Lab Matters Fall 2023 | Page 79

APHL 2023 POSTER ABSTRACTS

What ’ s in the Water ? A Prototype Environmental Metagenomics Database and Pipeline for Pathogen Detection , Microbial Composition , and Antimicrobial Resistance Detection

E . Proctor , H . Bird , A . Arfken , J . Mercante , M . Mattioli , S . S . Morrison ; US Centers for Disease Control and Prevention

Environmental water metagenomics remains challenging for public health pathogen detection and characterization due to ecological diversity and lack of relevant , robust publicly available bioinformatics analyses . Currently , there are limited well-curated environmental water metagenomic databases and few bioinformatic workflows available which perform community level comparisons that allow for rapid public health response . Available environmental databases lack critical subtype or strain information relevant to public health . The objectives of this project are to create a wellcurated , water-associated microorganism genomic database with a focus on public health-relevant organisms and to integrate the database into a bioinformatic workflow of open-source tools to optimize pathogen and antimicrobial resistance detection from shotgun metagenomic sequencing data of varying depths . Preliminary database curation is completed using genomic sequences of species ( N &# 3f8 ) included in the ZymoBIOMICS genomic community . These sequences are then processed through the ART data simulator to generate an in silico metagenomic read dataset , which is supplied to kmer-based approaches , Kraken 2 and Kalamari . These kmer-based approaches then match the in silico data with the database sequences . The predicted matches serve as an assessment of Kraken 2 and Kalamari . Our first version of the database will use the results from the two kmer-based approaches to build a comprehensive data which includes 11 genuses of critical public health interest to the Waterborne Diseases Prevention Branch . Bioinformatic tool development for pathogen detection and characterization from Illumina MiSeq and NovaSeq paired-end data is done using a kmer-based approach . The kmer-based approach is then supplemented with a distance-based sequence similarity method to recover approximate matches to the environmental water database . Sequences that are not classified through these processes are then assembled into contigs . Predicted gene features are then identified from the contigs to provide a potential functional composition of the environmental sample , including comparison to antimicrobial resistance databases for resistome profiling . Tools produced from this work could inform future environmental pathogen detection , source tracking , and outbreak response . Moreover , by evaluating the communities that occur alongside pathogens in environmental waters , these analyses may lead to identification of unknown microbial risk indicators . Finally , a well curated water environmental database will be made available via the Kalamari nomenclature to support more environmental sequence-based research .

Disclaimer : The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the US Centers for Disease Control and Prevention .

Presenter : Elizabeth Proctor , ued6 @ cdc . gov

Whole Genome Sequencing Analysis of Multiple Candida auris Outbreaks in Southern Nevada

A . Gorzalski 1 , F . Ambrosio 2 , M . Scribner 2 , S . Wright 2 , L . Massic 1 , K . Libuit 2 , J . Sevinsky 2 , M . Pandori 1 , D . Hess 1 ; 1 Nevada State Public Health Laboratory , 2 Theiagen Genomics LLC

A Candida auris outbreak has been ongoing in Southern Nevada since August 2021 . Over 800 patients have tested positive for C . auris at 20 facilities with dozens of associated patient deaths . This abstract describes whole genome sequencing of over 800 C . auris isolates from this outbreak . Genetically distinct subgroups of C . auris were detected from Clade I ( 2 distinct lineages ) and III ( 1 lineage ). Open-source bioinformatic tools were developed and implemented to aide in the epidemiologist investigation . This investigation compares three methods for C . auris whole genome analysis : nullarbor , MycoSNP and a new pipeline TheiaEuk . We also describe a novel analysis method focused on elucidating connections between isolates within an ongoing outbreak . This study places the ongoing outbreaks in a global context based on available whole genome sequencing . Lastly , we describe how the generated results were communicated to epidemiologists and infection control to generate public health interventions .

Presenter : David Hess , dhess @ unr . edu

Whole Genome Sequencing and Transcriptional Analysis of Pseudomonas aeruginosa from Patients with Cystic Fibrosis

E . Knorr , J . Paczkowski ; New York State Department of Health

Pseudomonas aeruginosa is an opportunistic pathogen and a major cause of morbidity and mortality in people with cystic fibrosis ( CF ). P . aeruginosa is one of the most common causes of nosocomial infections and was recently listed by the US Centers for Disease Control and Prevention as a pathogen of concern , as it can form chronic infections with resistance to many commonly used antibiotics . Therefore , it is important to understand the pathogenesis of P . aeruginosa in isolates from patients with CF . Quorum sensing ( QS ) is a mechanism for cell-cell communication in response to cell population densities that controls gene regulation . QS allows bacteria to communicate and coordinate functions such as biofilm formation and virulence factors . The two main QS pathways in P . aeruginosa are the LasI-LasR and RhlI-RhlR systems . The signal synthases LasI and RhlI produce acyl-homoserine lactones ( AHL ), also called autoinducers ( AI ). These AI bind to their respective transcription factors LasR and RhlR , triggering additional QS gene expression . In addition to these Las and RhlR systems , the enzyme PqsE directly interacts with RhlR to control virulence via enhanced binding of the RhlR-PqsE complex to DNA . To understand the pathogenesis of P . aeruginosa in CF patients , it is crucial we explore the variation in P . aeruginosa genomes , particularly genes associated with QS , and their expression . In this research we are interested in linking common polymorphisms in QS genes with altered transcriptional regulation in clinical isolates from patients with CF . We performed whole genome sequencing and transcriptional analysis of CF isolates to measure QS gene regulation under nutrient rich and nutrient limited conditions and correlated that with changes in the genomic sequences of key QS regulators . We will also insert a noninteracting PqsE variant into the CF isolate strains that is incapable of virulence phenotypes to