Dataset Information

The General Explanation Method with NMR Spectroscopy Enables the Identification of Metabolite Profiles Specific for Normal and Tumor Cell Lines.

ABSTRACT: Machine learning models in metabolomics, despite their great prediction accuracy, are still not widely adopted owing to the lack of an efficient explanation for their predictions. In this study, we propose the use of the general explanation method to explain the predictions of a machine learning model to gain detailed insight into metabolic differences between biological systems. The method was tested on a dataset of ¹ H NMR spectra acquired on normal lung and mesothelial cell lines and their tumor counterparts. Initially, the random forests and artificial neural network models were applied to the dataset, and excellent prediction accuracy was achieved. The predictions of the models were explained with the general explanation method, which enabled identification of discriminating metabolic concentration differences between individual cell lines and enabled the construction of their specific metabolic concentration profiles. This intuitive and robust method holds great promise for in-depth understanding of the mechanisms that underline phenotypes as well as for biomarker discovery in complex diseases.

SUBMITTER: Pecnik K

PROVIDER: S-EPMC6220813 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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The General Explanation Method with NMR Spectroscopy Enables the Identification of Metabolite Profiles Specific for Normal and Tumor Cell Lines.

Pečnik Klemen K Todorović Vesna V Bošnjak Maša M Čemažar Maja M Kononenko Igor I Serša Gregor G Plavec Janez J

Chembiochem : a European journal of chemical biology 20180914 19

Machine learning models in metabolomics, despite their great prediction accuracy, are still not widely adopted owing to the lack of an efficient explanation for their predictions. In this study, we propose the use of the general explanation method to explain the predictions of a machine learning model to gain detailed insight into metabolic differences between biological systems. The method was tested on a dataset of <sup>1</sup> H NMR spectra acquired on normal lung and mesothelial cell lines a ...[more]

PMID: 30067305

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Project description:A current challenge in metabolomics is the reliable quantitation of many metabolites. Limited resolution and sensitivity combined with the challenges associated with unknown metabolite identification have restricted both the number and the quantitative accuracy of blood metabolites. Focused on alleviating this bottleneck in NMR-based metabolomics, investigations of pooled human serum combining an array of 1D/2D NMR experiments at 800 MHz, database searches, and spiking with authentic compounds enabled the identification of 67 blood metabolites. Many of these (?1/3) are new compared with those reported previously as a part of the Human Serum Metabolome Database. In addition, considering both the high reproducibility and quantitative nature of NMR as well as the sensitivity of NMR chemical shifts to altered sample conditions, experimental protocols and comprehensive peak annotations are provided here as a guide for identification and quantitation of the new pool of blood metabolites for routine applications. Further, investigations focused on the evaluation of quantitation using organic solvents revealed a surprisingly poor performance for protein precipitation using acetonitrile. One-third of the detected metabolites were attenuated by 10-67% compared with methanol precipitation at the same solvent-to-serum ratio of 2:1 (v/v). Nearly 2/3 of the metabolites were further attenuated by up to 65% upon increasing the acetonitrile-to-serum ratio to 4:1 (v/v). These results, combined with the newly established identity for many unknown metabolites in the NMR spectrum, offer new avenues for human serum/plasma-based metabolomics. Further, the ability to quantitatively evaluate nearly 70 blood metabolites that represent numerous classes, including amino acids, organic acids, carbohydrates, and heterocyclic compounds, using a simple and highly reproducible analytical method such as NMR may potentially guide the evaluation of samples for analysis using mass spectrometry.

Dataset Information

The General Explanation Method with NMR Spectroscopy Enables the Identification of Metabolite Profiles Specific for Normal and Tumor Cell Lines.

Publications

The General Explanation Method with NMR Spectroscopy Enables the Identification of Metabolite Profiles Specific for Normal and Tumor Cell Lines.

Similar Datasets

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