Untargeted metabolomics study of Humulus lupulus brewing cultivars, for genetic origin classification task

Ikhalaynen, Y.A.; Rodin, I.A.; Plyushchenko, I.V.

Авторы: Ikhalaynen Y.A., Rodin I.A., Plyushchenko I.V.
Сборник: 13th Winter Symposium on Chemometrics. Book of Abstracts
Тезисы
Год издания: 2022
Место издания: Russian Chemometrics Society Moscow
Первая страница: 52
Последняя страница: 52
Аннотация: In plant analysis omics approaches help to solve a wide range of different problems – from botanicclassification to environment pollution studies. Genomic and metabolomic approaches were maininstruments in plant studies for many years. In spite of the fact that DNA based methods seem to be mostsuitable for plant classification tasks, the resulting phenotype of a species could vary because of differentfactors, such as different gene expression influence on phenotype and specifics of plant genome. In thissituation, an untargeted metabolomics approach seems convenient for confirmation of other types ofclassification and cross-omics studies, since it does not require difficult conditions of sample preparationand interpretation of raw data. In our study, we provide workflow of brewing Humulus lupulus cultivarsclassification according to previously obtained EST-SSR based results [1] by LC-HRMS profiling,including feature selection and identification of significant metabolites. 18 cultivars of hops (in pellets) were analyzed, each in 3 biological repeats (54 samples in total).Randomization of the experimental sequence and quality control (QC) samples were used to reduceinfluence of batch effect and systematic error. Sample preparation consisted of ultrasonic extraction ofhomogenised pellets using methanol. Mixture of all samples were used for QC samples preparation.LC-HRMS profiling was provided via Shimadzu LCMS-IT-TOF system, equipped with electrosprayionization ion source (ESI) and reversed-phase column Thermo Acclaim RSLC C18. Peak picking andchromatogram alignment were performed using xcms R package with parameters were optimized byIPO package. Random forest based missing value imputation and XGboost based signal correction wereperformed for the peaks table. abels, obtained in reference genomic study, were fine-tuned by hierarchicalcluster analysis based on principal components coordinates.Marker compounds were selected using rational cut off according to three tests: moderated t-test, foldchange and PLS-DA algorithm. Collision induced dissociation fragmentation mass-spectra were thanobtained for selected biomarkers and processed by various annotation tools, including MetFrag, CFM-IDand McSearch.References1. Josef Patzak, Alena Henychová. Czech Journal of Genetics and Plant Breeding 54.2 (2018): 86-91.
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Untargeted metabolomics study of Humulus lupulus brewing cultivars, for genetic origin classification taskтезисы доклада

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