UC Santa Cruz

Millions of people world-wide are affected by diseases and conditions related to microbial pathogens and the immune system. The demand for new therapies against antibiotic resistant bacteria and anti-inflammatories is urgent. My dissertation encompasses two distinct avenues of modern drug discovery: 1) A chemical biology approach to improve the physical properties and potency of novel cyclic peptide inhibitors to combat the virulence of aggressive gram negative bacteria by inhibiting the Type 3 Secretion System (T3SS) and 2) The development of a high-content image-based screening assay for the detection of anti-inflammatories. Chapter 1 describes the structural activity relationship studies of a novel cyclic peptide T3SS inhibitor and its potency against various gram negative bacteria T3SS. Chapter 2 describes the development of a high content image-based screen using macrophages and pro-inflammatory endotoxin lipopolysaccharide (LPS). We compare this screening platform to classic HeLa cytological profiling (CP) and observe the limitations of image based feature extraction techniques to cluster by MOA based on similarity profiles. Furthermore, we use supervised deep learning approaches to identify compounds that reverse the LPS-stimulated phenotype and confirm the selected deep learning hits exhibit an immunomodulatory response to LPS. Lastly we briefly describe our efforts in developing a costume-built online database for viewing the images generated from the HCS screen. This database brings side-by-side comparisons of macrophages treated with compounds +/- LPS stimuli and both stain sets. In addition, it's searchable and includes compound metadata associated with every image, allowing for quick inspection of the images.