Snyder Lab

Publications

All Publications (886)

Featured Publications (109)

Journal Articles (861)

Conference Proceedings (25)

Stanford W. Ascherman Professor of Genetics

Publications

  • Dynamic lipidome alterations associated with human health, disease and ageing. Nature metabolism Hornburg, D., Wu, S., Moqri, M., Zhou, X., Contrepois, K., Bararpour, N., Traber, G. M., Su, B., Metwally, A. A., Avina, M., Zhou, W., Ubellacker, J. M., Mishra, T., Schüssler-Fiorenza Rose, S. M., Kavathas, P. B., Williams, K. J., Snyder, M. P. 2023

    Abstract

    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.

    View details for DOI 10.1038/s42255-023-00880-1

    View details for PubMedID 37697054

    View details for PubMedCentralID 7736650

  • Biomarkers of aging for the identification and evaluation of longevity interventions. Cell Moqri, M., Herzog, C., Poganik, J. R., Biomarkers of Aging Consortium, Justice, J., Belsky, D. W., Higgins-Chen, A., Moskalev, A., Fuellen, G., Cohen, A. A., Bautmans, I., Widschwendter, M., Ding, J., Fleming, A., Mannick, J., Han, J. J., Zhavoronkov, A., Barzilai, N., Kaeberlein, M., Cummings, S., Kennedy, B. K., Ferrucci, L., Horvath, S., Verdin, E., Maier, A. B., Snyder, M. P., Sebastiano, V., Gladyshev, V. N. 2023; 186 (18): 3758-3775

    Abstract

    With the rapid expansion of aging biology research, the identification and evaluation of longevity interventions in humans have become key goals of this field. Biomarkers of aging are critically important tools in achieving these objectives over realistic time frames. However, the current lack of standards and consensus on the properties of a reliable aging biomarker hinders their further development and validation for clinical applications. Here, we advance a framework for the terminology and characterization of biomarkers of aging, including classification and potential clinical use cases. We discuss validation steps and highlight ongoing challenges as potential areas in need of future research. This framework sets the stage for the development of valid biomarkers of aging and their ultimate utilization in clinical trials and practice.

    View details for DOI 10.1016/j.cell.2023.08.003

    View details for PubMedID 37657418

  • Advances and prospects for the Human BioMolecular Atlas Program (HuBMAP). Nature cell biology Jain, S., Pei, L., Spraggins, J. M., Angelo, M., Carson, J. P., Gehlenborg, N., Ginty, F., Gonçalves, J. P., Hagood, J. S., Hickey, J. W., Kelleher, N. L., Laurent, L. C., Lin, S., Lin, Y., Liu, H., Naba, A., Nakayasu, E. S., Qian, W. J., Radtke, A., Robson, P., Stockwell, B. R., Van de Plas, R., Vlachos, I. S., Zhou, M., Börner, K., Snyder, M. P. 2023

    Abstract

    The Human BioMolecular Atlas Program (HuBMAP) aims to create a multi-scale spatial atlas of the healthy human body at single-cell resolution by applying advanced technologies and disseminating resources to the community. As the HuBMAP moves past its first phase, creating ontologies, protocols and pipelines, this Perspective introduces the production phase: the generation of reference spatial maps of functional tissue units across many organs from diverse populations and the creation of mapping tools and infrastructure to advance biomedical research.

    View details for DOI 10.1038/s41556-023-01194-w

    View details for PubMedID 37468756

    View details for PubMedCentralID 8238499

  • Organization of the human intestine at single-cell resolution. Nature Hickey, J. W., Becker, W. R., Nevins, S. A., Horning, A., Perez, A. E., Zhu, C., Zhu, B., Wei, B., Chiu, R., Chen, D. C., Cotter, D. L., Esplin, E. D., Weimer, A. K., Caraccio, C., Venkataraaman, V., Schürch, C. M., Black, S., Brbić, M., Cao, K., Chen, S., Zhang, W., Monte, E., Zhang, N. R., Ma, Z., Leskovec, J., Zhang, Z., Lin, S., Longacre, T., Plevritis, S. K., Lin, Y., Nolan, G. P., Greenleaf, W. J., Snyder, M. 2023; 619 (7970): 572-584

    Abstract

    The intestine is a complex organ that promotes digestion, extracts nutrients, participates in immune surveillance, maintains critical symbiotic relationships with microbiota and affects overall health1. The intesting has a length of over nine metres, along which there are differences in structure and function2. The localization of individual cell types, cell type development trajectories and detailed cell transcriptional programs probably drive these differences in function. Here, to better understand these differences, we evaluated the organization of single cells using multiplexed imaging and single-nucleus RNA and open chromatin assays across eight different intestinal sites from nine donors. Through systematic analyses, we find cell compositions that differ substantially across regions of the intestine and demonstrate the complexity of epithelial subtypes, and find that the same cell types are organized into distinct neighbourhoods and communities, highlighting distinct immunological niches that are present in the intestine. We also map gene regulatory differences in these cells that are suggestive of a regulatory differentiation cascade, and associate intestinal disease heritability with specific cell types. These results describe the complexity of the cell composition, regulation and organization for this organ, and serve as an important reference map for understanding human biology and disease.

    View details for DOI 10.1038/s41586-023-05915-x

    View details for PubMedID 37468586

    View details for PubMedCentralID PMC10356619

  • Dynamic monitoring of thousands of biochemical analytes using microsampling NATURE BIOMEDICAL ENGINEERING Kellogg, R., Snyder, M. 2023

    View details for DOI 10.1038/s41551-023-01005-5

    View details for Web of Science ID 000920584900001

    View details for PubMedID 36697922

  • Multi-omics microsampling for the profiling of lifestyle-associated changes in health. Nature biomedical engineering Shen, X., Kellogg, R., Panyard, D. J., Bararpour, N., Castillo, K. E., Lee-McMullen, B., Delfarah, A., Ubellacker, J., Ahadi, S., Rosenberg-Hasson, Y., Ganz, A., Contrepois, K., Michael, B., Simms, I., Wang, C., Hornburg, D., Snyder, M. P. 2023

    Abstract

    Current healthcare practices are reactive and use limited physiological and clinical information, often collected months or years apart. Moreover, the discovery and profiling of blood biomarkers in clinical and research settings are constrained by geographical barriers, the cost and inconvenience of in-clinic venepuncture, low sampling frequency and the low depth of molecular measurements. Here we describe a strategy for the frequent capture and analysis of thousands of metabolites, lipids, cytokines and proteins in 10 μl of blood alongside physiological information from wearable sensors. We show the advantages of such frequent and dense multi-omics microsampling in two applications: the assessment of the reactions to a complex mixture of dietary interventions, to discover individualized inflammatory and metabolic responses; and deep individualized profiling, to reveal large-scale molecular fluctuations as well as thousands of molecular relationships associated with intra-day physiological variations (in heart rate, for example) and with the levels of clinical biomarkers (specifically, glucose and cortisol) and of physical activity. Combining wearables and multi-omics microsampling for frequent and scalable omics may facilitate dynamic health profiling and biomarker discovery.

    View details for DOI 10.1038/s41551-022-00999-8

    View details for PubMedID 36658343

  • Recurrent repeat expansions in human cancer genomes. Nature Erwin, G. S., Gursoy, G., Al-Abri, R., Suriyaprakash, A., Dolzhenko, E., Zhu, K., Hoerner, C. R., White, S. M., Ramirez, L., Vadlakonda, A., Vadlakonda, A., von Kraut, K., Park, J., Brannon, C. M., Sumano, D. A., Kirtikar, R. A., Erwin, A. A., Metzner, T. J., Yuen, R. K., Fan, A. C., Leppert, J. T., Eberle, M. A., Gerstein, M., Snyder, M. P. 2022

    Abstract

    Expansion of a single repetitive DNA sequence, termed a tandem repeat (TR), is known to cause more than 50 diseases1,2. However, repeat expansions are often not explored beyond neurological and neurodegenerative disorders. In some cancers, mutations accumulate in short tracts of TRs, a phenomenon termed microsatellite instability; however, larger repeat expansions have not been systematically analysed in cancer3-8. Here we identified TR expansions in 2,622 cancer genomes spanning 29 cancer types. In seven cancer types, we found 160 recurrent repeat expansions (rREs), most of which (155/160) were subtype specific. We found that rREs were non-uniformly distributed in the genome with enrichment near candidate cis-regulatory elements, suggesting a potential role in gene regulation. One rRE, a GAAA-repeat expansion, located near a regulatory element in the first intron of UGT2B7 was detected in 34% of renal cell carcinoma samples and was validated by long-read DNA sequencing. Moreover, in preliminary experiments, treating cells that harbour this rRE with a GAAA-targeting molecule led to a dose-dependent decrease in cell proliferation. Overall, our results suggest that rREs may be an important but unexplored source of genetic variation in human cancer, and we provide a comprehensive catalogue for further study.

    View details for DOI 10.1038/s41586-022-05515-1

    View details for PubMedID 36517591

  • Distinct factors associated with short-term and long-term weight loss induced by low-fat or low-carbohydrate diet intervention. Cell reports. Medicine Li, X., Perelman, D., Leong, A. K., Fragiadakis, G., Gardner, C. D., Snyder, M. P. 2022: 100870

    Abstract

    To understand what determines the success of short- and long-term weight loss, we conduct a secondary analysis of dietary, metabolic, and molecular data collected from 609 participants before, during, and after a 1-year weight-loss intervention with either a healthy low-carbohydrate (HLC) or a healthy low-fat (HLF) diet. Through systematic analysis of multidomain datasets, we find that dietary adherence and diet quality, not just caloric restriction, are important for short-term weight loss in both diets. Interestingly, we observe minimal dietary differences between those who succeeded in long-term weight loss and those who did not. Instead, proteomic and gut microbiota signatures significantly differ between these two groups at baseline. Moreover, the baseline respiratory quotient may suggest a specific diet for better weight-loss outcomes. Overall, the identification of these dietary, molecular, and metabolic factors, common or unique to the HLC and HLF diets, provides a roadmap for developing individualized weight-loss strategies.

