Pathogen profiling for disease management and surveillance

Abstract

The usefulness of rapid pathogen genotyping is widely recognized, but its effective interpretation and application requires integration into clinical and public health decision-making. How can pathogen genotyping data best be translated to inform disease management and surveillance? Pathogen profiling integrates microbial genomics data into communicable disease control by consolidating phenotypic identity-based methods with DNA microarrays, proteomics, metabolomics and sequence-based typing. Sharing data on pathogen profiles should facilitate our understanding of transmission patterns and the dynamics of epidemics.

Figure 1. Interaction of the different 'omes' in a microbial cell.

Each 'ome' is a complex function of the other 'omes', and the amount of integration increases from the bottom to the top.

Figure 2. Relationships between MRSA as a concept (object) and determinants of the pathogen profile.

This data model defines major classes of attributes for an MRSA profile (for example, genotyping methods, virulence factors and clinical outcomes) and relationships between them. blaZ, β-lactamase gene; drfA, trimethoprim resistance gene; Ent, enterotoxin; erm, macrolide resistance gene; Et, exfoliative toxin; femA, gene encoding a cytoplasmic protein necessary for the expression of meticillin resistance; Luk-PV, Panton-Valentine leukocidin; mecA, gene encoding PBP2a, the low-binding-affinity penicillin-binding protein that mediates meticillin-resistance; MRSA, meticillin-resistant Staphylococcus aureus; SCCmec, Staphylococcus cassette chromosome; spa, staphylococcal protein A gene type; ST, sequence type; tetK, tetracycline resistance gene; tst, staphylococcal toxic shock toxin gene; vanA, vanB, vanC, vancomycin resistance genes.