[Show abstract][Hide abstract] ABSTRACT: Three benchtop high-throughput sequencing instruments are now available. The 454 GS Junior (Roche), MiSeq (Illumina) and Ion Torrent PGM (Life Technologies) are laser-printer sized and offer modest set-up and running costs. Each instrument can generate data required for a draft bacterial genome sequence in days, making them attractive for identifying and characterizing pathogens in the clinical setting. We compared the performance of these instruments by sequencing an isolate of Escherichia coli O104:H4, which caused an outbreak of food poisoning in Germany in 2011. The MiSeq had the highest throughput per run (1.6 Gb/run, 60 Mb/h) and lowest error rates. The 454 GS Junior generated the longest reads (up to 600 bases) and most contiguous assemblies but had the lowest throughput (70 Mb/run, 9 Mb/h). Run in 100-bp mode, the Ion Torrent PGM had the highest throughput (80–100 Mb/h). Unlike the MiSeq, the Ion Torrent PGM and 454 GS Junior both produced homopolymer-associated indel errors (1.5 and 0.38 errors per 100 bases, respectively).
[Show abstract][Hide abstract] ABSTRACT: Mycobacterium abscessus is a rapidly growing environmental mycobacterium commonly found in soil and water which is often also associated with infections in humans, particularly of the lung. We report herein the draft genome sequence of M. abscessus strain 47J26.
[Show abstract][Hide abstract] ABSTRACT: Currently there is limited information available on the accuracy and precision of relative isotopic abundance (RIA) measurements using high-resolution direct-infusion mass spectrometry (HR DIMS), and it is unclear if this information can benefit automated peak annotation in metabolomics. Here we characterize the accuracy of RIA measurements on the Thermo LTQ FT Ultra (resolution of 100,000-750,000) and LTQ Orbitrap (R = 100,000) mass spectrometers. This first involved reoptimizing the SIM-stitching method (Southam, A. D. Anal. Chem. 2007, 79, 4595-4602) for the LTQ FT Ultra, which achieved a ca. 3-fold sensitivity increase compared to the original method while maintaining a root-mean-squared mass error of 0.16 ppm. Using this method, we show the quality of RIA measurements is highly dependent on signal-to-noise ratio (SNR), with RIA accuracy increasing with higher SNR. Furthermore, a negative offset between the theoretical and empirically calculated numbers of carbon atoms was observed for both mass spectrometers. Increasing the resolution of the LTQ FT Ultra lowered both the sensitivity and the quality of RIA measurements. Overall, although the errors in the empirically calculated number of carbons can be large (e.g., 10 carbons), we demonstrate that RIA measurements do improve automated peak annotation, increasing the number of single empirical formula assignments by >3-fold compared to using accurate mass alone.
[Show abstract][Hide abstract] ABSTRACT: Metabolite identification is of central importance to metabolomics as it provides the route to new knowledge. Automated identification of the thousands of peaks detected by high resolution mass spectrometry is currently not possible, largely due to the finite mass accuracy of the spectrometer and the complexity that one peak can be assigned to one or more empirical formula(e) and each formula maps to one or more metabolites. Biological samples are not, however, composed of random metabolite mixtures, but instead comprise of thousands of compounds related through specific chemical transformations. Here we evaluate if prior biological knowledge of these transformations can improve metabolite identification accuracy.Our identification algorithm – which uses metabolite interconnectivity from the KEGG database to putatively identify metabolites by name – is based on mapping an experimentally-derived empirical formula difference for a pair of peaks to a known empirical formula difference between substrate-product pairs derived from KEGG, termed transformation mapping (TM). To maximize identification accuracy, we also developed a novel semi-automated method to calculate a mass error surface associated with experimental peak-pair differences. The TM algorithm with mass error surface has been extensively validated using simulated and experimental datasets by calculating false positive and false negative rates of metabolite identification. Compared to the traditional identification method of database searching accurate masses on a single-peak-by-peak basis, the TM algorithm reduces the false positive rate of identification by > 4-fold, while maintaining a minimal false negative rate. The mass error surface, putative identification of metabolite names, and calculation of false positive and false negative rates collectively advance and improve upon related previous research on this topic [1, 2]. We conclude that inclusion of prior biological knowledge in the form of metabolic pathways provides one route to more accurate metabolite identification.
Chemometrics and Intelligent Laboratory Systems 11/2010;
Source Code for Biology and Medicine 06/2010; 5:6.
