Retrospective pharmacogenetic study of psoriasis highlights the role of KLK7 in tumour necrosis factor signalling.

BACKGROUND: Multiple treatment options are available for the management of psoriasis, but clinical response varies among individual patients and no biomarkers are available to facilitate treatment selection for improved patient outcomes. OBJECTIVES: To utilize retrospective data to conduct a pharmacogenetic study to explore the potential genetic pathways associated with drug response in the treatment of psoriasis. METHODS: We conducted a retrospective pharmacogenetic study using self-evaluated treatment response from 1942 genotyped patients with psoriasis. We examined 6 502 658 genetic markers to model their associations with response to six treatment options using linear regression, adjusting for cohort variables and demographic features. We further utilized an integrative approach incorporating epigenomics, transcriptomics and a longitudinal clinical cohort to provide biological implications for the topmost signals associated with drug response. RESULTS: Two novel markers were revealed to be associated with treatment response: rs1991820 (P = 1.30 × 10-6) for anti-tumour necrosis factor (TNF) biologics; and rs62264137 (P = 2.94 × 10-6) for methotrexate, which was also associated with cutaneous mRNA expression levels of two known psoriasis-related genes KLK7 (P = 1.0 × 10-12) and CD200 (P = 5.4 × 10-6). We demonstrated that KLK7 expression was increased in the psoriatic epidermis, as shown by immunohistochemistry, as well as single-cell RNA sequencing, and its responsiveness to anti-TNF treatment was highlighted. By inhibiting the expression of KLK7, we further illustrated that keratinocytes have decreased proinflammatory responses to TNF. CONCLUSIONS: Our study implicates the genetic regulation of cytokine responses in predicting clinical drug response and supports the association between pharmacogenetic loci and anti-TNF response, as shown here for KLK7.

Prediction of acute appendicitis among patients with undifferentiated abdominal pain at emergency department.

BACKGROUND: Early screening and accurately identifying Acute Appendicitis (AA) among patients with undifferentiated symptoms associated with appendicitis during their emergency visit will improve patient safety and health care quality. The aim of the study was to compare models that predict AA among patients with undifferentiated symptoms at emergency visits using both structured data and free-text data from a national survey. METHODS: We performed a secondary data analysis on the 2005-2017 United States National Hospital Ambulatory Medical Care Survey (NHAMCS) data to estimate the association between emergency department (ED) patients with the diagnosis of AA, and the demographic and clinical factors present at ED visits during a patient's ED stay. We used binary logistic regression (LR) and random forest (RF) models incorporating natural language processing (NLP) to predict AA diagnosis among patients with undifferentiated symptoms. RESULTS: Among the 40,441 ED patients with assigned International Classification of Diseases (ICD) codes of AA and appendicitis-related symptoms between 2005 and 2017, 655 adults (2.3%) and 256 children (2.2%) had AA. For the LR model identifying AA diagnosis among adult ED patients, the c-statistic was 0.72 (95% CI: 0.69-0.75) for structured variables only, 0.72 (95% CI: 0.69-0.75) for unstructured variables only, and 0.78 (95% CI: 0.76-0.80) when including both structured and unstructured variables. For the LR model identifying AA diagnosis among pediatric ED patients, the c-statistic was 0.84 (95% CI: 0.79-0.89) for including structured variables only, 0.78 (95% CI: 0.72-0.84) for unstructured variables, and 0.87 (95% CI: 0.83-0.91) when including both structured and unstructured variables. The RF method showed similar c-statistic to the corresponding LR model. CONCLUSIONS: We developed predictive models that can predict the AA diagnosis for adult and pediatric ED patients, and the predictive accuracy was improved with the inclusion of NLP elements and approaches.

Multi-ancestry Genome-Wide Association Meta-Analysis Identifies Novel Loci in Atopic Dermatitis.

Atopic dermatitis (AD) is a highly heritable and common inflammatory skin condition affecting children and adults worldwide. Multi-ancestry approaches to AD genetic association studies are poised to boost power to detect genetic signal and identify ancestry-specific loci contributing to AD risk. Here, we present a multi-ancestry GWAS meta-analysis of twelve AD cohorts from five ancestral populations totaling 56,146 cases and 602,280 controls. We report 101 genomic loci associated with AD, including 15 loci that have not been previously associated with AD or eczema. Fine-mapping, QTL colocalization, and cell-type enrichment analyses identified genes and cell types implicated in AD pathophysiology. Functional analyses in keratinocytes provide evidence for genes that could play a role in AD through epidermal barrier function. Our study provides new insights into the etiology of AD by harnessing multiple genetic and functional approaches to unveil the mechanisms by which AD-associated variants impact genes and cell types.

