C-reactive protein as a novel biomarker for vitamin D deficiency in alopecia areata.

BACKGROUND: Alopecia areata (AA) is an autoimmune condition characterized by sudden and unpredictable hair loss, with a lifetime incidence of 2%. AA can be divided into three categories: patchy alopecia, alopecia totalis, and alopecia universalis. It can affect a person's psychological health and overall quality of life. Elevated C-reactive protein (CRP) levels in the liver may indicate an inflammatory response in autoimmune diseases. Vitamin D, essential for immune system control and skin health, may be related to AA. Hair follicles contain vitamin D receptors, which control immunological responses in the skin. However, no study has found a relationship between CRP and vitamin D in AA patients in our region. SUBJECTS AND METHODS: An analytical cross-sectional study with a case-control design research investigation of 82 AA patients and 81 healthy controls was carried out. Both groups' medical histories were taken. Biochemical analysis was done for both groups as well as the serum vitamin D levels, and CRP. Genetic analysis for CDX2 rs11568820 variant detected by PCR (T-ARMS-PCR) method and vitamin D receptor (VDR) gene expression measured by real-time PCR analysis for both patients and healthy subjects. RESULTS: CRP levels are higher in AA patients, AA patients with G/G genotypes exhibited higher concentrations of CRP when compared to those with A/A and A/G genotypes while patients with A/A genotypes have higher levels of Serum vitamin D as compared to the A/G and G/G genotypes. G allele was more abundant in AA patients. VDR gene expression was lower in AA compared to control and lower in ophiasis compared to localized and multiple patchy AA. An important inverse linear correlation was observed between vitamin D and CRP levels in ophiasis AA. CONCLUSION: CRP concentrations were found to be elevated in AA patients. The considerable accuracy of CRP in the diagnosis of AA is substantiated by a statistically significant al. A noteworthy inverse linear association was observed between serum vitamin D and CRP concentrations in ophiasis AA.

MiR-200c-3p as a novel genetic marker and therapeutic tool for alopecia areata.

BACKGROUND: MicroRNAs (miRNAs) are small RNA molecules that regulate gene expression in diverse biological processes. They hold promise as therapeutic candidates for targeting human disease pathways, although our understanding of their gene regulatory mechanism remains incomplete. Alopecia areata (AA) is a prevalent inflammatory ailment distinguished by the infiltration of T cells targeting the anagen-stage hair follicles. The scarcity of effective remedies for AA may stem from limited understanding regarding its precise cellular mechanism. AIM: To investigate and examine the importance and role of the miR-200c-3p as a genetic indicator for AA, and its possible impact on disease progression. SUBJECTS AND METHODS: Case-control study included 65 patients with AA and 65 matched healthy controls. A real-time PCR technique was used to measure the expression of miR-200c-3p for both groups. Bioinformatic tools were used for prediction with genes and gene-gene interaction, and protein-protein interaction. RESULTS: The expression levels of miR-200c-3p were significantly higher in AA patients than in healthy controls. We predicted that miR-200c-3p plays a markable role in the development of AA by its effect on the EGFR tyrosine kinase inhibitor resistance pathway. CONCLUSION: We were able to identify the influence of miR-200c-3p on both PLCG1 and RPS6KP1 genes which in turn regulate the EGFR tyrosine kinases resistance pathway that displayed the most substantial increase in activity. Our outcomes shed light on the era of the potential theranostic role of this innovative miRNA in AA.

Interleukin gene polymorphisms and alopecia areata: A systematic review and meta-analysis.