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[Show abstract][Hide abstract] ABSTRACT: To improve the outcome of orthotopic liver transplantation (OLT), knowledge of early molecular events occurring upon ischemia/reperfusion is essential. Powerful approaches for profiling metabolic changes in tissues and biofluids are now available. Our objective was to investigate the applicability of two technologies to a small but well-defined cohort of patients undergoing OLT: consecutive liver biopsies by Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) and microdialysates of extracellular fluid by coulometric electrochemical array detection (CEAD). FT-ICR MS detected reproducibly more than 4,000 peaks, revealing hundreds of significant metabolic differences between pre- and postreperfusion grafts. These included increased urea production, bile acid synthesis and clearance of preservation solution upon reperfusion, indicating a rapid resumption of biochemical function within the graft. FT-ICR MS also identified successfully the only graft obtained by donation-after-cardiac-death as a "metabolic outlier." CEAD time-profile analysis showed that there was considerable change in redox-active metabolites (up to 18 h postreperfusion), followed by their stabilization. Collectively these results verify the applicability of FT-ICR MS and CEAD for characterizing multiple metabolic pathways during OLT. The success of this proof-of-principle application of these technologies to a clinical setting, considering the potential metabolic heterogeneity across only eight donor livers, is encouraging.
Omics: a journal of integrative biology 03/2010; 14(2):143-50.
[Show abstract][Hide abstract] ABSTRACT: NMR spectroscopy remains one of the primary analytical approaches in metabolomics. Although 1D (1)H NMR spectroscopy is versatile, highly reproducible and currently the most widely used technique in NMR metabolomics, analysis of complex biological samples typically yields highly congested spectra with severely overlapping signals making unambiguous metabolite identification and quantification almost impossible. Consequently there is a growing use of 2D NMR methods, in particular (1)H J-resolved (JRES) spectroscopy, which spreads the high signal density into a second dimension. One potentially powerful method to deconvolute these JRES spectra, facilitating metabolite quantification, is via line-shape fitting. However, the mathematical functions describing the JRES NMR line-shape, in particular after applying apodisation functions and JRES specific processing, including tilting and symmetrisation, remain uncharacterised. Furthermore, possible quantitation errors arising from processing JRES spectra have not been evaluated, nor have the potentially adverse quantitative effects of overlapping dispersive tails of closely spaced signals in the 2D spectrum. Here we address these issues and evaluate the suitability of the JRES experiment for accurate complex mixture analysis. Specifically, we have examined changes in NMR line-shape and signal intensity after application of different apodisation functions (SINE and SEM) and JRES specific processing (tilting and symmetrising), comparing simulated and experimental data. We also report a significant quantitation error of up to 33%, dependent upon apodisation, due to overlap of the dispersive tails of closely spaced resonances. Finally, we have validated the use of these mathematical line-shape functions for metabolite quantitation of 2D JRES spectra, by comparison to corresponding 1D NMR datasets, using both gravimetrically-prepared chemically defined mixtures as well as biological tissue extracts.
Magnetic Resonance in Chemistry 08/2009; 47 Suppl 1(S1):S86-95.
[Show abstract][Hide abstract] ABSTRACT: We determined the genome sequence of the type strain of Helicobacter canadensis, an emerging human pathogen with diverse animal reservoirs. Potential virulence determinants carried by the genome include systems for N-linked glycosylation and capsular export. A protein-based phylogenetic analysis places H. canadensis close to Wolinella succinogenes.
[Show abstract][Hide abstract] ABSTRACT: Metabolomics datasets, by definition, comprise of measurements of large numbers of metabolites. Both technical (analytical) and biological factors will induce variation within these measurements that is not consistent across all metabolites. Consequently, criteria are required to assess the reproducibility of metabolomics datasets that are derived from all the detected metabolites. Here we calculate spectrum-wide relative standard deviations (RSDs; also termed coefficient of variation, CV) for ten metabolomics datasets, spanning a variety of sample types from mammals, fish, invertebrates and a cell line, and display them succinctly as boxplots. We demonstrate multiple applications of spectral RSDs for characterising technical as well as inter-individual biological variation: for optimising metabolite extractions, comparing analytical techniques, investigating matrix effects, and comparing biofluids and tissue extracts from single and multiple species for optimising experimental design. Technical variation within metabolomics datasets, recorded using one- and two-dimensional NMR and mass spectrometry, ranges from 1.6 to 20.6% (reported as the median spectral RSD). Inter-individual biological variation is typically larger, ranging from as low as 7.2% for tissue extracts from laboratory-housed rats to 58.4% for fish plasma. In addition, for some of the datasets we confirm that the spectral RSD values are largely invariant across different spectral processing methods, such as baseline correction, normalisation and binning resolution. In conclusion, we propose spectral RSDs and their median values contained herein as practical benchmarks for metabolomics studies.
The Analyst 04/2009; 134(3):478-85.
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