was initially reported as the first plasmid-mediated colistin resistance gene in clinical isolates of and in China and has subsequently been identified worldwide in various species of the family encodes a phosphoethanolamine transferase, and its expression has been shown to generate phosphoethanolamine-modified bis-phosphorylated hexa-acylated lipid A in Here, we investigated the effects of on colistin susceptibility and on lipopolysaccharide structures in laboratory and clinical strains of the Gram-negative ESKAPE (, ,, ,, and species) pathogens, which are often treated clinically by colistin. The effects of on colistin resistance were determined using MIC assays of laboratory and clinical strains of ,, , and Lipid A structural changes resulting from MCR-1 were analyzed by mass spectrometry. The introduction of led to colistin resistance in, , and but only moderately reduced susceptibility in Phosphoethanolamine modification of lipid A was observed consistently for all four species. These findings highlight the risk of colistin resistance as a consequence of expression among ESKAPE pathogens, especially in and Furthermore, the observation that lipid A structures were modified despite only modest increases in colistin MICs in some instances suggests more sophisticated surveillance methods may need to be developed to track the dissemination of or plasmid-mediated phosphoethanolamine transferases in general.

Rapid diagnostics that enable identification of infectious agents improve patient outcomes, antimicrobial stewardship, and length of hospital stay. Current methods for pathogen detection in the clinical laboratory include biological culture, nucleic acid amplification, ribosomal protein characterization, and genome sequencing. Pathogen identification from single colonies by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) analysis of high abundance proteins is gaining popularity in clinical laboratories. Here, we present a novel and complementary approach that utilizes essential microbial glycolipids as chemical fingerprints for identification of individual bacterial species. Gram-positive and negative bacterial glycolipids were extracted using a single optimized protocol. Extracts of the clinically significant ESKAPE pathogens: E nterococcus faecium, S taphylococcus aureus, K lebsiella pneumoniae, A cinetobacter baumannii, P seudomonas aeruginosa, and E nterobacter spp. were analyzed by MALDI-TOF-MS in negative ion mode to obtain glycolipid mass spectra. A library of glycolipid mass spectra from 50 microbial entries was developed that allowed bacterial speciation of the ESKAPE pathogens, as well as identification of pathogens directly from blood bottles without culture on solid medium and determination of antimicrobial peptide resistance. These results demonstrate that bacterial glycolipid mass spectra represent chemical barcodes that identify pathogens, potentially providing a useful alternative to existing diagnostics.

The pathway to colistin resistance in K. pneumoniae primarily involves lipid A modification with Ara4N in clinical settings.

Infectious diseases have a substantial global health impact. Clinicians need rapid and accurate diagnoses of infections to direct patient treatment and improve antibiotic stewardship. Current technologies employed in routine diagnostics are based on bacterial culture followed by morphological trait differentiation and biochemical testing, which can be time-consuming and labor-intensive. With advances in mass spectrometry (MS) for clinical diagnostics, the U.S. Food and Drug Administration has approved two microbial identification platforms based on matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MS analysis of microbial proteins. We recently reported a novel and complementary approach by comparing MALDI-TOF mass spectra of microbial membrane lipid fingerprints to identify ESKAPE pathogens. However, this lipid-based approach used a sample preparation method that required more than a working day from sample collection to identification. Here, we report a new method that extracts lipids efficiently and rapidly from microbial membranes using an aqueous sodium acetate (SA) buffer that can be used to identify clinically relevant Gram-positive and -negative pathogens and fungal species in less than an hour. The SA method also has the ability to differentiate antibiotic-susceptible and antibiotic-resistant strains, directly identify microbes from biological specimens, and detect multiple pathogens in a mixed sample. These results should have positive implications for the manner in which bacteria and fungi are identified in general hospital settings and intensive care units.

