Fluid shear stress regulates HepG2 cell migration though time-dependent integrin signaling cascade

Hepatocellular carcinoma (HCC) is a subtype of malignant liver cancer with poor prognosis and limited treatment options. It is noteworthy that mechanical forces in tumor microenvironment play a pivotal role in mediating the behaviors and functions of tumor cells. As an instrumental type of mechanical forces in vivo, fluid shear stress (FSS) has been reported having potent physiologic and pathologic effects on cancer progression. However, the time-dependent mechanochemical transduction in HCC induced by FSS remains unclear. In this study, hepatocellular carcinoma HepG2 cells were exposed to 1.4 dyn/cm² FSS for transient duration (15s and 30s), short duration (5 min, 15 min and 30 min) and long duration (1h, 2h and 4h), respectively. The expression and translocation of Integrins induced FAK-Rho GTPases signaling events were examined. Our results showed that FSS endowed HepG2 cells with higher migration ability via reorganizing cellular F-actin and disrupting intercellular tight junctions. We further demonstrated that FSS regulated the expression and translocation of Integrins and their downstream signaling cascade in time-dependent patterns. The FSS downregulated focal adhesion components (Paxillin, Vinculin and Talin) while upregulated the expression of Rho GTPases (Cdc42, Rac1 and RhoA) in long durations. These results indicated that FSS enhanced tumor cell migration through Integrins-FAK-Rho GTPases signaling pathway in time-dependent manners. Our in vitro findings shed new light on the role of FSS acting in physiologic and pathological processes during tumor progression, which has emerged as a promising clinical strategy for liver carcinoma.     

Binding of B‐cell maturation antigen to B‐cell activating factor induces survival of multiple myeloma cells by activating Akt and JNK signaling pathways

B‐cell maturation antigen (BCMA) is expressed on normal and malignant plasma cells and represents a potential target for therapeutic intervention. In this study, we characterized the mechanism underlying the protein kinase B (Akt) and c‐Jun N‐terminal kinase (JNK) pathways and BCMA interactions in regulating multiple myeloma (MM) cell survival. It was found that the expression levels of B cell‐activating factor (BAFF) and BCMA were increased in MM cells as compared with those in normal controls. The proliferation of U266 cells was induced by recombinant human BAFF (rhBAFF) and could also be decreased by BCMA siRNA. The expression of Bcl‐2 protein was up‐regulated, and Bax protein was down‐regulated after rhBAFF treatment, which could be reversed by BCMA siRNA. Similarly, the protein p‐JNK and p‐Akt were activated by rhBAFF and could be changed by BCMA siRNA. In addition, the BCMA mRNA and protein expression levels were decreased after treatment with Akt and JNK pathway inhibitors. These results suggest that Akt and JNK pathways are involved in the regulation of BCMA. A novel BAFF/BCMA signalling pathway in MM may be a new therapeutic target for MM. Copyright © 2016 John Wiley & Sons, Ltd.     

A Mathematical Framework for Combining Decisions of Multiple Experts toward Accurate and Remote Diagnosis of Malaria Using Tele-Microscopy

We propose a methodology for digitally fusing diagnostic decisions made by multiple medical experts in order to improve accuracy of diagnosis. Toward this goal, we report an experimental study involving nine experts, where each one was given more than 8,000 digital microscopic images of individual human red blood cells and asked to identify malaria infected cells. The results of this experiment reveal that even highly trained medical experts are not always self-consistent in their diagnostic decisions and that there exists a fair level of disagreement among experts, even for binary decisions (i.e., infected vs. uninfected). To tackle this general medical diagnosis problem, we propose a probabilistic algorithm to fuse the decisions made by trained medical experts to robustly achieve higher levels of accuracy when compared to individual experts making such decisions. By modelling the decisions of experts as a three component mixture model and solving for the underlying parameters using the Expectation Maximisation algorithm, we demonstrate the efficacy of our approach which significantly improves the overall diagnostic accuracy of malaria infected cells. Additionally, we present a mathematical framework for performing ‘slide-level’ diagnosis by using individual ‘cell-level’ diagnosis data, shedding more light on the statistical rules that should govern the routine practice in examination of e.g., thin blood smear samples. This framework could be generalized for various other tele-pathology needs, and can be used by trained experts within an efficient tele-medicine platform.     

Analysis of a common inheritable idiotype in IgA-deficient sera using monoclonal antibodies

In these studies we describe the production of three mAb raised to an idiotype on an IgG anticasein antibody isolated from the serum of one IgA-deficient blood donor. These are IgM kappa and block the binding of casein Ag to anticasein antibody. Sera of unrelated IgA-deficient donors were tested for the presence of the idiotype; 15 of 56 IgA-deficient sera (25%) contain the anticasein idiotype, whereas 1 of 45 normal sera was positive. Anticasein antibodies as a whole were predominantly of the IgG1 and IgG3 subclass; idiotype-positive anticaseins are predominantly of the IgG1 subclass. For IgA-deficient donors, the relative amount of idiotype-positive anticasein antibody was correlated with the level of anticasein present in the serum. Studies were done to investigate the potential inheritance of the idiotype in families; in three of four families the idiotype was inherited in an apparent autosomal dominant pattern. Our data show that a common cross-reactive idiotype can be detected in the sera of IgA-deficient individuals and their family members. This suggests that V region markers may be conserved in this humoral immunodeficiency disease.     

Predicting Metabolic Pathways of Small Molecules and Enzymes Based on Interaction Information of Chemicals and Proteins

Metabolic pathway analysis, one of the most important fields in biochemistry, is pivotal to understanding the maintenance and modulation of the functions of an organism. Good comprehension of metabolic pathways is critical to understanding the mechanisms of some fundamental biological processes. Given a small molecule or an enzyme, how may one identify the metabolic pathways in which it may participate? Answering such a question is a first important step in understanding a metabolic pathway system. By utilizing the information provided by chemical-chemical interactions, chemical-protein interactions, and protein-protein interactions, a novel method was proposed by which to allocate small molecules and enzymes to 11 major classes of metabolic pathways. A benchmark dataset consisting of 3,348 small molecules and 654 enzymes of yeast was constructed to test the method. It was observed that the first order prediction accuracy evaluated by the jackknife test was 79.56% in identifying the small molecules and enzymes in a benchmark dataset. Our method may become a useful vehicle in predicting the metabolic pathways of small molecules and enzymes, providing a basis for some further analysis of the pathway systems.     

On the classification of Calligonum juochiangense and C. pumilum (Polygonaceae)

After observing specimens of Calligonum pumilum Losinsk. and C. juochiangense Y. X. Liou in both the field and in herbarium collections, it was found that the morphological characters of these two species are quite different, especially with respect of the twisted direction of fruit ribs, number of bristle rows along each rib, rigidity and degree of interweaving of bristles, as well as their geographic distribution. Therefore, it is concluded that C. pumilum and C. juochiangense should be accepted as two independent species.