Urinary antihypertensive drug metabolite screening using molecular networking coupled to high-resolution mass spectrometry fragmentation

Introduction Mass spectrometry is the current technique of choice in studying drug metabolism. High-resolution mass spectrometry in combination with MS/MS gas-phase experiments has the potential to contribute to rapid advances in this field. However, the data emerging from such fragmentation spectral files pose challenges to downstream analysis, given their complexity and size. Objectives This study aims to detect and visualize antihypertensive drug metabolites in untargeted metabolomics experiments based on the spectral similarity of their fragmentation spectra. Furthermore, spectral clusters of endogenous metabolites were also examined. Methods Here we apply a molecular networking approach to seek drugs and their metabolites, in fragmentation spectra from urine derived from a cohort of 26 patients on antihypertensive therapy. The mass spectrometry data was collected on a Thermo Q-Exactive coupled to pHILIC chromatography using data dependent analysis (DDA) MS/MS gas-phase experiments. Results In total, 165 separate drug metabolites were found and structurally annotated (17 by spectral matching and 122 by classification based on a clustered fragmentation pattern). The clusters could be traced to 13 drugs including the known antihypertensives verapamil, losartan and amlodipine. The molecular networking approach also generated clusters of endogenous metabolites, including carnitine derivatives, and conjugates containing glutamine, glutamate and trigonelline. Conclusions The approach offers unprecedented capability in the untargeted identification of drugs and their metabolites at the population level and has great potential to contribute to understanding stratified responses to drugs where differences in drug metabolism may determine treatment outcome

Unexpected differential metabolic responses of Campylobacter jejuni to the abundant presence of glutamate and fucose

Introduction: Campylobacter jejuni is the leading cause of foodborne bacterial enteritis in humans, and yet little is known in regard to how genetic diversity and metabolic capabilities among isolates affect their metabolic phenotype and pathogenicity. Objectives: For instance, the C. jejuni 11168 strain can utilize both l-fucose and l-glutamate as a carbon source, which provides the strain with a competitive advantage in some environments and in this study we set out to assess the metabolic response of C. jejuni 11168 to the presence of l-fucose and l-glutamate in the growth medium. Methods: To achieve this, untargeted hydrophilic liquid chromatography coupled to mass spectrometry was used to obtain metabolite profiles of supernatant extracts obtained at three different time points up to 24 h. Results: This study identified both the depletion and the production and subsequent release of a multitude of expected and unexpected metabolites during the growth of C. jejuni 11168 under three different conditions. A large set of standards allowed identification of a number of metabolites. Further mass spectrometry fragmentation analysis allowed the additional annotation of substrate-specific metabolites. The results show that C. jejuni 11168 upon l-fucose addition indeed produces degradation products of the fucose pathway. Furthermore, methionine was faster depleted from the medium, consistent with previously-observed methionine auxotrophy. Conclusions: Moreover, a multitude of not previously annotated metabolites in C. jejuni were found to be increased specifically upon l-fucose addition. These metabolites may well play a role in the pathogenicity of this C. jejuni strain.</p

Microbiome-derived carnitine mimics as previously unknown mediators of gut-brain axis communication

Alterations to the gut microbiome are associated with various neurological diseases, yet evidence of causality and identity of microbiome-derived compounds that mediate gut-brain axis interaction remain elusive. Here, we identify two previously unknown bacterial metabolites 3-methyl-4-(trimethylammonio)butanoate and 4-(trimethylammonio)pentanoate, structural analogs of carnitine that are present in both gut and brain of specific pathogen–free mice but absent in germ-free mice. We demonstrate that these compounds are produced by anaerobic commensal bacteria from the family Lachnospiraceae (Clostridiales) family, colocalize with carnitine in brain white matter, and inhibit carnitine-mediated fatty acid oxidation in a murine cell culture model of central nervous system white matter. This is the first description of direct molecular inter-kingdom exchange between gut prokaryotes and mammalian brain cells, leading to inhibition of brain cell function

Annotation of Specialized Metabolites from High-Throughput and High Resolution Mass Spectrometry Metabolomics

High-throughput mass spectrometry (MS) metabolomics profiling of highly complex samples allows the comprehensive detection of hundreds to thousands of metabolites under a given condition and point in time and produces information-rich data sets on known and unknown metabolites. One of the main challenges is the identification and annotation of metabolites from these complex data sets since the number of authentic standards available for specialized metabolites is far lower than an account for the number of mass spectral features. Previously, we reported two novel tools, MetNet and MetCirc, for putative annotation and structural prediction on unknown metabolites using known metabolites as baits. MetNet employs differences between m/z values of MS1 features, which correspond to metabolic transformations, and statistical associations, while MetCirc uses MS/MS features as input and calculates similarity scores of aligned spectra between features to guide the annotation of metabolites. Here, we showcase the use of MetNet and MetCirc to putatively annotate metabolites and provide detailed instructions as to how those can be used. While our case studies are from plants, the tools find equal utility in studies on bacterial, fungal, or mammalian xenobiotic samples

A workflow for bacterial metabolic fingerprinting and lipid profiling: application to Ciprofloxacin challenged Escherichia coli

The field of lipidomics focuses upon the non-targeted analysis of lipid composition, the process of which follows similar routines to those applied in conventional metabolic profiling, however lipidomics differs with respect to the sample preparation steps and chosen analytical platform applied to the sample analysis. Conventionally, lipidomics has applied analytical techniques such as direct infusion mass spectrometry and more recently reverse phase liquid chromatography–mass spectrometry, for the detection of mono-, di-, and tri-acyl glycerols, phospholipids, and other complex lipophilic species such as sterols. The field is rapidly expanding, especially with respect to the clinical sciences where it is known that changes of lipid composition, especially phospholipids, are commonly associated with many disease processes. As a proof of principle study, a small number of Escherichia coli isolates were selected on the basis of their sensitivity to a second generation fluoroquinolone antibiotic, known as Ciprofloxacin (E. coli isolates 161 and 171, non-ST131 isolates, which are resistant and sensitive respectively: E. coli isolates 160 and 173, ST131 sequence isolates which are resistant and susceptible respectively). It has been proposed that Ciprofloxacin may be a surface active drug that interacts at the surface-water interface of the phospholipid bi-layer where the head groups reside. Further, antibiotic resistance through intracellular exclusion is known to result in remodelling of the phospholipid membrane. Therefore, to study the effects of Ciprofloxacin on both susceptible and resistant bacterial strains, lipid profiling would present an informative approach. Control and antibiotic challenged cultures for each of the isolates were compared for changes in metabolite and lipid composition as detected by FT-IR spectroscopy and RP-UHPLC–MS, and appraised with a variety of chemometric data analysis approaches. The developed bacterial lipidomics workflow was deemed to be highly reproducible (with respect to the employed technical and analytical routines) and led to the detection of a large array of lipid classes as well as highlighting a range of significant lipid alterations that differed in regulation between susceptible and resistant E. coli isolates