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Article Abstract
Diabetic kidney disease (DKD) is a serious complication of diabetes patients with long time duration, presenting with albuminuria and/or a reduced estimated glomerular filtration rate (eGFR), and without symptoms of other primary causes of kidney injury. Clinical studies showed matrix metalloproteinase 2 (MMP2) is the potential indicator for DKD diagnosis. However, the typical measurement of MMP2 is complicated and time-consuming. Therefore, it is necessary to develop an easy and reliable approach for MMP2 detection. Herein, we proposed a reliable and easy-to-use nanopore solution for the quantitative measurement of MMP2 at the single-molecule level using α-hemolysin nanopore. Assisted by machine learning, the peptide substrate and peptide products digested by MMP2 were classified with 100 % accuracy. The quantitative range of MMP2 concentration was 50-400 ng/ml. We further investigated the inhibitory effects of MMP2 activity by different chemicals including Cu, Ni, Zn, EDTA, and its inhibitor GM6001. Finally, MMP2 measurement was explored in the presence of simulated urine. Our research provides a new solution of quantification of MMP2 activity combined with machine learning for DKD diagnosis.
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