Showing papers by "Michael Snyder published in 2020"

Expanded encyclopaedias of DNA elements in the human and mouse genomes

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.

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

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.

Physiological blood-brain transport is impaired with age by a shift in transcytosis.

Abstract: The vascular interface of the brain, known as the blood–brain barrier (BBB), is understood to maintain brain function in part via its low transcellular permeability1–3. Yet, recent studies have demonstrated that brain ageing is sensitive to circulatory proteins4,5. Thus, it is unclear whether permeability to individually injected exogenous tracers—as is standard in BBB studies—fully represents blood-to-brain transport. Here we label hundreds of proteins constituting the mouse blood plasma proteome, and upon their systemic administration, study the BBB with its physiological ligand. We find that plasma proteins readily permeate the healthy brain parenchyma, with transport maintained by BBB-specific transcriptional programmes. Unlike IgG antibody, plasma protein uptake diminishes in the aged brain, driven by an age-related shift in transport from ligand-specific receptor-mediated to non-specific caveolar transcytosis. This age-related shift occurs alongside a specific loss of pericyte coverage. Pharmacological inhibition of the age-upregulated phosphatase ALPL, a predicted negative regulator of transport, enhances brain uptake of therapeutically relevant transferrin, transferrin receptor antibody and plasma. These findings reveal the extent of physiological protein transcytosis to the healthy brain, a mechanism of widespread BBB dysfunction with age and a strategy for enhanced drug delivery. Tagging and tracking the blood plasma proteome as a discovery tool reveals widespread endogenous transport of proteins into the healthy brain and the pharmacologically modifiable mechanisms by which the brain endothelium regulates this process with age.

Personal aging markers and ageotypes revealed by deep longitudinal profiling

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. Longitudinal multiomics profiling of a cohort of healthy people reveals distinct aging patterns—termed ageotypes—in different individuals.

A high-stringency blueprint of the human proteome

Abstract: The Human Proteome Organization (HUPO) launched the Human Proteome Project (HPP) in 2010, creating an international framework for global collaboration, data sharing, quality assurance and enhancing accurate annotation of the genome-encoded proteome. During the subsequent decade, the HPP established collaborations, developed guidelines and metrics, and undertook reanalysis of previously deposited community data, continuously increasing the coverage of the human proteome. On the occasion of the HPP’s tenth anniversary, we here report a 90.4% complete high-stringency human proteome blueprint. This knowledge is essential for discerning molecular processes in health and disease, as we demonstrate by highlighting potential roles the human proteome plays in our understanding, diagnosis and treatment of cancers, cardiovascular and infectious diseases.

Landscape of cohesin-mediated chromatin loops in the human genome

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. A map of cohesin-mediated chromatin loops in 24 types of human cells identifies loops that show cell-type-specific variation, indicating that chromatin loops may help to specify cell-specific gene expression programs and functions.

Perspectives on ENCODE.

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.

Systematic identification of silencers in human cells.

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 caspase 9. 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. A genome-wide screen identifies silencer regions in human cells. Deletion of silencers linked to the transporter genes ABCC2 and ABCG2 causes their up-regulation and chemo-resistance.

An integrative ENCODE resource for cancer genomics

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.

Multi-faceted epigenetic dysregulation of gene expression promotes esophageal squamous cell carcinoma.

Abstract: Epigenetic landscapes can shape physiologic and disease phenotypes. We used integrative, high resolution multi-omics methods to delineate the methylome landscape and characterize the oncogenic drivers of esophageal squamous cell carcinoma (ESCC). We found 98% of CpGs are hypomethylated across the ESCC genome. Hypo-methylated regions are enriched in areas with heterochromatin binding markers (H3K9me3, H3K27me3), while hyper-methylated regions are enriched in polycomb repressive complex (EZH2/SUZ12) recognizing regions. Altered methylation in promoters, enhancers, and gene bodies, as well as in polycomb repressive complex occupancy and CTCF binding sites are associated with cancer-specific gene dysregulation. Epigenetic-mediated activation of non-canonical WNT/β-catenin/MMP signaling and a YY1/lncRNA ESCCAL-1/ribosomal protein network are uncovered and validated as potential novel ESCC driver alterations. This study advances our understanding of how epigenetic landscapes shape cancer pathogenesis and provides a resource for biomarker and target discovery.

