Dataset Information

Pathogenesis of Alopecia Areata Based on Bioinformatics Analysis.

ABSTRACT: Background:Alopecia areata (AA) describes a sudden localized patchy alopecia. The cause of AA is not completely clear and its incidence may be related to genetic, autoimmune, and environmental factors. Aim:To explore the possible mechanisms of AA and to provide a basis for the early diagnosis and treatment of AA. Methods:Gene microarray data from 122 scalp skin biopsy tissue samples from patients with AA or healthy controls from the Gene-Cloud of Biotechnology Information database were analyzed using bioinformatics analysis methods. Molecular network analysis of the differentially expressed genes (DEGs) was conducted by Cytocluster using the Molecular Complex Detection (MCODE) algorithm. Results:The gene expression profile of skin lesions from patients with AA was significantly altered, with 111 DEGs found in the skin lesions of AA, compared with that of the healthy skin. The DEGs were mainly related to biological processes such as the development of the epidermis and inflammatory reaction. The protein-protein interaction network analysis of DEGs revealed bone morphogenetic protein 2 (BMP2) as a core protein interaction network. BMP2 acted not only via the inflammatory response but also via the signaling pathways in epithelial cell development and epidermal cell differentiation to affect the epidermal development. MCODE analysis further showed that keratins (KRTs) and keratin-associated proteins (KRTAPs) can affect the epidermal development via the epidermal development pathway. Conclusions:The abnormal development of the epidermis and inflammatory reactions in skin tissue play important roles in the pathogenesis of AA and are closely related to BMP2, KRTs, and KRTAPs genes. Limitations:Our study was limited by experimental verification.

SUBMITTER: Zhang Z

PROVIDER: S-EPMC6340232 | biostudies-literature | 2019 Jan-Feb

REPOSITORIES: biostudies-literature

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Pathogenesis of Alopecia Areata Based on Bioinformatics Analysis.

Zhang Zhigang Z Wang Xiaoli X Zhang Rongqiang R

Indian journal of dermatology 20190101 1

<h4>Background</h4>Alopecia areata (AA) describes a sudden localized patchy alopecia. The cause of AA is not completely clear and its incidence may be related to genetic, autoimmune, and environmental factors.<h4>Aim</h4>To explore the possible mechanisms of AA and to provide a basis for the early diagnosis and treatment of AA.<h4>Methods</h4>Gene microarray data from 122 scalp skin biopsy tissue samples from patients with AA or healthy controls from the Gene-Cloud of Biotechnology Information d ...[more]

PMID: 30745627

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Project description:One of the most common human autoimmune diseases, alopecia areata (AA), is characterized by sudden, often persisting and psychologically devastating hair loss. Animal models have helped greatly to elucidate critical cellular and molecular immune pathways in AA. The two most prominent ones are inbred C3H/HeJ mice which develop an AA-like hair phenotype spontaneously or after experimental induction, and healthy human scalp skin xenotransplanted onto SCID mice, in which a phenocopy of human AA is induced by injecting IL-2-stimulated PBMCs enriched for CD56+/NKG2D+ cells intradermally. The current review critically examines the pros and cons of the available AA animal models and how they have shaped our understanding of AA pathobiology, and the development of new therapeutic strategies. AA is thought to arise when the hair follicle's (HF) natural immune privilege (IP) collapses, inducing ectopic MHC class I expression in the HF epithelium and autoantigen presentation to autoreactive CD8+ T cells. In common with other autoimmune diseases, upregulation of IFN-γ and IL-15 is critically implicated in AA pathogenesis, as are NKG2D and its ligands, MICA, and ULBP3. The C3H/HeJ mouse model was used to identify key immune cell and molecular principles in murine AA, and proof-of-principle that Janus kinase (JAK) inhibitors are suitable agents for AA management in vivo, since both IFN-γ and IL-15 signal via the JAK pathway. Instead, the humanized mouse model of AA has been used to demonstrate the previously hypothesized key role of CD8+ T cells and NKG2D+ cells in AA pathogenesis and to discover human-specific pharmacologic targets like the potassium channel Kv1.3, and to show that the PDE4 inhibitor, apremilast, inhibits AA development in human skin. As such, AA provides a model disease, in which to contemplate general challenges, opportunities, and limitations one faces when selecting appropriate animal models in preclinical research for human autoimmune diseases.

Project description:IntroductionIndividuals with alopecia areata (AA) may experience significant impacts on their health-related quality of life. The novel Alopecia Areata Patient Priority Outcomes (AAPPO) questionnaire has been developed to assess hair loss signs, emotional symptoms, and activity limitations associated with AA. The objective of this study was to evaluate psychometric properties and establish scoring of the AAPPO in adults and adolescents with AA.MethodsScoring and measurement properties of the AAPPO were examined using baseline and 2-week follow-up data from a prospective, noninterventional, web-based study of 121 patients with AA (85 adults aged ≥ 18 years, 36 adolescents aged 12-17 years) with Severity of Alopecia Tool (SALT) ≥ 25% scalp hair loss.ResultsExploratory and confirmatory factor analysis supported four single Hair Loss (HL) items, an Emotional Symptoms domain (ES; 4 items), and an Activity Limitations domain (AL; 3 items). Among all patients, the multi-item ES and AL domains had strong internal consistency (α ≥ 0.87); all HL items and domain scores had strong test-retest reliability (weighted kappa or intraclass correlation coefficients ≥ 0.78). All HL item scores demonstrated strong construct validity (r ≥ 0.52) compared with the patient-reported Alopecia Areata Symptom and Impact Scale (AASIS) hair loss subscale score; ES and AL domain scores exhibited strong construct validity (r ≥ 0.66) compared with the SF-36 Mental Component Summary (MCS) score. Using SALT scores, HL mean item scores were better (lower) in the 25-49% SALT subgroup versus those with highest SALT scores (76-100%); however, ES mean domain scores were better in the SALT 76-100% subgroup in the same comparison (p < 0.0001). Using AASIS and MCS score-created subgroups, ES and AL mean domain scores demonstrated hypothesized differences across subgroups (all p values < 0.0001).ConclusionThe AAPPO questionnaire is a reliable, valid disease-specific measure of hair loss severity and impact in individuals with AA.

Dataset Information

Pathogenesis of Alopecia Areata Based on Bioinformatics Analysis.

Publications

Pathogenesis of Alopecia Areata Based on Bioinformatics Analysis.

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