Weighted edge based clustering to identify protein complexes in protein–protein interaction networks incorporating gene expression profile

Protein complex detection from protein–protein interaction (PPI) network has received a lot of focus in recent years. A number of methods identify protein complexes as dense sub-graphs using network information while several other methods detect protein complexes based on topological information. While the methods based on identifying dense sub-graphs are more effective in identifying protein complexes, not all protein complexes have high density. Moreover, existing methods focus more on static PPI networks and usually overlook the dynamic nature of protein complexes. Here, we propose a new method, Weighted Edge based Clustering (WEC), to identify protein complexes based on the weight of the edge between two interacting proteins, where the weight is defined by the edge clustering coefficient and the gene expression correlation between the interacting proteins. Our WEC method is capable of detecting highly inter-connected and co-expressed protein complexes. The experimental results of WEC on three real life data shows that our method can detect protein complexes effectively in comparison with other highly cited existing methods.Availability: The WEC tool is available at http://agnigarh.tezu.ernet.in/~rosy8/shared.html.     

Network regularised Cox regression and multiplex network models to predict disease comorbidities and survival of cancer

In cancer genomics, gene expression levels provide important molecular signatures for all types of cancer, and this could be very useful for predicting the survival of cancer patients. However, the main challenge of gene expression data analysis is high dimensionality, and microarray is characterised by few number of samples with large number of genes. To overcome this problem, a variety of penalised Cox proportional hazard models have been proposed. We introduce a novel network regularised Cox proportional hazard model and a novel multiplex network model to measure the disease comorbidities and to predict survival of the cancer patient. Our methods are applied to analyse seven microarray cancer gene expression datasets: breast cancer, ovarian cancer, lung cancer, liver cancer, renal cancer and osteosarcoma. Firstly, we applied a principal component analysis to reduce the dimensionality of original gene expression data. Secondly, we applied a network regularised Cox regression model on the reduced gene expression datasets. By using normalised mutual information method and multiplex network model, we predict the comorbidities for the liver cancer based on the integration of diverse set of omics and clinical data, and we find the diseasome associations (disease–gene association) among different cancers based on the identified common significant genes. Finally, we evaluated the precision of the approach with respect to the accuracy of survival prediction using ROC curves. We report that colon cancer, liver cancer and renal cancer share the CXCL5 gene, and breast cancer, ovarian cancer and renal cancer share the CCND2 gene. Our methods are useful to predict survival of the patient and disease comorbidities more accurately and helpful for improvement of the care of patients with comorbidity. Software in Matlab and R is available on our GitHub page: https://github.com/ssnhcom/NetworkRegularisedCox.git.     

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     

Characterizing mutation–expression network relationships in multiple cancers

BackgroundData made available through large cancer consortia like The Cancer Genome Atlas make for a rich source of information to be studied across and between cancers. In recent years, network approaches have been applied to such data in uncovering the complex interrelationships between mutational and expression profiles, but lack direct testing for expression changes via mutation. In this pan-cancer study we analyze mutation and gene expression information in an integrative manner by considering the networks generated by testing for differences in expression in direct association with specific mutations. We relate our findings among the 19 cancers examined to identify commonalities and differences as well as their characteristics.ResultsUsing somatic mutation and gene expression information across 19 cancers, we generated mutation–expression networks per cancer. On evaluation we found that our generated networks were significantly enriched for known cancer-related genes, such as skin cutaneous melanoma (p < 0.01 using Network of Cancer Genes 4.0). Our framework identified that while different cancers contained commonly mutated genes, there was little concordance between associated gene expression changes among cancers. Comparison between cancers showed a greater overlap of network nodes for cancers with higher overall non-silent mutation load, compared to those with a lower overall non-silent mutation load.ConclusionsThis study offers a framework that explores network information through co-analysis of somatic mutations and gene expression profiles. Our pan-cancer application of this approach suggests that while mutations are frequently common among cancer types, the impact they have on the surrounding networks via gene expression changes varies. Despite this finding, there are some cancers for which mutation-associated network behaviour appears to be similar: suggesting a potential framework for uncovering related cancers for which similar therapeutic strategies may be applicable. Our framework for understanding relationships among cancers has been integrated into an interactive R Shiny application, PAn Cancer Mutation Expression Networks (PACMEN), containing dynamic and static network visualization of the mutation–expression networks. PACMEN also features tools for further examination of network topology characteristics among cancers.     

