{"doi":"10.1111/1753-0407.70026","title":"β‐Cell gene expression stress signatures in types 1 and 2 diabetes","abstract":"Diabetes mellitus (DM) is a chronic metabolic disorder that occurs when pancreatic β-cells can no longer produce enough insulin to maintain normal blood glucose levels. DM presently affects 10.5% of the world adult population. While T1D is a disease of “mistaken identity,” where the immune system attacks and destroys pancreatic β-cells in the context of islet inflammation (insulitis),1 T2D is associated with sedentary lifestyles and high-fat diets, typically involving ineffective use of insulin and progressive loss of β-cell function.1 Both diseases result from multifaceted interactions between genetic and environmental factors, with β-cell failure as the core mechanism of pathogenesis. In T1D, the disease arises from a complex interaction between immune cells and β-cells, involving chemokine and cytokine release and signals from stressed or dying β-cells that attract and activate immune cells to the islets and lead to β-cell apoptosis.2 Beyond the destruction of β-cells by the immune system, it is now accepted that stress and impaired function of these cells significantly contribute to the onset and progression of the disease.1-3 In T2D, the disease is driven by an interplay between insulin resistance and β-cell dysfunction in genetically susceptible individuals, with metabolic stress and perhaps also inflammation impairing insulin secretion and eventually β-cell survival, although to a less degree than in T1D.1, 4, 5 The complexity of diabetes pathogenesis makes it very difficult to identify specific causes of the disease, which hampers the development of adequate therapies to protect β-cells and thus prevent disease. This difficulty was well described by Tolstoy, in his masterpiece “War and Peace,” published 1869 (in this case addressing the Napoleonic war against tsarist Russia): “…the impulse to seek causes is innate in the soul of man. And the human intellect, with no inkling on the immense variety and complexity of circumstances conditioning a phenomena, any one of which may be separately conceived of as the cause of it, snatches the first and most easily understood approximation, and says here is the cause.” In the context of pathophysiology, this had led to the simplistic view of “one gene, one protein, one disease.” However, with the sequencing of the human genome and the subsequent advent of omics technologies that allow interrogating the whole system in a parallel and often also in a sequential way, our understanding of complex diseases changed: we now focus on the dysfunction of gene and transcription factor networks and of post-transcriptional and post-translational mechanisms. The advent of single-cell RNA sequencing (scRNA-seq) has provided a new tool for dissecting the molecular intricacies underlying pancreatic islet cells stress and thus addressing mechanisms of disease closer to its real “immense variety and complexity of circumstances.” A recent study by Maestas et al. focused on β-cell stress by utilizing in vitro models to investigate the effects of ER stress inducers (thapsigargin, brefeldin A) and inflammatory cytokines (IFNγ, IL1β, TNFα, and their combination) on β-cells, using islets from five donors for the scRNA-seq analysis.6 This study provided very interesting information, but the limited number of donors and the use of in vitro stress conditions to model T1D and T2D may have not fully captured the in vivo disease context. To further investigate the stress signatures potentially present in the human disease, we presently analyzed data from the Human Pancreas Analysis Program (HPAP).7, 8 The HPAP provides an extensive public database with scRNA-seq data from islets from non-diabetic individuals or individuals affected by T1D or T2D, offering a valuable resource to study disease-specific transcriptional profiles of β-cells. We thus re-analyzed scRNA-seq data from the HPAP database up to 12.2023, which includes 10X Genomics data for islets from 27 non-diabetic, 7 T1D, and 10 T2D individuals, using ","journal":"Journal of Diabetes","year":2024,"id":443608,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":8,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9613,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":262820,"name":"Décio L. Eizirik","orcid":"0000-0003-2453-5889","position":1,"is_corresponding":false},{"id":956963,"name":"Xiaoyan Yi","orcid":"0000-0001-6995-8573","position":0,"is_corresponding":true}],"reference_count":20,"raw_metadata":null,"created_at":"2026-07-19T02:01:29.001453Z","pmid":"39505716","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}