Evaluation of automated electrospray-TOF mass spectrometryfor metabolic fingerprinting of the plant metabolome

Metabolic fingerprinting is increasingly employed in microbial and plant metabolomics. Identification and evaluation of analytical factors that influence mass spectra produced with automated electrospray time of flight mass spectrometry to support metabolic fingerprinting are described. Instrument resolution of 4000 (FWHM) at mass 200 Da provided detection of ions of the same nominal mass but different monoisotopic masses. Complex mass spectra were obtained from polar extracts of tomato fruit in positive and negative ion mode. These spectra consist of metabolite ions (molecular, adduct and fragment) and those derived from the extraction medium, largely in the form of [M+H]⁺, [M–H]^–, [M+Na]⁺, [M+K]⁺, [2M+H]⁺, [M+Cl]^– and [2M–H]^–. Ionisation suppression reduced sensitivity, although its effect was consistent for a wide range of metabolite concentrations. Variability in ion signal intensity was lower in analytical (2.2–30.1%) compared to biological (within fruit 9.6–27.6%; between-fruit 13.2–34.4%) replicates. The method is applicable to high throughput metabolic fingerprinting and, with accurate mass measurements, is able to provide reductions in data complexity and preliminary identification of metabolites.     

Mining for treatment-specific and general changes in target compounds and metabolic fingerprints in response to herbivory and phytohormones in Plantago lanceolata

Induction studies focusing on target metabolites may not reveal metabolic changes occurring in plants after various challenges. By contrast, metabolic fingerprinting can be a powerful tool to find patterns that are either treatment-specific or general and was therefore used to depict plant responses after various challenges. Plants of Plantago lanceolata were challenged by mechanical damage, specialist herbivores (aphids or sawfly larvae), generalist herbivores (Lepidopteran caterpillars) or phytohormones (jasmonic or salicylic acid). After 3 d of treatment, local and systemic leaves were analyzed for characteristic target metabolites (iridoid glucosides and verbascoside) by gas chromatography coupled with mass spectrometry (GC-MS) and for metabolic fingerprints by liquid chromatography coupled with time of flight mass spectrometry (LC-TOF-MS). Whereas only marginal changes in target metabolite concentrations were found, metabolic fingerprints were substantially affected especially by generalist and phytohormone treatments. By contrast, mechanical damage and specialist herbivory caused fewer changes. Responses to generalists partly overlapped with the changes caused by jasmonic acid, but many additional peaks were up-regulated. Furthermore, many peaks were co-induced by jasmonic and salicylic acid. The surprisingly high co-induction of peaks by both phytohormones suggests that the signaling pathways regulate a set of common targets. Furthermore, only metabolic fingerprinting could reveal that herbivores induce additional species-specific pathways beyond these phytohormone responses.     

Efficient DNA Fingerprinting Based on the Targeted Sequencing of Active Retrotransposon Insertion Sites Using a Bench-Top High-Throughput Sequencing Platform

In many crop species, DNA fingerprinting is required for the precise identification of cultivars to protect the rights of breeders. Many families of retrotransposons have multiple copies throughout the eukaryotic genome and their integrated copies are inherited genetically. Thus, their insertion polymorphisms among cultivars are useful for DNA fingerprinting. In this study, we conducted a DNA fingerprinting based on the insertion polymorphisms of active retrotransposon families (Rtsp-1 and LIb) in sweet potato. Using 38 cultivars, we identified 2,024 insertion sites in the two families with an Illumina MiSeq sequencing platform. Of these insertion sites, 91.4% appeared to be polymorphic among the cultivars and 376 cultivar-specific insertion sites were identified, which were converted directly into cultivar-specific sequence-characterized amplified region (SCAR) markers. A phylogenetic tree was constructed using these insertion sites, which corresponded well with known pedigree information, thereby indicating their suitability for genetic diversity studies. Thus, the genome-wide comparative analysis of active retrotransposon insertion sites using the bench-top MiSeq sequencing platform is highly effective for DNA fingerprinting without any requirement for whole genome sequence information. This approach may facilitate the development of practical polymerase chain reaction-based cultivar diagnostic system and could also be applied to the determination of genetic relationships.     

