Process for the integrated extraction, identification and quantification of metabolites, proteins and RNA to reveal their co-regulation in biochemical networks

A novel extraction protocol is described with which metabolites, proteins and RNA are sequentially extracted from the same sample, thereby providing a convenient procedure for the analysis of replicates as well as exploiting the inherent biological variation of independent samples for multivariate data analysis. A detection of 652 metabolites, 297 proteins and clear RNA bands in a single Arabidopsis thaliana leaf sample was validated by analysis with gas chromatography coupled to a time of flight mass spectrometer for metabolites, two-dimensional liquid chromatography coupled to mass spectrometry for proteins, and Northern blot analysis for RNA. A subset of the most abundant proteins and metabolites from replicate analysis of different Arabidopsis accessions was merged to form an integrative dataset allowing both classification of different genotypes and the unbiased analysis of the hierarchical organization of proteins and metabolites within a real biochemical network.     

Metabolic fingerprinting of rat urine by LC/MS Part 1. Analysis by hydrophilic interaction liquid chromatography-electrospray ionization mass spectrometry

Complex biological samples, such as urine, contain a very large number of endogenous metabolites reflecting the metabolic state of an organism. Metabolite patterns can provide a comprehensive signature of the physiological state of an organism as well as insights into specific biochemical processes. Although the metabolites excreted in urine are commonly highly polar, the samples are generally analyzed using reversed-phase liquid chromatography mass spectrometry (RP-LC/MS). In Part 1 of this work, a method for detecting highly polar metabolites by hydrophilic interaction liquid chromatography-electrospray ionization mass spectrometry (HILIC/ESI-MS) is described as a complement to RP-LC/ESI-MS. In addition, in an accompanying paper (Part 2), different multivariate approaches to extracting information from the resulting complex data are described to enable metabolic fingerprints to be obtained. The coverage of the method for the screening of as many metabolites as possible is highly improved by analyzing the urine samples using both a C(18) column and a ZIC-HILIC column. The latter was found to be a good alternative when analyzing highly polar compounds, e.g., hydroxyproline and creatinine, to columns typically used for reversed-phase liquid chromatography.     

Column-switching high-performance liquid chromatographic detection of pholcodine and its metabolites in urine with fluorescence and electrochemical detection

A sensitive and selective method for the detection of pholcodine and its metabolite morphine in urine using high-performance liquid chromatography is described. It involves on-line clean-up of urine on a trace enrichment column packed with a polymeric strong cation-exchange material. Pholcodine and its metabolites were separated on two analytical columns with different selectivities. Pholcodine was detected by a fluorescence detector and morphine was detected electrochemically. One system, based on reversed-phase chromatography, applied a polystyrene—divinylbenzene column and gradient elution. The other system was based on normal-phase chromatography with a silica column and isocratic elution. Morphine was confirmed to be a metabolite of pholcodine by reversed-phase chromatography and electrochemical detection. Two unidentified metabolites of pholcodine were separated from pholcodine by normal-phase chromatography and detected by fluorescence detection.     

Volatile compound fingerprinting of mixed-culture fermentations

With the advent of the -omics era, classical technology platforms, such as hyphenated mass spectrometry, are currently undergoing a transformation toward high-throughput application. These novel platforms yield highly detailed metabolite profiles in large numbers of samples. Such profiles can be used as fingerprints for the accurate identification and classification of samples as well as for the study of effects of experimental conditions on the concentrations of specific metabolites. Challenges for the application of these methods lie in the acquisition of high-quality data, data normalization, and data mining. Here, a high-throughput fingerprinting approach based on analysis of headspace volatiles using ultrafast gas chromatography coupled to time of flight mass spectrometry (ultrafast GC/TOF-MS) was developed and evaluated for classification and screening purposes in food fermentation. GC-MS mass spectra of headspace samples of milk fermented by different mixed cultures of lactic acid bacteria (LAB) were collected and preprocessed in MetAlign, a dedicated software package for the preprocessing and comparison of liquid chromatography (LC)-MS and GC-MS data. The Random Forest algorithm was used to detect mass peaks that discriminated combinations of species or strains used in fermentations. Many of these mass peaks originated from key flavor compounds, indicating that the presence or absence of individual strains or combinations of strains significantly influenced the concentrations of these components. We demonstrate that the approach can be used for purposes like the selection of strains from collections based on flavor characteristics and the screening of (mixed) cultures for the presence or absence of strains. In addition, we show that strain-specific flavor characteristics can be traced back to genetic markers when comparative genome hybridization (CGH) data are available.     

