Interleukin 10 promotor gene polymorphism in the pathogenesis of psoriasis

Expression of B lymphocyte-induced maturation protein 1 (Blimp-1) in keratinocyte and cytokine signalling drives human Th17 response in psoriasis

Transcriptional factor B lymphocyte-induced maturation protein 1 (Blimp-1) is pivotally implicated in T helper 17 (Th17) cell differentiation. This study investigated expression of the Blimp-1 protein, positive regulatory domain 1 (PRDM1), and cytokine genes in psoriasis (PsO). Affected (AS-PsO) and non-affected skin (nAS-PsO) samples were used to assess gene and protein expressions by reverse transcription-quantitative PCR (RT-qPCR), and immunostaining and confocal microscopy, respectively; the normalised public transcriptomic data permitted differential gene expression analyses. On RT-qPCR, PRDM1 and IL17A transcripts showed higher expression in AS-PsO than in nAS-PsO (n = 34) (p < 0.001; p < 0.0001, respectively). Confocal microscopy showed Blimp-1 protein expression in epidermal layer keratinocytes in AS-PsO, but not in nAS-PsO. Bioinformatic analysis of the transcriptomic dataset GSE13355 corroborated the increased PRDM1, signal transducer and activator of transcription 3 (STAT3), IL12B, TNF, IL17A, IL6, IL1B, IL22, and IL10 gene expression in AS-PsO, when compared to normal skin and nAS-PsO (p < 0.001). PRDM1 expression correlated positively (p < 0.0001) with that of IL17A (r = 0.7), IL1B (r = 0.67), IL12B (r = 0.6), IL6 (r = 0.59), IL22 (r = 0.53), IL23A (r = 0.47), IL21 (r = 0.47), IL27 (r = 0.34), IL23R (r = 0.32), S100 calcium binding protein A9 (r = 0.63), and lipocalin 2 (r = 0.50), and negatively with that of TGFB1 (r = - 0.28) and RORC (r = - 0.60). Blimp-1 may be critical in the pathogenesis of PsO dysregulation involving the Th17 inflammatory pathway. This knowledge may accelerate the development of new treatments.

GEE-TGDR: A Longitudinal Feature Selection Algorithm and Its Application to lncRNA Expression Profiles for Psoriasis Patients Treated with Immune Therapies

With the fast evolution of high-throughput technology, longitudinal gene expression experiments have become affordable and increasingly common in biomedical fields. Generalized estimating equation (GEE) approach is a widely used statistical method for the analysis of longitudinal data. Feature selection is imperative in longitudinal omics data analysis. Among a variety of existing feature selection methods, an embedded method-threshold gradient descent regularization (TGDR)-stands out due to its excellent characteristics. An alignment of GEE with TGDR is a promising area for the purpose of identifying relevant markers that can explain the dynamic changes of outcomes across time. We proposed a new novel feature selection algorithm for longitudinal outcomes-GEE-TGDR. In the GEE-TGDR method, the corresponding quasilikelihood function of a GEE model is the objective function to be optimized, and the optimization and feature selection are accomplished by the TGDR method. Long noncoding RNAs (lncRNAs) are posttranscriptional and epigenetic regulators and have lower expression levels and are more tissue-specific compared with protein-coding genes. So far, the implication of lncRNAs in psoriasis remains largely unexplored and poorly understood even though some evidence in the literature supports that lncRNAs and psoriasis are highly associated. In this study, we applied the GEE-TGDR method to a lncRNA expression dataset that examined the response of psoriasis patients to immune treatments. As a result, a list including 10 relevant lncRNAs was identified with a predictive accuracy of 70% that is superior to the accuracies achieved by two competitive methods and meaningful biological interpretation. A widespread application of the GEE-TGDR method in omics longitudinal data analysis is anticipated.