Whole-Genome Sequencing To Identify Drivers of Carbapenem-Resistant Klebsiella pneumoniae Transmission within and between Regional Long-Term Acute-Care Hospitals

Han, Jennifer; Lapp, Zena; Bushman, Frederic D.; Lautenbach, Ebbing; Goldstein, Ellie J. C.; Mattei, Lisa M.; Hofstaedter, Casey E.; Kim, Dorothy; Nachamkin, Irving; Garrigan, Charles; Jain, Tanya; Bilker, Warren B.; Wolford, Hannah; Slayton, Rachel B.; Wise, Jacqueleen; Tolomeo, Pam; Snitkin, Evan S.

doi:10.1128/aac.01622-19

Cited by 28 publications

(82 citation statements)

References 42 publications

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“…Core genome variants were identified using a reference genome, and accessory genes were identified using Roary (29). Details about the clinical data, analysis pipeline (30), genomic data curation (15,18,23,29,(31)(32)(33)(34), and phylogenetic reconstruction (35)(36)(37)(38) are provided in the supplemental material. While most clinical data cannot be shared, the deidentified patient ID, hospital of sample isolation, and isolation site are included in the Sequence Read Archive metadata for the BioProject.…”

Section: Methodsmentioning

confidence: 99%

Patient and Microbial Genomic Factors Associated with Carbapenem-Resistant Klebsiella pneumoniae Extraintestinal Colonization and Infection

et al. 2021

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Carbapenem-resistant Klebsiella pneumoniae (CRKP) is a critical-priority antibiotic resistance threat that has emerged over the past several decades, spread across the globe, and accumulated resistance to last-line antibiotic agents. While CRKP infections are associated with high mortality, only a subset of patients acquiring CRKP extraintestinal colonization will develop clinical infection. Here, we sought to ascertain the relative importance of patient characteristics and CRKP genetic background in determining patient risk of infection. Machine learning models classifying colonization versus infection were built using whole-genome sequences and clinical metadata from a comprehensive set of 331 CRKP extraintestinal isolates collected across 21 long-term acute-care hospitals over the course of a year. Model performance was evaluated based on area under the receiver operating characteristic curve (AUROC) on held-out test data. We found that patient and genomic features were predictive of clinical CRKP infection to similar extents (AUROC interquartile ranges [IQRs]: patient = 0.59 to 0.68, genomic = 0.55 to 0.61, combined = 0.62 to 0.68). Patient predictors of infection included the presence of indwelling devices, kidney disease, and length of stay. Genomic predictors of infection included presence of the ICEKp10 mobile genetic element carrying the yersiniabactin iron acquisition system and disruption of an O-antigen biosynthetic gene in a sublineage of the epidemic ST258 clone. Altered O-antigen biosynthesis increased association with the respiratory tract, and subsequent ICEKp10 acquisition was associated with increased virulence. These results highlight the potential of integrated models including both patient and microbial features to provide a more holistic understanding of patient clinical trajectories and ongoing within-lineage pathogen adaptation. IMPORTANCE Multidrug-resistant organisms, such as carbapenem-resistant Klebsiella pneumoniae (CRKP), colonize alarmingly large fractions of patients in regions of endemicity, but only a subset of patients develop life-threatening infections. While patient characteristics influence risk for infection, the relative contribution of microbial genetic background to patient risk remains unclear. We used machine learning to determine whether patient and/or microbial characteristics can discriminate between CRKP extraintestinal colonization and infection across multiple health care facilities and found that both patient and microbial factors were predictive. Examination of informative microbial genetic features revealed variation within the ST258 epidemic lineage that was associated with respiratory tract colonization and increased rates of infection. These findings indicate that circulating genetic variation within a highly prevalent epidemic lineage of CRKP influences patient clinical trajectories. In addition, this work supports the need for future studies examining the microbial genetic determinants of clinical outcomes in human populations, as well as epidemiologic and experimental follow-ups of identified features to discern generalizability and biological mechanisms.

