Core Director: Nagireddy Putluri, Ph.D.
The Metabolomics section of the CPRIT Core is an analytical facility specializing in liquid chromatography coupled with mass spectrometry platforms. The main goal is to support researchers in the identification and understanding of the changes in metabolism that contribute to cancer biology, which will ideally lead to novel detection and treatment options. The core has established methods for targeted measurement of over 500 metabolites, and metabolic flux of specific pathways, in diverse biological specimens using established protocols and methods.
Core directors provide upfront consultation on experimental design and then the research staff perform all the necessary sample preparations and mass spectrometry analysis. This includes extraction of metabolites from biological samples using a combination of aqueous and organic solvents, liquid chromatography/mass spectrometry analysis, pre-processing of the mass spectral data and primary data analysis. The process starting from sample preparation to mass spectrometry is monitored using spiked isotopic standards that have been characterized for their chromatographic behavior and fragmentation profile. Once the mass spectral data is obtained, data analytics steps are performed using Mass Hunter software including scaling, imputation and normalization. The resulting data output then undergoes analysis by an in-house biostatistics pipeline, that determines metabolite levels, and defines differential levels of compounds in experimental groups. This primary data analysis is provided as a package in with the MS analysis services. Upon request, additional higher level data analysis can be provided separately by our bioinformatics group,which includes pathway mapping using multiple enrichment methods such as Oncomine Concept Map (OCM), Ingenuity (IPA), Gene Set Analysis (GSA), and Network-based Gene Set Enrichment Analysis (NETGSEA). The bioinformatics group can also support integration of transcriptomic and proteomic data with the metabolomics data.
The main instrumentation platforms are Agilent triple quadrupole (QQQ) mass spectrometers connected to an Agilent UPLC. The QQQ has a wide dynamic range of mass detection from 5-1,400 amu, six orders of linearity and a high mass resolution of ~0.4 amu, for detection of metabolites in a targeted approach. In addition, we have a 2000 compounds MS2 library from Agilent.
1. Targeted Metabolomics:
Targeted metabolomics performs quantitative measurements of up to 500 known compounds through the use of high pressure liquid chromatography and mass spectrometry.( By MRM methods?). This provides measurements of concentrations of steady-state levels of metabolites. To cover 500 metabolites requires 14 different methods that are a combination of distinct extraction procedures and LC systems. Listed below are the different classes of metabolites associated with each method. A complete list of all 500 metabolites can be found on the side bar link.
Class of Metabolomics (>500 metabolites):
Method 1. Amino Sugar – Positive ionization
Method 2. Amino Sugar – Negative ionization
Method 3. Amino acids and related metabolites – Positive ionization
Method 4. Amino acids and related metabolites – Negative ionization
Method 5. Prostaglandins
Method 6. Carnitines (Short chain)
Method 7. Polyamines
Method 8. TCA metabolites
Method 9. CoA's and Carnitines (Long chain)
Method 10. Sugars
Method 11. Nucleotides / Nucleic acid
Method 12. Vitamins and Steroids
Method 13. Bile acids
2. Metabolic Flux:
To test mechanisms that are responsible for altered regulation of steady-state levels of metabolites, in the cell, measurements of metabolic flux through pathways is required. By providing 13C or 15N isotope labeled metabolic substrates (such as isotopic labeled glucose, glutamine, or lactate) to living cells, isotopomer patterns of key metabolites can be precisely measured using mass spectrometry. These analyses can provide valuable information on both pathway activities and metabolite pool sizes. Since all metabolites are either reactants or products in metabolic pathways, changes in their levels due to either altered production or altered disposal are determined by the kinetic rates of key steps within those pathways. Services available currently use cell lines only and are confined to the targeted analysis of pathways as indicated below:
A. Glucose metabolic flux
B. Citric acid cycle (TCA cycle) metabolic flux
C. Glutamine Flux
D. Lipid or Fatty acid Metabolism
3. Primary Data Analysis:
Targeted Mass Spectrometry: For MRM data acquired using Agilent 6430 or 6490 QQQ, data handling will be performed using both Qualitative and Quantitative Analysis software from Agilent Technologies, that will ascertain retention time, verify MRM transitions and calculate absolute levels of metabolites using calibration curves.
The initial low level analysis of metabolomic data will use a series of steps including preliminary review of data, visualization for spotting patterns, data cleaning, imputation and normalization. Data cleaning and basic statistical analyses will include identifying potential outliers, checking for normality, examining the proportion and/or variance for each variable. Depending on the study design several different approaches will be available, ranging from simple median centering, to centering and scaling based on the values of internally spiked standards, to employing more advanced fixed effects analysis of variance procedure that use factors, data platform, batch information, ionization mode, etc.
