Emilio Manuel Serrano-López

Alopecia areata (AA) is a chronic autoimmune disease characterized by non-scarring hair loss, where pathogenesis is closely linked to the collapse of hair follicle immune privilege and dysregulated T-cell responses. Increasing evidence suggests that gut dysbiosis may contribute to systemic immune alterations relevant to autoimmune disorders, yet its role in AA remains largely unexplored. In this study, we aimed to characterize the gut microbiota composition of AA patients and evaluate its potential as a biomarker for disease discrimination. Fecal samples from patients with AA and healthy controls were analyzed by 16S rRNA sequencing and processed through QIIME2 and MicrobiomeAnalyst platforms. Diversity metrics, differential abundance, and microbial network correlations were assessed, and supervised machine learning models were developed to classify AA versus control profiles. Our results revealed distinct microbial signatures in AA, with enrichment of pro-inflammatory genera such as Methanobrevibacter, Collinsella, and Ruminococcus gnavus, and depletion of immunoregulatory commensals, including Faecalibacterium and Eubacterium eligens group. Network analyses showed more complex microbial interactions in AA, and Random Forest models achieved 92% accuracy in discriminating AA from controls. These findings indicate that gut dysbiosis may play a role in AA pathogenesis, providing potential diagnostic biomarkers and supporting microbiota-targeted interventions as future therapeutic strategies.

Background/Objectives: Growing evidence highlights the pivotal role of gut dysbiosis in the pathophysiology of irritable bowel syndrome (IBS). Despite this, the identification of an “IBS microbiota signature” remains elusive, primarily due to the influence of genetic, dietary, and environmental factors. To address these confounding variables, it is critical to perform comparative analyses using a control group derived from the same community as the IBS patients. This study aimed to evaluate and contrast the gut microbiota composition of IBS patients with healthy controls. Methods: We compared the gut microbiota from stool samples of 25 IBS patients diagnosed according to the Rome IV criteria, and 110 healthy subjects without acute or chronic diseases and not on continuous medication. The high-throughput sequencing of the V3–V4 regions of the 16S rRNA gene was conducted for microbiota analysis. Results: The IBS gut microbiota was richer but exhibited lower alpha diversity compared to the control group, suggesting simplification and imbalance. A beta diversity analysis revealed overall compositional differences between the two groups. A heat tree analysis highlighted key IBS-associated changes, including a decrease in Firmicutes, mainly due to Clostridia, and an increase in Bacteroidota, driven by an expansion of Bacteroidales families. Differential expression analyses identified important genera within these taxa like Bacteroides, Faecalibacterium, and Blautia, which could serve as microbiota-based biomarkers for IBS. Conclusions: Our results reveal both statistically and clinically significant differences in gut microbiota composition and diversity between IBS patients and healthy controls from the same community. These findings provide a deeper understanding of how alterations in the gut microbiota may contribute to IBS symptoms, offering new insights into the diagnosis and potential treatments.