SEP 13, 2017 12:00 PM PDT
High Resolution Outbreak Tracing and Resistance Detection using Whole Genome Sequencing in the case of a Mycobacterium tuberculosis Outbreak
Presented at the Microbiology & Immunology 2017 Virtual Event
SPONSORED BY: QIAGEN Molecular Diagnostics
CONTINUING EDUCATION (CME/CE/CEU) CREDITS: P.A.C.E. CE | Florida CE
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Speakers:
  • Senior Scientist, Microbial Genomics, QIAGEN
    Biography
      Winnie Ridderberg, a microbiologist, received her doctoral degree from Aarhus University, Denmark. Winnie has almost a decade's experience in clinical microbiology, from working at the Department of Clinical Microbiology at Aarhus University Hospital. Winnie's primary research interest has been the microbiology of cystic fibrosis with specific focus on bacterial evolution and microbiome studies. Winnie joined QIAGEN Bioinformatics in early 2017 as a research scientist at the Microbial Genomics team.

    Abstract:

    Background: In March 2014, a molecular cluster of five multidrug resistant Mycobacterium tuberculosis was detected by the Austrian National Reference Laboratory. An investigation was initiated to determine if transmission had occurred within Austria. Epidemiological links to Germany and Romania prompted a multi-national joint investigation, tracing the outbreak. The results were published by Fiebig and co-workers in 2017.

    Methods: CLC Microbial Genomics Module, an extension of CLC Genomics Workbench, contain tools required for typing pathogenic bacteria, tracing outbreaks, and detecting antimicrobial resistance. Preconfigured, but customisable, workflows ensure ease-of-use, allowing users to focus on interpreting analysis results.

    Results: Whole genome SNP analysis showed high resolution clustering of isolates. Information on the whole genome further permitted simultaneous detection of resistance causing variants. Using an improved variant detection pipeline, we identified additional variants associated to antimicrobial resistance compared to the original study. Novel variants were explored in the context of 3D protein models to predict and qualify the effect of the variants on antimicrobial susceptibility. 

    Conclusion: Using data published by Fiebig at al., 2017, we demonstrate how CLC Microbial Genomics Module can be used to trace pathogen outbreaks and detect resistance causing variants. User-friendly tools and preconfigured workflows ensure ease of use and reproducibility.


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