Mammary duct luminal epithelium controls adipocyte thermogenic programme.

Sympathetic activation during cold exposure increases adipocyte thermogenesis via the expression of mitochondrial protein uncoupling protein 1 (UCP1)1. The propensity of adipocytes to express UCP1 is under a critical influence of the adipose microenvironment and varies between sexes and among various fat depots2-7. Here we report that mammary gland ductal epithelial cells in the adipose niche regulate cold-induced adipocyte UCP1 expression in female mouse subcutaneous white adipose tissue (scWAT). Single-cell RNA sequencing shows that glandular luminal epithelium subtypes express transcripts that encode secretory factors controlling adipocyte UCP1 expression under cold conditions. We term these luminal epithelium secretory factors 'mammokines'. Using 3D visualization of whole-tissue immunofluorescence, we reveal sympathetic nerve-ductal contact points. We show that mammary ducts activated by sympathetic nerves limit adipocyte UCP1 expression via the mammokine lipocalin 2. In vivo and ex vivo ablation of mammary duct epithelium enhance the cold-induced adipocyte thermogenic gene programme in scWAT. Since the mammary duct network extends throughout most of the scWAT in female mice, females show markedly less scWAT UCP1 expression, fat oxidation, energy expenditure and subcutaneous fat mass loss compared with male mice, implicating sex-specific roles of mammokines in adipose thermogenesis. These results reveal a role of sympathetic nerve-activated glandular epithelium in adipocyte UCP1 expression and suggest that mammary duct luminal epithelium has an important role in controlling glandular adiposity.

Sequential regression and simulation: a method for estimating causal effects from heterogeneous clinical trials without a common control group.

BACKGROUND: The advent of clinical trial data sharing platforms has created opportunities for making new discoveries and answering important questions using already collected data. However, existing methods for meta-analyzing these data require the presence of shared control groups across studies, significantly limiting the number of questions that can be confidently addressed. We sought to develop a method for meta-analyzing potentially heterogeneous clinical trials even in the absence of a common control group. METHODS: This work was conducted within the context of a broader effort to study comparative efficacy in Crohn's disease. Following a search of clnicaltrials.gov we obtained access to the individual participant data from nine trials of FDA-approved treatments in Crohn's Disease (N = 3392). We developed a method involving sequences of regression and simulation to separately model the placebo- and drug-attributable effects, and to simulate head-to-head trials against an appropriately normalized background. We validated this method by comparing the outcome of a simulated trial comparing the efficacies of adalimumab and ustekinumab against the recently published results of SEAVUE, an actual head-to-head trial of these drugs. This study was pre-registered on PROSPERO (#157,827) prior to the completion of SEAVUE. RESULTS: Using our method of sequential regression and simulation, we compared the week eight outcomes of two virtual cohorts subject to the same patient selection criteria as SEAVUE and treated with adalimumab or ustekinumab. Our primary analysis replicated the corresponding published results from SEAVUE (p = 0.9). This finding proved stable under multiple sensitivity analyses. CONCLUSIONS: This new method may help reduce the bias of individual participant data meta-analyses, expand the scope of what can be learned from these already-collected data, and reduce the costs of obtaining high-quality evidence to guide patient care.

Systems spatiotemporal dynamics of traumatic brain injury at single-cell resolution reveals humanin as a therapeutic target.

BACKGROUND: The etiology of mild traumatic brain injury (mTBI) remains elusive due to the tissue and cellular heterogeneity of the affected brain regions that underlie cognitive impairments and subsequent neurological disorders. This complexity is further exacerbated by disrupted circuits within and between cell populations across brain regions and the periphery, which occur at different timescales and in spatial domains. METHODS: We profiled three tissues (hippocampus, frontal cortex, and blood leukocytes) at the acute (24-h) and subacute (7-day) phases of mTBI at single-cell resolution. RESULTS: We demonstrated that the coordinated gene expression patterns across cell types were disrupted and re-organized by TBI at different timescales with distinct regional and cellular patterns. Gene expression-based network modeling implied astrocytes as a key regulator of the cell-cell coordination following mTBI in both hippocampus and frontal cortex across timepoints, and mt-Rnr2, which encodes the mitochondrial peptide humanin, as a potential target for intervention based on its broad regional and dynamic dysregulation following mTBI. Treatment of a murine mTBI model with humanin reversed cognitive impairment caused by mTBI through the restoration of metabolic pathways within astrocytes. CONCLUSIONS: Our results offer a systems-level understanding of the dynamic and spatial regulation of gene programs by mTBI and pinpoint key target genes, pathways, and cell circuits that are amenable to therapeutics.