    View details for DOI 10.1016/j.xcrm.2022.100870

    View details for PubMedID 36516846

  • Longitudinally tracking personal physiomes for precision management of childhood epilepsy. PLOS digital health Jiang, P., Gao, F., Liu, S., Zhang, S., Zhang, X., Xia, Z., Zhang, W., Jiang, T., Zhu, J. L., Zhang, Z., Shu, Q., Snyder, M., Li, J. 2022; 1 (12): e0000161

    Abstract

    Our current understanding of human physiology and activities is largely derived from sparse and discrete individual clinical measurements. To achieve precise, proactive, and effective health management of an individual, longitudinal, and dense tracking of personal physiomes and activities is required, which is only feasible by utilizing wearable biosensors. As a pilot study, we implemented a cloud computing infrastructure to integrate wearable sensors, mobile computing, digital signal processing, and machine learning to improve early detection of seizure onsets in children. We recruited 99 children diagnosed with epilepsy and longitudinally tracked them at single-second resolution using a wearable wristband, and prospectively acquired more than one billion data points. This unique dataset offered us an opportunity to quantify physiological dynamics (e.g., heart rate, stress response) across age groups and to identify physiological irregularities upon epilepsy onset. The high-dimensional personal physiome and activity profiles displayed a clustering pattern anchored by patient age groups. These signatory patterns included strong age and sex-specific effects on varying circadian rhythms and stress responses across major childhood developmental stages. For each patient, we further compared the physiological and activity profiles associated with seizure onsets with the personal baseline and developed a machine learning framework to accurately capture these onset moments. The performance of this framework was further replicated in another independent patient cohort. We next referenced our predictions with the electroencephalogram (EEG) signals on selected patients and demonstrated that our approach could detect subtle seizures not recognized by humans and could detect seizures prior to clinical onset. Our work demonstrated the feasibility of a real-time mobile infrastructure in a clinical setting, which has the potential to be valuable in caring for epileptic patients. Extension of such a system has the potential to be leveraged as a health management device or longitudinal phenotyping tool in clinical cohort studies.

    View details for DOI 10.1371/journal.pdig.0000161

    View details for PubMedID 36812648

    View details for PubMedCentralID PMC9931296

  • Identification of non-coding silencer elements and their regulation of gene expression. Nature reviews. Molecular cell biology Pang, B., van Weerd, J. H., Hamoen, F. L., Snyder, M. P. 2022

    Abstract

    Cell type- and differentiation-specific gene expression is precisely controlled by genomic non-coding regulatory elements (NCREs), which include promoters, enhancers, silencers and insulators. It is estimated that more than 90% of disease-associated sequence variants lie within the non-coding part of the genome, potentially affecting the activity of NCREs. Consequently, the functional annotation of NCREs is a major driver of genome research. Compared with our knowledge of other regulatory elements, our knowledge of silencers, which are NCREs that repress the transcription of genes, is largely lacking. Multiple recent studies have reported large-scale identification of transcription silencer elements, indicating their importance in homeostasis and disease. In this Review, we discuss the biology of silencers, including methods for their discovery, epigenomic and other characteristics, and modes of function of silencers. We also discuss important silencer-relevant considerations in assessing data from genome-wide association studies and shed light on potential future silencer-based therapeutic applications.

    View details for DOI 10.1038/s41580-022-00549-9

    View details for PubMedID 36344659

  • Performance effectiveness of vital parameter combinations for early warning of sepsis-an exhaustive study using machine learning JAMIA OPEN Rangan, E., Pathinarupothi, R., Anand, K. S., Snyder, M. P. 2022; 5 (4): ooac080

    Abstract

    To carry out exhaustive data-driven computations for the performance of noninvasive vital signs heart rate (HR), respiratory rate (RR), peripheral oxygen saturation (SpO2), and temperature (Temp), considered both independently and in all possible combinations, for early detection of sepsis.By extracting features interpretable by clinicians, we applied Gradient Boosted Decision Tree machine learning on a dataset of 2630 patients to build 240 models. Validation was performed on a geographically distinct dataset. Relative to onset, predictions were clocked as per 16 pairs of monitoring intervals and prediction times, and the outcomes were ranked.The combination of HR and Temp was found to be a minimal feature set yielding maximal predictability with area under receiver operating curve 0.94, sensitivity of 0.85, and specificity of 0.90. Whereas HR and RR each directly enhance prediction, the effects of SpO2 and Temp are significant only when combined with HR or RR. In benchmarking relative to standard methods Systemic Inflammatory Response Syndrome (SIRS), National Early Warning Score (NEWS), and quick-Sequential Organ Failure Assessment (qSOFA), Vital-SEP outperformed all 3 of them.It can be concluded that using intensive care unit data even 2 vital signs are adequate to predict sepsis upto 6 h in advance with promising accuracy comparable to standard scoring methods and other sepsis predictive tools reported in literature. Vital-SEP can be used for fast-track prediction especially in limited resource hospital settings where laboratory based hematologic or biochemical assays may be unavailable, inaccurate, or entail clinically inordinate delays. A prospective study is essential to determine the clinical impact of the proposed sepsis prediction model and evaluate other outcomes such as mortality and duration of hospital stay.

    View details for DOI 10.1093/jamiaopen/ooac080

    View details for Web of Science ID 000868349400001

    View details for PubMedID 36267121

    View details for PubMedCentralID PMC9566305

  • Systems analysis of de novo mutations in congenital heart diseases identified a protein network in the hypoplastic left heart syndrome. Cell systems Wang, Y. J., Zhang, X., Lam, C. K., Guo, H., Wang, C., Zhang, S., Wu, J. C., Snyder, M., Li, J. 2022

    Abstract

    Despite a strong genetic component, only a few genes have been identified in congenital heart diseases (CHDs). We introduced systems analyses to uncover the hidden organization on biological networks of mutations in CHDs and leveraged network analysis to integrate the protein interactome, patient exomes, and single-cell transcriptomes of the developing heart. We identified a CHD network regulating heart development and observed that a sub-network also regulates fetal brain development, thereby providing mechanistic insights into the clinical comorbidities between CHDs and neurodevelopmental conditions. At a small scale, we experimentally verified uncharacterized cardiac functions of several proteins. At a global scale, our study revealed developmental dynamics of the network and observed its association with the hypoplastic left heart syndrome (HLHS), which was further supported by the dysregulation of the network in HLHS endothelial cells. Overall, our work identified previously uncharacterized CHD factors and provided a generalizable framework applicable to studying many other complex diseases. A record of this paper's Transparent Peer Review process is included in the supplemental information.

    View details for DOI 10.1016/j.cels.2022.09.001

    View details for PubMedID 36167075

  • Chimpanzee and pig-tailed macaque iPSCs: Improved culture and generation of primate cross-species embryos. Cell reports Roodgar, M., Suchy, F. P., Nguyen, L. H., Bajpai, V. K., Sinha, R., Vilches-Moure, J. G., Van Bortle, K., Bhadury, J., Metwally, A., Jiang, L., Jian, R., Chiang, R., Oikonomopoulos, A., Wu, J. C., Weissman, I. L., Mankowski, J. L., Holmes, S., Loh, K. M., Nakauchi, H., VandeVoort, C. A., Snyder, M. P. 2022; 40 (9): 111264

    Abstract

    As our closest living relatives, non-human primates uniquely enable explorations of human health, disease, development, and evolution. Considerable effort has thus been devoted to generating induced pluripotent stem cells (iPSCs) from multiple non-human primate species. Here, we establish improved culture methods for chimpanzee (Pan troglodytes) and pig-tailed macaque (Macaca nemestrina) iPSCs. Such iPSCs spontaneously differentiate in conventional culture conditions, but can be readily propagated by inhibiting endogenous WNT signaling. As a unique functional test of these iPSCs, we injected them into the pre-implantation embryos of another non-human species, rhesus macaques (Macaca mulatta). Ectopic expression of gene BCL2 enhances the survival and proliferation of chimpanzee and pig-tailed macaque iPSCs within the pre-implantation embryo, although the identity and long-term contribution of the transplanted cells warrants further investigation. In summary, we disclose transcriptomic and proteomic data, cell lines, and cell culture resources that may be broadly enabling for non-human primate iPSCs research.

    View details for DOI 10.1016/j.celrep.2022.111264

    View details for PubMedID 36044843

  • massDatabase: utilities for the operation of the public compound and pathway database. Bioinformatics (Oxford, England) Shen, X., Wang, C., Snyder, M. P. 2022

    Abstract

    SUMMARY: One of the major challenges in LC-MS data is converting many metabolic feature entries to biological function information, such as metabolite annotation and pathway enrichment, which are based on the compound and pathway databases. Multiple online databases have been developed. However, no tool has been developed for operating all these databases for biological analysis. Therefore, we developed massDatabase, an R package that operates the online public databases and combines with other tools for streamlined compound annotation and pathway enrichment. massDatabase is a flexible, simple, and powerful tool that can be installed on all platforms, allowing the users to leverage all the online public databases for biological function mining. A detailed tutorial and a case study are provided in the Supplementary Materials.AVAILABILITY AND IMPLEMENTATION: https://massdatabase.tidymass.org/.SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

    View details for DOI 10.1093/bioinformatics/btac546

    View details for PubMedID 35944213

  • TidyMass an object-oriented reproducible analysis framework for LC-MS data. Nature communications Shen, X., Yan, H., Wang, C., Gao, P., Johnson, C. H., Snyder, M. P. 2022; 13 (1): 4365

    Abstract

    Reproducibility, traceability, and transparency have been long-standing issues for metabolomics data analysis. Multiple tools have been developed, but limitations still exist. Here, we present the tidyMass project ( https://www.tidymass.org/ ), a comprehensive R-based computational framework that can achieve the traceable, shareable, and reproducible workflow needs of data processing and analysis for LC-MS-based untargeted metabolomics. TidyMass is an ecosystem of R packages that share an underlying design philosophy, grammar, and data structure, which provides a comprehensive, reproducible, and object-oriented computational framework. The modular architecture makes tidyMass a highly flexible and extensible tool, which other users can improve and integrate with other tools to customize their own pipeline.

