Screening of Drug Metabolizing Enzymes for the Ginsenoside Compound K In Vitro: An Efficient Anti-Cancer Substance Originating from Panax Ginseng

Ginsenoside compound K (CK), a rare ginsenoside originating from Panax Ginseng, has been found to possess unique pharmacological activities specifically as anti-cancers. However, the role of cytochrome P450s (CYPs) in the metabolism of CK is unclear. In this study, we screened the CYPs for the metabolism of CK in vitro using human liver microsomes (HLMs) or human recombinant CYPs. The results showed that CK inhibited the enzyme activities of CYP2C9 and CYP3A4 in the HLMs. The K_m and V_max values of CK were 84.20±21.92 μM and 0.28±0.04 nmol/mg protein/min, respectively, for the HLMs; 34.63±10.48 μM and 0.45±0.05 nmol/nmol P450/min, respectively, for CYP2C9; and 27.03±5.04 μM and 0.68±0.04 nmol/nmol P450/min, respectively, for CYP3A4. The IC₅₀ values were 16.00 μM and 9.83 μM, and K_i values were 14.92 μM and 11.42μM for CYP2C9 and CYP3A4, respectively. Other human CYP isoforms, including CYP1A2, CYP2A6, CYP2D6, CYP2E1, and CYP2C19, showed minimal or no effect on CK metabolism. The results suggested that CK was a substrate and also inhibitors for both CYP2C9 and CYP3A4. Patients using CK in combination with therapeutic drugs that are substrates of CYP2C9 and CYP3A4 for different reasons should be careful, although the inhibiting potency of CK is much poorer than that of enzyme-specific inhibitors.     

In memoriam: Professor Ray WU

<正>During the Chinese New Year holidays in 2008, Professor Zhang Nai-Heng, former chair of the Department of Biochemistry, Beijing Medical College, informed me of the sad news that Professor Ray Wu had passed away in Ithaca, New York on     

Extracellular vesicles in bone and tooth: A state-of-art paradigm in skeletal regeneration

Bone and tooth, fundamental parts of the craniofacial skeleton, are anatomically and developmentally interconnected structures. Notably, pathological processes in these tissues underwent together and progressed in multilevels. Extracellular vesicles (EVs) are cell-released small organelles and transfer proteins and genetic information into cells and tissues. Although EVs have been identified in bone and tooth, particularly EVs have been identified in the bone formation and resorption, the concrete roles of EVs in bone and tooth development and diseases remain elusive. As such, we review the recent progress of EVs in bone and tooth to highlight the novel findings of EVs in cellular communication, tissue homeostasis, and interventions. This will enhance our comprehension on the skeletal biology and shed new light on the modulation of skeletal disorders and the potential of genetic treatment.     

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.