A vast resource of allelic expression data spanning human tissues

Abstract: Allele expression (AE) analysis robustly measures cis-regulatory effects. Here, we present and demonstrate the utility of a vast AE resource generated from the GTEx v8 release, containing 15,253 samples spanning 54 human tissues for a total of 431 million measurements of AE at the SNP level and 153 million measurements at the haplotype level. In addition, we develop an extension of our tool phASER that allows effect sizes of cis-regulatory variants to be estimated using haplotype-level AE data. This AE resource is the largest to date, and we are able to make haplotype-level data publicly available. We anticipate that the availability of this resource will enable future studies of regulatory variation across human tissues.

Remodeling of active endothelial enhancers is associated with aberrant gene-regulatory networks in pulmonary arterial hypertension.

Abstract: Environmental and epigenetic factors often play an important role in polygenic disorders. However, how such factors affect disease-specific tissues at the molecular level remains to be understood. Here, we address this in pulmonary arterial hypertension (PAH). We obtain pulmonary arterial endothelial cells (PAECs) from lungs of patients and controls (n = 19), and perform chromatin, transcriptomic and interaction profiling. Overall, we observe extensive remodeling at active enhancers in PAH PAECs and identify hundreds of differentially active TFs, yet find very little transcriptomic changes in steady-state. We devise a disease-specific enhancer-gene regulatory network and predict that primed enhancers in PAH PAECs are activated by the differentially active TFs, resulting in an aberrant response to endothelial signals, which could lead to disturbed angiogenesis and endothelial-to-mesenchymal-transition. We validate these predictions for a selection of target genes in PAECs stimulated with TGF-β, VEGF or serotonin. Our study highlights the role of chromatin state and enhancers in disease-relevant cell types of PAH. Pulmonary arterial hypertension (PAH) is a heterogeneous disease, causing severe breathing problems and cardiac morbidity. Here, the authors study chromatin marks in pulmonary arterial endothelial cells from PAH patients and controls and find changes in transcription factor and enhancer activity that suggest an aberrant response to signalling in PAH.

Cumulative Lifetime Burden of Cardiovascular Disease From Early Exposure to Air Pollution

Abstract: The disease burden associated with air pollution continues to grow. The World Health Organization (WHO) estimates ≈7 million people worldwide die yearly from exposure to polluted air, half of which-3.3 million-are attributable to cardiovascular disease (CVD), greater than from major modifiable CVD risks including smoking, hypertension, hyperlipidemia, and diabetes mellitus. This serious and growing health threat is attributed to increasing urbanization of the world's populations with consequent exposure to polluted air. Especially vulnerable are the elderly, patients with pre-existing CVD, and children. The cumulative lifetime burden in children is particularly of concern because their rapidly developing cardiopulmonary systems are more susceptible to damage and they spend more time outdoors and therefore inhale more pollutants. World Health Organization estimates that 93% of the world's children aged <15 years-1.8 billion children-breathe air that puts their health and development at risk. Here, we present growing scientific evidence, including from our own group, that chronic exposure to air pollution early in life is directly linked to development of major CVD risks, including obesity, hypertension, and metabolic disorders. In this review, we surveyed the literature for current knowledge of how pollution exposure early in life adversely impacts cardiovascular phenotypes, and lay the foundation for early intervention and other strategies that can help prevent this damage. We also discuss the need for better guidelines and additional research to validate exposure metrics and interventions that will ultimately help healthcare providers reduce the growing burden of CVD from pollution.

Multiomic immune clockworks of pregnancy

Abstract: Preterm birth is the leading cause of mortality in children under the age of five worldwide. Despite major efforts, we still lack the ability to accurately predict and effectively prevent preterm birth. While multiple factors contribute to preterm labor, dysregulations of immunological adaptations required for the maintenance of a healthy pregnancy is at its pathophysiological core. Consequently, a precise understanding of these chronologically paced immune adaptations and of the biological pacemakers that synchronize the pregnancy "immune clock" is a critical first step towards identifying deviations that are hallmarks of peterm birth. Here, we will review key elements of the fetal, placental, and maternal pacemakers that program the immune clock of pregnancy. We will then emphasize multiomic studies that enable a more integrated view of pregnancy-related immune adaptations. Such multiomic assessments can strengthen the biological plausibility of immunological findings and increase the power of biological signatures predictive of preterm birth.

Multiomics Characterization of Preterm Birth in Low- and Middle-Income Countries.

