Multi-omics analysis reveals drivers of loss of β-cell function after newly diagnosed autoimmune type 1 diabetes: An INNODIA multicenter study.
disease progression
multi‐omics
type 1 diabetes
Journal
Diabetes/metabolism research and reviews
ISSN: 1520-7560
Titre abrégé: Diabetes Metab Res Rev
Pays: England
ID NLM: 100883450
Informations de publication
Date de publication:
Jul 2024
Jul 2024
Historique:
revised:
30
04
2024
received:
06
12
2023
accepted:
07
05
2024
medline:
4
7
2024
pubmed:
4
7
2024
entrez:
4
7
2024
Statut:
ppublish
Résumé
Heterogeneity in the rate of β-cell loss in newly diagnosed type 1 diabetes patients is poorly understood and creates a barrier to designing and interpreting disease-modifying clinical trials. Integrative analyses of baseline multi-omics data obtained after the diagnosis of type 1 diabetes may provide mechanistic insight into the diverse rates of disease progression after type 1 diabetes diagnosis. We collected samples in a pan-European consortium that enabled the concerted analysis of five different omics modalities in data from 97 newly diagnosed patients. In this study, we used Multi-Omics Factor Analysis to identify molecular signatures correlating with post-diagnosis decline in β-cell mass measured as fasting C-peptide. Two molecular signatures were significantly correlated with fasting C-peptide levels. One signature showed a correlation to neutrophil degranulation, cytokine signalling, lymphoid and non-lymphoid cell interactions and G-protein coupled receptor signalling events that were inversely associated with a rapid decline in β-cell function. The second signature was related to translation and viral infection was inversely associated with change in β-cell function. In addition, the immunomics data revealed a Natural Killer cell signature associated with rapid β-cell decline. Features that differ between individuals with slow and rapid decline in β-cell mass could be valuable in staging and prediction of the rate of disease progression and thus enable smarter (shorter and smaller) trial designs for disease modifying therapies as well as offering biomarkers of therapeutic effect.
Substances chimiques
Biomarkers
0
C-Peptide
0
Types de publication
Journal Article
Multicenter Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
e3833Subventions
Organisme : Innovative Medicine Initiative 2 Joint Undertaking
Organisme : European Federation of Pharmaceutical Industries and Associations
Organisme : European Union's Horizon 2020 research and innovation program
Organisme : Leona M. and Harry B. Helmsley Charitable Trust
Organisme : JDRF
Informations de copyright
© 2024 The Author(s). Diabetes/Metabolism Research and Reviews published by John Wiley & Sons Ltd.
Références
Warshauer JT, Bluestone JA, Anderson MS. New frontiers in the treatment of type 1 diabetes. Cell Metab. 2020;31(1):46‐61. https://doi.org/10.1016/j.cmet.2019.11.017
International Diabetes Federation. IDF Diabetes Atlas. 10th ed.; 2021. Published online.
