Rapid metabolic fingerprinting with the aid of chemometric models to identify authenticity of natural medicines: Turmeric, Ocimum, and Withania somnifera study

Herbal medicines are popular natural medicines that have been used for decades.The use of alternative medicines continues to expand rapidly across the world.The World Health Organization suggests that quality assessment of natural medicines is essential for any therapeutic or health care applications,as their therapeutic potential varies between different geographic origins,plant species,and varieties.Classification of herbal medicines based on a limited number of secondary metabolites is not an ideal approach.Their quality should be considered based on a complete metabolic profile,as their pharmacological activity is not due to a few specific secondary metabolites but rather a larger group of bioactive compounds.A holistic and integrative approach using rapid and nondestructive analytical strategies for the screening of herbal medicines is required for robust characterization.In this study,a rapid and effective quality assessment system for geographical traceability,species,and variety-specific authenticity of the widely used natural medicines turmeric,Ocimum,and Withania somnifera was investigated using Fourier transform near-infrared(FT-NIR)spectroscopy-based metabolic fingerprinting.Four different geographical origins of turmeric,five different Ocimum species,and three different varieties of roots and leaves of Withania somnifera were studied with the aid of machine learning approaches.Extremely good discrimination(R^(2)>0.98,Q^(2)>0.97,and accuracy=1.0)with sensitivity and specificity of 100%was achieved using this metabolic fingerprinting strategy.Our study demonstrated that FT-NIR-based rapid metabolic fingerprinting can be used as a robust analytical method to authenticate several important medicinal herbs.

Chemometrics-aided Metabolic Fingerprint Method Applied in Bladder Cancer Stages Differentiating

Objective To establish early detection and diagnosis for bladder cancer.Methods In the current study,a metabolomics strategy was used to profile bladder cancer urine metabolites in mice and to further characterize the disease status at different stages.In addition,some chemometrics algorithms were adopted to analyze the metabolites fingerprints,including baseline removal and retention time shift,to overcome variations in the experimental process.After processing,metabolites were qualitatively and quantitatively analyzed in each sample at different stages.Finally,a random forest algorithm was used to discriminate the differences among different groups.Results Four potential biomarkers,including glyceric acid,(R*,R*)-2,3-Dihydroxybutanoic acid,N-(1-oxohexyl)-glycine and D-Turanose,were discovered by exploring the characteristics of different groups.Conclusion These results suggest that combining chemometrics with the metabolites profile is an effective approach to aid in clinical diagnosis.

Utilizing metabolomic approach to study the mode of action of fungicides and corresponding resistance in plant pathogens

Fungicides are an indispensable tool in plant disease control.Various modes of action(MOAs)have been identified in different fungicides to suppress plant pathogens.The combined use of fungicides with distinct MOAs has been recommended to prevent the development of pathogen resistance.In studying MOAs,metabolomics has been proven to be a robust and high-throughput method.Because metabolites are unique and distinct depending on the biological activities of an organism,MOAs can be identified and classified by establishing metabolic fingerprinting and metabolic profiles.Similarly,if fungicide resistance is developed in a pathogen,the metabolome will change,which can be identified.In this review,we have discussed the principles and advanced applications of metabolomics in the study of MOAs and resistance mechanisms of fungicides,and the potential of metabolic data in understanding the interaction between fungicides and pathogens.Challenges are also discussed in the application of metabolomics,improvement of the study on the mechanism of fungicides in their functions against pathogens and advancing the development of novel fungicides.

Selection in monoculture vs. mixture alters plant metabolic fingerprints

Aims In grassland biodiversity experiments,positive biodiversity effects on primary productivity increase over time.recent research has shown that differential selection in monoculture and mixed-species communities leads to the rapid emergence of monoculture and mix-ture types,adapted to their own biotic community.We used eight plant species selected for 8 years in such a biodiversity experiment to test if monoculture and mixture types differed in metabolic pro-files using infrared spectroscopy.Methods Fourier transform infrared spectroscopy(FTIr)was used to assess metabolic fingerprints of leaf samples of 10 individuals of each species from either monocultures or mixtures.The FTIr spectra were analyzed using multivariate procedures to assess(i)whether indi-viduals within species could be correctly assigned to monoculture or mixture history based on the spectra alone and(ii)which parts of the spectra drive the group assignment,i.e.which metabolic groups were subject to differential selection in monocultures vs.mixtures.Important Findings Plant individuals within each of the eight species could be classified as either from monoculture or mixture selection history based on their FTIr spectra.Different metabolic groups were differentially selected in the different species;some of them may be related to defense of patho-gens accumulating more strongly in monocultures than in mixtures.The rapid selection of the monoculture and mixture types within the eight study species could have been due to a sorting-out process based on large initial genetic or epigenetic variation within the species.

Use of the Metabolomics Approach to Characterize Chinese Medicinal Material Huangqi

Integration of the genetic and metabolic fingerprinting can provide a new approach to differentiate similar Traditional Chinese Medical （TCM） materials. Two leguminous plants, Mojia Huangqi and Menggu Huangqi, are important medical herbs and share great similarities in morphology, chemical constituent, and genomic DNA sequence. The taxonomy of Mojia Huangqi and Menggu Huangqi has been debated for more than 50 years and discrimination of TCM materials directly affects the pharmacological and clinical effects. AFLP based genetic fingerprinting and GC-TOF/MS-based meta- bolic fingerprinting were used to successfully discriminate the two species. The results of AFLP supported the opinion that Menggu Huangqi was a variant of Mojia Huangqi. The metabolic fingerprinting showed growth locations have greater impacts on the metabolite composition and quantity than the genotypes （cultivated versus wild） in Menggu Huangqi. The difference of some soluble sugars, fatty acids, proline, and polyamine reflected plant adaptation to different growth environments. Using multivariate and univariate statistical analysis, three AFLP markers and eight metabolites were identified as candidate DNA and metabolic markers to distinguish the two herb materials. The correlation network between AFLP markers and metabolites revealed a complex correlation network, which indicated the special metabolic pathways and the regulation networks of Huangqi.

Quantitative resistance against Bemisia tabaci in Solanum pennellii:Genetics and metabolomics

The whitefly Bemisia tabaci is a serious threat in tomato cultivation worldwide as all varieties grown today are highly susceptible to this devastating herbivorous insect.Many accessions of the tomato wild relative Solanum pennellii show a high resistance towards B. tabaci. A mapping approach was used to elucidate the genetic background of whiteflyresistance related traits and associated biochemical traits in this species. Minor quantitative trait loci（QTLs） for whitefly adult survival（AS） and oviposition rate（OR） were identified and some were confirmed in an F2BC1 population, where they showed increased percentages of explained variance（more than 30%）. Bulked segregant analyses on pools of whiteflyresistant and-susceptible F2 plants enabled the identification of metabolites that correlate either with resistance or susceptibility. Genetic mapping of these metabolites showed that a large number of them co-localize with whiteflyresistance QTLs. Some of these whitefly-resistance QTLs are hotspots for metabolite QTLs. Although a large number of metabolite QTLs correlated to whitefly resistance or susceptibility, most of them are yet unknown compounds and further studies are needed to identify the metabolic pathways and genes involved. The results indicate a direct genetic correlation between biochemical-based resistance characteristics and reduced whitefly incidence in S. pennellii.