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Endocrine, Metabolic & Immune Disorders - Drug Targets

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ISSN (Print): 1871-5303
ISSN (Online): 2212-3873

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Review Article

CD163 in Macrophages: A Potential Biomarker for Predicting the Progression of Diabetic Nephropathy based on Bioinformatics Analysis

Author(s): Xiaoxia Wang, Rui Li, Ting Liu, Yanyan Jia, Xingxing Gao and Xiaodong Zhang*

Volume 23, Issue 3, 2023

Published on: 09 September, 2022

Page: [294 - 303] Pages: 10

DOI: 10.2174/1871530322666220616102754

Price: $65

Abstract

Objective: This study aimed to identify the potential biomarkers in DN.

Methods: DN datasets GSE30528 and GSE47183 were downloaded from the Gene Expression Omnibus database. Immune cell infiltration was analyzed using CIBERSORT. Weighted gene co-expression network analysis (WGCNA) was performed to obtain the module genes specific to DN. The relevant genes were identified intersecting the module genes and differentially expressed genes (DEGs). The core genes were identified using the MCC algorithm in Cytoscape software. ROC and Pearson analyses alongside gene set enrichment analysis (GSEA) were performed to identify the key gene for the core genes. Finally, we performed the Spearman to analyze the correlation between key gene and glomerular filtration rate (GFR), serum creatinine (Scr), age and sex in DN.

Results: CIBERSORT analysis revealed the immune cell infiltration in the DN renal tissue and Venn identified 12 relevant genes. Among these, 5 core genes, namely TYROBP, C1QA, C1QB, CD163 and MS4A6A, were identified. Pearson analyses revealed that immune cell infiltration and expression of core genes are related. The key genes with high diagnostic values for DN were identified to be CD163 via ROC analyses. After Spearman correlation analysis, the expression level of CD163 was correlated with GFR (r =0.27), a difference that nearly reached statistical significance (P =0.058). However, there was no correlation between the level of CD163 and age (r =-0.24, P =0.09), sex (r =-0.11, P=0.32) and Scr (r=0.15, P=0.4).

Conclusion: We found that CD163 in macrophages may be a potential biomarker in predicting and treating DN.

Keywords: Weight gene co-expression network analysis, diabetic nephropathy, key genes, CD163, biomarkers, immune cell.

