Capturing the finer points of gene expression in psoriasis: beaming in on the CCL19/CCR7 axis

Single-cell sequencing analysis and multiple machine-learning models revealed the cellular crosstalk of dendritic cells and identified FABP5 and KLRB1 as novel biomarkers for psoriasis

Background

Psoriasis is an immune-mediated disorder influenced by environmental factors on a genetic basis. Despite advancements, challenges persist, including the diminishing efficacy of biologics and small-molecule targeted agents, alongside managing recurrence and psoriasis-related comorbidities. Unraveling the underlying pathogenesis and identifying valuable biomarkers remain pivotal for diagnosing and treating psoriasis.

Methods

We employed a series of bioinformatics (including single-cell sequencing data analysis and machine learning techniques) and statistical methods to integrate and analyze multi-level data. We observed the cellular changes in psoriatic skin tissues, screened the key genes Fatty acid binding protein 5 (FABP5) and The killer cell lectin-like receptor B1 (KLRB1), evaluated the efficacy of six widely prescribed drugs on psoriasis treatment in modulating the dendritic cell-associated pathway, and assessed their overall efficacy. Finally, RT-qPCR, immunohistochemistry, and immunofluorescence assays were used to validate.

Results

The regulatory influence of dendritic cells (DCs) on T cells through the CD70/CD27 signaling pathway may emerge as a significant facet of the inflammatory response in psoriasis. Notably, FABP5 and KLRB1 exhibited up-regulation and co-localization in psoriatic skin tissues and M5-induced HaCaT cells, serving as potential biomarkers influencing psoriasis development.

Conclusion

Our study analyzed the impact of DC-T cell crosstalk in psoriasis, elucidated the characterization of two biomarkers, FABP5 and KLRB1, in psoriasis, and highlighted the promise and value of tofacitinib in psoriasis therapy targeting DCs.

Altered T cell subpopulations and serum anti-TYRP2 and tyrosinase antibodies in the acute and chronic phase of alopecia areata in the C3H/HeJ mouse model

BACKGROUND

C3H/HeJ mouse models progress gradually in hair loss from acute to chronic phase and reflect the symptoms of patients with alopecia areata (AA). However, the underlying pathological characteristics alteration associated with disease progression and autoantigens remain unclear.

OBJECTIVE

We aimed at elucidating the pathological differences between acute and chronic-AA in the C3H/HeJ mouse model.

METHODS

We analyzed populations of PBMCs, skin-draining lymph node (SDLN) cells, and cutaneous cells of AA mice using flow cytometry. The cytokine and chemokine expressions in the serum and skin were determined using multiplex assay and qPCR. The antibody serum levels were determined using ELISA and the antigen-specific T cells were detected using the MHC class I tetramer.

RESULTS

The CD8⁺NKG2D⁺ T and CD8⁺ T_EM cell percentage in the chronic-AA SDLNs or among the unaffected and acute-AA mice PBMCs increased. The Th1 and CD4⁺ T_EM cell percentage in the SDLNs and among PBMCs increased in the unaffected and AA mice. The percentage of CD8⁺ T_EM/T_RM cells and MHC class I expression increased in the lesions of acute-AA or the non-lesions and lesions of chronic-AA. The Th1 cells, dendritic cell-related cytokines, CD11c⁺ cells and MHC class II expression increased in the skin of AA mice. The antibody levels and TYRP2 and tyrosinase-specific CD8⁺ T cell percentages were upregulated in AA mice.

CONCLUSION

These results suggest that the CD8⁺ and CD4⁺ T cell subpopulations, cytokine and chemokine expressions differ between the disease phases. Moreover, TYRP2 and tyrosinase are potential autoreactive targets in the AA mouse model.

Review-Current Concepts in Inflammatory Skin Diseases Evolved by Transcriptome Analysis: In-Depth Analysis of Atopic Dermatitis and Psoriasis

During the last decades, high-throughput assessment of gene expression in patient tissues using microarray technology or RNA-Seq took center stage in clinical research. Insights into the diversity and frequency of transcripts in healthy and diseased conditions provide valuable information on the cellular status in the respective tissues. Growing with the technique, the bioinformatic analysis toolkit reveals biologically relevant pathways which assist in understanding basic pathophysiological mechanisms. Conventional classification systems of inflammatory skin diseases rely on descriptive assessments by pathologists. In contrast to this, molecular profiling may uncover previously unknown disease classifying features. Thereby, treatments and prognostics of patients may be improved. Furthermore, disease models in basic research in comparison to the human disease can be directly validated. The aim of this article is not only to provide the reader with information on the opportunities of these techniques, but to outline potential pitfalls and technical limitations as well. Major published findings are briefly discussed to provide a broad overview on the current findings in transcriptomics in inflammatory skin diseases.

Prediction of crucial epigenetically‑associated, differentially expressed genes by integrated bioinformatics analysis and the identification of S100A9 as a novel biomarker in psoriasis

Psoriasis is one of the most common immune-mediated inflammatory diseases of the skin. The identification of the pivotal molecular mechanisms responsible for the disease pathogenesis may lead to the development of novel therapeutic options. The present study aimed to identify pivotal differentially expressed genes (DEGs) and methylated DEGs in psoriasis. The raw data from gene microarrays were obtained from the Gene Expression Omnibus database. The data were processed using packages in Bioconductor. In total, 352 upregulated and 137 downregulated DEGs were identified. The upregulated DEGs were primarily enriched in the 'innate immune defense' response and the 'cell cycle'. The down-regulated DEGs were primarily enriched in 'cell adhesion' and 'tight junction pathways'. A total of 95 methylated DEGs were identified, which were significantly enriched in the 'interleukin (IL)-17 signaling pathway' and the 'response to interferon'. Based on a comprehensive evaluation of all algorithms in cytoHubba, the key epigenetic-associated hub genes (S100A9, SELL, FCGR3B, MMP9, S100A7, IL7R, IRF7, CCR7, IFI44, CXCL1 and LCN2) were screened out. In order to further validate these genes, the present study constructed a model of imiquimod (IMQ)-induced psoriasiform dermatitis using mice. The levels of these hub genes were increased in the IMQ group. The knockdown of methylation-regulating enzyme ten-eleven translocation (TET) 2 expression in mice attenuated the expression levels of S100A9, SELL, IL7R, MMP9, CXCL1 and LCN2. Furthermore, the hydroxymethylated level of S100A9 was highly expressed in the IMQ group and was significantly decreased by TET2 deficiency in mice. On the whole, using an integrative system bioinformatics approach, the present study identified a series of characteristic enrichment pathways and key genes that may serve as potential biomarkers in psoriasis.