Characterization of aerobic heterotrophic bacteria in cold and nutrient-poor freshwater ecosystems

Aerobic heterotrophic bacteria present in the surface water of three cold and nutrient-poor lakes in the Chilean Patagonia (Alto Reino, Las Dos Torres and Venus) were analysed for genetic similarity and metabolic diversity using 16S ribosomal DNA and the Biolog EcoPlate^TM system, respectively. Bacterial fingerprints of water samples in enriched and non-enriched nutrient broth demonstrated a >50% fingerprinting similarity between the lakes. Metabolic activity was also similar. However, the Biolog EcoPlate^TM system carbon substrates revealed functional diversity. Lake Las Dos Torres showed the most fingerprinting similarity between enriched and non-enriched cold water samples. The amounts of living and viable bacteria were also higher in this lake’s water sample, suggesting a predominance of facultative oligotrophic groups. DNA sequencing analysis demonstrated the presence of phylum Bacteroidetes in Lake Alto Reino; phyla Bacteroidetes and Gammaproteobacteria in Lake Las Dos Torres; and phyla Bacteroidetes, Alphaproteobacteria, and Gammaproteobacteria in Lake Venus. Although each lake had a unique bacterial community structure, the different bacterial groups may be performing similar metabolic functions, given the similarity in extreme environmental conditions.     

Rapid classification of phenotypic mutants of Arabidopsis via metabolite fingerprinting

We evaluated the application of gas chromatography-mass spectrometry metabolic fingerprinting to classify forward genetic mutants with similar phenotypes. Mutations affecting distinct metabolic or signaling pathways can result in common phenotypic traits that are used to identify mutants in genetic screens. Measurement of a broad range of metabolites provides information about the underlying processes affected in such mutants. Metabolite profiles of Arabidopsis (Arabidopsis thaliana) mutants defective in starch metabolism and uncharacterized mutants displaying a starch-excess phenotype were compared. Each genotype displayed a unique fingerprint. Statistical methods grouped the mutants robustly into distinct classes. Determining the genes mutated in three uncharacterized mutants confirmed that those clustering with known mutants were genuinely defective in starch metabolism. A mutant that clustered away from the known mutants was defective in the circadian clock and had a pleiotropic starch-excess phenotype. These results indicate that metabolic fingerprinting is a powerful tool that can rapidly classify forward genetic mutants and streamline the process of gene discovery.     

M13-based genotyping of marine cyanobacterial strains from the Indian subcontinent and maintained in the NFMC germplasm collection

The aim of this study was to analyze marine cyanobacterial culture collections strains of the Indian subcontinent at the level below species. This is important to improve the abilities of service culture collections to provide their user community with correctly identified and clean organisms. A total of 50 marine cyanobacterial strains were genotyped with M13 polymerase chain reaction (PCR) fingerprinting to provide diagnostic fingerprints for each culture. Depending on the strains, 9 to 26 bands were observed for the primer tested. Within the species, strains representing different isolates were genetically clearly different. Data obtained from genomic fingerprinting were used to construct binary distance matrix, and the neighbor-joining tree constructed demonstrated the ability of this method to differentiate strains at the intraspecific level. An important and useful result obtained in this study is the application of the M13 PCR fingerprinting method on almost all forms of cyanobacteria for strain and species discrimination.     

Molecular assessment of microbiota structure and dynamics along mixed olive oil and winery wastewaters biotreatment

The major parcel of the degradation occurring along wastewater biotreatments is performed either by the native microbiota or by added microbial inocula. The main aim of this study was to apply two fingerprinting methods, temperature gradient gel electrophoresis (TGGE) and length heterogeneity-PCR (LH-PCR) analysis of 16S rRNA gene fragments, in order to assess the microbiota structure and dynamics during mixed olive oil and winery wastewaters aerobic biotreatment performed in a jet-loop reactor (JLR). Sequence homology analysis showed the presence of bacterial genera Gluconacetobacter, Klebsiella, Lactobacillus, Novosphingobium, Pseudomonas, Prevotella, Ralstonia, Sphingobium and Sphingomonas affiliated with five main phylogenetic groups: alpha-, beta- and gamma-Proteobacteria, Firmicutes and Bacteroidetes. LH-PCR analysis distinguished eight predominant DNA fragments correlated with the samples showing highest performance (COD removal rates of 67 up to 75%). Cluster analysis of both TGGE and LH-PCR fingerprinting profiles established five main clusters, with similarity coefficients higher than 79% (TGGE) and 62% (LH-PCR), and related with hydraulic retention time, indicating that this was the main factor responsible for the shifts in the microbiota structure. Canonical correspondence analysis revealed that changes observed on temperature and O₂ level were also responsible for shifts in microbiota composition. Community level metabolic profile analysis was used to test metabolic activities in samples. Integrated data revealed that the microbiota structure corresponds to bacterial groups with high degradative potential and good suitability for this type of effluents biotreatments.     

