The Molecular Revolution in Cutaneous Biology: The Era of Global Transcriptional Analysis

Clinical and molecular diagnosis of genodermatoses: Review and perspectives

Genodermatoses are a complex and heterogeneous group of genetic skin disorders characterized by variable expression and clinical and genetic heterogeneity, rendering their diagnosis challenging. DNA-based techniques, like whole-exome sequencing, can establish a diagnosis in 50% of cases. RNA-sequencing is emerging as an attractive tool that can obtain information regarding gene expression while integrating functional genomic data with regard to the interpretation of variants. This increases the diagnostic rate by an additional 10-15%. In the present review, we detail the clinical steps involved in the diagnosis of genodermatoses, as well as the current DNA-based technologies available to clinicians. Herein, the intention is to facilitate a better understanding of the possibilities and limitations of these diagnostic technologies. In addition, this review could guide dermatologists through new emerging techniques, such as RNA-sequencing and its applications to familiarizing them with future techniques. Currently, this multi-omics approach is likely the best strategy designed to promote the diagnosis of patients with genodermatoses and discover new skin disease genes that could result in novel targeted therapies.

Clinical trait-connected network analysis reveals transcriptional markers of active psoriasis treatment with Liangxue-Jiedu decoction

ETHNOPHARMACOLOGICAL RELEVANCE

Psoriasis is a complex recurrent inflammatory skin disease with different pathological changes in different stages. Psoriasis in its active stage, which is comparable to the blood-heat type in traditional Chinese medicine (TCM), has been treated by Liangxue Jiedu Decoction (LJD) in TCM for decades, with proven efficacy. According to TCM theories, LJD has the function of removing heat and pathogenic factors from the blood.

AIM OF THE STUDY

We aimed to investigate the molecular features associated with the active stage psoriasis and identify genes responding to LJD treatment accompanied by lesion remission.

MATERIALS AND METHODS

Healthy volunteers and psoriasis patients who met specific diagnostic criteria were recruited. Twenty-six transcriptomes were profiled from the peripheral blood mononuclear cells (PBMCs) of 10 psoriasis patients (pre- and post-treatment) and 6 healthy volunteers. RNA sequencing data were analyzed using an integrated approach combining differential gene expression analysis (DGEA) and weighted gene co-expression network analysis (WGCNA), by which gene expression was linked to multiple clinical traits, including psoriasis area and severity index (PASI), as well as the improvement rate of skin lesions (ΔPASI). The actions of LJD were then verified using an in vitro cell assay coupled to flow cytometric analysis and RT-PCR.

RESULTS

We identified four network modules with statistical significance (P < 0.05), two of which connected to the PASI score, while the other two connected to 8-week treatment and ΔPASI, respectively. In psoriasis patients, activated inflammatory pathways and inhibited G-protein signaling genes (GTPase IMAP family member and G protein-coupled receptor) co-occurred, with high expression of CD83 and CD69, and low expression of CD160 and CD180, compared with the health. Accompanying LJD treatment and lesion remission, the expression of CD69 and cell cycle-related genes, including CCNA2, CCNB2, CDK1, and TOP2A, was down-regulated. The inhibitory role of LJD on CD69 expression was confirmed by the decline of activating naïve CD4⁺ T lymphocytes.

CONCLUSION

Our study suggests that active psoriasis is characterized by unbalanced immune status with dendrite cell and lymphocyte-associated inflammatory activation as well as NK cell- and B cell-associated defense response aberrance. LJD played an inhibitory role in T cell activation, a process located downstream pathological cascade of psoriasis.

Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations

Machine learning (ML) has the potential to improve the dermatologist's practice from diagnosis to personalized treatment. Recent advancements in access to large datasets (e.g., electronic medical records, image databases, omics), faster computing, and cheaper data storage have encouraged the development of ML algorithms with human-like intelligence in dermatology. This article is an overview of the basics of ML, current applications of ML, and potential limitations and considerations for further development of ML. We have identified five current areas of applications for ML in dermatology: (1) disease classification using clinical images; (2) disease classification using dermatopathology images; (3) assessment of skin diseases using mobile applications and personal monitoring devices; (4) facilitating large-scale epidemiology research; and (5) precision medicine. The purpose of this review is to provide a guide for dermatologists to help demystify the fundamentals of ML and its wide range of applications in order to better evaluate its potential opportunities and challenges.

A Framework for Multi-Omic Prediction of Treatment Response to Biologic Therapy for Psoriasis

Biologic therapies have shown high efficacy in psoriasis, but individual response varies and is poorly understood. To inform biomarker discovery in the Psoriasis Stratification to Optimise Relevant Therapy (i.e., PSORT) study, we evaluated a comprehensive array of omics platforms across three time points and multiple tissues in a pilot investigation of 10 patients with severe psoriasis, treated with the tumor necrosis factor (TNF) inhibitor, etanercept. We used RNA sequencing to analyze mRNA and small RNA transcriptome in blood, lesional and nonlesional skin, and the SOMAscan platform to investigate the serum proteome. Using an integrative systems biology approach, we identified signals of treatment response in genes and pathways associated with TNF signaling, psoriasis pathology, and the major histocompatibility complex region. We found association between clinical response and TNF-regulated genes in blood and skin. Using a combination of differential expression testing, upstream regulator analysis, clustering techniques, and predictive modeling, we show that baseline samples are indicative of patient response to biologic therapies, including signals in blood, which have traditionally been considered unreliable for inference in dermatology. In conclusion, our pilot study provides both an analytical framework and empirical basis to estimate power for larger studies, specifically the ongoing PSORT study, which we show as powered for biomarker discovery and patient stratification.

Research Techniques Made Simple: Using Genome-Wide Association Studies to Understand Complex Cutaneous Disorders

Complex cutaneous disorders result from the combined effect of many different genes and environmental factors, with individual genetic variants often having only a modest effect on disease risk. The ability to examine large numbers of samples is required for correlating genetic variants with diseases/traits. Technological advances in high-throughput genotyping, along with mapping of the human genome and its associated inter-individual variation, have allowed genetic variants to be analyzed at high density in large case-control cohorts for many diseases, including several major skin diseases. These genome-wide association studies focus on showing differences in the frequencies of variants between case and control groups, rather than co-transmission of a variant and disease through a family, as is done in linkage studies. In this review, we provide overall guidance for genome-wide association study analysis and interpreting the results. Additionally, we discuss challenges and future directions for genome-wide association studies, focusing on translation of findings to provide biological and clinical implications for dermatology.

Research Techniques Made Simple: Bioinformatics for Genome-Scale Biology

High-throughput biology presents unique opportunities and challenges for dermatological research. Drawing on a small handful of exemplary studies, we review some of the major lessons of these new technologies. We caution against several common errors and introduce helpful statistical concepts that may be unfamiliar to researchers without experience in bioinformatics. We recommend specific software tools that can aid dermatologists at varying levels of computational literacy, including platforms with command line and graphical user interfaces. The future of dermatology lies in integrative research, in which clinicians, laboratory scientists, and data analysts come together to plan, execute, and publish their work in open forums that promote critical discussion and reproducibility. In this article, we offer guidelines that we hope will steer researchers toward best practices for this new and dynamic era of data intensive dermatology.