Pathogenetic role of tissue factor in graft-versus-host disease

Graft-versus-host disease (GVHD) is a serious complication after allogeneic stem cell transplantation, the mechanism of it is still not elucidated. Recent findings suggest that host endothelial cells are a target of alloreactive donor cytotoxic T lymphocytes in GVHD and tissue factor (TF) plays an important role not only in coagulation-inflammation cycle, but also in transplant immunology. We postulate TF expression in vascular endothelial cells(VEC) may play an pivotal role in the pathogenesis of GVHD. TF gene andprotein expression in target organs of GVHD in aGVHD mice was significantly elevated compared to that of controls as determined by real-time PCR and Western blotting. Allogeneic CD4^+^T cell and CD8^+^T cells enhanced TF, VCAM-1, TNF-[alpha], IFN-[gamma] and IL-6 expression in TNF-[alpha] prestimulated HUVECs compared to controls as determined by flowcytometry and real-time PCR. JNK and p38MAPK mediated allogeneic T cells-induced TF expression in HUVECs. These effects were largely prevented by monoclonal antibody against TF, SB203580 and SP600125. In concert, these data provide strong evidence that upregulated TF expression is related to tissue damage caused by GVHD, TF isthe key factor in GVHD mediated by endothelial cells and allogeneic T cells-induced TF and consecutive proinflammatory cytokines expression in VEC contribute to the pathogenesis of GVHD

Improvements on Seeding Based Protein Sequence Similarity Search

The primary goal of bioinformatics is to increase an understanding in the biology of organisms. Computational, statistical, and mathematical theories and techniques have been developed on formal and practical problems that assist to achieve this primary goal. For the past three decades, the primary application of bioinformatics has been biological data analysis. The DNA or protein sequence similarity search is perhaps the most common, yet vitally important task for analyzing biological data. The sequence similarity search is a process of finding optimal sequence alignments. On the theoretical level, the problem of sequence similarity search is complex. On the applicational level, the sequences similarity search onto a biological database has been one of the most basic tasks today. Using traditional quadratic time complexity solutions becomes a challenge due to the size of the database. Seeding (or filtration) based approaches, which trade sensitivity for speed, are a popular choice among those available. Two main phases usually exist in a seeding based approach. The first phase is referred to as the hit generation, and the second phase is referred to as the hit extension. In this thesis, two improvements on the seeding based protein sequence similarity search are presented. First, for the hit generation, a new seeding idea, namely spaced k-mer neighbors, is presented. We present our effective algorithms to find a good set of spaced k-mer neighbors. Secondly, for the hit generation, a new method, namely HexFilter, is proposed to reduce the number of hit extensions while achieving better selectivity. We show our HexFilters with optimized configurations