Yubo Li‡
a,
Xiuxiu Zhang‡a,
Huifang Zhou‡b,
Simiao Fana,
Yuming Wanga,
Lu Zhanga,
Liang Jua,
Xin Wua,
Huanyu Wua and
Yanjun Zhang*a
aTianjin State Key Laboratory of Modern Chinese Medicine, School of Traditional Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, 312 Anshan west Road, Tianjin 300193, China. E-mail: tianjin_tcm001@sina.com; Fax: +86-22-59596223; Tel: +86-22-59596223
bDepartment of experimental teaching, Tianjin University of Traditional Chinese Medicine, 88 Yuquan Road, Tianjin 300193, China
First published on 6th January 2014
Cisplatin is a well-known chemotherapeutic agent in cancer therapy. It is commonly administered intraperitoneally and intravenously in the clinic. The use of cisplatin is limited by its side effects, particularly its nephrotoxicity. In this study, mass spectrometry-based metabonomics coupled with multivariate statistical analysis was used to find biomarkers of kidney injury and further applied to investigate on the disturbed metabolic pathways, which were induced by single intraperitoneal or intravenous injection of cisplatin to rats with the dosage of 6 mg kg−1. It was found that sixteen biomarkers were changed because of drug administration. Among these sixteen biomarkers, eight biomarkers, including LPC(20:3), creatinine, LPC(14:0), LPC(18:3), LPC(22:5), arachidonic acid, proline and tryptophan, were found to be related to biochemical indicators of nephrotoxicity using Pearson correlation analysis. The identified biomarkers were mainly involved in valine, leucine and isoleucine biosynthesis, metabolism of sphingolipid, arginine and proline, glycerophospholipid, tryptophan, and arachidonic acid. In addition, the disturbed pathways were found to be time- and intraperitoneal or intravenous administration-dependent. The present result shows that mass spectrometry-based metabonomics approaches could be applied to study changes in metabolites and metabolic pathways associated with intraperitoneal or intravenous injection of cisplatin.
Metabonomics is a branch of systems biology that mainly includes genomics, proteomics, transcriptomics, and metabonomics.16 Metabonomics is a rapidly developing subject applied in the study of numerous fields, such as toxicology, therapeutic efficacy, and natural products.16–19 Metabonomics technology can detect and quantify a large amount of metabolites such as lipids, amino acids, peptides, organic acids, and vitamins.18 These small and low-weight molecules are the final products in biological metabolite pathways and they have an important function in metabolism.18 Metabonomics currently has an important function in predicting drug-induced toxicity because it can provide essential information on metabolic profiles in biofluid and organs derived from drug administration.20,21 Moreover, the biomarkers identified by metabonomics can predict toxicity earlier than general clinical chemical methods, improving the development of toxic detection, which is beneficial to human health.
The foundation and core of metabonomics is high quality data that requires an advanced analysis platform, in which nuclear magnetic resonance (NMR) and mass spectrometry (MS) are commonly used.22,23 MS has been a powerful tool in metabonomics study with its high sensitivity, greater accuracy, and higher resolution. Rapid resolution liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry (RRLC-Q-TOF-MS) provides high sensitivity with low detection limits, high resolution, good separation, and high accuracy and has become an important analysis platform for detecting metabolic changes in metabonomics.24,25 In addition, RRLC-Q-TOF-MS can also provide ion fragment information through MS/MS data, which is significant for the identification of potential biomarkers.
In this study, we built a method based on metabonomics coupled with RRLC-Q-TOF-MS and multivariate statistical analysis to discover plasma biomarkers of kidney injury caused by IP and IV cisplatin administration. We also speculated on the disturbed pathway. This study aims to find the characteristic of nephrotoxicity induced by two common used cisplatin administration at the metabolic level, trying to offer information for further investigation on mechanism of toxicity cisplatin.
