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Research Paper

Identification of fibronectin 1 (FN1) and complement component 3 (C3) as immune infiltration-related biomarkers for diabetic nephropathy using integrated bioinformatic analysis

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Pages 5386-5401 | Received 12 May 2021, Accepted 22 Jul 2021, Published online: 23 Aug 2021

References

  • Xie Y, Bowe B, Mokdad AH, et al. Analysis of the global burden of disease study highlights the global, regional, and national trends of chronic kidney disease epidemiology from 1990 to 2016. Kidney Int. 2018;94(3):567–581.
  • Alicic RZ, Rooney MT, Tuttle KR. Diabetic kidney disease: challenges, progress, and possibilities. Clin J Am Soc Nephrol. 2017;12(12):2032–2045.
  • Muskiet MHA, Wheeler DC, Heerspink HJL. New pharmacological strategies for protecting kidney function in type 2 diabetes. Lancet Diabetes Endocrinol. 2019;7(5):397–412.
  • Tuomi T, Santoro N, Caprio S, et al. The many faces of diabetes: a disease with increasing heterogeneity. Lancet. 2014;383(9922):1084–1094.
  • Tang SCW, Yiu WH. Innate immunity in diabetic kidney disease. Nat Rev Nephrol. 2020;16(4):206–222.
  • Perlman AS, Chevalier JM, Wilkinson P, et al. Serum inflammatory and immune mediators are elevated in early stage diabetic nephropathy. Ann Clin Lab Sci. 2015;45(3):256–263.
  • Hickey FB, Martin F. Role of the immune system in diabetic kidney disease. Curr Diab Rep. 2018;18(4):20.
  • Pichler R, Afkarian M, Dieter BP, et al. Immunity and inflammation in diabetic kidney disease: translating mechanisms to biomarkers and treatment targets. Am J Physiol Renal Physiol. 2017;312(4):F716–F31.
  • Araujo LS, Torquato BGS, Da Silva CA, et al. Renal expression of cytokines and chemokines in diabetic nephropathy. BMC Nephrol. 2020;21(1):308.
  • Lin M, Tang SC. Toll-like receptors: sensing and reacting to diabetic injury in the kidney. Nephrol Dial Transplant. 2014;29(4):746–754.
  • Lin M, Yiu WH, Wu HJ, et al. Toll-like receptor 4 promotes tubular inflammation in diabetic nephropathy. J Am Soc Nephrol. 2012;23(1):86–102.
  • Banba N, Nakamura T, Matsumura M, et al. Possible relationship of monocyte chemoattractant protein-1 with diabetic nephropathy. Kidney Int. 2000;58(2):684–690.
  • Wada T, Furuichi K, Sakai N, et al. Up-regulation of monocyte chemoattractant protein-1 in tubulointerstitial lesions of human diabetic nephropathy. Kidney Int. 2000;58(4):1492–1499.
  • Segerer S, Nelson PJ, Schlondorff D. Chemokines, chemokine receptors, and renal disease: from basic science to pathophysiologic and therapeutic studies. J Am Soc Nephrol. 2000;11(1):152–176.
  • Huang R, Zheng X, Wang J. Bioinformatic exploration of the immune related molecular mechanism underlying pulmonary arterial hypertension. Bioengineered. 2021;12(1):3137–3147.
  • Liu Z, Sun D, Zhu Q, et al. The screening of immune-related biomarkers for prognosis of lung adenocarcinoma. Bioengineered. 2021;12(1):1273–1285.
  • Conte F, Fiscon G, Licursi V, et al. A paradigm shift in medicine: a comprehensive review of network-based approaches. Biochim Biophys Acta Gene Regul Mech. 2020;1863(6):194416.
  • Silverman EK, Schmidt H, Anastasiadou E, et al. Molecular networks in network medicine: development and applications. Wiley Interdiscip Rev Syst Biol Med. 2020;12(6):e1489.
  • 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–D12.
  • Paci P, Fiscon G, Conte F, et al. Gene co-expression in the interactome: moving from correlation toward causation via an integrated approach to disease module discovery. NPJ Syst Biol Appl. 2021;7(1):3.
  • Zhang B, Horvath S. A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol. 2005;4:Article17.
  • Paci P, Fiscon G, Conte F, et al. Integrated transcriptomic correlation network analysis identifies COPD molecular determinants. Sci Rep. 2020;10(1):3361.
  • Fiscon G, Pegoraro S, Conte F, et al. Gene network analysis using SWIM reveals interplay between the transcription factor-encoding genes HMGA1, FOXM1, and MYBL2 in triple-negative breast cancer. FEBS Lett. 2021;595(11):1569–1586.
  • Falcone R, Conte F, Fiscon G, et al. BRAF(V600E)-mutant cancers display a variety of networks by SWIM analysis: prediction of vemurafenib clinical response. Endocrine. 2019;64(2):406–413.
  • Fiscon G, Conte F, Licursi V, et al. Computational identification of specific genes for glioblastoma stem-like cells identity. Sci Rep. 2018;8(1):7769.
