An expression meta-analysis of predicted microRNA targets identifies a diagnostic signature for lung cancer

<p>Abstract</p> <p>Background</p> <p>Patients diagnosed with lung adenocarcinoma (AD) and squamous cell carcinoma (SCC), two major histologic subtypes of lung cancer, currently receive similar standard treatments, but resistance to adjuvant chemotherapy is prevalent. Identification of differentially expressed genes marking AD and SCC may prove to be of diagnostic value and help unravel molecular basis of their histogenesis and biologies, and deliver more effective and specific systemic therapy.</p> <p>Methods</p> <p>MiRNA target genes were predicted by union of miRanda, TargetScan, and PicTar, followed by screening for matched gene symbols in NCBI human sequences and Gene Ontology (GO) terms using the PANTHER database that was also used for analyzing the significance of biological processes and pathways within each ontology term. Microarray data were extracted from Gene Expression Omnibus repository, and tumor subtype prediction by gene expression used Prediction Analysis of Microarrays.</p> <p>Results</p> <p>Computationally predicted target genes of three microRNAs, miR-34b/34c/449, that were detected in human lung, testis, and fallopian tubes but not in other normal tissues, were filtered by representation of GO terms and their ability to classify lung cancer subtypes, followed by a meta-analysis of microarray data to classify AD and SCC. Expression of a minimal set of 17 predicted miR-34b/34c/449 target genes derived from the developmental process GO category was identified from a training set to classify 41 AD and 17 SCC, and correctly predicted in average 87% of 354 AD and 82% of 282 SCC specimens from total 9 independent published datasets. The accuracy of prediction still remains comparable when classifying 103 AD and 79 SCC samples from another 4 published datasets that have only 14 to 16 of the 17 genes available for prediction (84% and 85% for AD and SCC, respectively). Expression of this signature in two published datasets of epithelial cells obtained at bronchoscopy from cigarette smokers, if combined with cytopathology of the cells, yielded 89–90% sensitivity of lung cancer detection and 87–90% negative predictive value to non-cancer patients.</p> <p>Conclusion</p> <p>This study focuses on predicted targets of three lung-enriched miRNAs, compares their expression patterns in lung cancer by their GO terms, and identifies a minimal set of genes differentially expressed in AD and SCC, followed by validating this gene signature in multiple published datasets. Expression of this gene signature in bronchial epithelial cells of cigarette smokers also has a great sensitivity to predict the patients having lung cancer if combined with cytopathology of the cells.</p

Community landscapes: an integrative approach to determine overlapping network module hierarchy, identify key nodes and predict network dynamics

Background: Network communities help the functional organization and evolution of complex networks. However, the development of a method, which is both fast and accurate, provides modular overlaps and partitions of a heterogeneous network, has proven to be rather difficult. Methodology/Principal Findings: Here we introduce the novel concept of ModuLand, an integrative method family determining overlapping network modules as hills of an influence function-based, centrality-type community landscape, and including several widely used modularization methods as special cases. As various adaptations of the method family, we developed several algorithms, which provide an efficient analysis of weighted and directed networks, and (1) determine pervasively overlapping modules with high resolution; (2) uncover a detailed hierarchical network structure allowing an efficient, zoom-in analysis of large networks; (3) allow the determination of key network nodes and (4) help to predict network dynamics. Conclusions/Significance: The concept opens a wide range of possibilities to develop new approaches and applications including network routing, classification, comparison and prediction.Comment: 25 pages with 6 figures and a Glossary + Supporting Information containing pseudo-codes of all algorithms used, 14 Figures, 5 Tables (with 18 module definitions, 129 different modularization methods, 13 module comparision methods) and 396 references. All algorithms can be downloaded from this web-site: http://www.linkgroup.hu/modules.ph

In silico modeling of the specific inhibitory potential of thiophene-2,3-dihydro-1,5-benzothiazepine against BChE in the formation of β-amyloid plaques associated with Alzheimer's disease