    View details for DOI 10.1038/s41467-022-32155-w

    View details for PubMedID 35902589

  • Single-cell analyses define a continuum of cell state and composition changes in the malignant transformation of polyps to colorectal cancer. Nature genetics Becker, W. R., Nevins, S. A., Chen, D. C., Chiu, R., Horning, A. M., Guha, T. K., Laquindanum, R., Mills, M., Chaib, H., Ladabaum, U., Longacre, T., Shen, J., Esplin, E. D., Kundaje, A., Ford, J. M., Curtis, C., Snyder, M. P., Greenleaf, W. J. 2022

    Abstract

    To chart cell composition and cell state changes that occur during the transformation of healthy colon to precancerous adenomas to colorectal cancer (CRC), we generated single-cell chromatin accessibility profiles and single-cell transcriptomes from 1,000 to 10,000 cells per sample for 48 polyps, 27 normal tissues and 6 CRCs collected from patients with or without germline APC mutations. A large fraction of polyp and CRC cells exhibit a stem-like phenotype, and we define a continuum of epigenetic and transcriptional changes occurring in these stem-like cells as they progress from homeostasis to CRC. Advanced polyps contain increasing numbers of stem-like cells, regulatory T cells and a subtype of pre-cancer-associated fibroblasts. In the cancerous state, we observe T cell exhaustion, RUNX1-regulated cancer-associated fibroblasts and increasing accessibility associated with HNF4A motifs in epithelia. DNA methylation changes in sporadic CRC are strongly anti-correlated with accessibility changes along this continuum, further identifying regulatory markers for molecular staging of polyps.

    View details for DOI 10.1038/s41588-022-01088-x

    View details for PubMedID 35726067

  • A genome-wide atlas of recurrent repeat expansions in human cancer genomes Erwin, G. S., Gursoy, G., Al-Abri, R., Hoerner, C., Dolzhenko, E., Eberle, M., Fan, A., Leppert, J., Gerstein, M., Snyder, M. P. AMER ASSOC CANCER RESEARCH. 2022
  • Precision environmental health monitoring by longitudinal exposome and multi-omics profiling. Genome research Gao, P., Shen, X., Zhang, X., Jiang, C., Zhang, S., Zhou, X., Schüssler-Fiorenza Rose, S. M., Snyder, M. 2022

    Abstract

    Conventional environmental health studies have primarily focused on limited environmental stressors at the population level, which lacks the power to dissect the complexity and heterogeneity of individualized environmental exposures. Here, as a pilot case study, we integrated deep-profiled longitudinal personal exposome and internal multi-omics to systematically investigate how the exposome shapes a single individual's phenome. We annotated thousands of chemical and biological components in the personal exposome cloud and found they were significantly correlated with thousands of internal biomolecules, which was further cross-validated using corresponding clinical data. Our results showed that agrochemicals and fungi predominated in the highly diverse and dynamic personal exposome, and the biomolecules and pathways related to the individual's immune system, kidney, and liver were highly associated with the personal external exposome. Overall, this data-driven longitudinal monitoring study shows the potential dynamic interactions between the personal exposome and internal multi-omics, as well as the impact of the exposome on precision health by producing abundant testable hypotheses.

    View details for DOI 10.1101/gr.276521.121

    View details for PubMedID 35667843

  • Multiomic analysis reveals cell-type-specific molecular determinants of COVID-19 severity. Cell systems Zhang, S., Cooper-Knock, J., Weimer, A. K., Shi, M., Kozhaya, L., Unutmaz, D., Harvey, C., Julian, T. H., Furini, S., Frullanti, E., Fava, F., Renieri, A., Gao, P., Shen, X., Timpanaro, I. S., Kenna, K. P., Baillie, J. K., Davis, M. M., Tsao, P. S., Snyder, M. P. 2022

    Abstract

    The determinants of severe COVID-19 in healthy adults are poorly understood, which limits the opportunity for early intervention. We present a multiomic analysis using machine learning to characterize the genomic basis of COVID-19 severity. We use single-cell multiome profiling of human lungs to link genetic signals to cell-type-specific functions. We discover >1,000 risk genes across 19 cell types, which account for 77% of the SNP-based heritability for severe disease. Genetic risk is particularly focused within natural killer (NK) cells and T cells, placing the dysfunction of these cells upstream of severe disease. Mendelian randomization and single-cell profiling of human NK cells support the role of NK cells and further localize genetic risk to CD56bright NK cells, which are key cytokine producers during the innate immune response. Rare variant analysis confirms the enrichment of severe-disease-associated genetic variation within NK-cell risk genes. Our study provides insights into the pathogenesis of severe COVID-19 with potential therapeutic targets.

    View details for DOI 10.1016/j.cels.2022.05.007

    View details for PubMedID 35690068

  • Global, distinctive, and personal changes in molecular and microbial profiles by specific fibers in humans. Cell host & microbe Lancaster, S. M., Lee-McMullen, B., Abbott, C. W., Quijada, J. V., Hornburg, D., Park, H., Perelman, D., Peterson, D. J., Tang, M., Robinson, A., Ahadi, S., Contrepois, K., Hung, C., Ashland, M., McLaughlin, T., Boonyanit, A., Horning, A., Sonnenburg, J. L., Snyder, M. P. 2022

    Abstract

    Dietary fibers act through the microbiome to improve cardiovascular health and prevent metabolic disorders and cancer. To understand the health benefits of dietary fiber supplementation, we investigated two popular purified fibers, arabinoxylan (AX) and long-chain inulin (LCI), and a mixture of five fibers. We present multiomic signatures of metabolomics, lipidomics, proteomics, metagenomics, a cytokine panel, and clinical measurements on healthy and insulin-resistant participants. Each fiber is associated with fiber-dependent biochemical and microbial responses. AX consumption associates with a significant reduction in LDL and an increase in bile acids, contributing to its observed cholesterol reduction. LCI is associated with an increase in Bifidobacterium. However, at the highest LCI dose, there is increased inflammation and elevation in the liver enzyme alanine aminotransferase. This study yields insights into the effects of fiber supplementation and the mechanisms behind fiber-induced cholesterol reduction, and it shows effects of individual, purified fibers on the microbiome.

    View details for DOI 10.1016/j.chom.2022.03.036

    View details for PubMedID 35483363

  • Adverse childhood experiences, diabetes and associated conditions, preventive care practices and healthcare access: A population-based study. Preventive medicine Rose, S. M., Slavich, G. M., Snyder, M. P. 2022: 107044

    Abstract

    Our objective was to examine associations between Adverse Childhood Experiences (ACEs) and diabetes mellitus, including related conditions and preventive care practices. We used data from the Behavioral Risk Factor Surveillance System (BRFSS) 2009-2012, a cross-sectional, population-based survey, to assess ACEs, diabetes, and healthcare access in 179,375 adults. In those with diabetes (n = 21,007), we assessed the association of ACEs with myocardial infarction, stroke, and five Healthy People 2020 (HP2020) diabetes-related preventive-care objectives (n = 13,152). Healthcare access indicators included lack of a regular healthcare provider, insurance, and difficulty affording healthcare. Regression analyses adjusted for age, sex, and race. The adjusted odds ratio (AOR) of diabetes increased in a stepwise fashion by ACE exposure, ranging from 1.2 (95% CI 1.1-1.3) for 1 ACE to 1.7 (95% CI 1.6-1.9) for ≥4 ACEs, versus having no ACEs. In persons with diabetes, those with ≥4 ACEs had an elevated adjusted odds of myocardial infarction (AOR = 1.6, 95% CI 1.2-2.0) and stroke (AOR = 1.8, 95% CI 1.3-2.4), versus having no ACEs. ACEs were also associated with a reduction in the adjusted percent of HP2020 diabetes objectives met: 72.9% (95% CI 71.3-74.5) for those with no ACEs versus only 66.5% (95% CI 63.8-69.3%) for those with ≥4 ACEs (p = 0.0002). Finally, ACEs predicted worse healthcare access in a stepwise fashion for all indicators. In conclusion, ACEs are associated with greater prevalence of diabetes and associated conditions, and with meeting fewer HP2020 prevention goals. ACEs screening and trauma-informed care practices are thus recommended.

    View details for DOI 10.1016/j.ypmed.2022.107044

    View details for PubMedID 35398366

  • Genome-wide identification of the genetic basis of amyotrophic lateral sclerosis. Neuron Zhang, S., Cooper-Knock, J., Weimer, A. K., Shi, M., Moll, T., Marshall, J. N., Harvey, C., Nezhad, H. G., Franklin, J., Souza, C. D., Ning, K., Wang, C., Li, J., Dilliott, A. A., Farhan, S., Elhaik, E., Pasniceanu, I., Livesey, M. R., Eitan, C., Hornstein, E., Kenna, K. P., Project MinE ALS Sequencing Consortium, Veldink, J. H., Ferraiuolo, L., Shaw, P. J., Snyder, M. P., Blair, I., Wray, N. R., Kiernan, M., Mitne Neto, M., Chio, A., Cauchi, R., Robberecht, W., van Damme, P., Corcia, P., Couratier, P., Hardiman, O., McLaughin, R., Gotkine, M., Drory, V., Ticozzi, N., Silani, V., Veldink, J. H., van den Berg, L. H., de Carvalho, M., Mora Pardina, J. S., Povedano, M., Andersen, P., Weber, M., Basak, N. A., Al-Chalabi, A., Shaw, C., Shaw, P. J., Morrison, K. E., Landers, J. E., Glass, J. D. 1800

    Abstract

    Amyotrophic lateral sclerosis (ALS) is a complex disease that leads to motor neuron death. Despite heritability estimates of 52%, genome-wide association studies (GWASs) have discovered relatively few loci. We developed a machine learning approach called RefMap, which integrates functional genomics with GWAS summary statistics for gene discovery. With transcriptomic and epigenetic profiling of motor neurons derived from induced pluripotent stem cells (iPSCs), RefMap identified 690 ALS-associated genes that represent a 5-fold increase in recovered heritability. Extensive conservation, transcriptome, network, and rare variant analyses demonstrated the functional significance of candidate genes in healthy and diseased motor neurons and brain tissues. Genetic convergence between common and rare variation highlighted KANK1 as a new ALS gene. Reproducing KANK1 patient mutations in human neurons led to neurotoxicity and demonstrated that TDP-43 mislocalization, a hallmark pathology of ALS, is downstream of axonal dysfunction. RefMap can be readily applied to other complex diseases.

