Infectious diseases spread by pathogenic bacteria continue to pose a significant threat to global public health. The severity of this threat is further exacerbated by the emergence and proliferation of drug-resistant bacteria. Effective and targeted treatment of infectious diseases necessitates knowing the identity of the responsible pathogen (or multiple pathogens, in the case of polymicrobial infections) and its antimicrobial susceptibility profile. Current clinical methods used to determine bacterial identity and its antimicrobial susceptibility rely on bulk bacterial culture, which can take from several days to weeks to complete. The long duration needed for definitive diagnosis results in common use of broad-spectrum antibiotics, which subsequently promotes the development and spread of antimicrobial resistance. Thus, rapid diagnostics are essential for delivering effective and targeted antimicrobial treatments and improving patient care. Specifically, there remains a critical need for finding the identity of the infecting bacteria and its antimicrobial susceptibility profile from patient samples rapidly, such that healthcare providers could initiate effective treatments in a timely manner.
In this thesis, we present a rapid bacterial diagnostic approach that is capable of automated bacterial identification (ID) and antimicrobial susceptibility testing (AST) in heterogeneous samples by using molecular-based techniques, i.e. polymerase chain reaction (PCR) and high-resolution melt (HRM) analysis. A machine learning algorithm is employed to automatically identify bacterial species based on melt profiles of amplicons generated through universal PCR on 16S rRNA gene. For AST, we introduce “pheno-molecular” approach, which combines phenotypic growth (i.e., in vitro bacteria culture with antibiotics) with molecular-based detection method. PCR is utilized to quantify nucleic acids after brief incubation, which correlates to phenotypic growth responses of individual pathogens in samples. By comparing bacterial growth in the presence or absence of antibiotics after brief cultivation, susceptibility profile of each bacterial species can be revealed.
We start with an introduction to diagnosing bacterial infections, including the current gold standard of diagnostics and discuss on some existing alternative methods that try to address these shortcomings. We also describe the fundamentals of molecular techniques that we employ, universal PCR and HRM, and their utilities in detail (Chapter 1). Then, we aim to develop an automated and supervised melt curve-based classification by exploring various well-known machine learning techniques (Chapter 2). Next, we develop a universal amplification reaction of long amplicon PCR covering 16S gene loci for HRM-based bacterial species identification. Nested support vector machine is described in detail as it pertains to automatically identify bacterial species based on our melt curve database of 37 clinically-relevant bacterial species (Chapter 3). We then further expand the assay’s application to perform both ID and AST in a streamlined process by including brief bacterial culture prior to PCR amplification (Chapter 4). Finally, we utilize limiting dilutions and a microfluidic platform to enable single molecule analysis, which allows for individual bacterial species investigation and also improves assay turnaround time (Chapter 5)