Epigenetic Alteration of Smooth Muscle Cells Regulates Endothelin-Dependent Blood Pressure and Hypertensive Arterial Remodeling.

Long-standing hypertension (HTN) affects multiple organ systems and leads to pathologic arterial remodeling, which is driven largely by smooth muscle cell (SMC) plasticity. Although genome wide association studies (GWAS) have identified numerous variants associated with changes in blood pressure in humans, only a small percentage of these variants actually cause HTN. In order to identify relevant genes important in SMC function in HTN, we screened three separate human GWAS and Mendelian randomization studies to identify SNPs located within non-coding gene regions, focusing on genes encoding epigenetic enzymes, as these have been recently identified to control SMC fate in cardiovascular disease. We identified SNPs rs62059712 and rs74480102 in the promoter of the human JMJD3 gene and show that the minor C allele increases JMJD3 transcription in SMCs via increased SP1 binding to the JMJD3 promoter. Using our novel SMC-specific Jmjd3-deficient murine model ( Jmjd3 flox/flox Myh11 CreERT ), we show that loss of Jmjd3 in SMCs results in HTN, mechanistically, due to decreased EDNRB expression and a compensatory increase in EDNRA expression. As a translational corollary, through single cell RNA-sequencing (scRNA-seq) of human arteries, we found strong correlation between JMJD3 and EDNRB expression in SMCs. Further, we identified that JMJD3 is required for SMC-specific gene expression, and loss of JMJD3 in SMCs in the setting of HTN results in increased arterial remodeling by promoting the SMC synthetic phenotype. Our findings link a HTN-associated human DNA variant with regulation of SMC plasticity, revealing therapeutic targets that may be used in the screening and/or personalized treatment of HTN.

Large-scale epidemiological analysis of common skin diseases to identify shared and unique comorbidities and demographic factors.

Introduction: The utilization of large-scale claims databases has greatly improved the management, accessibility, and integration of extensive medical data. However, its potential for systematically identifying comorbidities in the context of skin diseases remains unexplored. Methods: This study aims to assess the capability of a comprehensive claims database in identifying comorbidities linked to 14 specific skin and skin-related conditions and examining temporal changes in their association patterns. This study employed a retrospective case-control cohort design utilizing 13 million skin/skin-related patients and 2 million randomly sampled controls from Optum's de-identified Clinformatics® Data Mart Database spanning the period from 2001 to 2018. A broad spectrum of comorbidities encompassing cancer, diabetes, respiratory, mental, immunity, gastrointestinal, and cardiovascular conditions were examined for each of the 14 skin and skin-related disorders in the study. Results: Using the established type-2 diabetes (T2D) and psoriasis comorbidity as example, we demonstrated the association is significant (P-values<1x10-15) and stable across years (OR=1.15-1.31). Analysis of the 2014-2018 data reveals that celiac disease, Crohn's disease, and ulcerative colitis exhibit the strongest associations with the 14 skin/skin-related conditions. Systemic lupus erythematosus (SLE), leprosy, and hidradenitis suppurativa show the strongest associations with 30 different comorbidities. Particularly notable associations include Crohn's disease with leprosy (odds ratio [OR]=6.60, 95% confidence interval [CI]: 3.09-14.08), primary biliary cirrhosis with SLE (OR=6.07, 95% CI: 4.93-7.46), and celiac disease with SLE (OR=6.06, 95% CI: 5.49-6.69). In addition, changes in associations were observed over time. For instance, the association between atopic dermatitis and lung cancer demonstrates a marked decrease over the past decade, with the odds ratio decreasing from 1.75 (95% CI: 1.47-2.07) to 1.02 (95% CI: 0.97-1.07). The identification of skin-associated comorbidities contributes to individualized healthcare and improved clinical management, while also enhancing our understanding of shared pathophysiology. Moreover, tracking these associations over time aids in evaluating the progression of clinical diagnosis and treatment. Discussion: The findings highlight the potential of utilizing comprehensive claims databases in advancing research and improving patient care in dermatology.