Taxonomic discrimination of cyanobacteria by metabolic fingerprinting using proton nuclear magnetic resonance spectra and multivariate statistical analysis

When whole-cell extracts are analyzed, proton nuclear magnetic resonance (¹H NMR) spectroscopy provides biochemical profiles that contain overlapping signals of the majority of the compounds. To determine whether cyanobacteria could be taxonomically discriminated on the basis of metabolic fingerprinting, we subjected whole-cell extracts of the cyanobacteria to¹H NMR. The¹H NMR spectra revealed a predominance of signals in the aliphatic region. Principal component analysis (PCA) of the data then enabled discrimination of the cyanobacteria. The hierarchical dendrogram, based on PCA of the aliphatic region data, showed that six cyanobacterial taxa were discriminated from two eukaryotic microalgal species, and that the six taxa could be subsequently divided into three groups. This agrees with the current taxonomy of cyanobacteria. Therefore, our overall results indicate that metabolic fingerprinting using¹H NMR spectra and multivariate statistical analysis provide a simple, rapid method for the taxonomical discrimination of cyanobacteria.     

On-line microbial biosensing and fingerprinting of water pollutants

The potential for biosensors to contribute to on-line toxicity testing for monitoring of water quality is currently constrained both by the relevance of the biosensors available and the technology for biosensor delivery. This paper reports the use of novel slow release biosensor delivery for on-line monitoring instrumentation, with environmentally relevant bacteria for both simple toxicity testing and more complex toxicity fingerprinting of industrial effluents. The on-line toxicity test, using bioluminescence-based biosensors, proved to be as sensitive and reliable as the corresponding batch test, with comparable contaminant EC(50) values from both methods. Toxicity fingerprinting through the investigation of the kinetics (dose-response) and the dynamics (response with time) of the biosensor test response proved to be diagnostic of both effluent type and composition. Furthermore, the slow release of biosensors immobilised in a polyvinyl alcohol (PVA) matrix greatly improved biosensor delivery, did not affect the sensitivity of toxicity testing, and demonstrated great potential for inclusion in on-line monitoring instrumentation.     

Functional analysis of colonic bacterial metabolism: relevant to health?

With the use of molecular techniques, numerous studies have evaluated the composition of the intestinal microbiota in health and disease. However, it is of major interest to supplement this with a functional analysis of the microbiota. In this review, the different approaches that have been used to characterize microbial metabolites, yielding information on the functional end products of microbial metabolism, have been summarized. To analyze colonic microbial metabolites, the most conventional way is by application of a hypothesis-driven targeted approach, through quantification of selected metabolites from carbohydrate (e.g., short-chain fatty acids) and protein fermentation (e.g., p-cresol, phenol, ammonia, or H(2)S), secondary bile acids, or colonic enzymes. The application of stable isotope-labeled substrates can provide an elegant solution to study these metabolic pathways in vivo. On the other hand, a top-down approach can be followed by applying metabolite fingerprinting techniques based on (1)H-NMR or mass spectrometric analysis. Quantification of known metabolites and characterization of metabolite patterns in urine, breath, plasma, and fecal samples can reveal new pathways and give insight into physiological regulatory processes of the colonic microbiota. In addition, specific metabolic profiles can function as a diagnostic tool for the identification of several gastrointestinal diseases, such as ulcerative colitis and Crohn's disease. Nevertheless, future research will have to evaluate the relevance of associations between metabolites and different disease states.     

