Discovery of Potential Biomarkers with Dose- and Time-Dependence in Cisplatin-Induced Nephrotoxicity Using Metabolomics Integrated with a Principal Component-Based Area Calculation Strategy

Cisplatin is a potent chemotherapeutic agent. However, its clinical usage is restricted by serious adverse effects, especially nephrotoxicity. For revealing the dose- and time-dependence of cisplatin-induced nephrotoxicity, mass spectrometry-based metabolomics integrated with a principal component-based area calculation (PCAC) strategy was proposed in the present study. Area plots based on the first two principal components of the principal component analysis model were constructed first. Then, the sums of cumulative areas under PC-T curves (AUC_PC‑T) were calculated. Finally, the fold change of AUC_PC‑T between experimental and control groups at different time points was calculated and used as an indicative parameter. With the PCAC approach, dose- and time-dependence of cisplatin-induced metabolic change was quantitatively confirmed for the first time. Furthermore, 27 potential biomarkers with dose- and time-dependence related to nephrotoxicity induced by cisplatin were screened out and tentatively identified. Metabolic pathways interrupted by cisplatin mainly included energy, amino acid, and lipid metabolism

Twin Derivatization Strategy for High-Coverage Quantification of Free Fatty Acids by Liquid Chromatography–Tandem Mass Spectrometry

Free fatty acids (FFAs) are vitally important components of lipids that modulate biological metabolism in various ways. Although the molecular structures are simple, the analysis of FFAs is still challenging due to their unique properties and wide concentration range. In the present study, a high-coverage liquid chromatography-tandem mass spectrometry (LC-MS/MS) method was established for the quantification of FFAs in serum samples using two structural analogues 5-(dimethylamino)­naphthalene-1-sulfonyl piperazine (Dns-PP) and (diethylamino)­naphthalene-1-sulfonyl piperazine (Dens-PP) as twin derivatization reagents. The Dns labeling of FFAs could significantly enhance their MS response via the introduction of the easily ionizable moiety of a tertiary amine-containing part and aid fragmentation in the multiple reaction monitoring (MRM) mode. Our results demonstrated that the detection sensitivities of FFAs were increased by 50–1500 fold compared with the nonderivatization method. At the same time, Dens-labeled standards were used as one-to-one internal standards to ensure accurate quantifications. Thirty-eight FFAs, covering short-, medium-, and long-chain, could be quantified in wide dynamic range with the lower limit of quantification (LLOQ) varied from 2 to 20 nM. Using this method, we analyzed serum FFAs in rat models of cisplatin-induced nephrotoxicity and irinotecan-induced gastrointestinal toxicity, respectively. The findings were further compared with those revealed by previous untargeted metabolomics. The results indicate that twin derivatization-based LC-MS provides a more accurate view of global FFA alternation and has great application potential in the fields of targeted metabolomics

Discovery of Metabolite Biomarkers for Acute Ischemic Stroke Progression

Stroke remains a major public health problem worldwide; it causes severe disability and is associated with high mortality rates. However, early diagnosis of stroke is difficult, and no reliable biomarkers are currently established. In this study, mass-spectrometry-based metabolomics was utilized to characterize the metabolic features of the serum of patients with acute ischemic stroke (AIS) to identify novel sensitive biomarkers for diagnosis and progression. First, global metabolic profiling was performed on a training set of 80 human serum samples (40 cases and 40 controls). The metabolic profiling identified significant alterations in a series of 26 metabolites with related metabolic pathways involving amino acid, fatty acid, phospholipid, and choline metabolism. Subsequently, multiple algorithms were run on a test set consisting of 49 serum samples (26 cases and 23 controls) to develop different classifiers for verifying and evaluating potential biomarkers. Finally, a panel of five differential metabolites, including serine, isoleucine, betaine, PC­(5:0/5:0), and LysoPE(18:2), exhibited potential to differentiate AIS samples from healthy control samples, with area under the receiver operating characteristic curve values of 0.988 and 0.971 in the training and test sets, respectively. These findings provided insights for the development of new diagnostic tests and therapeutic approaches for AIS