Single‐wall and multi‐wall carbon nanotubes promote rice root growth by eliciting the similar molecular pathways and epigenetic regulation

Organisms are constantly exposed to environmental stimuli and have evolved mechanisms of protection and adaptation. Various effects of nanoparticles (NPs) on crops have been described and some results confirm that NPs could enhance plant growth at the physiological and genetic levels. This study comparatively analysed the effect of carbon nanotubes (CNTs) on rice growth. The results showed that single‐wall CNTs were located in the intercellular space while multi‐wall CNTs penetrated cell walls in roots. CNTs could promote rice root growth through the regulation of expression of the root growth related genes and elevated global histone acetylation in rice root meristem zones. These responses were returned to normal levels after CNTs were removed from medium. CNTs caused the similar histone acetylation and methylation statuses across the local promoter region of the Cullin‐RING ligases 1 (CRL1) gene and increased micrococcal nuclease accessibility of this region, which enhanced this gene expression. The authors results suggested that CNTs could cause plant responses at the cellular, genetic, and epigenetic levels and these responses were independent on interaction modes between root cells and CNTs.Inspec keywords: crops, multi‐wall carbon nanotubes, single‐wall carbon nanotubes, nanobiotechnology, cellular biophysics, genetics, enzymes, biochemistry, molecular biophysicsOther keywords: single‐wall carbon nanotubes, multiwall carbon nanotubes, rice root growth, molecular pathways, epigenetic regulation, environmental stimuli, crops, intercellular space, cell walls, global histone acetylation, rice root meristem zones, histone acetylation, methylation statuses, local promoter region, CRL1 gene, micrococcal nuclease accessibility, root growth related gene expression, plant responses, cellular levels, epigenetic levels, genetic levels, interaction modes, C     

Analysis of healthy and tumour DNA methylation distributions in kidney‐renal‐clear‐cell‐carcinoma using Kullback–Leibler and Jensen–Shannon distance measures

DNA methylation is an epigenetic phenomenon in which methyl groups get bonded to the cytosines of the DNA molecule altering the expression of the associated genes. Cancer is linked with hypo or hyper‐methylation of specific genes as well as global changes in DNA methylation. In this study, the authors study the probability density function distribution of DNA methylation in various significant genes and across the genome in healthy and tumour samples. They propose a unique ‘average healthy methylation distribution’ based on the methylation values of several healthy samples. They then obtain the Kullback–Leibler and Jensen–Shannon distances between methylation distributions of the healthy and tumour samples and the average healthy methylation distribution. The distance measures of the healthy and tumour samples from the average healthy methylation distribution are compared and the differences in the distances are analysed as possible parameters for cancer. A classifier trained on these values was found to provide high values of sensitivity and specificity. They consider this to be a computationally efficient approach to predict tumour samples based on DNA methylation data. This technique can also be improvised to consider other differentially methylated genes significant in cancer or other epigenetic diseases.Inspec keywords: cancer, tumours, DNA, genetics, molecular biophysicsOther keywords: tumour DNA methylation distributions, kidney‐renal‐clear‐cell‐carcinoma, Kullback–Leibler distance measure, Jensen–Shannon distance measure, epigenetic phenomenon, methyl groups, cytosines, hyper‐methylation, probability density function distribution, average healthy methylation distribution     

Identify signature regulatory network for glioblastoma prognosis by integrative mRNA and miRNA co‐expression analysis

Glioblastoma multiforme (GBM) is the most common and aggressive type of primary brain tumor in adults. Patients with this disease have a poor prognosis. The objective of this study is to identify survival‐related individual genes (or miRNAs) and miRNA ‐mRNA pairs in GBM using a multi‐step approach. First, the weighted gene co‐expression network analysis and survival analysis are applied to identify survival‐related modules from mRNA and miRNA expression profiles, respectively. Subsequently, the role of individual genes (or miRNAs) within these modules in GBM prognosis are highlighted using survival analysis. Finally, the integration analysis of miRNA and mRNA expression as well as miRNA target prediction is used to identify survival‐related miRNA ‐mRNA regulatory network. In this study, five genes and two miRNA modules that significantly correlated to patient''s survival. In addition, many individual genes (or miRNAs) assigned to these modules were found to be closely linked with survival. For instance, increased expression of neuropilin‐1 gene (a member of module turquoise) indicated poor prognosis for patients and a group of miRNA ‐mRNA regulatory networks that comprised 38 survival‐related miRNA ‐mRNA pairs. These findings provide a new insight into the underlying molecular regulatory mechanisms of GBM.Inspec keywords: RNA, molecular biophysics, genetics, cancerOther keywords: signature regulatory network, glioblastoma prognosis, mRNA coexpression analysis, miRNA coexpression analysis, glioblastoma multiforme, brain tumour, microRNAs, pathogenesis, genome‐wide regulatory networks, miRNA‐mRNA pairs, weighted gene coexpression network analysis, survival analysis, GBM prognosis, integration analysis, neuropilin‐1 gene, module turquoise, molecular regulatory mechanisms     

