HPLC Retention time prediction for metabolome analysis

Hagiwara, Toshiyuki; Saito, Seiji; Ujiie, Yoshifumi; Imai, Kanae; Kakuta, Masanori; Kadota, Koji; Terada, Tohru; Sumikoshi, Kazuya; Shimizu, Kentaro; Nishi, Tatsunari

doi:10.6026/97320630005255

Cited by 18 publications

(13 citation statements)

References 6 publications

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“…There remain, however, many currently unidentifiable features that were modulated by prebiotic and/or stress. With continued improvements in available reference samples 44 , future work will expand the identification of potential gut derived biomolecular pathways and could facilitate the discovery of additional novel molecules capable of impacting physiology and complex behavior.…”

Section: Discussionmentioning

confidence: 99%

Dietary prebiotics alter novel microbial dependent fecal metabolites that improve sleep

Thompson

Vargas

Dorrestein

et al. 2020

Sci Rep

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Dietary prebiotics produce favorable changes in the commensal gut microbiome and reduce host vulnerability to stress-induced disruptions in complex behaviors such as sleep. the mechanisms for how prebiotics modulate stress physiology remain unclear; however, emerging evidence suggests that gut microbes and their metabolites may play a role. this study tested if stress and/or dietary prebiotics (test diet) alter the fecal metabolome; and explored if these changes were related to sleep and/or gut microbial alpha diversity. Male F344 rats on either Test or Control diet were instrumented for electroencephalography biotelemetry measures of sleep/wake. After 5 weeks on diet, rats were either stressed or remained in home cages. Based on untargeted mass spectrometry and 16S rRNA gene sequencing, both stress and Test diet altered the fecal metabolome/microbiome. In addition, Test diet prevented the stress-induced reduction in microbial alpha diversity based on PD_Whole_Tree, which has been previously published. network propagation analysis revealed that stress increased members of the neuroactive steroidal pregnane molecular family; and that Test diet reduced this effect. We also discovered links between sleep, alpha diversity, and pyrimidine, secondary bile acid, and neuroactive glucocorticoid/pregnanolone-type steroidal metabolites. these results reveal novel microbialdependent metabolites that may modulate stress physiology and sleep. Evidence in both the human and animal literatures suggest that stressor exposure can negatively impact sleep 1-4 and the gut microbiota 5-7. We have previously reported, for example, that male rats exposed to an acute stressor experience disruptions in sleep architecture, have flattened diurnal rhythmicity of core body temperature, learning deficits, express depressive-like behaviors, and altered gut microbial alpha diversity 4,8-10. Ad libitum consumption of prebiotic diets, compared to nutrient/calorically matched control diets, prior to stressor exposure attenuates many of these effects 7,11. In addition, ingestion of prebiotic diet also improves non-rapid eye movement (NREM) sleep, promotes increases in rapid eye movement (REM) sleep after stressor exposure (REM rebound), and prevents stress-induced reductions in gut microbial alpha diversity 7. The mechanisms for how prebiotic diet-induced changes in the gut microbiota impact stress physiology remain unclear; however, there is emerging evidence that gut microbial metabolites likely play a role 12,13. Bacterial dependent metabolites, such as short chain fatty acids and secondary bile acids, can signal the brain through the blood and/or vagal afferents 14-16. It is feasible, therefore, that gut microbial modulatory diets produce changes in the gut metabolome that signal the brain and impact complex brain functions. Fecal samples were collected both before and after stressor exposure and impacts on overall gut metabolomic structure were determined using untargeted mass spectrometry chromatography (LC-MS/MS). Those features driving the c...

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Section: Discussionmentioning

confidence: 99%

Dietary prebiotics alter novel microbial dependent fecal metabolites that improve sleep

Thompson

Vargas

Dorrestein

et al. 2020

Sci Rep

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“…In untargeted metabolomics, retention time prediction can be used to validate mass-based compound annotations (Hagiwara et al, 2010;Cao et al, 2015). Lipids of the same class elute at similar retention times .…”

Section: Retention Filtermentioning

confidence: 99%

Structure Annotation and Quantification of Wheat Seed Oxidized Lipids by High-Resolution LC-MS/MS

