts, gene expression experiments conducted in spaceflight have not experienced the same pace of development. Out of the hundreds of spaceflight gene expression datasets available, only a few used scRNA-seq and SRT. In this perspective piece, we explore the growing importance of scRNA-seq and SRT in space biology and discuss the challenges and considerations relevant to robust experimental design to enable growth of these methods in the field.

Integrating bioinformatic strategies in spatial life science research

As space exploration programs progress, manned space missions will become more frequent and farther away from Earth, putting a greater emphasis on astronaut health. Through the collaborative efforts of researchers from various countries, the effect of the space environment factors on living systems is gradually being uncovered. Although a large number of interconnected research findings have been produced, their connection seems to be confused, and many unknown effects are left to be discovered. Simultaneously, several valuable data resources have emerged, accumulating data measuring biological effects in space that can be used to further investigate the unknown biological adaptations. In this review, the previous findings and their correlations are sorted out to facilitate the understanding of biological adaptations to space and the design of countermeasures. The biological effect measurement methods/data types are also organized to provide references for experimental design and data analysis. To aid deeper exploration of the data resources, we summarized common characteristics of the data generated from longitudinal experiments, outlined challenges or caveats in data analysis and provided corresponding solutions by recommending bioinformatics strategies and available models/tools.

Database of space life investigations and bioinformatics of microbiology in extreme environments

Biological experiments performed in space crafts like space stations, space shuttles, and recoverable satellites has enabled extensive spaceflight life investigations (SLIs). In particular, SLIs have revealed distinguished space effects on microbial growth, survival, metabolite production, biofilm formation, virulence development and drug resistant mutations. These provide unique perspectives to ground-based microbiology and new opportunities for industrial pharmaceutical and metabolite productions. SLIs are with specialized experimental setups, analysis methods and research outcomes, which can be accessed by established databases National Aeronautics and Space Administration (NASA) Life Science Data Archive, Erasmus Experiment Archive, and NASA GeneLab. The increasing research across diverse fields may be better facilitated by databases of convenient search facilities and categorized presentation of comprehensive contents. We therefore developed the Space Life Investigation Database (SpaceLID) http://bidd.group/spacelid/, which collected SLIs from published academic papers. Currently, this database provides detailed menu search facilities and categorized contents about the studied phenomena, materials, experimental procedures, analysis methods, and research outcomes of 448 SLIs of 90 species (microbial, plant, animal, human), 81 foods and 106 pharmaceuticals, including 232 SLIs not covered by the established databases. The potential applications of SpaceLID are illustrated by the examples of published experimental design and bioinformatic analysis of spaceflight microbial phenomena.

A multi-omics longitudinal study of the murine retinal response to chronic low-dose irradiation and simulated microgravity

The space environment includes unique hazards like radiation and microgravity which can adversely affect biological systems. We assessed a multi-omics NASA GeneLab dataset where mice were hindlimb unloaded and/or gamma irradiated for 21 days followed by retinal analysis at 7 days, 1 month or 4 months post-exposure. We compared time-matched epigenomic and transcriptomic retinal profiles resulting in a total of 4178 differentially methylated loci or regions, and 457 differentially expressed genes. Highest correlation in methylation difference was seen across different conditions at the same time point. Nucleotide metabolism biological processes were enriched in all groups with activation at 1 month and suppression at 7 days and 4 months. Genes and processes related to Notch and Wnt signaling showed alterations 4 months post-exposure. A total of 23 genes showed significant changes in methylation and expression compared to unexposed controls, including genes involved in retinal function and inflammatory response. This multi-omics analysis interrogates the epigenomic and transcriptomic impacts of radiation and hindlimb unloading on the retina in isolation and in combination and highlights important molecular mechanisms at different post-exposure stages.

Validating Causal Diagrams of Human Health Risks for Spaceflight: An Example Using Bone Data from Rodents

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.