  • Grimaldi AM, Conte F, Pane K, et al. The new paradigm of network medicine to analyze breast cancer phenotypes. Int J Mol Sci. 2020;21:18.
  • Fiscon G, Conte F, Farina L, et al. SAveRUNNER: a network-based algorithm for drug repurposing and its application to COVID-19. PLoS Comput Biol. 2021;17(2):e1008686.
  • Pan Y, Jiang S, Hou Q, et al. Dissection of glomerular transcriptional profile in patients with diabetic nephropathy: SRGAP2a protects podocyte structure and function. Diabetes. 2018;67(4):717–730.
  • Sircar M, Rosales IA, Selig MK, et al. Complement 7 is up-regulated in human early diabetic kidney disease. Am J Pathol. 2018;188(10):2147–2154.
  • Grayson PC, Eddy S, Taroni JN, et al. Metabolic pathways and immunometabolism in rare kidney diseases. Ann Rheum Dis. 2018;77(8):1226–1233.
  • Fan Y, Yi Z, D’Agati VD, et al. Comparison of kidney transcriptomic profiles of early and advanced diabetic nephropathy reveals potential new mechanisms for disease progression. Diabetes. 2019;68(12):2301–2314.
  • Parker HS, Leek JT, Favorov AV, et al. Preserving biological heterogeneity with a permuted surrogate variable analysis for genomics batch correction. Bioinformatics. 2014;30(19):2757–2763.
  • Bhattacharya S, Dunn P, Thomas CG, et al. ImmPort, toward repurposing of open access immunological assay data for translational and clinical research. Sci Data. 2018;5:180015.
  • Ru B, Wong CN, Tong Y, et al. TISIDB: an integrated repository portal for tumor-immune system interactions. Bioinformatics. 2019;35(20):4200–4202.
  • Breuer K, Foroushani AK, Laird MR, et al. InnateDB: systems biology of innate immunity and beyond–recent updates and continuing curation. Nucleic Acids Res. 2013;41( Databaseissue):D1228–33.
  • Ritchie ME, Phipson B, Wu D, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47.
  • Jiao X, Sherman BT, Huang Da W, et al. DAVID-WS: a stateful web service to facilitate gene/protein list analysis. Bioinformatics. 2012;28(13):1805–1806.
  • Ashburner M, Ball CA, Blake JA, et al. Gene ontology: tool for the unification of biology. The gene ontology consortium. Nat Genet. 2000;25(1):25–29.
  • Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27–30.
  • Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9:559.
  • Bader GD, Hogue CW. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics. 2003;4:2.
  • Yu G, Li F, Qin Y, et al. GOSemSim: an R package for measuring semantic similarity among GO terms and gene products. Bioinformatics. 2010;26(7):976–978.
  • Licursi V, Conte F, Fiscon G, et al. MIENTURNET: an interactive web tool for microRNA-target enrichment and network-based analysis. BMC Bioinformatics. 2019;20(1):545.
  • Tong Z, Cui Q, Wang J, et al. TransmiR v2.0: an updated transcription factor-microRNA regulation database. Nucleic Acids Res. 2019;47(D1):D253–D8.
  • Woroniecka KI, Park AS, Mohtat D, et al. Transcriptome analysis of human diabetic kidney disease. Diabetes. 2011;60(9):2354–2369.
  • ssSubramanian A, Narayan R, Corsello SM, et al. A next generation connectivity map: L1000 platform and the first 1,000,000 profiles. Cell. 2017;171(6):1437–52 e17.
  • Cooper ME. Interaction of metabolic and haemodynamic factors in mediating experimental diabetic nephropathy. Diabetologia. 2001;44(11):1957–1972.
  • Tesch GH. Diabetic nephropathy - is this an immune disorder? Clin Sci (Lond). 2017;131(16):2183–2199.
  • Wilson PC, Wu H, Kirita Y, et al. The single-cell transcriptomic landscape of early human diabetic nephropathy. Proc Natl Acad Sci U S A. 2019;116(39):19619–19625.
  • Wada J, Makino H. Inflammation and the pathogenesis of diabetic nephropathy. Clin Sci (Lond). 2013;124(3):139–152.
  • Navarro-Gonzalez JF, Mora-Fernandez C, Muros de Fuentes M, et al. Inflammatory molecules and pathways in the pathogenesis of diabetic nephropathy. Nat Rev Nephrol. 2011;7(6):327–340.
  • Sassy-Prigent C, Heudes D, Mandet C, et al. Early glomerular macrophage recruitment in streptozotocin-induced diabetic rats. Diabetes. 2000;49(3):466–475.
  • Nguyen D, Ping F, Mu W, et al. Macrophage accumulation in human progressive diabetic nephropathy. Nephrology (Carlton). 2006;11(3):226–231.
  • Klessens CQF, Zandbergen M, Wolterbeek R, et al. Macrophages in diabetic nephropathy in patients with type 2 diabetes. Nephrol Dial Transplant. 2017;32(8):1322–1329.