Miller RG, Orchard TJ. Understanding metabolic memory: a tale of two studies. Diabetes. 2020;69(3):291‐299. https://doi.org/10.2337/db19‐0514
Rawshani A, Sattar N, Franzén S, et al. Excess mortality and cardiovascular disease in young adults with type 1 diabetes in relation to age at onset: a nationwide, register‐based cohort study. Lancet. 2018;392(10146):477‐486. https://doi.org/10.1016/S0140‐6736(18)31506‐X
Tatovic D, Dayan CM. Replacing insulin with immunotherapy: time for a paradigm change in Type 1 diabetes. Diabet Med J Br Diabet Assoc. 2021;38(12):e14696. https://doi.org/10.1111/dme.14696
Battaglia M, Ahmed S, Anderson MS, et al. Introducing the endotype concept to address the challenge of disease heterogeneity in type 1 diabetes. Diabetes Care. 2020;43(1):5‐12. https://doi.org/10.2337/dc19‐0880
Weston CS, Boehm BO, Pozzilli P. Type 1 diabetes: a new vision of the disease based on endotypes. Diabetes Metab Res Rev. 2024;40(2):e3770. https://doi.org/10.1002/dmrr.3770
Limonte CP, Valo E, Montemayor D, et al. A targeted multiomics approach to identify biomarkers associated with rapid eGFR decline in type 1 diabetes. Am J Nephrol. 2020;51(10):839‐848. https://doi.org/10.1159/000510830
Speake C, Skinner SO, Berel D, et al. A composite immune signature parallels disease progression across T1D subjects. JCI Insight. 2019;4(23). https://doi.org/10.1172/jci.insight.126917
Yeo L, Woodwyk A, Sood S, et al. Autoreactive T effector memory differentiation mirrors β cell function in type 1 diabetes. J Clin Invest. 2018;128(8):3460‐3474. https://doi.org/10.1172/JCI120555
Dunger DB, Bruggraber SFA, Mander AP, et al. INNODIA Master Protocol for the evaluation of investigational medicinal products in children, adolescents and adults with newly diagnosed type 1 diabetes. Trials. 2022;23(1):414. https://doi.org/10.1186/s13063‐022‐06259‐z
Besser REJ, Shields BM, Casas R, Hattersley AT, Ludvigsson J. Lessons from the mixed‐meal tolerance test. Diabetes Care. 2013;36(2):195‐201. https://doi.org/10.2337/dc12‐0836
Maddaloni E, Bolli GB, Frier BM, et al. C‐peptide determination in the diagnosis of type of diabetes and its management: a clinical perspective. Diabetes Obes Metab. 2022;24(10):1912‐1926. https://doi.org/10.1111/dom.14785
Bates D, Mächler M, Bolker B, Walker S. Fitting linear mixed‐effects models using lme4. J Stat Softw. 2015;67:1‐48. https://doi.org/10.18637/jss.v067.i01
Knip M, Virtanen SM, Seppä K, et al. Dietary intervention in infancy and later signs of beta‐cell autoimmunity. N Engl J Med. 2010;363(20):1900‐1908. https://doi.org/10.1056/NEJMoa1004809
Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA‐seq data with DESeq2. Genome Biol. 2014;15(12):550. https://doi.org/10.1186/s13059‐014‐0550‐8
Multi‐Omics Factor Analysis—a framework for unsupervised integration of multi‐omics data sets Mol Syst Biol. 2023;Accessed November 30, 2023. https://www.embopress.org/doi/full/10.15252/msb.20178124
Gene set enrichment analysis: a knowledge‐based approach for interpreting genome‐wide expression profiles | Proc Natl Acad Sci USA. 2023;Accessed November 30, 2023. https://www.pnas.org/doi/full/10.1073/pnas.0506580102
Frost HR, Li Z, Moore JH. Principal component gene set enrichment (PCGSE). BioData Min. 2015;8(1):25. https://doi.org/10.1186/s13040‐015‐0059‐z
Szklarczyk D, Gable AL, Nastou KC, et al. The STRING database in 2021: customizable protein–protein networks, and functional characterization of user‐uploaded gene/measurement sets. Nucleic Acids Res. 2021;49(D1):D605‐D612. https://doi.org/10.1093/nar/gkaa1074
Huang HY, Lin YCD, Cui S, et al. miRTarBase update 2022: an informative resource for experimentally validated miRNA–target interactions. Nucleic Acids Res. 2022;50(D1):D222‐D230. https://doi.org/10.1093/nar/gkab1079