Extraterrestrial Gynecology: Could Spaceflight Increase the Risk of Developing Cancer in Female Astronauts? An Updated Review

Outer space is an extremely hostile environment for human life, with ionizing radiation from galactic cosmic rays and microgravity posing the most significant hazards to the health of astronauts. Spaceflight has also been shown to have an impact on established cancer hallmarks, possibly increasing carcinogenic risk. Terrestrially, women have a higher incidence of radiation-induced cancers, largely driven by lung, thyroid, breast, and ovarian cancers, and therefore, historically, they have been permitted to spend significantly less time in space than men. In the present review, we focus on the effects of microgravity and radiation on the female reproductive system, particularly gynecological cancer. The aim is to provide a summary of the research that has been carried out related to the risk of gynecological cancer, highlighting what further studies are needed to pave the way for safer exploration class missions, as well as postflight screening and management of women astronauts following long-duration spaceflight.

FAIR Genomes metadata schema promoting Next Generation Sequencing data reuse in Dutch healthcare and research

The genomes of thousands of individuals are profiled within Dutch healthcare and research each year. However, this valuable genomic data, associated clinical data and consent are captured in different ways and stored across many systems and organizations. This makes it difficult to discover rare disease patients, reuse data for personalized medicine and establish research cohorts based on specific parameters. FAIR Genomes aims to enable NGS data reuse by developing metadata standards for the data descriptions needed to FAIRify genomic data while also addressing ELSI issues. We developed a semantic schema of essential data elements harmonized with international FAIR initiatives. The FAIR Genomes schema v1.1 contains 110 elements in 9 modules. It reuses common ontologies such as NCIT, DUO and EDAM, only introducing new terms when necessary. The schema is represented by a YAML file that can be transformed into templates for data entry software (EDC) and programmatic interfaces (JSON, RDF) to ease genomic data sharing in research and healthcare. The schema, documentation and MOLGENIS reference implementation are available at https://fairgenomes.org .

Physiological adaptations affecting drug pharmacokinetics in space: what do we really know? A critical review of the literature

As human spaceflight progresses with extended mission durations, the demand for effective and safe drugs will necessarily increase. To date, the accepted medications used during missions (for space motion sickness, sleep disturbances, allergies, pain, and sinus congestion) are administered under the assumption that they act as safely and efficaciously as on Earth. However, physiological changes have been documented in human subjects in spaceflight involving fluid shifts, muscle and bone loss, immune system dysregulation, and adjustments in the gastrointestinal tract and metabolism. These alterations may change the pharmacokinetics (PK) and pharmacodynamics of commonly used medications. Frustratingly, the information gained from bed rest studies and from in‐flight observations is incomplete and also demonstrates a high variability in drug PK. Therefore, the objectives of this review are to report (i) the impact of the space environmental stressors on human physiology in relation to PK; (ii) the state‐of‐the‐art on experimental data in space and/or in ground‐based models; (iii) the validation of ground‐based models for PK studies; and (iv) the identification of research gaps.

The transcriptomic revolution and radiation biology

PURPOSE

This article will briefly review the origins and evolution of functional genomics, first describing the experimental technology, and then some of the approaches applied to data analysis and visualization. It will emphasize application of functional genomics to radiation biology, using examples from the author's work to illustrate several key types of analysis. It concludes with a look at non-coding RNA, alternative reading of the genome, and single-cell transcriptomics, some of the innovative areas that may help to shape future research in radiation biology and oncology.

CONCLUSIONS

Transcriptomic approaches have provided insight into many areas of radiation biology and medicine, and innovations in technology and data analysis approaches promise continued contributions to radiation science in the future.