Abstract: Importance Worldwide, preterm birth (PTB) is the single largest cause of deaths in the perinatal and neonatal period and is associated with increased morbidity in young children. The cause of PTB is multifactorial, and the development of generalizable biological models may enable early detection and guide therapeutic studies. Objective To investigate the ability of transcriptomics and proteomics profiling of plasma and metabolomics analysis of urine to identify early biological measurements associated with PTB. Design, setting, and participants This diagnostic/prognostic study analyzed plasma and urine samples collected from May 2014 to June 2017 from pregnant women in 5 biorepository cohorts in low- and middle-income countries (LMICs; ie, Matlab, Bangladesh; Lusaka, Zambia; Sylhet, Bangladesh; Karachi, Pakistan; and Pemba, Tanzania). These cohorts were established to study maternal and fetal outcomes and were supported by the Alliance for Maternal and Newborn Health Improvement and the Global Alliance to Prevent Prematurity and Stillbirth biorepositories. Data were analyzed from December 2018 to July 2019. Exposures Blood and urine specimens that were collected early during pregnancy (median sampling time of 13.6 weeks of gestation, according to ultrasonography) were processed, stored, and shipped to the laboratories under uniform protocols. Plasma samples were assayed for targeted measurement of proteins and untargeted cell-free ribonucleic acid profiling; urine samples were assayed for metabolites. Main outcomes and measures The PTB phenotype was defined as the delivery of a live infant before completing 37 weeks of gestation. Results Of the 81 pregnant women included in this study, 39 had PTBs (48.1%) and 42 had term pregnancies (51.9%) (mean [SD] age of 24.8 [5.3] years). Univariate analysis demonstrated functional biological differences across the 5 cohorts. A cohort-adjusted machine learning algorithm was applied to each biological data set, and then a higher-level machine learning modeling combined the results into a final integrative model. The integrated model was more accurate, with an area under the receiver operating characteristic curve (AUROC) of 0.83 (95% CI, 0.72-0.91) compared with the models derived for each independent biological modality (transcriptomics AUROC, 0.73 [95% CI, 0.61-0.83]; metabolomics AUROC, 0.59 [95% CI, 0.47-0.72]; and proteomics AUROC, 0.75 [95% CI, 0.64-0.85]). Primary features associated with PTB included an inflammatory module as well as a metabolomic module measured in urine associated with the glutamine and glutamate metabolism and valine, leucine, and isoleucine biosynthesis pathways. Conclusions and relevance This study found that, in LMICs and high PTB settings, major biological adaptations during term pregnancy follow a generalizable model and the predictive accuracy for PTB was augmented by combining various omics data sets, suggesting that PTB is a condition that manifests within multiple biological systems. These data sets, with machine learning partnerships, may be a key step in developing valuable predictive tests and intervention candidates for preventing PTB.

The MEK5-ERK5 Kinase Axis Controls Lipid Metabolism in Small-Cell Lung Cancer.

Abstract: Small-cell lung cancer (SCLC) is an aggressive form of lung cancer with dismal survival rates. While kinases often play key roles driving tumorigenesis, there are strikingly few kinases known to promote the development of SCLC. Here, we investigated the contribution of the MAPK module MEK5-ERK5 to SCLC growth. MEK5 and ERK5 were required for optimal survival and expansion of SCLC cell lines in vitro and in vivo. Transcriptomics analyses identified a role for the MEK5-ERK5 axis in the metabolism of SCLC cells, including lipid metabolism. In-depth lipidomics analyses showed that loss of MEK5/ERK5 perturbs several lipid metabolism pathways, including the mevalonate pathway that controls cholesterol synthesis. Notably, depletion of MEK5/ERK5 sensitized SCLC cells to pharmacologic inhibition of the mevalonate pathway by statins. These data identify a new MEK5-ERK5-lipid metabolism axis that promotes the growth of SCLC. SIGNIFICANCE: This study is the first to investigate MEK5 and ERK5 in SCLC, linking the activity of these two kinases to the control of cell survival and lipid metabolism.

