Enhanced Sensitivity of Nonsmall Cell Lung Cancer with Acquired Resistance to Epidermal Growth Factor Receptor-Tyrosine Kinase Inhibitors to Phenformin: The Roles of a Metabolic Shift to Oxidative Phosphorylation and Redox Balance.

Background: Although the efficacy of epidermal growth factor receptor-tyrosine kinase inhibitor (EGFR- TKI) therapy has been proven in non-small cell lung cancer (NSCLC) patients, acquired resistance to EGFR-TKIs presents a serious clinical problem. Hence, the identification of new therapeutic strategy is needed to treat EGFR-TKI-resistant NSCLC.

Methods: Acquired EGFR-TKI-resistant lung cancer cell lines (HCC827, H1993, and H292 cells with acquired resistance to gefitinib or erlotinib) were used for cell-based studies.

Erratum: Modulation of mRNA and lncRNA expression dynamics by the Set2-Rpd3S pathway.

This corrects the article DOI: 10.1038/ncomms13534.

Modulation of mRNA and lncRNA expression dynamics by the Set2-Rpd3S pathway.

H3K36 methylation by Set2 targets Rpd3S histone deacetylase to transcribed regions of mRNA genes, repressing internal cryptic promoters and slowing elongation. Here we explore the function of this pathway by analysing transcription in yeast undergoing a series of carbon source shifts. Approximately 80 mRNA genes show increased induction upon SET2 deletion.

Transcriptomic analysis of insulin-sensitive tissues from anti-diabetic drug treated ZDF rats, a T2DM animal model.

Gene expression changes have been associated with type 2 diabetes mellitus (T2DM); however, the alterations are not fully understood. We investigated the effects of anti-diabetic drugs on gene expression in Zucker diabetic fatty (ZDF) rats using oligonucleotide microarray technology to identify gene expression changes occurring in T2DM. Global gene expression in the pancreas, adipose tissue, skeletal muscle, and liver was profiled from Zucker lean control (ZLC) and anti-diabetic drug treated ZDF rats compared with those in ZDF rats.

Ogura-CMS in Chinese cabbage (Brassica rapa ssp. pekinensis) causes delayed expression of many nuclear genes.

We investigated the mechanism regulating cytoplasmic male sterility (CMS) in Brassica rapa ssp. pekinensis using floral bud transcriptome analyses of Ogura-CMS Chinese cabbage and its maintainer line in B. rapa 300-K oligomeric probe (Br300K) microarrays.

hiPathDB: a human-integrated pathway database with facile visualization.

One of the biggest challenges in the study of biological regulatory networks is the systematic organization and integration of complex interactions taking place within various biological pathways. Currently, the information of the biological pathways is dispersed in multiple databases in various formats. hiPathDB is an integrated pathway database that combines the curated human pathway data of NCI-Nature PID, Reactome, BioCarta and KEGG.

GARNET--gene set analysis with exploration of annotation relations.

Background: Gene set analysis is a powerful method of deducing biological meaning for an a priori defined set of genes. Numerous tools have been developed to test statistical enrichment or depletion in specific pathways or gene ontology (GO) terms. Major difficulties towards biological interpretation are integrating diverse types of annotation categories and exploring the relationships between annotation terms of similar information.

Clinical validation of colorectal cancer biomarkers identified from bioinformatics analysis of public expression data.

Purpose: Identification of novel biomarkers of cancer is important for improved diagnosis, prognosis, and therapeutic intervention. This study aimed to identify marker genes of colorectal cancer (CRC) by combining bioinformatics analysis of gene expression data and validation experiments using patient samples and to examine the potential connection between validated markers and the established oncogenes such as c-Myc and K-ras.

Experimental Design: Publicly available data from GenBank and Oncomine were meta-analyzed leading to 34 candidate marker genes of CRC.

Age-dependent evolution of the yeast protein interaction network suggests a limited role of gene duplication and divergence.

