Elucidating mechanistic insights into drug action for atopic dermatitis: a systems biology approach

Combination of UHPLC-MS/MS with context-specific network and cheminformatic approaches for identifying bioactivities and active components of propolis

Discovering new bioactivities and identifying active compounds of food materials are major fields of study in food science. However, the process commonly requires extensive experiments and can be technically challenging. In the current study, we employed network biology and cheminformatic approaches to predict new target diseases, active components, and related molecular mechanisms of propolis. Applying UHPLC-MS/MS analysis results of propolis to Context-Oriented Directed Associations (CODA) and Combination-Oriented Natural Product Database with Unified Terminology (COCONUT) systems indicated atopic dermatitis as a novel target disease. Experimental validation using cell- and human tissue-based models confirmed the therapeutic potential of propolis against atopic dermatitis. Moreover, we were able to find the major contributing compounds as well as their combinatorial effects responsible for the bioactivity of propolis. The CODA/COCONUT system also provided compound-associated genes explaining the underlying molecular mechanism of propolis. These results highlight the potential use of big data-driven network biological approaches to aid in analyzing the impact of food constituents at a systematic level.

Boolean model for melanogenesis

Melanogenesis is a highly regulated process through which the pigment melanin is produced in skin cells. Irregularities in the molecular events that govern the process of skin pigmentation can cause disorders like vitiligo. In order to understand the biology of disease progression, it is important to have an in depth understanding of intracellular events. Mathematical models provide an integrated view of intracellular signalling. There are very few models to date that incorporate intracellular processes relevant to melanogenesis and only one to our knowledge that simulates the dynamics of response to varying levels of input. Here, we report the formulation of the largest Boolean model (265 nodes) for melanogenesis to date. The model was built on the basis of a detailed interaction network graph published by Raghunath et al. Through additional manual curation of the reported interactions, we converted the graph into a set of Boolean rules, following the procedure of the first Boolean model (62 nodes) for melanogenesis published by Lee et al. Simulations show that the predicted response to varying UV levels for most of the nodes is similar to the predictions of the existing model. The greater complexity allows investigation of the sensitivity of melanin to additional nodes. We carried out perturbation analysis of the network through node deletion and constitutive activation to identify sensitivity of outcomes, and compared the nodes identified as sensitive to previous reports.

Atopic eczema: burden of disease and individual suffering - results from a large EU study in adults

BACKGROUND

Atopic eczema (AE, atopic dermatitis) is one of the most common non-communicable inflammatory skin diseases affecting 1-5% of the adult population in Europe with marked impairment in quality of life. In spite of great progress in understanding the pathophysiology of disturbed skin barrier and immune deviation, AE still represents a problem in daily clinical practice. Furthermore, the true impact of AE on individual suffering is often not recognized.

OBJECTIVES

With a large European study, we wanted to provide insights into the actual suffering and individual burden of disease in adult patients with AE.

METHODS

A total of 1189 adult patients (18-87 years, 56% female) with moderate to severe AE were recruited in nine European countries by dermatologists or allergists together with the help of patient organizations. A computer-assisted telephone interview was performed by experienced interviewers between October 2017 and March 2018. The following instruments were used to assess severity or measure quality of life: Patient-Oriented Eczema Measure (POEM), Dermatology Life Quality Index (DLQI), Hospital Anxiety and Depression Scale (HADS-D) and a newly developed Atopic Eczema Score of Emotional Consequences (AESEC). Patients were also asked to self-assess the severity of their disease.

RESULTS

Despite current treatment, 45% of participants still had actual moderate to very severe AE in POEM. Due to their skin disease, 57% missed at least 1 day of work in the preceding year. DLQI showed moderate to extremely large impairment in 55%. According to HADS-D, 10% scored on or above the threshold of eight points with signs of depressive symptoms. Assessed with AESEC, 57% were emotionally burdened with feelings such as 'trying to hide the eczema', 'feeling guilty about eczema', having 'problems with intimacy' and more. Of persons actually suffering from severe AE, 88% stated that their AE at least partly compromised their ability to face life.

CONCLUSIONS

This real-life study shows that adults with a moderate to severe form of AE are suffering more than what would be deemed acceptable. There is a need for increased awareness of this problem among healthcare professionals, policymakers and the general public to support research in the development of new and more effective treatments and provide access to better and affordable health care for affected patients.

Human and computational models of atopic dermatitis: A review and perspectives by an expert panel of the International Eczema Council

Atopic dermatitis (AD) is a prevalent disease worldwide and is associated with systemic comorbidities representing a significant burden on patients, their families, and society. Therapeutic options for AD remain limited, in part because of a lack of well-characterized animal models. There has been increasing interest in developing experimental approaches to study the pathogenesis of human AD in vivo, in vitro, and in silico to better define pathophysiologic mechanisms and identify novel therapeutic targets and biomarkers that predict therapeutic response. This review critically appraises a range of models, including genetic mutations relevant to AD, experimental challenge of human skin in vivo, tissue culture models, integration of “omics” data sets, and development of predictive computational models. Although no one individual model recapitulates the complex AD pathophysiology, our review highlights insights gained into key elements of cutaneous biology, molecular pathways, and therapeutic target identification through each approach. Recent developments in computational analysis, including application of machine learning and a systems approach to data integration and predictive modeling, highlight the applicability of these methods to AD subclassification (endotyping), therapy development, and precision medicine. Such predictive modeling will highlight knowledge gaps, further inform refinement of biological models, and support new experimental and systems approaches to AD. (J Allergy Clin Immunol 2019;143:36–45.)