Effects of heat-treatment on the stability and composition of metabolomic extracts from the earthworm <Emphasis Type="Italic">Eisenia fetida</Emphasis>

Environmental metabolomics studies employing earthworms as sentinels for soil contamination are numerous, but the instability of the metabolite extracts from these organisms has been minimally addressed. This study evaluated the efficacy of adding a heat-treatment step in two commonly used extraction protocols (Bligh and Dyer and D₂O phosphate buffer) as a pre-analytical stabilization method. The resulting metabolic profiles of Eisenia fetida were assessed using principal component analysis and NMR spectral evaluations. The heated Bligh and Dyer extractions produced stabilized profiles with minimal variation of the extracted metabolomic profiles over time, providing a more suitable method for metabolomic analysis of earthworm extracts.     

Application of 1H-NMR Metabolomic Profiling for Reef-Building Corals

In light of global reef decline new methods to accurately, cheaply, and quickly evaluate coral metabolic states are needed to assess reef health. Metabolomic profiling can describe the response of individuals to disturbance (i.e., shifts in environmental conditions) across biological models and is a powerful approach for characterizing and comparing coral metabolism. For the first time, we assess the utility of a proton-nuclear magnetic resonance spectroscopy (¹H-NMR)-based metabolomics approach in characterizing coral metabolite profiles by 1) investigating technical, intra-, and inter-sample variation, 2) evaluating the ability to recover targeted metabolite spikes, and 3) assessing the potential for this method to differentiate among coral species. Our results indicate ¹H-NMR profiling of Porites compressa corals is highly reproducible and exhibits low levels of variability within and among colonies. The spiking experiments validate the sensitivity of our methods and showcase the capacity of orthogonal partial least squares discriminate analysis (OPLS-DA) to distinguish between profiles spiked with varying metabolite concentrations (0 mM, 0.1 mM, and 10 mM). Finally, ¹H-NMR metabolomics coupled with OPLS-DA, revealed species-specific patterns in metabolite profiles among four reef-building corals (Pocillopora damicornis, Porites lobata, Montipora aequituberculata, and Seriatopora hystrix). Collectively, these data indicate that ¹H-NMR metabolomic techniques can profile reef-building coral metabolomes and have the potential to provide an integrated picture of the coral phenotype in response to environmental change.     

Metabolomics Unravel Contrasting Effects of Biodiversity on the Performance of Individual Plant Species

In spite of evidence for positive diversity-productivity relationships increasing plant diversity has highly variable effects on the performance of individual plant species, but the mechanisms behind these differential responses are far from being understood. To gain deeper insights into the physiological responses of individual plant species to increasing plant diversity we performed systematic untargeted metabolite profiling on a number of herbs derived from a grassland biodiversity experiment (Jena Experiment). The Jena Experiment comprises plots of varying species number (1, 2, 4, 8, 16 and 60) and number and composition of functional groups (1 to 4; grasses, legumes, tall herbs, small herbs). In this study the metabolomes of two tall-growing herbs (legume: Medicago x varia; non-legume: Knautia arvensis) and three small-growing herbs (legume: Lotus corniculatus; non-legumes: Bellis perennis, Leontodon autumnalis) in plant communities of increasing diversity were analyzed. For metabolite profiling we combined gas chromatography coupled to time-of-flight mass spectrometry (GC-TOF-MS) and UPLC coupled to FT-ICR-MS (LC-FT-MS) analyses from the same sample. This resulted in several thousands of detected m/z-features. ANOVA and multivariate statistical analysis revealed 139 significantly changed metabolites (30 by GC-TOF-MS and 109 by LC-FT-MS). The small-statured plants L. autumnalis, B. perennis and L. corniculatus showed metabolic response signatures to increasing plant diversity and species richness in contrast to tall-statured plants. Key-metabolites indicated C- and N-limitation for the non-leguminous small-statured species B. perennis and L. autumnalis, while the metabolic signature of the small-statured legume L. corniculatus indicated facilitation by other legumes. Thus, metabolomic analysis provided evidence for negative effects of resource competition on the investigated small-statured herbs that might mechanistically explain their decreasing performance with increasing plant diversity. In contrast, taller species often becoming dominant in mixed plant communities did not show modified metabolite profiles in response to altered resource availability with increasing plant diversity. Taken together, our study demonstrates that metabolite profiling is a strong diagnostic tool to assess individual metabolic phenotypes in response to plant diversity and ecophysiological adjustment.     