    View details for DOI 10.1016/j.neuron.2021.12.019

    View details for PubMedID 35045337

  • Phenotypic characteristics of peripheral immune cells of Myalgic encephalomyelitis/chronic fatigue syndrome via transmission electron microscopy: A pilot study. PloS one Jahanbani, F., Maynard, R. D., Sing, J. C., Jahanbani, S., Perrino, J. J., Spacek, D. V., Davis, R. W., Snyder, M. P. 2022; 17 (8): e0272703

    Abstract

    Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex chronic multi-systemic disease characterized by extreme fatigue that is not improved by rest, and worsens after exertion, whether physical or mental. Previous studies have shown ME/CFS-associated alterations in the immune system and mitochondria. We used transmission electron microscopy (TEM) to investigate the morphology and ultrastructure of unstimulated and stimulated ME/CFS immune cells and their intracellular organelles, including mitochondria. PBMCs from four participants were studied: a pair of identical twins discordant for moderate ME/CFS, as well as two age- and gender- matched unrelated subjects-one with an extremely severe form of ME/CFS and the other healthy. TEM analysis of CD3/CD28-stimulated T cells suggested a significant increase in the levels of apoptotic and necrotic cell death in T cells from ME/CFS patients (over 2-fold). Stimulated Tcells of ME/CFS patients also had higher numbers of swollen mitochondria. We also found a large increase in intracellular giant lipid droplet-like organelles in the stimulated PBMCs from the extremely severe ME/CFS patient potentially indicative of a lipid storage disorder. Lastly, we observed a slight increase in platelet aggregation in stimulated cells, suggestive of a possible role of platelet activity in ME/CFS pathophysiology and disease severity. These results indicate extensive morphological alterations in the cellular and mitochondrial phenotypes of ME/CFS patients' immune cells and suggest new insights into ME/CFS biology.

    View details for DOI 10.1371/journal.pone.0272703

    View details for PubMedID 35943990

  • Real-time alerting system for COVID-19 and other stress events using wearable data. Nature medicine Alavi, A., Bogu, G. K., Wang, M., Rangan, E. S., Brooks, A. W., Wang, Q., Higgs, E., Celli, A., Mishra, T., Metwally, A. A., Cha, K., Knowles, P., Alavi, A. A., Bhasin, R., Panchamukhi, S., Celis, D., Aditya, T., Honkala, A., Rolnik, B., Hunting, E., Dagan-Rosenfeld, O., Chauhan, A., Li, J. W., Bejikian, C., Krishnan, V., McGuire, L., Li, X., Bahmani, A., Snyder, M. P. 2021

    Abstract

    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.

    View details for DOI 10.1038/s41591-021-01593-2

    View details for PubMedID 34845389

  • A scalable, secure, and interoperable platform for deep data-driven health management. Nature communications Bahmani, A., Alavi, A., Buergel, T., Upadhyayula, S., Wang, Q., Ananthakrishnan, S. K., Alavi, A., Celis, D., Gillespie, D., Young, G., Xing, Z., Nguyen, M. H., Haque, A., Mathur, A., Payne, J., Mazaheri, G., Li, J. K., Kotipalli, P., Liao, L., Bhasin, R., Cha, K., Rolnik, B., Celli, A., Dagan-Rosenfeld, O., Higgs, E., Zhou, W., Berry, C. L., Van Winkle, K. G., Contrepois, K., Ray, U., Bettinger, K., Datta, S., Li, X., Snyder, M. P. 2021; 12 (1): 5757

    Abstract

    The large amount of biomedical data derived from wearable sensors, electronic health records, and molecular profiling (e.g., genomics data) is rapidly transforming our healthcare systems. The increasing scale and scope of biomedical data not only is generating enormous opportunities for improving health outcomes but also raises new challenges ranging from data acquisition and storage to data analysis and utilization. To meet these challenges, we developed the Personal Health Dashboard (PHD), which utilizes state-of-the-art security and scalability technologies to provide an end-to-end solution for big biomedical data analytics. The PHD platform is an open-source software framework that can be easily configured and deployed to any big data health project to store, organize, and process complex biomedical data sets, support real-time data analysis at both the individual level and the cohort level, and ensure participant privacy at every step. In addition to presenting the system, we illustrate the use of the PHD framework for large-scale applications in emerging multi-omics disease studies, such as collecting and visualization of diverse data types (wearable, clinical, omics) at a personal level, investigation of insulin resistance, and an infrastructure for the detection of presymptomatic COVID-19.

    View details for DOI 10.1038/s41467-021-26040-1

    View details for PubMedID 34599181

  • Chromatin accessibility associates with protein-RNA correlation in human cancer. Nature communications Sanghi, A., Gruber, J. J., Metwally, A., Jiang, L., Reynolds, W., Sunwoo, J., Orloff, L., Chang, H. Y., Kasowski, M., Snyder, M. P. 2021; 12 (1): 5732

    Abstract

    Although alterations in chromatin structure are known to exist in tumors, how these alterations relate to molecular phenotypes in cancer remains to be demonstrated. Multi-omics profiling of human tumors can provide insight into how alterations in chromatin structure are propagated through the pathway of gene expression to result in malignant protein expression. We applied multi-omics profiling of chromatin accessibility, RNA abundance, and protein abundance to 36 human thyroid cancer primary tumors, metastases, and patient-match normal tissue. Through quantification of chromatin accessibility associated with active transcription units and global protein expression, we identify a local chromatin structure that is highly correlated with coordinated RNA and protein expression. In particular, we identify enhancers located within gene-bodies as predictive of correlated RNA and protein expression, that is independent of overall transcriptional activity. To demonstrate the generalizability of these findings we also identify similar results in an independent cohort of human breast cancers. Taken together, these analyses suggest that local enhancers, rather than distal enhancers, are likely most predictive of cancer gene expression phenotypes. This allows for identification of potential targets for cancer therapeutic approaches and reinforces the utility of multi-omics profiling as a methodology to understand human disease.

    View details for DOI 10.1038/s41467-021-25872-1

    View details for PubMedID 34593797

  • metID: a R package for automatable compound annotation for LC-MS-based data. Bioinformatics (Oxford, England) Shen, X., Wu, S., Liang, L., Chen, S., Contrepois, K., Zhu, Z., Snyder, M. 2021

    Abstract

    SUMMARY: Accurate and efficient compound annotation is a long-standing challenge for LC-MS-based data (e.g., untargeted metabolomics and exposomics). Substantial efforts have been devoted to overcoming this obstacle, whereas current tools are limited by the sources of spectral information used (in-house and public databases) and are not automated and streamlined. Therefore, we developed metID, an R package that combines information from all major databases for comprehensive and streamlined compound annotation. metID is a flexible, simple, and powerful tool that can be installed on all platforms, allowing the compound annotation process to be fully automatic and reproducible. A detailed tutorial and a case study are provided in Supplementary Materials.AVAILABILITY AND IMPLEMENTATION: https://jaspershen.github.io/metID.SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

    View details for DOI 10.1093/bioinformatics/btab583

    View details for PubMedID 34432001

  • Five-year pediatric use of a digital wearable fitness device: lessons from a pilot case study. JAMIA open Butte, K. D., Bahmani, A., Butte, A. J., Li, X., Snyder, M. P. 2021; 4 (3): ooab054

    Abstract

    Objectives: Wearable fitness devices are increasingly being used by the general population, with many new applications being proposed for healthy adults as well as for adults with chronic diseases. Fewer, if any, studies of these devices have been conducted in healthy adolescents and teenagers, especially over a long period of time. The goal of this work was to document the successes and challenges involved in 5 years of a wearable fitness device use in a pediatric case study.Materials and methods: Comparison of 5 years of step counts and minutes asleep from a teenaged girl and her father.Results: At 60 months, this may be the longest reported pediatric study involving a wearable fitness device, and the first simultaneously involving a parent and a child. We find step counts to be significantly higher for both the adult and teen on school/work days, along with less sleep. The teen walked significantly less towards the end of the 5-year study. Surprisingly, many of the adult's and teen's sleeping and step counts were correlated, possibly due to coordinated behaviors.Discussion: We end with several recommendations for pediatricians and device manufacturers, including the need for constant adjustments of stride length and calorie counts as teens are growing.Conclusion: With periodic adjustments for growth, this pilot study shows these devices can be used for more accurate and consistent measurements in adolescents and teenagers over longer periods of time, to potentially promote healthy behaviors.

    View details for DOI 10.1093/jamiaopen/ooab054

    View details for PubMedID 34350390

  • Wearable sensors enable personalized predictions of clinical laboratory measurements. Nature medicine Dunn, J., Kidzinski, L., Runge, R., Witt, D., Hicks, J. L., Schussler-Fiorenza Rose, S. M., Li, X., Bahmani, A., Delp, S. L., Hastie, T., Snyder, M. P. 2021

    Abstract

    Vital signs, including heart rate and body temperature, are useful in detecting or monitoring medical conditions, but are typically measured in the clinic and require follow-up laboratory testing for more definitive diagnoses. Here we examined whether vital signs as measured by consumer wearable devices (that is, continuously monitored heart rate, body temperature, electrodermal activity and movement) can predict clinical laboratory test results using machine learning models, including random forest and Lasso models. Our results demonstrate that vital sign data collected from wearables give a more consistent and precise depiction of resting heart rate than do measurements taken in the clinic. Vital sign data collected from wearables can also predict several clinical laboratory measurements with lower prediction error than predictions made using clinically obtained vital sign measurements. The length of time over which vital signs are monitored and the proximity of the monitoring period to the date of prediction play a critical role in the performance of the machine learning models. These results demonstrate the value of commercial wearable devices for continuous and longitudinal assessment of physiological measurements that today can be measured only with clinical laboratory tests.