Metabolite fingerprinting and profiling in plants using NMR

Although less sensitive than mass spectrometry (MS), nuclear magnetic resonance (NMR) spectroscopy provides a powerful complementary technique for the identification and quantitative analysis of plant metabolites either in vivo or in tissue extracts. In one approach, metabolite fingerprinting, multivariate analysis of unassigned 1H NMR spectra is used to compare the overall metabolic composition of wild-type, mutant, and transgenic plant material, and to assess the impact of stress conditions on the plant metabolome. Metabolite fingerprinting by NMR is a fast, convenient, and effective tool for discriminating between groups of related samples and it identifies the most important regions of the spectrum for further analysis. In a second approach, metabolite profiling, the 1H NMR spectra of tissue extracts are assigned, a process that typically identifies 20-40 metabolites in an unfractionated extract. These profiles may also be used to compare groups of samples, and significant differences in metabolite concentrations provide the basis for hypotheses on the underlying causes for the observed segregation of the groups. Both approaches generate a metabolic phenotype for a plant, based on a system-wide but incomplete analysis of the plant metabolome. However, a review of the literature suggests that the emphasis so far has been on the accumulation of analytical data and sample classification, and that the potential of 1H NMR spectroscopy as a tool for probing the operation of metabolic networks, or as a functional genomics tool for identifying gene function, is largely untapped.     

Metabolite fingerprinting in transgenic lettuce

Metabolite fingerprinting has been achieved using direct atmospheric pressure chemical ionization-mass spectrometry (APCI-MS) and linked gas chromatography (GC-APCI/EI-MS) for transgenic lettuce (Lactuca sativa L. cv. Evola) plants expressing an IPT gene under the control of the senescence-specific SAG12 promoter from Arabidopsis thaliana (P(SAG12)-IPT). Mature heads of transgenic lettuce and their azygous controls were maintained under defined conditions to assess their shelf life. Transgenic lettuce plants exhibited delayed senescence and significant increases (up to a maximum of threefold) in the concentrations of three volatile organic compounds (VOCs), corresponding to molecular masses of 45, 47 and 63, when compared with heads from azygous plants. These VOCs were identified as acetaldehyde (45), ethanol (47) and dimethyl sulphide (63). The increase in dimethyl sulphide was paralleled by an accumulation of reactive oxygen species (ROS) in the heads of transgenic plants. These results demonstrate the applicability of metabolic fingerprinting techniques to elucidate the underlying pleiotropic responses of plants to transgene expression.     

Use of the Local False Discovery Rate for Identification of Metabolic Biomarkers in Rat Urine Following Genkwa Flos-Induced Hepatotoxicity

Metabolomics is concerned with characterizing the large number of metabolites present in a biological system using nuclear magnetic resonance (NMR) and HPLC/MS (high-performance liquid chromatography with mass spectrometry). Multivariate analysis is one of the most important tools for metabolic biomarker identification in metabolomic studies. However, analyzing the large-scale data sets acquired during metabolic fingerprinting is a major challenge. As a posterior probability that the features of interest are not affected, the local false discovery rate (LFDR) is a good interpretable measure. However, it is rarely used to when interrogating metabolic data to identify biomarkers. In this study, we employed the LFDR method to analyze HPLC/MS data acquired from a metabolomic study of metabolic changes in rat urine during hepatotoxicity induced by Genkwa flos (GF) treatment. The LFDR approach was successfully used to identify important rat urine metabolites altered by GF-stimulated hepatotoxicity. Compared with principle component analysis (PCA), LFDR is an interpretable measure and discovers more important metabolites in an HPLC/MS-based metabolomic study.     

Use of DNA fingerprinting to determine parentage in muskrats (Ondatra zibethicus).

The detection of high levels of genetic variability by DNA fingerprinting probes has allowed researchers to accurately assess relatedness. Multiple-mating strategies are characteristic of the mating systems of small mammals. As such, techniques that provide an accurate indication of how individuals are related genetically is of great importance to assess the mating system of a species. In this study, we applied the DNA fingerprinting technique to captive and wild muskrats (Ondatra zibethicus) to determine its usefulness for parentage analysis in wild populations. We found that DNA digested with the restriction enzyme Hae III and probed with Jeffrey's minisatellite 33. 15 identified a large amount of polymorphism in both groups of muskrats. The DNA fingerprinting technique correctly assessed parentage within the captive group. In the wild population, paternity was assigned between two adult males based on diagnostic fragments and similarity of banding patterns. The likelihood that paternity could be misassigned to a full sibling was high in this free-ranging population. However, because natal dispersal in muskrats is male biased, it is unlikely that two brothers would associate with the same female.     

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.     

