A model for the prediction of antimicrobial resistance in Escherichia coli based on a comparative evaluation of fatty acid profiles

Comparison of freeze-thaw and sonication cycle-based methods for extracting AMR-associated metabolites from Staphylococcus aureus

Emerging antimicrobial resistance (AMR) among Gram-positive pathogens, specifically in Staphylococcus aureus (S. aureus), is becoming a leading public health concern demanding effective therapeutics. Metabolite modulation can improve the efficacy of existing antibiotics and facilitate the development of effective therapeutics. However, it remained unexplored for drug-resistant S. aureus (gentamicin and methicillin-resistant), primarily due to the dearth of optimal metabolite extraction protocols including a protocol for AMR-associated metabolites. Therefore, in this investigation, we have compared the performance of the two most widely used methods, i.e., freeze-thaw cycle (FTC) and sonication cycle (SC), alone and in combination (FTC + SC), and identified the optimal method for this purpose. A total of 116, 119, and 99 metabolites were identified using the FTC, SC, and FTC + SC methods, respectively, leading to the identification of 163 metabolites cumulatively. Out of 163, 69 metabolites were found to be associated with AMR in published literature consisting of the highest number of metabolites identified by FTC (57) followed by SC (54) and FTC + SC (40). Thus, the performances of FTC and SC methods were comparable with no additional benefits of combining both. Moreover, each method showed biasness toward specific metabolite(s) or class of metabolites, suggesting that the choice of metabolite extraction method shall be decided based on the metabolites of interest in the investigation.

Colistin Treatment Affects Lipid Composition of Acinetobacter baumannii

Multidrug-resistant Acinetobacter baumannii (A. baumannii) causes severe and often fatal healthcare-associated infections due partly to antibiotic resistance. There are no studies on A. baumannii lipidomics of susceptible and resistant strains grown at lethal and sublethal concentrations. Therefore, we analyzed the impact of colistin resistance on glycerolipids’ content by using untargeted lipidomics on clinical isolate. Nine lipid sub-classes were annotated, including phosphatidylcholine, rarely detected in the bacterial membrane among 130 different lipid species. The other lipid sub-classes detected are phosphatidylethanolamine (PE), phosphatidylglycerol (PG), lysophosphatidylethanolamine, hemibismonoacylglycerophosphate, cardiolipin, monolysocardiolipin, diacylglycerol, and triacylglycerol. Under lethal and sublethal concentrations of colistin, significant reduction of PE was observed on the resistant and susceptible strain, respectively. Palmitic acid percentage was higher at colistin at low concentration but only for the susceptible strain. When looking at individual lipid species, the most abundant PE and PG species (PE 34:1 and PG 34:1) are significantly upregulated when the susceptible and the resistant strains are cultivated with colistin. This is, to date, the most exhaustive lipidomics data compilation of A. baumannii cultivated in the presence of colistin. This work is highlighting the plasma membrane plasticity used by this gram-negative bacterium to survive colistin treatment.