Joint cell segmentation and cell type annotation for spatial transcriptomics.

RNA hybridization-based spatial transcriptomics provides unparalleled detection sensitivity. However, inaccuracies in segmentation of image volumes into cells cause misassignment of mRNAs which is a major source of errors. Here, we develop JSTA, a computational framework for joint cell segmentation and cell type annotation that utilizes prior knowledge of cell type-specific gene expression. Simulation results show that leveraging existing cell type taxonomy increases RNA assignment accuracy by more than 45%. Using JSTA, we were able to classify cells in the mouse hippocampus into 133 (sub)types revealing the spatial organization of CA1, CA3, and Sst neuron subtypes. Analysis of within cell subtype spatial differential gene expression of 80 candidate genes identified 63 with statistically significant spatial differential gene expression across 61 (sub)types. Overall, our work demonstrates that known cell type expression patterns can be leveraged to improve the accuracy of RNA hybridization-based spatial transcriptomics while providing highly granular cell (sub)type information. The large number of newly discovered spatial gene expression patterns substantiates the need for accurate spatial transcriptomic measurements that can provide information beyond cell (sub)type labels.

Single-Cell Study of Two Rat Models of Pulmonary Arterial Hypertension Reveals Connections to Human Pathobiology and Drug Repositioning.

Rationale: The cellular and molecular landscape and translational value of commonly used models of pulmonary arterial hypertension (PAH) are poorly understood. Single-cell transcriptomics can enhance molecular understanding of preclinical models and facilitate their rational use and interpretation.Objectives: To determine and prioritize dysregulated genes, pathways, and cell types in lungs of PAH rat models to assess relevance to human PAH and identify drug repositioning candidates.Methods: Single-cell RNA sequencing was performed on the lungs of monocrotaline (MCT), Sugen-hypoxia (SuHx), and control rats to identify altered genes and cell types, followed by validation using flow-sorted cells, RNA in situ hybridization, and immunofluorescence. Relevance to human PAH was assessed by histology of lungs from patients and via integration with human PAH genetic loci and known disease genes. Candidate drugs were predicted using Connectivity Map.Measurements and Main Results: Distinct changes in genes and pathways in numerous cell types were identified in SuHx and MCT lungs. Widespread upregulation of NF-κB signaling and downregulation of IFN signaling was observed across cell types. SuHx nonclassical monocytes and MCT conventional dendritic cells showed particularly strong NF-κB pathway activation. Genes altered in SuHx nonclassical monocytes were significantly enriched for PAH-associated genes and genetic variants, and candidate drugs predicted to reverse the changes were identified. An open-access online platform was developed to share single-cell data and drug candidates (http://mergeomics.research.idre.ucla.edu/PVDSingleCell/).Conclusions: Our study revealed the distinct and shared dysregulation of genes and pathways in two commonly used PAH models for the first time at single-cell resolution and demonstrated their relevance to human PAH and utility for drug repositioning.

Single-cell transcriptomic profiling of satellite glial cells in stellate ganglia reveals developmental and functional axial dynamics.

Stellate ganglion neurons, important mediators of cardiopulmonary neurotransmission, are surrounded by satellite glial cells (SGCs), which are essential for the function, maintenance, and development of neurons. However, it remains unknown whether SGCs in adult sympathetic ganglia exhibit any functional diversity, and what role this plays in modulating neurotransmission. We performed single-cell RNA sequencing of mouse stellate ganglia (n = 8 animals), focusing on SGCs (n = 11,595 cells). SGCs were identified by high expression of glial-specific transcripts, S100b and Fabp7. Microglia and Schwann cells were identified by expression of C1qa/C1qb/C1qc and Ncmap/Drp2, respectively, and excluded from further analysis. Dimensionality reduction and clustering of SGCs revealed six distinct transcriptomic subtypes, one of which was characterized the expression of pro-inflammatory markers and excluded from further analyses. The transcriptomic profiles and corresponding biochemical pathways of the remaining subtypes were analyzed and compared with published astrocytic transcriptomes. This revealed gradual shifts of developmental and functional pathways across the subtypes, originating from an immature and pluripotent subpopulation into two mature populations of SGCs, characterized by upregulated functional pathways such as cholesterol metabolism. As SGCs aged, these functional pathways were downregulated while genes and pathways associated with cellular stress responses were upregulated. These findings were confirmed and furthered by an unbiased pseudo-time analysis, which revealed two distinct trajectories involving the five subtypes that were studied. These findings demonstrate that SGCs in mouse stellate ganglia exhibit transcriptomic heterogeneity along maturation or differentiation axes. These subpopulations and their unique biochemical properties suggest dynamic physiological adaptations that modulate neuronal function.