Adaptive modelling of gene regulatory network using Bayesian information criterion‐guided sparse regression approach

Inferring gene regulatory networks (GRNs) from microarray expression data are an important but challenging issue in systems biology. In this study, the authors propose a Bayesian information criterion (BIC)‐guided sparse regression approach for GRN reconstruction. This approach can adaptively model GRNs by optimising the l ₁ ‐norm regularisation of sparse regression based on a modified version of BIC. The use of the regularisation strategy ensures the inferred GRNs to be as sparse as natural, while the modified BIC allows incorporating prior knowledge on expression regulation and thus avoids the overestimation of expression regulators as usual. Especially, the proposed method provides a clear interpretation of combinatorial regulations of gene expression by optimally extracting regulation coordination for a given target gene. Experimental results on both simulation data and real‐world microarray data demonstrate the competent performance of discovering regulatory relationships in GRN reconstruction.Inspec keywords: genetics, Bayes methods, genomics, regression analysis, inference mechanisms, bioinformaticsOther keywords: adaptive modelling, gene regulatory network, Bayesian information criterion‐guided sparse regression approach, GRN, microarray expression data, systems biology, GRN reconstruction, optimisation, l₁ ‐norm regularisation     

Gaussian graphical model for identifying significantly responsive regulatory networks from time course high‐throughput data

With rapid accumulation of functional relationships between biological molecules, knowledge‐based networks have been constructed and stocked in many databases. These networks provide curated and comprehensive information for functional linkages among genes and proteins, whereas their activities are highly related with specific phenotypes and conditions. To evaluate a knowledge‐based network in a specific condition, the consistency between its structure and conditionally specific gene expression profiling data are an important criterion. In this study, the authors propose a Gaussian graphical model to evaluate the documented regulatory networks by the consistency between network architectures and time course gene expression profiles. They derive a dynamic Bayesian network model to evaluate gene regulatory networks in both simulated and true time course microarray data. The regulatory networks are evaluated by matching network structure with gene expression to achieve consistency measurement. To demonstrate the effectiveness of the authors method, they identify significant regulatory networks in response to the time course of circadian rhythm. The knowledge‐based networks are screened and ranked by their structural consistencies with dynamic gene expression profiling.Inspec keywords: Bayes methods, biology computing, circadian rhythms, Gaussian processes, genetics, genomics, graphs, molecular biophysics, proteinsOther keywords: Gaussian graphical model, responsive regulatory networks, time course high‐throughput data, biological molecules, dynamic gene expression proflling, circadian rhythm, consistency measurement, matching network structure, simulated time course microarray data, true time course microarray data, dynamic Bayesian network model, time course gene expression proflles, network architectures, documented regulatory networks, speciflc gene expression proflling data, phenotypes, proteins, functional linkages, databases, knowledge‐based networks     

Detecting small attractors of large Boolean networks by function‐reduction‐based strategy

Boolean networks (BNs) are widely used to model gene regulatory networks and to design therapeutic intervention strategies to affect the long‐term behaviour of systems. A central aim of Boolean‐network analysis is to find attractors that correspond to various cellular states, such as cell types or the stage of cell differentiation. This problem is NP‐hard and various algorithms have been used to tackle it with considerable success. The idea is that a singleton attractor corresponds to n consistent subsequences in the truth table. To find these subsequences, the authors gradually reduce the entire truth table of Boolean functions by extending a partial gene activity profile (GAP). Not only does this process delete inconsistent subsequences in truth tables, it also directly determines values for some nodes not extended, which means it can abandon the partial GAPs that cannot lead to an attractor as early as possible. The results of simulation show that the proposed algorithm can detect small attractors with length p = 4 in BNs of up to 200 nodes with average indegree K = 2.Inspec keywords: Boolean functions, genetics, cellular biophysicsOther keywords: detecting small attractors, function‐reduction‐based strategy, model gene regulatory networks, therapeutic intervention strategies, Boolean‐network analysis, cellular states, NP‐hard, singleton attractor, Boolean functions, partial gene activity profile, cell differentiation     

Properties of sparse penalties on inferring gene regulatory networks from time‐course gene expression data

