Niche Preference of Escherichia coli in a Peri-Urban Pond Ecosystem.
Gitanjali NandaKafleTaylor HuegenSarah C PotgieterEmma SteenkampStephanus N VenterVolker S BrözelPublished in: Life (Basel, Switzerland) (2021)
Escherichia coli comprises diverse strains with a large accessory genome, indicating functional diversity and the ability to adapt to a range of niches. Specific strains would display greatest fitness in niches matching their combination of phenotypic traits. Given this hypothesis, we sought to determine whether E. coli in a peri-urban pond and associated cattle pasture display niche preference. Samples were collected from water, sediment, aquatic plants, water snails associated with the pond, as well as bovine feces from cattle in an adjacent pasture. Isolates (120) were obtained after plating on Membrane Lactose Glucuronide Agar (MLGA). We used the uidA and mutS sequences for all isolates to determine phylogeny by maximum likelihood, and population structure through gene flow analysis. PCR was used to allocate isolates to phylogroups and to determine the presence of pathogenicity/virulence genes (stxI, stxII, eaeA, hlyA, ST, and LT). Antimicrobial resistance was determined using a disk diffusion assay for Tetracycline, Gentamicin, Ciprofloxacin, Meropenem, Ceftriaxone, and Azithromycin. Our results showed that isolates from water, sediment, and water plants were similar by phylogroup distribution, virulence gene distribution, and antibiotic resistance while both snail and feces populations were significantly different. Few of the feces isolates were significantly similar to aquatic ones, and most of the snail isolates were also different. Population structure analysis indicated three genetic backgrounds associated with bovine, snail, and aquatic environments. Collectively these data support niche preference of E. coli isolates occurring in this ecosystem.
Keyphrases
- escherichia coli
- genetic diversity
- antimicrobial resistance
- genome wide
- biofilm formation
- epithelial mesenchymal transition
- pseudomonas aeruginosa
- risk assessment
- heavy metals
- climate change
- klebsiella pneumoniae
- copy number
- machine learning
- dna methylation
- body composition
- staphylococcus aureus
- gene expression
- genome wide identification
- high throughput
- artificial intelligence
- polycyclic aromatic hydrocarbons
- single cell
- data analysis