1
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Hardaway AL, Goudarzi M, Berk M, Chung YM, Zhang R, Li J, Klein E, Sharifi N. 5-Hydroxyeicosatetraenoic Acid Controls Androgen Reduction in Diverse Types of Human Epithelial Cells. Endocrinology 2022; 164:bqac191. [PMID: 36412122 PMCID: PMC9923800 DOI: 10.1210/endocr/bqac191] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 11/14/2022] [Accepted: 11/16/2022] [Indexed: 11/24/2022]
Abstract
Androgens regulate broad physiologic and pathologic processes, including external genitalia development, prostate cancer progression, and anti-inflammatory effects in both cancer and asthma. In prostate cancer, several lines of evidence have implicated dietary and endogenous fatty acids in cell invasion, angiogenesis, and treatment resistance. However, the role of fatty acids in steroidogenesis and the mechanisms by which alterations in this pathway occur are not well understood. Here, we show that, of a panel of fatty acids tested, arachidonic acid and its specific metabolite 5-hydroxyeicosatetraenoic acid (5-HETE) regulate androgen metabolism. Arachidonic acid is metabolized to 5-HETE and reduces androgens by inducing aldo-keto reductase (AKR) family members AKR1C2 and AKR1C3 expression in human prostate, breast, and lung epithelial cells. Finally, we provide evidence that these effects require the expression of the antioxidant response sensor, nuclear factor erythroid 2-related factor 2 (Nrf2). Our findings identify an interconnection between conventional fatty acid metabolism and steroid metabolism that has broad relevance to androgen physiology and inflammatory regulation.
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Affiliation(s)
- Aimalie L Hardaway
- Genitourinary Malignancies Research Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Maryam Goudarzi
- Proteomics and Metabolomics Core, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Michael Berk
- Genitourinary Malignancies Research Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Yoon-Mi Chung
- Genitourinary Malignancies Research Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Renliang Zhang
- Proteomics and Metabolomics Core, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Jianneng Li
- Genitourinary Malignancies Research Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Eric Klein
- Department of Urology, Glickman Urological and Kidney Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Nima Sharifi
- Genitourinary Malignancies Research Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Urology, Glickman Urological and Kidney Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Hematology and Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH 44195, USA
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2
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Yan S, Bhawal R, Yin Z, Thannhauser TW, Zhang S. Recent advances in proteomics and metabolomics in plants. MOLECULAR HORTICULTURE 2022; 2:17. [PMID: 37789425 PMCID: PMC10514990 DOI: 10.1186/s43897-022-00038-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Accepted: 06/20/2022] [Indexed: 10/05/2023]
Abstract
Over the past decade, systems biology and plant-omics have increasingly become the main stream in plant biology research. New developments in mass spectrometry and bioinformatics tools, and methodological schema to integrate multi-omics data have leveraged recent advances in proteomics and metabolomics. These progresses are driving a rapid evolution in the field of plant research, greatly facilitating our understanding of the mechanistic aspects of plant metabolisms and the interactions of plants with their external environment. Here, we review the recent progresses in MS-based proteomics and metabolomics tools and workflows with a special focus on their applications to plant biology research using several case studies related to mechanistic understanding of stress response, gene/protein function characterization, metabolic and signaling pathways exploration, and natural product discovery. We also present a projection concerning future perspectives in MS-based proteomics and metabolomics development including their applications to and challenges for system biology. This review is intended to provide readers with an overview of how advanced MS technology, and integrated application of proteomics and metabolomics can be used to advance plant system biology research.
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Affiliation(s)
- Shijuan Yan
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou, China
| | - Ruchika Bhawal
- Proteomics and Metabolomics Facility, Institute of Biotechnology, Cornell University, 139 Biotechnology Building, 526 Campus Road, Ithaca, NY, 14853, USA
| | - Zhibin Yin
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou, China
| | | | - Sheng Zhang
- Proteomics and Metabolomics Facility, Institute of Biotechnology, Cornell University, 139 Biotechnology Building, 526 Campus Road, Ithaca, NY, 14853, USA.
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3
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Schugar RC, Gliniak CM, Osborn LJ, Massey W, Sangwan N, Horak A, Banerjee R, Orabi D, Helsley RN, Brown AL, Burrows A, Finney C, Fung KK, Allen FM, Ferguson D, Gromovsky AD, Neumann C, Cook K, McMillan A, Buffa JA, Anderson JT, Mehrabian M, Goudarzi M, Willard B, Mak TD, Armstrong AR, Swanson G, Keshavarzian A, Garcia-Garcia JC, Wang Z, Lusis AJ, Hazen SL, Brown JM. Gut microbe-targeted choline trimethylamine lyase inhibition improves obesity via rewiring of host circadian rhythms. eLife 2022; 11:63998. [PMID: 35072627 PMCID: PMC8813054 DOI: 10.7554/elife.63998] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 01/20/2022] [Indexed: 11/13/2022] Open
Abstract
Obesity has repeatedly been linked to reorganization of the gut microbiome, yet to this point obesity therapeutics have been targeted exclusively toward the human host. Here, we show that gut microbe-targeted inhibition of the trimethylamine N-oxide (TMAO) pathway protects mice against the metabolic disturbances associated with diet-induced obesity (DIO) or leptin deficiency (Lepob/ob). Small molecule inhibition of the gut microbial enzyme choline TMA-lyase (CutC) does not reduce food intake but is instead associated with alterations in the gut microbiome, improvement in glucose tolerance, and enhanced energy expenditure. We also show that gut microbial CutC inhibition is associated with reorganization of host circadian control of both phosphatidylcholine and energy metabolism. This study underscores the relationship between microbe and host metabolism and provides evidence that gut microbe-derived trimethylamine (TMA) is a key regulator of the host circadian clock. This work also demonstrates that gut microbe-targeted enzyme inhibitors have potential as anti-obesity therapeutics.
