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Journal of Translational Medicine
Published in: Journal of Translational Medicine 1/2021

Open Access 01-12-2021 | Biomarkers | Research

Extracellular vesicle-derived AEBP1 mRNA as a novel candidate biomarker for diabetic kidney disease

Authors: Yiying Tao, Xing Wei, Yue Yue, Jiaxin Wang, Jianzhong Li, Lei Shen, Guoyuan Lu, Yang He, Shidi Zhao, Fan Zhao, Zhen Weng, Xiahong Shen, Ling Zhou

Published in: Journal of Translational Medicine | Issue 1/2021

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Abstract

Background

A novel and improved methodology is still required for the diagnosis of diabetic kidney disease (DKD). The aim of the present study was to identify novel biomarkers using extracellular vesicle (EV)-derived mRNA based on kidney tissue microarray data.

Methods

Candidate genes were identified by intersecting the differentially expressed genes (DEGs) and eGFR-correlated genes using the GEO datasets GSE30528 and GSE96804, followed by clinical parameter correlation and diagnostic efficacy assessment.

Results

Fifteen intersecting genes, including 8 positively correlated genes, B3GALT2, CDH10, MIR3916, NELL1, OCLM, PRKAR2B, TREM1 and USP46, and 7 negatively correlated genes, AEBP1, CDH6, HSD17B2, LUM, MS4A4A, PTN and RASSF9, were confirmed. The expression level assessment results revealed significantly increased levels of AEBP1 in DKD-derived EVs compared to those in T2DM and control EVs. Correlation analysis revealed that AEBP1 levels were positively correlated with Cr, 24-h urine protein and serum CYC and negatively correlated with eGFR and LDL, and good diagnostic efficacy for DKD was also found using AEBP1 levels to differentiate DKD patients from T2DM patients or controls.