The potential of metabolite profiling as a selection tool for genotype discrimination in Populus

Differences between wild-type Populus tremulaxalba and two transgenic lines with modified lignin monomer composition, were interrogated using metabolic profiling. Analysis of metabolite abundance data by GC-MS, coupled with principal components analysis (PCA), successfully differentiated between lines that had distinct phenotypes, whether samples were taken from the cambial zone or non-lignifying suspension tissue cultures. Interestingly, the GC-MS analysis detected relatively few phenolic metabolites in cambial extracts, although a single metabolite associated with the differentiation between lines was directly related to the phenylpropanoid pathway or other down-stream aspects of lignin biosynthesis. In fact, carbohydrates, which have only an indirect relationship with the modified lignin monomer composition, featured strongly in the line-differentiating aspects of the statistical analysis. Traditional HPLC analysis was employed to verify the GC-MS data. These findings demonstrate that metabolic traits can be dissected reliably and accurately by metabolomic analyses, enabling the discrimination of individual genotypes of the same tree species that exhibit marked differences in industrially relevant wood traits. Furthermore, this validates the potential of using metabolite profiling techniques for marker generation in the context of plant/tree breeding for industrial applications.     

1H-NMR metabolite profiles of different strains of Plasmodium falciparum

Although efforts to understand the basis for inter-strain phenotypic variation in the most virulent malaria species, Plasmodium falciparum, have benefited from advances in genomic technologies, there have to date been few metabolomic studies of this parasite. Using ¹H-NMR spectroscopy, we have compared the metabolite profiles of red blood cells infected with different P. falciparum strains. These included both chloroquine-sensitive and chloroquine-resistant strains, as well as transfectant lines engineered to express different isoforms of the chloroquine-resistance-conferring pfcrt (P. falciparum chloroquine resistance transporter). Our analyses revealed strain-specific differences in a range of metabolites. There was marked variation in the levels of the membrane precursors choline and phosphocholine, with some strains having >30-fold higher choline levels and >5-fold higher phosphocholine levels than others. Chloroquine-resistant strains showed elevated levels of a number of amino acids relative to chloroquine-sensitive strains, including an approximately 2-fold increase in aspartate levels. The elevation in amino acid levels was attributable to mutations in pfcrt. Pfcrt-linked differences in amino acid abundance were confirmed using alternate extraction and detection (HPLC) methods. Mutations acquired to withstand chloroquine exposure therefore give rise to significant biochemical alterations in the parasite.     

Towards large-scale FAME-based bacterial species identification using machine learning techniques

In the last decade, bacterial taxonomy witnessed a huge expansion. The swift pace of bacterial species (re-)definitions has a serious impact on the accuracy and completeness of first-line identification methods. Consequently, back-end identification libraries need to be synchronized with the List of Prokaryotic names with Standing in Nomenclature. In this study, we focus on bacterial fatty acid methyl ester (FAME) profiling as a broadly used first-line identification method. From the BAME@LMG database, we have selected FAME profiles of individual strains belonging to the genera Bacillus, Paenibacillus and Pseudomonas. Only those profiles resulting from standard growth conditions have been retained. The corresponding data set covers 74, 44 and 95 validly published bacterial species, respectively, represented by 961, 378 and 1673 standard FAME profiles. Through the application of machine learning techniques in a supervised strategy, different computational models have been built for genus and species identification. Three techniques have been considered: artificial neural networks, random forests and support vector machines. Nearly perfect identification has been achieved at genus level. Notwithstanding the known limited discriminative power of FAME analysis for species identification, the computational models have resulted in good species identification results for the three genera. For Bacillus, Paenibacillus and Pseudomonas, random forests have resulted in sensitivity values, respectively, 0.847, 0.901 and 0.708. The random forests models outperform those of the other machine learning techniques. Moreover, our machine learning approach also outperformed the Sherlock MIS (MIDI Inc., Newark, DE, USA). These results show that machine learning proves very useful for FAME-based bacterial species identification. Besides good bacterial identification at species level, speed and ease of taxonomic synchronization are major advantages of this computational species identification strategy.     

