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. 2018 Apr 27;7(3):529–537. doi: 10.1039/c7tx00345e

Plasma metabolic profiling analysis of neurotoxicity induced by oxaliplatin using metabonomics and multivariate data analysis

Yanyan Xu a,b,†, Yiwei Zhao a,b,†, Xuejun Guo a,b, Yubo Li a,b,✉, Yanjun Zhang b,✉
PMCID: PMC6060739  PMID: 30090603

graphic file with name c7tx00345e-ga.jpgOxaliplatin is a third generation antitumor agent, which is often used in treating advanced colorectal cancer, but the use of oxaliplatin is limited by its side effects, especially peripheral nerve toxicity.

Abstract

Oxaliplatin is a third generation antitumor agent, which is often used in treating advanced colorectal cancer, but the use of oxaliplatin is limited by its side effects, especially peripheral nerve toxicity. Metabonomics techniques, as a holistic analytical technique, could provide basic information on the metabolic profile of biological fluids during drug administration. In this study, we used the ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS) technique to analyze rat plasma samples collected seven days after oxaliplatin administration. The changes of metabolites in plasma samples were evaluated by principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA), and 15 kinds of neurotoxicity-related biomarkers were screened. The metabolic pathways of interference involved amino acid biosynthesis and metabolism, glycerophospholipid metabolism, sphingolipid metabolism and so on. The biomarkers found in this study are significant for the study of neurotoxicity.

1. Introduction

Oxaliplatin is a platinum-based chemotherapy drug, commonly used in the first-line treatment of metastatic colorectal cancer,1 gastric cancer2 and breast cancer.3 Oxaliplatin treatment has some beneficial effects, but its peripheral neurotoxicity has aroused widespread concerns.4 According to clinical reports, 80% of patients suffered from acute neurological symptoms after oxaliplatin administration, such as paresthesia or insensitivity,5 which reduced the quality of people's lives seriously. At present, behaviour measurements and electrophysiological testing of nerves have been used as indicators of neurological damage,6 but these methods lack sensitivity and accuracy, and the characteristics of neurotoxicity remain unclear. Therefore, it is urgent to establish an effective method to find out the markers and metabolic pathways of neurotoxicity, which could lay a foundation for the study of the oxaliplatin toxicity mechanism.

Following the development of genomics, transcriptomics and proteomics, metabonomics has become the focus of modern systems biology.7 Metabonomics could be used to speculate on the body's pathological mechanisms mainly through the analysis of cells, tissues or organisms affected by the external effects of metabolic changes.8 Now, it has been applied in many fields, such as disease diagnosis, drug safety evaluation, toxicology research, food nutrition research, plant research, etc.9–13 Besides, by comprehensive analysis of multiple indicators, metabonomics enables the study of endogenous small molecule metabolite species, the number of changes and the relationship.14,15 Metabolites as the products of metabolic pathways play an important role in metabolism.16 Ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) has the characteristics of high sensitivity, wide range and good resolution and therefore UPLC-MS-based metabonomics has great potential for the identification of biomarkers for disease diagnosis (such as Alzheimer's disease and schizophrenia)17 and drug-induced toxicity assessment (such as cardiotoxicity, hepatotoxicity, nephrotoxicity, etc.).18–20 There have been reports on the application of metabonomics in neurotoxicity studies, such as the study of acrylamide neurotoxicity.21 Thus, metabonomics can predict the early generation of toxicity and helps in finding the potential mechanism of toxicity.

In this study, we applied the UPLC-Q-TOF/MS technique to analyze rat plasma samples after oxaliplatin administration and established a multivariate statistical analysis method based on metabonomics to detect potential oxaliplatin-induced biomarkers, and to find the relevant interfering pathways for the neurotoxicity of oxaliplatin.

