Technical Field of Invention

The present invention deals with the process of isolation/ extraction, separation and identification of low molecular mass chemicals (LMC) including the biomarkers from the human blood plasma by Liquid chromatography - mass spectrometry (LC-MS/MS).

Metabolite Biomarker(s) is a metabolite or metabolite profile that may be a known metabolite(s) with altered concentration(s) or a modified chemical of divergent pathway, which is an indicative of specific disease at an early stage of disease or abnormality. There is a great demand for biomarker discovery for various diseases such as cancers, arthritis, ischeima, inborn errors of metabolism etc., for the early disease detection. The metabolites/ modified metabolites that serve as biomarkers for disease diagnosis require sensitive, correct detection and accurate quantification system which is complement^ in our invention by novel sample preparation, LC separation followed by detection by ESl-MS/MS method.

Prior Art

Diagnosis of disease at early stage relies on the discovery of metabolites or the pattern of metabolites that is generated due to the specific physiological changes such signature metabolites are known as biomarkers. The identification of biomarkers/ metabolome that are indicative of a disease at an early stage require development of methods, sensitive enough to measure these biomarkers. Metabolomics is the key technology that make use of the hyphenation technology of LC-MS/MS, GC-MS/MS, CE-MS/MS, LC-MS/MS/TOF, CE-MS/MS-TOF, that aims al the unbiased analysis of all the low molecular mass chemicals (LMCs) present in a given biological sample accurately and quantitatively. The concept that metabolic state is representative of the overall physiologic status of the organism lies at the heart of metabolomics. Metabolomic studies capture global biochemical events by assaying thousands of small molecules in cells, tissues, organs, or biological fluids-—followed by the application of informatics techniques to define metabolomic signatures. Metabolomic studies can lead to enhanced understanding of disease mechanisms and to new diagnostic markers as well as enhanced understanding of mechanisms for drug effect and increased ability to predict individual variation in drug response phenotypes (pharmaco metabolomics).

Metabolomics offers a means of deciphering cellular metabolism and metabolic regulation, which is downstream to genomic and is considered to be highly significant as it aims for the non-targeted identification of all low molecular mass chemicals (LMCs) present in a specific biological sample. Metabolomics reveal novel metabolomic profiles, modified metabolites, altered fluxes, addresses of glotal metabolites and also their quantitative dynamics. Unique features of persistence phenotype unravel the biochemical basis of the cellular processes, which, up on correct quantitation cater to serve as unique biomarker for diagnosis. Due to a wide range of metabolites in the metabolic network, eg, approximately 600 metabolites in Saccharomyces cerevisiae, 1692 metabolites in Bacillus subtilis and up to 200,000 metabolites in plant kingdom, it is a very challenging task to establish analytical tools for identifying and quantifying all of them. The overall size of the human metabolome remains a subject of debate and depends on the definition of exactly what components should be included and on the analytical platform used.

The objective of this invention is to develop a comprehensive method using Liquid chromatography - mass spectrometry (LC-MS/MS) to obtain a wide spectrum of low molecular mass chemicals (LMC) from human blood plasma and construction of disease specific metabolite library. This invention contributes significantly to biomedical research and, ultimately to clinical medical diagnosis.

Disease disrupts metabolism and, as a result, causes changes that are long lasting and can be captured as metabolic signatures. Initial metabolomic signatures have already been reported for several disease states, including motor neuron disease, depression, schizophrenia, Alzheimer, s disease, cardiovascular and coronary artery disease, hypertension, subarachnoid hemorrhage, preeclampsia, type-2 diabetes, liver cancer, ovarian cancer, breast cancer, and Huntington's disease. Few investigators reported the plausible metabolites in various disease conditions such as, Musarrat et al., 1996, (1), Zheng e( al.. 2005 (2) and Grace et al., 2007, (3) showed the increased levels of 8-bydroxyguanosine, 1-methyIguanosine and enterolactone respectively during prognosis of human breast cancer. Similarly, Markis et a. I, 2001, (4) reported increased homocystein levels in various cancers; Cauley et al, 2003, (5) reported the levels of o-hydroxysterone associated with the breast cancer in elderly women. Byun et al, 2008, (6) showed the variation in polyamine concentrations such as, N'-, and N*-acetyl spermidine and the increased 1,3-diaminopropane in breast cancer patients.

