Longitudinal multi-omics of host-microbe dynamics in prediabetes.

Type 2 diabetes mellitus (T2D) is a growing health problem, but little is known about its early disease stages, its effects on biological processes or the transition to clinical T2D. To understand the earliest stages of T2D better, we obtained samples from 106 healthy individuals and individuals with prediabetes over approximately four years and performed deep profiling of transcriptomes, metabolomes, cytokines, and proteomes, as well as changes in the microbiome. This rich longitudinal data set revealed many insights: first, healthy profiles are distinct among individuals while displaying diverse patterns of intra- and/or inter-personal variability. Second, extensive host and microbial changes occur during respiratory viral infections and immunization, and immunization triggers potentially protective responses that are distinct from responses to respiratory viral infections. Moreover, during respiratory viral infections, insulin-resistant participants respond differently than insulin-sensitive participants. Third, global co-association analyses among the thousands of profiled molecules reveal specific host-microbe interactions that differ between insulin-resistant and insulin-sensitive individuals. Last, we identified early personal molecular signatures in one individual that preceded the onset of T2D, including the inflammation markers interleukin-1 receptor agonist (IL-1RA) and high-sensitivity C-reactive protein (CRP) paired with xenobiotic-induced immune signalling. Our study reveals insights into pathways and responses that differ between glucose-dysregulated and healthy individuals during health and disease and provides an open-access data resource to enable further research into healthy, prediabetic and T2D states.

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.

SBR-Blood: systems biology repository for hematopoietic cells.

Extensive research into hematopoiesis (the development of blood cells) over several decades has generated large sets of expression and epigenetic profiles in multiple human and mouse blood cell types. However, there is no single location to analyze how gene regulatory processes lead to different mature blood cells. We have developed a new database framework called hematopoietic Systems Biology Repository (SBR-Blood), available online at http://sbrblood.nhgri.nih.gov, which allows user-initiated analyses for cell type correlations or gene-specific behavior during differentiation using publicly available datasets for array- and sequencing-based platforms from mouse hematopoietic cells. SBR-Blood organizes information by both cell identity and by hematopoietic lineage. The validity and usability of SBR-Blood has been established through the reproduction of workflows relevant to expression data, DNA methylation, histone modifications and transcription factor occupancy profiles.

Dynamic shifts in occupancy by TAL1 are guided by GATA factors and drive large-scale reprogramming of gene expression during hematopoiesis.

We used mouse ENCODE data along with complementary data from other laboratories to study the dynamics of occupancy and the role in gene regulation of the transcription factor TAL1, a critical regulator of hematopoiesis, at multiple stages of hematopoietic differentiation. We combined ChIP-seq and RNA-seq data in six mouse cell types representing a progression from multilineage precursors to differentiated erythroblasts and megakaryocytes. We found that sites of occupancy shift dramatically during commitment to the erythroid lineage, vary further during terminal maturation, and are strongly associated with changes in gene expression. In multilineage progenitors, the likely target genes are enriched for hematopoietic growth and functions associated with the mature cells of specific daughter lineages (such as megakaryocytes). In contrast, target genes in erythroblasts are specifically enriched for red cell functions. Furthermore, shifts in TAL1 occupancy during erythroid differentiation are associated with gene repression (dissociation) and induction (co-occupancy with GATA1). Based on both enrichment for transcription factor binding site motifs and co-occupancy determined by ChIP-seq, recruitment by GATA transcription factors appears to be a stronger determinant of TAL1 binding to chromatin than the canonical E-box binding site motif. Studies of additional proteins lead to the model that TAL1 regulates expression after being directed to a distinct subset of genomic binding sites in each cell type via its association with different complexes containing master regulators such as GATA2, ERG, and RUNX1 in multilineage cells and the lineage-specific master regulator GATA1 in erythroblasts.

Divergent functions of hematopoietic transcription factors in lineage priming and differentiation during erythro-megakaryopoiesis.

