Autoimmunity最新文献

The pathogenesis of autoimmune thyroiditis (AIT) is intricately linked to immune dysregulation, endocrine imbalance, and gut microbiota dysbiosis. The immune system drives autoimmune attacks against thyroid tissue through Th1/Th2 cell imbalance, Treg dysfunction, and excessive release of proinflammatory cytokines. Thyroid hormone regulation primarily occurs via the hypothalamic-pituitary-thyroid (HPT) axis. Elevated levels of TPOAb and TgAb in AIT patients can lead to hypothyroidism by affecting the HPT feedback loop. Thyroid hormone regulation of immune cell metabolism and differentiation, in turn, affects immune homeostasis, forming a bidirectional regulatory network. Recent studies further reveal that the gut microbiota influences systemic immune tolerance by regulating intestinal barrier integrity and metabolites (e.g. short-chain fatty acids and secondary bile acids). Abnormal abundance of specific genera (e.g. Bacteroides and Prevotella) can promote the production of thyroid autoantibodies (TPOAb/TgAb), and increased intestinal permeability caused by microbiota dysbiosis may facilitate cross-reactivity between microbial antigens and thyroid antigens. Furthermore, the gut microbiota indirectly regulates thyroid function through the HPT axis. This review aims to summarize the current knowledge regarding the specific molecular mechanisms of gut microbiota-immune-endocrine interactions in AIT, offer important references for researching the treatment directions of AIT.

The incidence of prostate cancer continues to increase, making it the second most common malignant tumor among men worldwide. Immunotherapy has emerged as a key therapeutic strategy for treating tumors. Numerous studies have established that the efficacy of tumor immunotherapy is closely associated with the tumor microenvironment and T cell subsets. However, the specific functions of certain T cell subsets in prostate cancer remain incompletely characterized. Therefore, this study aimed to systematically investigate the distribution patterns of T cell subsets within the tumor microenvironment of prostate cancer patients and their correlations with clinicopathological parameters. Therefore, we investigated the impact of T cells on the tumor microenvironment of prostate cancer at the single-cell level. We employed a variety of analytical methods to reveal the functions of T cells, including cell interaction analysis, time-series analysis, enrichment analysis, immune infiltration analysis, and other analytical approaches. By integrating bulk RNA-seq data, we constructed and validated a prognostic risk model based on T cell marker genes. Finally, we utilized the ssGSEA and ESTIMATE algorithms to explore the relationship between the prognostic risk model and immunotherapy. After quality control, 16,999 cells from the single-cell data were retained for downstream analysis. Our study focused on T cells, revealing the communication between various cell types and T cells. Pseudotime analysis showed that different T cell marker genes exhibited differential expression at various time points, corresponding to distinct biological processes. Enrichment analysis indicated that T cell marker genes were enriched in several immune-related pathways. From our analysis, BCAS2, EIF2S2, RIOK3, and ATP6V1E1 were ultimately identified as prognostic markers. Immune infiltration analysis revealed that high-risk patients had lower immune scores, stromal scores, and ESTIMATE scores and greater tumor purity compared to low-risk patients. We analyzed the mechanisms involving T cells in prostate cancer from multiple perspectives, constructed a prognostic model, and conducted immune infiltration analysis. Our findings contribute to the understanding of prostate cancer and its prognosis, providing valuable insights for future research and prognostic assessments in prostate cancer.

Autoimmune gastritis (AIG) is an autoimmune disease characterized primarily by the destruction of gastric parietal cell structure and atrophy of the gastric fundic and body mucosa. The global prevalence of AIG is approximately 3.85%. Its main complications include pernicious anemia, gastric neuroendocrine tumors, and gastric cancer, which pose significant health risks. Currently, targeted treatment options for AIG are lacking worldwide. Animal models of AIG are crucial for investigating its pathogenesis and for developing drug therapies. However, reproducible methods for establishing AIG animal models remain scarce. This article provides a systematic review of internationally employed methods for modeling AIG in animals. These methods are categorized and discussed based on the modeling approaches and mechanisms, including neonatal thymectomy, genetically modified animals such as TxA23 and Ctox mice, inducer-based methods such as H⁺/K⁺-ATPase immunization, viral infection, and combined modeling strategies. In addition, the types of modeling agents and the time required for model establishment are also examined. This review highlights existing challenges, such as the lack of uniform modeling standards and evaluation criteria, and aims to provide a foundation for further exploration of AIG.

