Neighbor-favoring weight reinforcement to improve random walk-based disease gene prioritization

BackgroundFinding candidate genes associated with a disease is an important issue in biomedical research. Recently, many network-based methods have been proposed that implicitly utilize the modularity principle, which states that genes causing the same or similar diseases tend to form physical or functional modules in gene/protein relationship networks. Of these methods, the random walk with restart (RWR) algorithm is considered to be a state-of-the-art approach, but the modularity principle has not been fully considered in traditional RWR approaches. Therefore, we propose a novel method called ORIENT (neighbor-favoring weight reinforcement) to improve the performance of RWR through proper intensification of the weights of interactions close to the known disease genes.ResultsThrough extensive simulations over hundreds of diseases, we observed that our approach performs better than the traditional RWR algorithm. In particular, our method worked best when the weights of interactions involving only the nearest neighbor genes of the disease genes were intensified. Interestingly, the performance of our approach was negatively related to the probability with which the random walk will restart, whereas the performance of RWR without the weight-reinforcement was positively related in dense gene/protein relationship networks. We further found that the density of the disease gene-projected sub-graph and the number of paths between the disease genes in a gene/protein relationship network may be explanatory variables for the RWR performance. Finally, a comparison with other well-known gene prioritization tools including Endeavour, ToppGene, and BioGraph, revealed that our approach shows significantly better performance.ConclusionTaken together, these findings provide insight to efficiently guide RWR in disease gene prioritization.     

Semantically predicting protein functions based on protein functional connectivity

BackgroundThe current availability of public protein–protein interaction (PPI) databases which are usually modelled as PPI networks has led to the rapid development of protein function prediction approaches. The existing network-based prediction approaches mainly focus on the topological similarities between immediately interacting proteins, neglecting the protein functional connectivity which is the functional tightness between proteins. In this paper, we attempt to predict the functions of unannotated proteins based on PPI networks by incorporating the protein functional connectivity, as well as the similarity of protein functions, into the prediction procedure.ResultsAn approach named Semantic protein function Prediction based on protein Functional Connectivity (SPFC) is proposed to achieve a higher accuracy in predicting functions of unannotated protein. We define the functional connectivity and function addition for each protein, and incorporate them into the prediction. We evaluated the SPFC on real PPI datasets and the experiment results show that the SPFC method is more effective in function prediction than other network-based approaches.ConclusionIncorporating the functional connectivity of each protein into the function prediction can significantly improve the accuracy of protein prediction.     

The Potential Effects of Curcumin on Pulmonary Fibroblasts of Idiopathic Pulmonary Fibrosis (IPF)—Approaching with Next-Generation Sequencing and Bioinformatics

Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive interstitial lung disease. Currently, therapeutic options are limited for this fatal disease. Curcumin, with its pleiotropic effects, has been studied for its potential therapeutic utilities in various diseases, including pulmonary fibrosis. However, the detailed mechanisms have not been studied comprehensively. We conducted a next-generation sequencing and bioinformatics study to investigate changes in the profiles of mRNA and microRNA after curcumin treatment in IPF fibroblasts. We identified 23 downregulated and 8 upregulated protein-coding genes in curcumin-treated IPF fibroblasts. Using STRING and IPA, we identified that suppression of cell cycle progression was the main cellular function associated with these differentially expressed genes. We also identified 13 downregulated and 57 upregulated microRNAs in curcumin-treated IPF fibroblasts. Further analysis identified a potential microRNA-mediated gene expression alteration in curcumin-treated IPF fibroblasts, namely, downregulated hsa-miR-6724-5p and upregulated KLF10. Therefore, curcumin might decrease the level of hsa-miR-6724-5p, leading to increased KLF10 expression, resulting in cell cycle arrest in curcumin-treated IPF fibroblasts. In conclusion, our findings might support the potential role of curcumin in the treatment of IPF, but further in-depth study is warranted to confirm our findings.     