2. Materials and methods

2.1. Reagents and materials

HPLC-grade acetonitrile and formic acid were purchased from Oceanpak (Gothenburg, Sweden) and ROE (USA), respectively. Distilled water was obtained from Wahaha Company (Hangzhou, China). Normal saline and 5% glucose solution were obtained from Shandong Qi Du Pharmaceutical Co., Ltd (Zibo, China). Oxaliplatin was obtained from Dalian Meilun Biotechnology Co., Ltd (Dalian, China). Creatine and tyrosine were purchased from Sichuan Victory Biological Technology Co., Ltd (Chengdu, China) with purity greater than 98%.

2.2. Animal treatment

Male Wistar rats weighing 200 g to 220 g were purchased from Beijing Weitong Lihua Company (license no. SCXK(Jing) 2012-0001, Beijing, China), and were raised at the Institute of Radiation Medicine Chinese Academy of Medical Sciences (Tianjin, China). The animals were housed in a room under controlled light (12/12 h light/dark cycle), temperature (23 ± 2 °C), and relative humidity (60%). Before experimentation, all animals were acclimated for 1 week, with access to free diet and clear water. Then twenty-four rats were randomly divided into three groups: control group (8 rats,1 ml day–1, intraperitoneal injection of 5% glucose solution for 7 days, NC), and oxaliplatin low dose (OLD) group (8 rats, 3 mg kg–1, daily, continuous intraperitoneal injection administration for 7 days, OLD group), oxaliplatin high dose (OHD) group (8 rats, 6 mg kg–1, daily, continuous intraperitoneal injection administration for 7 days, OHD group).22 This study was approved by the Animal Ethics Committee of Tianjin University of Traditional Chinese Medicine under permit number TCM-2012.078F01. All experimental procedures were conducted in accordance with China's national legislation and local guidelines.

2.3. Biochemical and pathological examination

The concentrations of nerve growth factor (NGF) and neuron-specific enolase (NSE) in serum were determined using an automatic chemical analyzer. The pathological features of the dorsal root ganglia (DRG) and sciatic nerve (SN) were examined by hematoxylin–eosin (H&E) staining. Tissues were embedded in paraffin wax, cut into 4 μm sections, and fixed on glass slides. The sections were deparaffinized with xylene, hydrated, stained with haematoxylin, differentiated with hydrochloric alcohol, stained with eosin and dehydrated in a graded alcohol series. Then, the slides were cleaned with xylene and viewed under an optical microscope with 400 times magnification. The pathological changes of tissues were observed.

2.4. Sample collection and preparation

After treatment with the corresponding drugs, the blood samples were collected 7d after administration. In order to avoid the effects of food on the final results, all animals were fasted for 12 h before sample collection. After collecting blood, all animals were sacrificed. Blood collected in heparinized tubes was centrifuged at 760g for 15 min at 4 °C, and the obtained supernatant was centrifuged at 1040g for 8 min to separate the plasma. The plasma samples that were stored at –80 °C were collected for metabonomic analysis. Another set of blood samples was collected in ordinary test tubes using the same approach to isolate serum which was used for biochemical tests. Before analysis, the plasma sample was extracted from the –80 °C refrigerator, and thawed at room temperature. 300 μL of acetonitrile were added into 100 μL of plasma to remove the protein. The final mixture was ultrasonicated in an ice water bath for 10 min, vortexed for 1 min, and then centrifuged at 14 360g for 15 min at 4 °C. The supernatant was removed and analyzed by UPLC-Q-TOF/MS. An aliquot of 20 μL of plasma was taken from each plasma sample into a centrifuge tube, vortexed for 1 min, centrifuged at 14 360g for 10 min at 4 °C and the supernatant was used to prepare the quality control (QC) sample.

2.5. Method validation

Before sample analysis, the QC samples were injected to validate the method including precision, repeatability and stability.

2.5.1. Instrument precision test

A QC sample was injected six times consecutively. 20 chromatographic peaks were selected randomly, and RSD values of the areas and retention times of these peaks were calculated.

2.5.2. Method repeatability test

Six QC samples were injected consecutively. 20 chromatographic peaks were selected randomly, and the RSD values of the areas and retention times of these peaks were calculated.

