—Establishment of Metabolite Extraction Methods from Tissue, Cell, and Medium Samples and
Applications
of Metabolome Analysis on Cancer Research—
所属： 政策・メディア研究科 修士2年 バイオインフォマティクスプログラム
研究成果報告書： 2005年度
Metabolome analysis using capillary electrophoresis mass spectrometry (CE-MS) is a novel and promising approach that can reveal phenotypic information of cells by a most direct manner. In order to realize the accurate and reliable quantification of metabolites by CE-MS, the development of metabolite extraction methods specifically designed for different types of biological samples is crucial; therefore, the step-by-step metabolite extraction protocols for tissues, cultured adherent cells, and medium were established and optimized for CE-MS analysis. In particular, the appropriate homogenization method was developed and the linearity between the weight and metabolite concentrations was examined for tissue samples. For adherent cells, (1) the advantages of using mannitol solution as washing buffer, (2) the adequacy of washing twice to thoroughly remove the trace of media, (3) the inadequacy of trypsinization of cells, (4) the linearity between the number of cells and metabolite concentrations, and (5) the unnecessity of collecting cell debris for the metabolite extraction were demonstrated. Proper ratios of sample, methanol, and chloroform volumes to efficiently extract metabolites from medium sample were also investigated. Subsequently, metabolome analysis was applied on cancer research using both tumor tissues and cell line samples. In particular, comparative metabolome analysis was conducted on mouse tumor tissues that were treated or untreated by arctigenin, an antitumor drug candidate, and revealed several key metabolites that are useful to interpret the biochemical mechanism of arctigenin-induced cytotoxicity. Moreover, time-course metabolome analysis of DLD-1, a human colon cancer cell line, during its exponential growth was carried out, and several metabolites that seemed to take primary roles for its growth were identified. Although a sole use of metabolome analysis is incapable of realizing a complete elucidation of cancer cell-specific molecular mechanisms, the potential of metabolomics approach on cancer research by way of providing numerous clues to interpret biochemical properties of cancer cells was clearly demonstrated.
Introduction
The development of quantitative and integrative metabolome analysis using capillary electrophoresis mass spectrometry (CE-MS)1 realized the measurement of literally all the ionic metabolite levels in plasma, serum, urine, erythrocytes, and bacteria. Clinical applications of CE-MS also have been conducted recently2-4. Among them, metabolome analysis using CE-MS for lifestyle diseases such as atherosclerosis, hypertension, diabetes, and cancer attracts significant attention with the expectation of the discovery of biomarker and key metabolites to elucidate the development mechanism of diseases. Promising the development of a novel method to prevent and treat cancer, CE-MS application on cancer research has recently been initiated5,6. Comparative metabolome analysis of clinical tumor and healthy tissue samples, for example, may disclose a crucial metabolic target that should be appropriately controlled to eliminate tumor. Likewise, metabolic profiling using cell lines will be extremely useful in a sense that it is convenient to virtually reproduce the actual microenvironment of malignant cells and enable to demonstrate the effects of dynamic perturbation on the cells. Nevertheless, no study that conducted the quantitative metabolome analysis of cancer tissues or cell lines using CE-MS on a broad scale has been published to the best of the author's knowledge. Therefore, method developments of metabolite extraction from tissue and cell line samples for CE-MS analysis were the first step toward the application of quantitative metabolome analysis on cancer research.
Upon the establishment of metabolite extraction methods from tissue and cell line samples, the metabolome analysis was applied to a series of experiments using mouse tissue and cell line samples. First, as a collaborative project with National Cancer Center (NCC), the effect of arctigenin, which is an anticancer drug candidate, on the metabolic profiles of tumor tissues of mice was analyzed by CE-MS. It was recently found that arctigenin imposes cytotoxic effects on the cells only in the nutrition-deprived condition7. Based on the fact that several cancer types, pancreatic cancer as an example, are highly malignant even in the hypovascular microenvironment, i.e., hypoxic and nutrition-deprived conditions8, the elucidation of energy management mechanism of cancer cells in such severe conditions is believed to provide a crucial information to selectively and completely eliminate tumors from patients. During the process of carcinogenesis, cancer cells are hypothesized to acquire the resistance to hypoxic and nutrient-deprived environment, and this has been demonstrated in in vitro experiments using several cancer cell lines including pancreatic cancer9. In this experiment, Panc-1, a pancreatic cancer cell line, was injected into mice to form a tumor, and subsequently, arctigenin was administrated. The elucidation of the molecular mechanism of arctigenin-derived cytotoxicity and the potential side effects upon the arctigenin administration was attempted by comparative metabolome analysis. Next, as another collaborative project with NCC, the metabolites that are crucial for the growth of cancer cells were investigated by way of metabolome analysis using cell line samples. From the observation that pancreatic cells are more tolerant than normal fibroblast cells in glucose deprived condition10, it is reasonable that pancreatic cells should possess a superior method of energy production to fibroblast cells. Similarly, DLD-1, a human colon cancer, is known to be tolerant to glucose-deprived condition but not comparably to amino acid-deprived condition (by direct conversation with Dr. Kazunori Sato at NCC). Accordingly, the time-course analysis of metabolic profile on DLD-1 during its exponential growth was conducted in order to identify a series of metabolites that are crucial for its growth. Again, the identification of crucial metabolite source for the growth of cancer cell may provide a critical information to develop a novel strategy that can specifically and selectively eliminate cancer cells from patients.
Conclusive Remarks
Procedures to extract metabolites from tissues, adherent cells, and medium were developed and optimized for CE-MS analysis, and the developed techniques were applied on the metabolome analysis of biological tissue and cell samples for cancer research. In particular, (1) it was found that the use of Multi-Beads Shocker for the complete homogenation is necessary for certain types of hard tissues, though ultrasonicator works sufficiently for soft tissues, (2) when Multi-Beads Shocker is used for the homogenization, the weight of the tissue samples should be in the range of approximately 30 ~ 100 mg and the appropriate setting of the device is crucial, (3) the use of 5% mannitol in order to wash away the trace amount of medium from adherent cells is appropriate since it can maintain the osmotic pressure, improve the signals of CE-MS measurement, and cannot be metabolized by cells, (4) washing twice with 5% mannitol is sufficient and adequate to remove 99% of the medium-derived metabolites, (5) the use of trypsin for the extraction of metabolites is not suitable since it significantly changes the metabolic profiles of cells, (6) the linearity between the number of cells and the concentration of most of the metabolites measured by CE-MS is adequately high (R2 > 0.9) when the number of cells is in the range of 2 x 106 and 10 x 106, yet occasionally the linearity is considerably different depending on compounds and cell types, (7) metabolites are eluted out from the cells instantaneously when methanol is added and therefore the scrape of cells from the plate is not necessary to extract metabolites, (8) there is a range of appropriate ratio among sample, methanol, and chloroform volume to be mixed in order to extract metabolites from medium, and desalination, if possible, should greatly improve the resolution of peaks when measured by CE-MS (9) based on several metabolic features between sample groups, the involvement of TCA cycle and certain amino acids on the biochemical effect of an antitumor drug candidate, arctigenin, was indicated by metabolome analysis of tumor tissue samples of mice, though repetitive measurements on numerous samples are desirable for reproducible and reliable results, (10) several metabolites that seem to be crucial for the exponential growth of DLD-1 cells were identified by serum-stimulating and analyzing the time-course metabolic profiles of the cells.
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