title: Commonly used molecular epidemiology markers of Streptococcus agalactiae do not appear to predict virulence.
abstract: OBJECTIVE
  Several virulent clones of group B streptococcus (GBS) are known to be associated with certain serotypes and molecular epidemiological markers. It is unclear, however, whether the clinical significance of GBS can be predicted based solely on such molecular markers. The aim of this study was to test the hypothesis that GBS virulence can be predicted by using the molecular epidemiology markers.
  
  
  METHODS
  We examined 912 human GBS isolates in which 18 distinct molecular markers (including virulence-associated mobile genetic elements, polysaccharide capsule determinants, variants of a surface antigen and invasin, and antibiotic resistance-related genes) were characterised using multiplex PCR based reverse line blot assay. All strains were classified in clinically relevant invasive and colonising categories. Relationships between molecular markers and clinical phenotypes were tested using statistical and machine learning analyses. Classifier performance was evaluated by the area under receiver operator characteristic curve (AUC).
  
  
  RESULTS
  The distribution of serotypes was comparable with those in previous reports (Ia, 22.1%; III, 34.7%; V, 17.7%). From single marker analyses, only alp3 (which encodes a surface protein antigen, commonly associated with serotype V) showed an increased association with invasive diseases (OR = 2.93, p = 0.0003). Molecular serotype (MS) II (OR = 10.0, p = 0.0007) had a significant association with early-onset neonatal disease when compared with late-onset diseases. Predictive analysis with logistic regression and machine learning classifiers, however, only yielded weak predictive power (AUC 0.56-0.71, stratified 10-fold cross-validation) across all the subgroups.
  
  
  CONCLUSIONS
  While some molecular epidemiological markers are important in defining GBS clusters, a definitive predictive relationship between the molecular markers and clinical outcomes may be lacking.
authors.affiliation: Centre for Health Informatics, University of New South Wales, Sydney, New South Wales, Australia.
authors.forename: Frank
authors.initials: F
authors.lastname: Lin
authors.forename: Vitali
authors.initials: V
authors.lastname: Sintchenko
authors.forename: Fanrong
authors.initials: F
authors.lastname: Kong
authors.forename: Gwendolyn L
authors.initials: GL
authors.lastname: Gilbert
authors.forename: Enrico
authors.initials: E
authors.lastname: Coiera
mesh_terms: D000293:Adolescent; D000328:Adult; D001185:Artificial Intelligence; D001426:Bacterial Proteins / Q000235:genetics*; D016000:Cluster Analysis; D005260:Female; D005798:Genes, Bacterial; D005819:Genetic Markers; D044404:Genomic Islands / Q000235:genetics; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008297:Male; D008875:Middle Aged; D017720:Molecular Epidemiology; D011237:Predictive Value of Tests; D011247:Pregnancy; D012372:ROC Curve; D012703:Serotyping; D013290:Streptococcal Infections / Q000382:microbiology*; D013292:Streptococcus agalactiae / Q000737:chemistry / Q000235:genetics* / Q000472:pathogenicity*; D014774:Virulence; D055815:Young Adult
chemical_list: D001426:Bacterial Proteins; D005819:Genetic Markers