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Affiliation(s)
- Rebecca C Schugar
- Department of Cardiovascular and Metabolic Sciences, Cleveland Clinic Lerner College of Medicine
| | | | - Lucas J Osborn
- Department of Cardiovascular and Metabolic Sciences, Cleveland Clinic Lerner College of Medicine
| | - William Massey
- Department of Cardiovascular and Metabolic Sciences, Cleveland Clinic Lerner College of Medicine
| | - Naseer Sangwan
- Department of Cardiovascular and Metabolic Sciences, Cleveland Clinic Lerner College of Medicine
| | - Anthony Horak
- Department of Cardiovascular and Metabolic Sciences, Cleveland Clinic Lerner College of Medicine
| | - Rakhee Banerjee
- Department of Cardiovascular and Metabolic Sciences, Cleveland Clinic Lerner College of Medicine
| | - Danny Orabi
- Department of Cardiovascular and Metabolic Sciences, Cleveland Clinic Lerner College of Medicine
| | - Robert N Helsley
- Department of Cardiovascular and Metabolic Sciences, Cleveland Clinic Lerner College of Medicine
| | - Amanda L Brown
- Department of Cardiovascular and Metabolic Sciences, Cleveland Clinic Lerner College of Medicine
| | - Amy Burrows
- Department of Cardiovascular and Metabolic Sciences, Cleveland Clinic Lerner College of Medicine
| | - Chelsea Finney
- Department of Cardiovascular and Metabolic Sciences, Cleveland Clinic Lerner College of Medicine
| | - Kevin K Fung
- Department of Cardiovascular and Metabolic Sciences, Cleveland Clinic Lerner College of Medicine
| | - Frederick M Allen
- Department of Cardiovascular and Metabolic Sciences, Cleveland Clinic Lerner College of Medicine
| | - Daniel Ferguson
- Department of Cardiovascular and Metabolic Sciences, Cleveland Clinic Lerner College of Medicine
| | - Anthony D Gromovsky
- Department of Cardiovascular and Metabolic Sciences, Cleveland Clinic Lerner College of Medicine
| | - Chase Neumann
- Department of Cardiovascular and Metabolic Sciences, Cleveland Clinic Lerner College of Medicine
| | - Kendall Cook
- Department of Cardiovascular and Metabolic Sciences, Cleveland Clinic Lerner College of Medicine
| | - Amy McMillan
- Department of Cardiovascular and Metabolic Sciences, Cleveland Clinic Lerner College of Medicine
| | - Jennifer A Buffa
- Department of Cardiovascular and Metabolic Sciences, Cleveland Clinic Lerner College of Medicine
| | - James T Anderson
- Department of Cardiovascular and Metabolic Sciences, Cleveland Clinic Lerner College of Medicine
| | | | - Maryam Goudarzi
- Department of Cardiovascular and Metabolic Sciences, Cleveland Clinic Lerner College of Medicine
| | - Belinda Willard
- Research Core Services, Cleveland Clinic Lerner College of Medicine
| | - Tytus D Mak
- Mass Spectromety Data Center, National Institute of Standards and Technology (NIST)
| | | | - Garth Swanson
- Department of Internal Medicine, Rush University Medical Center
| | | | | | - Zeneng Wang
- Department of Cardiovascular and Metabolic Sciences, Cleveland Clinic Lerner College of Medicine
| | - Aldons J Lusis
- Department of Medicine, University of California, Los Angeles
| | - Stanley L Hazen
- Department of Cellular and Molecular Medicine, Cleveland Clinic Lerner College of Medicine
| | - Jonathan Mark Brown
- Department of Cardiovascular and Metabolic Sciences, Cleveland Clinic Lerner College of Medicine
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4
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Liu S, You Y, Tong Z, Zhang L. Developing an Embedding, Koopman and Autoencoder Technologies-Based Multi-Omics Time Series Predictive Model (EKATP) for Systems Biology research. Front Genet 2021; 12:761629. [PMID: 34764986 PMCID: PMC8576451 DOI: 10.3389/fgene.2021.761629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 09/27/2021] [Indexed: 11/13/2022] Open
Abstract
It is very important for systems biologists to predict the state of the multi-omics time series for disease occurrence and health detection. However, it is difficult to make the prediction due to the high-dimensional, nonlinear and noisy characteristics of the multi-omics time series data. For this reason, this study innovatively proposes an Embedding, Koopman and Autoencoder technologies-based multi-omics time series predictive model (EKATP) to predict the future state of a high-dimensional nonlinear multi-omics time series. We evaluate this EKATP by using a genomics time series with chaotic behavior, a proteomics time series with oscillating behavior and a metabolomics time series with flow behavior. The computational experiments demonstrate that our proposed EKATP can substantially improve the accuracy, robustness and generalizability to predict the future state of a time series for multi-omics data.