2.5.3. Sample stability test

A QC sample was stored at 4 °C and injected at 0, 2, 6, 12 and 24 h, respectively. 20 chromatographic peaks were selected randomly, and RSD values of the areas and retention times of these peaks were calculated.

2.6. Chromatographic and mass spectrometric conditions

Data acquisition was performed on an UPLC-Q-TOF/MS system (Waters, USA). We used an ACQUITY UPLC BEH C18 column (2.1 mm × 100 mm, 1.7 μm, Waters, USA). The column temperature was set to 40 °C, and the flow rate was 0.3 mL min–1. The injection volume was 5 μL. The UPLC separation system included a binary solvent system with mobile phase A (0.1% formic acid in water) and mobile phase B (0.1% formic acid in acetonitrile) using a gradient elution program: 0–0.5 min, A: 99–99%; 0.5–2 min, A: 99–50%; 2–9 min, A: 50–1%; 9–10 min, A: 1–1%; 10–10.5 min, A:1–99%; and 10.5–12 min, A: 99–99%.

A Xevo G2-Q-TOF/MS (Waters, USA) was used with an electrospray ionization source in positive ion mode. The MS parameters were as follows: nitrogen was used as the drying gas; drying gas temperature, 325 °C; drying gas flow, 10 mL min–1; desolvation gas flow, 600 L h–1; capillary voltage, 2.1 kV; nebulizer pressure, 350 psi; and evaporative and auxiliary gases, high-purity nitrogen. The Leucine enkephalin was used to ensure accurate mass measurement. The data acquisition range was limited during 50 Da to 1000 Da. All samples were randomly injected.

2.7. Data processing

A multivariate statistical analysis was used to find the biomarkers associated with the oxaliplatin treatment. The raw data were collected with MarkerLynx applications manager Version 4.1 (Waters Corp., Manchester, UK). Potential discriminatory variables were determined after the raw data were processed for peak discovery and peak alignment and filtered. SIMCA-P + 12.0 software (Umetrics AB, Umea, Sweden) was used to perform PCA and PLS-DA, which was commonly used for metabolic studies. Candidate biomarkers were filtered out with a variable-importance plot (VIP) value >1.5. After Student's t test, biomarkers with significant differences (P < 0.05) were selected as neurotoxicity biomarkers. The peak area was used to describe the content change of the differential metabolites. The biomarkers were eventually authenticated by MS/MS analysis and metabolite databases HMDB (; http://www.hmdb.ca/) and KEGG (; http://www.genome.jp/kegg/). Finally, the perturbation was described by MetPA (; http://www.metpa.metabolomics.ca./MetPA/faces/Home.jsp), an important tool in metabonomic pathway analysis. A permutation test was performed to ensure no overfitting of the model occurred.

3. Results and discussion

3.1. Biochemical analysis and histopathological assessment

In this study, NGF and NSE values in the administration and normal control were compared to evaluate the drug-induced neurotoxicity. The results are shown in Fig. 1. Compared with the NC group, the NGF level was significantly decreased (p < 0.05) only in the OHD group. NGF can promote the growth, development, differentiation and maturation of peripheral neurons, maintain the normal function of the nervous system, and accelerate the repair of the nervous system after injury. When the nervous system gets damaged, it causes a decline in NGF. The NSE level was increased in the OHD and OLD groups, but there was no significant difference. The change trend in the two indices was consistent with the description in the literature.23,24

Fig. 1. NGF and NSE levels in serum samples (*P < 0.05, compared with the NC group).

Fig. 1

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The histopathological results are shown in Fig. 2. Compared with the NC group, there was no obvious damage in the OLD and OHD groups in the DRG. However, the OLD and OHD groups of the SN were changed. Parts of the nucleus and nucleoli showed shrinkage, the nucleus stained lighter, and the outline of the nuclear membrane was blurred.