These signatures are made up of number of metabolites that are deregulated, with concentrations that are modified in the disease state or after drug exposure. As a resuh, analysis of these signatures and their components can potentially provide information with regard to disease patho-physiology. Metabolic signatures have also been identified for several drugs where the signatures represent changes that occur secondary to drug treatment and in which those signatures capture information from pathways that are targets for, or are affected by. drug therapy.

In summary, metabolomics approach promises to have broad implications for both basic biomedical research and medical practice because it can capture information with regard to mechanisms of disease and of drug action, making it possible to map disease risk or drug action to metabolic pathways. Therefore, since several years there has been a requisition to develop a robust, reproducible, quick and easy procedure for extraction, separation, sensitive detection and cost effective metabolite biomarker for various diseases including breast cancer.

The construction of specific Metabolite Library is highly remarkable for the unilateral and precise screening of the entire metabolome of an individual. The metabolite library enables clinicians and psychologists to target Patho-physiology, and Pharmaco-genomics condition of an individual for the early diagnosis of wide spectrum of disease and also understand the prognosis/ clinical response. It also helps in understanding the pattern of LMCs in different physiological conditions. The differential expression, diversion of metabolic pathway, regulation of metabolites and their influence on divergent pathways are viewed as signals that will help to Figure out potential biomarkers for specific target disease diagnosis.

References:

1. Byun J.A, Lee S.H., Jung B.H, Choi M.H, Moon M.H, Chung BC. Analysis of polyamines as carbamoyl derivatives in urine and serum by liquid chromatography- tandem mass spectrometry. Biomed Chromatogr. 2008 Jan; 22{l):73-8a

2. Cauley JA, Zmuda JM, Danielson ME, Ljung BM, Bauer DC. Cummings SR, Kuller LH. Estrogen metabolites and the risk of breast cancer in older women. Epidemiology. 2003 Nov; 14(6): 740-4.
3. Zheng YF, Kong HW, Xiong JH, Lv S, Xu GW. Clinical significance and prognostic value of urinary nuclcocytes in breast cancer, patients. Clin. Biochem. 2005 Jan; 38(1): 24-30.

4. Grace PB, Mistry NS, Carter MH, Leathern AJ, Teale P. High throughput quantification of phytoestrogens in human urine and serum using iiquid chromatography- tandem mass spectrometry. J.Chromatogr B analyt Techno! Biomed Life Sci. 2007 Jun 15: 853.

5. Musarrat J; Arezina-Wilson ). Prognostic and Aetiological relevance of 8-hydroxyguanosine in human breast carcinogenesis. European Journal of Cancer, Vol 32 number 7, Jun 1996, pp. 1209-1214(6).

6. Andreas Makris, Helen Cladd, Russell J. Burcombe, James M. Smith, Michael Makris, Raised plasma homocysteine levels in women with metastatic breast cancer. Am Soc Clin Oncol 20; 2001 (abstr 179).

7. Jiye A, Johan Trygg, Jonas Gullberg,, Annika I. Johansson, Pal R Jonsson, Henrik Antti, Stefan L. Marklund, and Thomas Moritz. Extraction and GC/MS Analysis of the Human Blood Plasma Metabolome. Anal. Chem. 2005, 77, pp80g6-8094

Summary of Present Invention

The present invention deals with the process of isolation/ extraction, separation and identification of low molecular mass chemicals (LMC)/melabolite including the biomarkers from the human blood plasma by Liquid chromatography - mass spectrometry (LC-MS/MS). Disease disrupts metabolism and, as a result, causes changes that are long lasting and can be captured as metabolic signatures. The metabolite signature or the profile is an indicative of specific disease, such altered metabolite(s), changed levels of metabolites during specific physiologic change arc known as biomarkers. The biomarkers are significant and highly remarkable for disease diagnosis at an early stage as they are or their changed concentrations are an indicative of specific disease. In this present investigation a robust, sensitive experimental method for the extraction, separation detection and characterization of metabolite biomarkers for various diseases such as breast cancer, diabetes, arthritis, etc., from biological samples has been developed.