Mergeomics: a web server for identifying pathological pathways, networks, and key regulators via multidimensional data integration.

BACKGROUND: Human diseases are commonly the result of multidimensional changes at molecular, cellular, and systemic levels. Recent advances in genomic technologies have enabled an outpour of omics datasets that capture these changes. However, separate analyses of these various data only provide fragmented understanding and do not capture the holistic view of disease mechanisms. To meet the urgent needs for tools that effectively integrate multiple types of omics data to derive biological insights, we have developed Mergeomics, a computational pipeline that integrates multidimensional disease association data with functional genomics and molecular networks to retrieve biological pathways, gene networks, and central regulators critical for disease development. RESULTS: To make the Mergeomics pipeline available to a wider research community, we have implemented an online, user-friendly web server ( http://mergeomics. RESEARCH: idre.ucla.edu/ ). The web server features a modular implementation of the Mergeomics pipeline with detailed tutorials. Additionally, it provides curated genomic resources including tissue-specific expression quantitative trait loci, ENCODE functional annotations, biological pathways, and molecular networks, and offers interactive visualization of analytical results. Multiple computational tools including Marker Dependency Filtering (MDF), Marker Set Enrichment Analysis (MSEA), Meta-MSEA, and Weighted Key Driver Analysis (wKDA) can be used separately or in flexible combinations. User-defined summary-level genomic association datasets (e.g., genetic, transcriptomic, epigenomic) related to a particular disease or phenotype can be uploaded and computed real-time to yield biologically interpretable results, which can be viewed online and downloaded for later use. CONCLUSIONS: Our Mergeomics web server offers researchers flexible and user-friendly tools to facilitate integration of multidimensional data into holistic views of disease mechanisms in the form of tissue-specific key regulators, biological pathways, and gene networks.

Characterisation of digital therapeutic clinical trials: a systematic review with natural language processing.

Digital therapeutics (DTx) are a somewhat novel class of US Food and Drug Administration-regulated software that help patients prevent, manage, or treat disease. Here, we use natural language processing to characterise registered DTx clinical trials and provide insights into the clinical development landscape for these novel therapeutics. We identified 449 DTx clinical trials, initiated or expected to be initiated between 2010 and 2030, from ClinicalTrials.gov using 27 search terms, and available data were analysed, including trial durations, locations, MeSH categories, enrolment, and sponsor types. Topic modelling of eligibility criteria, done with BERTopic, showed that DTx trials frequently exclude patients on the basis of age, comorbidities, pregnancy, language barriers, and digital determinants of health, including smartphone or data plan access. Our comprehensive overview of the DTx development landscape highlights challenges in designing inclusive DTx clinical trials and presents opportunities for clinicians and researchers to address these challenges. Finally, we provide an interactive dashboard for readers to conduct their own analyses.

Personalizing treatment selection in Crohn's disease: a meta-analysis of individual participant data from fifteen randomized controlled trials.

BACKGROUND: Meta-analyses have found anti-TNF drugs to be the best treatment, on average, for Crohn's disease. We performed a subgroup analysis to determine if it is possible to achieve more efficacious outcomes by individualizing treatment selection. METHODS: We obtained participant-level data from 15 trials of FDA-approved treatments (N=5703). We used sequential regression and simulation to model week six disease activity as a function of drug class, demographics, and disease-related features. We performed hypothesis testing to define subgroups based on rank-ordered preferences for treatments. We queried health records from University of California Health (UCH) to estimate the impacts these models could have on practice. We computed the sample size needed to prospectively test a prediction of our models. RESULTS: 45% of the participants (N=2561) showed greater efficacy with at least one drug class (anti-TNF, anti-IL-12/23, anti-integrin) over another. They were classifiable into 6 subgroups, two showing greatest efficacy with anti-TNFs (36%, N=2064). Women over 50 showed superior responses with anti-IL-12/23s. Although they represented only 2% of the trial-based cohort, 25% of Crohn's patients at UCH are women over 50 (N=5,647), consistent with potential selection bias in trials. Moreover, 75% of biologic-exposed women over 50 did not receive an anti-IL12/23 first-line, supporting the potential value of these models. A future trial with 250 patients per arm will have 97% power to confirm the superiority of anti-IL-12/23s over anti-TNFs in these patients. A treatment recommendation tool is available at https://crohnsrx.org. CONCLUSIONS: Personalizing treatment can improve outcomes in Crohn's disease. Future work is needed to confirm these findings, and improve representativeness in Crohn's trials.

