Identifying and quantifying metabolites by scoring peaks of GC-MS data.

BACKGROUND: Metabolomics is one of most recent omics technologies. It has been applied on fields such as food science, nutrition, drug discovery and systems biology. For this, gas chromatography-mass spectrometry (GC-MS) has been largely applied and many computational tools have been developed to support the analysis of metabolomics data. Among them, AMDIS is perhaps the most used tool for identifying and quantifying metabolites. However, AMDIS generates a high number of false-positives and does not have an interface amenable for high-throughput data analysis. Although additional computational tools have been developed for processing AMDIS results and to perform normalisations and statistical analysis of metabolomics data, there is not yet a single free software or package able to reliably identify and quantify metabolites analysed by GC-MS. RESULTS: Here we introduce a new algorithm, PScore, able to score peaks according to their likelihood of representing metabolites defined in a mass spectral library. We implemented PScore in a R package called MetaBox and evaluated the applicability and potential of MetaBox by comparing its performance against AMDIS results when analysing volatile organic compounds (VOC) from standard mixtures of metabolites and from female and male mice faecal samples. MetaBox reported lower percentages of false positives and false negatives, and was able to report a higher number of potential biomarkers associated to the metabolism of female and male mice. CONCLUSIONS: Identification and quantification of metabolites is among the most critical and time-consuming steps in GC-MS metabolome analysis. Here we present an algorithm implemented in a R package, which allows users to construct flexible pipelines and analyse metabolomics data in a high-throughput manner.

Toxin-antitoxin systems of Mycobacterium smegmatis are essential for cell survival.

The role of chromosomal toxin-antitoxin (TA) modules in bacterial physiology remains enigmatic despite their abundance in the genomes of many bacteria. Mycobacterium smegmatis contains three putative TA systems, VapBC, MazEF, and Phd/Doc, and previous work from our group has shown VapBC to be a bona fide TA system. In this study, we show that MazEF and Phd/Doc are also TA systems that are constitutively expressed, transcribed as leaderless transcripts, and subject to autoregulation, and expression of the toxin component leads to growth inhibition that can be rescued by the cognate antitoxin. No phenotype was identified for deletions of the individual TA systems, but a triple deletion strain (ΔvapBC, mazEF, phd/doc), designated ΔTA(triple), exhibited a survival defect in complex growth medium demonstrating an essential role for these TA modules in mycobacterial survival. Transcriptomic analysis revealed no significant differences in gene expression between wild type and the ΔTA(triple) mutant under these conditions suggesting that the growth defect was not at a transcriptional level. Metabolomic analysis demonstrated that in response to starvation in complex medium, both the wild type and ΔTA(triple) mutant consumed a wide range of amino acids from the external milieu. Analysis of intracellular metabolites revealed a significant difference in the levels of branched-chain amino acids between the wild type and ΔTA(triple) mutant, which are proposed to play essential roles in monitoring the nutritional supply and physiological state of the cell and linking catabolic with anabolic reactions. Disruption of this balance in the ΔTA(triple) mutant may explain the survival defect in complex growth medium.

Transcriptional and metabolomic consequences of LuxS inactivation reveal a metabolic rather than quorum-sensing role for LuxS in Lactobacillus reuteri 100-23.

Autoinducer-2 (AI-2)-mediated quorum sensing has been extensively studied in relation to the regulation of microbial behavior. There are, however, two potential roles for the AI-2 synthase (LuxS). The first is in the production of AI-2 and the second is as an enzyme in the activated methyl cycle, where it catalyzes the conversion of S-ribosylhomocysteine to homocysteine. The by-product of the reaction catalyzed by LuxS is (S)-4,5-dihydroxy-2,3-pentanedione, which spontaneously forms the furanones known collectively as AI-2. The mammalian gut contains a complex collection of bacterial species so a method of interspecies communication might influence community structure and function. Lactobacillus reuteri 100-23 is an autochthonous inhabitant of the rodent forestomach, where it adheres to the nonsecretory epithelium, forming a biofilm. Microarray comparisons of gene expression profiles of the L. reuteri 100-23 wild type and a luxS mutant under different culture conditions revealed altered transcription of genes encoding proteins associated with cysteine biosynthesis/oxidative stress response, urease activity, and sortase-dependent proteins. Metabolomic analysis showed that the luxS mutation affected cellular levels of fermentation products, fatty acids and amino acids. Cell density-dependent changes (log phase versus stationary phase growth) in gene transcription were not detected, indicating that AI-2 was unlikely to be involved in gene regulation mediated by quorum sensing in L. reuteri 100-23.

