MetAssign: probabilistic annotation of metabolites from LC–MS data using a Bayesian clustering approach

Motivation: The use of liquid chromatography coupled to mass spectrometry (LC–MS) has enabled the high-throughput profiling of the metabolite composition of biological samples. However, the large amount of data obtained can be difficult to analyse and often requires computational processing to understand which metabolites are present in a sample. This paper looks at the dual problem of annotating peaks in a sample with a metabolite, together with putatively annotating whether a metabolite is present in the sample. The starting point of the approach is a Bayesian clustering of peaks into groups, each corresponding to putative adducts and isotopes of a single metabolite.<p></p> Results: The Bayesian modelling introduced here combines information from the mass-to-charge ratio, retention time and intensity of each peak, together with a model of the inter-peak dependency structure, to increase the accuracy of peak annotation. The results inherently contain a quantitative estimate of confidence in the peak annotations and allow an accurate trade off between precision and recall. Extensive validation experiments using authentic chemical standards show that this system is able to produce more accurate putative identifications than other state-of-the-art systems, while at the same time giving a probabilistic measure of confidence in the annotations.<p></p> Availability: The software has been implemented as part of the mzMatch metabolomics analysis pipeline, which is available for download at http://mzmatch.sourceforge.net/

The emergence of proton nuclear magnetic resonance metabolomics in the cardiovascular arena as viewed from a clinical perspective

The ability to phenotype metabolic profiles in serum has increased substantially in recent years with the advent of metabolomics. Metabolomics is the study of the metabolome, defined as those molecules with an atomic mass less than 1.5 kDa. There are two main metabolomics methods: mass spectrometry (MS) and proton nuclear magnetic resonance (1H NMR) spectroscopy, each with its respective benefits and limitations. MS has greater sensitivity and so can detect many more metabolites. However, its cost (especially when heavy labelled internal standards are required for absolute quantitation) and quality control is sub-optimal for large cohorts. 1H NMR is less sensitive but sample preparation is generally faster and analysis times shorter, resulting in markedly lower analysis costs. 1H NMR is robust, reproducible and can provide absolute quantitation of many metabolites. Of particular relevance to cardio-metabolic disease is the ability of 1H NMR to provide detailed quantitative data on amino acids, fatty acids and other metabolites as well as lipoprotein subparticle concentrations and size. Early epidemiological studies suggest promise, however, this is an emerging field and more data is required before we can determine the clinical utility of these measures to improve disease prediction and treatment. This review describes the theoretical basis of 1H NMR; compares MS and 1H NMR and provides a tabular overview of recent 1H NMR-based research findings in the atherosclerosis field, describing the design and scope of studies conducted to date. 1H NMR metabolomics-CVD related research is emerging, however further large, robustly conducted prospective, genetic and intervention studies are needed to advance research on CVD risk prediction and to identify causal pathways amenable to intervention

Schistosoma haematobium infection is associated with alterations in energy and purine-related metabolism in preschool-aged children

Helminths are parasitic worms that infect over a billion people worldwide. The pathological consequences from infection are due in part, to parasite-induced changes in host metabolic pathways. Here, we analyse the changes in host metabolic profiles, in response to the first Schistosoma haematobium infection and treatment in Zimbabwean children. A cohort of 83 schistosome-negative children (2-5 years old) as determined by parasitological examination, guardian interviews and examination of medical records, was recruited at baseline. Children were followed up after three months for parasitological diagnosis of their first S. haematobium infection, by detection of parasite eggs excreted in urine. Children positive for infection were treated with the antihelminthic drug praziquantel, and treatment efficacy checked three months after treatment. Blood samples were taken at each time point, and capillary electrophoresis mass spectrometry in conjunction with multivariate analysis were used to compare the change in serum metabolite profiles in schistosome-infected versus uninfected children. Following baseline at the three-month follow up, 11 children had become infected with S. haematobium (incidence = 13.3%). Our results showed that infection with S. haematobium was associated with significant increases (>2-fold) in discriminatory metabolites, linked primarily with energy (G6P, 3-PG, AMP, ADP) and purine (AMP, ADP) metabolism. These observed changes were commensurate with schistosome infection intensity, and levels of the affected metabolites were reduced following treatment, albeit not significantly. This study demonstrates that early infection with S. haematobium is associated with alterations in host energy and purine metabolism. Taken together, these changes are consistent with parasite-related clinical manifestations of malnutrition, poor growth and poor physical and cognitive performance observed in schistosome-infected children

Rapid HILIC-Z ion mobility mass spectrometry (RHIMMS) method for untargeted metabolomics of complex biological samples