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Affiliation(s)
- Suran Liu
- College of Computer Science, Sichuan University, Chengdu, China
| | - Yujie You
- College of Computer Science, Sichuan University, Chengdu, China
| | - Zhaoqi Tong
- College of Software Engineering, Sichuan University, Chengdu, China
| | - Le Zhang
- College of Computer Science, Sichuan University, Chengdu, China
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5
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Ferreira AE, Sousa Silva M, Cordeiro C. Metabolic Network Inference from Time Series. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11347-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022] Open
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6
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Vicente E, Vujaskovic Z, Jackson IL. A Systematic Review of Metabolomic and Lipidomic Candidates for Biomarkers in Radiation Injury. Metabolites 2020; 10:E259. [PMID: 32575772 PMCID: PMC7344731 DOI: 10.3390/metabo10060259] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 06/09/2020] [Accepted: 06/13/2020] [Indexed: 12/16/2022] Open
Abstract
A large-scale nuclear event has the ability to inflict mass casualties requiring point-of-care and laboratory-based diagnostic and prognostic biomarkers to inform victim triage and appropriate medical intervention. Extensive progress has been made to develop post-exposure point-of-care biodosimetry assays and to identify biomarkers that may be used in early phase testing to predict the course of the disease. Screening for biomarkers has recently extended to identify specific metabolomic and lipidomic responses to radiation using animal models. The objective of this review was to determine which metabolites or lipids most frequently experienced perturbations post-ionizing irradiation (IR) in preclinical studies using animal models of acute radiation sickness (ARS) and delayed effects of acute radiation exposure (DEARE). Upon review of approximately 65 manuscripts published in the peer-reviewed literature, the most frequently referenced metabolites showing clear changes in IR induced injury were found to be citrulline, citric acid, creatine, taurine, carnitine, xanthine, creatinine, hypoxanthine, uric acid, and threonine. Each metabolite was evaluated by specific study parameters to determine whether trends were in agreement across several studies. A select few show agreement across variable animal models, IR doses and timepoints, indicating that they may be ubiquitous and appropriate for use in diagnostic or prognostic biomarker panels.
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Affiliation(s)
| | | | - Isabel L. Jackson
- Division of Translational Radiation Sciences, Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD 21201, USA; (E.V.); (Z.V.)
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7
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Tang J, Wang Y, Fu J, Zhou Y, Luo Y, Zhang Y, Li B, Yang Q, Xue W, Lou Y, Qiu Y, Zhu F. A critical assessment of the feature selection methods used for biomarker discovery in current metaproteomics studies. Brief Bioinform 2019; 21:1378-1390. [DOI: 10.1093/bib/bbz061] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Revised: 04/14/2019] [Indexed: 02/06/2023] Open
Abstract
Abstract
Microbial community (MC) has great impact on mediating complex disease indications, biogeochemical cycling and agricultural productivities, which makes metaproteomics powerful technique for quantifying diverse and dynamic composition of proteins or peptides. The key role of biostatistical strategies in MC study is reported to be underestimated, especially the appropriate application of feature selection method (FSM) is largely ignored. Although extensive efforts have been devoted to assessing the performance of FSMs, previous studies focused only on their classification accuracy without considering their ability to correctly and comprehensively identify the spiked proteins. In this study, the performances of 14 FSMs were comprehensively assessed based on two key criteria (both sample classification and spiked protein discovery) using a variety of metaproteomics benchmarks. First, the classification accuracies of those 14 FSMs were evaluated. Then, their abilities in identifying the proteins of different spiked concentrations were assessed. Finally, seven FSMs (FC, LMEB, OPLS-DA, PLS-DA, SAM, SVM-RFE and T-Test) were identified as performing consistently superior or good under both criteria with the PLS-DA performing consistently superior. In summary, this study served as comprehensive analysis on the performances of current FSMs and could provide a valuable guideline for researchers in metaproteomics.
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Affiliation(s)
- Jing Tang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
- Department of Bioinformatics, Chongqing Medical University, Chongqing, China
| | - Yunxia Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Jianbo Fu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Ying Zhou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Yongchao Luo
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Ying Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Bo Li
- School of Pharmaceutical Sciences, Chongqing University, Chongqing, China
| | - Qingxia Yang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
- School of Pharmaceutical Sciences, Chongqing University, Chongqing, China
| | - Weiwei Xue
- School of Pharmaceutical Sciences, Chongqing University, Chongqing, China
| | - Yan Lou
- Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yunqing Qiu
- Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
- School of Pharmaceutical Sciences, Chongqing University, Chongqing, China
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8
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Tebani A, Afonso C, Bekri S. Advances in metabolome information retrieval: turning chemistry into biology. Part II: biological information recovery. J Inherit Metab Dis 2018; 41:393-406. [PMID: 28842777 PMCID: PMC5959951 DOI: 10.1007/s10545-017-0080-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2017] [Revised: 07/27/2017] [Accepted: 07/28/2017] [Indexed: 12/11/2022]
Abstract
This work reports the second part of a review intending to give the state of the art of major metabolic phenotyping strategies. It particularly deals with inherent advantages and limits regarding data analysis issues and biological information retrieval tools along with translational challenges. This Part starts with introducing the main data preprocessing strategies of the different metabolomics data. Then, it describes the main data analysis techniques including univariate and multivariate aspects. It also addresses the challenges related to metabolite annotation and characterization. Finally, functional analysis including pathway and network strategies are discussed. The last section of this review is devoted to practical considerations and current challenges and pathways to bring metabolomics into clinical environments.