Validation of Metabolic Alterations in Microscale Cell Culture Lysates Using Hydrophilic Interaction Liquid Chromatography (HILIC)-Tandem Mass Spectrometry-Based Metabolomics

By standard convention, in order to increase the efficacy of metabolite detection from cell culture lysates, metabolite extracts from a large quantity of cells are utilized for multiple reaction monitoring-based metabolomic studies. Metabolomics from a small number of cell extracts offers a potential economical alternative to increased cell numbers, in turn increasing the utility of cell culture-based metabolomics. However, the effect of reduced cell numbers on targeted metabolomic profiling is relatively unstudied. Considering the limited knowledge available of the feasibility and accuracy of microscale cell culture metabolomics, the present study analyzes differences in metabolomic profiles of different cell numbers of three pancreatic cancer cell lines. Specifically, it examines the effects of reduced cell numbers on metabolite profiles by obtaining extracts either directly from microscale culture plates or through serial dilution of increased numbers of cellular metabolite extracts. Our results indicate reduced cell numbers only modestly affect the number of metabolites detected (93% of metabolites detected in cell numbers as low as 10⁴ cells and 97% for 10⁵ cells), independent of the method used to obtain the cells. However, metabolite peak intensities were differentially affected by the reduced cell numbers, with some peak intensities inversely proportional to the cell numbers. To help eliminate such potential inverse relationships, peak intensities for increased cell numbers were excluded from the comparative analysis. Overall, metabolite profiles from microscale culture plates were observed to differ from the serial dilution samples, which may be attributable to the medium-to-cell-number ratios. Finally, findings identify perturbations in metabolomic profiling for cellular extracts from reduced cell numbers, which offer future applications in microscale metabolomic evaluations.     

A metabolomics approach to characterise and identify various Mycobacterium species

We investigated the potential use of gas chromatography mass spectrometry (GC-MS), in combination with multivariate statistical data processing, to build a model for the classification of various tuberculosis (TB) causing, and non-TB Mycobacterium species, on the basis of their characteristic metabolite profiles. A modified Bligh-Dyer extraction procedure was used to extract lipid components from Mycobacterium tuberculosis, Mycobacterium avium, Mycobacterium bovis, and Mycobacterium kansasii cultures. Principle component analyses (PCA) of the GC-MS generated data showed a clear differentiation between all the Mycobacterium species tested. Subsequently, the 12 compounds best describing the variation between the sample groups were identified as potential metabolite markers, using PCA and partial least-squares discriminant analysis (PLS-DA). These metabolite markers were then used to build a discriminant classification model based on Bayes' theorem, in conjunction with multivariate kernel density estimation. This model subsequently correctly classified 2 "unknown" samples for each of the Mycobacterium species analysed, with probabilities ranging from 72 to 100%. Furthermore, Mycobacterium species classification could be achieved in less than 16 h, and the detection limit for this approach was 1×10(3)bacteriamL(-1). This study proves the capacity of a GC-MS, metabolomics pattern recognition approach for its possible use in TB diagnostics and disease characterisation.     

Alterations in the gut microbiota and metabolite profiles of patients with Kashin-Beck disease,an endemic osteoarthritis in China

Kashin-Beck disease (KBD) is a severe osteochondral disorder that may be driven by the interaction between genetic and environmental factors. We aimed to improve our understanding of the gut microbiota structure in KBD patients of different grades and the relationship between the gut microbiota and serum metabolites. Fecal and serum samples collected from KBD patients and normal controls (NCs) were used to characterize the gut microbiota using 16S rDNA gene and metabolomic sequencing via liquid chromatography-mass spectrometry (LC/MS). To identify whether gut microbial changes at the species level are associated with the genes or functions of the gut bacteria in the KBD patients, metagenomic sequencing of fecal samples from grade I KBD, grade II KBD and NC subjects was performed. The KBD group was characterized by elevated levels of Fusobacteria and Bacteroidetes. A total of 56 genera were identified to be significantly differentially abundant between the two groups. The genera Alloprevotella, Robinsoniella, Megamonas, and Escherichia_Shigella were more abundant in the KBD group. Consistent with the 16S rDNA analysis at the genus level, most of the differentially abundant species in KBD subjects belonged to the genus Prevotella according to metagenomic sequencing. Serum metabolomic analysis identified some differentially abundant metabolites among the grade I and II KBD and NC groups that were involved in lipid metabolism metabolic networks, such as that for unsaturated fatty acids and glycerophospholipids. Furthermore, we found that these differences in metabolite levels were associated with altered abundances of specific species. Our study provides a comprehensive landscape of the gut microbiota and metabolites in KBD patients and provides substantial evidence of a novel interplay between the gut microbiome and metabolome in KBD pathogenesis.Subject terms: Metagenomics, Metabolomics     