State-of-the art data normalization methods improve NMR-based metabolomic analysis

Extracting biomedical information from large metabolomic datasets by multivariate data analysis is of considerable complexity. Common challenges include among others screening for differentially produced metabolites, estimation of fold changes, and sample classification. Prior to these analysis steps, it is important to minimize contributions from unwanted biases and experimental variance. This is the goal of data preprocessing. In this work, different data normalization methods were compared systematically employing two different datasets generated by means of nuclear magnetic resonance (NMR) spectroscopy. To this end, two different types of normalization methods were used, one aiming to remove unwanted sample-to-sample variation while the other adjusts the variance of the different metabolites by variable scaling and variance stabilization methods. The impact of all methods tested on sample classification was evaluated on urinary NMR fingerprints obtained from healthy volunteers and patients suffering from autosomal polycystic kidney disease (ADPKD). Performance in terms of screening for differentially produced metabolites was investigated on a dataset following a Latin-square design, where varied amounts of 8 different metabolites were spiked into a human urine matrix while keeping the total spike-in amount constant. In addition, specific tests were conducted to systematically investigate the influence of the different preprocessing methods on the structure of the analyzed data. In conclusion, preprocessing methods originally developed for DNA microarray analysis, in particular, Quantile and Cubic-Spline Normalization, performed best in reducing bias, accurately detecting fold changes, and classifying samples.     

Determination of dopamine, homovanillic acid and 3,4-dihydroxyphenylacetic acid in rat brain striatum by high-performance liquid chromatography with electrochemical detection

Two procedures using liquid chromatography with electrochemical detection are described for the determination of dopamine (DA) and its two acidic metabolites, homovanillic acid (HVA) and 3,4-dihydroxyphenylacetic acid (DOPAC), in subregions of rat striatum and nucleus accumbens. A strong cation-exchange column was used for DA analysis and a C₁ reversed-phase column was used for the analysis of the metabolites. Effects of pH, temperature and percentage of methanol on the retention time of HVA and DOPAC were studied. Levels of these compounds in the subregions of rat striatum and nucleus accumbens are reported.     

Improved protocols for quantitative determination of metabolites from biological samples using high performance ionic-exchange chromatography with conductimetric and pulsed amperometric detection

Simple and reliable protocols are described for an extensive analysis of metabolites in extracts from different biological sources. The separation was performed by high performance ionic-exchange chromatography (HPIC) at alkaline pH using two types of chromatography columns and two detection methods. Organic acids and inorganic anions were separated on an ionPac AS11 column using a 0.5 to 35 mM Na0H gradient. Detection limits in the range of milligrams per liter were achieved by use of a conductivity detector equipped with an anion self-regenerating suppressor. Twelve phosphorylated compounds belonging to the glycolytic and the pentose phosphate pathways could be resolved on a CarboPac PA1 column using a Na0H/Na-acetate gradient. Quantification was achieved by pulsed amperometry with detection limits in the micromolar range. Cell extracts obtained by extraction in boiling buffered ethanol described previously could be directly injected onto HPIC columns for the separation of metabolites because the extraction procedure affected neither the retention time nor the stability of most of the metabolites, and yielded very clean chromatograms. These improved protocols were applied for a dynamic analysis of intracellular metabolites in Saccharomyces cerevisiae in response to a glucose pulse.     