Combinatorial actions of relatively few transcription factors control hematopoietic differentiation. To investigate this process in erythro-megakaryopoiesis, we correlated the genome-wide chromatin occupancy signatures of four master hematopoietic transcription factors (GATA1, GATA2, TAL1, and FLI1) and three diagnostic histone modification marks with the gene expression changes that occur during development of primary cultured megakaryocytes (MEG) and primary erythroblasts (ERY) from murine fetal liver hematopoietic stem/progenitor cells. We identified a robust, genome-wide mechanism of MEG-specific lineage priming by a previously described stem/progenitor cell-expressed transcription factor heptad (GATA2, LYL1, TAL1, FLI1, ERG, RUNX1, LMO2) binding to MEG-associated cis-regulatory modules (CRMs) in multipotential progenitors. This is followed by genome-wide GATA factor switching that mediates further induction of MEG-specific genes following lineage commitment. Interaction between GATA and ETS factors appears to be a key determinant of these processes. In contrast, ERY-specific lineage priming is biased toward GATA2-independent mechanisms. In addition to its role in MEG lineage priming, GATA2 plays an extensive role in late megakaryopoiesis as a transcriptional repressor at loci defined by a specific DNA signature. Our findings reveal important new insights into how ERY and MEG lineages arise from a common bipotential progenitor via overlapping and divergent functions of shared hematopoietic transcription factors.

Lineage and species-specific long noncoding RNAs during erythro-megakaryocytic development.

Mammals express thousands of long noncoding (lnc) RNAs, a few of which are known to function in tissue development. However, the entire repertoire of lncRNAs in most tissues and species is not defined. Indeed, most lncRNAs are not conserved, raising questions about function. We used RNA sequencing to identify 1109 polyadenylated lncRNAs expressed in erythroblasts, megakaryocytes, and megakaryocyte-erythroid precursors of mice, and 594 in erythroblasts of humans. More than half of these lncRNAs were unannotated, emphasizing the opportunity for new discovery through studies of specialized cell types. Analysis of the mouse erythro-megakaryocytic polyadenylated lncRNA transcriptome indicates that ~75% arise from promoters and 25% from enhancers, many of which are regulated by key transcription factors including GATA1 and TAL1. Erythroid lncRNA expression is largely conserved among 8 different mouse strains, yet only 15% of mouse lncRNAs are expressed in humans and vice versa, reflecting dramatic species-specificity. RNA interference assays of 21 abundant erythroid-specific murine lncRNAs in primary mouse erythroid precursors identified 7 whose knockdown inhibited terminal erythroid maturation. At least 6 of these 7 functional lncRNAs have no detectable expression in human erythroblasts, suggesting that lack of conservation between mammalian species does not predict lack of function.

Identification of biologically relevant enhancers in human erythroid cells.

Identification of cell type-specific enhancers is important for understanding the regulation of programs controlling cellular development and differentiation. Enhancers are typically marked by the co-transcriptional activator protein p300 or by groups of cell-expressed transcription factors. We hypothesized that a unique set of enhancers regulates gene expression in human erythroid cells, a highly specialized cell type evolved to provide adequate amounts of oxygen throughout the body. Using chromatin immunoprecipitation followed by massively parallel sequencing, genome-wide maps of candidate enhancers were constructed for p300 and four transcription factors, GATA1, NF-E2, KLF1, and SCL, using primary human erythroid cells. These data were combined with gene expression analyses, and candidate enhancers were identified. Consistent with their predicted function as candidate enhancers, there was statistically significant enrichment of p300 and combinations of co-localizing erythroid transcription factors within 1-50 kb of the transcriptional start site (TSS) of genes highly expressed in erythroid cells. Candidate enhancers were also enriched near genes with known erythroid cell function or phenotype. Candidate enhancers exhibited moderate conservation with mouse and minimal conservation with nonplacental vertebrates. Candidate enhancers were mapped to a set of erythroid-associated, biologically relevant, SNPs from the genome-wide association studies (GWAS) catalogue of NHGRI, National Institutes of Health. Fourteen candidate enhancers, representing 10 genetic loci, mapped to sites associated with biologically relevant erythroid traits. Fragments from these loci directed statistically significant expression in reporter gene assays. Identification of enhancers in human erythroid cells will allow a better understanding of erythroid cell development, differentiation, structure, and function and provide insights into inherited and acquired hematologic disease.

