Dissecting hair breakage in alopecia areata: the central role of dysregulated cysteine homeostasis.

In the initial stages of Alopecia Areata (AA), the predominance of hair breakage or exclamation mark hairs serves as vital indicators of disease activity. These signs are non-invasive and are commonly employed in dermatoscopic examinations. Despite their clinical salience, the underlying etiology precipitating this hair breakage remains largely uncharted territory. Our exhaustive review of the existing literature points to a pivotal role for cysteine-a key amino acid central to hair growth-in these mechanisms. This review will probe and deliberate upon the implications of aberrant cysteine metabolism in the pathogenesis of AA. It will examine the potential intersections of cysteine metabolism with autophagy, ferroptosis, immunity, and psychiatric manifestations associated with AA. Such exploration could illuminate new facets of the disease's pathophysiology, potentially paving the way for innovative therapeutic strategies.

Identification of SLC40A1, LCN2, CREB5, and SLC7A11 as ferroptosis-related biomarkers in alopecia areata through machine learning.

Alopecia areata (AA) is a common non-scarring hair loss condition driven by the collapse of immune privilege and oxidative stress. The role of ferroptosis, a type of cell death linked to oxidative stress, in AA is yet to be explored, even though it's implicated in various diseases. Using transcriptome data from AA patients and controls from datasets GSE68801 and GSE80342, we aimed to identify AA diagnostic marker genes linked to ferroptosis. We employed Single-sample gene set enrichment analysis (ssGSEA) for immune cell infiltration evaluation. Correlations between ferroptosis-related differentially expressed genes (FRDEGs) and immune cells/functions were identified using Spearman analysis. Feature selection was done through Support vector machine-recursive feature elimination (SVM-RFE) and LASSO regression models. Validation was performed using the GSE80342 dataset, followed by hierarchical internal validation. We also constructed a nomogram to assess the predictive ability of FRDEGs in AA. Furthermore, the expression and distribution of these molecules were confirmed through immunofluorescence. Four genes, namely SLC40A1, LCN2, CREB5, and SLC7A11, were identified as markers for AA. A prediction model based on these genes showed high accuracy (AUC = 0.9052). Immunofluorescence revealed reduced expression of these molecules in AA patients compared to normal controls (NC), with SLC40A1 and CREB5 showing significant differences. Notably, they were primarily localized to the outer root sheath and in proximity to the sebaceous glands. Our study identified several ferroptosis-related genes associated with AA. These findings, emerging from the integration of immune cell infiltration analysis and machine learning, contribute to the evolving understanding of diagnostic and therapeutic strategies in AA. Importantly, this research lays a solid foundation for subsequent studies exploring the intricate relationship between AA and ferroptosis.

Causal role of immune cells in alopecia areata: A two-sample Mendelian randomization study.

BACKGROUND: Previous research has highlighted an association between alopecia areata (AA) and the collapse of hair follicle immune privilege, however, the causal linkage to specific immune cell traits remains to be elucidated. This study aimed to investigate the causal influence of immune cell traits on AA utilizing a two-sample Mendelian randomization (MR) approach. METHODS: Leveraging GWAS summary statistics of 731 immunological traits (n = 3757) and AA data (n = 211,428), MR analyses were conducted employing inverse-variance weighted (IVW), weighted median, and MR-Egger regression methodologies. Sensitivity analyses were undertaken using Cochran's Q test, MR-Egger intercept test, and MR-PRESSO analysis. A reverse MR analysis was performed for immune cell traits identified in the initial MR analysis. RESULTS: Our study unveiled multiple immune traits associated with AA. Protective associations were observed for CD62L- CD86+ myeloid dendritic cells (DCs), TD CD4+%CD4+ T cells, and others, with ORs ranging from 0.63 to 0.78. Conversely, traits like CD62L on CD62L+ plasmacytoid DCs, HLA-DR on CD14- CD16+ monocytes, HLA-DR on monocytes, and others, were determined to augment the risk of AA, with ORs ranging from 1.13 to 1.46. Reverse MR analysis signified a reduction in BAFF-R on IgD-CD24-B cells post-AA onset (OR: 0.97, 95% CI: 0.95-1.00), with no identified heterogeneity or horizontal pleiotropy among the instrumental variables (IVs). CONCLUSIONS: Our findings suggests that CD62L on certain subpopulations of DCs and HLA-DR on monocytes may epitomize risk factors for AA, offering potential therapeutic targets for alleviating AA.

Efficacy and safety of different JAK inhibitors in the treatment of alopecia areata: a network meta-analysis.

Background: Alopecia areata (AA) is an immune disease characterized by non-scarring hair loss. With the widespread application of JAK inhibitors in immune-related diseases, attention is being given to their role in the treatment of AA. However, it is unclear which JAK inhibitors have a satisfactory or positive effect on AA. This network meta-analysis aimed to compare the efficacy and safety of different JAK inhibitors in the treatment of AA. Methods: The network meta-analysis was performed according to the PRISMA guidelines. We included randomized controlled trials as well as a small number of cohort studies. The differences in efficacy and safety between the treatment and control groups were compared. Results: Five randomized controlled trials, two retrospective studies, and two prospective studies involving 1689 patients were included in this network meta-analysis. In terms of efficacy, oral baricitinib and ruxolitinib significantly improved the response rate of patients compared to placebo [MD = 8.44, 95% CI (3.63, 19.63)] and [MD = 6.94, 95% CI, (1.72, 28.05)],respectively. Oral baricitinib treatment significantly improved the response rate compared to non-oral JAK inhibitor treatment [MD=7.56, 95% CI (1.32,43.36)]. Oral baricitinib, tofacitinib, and ruxolitinib treatments significantly improved the complete response rate compared to placebo [MD = 12.21, 95% CI (3.41, 43.79)], [MD = 10.16, 95% CI (1.02, 101.54)], and [MD = 9.79, 95% CI, (1.29, 74.27)], respectively. In terms of safety, oral baricitinib, tofacitinib, and ruxolitinib treatments significantly reduced treatment-emergent adverse event rates compared with conventional steroid treatment [MD = 0.08, 95% CI (0.02, 0.42)], [MD = 0.14, 95% CI (0.04, 0.55)], and [MD = 0.35, 95% CI, (0.14, 0.88)], respectively. Conclusion: Oral baricitinib and ruxolitinib are excellent options for the treatment of AA owing to their good efficacy and safety profiles. In contrast, non-oral JAK inhibitors do not appear to have satisfactory efficacy in treating AA. However, further studies are required to verify the optimal dose of JAK inhibitors for AA therapy.