Description of Figures:

Figure I: Total Ion Chromatogram of+ EMS of normal blood Plasma extract

Figure 2: Total Ion Chromatogram of-EMS of normal blood Plasma extract

Figure 3: Total Ion Chromatogram of+EMS of breast cancer patient's blood plasma extract

Figure 4: Total Ion Chromatogram of-BMS of breast cancer patient's blood plasma extract

Figure 5: Total Ion Chromatogram of+EMS of diseased blood Plasma extract

Figure 6: Total Ion Chromatogram of -EMS of diseased blood Plasma extract

Figure 7; Mass Spectra of l-Methylguanosine

Figure 8: Mass Spectra of 16-alpha-hydroxysterone

Figure 9: Mass Spectra of Acetylspermidine

Figure 10: Mass Spectra of Diacetylspermine

Figure 11: Mass Spectra of 1,3-Diaminopopane

Figure 12: Mass Spectra of Enterolactone

Figure 13: Mass Spectra of Estradiol

Figure 14: Mass Spectra of Homocysteine

Figure 15: Mass Spectra of Hydroxyguanosine

Figure 16: Mass Spectra of Ribothymidine

Figure 17: Mass Spectra of S-Adenosylmethionine

Detailed Description

The objective of this invention is to develop a comprehensive method using Liquid chromatography - mass spectrometry (MDS SCIEX 4000 Q-Trap MS/MS. Applied Biosystems, synchronized with Shimadzu UFLC, Prominence), to obtain a wide spectrum of low molecular weight chemicals (LMC) from human blood plasma. This invention claims the method for the sample preparation, separation and the detection of the entire spectrum of LMC including biomarkers and construction of disease specific metabolite library. This invention contributes significantly to biomedical research and, ultimately to clinical medical diagnosis.

The following experiments were performed to derive the method described herein:

L Method Development for extraction, separation and detection of metabolites from human blood plasma by Liquid chromatography - Mass spectrometry (LC-MS/MS)

Material Information: All the chemicals used for this investigation are of highest purity grade. The Acetonitrile and methanol were of Mass grade and was purchased from JT Bakers, Chlorofbrm, Formic acid and Ethyl alcohol was procured from Fluka. The Milli-Q water of 1S.30M, was used throughout this investigation (Millipore)

Equipment: The equipments used for this investigation are: Mass spectrometer, MDS SCIEX 4000 Q Trap MS/MS (Applied Biosystems), and Ultra Flow Liquid Chromatography (UFLC, Shimadzu, prominence) consisting of Shimadzu LC20AB Pump, Shimadzu SIL20AC Autosampler, Shimadzu CTO20AC Column oven and UFLC system controller Model CBM20A were synchronized and the LC-MS/MS used.

Metabolite extraction from human blood plasma:

A. Preparation of plasma from human blood:

The blood samples from normal (control) and breast cancer, other disease were collected from Parsi population and were immediately transferred to ice cooled sterile BD vacutainers (containing 3.6 mg of K2-EDTA as an anti coagulant) kept in ice bucket. The contents of the tubes were mixed and the platelet-free plasma was separated from the whole blood by centrifugation at 4000 g for 10 minutes in a refrigerated centrifuge (Eppendorf- 5810R) at 4°C. 100 µl aliquots of the separated plasma were then transferred aseptically into 1.5 ml cryo-vials and the blood plasma samples were immediately used for further investigation or stored at -80"C until further use.

B. Development and optimization of the extraction process:
The extraction of plasma was performed by modification of the method as described by jiye ct al., (7). A total of nine different combinations of various organic solvents were investigated for the extraction of low molecular mass (LMC) metabolites from the human blood-plasma sample by precipitation method.

