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. 2022 Nov 16;27(22):7929.
doi: 10.3390/molecules27227929.

An Untargeted Metabolomics Approach on Carfilzomib-Induced Nephrotoxicity

Ioanna Barla  1 , Panagiotis Efentakis  2 , Sofia Lamprou  2 , Maria Gavriatopoulou  3 , Meletios-Athanasios Dimopoulos  3 , Evangelos Terpos  3 , Ioanna Andreadou  2 , Nikolaos Thomaidis  1 , Evangelos Gikas  1
Affiliations

An Untargeted Metabolomics Approach on Carfilzomib-Induced Nephrotoxicity

Ioanna Barla et al. Molecules. .

Abstract

Background: Carfilzomib (Cfz) is an anti-cancer drug related to cardiorenal adverse events, with cardiovascular and renal complications limiting its clinical use. Despite the important progress concerning the discovery of the underlying causes of Cfz-induced nephrotoxicity, the molecular/biochemical background is still not well clarified. Furthermore, the number of metabolomics-based studies concerning Cfz-induced nephrotoxicity is limited.

Methods: A metabolomics UPLC-HRMS-DIA methodology was applied to three bio-sample types i.e., plasma, kidney, and urine, obtained from two groups of mice, namely (i) Cfz (8 mg Cfz/ kg) and (ii) Control (0.9% NaCl) (n = 6 per group). Statistical analysis, involving univariate and multivariate tools, was applied for biomarker detection. Furthermore, a sub-study was developed, aiming to estimate metabolites' correlation among bio-samples, and to enlighten potential mechanisms.

Results: Cfz mostly affects the kidneys and urine metabolome. Fifty-four statistically important metabolites were discovered, and some of them have already been related to renal diseases. Furthermore, the correlations between bio-samples revealed patterns of metabolome alterations due to Cfz.

Conclusions: Cfz causes metabolite retention in kidney and dysregulates (up and down) several metabolites associated with the occurrence of inflammation and oxidative stress.

Keywords: HRMS; carfilzomib; metabolomics; nephrotoxicity.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
PCA and PLS-DA score plots. The red and the blue points of the score plots represent the Cfz group samples and the Control group samples, respectively. (a) PCA of kidney (+) dataset; (b) PCA of urine (+) dataset; (c) PCA of plasma (+) dataset; (d) PLS-DA of kidney (+) dataset; (e) PLS-DA of urine (+) dataset; (f) PLS-DA of plasma (+) dataset.
Figure 2
Figure 2
Representation of plasma (+)’s most important variables as extracted ion chromatograms. The first segment of these EICs corresponds to signals obtained from the infusion of calibrant solution. The chromatographic peaks at RT = 7.16 correspond to the adducts of formate detected in Cfz plasma samples.
Figure 3
Figure 3
Bar charts representing D-serine (blue color) and 2-aminoisobutyric acid (orange color) content in the Cfz (a) and Control (b) kidney samples. Aminoisobutyric acid is decreased in Cfz samples and D-serine is increased, while the later was not detected in Control samples. This suggested that in the Cfz case, the 2-Aminoisobutyric acid is decreased and is not able to inhibit D-Serine from H2O2 production and, therefore, cannot protect the kidneys from oxidative stress.
Figure 4
Figure 4
Diagrams of metabolite content (expressed via mean and SD) in the plasma, kidneys, and urine of both Cfz (red) and Control (blue) groups. Each diagram represents a type of the revealed patterns of metabolite distribution among bio-samples: (a) metabolites detected in all biosamples and statistically differentiated in kidney; (b) metabolites detected and statistically differentiated in kidney; (c) metabolites detected in all biosamples and statistically differentiated in urine; (d) metabolites detected and statistically differentiated in kidney (e) metabolites detected in all biosamples and statistically differentiated in kidney and urine.
Figure 5
Figure 5
ADMA had been detected as an intact metabolite and also in two forms of its metabolism. Here, ADMA, per se, is increased in Cfz kidneys; however, it is decreased in Cfz plasma and urine, suggesting an increased rate of ADMA production or ADMA’s strong retention at the kidney level. Furthermore, ADMA’s metabolites (ADMA + C5H3N5 and ADMA + SO3) have been highly detected in Cfz kidneys and urine.
Figure 6
Figure 6
Graphical description of data pre-processing workflow. The low and high CE–MS before (a) and after (b) their separation; (c) set of noise levels for the mass detection; (d) one peak which resulted from the chromatogram building; (e) the chromatogram deconvolution; (f) the result of the alignment; the PCAs before (g) and after (h) signal correction.
Figure 7
Figure 7
Graphical description of the statistical workflow for kidney (+), as follows: (a) the final peak list; (b) PCA; (c) PLS-DA; ( the number of PCs; the scores-plot the loadings plot; the content plot of a discriminant variable; the result of permutations test); (d) the ROC curve of a biomarker; (e) the fold change plot; (f) FDR-TT representation; (g) volcano plot.
Figure 8
Figure 8
Graphical representation of identification workflow, employing the example of variable 254.1014_3.15. (a) Extraction of ion chromatogram, m/z = 245.1014, tR = 3.14; (b,c) refer to MS and MS2 spectra, respectively, corresponding to this peak area; (d) the area removed as background spectra; (e,f) represent the MS2 spectra before and after background subtraction, respectively; (g) MSMS match graph obtained from MCID, during the identification procedure; (h) the structural representation of biotin.
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