Study of in vivo interactions between penicillin-binding proteins of Staphylococcus aureus

Staphylococcus aureus (S. aureus) is a major human pathogen that has acquired resistance to practically all classes of β-lactam antibiotics, being responsible of Multidrug resistant S. aureus (MRSA) associated infections both in healthcare (HA-MRSA) and community settings (CA-MRSA). The emergence of laboratory strains with high-resistance (VRSA) to the last resort antibiotic, vancomycin, is a warning of what is to come in clinical strains. Penicillin binding proteins (PBPs) target β-lactams and are responsible for catalyzing the last steps of synthesis of the main component of cell wall, peptidoglycan. As in Escherichia coli, it is suggested that S. aureus uses a multi-protein complex that carries out cell wall synthesis. In the presence of β-lactams, PBP2A and PBP2 perform a joint action to build the cell wall and allow cell survival. Likewise, PBP2 cooperates with PBP4 in cell wall cross-linking. However, an actual interaction between PBP2 and PBP4 and the location of such interaction has not yet been determined. Therefore, investigation of the existence of a PBP2-PBP4 interaction and its location(s) in vivo is of great interest, as it should provide new insights into the function of the cell wall synthesis machinery in S. aureus. The aim of this work was to develop Split-GFPP7 system to determine interactions between PBP2 and PBP4. GFPP7 was split in a strategic site and fused to proteins of interest. When each GFPP7 fragment, fused to proteins, was expressed alone in staphylococcal cells, no fluorescence was detectable. When GFPP7 fragments fused to different peptidoglycan synthesis (PBP2 and PBP4) or cell division (FtsZ and EzrA) proteins were co-expressed together, fluorescent fusions were localized to the septum. However, further analysis revealed that this positive result is mediated by GFPP7 self-association. We then interpret the results in light of such event and provide insights into ways of improving this system.

Unsupervised classification of remote sensing images combining Self Organizing Maps and segmentation techniques

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.