Conclusions

Our results confirmed that the AEBP1 level from plasma EVs could differentiate DKD patients from T2DM patients and control subjects and was a good indication of the function of multiple critical clinical parameters. The AEBP1 level of EVs may serve as a novel and efficacious biomarker for DKD diagnosis.
Appendix
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Literature
1.
Anders H-J, Huber TB, Isermann B, Schiffer M. CKD in diabetes: diabetic kidney disease versus nondiabetic kidney disease. Nat Rev Nephrol. 2018;14:361–77.PubMedCrossRef
2.
Thomas MC, Brownlee M, Susztak K, Sharma K, Jandeleit-Dahm KA, Zoungas S, Rossing P, Groop P-H, Cooper ME. Diabetic kidney disease. Nat Rev Dis Primers. 2015;1:1–20.
3.
Harjutsalo V, Groop P-H. Epidemiology and risk factors for diabetic kidney disease. Adv Chronic Kidney Dis. 2014;21:260–6.PubMedCrossRef
4.
5.
Khoury CC, Chen S, Ziyadeh FN: Pathophysiology of diabetic nephropathy. In: Chronic renal disease. Elsevier; 2020: 279–296
6.
7.
8.
MacIsaac RJ, Ekinci EI, Jerums G. Progressive diabetic nephropathy How useful is microalbuminuria?: contra. Kidney Int. 2014;86:50–7.PubMedCrossRef
9.
Coresh J, Heerspink HJ, Sang Y, Matsushita K, Arnlov J, Astor BC, Black C, Brunskill NJ, Carrero J-J, Feldman HI. Change in albuminuria and subsequent risk of end-stage kidney disease: an individual participant-level consortium meta-analysis of observational studies. Lancet Diabetes Endocrinol. 2019;7:115–27.PubMedPubMedCentralCrossRef
10.
Colombo M, Raposo G, Théry C. Biogenesis, secretion, and intercellular interactions of exosomes and other extracellular vesicles. Annu Rev Cell Dev Biol. 2014;30:255–89.PubMedCrossRef
11.
De Toro J, Herschlik L, Waldner C, Mongini C. Emerging roles of exosomes in normal and pathological conditions: new insights for diagnosis and therapeutic applications. Front Immunol. 2015;6:203.PubMedPubMedCentralCrossRef
12.
Gangoda L, Boukouris S, Liem M, Kalra H, Mathivanan S. Extracellular vesicles including exosomes are mediators of signal transduction: are they protective or pathogenic? Proteomics. 2015;15:260–71.PubMedCrossRef
13.
Lee Y, El Andaloussi S, Wood MJ. Exosomes and microvesicles: extracellular vesicles for genetic information transfer and gene therapy. Hum Mol Genet. 2012;21:R125–34.PubMedCrossRef
14.
Xu W-C, Qian G, Liu A-Q, Li Y-Q, Zou H-Q. Urinary extracellular vesicle: a potential source of early diagnostic and therapeutic biomarker in diabetic kidney disease. Chin Med J. 2018;131:1357.PubMedPubMedCentralCrossRef
15.
16.
Zhang W, Zhou X, Zhang H, Yao Q, Liu Y, Dong Z. Extracellular vesicles in diagnosis and therapy of kidney diseases. Am J Physiol Renal Physiol. 2016;311:F844–51.PubMedPubMedCentralCrossRef
17.
National Kidney F. KDOQI Clinical practice guideline for diabetes and CKD: 2012 Update. Am J Kidney Dis. 2012;60:850–86.CrossRef
18.
Chinese Diabetes Society The consensus for prevention and treatment of diabetic kidney disease. Chin J Diabetes Mellitus 2014, 11:792.
19.
American Diabetes A. Standards of medical care in diabetes–2010. Diabetes Care. 2010;33(Suppl 1):S11-61.CrossRef
20.
Gautier L, Cope L, Bolstad BM, Irizarry RA. affy–analysis of Affymetrix GeneChip data at the probe level. Bioinformatics. 2004;20:307–15.CrossRefPubMed
21.
Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43:e47.PubMedPubMedCentralCrossRef
22.
Raivo K: pheatmap: Pretty Heatmaps. R package version 2015, 1.
23.
24.
Enderle D, Spiel A, Coticchia CM, Berghoff E, Mueller R, Schlumpberger M, Sprenger-Haussels M, Shaffer JM, Lader E, Skog J, Noerholm M. Characterization of RNA from Exosomes and Other Extracellular Vesicles Isolated by a Novel Spin Column-Based Method. PLoS ONE. 2015;10:e0136133.PubMedPubMedCentralCrossRef
25.
Dong L, Lin W, Qi P, Xu MD, Wu X, Ni S, Huang D, Weng WW, Tan C, Sheng W, et al. Circulating long RNAs in serum extracellular vesicles: their characterization and potential application as biomarkers for diagnosis of colorectal cancer. Cancer Epidemiol Biomarkers Prev. 2016;25:1158–66.PubMedCrossRef
26.
Thongboonkerd V. Roles for exosome in various kidney diseases and disorders. Front Pharmacol. 2019;10:1655.PubMedCrossRef
27.
28.
29.
Eissa S, Matboli M, Bekhet MM. Clinical verification of a novel urinary microRNA panal: 133b, -342 and -30 as biomarkers for diabetic nephropathy identified by bioinformatics analysis. Biomed Pharmacother. 2016;83:92–9.PubMedCrossRef
30.
Eissa S, Matboli M, Aboushahba R, Bekhet MM, Soliman Y. Urinary exosomal microRNA panel unravels novel biomarkers for diagnosis of type 2 diabetic kidney disease. J Diabetes Complications. 2016;30:1585–92.PubMedCrossRef
31.
Zubiri I, Posada-Ayala M, Sanz-Maroto A, Calvo E, Martin-Lorenzo M, Gonzalez-Calero L, de la Cuesta F, Lopez JA, Fernandez-Fernandez B, Ortiz A, et al. Diabetic nephropathy induces changes in the proteome of human urinary exosomes as revealed by label-free comparative analysis. J Proteomics. 2014;96:92–102.PubMedCrossRef
32.
