Metabolomics is the profiling of induced small-molecule metabolites.
Irradiation in vivo triggers the expression of many genes involved in intercellular signaling, whose proteins can have wide-ranging effects on cellular metabolism.
These changes are reflected in alterations in the spectrum of small-molecule metabolites in blood, urine, and saliva.
Such metabolomic analyses offer several key advantages, particularly simple, non-invasive collection, and thus the potential for very high-throughput radiation biodosimeter screening
This project combines our metabolomics and stress-signaling expertise with the sensor-chip expertise of Sionex Corporation, to develop instrumentation for rapid non-invasive assessment of radiation exposure and injury using metabolic markers, thus addressing the overall theme of the Center for High-Throughput Minimally-Invasive Radiation Biodosimetry. The project is integrated with other components of the Center by providing patient blood for functional genomics analyses by Core B, and for cytogenetic analyses by Project 1. The Project 3 team will analyze urine, blood, saliva, sebum, and sweat from the same patients, and from mice, looking for unique changes in metabolites that indicate radiation exposure. This analysis is being performed on the UPLC-MS(TOF) (ultra performance liquid chromatograph plus time-of-flight mass spectrometry). The use of a genetic approach with mouse models will complement gene expression profiling efforts by Core B. Mouse models with disruption of relevant injury response pathways will help delineate injury signatures for key target tissues such as lymphoid, hematopoietic, and gastrointestinal. The ultimate goal for this project is to design and construct a completely non-invasive screening tool for radiation exposure, perhaps even avoiding the smallest finger prick for a blood sample.
Irradiation in vivo triggers the expression of many genes involved in intercellular signaling, whose proteins can have wide-ranging effects on cellular metabolism. Our data from a modern metabolomics approach indicate that these changes are reflected in alterations in the spectrum of metabolites in mouse urine as well as in cultured human cells. Such metabolomic analyses offer several key advantages including simple, non-invasive collection, and thus the potential for very high-throughput biodosimetry screening. We will also investigate the potential for using a metabolomic signature in saliva, sebum, and/or sweat, which would increase throughput still further.
The use of global profiling technologies has contributed substantially to the understanding of the radiation cellular stress response and has contributed to the elucidation of many of the complex biological networks associated with gene expression and signal transduction. On a similar level, global understanding of how ionizing radiation exposure affects small molecule concentrations (such as metabolites) would be expected to lead to the identification of metabolites that can be used to monitor for exposure and extent of injury. Metabolomics is a rapidly advancing field that aims to identify and quantify the concentration changes of all metabolites (i.e., the metabolome) in a given biofluid or model system. In order to assess the metabolic changes associated with ionizing radiation exposure, our approach employs ultra-pressure liquid chromatography (UPLC) coupled with highly sensitive time-of-flight (TOF) mass spectrometry (MS) to profile small molecules (<1 kDa) from cultured cells, mice, and patient samples. We are utilizing the Waters ACQUITY UPLC-MS(TOF) system with multivariate data analysis by both MarkerLynx (Waters) and SIMCA-P (Umetrics) software packages. The overall strategy is to develop metabolomic signatures of radiation exposure using mouse models and to a limited extent with cultured human cells, and to integrate these results with those from patients undergoing total body irradiation at the University of Pittsburgh; this approach is summarized below.
