Dataset Information

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.

SUBMITTER: Federico A

PROVIDER: S-EPMC10792836 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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Publications

Integrative network analysis suggests prioritised drugs for atopic dermatitis.

Federico Antonio A Möbus Lena L Al-Abdulraheem Zeyad Z Pavel Alisa A Fortino Vittorio V Del Giudice Giusy G Alenius Harri H Fyhrquist Nanna N Greco Dario D

Journal of translational medicine 20240116 1

<h4>Background</h4>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 a ...[more]

PMID: 38229087

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Project description:BackgroundAtopic dermatitis (AD) is an allergic inflammatory skin disease caused by aberrant and over-reactive immune response. Although circadian rhythm disruption is implicated in multiple immunoinflammatory conditions, including AD, the mechanisms at the molecular level underlying AD and circadian rhythms remain elusive.MethodsBulk and single-cell RNA-sequencing data of AD patients were acquired from the Gene Expression Omnibus, including GSE121212, GSE120721, and GSE153760 datasets. A single-sample gene set enrichment analysis was performed to estimate circadian rhythm gene expression levels. A differential expression analysis was utilized to identify the key candidate genes in AD. CIBERSORT was used to quantify the proportions of immune cells, and the R package "Seurat" was utilized to investigate single-cell RNA-sequencing data.ResultsCircadian rhythm gene expression levels were lower in AD skin samples than in normal skin samples. Dendritic cells were significantly upregulated and negatively correlated with circadian rhythm gene expression levels in AD patients. Compared with circadian rhythm-related genes in the control samples, ARNTL2, NOCT, and RORC were differentially expressed in AD; ARNTL2 and NOCT were significantly upregulated, whereas RORC was significantly downregulated in AD. ARNTL2, NOCT, and RORC also showed robust abilities to diagnose AD. We validated that the abundance of the dendritic cell was positively correlated with the ARNTL2 and NOCT expression levels using bulk RNA-sequencing data of the GSE121212 and single-cell RNA-sequencing data of the GSE153760. Moreover, the functional enrichment analysis showed that the IL-17 and NF-κB signaling pathways, Th1 and Th2 cell differentiations, and primary immunodeficiency, were enriched in AD patients.ConclusionThe findings of this study suggested that the circadian rhythm is involved in the progression of AD, and RNTL2, NOCT, and RORC as well as dendritic cells are differentially expressed in AD. These findings could be used to introduce diagnostic and chronotherapeutic modalities for AD.

Project description:BackgroundAtopic dermatitis (AD) is a chronic inflammatory skin condition with a highly variable clinical phenotype.ObjectiveThis study aimed to identify historical and clinical features and biomarkers associated with AD severity.MethodsA US registry of extensively phenotyped AD participants (aged 0.73-80 years) were enrolled at 9 academic centers. Information on family and personal medical history, examination, skin swabs (culture), and serum biomarkers was collected to evaluate their association with AD severity.ResultsParticipants with AD (N = 2862) whose disease was categorized as mild (11.6%), moderate (58.0%), or severe (30.4%) based on Rajka-Langeland scoring were enrolled. The trend test, when adjusting for gender, race, and age, demonstrated that severity was strongly (P ≤ .04) associated with a personal/family history of allergic disorders, history of alopecia, exposure to passive smoke, ocular herpes infection, skin bacterial and viral infections, and history of arrhythmia. Features observed more frequently (P ≤ .002), as a function of severity, included skin infections (impetigo, human papillomavirus, and molluscum contagiosum virus), Staphylococcus aureus colonization, excoriations, hyperlinear palms, ichthyosis, blepharitis, conjunctivitis, ectropion, and wheezing. Serum IgE, allergen and food (≤6 years) Phadiatop, and eosinophilia were strongly linked to severity (P < .001).ConclusionsIn a diverse US AD population, severity was associated with a history of atopic disorders, skin and extracutaneous bacterial and viral infections (by history and physical examination), higher IgE, eosinophilia and allergen sensitization, atopic skin manifestations (ie, excoriation, hyperlinear palms, and ichthyosis), and atopic ocular features (ie, blepharitis, conjunctivitis, and ectropion) as well as asthma findings (ie, wheezing). Data from our prospective registry significantly advance our understanding of AD phenotypes and endotypes, which is critical to achieve optimal management.

Dataset Information

Integrative network analysis suggests prioritised drugs for atopic dermatitis.

Background

Methods

Results

Conclusions

Publications

Integrative network analysis suggests prioritised drugs for atopic dermatitis.

Similar Datasets

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