The current clinical classification of coronavirus disease 2019 (COVID-19) does not adequately capture the biological heterogeneity observed among patients. To address this gap, the present study aimed to identify distinct subtypes of severe COVID-19 through unsupervised clustering analysis. We analyzed nine publicly available RNA sequencing datasets of peripheral blood samples from the GEO database. After identifying differentially expressed genes (DEGs), we applied a consensus clustering algorithm to classify the samples into distinct subtypes. To further characterize these subtypes, we performed gene set enrichment analysis and assessed immune cell infiltration to understand their underlying biological mechanisms. Based on the 139 upregulated DEGs of severe COVID-19 infection, patients were divided into subtype A, subtype B, and subtype C, each with different molecular and cellular characteristics. Subtype A was characterized by activated neutrophils that undergo degranulation and respond to bacteria or fungi. Subtype B showed significant activation in canonical pathways associated with interferon-alpha/beta signaling. Subtype C was characterized by immune cell activation associated with pathways of mitotic and cell cycle. These results facilitate the development of a precise classification framework, which informs the design of molecular diagnosis and provides actionable guidelines for stratified therapy in severe COVID-19 infection in the future.

The current diagnostic criteria for systemic juvenile idiopathic arthritis (sJIA) lack specificity. Diagnostic biomarkers are in urgent need to help with the early diagnosis of sJIA. Gene expression data of a JIA cohort study from Gene Expression Omnibus (GEO) database was adopted to get hub genes of sJIA by using integrated bioinformatic analysis including differentially expressed gene (DEG) analysis, weighted coexpression network analysis (WGCNA) and protein‒protein network interaction (PPI) analysis. Least absolute shrinkage and selection operator (LASSO) regression analysis was subsequently applied to identify biomarkers with the highest diagnostic potential for sJIA among these hub genes. A prediction model based on the identified biomarkers was constructed and subsequently validated in three additional independent GEO cohorts. Totally 761 DEGs were obtained by comparing gene expression profiles between sJIA patients and healthy controls. Twenty-two hub genes were identified by integrating WGCNA and PPI network analysis. All hub genes underwent LASSO regression analysis and three genes-ALAS2, S100A9, and S100A12-were eventually identified as the most promising diagnostic biomarkers. A prediction nomogram model based on these three genes was constructed, yielding an area under the curve (AUC) of 0.9337, and was subsequently validated in independent validation datasets, achieving AUC values of 0.9412, 0.9018, and 0.7064. The genes ALAS2, S100A9, and S100A12 showed significant association to sJIA and may serve as candidate diagnostic biomarkers pending further clinical validation.

Systemic lupus erythematosus (SLE) is a prototypical autoimmune connective tissue disease in which the immune system is aberrantly activated, producing various autoantibodies that target the body's tissues, particularly the kidneys. Approximately 50% of patients with lupus nephritis (LN) eventually progress to end-stage renal disease. Despite the availability of multiple therapies, their effectiveness is often limited by individual patient differences. Recent studies have identified a new signaling pathway, the cyclic GMP-AMP synthetase (cGAS)-interferon gene stimulating factor (STING) innate immune pathway. Research has indicated that the serum levels of cGAS, STING, and type I interferon are significantly higher in mice with SLE or LN compared with normal populations. Experimental findings also suggest that the absence of cGAS and STING significantly reduces autoantibody production and alleviates tissue inflammation in autoimmune mouse models. These observations highlight the potential of targeting this pathway as a treatment for patients with SLE and LN. However, significant translational hurdles remain, including contradictory evidence from murine models, a complete lack of human trial data for leading candidates, and the inherent risk of viral reactivation owing to systemic immunosuppression. This article provides an overview of the cGAS-STING pathway, discusses its relevance to SLE and LN, and summarizes over 20 inhibitors targeting cGAS/STING. Furthermore, we emphasize the key clinical challenges and prospects of targeted drugs in this pathway.