Measure all small molecules in select accessible fluids, such as urine, blood, saliva, sebum, and sweat
Identify exposure-specific elevated concentrations
Identify a candidate marker set
Move to quantitative approaches
Development of in-field device(s) to measure select metabolites
Deployment of in-field device as a component of integrative approach to screen for exposure
Many metabolites are concentrated in the urine and this offers a convenient starting point to develop radiation metabolomic profiles. Our approach for metabolomic profiling of urinary metabolites has already been demonstrated in a variety of mouse model systems (1-6). Urinary end-products of metabolism are invariably acidic and therefore anionic. We have analyzed over 2,000 24-hour mouse urines by UPLC-MS(TOF) for their content of anionic species. The chromatogram obtained from UPLC-MS(TOF) analysis of each urine sample contains data from between 4,000 and 6,000 ions, the majority of which represent individual urinary constituents, and the remainder result from unintentional fragmentation of parent ions in the source of the mass spectrometer. The mass spectrometer can be set to generate and analyze either positively or negatively charged ions. Typically, experiments are carried out in –ve ion mode. Each sample therefore has an associated data set of, say, 5,000 ions, each of which has a known accurate mass (to four decimal places), intensity, and retention time on the UPLC column. This means that ~15,000 data are typically collected for each sample. Therefore, our current dataset for metabolomic analysis, including both +ve and –ve ions, is approximately 764 X 5,000 X 3 X 2, equivalent to in excess of 20 million datapoints. Because each ion has a measured retention time and a determined accurate mass, these data can be used to identify the chemical nature of any biomarkers that are associated with radiation.
We have applied multivariate data analysis (specifically principal components analysis, partial least-squares discriminant analysis, and batch analysis, using SIMCA-P+) to distinguish metabolites changing after radiation exposures (0.1 to 11 Gy) of mice. Dose-dependent anionic biomarkers were revealed and subjected to further validation using liquid chromatography-tandem mass spectrometry and gas chromatography-mass spectrometry. Targeted metabolic profiling is also being employed for urinary products of protein and lipid interactions with reactive oxygen/nitrogen species. Overall, candidate urinary biomarkers of radiation exposure included Krebs’ cycle intermediates, products of impaired lipid metabolism, DNA damage, and abnormal gut floral metabolites. Cutting-edge informatics analyses, in collaboration with the Bioinformatics Core, will be used to select thoroughly characterized metabolomics markers to develop an optimal radiation metabolomics signature. Our results in mice indicate that metabolomics can provide high-throughput non-invasive protocols capable of detecting individuals who have received non-lethal doses of ionizing radiation.
Complementary metabolomic analyses are also being carried out in human cells, which offer the advantages of high-throughput and expanded dose range compared to patient samples. We have already demonstrated that gene expression profiles can be used to develop signatures to distinguish responses to ionizing radiation compared to other types of stress (7) as shown in the figure left. We have performed multivariate data analysis using principal components analysis and supervised orthogonal projection to latent structures analysis in order to identify perturbed metabolic pathways and differences at the metabolomic level between irradiated and untreated cells. As shown in the animation below, irradiated samples could be distinguished from unirradiated. Translational studies will extend these signatures into humans using samples from patients having total body irradiation.
Principal components analysis (PCA) separation of radiation from untreated cells. Representative results are shown for separation of metabolomic profiles for human cells irradiated with 1 Gy. Each point represents results for a single sample; red indicates results for irradiated cells and black for unirradiated.
Device Technology
The technology that we have chosen for the metabolomics radiation biodosimetry device, from Sionex Corporation, is radio-frequency differential ion mobility spectrometry (DMS). This device operates like a gas chromatograph, in that a carrier gas (air) is employed in the separation, but it is ions that are separated and detected, as in a mass spectrometer.
This chip is the underlying technology currently used in the Thermo Electron EGIS™ Defender trace explosive and drug detection system, currently deployed in several airports.
Sionex Corporation (Bedford, MA) is developing and deploying miniaturized chemical and biological detectors in high unit volume markets such as Homeland Security, Military Chemical/Bio-warfare detection, Process Control, Analytical Instruments and Life Sciences by making use of its proprietary, chip-based micro-Differential Mobility Sensor (microDMx™) technology alone and in combination with other technologies, as described in more detail on the company web site.
The Sionex microDMx technology is uniquely powerful because it provides 10-100x improvement in sensitivity, 5-10x improvement in selectivity, 100x smaller size, ability to tune the sensor to detect novel chemicals on the fly, and a richer data-set than existing chemical detectors, making it easier to identify chemical compounds. Sionex has received 20 patents and has additional applications filed with the US Patent Office, all with respect to the core technology and its applications.