For samples run on days that are fairly apart, batch effects occur that need to be corrected. To correct such batch effects, we either use analysis of variance techniques, or when the focus is on unsupervised techniques such as clustering and principal components analysis, the function “removeBatchEffect” available in the R package “limma” is employed.
Metabolic Flux: This will include calculations of the percentage incorporation of C13 into specific each metabolites of interest including time points and biological replicates respectively and data are normalized for natural abundance in each isotope. Statistical significance is obtained using two sided t-test.
Overview of Metabolites Measured
Figure 2 gives the overview of the metabolites measured by the core in the setting of biochemical pathways.
User Publications Supported by the Core
Terunuma, A., et al., MYC-driven accumulation of 2-hydroxyglutarate is associated with breast cancer prognosis. J Clin Invest, 2014. 124(1): p. 398-412.
Stashi, E., et al., SRC-2 is an essential coactivator for orchestrating metabolism and circadian rhythm. Cell Rep, 2014. 6(4): p. 633-45.
Lisewski, A.M., et al., Supergenomic network compression and the discovery of EXP1 as a glutathione transferase inhibited by artesunate. Cell, 2014. 158(4): p. 916-28.
Bhowmik, S.K., et al., EMT-Induced Metabolite Signature Identifies Poor Clinical Outcome. Oncotarget, 2015.
Putluri, N., et al., Pathway-centric integrative analysis identifies RRM2 as a prognostic marker in breast cancer associated with poor survival and tamoxifen resistance. Neoplasia, 2014. 16(5): p. 390-402.
Kommagani, R., et al., Acceleration of the glycolytic flux by steroid receptor coactivator-2 is essential for endometrial decidualization. PLoS Genet, 2013. 9(10): p. e1003900.
Kaushik, A.K., et al., Metabolomic profiling identifies biochemical pathways associated with castration-resistant prostate cancer. J Proteome Res, 2014. 13(2): p. 1088-100.
Shafi, A.A., et al., Differential regulation of metabolic pathways by androgen receptor (AR) and its constitutively active splice variant, AR-V7, in prostate cancer cells. Oncotarget, 2015. 6(31): p. 31997-2012.
Kang, Y.K., et al., CAPER is vital for energy and redox homeostasis by integrating glucose-induced mitochondrial functions via ERR-alpha-Gabpa and stress-induced adaptive responses via NF-kappaB-cMYC. PLoS Genet, 2015. 11(4): p. e1005116.
Dasgupta, S., et al., Coactivator SRC-2-dependent metabolic reprogramming mediates prostate cancer survival and metastasis. J Clin Invest, 2015. 125(3): p. 1174-88.
Zaslavsky, A.B., et al., Platelet-Synthesized Testosterone in Men with Prostate Cancer Induces Androgen Receptor Signaling. Neoplasia, 2015. 17(6): p. 490-6.
Methods and Reviews:
Feng Jin, Salil Kumar Bhowmik, Vasanta Putluri, Franklin Gu, Jie Gohlke, Friedrich Carl Von Rundstedt, Subhamoy Dasgupta, Rashmi Krishnapuram, Bert W. O’Malley, Arun Sreekumar and Nagireddy Putluri. A novel [15N] Glutamine Flux using LC-MS/MS-SRM for determination of nucleosides and nucleobases. Journal of Analytical & Bioanalytical Techniques 2015, 6, 5, 1000267.
Salil Kumar Bhowmik,Vasanta Putluri, Ramakrishna Kommagani, Sai Aparna Konde, John P. Lydon, Arun Sreekumar and Nagireddy Putluri; Application of 13C isotope labeling using liquid chromatography mass spectrometry (LC-MS) to determining phosphate-containing metabolic incorporation. J Mass Spectrom. 2013 Dec;48(12):1270-5. PMID: 24338880
Lloyd, S. M., Arnold, J. & Sreekumar, A. Metabolomic Profiling of Hormone-Dependent Cancers: A Bird’s Eye View. Trends Endocrinol Metab. 2015 Sep;26(9):477-85 PMID: 26242817.
Arnold, J. M., Choi, W. T., Sreekumar, A. & Maletic-Savatic, M. Analytical strategies for studying stem cell metabolism. Frontiers in biology 10, 141-15 (2015). PMID: 26213533.