Center for High-Throughput
Minimally-Invasive Radiation Biodosimetry

Project 3: Rapid Non-Invasive Radiation Biodosimetry through Metabolomics

P.I. Albert J. Fornace Jr., Georgetown University

 

Overview

In order to prepare for the possible detonation of a radiological dispersal device (RDD or so-called "dirty bomb") or improvised nuclear device (IND), the development of rapid, minimally invasive, and field-deployable biodosimetry is a high priority.

Our project addresses this priority with the powerful global profiling capabilities of metabolomics, a biomarker discovery platform uniquely suited for the analysis of biofluids, such as blood and urine, that require minimally- or non-invasive procedures to acquire. We have established the new field of radiation metabolomics, and have published a series of seminal papers on responses at the small molecule level after radiation. We have used metabolomics to define a urinary radiation response in mice and rats and are using these findings to guide discovery in humans.

Our studies are conducted using several inbred strains of mice as well as three genetically modified mouse strains, all of which have varying sensitivities to ionizing radiation. For comparison, human peripheral white blood cell samples will also be analyzed after ex vivo irradiation. Our approach is to harness the exquisite resolution and accurate mass measurement capabilities of the Ultra-Performance Liquid Chromatography–time-of-flight mass spectrometry metabolomics platform that has proven enormously useful to date. Our metabolomics analyses will be run in parallel with transcriptomics analyses (Project 2) and cellular endpoints (Project 1), sharing many of the same samples. Our goals are to define a strategy for minimally invasive biodosimetry for relevant real-world radiation exposures and to find biomarkers by which the most severe radiation-related injuries may be identified as early as possible.

Current Research

Having determined the feasibility of small-molecule profiling for radiation biodosimetry with total body, high dose-rate, external-beam γ-ray exposures and having established the field of radiation metabolomics using cultured cells and animals, our present efforts extend radiation metabolomics to studies of radiation exposures that will typically occur during a radiologic or nuclear event. Real-world, population-level exposure scenarios will include various qualities of radiation and differentially exposed tissues and organs. In particular, we are expanding our research into more real-world scenarios such as (1) low dose-rate exposures, (2) partial body exposures resulting from shielding of certain organs and tissues, (3) exposures to radioisotopes following an IND or RDD, and (4) mixed exposures to neutrons and low linear energy transfer (LET) radiation typical of an IND. We are also focusing attention on the development of prognostic biomarkers to predict individual outcomes from near lethal exposures as well as the mechanisms involved in biomarker responses.

Understanding Response Mechanisms

We have already been able to integrate some radiation signaling events at the metabolomics and proteomics levels, and an important focus will be to integrate radiation responses at multiple “omics” levels. From a basic science perspective, this will contribute to a more thorough understanding of the complex molecular responses to radiation exposures, and to the development of a systems biology model to assess global changes by radiation at multiple levels. From a practical standpoint, the use of combined cellular, transcriptomics and metabolomics biomarkers should increase the accuracy of “point-of-care” measurements for biodosimetry. We will take an integrative and complementary approach where many of the same samples will be assessed at the cellular (Project 1), transcriptomics (Project 2) and metabolomics levels for the complex signaling events that occur after radiation injury.

About Metabolomics

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; 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. 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, is 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 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.

 

Patents

Fornace A.J., Amundson S.A., and Trent J.M. Method for Detecting Radiation Exposure. US Patent Number: 7,008,768, granted March 7, 2006 (Submitted February 25, 2000).

Fornace A.J., Idle J., Nazarov E., Gonzalez F., Coy S. Systems and Methods for High-Throughput, Minimally-Invasive Radiation Biodosimetry. Application Number: PCT/US07/76825.

 

Publication Based on This Work

Johnson, C H, Patterson, A D, Krausz, K W, Kalinich, J F, Tyburski, J B, Kang, D W, Leucke, H, Gonzalez, F J, Blakely, W F, Idle, J R Radiation Metabolomics. 5. Identification of urinary biomarkers of ionizing radiation exposure in non-human primates by MS-based metabolomics. Radiat Res in press, 2012.

Hyduke, DR, Laiakis, EC, Li, H H, Fornace, A J, Jr. Identifying radiation exposure biomarkers from mouse blood transcriptome. Int J Bioinform Res Appl. in press, 2012.

Laiakis, EC, Hyduke, DR, Fornace, AJ, Jr. Comparison of Mouse Urinary Metabolic Profiles after Exposure to the Inflammatory Stressors gamma Radiation and Lipopolysaccharide. Radiat. Res. 177: 187-199, 2012.

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]

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.

Coy, SL, Cheema, AK, Tyburski, JB, Laiakis, EC, Collins, SP, Fornace, AJ, Jr Radiation metabolomics and its potential in biodosimetry. Int. J. Radiat. Biol. 87: 802-823, 2011 (August).

Schneider, B.B., Covey, T.R., Coy, S.L., Krylov, E.V. and Nazarov, E.G. Planar differential mobility spectrometer as a pre-filter for atmospheric pressure ionization mass spectrometry. Int. J. Mass Spectrom. (in press). PMC Journal - in process.

Schneider, B.B., Covey, T.R., Coy, S.L., Krylov, E.V. and Nazarov, E.G. Control of chemical effects in the separation process of a differential mobility mass spectrometer system. Eur J Mass Spectrom (Chichester, Eng) 16:57-71, 2010. [abstract]

Tyburski, J.B., Patterson, A.D., Krausz, K.W., Slavik, J., Fornace, A.J. Jr., 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]

Coy, S.L., Krylov, E.V. and Nazarov E.G. DMS-prefiltered mass spectrometry for the detection of biomarkers. Proc. SPIE 6954: 695411, 2008. [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]

 

Other Related Publications

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.

Buryakov, I.A., 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.

Buryakov, I.A., 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.

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.

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.

Eiceman, G.A., 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.

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.

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.

Krebs, M.D., 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.

Lampertus, G.R., 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.

Levin, D.S., 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.

Levin, D.S., 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.

Levin, D.S., 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.

Ma, X., Idle, J. R., Krausz, K. W. & Gonzalez, F. J. Metabolism of Melatonin by Human Cytochromes P450. Drug Metab Dispos 2004.

Miller, R.A., 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.

Miller, R.A., 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.

Miller, R.A., 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.

Nazarov, E. G., S. L. Coy, E.V. Krylov, R.A. Miller, GA. Eiceman. Pressure Effects in Differential Mobility Spectrometry. Anal. Chemistry, 78: 7697-06, 2006.

Schmidt, H., 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.

 

Collaborating Institutions

Georgetown University

National Cancer Institute



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