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[1]
DCCT/EDIC research group. Effect of intensive diabetes treatment on albuminuria in type 1 diabetes: Long-term follow-up of the Diabetes Control and Complications Trial and Epidemiology of Diabetes Interventions and Complications study. Lancet Diabetes Endocrinol., 2014, 2(10), 793-800.
[http://dx.doi.org/10.1016/S2213-8587(14)70155-X] [PMID: 25043685]
[2]
Wang, Y.; Zhou, T.; Zhang, Q.; Fei, Y.; Li, Z.; Li, S.; He, L.; Zhang, Q.; Dong, Y.; Fan, Y.; Wang, N. Poor renal and cardiovascular outcomes in patients with biopsy-proven diabetic nephropathy. Kidney Blood Press. Res., 2020, 45(3), 378-390.
[http://dx.doi.org/10.1159/000505919] [PMID: 32209792]
[3]
Liu, S.; Wang, C.; Yang, H.; Zhu, T.; Jiang, H.; Chen, J. Weighted gene co-expression network analysis identifies FCER1G as a key gene associated with diabetic kidney disease. Ann. Transl. Med., 2020, 8(21), 1427.
[http://dx.doi.org/10.21037/atm-20-1087] [PMID: 33313172]
[4]
Feng, S.; Gao, Y.; Yin, D.; Lv, L.; Wen, Y.; Li, Z.; Wang, B.; Wu, M.; Liu, B. Identification of lumican and fibromodulin as hub genes associated with accumulation of extracellular matrix in diabetic nephropathy. Kidney Blood Press. Res., 2021, 46(3), 275-285.
[http://dx.doi.org/10.1159/000514013] [PMID: 33887734]
[5]
Gurley, S.B.; Ghosh, S.; Johnson, S.A.; Azushima, K.; Sakban, R.B.; George, S.E.; Maeda, M.; Meyer, T.W.; Coffman, T.M. Inflammation and immunity pathways regulate genetic susceptibility to diabetic nephropathy. Diabetes, 2018, 67(10), 2096-2106.
[http://dx.doi.org/10.2337/db17-1323] [PMID: 30065034]
[6]
Bonacina, F.; Baragetti, A.; Catapano, A.L.; Norata, G.D. The interconnection between immuno-metabolism, diabetes, and CKD. Curr. Diab. Rep., 2019, 19(5), 21.
[http://dx.doi.org/10.1007/s11892-019-1143-4] [PMID: 30888513]
[7]
Li, R.X.; Yiu, W.H.; Wu, H.J.; Wong, D.W.; Chan, L.Y.; Lin, M.; Leung, J.C.; Lai, K.N.; Tang, S.C. BMP7 reduces inflammation and oxidative stress in diabetic tubulopathy. Clin. Sci. (Lond.), 2015, 128(4), 269-280.
[http://dx.doi.org/10.1042/CS20140401] [PMID: 25200314]
[8]
Moratal, C.; Laurain, A.; Naïmi, M.; Florin, T.; Esnault, V.; Neels, J.G.; Chevalier, N.; Chinetti, G.; Favre, G. Regulation of monocytes/macrophages by the renin-angiotensin system in diabetic nephropathy: State of the art and results of a pilot study. Int. J. Mol. Sci., 2021, 22(11), 6009.
[http://dx.doi.org/10.3390/ijms22116009] [PMID: 34199409]
[9]
Pichler, R.; Afkarian, M.; Dieter, B.P.; Tuttle, K.R. Immunity and inflammation in diabetic kidney disease: Translating mechanisms to biomarkers and treatment targets. Am. J. Physiol. Renal Physiol., 2017, 312(4), F716-F731.
[http://dx.doi.org/10.1152/ajprenal.00314.2016] [PMID: 27558558]
[10]
Kim, H.; Kim, M.; Lee, H.Y.; Park, H.Y.; Jhun, H.; Kim, S. Role of dendritic cell in diabetic nephropathy. Int. J. Mol. Sci., 2021, 22(14), 7554.
[http://dx.doi.org/10.3390/ijms22147554] [PMID: 34299173]
[11]
Gonzalez-Duque, S.; Azoury, M.E.; Colli, M.L.; Afonso, G.; Turatsinze, J.V.; Nigi, L.; Lalanne, A.I.; Sebastiani, G.; Carré, A.; Pinto, S.; Culina, S.; Corcos, N.; Bugliani, M.; Marchetti, P.; Armanet, M.; Diedisheim, M.; Kyewski, B.; Steinmetz, L.M.; Buus, S.; You, S.; Dubois-Laforgue, D.; Larger, E.; Beressi, J.P.; Bruno, G.; Dotta, F.; Scharfmann, R.; Eizirik, D.L.; Verdier, Y.; Vinh, J.; Mallone, R. Conventional and neo-antigenic peptides presented by β cells are targeted by circulating naïve CD8+ T cells in type 1 diabetic and healthy donors. Cell Metab., 2018, 28(6), 946-960.e6.
[http://dx.doi.org/10.1016/j.cmet.2018.07.007] [PMID: 30078552]
[12]
Zhang, J.; Wang, Y.; Zhang, R.; Li, H.; Han, Q.; Wu, Y.; Wang, S.; Guo, R.; Wang, T.; Li, L.; Liu, F. Serum levels of immunoglobulin G and complement 3 differentiate non-diabetic renal disease from diabetic nephropathy in patients with type 2 diabetes mellitus. Acta Diabetol., 2019, 56(8), 873-881.
[http://dx.doi.org/10.1007/s00592-019-01339-0] [PMID: 31004313]
[13]
Zhang, F.; Wang, C.; Wen, X.; Chen, Y.; Mao, R.; Cui, D.; Li, L.; Liu, J.; Chen, Y.; Cheng, J.; Lu, Y. Mesenchymal stem cells alleviate rat diabetic nephropathy by suppressing CD103(+) DCs-mediated CD8(+) T cell responses. J. Cell. Mol. Med., 2020, 24(10), 5817-5831.
[http://dx.doi.org/10.1111/jcmm.15250]
[14]