Incorporated in 2001, the Company is led by a seasoned management team drawn from a wide range of disciplines in the fields of scientific instrumentation, chemistry, biotechnology, manufacturing and entrepreneurial management.
Sionex microDMx Technology
Sionex microDMx technology is based upon micro-fabricated differential-ion-mobility spectrometry (DMS). As in mass-spectrometry, ions are separated and detected, but the separation occurs at atmospheric pressure and the principle of separation is based on molecular properties affecting ion mobility, rather than the m/z ratio of mass spectrometry. Ion mobility is a measure of how easily an ion travels through the air in response to electrostatic forces, and is dependent on molecular conformation, geometric size, clustering properties, and charge state. DMS further measures the difference between high and low field ion mobility so that changes in conformation and clustering properties are also important. Ions are selected electrically by two voltages, an RF separation voltage, and a low-frequency compensation voltage. The microDMx ion filter operation is illustrated in this figure.
For many applications, microDMx DMS technology can provide a stand-alone solution, but for the most complex applications, DMS can be combined with mass spectrometry and other techniques. Because the microDMx technology provides rapid and effective separation of ions from other interfering species, it can be used as a new ion source to replace time-consuming pre-separation techniques such as HPLC and reduce the resolution burden of the mass spectrometer. Results for a mixture of caffeine and polyethylene glycol species are shown in the figures below. When compared to mass-spectra obtained without a DMS-prefilter, chemical noise is suppressed by a factor of 50-100.
The illustration below shows results from a nano-electrospray DMS-MS interface with Angiotensin I mixed with poly-ethylene glycol species with masses ranging from 200 Da to 600 Da. At left, is the unfiltered mass spec, in the center, ion current tuned by a scanned DMS compensation voltage is shown, and at right is shown the supression of chemical noise in mass spectra prefiltered by DMS tuned to pass only one ion species.
The most recent advance from Sionex combines a rapid micro-scale GC with microDMx technology. The microAnalyzer sub-system offers the sensitivity and selectivity associated with microDMx based products with the added benefits associated with preconcentration and separation in a complete self contained sub-system requiring no external gases. In many applications, the sub-system will be capable of detecting and identifying chemicals down to parts per trillion (ppt) levels in complex matrices. The complete system is enclosed in a 9x5x4 inch package, and is capable of separating complex mixtures, as is illustrated below for a 45-component mixture.
Summary
Identification of radiation-induced metabolic changes and an understanding of the signaling pathways involved is necessary for development of reliable metabolomic markers to assess radiation exposure and extent of injury. We have demonstrated the feasibility of our state-of-the-art approach employing UPLC-MS(TOF) with the mouse urinary metabolome. We have identified confounding variables and are now able to monitor and control them. Our mouse model approach has identified a variety of important cytokine and growth factor responses to irradiation. The addition of injury-specific markers to our developing biodosimetric signature may help to identify unusually sensitive individuals who have suffered an organ-specific radiation injury disproportionate to that which would be expected based solely on their dose. Current progress will lay the groundwork for development of markers for use in dosimetry devices being developed with Sionex Corporation. Our immediate studies are focusing on the following:
Analysis of alternate metabolomes (blood plasma, sweat, saliva), allowing definition of the maximum set of molecules that may be informative for radiation exposure.
Radiation specificity studies to allow removal from the “radiation signature” of metabolites indicative of generalized stress, enhancing the radiation specificity.
Publication Based on This Work
Patterson, A. D., and J. R. Idle. A metabolomic perspective of small molecule toxicity. In T. C. M. B. Ballantyne, and T. Syversen (eds.), General and Applied Toxicology. John Wiley & Sons, Chichester, UK, 2009.
