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Teo, J.

Publications and source records attributed to Teo, J..

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The Acquisition of Resistance to Carbapenem and Macrolide-mediated Quorum Sensing Inhibition by Pseudomonas aeruginosa via a Novel Integrative and Conjugative Element ICETn43716385

Pseudomonas aeruginosa can cause persistant and life-threatening infections in immunocompromised patients. Carbapenems are the first-line agents to treat P. aeruginosa infections; therefore, the emergence of carbapenem-resistant P. aeruginosa strains has greatly challenged effective antibiotic therapy. In this study, we characterised the full-length genomes of two carbapenem resistant P. aeruginosa clinical isolates that produce the carbapebemase New Delhi metallo-{beta}-lactamase-1 (NDM-1). We found that the blaNDM-1 gene is encoded by a novel intergrative and conjugative element (ICE) ICETn43716385, which also carries the macrolide resistance gene msr(E) and the florfenicol resistance gene floR. The msr(E) gene has rarely been described in P. aeruginosa genomes. To investigate the functional roles of msr(E) in P. aeruginosa, we exogeneously expressed this gene in P. aeruginosa laboratory strains and found that the acquisition of msr(E) could abolish the azithromycin-mediated quorum sensing inhibition in vitro and the anti-Pseudomonas effect of azithromycin in vivo. In addition, the expression of msr(E) almost completely restored the azithromycin-affected P. aeruginosa transcriptome, as shown by our RNA sequencing analysis. We present the first evidence of blaNDM-1 to be carried by intergrative and conjugative elements, and the first evidence of co-transfer of carbapenem resistance and the resistance to macrolide-mediated quorum sensing inhibition into P. aeruginosa genomes.\n\nImportanceCarbapenem resistant P. aeruginosa has recently been listed as the top three most dangerous superbugs by World Health Organisation. The transmission of blaNDM-1 gene into P. aeruginosa can cause extreme resistance to carbapenems and fourth generation cephalosporins, which greatly compromises the effectiveness of these antibiotics against Pseudomonas infections. However, the lack of complete genome sequence of NDM-1-producing P. aeruginosa has limited our understanding of the transmisibility of blaNDM-1 in this organism. Here we showed the co-transfer of blaNDM-1 and msr(E) into P. aeruginosa genome by a novel integrative and conjugative element (ICE). The acquisition of these two genes confers P. aeruginosa with resistance to carbapenem and macrolide-mediated quorum sensing inhibition, both of which are important treatment stretagies for P. aeruginosa infections. Our findings highlight the potential of ICEs in transmitting carbapenem resistance, and that the anti-virulence treatment of P. aeruginosa infections by macrolides can be challenged by horizontal gene transfer.

genomics

Network analysis of patient flow in two UK acute care hospitals identifies key sub-networks for A&E performance

The topology of the patient flow network in a hospital is complex, comprising hundreds of overlapping patient journeys, and is a determinant of operational efficiency. To understand the network architecture of patient flow, we performed a data-driven network analysis of patient flow through two acute hospital sites of Kings College Hospital NHS Foundation Trust. Administration databases were queried for all intra-hospital patient transfers in an 18-month period and modelled as a dynamic weighted directed graph. A core subnetwork containing only 13-17% of all edges channelled 83-90% of the patient flow, while an ephemeral network constituted the remainder. Unsupervised cluster analysis and differential network analysis identified sub-networks where traffic is most associated with A&E performance the following day. Increased flow to clinical decision units was associated with the best A&E performance in both sites. The component analysis also detected a weekend effect on patient transfers which was not associated with performance. We have performed the first data-driven hypothesis-free analysis of patient flow which can enhance understanding of whole healthcare systems. Such analysis can drive transformation in healthcare as it has in industries such as manufacturing.

bioinformatics