2017

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Lipid oxidation is a process ubiquitous in life, but the direct and comprehensive analysis of oxidized lipids has been limited by available analytical methods. We applied high-resolution liquid chromatography-mass spectrometry (LC-MS) and tandem mass spectrometry (MS/MS) to quantify oxidized lipids (glycerides, fatty acids, phospholipids, lysophospholipids, and galactolipids) and implemented a platform-independent high-throughput-amenable analysis pipeline for the high-confidence annotation and acyl composition analysis of oxidized lipids. Lipid contents of 90 different naturally aged wheat (Triticum aestivum) seed stocks were quantified in an untargeted high-resolution LC-MS experiment, resulting in 18,556 quantitative mass-to-charge ratio features. In a posthoc liquid chromatography-tandem mass spectrometry experiment, high-resolution MS/MS spectra (5 mD accuracy) were recorded for 8,957 out of 12,080 putatively monoisotopic features of the LC-MS data set. A total of 353 nonoxidized and 559 oxidized lipids with up to four additional oxygen atoms were annotated based on the accurate mass recordings (1.5 ppm tolerance) of the LC-MS data set and filtering procedures. MS/MS spectra available for 828 of these annotations were analyzed by translating experimentally known fragmentation rules of lipids into the fragmentation of oxidized lipids. This led to the identification of 259 nonoxidized and 365 oxidized lipids by both accurate mass and MS/MS spectra and to the determination of acyl compositions for 221 nonoxidized and 295 oxidized lipids. Analysis of 15-year aged wheat seeds revealed increased lipid oxidation and hydrolysis in seeds stored in ambient versus cold conditions. Lipid oxidation is a process inevitably connected to life. Although essential for all respiring organisms, oxygen can form reactive species (ROS). The accumulation of these compounds leads to the phenomenon commonly referred to as oxidative stress in microbes and plant and animal tissues. ROS play a role in the attack against microbial pathogens by host cells (Imlay, 2013), time-dependent death of dormant seeds (Lee et al., 2010), human diseases (Coyle and Puttfarcken, 1993;Giugliano et al., 1996), in aging (Sohal and Weindruch, 1996;Sohal et al., 2002), and in signaling processes (Gilroy et al., 2016;Mignolet-Spruyt et al., 2016) Unlike oxidized fatty acids (FAs), oxidized lipids are hardly available commercially. Targeted liquid chromatography-mass spectrometry (LC-MS) analysis of oxidized lipids using authentic standards is not very common (Hui et al., 2010;Ravandi et al., 2014). Often, oxidized lipids are detected using tandem mass spectrometry (MS/MS), where the collision-induced products of precursor ions (PRs) are scanned for fragments or neutral losses (NLs) that correspond to oxidized acyl residues expected in oxidized lipids (Spickett and Pitt, 2015), and in some cases, higher resolution MS/MS spectra are provided to support the identification (Buseman et al., 2006;Vu et al., 2012).High-resolution mass spectrometry (MS) allows for ...

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“…QSRR modelling has usually been carried out on a particular class of compounds measured in respective analytical platforms (Sarkhosh et al 2012 ; Tyrkkö et al 2012 ; Meek 1980 ). Only recently has QSRR modelling found an application in metabolomics (Creek et al 2011 ; Hagiwara et al 2010 ) because there is a demand to assign chemical identities to many unknown peaks through improved utilization of retention time, along with mass spectral features.…”

Section: Introductionmentioning

confidence: 99%

Predicting retention time in hydrophilic interaction liquid chromatography mass spectrometry and its use for peak annotation in metabolomics

et al. 2014

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Liquid chromatography coupled to mass spectrometry (LCMS) is widely used in metabolomics due to its sensitivity, reproducibility, speed and versatility. Metabolites are detected as peaks which are characterised by mass-over-charge ratio (m/z) and retention time (rt), and one of the most critical but also the most challenging tasks in metabolomics is to annotate the large number of peaks detected in biological samples. Accurate m/z measurements enable the prediction of molecular formulae which provide clues to the chemical identity of peaks, but often a number of metabolites have identical molecular formulae. Chromatographic behaviour, reflecting the physicochemical properties of metabolites, should also provide structural information. However, the variation in rt between analytical runs, and the complicating factors underlying the observed time shifts, make the use of such information for peak annotation a non-trivial task. To this end, we conducted Quantitative Structure–Retention Relationship (QSRR) modelling between the calculated molecular descriptors (MDs) and the experimental retention times (rts) of 93 authentic compounds analysed using hydrophilic interaction liquid chromatography (HILIC) coupled to high resolution MS. A predictive QSRR model based on Random Forests algorithm outperformed a Multiple Linear Regression based model, and achieved a high correlation between predicted rts and experimental rts (Pearson’s correlation coefficient = 0.97), with mean and median absolute error of 0.52 min and 0.34 min (corresponding to 5.1 and 3.2 % error), respectively. We demonstrate that rt prediction with the precision achieved enables the systematic utilisation of rts for annotating unknown peaks detected in a metabolomics study. The application of the QSRR model with the strategy we outlined enhanced the peak annotation process by reducing the number of false positives resulting from database queries by matching accurate mass alone, and enriching the reference library. The predicted rts were validated using either authentic compounds or ion fragmentation patterns.Electronic supplementary materialThe online version of this article (doi:10.1007/s11306-014-0727-x) contains supplementary material, which is available to authorized users.

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HPLC Retention time prediction for metabolome analysis

Cited by 18 publications

References 6 publications

Dietary prebiotics alter novel microbial dependent fecal metabolites that improve sleep

Dietary prebiotics alter novel microbial dependent fecal metabolites that improve sleep

Structure Annotation and Quantification of Wheat Seed Oxidized Lipids by High-Resolution LC-MS/MS

Predicting retention time in hydrophilic interaction liquid chromatography mass spectrometry and its use for peak annotation in metabolomics

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