CoChat
Sign Up
Cited by 10Diabetes and associated disordersBioinformatics and Genomic NetworksDiabetes Management and Research

Elucidate multidimensionality of type 1 diabetes mellitus heterogeneity by multifaceted information

Open access
Chen, Shaw-Ji, Cheng, Jen-Liang, Lee, Sheng-An, Wang, Tse-Yi, Jang, Jyy-Yu, Chen, Kuang-Chi·2021·Scientific Reports
DOI 10.1038/s41598-021-00388-2PMC PMC8545927
Open this paper in CoChat — free accountOpen PDF

Abstract

Type 1 diabetes (T1D) is an autoimmune disease. Different factors, including genetics and viruses may contribute to T1D, but the causes of T1D are not fully known, and there is currently no cure. The advent of high-throughput technologies has revolutionized the field of medicine and biology, and analysis of multi-source data along with clinical information has brought a better understanding of the mechanisms behind disease pathogenesis. The aim of this work was the development of a data repository linking clinical information and interactome studies in T1D. To address this goal, we analyzed the electronic health records and online databases of genes, proteins, miRNAs, and pathways to have a global view of T1D. There were common comorbid diseases such as anemia, hypertension, vitreous diseases, renal diseases, and atherosclerosis in the phenotypic disease networks. In the protein-protein interaction network, CASP3 and TNF were date-hub proteins involved in several pathways. Moreover, CTNNB1, IGF1R, and STAT3 were hub proteins, whereas miR-155-5p, miR-34a-5p, miR-23-3p, and miR-20a-5p were hub miRNAs in the gene-miRNA interaction network. Multiple levels of information including genetic, protein, miRNA and clinical data resulted in multiple results, which suggests the complementarity of multiple sources. With the integration of multifaceted information, it will shed light on the mechanisms underlying T1D; the provided data and repository has utility in understanding phenotypic disease networks for the potential development of comorbidities in T1D patients as well as the clues for further research on T1D comorbidities.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41598-021-00388-2

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • miR-23-3p is implicated in the occurrence and development of diabetes as a regulatory hub.

    “Additionally, miR-23–3p is implicated in the occurrence and development of diabetes as a regulatory hub [14,15].”

    From Recent advances of miR-23 in human diseases and growth development (2025) · cites this paper as [15]

    The Results section, “MiRNAs related to T1D” (page unavailable), associates miR-23a-3p and miR-23b-3p with type 1 diabetes and describes their role in β-cell destruction. The copy does not identify an unsuffixed “miR-23-3p” as a regulatory hub, nor does this T1D-specific discussion establish the claim for diabetes generally. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