Serum was used to detect the level of BUN and Scr, which were measurement by automatic biochemical analyzer (BIOSINO, Ltd). Plasma was stored at minus 80 degrees centigrade prior to metabonomics analysis. The kidney were stained with hematoxylin and eosin (H&E) to observe the kidney pathological features. All the procedures were conducted in accordance with the Chinese national legislation and local guidelines. For convenient group labeling, IP-DDP-24h group was used to represent the group of samples collected at 24 h after single IP cisplatin administration. IP-DDP-72h group represented the group of samples collected 72 h after single IP cisplatin administration. IP-NS-24h and IP-NS-72h represented the groups of samples collected at 24 and 72 h, respectively, after single IP normal saline administration. IV-DDP-24h and IV-DDP-72h represented the groups of samples collected at 24 and 72 h, respectively, after single IV cisplatin administration. IV-NS-24h and IV-NS-72h represented the groups of samples collected at 24 and 72 h, respectively, after single IV normal saline administration.
Further investigation on the multivariate statistical analysis, including principal component analysis (PCA) and partial least-squares-discriminant analysis (PLS-DA), was loaded into SIMCA-P+11.5 software (Umetrics AB, Umea, Sweden). The data was scaled prior to multivariate data analysis. We used Pareto scaling in performing further analysis. The general trends and groupings of the total samples can be obtained, and remove outliers beyond 95% confidence level through PCA. PLS-DA, a supervised analysis technique, is a better method in removing undesirable information when building a model.26 To ensure that the established analysis model actually reflected discrimination of metabolites induced by DDP, the feasibility of the analysis model was verified through cross validation by taking out one-seventh of the samples in each group.27 The potential biomarkers were selected through S-plot, loading plot and variable importance plot (VIP). The selected potential biomarkers were further tested through independent sample t test using SPSS 17.0, and only biomarkers with p < 0.05 were considered in the next step. The biomarkers were finally identified using standard or MS/MS fragment information coupled with the available database, such as HMDB (http://www.hmdb.ca/), KEGG (http://www.genome.jp/kegg/), and MassBank (http://www.massbank.jp/).
The Pearson correlation analysis was used to explore the relationship between identified biomarkers and the result of the biochemical analysis. Pearson correlation coefficient is a linear correlation coefficient used to reflect the linear correlation of the two variables. The correlation coefficient r > 0 indicates that the two variables are positively correlated. The correlation coefficient r < 0 indicates they are negatively correlated. The higher absolute value of r, the stronger correlation of two variables is. Pearson correlation analysis were conducted using SPSS 17.0 in this study.
The interaction, construction, and pathway analysis of potential biomarkers was performed through MetPA (http://www.metpa.metabolomics.ca./MetPA/faces/Home.jsp). MetPA is a free, web-based tool aiming for metabonomics studies, which is convenient and easy-used for researchers to analyze the most relevant metabolic pathways affected by specific factor.
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Fig. 1 Effect of cisplatin on the level of Scr and BUN. (A) Changes in Scr level. (B) Changes in BUN level. Data are the means ± SD. *P < 0.05 compared to their control group respectively. |
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Fig. 2 Effect of cisplatin administration on the kidney tissue assessed by histopathology. Histological sections of the kidney were stained with H&E; 200× magnification. |
Multivariate statistical analysis was performed to better visualize the differences among the obtained complex data. Here, PCA and PLS-DA were used to filter out potential biomarkers between different groups. In PCA and PLS-DA plots, each point stands for a sample and each sample includes the information of metabolic profile. PCA is an unsupervised analysis technique, aiming to demonstrate the natural inter-relationship among different groups.28 The plasma samples were divided into different regions in PCA score plot, indicating that the plasma endogenous substance changed because of IP and IV injecting cisplatin and this change showed time-dependency (Fig. 3A and B). To further distinguish between the DDP and control groups, PLS-DA was used to determine the potential biomarkers related to nephrotoxicity. Fig. 3C–E respectively showed the score plot, S-plot and loading plot of PLS-DA model established in IP-DDP-24h group versus IP-NS-24h. The potential biomarkers were selected through S-plot, loadings plot, and variable importance plot. What's more, the different samples that appeared in different blocks indicated the presence of a time-dependent metabolic distinction in plasma collected at 24 and 72 h after single IV cisplatin administration (Fig. S2A, ESI†). Differences in the plasma metabolites between IP and IV cisplatin administration at the same time point were evident from PLS-DA analysis (Fig. S2B, ESI†). R2 (cum) and Q2 (cum) parameters usually indicate the fitness and prediction of the model. These two parameters are below 1 and can show a proper and good model when close to 1.29 In this study, The established model were reasonable with high value of R2 and Q2 (Table S1, ESI†). Metabolites with VIP >1 and variations far away from the S-plot and the loading plot were chosen as potential biomarkers. According to the criterion of data processing established above, endogenous metabolites significantly changed were summarized for preferential study.
