| Autor: |
Zhong, Ting, Li, Xinyu, Lei, Kang, Tang, Rong, Deng, Qiaolin, Love, Paul E, Zhou, Zhiguang, Zhao, Bin, Li, Xia |
| Předmět: |
|
| Zdroj: |
Nature Communications; 10/16/2024, Vol. 15 Issue 1, p1-18, 18p |
| Abstrakt: |
Type 1 diabetes (T1D) is a chronic autoimmune condition characterized by hyperglycemia resulting from the destruction of insulin-producing β-cells that is traditionally deemed irreversible, but partial remission (PR) with temporary reversal of hyperglycemia is sometimes observed. Here we use single-cell RNA sequencing to delineate the immune cell landscape across patients in different T1D stages. Together with cohort validation and functional assays, we observe dynamic changes in TIGIT+CCR7− Tregs and CD226+CCR7−CD8+ cytotoxic T cells during the peri-remission phase. Machine learning algorithms further identify TIGIT+CCR7− Tregs and CD226+CD8+ T cells as biomarkers for β-cell function decline in a predictive model, while cell communication analysis and in vitro assays suggest that TIGIT+CCR7− Tregs may inhibit CD226+CCR7−CD8+ T cells via TGF-β signaling. Lastly, in both cyclophosphamide-induced and streptozotocin (STZ)-induced mouse diabetes models, CD226 inhibition postpones insulitis onset and reduces hyperglycemia severity. Our results thus identify two interrelated immune cell subsets that may serve as biomarkers for monitoring disease progression and targets for therapeutic intervention of T1D. Type 1 diabetes (T1D) manifests as hyperglycemia, with spontaneous remission occasionally observed, but the underneath mechanisms are unclear. Here the authors analyses patients at different stages to find two populations of immune cells correlating with disease progression or remission, while results from mouse diabetes models validate these observations. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
|
Full text from SpringerLink