Physical exercise is a risk factor for amyotrophic lateral sclerosis: Convergent evidence from mendelian randomisation, transcriptomics and risk genotypes

Abstract: Background Amyotrophic lateral sclerosis (ALS) is a universally fatal neurodegenerative disease. ALS is determined by gene-environment interactions and improved understanding of these interactions may lead to effective personalised medicine. The role of physical exercise in the development of ALS is currently controversial. Methods We dissected the exercise-ALS relationship in a series of two-sample Mendelian randomisation (MR) experiments. We then we tested for enrichment of ALS genetic risk within exercise-associated transcriptome changes. Finally, we applied a validated physical activity (PA) questionnaire in a small cohort of genetically selected ALS patients. Findings We present MR evidence supporting a causal relationship between genetic liability to strenuous leisure-time exercise and ALS (multiplicative random effects IVW, p=0.01). Transcriptomic analysis revealed that genes with altered expression in response to acute exercise are enriched with known ALS risk genes (permutation test, p=0.013) including C9ORF72, and with ALS-associated rare variants of uncertain significance. Questionnaire evidence revealed that age of onset is inversely proportional to historical PA for C9ORF72-ALS (linear regression, t=-2.28, p=0.036) but not for non-C9ORF72-ALS. Moreover, compared to non-C9ORF72-ALS patients and neurologically normal controls, C9ORF72-ALS cases reported the highest minimum average PA (20.9kJ/kg/day) consistent with an exercise threshold for penetrance. Interpretation Our MR approach suggests a positive causal relationship between ALS and physical exercise. Exercise is likely to cause motor neuron injury only in patients with a risk-genotype. Consistent with this we have shown that ALS risk genes are activated in response to exercise. In particular, we propose that G4C2-repeat expansion of C9ORF72 predisposes to exercise-induced ALS. Funding We acknowledge support from the Wellcome Trust (JCK, 216596/Z/19/Z), NIHR (PJS, NF-SI-0617-10077; IS-BRC-1215-20017) and NIH (MPS, CEGS 5P50HG00773504, 1P50HL083800, 1R01HL101388, 1R01-HL122939, S10OD025212, and P30DK116074, UM1HG009442). RESEARCH IN CONTEXT Evidence before this study The role of physical activity (PA) as a risk factor for ALS was evaluated in a systematic review of 26 studies performed by Lacorte et al. in 2016. The authors concluded that there was insufficient evidence to draw a firm conclusion. The authors highlighted limitations of previous studies relating to heterogeneous classification of PA and ALS. They noted that none of the published literature achieved the highest quality rating in the Newcastle Ottawa Scale, which they attribute to methodological challenges posed by the rarity and severity of the disease. Failure to address genetic subtypes of ALS was proposed as a shortcoming in the studies surveyed. To identify more recent publications, we conducted a literature search using the PubMed database for articles published between 01/01/2015 - 11/11/2020. The search terms used were (“Amyotrophic lateral sclerosis”[Title/Abstract] OR “motor neuron disease”[Title/Abstract] OR MND[Title/Abstract] OR ALS[Title/Abstract]) AND (PA[Title/Abstract] OR exercise[Title/Abstract] OR “physical activity”[Title/Abstract] OR sport[Title/Abstract]). This search strategy yielded 182 results and we filtered for original, observational, human-subject studies but we excluded case series with Added value of this study In the present study, we have exploited the methodological advantages of mendelian randomisation (MR) to counter bias, together with a tailored approach to PA exposure aimed at isolating strenuous, frequent physical exercise. We achieved this by selecting and combining UK biobank questionnaire items. In contrast to previous studies, we have addressed the gene-environment interaction by measuring the effect of exercise on expression of ALS risk genes. Furthermore, we have considered in detail the relationship between PA and the most frequent genetic risk factor for ALS: hexanucleotide (G4C2) repeat expansion of C9ORF72. Our data suggests that genetic liability to leisure time physical activity is a risk factor for ALS and C9ORF72-ALS in particular. In addition, we offer evidence that a number of known ALS-associated genetic variants are functionally linked to the physiological response to exercise. Implications of all the available evidence Our results indicate that participation in leisure time physical activity is a risk factor for ALS particularly in the context of certain risk genotypes. This could explain some of the controversy in previous studies which have largely neglected genetic heterogeneity within ALS patients. Our results form a platform for future research to explore the interaction between specific genotypes and exercise-induced ALS in a prospective manner with larger numbers, and in selected pedigrees. Ultimately this could lead to the design of personalised medicine including lifestyle advice regarding physical activity, to patients with ALS and their family members.

Genome-Wide Identification of the Genetic Basis of Amyotrophic Lateral Sclerosis

Abstract: Amyotrophic lateral sclerosis (ALS) is a complex disease centered on progressive death of motor neurons. Despite heritability estimates of 52%, GWAS studies have discovered only seven genome-wide significant hits. We developed a new machine learning approach called RefMap that integrates functional genomics with ALS genetics. Comprehensive transcriptomic and epigenetic profiling of iPSC-derived motor neurons enabled RefMap to identify 690 genes associated with ALS, the majority of which are novel. Extensive conservation, transcriptome and network analyses demonstrated the functional significance of these candidate genes in motor neurons and disease progression, and our genetic results support initiation of ALS neurotoxicity in the distal axon. KANK1 is enriched with coding and noncoding, common and rare ALS-associated variants. Reproducing patient KANK1 mutations in human neurons led to neurotoxicity with disruption of the distal axon. RefMap can be applied broadly to increase the discovery power in genetic association studies of human complex traits and diseases.