Following solvents/ solvent systems were tested for the extraction of LMCs from plasma samples

1. Methanol (-20''C)
2. Acetonitrile (-20°C)
3. Methanol: Water, 9:1 (-20''C)
4. Acetonitrile: Water, 9:1 (-20''C)
5. Methanol: Chloroform, 9:1 (-20"C)
6. Methanol: Chloroform, 8:2 (-20'*C)
7. Acetonitrile: Methanol, 50:50 (-20''C)
8. Methanol: Chloroform: Water, 8:1:1 (-20°C)and
9. Ethanol

Metabolite extraction procedure:

The 100-µl blood-plasma samples from were processed with 900 1 of the above solvent/ solvent mixture (pre-chilled to -20"C). The metabolites were extracted by vortexing the samples (4*'C) for 30 sec by a cyclo-mixer after every 15 min for an hour. The samples were centrifuged at 14000 rpm for 15 min and 4''C. The supernatant was then carefully transferred into 1 ml Shimadzu HPLC vials.

II Liquid chromatography - Mass spectrometry (LC-MS/MS) analysis of metabolites

Liquid chromatography parameters:
The analysis of metabolites was performed on an Ultra-Flow Liquid Chromatography (UFLC, Prominence, Shimadzu) coupled to mass spectrometer (MDS SCIEX 4000 Q-Trap MS/MS, Applied Biosystems).

The processed samples were transferred to the autosampler (Shimadzu, model SIL-20AC) for Ultra Flow Liquid Chromatography (UFLC) separation. A 10-ul volume of the plasma sample was injected into the Symmetry'* CI8, 3.5 um, 2.1 x 100mm column (Part No. WAT058965, column serial No. 01673522911106, Waters, Ireland) by an auto sampler. The column temperature was maintained at 40'^C through out the experiment. Each sample

was separated at a total flow rate of 0.4 ml per min with a linear gradient of 0.1% formic acid in water (A) and 0.1% formic acid in acetonitrile (B). The gradient consisted 0-20% B over 0-2 min, 20-40% B over 2-4 min, 40-95% B over 4-6 min, the composition was held at 95% for 2 min. and returned to 1% B at 9 min. the composition was kept at 1% B for further 2 min before next injection.

Mass spectrometer parameters:

The UFLC was coupled to mass spectrometer equipped with electrospray ionization operating in positive or negative mode using Analyst 1.4.2 soft ware for data acquisition and data processing. The nebulizer gas GSl and corona discharge gas GS2 were set to 60 and collision energy to l0eV (Ql-MS or EMS acquisition). Ion source gas in positive mode and negative mode was set to 4000 and 2875 respectively where as the source temperature was set at 400°C and 300C in positive and negative polarity respectively with CAD gas kept on medium mode. The mass spectra were acquired in full scan mode from 50 amu to 1500 amu for 6 msec and 557 cycles. The run was conducted for 10 min at excitation energy of 10. For the fragmentation of parent ion the data was acquired using Enhanced Product Ion (EPI) or MS/MS/MS mode with collision energy set to 25, 35 and 50 and collision energy spread (CES) 5.

The Ql-MS and the Information Dependent Analysis (IDA), Enhanced Charge, Neutral loss scan, parent Ion Scan, Product Ion Scan, MS/MS, MS/MS/MS was obtained for both the test sample and the blank as indicated above. The IDA was performed both in positive and in negative mode. The nebulizer gas GSl and corona discharge gas GS2 were set to 60, collision energy for patent ions and daughter ions were set to 10 +/- 0 and 40 +/• 20 respectively. Ion source gas, source temperature in positive mode and negative mode were set to 4000, 400''C and 2875, 300*^0 respectively with medium CAD gas. The data was acquired from start mass 50 amu to stop mass 1500 amu.

Data Processing:

For the processing, the total ion chromatogram (TIC) of blank (solvent) and disease samples were Gaussian smooth, base line subtracted and noise to be set to 1%. The TIC of blank was subtracted from that of the TIC of test and the spectrum was generated using Analyst Software1 .4.2. The noise level of spectrum was set to 1%. The processed spectrum is also manually verified. The data list is then generated to check the number of ions present with their m/z, centroid m/z, peak intensities, resolution, peak area and their charge specification. Next level of processing involves the elimination of the multiple charge ions by checking their singly charged ions. The low intense ions are further extracted to obtain Extracted ion chromatogram (XTC) or amplified.

The metabolites were primarily identified by comparing their molecular mass with HMDB KEGG, NIST database and were confirmed finally based on their individual fragmentation pattern.