Methods and Advances

Cover illustration: Methods and Advances in Biotech. Cover images depict tools for metabolite analysis in biocatalytic synthesis (see p. 1253). Direct infusion mass spectrometry and LC-MS (bottom) have been particularly useful for the rapid analysis of complex metabolites, derivatives and homologues. The complementary separation power of capillary electrophoresis (middle) combined with sensitive detection is very useful for the analysis of charged metabolites. Chromatographic techniques like GC, LC and TLC are the true workhorses for all classes of metabolites and advanced separations using high-performance liquid chromatography instruments (top) with adequate metabolite detection push the limits even further. Images courtesy from R. Wohlgemuth.     

State-of-the art data normalization methods improve NMR-based metabolomic analysis

Extracting biomedical information from large metabolomic datasets by multivariate data analysis is of considerable complexity. Common challenges include among others screening for differentially produced metabolites, estimation of fold changes, and sample classification. Prior to these analysis steps, it is important to minimize contributions from unwanted biases and experimental variance. This is the goal of data preprocessing. In this work, different data normalization methods were compared systematically employing two different datasets generated by means of nuclear magnetic resonance (NMR) spectroscopy. To this end, two different types of normalization methods were used, one aiming to remove unwanted sample-to-sample variation while the other adjusts the variance of the different metabolites by variable scaling and variance stabilization methods. The impact of all methods tested on sample classification was evaluated on urinary NMR fingerprints obtained from healthy volunteers and patients suffering from autosomal polycystic kidney disease (ADPKD). Performance in terms of screening for differentially produced metabolites was investigated on a dataset following a Latin-square design, where varied amounts of 8 different metabolites were spiked into a human urine matrix while keeping the total spike-in amount constant. In addition, specific tests were conducted to systematically investigate the influence of the different preprocessing methods on the structure of the analyzed data. In conclusion, preprocessing methods originally developed for DNA microarray analysis, in particular, Quantile and Cubic-Spline Normalization, performed best in reducing bias, accurately detecting fold changes, and classifying samples.

     

An h.p.l.c. method for determining chain-length distribution in some glycogens.

Human, oyster, Streptococcus mitis, and phyto-glycogen samples were debranched using Pseudomonas amylodermosa isoamylase (EC 3.2.1.68). The distribution of chain lengths was studied by high-performance liquid chromatography on reversed-phase columns, with water as eluent. Quantitative data was obtained over the degree of polymerisation range three to eighteen (d.p. 3-18), and oligosaccharides up to d.p. 26 were detected. No single column was found suitable for the resolution of the complete range of oligosaccharides, two columns being necessary for the quantitative analysis. The resulting "fingerprints" of chain lengths are characteristic of the glycogen source and should be useful for both comparison purposes among glycogens and for monitoring procedures of glycogen isolation.     

Metabolic fingerprinting of Leontopodium species (Asteraceae) by means of ¹H NMR and HPLC-ESI-MS

The genus Leontopodium, mainly distributed in Central and Eastern Asia, consists of ca. 34-58 different species. The European Leontopodium alpinum, commonly known as Edelweiss, has a long tradition in folk medicine. Recent research has resulted in the identification of prior unknown secondary metabolites, some of them with interesting biological activities. Despite this, nearly nothing is known about the Asian species of the genus. In this study, we applied proton nuclear magnetic resonance (1H NMR) spectroscopy and liquid chromatography-mass spectrometry (LC-MS) metabolic fingerprinting to reveal insights into the metabolic patterns of 11 different Leontopodium species, and to conclude on their taxonomic relationship. Principal component analysis (PCA) of 1H NMR fingerprints revealed two species groups. Discriminators for these groups were identified as fatty acids and sucrose for group A, and ent-kaurenoic acid and derivatives thereof for group B. Five diterpenes together with one sesquiterpene were isolated from Leontopodium franchetii roots; the compounds were described for the first time for L. franchetii: ent-kaur-16-en-19-oic acid, methyl-15α-angeloyloxy-ent-kaur-16-en-19-oate, methyl-ent-kaur-16-en-19-oate, 8-acetoxymodhephene, 19-acetoxy-ent-kaur-16-ene, methyl-15β-angeloyloxy-16,17-epoxy-ent-kauran-19-oate. In addition, differences in the metabolic profile between collected and cultivated species could be observed using a partial least squares-discriminant analysis (PLS-DA). PCA of the LC-MS fingerprints revealed three groups. Discriminating signals were compared to literature data and identified as two bisabolane derivatives responsible for discrimination of group A and C, and one ent-kaurenoic acid derivative, discriminating group B. A taxonomic relationship between a previously unidentified species and L. franchetii and Leontopodium sinense could be determined by comparing NMR fingerprints. This finding supports recent molecular data. Furthermore, Leontopodium dedekensii and L. sinense, two closely related species in terms of morphology and DNA-fingerprints, could be distinguished clearly using 1H NMR and LC-MS metabolic fingerprinting.     