The citation lists haven’t been fetched yet

References 64 · Cited by 10

Research works printed but not linked: 60

  1. [1]American Diabetes Association Classification and diagnosis of diabetes Diabetes Care 2016 39 Suppl 1 S13 22 10.2337/dc16-S005 26696675 · DOI
  2. [3]Jain P Systems biology approach reveals genome to phenome correlation in type 2 diabetes PLoS ONE 2013 8 e53522 10.1371/journal.pone.0053522 23308243 PMC3538588 · DOI
  3. [4]Safari-Alighiarloo N Taghizadeh M Mohammad Tabatabaei S Namaki S Rezaei-Tavirani M Identification of common key genes and pathways between type 1 diabetes and multiple sclerosis using transcriptome and interactome analysis Endocrine 2020 68 81 92 10.1007/s12020-019-02181-8 31912409 · DOI
  4. [5]Capobianco E Systems and precision medicine approaches to diabetes heterogeneity: a Big Data perspective Clin. Transl. Med. 2017 6 23 10.1186/s40169-017-0155-4 28744848 PMC5526830 · DOI · Paper page
  5. [6]Barabasi AL Network medicine–from obesity to the "diseasome" N. Engl. J. Med. 2007 357 404 407 10.1056/NEJMe078114 17652657 · DOI
  6. [7]Barabasi AL Gulbahce N Loscalzo J Network medicine: a network-based approach to human disease Nat. Rev. Genet. 2011 12 56 68 10.1038/nrg2918 21164525 PMC3140052 · DOI · Paper page
  7. [8]Vidal M Cusick ME Barabasi AL Interactome networks and human disease Cell 2011 144 986 998 10.1016/j.cell.2011.02.016 21414488 PMC3102045 · DOI · Paper page
  8. [9]McColl ER Asthana R Paine MF Piquette-Miller M The age of omics-driven precision medicine Clin. Pharmacol. Ther. 2019 106 477 481 10.1002/cpt.1532 31407329 · DOI
  9. [10]Hidalgo CA Blumm N Barabasi AL Christakis NA A dynamic network approach for the study of human phenotypes PLoS Comput. Biol. 2009 5 e1000353 10.1371/journal.pcbi.1000353 19360091 PMC2661364 · DOI
  10. [11]Klimek P Kautzky-Willer A Chmiel A Schiller-Fruhwirth I Thurner S Quantification of diabetes comorbidity risks across life using nation-wide big claims data PLoS Comput. Biol. 2015 11 e1004125 10.1371/journal.pcbi.1004125 25855969 PMC4391714 · DOI
  11. [12]von Mering C Comparative assessment of large-scale data sets of protein-protein interactions Nature 2002 417 399 403 10.1038/nature750 12000970 · DOI
  12. [13]Ran J Construction and analysis of the protein-protein interaction network related to essential hypertension BMC Syst. Biol. 2013 7 32 10.1186/1752-0509-7-32 23587307 PMC3641020 · DOI
  13. [14]Chen SJ Liao DL Chen CH Wang TY Chen KC Construction and analysis of protein-protein interaction network of heroin use disorder Sci. Rep. 2019 9 4980 10.1038/s41598-019-41552-z 30899073 PMC6428805 · DOI
  14. [15]Navarro S Molecular Biology Gene to Proteins 2019 ED-Tech Press
  15. [16]Stelzl U A human protein-protein interaction network: a resource for annotating the proteome Cell 2005 122 957 968 10.1016/j.cell.2005.08.029 16169070 · DOI
  16. [17]Bartel DP MicroRNAs: genomics, biogenesis, mechanism, and function Cell 2004 116 281 297 10.1016/s0092-8674(04)00045-5 14744438 · DOI
  17. [18]Baltimore D Boldin MP O'Connell RM Rao DS Taganov KD MicroRNAs: new regulators of immune cell development and function Nat. Immunol. 2008 9 839 845 10.1038/ni.f.209 18645592 · DOI
  18. [20]Kann MG Protein interactions and disease: computational approaches to uncover the etiology of diseases Brief Bioinform. 2007 8 333 346 10.1093/bib/bbm031 17638813 · DOI
  19. [21]The Gene Ontology Consortium The gene ontology resource: 20 years and still going strong Nucleic Acids Res. 2019 47 D330 D338 10.1093/nar/gky1055 30395331 PMC6323945 · DOI · Paper page
  20. [22]Kanehisa M Goto S KEGG: Kyoto encyclopedia of genes and genomes Nucleic Acids Res. 2000 28 27 30 10.1093/nar/28.1.27 10592173 PMC102409 · DOI · Paper page
  21. [23]Kanehisa M Sato Y Kawashima M Furumichi M Tanabe M KEGG as a reference resource for gene and protein annotation Nucleic Acids Res 2016 44 D457 462 10.1093/nar/gkv1070 26476454 PMC4702792 · DOI · Paper page
  22. [24]Laakso M Pyorala K Age of onset and type of diabetes Diabetes Care 1985 8 114 117 10.2337/diacare.8.2.114 3873328 · DOI
  23. [25]Amberger JS Bocchini CA Schiettecatte F Scott AF Hamosh A OMIM.org: Online Mendelian Inheritance in Man (OMIM(R)), an online catalog of human genes and genetic disorders Nucleic Acids Res. 2015 43 D789 D798 10.1093/nar/gku1205 25428349 PMC4383985 · DOI · Paper page
  24. [26]Szklarczyk D STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets Nucleic Acids Res. 2019 47 D607 D613 10.1093/nar/gky1131 30476243 PMC6323986 · DOI · Paper page
  25. [27]Yu H Kim PM Sprecher E Trifonov V Gerstein M The importance of bottlenecks in protein networks: correlation with gene essentiality and expression dynamics PLoS Comput. Biol. 2007 3 e59 10.1371/journal.pcbi.0030059 17447836 PMC1853125 · DOI
  26. [28]Watts DJ Strogatz SH Collective dynamics of 'small-world' networks Nature 1998 393 440 442 10.1038/30918 9623998 · DOI · Paper page
  27. [29]Bastian, M., Heymann, S. & Jacomy, M. in International AAAI Conference on Weblogs and Social Media (AAAI, 2009).
  28. [30]Assmann TS Recamonde-Mendoza M De Souza BM Crispim D MicroRNA expression profiles and type 1 diabetes mellitus: systematic review and bioinformatic analysis Endocr. Connect. 2017 6 773 790 10.1530/EC-17-0248 28986402 PMC5682418 · DOI
  29. [31]Assmann TS MicroRNA expression profile in plasma from type 1 diabetic patients: case-control study and bioinformatic analysis Diabetes Res. Clin. Pract. 2018 141 35 46 10.1016/j.diabres.2018.03.044 29679626 · DOI
  30. [32]Huang Z HMDD v3.0: a database for experimentally supported human microRNA-disease associations Nucleic Acids Res. 2019 47 D1013 D1017 10.1093/nar/gky1010 30364956 PMC6323994 · DOI
  31. [33]Kanehisa M Furumichi M Tanabe M Sato Y Morishima K KEGG: new perspectives on genomes, pathways, diseases and drugs Nucleic Acids Res. 2017 45 D353 D361 10.1093/nar/gkw1092 27899662 PMC5210567 · DOI · Paper page
  32. [34]Han JD Evidence for dynamically organized modularity in the yeast protein-protein interaction network Nature 2004 430 88 93 10.1038/nature02555 15190252 · DOI