Multi-ancestry and multi-trait genome-wide association meta-analyses inform clinical risk prediction for systemic lupus erythematosus.

Systemic lupus erythematosus is a heritable autoimmune disease that predominantly affects young women. To improve our understanding of genetic etiology, we conduct multi-ancestry and multi-trait meta-analysis of genome-wide association studies, encompassing 12 systemic lupus erythematosus cohorts from 3 different ancestries and 10 genetically correlated autoimmune diseases, and identify 16 novel loci. We also perform transcriptome-wide association studies, computational drug repurposing analysis, and cell type enrichment analysis. We discover putative drug classes, including a histone deacetylase inhibitor that could be repurposed to treat lupus. We also identify multiple cell types enriched with putative target genes, such as non-classical monocytes and B cells, which may be targeted for future therapeutics. Using this newly assembled result, we further construct polygenic risk score models and demonstrate that integrating polygenic risk score with clinical lab biomarkers improves the diagnostic accuracy of systemic lupus erythematosus using the Vanderbilt BioVU and Michigan Genomics Initiative biobanks.

Shared genetic risk factors and causal association between psoriasis and coronary artery disease.

Psoriasis and coronary artery disease (CAD) are related comorbidities that are well established, but whether a genetic basis underlies this is not well studied. We apply trans-disease meta-analysis to 11,024 psoriasis and 60,801 CAD cases, along with their associated controls, identifying one opposing and three shared genetic loci, which are confirmed through colocalization analysis. Combining results from Bayesian credible interval analysis with independent information from genomic, epigenomic, and spatial chromatin organization, we prioritize genes (including IFIH1 and IL23A) that have implications for common molecular mechanisms involved in psoriasis and CAD inflammatory signaling. Chronic systemic inflammation has been associated with CAD and myocardial infarction, and Mendelian randomization analysis finds that CAD as an exposure can have a significant causal effect on psoriasis (OR = 1.11; p = 3×10-6) following adjustment for BMI and waist-hip ratio. Together, these findings suggest that systemic inflammation which causes CAD can increase the risk of psoriasis.

Roles Played by Stress-Induced Pathways in Driving Ethnic Heterogeneity for Inflammatory Skin Diseases.

Immune-mediated skin conditions (IMSCs) are a diverse group of autoimmune diseases associated with significant disease burden. Atopic dermatitis and psoriasis are among the most common IMSCs in the United States and have disproportionate impact on racial and ethnic minorities. African American patients are more likely to develop atopic dermatitis compared to their European American counterparts; and despite lower prevalence of psoriasis among this group, African American patients can suffer from more extensive disease involvement, significant post-inflammatory changes, and a decreased quality of life. While recent studies have been focused on understanding the heterogeneity underlying disease mechanisms and genetic factors at play, little emphasis has been put on the effect of psychosocial or psychological stress on immune pathways, and how these factors contribute to differences in clinical severity, prevalence, and treatment response across ethnic groups. In this review, we explore the heterogeneity of atopic dermatitis and psoriasis between African American and European American patients by summarizing epidemiological studies, addressing potential molecular and environmental factors, with a focus on the intersection between stress and inflammatory pathways.

Prediction of human-virus protein-protein interactions through a sequence embedding-based machine learning method.

The identification of human-virus protein-protein interactions (PPIs) is an essential and challenging research topic, potentially providing a mechanistic understanding of viral infection. Given that the experimental determination of human-virus PPIs is time-consuming and labor-intensive, computational methods are playing an important role in providing testable hypotheses, complementing the determination of large-scale interactome between species. In this work, we applied an unsupervised sequence embedding technique (doc2vec) to represent protein sequences as rich feature vectors of low dimensionality. Training a Random Forest (RF) classifier through a training dataset that covers known PPIs between human and all viruses, we obtained excellent predictive accuracy outperforming various combinations of machine learning algorithms and commonly-used sequence encoding schemes. Rigorous comparison with three existing human-virus PPI prediction methods, our proposed computational framework further provided very competitive and promising performance, suggesting that the doc2vec encoding scheme effectively captures context information of protein sequences, pertaining to corresponding protein-protein interactions. Our approach is freely accessible through our web server as part of our host-pathogen PPI prediction platform (http://zzdlab.com/InterSPPI/). Taken together, we hope the current work not only contributes a useful predictor to accelerate the exploration of human-virus PPIs, but also provides some meaningful insights into human-virus relationships.