Narrow-bore sample trapping and chromatography combined with quadrupole/time-of-flight mass spectrometry for ultra-sensitive identification of in vivo and in vitro metabolites

The identification of in vitro and in vivo metabolites is vital to the discovery and development of new pharmaceutical therapies. Analytical strategies to identify metabolites at different stages of this process vary, but all involve the use of liquid chromatography separations combined with detection via mass spectrometry (HPLC/MS). Reported here is the use of narrow-bore column (0.5-1.0 mm i.d.) trapping of metabolites, followed by back-flushing onto a matching analytical column. Separated metabolites were then identified using quadrupole time-of-flight mass spectrometry (MS) and tandem MS. Metabolites in human plasma and from low-level in vitro incubations, that were not identified using standard HPLC/MS approaches, were characterized using the instrumental configuration described here.     

Simultaneous analysis of homovanillic acid, 5-hydroxyindoleacetic acid, 3-methoxy-4-hydroxy-phenylethylene glycol and vanilmandelic acid in plasma from alcoholics by high-performance liquid chromatography with electrochemical detection Critical comparison of solid-phase and liquid—liquid extraction methods

A method is described for the simultaneous determination of vanilmandelic acid, 3-methoxy-4-hydroxyphenylethylene glycol, 5-hydroxyindoleacetic acid, and homovanillic acid in a human plasma sample using reversed-phase high-performance liquid chromatography with column switching and amperometric detection. Two methods of sample preparation were tested. Liquid—liquid extraction yields better recoveries, is more selective and precise than solid-phase extraction and allows a shorter time of chromatographic analysis. Estimated plasma values of the metabolites from healthy controls are in good agreement with previously reported levels. Studies of alcoholics at the beginning of the delirium tremens provided different plasma levels of the metabolites, dependent on the different duration — and hence the severity — of the delirium.     

Identification of some Bioactive Metabolites in a Fractionated Methanol Extract from Ipomoea aquatica (Aerial Parts) through TLC,HPLC, UPLC‐ESI‐QTOF‐MS and LC‐SPE‐NMR Fingerprints Analyses

Introduction

The plant species Ipomoea aquatica contains various bioactive constituents, e.g. phenols and flavonoids, which have several medical uses. All previous studies were executed in Asia; however, no reports are available from Africa, and the secondary metabolites of this plant species from Africa are still unknown.

Objective

The present study aims finding suitable conditions to identify the bioactive compounds from different fractions.

Methodology

Chromatographic fingerprint profiles of different fractions were developed using high‐performance liquid chromatography (HPLC) and then these conditions were transferred to thin‐layer chromatography (TLC). Subsequently, the chemical structure of some bioactive compounds was elucidated using ultra‐performance liquid chromatography‐quadrupole time of flight‐tandem mass spectrometry (UPLC‐QTOF‐MS) and liquid chromatography‐solid phase extraction‐nuclear magnetic resonance (LC‐SPE‐NMR) spectroscopy.