  33. [35]Grieco FA MicroRNAs miR-23a-3p, miR-23b-3p, and miR-149-5p regulate the expression of proapoptotic BH3-only proteins DP5 and PUMA in human pancreatic beta-cells Diabetes 2017 66 100 112 10.2337/db16-0592 27737950 PMC5204315 · DOI
  34. [36]Pan W miR-192 is upregulated in T1DM, regulates pancreatic beta-cell development and inhibits insulin secretion through suppressing GLP-1 expression Exp. Ther. Med. 2018 16 2717 2724 10.3892/etm.2018.6453 30186503 PMC6122452 · DOI
  35. [37]Malachowska B Temporal dynamics of serum let-7g expression mirror the decline of residual beta-cell function in longitudinal observation of children with type 1 diabetes Pediatr. Diabetes 2018 19 1407 1415 10.1111/pedi.12783 30259606 · DOI
  36. [38]Azhir Z Dehghanian F Hojati Z Increased expression of microRNAs, miR-20a and miR-326 in PBMCs of patients with type 1 diabetes Mol. Biol. Rep. 2018 45 1973 1980 10.1007/s11033-018-4352-z 30194557 · DOI
  37. [39]Chang L Zhou G Soufan O Xia J miRNet 2.0: network-based visual analytics for miRNA functional analysis and systems biology Nucleic Acids Res. 2020 48 W244 W251 10.1093/nar/gkaa467 32484539 PMC7319552 · DOI
  38. [40]Rewers M The Environmental Determinants of Diabetes in the Young (TEDDY) study: 2018 update Curr. Diab. Rep. 2018 18 136 10.1007/s11892-018-1113-2 30353256 PMC6415767 · DOI
  39. [41]Mandrup-Poulsen T beta-cell apoptosis: stimuli and signaling Diabetes 2001 50 Suppl 1 S58 63 10.2337/diabetes.50.2007.s58 11272204 · DOI
  40. [42]Liadis N Caspase-3-dependent beta-cell apoptosis in the initiation of autoimmune diabetes mellitus Mol Cell Biol 2005 25 3620 3629 10.1128/MCB.25.9.3620-3629.2005 15831467 PMC1084289 · DOI
  41. [43]Liadis N Distinct in vivo roles of caspase-8 in beta-cells in physiological and diabetes models Diabetes 2007 56 2302 2311 10.2337/db06-1771 17563067 · DOI
  42. [44]Zorena K Relationship between serum transforming growth factor beta 1 concentrations and the duration of type 1 diabetes mellitus in children and adolescents Mediators Inflamm. 2013 2013 849457 10.1155/2013/849457 24222720 PMC3810184 · DOI
  43. [45]Flores L Transforming growth factor beta at clinical onset of type 1 diabetes mellitus. A pilot study Diabet. Med. 2004 21 818 822 10.1111/j.1464-5491.2004.01242.x 15270783 · DOI
  44. [46]Zamani M Tabatabaiefar MA Mosayyebi S Mashaghi A Mansouri P Possible association of the CD4 gene polymorphism with vitiligo in an Iranian population Clin. Exp. Dermatol. 2010 35 521 524 10.1111/j.1365-2230.2009.03667.x 19843086 · DOI
  45. [47]Ishigame H Excessive Th1 responses due to the absence of TGF-beta signaling cause autoimmune diabetes and dysregulated Treg cell homeostasis Proc. Natl. Acad. Sci. U. S. A. 2013 110 6961 6966 10.1073/pnas.1304498110 23569233 PMC3637710 · DOI
  46. [48]Arroyo-Jousse V Garcia-Diaz DF Codner E Perez-Bravo F Epigenetics in type 1 diabetes: TNFa gene promoter methylation status in Chilean patients with type 1 diabetes mellitus Br. J. Nutr. 2016 116 1861 1868 10.1017/S0007114516003846 27890035 · DOI
  47. [49]Gohda T Circulating TNF receptors 1 and 2 predict stage 3 CKD in type 1 diabetes J. Am. Soc. Nephrol. 2012 23 516 524 10.1681/ASN.2011060628 22266664 PMC3294299 · DOI
  48. [51]Vidotti DB High glucose concentration stimulates intracellular renin activity and angiotensin II generation in rat mesangial cells Am. J. Physiol. Renal. Physiol. 2004 286 F1039 1045 10.1152/ajprenal.00371.2003 14722017 · DOI
  49. [52]Boucher J Tseng YH Kahn CR Insulin and insulin-like growth factor-1 receptors act as ligand-specific amplitude modulators of a common pathway regulating gene transcription J. Biol. Chem. 2010 285 17235 17245 10.1074/jbc.M110.118620 20360006 PMC2878077 · DOI
  50. [53]Azadegan-Dehkordi F Bagheri N Shirzad H Rafieian-Kopaei M The role of Th1 and Th17 cells in glomerulonephritis J. Nephropathol. 2015 4 32 37 10.12860/jnp.2015.07 25964886 PMC4417667 · DOI
  51. [54]Song N Thaiss F Guo L NFkappaB and kidney injury Front. Immunol. 2019 10 815 10.3389/fimmu.2019.00815 31040851 PMC6477040 · DOI
  52. [55]Al-Lamki RS Mayadas TN TNF receptors: signaling pathways and contribution to renal dysfunction Kidney Int. 2015 87 281 296 10.1038/ki.2014.285 25140911 · DOI
  53. [56]Lee HH Relative prevalence and risk factors of HTLV-I and HTLV-II infection in US blood donors Lancet 1991 337 1435 1439 10.1016/0140-6736(91)93126-t 1675317 · DOI
  54. [57]Zendehdel K Cancer incidence in patients with type 1 diabetes mellitus: a population-based cohort study in Sweden J. Natl. Cancer Inst. 2003 95 1797 1800 10.1093/jnci/djg105 14652242 · DOI
  55. [58]Wang K Comparative genetic analysis of inflammatory bowel disease and type 1 diabetes implicates multiple loci with opposite effects Hum. Mol. Genet. 2010 19 2059 2067 10.1093/hmg/ddq078 20176734 PMC2860894 · DOI
  56. [59]Coppieters KT Boettler T von Herrath M Virus infections in type 1 diabetes Cold Spring Harb. Perspect. Med. 2012 2 a007682 10.1101/cshperspect.a007682 22315719 PMC3253029 · DOI
  57. [60]Sur S In silico analysis reveals interrelation of enriched pathways and genes in type 1 diabetes Immunogenetics 2020 72 399 412 10.1007/s00251-020-01177-3 32860078 · DOI
  58. [61]Safari-Alighiarloo N Identification of new key genes for type 1 diabetes through construction and analysis of protein-protein interaction networks based on blood and pancreatic islet transcriptomes J. Diabetes 2017 9 764 777 10.1111/1753-0407.12483 27625010 · DOI
  59. [62]Lu JM System network analysis of genomics and transcriptomics data identified type 1 diabetes-associated pathway and genes Genes Immun. 2019 20 500 508 10.1038/s41435-018-0045-9 30245508 PMC6431577 · DOI
  60. [63]Szymczak F Colli ML Mamula MJ Evans-Molina C Eizirik DL Gene expression signatures of target tissues in type 1 diabetes, lupus erythematosus, multiple sclerosis, and rheumatoid arthritis Sci. Adv. 2021 7 eabd7600 10.1126/sciadv.abd7600 33523973 PMC7787485 · DOI