Human ether-á-go-go–related gene (hERG) potassium channels are critical for cardiac action potential repolarization. Cardiac hERG channels comprise two primary isoforms: hERG1a, which has a regulatory N-terminal Per-Arnt-Sim (PAS) domain, and hERG1b, which does not. Isolated, PAS-containing hERG1a N-terminal regions (NTRs) directly regulate NTR-deleted hERG1a channels; however, it is unclear whether hERG1b isoforms contain sufficient machinery to support regulation by hERG1a NTRs. To test this, we constructed a series of PAS domain–containing hERG1a NTRs (encoding amino acids 1–181, 1–228, 1–319, and 1–365). The NTRs were also predicted to form from truncation mutations that were linked to type 2 long QT syndrome (LQTS), a cardiac arrhythmia disorder associated with mutations in the hERG gene. All of the hERG1a NTRs markedly regulated heteromeric hERG1a/hERG1b channels and homomeric hERG1b channels by decreasing the magnitude of the current–voltage relationship and slowing the kinetics of channel closing (deactivation). In contrast, NTRs did not measurably regulate hERG1a channels. A short NTR (encoding amino acids 1–135) composed primarily of the PAS domain was sufficient to regulate hERG1b. These results suggest that isolated hERG1a NTRs directly interact with hERG1b subunits. Our results demonstrate that deactivation is faster in hERG1a/hERG1b channels compared to hERG1a channels because of fewer PAS domains, not because of an inhibitory effect of the unique hERG1b NTR. A decrease in outward current density of hERG1a/hERG1b channels by hERG1a NTRs may be a mechanism for LQTS.

Rapid infection diagnosis is critical to improving patient treatment and outcome. Recent studies have shown microbial lipids to be sensitive and selective biomarkers for identifying bacterial and fungal species and antimicrobial resistance. Practical procedures for microbial lipid biomarker analysis will therefore improve patient outcomes and antimicrobial stewardship. However, current lipid extraction methods require significant hands-on time and are thus not suited for direct adoption as a clinical assay for microbial identification. Here, we have developed a method for lipid extraction directly on the surface of stainless-steel matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) plates, termed fast lipid analysis technique or FLAT, which facilitates the identification of bacterial and fungal species using a sub-60-minute workflow. Additionally, our method detects lipid A modifications in Gram-negative bacteria that are associated with antimicrobial resistance, including to colistin.

Acinetobacter baumannii is a prevalent nosocomial pathogen with a high incidence of multidrug resistance. Treatment of infections due to this organism with colistin, a last-resort antibiotic of the polymyxin class, can result in the emergence of colistin-resistant strains. Colistin resistance primarily occurs via modifications of the terminal phosphate moieties of lipopolysaccharide-derived lipid A, which reduces overall membrane electronegativity. These modifications are readily identified by mass spectrometry (MS). In this study, we prospectively collected Acinetobacter baumannii complex clinical isolates from a hospital system in Pennsylvania over a 3-year period. All isolates were evaluated for colistin resistance using standard MIC testing by both agar dilution and broth microdilution, as well as genospecies identification and lipid A profiling using MS analyses. Overall, an excellent correlation between colistin susceptibility and resistance, determined by MIC testing, and the presence of a lipid A modification, determined by MS, was observed with a sensitivity of 92.9% and a specificity of 94.0%. Additionally, glycolipid profiling was able to differentiate A. baumannii complex organisms based on their membrane lipids. With the growth of MS use in clinical laboratories, a reliable MS-based glycolipid phenotyping method that identifies colistin resistance in A. baumannii complex clinical isolates, as well as other Gram-negative organisms, represents an alternative or complementary approach to existing diagnostics.

With the increased prevalence of multidrug-resistant Gram-negative bacteria, the use of colistin and other last-line antimicrobials is being revisited clinically. As a result, there has been an emergence of colistin-resistant bacterial species, including Acinetobacter baumannii and Klebsiella pneumoniae. The rapid identification of such pathogens is vitally important for the effective treatment of patients. We previously demonstrated that mass spectrometry of bacterial glycolipids has the capacity to identify and detect colistin resistance in a variety of bacterial species. In this study, we present a machine learning paradigm that is capable of identifying A. baumannii, K. pneumoniae and their colistin-resistant forms using a manually curated dataset of lipid mass spectra from 48 additional Gram-positive and -negative organisms. We demonstrate that these classifiers detect A. baumannii and K. pneumoniae in isolate and polymicrobial specimens, establishing a framework to translate glycolipid mass spectra into pathogen identifications.