Electrical breakdown in high‐density polyethylene/graphite nanosheets conductive composites

The electrical breakdown is investigated in high‐density polyethylene/graphite nanosheets (HDPE/GNs) conductive composites. A sample suffers electrical breakdown when the applied field exceeds the critical electrical breakdown field below which the sample reaches a stable state. It is found that the ratio of the critical electrical breakdown resistance to the linear resistance assumes a fixed value Y, which is found to be about 1.30 in HDPE/GNs conductive composites. The value Y is independent of GNs' content, sample size, breakdown cycle, but depends on the property of GNs. The critical breakdown field E_b scales with the resistance R₀ as with the exponent y_b = 0.46 ± 0.02, which is close to the value predicted in mean‐field theory but higher than the value given by the singly connected bonds networks. © 2009 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 47: 576–582, 2009     

A Multiple‐Responsive Self‐Healing Supramolecular Polymer Gel Network Based on Multiple Orthogonal Interactions

Supramolecular polymer networks have attracted considerable attention not only due to their topological importance but also because they can show some fantastic properties such as stimuli‐responsiveness and self‐healing. Although various supramolecular networks are constructed by supramolecular chemists based on different non‐covalent interactions, supramolecular polymer networks based on multiple orthogonal interactions are still rare. Here, a supramolecular polymer network is presented on the basis of the host–guest interactions between dibenzo‐24‐crown‐8 (DB24C8) and dibenzylammonium salts (DBAS), the metal–ligand coordination interactions between terpyridine and Zn(OTf)₂, and between 1,2,3‐triazole and PdCl₂(PhCN)₂. The topology of the networks can be easily tuned from monomer to main‐chain supramolecular polymer and then to the supramolecular networks. This process is well studied by various characterization methods such as ¹H NMR, UV–vis, DOSY, viscosity, and rheological measurements. More importantly, a supramolecular gel is obtained at high concentrations of the supramolecular networks, which demonstrates both stimuli‐responsiveness and self‐healing properties.

     

Reconstructing gene regulatory networks from knock-out data using Gaussian Noise Model and Pearson Correlation Coefficient

A gene regulatory network (GRN) is a large and complex network consisting of interacting elements that, over time, affect each other’s state. The dynamics of complex gene regulatory processes are difficult to understand using intuitive approaches alone. To overcome this problem, we propose an algorithm for inferring the regulatory interactions from knock-out data using a Gaussian model combines with Pearson Correlation Coefficient (PCC). There are several problems relating to GRN construction that have been outlined in this paper. We demonstrated the ability of our proposed method to (1) predict the presence of regulatory interactions between genes, (2) their directionality and (3) their states (activation or suppression). The algorithm was applied to network sizes of 10 and 50 genes from DREAM3 datasets and network sizes of 10 from DREAM4 datasets. The predicted networks were evaluated based on AUROC and AUPR. We discovered that high false positive values were generated by our GRN prediction methods because the indirect regulations have been wrongly predicted as true relationships. We achieved satisfactory results as the majority of sub-networks achieved AUROC values above 0.5.     

Recurrent neural network based hybrid model for reconstructing gene regulatory network