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Affiliation(s)
- Abdellah Tebani
- Department of Metabolic Biochemistry, Rouen University Hospital, 76000, Rouen, France
- Normandie Université, UNIROUEN, CHU Rouen, IRIB, INSERM U1245, 76000, Rouen, France
- Normandie Université, UNIROUEN, INSA Rouen, CNRS, COBRA, 76000, Rouen, France
| | - Carlos Afonso
- Normandie Université, UNIROUEN, INSA Rouen, CNRS, COBRA, 76000, Rouen, France
| | - Soumeya Bekri
- Department of Metabolic Biochemistry, Rouen University Hospital, 76000, Rouen, France.
- Normandie Université, UNIROUEN, CHU Rouen, IRIB, INSERM U1245, 76000, Rouen, France.
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9
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Zhao M, Lau KK, Zhou X, Wu J, Yang J, Wang C. Urinary metabolic signatures and early triage of acute radiation exposure in rat model. MOLECULAR BIOSYSTEMS 2017; 13:756-766. [PMID: 28225098 DOI: 10.1039/c6mb00785f] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
After a large-scale radiological accident, early-response biomarkers to assess radiation exposure over a broad dose range are not only the basis of rapid radiation triage, but are also the key to the rational use of limited medical resources and to the improvement of treatment efficiency. Because of its high throughput, rapid assays and minimally invasive sample collection, metabolomics has been applied to research into radiation exposure biomarkers in recent years. Due to the complexity of radiobiological effects, most of the potential biomarkers are both dose-dependent and time-dependent. In reality, it is very difficult to find a single biomarker that is both sensitive and specific in a given radiation exposure scenario. Therefore, a multi-parameters approach for radiation exposure assessment is more realistic in real nuclear accidents. In this study, untargeted metabolomic profiling based on gas chromatography-mass spectrometry (GC-MS) and targeted amino acid profiling based on LC-MS/MS were combined to investigate early urinary metabolite responses within 48 h post-exposure in a rat model. A few of the key early-response metabolites for radiation exposure were identified, which revealed the most relevant metabolic pathways. Furthermore, a panel of potential urinary biomarkers was selected through a multi-criteria approach and applied to early triage following irradiation. Our study suggests that it is feasible to use a multi-parameters approach to triage radiation damage, and the urinary excretion levels of the relevant metabolites provide insights into radiation damage and repair.
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Affiliation(s)
- Mingxiao Zhao
- School of Radiation Medicine and Protection, Medical College of Soochow University, School for Radiological and Interdisciplinary Sciences (RAD-X), Jiangsu Provincial Key Laboratory of Radiation Medicine and Protection, Suzhou Industrial Park Ren'ai Road 199, Suzhou 215123, P. R. China.
| | - Kim Kt Lau
- Department of Applied Chemistry, Xi'an Jiaotong-Liverpool University, Suzhou Industrial Park Ren'ai Road 111, Suzhou 215123, P. R. China
| | - Xian Zhou
- School of Radiation Medicine and Protection, Medical College of Soochow University, School for Radiological and Interdisciplinary Sciences (RAD-X), Jiangsu Provincial Key Laboratory of Radiation Medicine and Protection, Suzhou Industrial Park Ren'ai Road 199, Suzhou 215123, P. R. China.
| | - Jianfang Wu
- Department of Applied Chemistry, Xi'an Jiaotong-Liverpool University, Suzhou Industrial Park Ren'ai Road 111, Suzhou 215123, P. R. China
| | - Jun Yang
- Department of Entomology and Nematology, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA.
| | - Chang Wang
- School of Radiation Medicine and Protection, Medical College of Soochow University, School for Radiological and Interdisciplinary Sciences (RAD-X), Jiangsu Provincial Key Laboratory of Radiation Medicine and Protection, Suzhou Industrial Park Ren'ai Road 199, Suzhou 215123, P. R. China.
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10
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Perez de Souza L, Naake T, Tohge T, Fernie AR. From chromatogram to analyte to metabolite. How to pick horses for courses from the massive web resources for mass spectral plant metabolomics. Gigascience 2017; 6:1-20. [PMID: 28520864 PMCID: PMC5499862 DOI: 10.1093/gigascience/gix037] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2017] [Revised: 05/08/2017] [Accepted: 05/12/2017] [Indexed: 01/19/2023] Open
Abstract
The grand challenge currently facing metabolomics is the expansion of the coverage of the metabolome from a minor percentage of the metabolic complement of the cell toward the level of coverage afforded by other post-genomic technologies such as transcriptomics and proteomics. In plants, this problem is exacerbated by the sheer diversity of chemicals that constitute the metabolome, with the number of metabolites in the plant kingdom generally considered to be in excess of 200 000. In this review, we focus on web resources that can be exploited in order to improve analyte and ultimately metabolite identification and quantification. There is a wide range of available software that not only aids in this but also in the related area of peak alignment; however, for the uninitiated, choosing which program to use is a daunting task. For this reason, we provide an overview of the pros and cons of the software as well as comments regarding the level of programing skills required to effectively exploit their basic functions. In addition, the torrent of available genome and transcriptome sequences that followed the advent of next-generation sequencing has opened up further valuable resources for metabolite identification. All things considered, we posit that only via a continued communal sharing of information such as that deposited in the databases described within the article are we likely to be able to make significant headway toward improving our coverage of the plant metabolome.