Pathway Activity Profiling (PAPi): from the metabolite profile to the metabolic pathway activity.

MOTIVATION: Metabolomics is one of the most recent omics-technologies and uses robust analytical techniques to screen low molecular mass metabolites in biological samples. It has evolved very quickly during the last decade. However, metabolomics datasets are considered highly complex when used to relate metabolite levels to metabolic pathway activity. Despite recent developments in bioinformatics, which have improved the quality of metabolomics data, there is still no straightforward method capable of correlating metabolite level to the activity of different metabolic pathways operating within the cells. Thus, this kind of analysis still depends on extremely laborious and time-consuming processes. RESULTS: Here, we present a new algorithm Pathway Activity Profiling (PAPi) with which we are able to compare metabolic pathway activities from metabolite profiles. The applicability and potential of PAPi was demonstrated using a previously published data from the yeast Saccharomyces cerevisiae. PAPi was able to support the biological interpretations of the previously published observations and, in addition, generated new hypotheses in a straightforward manner. However, PAPi is time consuming to perform manually. Thus, we also present here a new R-software package (PAPi) which implements the PAPi algorithm and facilitates its usage to quickly compare metabolic pathways activities between different experimental conditions. Using the identified metabolites and their respective abundances as input, the PAPi package calculates pathways' Activity Scores, which represents the potential metabolic pathways activities and allows their comparison between conditions. PAPi also performs principal components analysis and analysis of variance or t-test to investigate differences in activity level between experimental conditions. In addition, PAPi generates comparative graphs highlighting up- and down-regulated pathway activity. AVAILABILITY: These datasets are available in http://www.4shared.com/file/hTWyndYU/extra.html and http://www.4shared.com/file/VbQIIDeu/intra.html. PAPi package is available in: http://www.4shared.com/file/s0uIYWIg/PAPi_10.html CONTACT: s.villas-boas@auckland.ac.nz SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The use of a gas chromatography-sensor system combined with advanced statistical methods, towards the diagnosis of urological malignancies.

Prostate cancer is one of the most common cancers. Serum prostate-specific antigen (PSA) is used to aid the selection of men undergoing biopsies. Its use remains controversial. We propose a GC-sensor algorithm system for classifying urine samples from patients with urological symptoms. This pilot study includes 155 men presenting to urology clinics, 58 were diagnosed with prostate cancer, 24 with bladder cancer and 73 with haematuria and or poor stream, without cancer. Principal component analysis (PCA) was applied to assess the discrimination achieved, while linear discriminant analysis (LDA) and support vector machine (SVM) were used as statistical models for sample classification. Leave-one-out cross-validation (LOOCV), repeated 10-fold cross-validation (10FoldCV), repeated double cross-validation (DoubleCV) and Monte Carlo permutations were applied to assess performance. Significant separation was found between prostate cancer and control samples, bladder cancer and controls and between bladder and prostate cancer samples. For prostate cancer diagnosis, the GC/SVM system classified samples with 95% sensitivity and 96% specificity after LOOCV. For bladder cancer diagnosis, the SVM reported 96% sensitivity and 100% specificity after LOOCV, while the DoubleCV reported 87% sensitivity and 99% specificity, with SVM showing 78% and 98% sensitivity between prostate and bladder cancer samples. Evaluation of the results of the Monte Carlo permutation of class labels obtained chance-like accuracy values around 50% suggesting the observed results for bladder cancer and prostate cancer detection are not due to over fitting. The results of the pilot study presented here indicate that the GC system is able to successfully identify patterns that allow classification of urine samples from patients with urological cancers. An accurate diagnosis based on urine samples would reduce the number of negative prostate biopsies performed, and the frequency of surveillance cystoscopy for bladder cancer patients. Larger cohort studies are planned to investigate the potential of this system. Future work may lead to non-invasive breath analyses for diagnosing urological conditions.

