Draft Genome Sequence of Strain ATCC 33958, Reported To Be Elizabethkingia miricola

Matyi, Stephanie; Hoyt, Peter R.; Ayoubi-Canaan, Patricia; Hasan, Nabeeh A.; Gustafson, John E.

doi:10.1128/genomea.00828-15

Cited by 9 publications

(6 citation statements)

References 27 publications

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“…This isolate was reclassified as E. meningoseptica in the same study that misidentified E. miricola isolate CIP108653 as E. meningoseptica based on 16S rRNA sequences (Bernardet et al., 2005). Given the changes in nomenclature and the sometimes challenging identification of Elizabethkingia isolates (de Carvalho Filho, Marson, & Levy, 2017; Han et al., 2016; Matyi, Hoyt, Ayoubi‐Canaan, Hasan, & Gustafson, 2015; Rahim, Gupta, & Aggarwal, 2018), it cannot be excluded that some historic descriptions of bacterial infections similar to the condition described here (Chung, 1990; Green et al, 1999; Mauel, Miller, Frazier, & Hines, 2002; Olson, Gard, Brown, Hampton, & Morck, 1992; Taylor, Simmonds, & Loeffler, 1993) are in fact related to E. miricola infection. This would make E. miricola a more important amphibian pathogen than previously thought.…”

Section: Discussionmentioning

confidence: 99%

Elizabethkingia miricolainfection in multiple anuran species

Trimpert

Eichhorn

Vladimirova

et al. 2020

Transbounding Emerging Dis

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Infectious diseases have been linked to an unprecedented decline in amphibians worldwide. The largest infectious threats to amphibians are constituted by fungi of the genus Batrachochytrium (Fisher & Garner, 2020) and members of the family Iridoviridae known as ranaviruses (Price et al., 2017). Despite this dominance of fungi and viruses as infectious threats to amphibians, bacterial infection is an important and frequently diagnosed problem in amphibian medicine (Hemingway,

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Section: Discussionmentioning

confidence: 99%

Elizabethkingia miricolainfection in multiple anuran species

Trimpert

Eichhorn

Vladimirova

et al. 2020

Transbounding Emerging Dis

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“…While a comprehensive comparison of Elizabethkingia genomes is forthcoming, this preliminary analysis suggested that E. miricola is most similar to genomospecies 2, while both E. anophelis and E. endophytica are most similar to genomospecies 1. The 16S rRNA sequence from the recently published draft genome of Elizabethkingia strain ATCC 33958 ( 10 ) was an exact match to the gene from Elizabethkingia genomospecies 3.…”

Section: Genome Announcementmentioning

confidence: 92%

Draft Genome Sequences of Strains Representing Each of the Elizabethkingia Genomospecies Previously Determined by DNA-DNA Hybridization

et al. 2016

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“…Canu was chosen because it provides higher assembly sequence identity than competing long-read assemblers, such as miniasm (Koren et al, 2017). Minimum overlap length was 500 bp and suggested genome size was 3.8 Mb and 4.5 Mb for E. meningoseptica KC1913 (Matyi et al, 2013), and E. bruuniana ATCC 33958 (Matyi et al, 2015), respectively.…”

Section: Methodsmentioning

confidence: 99%

Pan-Genomic and Polymorphic Driven Prediction of Antibiotic Resistance in Elizabethkingia

et al. 2019

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The Elizabethkingia are a genetically diverse genus of emerging pathogens that exhibit multidrug resistance to a range of common antibiotics. Two representative species, Elizabethkingia bruuniana and E. meningoseptica , were phenotypically tested to determine minimum inhibitory concentrations (MICs) for five antibiotics. Ultra-long read sequencing with Oxford Nanopore Technologies (ONT) and subsequent de novo assembly produced complete, gapless circular genomes for each strain. Alignment based annotation with Prokka identified 5,480 features in E. bruuniana and 5,203 features in E. meningoseptica , where none of these identified genes or gene combinations corresponded to observed phenotypic resistance values. Pan-genomic analysis, performed with an additional 19 Elizabethkingia strains, identified a core-genome size of 2,658,537 bp, 32 uniquely identifiable intrinsic chromosomal antibiotic resistance core-genes and 77 antibiotic resistance pan-genes. Using core-SNPs and pan-genes in combination with six machine learning (ML) algorithms, binary classification of clindamycin and vancomycin resistance achieved f1 scores of 0.94 and 0.84, respectively. Performance on the more challenging multiclass problem for fusidic acid, rifampin and ciprofloxacin resulted in f1 scores of 0.70, 0.75, and 0.54, respectively. By producing two sets of quality biological predictors, pan-genome genes and core-genome SNPs, from long-read sequence data and applying an ensemble of ML techniques, our results demonstrated that accurate phenotypic inference, at multiple AMR resolutions, can be achieved.

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Draft Genome Sequence of Strain ATCC 33958, Reported To Be Elizabethkingia miricola

Cited by 9 publications

References 27 publications

Elizabethkingia miricolainfection in multiple anuran species

Elizabethkingia miricolainfection in multiple anuran species

Draft Genome Sequences of Strains Representing Each of the Elizabethkingia Genomospecies Previously Determined by DNA-DNA Hybridization

Pan-Genomic and Polymorphic Driven Prediction of Antibiotic Resistance in Elizabethkingia

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