<i>Staphylococcal </i>and other bacterial infections are a global cause of death, infecting upwards of 100,000 people each year and killing 10-30% of those infected. Exasperating this problem is the increasing number of bacteria that continue to develop resistance to common drug treatments. To combat drug-resistance, it is necessary to understand the mechanism by which bacteria develop antibiotic resistance so new treatments can be developed. One method for determining mechanisms of antibiotic resistance is metabolomics - the study of small molecules within a cell, tissue, or organism. Metabolites can be extracted from these biological samples, then identified and quantified to understand the modified metabolic processes that confer antibiotic resistance. Thus, there is an immediate need to develop standardized analytical methods and to validate results using authentic standards. Given the higher sensitivity and broader coverage of the metabolome, liquid chromatography coupled with mass spectrometry (LC-MS) has been an important analytical method for metabolomics. However, it is necessary to first establish an optimal chromatographic protocol to resolve the metabolites within a complex mixture that can then be readily detected using high-resolution MS. Towards this end, we present an effective LC separation method for metabolomics using a 150 mm C<sub>18</sub>-column with a 28-minute gradient using a mobile phase of water and acetonitrile (0.1% formic acid). Compared to HILIC BEH-Amide and different column lengths (50, 100 mm), as well as varied mobile phase gradients and times, our approach detected most of the metabolites of interest (amino-acid and TCA cycle metabolites) with improved peak shapes and separation. Fragmentation data was collected in a data-independent acquisition mode to facilitate metabolite identification, and the electrospray ion source energy was optimized to limit in-source fragmentation of phenylalanine and other metabolites. These optimized LC parameters are now being used to establish an in-house chemical library of validated spectra. A standardized LC-MS method will provide a quick and reliable resource for determining metabolite identities and concentrations in biological samples.

Methods for rapid metabolite classification

ACS Spring 2022

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