Identifying unknown metabolites using NMR-based metabolic profiling techniques

Abstract

Metabolic profiling of biological samples provides important insights into multiple physiological and pathological processes, but is hindered by a lack of automated annotation and standardised methods for structure elucidation of candidate disease biomarkers. Here, we describe a system for identifying molecular species derived from NMR spectroscopybased metabolic phenotyping studies, with detailed info on sample preparation, data acquisition, and data modelling. We provide eight different modular workflows to be followed in a recommended sequential order according to their level of difficulty. This multi-platform system involves the use of statistical spectroscopic tools such as STOCSY, STORM and RED-STORM to identify other signals in the NMR spectra relating to the same molecule. It also utilizes 2D-NMR spectroscopic analysis, separation and pre-concentration techniques, multiple hyphenated analytical platforms and data extraction from existing databases. The complete system, using all eight workflows, would take up to a month, as it includes multidimensional NMR experiments that require prolonged experiment times. However, easier identification cases using fewer steps would take two or three days. This approach to biomarker discovery is efficient, cost-effective and offers increased chemical space coverage of the metabolome, resulting in faster and more accurate assignment of NMR-generated biomarkers arising from metabolic phenotyping studies. Finally, it requires basic understanding of Matlab in order to perform statistical spectroscopic tools and analytical skills to perform Solid Phase Extraction, LC-fraction collection, LC-NMR-MS and 1D and 2D NMR experiments. STORM (and STOCSY): The code for executing both the STOCSY and STORM algorithms is in https://bitbucket.org/jmp111/storm/src. These can be executed in a Matlab environment. RED-STORM-The code for executing the RED-STORM algorithm is provided in https://bitbucket.org/jmp111/redstorm/src/. This can be executed in a Matlab environment.

Comparison of Metabolic Profiling of Arabidopsis Inflorescences Between Landsberg erecta and Columbia, and Meiosis-Defective Mutants by 1H-NMR Spectroscopy

“…An NMR-based metabolomics approach revealed distinct metabolic changes between the inflorescences of two Arabidopsis accessions, Col and Ler, compared to several meiosis-defective mutants. Due to the low sensitivity of NMR (Garcia-Perez et al 2020), the samples we used were inflorescences covering stages 1-12. The difference in metabolites between Col and Ler inflorescences reflects the true developmental difference.…”

“…However, few studies have employed metabolomics to systematically investigate the impact in meiosis on the metabolome (Li et al 2020). Metabolomics analysis has become a powerful technology to comprehensively clarify plant development (Wen et al 2015;Zhu et al 2018) and for molecular phenotyping (Dai et al 2010a;Ren et al 2009;Garcia-Perez et al 2020). Such approaches have already been successfully applied to study metabolite composition (Xiao et al 2008;Kusano et al 2015), systematic responses to abiotic stresses (Wang et al 2006;Dai et al 2010b;Zhang et al 2011), pathogen infection (Kumar et al 2016;Lima et al 2010;Choi et al 2004), and herbivore infestation in host plants (Widarto et al 2006;Liu et al 2017).…”

Cerebrospinal fluid metabolomics: detection of neuroinflammation in human central nervous system disease

“… 26 The strategies involved in molecular feature extractions and metabolite annotations have been previously reviewed. 27 , 28 , 29 , 30 Advanced statistical tools such as chemometrics have become an essential tool for the extraction of valuable metabolic signature information. Chemometrics has developed into a well‐established statistical tool in areas such as multivariate calibration, pattern recognition, multivariate statistical process control and quantitative structure modelling.…”

“…Data preprocessing software packages, metabolite databases and libraries available for NMR and mass spectrometry (MS) metabolomics research have expanded, with increased dependence on the usage of metabolome repositories and querying platforms 26 . The strategies involved in molecular feature extractions and metabolite annotations have been previously reviewed 27–30 . Advanced statistical tools such as chemometrics have become an essential tool for the extraction of valuable metabolic signature information.…”

Metabolomic Approaches to Study Chemical Exposure-Related Metabolism Alterations in Mammalian Cell Cultures

“…Detailed metabolite annotation strategies are specific to the analytical techniques and instrumental setup. Metabolite assignment is generally obtained by the comparison of peak features (such as multiplicity, coupling constants, and chemical shifts for NMR, and m/z ratios, isotope pattern, element composition, fragmentation and/or retention time for (LC-) MS) against public, commercial or in-house spectral libraries of known metabolites [ 60 , 116 , 117 , 118 ].…”