    View details for DOI 10.1038/s41591-021-01339-0

    View details for PubMedID 34031607

  • Pre-symptomatic detection of COVID-19 from smartwatch data. Nature biomedical engineering Mishra, T., Wang, M., Metwally, A. A., Bogu, G. K., Brooks, A. W., Bahmani, A., Alavi, A., Celli, A., Higgs, E., Dagan-Rosenfeld, O., Fay, B., Kirkpatrick, S., Kellogg, R., Gibson, M., Wang, T., Hunting, E. M., Mamic, P., Ganz, A. B., Rolnik, B., Li, X., Snyder, M. P. 2020

    Abstract

    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.

    View details for DOI 10.1038/s41551-020-00640-6

    View details for PubMedID 33208926

  • An integrative ENCODE resource for cancer genomics. Nature communications Zhang, J., Lee, D., Dhiman, V., Jiang, P., Xu, J., McGillivray, P., Yang, H., Liu, J., Meyerson, W., Clarke, D., Gu, M., Li, S., Lou, S., Xu, J., Lochovsky, L., Ung, M., Ma, L., Yu, S., Cao, Q., Harmanci, A., Yan, K., Sethi, A., Gursoy, G., Schoenberg, M. R., Rozowsky, J., Warrell, J., Emani, P., Yang, Y. T., Galeev, T., Kong, X., Liu, S., Li, X., Krishnan, J., Feng, Y., Rivera-Mulia, J. C., Adrian, J., Broach, J. R., Bolt, M., Moran, J., Fitzgerald, D., Dileep, V., Liu, T., Mei, S., Sasaki, T., Trevilla-Garcia, C., Wang, S., Wang, Y., Zang, C., Wang, D., Klein, R. J., Snyder, M., Gilbert, D. M., Yip, K., Cheng, C., Yue, F., Liu, X. S., White, K. P., Gerstein, M. 2020; 11 (1): 3696

    Abstract

    ENCODE comprises thousands of functional genomics datasets, and the encyclopedia covers hundreds of cell types, providing a universal annotation for genome interpretation. However, for particular applications, it may be advantageous to use a customized annotation. Here, we develop such a custom annotation by leveraging advanced assays, such as eCLIP, Hi-C, and whole-genome STARR-seq on a number of data-rich ENCODE cell types. A key aspect of this annotation is comprehensive and experimentally derived networks of both transcription factors and RNA-binding proteins (TFs and RBPs). Cancer, a disease of system-wide dysregulation, is an ideal application for such a network-based annotation. Specifically, for cancer-associated cell types, we put regulators into hierarchies and measure their network change (rewiring) during oncogenesis. We also extensively survey TF-RBP crosstalk, highlighting how SUB1, a previously uncharacterized RBP, drives aberrant tumor expression and amplifies the effect of MYC, a well-known oncogenic TF. Furthermore, we show how our annotation allows us to place oncogenic transformations in the context of a broad cell space; here, many normal-to-tumor transitions move towards a stem-like state, while oncogene knockdowns show an opposing trend. Finally, we organize the resource into a coherent workflow to prioritize key elements and variants, in addition to regulators. We showcase the application of this prioritization to somatic burdening, cancer differential expression and GWAS. Targeted validations of the prioritized regulators, elements and variants using siRNA knockdowns, CRISPR-based editing, and luciferase assays demonstrate the value of the ENCODE resource.

    View details for DOI 10.1038/s41467-020-14743-w

    View details for PubMedID 32728046

  • Perspectives on ENCODE. Nature ENCODE Project Consortium, Snyder, M. P., Gingeras, T. R., Moore, J. E., Weng, Z., Gerstein, M. B., Ren, B., Hardison, R. C., Stamatoyannopoulos, J. A., Graveley, B. R., Feingold, E. A., Pazin, M. J., Pagan, M., Gilchrist, D. A., Hitz, B. C., Cherry, J. M., Bernstein, B. E., Mendenhall, E. M., Zerbino, D. R., Frankish, A., Flicek, P., Myers, R. M., Abascal, F., Acosta, R., Addleman, N. J., Adrian, J., Afzal, V., Aken, B., Akiyama, J. A., Jammal, O. A., Amrhein, H., Anderson, S. M., Andrews, G. R., Antoshechkin, I., Ardlie, K. G., Armstrong, J., Astley, M., Banerjee, B., Barkal, A. A., Barnes, I. H., Barozzi, I., Barrell, D., Barson, G., Bates, D., Baymuradov, U. K., Bazile, C., Beer, M. A., Beik, S., Bender, M. A., Bennett, R., Bouvrette, L. P., Bernstein, B. E., Berry, A., Bhaskar, A., Bignell, A., Blue, S. M., Bodine, D. M., Boix, C., Boley, N., Borrman, T., Borsari, B., Boyle, A. P., Brandsmeier, L. A., Breschi, A., Bresnick, E. H., Brooks, J. A., Buckley, M., Burge, C. B., Byron, R., Cahill, E., Cai, L., Cao, L., Carty, M., Castanon, R. G., Castillo, A., Chaib, H., Chan, E. T., Chee, D. R., Chee, S., Chen, H., Chen, H., Chen, J., Chen, S., Cherry, J. M., Chhetri, S. B., Choudhary, J. S., Chrast, J., Chung, D., Clarke, D., Cody, N. A., Coppola, C. J., Coursen, J., D'Ippolito, A. M., Dalton, S., Danyko, C., Davidson, C., Davila-Velderrain, J., Davis, C. A., Dekker, J., Deran, A., DeSalvo, G., Despacio-Reyes, G., Dewey, C. N., Dickel, D. E., Diegel, M., Diekhans, M., Dileep, V., Ding, B., Djebali, S., Dobin, A., Dominguez, D., Donaldson, S., Drenkow, J., Dreszer, T. R., Drier, Y., Duff, M. O., Dunn, D., Eastman, C., Ecker, J. R., Edwards, M. D., El-Ali, N., Elhajjajy, S. I., Elkins, K., Emili, A., Epstein, C. B., Evans, R. C., Ezkurdia, I., Fan, K., Farnham, P. J., Farrell, N., Feingold, E. A., Ferreira, A., Fisher-Aylor, K., Fitzgerald, S., Flicek, P., Foo, C. S., Fortier, K., Frankish, A., Freese, P., Fu, S., Fu, X., Fu, Y., Fukuda-Yuzawa, Y., Fulciniti, M., Funnell, A. P., Gabdank, I., Galeev, T., Gao, M., Giron, C. G., Garvin, T. H., Gelboin-Burkhart, C. A., Georgolopoulos, G., Gerstein, M. B., Giardine, B. M., Gifford, D. K., Gilbert, D. M., Gilchrist, D. A., Gillespie, S., Gingeras, T. R., Gong, P., Gonzalez, A., Gonzalez, J. M., Good, P., Goren, A., Gorkin, D. U., Graveley, B. R., Gray, M., Greenblatt, J. F., Griffiths, E., Groudine, M. T., Grubert, F., Gu, M., Guigo, R., Guo, H., Guo, Y., Guo, Y., Gursoy, G., Gutierrez-Arcelus, M., Halow, J., Hardison, R. C., Hardy, M., Hariharan, M., Harmanci, A., Harrington, A., Harrow, J. L., Hashimoto, T. B., Hasz, R. D., Hatan, M., Haugen, E., Hayes, J. E., He, P., He, Y., Heidari, N., Hendrickson, D., Heuston, E. F., Hilton, J. A., Hitz, B. C., Hochman, A., Holgren, C., Hou, L., Hou, S., Hsiao, Y. E., Hsu, S., Huang, H., Hubbard, T. J., Huey, J., Hughes, T. R., Hunt, T., Ibarrientos, S., Issner, R., Iwata, M., Izuogu, O., Jaakkola, T., Jameel, N., Jansen, C., Jiang, L., Jiang, P., Johnson, A., Johnson, R., Jungreis, I., Kadaba, M., Kasowski, M., Kasparian, M., Kato, M., Kaul, R., Kawli, T., Kay, M., Keen, J. C., Keles, S., Keller, C. A., Kelley, D., Kellis, M., Kheradpour, P., Kim, D. S., Kirilusha, A., Klein, R. J., Knoechel, B., Kuan, S., Kulik, M. J., Kumar, S., Kundaje, A., Kutyavin, T., Lagarde, J., Lajoie, B. R., Lambert, N. J., Lazar, J., Lee, A. Y., Lee, D., Lee, E., Lee, J. W., Lee, K., Leslie, C. S., Levy, S., Li, B., Li, H., Li, N., Li, X., Li, Y. I., Li, Y., Li, Y., Li, Y., Lian, J., Libbrecht, M. W., Lin, S., Lin, Y., Liu, D., Liu, J., Liu, P., Liu, T., Liu, X. S., Liu, Y., Liu, Y., Long, M., Lou, S., Loveland, J., Lu, A., Lu, Y., Lecuyer, E., Ma, L., Mackiewicz, M., Mannion, B. J., Mannstadt, M., Manthravadi, D., Marinov, G. K., Martin, F. J., Mattei, E., McCue, K., McEown, M., McVicker, G., Meadows, S. K., Meissner, A., Mendenhall, E. M., Messer, C. L., Meuleman, W., Meyer, C., Miller, S., Milton, M. G., Mishra, T., Moore, D. E., Moore, H. M., Moore, J. E., Moore, S. H., Moran, J., Mortazavi, A., Mudge, J. M., Munshi, N., Murad, R., Myers, R. M., Nandakumar, V., Nandi, P., Narasimha, A. M., Narayanan, A. K., Naughton, H., Navarro, F. C., Navas, P., Nazarovs, J., Nelson, J., Neph, S., Neri, F. J., Nery, J. R., Nesmith, A. R., Newberry, J. S., Newberry, K. M., Ngo, V., Nguyen, R., Nguyen, T. B., Nguyen, T., Nishida, A., Noble, W. S., Novak, C. S., Novoa, E. M., Nunez, B., O'Donnell, C. W., Olson, S., Onate, K. C., Otterman, E., Ozadam, H., Pagan, M., Palden, T., Pan, X., Park, Y., Partridge, E. C., Paten, B., Pauli-Behn, F., Pazin, M. J., Pei, B., Pennacchio, L. A., Perez, A. R., Perry, E. H., Pervouchine, D. D., Phalke, N. N., Pham, Q., Phanstiel, D. H., Plajzer-Frick, I., Pratt, G. A., Pratt, H. E., Preissl, S., Pritchard, J. K., Pritykin, Y., Purcaro, M. J., Qin, Q., Quinones-Valdez, G., Rabano, I., Radovani, E., Raj, A., Rajagopal, N., Ram, O., Ramirez, L., Ramirez, R. N., Rausch, D., Raychaudhuri, S., Raymond, J., Razavi, R., Reddy, T. E., Reimonn, T. M., Ren, B., Reymond, A., Reynolds, A., Rhie, S. K., Rinn, J., Rivera, M., Rivera-Mulia, J. C., Roberts, B., Rodriguez, J. M., Rozowsky, J., Ryan, R., Rynes, E., Salins, D. N., Sandstrom, R., Sasaki, T., Sathe, S., Savic, D., Scavelli, A., Scheiman, J., Schlaffner, C., Schloss, J. A., Schmitges, F. W., See, L. H., Sethi, A., Setty, M., Shafer, A., Shan, S., Sharon, E., Shen, Q., Shen, Y., Sherwood, R. I., Shi, M., Shin, S., Shoresh, N., Siebenthall, K., Sisu, C., Slifer, T., Sloan, C. A., Smith, A., Snetkova, V., Snyder, M. P., Spacek, D. V., Srinivasan, S., Srivas, R., Stamatoyannopoulos, G., Stamatoyannopoulos, J. A., Stanton, R., Steffan, D., Stehling-Sun, S., Strattan, J. S., Su, A., Sundararaman, B., Suner, M., Syed, T., Szynkarek, M., Tanaka, F. Y., Tenen, D., Teng, M., Thomas, J. A., Toffey, D., Tress, M. L., Trout, D. E., Trynka, G., Tsuji, J., Upchurch, S. A., Ursu, O., Uszczynska-Ratajczak, B., Uziel, M. C., Valencia, A., Biber, B. V., van der Velde, A. G., Van Nostrand, E. L., Vaydylevich, Y., Vazquez, J., Victorsen, A., Vielmetter, J., Vierstra, J., Visel, A., Vlasova, A., Vockley, C. M., Volpi, S., Vong, S., Wang, H., Wang, M., Wang, Q., Wang, R., Wang, T., Wang, W., Wang, X., Wang, Y., Watson, N. K., Wei, X., Wei, Z., Weisser, H., Weissman, S. M., Welch, R., Welikson, R. E., Weng, Z., Westra, H., Whitaker, J. W., White, C., White, K. P., Wildberg, A., Williams, B. A., Wine, D., Witt, H. N., Wold, B., Wolf, M., Wright, J., Xiao, R., Xiao, X., Xu, J., Xu, J., Yan, K., Yan, Y., Yang, H., Yang, X., Yang, Y., Yardimci, G. G., Yee, B. A., Yeo, G. W., Young, T., Yu, T., Yue, F., Zaleski, C., Zang, C., Zeng, H., Zeng, W., Zerbino, D. R., Zhai, J., Zhan, L., Zhan, Y., Zhang, B., Zhang, J., Zhang, J., Zhang, K., Zhang, L., Zhang, P., Zhang, Q., Zhang, X., Zhang, Y., Zhang, Z., Zhao, Y., Zheng, Y., Zhong, G., Zhou, X., Zhu, Y., Zimmerman, J. 2020; 583 (7818): 693–98

