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Staphylococcus aureus (SA) is an opportunistic pathogen that can cause a wide spectrum of infections, from superficial skin inflammation to severe, and potentially fatal, invasive diseases. Due to the many pottential routes of infection, host derived environmental signals (oxygen availability, nutrients, etc) are vital for host colinzation and thus contribute to SA’s pathogenesis. To uncover the direct effects of environmental factors on SA metabolism, we performed a series of experiments in diverse culture environments and correlated our findings of SA’s metabolic adaptation to some of the pathogen’s known virulence factors. Untargeted metabolomics was conducted on a Thermo Q-Exactive(™) high-resolution mass spectrometer (HRMS). We detected 260 intracellular polar metabolites from our bacteria cultured in both aerobic and anaerobic conditions, as well as glucose and dextrin supplemented cultures. These metabolites were mapped to relevant metabolic pathways to elucidate the adaptive metabolic processes of both methicillin sensitive SA (MSSA) and methicillin resistant SA (MRSA). We also detected increased expression of virulence genes agr-I and sea of MRSA supplemented with both glucose and dextrin by qPCR. With the metabolic data collected that may be associated with the adaptive growth and virulence of SA, our study could set up the foundations for future work to identify metabolic inhibitors/modulators to mitigate SA infections in different growth environments.

In metabolomics, identification of metabolic pathways altered by disease, genetics, or environmental perturbations is crucial to uncover the underlying biological mechanisms. A number of pathway analysis methods are currently available, which are generally based on equal-probability, topological-centrality, or model-separability methods. In brief, prior identification of significant metabolites is needed for the first two types of methods, while each pathway is modeled separately in the model-separability-based methods. In these methods, interactions between metabolic pathways are not taken into consideration. The current study aims to develop a novel metabolic pathway identification method based on multi-block partial least squares (MB-PLS) analysis by including all pathways into a global model to facilitate biological interpretation. The detected metabolites are first assigned to pathway blocks based on their roles in metabolism as defined by the KEGG pathway database. The metabolite intensity or concentration data matrix is then reconstructed as data blocks according to the metabolite subsets. Then, a MB-PLS model is built on these data blocks. A new metric, named the pathway importance in projection (PIP), is proposed for evaluation of the significance of each metabolic pathway for group separation. A simulated dataset was generated by imposing artificial perturbation on four pre-defined pathways of the healthy control group of a colorectal cancer study. Performance of the proposed method was evaluated and compared with seven other commonly used methods using both an actual metabolomics dataset and the simulated dataset. For the real metabolomics dataset, most of the significant pathways identified by the proposed method were found to be consistent with the published literature. For the simulated dataset, the significant pathways identified by the proposed method are highly consistent with the pre-defined pathways. The experimental results demonstrate that the proposed method is effective for identification of significant metabolic pathways, which may facilitate biological interpretation of metabolomics data.

This study aimed to develop and incorporate a secondary electrospray ionization (SESI) setup in combination with both targeted tandem mass spectrometry (MS/MS) and a hybrid metabolomics technique, globally optimized targeted mass spectrometry (GOT-MS), to sensitively detect volatile metabolites from the headspace of in vitro gut microbial culture in a human colonic model (HCM). Two SESI-tandem mass spectrometry panels with a comparable number of targeted metabolites/features (77 compounds in the targeted SESI-MS/MS panel and 75 features in the SESI-GOT-MS/MS panel) were established. The analytical performance of the SESI-GOT-MS/MS method, as well as its biological capability, were examined and compared with the targeted SESI-MS/MS method. As a result, the SESI-GOT-MS/MS method detected a similar number of metabolic features with good reproducibility (coefficient of variation

Although higher intakes of dairy milk are associated with a lower risk of metabolic syndrome (MetS), the underlying protective mechanism remains unclear. This study investigated the dynamic metabolic profile shift following the ingestion of low-fat milk or an isocaloric volume of rice milk in obese individuals with metabolic syndrome (MetS). In a randomized, double-blind, crossover study, postprandial plasma samples ( n = 266) were collected from 19 MetS participants. Plasma samples were analyzed by a targeted metabolomics platform which specifically detects 117 metabolites from 25 metabolic pathways. The comprehensive time-course metabolic profiling in MetS participants indicated that the postprandial metabolic profiles distinguish low-fat milk and rice milk consumption in a time-dependent manner. Metabolic biomarkers, such as orotate, leucine/isoleucine and adenine, showed significantly different trends in the two test beverages. Bayesian statistics identified 12 metabolites associated with clinical characteristics of postprandial vascular endothelial function, such as flow-mediated dilation (FMD), postprandial plasma markers of oxidative stress and NO status. Furthermore, metabolic pathway analysis based on these metabolite data indicated the potential utility of metabolomics to provide mechanistic insights of dietary interventions to regulate postprandial metabolic excursions.

