Faculty Bibliography

Background/UNASSIGNED:The epidemiology of nosocomial bloodstream infections (NBSIs) in patients with coronavirus disease 2019 (COVID-19) is poorly understood, due in part to substantial disease heterogeneity resulting from multiple potential pathogens. Methods/UNASSIGNED:We identified risk factors for NBSIs and examined the association between NBSIs and mortality in a retrospective cohort of patients hospitalized with COVID-19 in 2 New York City hospitals during the height of the pandemic. We adjusted for the potential effects of factors likely to confound that association, including age, race, illness severity upon admission, and underlying health status. Results/UNASSIGNED:infections did not have an identifiable source and were not associated with common risk factors for infection by these organisms. Conclusions/UNASSIGNED:Pathogen species and mortality exhibited temporal differences. Early recognition of risk factors among COVID-19 patients could potentially decrease NBSI-associated mortality through early COVID-19 and antimicrobial treatment.

In this era of rising antibiotic resistance, in contrast to our increasing understanding of mechanisms that cause resistance, our understanding of mechanisms that influence the propensity to evolve resistance remains limited. Here, we identified genetic factors that facilitate the evolution of resistance to carbapenems, the antibiotic of 'last resort,' in Klebsiella pneumoniae, the major carbapenem resistant species. In clinical isolates, we found that high-level transposon insertional mutagenesis plays an important role in contributing to high-level resistance frequencies in several major and emerging carbapenem-resistant lineages. A broader spectrum of resistance-conferring mutations for select carbapenems such as ertapenem also enables higher resistance frequencies and importantly, creates stepping-stones to achieve high-level resistance to all carbapenems. These mutational mechanisms can contribute to the evolution of resistance, in conjunction with the loss of systems that restrict horizontal resistance gene uptake, such as the CRISPR-Cas system. Given the need for greater antibiotic stewardship, these findings argue that in addition to considering the current efficacy of an antibiotic for a clinical isolate in antibiotic selection, considerations of future efficacy are also important. The genetic background of a clinical isolate and the exact antibiotic identity can and should also be considered as it is a determinant of a strain's propensity to become resistant. Together, these findings thus provide a molecular framework for understanding acquisition of carbapenem resistance in K. pneumoniae with important implications for diagnosing and treating this important class of pathogens.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Among the most urgent public health threats is the worldwide emergence of carbapenem-resistant Enterobacteriaceae^1-4, which are resistant to the antibiotic class of 'last resort'. In the United States and Europe, carbapenem-resistant strains of the Klebsiella pneumoniae ST258 (ref. ⁵) sequence type are dominant, endemic^6-8 and associated with high mortality^6,9,10. We report the global evolution of pathogenicity in carbapenem-resistant K. pneumoniae, resulting in the repeated convergence of virulence and carbapenem resistance in the United States and Europe, dating back to as early as 2009. We demonstrate that K. pneumoniae can enhance its pathogenicity by adopting two opposing infection programs through easily acquired gain- and loss-of-function mutations. Single-nucleotide polymorphisms in the capsule biosynthesis gene wzc lead to hypercapsule production, which confers phagocytosis resistance, enhanced dissemination and increased mortality in animal models. In contrast, mutations disrupting capsule biosynthesis genes impair capsule production, which enhances epithelial cell invasion, in vitro biofilm formation and persistence in urinary tract infections. These two types of capsule mutants have emerged repeatedly and independently in Europe and the United States, with hypercapsule mutants associated with bloodstream infections and capsule-deficient mutants associated with urinary tract infections. In the latter case, drug-tolerant K. pneumoniae can persist to yield potentially untreatable, persistent infection.

Multidrug resistant organisms are a serious threat to human health^1,2. Fast, accurate antibiotic susceptibility testing (AST) is a critical need in addressing escalating antibiotic resistance, since delays in identifying multidrug resistant organisms increase mortality^3,4 and use of broad-spectrum antibiotics, further selecting for resistant organisms. Yet current growth-based AST assays, such as broth microdilution⁵, require several days before informing key clinical decisions. Rapid AST would transform the care of patients with infection while ensuring that our antibiotic arsenal is deployed as efficiently as possible. Growth-based assays are fundamentally constrained in speed by doubling time of the pathogen, and genotypic assays are limited by the ever-growing diversity and complexity of bacterial antibiotic resistance mechanisms. Here we describe a rapid assay for combined genotypic and phenotypic AST through RNA detection, GoPhAST-R, that classifies strains with 94-99% accuracy by coupling machine learning analysis of early antibiotic-induced transcriptional changes with simultaneous detection of key genetic resistance determinants to increase accuracy of resistance detection, facilitate molecular epidemiology and enable early detection of emerging resistance mechanisms. This two-pronged approach provides phenotypic AST 24-36 h faster than standard workflows, with <4 h assay time on a pilot instrument for hybridization-based multiplexed RNA detection implemented directly from positive blood cultures.

Accurate orthologue identification is a vital component of bacterial comparative genomic studies, but many popular sequence-similarity-based approaches do not scale well to the large numbers of genomes that are now generated routinely. Furthermore, most approaches do not take gene synteny into account, which is useful information for disentangling paralogues. Here, we present SynerClust, a user-friendly synteny-aware tool based on synergy that can process thousands of genomes. SynerClust was designed to analyse genomes with high levels of local synteny, particularly prokaryotes, which have operon structure. SynerClust's run-time is optimized by selecting cluster representatives at each node in the phylogeny; thus, avoiding the need for exhaustive pairwise similarity searches. In benchmarking against Roary, Hieranoid2, PanX and Reciprocal Best Hit, SynerClust was able to more completely identify sets of core genes for datasets that included diverse strains, while using substantially less memory, and with scalability comparable to the fastest tools. Due to its scalability, ease of installation and use, and suitability for a variety of computing environments, orthogroup clustering using SynerClust will enable many large-scale prokaryotic comparative genomics efforts.