Freeze-drying: an alternative method for the analysis of volatile organic compounds in the headspace of urine samples using solid phase micro-extraction coupled to gas chromatography - mass spectrometry.

BACKGROUND: Volatile organic compounds (VOCs) can be intermediates of metabolic pathways and their levels in biological samples may provide a better understanding about diseases in addition to potential methods for diagnosis. Headspace analysis of VOCs in urine samples using solid phase micro extraction (SPME) coupled to gas chromatography - mass spectrometry (GC-MS) is one of the most used techniques. However, it generally produces a limited profile of VOCs if applied to fresh urine. Sample preparation methods, such as addition of salt, base or acid, have been developed to improve the headspace-SPME-GC-MS analysis of VOCs in urine samples. These methods result in a richer profile of VOCs, however, they may also add potential contaminants to the urine samples, result in increased variability introduced by manually processing the samples and promote degradation of metabolites due to extreme pH levels. Here, we evaluated if freeze-drying can be considered an alternative sample preparation method for headspace-SPME-GC-MS analysis of urine samples. RESULTS: We collected urine from three volunteers and compared the performances of freeze-drying, addition of acid (HCl), addition of base (NaOH), addition of salt (NaCl), fresh urine and frozen urine when identifying and quantifying metabolites in 4 ml samples. Freeze-drying and addition of acid produced a significantly higher number of VOCs identified than any other method, with freeze-drying covering a slightly higher number of chemical classes, showing an improved repeatability and reducing siloxane impurities. CONCLUSION: In this work we compared the performance of sample preparation methods for the SPME-GC-MS analysis of urine samples. To the best of our knowledge, this is the first study evaluating the potential of freeze-dry as an alternative sample preparation method. Our results indicate that freeze-drying has potential to be used as an alternative method for the SPME-GC-MS analysis of urine samples. Additional studies using internal standard, synthetic urine and calibration curves will allow a more precise quantification of metabolites and additional comparisons between methods.Graphical abstractEnhancing VOC profiling from urine samples.

Pathway Activity Profiling (PAPi): a tool for metabolic pathway analysis.

Pathway Activity Profiling (PAPi) is a method developed to correlate levels of metabolites to the activity of metabolic pathways operating within biological systems. Based solely on a metabolomics data set and the Kyoto Encyclopedia of Genes and Genomes, PAPi predicts and compares the activity of metabolic pathways across experimental conditions, which considerably improves the hypothesis generation process for achieving the biological interpretation of biological studies. In this chapter, we describe how to apply PAPi to a metabolomics data set using the R-software.

Analytical platform for metabolome analysis of microbial cells using methyl chloroformate derivatization followed by gas chromatography-mass spectrometry.

This protocol describes an analytical platform for the analysis of intra- and extracellular metabolites of microbial cells (yeast, filamentous fungi and bacteria) using gas chromatography-mass spectrometry (GC-MS). The protocol is subdivided into sampling, sample preparation, chemical derivatization of metabolites, GC-MS analysis and data processing and analysis. This protocol uses two robust quenching methods for microbial cultures, the first of which, cold glycerol-saline quenching, causes reduced leakage of intracellular metabolites, thus allowing a more reliable separation of intra- and extracellular metabolites with simultaneous stopping of cell metabolism. The second, fast filtration, is specifically designed for quenching filamentous micro-organisms. These sampling techniques are combined with an easy sample-preparation procedure and a fast chemical derivatization reaction using methyl chloroformate. This reaction takes place at room temperature, in aqueous medium, and is less prone to matrix effect compared with other derivatizations. This protocol takes an average of 10 d to complete and enables the simultaneous analysis of hundreds of metabolites from the central carbon metabolism (amino and nonamino organic acids, phosphorylated organic acids and fatty acid intermediates) using an in-house MS library and a data analysis pipeline consisting of two free software programs (Automated Mass Deconvolution and Identification System (AMDIS) and R).