
The lipopolysaccharide (O) and flagellar (H) surface antigens of Escherichia coli are targets for serotyping that have traditionally been used to identify pathogenic lineages. These surface antigens are important for the survival of E. coli within mammalian hosts. However, traditional serotyping has several limitations, and public health reference laboratories are increasingly moving towards whole genome sequencing (WGS) to characterize bacterial isolates. Here we present a method to rapidly and accurately serotype E. coli isolates from raw, short read WGS data. Our approach bypasses the need for de novo genome assembly by directly screening WGS reads against a curated database of alleles linked to known and novel E. coli O-groups and H-types (the EcOH database) using the software package srst2 . We validated the approach by comparing in silico results for 197 enteropathogenic E. coli isolates with those obtained by serological phenotyping in an independent laboratory. We then demonstrated the utility of our method to characterize isolates in public health and clinical settings, and to explore the genetic diversity of >1500 E. coli genomes from multiple sources. Importantly, we showed that transfer of O- and H-antigen loci between E. coli chromosomal backbones is common, with little evidence of constraints by host or pathotype, suggesting that E. coli ‘ strain space’ may be virtually unlimited, even within specific pathotypes. Our findings show that serotyping is most useful when used in combination with strain genotyping to characterize microevolution events within an inferred population structure.
570, Antigens, Bacterial, Escherichia coli Proteins, 610, Genetic Variation, O Antigens, Sequence Analysis, DNA, Serogroup, Escherichia coli, Animals, Computer Simulation, Serotyping, Phylogeny, Research Paper
570, Antigens, Bacterial, Escherichia coli Proteins, 610, Genetic Variation, O Antigens, Sequence Analysis, DNA, Serogroup, Escherichia coli, Animals, Computer Simulation, Serotyping, Phylogeny, Research Paper
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 144 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 1% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
