Fundamental Biological Features of Spaceflight: Advancing the Field to Enable Deep-Space Exploration.

Research on astronaut health and model organisms have revealed six features of spaceflight biology that guide our current understanding of fundamental molecular changes that occur during space travel. The features include oxidative stress, DNA damage, mitochondrial dysregulation, epigenetic changes (including gene regulation), telomere length alterations, and microbiome shifts. Here we review the known hazards of human spaceflight, how spaceflight affects living systems through these six fundamental features, and the associated health risks of space exploration. We also discuss the essential issues related to the health and safety of astronauts involved in future missions, especially planned long-duration and Martian missions.

XACT-Seq Comprehensively Defines the Promoter-Position and Promoter-Sequence Determinants for Initial-Transcription Pausing.

Pausing by RNA polymerase (RNAP) during transcription elongation, in which a translocating RNAP uses a "stepping" mechanism, has been studied extensively, but pausing by RNAP during initial transcription, in which a promoter-anchored RNAP uses a "scrunching" mechanism, has not. We report a method that directly defines the RNAP-active-center position relative to DNA with single-nucleotide resolution (XACT-seq; "crosslink-between-active-center-and-template sequencing"). We apply this method to detect and quantify pausing in initial transcription at 411 (∼4,000,000) promoter sequences in vivo in Escherichia coli. The results show initial-transcription pausing can occur in each nucleotide addition during initial transcription, particularly the first 4 to 5 nucleotide additions. The results further show initial-transcription pausing occurs at sequences that resemble the consensus sequence element for transcription-elongation pausing. Our findings define the positional and sequence determinants for initial-transcription pausing and establish initial-transcription pausing is hard coded by sequence elements similar to those for transcription-elongation pausing.

CapZyme-Seq Comprehensively Defines Promoter-Sequence Determinants for RNA 5' Capping with NAD.

Nucleoside-containing metabolites such as NAD+ can be incorporated as 5' caps on RNA by serving as non-canonical initiating nucleotides (NCINs) for transcription initiation by RNA polymerase (RNAP). Here, we report CapZyme-seq, a high-throughput-sequencing method that employs NCIN-decapping enzymes NudC and Rai1 to detect and quantify NCIN-capped RNA. By combining CapZyme-seq with multiplexed transcriptomics, we determine efficiencies of NAD+ capping by Escherichia coli RNAP for ∼16,000 promoter sequences. The results define preferred transcription start site (TSS) positions for NAD+ capping and define a consensus promoter sequence for NAD+ capping: HRRASWW (TSS underlined). By applying CapZyme-seq to E. coli total cellular RNA, we establish that sequence determinants for NCIN capping in vivo match the NAD+-capping consensus defined in vitro, and we identify and quantify NCIN-capped small RNAs (sRNAs). Our findings define the promoter-sequence determinants for NCIN capping with NAD+ and provide a general method for analysis of NCIN capping in vitro and in vivo.

Promoter-sequence determinants and structural basis of primer-dependent transcription initiation in Escherichia coli.

Chemical modifications of RNA 5'-ends enable "epitranscriptomic" regulation, influencing multiple aspects of RNA fate. In transcription initiation, a large inventory of substrates compete with nucleoside triphosphates for use as initiating entities, providing an ab initio mechanism for altering the RNA 5'-end. In Escherichia coli cells, RNAs with a 5'-end hydroxyl are generated by use of dinucleotide RNAs as primers for transcription initiation, "primer-dependent initiation." Here, we use massively systematic transcript end readout (MASTER) to detect and quantify RNA 5'-ends generated by primer-dependent initiation for ∼410 (∼1,000,000) promoter sequences in E. coli The results show primer-dependent initiation in E. coli involves any of the 16 possible dinucleotide primers and depends on promoter sequences in, upstream, and downstream of the primer binding site. The results yield a consensus sequence for primer-dependent initiation, YTSS-2NTSS-1NTSSWTSS+1, where TSS is the transcription start site, NTSS-1NTSS is the primer binding site, Y is pyrimidine, and W is A or T. Biochemical and structure-determination studies show that the base pair (nontemplate-strand base:template-strand base) immediately upstream of the primer binding site (Y:RTSS-2, where R is purine) exerts its effect through the base on the DNA template strand (RTSS-2) through interchain base stacking with the RNA primer. Results from analysis of a large set of natural, chromosomally encoded Ecoli promoters support the conclusions from MASTER. Our findings provide a mechanistic and structural description of how TSS-region sequence hard-codes not only the TSS position but also the potential for epitranscriptomic regulation through primer-dependent transcription initiation.