BACKGROUND: Alopecia areata (AA) is an autoimmune disease which results in non-scarring hair loss on the scalp or any surface with hair. Several genetic polymorphisms of the interleukin genes have been linked with this disease but the results are inconsistent. This systematic review and meta-analysis were done to find the association between rs3118470, rs2275913, rs3212227, and rs10889677 of the IL2RA, IL17A, IL12B, and IL23R genes, respectively, of the interleukin family with alopecia areata. METHODS: A comprehensive search for relevant research articles was conducted in Pubmed, Google Scholar, and Embase databases. Our search yielded 8 relevant articles with 1940 cases and 1788 controls. The odds ratio with 95% confidence intervals was calculated using fixed effect and random effect models. Heterogeneity was determined using the Q-test and I2 test. Publication bias was determined and funnel plots were used to adjust the odds ratio. RESULTS: We found a significant risk effect for rs3118470 of the IL2RA gene with alopecia areata in the dominant model (CC + CT vs TT; OR = 1.54, 95% confidence interval = 1.05-2.26, P < .05, I2 = 69.03%) and homozygous model (CC vs TT; OR = 2.00, 95% confidence interval = 1.07-3.71, P < .05, I2 = 72.84%). For the other single nucleotide polymorphisms, we could not find any statistically significant association with the disease. CONCLUSION: Our analysis showed that mutation of rs3118470 of IL2RA gene possesses a significant risk effect for alopecia areata. Future studies with larger sample sizes and ethnic backgrounds are warranted to confirm our findings.

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.

Association between alopecia areata, anxiety, and depression: Insights from a bidirectional two-sample Mendelian randomization study.

BACKGROUND: As research progresses, there has been growing interest in the association between Alopecia areata (AA) and anxiety, as well as depression. However, there have been limited reports on the genetic variation level of AA in relation to mental disorders. METHOD: We performed large-scale Two sample Mendelian randomization (MR) analyses to examine whether there is a association between AA with anxiety and depression. The data utilized for AA analysis were sourced from the FinnGen release 9 databases, including 682 cases and 361,822 controls. Summary statistics for major depression disorder (MDD) were obtained from a genome-wide meta-analysis dataset, incorporating 170,756 cases and 329,443 controls. The anxiety disorder data was conducted by the Anxiety Neuro Genetics Study Consortium, including 5580 cases and 11,730 controls. We employed four distinct approaches, including MR-Egger, weighted median, random-effect inverse variance weighted (IVW), and weighted mode, to conduct the MR analysis. RESULTS: Genetic liability to AA was associated with an increased risk of Major depression disorder (MDD) and anxiety demonstrated an odds ratio (OR) of 1.01 (ßivw = 0.011, PIVW = 0.023) and OR of 1.16 (ßivw = 0.150, PIVW = 0.002). Upon conducting the Bonferroni correction, the P-values were 0.046 and 0.004, respectively. For reverse analysis, we observed no significant association between anxiety and MDD with the risk of AA. CONCLUSIONS: Our research unveil a unidirectional causal association whereby AA exerts a risk effect against MDD and anxiety, which serves as a valuable complement to prior meta-analyses, enriching the existing body of knowledge on the subject matter.

Association between genetically predicted rheumatoid arthritis and alopecia areata: a two-sample Mendelian randomization study.

Background: Although numerous observational studies have indicated a potential association between autoimmune diseases, such as rheumatoid arthritis (RA) and alopecia areata (AA), the research reports lack a clear causal relationship. In this study, our objective is to utilize the Mendelian randomization (MR) design to examine the potential causal association between RA and AA. Methods: To investigate the causal relationship between RA and AA, we utilized large-scale gene aggregation data from genome-wide association studies (GWAS), including RA (n=58,284) and AA (n=361,822) based on previous observational studies. In our analysis, we mainly employed the inverse variance-weighted (IVW) method of the random effects model, supplemented by the weighted median (WM) method and the MR Egger method. Results: The findings from the IVW methods revealed a significant association between genetically predicted RA and an increased likelihood of AA, as evidenced by an odds ratio of 1.21 (95%CI = 1.11-1.32; P < 0.001. Both the WM method and MR-Egger regression consistently showed significant directional outcomes (Both P < 0.05), indicating a robust association between RA and AA. Additionally, both the funnel plot and the MR-Egger intercepts provided evidence of the absence of directional pleiotropy, suggesting that the observed association is not influenced by other common genetic factors. Conclusions: The results of the study suggest a possible link between genetically predicted RA and AA. This finding highlights the importance for individuals diagnosed with RA to remain vigilant and aware of the potential development of AA. Regular monitoring and early detection can be crucial in managing and addressing this potential complication.