Ismailova A, Salehi‐Tabar R, Dimitrov V, Memari B, Barbier C, White JH. Identification of a forkhead box protein transcriptional network induced in human neutrophils in response to inflammatory stimuli. Front Immunol. 2023;14:1123344. https://doi.org/10.3389/fimmu.2023.1123344
Zou Y, Gong N, Cui Y, et al. Forkhead box P1 (FOXP1) transcription factor regulates hepatic glucose homeostasis. J Biol Chem. 2015;290(51):30607‐30615. https://doi.org/10.1074/jbc.M115.681627
Dupuis J, Langenberg C, Prokopenko I, et al. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat Genet. 2010;42(2):105‐116. https://doi.org/10.1038/ng.520
Koscielny G, An P, Carvalho‐Silva D, et al. Open Targets: a platform for therapeutic target identification and validation. Nucleic Acids Res. 2017;45(Database issue):D985‐D994. https://doi.org/10.1093/nar/gkw1055
Chen B, Khodadoust MS, Liu CL, Newman AM, Alizadeh AA. Profiling tumor infiltrating immune cells with CIBERSORT. In: vonStechow L, ed. Cancer Systems Biology: Methods and Protocols. Methods in Molecular Biology. Springer; 2018:243‐259. https://doi.org/10.1007/978‐1‐4939‐7493‐1_12
Harsunen MH, Puff R, D’Orlando O, et al. Reduced blood leukocyte and neutrophil numbers in the pathogenesis of type 1 diabetes. Horm Metab Res Horm Stoffwechselforschung Horm Metab. 2013;45(6):467‐470. https://doi.org/10.1055/s‐0032‐1331226
Valle A, Giamporcaro GM, Scavini M, et al. Reduction of circulating neutrophils precedes and accompanies type 1 diabetes. Diabetes. 2013;62(6):2072‐2077. https://doi.org/10.2337/db12‐1345
Wang Y, Xiao Y, Zhong L, et al. Increased neutrophil elastase and proteinase 3 and augmented NETosis are closely associated with β‐cell autoimmunity in patients with type 1 diabetes. Diabetes. 2014;63(12):4239‐4248. https://doi.org/10.2337/db14‐0480
Vecchio F, Buono NL, Stabilini A, et al. Abnormal neutrophil signature in the blood and pancreas of presymptomatic and symptomatic type 1 diabetes. JCI Insight. 2018;3(18). https://doi.org/10.1172/jci.insight.122146
Popp SK, Vecchio F, Brown DJ, et al. Circulating platelet‐neutrophil aggregates characterize the development of type 1 diabetes in humans and NOD mice. JCI Insight. 2022;7(2). https://doi.org/10.1172/jci.insight.153993
Roles of Neutrophil Granule Proteins in Orchestrating Inflammation and Immunity ‐ Othman ‐ 2022 ‐ the FEBS Journal ‐ Wiley Online Library. Accessed November 30, 2023. https://febs.onlinelibrary.wiley.com/doi/full/10.1111/febs.15803
Vehik K, Lynch KF, Wong MC, et al. Prospective virome analyses in young children at increased genetic risk for type 1 diabetes. Nat Med. 2019;25(12):1865‐1872. https://doi.org/10.1038/s41591‐019‐0667‐0
Moulder R, Välikangas T, Hirvonen MK, et al. Targeted serum proteomics of longitudinal samples from newly diagnosed youth with type 1 diabetes distinguishes markers of disease and C‐peptide trajectory. Diabetologia. 2023;66(11):1983‐1996. https://doi.org/10.1007/s00125‐023‐05974‐9
Suomi T, Starskaia I, Kalim UU, et al. Gene expression signature predicts rate of type 1 diabetes progression. EBioMedicine. 2023;92:104625. https://doi.org/10.1016/j.ebiom.2023.104625
Wilson RG, Anderson J, Shenton BK, White MD, Taylor RM, Proud G. Natural killer cells in insulin dependent diabetes mellitus. Br Med J Clin Res Ed. 1986;293(6541):244. https://doi.org/10.1136/bmj.293.6541.244
Fitas AL, Martins C, Borrego LM, et al. Immune cell and cytokine patterns in children with type 1 diabetes mellitus undergoing a remission phase: a longitudinal study. Pediatr Diabetes. 2018;19(5):963‐971. https://doi.org/10.1111/pedi.12671
Oras A, Peet A, Giese T, Tillmann V, Uibo R. A study of 51 subtypes of peripheral blood immune cells in newly diagnosed young type 1 diabetes patients. Clin Exp Immunol. 2019;198(1):57‐70. https://doi.org/10.1111/cei.13332