Massively Systematic Transcript End Readout, "MASTER": Transcription Start Site Selection, Transcriptional Slippage, and Transcript Yields.

We report the development of a next-generation sequencing-based technology that entails construction of a DNA library comprising up to at least 4(7) (∼ 16,000) barcoded sequences, production of RNA transcripts, and analysis of transcript ends and transcript yields (massively systematic transcript end readout, "MASTER"). Using MASTER, we define full inventories of transcription start sites ("TSSomes") of Escherichia coli RNA polymerase for initiation at a consensus core promoter in vitro and in vivo; we define the TSS-region DNA sequence determinants for TSS selection, reiterative initiation ("slippage synthesis"), and transcript yield; and we define effects of DNA topology and NTP concentration. The results reveal that slippage synthesis occurs from the majority of TSS-region DNA sequences and that TSS-region DNA sequences have profound, up to 100-fold, effects on transcript yield. The results further reveal that TSSomes depend on DNA topology, consistent with the proposal that TSS selection involves transcription-bubble expansion ("scrunching") and transcription-bubble contraction ("anti-scrunching").

Betacoronavirus-specific alternate splicing.

Viruses can subvert a number of cellular processes including splicing in order to block innate antiviral responses, and many viruses interact with cellular splicing machinery. SARS-CoV-2 infection was shown to suppress global mRNA splicing, and at least 10 SARS-CoV-2 proteins bind specifically to one or more human RNAs. Here, we investigate 17 published experimental and clinical datasets related to SARS-CoV-2 infection, datasets from the betacoronaviruses SARS-CoV and MERS, as well as Streptococcus pneumonia, HCV, Zika virus, Dengue virus, influenza H3N2, and RSV. We show that genes showing differential alternative splicing in SARS-CoV-2 have a similar functional profile to those of SARS-CoV and MERS and affect a diverse set of genes and biological functions, including many closely related to virus biology. Additionally, the differentially spliced transcripts of cells infected by coronaviruses were more likely to undergo intron-retention, contain a pseudouridine modification, and have a smaller number of exons as compared with differentially spliced transcripts in the control groups. Viral load in clinical COVID-19 samples was correlated with isoform distribution of differentially spliced genes. A significantly higher number of ribosomal genes are affected by differential alternative splicing and gene expression in betacoronavirus samples, and the betacoronavirus differentially spliced genes are depleted for binding sites of RNA-binding proteins. Our results demonstrate characteristic patterns of differential splicing in cells infected by SARS-CoV-2, SARS-CoV, and MERS. The alternative splicing changes observed in betacoronaviruses infection potentially modify a broad range of cellular functions, via changes in the functions of the products of a diverse set of genes involved in different biological processes.

Differentiating children with sepsis with and without acute respiratory distress syndrome using proteomics.

Both sepsis and acute respiratory distress syndrome (ARDS) rely on imprecise clinical definitions leading to heterogeneity, which has contributed to negative trials. Because circulating protein/DNA complexes have been implicated in sepsis and ARDS, we aimed to develop a proteomic signature of DNA-bound proteins to discriminate between children with sepsis with and without ARDS. We performed a prospective case-control study in 12 children with sepsis with ARDS matched to 12 children with sepsis without ARDS on age, severity of illness score, and source of infection. We performed co-immunoprecipitation and downstream proteomics in plasma collected ≤ 24 h of intensive care unit admission. Expression profiles were generated, and a random forest classifier was used on differentially expressed proteins to develop a signature which discriminated ARDS. The classifier was tested in six independent blinded samples. Neutrophil and nucleosome proteins were over-represented in ARDS, including two S100A proteins, superoxide dismutase (SOD), and three histones. Random forest produced a 10-protein signature that accurately discriminated between children with sepsis with and without ARDS. This classifier perfectly assigned six independent blinded samples as having ARDS or not. We validated higher expression of the most informative discriminating protein, galectin-3-binding protein, in children with ARDS. Our methodology has applicability to isolation of DNA-bound proteins from plasma. Our results support the premise of a molecular definition of ARDS, and give preliminary insight into why some children with sepsis, but not others, develop ARDS.

Scedar: A scalable Python package for single-cell RNA-seq exploratory data analysis.

