Dataset Information

Fabric Phase Sorptive Extraction-A Metabolomic Preprocessing Approach for Ionizing Radiation Exposure Assessment.

ABSTRACT: The modern application of mass spectrometry-based metabolomics to the field of radiation assessment and biodosimetry has allowed for the development of prompt biomarker screenings for radiation exposure. Our previous work on radiation assessment, in easily accessible biofluids (such as urine, blood, saliva), has revealed unique metabolic perturbations in response to radiation quality, dose, and dose rate. Nevertheless, the employment of swift injury assessment in the case of a radiological disaster still remains a challenge as current sample processing can be time consuming and cause sample degradation. To address these concerns, we report a metabolomics workflow using a mass spectrometry-compatible fabric phase sorptive extraction (FPSE) technique. FPSE employs a matrix coated with sol-gel poly(caprolactone-b-dimethylsiloxane-b-caprolactone) that binds both polar and nonpolar metabolites in whole blood, eliminating serum processing steps. We confirm that the FPSE preparation technique combined with liquid chromatography-mass spectrometry can distinguish radiation exposure markers such as taurine, carnitine, arachidonic acid, ?-linolenic acid, and oleic acid found 24 h after 8 Gy irradiation. We also note the effect of different membrane fibers on both metabolite extraction efficiency and the temporal stabilization of metabolites in whole blood at room temperature. These findings suggest that the FPSE approach could work in future technology to triage irradiated individuals accurately, via biomarker screening, by providing a novel method to stabilize biofluids between collection and sample analysis.

SUBMITTER: Taraboletti A

PROVIDER: S-EPMC7437658 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Fabric Phase Sorptive Extraction-A Metabolomic Preprocessing Approach for Ionizing Radiation Exposure Assessment.

Taraboletti Alexandra A Goudarzi Maryam M Kabir Abuzar A Moon Bo-Hyun BH Laiakis Evagelia C EC Lacombe Jerome J Ake Pelagie P Shoishiro Sueoka S Brenner David D Fornace Albert J AJ Zenhausern Frederic F

Journal of proteome research 20190522 8

The modern application of mass spectrometry-based metabolomics to the field of radiation assessment and biodosimetry has allowed for the development of prompt biomarker screenings for radiation exposure. Our previous work on radiation assessment, in easily accessible biofluids (such as urine, blood, saliva), has revealed unique metabolic perturbations in response to radiation quality, dose, and dose rate. Nevertheless, the employment of swift injury assessment in the case of a radiological disas ...[more]

PMID: 31090424

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Project description:A novel method based on fabric phase sorptive extraction (FPSE) followed by gas chromatography-tandem mass spectrometry (GC-MS/MS) has been validated for the simultaneous determination of 11 UV filters (ethylhexyl salicylate, benzyl salicylate, homosalate, benzophenone-3, isoamylmethoxycinnamate, 4-methylbenzylidenecamphor, methyl anthranilate, etocrylene, 2-ethylhexylmethoxycinnamate, 2-ethylhexyl p-dimethylaminobenzoate, and octocrylene), in natural and recreational waters. Major experimental parameters affecting FPSE procedure have been optimized to obtain the highest extraction efficiency. Different types and sizes of sol-gel coated FPSE media, sample volume, extraction time, and type and volume of desorption solvent were evaluated. The optimal conditions involved the use of a (2.0 × 2.5) cm2 FPSE device with PDMS based coating for the extraction of 20 mL of water for 20 min. The quantitative desorption of the target compounds was performed with 0.5-1 mL of ethyl acetate. The method was satisfactorily validated in terms of linearity, precision, repeatability and reproducibility. Recovery studies were performed at different concentration levels in real water matrices to show its suitability, obtaining mean values about 90% and satisfactory precision. LODs were at the low ng L-1 in all cases. Finally, the validated FPSE-GC-MS/MS method was applied to different real samples, including environmental water (lake, river, seawater) and recreational water (swimming-pool), where 8 out of the 11 studied compounds were detected at concentrations between 0.12-123 μg L-1. FPSE is proposed as an efficient and simple alternative to other extraction and microextraction techniques for the analysis of UV filters in waters. Since no matrix effects were observed, quantification could be carried out by conventional calibration with standard solutions, without the need to perform the complete FPSE procedure, thus allowing a higher throughput in comparison with other microextraction techniques.

Project description:Background: Gene signatures derived from transcriptomic data using machine learning methods have shown promise for biodosimetry testing. These signatures may not be sufficiently robust for large scale testing, as their performance has not been adequately validated on external, independent datasets. The present study develops human and murine signatures with biochemically-inspired machine learning that are strictly validated using k-fold and traditional approaches. Methods: Gene Expression Omnibus (GEO) datasets of exposed human and murine lymphocytes were preprocessed via nearest neighbor imputation and expression of genes implicated in the literature to be responsive to radiation exposure (n=998) were then ranked by Minimum Redundancy Maximum Relevance (mRMR). Optimal signatures were derived by backward, complete, and forward sequential feature selection using Support Vector Machines (SVM), and validated using k-fold or traditional validation on independent datasets. Results: The best human signatures we derived exhibit k-fold validation accuracies of up to 98% ( DDB2, PRKDC, TPP2, PTPRE, and GADD45A) when validated over 209 samples and traditional validation accuracies of up to 92% ( DDB2, CD8A, TALDO1, PCNA, EIF4G2, LCN2, CDKN1A, PRKCH, ENO1, and PPM1D) when validated over 85 samples. Some human signatures are specific enough to differentiate between chemotherapy and radiotherapy. Certain multi-class murine signatures have sufficient granularity in dose estimation to inform eligibility for cytokine therapy (assuming these signatures could be translated to humans). We compiled a list of the most frequently appearing genes in the top 20 human and mouse signatures. More frequently appearing genes among an ensemble of signatures may indicate greater impact of these genes on the performance of individual signatures. Several genes in the signatures we derived are present in previously proposed signatures. Conclusions: Gene signatures for ionizing radiation exposure derived by machine learning have low error rates in externally validated, independent datasets, and exhibit high specificity and granularity for dose estimation.

Dataset Information

Fabric Phase Sorptive Extraction-A Metabolomic Preprocessing Approach for Ionizing Radiation Exposure Assessment.

Publications

Fabric Phase Sorptive Extraction-A Metabolomic Preprocessing Approach for Ionizing Radiation Exposure Assessment.

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