Time Series Microarray Data Research Articles

Interplay of microRNAs, transcription factors and target genes: linking dynamic expression changes to function

MicroRNAs (miRNAs) are ubiquitously expressed small non-coding RNAs that, in most cases, negatively regulate gene expression at the post-transcriptional level. miRNAs are involved in fine-tuning fundamental cellular processes such as proliferation, cell death and cell cycle control and are believed to confer robustness to biological responses. Here, we investigated simultaneously the transcriptional changes of miRNA and mRNA expression levels over time after activation of the Janus kinase/Signal transducer and activator of transcription (Jak/STAT) pathway by interferon-γ stimulation of melanoma cells. To examine global miRNA and mRNA expression patterns, time-series microarray data were analysed. We observed delayed responses of miRNAs (after 24–48 h) with respect to mRNAs (12–24 h) and identified biological functions involved at each step of the cellular response. Inference of the upstream regulators allowed for identification of transcriptional regulators involved in cellular reactions to interferon-γ stimulation. Linking expression profiles of transcriptional regulators and miRNAs with their annotated functions, we demonstrate the dynamic interplay of miRNAs and upstream regulators with biological functions. Finally, our data revealed network motifs in the form of feed-forward loops involving transcriptional regulators, mRNAs and miRNAs. Additional information obtained from integrating time-series mRNA and miRNA data may represent an important step towards understanding the regulatory principles of gene expression.

Construction of GA-optimized Radial Basis Neural Network from HIV-1 Vpr Mutant Microarray Gene Expression Data

Abstract Finding the most significant gene from microarray time series data is important for designing drugs of particular disease. Con- struction of Neural Network through protein interactions is a vital and useful approach to develop new drugs target. Some of the computational tools are being utilized for predicting the viral-host interactions. The database of human HIV-1 Vpr mutant gene expression microarray time series expression value contain records of experimentally validated interactions. The main problem to analyze this type of microarray data is classification problem as because human HIV-1 Vpr mutant cell is an infected dendritic cell. We firstly, have clustered the gene microarray time series data using subtractive clustering method then construct Radial Basis Neural Network on cluster of HIV-1 Vpr mutant microarray time series data. The network output is optimized by using Genetic Algorithm and from the optimized value of network output we got a significant gene which lead to drug discovery in future.

Transcriptome Temporal and Functional Analysis of Liver Regeneration Termination

Decades of liver regeneration studies still left the termination phase least elucidated. However regeneration ending mechanisms are clinicaly relevant. We aimed to analyse the timing and transcriptional control of the latest phase of liver regeneration, both controversial. Male Wistar rats were subjected to 2/3 partial hepatectomy with recovery lasting from 1 to 14 days. Time-series microarray data were assessed by innovative combination of hierarchical clustering and principal component analysis and validated by real-time RT-PCR. Hierarchical clustering and principal component analysis in agreement distinguished three temporal phases of liver regeneration. We found 359 genes specifically altered during late phase regeneration. Gene enrichment analysis and manual review of microarray data suggested five pathways worth further study: PPAR signalling pathway; lipid metabolism; complement, coagulation and fibrinolytic cascades; ECM remodelling and xenobiotic biotransformation. Microarray findings pertinent for termination phase were substantiated by real-time RT-PCR. In conclusion, transcriptional profiling mapped late phase of liver regeneration beyond 5(th) day of recovery and revealed 5 pathways specifically acting at this time. Inclusion of longer post-surgery intervals brought improved coverage of regeneration time dynamics and is advisable for further works. Investigation into the workings of suggested pathways might prove helpful in preventing and managing liver tumours.

On Modeling and State Estimation for Genetic Regulatory Networks With Polytopic Uncertainties

It is widely believed that gene expression data contains rich information that could discover the higher-order structures of an organism and even interpret its behavior. The modeling problem of gene regulatory networks (GRNs) from the experimental data has recently received increasing research attention. In this paper, we investigate the uncertainty quantification and state estimation issues. The polytopic uncertainty model (PUM) is exploited for describing the GRNs where the parameter uncertainties are constrained in a convex polytope domain. To cope with the high-dimension problem for GRN models, the principal component plane (PCP) algorithm is proposed to construct a pruned polytope in order to use as less vertices as possible to maintain the essential information from original polytope. The so-called system equivalence transformation is developed to transform the original system into a simpler canonical form and therefore facilitate the subsequent state estimation problem. For the state estimation problem, a robust stability condition is incorporated with guaranteed H2 performance via the semi-definite programme method, and then a new sufficient condition is derived for the desired H2 estimators with several free slack matrices. Such a condition is vertex-dependent and therefore possesses less conservatism. It is shown, via simulation from real-world microarray time-series data, that the designed H2 estimators have strong capability of dealing with modeling and estimation problems for short but high-dimensional gene expression time series.