Dynamics of the epigenetic landscape during erythroid differentiation after GATA1 restoration.

Interplays among lineage-specific nuclear proteins, chromatin modifying enzymes, and the basal transcription machinery govern cellular differentiation, but their dynamics of action and coordination with transcriptional control are not fully understood. Alterations in chromatin structure appear to establish a permissive state for gene activation at some loci, but they play an integral role in activation at other loci. To determine the predominant roles of chromatin states and factor occupancy in directing gene regulation during differentiation, we mapped chromatin accessibility, histone modifications, and nuclear factor occupancy genome-wide during mouse erythroid differentiation dependent on the master regulatory transcription factor GATA1. Notably, despite extensive changes in gene expression, the chromatin state profiles (proportions of a gene in a chromatin state dominated by activating or repressive histone modifications) and accessibility remain largely unchanged during GATA1-induced erythroid differentiation. In contrast, gene induction and repression are strongly associated with changes in patterns of transcription factor occupancy. Our results indicate that during erythroid differentiation, the broad features of chromatin states are established at the stage of lineage commitment, largely independently of GATA1. These determine permissiveness for expression, with subsequent induction or repression mediated by distinctive combinations of transcription factors.

Dynamic lipidome alterations associated with human health, disease and ageing.

Lipids can be of endogenous or exogenous origin and affect diverse biological functions, including cell membrane maintenance, energy management and cellular signalling. Here, we report >800 lipid species, many of which are associated with health-to-disease transitions in diabetes, ageing and inflammation, as well as cytokine-lipidome networks. We performed comprehensive longitudinal lipidomic profiling and analysed >1,500 plasma samples from 112 participants followed for up to 9 years (average 3.2 years) to define the distinct physiological roles of complex lipid subclasses, including large and small triacylglycerols, ester- and ether-linked phosphatidylethanolamines, lysophosphatidylcholines, lysophosphatidylethanolamines, cholesterol esters and ceramides. Our findings reveal dynamic changes in the plasma lipidome during respiratory viral infection, insulin resistance and ageing, suggesting that lipids may have roles in immune homoeostasis and inflammation regulation. Individuals with insulin resistance exhibit disturbed immune homoeostasis, altered associations between lipids and clinical markers, and accelerated changes in specific lipid subclasses during ageing. Our dataset based on longitudinal deep lipidome profiling offers insights into personalized ageing, metabolic health and inflammation, potentially guiding future monitoring and intervention strategies.

Real-time alerting system for COVID-19 and other stress events using wearable data.

Early detection of infectious diseases is crucial for reducing transmission and facilitating early intervention. In this study, we built a real-time smartwatch-based alerting system that detects aberrant physiological and activity signals (heart rates and steps) associated with the onset of early infection and implemented this system in a prospective study. In a cohort of 3,318 participants, of whom 84 were infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), this system generated alerts for pre-symptomatic and asymptomatic SARS-CoV-2 infection in 67 (80%) of the infected individuals. Pre-symptomatic signals were observed at a median of 3 days before symptom onset. Examination of detailed survey responses provided by the participants revealed that other respiratory infections as well as events not associated with infection, such as stress, alcohol consumption and travel, could also trigger alerts, albeit at a much lower mean frequency (1.15 alert days per person compared to 3.42 alert days per person for coronavirus disease 2019 cases). Thus, analysis of smartwatch signals by an online detection algorithm provides advance warning of SARS-CoV-2 infection in a high percentage of cases. This study shows that a real-time alerting system can be used for early detection of infection and other stressors and employed on an open-source platform that is scalable to millions of users.

Real-time Alerting System for COVID-19 Using Wearable Data.

Early detection of infectious disease is crucial for reducing transmission and facilitating early intervention. We built a real-time smartwatch-based alerting system for the detection of aberrant physiological and activity signals (e.g. resting heart rate, steps) associated with early infection onset at the individual level. Upon applying this system to a cohort of 3,246 participants, we found that alerts were generated for pre-symptomatic and asymptomatic COVID-19 infections in 78% of cases, and pre-symptomatic signals were observed a median of three days prior to symptom onset. Furthermore, by examining over 100,000 survey annotations, we found that other respiratory infections as well as events not associated with COVID-19 (e.g. stress, alcohol consumption, travel) could trigger alerts, albeit at a lower mean period (1.9 days) than those observed in the COVID-19 cases (4.3 days). Thus this system has potential both for advanced warning of COVID-19 as well as a general system for measuring health via detection of physiological shifts from personal baselines. The system is open-source and scalable to millions of users, offering a personal health monitoring system that can operate in real time on a global scale.

