Abstract

Objectives: Efforts to treat Escherichia coli infections are increasingly being compromised by the rapid, global spread of antimicrobial resistance (AMR). Whilst AMR in E. coli has been extensively investigated in resource-rich settings, in sub-Saharan Africa molecular patterns of AMR are not well described. In this study, we have begun to explore the population structure and molecular determinants of AMR amongst E. coli isolates from Malawi. Methods: Ninety-four E. coli isolates from patients admitted to Queen’s Hospital, Malawi, were whole-genome sequenced. The isolates were selected on the basis of diversity of phenotypic resistance profiles and clinical source of isolation (blood, CSF and rectal swab). Sequence data were analysed using comparative genomics and phylogenetics. Results: Our results revealed the presence of five clades, which were strongly associated with E. coli phylogroups A, B1, B2, D and F. We identified 43 multilocus STs, of which ST131 (14.9%) and ST12 (9.6%) were the most common. We identified 25 AMR genes. The most common ESBL gene was blaCTX-M-15 and it was present in all five phylogroups and 11 STs, and most commonly detected in ST391 (4/4 isolates), ST648 (3/3 isolates) and ST131 [3/14 (21.4%) isolates]. Conclusions: This study has revealed a high diversity of lineages associated with AMR, including ESBL and fluoroquinolone resistance, in Malawi. The data highlight the value of longitudinal bacteraemia surveillance coupled with detailed molecular epidemiology in all settings, including low-income settings, in describing the global epidemiology of ESBL resistance.

Highlights

  • By normalising the number of genes obtained in the 10 iterative samples per given set of genome sequences as described in Figure S1, the values of and γ were estimated to obtain the power-law regression equation F(N) = e8.4446N0.3551depicted by the red line-graph

  • The points in black represent the average number of genes in the pan-genome of a given set of genomes

  • Blood Blood Blood Blood Blood Blood Blood RS CSF Blood Blood Blood Blood RS Blood

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Summary

Introduction

ΒNγ illustrating an open pan-genome of the E. coli isolates. By normalising the number of genes obtained in the 10 iterative samples per given set of genome sequences as described, the values of and γ were estimated to obtain the power-law regression equation F(N) = e8.4446N0.3551depicted by the red line-graph.

Results
Conclusion

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