Intracellular metabolite profiling and the evaluation of metabolite extraction solvents for Clostridium carboxidivorans fermenting carbon monoxide

Clostridium carboxidivorans ferments CO, CO₂, and H₂ via the Wood-Ljungdahl pathway. CO, CO_2, and H₂ are unique substrates, unlike other carbon sources like glucose, so it is necessary to analyze intracellular metabolite profiles for gas fermentation by C. carboxidivorans for metabolic engineering. Moreover, it is necessary to optimize the metabolite extraction solvent specifically for C. carboxidivorans fermenting syngas. In comparison with glucose media, the gas media allowed significant abundance changes of 38 and 34 metabolites in the exponential and stationary phases, respectively. Especially, C. carboxidivorans cultivated in the gas media showed changes of fatty acid metabolism and higher levels of intracellular fatty acid synthesis possibly due to cofactor imbalance and slow metabolism. Meanwhile, the evaluation of extraction solvents revealed the mixture of water-isopropanol-methanol (2:2:5, v/v/v) to be the best extraction solvent, which showed a higher extraction capability and reproducibility than pure methanol, the conventional extraction solvent. This is the first metabolomic study to demonstrate the unique intracellular metabolite profiles of the gas fermentation compared to glucose fermentation, and to evaluate water-isopropanol-methanol as the optimal metabolite extraction solvent for C. carboxidivorans on gas fermentation.     

Occurrence of N-acyl-L-homoserine lactones in extracts of some Gram-negative bacteria evaluated by gas chromatography-mass spectrometry

Acylated homoserine lactones (AHLs) are self-generated signal molecules that mediate population density-dependent gene expression (quorum sensing) in a variety of Gram-negative bacteria. These signal molecules diffuse from bacterial cells and accumulate in the medium as a function of cell growth. In selected foods AHLs contribute to product spoilage. As different bacterial species produce AHL analogs that differ in length of the N-acyl chain, ranging from 4 to 14 carbons and in the substitution at the C-3 position of the side chain (i.e., oxo or hydroxyl group), the suitability and applicability of a gas chromatography-mass spectrometry direct method for characterizing trace amounts of AHLs was evaluated using N-heptanoyl-homoserine lactone as internal standard. Crude cell-free supernatants of bacterial cultures of Aeromonas hydrophila, Aeromonas salmonicida, Pseudomonas aeruginosa, Pseudomonas fluorescens, Yersinia enterocolitica, and Serratia liquefaciens were screened for AHL production in selected ion monitoring mode, using the prominent fragment at m/z 143. The observed profiles of distinguishable N-acyl-homoserine lactones occurring in bacterial extracts were compared and discussed. The presence of a labile 3-oxo-hexanoylhomoserine lactone was evidenced but serious difficulties arose in estimating its concentration as thermal degradation occurs during the gas chromatographic separation. Its electron impact mass spectra was, however, given and interpreted.     

Bacterial artificial chromosome fingerprint arrays for the differentiation of transcriptomic differences in mycobacteria