Authors
Chen, S.-J., Cheng, J.-L., Lee, S.-A., Wang, T.-Y., Jang, J.-Y., & Chen, K.-C.
Affiliations
  • Mackay Memorial Hospital (TW)
  • Tzu Chi University (TW)
  • Kainan University (TW)
Corresponding author
Chen, Kuang-Chi
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2021-10-25
Volume
11
Issue
1
Pages
20965-20965
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/s41598-021-00388-2
PMC
PMC8545927
OpenAlex
W3208689631
Cited by
10
References
64
Citations by year
2024: 2 · 2023: 7 · 2021: 1
FWCI
1.41
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, other-oa)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Mackay Memorial Hospital (TTMMH-107-11)
  • Ministry of Science and Technology, Taiwan (NSC 102-2221-E-320-008)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Diabetes and associated disorders
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
Keywords
type 1 diabetes, disease heterogeneity, comorbidities, protein-protein interaction network, hub proteins, hub miRNAs, caspase-3, TNF-α, CTNNB1, IGF-1 receptor, STAT3, microRNA, electronic health records, data integration, pathogenesis
MeSH terms
Diabetes Mellitus, Type 1, Female, Humans, Male, Sex Characteristics, Comorbidity, Databases, Factual, MicroRNAs, Genetic Association Studies, Electronic Health Records, Protein Interaction Maps

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review