INTRODUCTION: Recent advances in high-throughput methodologies in the ‘omics’ and synthetic biology fields call for rapid and sensitive workflows in the metabolic phenotyping of complex biological samples. OBJECTIVE: The objective of this research was to evaluate a straightforward to implement LC–MS metabolomics method using a commercially available chromatography column that provides increased throughput. Reducing run time can potentially impact chromatography and therefore the effects of ion mobility spectrometry to expand peak capacity were also evaluated. Additional confidence provided via collision cross section measurements for detected features was also explored. METHODS: A rapid untargeted metabolomics workflow was developed with broad metabolome coverage, combining zwitterionic-phase hydrophilic interaction chromatography (HILIC-Z) with drift tube ion mobility-quadrupole time-of-flight (DTIM-qTOF) mass spectrometry. The analytical performance of our method was explored using extracts from complex biological samples, including a reproducibility study on chicken serum and a simple comparative study on a bacterial metabolome. RESULTS: The method is acronymised RHIMMS for rapid HILIC-Z ion mobility mass spectrometry. We present the RHIMMS workflow starting with data acquisition, followed by data processing and analysis. RHIMMS demonstrates improved chromatographic separation for a selection of metabolites with wide physicochemical properties while maintaining reproducibility at better than 20% over 200 injections at 3.5 min per sample for the selected metabolites, and a mean of 13.9% for the top 50 metabolites by intensity. Additionally, the combination of rapid chromatographic separation with ion mobility allows improved annotation and the ability to distinguish isobaric compounds. CONCLUSION: Our results demonstrate RHIMMS to be a rapid, reproducible, sensitive and high-resolution analytical platform that is highly applicable to the untargeted metabolomics analysis of complex samples. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11306-022-01871-1

Draft genome sequence of isolate Staphylococcus aureus LHSKBClinical, isolated from an infected hip

We report here the genome sequence of a clinical isolate of <i>Staphylococcus aureus</i> from an orthopedic infection. Phenotypically diverse <i>Staphylococcus aureus</i> strains are associated with orthopedic infections and subsequent implant failure, and some are highly resistant to antibiotics. This genome sequence will support further analyses of strains causing orthopedic infections

Recovery from Rapamycin: drug-insensitive activity of yeast target of rapamycin complex 1 (torc1) supports residual proliferation that dilutes rapamycin among progeny cells

The target of rapamycin complex 1 (TORC1) is a key conserved regulator of eukaryotic cell growth. The xenobiotic rapamycin is a potent inhibitor of the yeast complex. Surprisingly, the EGO complex, a nonessential in vivo activator of TORC1, is somehow required for yeast cells to recover efficiently from a period of treatment with rapamycin. Why? Here, we found that rapamycin is only a partial inhibitor of TORC1. We confirmed that saturating amounts of rapamycin do not fully inhibit proliferation of wild-type cells, and we found that the residual proliferation in the presence of the drug is dependent on the EGO complex and on the activity of TORC1. We found that this residual TORC1-dependent proliferation is key to recovery from rapamycin treatment. First, the residual proliferation rate correlates with the ability of cells to recover from treatment. Second, the residual proliferation rate persists long after washout of the drug and until cells recover. Third, the total observable pool of cell-associated rapamycin is extremely stable and decreases only with increasing cell number after washout of the drug. Finally, consideration of the residual proliferation rate alone accurately and quantitatively accounts for the kinetics of recovery of wild-type cells and for the nature and severity of the ego− mutant defect. Overall, our results revealed that rapamycin is a partial inhibitor of yeast TORC1, that persistence of the drug limits recovery, and that rapamycin is not detoxified by yeast but is passively diluted among progeny cells because of residual proliferation

A fluorescence-based assay for the uptake of CPD0801 (DB829) by African trypanosomes

Drug therapies currently used for second stage Human African Trypanosomiasis (HAT) exhibit problems with toxicity, difficulty of administration, and resistance linked to the loss of transporter function. Key to the development of new drugs for HAT is a better understanding of the transport properties of candidate compounds. Standard methods for studying transport utilize radio-labelled permeant or HPLC-MS, however the natural fluorescence of many trypanocidal compounds can be exploited. Here we present a fluorescence-based assay for measuring uptake, by trypanosomes, of CPD0801, a drug candidate for second stage HAT. Sample fluorescence is measured in a 96-well format using a benchtop fluorimeter. Our method is directly applicable to the study of other diamidines with similar fluorescent properties and readily adapted for use with other cell types or fluorescent molecules as we demonstrate for the veterinary trypanocide ethidiu

Stable Isotope-Assisted Metabolomics for Network-Wide Metabolic Pathway Elucidation

The combination of high-resolution LC−MS-based untargeted metabolomics with stable isotope tracing provides a global overview of the cellular fate of precursor metabolites. This methodology enables detection of putative metabolites from biological samples and simultaneous quantification of the pattern and extent of isotope labeling. Labeling of Trypanosoma brucei cell cultures with 50% uniformly 13C-labeled glucose demonstrated incorporation of glucose-derived carbon into 187 of 588 putatively identified metabolites in diverse pathways including carbohydrate, nucleotide, lipid, and amino acid metabolism. Labeling patterns confirmed the metabolic pathways responsible for the biosynthesis of many detected metabolites, and labeling was detected in unexpected metabolites, including two higher sugar phosphates annotated as octulose phosphate and nonulose phosphate. This untargeted approach to stable isotope tracing facilitates the biochemical analysis of known pathways and yields rapid identification of previously unexplored areas of metabolism.