Differential Banding Pattern Based Identification of Urinary Tract Infection Causing Bacteria
Poulomi Nandy, Md Aftabuddin, Ashoke Ranjan Thakur and Shaon RayChaudhuri
DOI : 10.3844/ajbbsp.2013.124.132
American Journal of Biochemistry and Biotechnology
Volume 9, Issue 2
Urinary Tract Infection (UTI) affects all age groups, but women are more susceptible than men. These infections are typically caused by E coli, Proteus mirabilis, Staphylococcus aureus, Klebsiella pneumoniae, Pseudomonas aeruginosa, Enterococcus fecalis and so on. Since uncultivable microbes are numerically abundant in urine samples, culture independent detection seems to be the method of choice for diagnosing UTI. This study was an attempt to design a database of banding pattern of microbial variety inhabiting normal and infected subjects. The 16S rDNA Polymerase Chain Reaction (PCR) product was digested with 14 different restriction enzymes and run on a 2% agarose gel. From the restriction digestion images, their banding pattern and dendogram analysis, it was possible to differentiate and distinguish between E.coli, Pseudomonas, Klebsiella, Staphylococcus and Enterococci genus. Most of the enzymes like XbaI, ApaI, KpnI, PstI gave similar banding patterns for Klebsiella, E.coli and Pseudomonas, which could be differentiated from the Staphylococcus members. BgII and SmaI gave similar patterns for Klebsiella and E.coli, which was in turn different from that of Pseudomonas and Staphylococcus. Enzyme BamHI not only differentiated among Staphylococcus and the other three groups but was also able to show a distinct variation in banding pattern among Staphylococcus members. The database generated was used to identify pathogens from unknown patient samples without cultivating them. HindIII and HinfI can be used as two separate potential enzymes to differentiate and distinguish between the various microbes.
© 2013 Poulomi Nandy, Md Aftabuddin, Ashoke Ranjan Thakur and Shaon RayChaudhuri. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.