Zhang, J.; Yang, X.; Zhang, X.; Lu, D.; Guo, R. Electro-acupuncture protects diabetic nephropathy-induced inflammation through suppression of NLRP3 inflammasome in Renal macrophage isolation. Endocr. Metab. Immune Disord. Drug Targets, 2021, 21(11), 2075-2083.
[http://dx.doi.org/10.2174/1871530321666210118161721]
[15]
Yang, X.; Mou, S. Role of immune cells in diabetic kidney disease. Curr. Gene Ther., 2017, 17(6), 424-433.
[http://dx.doi.org/10.2174/1566523218666180214100351] [PMID: 29446740]
[16]
Li, Y.; Yu, W.; He, M.; Yuan, F. The effects of M1/M2 macrophages on the mRNA expression profile of diabetic glomerular endothelial cells. Nephron, 2021, 145(5), 568-578.
[http://dx.doi.org/10.1159/000513268] [PMID: 33957627]
[17]
Zhu, M.; Sun, X.; Qi, X.; Xia, L.; Wu, Y. Exosomes from high glucose-treated macrophages activate macrophages andinduce inflammatory responses via NF-κB signaling pathway in vitro and in vivo. Int. Immunopharmacol., 2020, 84, 106551.
[http://dx.doi.org/10.1016/j.intimp.2020.106551] [PMID: 32388490]
[18]
Songyan, Y.; Cheng, Y.; Li, B.; Xue, J.; Yin, Y.; Gao, J.; Gong, Z.; Wang, J.; Mu, Y. M1 macrophages accelerate renal glomerular endothelial cell senescence through reactive oxygen species accumulation in streptozotocin-induced diabetic mice. Int. Immunopharmacol., 2020, 81, 106294.
[http://dx.doi.org/10.1016/j.intimp.2020.106294] [PMID: 32062081]
[19]
Kim, M.G.; Kim, S.C.; Ko, Y.S.; Lee, H.Y.; Jo, S.K.; Cho, W. The Role of M2 macrophages in the progression of chronic kidney disease following acute kidney injury. PLoS One, 2015, 10(12), e0143961.
[http://dx.doi.org/10.1371/journal.pone.0143961] [PMID: 26630505]
[20]
Kristiansen, M.; Graversen, J.H.; Jacobsen, C.; Sonne, O.; Hoffman, H.J.; Law, S.K.; Moestrup, S.K. Identification of the haemoglobin scavenger receptor. Nature, 2001, 409(6817), 198-201.
[http://dx.doi.org/10.1038/35051594] [PMID: 11196644]
[21]
Mejia-Vilet, J.M.; Zhang, X.L.; Cruz, C.; Cano-Verduzco, M.L.; Shapiro, J.P.; Nagaraja, H.N.; Morales-Buenrostro, L.E.; Rovin, B.H. Urinary soluble CD163: A novel noninvasive biomarker of activity for lupus nephritis. J. Am. Soc. Nephrol., 2020, 31(6), 1335-1347.
[http://dx.doi.org/10.1681/ASN.2019121285] [PMID: 32300067]
[22]
Frantz, C.; Pezet, S.; Avouac, J.; Allanore, Y. Soluble CD163 as a potential biomarker in systemic sclerosis. Dis. Markers, 2018, 2018, 8509583.
[http://dx.doi.org/10.1155/2018/8509583] [PMID: 29805720]
[23]
Møller, H.J. Soluble CD163. Scand. J. Clin. Lab. Invest., 2012, 72(1), 1-13.
[http://dx.doi.org/10.3109/00365513.2011.626868] [PMID: 22060747]
[24]
Wiktoria, R.; Atkinson, S.D.; Kelly, C. The TWEAK/Fn14/CD163 axis-implications for metabolic disease. Rev. Endocr. Metab. Disord., 2021.
[25]
Villacorta, J.; Lucientes, L.; Goicoechea, E.; Acevedo, M.; Cavero, T.; Sanchez-Camara, L.; Díaz-Crespo, F.; Gimenez-Moyano, S.; García-Bermejo, L.; Fernandez-Juarez, G. Urinary soluble CD163 as a biomarker of disease activity and relapse in antineutrophil cytoplasm antibody-associated glomerulonephritis. Clin. Kidney J., 2020, 14(1), 212-219.
[http://dx.doi.org/10.1093/ckj/sfaa043] [PMID: 33564421]
[26]
Aendekerk, J.P.; Timmermans, S.A.M.E.G.; Busch, M.H.; Potjewijd, J.; Heeringa, P.; Damoiseaux, J.G.M.C.; Reutelingsperger, C.P.; van Paassen, P. Urinary Soluble CD163 and disease activity in biopsy-proven ANCA-associated glomerulonephritis. Clin. J. Am. Soc. Nephrol., 2020, 15(12), 1740-1748.
[http://dx.doi.org/10.2215/CJN.07210520] [PMID: 33203735]
[27]
Moran, S.M.; Scott, J.; Clarkson, M.R.; Conlon, N.; Dunne, J.; Griffin, M.D.; Griffin, T.P.; Groarke, E.; Holian, J.; Judge, C.; Wyse, J.; McLoughlin, K.; O’Hara, P.V.; Little, M.A.; Kretzler, M. The clinical application of urine soluble CD163 in ANCA-associated vasculitis. J. Am. Soc. Nephrol., 2021, 32(11), 2920-2932.
[http://dx.doi.org/10.1681/ASN.2021030382] [PMID: 34518279]
[28]
O’Reilly, V.P.; Wong, L.; Kennedy, C.; Elliot, L.A.; O’Meachair, S.; Coughlan, A.M.; O’Brien, E.C.; Ryan, M.M.; Sandoval, D.; Connolly, E.; Dekkema, G.J.; Lau, J.; Abdulahad, W.H.; Sanders, J.S.; Heeringa, P.; Buckley, C.; O’Brien, C.; Finn, S.; Cohen, C.D.; Lindemeyer, M.T.; Hickey, F.B.; O’Hara, P.V.; Feighery, C.; Moran, S.M.; Mellotte, G.; Clarkson, M.R.; Dorman, A.J.; Murray, P.T.; Little, M.A. Urinary soluble CD163 in active renal vasculitis. J. Am. Soc. Nephrol., 2016, 27(9), 2906-2916.
[http://dx.doi.org/10.1681/ASN.2015050511] [PMID: 26940094]

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