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.

Deep longitudinal multiomics profiling reveals two biological seasonal patterns in California.

The influence of seasons on biological processes is poorly understood. In order to identify biological seasonal patterns based on diverse molecular data, rather than calendar dates, we performed a deep longitudinal multiomics profiling of 105 individuals over 4 years. Here, we report more than 1000 seasonal variations in omics analytes and clinical measures. The different molecules group into two major seasonal patterns which correlate with peaks in late spring and late fall/early winter in California. The two patterns are enriched for molecules involved in human biological processes such as inflammation, immunity, cardiovascular health, as well as neurological and psychiatric conditions. Lastly, we identify molecules and microbes that demonstrate different seasonal patterns in insulin sensitive and insulin resistant individuals. The results of our study have important implications in healthcare and highlight the value of considering seasonality when assessing population wide health risk and management.

Pre-symptomatic detection of COVID-19 from smartwatch data.

Consumer wearable devices that continuously measure vital signs have been used to monitor the onset of infectious disease. Here, we show that data from consumer smartwatches can be used for the pre-symptomatic detection of coronavirus disease 2019 (COVID-19). We analysed physiological and activity data from 32 individuals infected with COVID-19, identified from a cohort of nearly 5,300 participants, and found that 26 of them (81%) had alterations in their heart rate, number of daily steps or time asleep. Of the 25 cases of COVID-19 with detected physiological alterations for which we had symptom information, 22 were detected before (or at) symptom onset, with four cases detected at least nine days earlier. Using retrospective smartwatch data, we show that 63% of the COVID-19 cases could have been detected before symptom onset in real time via a two-tiered warning system based on the occurrence of extreme elevations in resting heart rate relative to the individual baseline. Our findings suggest that activity tracking and health monitoring via consumer wearable devices may be used for the large-scale, real-time detection of respiratory infections, often pre-symptomatically.

Cell-free DNA (cfDNA) and Exosome Profiling from a Year-Long Human Spaceflight Reveals Circulating Biomarkers.

Liquid biopsies based on cell-free DNA (cfDNA) or exosomes provide a noninvasive approach to monitor human health and disease but have not been utilized for astronauts. Here, we profile cfDNA characteristics, including fragment size, cellular deconvolution, and nucleosome positioning, in an astronaut during a year-long mission on the International Space Station, compared to his identical twin on Earth and healthy donors. We observed a significant increase in the proportion of cell-free mitochondrial DNA (cf-mtDNA) inflight, and analysis of post-flight exosomes in plasma revealed a 30-fold increase in circulating exosomes and patient-specific protein cargo (including brain-derived peptides) after the year-long mission. This longitudinal analysis of astronaut cfDNA during spaceflight and the exosome profiles highlights their utility for astronaut health monitoring, as well as cf-mtDNA levels as a potential biomarker for physiological stress or immune system responses related to microgravity, radiation exposure, and the other unique environmental conditions of spaceflight.

A longitudinal big data approach for precision health.

Precision health relies on the ability to assess disease risk at an individual level, detect early preclinical conditions and initiate preventive strategies. Recent technological advances in omics and wearable monitoring enable deep molecular and physiological profiling and may provide important tools for precision health. We explored the ability of deep longitudinal profiling to make health-related discoveries, identify clinically relevant molecular pathways and affect behavior in a prospective longitudinal cohort (n = 109) enriched for risk of type 2 diabetes mellitus. The cohort underwent integrative personalized omics profiling from samples collected quarterly for up to 8 years (median, 2.8 years) using clinical measures and emerging technologies including genome, immunome, transcriptome, proteome, metabolome, microbiome and wearable monitoring. We discovered more than 67 clinically actionable health discoveries and identified multiple molecular pathways associated with metabolic, cardiovascular and oncologic pathophysiology. We developed prediction models for insulin resistance by using omics measurements, illustrating their potential to replace burdensome tests. Finally, study participation led the majority of participants to implement diet and exercise changes. Altogether, we conclude that deep longitudinal profiling can lead to actionable health discoveries and provide relevant information for precision health.

