APHL 2022 POSTER ABSTRACTS
Carbapenemase-encoding Genes
J Socea , V Stone , C Moore , L Thomas , JA Burks , X Qian , P Gibbs and R Steece , Tennessee Department of Health Laboratory Services
The Tennessee Department of Health ( TN DoH ) serves as the Southeast Regional Laboratory for the Antibiotic Resistance Laboratory Network ( AR Lab Network ). A central goal of the AR Lab Network is to enhance the detection of antimicrobial resistance ( AR ) genes in various pathogens to aid in the generation of public health solutions that can work toward addressing the building problem of antimicrobial resistant infections . One target of this arm of detection are genes that encode carbapenemases , including KPC , NDM , VIM , IMP and OXA , coined “ the big 5 .” These genes are often found in genera within the Enterobacterales and Pseudomonadales order . To screen for carbapenem-resistant organisms , patient samples are tested using the Modified Carbapenem Inactivation Method ( mCIM ). This phenotypic assay is a quick and cost-effective method for visualization of isolates that produce carbapenemases . Those that are considered positive ( inactive antimicrobial disks ) are then subject to PCR ( polymerase chain reaction ) to confirm the presence of one of the target genes . Interestingly , there are instances where organisms present an mCIM positive result , but none of the target genes are detected by PCR . The focus of this project was to identify other AR genes within the genome of these organisms that may be responsible for the phenotypic result observed by using whole genome sequencing ( WGS ). Upon analysis of the WGS data , we may be able to identify additional AR genes that play a role in generating an mCIM positive result but were not detected through PCR because of their dissimilarity to the target carbapenemase genes . Additionally , there is scope to gain understanding of the sources from which the AR genes were acquired by these organisms through phylogenetic analysis . Twenty-one isolates submitted to the TN DoH that were mCIM positive but PCR negative were sequenced using Illumina MiSeq , and paired-end sequencing data were analyzed with an in-house pipeline . Antimicrobial resistance genes were identified using the NCBI AMRFinderPlus . Gene annotation was performed by Prokka v1.14 . The resulting data was used to identify additional potential carbapenemase resistance mechanisms . The overall goal of these studies is to create a clearer picture on AR within these pathogens and the importance of accurate detection to monitor this growing public health problem .
Presenter : Jillian Socea , Tennessee Department of Health Laboratory Services , jillian . socea @ tn . gov
WBE of Enteric Viruses using Bio-Rad Droplet Digital PCR Expert Design Assays
D Spina , Bio-Rad Laboratories
ddPCR assays have been developed using Bio-Rad ’ s Droplet Digital Expert Design tool for the following enteric pathogens : Adenovirus , Norovirus , Enterovirus , Respiratory Syncytial Virus A / B , HF183 , & Legionella . Bio-Rad ’ s ddPCR offers many benefits over traditional qPCR for wastewater testing , including its high sensitivity and accuracy in quantifying DNA and RNA viruses found in very low concentrations , as well as the ability to handle inhibitory substances . Droplet Digital PCR also offers a method of absolute quantification without relying on standard curves , as is the case with qPCR . The data presented shows absolute quantification and assay dilution series for a selection of two known DNA virus ( Adenovirus and Enterovirus ) and an RNA virus ( Norovirus ) commonly found in wastewater in varying concentrations . The data illustrates that the Norovirus , Adenovirus and Enterovirus assays are able to detect concentrations as low as a 1 cp / μL of viral pathogen in wastewater . The results demonstrate how Droplet Digital PCR accurately quantifies these DNA and RNA viruses in wastewater and can serve as an accurate warning system to monitor disease outbreaks in a particular community long before the first cases are reported in health facilities .
Presenter : Abiodun Bodunrin & David Spina , Bio-Rad Laboratories , abiodun _ bodunrin @ bio-rad . com & david _ spina @ bio-rad . com
HantaNet : A MicrobeTrace Application for Variant Classification and Outbreak Investigations of Hantaviruses
R Cintron-Moret , S Whitmer , KW Chiu , E Moscoso , E Talundzic , M Mobley , J Montgomery , J Klena , E Campbell and WM Switzer , Centers for Disease Control and Prevention , Atlanta , GA
Introduction : Hantaviruses cause human infections worldwide and are transmitted by infected rodents that shed virus in urine and feces . Genetic evidence suggests that viral sub-strains are geographically confined , but the lack of an organized clearinghouse for hantavirus data integration and sharing hinders virus surveillance and outbreak response . To overcome this challenge , we developed HantaNet , a new MicrobeTrace application for rapid variant classification of hantaviruses . CDC and public health labs use MicrobeTrace for HIV and COVID-19 cluster detection and response by combining genomic and epidemiologic data for visualization and analysis of transmission networks .
Methods : We built three reference modules in MicrobeTrace for the nucleocapsid ( S ), glycoprotein ( M ) and RNA-dependent RNA polymerase ( L ) for the hantavirus segmented genome . First , we searched the NCBI nucleotide database for hantaviruses collected since 1982 , curated the metadata in Tableau and performed sequence alignments using MAFTT . Hantavirus strain-specific references were selected based on good sequence quality and completeness and were imported into MicrobeTrace to generate clusters with the Tamura-Nei 93 ( TN93 ) nucleotide substitution model . To validate the HantaNet reference modules , the genetic threshold was changed to modify cluster size and specificity compared to published S , M and L gene phylogenies . Transmission networks in HantaNet were validated using contact tracing data and hantavirus sequences collected during a recent Andes virus ( ANDV ) outbreak in Argentina .
Results : We determined a TN93 distance threshold of 15 – 20 % ( 0.15 – 0.20 nucleotide substitutions / site ) was optimal for viral strain-specific clustering in HantaNet . Lower genetic thresholds resulted in specific clustering of ANDV viruses isolated during the 2018 – 19 outbreak in Argentina . With HantaNet , users can classify , build and visualize outbreak clusters and transmission networks , and save and share datasets .
Conclusions : HantaNet is a new adaption of MicrobeTrace for the classification of hantaviruses and analysis of transmission networks making it a versatile tool for rapid deployment during surveillance and outbreak response of viruses containing segmented genomes , such as hantavirus and influenza .
Infectious Diseases