Tiered Sympathetic Control of Cardiac Function Revealed by Viral Tracing and Single Cell Transcriptome Profiling.

The cell bodies of postganglionic sympathetic neurons innervating the heart primarily reside in the stellate ganglion (SG), alongside neurons innervating other organs and tissues. Whether cardiac-innervating stellate ganglionic neurons (SGNs) exhibit diversity and distinction from those innervating other tissues is not known. To identify and resolve the transcriptomic profiles of SGNs innervating the heart we leveraged retrograde tracing techniques using adeno-associated virus (AAV) expressing fluorescent proteins (GFP or Td-tomato) with single cell RNA sequencing. We investigated electrophysiologic, morphologic, and physiologic roles for subsets of cardiac-specific neurons and found that three of five adrenergic SGN subtypes innervate the heart. These three subtypes stratify into two subpopulations; high (NA1a) and low (NA1b and NA1c) Npy-expressing cells, exhibit distinct morphological, neurochemical, and electrophysiologic characteristics. In physiologic studies in transgenic mouse models modulating NPY signaling, we identified differential control of cardiac responses by these two subpopulations to high and low stress states. These findings provide novel insights into the unique properties of neurons responsible for cardiac sympathetic regulation, with implications for novel strategies to target specific neuronal subtypes for sympathetic blockade in cardiac disease.

Tiered sympathetic control of cardiac function revealed by viral tracing and single cell transcriptome profiling.

The cell bodies of postganglionic sympathetic neurons innervating the heart primarily reside in the stellate ganglion (SG), alongside neurons innervating other organs and tissues. Whether cardiac-innervating stellate ganglionic neurons (SGNs) exhibit diversity and distinction from those innervating other tissues is not known. To identify and resolve the transcriptomic profiles of SGNs innervating the heart, we leveraged retrograde tracing techniques using adeno-associated virus (AAV) expressing fluorescent proteins (GFP or Td-tomato) with single cell RNA sequencing. We investigated electrophysiologic, morphologic, and physiologic roles for subsets of cardiac-specific neurons and found that three of five adrenergic SGN subtypes innervate the heart. These three subtypes stratify into two subpopulations; high (NA1a) and low (NA1b and NA1c) neuropeptide-Y (NPY) -expressing cells, exhibit distinct morphological, neurochemical, and electrophysiologic characteristics. In physiologic studies in transgenic mouse models modulating NPY signaling, we identified differential control of cardiac responses by these two subpopulations to high and low stress states. These findings provide novel insights into the unique properties of neurons responsible for cardiac sympathetic regulation, with implications for novel strategies to target specific neuronal subtypes for sympathetic blockade in cardiac disease.

PharmOmics: A species- and tissue-specific drug signature database and gene-network-based drug repositioning tool.

Drug development has been hampered by a high failure rate in clinical trials due to our incomplete understanding of drug functions across organs and species. Therefore, elucidating species- and tissue-specific drug functions can provide insights into therapeutic efficacy, potential adverse effects, and interspecies differences necessary for effective translational medicine. Here, we present PharmOmics, a drug knowledgebase and analytical tool that is hosted on an interactive web server. Using tissue- and species-specific transcriptome data from human, mouse, and rat curated from different databases, we implemented a gene-network-based approach for drug repositioning. We demonstrate the potential of PharmOmics to retrieve known therapeutic drugs and identify drugs with tissue toxicity using in silico performance assessment. We further validated predicted drugs for nonalcoholic fatty liver disease in mice. By combining tissue- and species-specific in vivo drug signatures with gene networks, PharmOmics serves as a complementary tool to support drug characterization and network-based medicine.

Case-control study of the association of chronic acid suppression and social determinants of health with COVID-19 infection.

