The usefulness of multiple antibiotic resistance (MAR) indexing technique in differentiating fecal coliform bacteria from different sources
Pollution of water sources with human fecal matter and associated intestinal pathogens poses a great risk to public health. Fecal contamination of water is not the only problem to communities that consume untreated water. The extent of the microbial contamination of water sources also needs to be considered when designing treatment regimes for the production of potable water. The more polluted the source of drinking water is, the more extensive and expensive treatment regimes have to be used to produce microbial risk-free water. For decades fecal coliform counts have been used as indicators of fecal contamination and the potential presence of intestinal pathogens in surface waters. However, fecal coliforms fail to provide information about the source of fecal contamination. Knowing the source of fecal contamination is vital in managing this problem in surface waters. This study explored the use of two techniques, multiple antibiotic resistance (MAR) indexing and caffeine detection as means of differentiating E. coli isolates from various sources. A total of 322 E. coli were isolated from domestic and wild animals as well as human sewage by using conventional culture methods. Standard chemical and biochemical tests were used to identify these isolates. All isolates were assayed against a battery of 10 antibiotics using the micro-dilution method. The results obtained were used to generate antibiotic resistance profiles which in turn were used to statistically group the isolates into different subsets. Caffeine detection by Thin Layer Chromatography (TLC) was used to differentiate between human and non-human derived E. coli isolates. The correct classification rate was 78% when MAR indexing was used and 50% when using caffeine detection. Sixty percent of E. coli from humans were correctly classified and 95.5% of E. coli from animals were correctly classified as non-humans sources respectively. The results of this study underscore the validity of MAR indexing as a method of bacterial source tracking. MAR indexing has great discriminatory power without the complexities and the high costs often associated with established genotype-based methods. Caffeine detection indicated an average classification rate (50%). With further research, caffeine detection may give another option for source tracking when genotyping methods are limited by either costs or lack of expertise. The use of combined techniques may provide a much more reliable and cost-effective option for bacterial source tracking when each technique used provide similar results.
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