Gemcitabine enhances Wilms�� tumor gene WT1 expression and sensitizes human pancreatic cancer cells with WT1-specific T-cell-mediated antitumor immune response

Wilms' tumor gene (WT1), which is expressed in human pancreatic cancer (PC), is a unique tumor antigen recognized by T-cell-mediated antitumor immune response. Gemcitabine (GEM), a standard therapeutic drug for PC, was examined for the regulation of WT1 expression and the sensitizing effect on PC cells with WT1-specific antitumor immune response. Expression of WT1 was examined by quantitative PCR, immunoblot analysis, and confocal microscopy. Antigenic peptide of WT1 presented on HLA class I molecules was detected by mass spectrometry. WT1-specific T-cell receptor gene-transduced human T cells were used as effecter T cells for the analysis of cytotoxic activity. GEM treatment of human MIAPaCa2 PC cells enhanced WT1 mRNA levels, and this increase is associated with nuclear factor kappa B activation. Tumor tissue from GEM-treated MIAPaCa2-bearing SCID mice also showed an increase in WT1 mRNA. Some human PC cell lines other than MIAPaCa2 showed up-regulation of WT1 mRNA levels following GEM treatment. GEM treatment shifted WT1 protein from the nucleus to the cytoplasm, which may promote proteasomal processing of WT1 protein and generation of antigenic peptide. In fact, presentation of HLA-A*2402-restricted antigenic peptide of WT1 (CMTWNQMNL) increased in GEM-treated MIAPaCa2 cells relative to untreated cells. WT1-specific cytotoxic T cells killed MIAPaCa2 cells treated with an optimal dose of GEM more efficiently than untreated MIAPaCa2 cells. GEM enhanced WT1 expression in human PC cells and sensitized PC cells with WT1-specific T-cell-mediated antitumor immune response.     

Stretching actin filaments within cells enhances their affinity for the myosin II motor domain

To test the hypothesis that the myosin II motor domain (S1) preferentially binds to specific subsets of actin filaments in vivo, we expressed GFP-fused S1 with mutations that enhanced its affinity for actin in Dictyostelium cells. Consistent with the hypothesis, the GFP-S1 mutants were localized along specific portions of the cell cortex. Comparison with rhodamine-phalloidin staining in fixed cells demonstrated that the GFP-S1 probes preferentially bound to actin filaments in the rear cortex and cleavage furrows, where actin filaments are stretched by interaction with endogenous myosin II filaments. The GFP-S1 probes were similarly enriched in the cortex stretched passively by traction forces in the absence of myosin II or by external forces using a microcapillary. The preferential binding of GFP-S1 mutants to stretched actin filaments did not depend on cortexillin I or PTEN, two proteins previously implicated in the recruitment of myosin II filaments to stretched cortex. These results suggested that it is the stretching of the actin filaments itself that increases their affinity for the myosin II motor domain. In contrast, the GFP-fused myosin I motor domain did not localize to stretched actin filaments, which suggests different preferences of the motor domains for different structures of actin filaments play a role in distinct intracellular localizations of myosin I and II. We propose a scheme in which the stretching of actin filaments, the preferential binding of myosin II filaments to stretched actin filaments, and myosin II-dependent contraction form a positive feedback loop that contributes to the stabilization of cell polarity and to the responsiveness of the cells to external mechanical stimuli.     

SiGN-SSM: open source parallel software for estimating gene networks with state space models

SiGN-SSM is an open-source gene network estimation software able to run in parallel on PCs and massively parallel supercomputers. The software estimates a state space model (SSM), that is a statistical dynamic model suitable for analyzing short time and/or replicated time series gene expression profiles. SiGN-SSM implements a novel parameter constraint effective to stabilize the estimated models. Also, by using a supercomputer, it is able to determine the gene network structure by a statistical permutation test in a practical time. SiGN-SSM is applicable not only to analyzing temporal regulatory dependencies between genes, but also to extracting the differentially regulated genes from time series expression profiles. AVAILABILITY: SiGN-SSM is distributed under GNU Affero General Public Licence (GNU AGPL) version 3 and can be downloaded at http://sign.hgc.jp/signssm/. The pre-compiled binaries for some architectures are available in addition to the source code. The pre-installed binaries are also available on the Human Genome Center supercomputer system. The online manual and the supplementary information of SiGN-SSM is available on our web site. CONTACT: tamada@ims.u-tokyo.ac.jp.     

Systems biology model repository for macrophage pathway simulation

SUMMARY: The Macrophage Pathway Knowledgebase (MACPAK) is a computational system that allows biomedical researchers to query and study the dynamic behaviors of macrophage molecular pathways. It integrates the knowledge of 230 reviews that were carefully checked by specialists for their accuracy and then converted to 230 dynamic mathematical pathway models. MACPAK comprises a total of 24 009 entities and 12 774 processes and is described in the Cell System Markup Language (CSML), an XML format that runs on the Cell Illustrator platform and can be visualized with a customized Cytoscape for further analysis. AVAILABILITY: MACPAK can be accessed via an interactive web site at http://macpak.csml.org. The CSML pathway models are available under the Creative Commons license.     