Identifying immuno-related diagnostic genes and immune infiltration signatures for periodontitis and alopecia areata.

BACKGROUND: Although there have been indications that periodontitis (PD) may be susceptible to alopecia areata (AA), the underlying mechanism of its pathogenesis remains poorly understood. The objective of our study is to conduct further research into the occurrence of this complication. METHODS: The gene expression omnibus (GEO) database was the source of acquisition for both PD and AA datasets. Various methods, including the differentially expressed genes (DEGs) analysis, functional enrichment analysis, protein-protein interaction (PPI) network construction, Cytohubba algorithms, and RandomForest algorithms, were utilized to identify candidate hub immuno-related genes (IRGs) for diagnosing AA with PD. The diagnostic efficacy was assessed by constructing receiver operating characteristic (ROC) curves. To further deepen our understanding, immune cell infiltration, flow cytometry assay, and immunofluorescence techniques were employed to uncover immune cell dysregulation in PD and AA. RESULTS: 899 and 803 DEGs were detected in AA and PD, respectively, with an intersection of 150 common DEGs enriched in immune regulation. Further analysis of the junction of shared DEGs and IRGs was analyzed using the PPI network, Mcode, and Cytohubba algorithms. Three hub genes (CTSS, IL2RG, and ITGAL) were subsequently selected by Cytohubba and RandomForest algorithms and were found to be promising candidate hub genes with high diagnostic values (AUC ranging from 0.776 to 0.909) for diagnosing AA with PD. Additionally, various dysregulated immune cells were observed, with mast cells potentially serving as markers for AA and plasma for PD. CONCLUSION: Three candidate hub IRGs (CTSS, IL2RG, and ITGAL) were identified with considerable diagnostic values. Besides, mast cells could serve as markers for AA, while plasma may indicate PD. Our research has the potential to identify shared diagnostic candidate genes and immune cells for AA and PD patients.

Clinical and Genetic Aspects of Alopecia Areata: A Cutting Edge Review.

Alopecia areata (AA) is a chronic, non-scarring, immune-mediated skin disease that affects approximately 0.5-2% of the global population. The etiology of AA is complex and involves genetic and environmental factors, with significant advancements in genetic research occurring in recent years. In addition to well-known genes such as PTPN22, CTLA4, and IL2, which have been widely supported as being associated with AA, an increasing number of specific gene-related loci have been discovered through advances in genetic research. For instance, gene analysis of microRNAs can reveal the critical role of miRNAs in regulating gene expression, aiding in the understanding of cellular and organismal functional regulatory mechanisms. Furthermore, numerous studies have confirmed the existence of correlations between AA and other immune-related diseases. Examples include hyperthyroidism and rheumatoid arthritis. By understanding the interrelationships between AA and other immune diseases, we can further comprehend potential shared genetic foundations or pathogenic mechanisms among different diseases. Genetic research plays a crucial role in unraveling the pathogenesis of AA, as the identification of genetic variations associated with AA can assist in formulating more effective and targeted treatment strategies.

Functional interrogation of lymphocyte subsets in alopecia areata using single-cell RNA sequencing.

