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Volume 21, Issue 2
  • ISSN: 1573-3998
  • E-ISSN: 1875-6417

Abstract

Background

Renal disease in T2DM could arise independently of hyperglycemia, aka non diabetic kidney disease. Its prevalence ranges from 33% to 72.5% among T2DM patients. Specific molecular signatures that distinguish Diabetic Nephropathy from NDKD (FSGS) in T2DM might provide new targets for CKD management.

Methods

Five original GEO microarray DN and FSGS datasets were evaluated (GSE111154, GSE96804, GSE125779, GSE129973 and GSE121233). Each of the three groups (DN, FSGS, and Controls) had equal renal transcriptome data (n=32) included in the analysis to eliminate bias. The DEGs were identified using TAC4.0. Pathway analysis was performed on the discovered genes aligned to official gene symbols using Reactome, followed by functional gene enrichment analysis using Funrich, Enrichr. STRING and Network analyst investigated PPI, followed by Webgestalt's pathway erichment. Finally, using the Targetscan 7.0 and DIANA tools, filtered differential microRNAs downregulated in DN were evaluated for target identification.

Results

Between the three groups, DN, FSGS, and Control, a total of 194 DEGs with foldchange, >2 & <-2 and value 0.01 were found in the renal transcriptome. In comparison to control, 45 genes were elevated, particularly in DN, whereas 43 were upregulated specifically in FSGS. DN datasets were compared to FSGS in a separate analysis. FABP4, EBF1, ADIRF, and ART4 were shown to be among the substantially uregulated genes unique to DN in both analyses. The transcriptional regulation of white adipocytes was discovered by pathway analysis.

Conclusion

The molecular markers revealed might be employed as specific targets in the aetiology of DN, as well as in T2DM patients' therapeutic care.

© 2025 Bentham Science Publishers
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2024-11-19