RESULTS

Of the nine different solvent systems tested for the extraction of LMC(s), the methanol, chloroform, water (8:1:!) extraction process showed highest number of LMCs/ molecular ions with good ionization and resolution. This solvent system is shown to cover broad range of metabolites of different polarity ranging from polarity -Log 2 to +Log 6. The chloroform at still higher levels at 150µl and 200 L methanol, chloroform, water (7.5:1.5:1) and (7:2:1), suppressed the ionisation of metabolites in the plasma.
The Total Ion Chromatogram (TIC) of positive (+) EMS by ESI-LC-MS/MS spectrum, the EMS spectrum showed the presence of 1276 (Fig. 1) and 1189 (Fig. 3) compounds and the negative (-) EMS of TIC showed 2210 (Fig. 2) and 2316 (Fig. 4) metabolites in normal, and blood plasma samples of breast cancer patient respectively. The processed m/z spectrum of the TIC of positive EMS, m/z spectrum of positive EMS and data list and the TIC of negative EMS m/z spectrum of negative EMS and data list of other disease samples are shown in Figure 5 and Figure 6 respectively.

The m/z spectrum retrieved from the TIC of EMS was normalized, processed and subtracted with the spectrum of blank. Similarly the m/z spectrum of breast cancer samples were also normalized and processed. The m/z data of diseased samples were compared with that of the normal samples to detect the differentially expressed metabolites in the diseased blood plasma sample. The processed data list of differential expressed metabolites show the presence of several metabolites such as, l-Methylguanosine (Fig.9), 16-alpha-hydroxysterone (Fig. 8), Acetylspermidine (Fig. 9), Diacetylspermine (Fig- 10), U3-Diaminopopane (Fig. 11), Enterolactone (Fig. 12), Estradiol (Fig. 13), Homocysteine (Fig. 14), Hydroxyguanosine (Fig. 15), Ribothymidine(Fig. I6)andS'Adenosylmethionine(Fig. 17).

Claim

1. In a metabolite analysis system, a method, comprising the steps of: Solvent composition used in extraction of the metabolites from human blood plasma for identifying chromatography and mass spectrometry peaks from the sample run; mass spectrometry peak being one of an MS peak and MS/MS peak and using nominal or exact mass; generating a list of sample data having said identified peaks; performing chemometric analysis on said sample data to identify biomarkers; said chemometric analysis performed without loss of retention time data by the same application performing the programmatic identification of said chromatography and mass spectrometry peaks.

2. The method of claim 1 wherein said metabolite biomarker is a metabolite or metabolite profile that may be a known metabolite with altered concentration or a modified chemical of divergent pathway, which is an indicative of specific disease at an early stage of disease or abnormality.

3. A medium in a metabolite analysis system, said medium holding executable steps for a method, said method comprising the steps of: identifying chromatography peaks and mass spectrometry peaks from a sample run; said mass spectrometry peak being one of an MS peak and MS/MS peak and using nominal or exact mass; generating a list of sample data having said identified peaks; performing chemometric analysis on said sample data to identify biomarkers;

4. The method of claim 1, comprising the further steps of: comparing said identified biomarkers using novel algorithm with a database of known compounds.

5. The method of claim 1 wherein said chemometric analysis is performed using one of Principle Component Analysis (PCA) and Partial Least Squares Discriminate Analysis (PLS-DA).

6. The method of claim 1, comprising the further step of: removing unwanted material traces from the sample data prior to performing chemometric analysis.

7. The method of claim 1 wherein said sample data includes mass data, retention time and signal intensity values.

8. The method of claim 1 wherein said biomarkers are used in Systems Biology.

9. The method of claim 1 wherein said chemometric analysis further comprises the steps of: plotting said sample data on an n-dimensional plot, said n-dimensional plot indicating the analyte peaks of a plurality of said biomarkers.

10. The system of claim 1 wherein said toxicological and biomarker identification facility is implemented in diagnostic kit or chip and the said biomarkers are used in at least one of Metabolomics, Metabonomics, Proteomics, Functional Genomics, Lipidomics, Glycomics.