Proteins interact in complex protein-protein interaction (PPI) networks whose topological properties-such as scale-free topology, hierarchical modularity, and dissortativity-have suggested models of network evolution. Currently preferred models invoke preferential attachment or gene duplication and divergence to produce networks whose topology matches that observed for real PPIs, thus supporting these as likely models for network evolution. Here, we show that the interaction density and homodimeric frequency are highly protein age-dependent in real PPI networks in a manner which does not agree with these canonical models.

Inferring mouse gene functions from genomic-scale data using a combined functional network/classification strategy.

The complete set of mouse genes, as with the set of human genes, is still largely uncharacterized, with many pieces of experimental evidence accumulating regarding the activities and expression of the genes, but the majority of genes as yet still of unknown function. Within the context of the MouseFunc competition, we developed and applied two distinct large-scale data mining approaches to infer the functions (Gene Ontology annotations) of mouse genes from experimental observations from available functional genomics, proteomics, comparative genomics, and phenotypic data. The two strategies - the first using classifiers to map features to annotations, the second propagating annotations from characterized genes to uncharacterized genes along edges in a network constructed from the features - offer alternative and possibly complementary approaches to providing functional annotations.

A critical assessment of Mus musculus gene function prediction using integrated genomic evidence.

Background: Several years after sequencing the human genome and the mouse genome, much remains to be discovered about the functions of most human and mouse genes. Computational prediction of gene function promises to help focus limited experimental resources on the most likely hypotheses. Several algorithms using diverse genomic data have been applied to this task in model organisms; however, the performance of such approaches in mammals has not yet been evaluated.

Using structural motif descriptors for sequence-based binding site prediction.

Background: Many protein sequences are still poorly annotated. Functional characterization of a protein is often improved by the identification of its interaction partners. Here, we aim to predict protein-protein interactions (PPI) and protein-ligand interactions (PLI) on sequence level using 3D information.

Hispidin derivatives from the mushroom Inonotus xeranticus and their antioxidant activity.

In an effort to identify antioxidants from edible and medicinal mushrooms, three new hispidin derivatives, methylinoscavin A (2), inoscavin B (4), and methylinoscavin B (5), together with the known compounds inoscavin A and phelligridin F, were isolated from the methanolic extract of the fruiting bodies of Inonotus xeranticus. Their structures were determined on the basis of spectroscopic analyses.

SCOPPI: a structural classification of protein-protein interfaces.

SCOPPI, the structural classification of protein-protein interfaces, is a comprehensive database that classifies and annotates domain interactions derived from all known protein structures. SCOPPI applies SCOP domain definitions and a distance criterion to determine inter-domain interfaces. Using a novel method based on multiple sequence and structural alignments of SCOP families, SCOPPI presents a comprehensive geometrical classification of domain interfaces.

Equivalent binding sites reveal convergently evolved interaction motifs.

Motivation: Much research has been devoted to the characterization of interaction interfaces found in complexes with known structure. In this context, the interactions of non-homologous domains at equivalent binding sites are of particular interest, as they can reveal convergently evolved interface motifs. Such motifs are an important source of information to formulate rules for interaction specificity and to design ligands based on the common features shared among diverse partners.

Survey of the geometric association of domain-domain interfaces.

Considering the limited success of the most sophisticated docking methods available and the amount of computation required for systematic docking, cataloging all the known interfaces may be an alternative basis for the prediction of protein tertiary and quaternary structures. We classify domain interfaces according to the geometry of domain-domain association. By applying a simple and efficient method called "interface tag clustering," more than 4,000 distinct types of domain interfaces are collected from Protein Quaternary Structure Server and Protein Data Bank.

Large scale statistical prediction of protein-protein interaction by potentially interacting domain (PID) pair.

Protein-protein interaction plays a critical role in biological processes. The identification of interacting proteins by computational methods can provide new leads in functional studies of uncharacterized proteins without performing extensive experiments. We developed a database for the potentially interacting domain pairs (PID) extracted from a dataset of experimentally identified interacting protein pairs (DIP: database of interacting proteins) with InterPro, an integrated database of protein families, domains and functional sites.