Project description:Phages are viruses that specifically infect and kill bacteria. Bacterial fermentation and biotechnology industries see them as enemies, however, they are also investigated for the treatment or prevention of infections caused by multidrug resistant bacteria. Whether foes or allies, their importance is undeniable. Despite decades of research some aspects of phage biology are still poorly understood. In this study, we used label-free quantitative proteomics to reveal the proteotypes of Lactococcus lactis MG1363 during infection by the virulent phage p2, a model for studying the biology of phages infecting Gram-positive bacteria. Our approach resulted in the high-confidence detection and quantification of 59% of the theoretical bacterial proteome, including 226 bacterial proteins detected only during phage infection and 6 proteins unique to uninfected bacteria. We also identified many bacterial proteins of differing abundance during the infection. Using this high-throughput proteomic datasets, we selected specific bacterial genes for inactivation using CRISPR-Cas9 to investigate their involvement in phage replication. One knockout mutant lacking gene llmg_0219 showed resistance to phage p2 due to a deficiency in phage adsorption. Furthermore, we detected and quantified 78% of the theoretical phage proteome and identified many proteins of phage p2 that had not been previously detected. Among others, we uncovered a conserved small phage protein (ORFN1) coded by an unannotated gene. We also applied a targeted approach to achieve greater sensitivity and identify undetected phage proteins that were expected to be present. This allowed us to follow the fate of ORF46, a small phage protein of low abundance. In summary, this work offers a unique view of the virulent phages’ takeover of bacterial cells and provides novel information on phage-host interactions.
Project description:Sequence overlap between two genes is common across all genomes, with viruses having particularly high proportions of these gene overlaps. The natural biological function and effects on fitness of gene overlaps are not fully understood and their effects on gene cluster and genome-level refactoring are unknown.The model bacteriophage φX174 genome displays complex sequence architecture in which ~26% of nucleotides are involved in encoding more than one gene. In this study we use an engineered φX174 phage containing a genome with all gene overlaps removed.
Here we have temporally measured the proteome of a synthetically engineered and wild-type φX174 during infection. We find that almost half of all phage proteins (5/11) have abnormal expression profiles after genome modularisation.