Molecular Detection Of Human Bacterial Pathogens PDF Download

Bacteria are all around us. In our environment, on our bodies, and even in the air we breathe. While usually benign, their invasion into the bloodstream can pose severe risks to human health making detection of these microbes of the utmost importance. Typical bacterial detection techniques for clinical diagnostics are limited by long testing times, high rates of false positive and negative detection, and assumptions about the pathogen. This dissertation is centered around the development of a novel method based on digital PCR (dPCR) to accurately and agnostically detect, identify, and quantify bacteria for applications in clinical microbiology. In chapter 1, I developed a novel high-resolution melt (HRM)-based internal amplification control (IAC) strategy for dPCR to provide assurance that clinical specimens are successfully amplified and accurately quantified. I showed that detection of an exogenous IAC using digital HRM (dHRM) reduces the inclusion of false negative partitions, changing the calculated DNA concentration up to 52%. The integration of dHRM enabled classification of partitions that would otherwise be considered ambiguous "rain", which accounts for up to ~3% and ~10% of partitions in intercalating dye and hydrolysis probe dPCR respectively. The IAC was developed for use with a broad-based bacterial primer set for microbial profiling, but can be applied to any dPCR application. I then took these concepts and used them to develop a novel method to detect, quantify, and identify bacteria in human blood samples. This first involved optimizing the assay developed in chapter 1 to avoid interference from human DNA. I then developed a melt curve database for bacterial pathogens using dHRM, quantified the lower limit of detection for our method, and used the assay to quantify bacterial DNA in whole blood samples that were matched to blood culture positives. This study demonstrated that dHRM can be reliably used to differentiate blood culture positive and negative samples from patients with 90% concordance when compared with clinical adjudication. This work resulted in a technique that can be applied to a number of DNA profiling applications and advances the utility of dPCR in clinical applications where accurate quantification is imperative.