Resistance to colistin, a polypeptide drug used as an agent of last resort for the treatment of infections caused by multidrug-resistant (MDR) and extensively drug-resistant (XDR) Gram-negative bacteria, including carbapenem-resistant Enterobacteriaceae (CRE), severely limits treatment options and may even transform an XDR organism into one that is pan-resistant. We investigated the synergistic activity of colistin in combination with 19 antibiotics against a collection of 20 colistin-resistant Enterobacteriaceae isolates, 15 of which were also CRE. All combinations were tested against all strains using an inkjet printer-assisted digital dispensing checkerboard array, and the activities of those that demonstrated synergy by this method were evaluated against a single isolate in a time-kill synergy study. Eighteen of 19 combinations demonstrated synergy against two or more isolates, and the 4 most highly synergistic combinations (colistin combined with linezolid, rifampin, azithromycin, and fusidic acid) were synergistic against ≥90% of strains. Sixteen of 18 combinations (88.9%) that were synergistic in the checkerboard array were also synergistic in a time-kill study. Our findings demonstrate that colistin in combination with a range of antibiotics, particularly protein and RNA synthesis inhibitors, exhibits synergy against colistin-resistant strains, suggesting that colistin may exert a subinhibitory permeabilizing effect on the Gram-negative bacterial outer membrane even in isolates that are resistant to it. These findings suggest that colistin combination therapy may have promise as a treatment approach for patients infected with colistin-resistant XDR Gram-negative pathogens.

Identifying resistance to antiretroviral drugs is crucial for ensuring the successful treatment of patients infected with viruses such as human immunodeficiency virus (HIV) or hepatitis C virus (HCV). In contrast to Sanger sequencing, next-generation sequencing (NGS) can detect resistance mutations in minority populations. Thus, genotypic resistance testing based on NGS data can offer novel, treatment-relevant insights. Since existing web services for analyzing resistance in NGS samples are subject to long processing times and follow strictly rules-based approaches, we developed geno2pheno[ngs-freq], a web service for rapidly identifying drug resistance in HIV-1 and HCV samples. By relying on frequency files that provide the read counts of nucleotides or codons along a viral genome, the time-intensive step of processing raw NGS data is eliminated. Once a frequency file has been uploaded, consensus sequences are generated for a set of user-defined prevalence cutoffs, such that the constructed sequences contain only those nucleotides whose codon prevalence exceeds a given cutoff. After locally aligning the sequences to a set of references, resistance is predicted using the well-established approaches of geno2pheno[resistance] and geno2pheno[hcv]. geno2pheno[ngs-freq] can assist clinical decision making by enabling users to explore resistance in viral populations with different abundances and is freely available at http://ngs.geno2pheno.org.

BACKGROUND:Quality Assurance (QA) programs are essential to evaluate performance in diagnostics laboratories. OBJECTIVES:We present the results from the first QA for HIV-1 genotypic tropism testing, organized and coordinated by the Institute of Virology at the University of Cologne. STUDY DESIGN:12 cell culture-derived viral strains of different HIV-1 clades from the NIH AIDS Reagent Program were sent to the participants to be processed with their standard diagnostic methods Fasta files containing the V3 region sequence were centrally analyzed at the Institute of Virology, Cologne. All samples were sent in parallel for phenotypic testing. RESULTS:and PhenXR only achieved results for 58.3% of the samples. All four X4 samples were identified by 31/36 laboratories, two laboratories amplified 3/4×4 samples, and three detected 2/4×4 samples. There was high concordance among the genotypic and phenotypic results, although differences in FPR values were detected. Most deficiencies in sequence editing did not result in wrong classification of X4 viruses as R5, with the exception of sample NRZ05 by laboratory 38, but in an overestimation of CXCR4 use. CONCLUSIONS:This demonstrates that genotypic tropism prediction is a safe procedure for clinical purposes. As we used homogeneous cell culture samples and all sequence fasta files were centrally analyzed, variations in FPR values can only be attributed to sample preparation, RT-PCR or sequence editing protocols.

BACKGROUND:HIV-1 drug resistance can be measured with phenotypic drug-resistance tests. However, the output of these tests, the resistance factor (RF), requires interpretation with respect to the in vivo activity of the tested variant. Specifically, the dynamic range of the RF for each drug has to be divided into a suitable number of clinically meaningful intervals. METHODS:We calculated a susceptible-to-intermediate and an intermediate-to-resistant cutoff per drug for RFs predicted by geno2pheno[resistance]. Probability densities for therapeutic success and failure were estimated from 10,444 treatment episodes. The density estimation procedure corrects for the activity of the backbone drug compounds and for therapy failure without drug resistance. For estimating the probability of therapeutic success given an RF, we fit a sigmoid function. The cutoffs are given by the roots of the third derivative of the sigmoid function. RESULTS:For performance assessment, we used geno2pheno[resistance] RF predictions and the cutoffs for predicting therapeutic success in 2 independent sets of therapy episodes. HIVdb was used for performance comparison. On one test set (n = 807), our cutoffs and HIVdb performed equally well receiver operating characteristic curve [(ROC)-area under the curve (AUC): 0.68]. On the other test set (n = 917), our cutoffs (ROC-AUC: 0.63) and HIVdb (ROC-AUC: 0.65) performed comparatively well. CONCLUSIONS:Our method can be used for calculating clinically relevant cutoffs for (predicted) RFs. The method corrects for the activity of the backbone drug compounds and for therapy failure without drug resistance. Our method's performance is comparable with that of HIVdb. RF cutoffs for the latest version of geno2pheno[resistance] have been estimated with this method.