APHL 2021 Poster Abstracts
Infectious Disease transmitted from the instrument output file to ChromaCode Cloud and transformed into a reduced data representation containing only the qPCR intensity data and essential metadata . Data is de-identified to remove any institution-specific information and processed through ChromaCode ’ s proprietary analytics algorithms to offer key insights . The HDPCR TBP RUO assay , not for diagnostic use , is a multiplex real-time PCR ( qPCR ) test for the simultaneous detection and identification of nine of the most common tickborne pathogens from whole blood , including Anaplasma phagocytophilum , Babesia microti , Borrelia miyamotoi , Borrelia Group 1 ( B . burgdorferi , B . mayonii ), Borrelia Group 2 ( B . hermsii , B . parkeri , B . turicatae ), Ehrlichia chaffeensis , Ehrlichia ewingii , Ehrlichia muris eauclarensis , and Rickettsia spp .
Results : In a cohort inclusive of contrived samples ( n = 4768 ), 56.6 % of the samples tested were infected with at least one pathogen with 11.0 % harboring two or more pathogens . The top five single targets detected among this population included Anaplasma phagocytophilum , Borrelia Group 1 , Babesia microti , Ehrlichia chaffeensis , and Rickettsia spp .
Conclusion : The ChromaCode Cloud Data Analytics platform is a powerful surveillance tool that provides the ability to inform key insights and track wider epidemiological trends of tick-borne disease .
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Centers for Disease Control and Prevention . “ Tickborne Disease Surveillance Data Summary ”. https :// www . cdc . gov / ticks / datasummary / index . html
Presenter : Mimi Wang , ChromaCode , mwang @ chromacode . com
Assessing the Genetic Diversity of Carbapenemaseproducing Organisms Harbored by Individuals and Implications for Infection Control
J . Chan , W . Haas , E . Snavely , K . Cummings , P . Weeber , J . Bodnar , C . Wagner , S . Morris , E . Nazarian and K . Musser , New York State Department of Health / Wadsworth Center , Albany , NY
The rapid identification of carbapenemase-producing carbapenemresistant organisms ( CP-CROs ) from patients is crucial to interrupting transmission of these organisms within healthcare settings . WGS technology allows for the high-resolution analysis of bacterial isolates . Among CP-CROs , the number of nucleotide differences defining the likelihood of genetic relatedness or unrelatedness has not been well established . We evaluated the genetic diversity of CP-CROs , isolated from a subset of colonized individuals in healthcare facilities . Patient swabs routinely submitted through the Northeast Antibiotic Resistance ( AR ) Laboratory Network are tested using the Cepheid Xpert ® Carba-R assay that detects the presence of beta-lactamase ( bla ) AR genes : blaKPC , blaNDM , blaVIM , blaIMP , and blaOXA-48 . Laboratory developed tests are also used to screen specimens for all blaIMP or blaOXA-23 , -24 / 40 , -58-like gene variants . Positive specimens are cultured to isolate CP-CROs . In this study , sets of phenotypically identical isolates ( median of six colonies , range 3 to 11 ), cultured from 16 colonized individuals ( rectal , sputum , or tracheostomy swab ), were sequenced and analyzed with bioinformatic pipelines to assess AR genes , multilocus sequence type ( MLST ), and mutation event ( ME ) separation . CP-CROs assessed included Enterobacterales , Acinetobacter baumannii , and Pseudomonas aeruginosa . Twelve of 18 ( 67 %) CP-CRO sets were < 10 MEs apart and 6 of 18 ( 33 %) CP-CRO sets had maximum differences in MEs from 15 up to 357 , when compared amongst themselves . In some instances , patients harbored CP-CROs of the same species displaying distinct AR gene variants or MLSTs . The assessment of WGS MEs , along with phylogenetic and epidemiologic data , will help to evolve the framework for interpreting and reporting CP-CRO WGS data in an outbreak setting .
Presenter : June Chan , New York State Department of Health / Wadsworth Center , Albany , NY , june . chan @ health . ny . gov
BugSeq 16S : Automated , Highly Accurate Nanopore 16S Sequencing Analysis in a Scalable , Secure Cloud Environment
A . Jung and S . Chorlton , BugSeq Bioinformatics , Vancouver , BC , Canada
Introduction : Sequencing of the bacterial 16S ribosomal subunit remains a cornerstone of bacterial identification and an important test in the armamentarium of reference laboratories . NanoCLUST is currently the only published tool that can perform species-level taxonomic classification from nanopore 16S sequencing data . However , NanoCLUST takes BLAST ’ s top hit to classify consensus sequences , which is highly non-specific , and requires command line knowledge to execute . BugSeq 16S builds on NanoCLUST to provide significantly more accurate species-level classification from 16S nanopore sequencing data . Additionally , BugSeq 16S is deployed to the cloud for rapid and easy sequencing analysis .
Methods : Raw nanopore sequencing data is uploaded to bugseq . com and undergoes automated analysis . First , reads are demultiplexed and adapter trimmed with qcat , followed by quality filtering and primer trimming . Reads are then clustered and corrected using the same approach as NanoCLUST . After cluster consensus sequence generation , consensus sequences from multiple samples are combined , imported into QIIME2 artifacts , and classified using the QIIME2 vsearch classifier . Results are visualized using QIIME2 ’ s artifact viewer .
Results : We evaluate BugSeq 16S against NanoCLUST using real nanopore 16S sequencing data of a known microbial community . Previously generated duplicate 16S sequencing data from the ZymoBIOMICS mock community , which contains 8 bacteria , was downloaded and analysed . In both duplicates , BugSeq 16S detected seven out of eight species in the mock microbial community correctly , with one false positive species additionally identified . Conversely , NanoCLUST detected 6 / 8 and 5 / 8 species correctly , with four and two false positive species additionally identified in each sample , respectively . Furthermore , when applied to two eye samples from patients with suspected bacterial keratitis , BugSeq 16S produced results concordant with bacterial culture ( Serratia marcescens ) and Illumina 16S sequencing ( Bacillus subtilis ), respectively .
Conclusion : BugSeq 16S provides significantly more accurate bacterial identification from nanopore 16S sequencing data as compared to alternative tools . BugSeq 16S is deployed to the cloud for easy access in labs with any level of sequencing expertise and experience . The combination of ease and accuracy of BugSeq 16S may enable increased adoption of routine 16S sequencing in reference laboratories .
Presenter : Sam Chorlton , BugSeq Bioinformatics , Vancouver , BC , Canada , sam @ bugseq . com
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LAB MATTERS Summer 2021 |
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