Fig. 2. Histopathological examination of the DRG and SN by H&E staining (400× magnification). (a) NC SN group, (b) OLD SN group, (c) OHD SN group; (d) NC DRG group, (e) OLD DRG group, and (f) OHD DRG group.

Fig. 2

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3.2. Method validation

Instrument precision test results showed that for the peak area and retention time, the RSD was less than 15% and 1%, respectively, indicating that the precision of the instrument was good. The method repeatability test showed RSD below 14.3% and 1%, respectively, indicating that the sample preparation was accurate. In addition, the RSDs of the peak area and retention time of the plasma samples for the stability test were less than 15% and 1%, respectively, demonstrating good stability of the pre-treated plasma samples within 24 hours. The method validation results were in accordance with the requirements of metabonomics.

3.3. Metabolic profiling and data processing

The details of ion chromatograms and mass spectra are shown in Fig. 3. Through the plasma metabolic fingerprints, some differences can be found between the control group and oxaliplatin administration group. Multivariate statistical analysis was used to better visualize the differences among the obtained complex data. PCA and PLS-DA are the most commonly used multivariate statistical analysis methods. PCA was used to remove outlier samples, with a circle representing a 95% confidence interval, and the sample outside the circle representing the outliers, which is meaningless. PLS-DA was used to screen for high contribution variables between the control group and the administration group. The PCA and PLS-DA scatter plots are shown in Fig. 4. In the PLS-DA model, R2 (cum) and Q2 (cum) parameters usually indicate the fitness and prediction of the model. The model is stable and reasonable when R2 and Q2 are less than 1 and the two parameters are close to 1.25R2Y = 0.99, Q2 = 0.32, the values are higher in this study, thus ensuring the accuracy of the results. The permutation test (200 random permutations) also validated the model: the calculated R2 = 0.91 and Q2 = –0.12 values were lower than the original ones, and the Q2 intercepted the vertical axis below zero; no data overfitting was observed (Fig. 5).

Fig. 3. (A) The base peak intensity (BPI) chromatogram of plasma in the QC sample in positive mode gained based on UPLC-Q-TOF/MS. (B) BPI chromatogram of plasma in the NC and administration groups in positive mode gained based on UPLC-Q-TOF/MS.

Fig. 3

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Fig. 4. Results of multivariate statistical analysis for the administration and NC groups. (A) PCA score plot; (B) PLS-DA score plot.

Fig. 4

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Fig. 5. Permutation analysis of the model (200 times).

Fig. 5

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3.4. Identification of potential biomarkers

Through the multivariate statistical analysis, the observed m/z values of the biomarkers were determined. Using the observed m/z values, the HMDB database retrieved some of the compounds, among which some exogenous substances are removed and the resulting endogenous substances were screened for further identification of the biomarkers. To better understand this identification, we took the ion (tR = 4.71 min, m/z = 468.3088) as an example to describe in detail. The molecular formula was believed to be C22H46NO7P by searching the HMDB database for a compound with m/z = 468.3088. In addition, the main fragment ions in the positive MS/MS spectrum were found at m/z = 468.3, 184.0 and 104.1. The corresponding ions were [M + H]+, [M + H – C17H32O3]+ and [M + H – C17H33O6P]+, respectively. Based on the MS/MS information, the metabolite was finally identified as lysophosphatidylcholine (14 : 0) [LPC (14 : 0)]. Finally, we totally identified 15 biomarkers, as shown in Table 1.

Table 1. Identified metabolites related to oxaliplatin neurotoxicity based on UPLC-Q-TOF/MS.