    Abstract

    The Encylopedia of DNA Elements (ENCODE) Project launched in 2003 with the long-term goal of developing a comprehensive map of functional elements in the human genome. These included genes, biochemical regions associated with gene regulation (for example, transcription factor binding sites, open chromatin, and histone marks) and transcript isoforms. The marks serve as sites for candidate cis-regulatory elements (cCREs) that may serve functional roles in regulating gene expression1. The project has been extended to model organisms, particularly the mouse. In the third phase of ENCODE, nearly a million and more than 300,000 cCRE annotations have been generated for human and mouse, respectively, and these have provided a valuable resource for the scientific community.

    View details for DOI 10.1038/s41586-020-2449-8

    View details for PubMedID 32728248

  • Metabolic Dynamics and Prediction of Gestational Age and Time to Delivery in Pregnant Women. Cell Liang, L., Rasmussen, M. H., Piening, B., Shen, X., Chen, S., Rost, H., Snyder, J. K., Tibshirani, R., Skotte, L., Lee, N. C., Contrepois, K., Feenstra, B., Zackriah, H., Snyder, M., Melbye, M. 2020; 181 (7): 1680

    Abstract

    Metabolism during pregnancy is a dynamic and precisely programmed process, the failure of which can bring devastating consequences to the mother and fetus. To define a high-resolution temporal profile of metabolites during healthy pregnancy, we analyzed the untargeted metabolome of 784weekly blood samples from 30 pregnant women. Broad changes and a highly choreographed profile were revealed: 4,995 metabolic features (of 9,651 total), 460 annotated compounds (of 687 total), and 34 human metabolic pathways (of 48 total) were significantly changed during pregnancy. Using linear models, we built a metabolic clock with five metabolites that time gestational age in high accordance with ultrasound (R= 0.92). Furthermore, two to three metabolites can identify when labor occurs (time to delivery within two, four, and eight weeks, AUROC ≥ 0.85). Our study represents a weekly characterization of the human pregnancy metabolome, providing a high-resolution landscape for understanding pregnancy with potential clinical utilities.

    View details for DOI 10.1016/j.cell.2020.05.002

    View details for PubMedID 32589958

  • Systematic identification of silencers in human cells. Nature genetics Pang, B., Snyder, M. P. 2020

    Abstract

    The majority of the human genome does not encode proteins. Many of these noncoding regions contain important regulatory sequences that control gene expression. To date, most studies have focused on activators such as enhancers, but regions that repress gene expression-silencers-have not been systematically studied. We have developed a system that identifies silencer regions in a genome-wide fashion on the basis of silencer-mediated transcriptional repression of caspase9. We found that silencers are widely distributed and may function in a tissue-specific fashion. These silencers harbor unique epigenetic signatures and are associated with specific transcription factors. Silencers also act at multiple genes, and at the level of chromosomal domains and long-range interactions. Deletion of silencer regions linked to the drug transporter genes ABCC2 and ABCG2 caused chemo-resistance. Overall, our study demonstrates that tissue-specific silencing is widespread throughout the human genome and probably contributes substantially to the regulation of gene expression and human biology.

    View details for DOI 10.1038/s41588-020-0578-5

    View details for PubMedID 32094911

  • Deep Characterization of the Human Antibody Response to Natural Infection Using Longitudinal Immune Repertoire Sequencing. Molecular & cellular proteomics : MCP Mitsunaga, E. M., Snyder, M. P. 2020; 19 (2): 278-293

    Abstract

    Human antibody response studies are largely restricted to periods of high immune activity (e.g. vaccination). To comprehensively understand the healthy B cell immune repertoire and how this changes over time and through natural infection, we conducted immune repertoire RNA sequencing on flow cytometry-sorted B cell subsets to profile a single individual's antibodies over 11 months through two periods of natural viral infection. We found that 1) a baseline of healthy variable (V) gene usage in antibodies exists and is stable over time, but antibodies in memory cells consistently have a different usage profile relative to earlier B cell stages; 2) a single complementarity-determining region 3 (CDR3) is potentially generated from more than one VJ gene combination; and 3) IgG and IgA antibody transcripts are found at low levels in early human B cell development, suggesting that class switching may occur earlier than previously realized. These findings provide insight into immune repertoire stability, response to natural infections, and human B cell development.

    View details for DOI 10.1074/mcp.RA119.001633

    View details for PubMedID 33451388

  • Personal aging markers and ageotypes revealed by deep longitudinal profiling. Nature medicine Ahadi, S., Zhou, W., Schussler-Fiorenza Rose, S. M., Sailani, M. R., Contrepois, K., Avina, M., Ashland, M., Brunet, A., Snyder, M. 2020; 26 (1): 83–90

    Abstract

    The molecular changes that occur with aging are not well understood1-4. Here, we performed longitudinal and deep multiomics profiling of 106 healthy individuals from 29 to 75 years of age and examined how different types of 'omic' measurements, including transcripts, proteins, metabolites, cytokines, microbes and clinical laboratory values, correlate with age. We identified both known and new markers that associated with age, as well as distinct molecular patterns of aging in insulin-resistant as compared to insulin-sensitive individuals. In a longitudinal setting, we identified personal aging markers whose levels changed over a short time frame of 2-3 years. Further, we defined different types of aging patterns in different individuals, termed 'ageotypes', on the basis of the types of molecular pathways that changed over time in a given individual. Ageotypes may provide a molecular assessment of personal aging, reflective of personal lifestyle and medical history, that may ultimately be useful in monitoring and intervening in the aging process.

