Week 3: Identifying E. coli Strains

Hi everyone, welcome back to my blog! This week, I will be identifying and analyzing the specific strains of E. Coli that I will be including in my dataset for my computer algorithms while further expanding on the biological importance behind each. 

Data Point Zero: Non-mutated E. coli Strain

The purpose of this computer algorithm is to determine the presence of mutations within strains; therefore, the control, or base, strain should contain no mutations. There are records of many wild-type E. coli strains, but it’s important to select the correct one. E. coli K-12 is widely used in laboratories as its substrains, MG1655 and W3110, are well-characterized and free of major mutations. Overall, Scientists prefer E. coli K-12 due to its genetic stability, ease of manipulation, and lack of harmful virulence factors. However, E. coli NCM3722 is a wild-type derivative of the E. coli K-12 strain, closely related to MG1655 but with traits more representative of natural E. coli isolates. Researchers often use NCM3722 because it exhibits traits more representative of wild-type E. coli strains, so it offers a reliable baseline for studying bacterial physiology under various environmental conditions. Therefore, for this computer algorithm, E. coli NCM3722 will be utilized as the base sequence. 

Additional Data Points for Training

To train this computer algorithm, it’s important to use mutated strains that have been implicated in previous public health crises due to their virulence, antibiotic resistance, or toxin production. E. coli O157:H7, a Shiga toxin-producing strain, caused severe foodborne outbreaks leading to hemolytic uremic syndrome and kidney failure. Similarly, E. coli O104:H4 was responsible for the 2011 outbreak in Germany, acquiring Shiga toxin genes and antibiotic resistance through horizontal gene transfer. Another concerning strain, E. coli ST131, is a multidrug-resistant pathogen linked to urinary tract and bloodstream infections, with mutations conferring resistance to extended-spectrum β-lactamases (ESBLs). These three strains are what I will use when I begin building my computer algorithm, and they will be compared to the original base strain for mutation identification practice.