Alopecia areata (AA) is among the most prevalent autoimmune diseases, but the development of innovative therapeutic strategies has lagged due to an incomplete understanding of the immunological underpinnings of disease. Here, we performed single-cell RNA sequencing (scRNAseq) of skin-infiltrating immune cells from the graft-induced C3H/HeJ mouse model of AA, coupled with antibody-based depletion to interrogate the functional role of specific cell types in AA in vivo. Since AA is predominantly T cell-mediated, we focused on dissecting lymphocyte function in AA. Both our scRNAseq and functional studies established CD8+ T cells as the primary disease-driving cell type in AA. Only the depletion of CD8+ T cells, but not CD4+ T cells, NK, B, or γδ T cells, was sufficient to prevent and reverse AA. Selective depletion of regulatory T cells (Treg) showed that Treg are protective against AA in C3H/HeJ mice, suggesting that failure of Treg-mediated immunosuppression is not a major disease mechanism in AA. Focused analyses of CD8+ T cells revealed five subsets, whose heterogeneity is defined by an "effectorness gradient" of interrelated transcriptional states that culminate in increased effector function and tissue residency. scRNAseq of human AA skin showed that CD8+ T cells in human AA follow a similar trajectory, underscoring that shared mechanisms drive disease in both murine and human AA. Our study represents a comprehensive, systematic interrogation of lymphocyte heterogeneity in AA and uncovers a novel framework for AA-associated CD8+ T cells with implications for the design of future therapeutics.

Mitochondrial DNA copy number as a diagnostic marker and indicator of degree of severity in alopecia areata.

Alopecia areata (AA) is a disorder with several etiologies. The evidence suggests that the absolute copy number of mitochondrial deoxyribonucleic acid (mtDNA), as well as proportion of mutated mtDNA copies, determines disease onset. This study aims to quantify the relative index of the mtDNA copy number in patients with AA and healthy controls and correlate the results with the existing clinical information. This case-control study included 50 patients with AA and 50 age- and sex-coordinated healthy persons as controls. The severity of AA was weighed using the Severity of Alopecia Tool and Kavak's classification. The relative index of the mtDNA copy number was measured by real-time qPCR. Significant statistical difference was observed between cases and controls regarding mean mtDNA copy number, p < .001. There was significant positive correlation with SALT score (p = â€…0.001). A cutoff value of >1.619 N/µL could significantly diagnose AA cases (p < .001), and a cutoff value of > 1.36 N/µL could discriminate mild AA cases from those with moderate AA (p = â€…0.007). The relative index of mtDNA copy number is significantly elevated in AA cases and could be helpful in diagnosing and evaluating AA severity.

Mendelian randomization study highlights hypothyroidism as a causal determinant of alopecia areata.

Background: Although observational studies have found an association between hypothyroidism and alopecia areata, the causality of this relationship remains unclear. Objectives: This study aimed to investigate the genetic variants associated with hypothyroidism and their potential impact on the risk of developing alopecia areata. Methods: genome-wide association study summary statistics for hypothyroidism (30,155 cases and 379,986 controls) and alopecia areata (289 cases and 211,139 controls) were obtained from the IEU OpenGwas project. The inverse variance-weighted method was used as the primary analysis method to evaluate the causality between hypothyroidism and alopecia areata, supplemented by the weighted median, MR-Egger, simple mode and weighted mode. Furthermore, the function of causal SNPs was evaluated by gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and protein-protein interaction networks. Result: Utilizing two-sample Mendelian randomization analysis, we found that the single-nucleotide polymorphisms (SNPs) of hypothyroidism (OR = 1.40, 95% CI: 1.12-1.75, p = 3.03×10-3) significantly increased the risk of alopecia areata ( 289 cases and 211,139 controls ). KEGG pathway analysis showed that the candidate genes were mainly enriched in virion-herpesvirus, Th1 and Th2 cell differentiation, Th17 cell differentiation, T-cell receptor signaling pathway, PD-L1/PD-1 checkpoint pathway in cancer and Toll-like receptor signaling pathway. Protein-protein interaction networks results showed that CTLA4, STAT4, IL2RA, TYK2, IRF7, SH2B3, BACH2, TLR3, NOD2, and FLT3. Conclusion: This study provided compelling genetic evidence supporting a causative association between hypothyroidism and alopecia areata, which could potentially inform the development of more efficacious treatment strategies for patients afflicted by alopecia areata.

