“Notame”: Workflow for Non-Targeted LC–MS Metabolic Profiling

Metabolites, Год журнала: 2020, Номер 10(4), С. 135 - 135

Опубликована: Март 31, 2020

Metabolomics analysis generates vast arrays of data, necessitating comprehensive workflows involving expertise in analytics, biochemistry and bioinformatics order to provide coherent high-quality data that enable discovery robust biologically significant metabolic findings. In this protocol article, we introduce notame, an analytical workflow for non-targeted profiling approaches, utilizing liquid chromatography–mass spectrometry analysis. We overview lab protocols statistical methods commonly practice the nutritional metabolomics data. The paper is divided into three main sections: first second sections introducing background study designs available research third section describing detail steps used produce, preprocess statistically analyze and, finally, identify interpret compounds have emerged as interesting.

Язык: Английский

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Metabolomic Strategies in Biomarker Research–New Approach for Indirect Identification of Drug Consumption and Sample Manipulation in Clinical and Forensic Toxicology?

Frontiers in Chemistry, Год журнала: 2019, Номер 7

Опубликована: Май 10, 2019

Drug of abuse (DOA) consumption is a growing problem worldwide, particularly with increasing numbers new psychoactive substances (NPS) entering the drug market. Generally, little information on their adverse effects and toxicity are available. The direct detection identification NPS an analytical challenge due to ephemerality scene. An approach that does not directly focus structural analyte or its metabolites, would be beneficial for this complex scenario development alternative screening methods could help provide fast response suspected consumption. A metabolomics might represent such strategy biomarkers different questions in DOA testing. Metabolomics monitoring changes small (endogenous) molecules (<1,000 Da) certain stimulus, e.g., For review, literature search targeting "metabolomics" DOAs was conducted. Thereby, applications metabolomic strategies biomarker research were identified: (a) as additional tool metabolism studies bearing major advantage priori unknown unexpected metabolites can identified; (b) endogenous metabolite patterns, synthetic cannabinoids also indirectly detect urine manipulation attempts by chemical adulteration replacement artificial samples. majority currently available field, however, deals better assess acute chronic find addiction tolerance. Certain compounds detected all studied DOAs, but often similar compounds/pathways influenced. When evaluating these regard possible consumption, observed appear, albeit statistically significant, too reliably work Further, drugs shown affect same pathways. In conclusion, approaches possess potential indicating More studies, including more sensitive targeted analyses, multi-variant statistical models deep-learning needed fully explore omics science

Язык: Английский

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Deep learning meets metabolomics: a methodological perspective

Briefings in Bioinformatics, Год журнала: 2020, Номер 22(2), С. 1531 - 1542

Опубликована: Авг. 11, 2020

Deep learning (DL), an emerging area of investigation in the fields machine and artificial intelligence, has markedly advanced over past years. DL techniques are being applied to assist medical professionals researchers improving clinical diagnosis, disease prediction drug discovery. It is expected that will help provide actionable knowledge from a variety 'big data', including metabolomics data. In this review, we discuss applicability metabolomics, while presenting discussing several examples recent research. We emphasize use tackling bottlenecks data acquisition, processing, metabolite identification, as well metabolic phenotyping biomarker Finally, how used genome-scale modelling interpretation The DL-based approaches discussed here may computational biologists with integration, drawing statistical inference about biological outcomes, based on

Язык: Английский

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Data normalization strategies in metabolomics: Current challenges, approaches, and tools

European Journal of Mass Spectrometry, Год журнала: 2020, Номер 26(3), С. 165 - 174

Опубликована: Апрель 10, 2020

Data normalization is a big challenge in quantitative metabolomics approaches, whether targeted or untargeted. Without proper normalization, the mass-spectrometry and spectroscopy data can provide erroneous, sub-optimal data, which lead to misleading confusing biological results thereby result failed application human healthcare, clinical, other research avenues. To address this issue, number of statistical approaches software tools have been proposed literature implemented over years, providing multitude choose from – either sample-based data-based strategies. In recent new dedicated for surfaced as well. account article, I summarize existing discoveries findings area introduce some that aid normalization.

Язык: Английский

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Untargeted metabolomics of COVID-19 patient serum reveals potential prognostic markers of both severity and outcome

Metabolomics, Год журнала: 2021, Номер 18(1)

Опубликована: Дек. 20, 2021

Abstract Introduction The diagnosis of COVID-19 is normally based on the qualitative detection viral nucleic acid sequences. Properties host response are not measured but key in determining outcome. Although metabolic profiles well suited to capture state, most metabolomics studies either underpowered, measure only a restricted subset metabolites, compare infected individuals against uninfected control cohorts that suitably matched, or do provide compact predictive model. Objectives Here we well-powered, untargeted assessment 120 patient samples acquired at hospital admission. study aims predict patient’s infection severity (i.e., mild severe) and potential outcome discharged deceased). Methods High resolution UHPLC-MS/MS analysis was performed serum using both positive negative ionization modes. A 20 intermediary metabolites were selected univariate statistical significance multiple predictor Bayesian logistic regression model created. Results predictors for their relevant biological function include deoxycytidine ureidopropionate (indirectly reflecting load), kynurenine (reflecting inflammatory response), short chain acylcarnitines (energy metabolism) among others. Currently, this approach predicts with Monte Carlo cross validated area under ROC curve 0.792 (SD 0.09) 0.793 0.08), respectively. blind validation an additional 90 patients predicted AUC 0.83 (CI 0.74–0.91) 0.76 0.67–0.86). Conclusion Prognostic tests markers discussed paper could allow improvement planning treatment.

Язык: Английский

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