APHL 2022 POSTER ABSTRACTS response to endemic and emerging arboviruses , including West Nile virus outbreaks and local transmission of Zika virus .
Presenter : Bethany Bolling , Texas Department of State Health Services , bethany . bolling @ dshs . texas . gov
Establishment of Methods for the Detection of Antimicrobial Resistant Organisms in Wastewater Surveillance Samples
J Chavez , E Young , M Vowles , N La Crosse , K Oakeson and A Rossi , Utah Public Health Laboratory
Infections with antimicrobial resistant organisms are an important cause of morbidity and mortality worldwide and an ever-growing public health concern . Tracking the geographical distribution and spread of antimicrobial resistance ( AR ) allows the implementation of infection prevention strategies such as targeted admission screenings . Localization of specific threats is also useful in raising awareness in the healthcare community . Here we present preliminary data on the use of wastewater samples to create endemicity maps of established AR threats such as Carbapenem-resistant Enterobacterales ( CREs ) and to facilitate early detection of non-autochthonous pathogens such as Candida auris . Untreated influent samples from 32 wastewater treatment plants ( WTPs ) throughout Utah were collected twice weekly for COVID-19 surveillance and aliquots used for the isolation of CREs and yeasts . CREs were selected on CHROMagar™ mSuperCARBA™ plates containing meropenem . Presumptive CRE colonies were evaluated for oxidase activity to screen out Aeromonas sp . and other non-fermenters abundant in wastewater . Species-level identification of oxidase-negative colonies was performed by MALDI-TOF ( Bruker ). Carbapenemase production was evaluated by the modified carbapenamase inactivation method ( mCIM ). mCIMpositive organisms were analyzed by whole genome sequencing ( WGS ) on an Illumina MiSeq platform . Antibiotic resistance markers were identified by using the NCBI National Database of Antibiotic Resistant Organisms . Yeasts sedimented from wastewater were selected on HardyCHROM™ Candida agar after incubation at 40 ° C . Colonies with a phenotype compatible with C . auris were subjected to MALDI-TOF . 300 carbapenamase-producing CREs were recovered from 19 of the 33 WTPs and included the following genera : Escherichia , Enterobacter , Citrobacter , Klebsiella , Serratia , Pseudocitrobacter and Raoultella . CREs seem to be concentrated in urban rather than rural areas . WGS was obtained from 268 isolates recovered from 25 WTPs and revealed the presence of the following carbapenem resistance genes : KPC-2 , KPC-3 , NDM-1 , NDM-5 , VIM- 2 , VIM-4 , OXA-181 , GES-5 , GES-2 . Interestingly , the detected KPC , NDM and VIM-2 subtypes have also been identified in State resident colonized individuals . Yeasts could be recovered at a concentration of about 80 CFU per 100 mL of wastewater . The most abundant Candida species isolated from wastewater was C . glabrata . We analyzed samples from 32 WTPs and we were not able to detect any C . auris isolates , a pathogen not currently detected in Utah based on clinical sampling . This preliminary work forms the basis for drawing complete CRE endemicity maps in Utah , comparing of environmental and clinical sampling and the exploration of wastewater surveillance for antibiotic resistant organisms at the healthcare facility-level .
Presenter : Jorge Chavez , Utah Public Health Laboratory , aphlidlbjc1 @ utah . gov
Improving Surveillance and Cluster Detection of Carbapenem-resistant Organisms Using Whole Genome Sequencing
K Gali and R St . Jacques , Virginia Division of Consolidated Laboratory Services
According to the Centers for Disease Control and Prevention Antibiotic Resistance Threats in the United States 2019 report , Gram-negative Carbapenem-resistant organisms ( CRO ) are two of the top five most urgent antimicrobial resistance threats . Improving the surveillance of antibiotic resistance genes within the population and identifying disease clusters is a vital part of the continued effort to control and prevent these infections . Whole-genome sequencing ( WGS ) enables the comparison of pathogens at the genome-level to determine how closely related isolates are to one another . Identifying disease clusters through genomic comparison enhances the public health response through targeted intervention and infection control measures . DCLS has performed WGS of select CRO isolates for several years , but the process for utilizing that data has needed additional development . By examining the workflow and bioinformatics tools , we developed a method to enhance the analysis of our WGS data to detect resistance genes and disease clusters . Submission to the National Center for Biotechnology Information ( NCBI ) Pathogen Detection Project provides the initial phylogenetic and resistance gene analysis . Additional bioinformatics analysis using the Dryad pipeline and Gamma software generates data on antimicrobial resistance and hypervirulence genes , as well as resistance to disinfectants . If clusters of illness are indicated in NCBI Pathogen Detection , then the Dryad pipeline is used to assess SNP distances between the isolates . Knowing the SNP distance between different isolates allows us to determine if the cases are closely linked . Linking disease cases improves epidemiologic investigations by providing evidence that two infections are more likely to be caused by the same source or spread from one individual to another . While improving the CRO cluster detection process , DCLS began an investigation of a possible outbreak at the request of the Virginia Department of Health . Through WGS and bioinformatics analysis , the requested isolates proved to be closely related to one another . In addition , we identified genetically similar isolates from three additional cases that were previously unknown to the health department . In another investigation , several isolates with similar phenotypic characteristics to the disease case were identified . Through WGS , it was determined that some of these cases had genomically distinct bacterial strains , suggesting that a common source of infection was unlikely . Additionally , two isolates of the same multiple locus sequence type were not closely related by a pairwise SNP comparison . Our analysis enabled us to show that these cases were not linked to the isolate under investigation . We have developed a workflow that has enhanced surveillance of disease clusters and identification of resistance genes .
Presenter : Katelin Gali , Virginia Division of Consolidated Laboratory Services , katelin . gali @ dgs . virginia . gov
Infectious Diseases