    View details for DOI 10.1038/s41591-019-0719-5

    View details for PubMedID 31932806

  • Candidate variants in TUB are associated with familial tremor. PLoS genetics Sailani, M. R., Jahanbani, F. n., Abbott, C. W., Lee, H. n., Zia, A. n., Rego, S. n., Winkelmann, J. n., Hopfner, F. n., Khan, T. N., Katsanis, N. n., Müller, S. H., Berg, D. n., Lyman, K. M., Mychajliw, C. n., Deuschl, G. n., Bernstein, J. A., Kuhlenbäumer, G. n., Snyder, M. P. 2020; 16 (9): e1009010

    Abstract

    Essential tremor (ET) is the most common adult-onset movement disorder. In the present study, we performed whole exome sequencing of a large ET-affected family (10 affected and 6 un-affected family members) and identified a TUB p.V431I variant (rs75594955) segregating in a manner consistent with autosomal-dominant inheritance. Subsequent targeted re-sequencing of TUB in 820 unrelated individuals with sporadic ET and 630 controls revealed significant enrichment of rare nonsynonymous TUB variants (e.g. rs75594955: p.V431I, rs1241709665: p.Ile20Phe, rs55648406: p.Arg49Gln) in the ET cohort (SKAT-O test p-value = 6.20e-08). TUB encodes a transcription factor predominantly expressed in neuronal cells and has been previously implicated in obesity. ChIP-seq analyses of the TUB transcription factor across different regions of the mouse brain revealed that TUB regulates the pathways responsible for neurotransmitter production as well thyroid hormone signaling. Together, these results support the association of rare variants in TUB with ET.

    View details for DOI 10.1371/journal.pgen.1009010

    View details for PubMedID 32956375

  • A Quantitative Proteome Map of the Human Body. Cell Jiang, L. n., Wang, M. n., Lin, S. n., Jian, R. n., Li, X. n., Chan, J. n., Dong, G. n., Fang, H. n., Robinson, A. E., Snyder, M. P. 2020

    Abstract

    Determining protein levels in each tissue and how they compare with RNA levels is important for understanding human biology and disease as well as regulatory processes that control protein levels. We quantified the relative protein levels from over 12,000 genes across 32 normal human tissues. Tissue-specific or tissue-enriched proteins were identified and compared to transcriptome data. Many ubiquitous transcripts are found to encode tissue-specific proteins. Discordance of RNA and protein enrichment revealed potential sites of synthesis and action of secreted proteins. The tissue-specific distribution of proteins also provides an in-depth view of complex biological events that require the interplay of multiple tissues. Most importantly, our study demonstrated that protein tissue-enrichment information can explain phenotypes of genetic diseases, which cannot be obtained by transcript information alone. Overall, our results demonstrate how understanding protein levels can provide insights into regulation, secretome, metabolism, and human diseases.

    View details for DOI 10.1016/j.cell.2020.08.036

    View details for PubMedID 32916130

  • Landscape of cohesin-mediated chromatin loops in the human genome. Nature Grubert, F. n., Srivas, R. n., Spacek, D. V., Kasowski, M. n., Ruiz-Velasco, M. n., Sinnott-Armstrong, N. n., Greenside, P. n., Narasimha, A. n., Liu, Q. n., Geller, B. n., Sanghi, A. n., Kulik, M. n., Sa, S. n., Rabinovitch, M. n., Kundaje, A. n., Dalton, S. n., Zaugg, J. B., Snyder, M. n. 2020; 583 (7818): 737–43

    Abstract

    Physical interactions between distal regulatory elements have a key role in regulating gene expression, but the extent to which these interactions vary between cell types and contribute to cell-type-specific gene expression remains unclear. Here, to address these questions as part of phase III of the Encyclopedia of DNA Elements (ENCODE), we mapped cohesin-mediated chromatin loops, using chromatin interaction analysis by paired-end tag sequencing (ChIA-PET), and analysed gene expression in 24 diverse human cell types, including core ENCODE cell lines. Twenty-eight per cent of all chromatin loops vary across cell types; these variations modestly correlate with changes in gene expression and are effective at grouping cell types according to their tissue of origin. The connectivity of genes corresponds to different functional classes, with housekeeping genes having few contacts, and dosage-sensitive genes being more connected to enhancer elements. This atlas of chromatin loops complements the diverse maps of regulatory architecture that comprise the ENCODE Encyclopedia, and will help to support emerging analyses of genome structure and function.

    View details for DOI 10.1038/s41586-020-2151-x

    View details for PubMedID 32728247

  • Molecular Choreography of Acute Exercise. Cell Contrepois, K. n., Wu, S. n., Moneghetti, K. J., Hornburg, D. n., Ahadi, S. n., Tsai, M. S., Metwally, A. A., Wei, E. n., Lee-McMullen, B. n., Quijada, J. V., Chen, S. n., Christle, J. W., Ellenberger, M. n., Balliu, B. n., Taylor, S. n., Durrant, M. G., Knowles, D. A., Choudhry, H. n., Ashland, M. n., Bahmani, A. n., Enslen, B. n., Amsallem, M. n., Kobayashi, Y. n., Avina, M. n., Perelman, D. n., Schüssler-Fiorenza Rose, S. M., Zhou, W. n., Ashley, E. A., Montgomery, S. B., Chaib, H. n., Haddad, F. n., Snyder, M. P. 2020; 181 (5): 1112–30.e16

    Abstract

    Acute physical activity leads to several changes in metabolic, cardiovascular, and immune pathways. Although studies have examined selected changes in these pathways, the system-wide molecular response to an acute bout of exercise has not been fully characterized. We performed longitudinal multi-omic profiling of plasma and peripheral blood mononuclear cells including metabolome, lipidome, immunome, proteome, and transcriptome from 36 well-characterized volunteers, before and after a controlled bout of symptom-limited exercise. Time-series analysis revealed thousands of molecular changes and an orchestrated choreography of biological processes involving energy metabolism, oxidative stress, inflammation, tissue repair, and growth factor response, as well as regulatory pathways. Most of these processes were dampened and some were reversed in insulin-resistant participants. Finally, we discovered biological pathways involved in cardiopulmonary exercise response and developed prediction models revealing potential resting blood-based biomarkers of peak oxygen consumption.

    View details for DOI 10.1016/j.cell.2020.04.043

    View details for PubMedID 32470399

  • Deep longitudinal multiomics profiling reveals two biological seasonal patterns in California. Nature communications Sailani, M. R., Metwally, A. A., Zhou, W. n., Rose, S. M., Ahadi, S. n., Contrepois, K. n., Mishra, T. n., Zhang, M. J., Kidziński, Ł. n., Chu, T. J., Snyder, M. P. 2020; 11 (1): 4933

    Abstract

    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.

    View details for DOI 10.1038/s41467-020-18758-1

    View details for PubMedID 33004787

  • Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature Moore, J. E., Purcaro, M. J., Pratt, H. E., Epstein, C. B., Shoresh, N. n., Adrian, J. n., Kawli, T. n., Davis, C. A., Dobin, A. n., Kaul, R. n., Halow, J. n., Van Nostrand, E. L., Freese, P. n., Gorkin, D. U., Shen, Y. n., He, Y. n., Mackiewicz, M. n., Pauli-Behn, F. n., Williams, B. A., Mortazavi, A. n., Keller, C. A., Zhang, X. O., Elhajjajy, S. I., Huey, J. n., Dickel, D. E., Snetkova, V. n., Wei, X. n., Wang, X. n., Rivera-Mulia, J. C., Rozowsky, J. n., Zhang, J. n., Chhetri, S. B., Zhang, J. n., Victorsen, A. n., White, K. P., Visel, A. n., Yeo, G. W., Burge, C. B., Lécuyer, E. n., Gilbert, D. M., Dekker, J. n., Rinn, J. n., Mendenhall, E. M., Ecker, J. R., Kellis, M. n., Klein, R. J., Noble, W. S., Kundaje, A. n., Guigó, R. n., Farnham, P. J., Cherry, J. M., Myers, R. M., Ren, B. n., Graveley, B. R., Gerstein, M. B., Pennacchio, L. A., Snyder, M. P., Bernstein, B. E., Wold, B. n., Hardison, R. C., Gingeras, T. R., Stamatoyannopoulos, J. A., Weng, Z. n. 2020; 583 (7818): 699–710

    Abstract

    The human and mouse genomes contain instructions that specify RNAs and proteins and govern the timing, magnitude, and cellular context of their production. To better delineate these elements, phase III of the Encyclopedia of DNA Elements (ENCODE) Project has expanded analysis of the cell and tissue repertoires of RNA transcription, chromatin structure and modification, DNA methylation, chromatin looping, and occupancy by transcription factors and RNA-binding proteins. Here we summarize these efforts, which have produced 5,992 new experimental datasets, including systematic determinations across mouse fetal development. All data are available through the ENCODE data portal (https://www.encodeproject.org), including phase II ENCODE1 and Roadmap Epigenomics2 data. We have developed a registry of 926,535 human and 339,815 mouse candidate cis-regulatory elements, covering 7.9 and 3.4% of their respective genomes, by integrating selected datatypes associated with gene regulation, and constructed a web-based server (SCREEN; http://screen.encodeproject.org) to provide flexible, user-defined access to this resource. Collectively, the ENCODE data and registry provide an expansive resource for the scientific community to build a better understanding of the organization and function of the human and mouse genomes.