Ranking of microRNA target prediction scores by Pareto front analysis

Over the past ten years, a variety of microRNA target prediction methods has been developed, and many of the methods are constantly improved and adapted to recent insights into miRNA-mRNA interactions. In a typical scenario, different methods return different rankings of putative targets, even if the ranking is reduced to selected mRNAs that are related to a specific disease or cell type. For the experimental validation it is then difficult to decide in which order to process the predicted miRNA-mRNA bindings, since each validation is a laborious task and therefore only a limited number of mRNAs can be analysed. We propose a new ranking scheme that combines ranked predictions from several methods and - unlike standard thresholding methods - utilises the concept of Pareto fronts as defined in multi-objective optimisation. In the present study, we attempt a proof of concept by applying the new ranking scheme to hsa-miR-21, hsa-miR-125b, and hsa-miR-373 and prediction scores supplied by PITA and RNAhybrid. The scores are interpreted as a two-objective optimisation problem, and the elements of the Pareto front are ranked by the STarMir score with a subsequent re-calculation of the Pareto front after removal of the top-ranked mRNA from the basic set of prediction scores. The method is evaluated on validated targets of the three miRNA, and the ranking is compared to scores from DIANA-microT and TargetScan. We observed that the new ranking method performs well and consistent, and the first validated targets are elements of Pareto fronts at a relatively early stage of the recurrent procedure, which encourages further research towards a higher-dimensional analysis of Pareto fronts.     

Prediction of drug-target interaction by integrating diverse heterogeneous information source with multiple kernel learning and clustering methods

BackgroundIdentification of potential drug-target interaction pairs is very important for pharmaceutical innovation and drug discovery. Numerous machine learning-based and network-based algorithms have been developed for predicting drug-target interactions. However, large-scale pharmacological, genomic and chemical datum emerged recently provide new opportunity for further heightening the accuracy of drug-target interactions prediction.ResultsIn this work, based on the assumption that similar drugs tend to interact with similar proteins and vice versa, we developed a novel computational method (namely MKLC-BiRW) to predict new drug-target interactions. MKLC-BiRW integrates diverse drug-related and target-related heterogeneous information source by using the multiple kernel learning and clustering methods to generate the drug and target similarity matrices, in which the low similarity elements are set to zero to build the drug and target similarity correction networks. By incorporating these drug and target similarity correction networks with known drug-target interaction bipartite graph, MKLC-BiRW constructs the heterogeneous network on which Bi-random walk algorithm is adopted to infer the potential drug-target interactions.ConclusionsCompared with other existing state-of-the-art methods, MKLC-BiRW achieves the best performance in terms of AUC and AUPR. MKLC-BiRW can effectively predict the potential drug-target interactions.     

Serum exosomal microRNAs as novel biomarkers for hepatocellular carcinoma

Recent studies have shown that circulating microRNAs are a potential biomarker in various types of malignancies. The aim of this study was to investigate the feasibility of using serum exosomal microRNAs as novel serological biomarkers for hepatocellular carcinoma (HCC) in patients with chronic hepatitis B (CHB). We measured the serum exosomal microRNAs and serum circulating microRNAs in patients with CHB (n=20), liver cirrhosis (LC) (n=20) and HCC (n=20). Serum exosomal microRNA was extracted from 500 μl of serum using an Exosome RNA Isolation kit. The expression levels of microRNAs were quantified by real-time PCR. The expression levels of selected microRNAs were normalized to Caenorhabditis elegans microRNA (Cel-miR-39). The serum levels of exosomal miR-18a, miR-221, miR-222 and miR-224 were significantly higher in patients with HCC than those with CHB or LC (P<0.05). Further, the serum levels of exosomal miR-101, miR-106b, miR-122 and miR-195 were lower in patients with HCC than in patients with CHB (P=0.014, P<0.001, P<0.001 and P<0.001, respectively). There was no significant difference in the levels of miR-21 and miR-93 among the three groups. Additionally, the serum levels of circulating microRNAs showed a smaller difference between HCC and either CHB or LC. This study suggests that serum exosomal microRNAs may be used as novel serological biomarkers for HCC.     