The dynamic, combinatorial cis-regulatory lexicon of epidermal differentiation

Abstract: Transcription factors (TFs) bind DNA sequence motif vocabularies in cis-regulatory elements (CREs) to modulate chromatin state and gene expression during cell state transitions. A quantitative understanding of how motif lexicons influence dynamic regulatory activity has been elusive due to the combinatorial nature of the cis-regulatory code. To address this, we undertook multi-omic data profiling of chromatin and expression dynamics across epidermal differentiation to identify 40,103 dynamic CREs associated with 3,609 dynamically expressed genes, then applied an interpretable deep learning framework to model the cis-regulatory logic of chromatin accessibility. This identified cooperative DNA sequence rules in dynamic CREs regulating synchronous gene modules with diverse roles in skin differentiation. Massively parallel reporter analysis validated temporal dynamics and cooperative cis-regulatory logic. Variants linked to human polygenic skin disease were enriched in these time-dependent combinatorial motif rules. This integrative approach reveals the combinatorial cis-regulatory lexicon of epidermal differentiation and represents a general framework for deciphering the organizational principles of the cis-regulatory code in dynamic gene regulation.

Research on the Human Proteome Reaches a Major Milestone: >90% of Predicted Human Proteins Now Credibly Detected, According to the HUPO Human Proteome Project.

Abstract: According to the 2020 Metrics of the HUPO Human Proteome Project (HPP), expression has now been detected at the protein level for >90% of the 19 773 predicted proteins coded in the human genome. The HPP annually reports on progress made throughout the world toward credibly identifying and characterizing the complete human protein parts list and promoting proteomics as an integral part of multiomics studies in medicine and the life sciences. NeXtProt release 2020-01 classified 17 874 proteins as PE1, having strong protein-level evidence, up 180 from 17 694 one year earlier. These represent 90.4% of the 19 773 predicted coding genes (all PE1,2,3,4 proteins in neXtProt). Conversely, the number of neXtProt PE2,3,4 proteins, termed the "missing proteins" (MPs), was reduced by 230 from 2129 to 1899 since the neXtProt 2019-01 release. PeptideAtlas is the primary source of uniform reanalysis of raw mass spectrometry data for neXtProt, supplemented this year with extensive data from MassIVE. PeptideAtlas 2020-01 added 362 canonical proteins between 2019 and 2020 and MassIVE contributed 84 more, many of which converted PE1 entries based on non-MS evidence to the MS-based subgroup. The 19 Biology and Disease-driven B/D-HPP teams continue to pursue the identification of driver proteins that underlie disease states, the characterization of regulatory mechanisms controlling the functions of these proteins, their proteoforms, and their interactions, and the progression of transitions from correlation to coexpression to causal networks after system perturbations. And the Human Protein Atlas published Blood, Brain, and Metabolic Atlases.

Early Detection Of COVID-19 Using A Smartwatch

Abstract: Wearable devices digitally measuring vital signs have been used for monitoring health and illness onset and have high potential for real-time monitoring and disease detection. As such they are potentially useful during public health crises, such as the current COVID-19 global pandemic. Using smartwatch data from 31 infected individuals identified from a cohort of over 5000 participants, we investigated the use of wearables for early, presymptomatic detection of COVID-19. From physiological and activity data, we first demonstrate that COVID-19 infections are associated with alterations in heart rate, steps and sleep in 80% of COVID-19 infection cases. Failure to detect these changes in the remaining patients often occurred in those with chronic respiratory/lung disease. Importantly the physiological alterations were detected prior to, or at, symptom onset in over 85% of the positive cases (21/24), in some cases nine or more days before symptoms. Through daily surveys we can track physiological changes with symptom onset and severity. Finally, we develop a method to detect onset of COVID-19 infection in real-time which detects 67% of infection cases at or before symptom onset. Our study provides a roadmap to a rapid and universal diagnostic method for the large-scale detection of respiratory viral infections in advance of symptoms, highlighting a useful approach for managing epidemics using digital tracking and health monitoring.