This study examined the ability of Lactobacillus acidophilus (LA) to ferment black tea extract (BTE) and the enhancement of Escherichia coli cellular uptake of phenolic compounds when these bacteria were incubated with fermented BTE. The inhibitory effects of BTE to E. coli bacteria with and without fermentation were compared. Several intracellular phenolic compounds as well as metabolic profiles of E. coli with and without treatments were also determined using a high-performance liquid chromatography-tandem mass spectrometry-based approach. Our results showed that of three concentrations from the non-fermented BTE treatment, only the extract from the 25 mg/mL tea leaves solution could inhibit E. coli survival, while LA-fermented BTE extract from 5, 10, and 25 mg/mL tea leaves solutions all inhibited E. coli growth significantly. Intracellular concentrations of (+)-catechin-3-gallate/(-)-epicatechin-3-gallate and (+)-catechin/(-)-epicatechin were significantly higher when E. coli was treated with fermented BTE in comparison to non-fermented BTE. Scanning electron microscopy images indicated that the intracellular phenolic compounds inhibited E. coli growth by increasing endogenous oxidative stress. Metabolic profiles of E. coli were also investigated to understand their metabolic response when treated with BTE, and significant metabolic changes of E. coli were observed. Metabolic profile data were further analyzed using partial least squares discriminant analysis to distinguish the fermented BTE treatment group from the control group and the non-fermented BTE treatment group. The results indicated a large-scale E. coli metabolic dysregulation induced by the fermented BTE. Our findings showed that LA fermentation can be an efficient approach to enhance phenolic inhibition of bacterial cells through increased endogenous oxidative stress and dysregulated metabolic activities.

Targeted detection is one of the most important methods in mass spectrometry (MS)-based metabolomics; however, its major limitation is the reduced metabolome coverage that results from the limited set of targeted metabolites typically used in the analysis. In this study we describe a new approach, globally optimized targeted (GOT)-MS, that combines many of the advantages of targeted detection and global profiling in metabolomics analysis, including the capability to detect unknowns, broad metabolite coverage, and excellent quantitation. The key step in GOT-MS is a global search of precursor and product ions using a single liquid chromatography–triple quadrupole (LC–QQQ) mass spectrometer. Here, focused on measuring serum metabolites, we obtained 595 precursor ions and 1 890 multiple reaction monitoring (MRM) transitions, under positive and negative ionization modes in the mass range of 60–600 Da. For many of the MRMs/metabolites under investigation, the analytical performance of GOT-MS is better than or at least comparable to that obtained by global profiling using a quadrupole-time-of-flight (Q-TOF) instrument of similar vintage. Using a study of serum metabolites in colorectal cancer (CRC) as a representative example, GOT-MS significantly outperformed a large targeted MS assay containing ∼160 biologically important metabolites and provided a complementary approach to traditional global profiling using Q-TOF-MS. GOT-MS thus expands and optimizes the detection capabilities for QQQ-MS through a novel approach and should have the potential to significantly advance both basic and clinical metabolic research.

Colorectal cancer (CRC) is one of the most prevalent and deadly cancers in the world. Despite an expanding knowledge of its molecular pathogenesis during the past two decades, robust biomarkers to enable screening, surveillance, and therapy monitoring of CRC are still lacking. In this study, we present a targeted liquid chromatography-tandem mass spectrometry-based metabolic profiling approach for identifying biomarker candidates that could enable highly sensitive and specific CRC detection using human serum samples. In this targeted approach, 158 metabolites from 25 metabolic pathways of potential significance were monitored in 234 serum samples from three groups of patients (66 CRC patients, 76 polyp patients, and 92 healthy controls). Partial least-squares-discriminant analysis (PLS-DA) models were established, which proved to be powerful for distinguishing CRC patients from both healthy controls and polyp patients. Receiver operating characteristic curves generated based on these PLS-DA models showed high sensitivities (0.96 and 0.89, respectively, for differentiating CRC patients from healthy controls or polyp patients), good specificities (0.80 and 0.88), and excellent areas under the curve (0.93 and 0.95). Monte Carlo cross validation was also applied, demonstrating the robust diagnostic power of this metabolic profiling approach.