<?xml version="1.0" encoding="UTF-8"?>
<STUDY_SET xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
  <STUDY accession="ERP140558" alias="cb99ebcd-4301-423d-9f56-82e0c84170fc" center_name="FRIEDRICH-LOEFFLER-INSTITUT (FLI) Institute for Bacterial Infections and Zoonoses (IBIZ)">
    <IDENTIFIERS>
      <PRIMARY_ID>ERP140558</PRIMARY_ID>
      <EXTERNAL_ID namespace="BioProject">PRJEB55640</EXTERNAL_ID>
      <SUBMITTER_ID namespace="FRIEDRICH-LOEFFLER-INSTITUT (FLI) Institute for Bacterial Infections and Zoonoses (IBIZ)">cb99ebcd-4301-423d-9f56-82e0c84170fc</SUBMITTER_ID>
    </IDENTIFIERS>
    <DESCRIPTOR>
      <STUDY_TITLE>Genetic diversity of Campylobacter jejuni using WGS</STUDY_TITLE>
      <STUDY_TYPE existing_study_type="Other"/>
      <STUDY_ABSTRACT>Campylobacter jejuni (C. jejuni) is a zoonotic bacterium of public health significance. The present investigation was designed to assess the epidemiology and genetic heterogenicity of C. jejuni isolated from commercial turkey flocks in Germany using whole-genome sequencing. The Illumina MiSeq® technology was used to sequence 66 C. jejuni isolated between 2010 and 2011 from commercial meat turkey flocks located in ten German federal states. Phenotypic antimicrobial resistance was determined. The phylogeny, resistome, plasmidome, and virulome profile was analysed on basis of whole-genome sequencing (WGS) data. Resistance genotypes were identified by checking of sequences data with different tools (AMRFinder, ResFinder, NCBI and ABRicate) and compared with the phenotypic antimicrobial resistance. The isolates were classified into 28 different sequence types and 11 clonal complexes. The high genetic diversity was further indicated by an average pairwise single nucleotide-polymorphisms (SNPs) distance of 14.585 SNPs (range: 0-26540 SNPs).  In Campylobacter jejuni isolates, 30 virulence genes were identified. Most of the strains harboured the genes flaA (83.3%) and flaB (78.8%). The wlaN gene involved in the Guillain–Barré syndrome was found in 9 (13.6%) isolates. Out of 56, 36, 18 and 10 phenotypically resistant isolates against ampicillin, tetracycline, neomycin and streptomycin , respectively, 51 (91.1%), 34 (94.4%), 6 (33.3%) and 6 (60%) isolates harbored blaOXA, tet(O), aph(3')-IIIa and (sat4, ant(6)-1a and aad6) genes, respectively. The mutation in the housekeeping gene gyrA_T86I conferring resistance to quinolones was retrieved in 93.6% of phenotypically resistant isolates. A variety of 12 known ß-lactam resistance genes (blaOXA variants) were detected in 51 (91.1%) of 56 ampicillin-resistant isolates. Six (9.1%) isolates harbored undefined blaOXA genes. Out of 66 sequenced strains, 28 (42.4%) carried plasmids. Six isolates harboured a pTet plasmid which carries a tet(O) gene. This study highlighted the potential of whole-genome sequencing for improving routine surveillance of C. jejuni by enhancing outbreak detection and source-tracing. WGS can correctly identify and predict antimicrobial resistance with a high degree of accuracy and correlation between phenotypic resistance to  given antimicrobials. Updating of resistance gene databases is needed to avoid inaccuracy when using WGS-based analysis pipelines for AMR detection excluding or lacking of phenotypic data.</STUDY_ABSTRACT>
      <CENTER_PROJECT_NAME>undefined</CENTER_PROJECT_NAME>
      <STUDY_DESCRIPTION>Campylobacter jejuni (C. jejuni) is a zoonotic bacterium of public health significance. The present investigation was designed to assess the epidemiology and genetic heterogenicity of C. jejuni isolated from commercial turkey flocks in Germany using whole-genome sequencing. The Illumina MiSeq® technology was used to sequence 66 C. jejuni isolated between 2010 and 2011 from commercial meat turkey flocks located in ten German federal states. Phenotypic antimicrobial resistance was determined. The phylogeny, resistome, plasmidome, and virulome profile was analysed on basis of whole-genome sequencing (WGS) data. Resistance genotypes were identified by checking of sequences data with different tools (AMRFinder, ResFinder, NCBI and ABRicate) and compared with the phenotypic antimicrobial resistance. The isolates were classified into 28 different sequence types and 11 clonal complexes. The high genetic diversity was further indicated by an average pairwise single nucleotide-polymorphisms (SNPs) distance of 14.585 SNPs (range: 0-26540 SNPs).  In Campylobacter jejuni isolates, 30 virulence genes were identified. Most of the strains harboured the genes flaA (83.3%) and flaB (78.8%). The wlaN gene involved in the Guillain–Barré syndrome was found in 9 (13.6%) isolates. Out of 56, 36, 18 and 10 phenotypically resistant isolates against ampicillin, tetracycline, neomycin and streptomycin , respectively, 51 (91.1%), 34 (94.4%), 6 (33.3%) and 6 (60%) isolates harbored blaOXA, tet(O), aph(3')-IIIa and (sat4, ant(6)-1a and aad6) genes, respectively. The mutation in the housekeeping gene gyrA_T86I conferring resistance to quinolones was retrieved in 93.6% of phenotypically resistant isolates. A variety of 12 known ß-lactam resistance genes (blaOXA variants) were detected in 51 (91.1%) of 56 ampicillin-resistant isolates. Six (9.1%) isolates harbored undefined blaOXA genes. Out of 66 sequenced strains, 28 (42.4%) carried plasmids. Six isolates harboured a pTet plasmid which carries a tet(O) gene. This study highlighted the potential of whole-genome sequencing for improving routine surveillance of C. jejuni by enhancing outbreak detection and source-tracing. WGS can correctly identify and predict antimicrobial resistance with a high degree of accuracy and correlation between phenotypic resistance to  given antimicrobials. Updating of resistance gene databases is needed to avoid inaccuracy when using WGS-based analysis pipelines for AMR detection excluding or lacking of phenotypic data.</STUDY_DESCRIPTION>
    </DESCRIPTOR>
    <STUDY_ATTRIBUTES>
      <STUDY_ATTRIBUTE>
        <TAG>ENA-FIRST-PUBLIC</TAG>
        <VALUE>2023-01-27</VALUE>
      </STUDY_ATTRIBUTE>
      <STUDY_ATTRIBUTE>
        <TAG>ENA-LAST-UPDATE</TAG>
        <VALUE>2023-01-27</VALUE>
      </STUDY_ATTRIBUTE>
    </STUDY_ATTRIBUTES>
  </STUDY>
</STUDY_SET>