The Future of Personalized Medicine in Space: From Observations to Countermeasures

The aim of personalized medicine is to detach from a “one-size fits all approach” and improve patient health by individualization to achieve the best outcomes in disease prevention, diagnosis and treatment. Technological advances in sequencing, improved knowledge of omics, integration with bioinformatics and new in vitro testing formats, have enabled personalized medicine to become a reality. Individual variation in response to environmental factors can affect susceptibility to disease and response to treatments. Space travel exposes humans to environmental stressors that lead to physiological adaptations, from altered cell behavior to abnormal tissue responses, including immune system impairment. In the context of human space flight research, human health studies have shown a significant inter-individual variability in response to space analogue conditions. A substantial degree of variability has been noticed in response to medications (from both an efficacy and toxicity perspective) as well as in susceptibility to damage from radiation exposure and in physiological changes such as loss of bone mineral density and muscle mass in response to deconditioning. At present, personalized medicine for astronauts is limited. With the advent of longer duration missions beyond low Earth orbit, it is imperative that space agencies adopt a personalized strategy for each astronaut, starting from pre-emptive personalized pre-clinical approaches through to individualized countermeasures to minimize harmful physiological changes and find targeted treatment for disease. Advances in space medicine can also be translated to terrestrial applications, and vice versa. This review places the astronaut at the center of personalized medicine, will appraise existing evidence and future preclinical tools as well as clinical, ethical and legal considerations for future space travel.

Histone deacetylase HDA-4-mediated epigenetic regulation in space-flown C. elegans

Epigenetic changes during long-term spaceflight are beginning to be studied by NASA's twin astronauts and other model organisms. Here, we evaluate the epigenetic regulation of gene expression in space-flown C. elegans by comparing wild type and histone deacetylase (hda)-4 mutants. Expression levels of 39 genes were consistently upregulated in all four generations of adult hda-4 mutants grown under microgravity compared with artificial Earth-like gravity (1G). In contrast, in the wild type, microgravity-induced upregulation of these genes occurred a little. Among these genes, 11 contain the domain of unknown function 19 (DUF-19) and are located in a cluster on chromosome V. When compared with the 1G condition, histone H3 trimethylation at lysine 27 (H3K27me3) increased under microgravity in the DUF-19 containing genes T20D4.12 to 4.10 locus in wild-type adults. On the other hand, this increase was also observed in the hda-4 mutant, but the level was significantly reduced. The body length of wild-type adults decreased slightly but significantly when grown under microgravity. This decrease was even more pronounced with the hda-4 mutant. In ground-based experiments, one of the T20D4.11 overexpressing strains significantly reduced body length and also caused larval growth retardation and arrest. These results indicate that under microgravity, C. elegans activates histone deacetylase HDA-4 to suppress overregulation of several genes, including the DUF-19 family. In other words, the expression of certain genes, including negative regulators of growth and development, is epigenetically fine-tuned to adapt to the space microgravity.

Mammalian and Invertebrate Models as Complementary Tools for Gaining Mechanistic Insight on Muscle Responses to Spaceflight

Bioinformatics approaches have proven useful in understanding biological responses to spaceflight. Spaceflight experiments remain resource intensive and rare. One outstanding issue is how to maximize scientific output from a limited number of omics datasets from traditional animal models including nematodes, fruitfly, and rodents. The utility of omics data from invertebrate models in anticipating mammalian responses to spaceflight has not been fully explored. Hence, we performed comparative analyses of transcriptomes of soleus and extensor digitorum longus (EDL) in mice that underwent 37 days of spaceflight. Results indicate shared stress responses and altered circadian rhythm. EDL showed more robust growth signals and Pde2a downregulation, possibly underlying its resistance to atrophy versus soleus. Spaceflight and hindlimb unloading mice shared differential regulation of proliferation, circadian, and neuronal signaling. Shared gene regulation in muscles of humans on bedrest and space flown rodents suggest targets for mitigating muscle atrophy in space and on Earth. Spaceflight responses of C. elegans were more similar to EDL. Discrete life stages of D. melanogaster have distinct utility in anticipating EDL and soleus responses. In summary, spaceflight leads to shared and discrete molecular responses between muscle types and invertebrate models may augment mechanistic knowledge gained from rodent spaceflight and ground-based studies.