One of the exciting problems in systems biology research is to decipher how genome controls the development of complex biological system. The gene regulatory networks (GRNs) help in the identification of regulatory interactions between genes and offer fruitful information related to functional role of individual gene in a cellular system. Discovering GRNs lead to a wide range of applications, including identification of disease related pathways providing novel tentative drug targets, helps to predict disease response, and also assists in diagnosing various diseases including cancer. Reconstruction of GRNs from available biological data is still an open problem. This paper proposes a recurrent neural network (RNN) based model of GRN, hybridized with generalized extended Kalman filter for weight update in backpropagation through time training algorithm. The RNN is a complex neural network that gives a better settlement between biological closeness and mathematical flexibility to model GRN; and is also able to capture complex, non-linear and dynamic relationships among variables. Gene expression data are inherently noisy and Kalman filter performs well for estimation problem even in noisy data. Hence, we applied non-linear version of Kalman filter, known as generalized extended Kalman filter, for weight update during RNN training. The developed model has been tested on four benchmark networks such as DNA SOS repair network, IRMA network, and two synthetic networks from DREAM Challenge. We performed a comparison of our results with other state-of-the-art techniques which shows superiority of our proposed model. Further, 5% Gaussian noise has been induced in the dataset and result of the proposed model shows negligible effect of noise on results, demonstrating the noise tolerance capability of the model.     

Assessing the similarity of ligand binding conformations with the Contact Mode Score

Structural and computational biologists often need to measure the similarity of ligand binding conformations. The commonly used root-mean-square deviation (RMSD) is not only ligand-size dependent, but also may fail to capture biologically meaningful binding features. To address these issues, we developed the Contact Mode Score (CMS), a new metric to assess the conformational similarity based on intermolecular protein-ligand contacts. The CMS is less dependent on the ligand size and has the ability to include flexible receptors. In order to effectively compare binding poses of non-identical ligands bound to different proteins, we further developed the eXtended Contact Mode Score (XCMS). We believe that CMS and XCMS provide a meaningful assessment of the similarity of ligand binding conformations. CMS and XCMS are freely available at http://brylinski.cct.lsu.edu/content/contact-mode-score and http://geaux-computational-bio.github.io/contact-mode-score/.     

A novel fuzzy set based multifactor dimensionality reduction method for detecting gene–gene interaction

BackgroundGene-gene interaction (GGI) is one of the most popular approaches for finding the missing heritability of common complex traits in genetic association studies. The multifactor dimensionality reduction (MDR) method has been widely studied for detecting GGIs. In order to identify the best interaction model associated with disease susceptibility, MDR compares all possible genotype combinations in terms of their predictability of disease status from a simple binary high(H) and low(L) risk classification. However, this simple binary classification does not reflect the uncertainty of H/L classification.MethodsWe regard classifying H/L as equivalent to defining the degree of membership of two risk groups H/L. By adopting the fuzzy set theory, we propose Fuzzy MDR which takes into account the uncertainty of H/L classification. Fuzzy MDR allows the possibility of partial membership of H/L through a membership function which transforms the degree of uncertainty into a [0,1] scale. The best genotype combinations can be selected which maximizes a new fuzzy set based accuracy measure.ResultsTwo simulation studies are conducted to compare the power of the proposed Fuzzy MDR with that of MDR. Our results show that Fuzzy MDR has higher power than MDR. We illustrate the proposed Fuzzy MDR by analysing bipolar disorder (BD) trait of the WTCCC dataset to detect GGI associated with BD.ConclusionsWe propose a novel Fuzzy MDR method to detect gene–gene interaction by taking into account the uncertainly of H/L classification and show that it has higher power than MDR. Fuzzy MDR can be easily extended to handle continuous phenotypes as well. The program written in R for the proposed Fuzzy MDR is available at https://statgen.snu.ac.kr/software/FuzzyMDR.     

PPM-Dom: A novel method for domain position prediction

Domains are the structural basis of the physiological functions of proteins, and the prediction of which is an advantageous process on the study of protein structure and function. This article proposes a new complete automatic prediction method, PPM-Dom (Domain Position Prediction Method), for predicting the particular positions of domains in a target protein via its atomic coordinate. The presented method integrates complex networks, community division, and fuzzy mean operator (FMO). The whole sequences are divided into potential domain regions by the complex network and community division, and FMO allows the final determination for the domain position. This method will suffice to predict regions that will form a domain structure and those that are unstructured based on completely new atomic coordinate information of the query sequence, and be able to separate different domains in the same query sequence from each other. On evaluating the performance using an independent testing dataset, PPM-Dom reached 91.41% for prediction accuracy, 96.12% for sensitivity and 92.86% for specificity. The tool bag of PPM-Dom is freely available at http://cic.scu.edu.cn/bioinformatics/PPMDom.zip.     