Results

The HPLC fingerprints, developed on two coupled Chromolith RP‐18e columns, using a gradient mobile phase (methanol/water/trifluoroacetic acid, 5:95:0.05, v/v/v), showed more peaks than the TLC profile. The TLC fingerprint allows the identification of the types of chemical constituents, e.g. flavonoids. Two flavonoids (nicotiflorin and ramnazin‐3‐O‐rutinoside) and two phenolic compounds (dihydroxybenzoic acid pentoside and di‐pentoside) were tentatively identified by QTOF‐MS, while NMR confirmed the structure of rutin and nicotiflorin.

Conclusion

The HPLC and TLC results showed that HPLC fingerprints give more and better separated peaks, but TLC helped in determining the class of the active compounds in some fractions. Bioactive constituents were identified as well using MS and NMR analyses. Two flavonoids and two phenolic compounds were tentatively identified in this species for the first time, to the best of our knowledge. Copyright © 2017 John Wiley & Sons, Ltd.     

The use of high-performance liquid chromatography and diode array detection in fungal chemotaxonomy based on profiles of secondary metabolites

FRISVAD, J. C, 1989. The use of high-performance liquid chromatography and diode array detection in fungal chemotaxonomy based on profiles of secondary metabolites. Fungal chemotaxonomy (that part dealing with secondary metabolites) has often been based on thin layer chromatography (TLC) and visual or UV inspection of separated spots, before and after different chemical treatments. The identity of a small proportion of the spots can be suggested based on known internal and external standards. In most chemotaxonomical studies it is impossible to isolate, purify and identify all secondary metabolites produced, due to restraints of time and resources. High performance liquid chromatography (HPLC) of fungal extracts may have some advantages over TLC, but the problems mentioned above remain. These problems have been approached by using an alkylphenone retention time index in a reversed phase HPLC system combined with the use of a diode array UV-VIS detector. High performance thin layer chromatography is used for further confirmation of identity of the secondary metabolites. A particular advantage of this method is that the number of biosynthetic families or groups ('chemosyndromes') can be detected, as biosynthetically related metabolites usually have the same chromophores and UV-VIS spectra. Results obtained from Penicillium, Aspergillus and Fusanum species have shown that each species produces 5 to 15 different biosynthetic families of secondaiy metabolites, indicating that good chromatography data may be sufficient to identify species in the three genera. The use of the technique is exemplified by data on Aspergillus and Talaromyces species.     

Simultaneous gas chromatographic determination of lorcainide hydrochloride and three of its principal metabolites in biological samples

A method is described for the determination of the antiarrhythmic drug lorcainide hydrochloride and its three main metabolites in plasma, urine, faeces and tissues from man and animals. The procedure involves the extraction of the parent drug, its metabolites and the internal standard from the biological materials at different alkaline pH values, back-extraction into sulphuric acid and re-extraction into the organic phase (heptane—isoamyl alcohol). After silylation of the different phenolic and the N-dealkylated metabolites, analyses were carried out by automated gas—liquid chromatography with electron-capture detection. The method has a sensitivity limit of 5 ng for lorcainide, and 10–20 ng for the various metabolites, per millilitre of plasma.The method was applied to urine, faeces, plasma and tissue samples from man and animals. It was also suitable for automatic sample analysis.     

Rapid analysis of naphthalene and its metabolites in biological systems: Determination by high-performance liquid chromatography/fluorescence detection and by plasma desorption/chemical ionizations mass spectometry

A rapid procedure for the determination of naphthalene and its metabolites in bile of rainbow trout and mice is described. The integrated analytical techniques combine high-performance liquid chromatography/ultraviolet fluorescence detection and plasma desotption/chemical ionization mass spectrometry for identification and quantitation. After separation by reverse-phase liquid chromatography, naphthalene and its metablolites are detected and quantitated by ultraviolet fluoresence spectometry. Identification of two metabolites is confirmed by mass spectometry. A direct insertion probe tip for a conventional chemical ionization mass spectometer was modified to obtain spectra of thermally labile compounds. A spectrum of less than 100 ng of naphthyl glucuronide, a labile glucuronic acid conjugate of 1-naphthol, was obtained with this system.     