Investigating plant-plant interference by metabolic fingerprinting

New analytical developments in post-genomic technologies are being introduced to the field of plant ecology. FT-IR fingerprinting coupled with chemometrics via cluster analysis is proposed as a tool for correlating global metabolic changes with abiotic or biotic perturbation and/or interactions. The current study concentrates on detecting chemical responses by inter-species competition between a monocotyledon Brachypodium distachyion and a dicotyledon Arabidopsis thaliana. Growth analysis of 42 days old plants showed differences in both species under competition. Clear changes in the FT-IR metabolic fingerprints of B. distachyion in competition with A. thaliana were observed, whilst there were no apparent chemical differences in the A. thaliana plant tissues. This study demonstrates the power of this approach in detecting changes in the global metabolic profiles of plants in response to biotic interactions, and we believe FT-IR is appropriate for rapid screening (10 s per sample) prior to targeted metabolite analyses.     

Fingerprinting closely related xanthomonas pathovars with random nonamer oligonucleotide microarrays

Current bacterial DNA-typing methods are typically based on gel-based fingerprinting methods. As such, they access a limited complement of genetic information and many independent restriction enzymes or probes are required to achieve statistical rigor and confidence in the resulting pattern of DNA fragments. Furthermore, statistical comparison of gel-based fingerprints is complex and nonstandardized. To overcome these limitations of gel-based microbial DNA fingerprinting, we developed a prototype, 47-probe microarray consisting of randomly selected nonamer oligonucleotides. Custom image analysis algorithms and statistical tools were developed to automatically extract fingerprint profiles from microarray images. The prototype array and new image analysis algorithms were used to analyze 14 closely related Xanthomonas pathovars. Of the 47 probes on the prototype array, 10 had diagnostic value (based on a chi-squared test) and were used to construct statistically robust microarray fingerprints. Analysis of the microarray fingerprints showed clear differences between the 14 test organisms, including the separation of X. oryzae strains 43836 and 49072, which could not be resolved by traditional gel electrophoresis of REP-PCR amplification products. The proof-of-application study described here represents an important first step to high-resolution bacterial DNA fingerprinting with microarrays. The universal nature of the nonamer fingerprinting microarray and data analysis methods developed here also forms a basis for method standardization and application to the forensic identification of other closely related bacteria.     

Supervised machine learning techniques for the classification of metabolic disorders in newborns

MOTIVATION: During the Bavarian newborn screening programme all newborns have been tested for about 20 inherited metabolic disorders. Owing to the amount and complexity of the generated experimental data, machine learning techniques provide a promising approach to investigate novel patterns in high-dimensional metabolic data which form the source for constructing classification rules with high discriminatory power. RESULTS: Six machine learning techniques have been investigated for their classification accuracy focusing on two metabolic disorders, phenylketo nuria (PKU) and medium-chain acyl-CoA dehydrogenase deficiency (MCADD). Logistic regression analysis led to superior classification rules (sensitivity >96.8%, specificity >99.98%) compared to all investigated algorithms. Including novel constellations of metabolites into the models, the positive predictive value could be strongly increased (PKU 71.9% versus 16.2%, MCADD 88.4% versus 54.6% compared to the established diagnostic markers). Our results clearly prove that the mined data confirm the known and indicate some novel metabolic patterns which may contribute to a better understanding of newborn metabolism.     

Discrimination of patients with different serological evolution of HIV and co‐infection with HCV using metabolic fingerprinting based on Fourier transform infrared

Human immunodeficiency virus (HIV) is a retrovirus that weakens the immune system and permits opportunistic diseases such as hepatitis C (HCV) to enter the body. These diseases induce metabolic disorders in the patients and it is therefore logical to approach them from a holistic, functional perspective, studying the metabolome comprehensively to identify metabolic signatures associated with certain disease states. The metabolomics strategy here proposed involves metabolic fingerprinting using Fourier transform infrared spectroscopy and chemometric tools on 72 plasma samples (subdivided into 63 training and 9 test samples) to differentiate between healthy subjects and patients with different disease stages. Several options, relating to the variable selection method used in linear discriminant analysis and the number of categories being considered, were explored to optimize discrimination ability. A total of 18 bands enabled differentiation between control subjects, HIV patients and the group that encompassed patients with acquired immune deficiency syndrome (AIDS), AIDS/HCV and HIV/HCV, providing overall classification and internal prediction rates of 97.67% and 93.65%, respectively. Only 9 bands were required to further discriminate between AIDS, AIDS/HCV and HIV/HCV, with 99.20% (training) and 89.66% (cross‐validation) correct classifications. The simplicity and effectiveness of the classification methodology proposed was reinforced by the satisfactory results obtained in external prediction.      

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.