Project description:To identify cell adhesion molecules (CAMs) targeting bacterial membrane proteins within a synthetic bacteria-displayed nanobody library, we present a comprehensive whole-cell screening platform. This involves targeted amplicon sequencing to discover nanobodies targeting the natural adhesin, TraN. Furthermore, we employ deep mutational engineering to enhance the binding affinity of these nanobodies toward TraN.
Project description:Purpose: The interaction between Legionella pneumophila and its host cell is not completly understood. The aim of this study was to identify eukaryotic-like bacterial factors which manipulate the host cell. Method: A stable overexpression of LPC_1677 was performed in THP-1 cells. The LPC_1677 overexpressing cells were infected with L.p. and RNA sequencing was performed. Key pro-inflammatory markers were validated via qPCR and ELISA. Results: Bioinformatic screening for eukaryotic-like motives in L.p. took us to LPC_1677 which deacetylates host histones and thereby attenuating gene transcription. Conclusion: This study demonstrated that the eukaryotic-like bacterial factor LPC_1677 de-acetylates Histones in the host cell.
Project description:Metabolic heterogeneity modulates productivity, antibiotic resistance and cancer aggressiveness. Since metabolic fluxes represent the functional output of metabolism, with glycolytic flux correlating with highly-productive phenotypes and cancer, such flux map will be indicative of the cellular metabolic state. Therefore, the quantification of metabolic fluxes is vital to identify the existence of metabolic subpopulations and to understand the process of their emergence at the single-cell level. However, so far inference of metabolic fluxes in individual cells is not possible as no method is available. Here, we developed a biosensor for glycolytic flux measurements in single yeast cells drawing on the robust correlation between fructose-1,6-bisphosphate (FBP) and flux levels in yeast, and using the B. subtilis FBP-binding transcription factor CggR. We followed a systematic engineering approach starting from promoter design, computational protein design and protein engineering, accompanied by strict characterization of the biosensor using different biochemical methods, proteomics, metabolomics and physiological analyses. As proof of principle, we applied the biosensor in vivo in the search for metabolic subpopulations in yeast cultures and, using fluorescence microscopy, we demonstrated that quiescent yeast cells have low glycolytic fluxes in comparison to coexisting dividing cells. We anticipate that our biosensor will contribute with unprecedented resolution for the study of metabolic subpopulations, to understand how and why metabolic subpopulations emerge and, very importantly, give clues on how to counteract the undesirable effects of such.
Project description:Multicenter Preparedness Exercise Enables Rapid Development of Cluster-Specific PCR-Based Screening Assays from Bacterial Genomic Data
