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Severe respiratory syncytial virus (RSV) bronchiolitis in early life is a significant risk factor for future recurrent wheeze (RW) and asthma. The goal of the Azithromycin to Prevent Wheezing following severe RSV bronchiolitis II (APW-RSV II) clinical trial is to evaluate if azithromycin treatment in infants hospitalized with RSV bronchiolitis reduces the occurrence of RW during the preschool years.The APW-RSV II clinical trial is a double-blind, placebo-controlled, parallel-group, randomized trial, including otherwise healthy participants, ages 30 days-18 months, who are hospitalized due to RSV bronchiolitis. The study includes an active randomized treatment phase with azithromycin or placebo for 2 weeks, and an observational phase of 18–48 months. Two hundred participants were enrolled during three consecutive RSV seasons beginning in the fall of 2016 and were randomized to receive oral azithromycin 10 mg/kg/day for 7 days followed by 5 mg/kg/day for an additional 7 days, or matched placebo. The study hypothesis is that in infants hospitalized with RSV bronchiolitis, the addition of azithromycin therapy to routine bronchiolitis care would reduce the likelihood of developing post-RSV recurrent wheeze (≥3 episodes). The primary clinical outcome is the occurrence of a third episode of wheezing, which is evaluated every other month by phone questionnaires and during yearly in-person visits.A secondary objective of the APW-RSV II clinical trial is to examine how azithromycin therapy changes the upper airway microbiome composition, and to determine if these changes are related to the occurrence of post-RSV RW. Microbiome composition is characterized in nasal wash samples obtained before and after the study treatments.This clinical trial may identify the first effective intervention applied during severe RSV bronchiolitis to reduce the risk of post-RSV RW and ultimately asthma.

ABSTRACT Daptomycin, a last-line-of-defense antibiotic for treating Gram-positive infections, is experiencing clinical failure against important infectious agents, including Corynebacterium striatum. The recent transition of daptomycin to generic status is projected to dramatically increase availability, use, and clinical failure. Here we confirm the genetic mechanism of high-level daptomycin resistance (HLDR; MIC = >256 µg/ml) in C. striatum, which evolved within a patient during daptomycin therapy, a phenotype recapitulated in vitro. In all 8 independent cases tested, loss-of-function mutations in phosphatidylglycerol synthase (pgsA2) were necessary and sufficient for high-level daptomycin resistance. Through lipidomic and biochemical analysis, we demonstrate that daptomycin’s activity is dependent on the membrane phosphatidylglycerol (PG) concentration. Until now, the verification of PG as the in vivo target of daptomycin has proven difficult since tested cell model systems were not viable without membrane PG. C. striatum becomes daptomycin resistant at a high level by removing PG from the membrane and changing the membrane composition to maintain viability. This work demonstrates that loss-of-function mutation in pgsA2 and the loss of membrane PG are necessary and sufficient to produce high-level resistance to daptomycin in C. striatum. IMPORTANCE Antimicrobial resistance threatens the efficacy of antimicrobial treatment options, including last-line-of-defense drugs. Understanding how this resistance develops can help direct antimicrobial stewardship efforts and is critical to designing the next generation of antimicrobial therapies. Here we determine how Corynebacterium striatum, a skin commensal and opportunistic pathogen, evolved high-level resistance to a drug of last resort, daptomycin. Through a single mutation, this pathogen was able to remove the daptomycin’s target, phosphatidylglycerol (PG), from the membrane and evade daptomycin’s bactericidal activity. We found that additional compensatory changes were not necessary to support the removal of PG and replacement with phosphatidylinositol (PI). The ease with which C. striatum evolved high-level resistance is cause for alarm and highlights the importance of screening new antimicrobials against a wide range of clinical pathogens which may harbor unique capacities for resistance evolution.

The time-to-result for culture-based microorganism recovery and phenotypic antimicrobial susceptibility testing necessitate initial use of empiric (frequently broad-spectrum) antimicrobial therapy. If the empiric therapy is not optimal, this can lead to adverse patient outcomes and contribute to increasing antibiotic resistance in pathogens. New, more rapid technologies are emerging to meet this need. Many of these are based on identifying resistance genes, rather than directly assaying resistance phenotypes, and thus require interpretation to translate the genotype into treatment recommendations. These interpretations, like other parts of clinical diagnostic workflows, are likely to be increasingly automated in the future. We set out to evaluate the two major approaches that could be amenable to automation pipelines: rules-based methods and machine learning methods. The rules-based algorithm makes predictions based upon current, curated knowledge of Enterobacteriaceae resistance genes. The machine-learning algorithm predicts resistance and susceptibility based on a model built from a training set of variably resistant isolates. As our test set, we used whole genome sequence data from 78 clinical Enterobacteriaceae isolates, previously identified to represent a variety of phenotypes, from fully-susceptible to pan-resistant strains for the antibiotics tested. We tested three antibiotic resistance determinant databases for their utility in identifying the complete resistome for each isolate. The predictions of the rules-based and machine learning algorithms for these isolates were compared to results of phenotype-based diagnostics. The rules based and machine-learning predictions achieved agreement with standard-of-care phenotypic diagnostics of 89.0% and 90.3%, respectively, across twelve antibiotic agents from six major antibiotic classes. Several sources of disagreement between the algorithms were identified. Novel variants of known resistance factors and incomplete genome assembly confounded the rules-based algorithm, resulting in predictions based on gene family, rather than on knowledge of the specific variant found. Low-frequency resistance caused errors in the machine-learning algorithm because those genes were not seen or seen infrequently in the test set. We also identified an example of variability in the phenotype-based results that led to disagreement with both genotype-based methods. Genotype-based antimicrobial susceptibility testing shows great promise as a diagnostic tool, and we outline specific research goals to further refine this methodology.