Lanz, C., Paterson, A. D., Slavik, J., Krausz, K. W., Ledermann, M., Gonzalez, F. J., and Idle, J. R. Radiation Metabolomics. 3. Biomarker Discovery in the Urine of Gamma-Irradiated Rats Using a Simplified Metabolomics Protocol of Gas Chromatography-Mass Spectrometry Combined with Random Forests Machine Learning Algorithm. Radiat Res 172: 198-212, 2009. [abstract] [PDF]
Tyburski, J. B., Patterson, A. D., Krausz, K. W., Slavik, J., Fornace, Jr, A. J., Gonzalez, F. J., and Idle, J. R. Radiation metabolomics. 2. Dose- and time-dependent urinary excretion of deaminated purines and pyrimidines after sublethal gamma-radiation exposure in mice. Radiat Res 172: 42-57, 2009. [abstract] [PDF]
Patterson, A.D., Li, H., Eichler, G.S., Krausz, K.W., Weinstein, J.N., Fornace, A.J., Jr., Gonzalez, F.J. and Idle, J.R. UPLC-ESI-TOFMS-based metabolomics and gene expression dynamics inspector self-organizing metabolomic maps as tools for understanding the cellular response to ionizing radiation. Anal Chem 80:665-74, 2008. [abstract] [PDF]
Tyburski, J.B., Patterson, A.D., Krausz, K.W., Slavik, J., Fornace, A.J., Jr., Gonzalez, F.J. and Idle, J.R. Radiation metabolomics. 1. Identification of minimally invasive urine biomarkers for gamma-radiation exposure in mice. Radiat Res 170:1-14, 2008. [abstract] [PDF]
Ku, W. W., Aubrecht, J., Mauthe, R. J., Schiestl, R. H., and Fornace, A. J. Jr. Why not start with a single test: a transformational alternative to genotoxicity hazard and risk assessment. Toxicol. Sci. 99: 20-5, 2007. [PDF]
Ma, X., Shah, Y., Cheung, C., Guo, G. L., Feigenbaum, L., Krausz, K. W., Idle, J. R. & Gonzalez, F. J. The PREgnane X receptor gene-humanized mouse: a model for investigating drug-drug interactions mediated by cytochromes P450 3A. Drug Metab Dispos 35: 194-200, 2007. [abstract] [PDF]
Giri, S., Krausz, K. W., Idle, J. R. & Gonzalez, F. J. The metabolomics of (+/-)-arecoline 1-oxide in the mouse and its formation by human flavin-containing monooxygenases. Biochem Pharmacol 73: 561, 2007.
Chen, C., Ma, X., Malfatti, M. A., Krausz, K. W., Kimura, S., Felton, J. S., Idle, J. R. & Gonzalez, F. J. A Comprehensive Investigation of 2-Amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP) Metabolism in the Mouse Using a Multivariate Data Analysis Approach. Chem Res Toxicol 2007.
R.A. Miller, E.G. Nazarov, and D. Levin: "Differential Mobility Spectrometry (FAIMS): powerful tool for rapid gas phase ion separation and detection” Chapter 10 in Book Achille Capiello (Editor), Advances in LC-MS Instrumentation. Journal of Chromatography Library, Vol.72 2007 , Elsevier.
D.S. Levin, P. Vouros, R.A. Miller, E.G. Nazarov using a Nanoelectrospray-Differential Mobility Spectrometer system for the Analysis of Oligosaccharides with solvent selected Control Over ESI Aggregate Ion Formation. Accepted in J. Am. Soc. Mass Specrom, 2007.
Giri, S., Idle, J. R., Chen, C., Zabriskie, M. T., Krasuz, K. W. & Gonzalez, F. J. A Metabolomic Approach to the Metabolism of the Areca Nut Alkaloids Arecoline and Arecaidine in the Mouse. Chem. Res. Toxicol. 19: 818, 2006.