In single-cell RNA-seq (scRNA-seq) experiments, the number of individual cells has increased exponentially, and the sequencing depth of each cell has decreased significantly. As a result, analyzing scRNA-seq data requires extensive considerations of program efficiency and method selection. In order to reduce the complexity of scRNA-seq data analysis, we present scedar, a scalable Python package for scRNA-seq exploratory data analysis. The package provides a convenient and reliable interface for performing visualization, imputation of gene dropouts, detection of rare transcriptomic profiles, and clustering on large-scale scRNA-seq datasets. The analytical methods are efficient, and they also do not assume that the data follow certain statistical distributions. The package is extensible and modular, which would facilitate the further development of functionalities for future requirements with the open-source development community. The scedar package is distributed under the terms of the MIT license at https://pypi.org/project/scedar.

A transcriptome-based classifier to determine molecular subtypes in medulloblastoma.

Medulloblastoma is a highly heterogeneous pediatric brain tumor with five molecular subtypes, Sonic Hedgehog TP53-mutant, Sonic Hedgehog TP53-wildtype, WNT, Group 3, and Group 4, defined by the World Health Organization. The current mechanism for classification into these molecular subtypes is through the use of immunostaining, methylation, and/or genetics. We surveyed the literature and identified a number of RNA-Seq and microarray datasets in order to develop, train, test, and validate a robust classifier to identify medulloblastoma molecular subtypes through the use of transcriptomic profiling data. We have developed a GPL-3 licensed R package and a Shiny Application to enable users to quickly and robustly classify medulloblastoma samples using transcriptomic data. The classifier utilizes a large composite microarray dataset (15 individual datasets), an individual microarray study, and an RNA-Seq dataset, using gene ratios instead of gene expression measures as features for the model. Discriminating features were identified using the limma R package and samples were classified using an unweighted mean of normalized scores. We utilized two training datasets and applied the classifier in 15 separate datasets. We observed a minimum accuracy of 85.71% in the smallest dataset and a maximum of 100% accuracy in four datasets with an overall median accuracy of 97.8% across the 15 datasets, with the majority of misclassification occurring between the heterogeneous Group 3 and Group 4 subtypes. We anticipate this medulloblastoma transcriptomic subtype classifier will be broadly applicable to the cancer research and clinical communities.

What Every Reader Should Know About Studies Using Electronic Health Record Data but May Be Afraid to Ask.

Coincident with the tsunami of COVID-19-related publications, there has been a surge of studies using real-world data, including those obtained from the electronic health record (EHR). Unfortunately, several of these high-profile publications were retracted because of concerns regarding the soundness and quality of the studies and the EHR data they purported to analyze. These retractions highlight that although a small community of EHR informatics experts can readily identify strengths and flaws in EHR-derived studies, many medical editorial teams and otherwise sophisticated medical readers lack the framework to fully critically appraise these studies. In addition, conventional statistical analyses cannot overcome the need for an understanding of the opportunities and limitations of EHR-derived studies. We distill here from the broader informatics literature six key considerations that are crucial for appraising studies utilizing EHR data: data completeness, data collection and handling (eg, transformation), data type (ie, codified, textual), robustness of methods against EHR variability (within and across institutions, countries, and time), transparency of data and analytic code, and the multidisciplinary approach. These considerations will inform researchers, clinicians, and other stakeholders as to the recommended best practices in reviewing manuscripts, grants, and other outputs from EHR-data derived studies, and thereby promote and foster rigor, quality, and reliability of this rapidly growing field.

The Genomics Research and Innovation Network: creating an interoperable, federated, genomics learning system.

PURPOSE: Clinicians and researchers must contextualize a patient's genetic variants against population-based references with detailed phenotyping. We sought to establish globally scalable technology, policy, and procedures for sharing biosamples and associated genomic and phenotypic data on broadly consented cohorts, across sites of care. METHODS: Three of the nation's leading children's hospitals launched the Genomic Research and Innovation Network (GRIN), with federated information technology infrastructure, harmonized biobanking protocols, and material transfer agreements. Pilot studies in epilepsy and short stature were completed to design and test the collaboration model. RESULTS: Harmonized, broadly consented institutional review board (IRB) protocols were approved and used for biobank enrollment, creating ever-expanding, compatible biobanks. An open source federated query infrastructure was established over genotype-phenotype databases at the three hospitals. Investigators securely access the GRIN platform for prep to research queries, receiving aggregate counts of patients with particular phenotypes or genotypes in each biobank. With proper approvals, de-identified data is exported to a shared analytic workspace. Investigators at all sites enthusiastically collaborated on the pilot studies, resulting in multiple publications. Investigators have also begun to successfully utilize the infrastructure for grant applications. CONCLUSIONS: The GRIN collaboration establishes the technology, policy, and procedures for a scalable genomic research network.

