Integrative network analysis suggests prioritised drugs for atopic dermatitis

Abstract

Background: Atopic dermatitis (AD) is a prevalent chronic inflammatory skin disease whose pathophysiology involves the interplay between genetic and environmental factors, ultimately leading to dysfunction of the epidermis. While several treatments are effective in symptom management, many existing therapies offer only temporary relief and often come with side effects. For this reason, the formulation of an effective therapeutic plan is challenging and there is a need for more effective and targeted treatments that address the root causes of the condition. Here, we hypothesise that modelling the complexity of the molecular buildup of the atopic dermatitis can be a concrete means to drive drug discovery.

Methods: We preprocessed, harmonised and integrated publicly available transcriptomics datasets of lesional and non-lesional skin from AD patients. We inferred co-expression network models of both AD lesional and non-lesional skin and exploited their interactional properties by integrating them with a priori knowledge in order to extrapolate a robust AD disease module. Pharmacophore-based virtual screening was then utilised to build a tailored library of compounds potentially active for AD.

Results: In this study, we identified a core disease module for AD, pinpointing known and unknown molecular determinants underlying the skin lesions. We identified skin- and immune-cell type signatures expressed by the disease module, and characterised the impaired cellular functions underlying the complex phenotype of atopic dermatitis. Therefore, by investigating the connectivity of genes belonging to the AD module, we prioritised novel putative biomarkers of the disease. Finally, we defined a tailored compound library by characterising the therapeutic potential of drugs targeting genes within the disease module to facilitate and tailor future drug discovery efforts towards novel pharmacological strategies for AD.

Conclusions: Overall, our study reveals a core disease module providing unprecedented information about genetic, transcriptional and pharmacological relationships that foster drug discovery in atopic dermatitis.

Keywords: Atopic dermatitis; Biomarkers; Disease module; Drug discovery; Network analysis.

Fig. 1

Overview of the analytical pipeline developed in this study. A Integration of publicly available transcriptomics datasets of lesional skin and non-lesional counterparts from atopic dermatitis patients. The data underwent meta-analysis and were utilised to infer co-expression network models for the lesional and non-lesional skin. Therefore, a gene rank was computed by exploiting information from data driven genes and genes with a priori knowledge of association with atopic dermatitis. B Identification of the atopic dermatitis disease module. C Characterisation of the disease module based on skin and immune cell transcriptional signatures, drug-target associations and intra-module connectivity. Druggability evaluation of the disease module, extrapolation of pharmacologically active pharmacophores and identification of novel compounds through virtual screening

Fig. 2

Pathways enriched by genes belonging to the AD disease module

Fig. 3

Results of the enrichment analysis of skin and immune cell type-specific genes within the AD disease module

Fig. 4

Selection process to retrieve AD-relevant edges

Fig. 5

Prioritisation of drug targets