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Affiliation(s)
- Leonardo Perez de Souza
- Max-Planck-Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany
| | - Thomas Naake
- Max-Planck-Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany
| | - Takayuki Tohge
- Max-Planck-Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany
| | - Alisdair R Fernie
- Max-Planck-Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany
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11
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Pannkuk EL, Fornace AJ, Laiakis EC. Metabolomic applications in radiation biodosimetry: exploring radiation effects through small molecules. Int J Radiat Biol 2017; 93:1151-1176. [PMID: 28067089 DOI: 10.1080/09553002.2016.1269218] [Citation(s) in RCA: 78] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
PURPOSE Exposure of the general population to ionizing radiation has increased in the past decades, primarily due to long distance travel and medical procedures. On the other hand, accidental exposures, nuclear accidents, and elevated threats of terrorism with the potential detonation of a radiological dispersal device or improvised nuclear device in a major city, all have led to increased needs for rapid biodosimetry and assessment of exposure to different radiation qualities and scenarios. Metabolomics, the qualitative and quantitative assessment of small molecules in a given biological specimen, has emerged as a promising technology to allow for rapid determination of an individual's exposure level and metabolic phenotype. Advancements in mass spectrometry techniques have led to untargeted (discovery phase, global assessment) and targeted (quantitative phase) methods not only to identify biomarkers of radiation exposure, but also to assess general perturbations of metabolism with potential long-term consequences, such as cancer, cardiovascular, and pulmonary disease. CONCLUSIONS Metabolomics of radiation exposure has provided a highly informative snapshot of metabolic dysregulation. Biomarkers in easily accessible biofluids and biospecimens (urine, blood, saliva, sebum, fecal material) from mouse, rat, and minipig models, to non-human primates and humans have provided the basis for determination of a radiation signature to assess the need for medical intervention. Here we provide a comprehensive description of the current status of radiation metabolomic studies for the purpose of rapid high-throughput radiation biodosimetry in easily accessible biofluids and discuss future directions of radiation metabolomics research.
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Affiliation(s)
- Evan L Pannkuk
- a Tumor Biology Program , Lombardi Comprehensive Cancer Center, Georgetown University , Washington DC , USA
| | - Albert J Fornace
- b Molecular Oncology , Lombardi Comprehensive Cancer Center, Georgetown University , Washington DC , USA.,c Department of Biochemistry and Molecular and Cellular Biology , Georgetown University , Washington DC , USA
| | - Evagelia C Laiakis
- c Department of Biochemistry and Molecular and Cellular Biology , Georgetown University , Washington DC , USA
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12
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Li B, Tang J, Yang Q, Cui X, Li S, Chen S, Cao Q, Xue W, Chen N, Zhu F. Performance Evaluation and Online Realization of Data-driven Normalization Methods Used in LC/MS based Untargeted Metabolomics Analysis. Sci Rep 2016; 6:38881. [PMID: 27958387 PMCID: PMC5153651 DOI: 10.1038/srep38881] [Citation(s) in RCA: 92] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2016] [Accepted: 11/15/2016] [Indexed: 02/06/2023] Open
Abstract
In untargeted metabolomics analysis, several factors (e.g., unwanted experimental & biological variations and technical errors) may hamper the identification of differential metabolic features, which requires the data-driven normalization approaches before feature selection. So far, ≥16 normalization methods have been widely applied for processing the LC/MS based metabolomics data. However, the performance and the sample size dependence of those methods have not yet been exhaustively compared and no online tool for comparatively and comprehensively evaluating the performance of all 16 normalization methods has been provided. In this study, a comprehensive comparison on these methods was conducted. As a result, 16 methods were categorized into three groups based on their normalization performances across various sample sizes. The VSN, the Log Transformation and the PQN were identified as methods of the best normalization performance, while the Contrast consistently underperformed across all sub-datasets of different benchmark data. Moreover, an interactive web tool comprehensively evaluating the performance of 16 methods specifically for normalizing LC/MS based metabolomics data was constructed and hosted at http://server.idrb.cqu.edu.cn/MetaPre/. In summary, this study could serve as a useful guidance to the selection of suitable normalization methods in analyzing the LC/MS based metabolomics data.