Genes regulate each other and form a gene regulatory network (GRN) to realise biological functions. Elucidating GRN from experimental data remains a challenging problem in systems biology. Numerous techniques have been developed and sparse linear regression methods become a promising approach to infer accurate GRNs. However, most linear methods are either based on steady‐state gene expression data or their statistical properties are not analysed. Here, two sparse penalties, adaptive least absolute shrinkage and selection operator and smoothly clipped absolute deviation, are proposed to infer GRNs from time‐course gene expression data based on an auto‐regressive model and their Oracle properties are proved under mild conditions. The effectiveness of those methods is demonstrated by applications to in silico and real biological data.Inspec keywords: genetics, autoregressive processesOther keywords: sparse penalties, gene regulatory networks, time‐course gene expression data, GRN, biological functions, systems biology, sparse linear regression methods, steady‐state gene expression data, adaptive least absolute shrinkage, selection operator, smoothly clipped absolute deviation, autoregressive model, Oracle properties     

Co‐condensation synthesis of well‐defined mesoporous silica nanoparticles: effect of surface chemical modification on plasmid DNA condensation and transfection

Chemically modified mesoporous silica nanoparticles (MSNs) are of interest due to their chemical and thermal stability with adjustable morphology and porosity; therefore, it was aimed to develop and compare the MCM‐41 MSNs functionalised with imidazole groups (MCM‐41‐Im) to unmodified (MCM‐41‐OH) and primary amine functionalised (MCM‐41‐NH₂) MSNs for experimental gene delivery. The results show efficient transfection of the complexes of the plasmid and either MCM‐41‐NH₂ or MCM‐41‐Im. Furthermore, following transfection of HeLa cells using MCM‐41‐Im, an enhanced GFP expression was achieved consistent with the noticeable DNase1 protection and endosomal escape properties of MCM‐41‐Im using carboxyfluorescein tracer.Inspec keywords: condensation, mesoporous materials, silicon compounds, nanoparticles, DNA, surface chemistry, porosity, gene therapy, cellular biophysics, biomedical materials, nanomedicine, nanofabrication, molecular biophysics, biochemistryOther keywords: co‐condensation synthesis, surface chemical modification, plasmid DNA condensation, plasmid DNA transfection, chemical modified mesoporous silica nanoparticles, chemical stability, thermal stability, adjustable morphology, porosity, MCM‐41 MSN functionalisation, imidazole groups, MCM‐41‐OH, primary amine functionalised MSN, gene delivery, HeLa cell transfection, GFP expression, DNase1 protection, endosomal escape properties, carboxyfluorescein tracer, SiO₂      

Gene expression feature selection for prostate cancer diagnosis using a two‐phase heuristic–deterministic search strategy

Here, a two‐phase search strategy is proposed to identify the biomarkers in gene expression data set for the prostate cancer diagnosis. A statistical filtering method is initially employed to remove the noisiest data. In the first phase of the search strategy, a multi‐objective optimisation based on the binary particle swarm optimisation algorithm tuned by a chaotic method is proposed to select the optimal subset of genes with the minimum number of genes and the maximum classification accuracy. Finally, in the second phase of the search strategy, the cache‐based modification of the sequential forward floating selection algorithm is used to find the most discriminant genes from the optimal subset of genes selected in the first phase. The results of applying the proposed algorithm on the available challenging prostate cancer data set demonstrate that the proposed algorithm can perfectly identify the informative genes such that the classification accuracy, sensitivity, and specificity of 100% are achieved with only nine biomarkers.Inspec keywords: cancer, biological organs, optimisation, feature extraction, search problems, particle swarm optimisation, pattern classification, geneticsOther keywords: biomarkers, gene expression feature selection, prostate cancer diagnosis, heuristic–deterministic search strategy, two‐phase search strategy, gene expression data, statistical filtering method, noisiest data, multiobjective optimisation, particle swarm optimisation algorithm, chaotic method, selection algorithm, discriminant genes, available challenging prostate cancer data, informative genes     

Degree‐adjusted algorithm for prioritisation of candidate disease genes from gene expression and protein interactome

Computational methods play an important role in the disease genes prioritisation by integrating many kinds of data sources such as gene expression, functional annotations and protein–protein interactions. However, the existing methods usually perform well in predicting highly linked genes, whereas they work quite poorly for loosely linked genes. Motivated by this observation, a degree‐adjusted strategy is applied to improve the algorithm that was proposed earlier for the prediction of disease genes from gene expression and protein interactions. The authors also showed that the modified method is good at identifying loosely linked disease genes and the overall performance gets enhanced accordingly. This study suggests the importance of statistically adjusting the degree distribution bias in the background network for network‐based modelling of complex diseases.Inspec keywords: biochemistry, bioinformatics, diseases, genetics, genomics, medical computing, physiological models, proteins, statistical analysis, proteomicsOther keywords: degree‐adjusted algorithm, candidate disease genes prioritisation, gene expression, protein interactome, computational method, functional annotation, protein–protein interaction, highly linked genes prediction, disease genes prediction, loosely linked disease genes identification, degree distribution bias statistical adjustment, complex disease network‐based modelling     