The NASA Twins Study: A multidimensional analysis of a year-long human spaceflight.

To understand the health impact of long-duration spaceflight, one identical twin astronaut was monitored before, during, and after a 1-year mission onboard the International Space Station; his twin served as a genetically matched ground control. Longitudinal assessments identified spaceflight-specific changes, including decreased body mass, telomere elongation, genome instability, carotid artery distension and increased intima-media thickness, altered ocular structure, transcriptional and metabolic changes, DNA methylation changes in immune and oxidative stress-related pathways, gastrointestinal microbiota alterations, and some cognitive decline postflight. Although average telomere length, global gene expression, and microbiome changes returned to near preflight levels within 6 months after return to Earth, increased numbers of short telomeres were observed and expression of some genes was still disrupted. These multiomic, molecular, physiological, and behavioral datasets provide a valuable roadmap of the putative health risks for future human spaceflight.

Distinct transcriptomic and exomic abnormalities within myelodysplastic syndrome marrow cells.

To provide biologic insights into mechanisms underlying myelodysplastic syndromes (MDS) we evaluated the CD34+ marrow cells transcriptome using high-throughput RNA sequencing (RNA-Seq). We demonstrated significant differential gene expression profiles (GEPs) between MDS and normal and identified 41 disease classifier genes. Additionally, two main clusters of GEPs distinguished patients based on their major clinical features, particularly between those whose disease remained stable versus patients who transformed into acute myeloid leukemia within 12 months. The genes whose expression was associated with disease outcome were involved in functional pathways and biologic processes highly relevant for MDS. Combined with exomic analysis we identified differential isoform usage of genes in MDS mutational subgroups, with consequent dysregulation of distinct biologic functions. This combination of clinical, transcriptomic and exomic findings provides valuable understanding of mechanisms underlying MDS and its progression to a more aggressive stage and also facilitates prognostic characterization of MDS patients.

Integrative Personal Omics Profiles during Periods of Weight Gain and Loss.

Advances in omics technologies now allow an unprecedented level of phenotyping for human diseases, including obesity, in which individual responses to excess weight are heterogeneous and unpredictable. To aid the development of better understanding of these phenotypes, we performed a controlled longitudinal weight perturbation study combining multiple omics strategies (genomics, transcriptomics, multiple proteomics assays, metabolomics, and microbiomics) during periods of weight gain and loss in humans. Results demonstrated that: (1) weight gain is associated with the activation of strong inflammatory and hypertrophic cardiomyopathy signatures in blood; (2) although weight loss reverses some changes, a number of signatures persist, indicative of long-term physiologic changes; (3) we observed omics signatures associated with insulin resistance that may serve as novel diagnostics; (4) specific biomolecules were highly individualized and stable in response to perturbations, potentially representing stable personalized markers. Most data are available open access and serve as a valuable resource for the community.

Dynamics of GATA1 binding and expression response in a GATA1-induced erythroid differentiation system.

During the maturation phase of mammalian erythroid differentiation, highly proliferative cells committed to the erythroid lineage undergo dramatic changes in morphology and function to produce circulating, enucleated erythrocytes. These changes are caused by equally dramatic alterations in gene expression, which in turn are driven by changes in the abundance and binding patterns of transcription factors such as GATA1. We have studied the dynamics of GATA1 binding by ChIP-seq and the global expression responses by RNA-seq in a GATA1-dependent mouse cell line model for erythroid maturation, in both cases examining seven progressive stages during differentiation. Analyses of these data should provide insights both into mechanisms of regulation (early versus late targets) and the consequences in cell physiology (e.g. distinctive categories of genes regulated at progressive stages of differentiation). The data are deposited in the Gene Expression Omnibus, series GSE36029, GSE40522, GSE49847, and GSE51338.