Rapid Transient Transcriptional Adaptation to Hypergravity in Jurkat T Cells Revealed by Comparative Analysis of Microarray and RNA-Seq Data

Cellular responses to micro- and hypergravity are rapid and complex and appear within the first few seconds of exposure. Transcriptomic analyses are a valuable tool to analyze these genome-wide cellular alterations. For a better understanding of the cellular dynamics upon altered gravity exposure, it is important to compare different time points. However, since most of the experiments are designed as endpoint measurements, the combination of cross-experiment meta-studies is inevitable. Microarray and RNA-Seq analyses are two of the main methods to study transcriptomics. In the field of altered gravity research, both methods are frequently used. However, the generation of these data sets is difficult and time-consuming and therefore the number of available data sets in this research field is limited. In this study, we investigated the comparability of microarray and RNA-Seq data and applied the results to a comparison of the transcriptomics dynamics between the hypergravity conditions during two real flight platforms and a centrifuge experiment to identify temporal adaptation processes. We performed a comparative study on an Affymetrix HTA2.0 microarray and a paired-end RNA-Seq data set originating from the same Jurkat T cell RNA samples from a short-term hypergravity experiment. The overall agreeability was high, with better sensitivity of the RNA-Seq analysis. The microarray data set showed weaknesses on the level of single upregulated genes, likely due to its normalization approach. On an aggregated level of biotypes, chromosomal distribution, and gene sets, both technologies performed equally well. The microarray showed better performance on the detection of altered gravity-related splicing events. We found that all initially altered transcripts fully adapted after 15 min to hypergravity and concluded that the altered gene expression response to hypergravity is transient and fully reversible. Based on the combined multiple-platform meta-analysis, we could demonstrate rapid transcriptional adaptation to hypergravity, the differential expression of the ATPase subunits ATP6V1A and ATP6V1D, and the cluster of differentiation (CD) molecules CD1E, CD2AP, CD46, CD47, CD53, CD69, CD96, CD164, and CD226 in hypergravity. We could experimentally demonstrate that it is possible to develop methodological evidence for the meta-analysis of individual data.

Rad-Bio-App: a discovery environment for biologists to explore spaceflight-related radiation exposures

In addition to microgravity, spaceflight simultaneously exposes biology to a suite of other stimuli. For example, in space, organisms experience ionizing radiation environments that significantly differ in both quality and quantity from those normally experienced on Earth. However, data on radiation exposure during space missions is often complex to access and to understand, limiting progress towards defining how radiation affects organisms against the unique background of spaceflight. To help address this challenge, we have developed the Rad-Bio-App. This web-accessible database imports radiation metadata from experiments archived in NASA’s GeneLab data repository, and then allows the user to explore these experiments both in the context of their radiation exposure and through their other metadata and results. Rad-Bio-App provides an easy-to-use, graphically-driven environment to enable both radiation biologists and non-specialist researchers to visualize, and understand the impact of ionizing radiation on various biological systems in the context of spaceflight.

NASA GeneLab RNA-seq consensus pipeline: standardized processing of short-read RNA-seq data

With the development of transcriptomic technologies, we are able to quantify precise changes in gene expression profiles from astronauts and other organisms exposed to spaceflight. Members of NASA GeneLab and GeneLab-associated analysis working groups (AWGs) have developed a consensus pipeline for analyzing short-read RNA-sequencing data from spaceflight-associated experiments. The pipeline includes quality control, read trimming, mapping, and gene quantification steps, culminating in the detection of differentially expressed genes. This data analysis pipeline and the results of its execution using data submitted to GeneLab are now all publicly available through the GeneLab database. We present here the full details and rationale for the construction of this pipeline in order to promote transparency, reproducibility, and reusability of pipeline data; to provide a template for data processing of future spaceflight-relevant datasets; and to encourage cross-analysis of data from other databases with the data available in GeneLab.