We took the ions at (tR = 11.58 min, m/z 468.3086) as an example to explain the identification. The molecular formula was supposed to be C22H46NO7P by searching on HMDB database utilizing m/z 468.3086, besides, the main fragment ions in positive MS/MS spectrum were found at m/z 450.3, 391.3, 285.2, 184.1, 125.0, 104.1, which could be the ions formed by the [M + H]+ of lost –H2O, –C3H10NO, –C5H13NO4P, –C17H33NO2, –C20H40NO3, and –C18H38NO4P respectively. To confirm the structure of this ion, according to the Chemspider database, the metabolite was finally identified as lysophosphatidylcholine (14:0) [LPC(14:0)]. The mass spectrum of LPC(14:0) were shown in Fig. 4. The detailed information of identified differential metabolites in plasma was provided in Table 1.
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Fig. 4 The MS/MS spectrum of LPC(14:0) showing its fragments in plasma samples obtained from RRLC-Q-TOF-MS/MS. |
No. | tR (min) | Metabolite | Obsd [M + H]+ | Calcd [M + H]+ | Error (ppm) | Formula | Molecular structure | Content changea (DDP/NS) | The groups detected this biomarkerd | Pathway (KEGG) |
---|---|---|---|---|---|---|---|---|---|---|
a The change of biomarker's content between DDP group and its corresponding control group, ↑, content increased; ↓, content decreased.b Identified by standards.c Identified by MS/MS information.d The groups in which biomarkers were identified, IP-DDP-24h group stands for the group of samples collected at 24 h after single intraperitoneal cisplatin administration; IP-DDP-72h group stands for the group of samples collected at 72 h after single intraperitoneal cisplatin administration; IV-DDP-24h group stands for the group of samples collected at 24 h after single intravenous cisplatin administration; IV-DDP-72h group stands for the group of samples collected at 72 h after single intravenous cisplatin administration. | ||||||||||
1 | 0.89 | Ureab | 61.0398 | 61.0396 | 3.3 | CH4N2O | 61.0 [M + H]+ | ↑ | IP-DDP-24h group | Arginine and proline metabolism |
IP-DDP-72h group | ||||||||||
IV-DDP-72h group | ||||||||||
2 | 0.92 | Creatinineb | 114.0661 | 114.0662 | −0.9 | C4H7N3O | 114.1 [M + H]+ | ↑ | IP-DDP-72h group | Arginine and proline metabolism |
IV-DDP-72h group | ||||||||||
3 | 0.93 | Prolineb | 116.0704 | 116.0706 | −1.7 | C5H9NO2 | 116.1 [M + H]+ | ↓ | IP-DDP-24h group | Arginine and proline metabolism |
IV-DDP-72h group | ||||||||||
4 | 0.89 | Valineb | 118.0859 | 118.0863 | −3.4 | C5H11NO2 | 118.1 [M + H]+ | ↓ | IP-DDP-24h group | Valine, leucine and isoleucine biosynthesis |
IP-DDP-72h group | ||||||||||
IV-DDP-24h group | ||||||||||
IV-DDP-72h group | ||||||||||
5 | 5.18 | Tryptophanc | 205.0968 | 205.0972 | −2.0 | C11H12N2O2 | 205.1 [M + H]+ | ↓ | IP-DDP-24h group | Tryptophan metabolism |
188.1 [M + H–NH2]+ | IP-DDP-72h group | |||||||||
IV-DDP-72h group | ||||||||||
6 | 9.08 | Sphinganinec | 302.3045 | 302.3054 | −3.0 | C18H39NO2 | 302.3 [M + H]+ | ↑ | IP-DDP-24h group | Sphingolipid metabolism |
284.3 [M + H–H2O]+ | IP-DDP-72h group | |||||||||
IV-DDP-72h group | ||||||||||
7 | 11.55 | Arachidonic acidb | 305.2471 | 305.2475 | −1.3 | C20H32O2 | 305.2 [M + H]+ | ↓ | IV-DDP-24h group | Arachidonic acid metabolism |
