No abstract

Streptococcus pyogenes (group A Streptococcus , GAS) is a strict human pathogen causing a broad spectrum of diseases and a variety of autoimmune sequelae. The pathogenesis of GAS infection mostly relies on the production of an extensive network of cell wall-associated and secreted virulence proteins, such as adhesins, toxins, and exoenzymes. PrsA, the only extracellular parvulin-type peptidyl-prolyl isomerase expressed ubiquitously in Gram-positive bacteria, has been suggested to assist the folding and maturation of newly exported proteins to acquire their native conformation and activity. Two PrsA proteins, PrsA1 and PrsA2, have been identified in GAS, but the respective contribution of each PrsA in GAS pathogenesis remains largely unknown. By combining comparative proteomic and phenotypic analysis approaches, we demonstrate that both PrsA isoforms are required to maintain GAS proteome homeostasis and virulence-associated traits in a unique and overlapping manner. The inactivation of both PrsA in GAS caused remarkable impairment in biofilm formation, host adherence, infection-induced cytotoxicity, and in vivo virulence in a murine soft tissue infection model. The concordance of proteomic and phenotypic data clearly features the essential role of PrsA in GAS full virulence.

A novel component-level dictionary learning framework that exploits image/video group characteristics based on sparse representation is introduced in this paper. Unlike the previous methods that select the dictionaries to best reconstruct the data, we present an energy minimization formulation that jointly optimizes the learning of both sparse dictionary and component-level importance within one unified framework to provide a discriminative and sparse representation for image/video groups. The importance measures how well each feature component represents the group property with the dictionary. Then, the dictionary is updated iteratively to reduce the influence of unimportant components, thus refining the sparse representation for each group. In the end, by keeping the top K important components, a compact representation is obtained for the sparse coding dictionary. Experimental results on several public image and video data sets are shown to demonstrate the superior performance of the proposed algorithm compared with the-state-of-the-art methods.