  • Kim SM, Lee SH, Kim YG, et al. Hyperuricemia-induced NLRP3 activation of macrophages contributes to the progression of diabetic nephropathy. Am J Physiol Renal Physiol. 2015;308(9):F993–F1003.
  • Chow FY, Nikolic-Paterson DJ, Ma FY, et al. Monocyte chemoattractant protein-1-induced tissue inflammation is critical for the development of renal injury but not type 2 diabetes in obese db/db mice. Diabetologia. 2007;50(2):471–480.
  • Chow FY, Nikolic-Paterson DJ, Ozols E, et al. Intercellular adhesion molecule-1 deficiency is protective against nephropathy in type 2 diabetic db/db mice. J Am Soc Nephrol. 2005;16(6):1711–1722.
  • Zhang X, Yang Y, Zhao Y. Macrophage phenotype and its relationship with renal function in human diabetic nephropathy. PLoS One. 2019;14(9):e0221991.
  • Huen SC, Cantley LG. Macrophages in renal injury and repair. Annu Rev Physiol. 2017;79:449–469.
  • Kushiyama T, Oda T, Yamada M, et al. Alteration in the phenotype of macrophages in the repair of renal interstitial fibrosis in mice. Nephrology (Carlton). 2011;16(5):522–535.
  • Nakazawa D, Marschner JA, Platen L, et al. Extracellular traps in kidney disease. Kidney Int. 2018;94(6):1087–1098.
  • Okon K, Stachura J. Increased mast cell density in renal interstitium is correlated with relative interstitial volume, serum creatinine and urea especially in diabetic nephropathy but also in primary glomerulonephritis. Pol J Pathol. 2007;58(3):193–197.
  • Moon JY, Jeong KH, Lee TW, et al. Aberrant recruitment and activation of T cells in diabetic nephropathy. Am J Nephrol. 2012;35(2):164–174.
  • Tampe B, Tampe D, Nyamsuren G, et al. Pharmacological induction of hypoxia-inducible transcription factor ARNT attenuates chronic kidney failure. J Clin Invest. 2018;128(7):3053–3070.
  • Zhou J, Zhou H, Liu Y, et al. Inhibition of CTCF-regulated miRNA-185-5p mitigates renal interstitial fibrosis of chronic kidney disease. Epigenomics. 2021;13(11):859–873.
  • Livingston MJ, Ding HF, Huang S, et al. Persistent activation of autophagy in kidney tubular cells promotes renal interstitial fibrosis during unilateral ureteral obstruction. Autophagy. 2016;12(6):976–998.
  • Alvarez ML, DiStefano JK. Functional characterization of the plasmacytoma variant translocation 1 gene (PVT1) in diabetic nephropathy. PLoS One. 2011;6(4):e18671.
  • Dong C, Greathouse KM, Beacham RL, et al. Fibronectin connecting segment-1 peptide inhibits pathogenic leukocyte trafficking and inflammatory demyelination in experimental models of chronic inflammatory demyelinating polyradiculoneuropathy. Exp Neurol. 2017;292:35–45.
  • Fernandes NRJ, Reilly NS, Schrock DC, et al. CD4(+) T cell interstitial migration controlled by fibronectin in the inflamed skin. Front Immunol. 2020;11:1501.
  • Cui J, Wu X, Song Y, et al. Complement C3 exacerbates renal interstitial fibrosis by facilitating the M1 macrophage phenotype in a mouse model of unilateral ureteral obstruction. Am J Physiol Renal Physiol. 2019;317(5):F1171–F82.
  • Liu Y, Wang K, Liang X, et al. Complement C3 produced by macrophages promotes renal fibrosis via IL-17A secretion. Front Immunol. 2018;9:2385.
  • Morigi M, Perico L, Corna D, et al. C3a receptor blockade protects podocytes from injury in diabetic nephropathy. JCI Insight. 2020;5:5.
  • Kelly KJ, Liu Y, Zhang J, et al. Renal C3 complement component: feed forward to diabetic kidney disease. Am J Nephrol. 2015;41(1):48–56.
  • Rasmussen KL, Nordestgaard BG, Nielsen SF. Complement C3 and risk of diabetic microvascular disease: a cohort study of 95202 individuals from the general population. Clin Chem. 2018;64(7):1113–1124.
  • Duan S, Sun L, Nie G, et al. Association of glomerular complement c4c deposition with the progression of diabetic kidney disease in patients with type 2 diabetes. Front Immunol. 2020;11:2073.
  • Tang X, Li H, Li L, et al. The clinical impact of glomerular immunoglobulin M deposition in patients with type 2 diabetic nephropathy. Am J Med Sci. 2018;356(4):365–373.
  • Sieber J, Wieder N, Clark A, et al. GDC-0879, a BRAF(V600E) inhibitor, protects kidney podocytes from death. Cell Chem Biol. 2018;25(2):175–84 e4.
  • Rios-Silva M, Trujillo X, Trujillo-Hernandez B, et al. Effect of chronic administration of forskolin on glycemia and oxidative stress in rats with and without experimental diabetes. Int J Med Sci. 2014;11(5):448–452.