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References

  1. RemuzziG. SchieppatiA. RuggenentiP. Clinical practice. Nephropathy in patients with type 2 diabetes.N. Engl. J. Med.2002346151145115110.1056/NEJMcp011773 11948275
     [Google Scholar]
  2. RitzE. RychlíkI. LocatelliF. HalimiS. End-stage renal failure in type 2 diabetes: A medical catastrophe of worldwide dimensions.Am. J. Kidney Dis.199934579580810.1016/S0272‑6386(99)70035‑1 10561134
     [Google Scholar]
  3. NabiO. BoursierJ. LapidusN. The burden of NAFLD in type 2 diabetic subjects from the general population: A Nationwide population-based follow-up study (NASHCO).Liver Int.202242359560610.1111/liv.15171 35066992
     [Google Scholar]
  4. XiongY. ZhouL. The signaling of cellular senescence in diabetic nephropathy.Oxid. Med. Cell. Longev.20192019749562910.1155/2019/7495629 31687085
     [Google Scholar]
  5. ZhangY. TingR.Z. YangW. Depression in Chinese patients with type 2 diabetes: Associations with hyperglycemia, hypoglycemia, and poor treatment adherence.J. Diabetes20157680080810.1111/1753‑0407.12238 25349949
     [Google Scholar]
  6. BrouwerS. van ZonS.K.R. BültmannU. RieseH. JeronimusB.F. Personality as a resource for labor market participation among individuals with chronic health conditions.Int. J. Environ. Res. Public Health20201717E624010.3390/ijerph17176240 32867344
     [Google Scholar]
  7. SadiqF. KazmiU.E.R. Comparison of social support, depression and anger in diabetic and cardiac patients.J. Pak. Med. Assoc.202171718141817 34410253
     [Google Scholar]
  8. SoniS.S. GowrishankarS. KishanA.G. RamanA. Non diabetic renal disease in type 2 diabetes mellitus.Nephrology (Carlton)200611653353710.1111/j.1440‑1797.2006.00681.x 17199793
     [Google Scholar]
  9. MogerV. KumarS.K. SakhujaV. Rapidly progressive renal failure in type 2 diabetes in the tropical environment: A clinico-pathological study.Ren. Fail.200527559560010.1080/08860220500200205 16152999
     [Google Scholar]
  10. PrakashJ. LodhaM. SinghS.K. VohraR. RajaR. Usha. Diabetic retinopathy is a poor predictor of type of nephropathy in proteinuric type 2 diabetic patients.J. Assoc. Physicians India200755412416 17879494
     [Google Scholar]
  11. PremalathaG. VidhyaK. DeepaR. RavikumarR. RemaM. MohanV. Prevalence of non-diabetic renal disease in type 2 diabetic patients in a diabetes centre in Southern India.J. Assoc. Physicians India20025011351139 12516695
     [Google Scholar]
  12. A Working Group of the International IgA Nephropathy Network and the Renal Pathology Society et al. The Oxford classification of IgA nephropathy: Pathology definitions, correlations, and reproducibility.Kidney Int200976546556
     [Google Scholar]
  13. D’AgatiV.D. KaskelF.J. FalkR.J. Focal segmental glomerulosclerosis.N. Engl. J. Med.2011365252398241110.1056/NEJMra1106556 22187987
     [Google Scholar]
  14. HricikD.E. Chung-ParkM. SedorJ.R. Glomerulonephritis.N. Engl. J. Med.19983391388889910.1056/NEJM199809243391306 9744974
     [Google Scholar]
  15. AndersH-J. HuberT.B. IsermannB. SchifferM. CKD in diabetes: Diabetic kidney disease versus nondiabetic kidney disease.Nat. Rev. Nephrol.201814636137710.1038/s41581‑018‑0001‑y 29654297
     [Google Scholar]
  16. DoshiS.M. FriedmanA.N. Diagnosis and management of type 2 diabetic kidney disease.Clin. J. Am. Soc. Nephrol.20171281366137310.2215/CJN.11111016 28280116
     [Google Scholar]
  17. RaiB. MishraP. AsifM.H. TiwariS. Identification of crucial degs and hub genes in focal segmental glomerulosclerosis: A bioinformatics study.Int J Appl Biol Pharm20211212420460
     [Google Scholar]
  18. BalakumarP. AroraM.K. ReddyJ. Anand-SrivastavaM.B. Pathophysiology of diabetic nephropathy: Involvement of multifaceted signalling mechanism.J. Cardiovasc. Pharmacol.200954212913810.1097/FJC.0b013e3181ad2190 19528810
     [Google Scholar]
  19. HuY. WangQ. WangZ. WangF. GuoX. LiG. Circulating microRNA profiles and the identification of miR-593 and miR-511 which directly target the PROP1 gene in children with combined pituitary hormone deficiency.Int. J. Mol. Med.201535235836610.3892/ijmm.2014.2016 25434367
