Atherosclerosis is a chronic, inflammatory disorder characterized by the deposition of excess lipids in the arterial intima. The formation of macrophage-derived foam cells in a plaque is a hallmark of the development of atherosclerosis. Lipid homeostasis, especially cholesterol homeostasis, plays a crucial role during the formation of foam cells. Recently, lipid droplet-associated proteins, including PAT and CIDE family proteins, have been shown to control the development of atherosclerosis by regulating the formation, growth, stabilization and functions of lipid droplets in macrophage-derived foam cells. This review focuses on the potential mechanisms of formation of macrophage-derived foam cells in atherosclerosis with particular emphasis on the role of lipid homeostasis and lipid droplet-associated proteins. Understanding the process of foam cell formation will aid in the future discovery of novel therapeutic interventions for atherosclerosis.

Polo-like kinase 1 (Plk1), a well-characterized member of serine/threonine kinases Plk family, has been shown to play pivotal roles in mitosis and cytokinesis in eukaryotic cells. Recent studies suggest that Plk1 not only controls the process of mitosis and cytokinesis, but also, going beyond those previously described functions, plays critical roles in DNA replication and Pten null prostate cancer initiation. In this review, we briefly summarize the functions of Plk1 in mitosis and cytokinesis, and then mainly focus on newly discovered functions of Plk1 in DNA replication and in Ptennull prostate cancer initiation. Furthermore, we briefly introduce the architectures of human and mouse prostate glands and the possible roles of Plk1 in human prostate cancer development. And finally, the newly chemotherapeutic development of small-molecule Plk1 inhibitors to target Plk1 in cancer treatment and their translational studies are also briefly reviewed.

Gene mutation (e.g. substitution, insertion and deletion) and related phenotype information are important biomedical knowledge. Many biomedical databases (e.g. OMIM) incorporate such data. However, few studies have examined the quality of this data. In the current study, we examined the quality of protein single-point mutations in the OMIM and identified whether the corresponding reference sequences align with the mutation positions. Our results show that close to 20% of mutation data cannot be mapped to a single reference sequence. The failed mappings are caused by position conflict, site shifting (peptide, N-terminal methionine) and other types of data error. We propose a preliminary model to resolve such inconsistency in the OMIM database.

A real time PCR assay for the detection of Vibrio parahaemolyticus in seafood samples was developed using a novel specific target and a competitive internal amplification control (IAC). The specificity of this assay was evaluated using 390 bacterial strains including V.parahaemolyticus, and other strains belonging to Vibrio and non-Vibrio species. The real time PCR assay unambiguously distinguished V. parahaemolyticus with a detection sensitivity of 4.8 fg per PCR with purified genomic DNA or 1 CFU per reaction by counting V. parahaemolyticus colonies. The assays of avoiding interference demonstrated that, even in the presence of 2.1 μg genomic DNA or 10⁷ CFU background bacteria, V. parahaemolyticus could still be accurately detected. In addition, the IAC was used to indicate false-negative results, and lower than 94 copies of IAC per reaction had no influence on the detection limit. Ninety-six seafood samples were tested, of which 58 (60.4%) were positive, including 3 false negative results. Consequently, the real time PCR assay is effective for the rapid detection of V. parahaemotyticus contaminants in seafood.

The self-renewal and multipotent potentials in neural stem cells (NSCs) maintain the normal physiological functions of central nervous system (CNS). The abnormal differentiation of NSCs would lead to CNS disorders. However, the mechanisms of how NSCs differentiate into astrocytes, oligodendrocytes (OLs) and neurons are still unclear, which is mainly due to the complexity of differentiation processes and the limitation of the cell separation method. In this study, we modeled the dynamics of neural cell interactions in a systemic approach by mining the high-throughput genomic and proteomic data, and identified 8615 genes that are involved in various biological processes and functions with significant changes during the differentiation processes. A total of 1559 genes are specifically expressed in neural cells, in which 242 genes are NSC specific, 215 are astrocyte specific, 551 are OL specific, and 563 are neuron specific. In addition, we proposed 57 transcriptional regulators specifically expressed in NSCs may play essential roles in the development courses. These findings provide more comprehensive analysis for better understanding the endogenous mechanisms of NSC fate determination.

BioNetSim, a Petri net-based software for modeling and simulating biochemistry processes, is developed, whose design and implement are presented in this paper, including logic construction, real-time access to KEGG (Kyoto Encyclopedia of Genes and Genomes), and BioModel database. Furthermore, glycolysis is simulated as an example of its application. BioNetSim is a helpful tool for researchers to download data, model biological network, and simulate complicated biochemistry processes. Gene regulatory networks, metabolic pathways, signaling pathways, and kinetics of cell interaction are all available in BioNetSim, which makes modeling more efficient and effective. Similar to other Petri net-based softwares, BioNetSim does well in graphic application and mathematic construction. Moreover, it shows several powerful predominances. (1) It creates models in database. (2) It realizes the real-time access to KEGG and BioModel and transfers data to Petri net. (3) It provides qualitative analysis, such as computation of constants. (4) It generates graphs for tracing the concentration of every molecule during the simulation processes.

Protein folding, stability, and function are usually influenced by pH. And free energy plays a fundamental role in analysis of such pH-dependent properties. Electrostatics-based theoretical framework using dielectric solvent continuum model and solving Poisson-Boltzmann equation numerically has been shown to be very successful in understanding the pH-dependent properties. However, in this approach the exact computation of pH-dependent free energy becomes impractical for proteins possessing more than several tens of ionizable sites (e.g.>30), because exact evaluation of the partition function requires a summation over a vast number of possible protonation microstates. Here we present a method which computes the free energy using the average energy and the protonation probabilities of ionizable sites obtained by the well-established Monte Carlo sampling procedure. The key feature is to calculate the entropy by using the protonation probabilities. We used this method to examine a well-studied protein (lysozyme) and produced results which agree very well with the exact calculations. Applications to the optimum pH of maximal stability of proteins and protein–DNA interactions have also resulted in good agreement with experimental data. These examples recommend our method for application to the elucidation of the pH-dependent properties of proteins.