Mechanisms and clinical relevance of bacterial heteroresistance

The rise of antibiotic-resistant bacteria has led to an urgent need for rapid detection of drug resistance in clinical samples, and improvements in global surveillance. Here we show how de Bruijn graph representation of bacterial diversity can be used to identify species and resistance profiles of clinical isolates. We implement this method for Staphylococcus aureus and Mycobacterium tuberculosis in a software package (‘Mykrobe predictor') that takes raw sequence data as input, and generates a clinician-friendly report within 3 minutes on a laptop. For S. aureus, the error rates of our method are comparable to gold-standard phenotypic methods, with sensitivity/specificity of 99.1%/99.6% across 12 antibiotics (using an independent validation set, n=470). For M. tuberculosis, our method predicts resistance with sensitivity/specificity of 82.6%/98.5% (independent validation set, n=1,609); sensitivity is lower here, probably because of limited understanding of the underlying genetic mechanisms. We give evidence that minor alleles improve detection of extremely drug-resistant strains, and demonstrate feasibility of the use of emerging single-molecule nanopore sequencing techniques for these purposes.

Colistin is the last resort for treatment of multidrug-resistant Acinetobacter baumannii. Unfortunately, resistance to colistin has been reported all over the world. The highest resistance rate was reported in Asia, followed by Europe. The heteroresistance rate of A. baumannii to colistin is generally higher than the resistance rate. The mechanism of resistance might be loss of lipopolysaccharide or/and the PmrAB two-component system. Pharmacokinetic/pharmacodynamic studies revealed that colistin monotherapy is unable to prevent resistance, and combination therapy might be the best antimicrobial strategy against colistin-resistant A. baumannii. Colistin/rifampicin and colistin/carbapenem are the most studied combinations that showed promising results in vitro, in vivo and in the clinic. New peptides showing good activity against colistin-resistant A. baumannii are also being investigated.

Quinolone antimicrobials are synthetic and widely used in clinical medicine. Resistance emerged with clinical use and became common in some bacterial pathogens. Mechanisms of resistance include two categories of mutation and acquisition of resistance-conferring genes. Resistance mutations in one or both of the two drug target enzymes, DNA gyrase and DNA topoisomerase IV, are commonly in a localized domain of the GyrA and ParE subunits of the respective enzymes and reduce drug binding to the enzyme-DNA complex. Other resistance mutations occur in regulatory genes that control the expression of native efflux pumps localized in the bacterial membrane(s). These pumps have broad substrate profiles that include quinolones as well as other antimicrobials, disinfectants, and dyes. Mutations of both types can accumulate with selection pressure and produce highly resistant strains. Resistance genes acquired on plasmids can confer low-level resistance that promotes the selection of mutational high-level resistance. Plasmid-encoded resistance is due to Qnr proteins that protect the target enzymes from quinolone action, one mutant aminoglycoside-modifying enzyme that also modifies certain quinolones, and mobile efflux pumps. Plasmids with these mechanisms often encode additional antimicrobial resistances and can transfer multidrug resistance that includes quinolones. Thus, the bacterial quinolone resistance armamentarium is large.