     [Google Scholar]
  20. HanR. HuS. QinW. C3a and suPAR drive versican V1 expression in tubular cells of focal segmental glomerulosclerosis.JCI Insight2019413e13098610.1172/jci.insight.130986
     [Google Scholar]
  21. WoronieckaK.I. ParkA.S. MohtatD. ThomasD.B. PullmanJ.M. SusztakK. Transcriptome analysis of human diabetic kidney disease.Diabetes20116092354236910.2337/db10‑1181 21752957
     [Google Scholar]
  22. MenonR. OttoE.A. HooverP. Single cell transcriptomics identifies focal segmental glomerulosclerosis remission endothelial biomarker.JCI Insight202056e13326710.1172/jci.insight.133267 32107344
     [Google Scholar]
  23. TongJ. XieJ. RenH. Comparison of glomerular transcriptome profiles of adult-onset steroid sensitive focal segmental glomerulosclerosis and minimal change disease.PLoS One20151011e014045310.1371/journal.pone.0140453 26536600
     [Google Scholar]
  24. TaoJ. MarianiL. EddyS. JAK-STAT signaling is activated in the kidney and peripheral blood cells of patients with focal segmental glomerulosclerosis.Kidney Int.201894479580810.1016/j.kint.2018.05.022 30093081
     [Google Scholar]
  25. LiW. SargsyanD. WuR. DNA methylome and transcriptome alterations in high glucose-induced diabetic nephropathy cellular model and identification of novel targets for treatment by tanshinone IIA.Chem. Res. Toxicol.201932101977198810.1021/acs.chemrestox.9b00117 31525975
     [Google Scholar]
  26. MulderS. HamidiH. KretzlerM. JuW. An integrative systems biology approach for precision medicine in diabetic kidney disease.Diabetes Obes. Metab.201820Suppl. 361310.1111/dom.13416 30294956
     [Google Scholar]
  27. SrivastavaM. RaiB. Molecular mechanisms of pathways in diabetic nephropathy development in patients with T2DM - A review.Int J Appl Biol Pharm202112380392
     [Google Scholar]
  28. RitchieM.E. PhipsonB. WuD. Limma powers differential expression analyses for RNA-sequencing and microarray studies.Nucleic Acids Res.2015437e47e710.1093/nar/gkv007 25605792
     [Google Scholar]
  29. WhistlerT. ChiangC-F. LinJ-M. LonerganW. ReevesW.C. The comparison of different pre- and post-analysis filters for determination of exon-level alternative splicing events using Affymetrix arrays.J. Biomol. Tech.20102114453 20357982
     [Google Scholar]
  30. RaychaudhuriS. StuartJ.M. AltmanR.B. Principal components analysis to summarize microarray experiments: Application to sporulation time series.Pac. Symp. Biocomput.200045546610.1142/9789814447331_0043 10902193
     [Google Scholar]
  31. SidiropoulosK. ViteriG. SevillaC. Reactome enhanced pathway visualization.Bioinformatics201733213461346710.1093/bioinformatics/btx441 29077811
     [Google Scholar]
  32. CroftD. MundoA.F. HawR. The Reactome pathway knowledgebase.Nucleic Acids Res.201442Database issueD472D47710.1093/nar/gkt1102 24243840
     [Google Scholar]
  33. PathanM. KeerthikumarS. AngC.S. FunRich: An open access standalone functional enrichment and interaction network analysis tool.Proteomics201515152597260110.1002/pmic.201400515 25921073
     [Google Scholar]
  34. ChenE.Y. TanC.M. KouY. Enrichr: Interactive and collaborative HTML5 gene list enrichment analysis tool.BMC Bioinformatics201314112810.1186/1471‑2105‑14‑128 23586463
     [Google Scholar]
  35. Enrichr: A comprehensive gene set enrichment analysis web server 2016 update | Nucleic Acids Research | Oxford Academic.Available from: https://academic.oup.com/nar/article/44/W1/W90/2499357?login=true
  36. LiaoY. WangJ. JaehnigE.J. ShiZ. ZhangB. WebGestalt 2019: Gene set analysis toolkit with revamped UIs and APIs.Nucleic Acids Res.201947W1W199-20510.1093/nar/gkz401 31114916
     [Google Scholar]
  37. WangJ. VasaikarS. ShiZ. GreerM. ZhangB. WebGestalt 2017: A more comprehensive, powerful, flexible and interactive gene set enrichment analysis toolkit.Nucleic Acids Res.201745W1W130-710.1093/nar/gkx356 28472511
     [Google Scholar]
  38. KhatriP. SirotaM. ButteA.J. Ten years of pathway analysis: Current approaches and outstanding challenges.PLOS Comput. Biol.201282e100237510.1371/journal.pcbi.1002375 22383865