8 | 11.58 | LPC(14:0)c | 468.3086 | 468.3085 | 0.2 | C22H46NO7P | 468.3 [M + H]+ | ↓ | IP-DDP-24h group | Glycerophospholipid metabolism |
450.3 [M + H–H2O]+ | ||||||||||
391.3 [M + H–C3H10NO]+ | ||||||||||
285.2 [M + H–C5H13NO4P]+ | ||||||||||
184.1 [M + H–C17H33NO2]+ | ||||||||||
125.0 [M + H–C20H40NO3]+ | ||||||||||
104.1 [M + H–C18H38NO4P]+ | ||||||||||
9 | 9.78 | LPC(16:1)c | 494.3243 | 494.3241 | 0.4 | C24H48NO7P | 494.3 [M + H]+ | ↑ | IP-DDP-24h group | Glycerophospholipid metabolism |
476.3 [M + H–H2O]+ | IP-DDP-72h group | |||||||||
184.1 [M + H–C19H35NO2]+ | IV-DDP-72h group | |||||||||
125.0 [M + H–C22H42NO3]+ | ||||||||||
104.0 [M + H–C20H40NO4P]+ | ||||||||||
10 | 11.66 | LPC(17:0)c | 510.3530 | 510.3554 | −4.7 | C25H52NO7P | 510.4 [M + H]+ | ↓ | IP-DDP-72h group | Glycerophospholipid metabolism |
492.4 [M + H–H2O]+ | IV-DDP-72h group | |||||||||
433.4 [M + H–C3H10NO]+ | ||||||||||
285.2 [M + H–C5H13NO4P]+ | ||||||||||
11 | 9.46 | LPC(18:3)c | 518.3239 | 518.3241 | −0.4 | C26H48NO7P | 518.3 [M + H]+ | ↓ | IP-DDP-24h group | Glycerophospholipid metabolism |
500.3 [M + H–H2O]+ | IP-DDP-72h group | |||||||||
184.1 [M + H–C16H32NO4P]+ | IV-DDP-24h group | |||||||||
125.0 [M + H–C24H42NO3]+ | ||||||||||
12 | 11.05 | LPC(18:2)c | 520.3376 | 520.3398 | −4.2 | C26H50NO7P | 520.3 [M + H]+ | ↑ | IP-DDP-72h group | Glycerophospholipid metabolism |
502.3 [M + H–H2O]+ | IV-DDP-24h group | |||||||||
184.1 [M + H–C21H37NO2]+ | ||||||||||
149.1 [M + H–C22H44NO3]+ | ||||||||||
104.1 [M + H–C22H42NO4P]+ | ||||||||||
13 | 9.85 | LPC(20:5)c | 542.3223 | 542.3241 | −3.3 | C28H48NO7P | 542.3 [M + H]+ | ↓ | IP-DDP-24h group | Glycerophospholipid metabolism |
524.3 [M + H–H2O]+ | ||||||||||
259.1 [M + H–C17H33NO2]+ | ||||||||||
185.1 [M + H–C19H36NO3P]+ | ||||||||||
126.0 [M + H–C26H42NO3]+ | ||||||||||
14 | 11.89 | LPC(20:3)c | 546.3529 | 546.3554 | −4.6 | C28H52NO7P | 546.4 [M + H]+ | ↑ | IP-DDP-72h group | Glycerophospholipid metabolism |
528.4 [M + H–H2O]+ | IV-DDP-24h group | |||||||||
184.1 [M + H–C18H36NO4P]+ | IV-DDP-72h group | |||||||||
125.1 [M + H–C26H46NO3]+ | ||||||||||
15 | 11.71 | LPC(20:2)c | 548.3714 | 548.3711 | 0.5 | C28H54NO7P | 548.4 [M + H]+ | ↓ | IP-DDP-72h group | Glycerophospholipid metabolism |
471.3 [M + H–C3H8O2]+ | IV-DDP-72h group | |||||||||
370.2 [M + H–C8H19NO5P]+ | ||||||||||
184.0 [M + H–C23H41NO]+ | ||||||||||
125.0 [M + H–C26H48NO3]+ | ||||||||||
104.0 [M + H–C24H46NO4P]+ | ||||||||||
16 | 11.62 | LPC(22:5)c | 570.3528 | 570.3554 | −4.6 | C30H52NO7P | 570.4 [M + H]+ | ↓ | IP-DDP-24h group | Glycerophospholipid metabolism |
552.3 [M + H–H2O]+ | IP-DDP-72h group | |||||||||
184.1 [M + H–C21H41NO3P]+ | IV-DDP-24h group | |||||||||
125.0 [M + H–C28H47NO3]+ | IV-DDP-72h group |
No. | Biomarker | The correlation between biomarkers and BUN level | The correlation between biomarkers and Scr level | ||
---|---|---|---|---|---|
r value | p value | r value | p value | ||
1 | LPC(20:3) | 0.950 | 0.003 | 0.980 | 0.001 |
2 | Creatinine | 0.970 | 0.001 | 0.930 | 0.007 |
3 | LPC(14:0) | −0.731 | 0.009 | −0.775 | 0.024 |
4 | LPC(18:3) | −0.633 | 0.012 | −0.626 | 0.097 |
5 | LPC(22:5) | −0.878 | 0.004 | −0.916 | 0.001 |
6 | Arachidonic acid | −0.756 | 0.030 | −0.724 | 0.042 |
7 | Proline | −0.868 | 0.005 | −0.893 | 0.003 |