Acid suppressants are widely-used classes of medications linked to increased risks of aerodigestive infections. Prior studies of these medications as potentially reversible risk factors for COVID-19 have been conflicting. We aimed to determine the impact of chronic acid suppression use on COVID-19 infection risk while simultaneously evaluating the influence of social determinants of health to validate known and discover novel risk factors. We assessed the association of chronic acid suppression with incident COVID-19 in a 1:1 case-control study of 900 patients tested across three academic medical centers in California, USA. Medical comorbidities and history of chronic acid suppression use were manually extracted from health records by physicians following a pre-specified protocol. Socio-behavioral factors by geomapping publicly-available data to patient zip codes were incorporated. We identified no evidence to support an association between chronic acid suppression and COVID-19 (adjusted odds ratio 1.04, 95% CI 0.92-1.17, P = 0.515). However, several medical and social features were positive (Latinx ethnicity, BMI ≥ 30, dementia, public transportation use, month of the pandemic) and negative (female sex, concurrent solid tumor, alcohol use disorder) predictors of new infection. These findings demonstrate the value of integrating publicly-available databases with medical data to identify critical features of communicable diseases.

Estrogen receptor alpha in the brain mediates tamoxifen-induced changes in physiology in mice.

Adjuvant tamoxifen therapy improves survival in breast cancer patients. Unfortunately, long-term treatment comes with side effects that impact health and quality of life, including hot flashes, changes in bone density, and fatigue. Partly due to a lack of proven animal models, the tissues and cells that mediate these negative side effects are unclear. Here, we show that mice undergoing tamoxifen treatment experience changes in temperature, bone, and movement. Single-cell RNA sequencing reveals that tamoxifen treatment induces widespread gene expression changes in the hypothalamus and preoptic area (hypothalamus-POA). These expression changes are dependent on estrogen receptor alpha (ERα), as conditional knockout of ERα in the hypothalamus-POA ablates or reverses tamoxifen-induced gene expression. Accordingly, ERα-deficient mice do not exhibit tamoxifen-induced changes in temperature, bone, or movement. These findings provide mechanistic insight into the effects of tamoxifen on the hypothalamus-POA and indicate that ERα mediates several physiological effects of tamoxifen treatment in mice.

Estrogen is a hormone often known for its role in female development and reproduction. Yet, it also has an impact on many biological processes such as immunity and the health of bones, the heart, or the brain. It usually works by attaching to receptor proteins in specific cells. For instance, estrogen-responsive cells are present in the hypothalamus, the brain area that controls energy levels as well as the body's temperature and internal clock. Breast cancer cells are also often sensitive to estrogen, with the hormone fuelling the growth of tumors. The drug tamoxifen blocks estrogen receptors, stopping cells from responding to the hormone. As such, it is often used to reduce the likelihood that estrogen-dependent breast cancer will come back after treatment. However, its use can induce hot flashes, changes in bone density, fatigue and other life-altering side effects. Here, Zhang et al. investigated how estrogen receptors in the hypothalamus and a related region known as the preoptic area could be responsible for these side effects in mice. When the rodents were given tamoxifen for 28 days, they experienced changes in temperature, bone density and movement similar to those found in humans. In fact, genetic analyses revealed that the drug altered the way genes were turned on and off in certain cells types in the hypothalamus. Crucially, mice whose hypothalamus and preoptic area lacked estrogen receptors did not experience these behavioral and biological alterations. The findings by Zhang et al. help to understand how the side effects of tamoxifen emerge, singling out estrogen receptors in particular brain regions. This result could help to develop new therapies so that breast cancer can be treated with a better quality of life.

MethylResolver-a method for deconvoluting bulk DNA methylation profiles into known and unknown cell contents.

Bulk tissue DNA methylation profiling has been used to examine epigenetic mechanisms and biomarkers of complex diseases such as cancer. However, heterogeneity of cellular content in tissues complicates result interpretation and utility. In silico deconvolution of cellular fractions from bulk tissue data offers a fast and inexpensive alternative to experimentally measuring such fractions. In this study, we report the design, implementation, and benchmarking of MethylResolver, a Least Trimmed Squares regression-based method for inferring leukocyte subset fractions from methylation profiles of tumor admixtures. Compared to previous approaches MethylResolver is more accurate as unknown cellular content in the mixture increases and is able to resolve tumor purity-scaled immune cell-type fractions without a cancer-specific signature. We also present a pan-cancer deconvolution of TCGA, recapitulating that high eosinophil fraction predicts improved cervical carcinoma survival and identifying elevated B cell fraction as a previously unreported predictor of poor survival for papillary renal cell carcinoma.