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Section: Methodsmentioning

confidence: 99%

Patient and Microbial Genomic Factors Associated with Carbapenem-Resistant Klebsiella pneumoniae Extraintestinal Colonization and Infection

et al. 2021

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“…SNP calling and phylogenetic tree reconstruction were performed on each dataset as described in [ 23 ]. The variant-calling pipeline can be found on GitHub ( https://github.com/Snitkin-Lab-Umich/variant_calling_pipeline ).…”

Section: Methodsmentioning

confidence: 99%

prewas: data pre-processing for more informative bacterial GWAS

et al. 2020

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While variant identification pipelines are becoming increasingly standardized, less attention has been paid to the pre-processing of variants prior to their use in bacterial genome-wide association studies (bGWAS). Three nuances of variant pre-processing that impact downstream identification of genetic associations include the separation of variants at multiallelic sites, separation of variants in overlapping genes, and referencing of variants relative to ancestral alleles. Here we demonstrate the importance of these variant pre-processing steps on diverse bacterial genomic datasets and present prewas, an R package, that standardizes the pre-processing of multiallelic sites, overlapping genes, and reference alleles before bGWAS. This package facilitates improved reproducibility and interpretability of bGWAS results. prewas enables users to extract maximal information from bGWAS by implementing multi-line representation for multiallelic sites and variants in overlapping genes. prewas outputs a binary SNP matrix that can be used for SNP-based bGWAS and will prevent the masking of minor alleles during bGWAS analysis. The optional binary gene matrix output can be used for gene-based bGWAS, which will enable users to maximize the power and evolutionary interpretability of their bGWAS studies. prewas is available for download from GitHub.

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“…We used whole-genome sequences of clinical (non-surveillance) CRKP isolates and associated patient metadata from a prospective observational study performed in 21 LTACHs from across the U.S. over the course of a year (BioProject accession no. PRJNA415194) (14). We included only the first clinical bloodstream, respiratory, or urinary isolate from each patient (n=355; Figure S1A ), and subset to only ST258 isolates for the majority of analyses (n=331; Table S1 ; see supplementary material for reasoning).…”

Section: Methodsmentioning

confidence: 99%

Machine learning models to identify patient and microbial genetic factors associated with carbapenem-resistantKlebsiella pneumoniaeinfection

Lapp¹,

Han

Wiens³

et al. 2020

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Background Among patients colonized with carbapenem-resistant Klebsiella pneumoniae (CRKP), only a subset develop clinical infection. While patient characteristics may influence risk for infection, it remains unclear if the genetic background of CRKP strains contributes to this risk. We applied machine learning to quantify the capacity of patient characteristics and microbial genotypes to discriminate infection and colonization, and identified patient and microbial features associated with infection across multiple healthcare facilities. Methods Machine learning models were built using whole-genome sequences and clinical metadata from 331 patients colonized or infected with CRKP across 21 long-term acute care hospitals. To quantify variation in performance, we built models using 100 different train/test splits of the entire dataset, and urinary and respiratory site-specific subsets, and evaluated predictive performance on each test split using the area under the receiver operating characteristics curve (AUROC). Patient and microbial features predictive of infection were identified as those consistently important for predicting infection based on average change in AUROC when included in the model. Findings We found that patient and genomic features were only weakly predictive of clinical CRKP infection vs. colonization (AUROC IQRs: patient=0.59-0.68, genomic=0.55-0.61, combined=0.62-0.68), and that one feature set did not consistently outperform the other (genomic vs. patient p=0.4). Comparable model performances were observed for anatomic site-specific models (combined AUROC IQRs: respiratory=0.61-0.71, urinary=0.54-0.64). Strong genomic predictors of infection included the presence of the ICEKp10 mobile genetic element carrying an iron acquisition system (yersiniabactin) and a toxin (colibactin), along with disruption of an O-antigen biosynthetic gene in a sub-lineage of the epidemic ST258 clone. Teasing apart sequential evolutionary steps in the context of clinical metadata indicated that altered O-antigen biosynthesis increased association with the respiratory tract, and subsequent acquisition of ICEKp10 was associated with increased virulence. Interpretation Our results support the need for rigorous machine learning frameworks to gain realistic estimates of the performance of clinical models of infection. Moreover, integrating microbial genomic and clinical data using such a framework can help tease apart the contribution of microbial genetic variation to clinical outcomes. Funding Centers for Disease Control and Prevention, National Institutes of Health, National Science Foundation

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Whole-Genome Sequencing To Identify Drivers of Carbapenem-Resistant Klebsiella pneumoniae Transmission within and between Regional Long-Term Acute-Care Hospitals

Cited by 28 publications

References 42 publications

Patient and Microbial Genomic Factors Associated with Carbapenem-Resistant Klebsiella pneumoniae Extraintestinal Colonization and Infection

Patient and Microbial Genomic Factors Associated with Carbapenem-Resistant Klebsiella pneumoniae Extraintestinal Colonization and Infection

prewas: data pre-processing for more informative bacterial GWAS

Machine learning models to identify patient and microbial genetic factors associated with carbapenem-resistantKlebsiella pneumoniaeinfection

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