8 | Tryptophan | −0.903 | 0.002 | −0.941 | 0.001 |
Other biomarkers were treated as the second group. The elevated level of creatinine in plasma reflects the damage in the function of glomerular filtration.32 Cisplatin may induce injury in the renal vasculature and result in decreased blood flow and ischemic injury of the kidneys, contributing to a decline in glomerular filtration rate.2 Arachidonic acid mediates inflammation widely perceived to be a side effect of cisplatin administration.2,33–35 Cisplatin-induced inflammation result in the development of kidney damage and renal injury.2 Proline participates in arginine and proline metabolism. Tryptophan is an essential amino acid and the precursor of serotonin.
In our study, the change in the contents of the same biomarkers compared with their respective control groups in the two routes of cisplatin administration significantly differed. For example, the plasma content of LPC(20:3) was unchanged in the IP-DDP-24h group, but it increased by threefold of its control group in the IV-DDP group at the same time point; the plasma LPC(18:3) level in the IP-DDP-24h group was reduced by 0.3 times and by 0.6 times at the same time point in the IV-DDP group. In addition, the cisplatin-disturbed pathways in the same time point of different administration were not consistent and existed an administration-dependency. Based on former mentioned fact, it can be speculated that the nephrotoxicity induced by two routes of cisplatin administration may do have some distinction. Meanwhile, this distinction may be related to the different mechanism of nephrotoxicity induced by the two routes of cisplatin, which will be further investigated.
DDP | Cisplatin |
IP | Intraperitoneal |
IV | Intravenous |
BUN | Blood urea nitrogen |
Scr | Serum creatinine |
NMR | Nuclear magnetic resonance |
MS | Mass spectrometry |
RRLC-Q-TOF-MS | Rapid resolution liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry |
IP-DDP-24h | The group of samples collected at 24 h after single intraperitoneal cisplatin administration |
IP-DDP-72h | The group of samples collected at 72 h after single intraperitoneal cisplatin administration |
IP-NS-24h | The groups of samples collected at 24 h after single intraperitoneal normal saline administration |
IP-NS-72h | The groups of samples collected at 72 h after single intraperitoneal normal saline administration |
IV-DDP-24h | The group of samples collected at 24 h after single intravenous cisplatin administration |
IV-DDP-72h | The group of samples collected at 72 h after single intravenous cisplatin administration |
IV-NS-24h | The groups of samples collected at 24 h after single intravenous normal saline administration |
IV-NS-72h | The groups of samples collected at 72 h after single intravenous normal saline administration |
ESI | Electrospray ionisation |
QC | Quality Control |
PCA | Principal component analysis |
PLS-DA | Partial least-squares-discriminant analysis |
VIP | Variable importance plot |
LPC | Lysophosphatidylcholine. |
Footnotes |
† Electronic supplementary information (ESI) available. See DOI: 10.1039/c3ra46920d |
‡ Yubo Li, Xiuxiu Zhang and Huifang Zhou contributed equally to the work as co-first authors. |
This journal is © The Royal Society of Chemistry 2014 |