The study of the relationship between the new topological index A(m) and the gas chromatographic retention indices of hydrocarbons by artificial neural networks

The newly developed topological indices A_m1-A_m3 and the molecular connectivity indices ^mX were applied to multivariate analysis in structure-property correlation studies. The topological indices calculated from the chemical structures of some hydrocarbons were used to represent the molecular structures. The prediction of the retention indices of the hydrocarbons on three different kinds of stationary phase in gas chromatography can be achieved applying artificial neural networks and multiple linear regression models. The results from the artificial neural networks approach were compared with those of multiple linear regression models. It is shown that the predictive ability of artificial neural networks is superior to that of multiple linear regression method under the experimental conditions in this paper. Both the topological indices ²X and A_m1 can improve the predicted results of the retention indices of the hydrocarbons on the stationary phase studied.     

MOEPGA: A novel method to detect protein complexes in yeast protein–protein interaction networks based on MultiObjective Evolutionary Programming Genetic Algorithm

The identification of protein complexes in protein–protein interaction (PPI) networks has greatly advanced our understanding of biological organisms. Existing computational methods to detect protein complexes are usually based on specific network topological properties of PPI networks. However, due to the inherent complexity of the network structures, the identification of protein complexes may not be fully addressed by using single network topological property. In this study, we propose a novel MultiObjective Evolutionary Programming Genetic Algorithm (MOEPGA) which integrates multiple network topological features to detect biologically meaningful protein complexes. Our approach first systematically analyzes the multiobjective problem in terms of identifying protein complexes from PPI networks, and then constructs the objective function of the iterative algorithm based on three common topological properties of protein complexes from the benchmark dataset, finally we describe our algorithm, which mainly consists of three steps, population initialization, subgraph mutation and subgraph selection operation. To show the utility of our method, we compared MOEPGA with several state-of-the-art algorithms on two yeast PPI datasets. The experiment results demonstrate that the proposed method can not only find more protein complexes but also achieve higher accuracy in terms of fscore. Moreover, our approach can cover a certain number of proteins in the input PPI network in terms of the normalized clustering score. Taken together, our method can serve as a powerful framework to detect protein complexes in yeast PPI networks, thereby facilitating the identification of the underlying biological functions.     

LIN28B confers radio-resistance through the posttranscriptional control of KRAS

To screen the differentially expressed microRNAs related to radio-resistance, we compared the microRNA profiles of lung cancer cells with different responses to ionizing radiation (IR). Of 328 microRNAs in microarray, 27 microRNAs were differentially expressed in NCI-H460 (H460) and NCI-H1299 (H1299) cells. Among them, let-7g was down-regulated in radio-resistant H1299 cells, and the level of let-7g was higher in radio-sensitive cells like Caski, H460, and ME180 in qRT-PCR analysis than in radio-resistant cells like A549, H1299, DLD1, and HeLa. Over-expression of let-7g in H1299 cells could suppress the translation of KRAS, and increase the sensitivity to IR. When we knockdown the expression of LIN28B, an upstream regulator of let-7g, the level of mature let-7g was increased in H1299 cells and the sensitivity to IR was also enhanced in LIN28B knockdown cells. From these data, we suggest that LIN28B plays an important role in radiation responses of lung cancer cells through inhibiting let-7g processing and increasing translation of KRAS.     