MIRACH: efficient model checker for quantitative biological pathway models

Model checking is playing an increasingly important role in systems biology as larger and more complex biological pathways are being modeled. In this article we report the release of an efficient model checker MIRACH 1.0, which supports any model written in popular formats such as CSML and SBML. MIRACH is integrated with a Petri-net-based simulation engine, enabling efficient online (on-the-fly) checking. In our experiment, by using Levchenko et al. model, we reveal that timesaving gains by using MIRACH easily surpass 400% compared with its offline-based counterpart. AVAILABILITY AND IMPLEMENTATION: MIRACH 1.0 was developed using Java and thus executable on any platform installed with JDK 6.0 (not JRE 6.0) or later. MIRACH 1.0, along with its source codes, documentation and examples are available at http://sourceforge.net/projects/mirach/ under the LGPLv3 license.     

Online model checking approach based parameter estimation to a neuronal fate decision simulation model in Caenorhabditis elegans with hybrid functional Petri net with extension

Mathematical modeling and simulation studies are playing an increasingly important role in helping researchers elucidate how living organisms function in cells. In systems biology, researchers typically tune many parameters manually to achieve simulation results that are consistent with biological knowledge. This severely limits the size and complexity of simulation models built. In order to break this limitation, we propose a computational framework to automatically estimate kinetic parameters for a given network structure. We utilized an online (on-the-fly) model checking technique (which saves resources compared to the offline approach), with a quantitative modeling and simulation architecture named hybrid functional Petri net with extension (HFPNe). We demonstrate the applicability of this framework by the analysis of the underlying model for the neuronal cell fate decision model (ASE fate model) in Caenorhabditis elegans. First, we built a quantitative ASE fate model containing 3327 components emulating nine genetic conditions. Then, using our developed efficient online model checker, MIRACH 1.0, together with parameter estimation, we ran 20-million simulation runs, and were able to locate 57 parameter sets for 23 parameters in the model that are consistent with 45 biological rules extracted from published biological articles without much manual intervention. To evaluate the robustness of these 57 parameter sets, we run another 20 million simulation runs using different magnitudes of noise. Our simulation results concluded that among these models, one model is the most reasonable and robust simulation model owing to the high stability against these stochastic noises. Our simulation results provide interesting biological findings which could be used for future wet-lab experiments.     

Identifying gene pathways associated with cancer characteristics via sparse statistical methods

We propose a statistical method for uncovering gene pathways that characterize cancer heterogeneity. To incorporate knowledge of the pathways into the model, we define a set of activities of pathways from microarray gene expression data based on the Sparse Probabilistic Principal Component Analysis (SPPCA). A pathway activity logistic regression model is then formulated for cancer phenotype. To select pathway activities related to binary cancer phenotypes, we use the elastic net for the parameter estimation and derive a model selection criterion for selecting tuning parameters included in the model estimation. Our proposed method can also reverse-engineer gene networks based on the identified multiple pathways that enables us to discover novel gene-gene associations relating with the cancer phenotypes. We illustrate the whole process of the proposed method through the analysis of breast cancer gene expression data.     

Intratracheal synthetic CpG oligodeoxynucleotide causes acute lung injury with systemic inflammatory response

Bacterial genome is characterized by frequent unmethylated cytosine-phosphate-guanine (CpG) motifs. Deleterious effects can occur when synthetic oligodeoxynucleotides (ODN) with unmethylated CpG dinucleotides (CpG-ODN) are administered in a systemic fashion. We aimed to evaluate the effect of intratracheal CpG-ODN on lung inflammation and systemic inflammatory response. C57BL/6J mice received intratracheal administration of CpG-ODN (0.01, 0.1, 1.0, 10, or 100 μM) or control ODN without CpG motif. Bronchoalveolar lavage (BAL) fluid was obtained 3 or 6 h or 1, 2, 7, or 14 days after the instillation and subjected to a differential cell count and cytokine measurement. Lung permeability was evaluated as the BAL fluid-to-plasma ratio of the concentration of human serum albumin that was injected 1 h before euthanasia. Nuclear factor (NF)-κB DNA binding activity was also evaluated in lung homogenates. Intratracheal administration of 10 μM or higher concentration of CpG-ODN induced significant inflammatory cell accumulation into the airspace. The peak accumulation of neutrophils and lymphocytes occurred 1 and 2 days after the CpG-ODN administration, respectively. Lung permeability was increased 1 day after the 10 μM CpG-ODN challenge. CpG-ODN also induced nuclear translocation of NF-κB and upregulation of various inflammatory cytokines in BAL fluid and plasma. Histopathology of the lungs and liver revealed acute lung injury and liver damage with necrosis, respectively. Control ODN without CpG motif did not induce any inflammatory change. Since intratracheal CpG-ODN induced acute lung injury as well as systemic inflammatory response, therapeutic strategies to neutralize bacterial DNA that is released after administration of bactericidal agents should be considered.