A Nosocomial Respiratory Infection Outbreak of Carbapenem-Resistant Escherichia coli ST131 With Multiple Transmissible blaKPC–2 Carrying Plasmids

Gong, Lin; Tang, Na; Chen, Dongke; Sun, Kaiwen; Lan, Ruiting; Zhang, Wen; Zhou, Haijian; Yuan, Min; Chen, Xia; Zhao, Xiaofei; Che, Jie; Bai, Xuemei; Zhang, Yunfei; Xu, Huji; Walsh, Timothy R.; Lu, Jinxing; Xu, Jianguo; Li, Juan; Feng, Jie

doi:10.3389/fmicb.2020.02068

Cited by 22 publications

(23 citation statements)

References 50 publications

(46 reference statements)

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“…A tool that is able to correctly predict E. coli plasmids will assist in identifying clinically relevant plasmids [ 41 , 42 , 43 , 44 ] and improve our understanding of the complex dynamics of ARG dissemination across different ecological niches [ 45 , 46 , 47 ]. From the vast offer of software to predict plasmids from short-read data we selected six tools and benchmarked their performances when attempting to reconstruct individual E. coli plasmids, with a special focus on plasmids that carry ARGs.…”

Section: Discussionmentioning

confidence: 99%

Recovering Escherichia coli Plasmids in the Absence of Long-Read Sequencing Data

et al. 2021

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The incidence of infections caused by multidrug-resistant E. coli strains has risen in the past years. Antibiotic resistance in E. coli is often mediated by acquisition and maintenance of plasmids. The study of E. coli plasmid epidemiology and genomics often requires long-read sequencing information, but recently a number of tools that allow plasmid prediction from short-read data have been developed. Here, we reviewed 25 available plasmid prediction tools and categorized them into binary plasmid/chromosome classification tools and plasmid reconstruction tools. We benchmarked six tools (MOB-suite, plasmidSPAdes, gplas, FishingForPlasmids, HyAsP and SCAPP) that aim to reliably reconstruct distinct plasmids, with a special focus on plasmids carrying antibiotic resistance genes (ARGs) such as extended-spectrum beta-lactamase genes. We found that two thirds (n = 425, 66.3%) of all plasmids were correctly reconstructed by at least one of the six tools, with a range of 92 (14.58%) to 317 (50.23%) correctly predicted plasmids. However, the majority of plasmids that carried antibiotic resistance genes (n = 85, 57.8%) could not be completely recovered as distinct plasmids by any of the tools. MOB-suite was the only tool that was able to correctly reconstruct the majority of plasmids (n = 317, 50.23%), and performed best at reconstructing large plasmids (n = 166, 46.37%) and ARG-plasmids (n = 41, 27.9%), but predictions frequently contained chromosome contamination (40%). In contrast, plasmidSPAdes reconstructed the highest fraction of plasmids smaller than 18 kbp (n = 168, 61.54%). Large ARG-plasmids, however, were frequently merged with sequences derived from distinct replicons. Available bioinformatic tools can provide valuable insight into E. coli plasmids, but also have important limitations. This work will serve as a guideline for selecting the most appropriate plasmid reconstruction tool for studies focusing on E. coli plasmids in the absence of long-read sequencing data.

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Section: Discussionmentioning

confidence: 99%

Recovering Escherichia coli Plasmids in the Absence of Long-Read Sequencing Data

et al. 2021

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“…Until recently, CPs were the agents of choice to treat infections due to E. coli ST131, well known for its MDR profile. However, CP resistance to emerged via CPase-encoding genes 86 . Transmission of a KPC-2-producing plasmid to ST131 E. coli led to infections in multiple wards in a hospital 86 .…”

Section: Carbapenemases (Cpases) and International Spreadmentioning

confidence: 99%

“…However, CP resistance to emerged via CPase-encoding genes 86 . Transmission of a KPC-2-producing plasmid to ST131 E. coli led to infections in multiple wards in a hospital 86 . Eight of 30 patients with respiratory infections died within 28 days of first isolation of ST131 E. coli.…”

Section: Carbapenemases (Cpases) and International Spreadmentioning

confidence: 99%

“…Eight of 30 patients with respiratory infections died within 28 days of first isolation of ST131 E. coli. Diverse MDR elements were transferred and rearranged between these plasmids 86 . Disturbingly, hypervirulence genes incorporated into CR-K. pneumoniae (CR-hvKp) have been increasingly reported 87,88 .…”

Section: Carbapenemases (Cpases) and International Spreadmentioning

confidence: 99%

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Escalating antimicrobial resistance among Enterobacteriaceae: focus on carbapenemases