Direct electrochemistry of glucose oxidase and sensing glucose using a screen-printed carbon electrode modified with graphite nanosheets and zinc oxide nanoparticles

We have studied the direct electrochemistry of glucose oxidase (GOx) immobilized on electrochemically fabricated graphite nanosheets (GNs) and zinc oxide nanoparticles (ZnO) that were deposited on a screen printed carbon electrode (SPCE). The GNs/ZnO composite was characterized by using scanning electron microscopy and elemental analysis. The GOx immobilized on the modified electrode shows a well-defined redox couple at a formal potential of ?0.4 V. The enhanced direct electrochemistry of GOx (compared to electrodes without ZnO or without GNs) indicates a fast electron transfer at this kind of electrode, with a heterogeneous electron transfer rate constant (K_s) of 3.75 s^?1. The fast electron transfer is attributed to the high conductivity and large edge plane defects of GNs and good conductivity of ZnO-NPs. The modified electrode displays a linear response to glucose in concentrations from 0.3 to 4.5 mM, and the sensitivity is 30.07 μA mM^?1 cm^?2. The sensor exhibits a high selectivity, good repeatability and reproducibility, and long term stability. Figure

Graphical representation for the fabrication of GNs/ZnO composite modified SPCE and the immobilization of GOx     

Scale-free networks versus evolutionary drift

Recent studies of properties of various biological networks revealed that many of them display scale-free characteristics. Since the theory of scale-free networks is applicable to evolving networks, one can hope that it provides not only a model of a biological network in its current state but also sheds some insight into the evolution of the network. In this work, we investigate the probability distributions and scaling properties underlying some models for biological networks and protein domain evolution. The analysis of evolutionary models for domain similarity networks indicates that models which include evolutionary drift are typically not scale free. Instead they adhere quite closely to the Yule distribution. This finding indicates that the direct applicability of scale-free models in understanding the evolution of biological network may not be as wide as it has been hoped for.     

Carbon Nanoparticles and Graphene Nanosheets as MALDI Matrices in Glycomics: a New Approach to Improve Glycan Profiling in Biological Samples

Glycomics continues to be a highly dynamic and interesting research area due to the need to comprehensively understand the biological attributes of glycosylation in many important biological functions such as the immune response, cell development, cell differentiation/adhesion, and host-pathogen interactions. Although matrix-assisted laser desorption ionization (MALDI) mass spectrometry (MS) has proven to be suitable for glycomic profiling studies, there is a need for improved sensitivity in the detection of native glycans, which ionize inefficiently. In this study, we investigated the efficiencies of graphene nanosheets (GNs) and carbon nanoparticles (CNPs) as MALDI matrices and co-matrices in glycan profiling. Our results indicated an enhancement of signal intensity by several orders of magnitude upon using GNs and CNPs in MALDI analysis of N-glycans derived from a variety of biological samples. Interestingly, increasing the amounts of CNPs and GNs improved not only the signal intensities but also prompted in-source decay (ISD) fragmentations, which produced extensive glycosidic and cross-ring cleavages. Our results indicated that the extent of ISD fragmentation could be modulated by CNP and GN concentrations, to obtain MS² and pseudo-MS³ spectra. The results for glycan profiling in high salt solutions confirmed high salt-tolerance capacities for both CNPs and GNs. Finally, the results showed that by using CNPs and GNs as co-matrices, DHB crystal formation was more homogeneous which improved shot-to-shot reproducibility and sensitivity.

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Aqueous Networks and Toroids of Amphiphilic Block Copolymer with Non‐ionic Surfactants

Network formation: Comicellization of polystyrene‐block‐polyethylene oxide (PS–PEO) with non‐ionic surfactants leads to the formation of networks and chains of spheres. The aqueous network solutions are responsive to dilution, undergoing morphological transformations to cylinders with Y junctions and both two‐ and three‐dimensional toroids (see figure).