Emerging new strategies for successful metabolite identification in metabolomics

Abstract

This review discusses strategies for the identification of metabolites in complex biological mixtures, as encountered in metabolomics, which have emerged in the recent past. These include NMR database-assisted approaches for the identification of commonly known metabolites as well as novel combinations of NMR and MS analysis methods for the identification of unknown metabolites. The use of certain chemical additives to the NMR tube can permit identification of metabolites with specific physical chemical properties.

Keywords: MS of metabolite mixtures; NMR of metabolite mixtures; complex mixture analysis; metabolite databases; metabolomics; nanoparticle-assisted metabolomics; paramagnetic relaxation enhancement.

Figure 1.. Metabolite identification by using the customized TOCSY ¹H(¹³C)-TOCCATA database.

In the 2D ¹H-¹H TOCSY spectrum of Escherichia coli cell lysate (orange), a ¹H TOCSY trace displayed as green cross-section is extracted (upper panel). Next, its cross-peaks are queried against the database using the webserver [61]. The query correctly and exclusively assigned the trace to the nicotinamide ring portion of NADP⁺ (see lower panel depicting a snapshot of the web server).

Figure 2.. Screenshots taken from COLMAR ¹³C-¹H HSQC web server. The HSQC peak list with 165 cross-peaks of Drosophila melanogaster metabolite extract (upper panel) is queried against the database.

List of matching compounds returned by the query (lower panel) containing the highest true positive and the lowest false-positive identification rate among ¹³C-¹H HSQC metabolomics web servers. COLMAR ¹³C-¹H HSQC is available for public use at [62]. Reproduced with permission from [33] © American Chemical Society (2015).

Figure 3.. Metabolite identification by using the recent 2D J-resolved NMR database SpinCouple.

The database contains ¹H chemical shift and ¹H-¹H J-coupling information of 598 metabolite standards. It is publically available for querying at [68]. Reproduced with permission from [67] © American Chemical Society (2016).

Figure 4.. Recently proposed combined MS/NMR approaches for the rapid and accurate identification of known and unknown metabolites in complex metabolite mixtures.

(A) The NMR/MS Translator strategy allows rapid identification of cataloged metabolites. (B) The SUMMIT MS/NMR strategy allows rapid identification of unknown metabolites. (A) Reproduced with permission from [75]. (B) Reproduced with permission from [76].

Figure 4.. Recently proposed combined MS/NMR approaches for the rapid and accurate identification of known and unknown metabolites in complex metabolite mixtures.

(A) The NMR/MS Translator strategy allows rapid identification of cataloged metabolites. (B) The SUMMIT MS/NMR strategy allows rapid identification of unknown metabolites. (A) Reproduced with permission from [75]. (B) Reproduced with permission from [76].

Figure 5.. The protocol integrating the SUMMIT MS/NMR with the NMR/MS Translator for the systematic and efficient identification of both known and unknown metabolites in complex metabolite mixtures.

Figure 6.. A chemo-selective approach to detect the same metabolites using NMR and MS by chemical modification.

¹⁵N-labeled cholamine attaches selectively and covalently to carboxyl group containing metabolites and enables their enhanced detection by both MS and NMR. Reprinted with permission from [87] © American Chemical Society (2013).

Figure 7.. The combined use of the paramagnetic spin relaxation agent gadolinium (Gd³⁺) and CPMG ¹H NMR to selectively suppress signals of metabolites in a complex mixture.

A low concentration of Gd³⁺ combined with a short T₂ filter only suppresses the signals from citric acid (blue). Next, a higher Gd³⁺ concentration along with a longer T₂ filter suppresses the signals from acetylcysteine (yellow). The remaining signals in the CPMG 1D ¹H NMR spectrum belong to mannitol (red), which is least affected by Gd³⁺ and the T₂ filter. Adapted with permission from [90] © American Chemical Society (2015).

Figure 8.. Effect of anionic silica nanoparticles on 1D ¹H and 2D ¹³C-¹H HSQC spectra of 10-compound metabolite model mixture consisting of lysine, arginine, histidine, citric acid, lactic acid, shikimic acid, alanine, dimethylglycine, glucose and valine 2 mM each (A) without and (B) with anionic silica nanoparticles.

Blue squares highlight the cross-peaks of lysine, arginine, histidine and dimethylglycine that are suppressed in the presence of silica nanoparticles (red squares). Reproduced with permission from [92] © American Chemical Society (2015).