Microscopic Characterization of the L10-FePt Nanoparticles Synthesized by the SiO2-Nanoreactor Method

We investigated magnetic properties of the L10-FePt nanoparticles synthesized by the SiO2-nanoreactor method by means of Moessbauer spectroscopy from the microscopic point of view. Almost all of the nanoparticles were revealed to have nearly the same Moessbauer hyperfine parameters as those of the bulk L10-FePt alloy, indicating that they have well-defined L10 structure equivalent to the bulk state in spite of their small size of 6.5 nm.Comment: 13 pages, 4 figure

Vasohibin-1 is identified as a master-regulator of endothelial cell apoptosis using gene network analysis.

BACKGROUND: Apoptosis is a critical process in endothelial cell (EC) biology and pathology, which has been extensively studied at protein level. Numerous gene expression studies of EC apoptosis have also been performed, however few attempts have been made to use gene expression data to identify the molecular relationships and master regulators that underlie EC apoptosis. Therefore, we sought to understand these relationships by generating a Bayesian gene regulatory network (GRN) model. RESULTS: ECs were induced to undergo apoptosis using serum withdrawal and followed over a time course in triplicate, using microarrays. When generating the GRN, this EC time course data was supplemented by a library of microarray data from EC treated with siRNAs targeting over 350 signalling molecules.The GRN model proposed Vasohibin-1 (VASH1) as one of the candidate master-regulators of EC apoptosis with numerous downstream mRNAs. To evaluate the role played by VASH1 in EC, we used siRNA to reduce the expression of VASH1. Of 10 mRNAs downstream of VASH1 in the GRN that were examined, 7 were significantly up- or down-regulated in the direction predicted by the GRN.Further supporting an important biological role of VASH1 in EC, targeted reduction of VASH1 mRNA abundance conferred resistance to serum withdrawal-induced EC death. CONCLUSION: We have utilised Bayesian GRN modelling to identify a novel candidate master regulator of EC apoptosis. This study demonstrates how GRN technology can complement traditional methods to hypothesise the regulatory relationships that underlie important biological processes

Gradual Increase of High Mobility Group Protein B1 in the Lungs after the Onset of Acute Exacerbation of Idiopathic Pulmonary Fibrosis

The pathogenesis of acute exacerbation of idiopathic pulmonary fibrosis (IPF) remains to be elucidated. To evaluate the roles of inflammatory mediators in acute exacerbation, the concentrations of high mobility group protein B1 (HMGB1), a chief mediator of acute lung injury, and 18 inflammatory cytokines were measured in the bronchoalveolar lavage fluid, serially sampled from seven IPF patients after the onset of acute exacerbation. HMGB1 gradually increased in the alveolar fluid after the onset of acute exacerbation, in positive correlation with monocytes chemotactic protein-1 (MCP-1), a potent fibrogenic mediator. In the lung tissues of eight IPF patients autopsied after acute exacerbation, intense cytoplasmic staining for HMGB1 was observed in the alveolar epithelial cells in alveolar capillary augmented lesions, where the capillary endothelial cells remarkably reduced the expression of thrombomodulin, an intrinsic antagonist of HMGB1. These results suggest pathogenic roles for HMGB1 and MCP-1 in the late phase of acute exacerbation of IPF

Gene network inference and visualization tools for biologists: application to new human transcriptome datasets.

Gene regulatory networks inferred from RNA abundance data have generated significant interest, but despite this, gene network approaches are used infrequently and often require input from bioinformaticians. We have assembled a suite of tools for analysing regulatory networks, and we illustrate their use with microarray datasets generated in human endothelial cells. We infer a range of regulatory networks, and based on this analysis discuss the strengths and limitations of network inference from RNA abundance data. We welcome contact from researchers interested in using our inference and visualization tools to answer biological questions

Elevated β-catenin pathway as a novel target for patients with resistance to EGF receptor targeting drugs

