Threshold-free genomic cluster detection to track transmission pathways in health-care settings: a genomic epidemiology analysis.

BACKGROUND: A crucial barrier to the routine application of whole-genome sequencing (WGS) for infection prevention is the insufficient criteria for determining whether a genomic linkage is consistent with transmission within the facility. We evaluated the use of single-nucleotide variant (SNV) thresholds, as well as a novel threshold-free approach, for inferring transmission linkages in a high-transmission setting. METHODS: We did a retrospective genomic epidemiology analysis of samples previously collected in the context of an intervention study at a long-term acute care hospital in the USA. We performed WGS on 435 isolates of Klebsiella pneumoniae harbouring the blaKPC carbapenemase (KPC-Kp) collected from 256 patients through admission and surveillance culturing (once every 2 weeks) of almost every patient who was admitted to hospital over a 1-year period. FINDINGS: Our analysis showed that the standard approach of using an SNV threshold to define transmission would lead to false-positive and false-negative inferences. False-positive inferences were driven by the frequent importation of closely related strains, which were presumably linked via transmission at connected health-care facilities. False-negative inferences stemmed from the diversity of colonising populations that were spread among patients, with multiple examples of hypermutator strain emergence within patients and, as a result, putative transmission links separated by large genetic distances. Motivated by limitations of an SNV threshold, we implemented a novel threshold-free transmission cluster inference approach, in which each of the acquired KPC-Kp isolates were linked back to the imported KPC-Kp isolate with which it shared the most variants. This approach yielded clusters that varied in levels of genetic diversity but where 105 (81%) of 129 unique strain acquisition events were associated with epidemiological links in the hospital. Of 100 patients who acquired KPC-Kp isolates that were included in a cluster, 47 could be linked to a single patient who was positive for KPC-Kp at admission, compared with 31 and 25 using 10 SNV and 20 SNV thresholds, respectively. Holistic examination of clusters highlighted extensive variation in the magnitude of onward transmission stemming from more than 100 importation events and revealed patterns in cluster propagation that could inform improvements to infection prevention strategies. INTERPRETATION: Our results show how the integration of culture surveillance data into genomic analyses can overcome limitations of cluster detection based on SNV-thresholds and improve the ability to track pathways of pathogen transmission in health-care settings. FUNDING: US Center for Disease Control and Prevention and University of Michigan.

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

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.

Outbreak of Murine Infection with Clostridium difficile Associated with the Administration of a Pre- and Perinatal Methyl Donor Diet.

Between October 2016 and June 2017, a C57BL/6J mouse colony that was undergoing a pre- and perinatal methyl donor supplementation diet intervention to study the impact of parental nutrition on offspring susceptibility to disease was found to suffer from an epizootic of unexpected deaths. Necropsy revealed the presence of severe colitis, and further investigation linked these outbreak deaths to a Clostridium difficile strain of ribotype 027 that we term 16N203. C. difficile infection (CDI) is associated with antibiotic use in humans. Current murine models of CDI rely on antibiotic pretreatment to establish clinical phenotypes. In this report, the C. difficile outbreak occurs in F1 mice linked to alterations in the parental diet. The diagnosis of CDI in the affected mice was confirmed by cecal/colonic histopathology, the presence of C. difficile bacteria in fecal/colonic culture, and detection of C. difficile toxins. F1 mice from parents fed the methyl supplementation diet also had significantly reduced survival (P < 0.0001) compared with F1 mice from parents fed the control diet. When we tested the 16N203 outbreak strain in an established mouse model of antibiotic-induced CDI, we confirmed that this strain is pathogenic. Our serendipitous observations from this spontaneous outbreak of C. difficile in association with a pre- and perinatal methyl donor diet suggest the important role that diet may play in host defense and CDI risk factors.IMPORTANCEClostridium difficile infection (CDI) has become the leading cause of infectious diarrhea in hospitals worldwide, owing its preeminence to the emergence of hyperendemic strains, such as ribotype 027 (RT027). A major CDI risk factor is antibiotic exposure, which alters gut microbiota, resulting in the loss of colonization resistance. Current murine models of CDI also depend on pretreatment of animals with antibiotics to establish disease. The outbreak that we report here is unique in that the CDI occurred in mice with no antibiotic exposure and is associated with a pre- and perinatal methyl supplementation donor diet intervention study. Our investigation subsequently reveals that the outbreak strain that we term 16N203 is an RT027 strain, and this isolated strain is also pathogenic in an established murine model of CDI (with antibiotics). Our report of this spontaneous outbreak offers additional insight into the importance of environmental factors, such as diet, and CDI susceptibility.

Genomic insights into the fate of colistin resistance and Acinetobacter baumannii during patient treatment.

Bacterial whole-genome sequencing (WGS) of human pathogens has provided unprecedented insights into the evolution of antibiotic resistance. Most studies have focused on identification of resistance mutations, leaving one to speculate on the fate of these mutants once the antibiotic selective pressure is removed. We performed WGS on longitudinal isolates of Acinetobacter baumannii from patients undergoing colistin treatment, and upon subsequent drug withdrawal. In each of the four patients, colistin resistance evolved via mutations at the pmr locus. Upon colistin withdrawal, an ancestral susceptible strain outcompeted resistant isolates in three of the four cases. In the final case, resistance was also lost, but by a compensatory inactivating mutation in the transcriptional regulator of the pmr locus. Notably, this inactivating mutation reduced the probability of reacquiring colistin resistance when subsequently challenged in vitro. On face value, these results supported an in vivo fitness cost preventing the evolution of stable colistin resistance. However, more careful analysis of WGS data identified genomic evidence for stable colistin resistance undetected by clinical microbiological assays. Transcriptional studies validated this genomic hypothesis, showing increased pmr expression of the initial isolate. Moreover, altering the environmental growth conditions of the clinical assay recapitulated the classification as colistin resistant. Additional targeted sequencing revealed that this isolate evolved undetected in a patient undergoing colistin treatment, and was then transmitted to other hospitalized patients, further demonstrating its stability in the absence of colistin. This study provides a unique window into mutational pathways taken in response to antibiotic pressure in vivo, and demonstrates the potential for genome sequence data to predict resistance phenotypes.