Lab Matters Fall 2023 | Page 71

APHL 2023 POSTER ABSTRACTS

( n = 138 ), 119 were further analyzed for virulence markers mpA ( 50 % similarity ( 1 / 2 )), mpA2 ( 100 % similarity ( 2 / 2 )), iroB ( 100 % similarity ( 1 / 1 )), iucA ( 94.1 % similarity ( 16 / 17 )) and peg-344 ( 100 % similarity ( 2 / 2 )) using both pipelines . Worth further investigation , the PHoeNIx pipeline interpreted additional genes such as ampH , penicillin binding protein , blaNMC and blaZn that were not detected by Abricate . Outputs generated by the PHoeNIx pipeline were also furnished with QC and contamination results . Unlike analysis by Abricate , the PHoeNIx pipeline can be conveniently run on a Linux server ( kernel . org ) using FASTQ files generated by the industry standard , Illumina sequencers ( Illumina , Inc ., San Diego , CA ); and as an open-source program , the software is available free of cost including upgrades . The PHoeNIx pipeline is more efficient with time and raw data quality checks . And further , the PHoeNIx pipeline provides additional useful analysis for identifying antibiotic resistance genes and virulence markers in surveillance isolates .

Presenter : Siddra Dar , siddra . dar @ maryland . gov

Establishment of Next Generation Sequencing for SARS- CoV-2 Utilizing Wastewater Samples from Rural Northeast Texas

A . A . Elbadawi , H . Allie ; Public Health Laboratory of East Texas

Background : One of the greatest hurdles to timely public health response , as exemplified by the COVID-19 pandemic , is disease monitoring and reporting . The rapid evolution of the SARS-CoV-2 variants makes it difficult to assess the evolution of the pathogen in a timely manner ; hence hindering public health response . Currently , the Public Health Laboratory of East Texas ( PHLET ) service area includes 35 counties within East Texas ; many of these counties are primarily rural in nature . The majority of these counties are largely underserved making them especially vulnerable to exposure of rapidly emerging diseases . Therefore , PHLET is validating the first wastewater based NGS ( next generation sequencing ) for rapid detection of SARS- CoV-2 variant circulating within the population near Smith and Gregg counties . Objectives : The primary aim of this project is to monitor and analyze the circulating and emerging SARS- CoV-2 variants in East Texas through utilization of regional wastewater samples . Additionally , we will create a pipeline for the data and share it with local , state and federal collaborators . Finally , PHLET plans to expand to other pathogens such as influenza and respiratory syncytial virus ( RSV ). Methods : The method followed the standard amplicon-based Illumina MiniSeq system , which can sequence up to 100 samples . For the validation phase , over 40 clinical samples containing SARS-CoV-2 were chosen spanning from March 2020 to August 2022 . They were extracted using the Qiagen Non-DSP Viral RNA kit . Next , the samples were prepared using the Illumina COVID Seq Assay ( 96 samples ). Sequencing was performed on the MiniSeq using the MiniSeq high-output reagent kit ( 300 cycles ). Finally , data was analyzed using the DRAGEN COVID Lineage App . Conclusions : Next generation sequencing of SARS-CoV-2 will enable the lab to expand variant detection and coordinate with our local partners to implement appropriate public health measures in response . In addition , by utilizing the Illumina instrument software for base-calling , the laboratory can import sequencing data for future collaboration with partners for bioinformatics pipeline development , as well as contribute the basic science of COVID-19 evolution through analysis of the data .

Presenter : Amna Elbadawi , amnaelbadawi18 @ gmail . com

Evaluating Whole-Genome Sequencing as an Approach for Antimicrobial Susceptibility Testing with AREScloud

C . Jossart 1 , M . Lasure 1 , A . Valley 1 , S . Beisken 2 , T . DeVos 2 , K . Florek 1 ;

1

Wisconsin State Laboratory of Hygiene , 2 Ares Genetics GmbH

Antimicrobial resistance is a growing public health threat that has been exacerbated during the SARS-CoV-2 pandemic , primarily due to the continued misuse of antimicrobials . The primary method to identify antimicrobial resistance and inform appropriate and targeted treatment is through antimicrobial susceptibility testing ( AST ). Determining the antimicrobial resistance profile is critical to patient care and antimicrobial resistance surveillance . Clinical and public health laboratories typically perform AST using broth microdilution or Etest and sometimes follow up with PCR and wholegenome sequencing ( WGS ) to further characterize the resistance mechanism . More recently , new methods are emerging that enable whole genome to be an effective alternative in antimicrobial susceptibility testing . Using machine learning models , WGS-AST methods can predict a wider variety of resistance phenotypes than possible in many laboratories and can provide a greater amount of additional data , including pathogen and resistance mechanism identification . Herein we compared the AREScloud web application ( Ares Genetics ), a cloud-based WGS-AST tool that utilizes machine-learning models established for various bacterialdrug combinations to the phenotypic AST profiles generated at the Wisconsin State Laboratory of Hygiene . We used AREScloud to compare the genotypic AST profiles for 16 public health reportable bacterial isolates , including five Acinetobacter baumannii , one Enterobacter cloacae complex , four Escherichia coli , three Klebsiella pneumoniae , and three Pseudomonas aeruginosa . Using phenotypic AST profiles as the reference , error designation and accuracy statistics were performed for 152 bacterial-drug combinations with an overall categorical agreement of 82.2 % and very major , major , and minor error rates of 2.6 %, 4.6 %, and 10.5 %, respectively . Here we demonstrate a strong potential for WGS-AST to be used as an effective complement to phenotypic AST and its use as a component in the overall effort of antimicrobial resistance surveillance .

Presenter : Christopher Jossart , cjjossart @ gmail . com

Exploring Viral Genetic Diversity by Deep Sequencing of Whole Genomes Following Target Enrichment : A Case Study of Universal Typing of Human Papillomavirus

B . Wakeman , T . Li , E . Unger , M . Rajeevan ; Division of High- Consequence Pathogens and Pathology , National Center for Emerging and Zoonotic Infectious Diseases , US Centers for Disease Control and Prevention

Introduction : The public health importance of information on viral genetic diversity has been highlighted by the COVID-19 pandemic . Intraspecific genetic variations ( strains , variants , integration and lineages ) may impact transmissibility and pathogenicity . While metagenomics has been the most widely used approach to generate genetic information on viruses , it requires extensive bioinformatics support , and in human samples with low viral load may lack required sensitivity , and specificity . In addition , PCRbased enrichment risks skewing the representation of viral genetic variations . RNA bait-based enrichment for all known types of a pathogen following whole genome deep sequencing ( eWGS ) is an alternative approach . Using Human Papillomavirus ( HPV ) as