    View details for DOI 10.1038/s41586-020-2493-4

    View details for PubMedID 32728249

  • The human body at cellular resolution: the NIH Human Biomolecular Atlas Program NATURE Snyder, M. P., Lin, S., Posgai, A., Atkinson, M., Regev, A., Rood, J., Rozenblatt-Rosen, O., Gaffney, L., Hupalowska, A., Satija, R., Gehlenborg, N., Shendure, J., Laskin, J., Harbury, P., Nystrom, N. A., Silverstein, J. C., Bar-Joseph, Z., Zhang, K., Borner, K., Lin, Y., Conroy, R., Procaccini, D., Roy, A. L., Pillai, A., Brown, M., Galis, Z. S., Cai, L., Shendure, J., Trapnell, C., Lin, S., Jackson, D., Snyder, M. P., Nolan, G., Greenleaf, W., Lin, Y., Plevritis, S., Ahadi, S., Nevins, S. A., Lee, H., Schuerch, C., Black, S., Venkataraaman, V., Esplin, E., Horning, A., Bahmani, A., Zhang, K., Sun, X., Jain, S., Hagood, J., Pryhuber, G., Kharchenko, P., Atkinson, M., Bodenmiller, B., Brusko, T., Clare-Salzler, M., Nick, H., Otto, K., Posgai, A., Wasserfall, C., Jorgensen, M., Brusko, M., Maffioletti, S., Caprioli, R. M., Spraggins, J. M., Gutierrez, D., Patterson, N., Neumann, E. K., Harris, R., deCaestecker, M., Fogo, A. B., van de Plas, R., Lau, K., Cai, L., Yuan, G., Zhu, Q., Dries, R., Yin, P., Saka, S. K., Kishi, J. Y., Wang, Y., Goldaracena, I., Laskin, J., Ye, D., Burnum-Johnson, K. E., Piehowski, P. D., Ansong, C., Zhu, Y., Harbury, P., Desai, T., Mulye, J., Chou, P., Nagendran, M., Bar-Joseph, Z., Teichmann, S. A., Paten, B., Murphy, R. F., Ma, J., Kiselev, V., Kingsford, C., Ricarte, A., Keays, M., Akoju, S. A., Ruffalo, M., Gehlenborg, N., Kharchenko, P., Vella, M., McCallum, C., Borner, K., Cross, L. E., Friedman, S. H., Heiland, R., Herr, B., Macklin, P., Quardokus, E. M., Record, L., Sluka, J. P., Weber, G. M., Nystrom, N. A., Silverstein, J. C., Blood, P. D., Ropelewski, A. J., Shirey, W. E., Scibek, R. M., Mabee, P., Lenhardt, W., Robasky, K., Michailidis, S., Satija, R., Marioni, J., Regev, A., Butler, A., Stuart, T., Fisher, E., Ghazanfar, S., Rood, J., Gaffney, L., Eraslan, G., Biancalani, T., Vaishnav, E. D., Conroy, R., Procaccini, D., Roy, A., Pillai, A., Brown, M., Galis, Z., Srinivas, P., Pawlyk, A., Sechi, S., Wilder, E., Anderson, J., HuBMAP Consortium 2019; 574 (7777): 187–92

    Abstract

    Transformative technologies are enabling the construction of three-dimensional maps of tissues with unprecedented spatial and molecular resolution. Over the next seven years, the NIH Common Fund Human Biomolecular Atlas Program (HuBMAP) intends to develop a widely accessible framework for comprehensively mapping the human body at single-cell resolution by supporting technology development, data acquisition, and detailed spatial mapping. HuBMAP will integrate its efforts with other funding agencies, programs, consortia, and the biomedical research community at large towards the shared vision of a comprehensive, accessible three-dimensional molecular and cellular atlas of the human body, in health and under various disease conditions.

    View details for DOI 10.1038/s41586-019-1629-x

    View details for Web of Science ID 000489784200035

    View details for PubMedID 31597973

    View details for PubMedCentralID PMC6800388

  • Big data and health. The Lancet. Digital health Snyder, M., Zhou, W. 2019; 1 (6): e252-e254

    View details for DOI 10.1016/S2589-7500(19)30109-8

    View details for PubMedID 33323249

  • HAT1 Coordinates Histone Production and Acetylation via H4 Promoter Binding. Molecular cell Gruber, J. J., Geller, B., Lipchik, A. M., Chen, J., Salahudeen, A. A., Ram, A. N., Ford, J. M., Kuo, C. J., Snyder, M. P. 2019

    Abstract

    The energetic costs of duplicating chromatin are large and therefore likely depend on nutrient sensing checkpoints and metabolic inputs. By studying chromatin modifiers regulated by epithelial growth factor, we identified histone acetyltransferase 1 (HAT1) as an induced gene that enhances proliferation through coordinating histone production, acetylation, and glucose metabolism. In addition to its canonical role as a cytoplasmic histone H4 acetyltransferase, we isolated a HAT1-containing complex bound specifically at promoters of H4 genes. HAT1-dependent transcription of H4 genes required an acetate-sensitive promoter element. HAT1 expression was critical for S-phase progression and maintenance of H3 lysine 9 acetylation at proliferation-associated genes, including histone genes. Therefore, these data describe a feedforward circuit whereby HAT1 captures acetyl groups on nascent histones and drives H4 production by chromatin binding to support chromatin replication and acetylation. These findings have important implications for human disease, since high HAT1 levels associate with poor outcomes across multiple cancer types.

    View details for DOI 10.1016/j.molcel.2019.05.034

    View details for PubMedID 31278053

  • The Integrative Human Microbiome Project NATURE Proctor, L. M., Creasy, H. H., Fettweis, J. M., Lloyd-Price, J., Mahurkar, A., Zhou, W., Buck, G. A., Snyder, M. P., Strauss, J. F., Weinstock, G. M., White, O., Huttenhower, C., Integrative HMP iHMP Res Network 2019; 569 (7758): 641–48

    Abstract

    The NIH Human Microbiome Project (HMP) has been carried out over ten years and two phases to provide resources, methods, and discoveries that link interactions between humans and their microbiomes to health-related outcomes. The recently completed second phase, the Integrative Human Microbiome Project, comprised studies of dynamic changes in the microbiome and host under three conditions: pregnancy and preterm birth; inflammatory bowel diseases; and stressors that affect individuals with prediabetes. The associated research begins to elucidate mechanisms of host-microbiome interactions under these conditions, provides unique data resources (at the HMP Data Coordination Center), and represents a paradigm for future multi-omic studies of the human microbiome.

    View details for DOI 10.1038/s41586-019-1238-8

    View details for Web of Science ID 000470144100031

    View details for PubMedID 31142853

  • A longitudinal big data approach for precision health NATURE MEDICINE Rose, S., Contrepois, K., Moneghetti, K. J., Zhou, W., Mishra, T., Mataraso, S., Dagan-Rosenfeld, O., Ganz, A. B., Dunn, J., Hornburg, D., Rego, S., Perelman, D., Ahadi, S., Sailani, M., Zhou, Y., Leopold, S. R., Chen, J., Ashland, M., Christle, J. W., Avina, M., Limcaoco, P., Ruiz, C., Tan, M., Butte, A. J., Weinstock, G. M., Slavich, G. M., Sodergren, E., McLaughlin, T. L., Haddad, F., Snyder, M. P. 2019; 25 (5): 792-+
  • Longitudinal multi-omics of host-microbe dynamics in prediabetes. Nature Zhou, W., Sailani, M. R., Contrepois, K., Zhou, Y., Ahadi, S., Leopold, S. R., Zhang, M. J., Rao, V., Avina, M., Mishra, T., Johnson, J., Lee-McMullen, B., Chen, S., Metwally, A. A., Tran, T. D., Nguyen, H., Zhou, X., Albright, B., Hong, B., Petersen, L., Bautista, E., Hanson, B., Chen, L., Spakowicz, D., Bahmani, A., Salins, D., Leopold, B., Ashland, M., Dagan-Rosenfeld, O., Rego, S., Limcaoco, P., Colbert, E., Allister, C., Perelman, D., Craig, C., Wei, E., Chaib, H., Hornburg, D., Dunn, J., Liang, L., Rose, S. M., Kukurba, K., Piening, B., Rost, H., Tse, D., McLaughlin, T., Sodergren, E., Weinstock, G. M., Snyder, M. 2019; 569 (7758): 663–71

    Abstract

    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 T2Dbetter, 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.

    View details for DOI 10.1038/s41586-019-1236-x

    View details for PubMedID 31142858

  • The NASA Twins Study: A multidimensional analysis of a year-long human spaceflight SCIENCE Garrett-Bakelman, F. E., Darshi, M., Green, S. J., Gur, R. C., Lin, L., Macias, B. R., McKenna, M. J., Meydan, C., Mishra, T., Nasrini, J., Piening, B. D., Rizzardi, L. F., Sharma, K., Siamwala, J. H., Taylor, L., Vitaterna, M., Afkarian, M., Afshinnekoo, E., Ahadi, S., Ambati, A., Arya, M., Bezdan, D., Callahan, C. M., Chen, S., Choi, A. K., Chlipala, G. E., Contrepois, K., Covington, M., Crucian, B. E., De Vivo, I., Dinges, D. F., Ebert, D. J., Feinberg, J. I., Gandara, J. A., George, K. A., Goutsias, J., Grills, G. S., Hargens, A. R., Heer, M., Hillary, R. P., Hoofnagle, A. N., Hook, V. H., Jenkinson, G., Jiang, P., Keshavarzian, A., Laurie, S. S., Lee-McMullen, B., Lumpkins, S. B., MacKay, M., Maienschein-Cline, M. G., Melnick, A. M., Moore, T. M., Nakahira, K., Patel, H. H., Pietrzyk, R., Rao, V., Saito, R., Salins, D. N., Schilling, J. M., Sears, D. D., Sheridan, C. K., Stenger, M. B., Tryggvadottir, R., Urban, A. E., Vaisar, T., Van Espen, B., Zhang, J., Ziegler, M. G., Zwart, S. R., Charles, J. B., Kundrot, C. E., Scott, G. I., Bailey, S. M., Basner, M., Feinberg, A. P., Lee, S. C., Mason, C. E., Mignot, E., Rana, B. K., Smith, S. M., Snyder, M. P., Turek, F. W. 2019; 364 (6436): 144-+
  • Gene-Environment Interaction in the Era of Precision Medicine CELL Li, J., Li, X., Zhang, S., Snyder, M. 2019; 177 (1): 38–44

Our Publications

Michael P. Snyder, PhD is the 14th most cited Molecular Biology & Genetics researcher in the world, cited ~20,000 times with an H index of ~200.