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Torraca V, Brokatzky D, Miles SL, Chong CE, De Silva PM, Baker S, Jenkins C, Holt KE, Baker KS, Mostowy S. Shigella Serotypes Associated With Carriage in Humans Establish Persistent Infection in Zebrafish. J Infect Dis 2023; 228:1108-1118. [PMID: 37556724 PMCID: PMC10582909 DOI: 10.1093/infdis/jiad326] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 08/03/2023] [Accepted: 08/08/2023] [Indexed: 08/11/2023] Open
Abstract
Shigella represents a paraphyletic group of enteroinvasive Escherichia coli. More than 40 Shigella serotypes have been reported. However, most cases within the men who have sex with men (MSM) community are attributed to 3 serotypes: Shigella sonnei unique serotype and Shigella flexneri 2a and 3a serotypes. Using the zebrafish model, we demonstrate that Shigella can establish persistent infection in vivo. Bacteria are not cleared by the immune system and become antibiotic tolerant. Establishment of persistent infection depends on the O-antigen, a key constituent of the bacterial surface and a serotype determinant. Representative isolates associated with MSM transmission persist in zebrafish, while representative isolates of a serotype not associated with MSM transmission do not. Isolates of a Shigella serotype establishing persistent infections elicited significantly less macrophage death in vivo than isolates of a serotype unable to persist. We conclude that zebrafish are a valuable platform to illuminate factors underlying establishment of Shigella persistent infection in humans.
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Affiliation(s)
- Vincenzo Torraca
- Department of Infection Biology, London School of Hygiene and Tropical Medicine, London, United Kingdom
- School of Life Sciences, University of Westminster, London, United Kingdom
- Department of Infectious Diseases, School of Immunology and Microbial Sciences, King’s College London, London, United Kingdom
| | - Dominik Brokatzky
- Department of Infection Biology, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Sydney L Miles
- Department of Infection Biology, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Charlotte E Chong
- Clinical Infection, Microbiology, and Immunology, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, United Kingdom
| | - P Malaka De Silva
- Clinical Infection, Microbiology, and Immunology, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, United Kingdom
| | - Stephen Baker
- Department of Medicine, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Claire Jenkins
- Gastrointestinal Bacterial Reference Unit, UK Health Security Agency, London, United Kingdom
| | - Kathryn E Holt
- Department of Infection Biology, London School of Hygiene and Tropical Medicine, London, United Kingdom
- Department of Infectious Diseases, Central Clinical School, Monash University, Melbourne, Australia
| | - Kate S Baker
- Clinical Infection, Microbiology, and Immunology, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, United Kingdom
- Department of Genetics, University of Cambridge, Cambridge, United Kingdom
| | - Serge Mostowy
- Department of Infection Biology, London School of Hygiene and Tropical Medicine, London, United Kingdom
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Monaci S, Qian S, Gillette K, Puyol-Antón E, Mukherjee R, Elliott MK, Whitaker J, Rajani R, O’Neill M, Rinaldi CA, Plank G, King AP, Bishop MJ. Non-invasive localization of post-infarct ventricular tachycardia exit sites to guide ablation planning: a computational deep learning platform utilizing the 12-lead electrocardiogram and intracardiac electrograms from implanted devices. Europace 2023; 25:469-477. [PMID: 36369980 PMCID: PMC9935046 DOI: 10.1093/europace/euac178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 09/08/2022] [Indexed: 11/13/2022] Open
Abstract
AIMS Existing strategies that identify post-infarct ventricular tachycardia (VT) ablation target either employ invasive electrophysiological (EP) mapping or non-invasive modalities utilizing the electrocardiogram (ECG). Their success relies on localizing sites critical to the maintenance of the clinical arrhythmia, not always recorded on the 12-lead ECG. Targeting the clinical VT by utilizing electrograms (EGM) recordings stored in implanted devices may aid ablation planning, enhancing safety and speed and potentially reducing the need of VT induction. In this context, we aim to develop a non-invasive computational-deep learning (DL) platform to localize VT exit sites from surface ECGs and implanted device intracardiac EGMs. METHODS AND RESULTS A library of ECGs and EGMs from simulated paced beats and representative post-infarct VTs was generated across five torso models. Traces were used to train DL algorithms to localize VT sites of earliest systolic activation; first tested on simulated data and then on a clinically induced VT to show applicability of our platform in clinical settings. Localization performance was estimated via localization errors (LEs) against known VT exit sites from simulations or clinical ablation targets. Surface ECGs successfully localized post-infarct VTs from simulated data with mean LE = 9.61 ± 2.61 mm across torsos. VT localization was successfully achieved from implanted device intracardiac EGMs with mean LE = 13.10 ± 2.36 mm. Finally, the clinically induced VT localization was in agreement with the clinical ablation volume. CONCLUSION The proposed framework may be utilized for direct localization of post-infarct VTs from surface ECGs and/or implanted device EGMs, or in conjunction with efficient, patient-specific modelling, enhancing safety and speed of ablation planning.
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Affiliation(s)
- Sofia Monaci
- Biomedical Engineering & Imaging Sciences, King’s College London, London SE1 7EH, United Kingdom
| | - Shuang Qian
- Biomedical Engineering & Imaging Sciences, King’s College London, London SE1 7EH, United Kingdom
| | | | - Esther Puyol-Antón
- Biomedical Engineering & Imaging Sciences, King’s College London, London SE1 7EH, United Kingdom
| | - Rahul Mukherjee
- Biomedical Engineering & Imaging Sciences, King’s College London, London SE1 7EH, United Kingdom
- Guy’s and St Thomas’ Hospital, London SE1 7EH, United Kingdom
| | - Mark K Elliott
- Biomedical Engineering & Imaging Sciences, King’s College London, London SE1 7EH, United Kingdom
- Guy’s and St Thomas’ Hospital, London SE1 7EH, United Kingdom
| | - John Whitaker
- Biomedical Engineering & Imaging Sciences, King’s College London, London SE1 7EH, United Kingdom
- Guy’s and St Thomas’ Hospital, London SE1 7EH, United Kingdom
| | - Ronak Rajani
- Biomedical Engineering & Imaging Sciences, King’s College London, London SE1 7EH, United Kingdom
- Guy’s and St Thomas’ Hospital, London SE1 7EH, United Kingdom
| | - Mark O’Neill
- Biomedical Engineering & Imaging Sciences, King’s College London, London SE1 7EH, United Kingdom
| | - Christopher A Rinaldi
- Biomedical Engineering & Imaging Sciences, King’s College London, London SE1 7EH, United Kingdom
- Guy’s and St Thomas’ Hospital, London SE1 7EH, United Kingdom
| | | | - Andrew P King
- Biomedical Engineering & Imaging Sciences, King’s College London, London SE1 7EH, United Kingdom
| | - Martin J Bishop
- Biomedical Engineering & Imaging Sciences, King’s College London, London SE1 7EH, United Kingdom
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