Lynch¹,

Clark

Zhanel

2021

Expert Opinion on Pharmacotherapy

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aminoglycosides (AG); antimicrobial resistance (AMR); -lactamase inhibitors (BLI); blood stream infections (BSIs) ; carbapenem (CP); carbapenemase (CPase); carbapenem-resistant (CR); carbapenemresistant Enterobacterales (CRE) ;-lactam inhibitor (BLI); CP-resistant K. pneumoniae (CRKP); CPsusceptible K. pneumoniae (CSKP); ceftazidime/avibactam (CAZ-AVI); cephalosporins (CEPHS); central line-associated (CLA); central venous catheter (CVC); community-acquired infection (CAI); complicated intra-abdominal infection (c-IAI); complicated urinary tract infection (c-UTI); defined daily dose (DDD) ; extended-spectrum -lactamases (ESBLs); extensively Drug Resistant (eXDR); fluoroquinolones (FQ); Gram negative bacteria (GNB); gross domestic product per capita (GDPPC); high-income countries (HICs); hospital acquired infection (HAI); imipenem-relebactam (IMI-REL); intensive care units (ICUs); Klebsiella pneumoniae carbapenemases (KPCs); long-term care facilities (LTCFs); low-and middleincome countries (LMICs); mechanical ventilation; metallo-lactamase (MBL); minimal inhibitory concentration (MIC); mobile genetic elements (MGEs); multidrug resistant (MDR); New Delhi Metalloproteinase-1 (NDM-1); non-Proteus Enterobacterales (NPE); organ transplant recipients (OTR); piperacillin/tazobactam (PTZ); solid organ transplant (SOT); tigecycline (TGC); ventilator-associated pneumonia (VAP)

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“…A tool that is able to correctly predict E.coli plasmids will assist in identifying clinically relevant plasmids [42][43][44][45], and improve our understanding of the complex dynamics of ARG dissemination across different ecological niches [46][47][48]. From the vast offer of software to predict plasmids from short-read data we selected six tools and benchmarked their performances when attempting to reconstruct individual E. coli plasmids, with a special focus on plasmids that carry ARGs.…”

Section: Discussionmentioning

confidence: 99%

Recovering Escherichia coli plasmids in the absence of long-read sequencing data

Paganini

Plantinga

Arredondo-Alonso

et al. 2021

Preprint

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The incidence of infections caused by multidrug-resistant Escherichia coli strains has risen in the past years. Antibiotic resistance in E. coli is often mediated by acquisition and maintenance of plasmids. The study of E. coli plasmid epidemiology and genomics often requires long-read sequencing information, but recently a number of tools that allow plasmid prediction from short-read data have been developed. Here, we reviewed 25 available plasmid prediction tools and categorized them into binary plasmid/chromosome classification tools and plasmid reconstruction tools. We benchmarked six tools that aim to reliably reconstruct distinct plasmids, with a special focus on plasmids carrying antibiotic resistance genes (ARGs) such as extended-spectrum beta-lactamase genes. They use either assembly graph information (plasmidSPAdes, gplas), reference databases (MOB-Suite, FishingForPlasmids) or both (HyAsP and SCAPP) to produce plasmid predictions. The benchmark data set consisted of 240 E. coli strains, harboring 631 plasmids, which were representative for the diversity of E. coli in public databases. Notably, these strains were not used for training any of the tools. We found that two thirds (n=425, 66.3.%) of all plasmids were correctly reconstructed by at least one of the six tools, with a range of 92 (14.58%) to 317 (50.23%) correctly predicted plasmids. However, the majority of plasmids that carried antibiotic resistance genes (n=85, 57.8%) could not be completely recovered as distinct plasmids by any of the tools. MOB-suite was the only tool that was able to correctly reconstruct the majority of plasmids (n=317, 50.23%), and performed best at reconstructing large plasmids (n=166, 46.37%) and ARG-plasmids (n=41, 27.9%), but predictions frequently contained chromosome contamination (40%). In contrast, plasmidSPAdes reconstructed the highest fraction of plasmids smaller than 18 kbp (n=168, 61.54%). Large ARG-plasmids, however, were recovered with small precision values (median=0.47, IQR=0.61), indicating that plasmidSPAdes frequently merged sequences derived from distinct replicons. Additionally, only 63% of all plasmid-borne ARGs were correctly predicted by plasmidSPAdes. The remaining four tools (FishingForPlasmids, HyAsP, SCAPP and gplas) were able to correctly reconstruct a combined total of 18 plasmids that were missed by MOB-suite and plasmidSPAdes. Available bioinformatic tools can provide valuable insight into E. coli plasmids, but also have important limitations. This work will serve as a guideline for selecting the most appropriate plasmid reconstruction tool for studies focusing on E. coli plasmids in the absence of long-read sequencing data.

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A Nosocomial Respiratory Infection Outbreak of Carbapenem-Resistant Escherichia coli ST131 With Multiple Transmissible blaKPC–2 Carrying Plasmids

Cited by 22 publications

References 50 publications

Recovering Escherichia coli Plasmids in the Absence of Long-Read Sequencing Data

Recovering Escherichia coli Plasmids in the Absence of Long-Read Sequencing Data

Escalating antimicrobial resistance among Enterobacteriaceae: focus on carbapenemases

Recovering Escherichia coli plasmids in the absence of long-read sequencing data

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