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Affiliation(s)
- Bo Li
- Innovative Drug Research and Bioinformatics Group, Innovative Drug Research Centre and School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Jing Tang
- Innovative Drug Research and Bioinformatics Group, Innovative Drug Research Centre and School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Qingxia Yang
- Innovative Drug Research and Bioinformatics Group, Innovative Drug Research Centre and School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Xuejiao Cui
- Innovative Drug Research and Bioinformatics Group, Innovative Drug Research Centre and School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Shuang Li
- Innovative Drug Research and Bioinformatics Group, Innovative Drug Research Centre and School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Sijie Chen
- College of Mathematics and Statistics, Chongqing University, Chongqing 401331, China
| | - Quanxing Cao
- Innovative Drug Research and Bioinformatics Group, Innovative Drug Research Centre and School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Weiwei Xue
- Innovative Drug Research and Bioinformatics Group, Innovative Drug Research Centre and School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Na Chen
- Innovative Drug Research and Bioinformatics Group, Innovative Drug Research Centre and School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Feng Zhu
- Innovative Drug Research and Bioinformatics Group, Innovative Drug Research Centre and School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
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Chen Z, Coy SL, Pannkuk EL, Laiakis EC, Hall AB, Fornace AJ, Vouros P. Rapid and High-Throughput Detection and Quantitation of Radiation Biomarkers in Human and Nonhuman Primates by Differential Mobility Spectrometry-Mass Spectrometry. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2016; 27:1626-36. [PMID: 27392730 PMCID: PMC5018447 DOI: 10.1007/s13361-016-1438-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2016] [Revised: 06/02/2016] [Accepted: 06/16/2016] [Indexed: 05/04/2023]
Abstract
Radiation exposure is an important public health issue due to a range of accidental and intentional threats. Prompt and effective large-scale screening and appropriate use of medical countermeasures (MCM) to mitigate radiation injury requires rapid methods for determining the radiation dose. In a number of studies, metabolomics has identified small-molecule biomarkers responding to the radiation dose. Differential mobility spectrometry-mass spectrometry (DMS-MS) has been used for similar compounds for high-throughput small-molecule detection and quantitation. In this study, we show that DMS-MS can detect and quantify two radiation biomarkers, trimethyl-L-lysine (TML) and hypoxanthine. Hypoxanthine is a human and nonhuman primate (NHP) radiation biomarker and metabolic intermediate, whereas TML is a radiation biomarker in humans but not in NHP, which is involved in carnitine synthesis. They have been analyzed by DMS-MS from urine samples after a simple strong cation exchange-solid phase extraction (SCX-SPE). The dramatic suppression of background and chemical noise provided by DMS-MS results in an approximately 10-fold reduction in time, including sample pretreatment time, compared with liquid chromatography-mass spectrometry (LC-MS). DMS-MS quantitation accuracy has been verified by validation testing for each biomarker. Human samples are not yet available, but for hypoxanthine, selected NHP urine samples (pre- and 7-d-post 10 Gy exposure) were analyzed, resulting in a mean change in concentration essentially identical to that obtained by LC-MS (fold-change 2.76 versus 2.59). These results confirm the potential of DMS-MS for field or clinical first-level rapid screening for radiation exposure. Graphical Abstract ᅟ.
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Affiliation(s)
- Zhidan Chen
- Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA, 02115, USA
| | - Stephen L Coy
- Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA, 02115, USA.
| | - Evan L Pannkuk
- Department of Biochemistry and Molecular and Cellular Biology, Georgetown University Medical Center, Washington, DC, 20057, USA
| | - Evagelia C Laiakis
- Department of Biochemistry and Molecular and Cellular Biology, Georgetown University Medical Center, Washington, DC, 20057, USA
| | - Adam B Hall
- Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA, 02115, USA
| | - Albert J Fornace
- Department of Biochemistry and Molecular and Cellular Biology, Georgetown University Medical Center, Washington, DC, 20057, USA
- Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, 20057, USA
- Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah, 22254, Saudi Arabia
| | - Paul Vouros
- Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA, 02115, USA.
- Barnett Institute of Chemical and Biological Analysis, Northeastern University, Boston, MA, 02115, USA.
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14
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A New Strategy for Analyzing Time-Series Data Using Dynamic Networks: Identifying Prospective Biomarkers of Hepatocellular Carcinoma. Sci Rep 2016; 6:32448. [PMID: 27578360 PMCID: PMC5006023 DOI: 10.1038/srep32448] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2015] [Accepted: 08/08/2016] [Indexed: 02/08/2023] Open
Abstract
Time-series metabolomics studies can provide insight into the dynamics of disease development and facilitate the discovery of prospective biomarkers. To improve the performance of early risk identification, a new strategy for analyzing time-series data based on dynamic networks (ATSD-DN) in a systematic time dimension is proposed. In ATSD-DN, the non-overlapping ratio was applied to measure the changes in feature ratios during the process of disease development and to construct dynamic networks. Dynamic concentration analysis and network topological structure analysis were performed to extract early warning information. This strategy was applied to the study of time-series lipidomics data from a stepwise hepatocarcinogenesis rat model. A ratio of lyso-phosphatidylcholine (LPC) 18:1/free fatty acid (FFA) 20:5 was identified as the potential biomarker for hepatocellular carcinoma (HCC). It can be used to classify HCC and non-HCC rats, and the area under the curve values in the discovery and external validation sets were 0.980 and 0.972, respectively. This strategy was also compared with a weighted relative difference accumulation algorithm (wRDA), multivariate empirical Bayes statistics (MEBA) and support vector machine-recursive feature elimination (SVM-RFE). The better performance of ATSD-DN suggests its potential for a more complete presentation of time-series changes and effective extraction of early warning information.
