Metabolomics Analysis Reveals the Association Between Lipid Abnormalities and Oxidative

Metabolomics analysis reveals the association between lipid abnormalities and oxidative stress, inflammation, fibrosis, and Nrf2 dysfunction in aristolochic acid-induced nephropathy

Ying-Yong Zhaoa,b*, Hui-Ling Wangf, Xian-Long Chengd, Feng Weid, Xu Baie, Rui-Chao Linc, Nosratola D Vazirib,*

aKey Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, the College of Life Sciences,Northwest University, No.229 Taibai North Road, Xi’an, Shaanxi710069, China Division of

bDivision ofNephrology and Hypertension, School of Medicine, University of California, Irvine, MedSci 1, C352, UCI Campus, Irvine, California,92897, USA

c School of Chinese Materia Medica, Beijing University of Chinese Medicine, No. 11 North Third Ring Road, Beijing 100029, China

dNational Institutes for Food and Drug Control, State Food and Drug Administration, No.2 Tiantan Xili, Beijing, 100050, China

eSolution Centre, Waters Technologies (Shanghai)Ltd., No. 1000Jinhai Road, Shanghai201203, China

f Department of nephrology, Shanghai Jimin Hospital,No. 338 Huaihai West Road, Shanghai 200052, China

SUPPLEMENTARY MATERIAL

Chromatographic separation of metabolomics analysis

The UPLC analysis was performed on a 2.1 mm × 100 mm ACQUITY 1.8 µm HSS T3 using a Waters AcquityTM UPLC system equipped with a WatersXevoTM G2 QTof MS. A gradient of 0.1% formic acid in acetonitrile (A) and 0.1% formic acid in water (B) used as follows: a linear gradient of 0–2.0min, 1–60% A; 2.0–6.0 min, 60–85% A; 6.0–8.0 min, 85-99% A; 8.0–10.0 min, 99-1% and 10.0–13.0 min, 1%. The flow rate was 0.45 ml/min. The autosampler was maintained at 4 °C. Every 2 µL sample solutionwasinjected for each run.

Mass spectrometry of the optimal conditions were as follows: capillary voltage of 1.5 kV for negative ion mode, cone voltage of15V, desolvation gastemperature of 450 °C, sourcetemperature of 120 °C, desolvation gasflow of 700 L/h, cone gas flow of 30 L/h, The scan rangewas from 50 to 1000m/z. Leucine–enkephalin was used for accurate mass acquisition. Waters MassLynx v4.1was used for all the acquisition and analysis ofdata.

Data processing and standardization metabolites

The raw data were analyzed using the Applied Waters Markerlynx XS software, this allowed deconvolution, alignment and data reduction to give a list of mass and retention time pairs with correspondingintensities for all the detected peaks from each data file in the dataset. The main parameters were set as follows: retention time range1– 10 min, mass range 50–1200 amu, mass tolerance 0.01, minimumintensity 1%, mass window 0.05, retention time window 0.20, andnoise elimination level 6.All of the data were normalized tothe summed total ion intensity per chromatogram, and theresultant data matrices were introduced to the EZinfo 2.0 softwarefor principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA).

TableS1

Identified biomarkers of AAN detected by UPLC-MS in positive ionization mode.

FA: fatty acyls; GL: glycerolipid; GP glycerophospholipid; PL: prenol lipid; SL: sphingolipid; ST: sterol lipid.

Figure S1NF-κB target protein expressions in kidney from model rats. Representative Western blots depicting p-IκB and nuclear content of p65 active subunit of NF-κB as well as expression of COX-2, iNOS, MCP-1, LOX-1, NOX4,p47phox, p22phox and 3-nitrotyrosinein the kidney tissues of the control group and model groups (n=8). *P<0.05, **P<0.01.

Figure S2Anti-oxidative stress Nrf2 and its repressor keap1 and Nrf2 target protein expression in kidney from control and model rats. Representative western blots of anti-oxidative stress proteins including keap1, Nrf2, HO-1, NQO1, GCLC, GCLM, Cu,Zn-SOD, Mn-SOD and catalase in the control and model rats (n=8). *P<0.05, **P<0.01.

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Figure S3 Results of multivariate analysis. The OPLS-DA score plots derived from the UPLC-MS profiles of kidney tissue extract in the control group and in the AAN group in the 4th week (A), 8th week (B), 12th week (C) and 24th week (D). S-plot based on kidney tissue profiling of the AAN and control group in the 4th week (E), 8th week (F), 12th week (G) and 24th week (H). The variables far from the origin contributed significantly to differentiate the clustering of AAN group from that of control group and were considered as potential biomarkers.

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Figure S4Heat maps for kidney tissue of the lipid metabolites. Heat maps for kidney tissue of the lipids in the 4th week (A), 8th week (B), 12th week (C) and 24th week (D). The color of each section is proportional to the significance of change of metabolites (red, upregulated; green, downregulated). Rows: lipid metabolites; Columns: kidney tissue samples.

Figure S5OPLS-DA loading plot from AAN group and control group in the 4th week (A), 8th week (B), 12th week (C) and 24th week (D). The loading plots represent which identified lipids were quantitatively higher or lower in AAN group compared with control group.

Figure S6Quantification of individual lipid molecular species of glycerophospholipids, glycerolipids and sphingolipids of control group and AAN group in the 4th week, 8th week, 12th week and 24th week. Abundance is represented as the relative intensity (y axis) of an identified lipid (x axis). Significantly increased LysoPC(18:0), LysoPC(18:1), LysoPC(18:2), LysoPC(20:4), LysoPE(0:0/20:4), LysoPE(0:0/22:5), LysoPE(20:2/0:0),PE(18:1/20:3), TG(14:0/20:5/14:1), TG(18:4/20:4/20:5), TG(18:4/22:6/22:6), TG(22:4/20:4/18:3) as well as significantly decreased PC(14:1/20:1), PC(20:5/18:1), PC(18:3/20:3), PC(22:4/14:0), PC(20:4/P-16:0) and PC(20:4/18:0) were observed in the AAN rats.

Figure S7 A typical example workflow of metabolomics using UPLC-QTOF/MS as research tools for discovering and quantifying metabolites in complex mixtures.

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