<p>Abstract</p> <p>Background</p> <p>Alzheimer's disease, known to be associated with the gradual loss of memory, is characterized by low concentration of acetylcholine in the hippocampus and cortex part of the brain. Inhibition of acetylcholinesterase has successfully been used as a drug target to treat Alzheimer's disease but drug resistance shown by butyrylcholinesterase remains a matter of concern in treating Alzheimer's disease. Apart from the many other reasons for Alzheimer's disease, its association with the genesis of fibrils by β-amyloid plaques is closely related to the increased activity of butyrylcholinesterase. Although few data are available on the inhibition of butyrylcholinesterase, studies have shown that that butyrylcholinesterase is a genetically validated drug target and its selective inhibition reduces the formation of β-amyloid plaques.</p> <p>Rationale</p> <p>We previously reported the inhibition of cholinesterases by 2,3-dihydro-1, 5-benzothiazepines, and considered this class of compounds as promising inhibitors for the cure of Alzheimer's disease. One compound from the same series, when substituted with a hydroxy group at C-3 in ring A and 2-thienyl moiety as ring B, showed greater activity against butyrylcholinesterase than to acetylcholinesterase. To provide insight into the binding mode of this compound (Compound A), molecular docking in combination with molecular dynamics simulation of 5000 ps in an explicit solvent system was carried out for both cholinesterases.</p> <p>Conclusion</p> <p>Molecular docking studies revealed that the potential of Compound A to inhibit cholinesterases was attributable to the cumulative effects of strong hydrogen bonds, cationic-π, π-π interactions and hydrophobic interactions. A comparison of the docking results of Compound A against both cholinesterases showed that amino acid residues in different sub-sites were engaged to stabilize the docked complex. The relatively high affinity of Compound A for butyrylcholinesterase was due to the additional hydrophobic interaction between the 2-thiophene moiety of Compound A and Ile69. The involvement of one catalytic triad residue (His438) of butyrylcholinesterase with the 3'-hydroxy group on ring A increases the selectivity of Compound A. C-C bond rotation around ring A also stabilizes and enhances the interaction of Compound A with butyrylcholinesterase. Furthermore, the classical network of hydrogen bonding interactions as formed by the catalytic triad of butyrylcholinesterase is disturbed by Compound A. This study may open a new avenue for structure-based drug design for Alzheimer's disease by considering the 3D-pharmacophoric features of the complex responsible for discriminating these two closely-related cholinesterases.</p

Preferential regulation of miRNA targets by environmental chemicals in the human genome

<p>Abstract</p> <p>Background</p> <p>microRNAs (miRNAs) represent a class of small (typically 22 nucleotides in length) non-coding RNAs that can degrade their target mRNAs or block their translation. Recent disease research showed the exposure to some environmental chemicals (ECs) can regulate the expression patterns of miRNAs, which raises the intriguing question of how miRNAs and their targets cope with the exposure to ECs throughout the genome.</p> <p>Results</p> <p>In this study, we comprehensively analyzed the properties of genes regulated by ECs (EC-genes) and found miRNA targets were significantly enriched among the EC-genes. Compared with the non-miRNA-targets, miRNA targets were roughly twice as likely to be EC-genes. By investigating the collection methods and other properties of the EC-genes, we demonstrated that the enrichment of miRNA targets was not attributed to either the potential collection bias of EC-genes, the presence of paralogs, longer 3'UTRs or more conserved 3'UTRs. Finally, we identified 1,842 significant concurrent interactions between 407 miRNAs and 497 ECs. This association network of miRNAs-ECs was highly modular and could be separated into 14 interconnected modules. In each module, miRNAs and ECs were closely connected, providing a good method to design accurate miRNA markers for ECs in toxicology research.</p> <p>Conclusions</p> <p>Our analyses indicated that miRNAs and their targets played important roles in cellular responses to ECs. Association analyses of miRNAs and ECs will help to broaden the understanding of the pathogenesis of such chemical components.</p