CovidCounties - an interactive, real-time tracker of the COVID-19 pandemic at the level of US counties.

Management of the COVID-19 pandemic has proven to be a significant challenge to policy makers. This is in large part due to uneven reporting and the absence of open-access visualization tools to present local trends and infer healthcare needs. Here we report the development of CovidCounties.org, an interactive web application that depicts daily disease trends at the level of US counties using time series plots and maps. This application is accompanied by a manually curated dataset that catalogs all major public policy actions made at the state-level, as well as technical validation of the primary data. Finally, the underlying code for the site is also provided as open source, enabling others to validate and learn from this work.

CovidCounties is an interactive real time tracker of the COVID19 pandemic at the level of US counties.

Management of the COVID-19 pandemic has proven to be a significant challenge to policy makers. This is in large part due to uneven reporting and the absence of open-access visualization tools to present local trends and infer healthcare needs. Here we report the development of CovidCounties.org, an interactive web application that depicts daily disease trends at the level of US counties using time series plots and maps. This application is accompanied by a manually curated dataset that catalogs all major public policy actions made at the state-level, as well as technical validation of the primary data. Finally, the underlying code for the site is also provided as open source, enabling others to validate and learn from this work.

Hypothalamic estrogen receptor alpha establishes a sexually dimorphic regulatory node of energy expenditure.

Estrogen receptor a (ERa) signaling in the ventromedial hypothalamus (VMH) contributes to energy homeostasis by modulating physical activity and thermogenesis. However, the precise neuronal populations involved remain undefined. Here, we describe six neuronal populations in the mouse VMH by using single-cell RNA transcriptomics and in situ hybridization. ERa is enriched in populations showing sex biased expression of reprimo (Rprm), tachykinin 1 (Tac1), and prodynorphin (Pdyn). Female biased expression of Tac1 and Rprm is patterned by ERa-dependent repression during male development, whereas male biased expression of Pdyn is maintained by circulating testicular hormone in adulthood. Chemogenetic activation of ERa positive VMH neurons stimulates heat generation and movement in both sexes. However, silencing Rprm gene function increases core temperature selectively in females and ectopic Rprm expression in males is associated with reduced core temperature. Together these findings reveal a role for Rprm in temperature regulation and ERa in the masculinization of neuron populations that underlie energy expenditure.

Network modeling of single-cell omics data: challenges, opportunities, and progresses.

Single-cell multi-omics technologies are rapidly evolving, prompting both methodological advances and biological discoveries at an unprecedented speed. Gene regulatory network modeling has been used as a powerful approach to elucidate the complex molecular interactions underlying biological processes and systems, yet its application in single-cell omics data modeling has been met with unique challenges and opportunities. In this review, we discuss these challenges and opportunities, and offer an overview of the recent development of network modeling approaches designed to capture dynamic networks, within-cell networks, and cell-cell interaction or communication networks. Finally, we outline the remaining gaps in single-cell gene network modeling and the outlooks of the field moving forward.

Multi-omics integration reveals molecular networks and regulators of psoriasis.

BACKGROUND: Psoriasis is a complex multi-factorial disease, involving both genetic susceptibilities and environmental triggers. Genome-wide association studies (GWAS) and epigenome-wide association studies (EWAS) have been carried out to identify genetic and epigenetic variants that are associated with psoriasis. However, these loci cannot fully explain the disease pathogenesis. METHODS: To achieve a comprehensive mechanistic understanding of psoriasis, we conducted a systems biology study, integrating multi-omics datasets including GWAS, EWAS, tissue-specific transcriptome, expression quantitative trait loci (eQTLs), gene networks, and biological pathways to identify the key genes, processes, and networks that are genetically and epigenetically associated with psoriasis risk. RESULTS: This integrative genomics study identified both well-characterized (e.g., the IL17 pathway in both GWAS and EWAS) and novel biological processes (e.g., the branched chain amino acid catabolism process in GWAS and the platelet and coagulation pathway in EWAS) involved in psoriasis. Finally, by utilizing tissue-specific gene regulatory networks, we unraveled the interactions among the psoriasis-associated genes and pathways in a tissue-specific manner and detected potential key regulatory genes in the psoriasis networks. CONCLUSIONS: The integration and convergence of multi-omics signals provide deeper and comprehensive insights into the biological mechanisms associated with psoriasis susceptibility.