No. t R (min) Compound Obsd m/z Calcd m/z Error (ppm) Formula MS/MS Content varied
1 0.80 Valine 118.0865 118.0868 –2.5 C5H11NO2 118.0 [M + H]+ ↓
72.0 [M + H – HCOOH]+
2 0.85 Proline 116.0705 116.0712 –6.0 C5H9NO2 116.1 [M + H]+ ↓
70.1 [M + H–HCOOH]+
3 0.89 Creatine a 154.0597 154.0592 3.2 C4H9N3O2 154.0 [M + Na]+ ↓
132.0 [M + H]+
90.0 [M + H – CH2N2]+
4 0.92 Tyrosine a 182.0813 182.0817 –2.2 C9H11NO3 182.0 [M + H]+ ↑
165.0 [M + H – NH3]+
136.0 [M + H – C2H6O]+
5 0.93 Norepinephrine 192.0648 192.0637 5.7 C8H11NO3 192.1 [M + Na]+ ↓
152.0 [M + H – H2O]+
107.0 [M + H – C2H9NO]+
6 2.16 Tryptophan 205.0973 205.0977 –2.0 C11H12N2O2 205.1 [M + H]+ ↓
188.1 [M + H – NH3]+
7 4.41 Sphinganine 302.3054 302.3059 –1.7 C18H39NO2 302.3 [M + H]+ ↓
284.3 [M + H – H2O]+
8 4.71 LysoPC (14 : 0) 468.3088 468.309 –0.4 C22H46NO7P 468.3 [M + H]+ ↓
450.3 [M + H – H2O]+
184.1 [M + H – C17H32O3]+
104.1 [M + H – C17H33O6P]+
9 5.02 LysoPC (16 : 1) 494.3244 494.3247 –0.6 C24H48NO7P 494.3 [M + H]+ ↓
476.3 [M + H – H2O]+
184.0 [M + H – C19H34O3]+
104.1[M + H – C19H35O6P]+
10 5.25 LysoPC (18 : 2) 520.3406 520.3403 0.6 C26H50NO7P 520.3 [M + H]+ ↓
502.3 [M + H – H2O]+
184.0 [M + H – C21H36O3]+
104.1 [M + H – C21H37O6P]+
11 5.31 LysoPC (15 : 0) 482.3246 482.3247 –0.2 C23H48NO7P 482.3 [M + H]+ ↓
184.0 [M + H – C18H34O3]+
104.1 [M + H – C18H35O6P]+
12 5.41 LysoPC (20 : 5) 542.3228 542.3247 –3.5 C28H48NO7P 542.3 [M + H]+ ↓
184.0 [M + H – C23H34O3]+
104.1 [M + H – C23H35O6P]+
13 5.63 LysoPC (22 : 5) 570.3555 570.3560 –0.9 C30H52NO7P 570.4 [M + H]+ ↓
552.3 [M + H – H2O]+
184.0 [M + H – C25H38O3]+
104.1 [M + H – C25H39O6P]+
14 5.70 LysoPC (18 : 3) 518.3224 518.3247 –4.4 C26H48NO7P 518.3 [M + H]+ ↓
500.3 [M + H – H2O]+
184.0 [M + H – C21H34O3]+
104.1 [M + H – C21H35O6P]+
15 5.91 Palmitoyl carnitine 400.3426 400.3427 –0.2 C23H45NO4 400.3 [M + H]+ ↓
341.2 [M + H – C3H9N]+
144.1 [M + H – C16H32O2]+
85.0 [M + H – C19H41NO2]+
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aMetabolites identified by comparison with standards.

3.5. Interpretation of selected biomarkers

LPC, a kind of endogenous phospholipid, is an important component of biofilms,26 which is related to the integrity of the neuronal cell membrane structure.27 Phosphatidylcholines (PCs) produce arachidonic acid and lysophosphatidylcholines (LPCs) through phospholipase A2 (PLA2). Mannelli L et al.28 suggested that oxaliplatin-induced nerve injury is the oxidative damage of the whole nervous system. Oxaliplatin induced these cells to produce large amounts of reactive oxygen species (ROS) and reactive nitrogen species (RNS), which elevated the levels of oxidative stress in vivo and made the oxidation and antioxidant system out of balance, thereby inducing cell apoptosis, manifested as neurotoxicity eventually. The PCs can remove the excess peroxides from the body, ROS and RNS. So when the nervous system is damaged, the body may need to consume a large amount of PCs to remove the peroxide in order to achieve normal levels of oxidative stress, so that once the metabolism of glycerol phospholipids is affected, the LPC content would be decreased.29 In addition, LPCs are related to lecithin metabolism. Lecithin is an important component of the cell membrane, involved in the synthesis of the neurotransmitter acetylcholine.30 Impaired lecithin metabolism may lead to nerve cell damage, and eventually evolve into neurotoxicity. Therefore, it was indicated that the abnormal changes in LPCs might be related to oxaliplatin neurotoxicity.

