Outer Membrane Vesicle Production by Helicobacter pylori Represents an Approach for the Delivery of Virulence Factors CagA, VacA and UreA into Human Gastric Adenocarcinoma (AGS) Cells.

Helicobacter pylori infection is the etiology of several gastric-related diseases including gastric cancer. Cytotoxin associated gene A (CagA), vacuolating cytotoxin A (VacA) and α-subunit of urease (UreA) are three major virulence factors of H. pylori, and each of them has a distinct entry pathway and pathogenic mechanism during bacterial infection. H. pylori can shed outer membrane vesicles (OMVs). Therefore, it would be interesting to explore the production kinetics of H. pylori OMVs and its connection with the entry of key virulence factors into host cells. Here, we isolated OMVs from H. pylori 26,695 strain and characterized their properties and interaction kinetics with human gastric adenocarcinoma (AGS) cells. We found that the generation of OMVs and the presence of CagA, VacA and UreA in OMVs were a lasting event throughout different phases of bacterial growth. H. pylori OMVs entered AGS cells mainly through macropinocytosis/phagocytosis. Furthermore, CagA, VacA and UreA could enter AGS cells via OMVs and the treatment with H. pylori OMVs would cause cell death. Comparison of H. pylori 26,695 and clinical strains suggested that the production and characteristics of OMVs are not only limited to laboratory strains commonly in use, but a general phenomenon to most H. pylori strains.

Active learning for semi-supervised clustering based on locally linear propagation reconstruction.

The success of semi-supervised clustering relies on the effectiveness of side information. To get effective side information, a new active learner learning pairwise constraints known as must-link and cannot-link constraints is proposed in this paper. Three novel techniques are developed for learning effective pairwise constraints. The first technique is used to identify samples less important to cluster structures. This technique makes use of a kernel version of locally linear embedding for manifold learning. Samples neither important to locally linear propagation reconstructions of other samples nor on flat patches in the learned manifold are regarded as unimportant samples. The second is a novel criterion for query selection. This criterion considers not only the importance of a sample to expanding the space coverage of the learned samples but also the expected number of queries needed to learn the sample. To facilitate semi-supervised clustering, the third technique yields inferred must-links for passing information about flat patches in the learned manifold to semi-supervised clustering algorithms. Experimental results have shown that the learned pairwise constraints can capture the underlying cluster structures and proven the feasibility of the proposed approach.