The integration of large-scale public data and network analysis uncovers molecular characteristics of psoriasis

Antonio Federico, Alisa Pavel, Lena Möbus, David McKean, Giusy del Giudice, Vittorio Fortino, Hanna Niehues, Joe Rastrick, Kilian Eyerich, Stefanie Eyerich, Ellen van den Bogaard, Catherine Smith, Stephan Weidinger, Emanuele de Rinaldis, Dario Greco

    Research output: Contribution to journalArticleScientificpeer-review

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    Abstract

    In recent years, a growing interest in the characterization of the molecular basis of psoriasis has been observed. However, despite the availability of a large amount of molecular data, many pathogenic mechanisms of psoriasis are still poorly understood. In this study, we performed an integrated analysis of 23 public transcriptomic datasets encompassing both lesional and uninvolved skin samples from psoriasis patients. We defined comprehensive gene co-expression network models of psoriatic lesions and uninvolved skin. Moreover, we curated and exploited a wide range of functional information from multiple public sources in order to systematically annotate the inferred networks. The integrated analysis of transcriptomics data and co-expression networks highlighted genes that are frequently dysregulated and show aberrant patterns of connectivity in the psoriatic lesion compared with the unaffected skin. Our approach allowed us to also identify plausible, previously unknown, actors in the expression of the psoriasis phenotype. Finally, we characterized communities of co-expressed genes associated with relevant molecular functions and expression signatures of specific immune cell types associated with the psoriasis lesion. Overall, integrating experimental driven results with curated functional information from public repositories represents an efficient approach to empower knowledge generation about psoriasis and may be applicable to other complex diseases.

    Original languageEnglish
    Article number62
    JournalHuman Genomics
    Volume16
    Issue number1
    DOIs
    Publication statusPublished - Dec 2022
    Publication typeA1 Journal article-refereed

    Keywords

    • Biomarkers
    • Druggability
    • Network analysis
    • Psoriasis
    • Public data
    • Transcriptomics

    Publication forum classification

    • Publication forum level 1

    ASJC Scopus subject areas

    • Molecular Medicine
    • Molecular Biology
    • Genetics
    • Drug Discovery

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