Lipase-mediated detoxification of host-derived antimicrobial fatty acids by Staphylococcus aureus.

Long-chain fatty acids with antimicrobial properties are abundant on the skin and mucosal surfaces, where they are essential to restrict the proliferation of opportunistic pathogens such as Staphylococcus aureus. These antimicrobial fatty acids (AFAs) elicit bacterial adaptation strategies, which have yet to be fully elucidated. Characterizing the pervasive mechanisms used by S. aureus to resist AFAs could open new avenues to prevent pathogen colonization. Here, we identify the S. aureus lipase Lip2 as a novel resistance factor against AFAs. Lip2 detoxifies AFAs via esterification with cholesterol. This is reminiscent of the activity of the fatty acid-modifying enzyme (FAME), whose identity has remained elusive for over three decades. In vitro, Lip2-dependent AFA-detoxification was apparent during planktonic growth and biofilm formation. Our genomic analysis revealed that prophage-mediated inactivation of Lip2 was rare in blood, nose, and skin strains, suggesting a particularly important role of Lip2 for host - microbe interactions. In a mouse model of S. aureus skin colonization, bacteria were protected from sapienic acid (a human-specific AFA) in a cholesterol- and lipase-dependent manner. These results suggest Lip2 is the long-sought FAME that exquisitely manipulates environmental lipids to promote bacterial growth in otherwise inhospitable niches.

Evaluation of Data-Dependent MS/MS Acquisition Parameters for Non-Targeted Metabolomics and Molecular Networking of Environmental Samples: Focus on the Q Exactive Platform.

Non-targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) is a widely used tool for metabolomics analysis, enabling the detection and annotation of small molecules in complex environmental samples. Data-dependent acquisition (DDA) of product ion spectra is thereby currently one of the most frequently applied data acquisition strategies. The optimization of DDA parameters is central to ensuring high spectral quality, coverage, and number of compound annotations. Here, we evaluated the influence of 10 central DDA settings of the Q Exactive mass spectrometer on natural organic matter samples from ocean, river, and soil environments. After data analysis with classical and feature-based molecular networking using MZmine and GNPS, we compared the total number of network nodes, multivariate clustering, and spectrum quality-related metrics such as annotation and singleton rates, MS/MS placement, and coverage. Our results show that automatic gain control, microscans, mass resolving power, and dynamic exclusion are the most critical parameters, whereas collision energy, TopN, and isolation width had moderate and apex trigger, monoisotopic selection, and isotopic exclusion minor effects. The insights into the data acquisition ergonomics of the Q Exactive platform presented here can guide new users and provide them with initial method parameters, some of which may also be transferable to other sample types and MS platforms.