Sphingolipids are the main lipid constituent of nerve tissue. Sphinganine can be directly phosphorylated by sphingosine kinase to generate sphingosine 1-phosphate, and on the other hand, it can be transformed into ceramide; ceramide can further be transformed under the action of enzymes to generate sphingomyelin, which has protective effects on neurons.31 The sphinganine content in this experiment is reduced, indicating that the nervous system may have been damaged.

Norepinephrine belongs to catecholamine neurotransmitters and is an important neurotransmitter in the human body. Norepinephrine is a recognized neuroprotective agent that exerts its neuroprotective effects, mainly by inhibiting the gene transcription of inflammatory factors and promoting the production of neuroprotective factors.32 In this study, the norepinephrine levels were reduced, thereby reducing the protective effect on nerve cells, causing nerve cells to suffer damage and producing toxicity.

Neurotoxicity is closely related to energy metabolism. When nerve cells are damaged, it affects energy metabolism. Carnitines are a reliable biomarker of abnormal energy metabolism.33 Creatine is commonly found in nerve cells,34 and acts as a stimulant for mitochondrial respiration that protects neurons from oxidative stress.35 When the body ATP is insufficient, it can quickly provide energy. Its consumption may cause cell fragility, such that the ATP synthesis becomes insufficient. Since amino acid metabolism is the material basis for protein synthesis and energy metabolism, changes in the amino acid content in this study reflected the abnormal metabolism of energy and amino acid metabolism. Related mechanisms are shown in Fig. 6.

Fig. 6. Different metabolites and the corresponding pathways in oxaliplatin-treated rats.

Fig. 6

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3.6. Disturbed metabolic pathways

A metabolic pathway analysis of potential biomarkers was performed with the MetPA database, which is a free web-based metabonomics metabolic pathway analysis tool that analyzes most relevant metabolic pathways affected under specific conditions. The results showed that amino acids biosynthesis and metabolism, glycerophospholipid metabolism, and sphingolipid metabolism were affected in oxaliplatin-treated rats (Fig. 7).

Fig. 7. A summary of pathway analysis with MetPA. The interrupted metabolic pathways in the treated group. (1) Phenylalanine, tyrosine and tryptophan biosynthesis; (2) valine, leucine and isoleucine biosynthesis; (3) tyrosine metabolism; (4) sphingolipid metabolism (5) arginine and proline metabolism (6) glycerophospholipid metabolism.

Fig. 7

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4. Conclusion

In the present study, a neurotoxicity model was established using rats treated with oxaliplatin. Metabonomics based on the UPLC-Q-TOF/MS technique was used to analyze the plasma samples obtained at seven days after oxaliplatin administration. By means of multivariate statistical analysis and integrated analysis, 15 biomarkers related to neurotoxicity induced by oxaliplatin were identified. These 15 potential biomarkers were involved in amino acid biosynthesis and metabolism, glycerophospholipid metabolism, sphingolipid metabolism and so on. Our results provided useful information on oxaliplatin-induced neurotoxicity, as well as a reference for the study of the neurotoxicity mechanism.

Conflicts of interest

There are no conflicts of interest to declare.

Acknowledgments

This project was supported by the Program for Changjiang Scholars and Innovative Research Team in University (IRT_14R41) and the Science and Technology Development Fund Program for Higher Education Institutions in Tianjin (No. 20140208).

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