There is a high death rate of lung cancer patients. Epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) are effective in some lung adenocarcinoma patients with EGFR mutations. However, a significant number of patients show primary and acquire resistance to EGFR-TKIs. Although the Akt kinase is commonly activated due to various resistance mechanisms, the key targets of Akt remain unclear. Here, we show that the Akt-β-catenin pathway may be a common resistance mechanism. We analyzed gene expression profiles of gefitinib-resistant PC9M2 cells that were derived from gefitinib-sensitive lung cancer PC9 cells and do not have known resistance mechanisms including EGFR mutation T790M. We found increased expression of Axin, a β-catenin target gene, increased phosphorylation of Akt and GSK3, accumulation of β-catenin in the cytoplasm/nucleus in PC9M2 cells. Both knockdown of β-catenin and treatment with a β-catenin inhibitor at least partially restored gefitinib sensitivity to PC9M2 cells. Lung adenocarcinoma tissues derived from gefitinib-resistant patients displayed a tendency to accumulate β-catenin in the cytoplasm. We provide a rationale for combination therapy that includes targeting of the Akt-β-catenin pathway to improve the efficacy of EGFR-TKIs

Cell Cycle Gene Networks Are Associated with Melanoma Prognosis

BACKGROUND: Our understanding of the molecular pathways that underlie melanoma remains incomplete. Although several published microarray studies of clinical melanomas have provided valuable information, we found only limited concordance between these studies. Therefore, we took an in vitro functional genomics approach to understand melanoma molecular pathways. METHODOLOGY/PRINCIPAL FINDINGS: Affymetrix microarray data were generated from A375 melanoma cells treated in vitro with siRNAs against 45 transcription factors and signaling molecules. Analysis of this data using unsupervised hierarchical clustering and Bayesian gene networks identified proliferation-association RNA clusters, which were co-ordinately expressed across the A375 cells and also across melanomas from patients. The abundance in metastatic melanomas of these cellular proliferation clusters and their putative upstream regulators was significantly associated with patient prognosis. An 8-gene classifier derived from gene network hub genes correctly classified the prognosis of 23/26 metastatic melanoma patients in a cross-validation study. Unlike the RNA clusters associated with cellular proliferation described above, co-ordinately expressed RNA clusters associated with immune response were clearly identified across melanoma tumours from patients but not across the siRNA-treated A375 cells, in which immune responses are not active. Three uncharacterised genes, which the gene networks predicted to be upstream of apoptosis- or cellular proliferation-associated RNAs, were found to significantly alter apoptosis and cell number when over-expressed in vitro. CONCLUSIONS/SIGNIFICANCE: This analysis identified co-expression of RNAs that encode functionally-related proteins, in particular, proliferation-associated RNA clusters that are linked to melanoma patient prognosis. Our analysis suggests that A375 cells in vitro may be valid models in which to study the gene expression modules that underlie some melanoma biological processes (e.g., proliferation) but not others (e.g., immune response). The gene expression modules identified here, and the RNAs predicted by Bayesian network inference to be upstream of these modules, are potential prognostic biomarkers and drug targets

セイブツ ジョウホウ カラノ イデンシ ネットワーク ノ スイテイ

京都大学0048新制・課程博士博士(情報学)甲第11957号情博第181号新制||情||40(附属図書館)23746UT51-2006-B136京都大学大学院情報学研究科知能情報学専攻(主査)教授 阿久津 達也, 教授 後藤 修, 教授 松田 哲也学位規則第4条第1項該当Doctor of InformaticsKyoto UniversityDA

System-Based Differential Gene Network Analysis for Characterizing a Sample-Specific Subnetwork

Gene network estimation is a method key to understanding a fundamental cellular system from high throughput omics data. However, the existing gene network analysis relies on having a sufficient number of samples and is required to handle a huge number of nodes and estimated edges, which remain difficult to interpret, especially in discovering the clinically relevant portions of the network. Here, we propose a novel method to extract a biomedically significant subnetwork using a Bayesian network, a type of unsupervised machine learning method that can be used as an explainable and interpretable artificial intelligence algorithm. Our method quantifies sample specific networks using our proposed Edge Contribution value (ECv) based on the estimated system, which realizes condition-specific subnetwork extraction using a limited number of samples. We applied this method to the Epithelial-Mesenchymal Transition (EMT) data set that is related to the process of metastasis and thus prognosis in cancer biology. We established our method-driven EMT network representing putative gene interactions. Furthermore, we found that the sample-specific ECv patterns of this EMT network can characterize the survival of lung cancer patients. These results show that our method unveils the explainable network differences in biological and clinical features through artificial intelligence technology