Metabolic fingerprinting as an indicator of biodiversity: towards understanding inter-specific relationships among Homoscleromorpha sponges

Sponges are an important source of secondary metabolites showing a great diversity of structures and biological activities. Secondary metabolites can display specificity on different taxonomic levels, from species to phylum, which can make them good taxonomic biomarkers. However, the knowledge available on the metabolome of non-model organisms is often poor. In this study, we demonstrate that sponge chemical diversity may be useful for fundamental issues in systematics or evolutionary biology, by using metabolic fingerprints as indicators of metabolomic diversity in order to assess interspecific relationships. The sponge clade Homoscleromorpha is particularly challenging because its chemistry has been little studied and its phylogeny is still debated. Identification at species level is often troublesome, especially for the highly diversified Oscarella genus which lacks the fundamental characters of sponge taxonomy. An HPLC–DAD–ELSD–MS metabolic fingerprinting approach was developed and applied to 10 Mediterranean Homoscleromorpha species as a rapid assessment of their chemical diversity. A first validation of our approach was to measure intraspecific variability, which was found significantly lower than interspecific variability obtained between two Oscarella sister-species. Interspecific relationships among Homoscleromorpha species were then inferred from the alignment of their metabolic fingerprints. The resulting classification is congruent with phylogenetic trees obtained for a DNA marker (mitochondrial COI) and demonstrates the existence of two distinct groups within Homoscleromorpha. Metabolic fingerprinting proves a useful complementary tool in sponge systematics. Our case study calls for a revision of Homoscleromorpha with further phylogenetic studies and identification of additional chemical synapomorphic characters.     

The yeast metabolome addressed by electrospray ionization mass spectrometry: Initiation of a mass spectral library and its applications for metabolic footprinting by direct infusion mass spectrometry

Mass spectrometry (MS) has been a major driver for metabolomics, and gas chromatography (GC)-MS has been one of the primary techniques used for microbial metabolomics. The use of liquid chromatography (LC)-MS has however been limited, but electrospray ionization (ESI) is very well suited for ionization of microbial metabolites without any previous derivatization needed. To address the capabilities of ESI-MS in detecting the metabolome of Saccharomyces cerevisiae, the in silico metabolome of this organism was used as a template to present a theoretical metabolome. This showed that in combination with the specificity of MS up to 84% of the metabolites can be identified in a high mass accuracy ESI-spectrum. A total of 66 metabolites were systematically analyzed by positive and negative ESI-MS/MS with the aim of initiating a spectral library for ESI of microbial metabolites. This systematic analysis gave insight into the ionization and fragmentation characteristics of the different metabolites. With this insight, a small study of metabolic footprinting with ESI-MS demonstrated that biological information can be extracted from footprinting spectra. Statistical analysis of the footprinting data revealed discriminating ions, which could be assigned using the in silico metabolome. By this approach metabolic footprinting can advance from a classification method that is used to derive biological information based on guilt-by-association, to a tool for extraction of metabolic differences, which can guide new targeted biological experiments. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

A novel method for the separation and quantitation of benzene metabolites using high-pressure liquid chromatography

A method for the separation of benzene metabolites using reverse-phase high-pressure liquid chromatography is described. The antoxidant, ascorbic acid is added to an aqueous mixture of 1,2,4-benzenetriol, hydroquinone, catechol, and phenol, to prevent autooxidation. The eluting solvents are equilibrated with nitrogen, degassed, and maintained under a nitrogen atmosphere during the analysis. A highly resolved and reproducible profile of the metabolites is achieved under these conditions. This method should prove useful in a number of pharmacokinetic studies where the biotransformation of the parent compound to autooxidizable species such as polyphenols and quinones precludes analysis under aerobic conditions.