Multiple Reaction Monitoring Profiling (MRM-Profiling) of Lipids To Distinguish Strain-Level Differences in Microbial Resistance in Escherichia coli

Abstract

The worldwide increase in antimicrobial resistance is due to antibiotic over-use in agriculture and over-prescription in medicine. For appropriate and timely patient support, faster diagnosis of antimicrobial resistance is required. Current methods for bacterial identification rely on genomics and proteomics and use comparisons with databases of known strains, but the diagnostic value of metabolites and lipids has not been explored significantly. Standard mass spectrometry/chromatography methods involve multiple dilutions during sample preparation and separation. To increase the amount of chemical information acquired and the speed of analysis of lipids, multiple reaction monitoring profiling (MRM-Profiling) has been applied. The MRM-Profiling workflow includes a discovery stage and a screening stage. The discovery stage employs precursor ion (PREC) and neutral loss (NL) scans to screen representative pooled samples for functional groups associated with particular lipid classes. The information from the first stage is organized in precursor/product ion pairs, or MRMs, and the screening stage rapidly interrogates individual samples for these MRMs. In this study, we performed MRM-Profiling of lipid extracts from four different strains of Escherichia coli cultured with amoxicillin or amoxicillin/clavulanate, a beta-lactam and beta-lactamase inhibitor, respectively. T-tests, analysis of variance (ANOVA) and receiver operating characteristic (ROC) curves were used to determine the significance of each MRM. Principal component analysis (PCA) was applied to distinguish different strains cultured under conditions that allowed or disallowed development of bacterial resistance. The results demonstrate that MRM-Profiling distinguishes the lipid profiles of resistant and non-resistant E. coli strains. Annually there are over 1.2 billion health care visits in the United States of which 12.6% result in ~154 million antibiotic prescriptions for bacterial infections. 1 Urinary, respiratory and skin infections are most common. 2 The top pathogens responsible for 80 to 90% of infections are Escherichia coli, Staphylococcus aureus, and Klebsiella pneumoniae, the latter two of which are part of the ESKAPE group of pathogens which also include Enterococcus faecium, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species. 3-5 Infections are especially common in certain care settings and patient populations where they cause high recurrence rates and great severity of infections. 6-8 Potential infections are frequently treated prior to confirmation of the presence of pathogens, leading to over-prescription which is strongly associated with the development of antibioticresistance and may eradicate potentially commensal microbiome populations, resulting in dysfunction against future pathogens. 9-12 High rates of infection recurrence and the emergence of pathogens with antimicrobial resistance are found among patients with frequent antibiotic dosing. 11,13,14 There therefore remains a need for rapid bacterial identificati...

Exacerbation of Nanoparticle-Induced Acute Pulmonary Inflammation in a Mouse Model of Metabolic Syndrome

“…lipid profiling results via LC-MS/MS (35,(37)(38)(39)(40)(41)(42)(43)(44). Our current study only evaluated transcriptional regulation of ALOX, future studies are needed to examine protein levels and activity.…”

“…MRM-profiling was originally proposed as a twophase (discovery and screening) method based on flow injection and chemical functional profiling for small molecule biomarker discovery (34,45). It has been applied to various studies (37)(38)(39)(40)(41)(42) and here, we used the approach to interrogate the samples for MRMs related to lipid mediators reported in the literature (46)(47)(48)(49). The ion intensities yielded were compared to a blank in order to eliminate the ones that were not informative.…”

Lipidomic Profiling of the Epidermis in a Mouse Model of Dermatitis Reveals Sexual Dimorphism and Changes in Lipid Composition before the Onset of Clinical Disease

“…The composite samples were analyzed using a previously described methodology of MRM-profiling discovery experiments [ 31 ]. Briefly, neutral loss (NL) and precursor ion (Prec) scans were used to profile phospholipids, acylcarnitines (AC), sulfatides, cholesteryl esters, ceramides, glycerolipids with diverse fatty acid acyl residues, triacylglycerides, and free fatty acids in positive and negative ion modes [ 31 , 71 , 72 , 73 , 74 , 75 ]. Using a micro-autosampler (G1367A), 8 µL of the sample was directly delivered into a QQQ6410 triple quadrupole mass spectrometer (Agilent Technologies, San Jose, CA, USA) equipped with an ESI ion source.…”

Multiple reaction monitoring profiling as an analytical strategy to investigate lipids in extracellular vesicles

“…Here, we report the application of MRM profiling 9,12 to explore lipids present in different types of EVs. We report discrimination between the lipid profiles of human lymphocyte EVs based on the effects of antibody stimulation and response to mycophenolate (MPA) exposure.…”

“…In order to access functional group information, the MRM profiling workflow starts with a discovery phase in which the functional group specific tandem mass spectrometry (MS) (MS/MS) scans-precursor (prec) and neutral loss (NL) scans-are applied to a sample that is representative of all those being studied. The choice of prec and NL scans is based on prior knowledge of the fragmentation of lipids both from experiment and from the literature, principally Here, we report the application of MRM profiling 9,12 to explore lipids present in different types of EVs. We report discrimination between the lipid profiles of human lymphocyte EVs based on the effects of antibody stimulation and response to mycophenolate (MPA) exposure.…”