A Relook into the Flavonoid Chemical Space of Lam. Leaves Through a Combination of LC-MS and Molecular Networking

Overview

Journal J Anal Methods Chem

Publisher Wiley

Specialty Chemistry

Date 2023 Oct 4

PMID 37790601

Authors

Dakalo Lorraine Ndou

Ashwell Rungano Ndhlala

Nikita Tawanda Tavengwa

Ntakadzeni Edwin Madala

Affiliations

Soon will be listed here.

Abstract

Lam. is a functional tree that is known to produce a variety of metabolites with purported pharmacological activities. It is frequently called the "miracle tree" due to its utilization in numerous nutraceutical and pharmacological contexts. This study was aimed at studying the chemical space of leaf extracts through molecular networking (MN), a tool that identifies metabolites by classifying them based on their MS-based fragmentation pattern similarities and signals. In this case, a special emphasis was placed on the flavonoid composition. The MN unraveled different molecular families such as flavonoids, carboxylic acids and derivatives, lignin glycosides, fatty acyls, and macrolactams that are found within the plant. In silico annotation tools such as network annotation propagation (NAP) and DEREPLICATOR, an unsupervised substructure identification tool (MS2LDA), and MolNet enhancer were also explored to further compliment the classic molecular networking output within the Global Natural Product Social (GNPS) site. In this study, common flavonoids found within were further annotated using MS2LDA. Utilizing computational tools allowed for the discovery of a wide range of structurally diverse flavonoid molecules within leaf extracts. The expansion of the flavonoid chemical repertoire in this plant arises from intricate glycosylation modifications, leading to the creation of structural isomers that manifest as isobaric ions during mass spectrometry (MS) analyses.

References

van der Hooft J, Wandy J, Young F, Padmanabhan S, Gerasimidis K, Burgess K . Unsupervised Discovery and Comparison of Structural Families Across Multiple Samples in Untargeted Metabolomics. Anal Chem. 2017; 89(14):7569-7577. PMC: 5524435. DOI: 10.1021/acs.analchem.7b01391. View

Li D, Ni R, Wang P, Zhang X, Wang P, Zhu T . Molecular Basis for Chemical Evolution of Flavones to Flavonols and Anthocyanins in Land Plants. Plant Physiol. 2020; 184(4):1731-1743. PMC: 7723094. DOI: 10.1104/pp.20.01185. View

Soltana H, De Rosso M, Lazreg H, Dalla Vedova A, Hammami M, Flamini R . LC-QTOF characterization of non-anthocyanic flavonoids in four Tunisian fig varieties. J Mass Spectrom. 2018; 53(9):817-823. DOI: 10.1002/jms.4209. View

Abu-Reidah I, Arraez-Roman D, Al-Nuri M, Warad I, Segura-Carretero A . Untargeted metabolite profiling and phytochemical analysis of Micromeria fruticosa L. (Lamiaceae) leaves. Food Chem. 2019; 279:128-143. DOI: 10.1016/j.foodchem.2018.11.144. View

Liu W, Song Q, Cao Y, Xie N, Li Z, Jiang Y . From H NMR-based non-targeted to LC-MS-based targeted metabolomics strategy for in-depth chemome comparisons among four Cistanche species. J Pharm Biomed Anal. 2018; 162:16-27. DOI: 10.1016/j.jpba.2018.09.013. View

Wandy J, Zhu Y, van der Hooft J, Daly R, Barrett M, Rogers S . Ms2lda.org: web-based topic modelling for substructure discovery in mass spectrometry. Bioinformatics. 2017; 34(2):317-318. PMC: 5860206. DOI: 10.1093/bioinformatics/btx582. View

Mosic M, Trifkovic J, Vovk I, Gasic U, Tesic Z, Sikoparija B . Phenolic Composition Influences the Health-Promoting Potential of Bee-Pollen. Biomolecules. 2019; 9(12). PMC: 6995608. DOI: 10.3390/biom9120783. View

Caudy A, Mulleder M, Ralser M . Metabolomics in Yeast. Cold Spring Harb Protoc. 2017; 2017(9):pdb.top083576. DOI: 10.1101/pdb.top083576. View

Yang J, Sanchez L, Rath C, Liu X, Boudreau P, Bruns N . Molecular networking as a dereplication strategy. J Nat Prod. 2013; 76(9):1686-99. PMC: 3936340. DOI: 10.1021/np400413s. View

10.

