Abstract: Extracytoplasmic proteins assume their native structure with the help of chaperones and folding catalysts en route to their final location. Disulfide bond formation plays an integral part in this process, contributing to protein stability and function. Despite their simple chemistry, disulfide bonds are introduced into substrate proteins by dedicated folding pathways in most organisms. In the periplasm of Gram-negative bacteria this is performed by the DSB system, and more specifically by the oxidase DsbA. Although the function of the DSB proteins is well characterized and their role in bacterial pathogenesis has been discussed, their contribution to antimicrobial resistance (AMR) has never been explored. We have demonstrated that disulfide bond formation is necessary for the function of key antimicrobial resistance determinants in the cell envelope of Enterobacteria. More specifically, we have found that deletion of DsbA abrogates or severely affects the breakdown of β-lactam antibiotics by β-lactamases, resistance to polymyxins due to the activity of mobile colistin resistance (MCR) enzymes and intrinsic resistance to multiple antibiotics arising from the action of Resistance-Nodulation-Division (RND) efflux pumps. Such diverse resistance determinants are impaired because the absence of DsbA compromises stability and folding of β-lactamases and MCR enzymes, whilst also leading to altered periplasmic proteostasis indirectly impacting efflux pump function. Importantly, the DSB system is a tractable AMR target, as chemical inhibition of disulfide bond formation sensitizes multidrug-resistant clinical isolates to several classes of existing antibiotics. We are currently focused on the discovery of improved DsbA inhibitors since their generation could lead to the development of next-generation antibiotic adjuvants that would function as broad-acting resistance breakers.

Speaker: Jakob Nikolas Kather (University Hospital Aachen & German Cancer Center Heidelberg, Germany)

Abstract: Precision oncology requires molecular and genetic testing of tumor tissue. For many tests, widespread implementation into clinical practice is limited because these biomarkers are costly, require significant expertise and are limited by tissue availability. However, virtually every cancer patient gets a biopsy as part of the diagnostic workup and this tissue is routinely stained with hematoxylin and eosin (HE). We have developed a deep learning-based technology to predict molecular features, prognosis and markers for treatment response directly from routine histology. This talk will summarize the state of the art in deep learning histopathology, demonstrate emerging use cases and discuss the clinical implications of deep learning based molecular testing of solid tumors.

Short Bio: Jakob Nikolas Kather is a clinician-scientist at University Hospital Aachen and the German Cancer Center Heidelberg, Germany. He leads a junior research group focused on deep-learning based prediction of treatment response in gastrointestinal cancer.