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Metformin suppresses β-cell apoptosis under ER stress by inhibiting protein translation

Metabolism. 2026 Apr 8:156607. doi: 10.1016/j.metabol.2026.156607. Online ahead of print.

ABSTRACT

Endoplasmic reticulum (ER) stress is a critical driver of pancreatic β-cell dysfunction and apoptosis. Although metformin, a drug used to treat type 2 diabetes, primarily decreases blood glucose levels by improving insulin sensitivity, its direct effects on β-cell survival remain unclear. Here, we investigated the effect of metformin on β-cell stress responses under ER stress conditions. Thapsigargin (Tg)-induced ER stress increased β-cell apoptosis in mouse islets, which was prevented by metformin in a dose-dependent manner. Treatment with metformin for 24 h suppressed the Tg-induced upregulation of unfolded protein response (UPR)-related genes, as confirmed by transcriptomic and pathway analyses. Quantitative proteomics revealed that Tg inhibited eIF2 signaling and protein translation, both of which were partially restored by metformin. Enrichment analysis further indicated the attenuation of apoptotic pathways in metformin-treated islets. Polysome profiling and puromycin incorporation assays demonstrated that metformin reduced protein translation independently of ER stress. Metformin promoted the dephosphorylation of 4E-BP1, a key initiator of cap-dependent protein translation that is activated by phosphorylation, and the antiapoptotic effect of metformin was abolished by 4E-BP1 knockdown in MIN6 cells. Phosphoproteomic analysis indicated that the activation of mTOR signaling, a kinase of 4E-BP1, in Tg-treated islets was mitigated by metformin. Taken together, these findings reveal a cytoprotective mechanism of metformin in β-cells, in which metformin suppresses ER stress-induced apoptosis through 4E-BP1-mediated inhibition of mRNA translation and modulation of mTOR signaling. This study highlights a β-cell-intrinsic action of metformin that may contribute to its long-term therapeutic benefits in diabetes management.

PMID:41962652 | DOI:10.1016/j.metabol.2026.156607

A Comprehensive Graph Pooling Benchmark: Effectiveness, Robustness and Generalizability

arXiv:2406.09031v5 Announce Type: replace-cross Abstract: Graph pooling has gained attention for its ability to obtain effective node and graph representations for various downstream tasks. Despite the recent surge in graph pooling approaches, there is a lack of standardized experimental settings and fair benchmarks to evaluate their performance. To address this issue, we have constructed a comprehensive benchmark that includes 17 graph pooling methods and 28 different graph datasets. This benchmark systematically assesses the performance of graph pooling methods in three dimensions, i.e., effectiveness, robustness, and generalizability. We first evaluate the performance of these graph pooling approaches across different tasks including graph classification, graph regression and node classification. Then, we investigate their performance under potential noise attacks and out-of-distribution shifts in real-world scenarios. We also involve detailed efficiency analysis, backbone analysis, parameter analysis and visualization to provide more evidence. Extensive experiments validate the strong capability and applicability of graph pooling approaches in various scenarios, which can provide valuable insights and guidance for deep geometric learning research. The source code of our benchmark is available at https://github.com/goose315/Graph_Pooling_Benchmark.
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