Although microarray technology has become more widespread as a discovery tool for bacterial pathogenesis, it remains a method available only to laboratories with access to expensive equipment and costly analysis software. Mycobacterium tuberculosis, the causative agent for tuberculosis (TB), afflicts one-third of the global population, and kills between 2 and 3 million people per year. While the majority of cases of TB occur in developing areas of the world, facilities in these regions may not be able to support microarray analysis. Additionally, a major limitation of microarrays is that only genes on the array are being assayed. With acquired virulence and drug resistance in microbes, a method less dependent on a predetermined list of gene targets is advantageous. We present a method of expression analysis based on bacterial artificial chromosomes (BACs) that can be applied with standard laboratory equipment and free analysis software. This technique, bacterial artificial chromosome fingerprint arrays (BACFA), was developed and utilised to identify expression differences between intracellular strains of M. tuberculosis, one virulent (H37Rv) and one attenuated (H37Ra). Southern blots of restriction-enzyme digested BAC fragments were sequentially hybridised with strain-specific cDNA probes to generate expression profiles that were used to isolate expression differences in broth grown and intracellular bacteria. Repeat comparisons of intracellular profiles via BACFA identified genomic regions differentially expressed by the two strains. Quantitative real-time PCR was used to assess the genes located in these fragments in order to confirm or deny the differential regulation of genes. In total, we identified six genes that were differentially regulated between strains inside the host cell (pks2, aceE, Rv1571, and frdBCD). We report that BACFA is an effective technique in the expression analysis of bacteria and can be considered complementary to the high-throughput analysis offered by microarrays.     

Volatile Emissions from Mycobacterium avium subsp. paratuberculosis Mirror Bacterial Growth and Enable Distinction of Different Strains

Control of paratuberculosis in livestock is hampered by the low sensitivity of established direct and indirect diagnostic methods. Like other bacteria, Mycobacterium avium subsp. paratuberculosis (MAP) emits volatile organic compounds (VOCs). Differences of VOC patterns in breath and feces of infected and not infected animals were described in first pilot experiments but detailed information on potential marker substances is missing. This study was intended to look for characteristic volatile substances in the headspace of cultures of different MAP strains and to find out how the emission of VOCs was affected by density of bacterial growth. One laboratory adapted and four field strains, three of MAP C-type and one MAP S-type were cultivated on Herrold’s egg yolk medium in dilutions of 10^-0, 10^-2, 10^-4 and 10^-6. Volatile substances were pre-concentrated from the headspace over the MAP cultures by means of Solid Phase Micro Extraction (SPME), thermally desorbed from the SPME fibers and separated and identified by means of GC-MS. Out of the large number of compounds found in the headspace over MAP cultures, 34 volatile marker substances could be identified as potential biomarkers for growth and metabolic activity. All five MAP strains could clearly be distinguished from blank culture media by means of emission patterns based on these 34 substances. In addition, patterns of volatiles emitted by the reference strain were significantly different from the field strains. Headspace concentrations of 2-ethylfuran, 2-methylfuran, 3-methylfuran, 2-pentylfuran, ethyl acetate, 1-methyl-1-H-pyrrole and dimethyldisulfide varied with density of bacterial growth. Analysis of VOCs emitted from mycobacterial cultures can be used to identify bacterial growth and, in addition, to differentiate between different bacterial strains. VOC emission patterns may be used to approximate bacterial growth density. In a perspective volatile marker substances could be used to diagnose MAP infections in animals and to identify different bacterial strains and origins.     

Targeted metabolomics profiles are strongly correlated with nutritional patterns in women

Nutrition plays an important role in human metabolism and health. Metabolomics is a promising tool for clinical, genetic and nutritional studies. A key question is to what extent metabolomic profiles reflect nutritional patterns in an epidemiological setting. We assessed the relationship between metabolomic profiles and nutritional intake in women from a large cross-sectional community study. Food frequency questionnaires (FFQs) were applied to 1,003 women from the TwinsUK cohort with targeted metabolomic analyses of serum samples using the Biocrates Absolute-IDQ? Kit p150 (163 metabolites). We analyzed seven nutritional parameters: coffee intake, garlic intake and nutritional scores derived from the FFQs summarizing fruit and vegetable intake, alcohol intake, meat intake, hypo-caloric dieting and a “traditional English” diet. We studied the correlation between metabolite levels and dietary intake patterns in the larger population and identified for each trait between 14 and 20 independent monozygotic twins pairs discordant for nutritional intake and replicated results in this set. Results from both analyses were then meta-analyzed. For the metabolites associated with nutritional patterns, we calculated heritability using structural equation modelling. 42 metabolite nutrient intake associations were statistically significant in the discovery samples (Bonferroni P < 4 × 10^?5) and 11 metabolite nutrient intake associations remained significant after validation. We found the strongest associations for fruit and vegetables intake and a glycerophospholipid (Phosphatidylcholine diacyl C38:6, P = 1.39 × 10^?9) and a sphingolipid (Sphingomyeline C26:1, P = 6.95 × 10^?13). We also found significant associations for coffee (confirming a previous association with C10 reported in an independent study), garlic intake and hypo-caloric dieting. Using the twin study design we find that two thirds the metabolites associated with nutritional patterns have a significant genetic contribution, and the remaining third are solely environmentally determined. Our data confirm the value of metabolomic studies for nutritional epidemiologic research.     