Chen, C., Meng, L., Ma, X., Krausz, K. W., Pommier, Y., Idle, J. R. & Gonzalez, F. J. Urinary metabolite profiling reveals CYP1A2-mediated metabolism of NSC686288 (aminoflavone). J Pharmacol Exp Ther 318: 1330, 2006.
D.S. Levin, P.A Vouros, R.A. Miller, E.G. Nazarov, J.C. Morris, Characterization of gas-phase molecular interactions on differential mobility ion behavior utilizing an electrospray ionization-differential mobility-mass spectrometer system. Analytical Chemistry, 78(1): 96-106, 2006.
E. G. Nazarov, S. L. Coy, E.V. Krylov, R.A. Miller, GA. Eiceman. Pressure Effects in Differential Mobility Spectrometry. Anal. Chemistry, 78: 7697-06, 2006.
D.S. Levin, R.A. Miller, E.G. Nazarov, P. Vouros. Rapid separation and quantitative analysis of peptides using a new nanoelectrospray-differential mobility spectrometer-mass spectrometer system. Anal. Chemistry, 78(15): 5443-5452, 2006.
Amundson, S. A., Do, K. T., Vinikoor, L., Koch-Paiz, C. A., Bittner, M. L., Trent, J. M., Meltzer, P. & Fornace, Jr, A. J. Stress-specific signatures: expression profiling of p53 wild-type and -null human cells. Oncogene 24: 4572, 2005.
M.D. Krebs, A.M. Zapata, E.G. Nazarov, R.A. Miller, I.S. Costa, A.L. Sonenshein, C.E. Davis. Detection of biological and chemical agents using differential mobility spectrometry (DMS) technology. IEEE SENSORS JOURNAL 5(4): 696-703, 2005.
G.R. Lampertus, C.S. Fix, S.M. Reidy, R.A. Miller, D. Wheeler, E. Nazarov, R. Sacks. Silicon Microfabricated Column with Microfabricated Differential Mobility Spectrometer for GC Analysis of Volatile Organic Compounds. Anal. Chem. 77: 7563-7571, 2005.
Ma, X., Idle, J. R., Krausz, K. W. & Gonzalez, F. J. Metabolism of Melatonin by Human Cytochromes P450. Drug Metab Dispos 2004.
H. Schmidt, F. Tadjimukhamedov, I.V. Mohrenz, G. B. Smith, G.A. Eiceman. Microfabricated Differential Mobility Spectrometry with Pyrolysis Gas Chromatography for Chemical Characterization of Bacteria. Anal. Chem. 76: 5208-5217, 2004.
G.A. Eiceman, E.G. Nazarov, R.A. Miller, E. Krylov, A. Zapata. A micro-mashined planar field assymmetric IMS as a gas chromatographic detector. Analyst 127: 466-471, 2002.
R.A. Miller, E.G. Nazarov, G.A. Eiceman, A.T. King. A MEMS radio-frequency ion mobility spectrometer for chemical vapor detection. Sensors and Actuators A 91, 301-312, 2001.
R.A. Miller, G.A. Eiceman, E.G. Nazarov, A.T. King: "A MEMS Radio- Frequency Ion Mobility Spectrometer for Chemical Agents." Draper technology digest, pp 36-43, 2000.
I.A. Buryakov, E.B. Krylov, E.G. Nazarov, and U.Kh. Rasulev. A New Method of Separation of Multi-Atomic Ions by Mobility at Atmospheric Pressure Using a High-Frequency Amplitude-Asymmetric Strong Electric Field. Int.J.of Mass-spectrometry and Ion Processes, 128: 143-148, 1993.
I.A. Buryakov, E.B. Krylov, A.L. Makas, E.G. Nazarov, V.V. Pervukhin and U.Kh. Rasulev. Separation of ions according to their mobility in a strong alternating current electric field. Pis’ma Zh.Tech.Fiz 17(12): 61-65, 1991.
Collaborating Institutions
University of Bern, Switzerland
Sionex Corporation, Bedford, MA