     [Google Scholar]
  39. SzklarczykD. FranceschiniA. WyderS. STRING v10: Protein-protein interaction networks, integrated over the tree of life.Nucleic Acids Res.201543D447D45210.1093/nar/gku1003 25352553
     [Google Scholar]
  40. SzklarczykD. GableA.L. LyonD. STRING v11: Protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets.Nucleic Acids Res.201947D1D607D61310.1093/nar/gky1131 30476243
     [Google Scholar]
  41. AgarwalV. BellG.W. NamJ-W. BartelD.P. Predicting effective microRNA target sites in mammalian mRNAs.eLife20154e0500510.7554/eLife.05005 26267216
     [Google Scholar]
  42. ParaskevopoulouM.D. GeorgakilasG. KostoulasN. DIANA-LncBase: Experimentally verified and computationally predicted microRNA targets on long non-coding RNAs.Nucleic Acids Res.201341D239D24510.1093/nar/gks1246 23193281
     [Google Scholar]
  43. DIANA TOOLS - microT-CDSAvailable from: http://diana.imis.athena-innovation.gr/DianaTools/index.php?r=microT_CDS/index
  44. FuseyaS. SuzukiR. OkadaR. Mice lacking core 1-derived O-glycan in podocytes develop transient proteinuria, resulting in focal segmental glomerulosclerosis.Biochem. Biophys. Res. Commun.202052310071013
     [Google Scholar]
  45. SerinoG. SallustioF. CoxS.N. PesceF. SchenaF.P. Abnormal miR-148b expression promotes aberrant glycosylation of IgA1 in IgA nephropathy.J. Am. Soc. Nephrol.201223581482410.1681/ASN.2011060567 22362909
     [Google Scholar]
  46. TrojnarM. Patro-MałyszaJ. Kimber-TrojnarŻ. Leszczyńska-GorzelakB. MosiewiczJ. Associations between fatty acid-binding protein 4−A proinflammatory adipokine and insulin resistance, gestational and type 2 diabetes mellitus.Cells20198322710.3390/cells8030227 30857223
     [Google Scholar]
  47. SeoD.H. NamM. JungM. Serum levels of adipocyte fatty acid-binding protein are associated with rapid renal function decline in patients with type 2 diabetes mellitus and preserved renal function.Diabetes Metab. J.202044687588610.4093/dmj.2019.0221 32662255
     [Google Scholar]
  48. Circulating Levels of Adipocyte and Epidermal Fatty Acid-Binding Proteins in Relation to Nephropathy Staging and Macrovascular Complications in Type 2 Diabetic Patients | Diabetes Care.Available from: https://care.diabetesjournals.org/content/32/1/132
  49. TorunerF. AltinovaA.E. AkturkM. The relationship between adipocyte fatty acid binding protein-4, retinol binding protein-4 levels and early diabetic nephropathy in patients with type 2 diabetes.Diabetes Res. Clin. Pract.201191220320710.1016/j.diabres.2010.11.011 21176857
     [Google Scholar]
  50. CintiS. Between brown and white: Novel aspects of adipocyte differentiation.Ann. Med.201143210411510.3109/07853890.2010.535557 21254898
     [Google Scholar]
  51. HeF. ShuY. WangX. Intensive glucose control reduces the risk effect of TRIB3, SMARCD3, and ATF6 genetic variation on diabetic vascular complications.Front. Pharmacol.20189142210.3389/fphar.2018.01422 30618737
     [Google Scholar]
  52. MicroRNA-3148 acts as molecular switch promoting malignant transformation and adipocytic differentiation of immortalized human bone marrow stromal cells via direct targeting of the SMAD2/TGFβ pathway | Cell Death Discovery.Available from: https://www.nature.com/articles/s41420-020-00312-z
  53. SchulteC. WestermannD. BlankenbergS. ZellerT. Diagnostic and prognostic value of circulating microRNAs in heart failure with preserved and reduced ejection fraction.World J. Cardiol.201571284386010.4330/wjc.v7.i12.843 26730290
     [Google Scholar]
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An In-Silico Approach to Identify Therapeutic Target and Markers Associated with Diabetic Nephropathy
Curr. Diabetes Rev. 21, E100622205872 (2025); https://doi.org/10.2174/1573399819666220610191935
/content/journals/cdr/10.2174/1573399819666220610191935
/content/journals/cdr/10.2174/1573399819666220610191935
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  • Article Type:     Research Article
Keyword(s): cytokines; diagnosis; kidney disease in diabetes; nephropathy; renal biopsy; Renal transcriptome

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