Stratification of alopecia areata reveals involvement of CD4 T cell populations and altered faecal microbiota.

Alopecia areata (AA) is an immune-mediated disease that causes non-scarring hair loss. Autoreactive CD8 T cells are key pathogenic effectors in the skin, and AA has been associated both with atopy and with perturbations in intestinal homeostasis. This study aimed to investigate mechanisms driving AA by characterizing the circulating immunophenotype and faecal microbiome, and by stratifying AA to understand how identified signatures associated with heterogeneous clinical features of the condition. Flow cytometric analyses identified alterations in circulating B cells and CD4 T cells, while 16S sequencing identified changes in alpha and beta diversity in the faecal microbiome in AA. The proportions of transitional and naïve B cells were found to be elevated in AA, particularly in AA samples from individuals with >50% hair loss and those with comorbid atopy, which is commonly associated with extensive hair loss. Although significant changes in circulating CD8 T cells were not observed, we found significant changes in CD4+ populations. In individuals with <50% hair loss higher frequencies of CCR6+CD4 ("Th17") and CCR6+CXCR3+CD4 ("Th1/17") T cells were found. While microbial species richness was not altered, AA was associated with reduced evenness and Shannon diversity of the intestinal microbiota, again particularly in those with <50% hair loss. We have identified novel immunological and microbial signatures in individuals with alopecia areata. Surprisingly, these are associated with lower levels of hair loss, and may therefore provide a rationale for improved targeting of molecular therapeutics.

How Our Microbiome Influences the Pathogenesis of Alopecia Areata.

Alopecia areata is a multifactorial autoimmune-based disease with a complex pathogenesis. As in all autoimmune diseases, genetic predisposition is key. The collapse of the immune privilege of the hair follicle leading to scalp loss is a major pathogenic event in alopecia areata. The microbiota considered a bacterial ecosystem located in a specific area of the human body could somehow influence the pathogenesis of alopecia areata, as it occurs in other autoimmune diseases. Moreover, the Next Generation Sequencing of the 16S rRNA bacterial gene and the metagenomic methodology have provided an excellent characterization of the microbiota. The aim of this narrative review is to examine the published literature on the cutaneous and intestinal microbiota in alopecia areata to be able to establish a pathogenic link. In this review, we summarize the influence of the microbiota on the development of alopecia areata. We first introduce the general pathogenic mechanisms that cause alopecia areata to understand the influence that the microbiota may exert and then we summarize the studies that have been carried out on what type of gut and skin microbiota is found in patients with this disease.

CHAC1 as a novel biomarker for distinguishing alopecia from other dermatological diseases and determining its severity.

Alopecia Areata (AA) is characterised by an autoimmune response to hair follicles (HFs) and its exact pathobiology remains unclear. The current study aims to look into the molecular changes in the skin of AA patients as well as the potential underlying molecular mechanisms of AA in order to identify potential candidates for early detection and treatment of AA. We applied Weighted Gene Co-expression Network Analysis (WGCNA) to identify key modules, hub genes, and mRNA-miRNA regulatory networks associated with AA. Furthermore, Chi2 as a machine-learning algorithm was used to compute the gene importance in AA. Finally, drug-target construction revealed the potential of repositioning drugs for the treatment of AA. Our analysis using four AA data sets established a network strongly correlated to AA pathogenicity based on GZMA, OXCT2, HOXC13, KRT40, COMP, CHAC1, and KRT83 hub genes. Interestingly, machine learning introduced these genes as important in AA pathogenicity. Besides that, using another ten data sets, we showed that CHAC1 could clearly distinguish AA from similar clinical phenotypes, such as scarring alopecia due to psoriasis. Also, two FDA-approved drug candidates and 30 experimentally validated miRNAs were identified that affected the co-expression network. Using transcriptome analysis, suggested CHAC1 as a potential diagnostic predictor to diagnose AA.