Correction: The Genomics Research and Innovation Network: creating an interoperable, federated, genomics learning system.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Derivation of a metabolic signature associated with bacterial meningitis in infants.

BACKGROUND: Diagnosis of bacterial meningitis (BM) is challenging in newborn infants. Presently, biomarkers of BM have limited diagnostic accuracy. Analysis of cerebrospinal fluid (CSF) metabolites may be a useful diagnostic tool in BM. METHODS: In a nested case-control study, we examined >400 metabolites in CSF of uninfected infants and infants with culture-confirmed BM using gas and liquid chromatography mass spectrometry. Preterm and full-term infants in a Level III or IV Neonatal Intensive Care Unit were prospectively enrolled when evaluated for serious bacterial infection. RESULTS: Over 200 CSF metabolites significantly differed in uninfected infants and infants with BM. Using machine learning, we found that as few as 6 metabolites distinguished infants with BM from uninfected infants in this pilot cohort. Further analysis demonstrated three metabolites associated with Group B Streptococcal meningitis. CONCLUSIONS: We report the first comprehensive metabolic analysis of CSF in infants with BM. In our pilot cohort, we derived a metabolic signature that predicted the presence or absence of BM, irrespective of gestational age, postnatal age, sex, race and ethnicity, presence of neurosurgical hardware, white blood cell count in CSF, and red blood cell contamination in CSF. Metabolic analysis may aid diagnosis of BM and facilitate clinical decision-making in infants. IMPACT: In a pilot cohort, metabolites in cerebrospinal fluid distinguished infants with bacterial meningitis from uninfected infants.We report the first comprehensive metabolic analysis of cerebrospinal fluid in infants with bacterial meningitis.Our findings may be used to improve diagnosis of bacterial meningitis and to offer mechanistic insights into the pathophysiology of bacterial meningitis in infants.

Interactions between RNA polymerase and the core recognition element are a determinant of transcription start site selection.

During transcription initiation, RNA polymerase (RNAP) holoenzyme unwinds ∼13 bp of promoter DNA, forming an RNAP-promoter open complex (RPo) containing a single-stranded transcription bubble, and selects a template-strand nucleotide to serve as the transcription start site (TSS). In RPo, RNAP core enzyme makes sequence-specific protein-DNA interactions with the downstream part of the nontemplate strand of the transcription bubble ("core recognition element," CRE). Here, we investigated whether sequence-specific RNAP-CRE interactions affect TSS selection. To do this, we used two next-generation sequencing-based approaches to compare the TSS profile of WT RNAP to that of an RNAP derivative defective in sequence-specific RNAP-CRE interactions. First, using massively systematic transcript end readout, MASTER, we assessed effects of RNAP-CRE interactions on TSS selection in vitro and in vivo for a library of 4(7) (∼16,000) consensus promoters containing different TSS region sequences, and we observed that the TSS profile of the RNAP derivative defective in RNAP-CRE interactions differed from that of WT RNAP, in a manner that correlated with the presence of consensus CRE sequences in the TSS region. Second, using 5' merodiploid native-elongating-transcript sequencing, 5' mNET-seq, we assessed effects of RNAP-CRE interactions at natural promoters in Escherichia coli, and we identified 39 promoters at which RNAP-CRE interactions determine TSS selection. Our findings establish RNAP-CRE interactions are a functional determinant of TSS selection. We propose that RNAP-CRE interactions modulate the position of the downstream end of the transcription bubble in RPo, and thereby modulate TSS selection, which involves transcription bubble expansion or transcription bubble contraction (scrunching or antiscrunching).

Astronaut omics and the impact of space on the human body at scale.

Future multi-year crewed planetary missions will motivate advances in aerospace nutrition and telehealth. On Earth, the Human Cell Atlas project aims to spatially map all cell types in the human body. Here, we propose that a parallel Human Cell Space Atlas could serve as an openly available, global resource for space life science research. As humanity becomes increasingly spacefaring, high-resolution omics on orbit could permit an advent of precision spaceflight healthcare. Alongside the scientific potential, we consider the complex ethical, cultural, and legal challenges intrinsic to the human space omics discipline, and how philosophical frameworks may benefit from international perspectives.

Inflammatory recruitment of healthy hematopoietic stem and progenitor cells in the acute myeloid leukemia niche.

