Clonal Lineages and Virulence Factors of Carbapenem Resistant E. coli in Alameda County, California, 2017-2019.
Samuel SlownNikolina WalasHeather K AmatoTyler LloydVici VargheseMonica BenderMark PandoriJay P GrahamPublished in: Antibiotics (Basel, Switzerland) (2022)
The prevalence of carbapenem-resistant Enterobacterales (CRE) has been increasing since the year 2000 and is considered a serious public health threat according to the Centers for Disease Control and Prevention. Limited studies have genotyped Carbapenem-resistant Escherichia coli using whole genome sequencing to characterize the most common lineages and resistance and virulence genes. The aim of this study was to characterize sequence data from carbapenem-resistant E. coli isolates ( n = 82) collected longitudinally by the Alameda County Public Health Laboratory (ACPHL) between 2017 and 2019. E. coli genomes were screened for antibiotic resistance genes (ARGs) and extraintestinal pathogenic E. coli virulence factor genes (VFGs). The carbapenem-resistant E. coli lineages were diverse, with 24 distinct sequence types (STs) represented, including clinically important STs: ST131, ST69, ST95, and ST73. All Ambler classes of Carbapenemases were present, with NDM-5 being most the frequently detected. Nearly all isolates (90%) contained genes encoding resistance to third-generation cephalosporins; bla CTX-M genes were most common. The number of virulence genes present within pandemic STs was significantly higher than the number in non-pandemic lineages ( p = 0.035). Virulence genes fim A (92%), tra t (71%), kps M (54%), and iut A (46%) were the most prevalent within the isolates. Considering the public health risk associated with CRE, these data enhance our understanding of the diversity of clinically important E. coli that are circulating in Alameda County, California.
Keyphrases
- wastewater treatment
- escherichia coli
- antibiotic resistance genes
- klebsiella pneumoniae
- public health
- genome wide
- biofilm formation
- bioinformatics analysis
- pseudomonas aeruginosa
- staphylococcus aureus
- health risk
- antimicrobial resistance
- genome wide identification
- sars cov
- healthcare
- gene expression
- heavy metals
- emergency department
- electronic health record
- dna methylation
- cystic fibrosis
- machine learning
- genetic diversity
- transcription factor
- big data
- drinking water
- artificial intelligence