Rossi L, Nicoletti MC, Carmosino M, Mastrofrancesco L, Di Franco A, Indrio F, Lella R, Laviola L, Giorgino F, Svelto M, et al. Urinary excretion of kidney aquaporins as possible diagnostic biomarker of diabetic nephropathy. J Diabetes Res. 2017;2017:4360357.PubMedPubMedCentralCrossRef
33.
Jager M, Lee MJ, Li C, Farmer SR, Fried SK, Layne MD. Aortic carboxypeptidase-like protein enhances adipose tissue stromal progenitor differentiation into myofibroblasts and is upregulated in fibrotic white adipose tissue. PLoS ONE. 2018;13:e0197777.PubMedPubMedCentralCrossRef
34.
Kim SW, Muise AM, Lyons PJ, Ro HS. Regulation of adipogenesis by a transcriptional repressor that modulates MAPK activation. J Biol Chem. 2001;276:10199–206.PubMedCrossRef
35.
Majdalawieh A, Zhang L, Fuki IV, Rader DJ, Ro HS. Adipocyte enhancer-binding protein 1 is a potential novel atherogenic factor involved in macrophage cholesterol homeostasis and inflammation. Proc Natl Acad Sci U S A. 2006;103:2346–51.PubMedPubMedCentralCrossRef
36.
Bogachev O, Majdalawieh A, Pan X, Zhang L, Ro HS. Adipocyte enhancer-binding protein 1 (AEBP1) (a novel macrophage proinflammatory mediator) overexpression promotes and ablation attenuates atherosclerosis in ApoE (−/−) and LDLR (−/−) mice. Mol Med. 2011;17:1056–64.PubMedPubMedCentralCrossRef
37.
Ren J, Han Y, Ren T, Fang H, Xu X, Lun Y, Jiang H, Xin S, Zhang J. AEBP1 promotes the occurrence and development of abdominal aortic aneurysm by modulating inflammation via the NF-kappaB pathway. J Atheroscler Thromb. 2020;27:255–70.PubMedPubMedCentralCrossRef
38.
Holloway RW, Bogachev O, Bharadwaj AG, McCluskey GD, Majdalawieh AF, Zhang L, Ro HS. Stromal adipocyte enhancer-binding protein (AEBP1) promotes mammary epithelial cell hyperplasia via proinflammatory and hedgehog signaling. J Biol Chem. 2012;287:39171–81.PubMedPubMedCentralCrossRef
39.
Gerhard GS, Hanson A, Wilhelmsen D, Piras IS, Still CD, Chu X, Petrick AT, DiStefano JK. AEBP1 expression increases with severity of fibrosis in NASH and is regulated by glucose, palmitate, and miR-372–3p. PLoS ONE. 2019;14:e0219764.PubMedPubMedCentralCrossRef
40.
Shijo M, Honda H, Suzuki SO, Hamasaki H, Hokama M, Abolhassani N, Nakabeppu Y, Ninomiya T, Kitazono T, Iwaki T. Association of adipocyte enhancer-binding protein 1 with Alzheimer’s disease pathology in human hippocampi. Brain Pathol. 2018;28:58–71.PubMedCrossRef
41.
Yorozu A, Yamamoto E, Niinuma T, Tsuyada A, Maruyama R, Kitajima H, Numata Y, Kai M, Sudo G, Kubo T, et al. Upregulation of adipocyte enhancer-binding protein 1 in endothelial cells promotes tumor angiogenesis in colorectal cancer. Cancer Sci. 2020;111:1631–44.PubMedPubMedCentralCrossRef
42.
Hebebrand M, Vasileiou G, Krumbiegel M, Kraus C, Uebe S, Ekici AB, Thiel CT, Reis A, Popp B. A biallelic truncating AEBP1 variant causes connective tissue disorder in two siblings. Am J Med Genet A. 2019;179:50–6.PubMed
43.
Hu W, Jin L, Jiang CC, Long GV, Scolyer RA, Wu Q, Zhang XD, Mei Y, Wu M. AEBP1 upregulation confers acquired resistance to BRAF (V600E) inhibition in melanoma. Cell Death Dis. 2013;4:e914.PubMedPubMedCentralCrossRef
44.
Vatseba T, Sokolova L, Pushkarev V, Kovzun O, Guda B, Pushkarev V, Tronko M. Activation of the PI3K/Akt/mTOR/p70S6K1 signaling cascade in the mononuclear cells of peripheral blood: association with insulin and insulin-like growth factor levels in the blood of patients with cancer and diabetes. Cytol Genet. 2019;53:489–93.CrossRef
45.
Chittka D, Banas B, Lennartz L, Putz FJ, Eidenschink K, Beck S, Stempfl T, Moehle C, Reichelt-Wurm S, Banas MC. Long-term expression of glomerular genes in diabetic nephropathy. Nephrol Dial Transplant. 2018;33:1533–44.PubMed
46.
Liang D, Liu S, Song Z, Liang W, Chang R, Li Y. Metformin ameliorates renal fibrosis in mice with unilateral ureteral obstruction and inhibits TGF-ß1-induced upregulation of cadherin-6 in renal proximal tubule epithelial cells. Am Diabetes Assoc 2018. https://​doi.​org/​10.​2337/​db18-2199-PUB
47.
Zhang H, Zhao T, Li Z, Yan M, Zhao H, Zhu B, Li P. Transcriptional profile of kidney from type 2 diabetic db/db mice. J Diabetes Res. 2017;2017:8391253.PubMedPubMedCentral
48.
Feng S-T, Gao Y-M, Yin D, Lv L-L, Wen Y, Li Z-L, Wang B, Wu M, Liu B-C. Identification of hub genes associated with accumulation of extracellular matrix as biomarkers of diabetic nephropathy. 2020.
49.
50.
Yokoi H, Kasahara M, Mori K, Ogawa Y, Kuwabara T, Imamaki H, Kawanishi T, Koga K, Ishii A, Kato Y, et al. Pleiotrophin triggers inflammation and increased peritoneal permeability leading to peritoneal fibrosis. Kidney Int. 2012;81:160–9.PubMedCrossRef
51.
Metadata
Title
Extracellular vesicle-derived AEBP1 mRNA as a novel candidate biomarker for diabetic kidney disease
Authors
Yiying Tao
Xing Wei
Yue Yue
Jiaxin Wang
Jianzhong Li
Lei Shen
Guoyuan Lu
Yang He
Shidi Zhao
Fan Zhao
Zhen Weng
Xiahong Shen
Ling Zhou
Publication date
01-12-2021
Publisher
BioMed Central
Keyword
Biomarkers
Published in
Journal of Translational Medicine / Issue 1/2021
Electronic ISSN: 1479-5876
DOI
https://doi.org/10.1186/s12967-021-03000-3

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