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15
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Menikarachchi LC, Dubey R, Hill DW, Brush DN, Grant DF. Development of Database Assisted Structure Identification (DASI) Methods for Nontargeted Metabolomics. Metabolites 2016; 6:metabo6020017. [PMID: 27258318 PMCID: PMC4931548 DOI: 10.3390/metabo6020017] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2016] [Revised: 05/26/2016] [Accepted: 05/27/2016] [Indexed: 01/06/2023] Open
Abstract
Metabolite structure identification remains a significant challenge in nontargeted metabolomics research. One commonly used strategy relies on searching biochemical databases using exact mass. However, this approach fails when the database does not contain the unknown metabolite (i.e., for unknown-unknowns). For these cases, constrained structure generation with combinatorial structure generators provides a potential option. Here we evaluated structure generation constraints based on the specification of: (1) substructures required (i.e., seed structures); (2) substructures not allowed; and (3) filters to remove incorrect structures. Our approach (database assisted structure identification, DASI) used predictive models in MolFind to find candidate structures with chemical and physical properties similar to the unknown. These candidates were then used for seed structure generation using eight different structure generation algorithms. One algorithm was able to generate correct seed structures for 21/39 test compounds. Eleven of these seed structures were large enough to constrain the combinatorial structure generator to fewer than 100,000 structures. In 35/39 cases, at least one algorithm was able to generate a correct seed structure. The DASI method has several limitations and will require further experimental validation and optimization. At present, it seems most useful for identifying the structure of unknown-unknowns with molecular weights <200 Da.
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Affiliation(s)
- Lochana C Menikarachchi
- Department of Pharmacy, Faculty of Allied Health Sciences, University of Peradeniya, Peradeniya 20400, Sri Lanka.
- Department of Pharmaceutical Sciences, University of Connecticut, 69 N Eagleville Rd, Storrs, CT 06269, USA.
| | - Ritvik Dubey
- Department of Pharmaceutical Sciences, University of Connecticut, 69 N Eagleville Rd, Storrs, CT 06269, USA.
| | - Dennis W Hill
- Department of Pharmaceutical Sciences, University of Connecticut, 69 N Eagleville Rd, Storrs, CT 06269, USA.
| | - Daniel N Brush
- Department of Pharmaceutical Sciences, University of Connecticut, 69 N Eagleville Rd, Storrs, CT 06269, USA.
| | - David F Grant
- Department of Pharmaceutical Sciences, University of Connecticut, 69 N Eagleville Rd, Storrs, CT 06269, USA.
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16
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Pannkuk EL, Laiakis EC, Authier S, Wong K, Fornace AJ. Targeted Metabolomics of Nonhuman Primate Serum after Exposure to Ionizing Radiation: Potential Tools for High-throughput Biodosimetry. RSC Adv 2016; 6:51192-51202. [PMID: 28367319 DOI: 10.1039/c6ra07757a] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
There is a need for research to rapidly determine an individual's absorbed dose and its potential health effects after a potential radiological or nuclear event that could expose large portions of a population to ionizing radiation (IR). Studies on biomarker identification after radiation exposure could aid in biodosimetry, identifying individual dose absorbed, as well as biologic response, and administering immediate and proper medical care. Metabolomics on easily accessible biofluids is an emerging field with potential for high-throughput biodosimetry. While tremendous effort has been put into obtaining discovery based global radiation signatures from a number of biofluids and model organisms, quantitative targeted analysis on a subset of known radiation biomarkers is required to develop an optimized panel of biomarkers for future clinical applications. The current study analyzes levels of several known broad chemical groups (acylcarnitines, amino acids, phosphatidylcholines, and biogenic amines) affected by IR in serum from nonhuman primates (NHP) 7 days after exposure through multiple reaction monitoring (MRM) analysis with a triple quadrupole mass spectrometry (MS) platform. We identified several novel metabolites affected by IR exposure through univariate and unsupervised multivariate analyses. Levels of acylcarnitines, amino acids, and phospholipids were perturbed indicating altered protein metabolism, fatty acid β-oxidation, and inflammation. Fold changes in carnitine and short-chain acylcarnitines (acetylcarnitine, propionylcarnitine, butyrylcarnitine, and valerylcarnitine) complement previous global radiation signatures on NHP; notably, the levels of change were lower than previously observed in urine. Decreased levels of glutamate, citrulline, and arginine after IR are biomarkers indicating gastrointestinal syndrome and perturbations to the urea cycle. Sex differences were also assessed and were more prevalent in circulating acylcarnitines and phospholipids after IR exposure. These biomarkers may be combined with previously described compounds from DNA damage to develop a defined metabolomic biodosimetry panel to be analyzed by MS platforms, which are increasingly available in clinical laboratories.