Inflammation in the bone marrow (BM) microenvironment is a constitutive component of leukemogenesis in acute myeloid leukemia (AML). Current evidence suggests that both leukemic blasts and stroma secrete proinflammatory factors that actively suppress the function of healthy hematopoietic stem and progenitor cells (HSPCs). HSPCs are also cellular components of the innate immune system, and we reasoned that they may actively propagate the inflammation in the leukemic niche. In two separate congenic models of AML we confirm by evaluation of the BM plasma secretome and HSPC-selective single-cell RNA sequencing (scRNA-Seq) that multipotent progenitors and long-lived stem cells adopt inflammatory gene expression programs, even at low leukemic infiltration of the BM. In particular, we observe interferon gamma (IFN-γ) pathway activation, along with secretion of its chemokine target, CXCL10. We show that AML-derived nanometer-sized extracellular vesicles (EVAML) are sufficient to trigger this inflammatory HSPC response, both in vitro and in vivo. Altogether, our studies indicate that HSPCs are an unrecognized component of the inflammatory adaptation of the BM by leukemic cells. The pro-inflammatory conversion and long-lived presence of HSPCs in the BM along with their regenerative re-expansion during remission may impact clonal selection and disease evolution.

BAP1 is required prenatally for differentiation and maintenance of postnatal murine enteric nervous system.

Epigenetic regulatory mechanisms are underappreciated, yet are critical for enteric nervous system (ENS) development and maintenance. We discovered that fetal loss of the epigenetic regulator Bap1 in the ENS lineage caused severe postnatal bowel dysfunction and early death in Tyrosinase-Cre Bap1fl/fl mice. Bap1-depleted ENS appeared normal in neonates; however, by P15, Bap1-deficient enteric neurons were largely absent from the small and large intestine of Tyrosinase-Cre Bap1fl/fl mice. Bowel motility became markedly abnormal with disproportionate loss of cholinergic neurons. Single-cell RNA sequencing at P5 showed that fetal Bap1 loss in Tyrosinase-Cre Bap1fl/fl mice markedly altered the composition and relative proportions of enteric neuron subtypes. In contrast, postnatal deletion of Bap1 did not cause enteric neuron loss or impaired bowel motility. These findings suggest that BAP1 is critical for postnatal enteric neuron differentiation and for early enteric neuron survival, a finding that may be relevant to the recently described human BAP1-associated neurodevelopmental disorder.

Spatially resolved multiomics on the neuronal effects induced by spaceflight in mice.

Impairment of the central nervous system (CNS) poses a significant health risk for astronauts during long-duration space missions. In this study, we employed an innovative approach by integrating single-cell multiomics (transcriptomics and chromatin accessibility) with spatial transcriptomics to elucidate the impact of spaceflight on the mouse brain in female mice. Our comparative analysis between ground control and spaceflight-exposed animals revealed significant alterations in essential brain processes including neurogenesis, synaptogenesis and synaptic transmission, particularly affecting the cortex, hippocampus, striatum and neuroendocrine structures. Additionally, we observed astrocyte activation and signs of immune dysfunction. At the pathway level, some spaceflight-induced changes in the brain exhibit similarities with neurodegenerative disorders, marked by oxidative stress and protein misfolding. Our integrated spatial multiomics approach serves as a stepping stone towards understanding spaceflight-induced CNS impairments at the level of individual brain regions and cell types, and provides a basis for comparison in future spaceflight studies. For broader scientific impact, all datasets from this study are available through an interactive data portal, as well as the National Aeronautics and Space Administration (NASA) Open Science Data Repository (OSDR).

Aging and putative frailty biomarkers are altered by spaceflight.

Human space exploration poses inherent risks to astronauts' health, leading to molecular changes that can significantly impact their well-being. These alterations encompass genomic instability, mitochondrial dysfunction, increased inflammation, homeostatic dysregulation, and various epigenomic changes. Remarkably, these changes bear similarities to those observed during the aging process on Earth. However, our understanding of the connection between these molecular shifts and disease development in space remains limited. Frailty syndrome, a clinical syndrome associated with biological aging, has not been comprehensively investigated during spaceflight. To bridge this knowledge gap, we leveraged murine data obtained from NASA's GeneLab, along with astronaut data gathered from the JAXA and Inspiration4 missions. Our objective was to assess the presence of biological markers and pathways related to frailty, aging, and sarcopenia within the spaceflight context. Through our analysis, we identified notable changes in gene expression patterns that may be indicative of the development of a frailty-like condition during space missions. These findings suggest that the parallels between spaceflight and the aging process may extend to encompass frailty as well. Consequently, further investigations exploring the utility of a frailty index in monitoring astronaut health appear to be warranted.