M‐matrix‐based stability conditions for genetic regulatory networks with time‐varying delays and noise perturbations

Stability is essential for designing and controlling any dynamic systems. Recently, the stability of genetic regulatory networks has been widely studied by employing linear matrix inequality (LMI) approach, which results in checking the existence of feasible solutions to high‐dimensional LMIs. In the previous study, the authors present several stability conditions for genetic regulatory networks with time‐varying delays, based on M ‐matrix theory and using the non‐smooth Lyapunov function, which results in determining whether a low‐dimensional matrix is a non‐singular M ‐matrix. However, the previous approach cannot be applied to analyse the stability of genetic regulatory networks with noise perturbations. Here, the authors design a smooth Lyapunov function quadratic in state variables and employ M ‐matrix theory to derive new stability conditions for genetic regulatory networks with time‐varying delays. Theoretically, these conditions are less conservative than existing ones in some genetic regulatory networks. Then the results are extended to genetic regulatory networks with time‐varying delays and noise perturbations. For genetic regulatory networks with n genes and n proteins, the derived conditions are to check if an n × n matrix is a non‐singular M ‐matrix. To further present the new theories proposed in this study, three example regulatory networks are analysed.Inspec keywords: genetics, linear matrix inequalities, Lyapunov matrix equations, molecular biophysics, noise, proteinsOther keywords: M‐matrix‐based stability condition, genetic regulatory networks, time‐varying delays, noise perturbations, linear matrix inequality approach, high‐dimensional LMI, Lyapunov function, state variables, M‐matrix theory, proteins, nonsingular M‐matrix     

Gene regulatory network discovery using pairwise Granger causality

Discovery of gene regulatory network from gene expression data can yield a useful insight to drug development. Among the methods applied to time‐series data, Granger causality (GC) has emerged as a powerful tool with several merits. Since gene expression data usually have a much larger number of genes than time points therefore a full model cannot be applied in a straightforward manner, GC is often applied to genes pairwisely. In this study, the authors first investigate with synthetic data how spurious causalities (false discoveries) may arise because of the use of pairwise rather than full‐model GC detection. Furthermore, spurious causalities may also arise if the order of the vector autoregressive model is not high enough. As a remedy, the authors demonstrate that model validation techniques can effectively reduce the number of false discoveries. Then, they apply pairwise GC with model validation to the real human HeLa cell‐cycle dataset. They find that Akaike information criterion is generally most suitable for determining model order, but precaution should be taken for extremely short time series. With the authors proposed implementation, degree distributions and network hubs are obtained and compared with existing results, giving a new observation that the hubs tend to act as sources rather than receivers of interactions.Inspec keywords: biology computing, cancer, causality, cellular biophysics, genetics, genomics, time seriesOther keywords: gene regulatory network discovery, pairwise Granger causality, gene expression data, drug development, time‐series data, synthetic data, spurious causalities, full‐model Granger causality detection, vector autoregressive model, real human HeLa cell‐cycle dataset, Akaike information criterion, degree distributions, network hubs     

Identification of self‐regulatory network motifs in reverse engineering gene regulatory networks using microarray gene expression data

Gene Regulatory Networks (GRNs) are reconstructed from the microarray gene expression data through diversified computational approaches. This process ensues in symmetric and diagonal interaction of gene pairs that cannot be modelled as direct activation, inhibition, and self‐regulatory interactions. The values of gene co‐expressions could help in identifying co‐regulations among them. The proposed approach aims at computing the differences in variances of co‐expressed genes rather than computing differences in values of mean expressions across experimental conditions. It adopts multivariate co‐variances using principal component analysis (PCA) to predict an asymmetric and non‐diagonal gene interaction matrix, to select only those gene pair interactions that exhibit the maximum variances in gene regulatory expressions. The asymmetric gene regulatory interactions help in identifying the controlling regulatory agents, thus lowering the false positive rate by minimizing the connections between previously unlinked network components. The experimental results on real as well as in silico datasets including time‐series RTX therapy, Arabidopsis thaliana, DREAM‐3, and DREAM‐8 datasets, in comparison with existing state‐of‐the‐art approaches demonstrated the enhanced performance of the proposed approach for predicting positive and negative feedback loops and self‐regulatory interactions. The generated GRNs hold the potential in determining the real nature of gene pair regulatory interactions.Inspec keywords: molecular biophysics, principal component analysis, genetics, biology computing, reverse engineeringOther keywords: controlling regulatory agents, interacting genes, unlinked network components, self‐regulatory interactions, gene pair regulatory interactions, self‐regulatory network motifs, reverse engineering gene regulatory networks, microarray gene expression data, diversified computational approaches, symmetric interaction, diagonal interaction, gene pairs, gene co‐expressions, co‐expressed genes, mean expressions, gene regulatory expressions, asymmetric gene regulatory interactions