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Affiliation(s)
- Evan L Pannkuk
- Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, Washington, DC
| | - Evagelia C Laiakis
- Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, Washington, DC
| | | | | | - Albert J Fornace
- Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, Washington, DC; Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC; Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, Saudi Arabia
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17
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Pannkuk EL, Laiakis EC, Mak TD, Astarita G, Authier S, Wong K, Fornace AJ. A Lipidomic and Metabolomic Serum Signature from Nonhuman Primates Exposed to Ionizing Radiation. Metabolomics 2016; 12:80. [PMID: 28220056 PMCID: PMC5314995 DOI: 10.1007/s11306-016-1010-0] [Citation(s) in RCA: 50] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
INTRODUCTION Due to dangers associated with potential accidents from nuclear energy and terrorist threats, there is a need for high-throughput biodosimetry to rapidly assess individual doses of radiation exposure. Lipidomics and metabolomics are becoming common tools for determining global signatures after disease or other physical insult and provide a "snapshot" of potential cellular damage. OBJECTIVES The current study assesses changes in the nonhuman primate (NHP) serum lipidome and metabolome 7 days following exposure to ionizing radiation (IR). METHODS Serum sample lipids and metabolites were extracted using a biphasic liquid-liquid extraction and analyzed by ultra performance liquid chromatography quadrupole time-of-flight mass spectrometry. Global radiation signatures were acquired in data-independent mode. RESULTS Radiation exposure caused significant perturbations in lipid metabolism, affecting all major lipid species, including free fatty acids, glycerolipids, glycerophospholipids and esterified sterols. In particular, we observed a significant increase in the levels of polyunsaturated fatty acids (PUFA)-containing lipids in the serum of NHPs exposed to 10 Gy radiation, suggesting a primary role played by PUFAs in the physiological response to IR. Metabolomics profiling indicated an increase in the levels of amino acids, carnitine, and purine metabolites in the serum of NHPs exposed to 10 Gy radiation, suggesting perturbations to protein digestion/absorption, biological oxidations, and fatty acid β-oxidation. CONCLUSIONS This is the first report to determine changes in the global NHP serum lipidome and metabolome following radiation exposure and provides information for developing metabolomic biomarker panels in human-based biodosimetry.
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Affiliation(s)
- Evan L. Pannkuk
- Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, Washington, DC
| | - Evagelia C. Laiakis
- Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, Washington, DC
| | - Tytus D. Mak
- Mass Spectrometry Data Center, National Institute of Standards and Technology, Gaithersburg, MD
| | - Giuseppe Astarita
- Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, Washington, DC
- Health Sciences, Waters Corporation, Milford, MA
| | | | | | - Albert J. Fornace
- Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, Washington, DC
- Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC
- Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, Saudi Arabia
- Address for correspondence: Georgetown University, 3970 Reservoir Road, NW, New, Research Building, Room E504, Washington, DC 20057, , Phone: 202-687-7843, Fax: 202-687-3140
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18
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Eke I, Makinde AY, Aryankalayil MJ, Ahmed MM, Coleman CN. Comprehensive molecular tumor profiling in radiation oncology: How it could be used for precision medicine. Cancer Lett 2016; 382:118-126. [PMID: 26828133 DOI: 10.1016/j.canlet.2016.01.041] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2015] [Revised: 01/21/2016] [Accepted: 01/26/2016] [Indexed: 12/16/2022]
Abstract
New technologies enabling the analysis of various molecules, including DNA, RNA, proteins and small metabolites, can aid in understanding the complex molecular processes in cancer cells. In particular, for the use of novel targeted therapeutics, elucidation of the mechanisms leading to cell death or survival is crucial to eliminate tumor resistance and optimize therapeutic efficacy. While some techniques, such as genomic analysis for identifying specific gene mutations or epigenetic testing of promoter methylation, are already in clinical use, other "omics-based" assays are still evolving. Here, we provide an overview of the current status of molecular profiling methods, including promising research strategies, as well as possible challenges, and their emerging role in radiation oncology.
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Affiliation(s)
- Iris Eke
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.
| | - Adeola Y Makinde
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Molykutty J Aryankalayil
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Mansoor M Ahmed
- Radiation Research Program, National Cancer Institute, National Institutes of Health, Rockville, MD 20850, USA
| | - C Norman Coleman
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA; Radiation Research Program, National Cancer Institute, National Institutes of Health, Rockville, MD 20850, USA
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Cajka T, Fiehn O. Toward Merging Untargeted and Targeted Methods in Mass Spectrometry-Based Metabolomics and Lipidomics. Anal Chem 2015; 88:524-45. [PMID: 26637011 DOI: 10.1021/acs.analchem.5b04491] [Citation(s) in RCA: 514] [Impact Index Per Article: 57.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Tomas Cajka
- UC Davis Genome Center-Metabolomics, University of California Davis , 451 Health Sciences Drive, Davis, California 95616, United States
| | - Oliver Fiehn
- UC Davis Genome Center-Metabolomics, University of California Davis , 451 Health Sciences Drive, Davis, California 95616, United States.,King Abdulaziz University , Faculty of Science, Biochemistry Department, P.O. Box 80203, Jeddah 21589, Saudi Arabia
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20
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Misra BB, van der Hooft JJJ. Updates in metabolomics tools and resources: 2014-2015. Electrophoresis 2015; 37:86-110. [DOI: 10.1002/elps.201500417] [Citation(s) in RCA: 100] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2015] [Revised: 10/04/2015] [Accepted: 10/05/2015] [Indexed: 12/12/2022]
Affiliation(s)
- Biswapriya B. Misra
- Department of Biology, Genetics Institute; University of Florida; Gainesville FL USA
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