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Analysis of omics data from different spaceflight studies presents unique challenges

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A standardized pipeline for RNA-seq analysis eliminates data processing variation

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The GeneLab RNA-seq pipeline includes QC, trimming, mapping, quantification, and DGE

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Space-relevant data processed with this pipeline are available at genelab.nasa.gov

A Meta-Analysis of the Effects of High-LET Ionizing Radiations in Human Gene Expression

The use of high linear energy transfer (LET) ionizing radiation (IR) is progressively being incorporated in radiation therapy due to its precise dose localization and high relative biological effectiveness. At the same time, these benefits of particle radiation become a high risk for astronauts in the case of inevitable cosmic radiation exposure. Nonetheless, DNA Damage Response (DDR) activated via complex DNA damage in healthy tissue, occurring from such types of radiation, may be instrumental in the induction of various chronic and late effects. An approach to elucidating the possible underlying mechanisms is studying alterations in gene expression. To this end, we identified differentially expressed genes (DEGs) in high Z and high energy (HZE) particle-, γ-ray- and X-ray-exposed healthy human tissues, utilizing microarray data available in public repositories. Differential gene expression analysis (DGEA) was conducted using the R programming language. Consequently, four separate meta-analyses were conducted, after DEG lists were grouped depending on radiation type, radiation dose and time of collection post-irradiation. To highlight the biological background of each meta-analysis group, functional enrichment analysis and biological network construction were conducted. For HZE particle exposure at 8–24 h post-irradiation, the most interesting finding is the variety of DNA repair mechanisms that were downregulated, a fact that is probably correlated with complex DNA damage formation. Simultaneously, after X-ray exposure during the same hours after irradiation, DNA repair mechanisms continue to take place. Finally, in a further comparison of low- and high-LET radiation effects, the most prominent result is that autophagy mechanisms seem to persist and that adaptive immune induction seems to be present. Such bioinformatics approaches may aid in obtaining an overview of the cellular response to high-LET particles. Understanding these response mechanisms can consequently aid in the development of countermeasures for future space missions and ameliorate heavy ion treatments.

Knowledge Network Embedding of Transcriptomic Data from Spaceflown Mice Uncovers Signs and Symptoms Associated with Terrestrial Diseases

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, Scalable Precision Medicine Oriented Knowledge Engine (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 2021 Nucleic Acids Research database issue and the online molecular biology database collection

The 2021 Nucleic Acids Research database Issue contains 189 papers spanning a wide range of biological fields and investigation. It includes 89 papers reporting on new databases and 90 covering recent changes to resources previously published in the Issue. A further ten are updates on databases most recently published elsewhere. Seven new databases focus on COVID-19 and SARS-CoV-2 and many others offer resources for studying the virus. Major returning nucleic acid databases include NONCODE, Rfam and RNAcentral. Protein family and domain databases include COG, Pfam, SMART and Panther. Protein structures are covered by RCSB PDB and dispersed proteins by PED and MobiDB. In metabolism and signalling, STRING, KEGG and WikiPathways are featured, along with returning KLIFS and new DKK and KinaseMD, all focused on kinases. IMG/M and IMG/VR update in the microbial and viral genome resources section, while human and model organism genomics resources include Flybase, Ensembl and UCSC Genome Browser. Cancer studies are covered by updates from canSAR and PINA, as well as newcomers CNCdatabase and Oncovar for cancer drivers. Plant comparative genomics is catered for by updates from Gramene and GreenPhylDB. The entire Database Issue is freely available online on the Nucleic Acids Research website (https://academic.oup.com/nar). The NAR online Molecular Biology Database Collection has been substantially updated, revisiting nearly 1000 entries, adding 90 new resources and eliminating 86 obsolete databases, bringing the current total to 1641 databases. It is available at https://www.oxfordjournals.org/nar/database/c/.