DEEPAligner: Deep encoding of pathways to align epigenetic signatures

Background and objectiveRecently, differential DNA Methylation is known to affect the regulatory mechanism of biological pathways. A pathway encompasses a set of interacting genes or gene products that altogether perform a given biological function. Pathways often encode strong methylation signatures that are capable of distinguishing biologically distinct subtypes. Even though Next Generation Sequencing techniques such as MeDIP-seq and MBD-isolated genome sequencing (MiGS) allow for genome-wide identification of clinical and biological subtypes, there is a pressing need for computational methods to compare epigenetic signatures across pathways.MethodsA novel alignment method, called DEEPAligner (Deep Encoded Epigenetic Pathway Aligner), is proposed in this paper that finds functionally consistent and topologically sound alignments of epigenetic signatures from pathway networks. A deep embedding framework is used to obtain epigenetic signatures from pathways which are then aligned for functional consistency and local topological similarity.ResultsExperiments on four benchmark cancer datasets reveal epigenetic signatures that are conserved in cancer-specific and across-cancer subtypes.ConclusionThe proposed deep embedding framework obtains highly coherent signatures that are aligned for biological as well as structural orthology. Comparison with state-of-the-art network alignment methods clearly suggest that the proposed method obtains topologically and functionally more consistent alignments.Availabilityhttp://bdbl.nitc.ac.in/DEEPAligner     

Hierarchical closeness efficiently predicts disease genes in a directed signaling network

BackgroundMany structural centrality measures were proposed to predict putative disease genes on biological networks. Closeness is one of the best-known structural centrality measures, and its effectiveness for disease gene prediction on undirected biological networks has been frequently reported. However, it is not clear whether closeness is effective for disease gene prediction on directed biological networks such as signaling networks.ResultsIn this paper, we first show that closeness does not significantly outperform other well-known centrality measures such as Degree, Betweenness, and PageRank for disease gene prediction on a human signaling network. In addition, we observed that prediction accuracy by the closeness measure was worse than that by a reachability measure, but closeness could efficiently predict disease genes among a set of genes with the same reachability value. Based on this observation, we devised a novel structural measure, hierarchical closeness, by combining reachability and closeness such that all genes are first ranked by the degree of reachability and then the tied genes are further ranked by closeness. We discovered that hierarchical closeness outperforms other structural centrality measures in disease gene prediction. We also found that the set of highly ranked genes in terms of hierarchical closeness is clearly different from that of hub genes with high connectivity. More interestingly, these findings were consistently reproduced in a random Boolean network model. Finally, we found that genes with relatively high hierarchical closeness are significantly likely to encode proteins in the extracellular matrix and receptor proteins in a human signaling network, supporting the fact that half of all modern medicinal drugs target receptor-encoding genes.ConclusionTaken together, hierarchical closeness proposed in this study is a novel structural measure to efficiently predict putative disease genes in a directed signaling network.     

Multi-scale top-down approach for modelling epileptic protein-protein interaction network analysis to identify driver nodes and pathways

Protein - Protein Interaction Network (PPIN) analysis unveils molecular level mechanisms involved in disease condition. To explore the complex regulatory mechanisms behind epilepsy and to address the clinical and biological issues of epilepsy, in silico techniques are feasible in a cost- effective manner. In this work, a hierarchical procedure to identify influential genes and regulatory pathways in epilepsy prognosis is proposed. To obtain key genes and pathways causing epilepsy, integration of two benchmarked datasets which are exclusively devoted for complex disorders is done as an initial step. Using STRING database, PPIN is constructed for modelling protein-protein interactions. Further, key interactions are obtained from the established PPIN using network centrality measures followed by network propagation algorithm -Random Walk with Restart (RWR). The outcome of the method reveals some influential genes behind epilepsy prognosis, along with their associated pathways like PI3 kinase, VEGF signaling, Ras, Wnt signaling etc. In comparison with similar works, our results have shown improvement in identifying unique molecular functions, biological processes, gene co-occurrences etc. Also, CORUM provides an annotation for approximately 60% of similarity in human protein complexes with the obtained result. We believe that the formulated strategy can put-up the vast consideration of indigenous drugs towards meticulous identification of genes encoded by protein against several combinatorial disorders.     

On the relationship between GC content and the number of predicted microRNA binding sites by MicroInspector

MicroRNA GC content and length is believed to play a role in the prediction of putative microRNA targets. MicroInspector was evaluated to determine the extent to which these characteristics of microRNAs play a role in binding site predictive accuracy. A strong bias towards under predicting the number of expected bindings sites for low GC content sequences was observed, especially for microRNAs with <50% GC content. Researchers working with organisms with unusually low GC content should be aware of this bias.