Triggering receptor expressed on myeloid cells-1 (TREM-1) and TREM-2, cell surface receptors involved in innate and adaptive immunity, exist in soluble (sTREM-1 and sTREM-2) and membrane-bound forms, with the soluble form serve as biomarkers in many immune diseases. However, the role of TREM-1 and TREM-2 in myasthenia gravis (MG) remains unclear. This study aims to investigate the expression of TREM-1 and TREM-2 in MG, as well as their potential role in the immunopathological mechanism of MG. We enrolled a total of 85 MG patients and 43 healthy controls (HC). Enzyme-linked immunosorbent assay and flow cytometry were used to quantify the expression of TREM-1 and TREM-2 in MG patients and HC. The levels of TREM-1 and TREM-2 mRNA of peripheral blood mononuclear cells were measured by reverse transcription quantitative real-time polymerase chain reaction (RT‒qPCR) in MG and HC. In vitro experiments were performed to explore the functional effects of intervening TREM-1 on CD4⁺T cells. We found that serum sTREM-1 levels were significantly elevated in MG patients Compared to HC, whereas sTREM-2 showed no difference. Additionally, sTREM-1 levels in MG patients positively correlated with disease severity and memory B-cell proportions. TREM-1 expression was reduced and most significantly on CD4⁺T and CD8⁺T cells in MG patients. Inhibition of TREM-1 inhibited Treg cell differentiation but had no significant effect on Th1, Th2, and Th17, indicating its pathogenic role in MG.

This case report presents the clinical course of an 80-year-old patient diagnosed with dermatomyositis in 2014, initially managed successfully with methotrexate (MTX). In March 2022, the patient experienced a severe SARS-CoV-2 infection with significant pulmonary involvement, necessitating discontinuation of MTX. Notably, the patient maintained complete remission of dermatomyositis post-infection without relapse. Sustained negativization of autoantibodies, confirmed through multiple evaluations, was observed, while persistent capillaroscopic abnormalities suggested ongoing subclinical disease activity. This case underscores the potential immunomodulatory effects of SARS-CoV-2 on autoimmune diseases and highlights the need for further investigation into its role in immune modulation and disease quiescence.

Cell stress, including endoplasmic reticulum (ER) stress, heat shock, and hypoxia, plays a pivotal role in cellular homeostasis and immune regulation, particularly in the intestine. ER stress, a key aspect of cell stress, triggers the unfolded protein response (UPR) to restore balance by managing misfolded proteins or inducing apoptosis if unresolved. The activation of these stress responses has emerged as a critical contributor to intestinal inflammation in conditions like inflammatory bowel disease (IBD). Regulatory T cells (Tregs), vital for maintaining mucosal immune tolerance, are strongly influenced by cellular stress, with the UPR shaping their stability, metabolic programming, and function. Microbial dysbiosis and reduced short-chain fatty acids (SCFAs) disrupt these adaptive pathways, further impairing Treg function. In this review, we explore how UPR signaling shapes Treg metabolism and intestinal inflammation. Identifying gaps in UPR-mediated adaptation and stress thresholds, we thus propose microbiome- and ER-stress-based therapeutic strategies as putative strategies for restoring immune balance in IBD.

Thyroid eye disease (TED) is an autoimmune inflammatory disease involving the extraocular tissues. It often occurs in patients with hyperthyroidism or a history of hyperthyroidism; however, it can also occur in euthyroid or hypothyroid patients. The immune cells involved in TED have been vigorously investigated, most notably the role of multiple T cell subpopulations. A comprehensive understanding of T cells in both animal and human studies can help us better understand the disease. This review aims to understand the roles of specific T cell subpopulations in TED. By discussing the effects of T cell subpopulations in animal models and human studies of TED, this review highlights the important role of imbalances of T cell subpopulations in TED. The discrepancies between human and animal studies of TED are also discussed in this review. Novel treatments targeting various T cell subpopulations could be developed to manage the disease.