Metabolomic Profiling of Faecal Extracts from Cryptosporidium parvum Infection in Experimental Mouse Models

Cryptosporidiosis is a gastrointestinal disease in humans and animals caused by infection with the protozoan parasite Cryptosporidium. In healthy individuals, the disease manifests mainly as acute self-limiting diarrhoea, but may be chronic and life threatening for those with compromised immune systems. Control and treatment of the disease is challenged by the lack of sensitive diagnostic tools and broad-spectrum chemotherapy. Metabolomics, or metabolite profiling, is an emerging field of study, which enables characterisation of the end products of regulatory processes in a biological system. Analysis of changes in metabolite patterns reflects changes in biochemical regulation, production and control, and may contribute to understanding the effects of Cryptosporidium infection in the host environment. In the present study, metabolomic analysis of faecal samples from experimentally infected mice was carried out to assess metabolite profiles pertaining to the infection. Gas-chromatography mass spectrometry (GC-MS) carried out on faecal samples from a group of C. parvum infected mice and a group of uninfected control mice detected a mean total of 220 compounds. Multivariate analyses showed distinct differences between the profiles of C. parvum infected mice and uninfected control mice,identifying a total of 40 compounds, or metabolites that contributed most to the variance between the two groups. These metabolites consisted of amino acids (n = 17), carbohydrates (n = 8), lipids (n = 7), organic acids (n = 3) and other various metabolites (n = 5), which showed significant differences in levels of metabolite abundance between the infected and uninfected mice groups (p < 0.05). The metabolites detected in this study as well as the differences in abundance between the C. parvum infected and the uninfected control mice, highlights the effects of the infection on intestinal permeability and the fate of the metabolites as a result of nutrient scavenging by the parasite to supplement its streamlined metabolism.     

Production of BMAA and DAB by diatoms (Phaeodactylum tricornutum,Chaetoceros sp., Chaetoceros calcitrans and,Thalassiosira pseudonana) and bacteria isolated from a diatom culture

Microalgae have previously been reported to contain β-N-methylamino-l-alanine (BMAA), and the global presence of these primary producers has been associated with the widespread occurrence of BMAA in marine organisms. It has been repeatedly shown that filter-feeding bivalves accumulate phytoplankton species and their toxins. In this study, the concentrations of total soluble BMAA and DAB as a function of growth phase were observed for four non-axenic diatom species (i.e. Phaeodactylum tricornutum, Chaetoceros sp., Chaetoceros calcitrans and Thalassiosira pseudonana). These strains had previously been shown to contain BMAA using a highly selective HILIC-MS/MS method. BMAA cell quota appeared to be species-specific, however, highest BMAA concentrations were always obtained during the stationary growth phase, for all four species, suggesting that BMAA is a secondary metabolite. While DAB was detected in a bacterial culture isolated from a culture of P. tricornutum, the presence or absence of a bacterial population did not influence production of BMAA and DAB by P. tricornutum, i.e. no significant difference was noted for BMAA and DAB production between axenic and non-axenic cultures. The presence of DAB in bacteria had previously been shown, and raised the question as to whether DAB observed in many species of microalgae may arise from the non-axenic culture conditions or from the microalgae themselves.     

Design of a PCR test based on the gyrA gene sequence for the identification of closely related species of the Bacillus subtilis group

A method for the taxonomic identification of seven closely related bacterial species of the Bacillus subtilis group (B. subtilis, B. amyloliquefaciens, B. licheniformis, B. vallismortis, B. atrophaeus, B. sonorensis, and B. mojavensis) using specific primers selected on the basis of the gyrA gene sequences was developed. The effectiveness of this method both for the identification of pure cultures of type strains of this group and for the precise species identification of collection and industrial bacterial strains was demonstrated. The principal possibility of using this method for detecting B. subtilis group bacteria in mixed cultures was shown.