Aksay O, Selli S, Kelebek H . LC-DAD-ESI-MS/MS-based assessment of the bioactive compounds in fresh and fermented caper (Capparis spinosa) buds and berries. Food Chem. 2020; 337:127959. DOI: 10.1016/j.foodchem.2020.127959. View

11.

Pascale R, Acquavia M, Cataldi T, Onzo A, Coviello D, Bufo S . Profiling of quercetin glycosides and acyl glycosides in sun-dried peperoni di Senise peppers (Capsicum annuum L.) by a combination of LC-ESI(-)-MS/MS and polarity prediction in reversed-phase separations. Anal Bioanal Chem. 2020; 412(12):3005-3015. DOI: 10.1007/s00216-020-02547-2. View

12.

Wu L, Zhang X, Xu X, Zheng Q, Yang J, Ding W . Characterization of aromatic glycosides in the extracts of Trollius species by ultra high-performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight tandem mass spectrometry. J Pharm Biomed Anal. 2013; 75:55-63. DOI: 10.1016/j.jpba.2012.11.015. View

13.

Du W, An Y, He X, Zhang D, He W . Protection of Kaempferol on Oxidative Stress-Induced Retinal Pigment Epithelial Cell Damage. Oxid Med Cell Longev. 2018; 2018:1610751. PMC: 6280232. DOI: 10.1155/2018/1610751. View

14.

Vincenti F, Montesano C, Di Ottavio F, Gregori A, Compagnone D, Sergi M . Molecular Networking: A Useful Tool for the Identification of New Psychoactive Substances in Seizures by LC-HRMS. Front Chem. 2020; 8:572952. PMC: 7723841. DOI: 10.3389/fchem.2020.572952. View

15.

Brilhante R, Sales J, Pereira V, Castelo-Branco D, de Aguiar Cordeiro R, de Souza Sampaio C . Research advances on the multiple uses of Moringa oleifera: A sustainable alternative for socially neglected population. Asian Pac J Trop Med. 2017; 10(7):621-630. DOI: 10.1016/j.apjtm.2017.07.002. View

16.

Ivanisevic J, Want E . From Samples to Insights into Metabolism: Uncovering Biologically Relevant Information in LC-HRMS Metabolomics Data. Metabolites. 2019; 9(12). PMC: 6950334. DOI: 10.3390/metabo9120308. View

17.

Aziz N, Kim M, Cho J . Anti-inflammatory effects of luteolin: A review of in vitro, in vivo, and in silico studies. J Ethnopharmacol. 2018; 225:342-358. DOI: 10.1016/j.jep.2018.05.019. View

18.

Salehi B, Venditti A, Sharifi-Rad M, Kregiel D, Sharifi-Rad J, Durazzo A . The Therapeutic Potential of Apigenin. Int J Mol Sci. 2019; 20(6). PMC: 6472148. DOI: 10.3390/ijms20061305. View

19.

Senn T, Hazen S, Tang W . Translating metabolomics to cardiovascular biomarkers. Prog Cardiovasc Dis. 2012; 55(1):70-6. PMC: 3414418. DOI: 10.1016/j.pcad.2012.06.004. View

20.

Aron A, Gentry E, McPhail K, Nothias L, Nothias-Esposito M, Bouslimani A . Reproducible molecular networking of untargeted mass spectrometry data using GNPS. Nat Protoc. 2020; 15(6):1954-1991. DOI: 10.1038/s41596-020-0317-5. View