Reverse engineering of gene regulatory networks using flexible neural tree models

The advances on DNA microarray technologies have enabled researchers to gain hundreds to thousands of gene expression levels. Much effect has been devoted over the past decade to analyze the gene expression data. In this study, flexible neural tree (FNT) model is used for gene regulatory network reconstruction and time-series prediction from gene expression profiling. We use voting strategy and Akaike information criterion (AIC) as two methods to identifying minimal regulatory elements of a target gene. A simulated dataset and three real biological datasets are used to test the validity of the FNT model. Results reveal that the FNT model can improve the prediction accuracy of microarray time-series data effectively and reconstruct gene regulatory network accurately.

Organic Anion and Cation SLC22 “Drug” Transporter (Oat1, Oat3, and Oct1) Regulation during Development and Maturation of the Kidney Proximal Tubule

Proper physiological function in the pre- and post-natal proximal tubule of the kidney depends upon the acquisition of selective permeability, apical-basolateral epithelial polarity and the expression of key transporters, including those involved in metabolite, toxin and drug handling. Particularly important are the SLC22 family of transporters, including the organic anion transporters Oat1 (originally identified as NKT) and Oat3 as well as the organic cation transporter Oct1. In ex vivo cultures of metanephric mesenchyme (MM; the embryonic progenitor tissue of the nephron) Oat function was evident before completion of nephron segmentation and corresponded with the maturation of tight junctions as measured biochemically by detergent extractability of the tight junction protein, ZO-1. Examination of available time series microarray data sets in the context of development and differentiation of the proximal tubule (derived from both in vivo and in vitro/ex vivo developing nephrons) allowed for correlation of gene expression data to biochemically and functionally defined states of development. This bioinformatic analysis yielded a network of genes with connectivity biased toward Hnf4α (but including Hnf1α, hyaluronic acid-CD44, and notch pathways). Intriguingly, the Oat1 and Oat3 genes were found to have strong temporal co-expression with Hnf4α in the cultured MM supporting the notion of some connection between the transporters and this transcription factor. Taken together with the ChIP-qPCR finding that Hnf4α occupies Oat1, Oat3, and Oct1 proximal promoters in the in vivo differentiating rat kidney, the data suggest a network of genes with Hnf4α at its center plays a role in regulating the terminal differentiation and capacity for drug and toxin handling by the nascent proximal tubule of the kidney.

Methods and Recent Patents for Modeling and Uncovering Gene Regulatory Networks

The potential of microarray experiments to profile the expression of thousand genes simultaneously has focused the interest of scientists during last twenty years. Microarray experiments provide valuable information about how genes compete and are associated to produce complex responses and cooperative effects. The modeling and analysis of such rich data sets has attracted researchers from different fields such as Statistics, Data Mining or Signal Processing. The results of this kind of analysis is a good candidate to undercover valuable information regarding the diagnosis, prognosis and drug design for specific diseases. In this work, we have revised recent patents that address the problem of model and infer the gene regulatory network from a microarray time-series data sets. Moreover we have compared these approaches with an inference method developed by the authors. Our methodology establishes a novel approach that combines a Markov linear model with the variational-Bayesian framework to undercover the Gene Regulatory Network.

Frequency-based time-series gene expression recomposition using PRIISM

BackgroundCircadian rhythm pathways influence the expression patterns of as much as 31% of the Arabidopsis genome through complicated interaction pathways, and have been found to be significantly disrupted by biotic and abiotic stress treatments, complicating treatment-response gene discovery methods due to clock pattern mismatches in the fold change-based statistics. The PRIISM (Pattern Recomposition for the Isolation of Independent Signals in Microarray data) algorithm outlined in this paper is designed to separate pattern changes induced by different forces, including treatment-response pathways and circadian clock rhythm disruptions.ResultsUsing the Fourier transform, high-resolution time-series microarray data is projected to the frequency domain. By identifying the clock frequency range from the core circadian clock genes, we separate the frequency spectrum to different sections containing treatment-frequency (representing up- or down-regulation by an adaptive treatment response), clock-frequency (representing the circadian clock-disruption response) and noise-frequency components. Then, we project the components’ spectra back to the expression domain to reconstruct isolated, independent gene expression patterns representing the effects of the different influences.By applying PRIISM on a high-resolution time-series Arabidopsis microarray dataset under a cold treatment, we systematically evaluated our method using maximum fold change and principal component analyses. The results of this study showed that the ranked treatment-frequency fold change results produce fewer false positives than the original methodology, and the 26-hour timepoint in our dataset was the best statistic for distinguishing the most known cold-response genes. In addition, six novel cold-response genes were discovered. PRIISM also provides gene expression data which represents only circadian clock influences, and may be useful for circadian clock studies.ConclusionPRIISM is a novel approach for overcoming the problem of circadian disruptions from stress treatments on plants. PRIISM can be integrated with any existing analysis approach on gene expression data to separate circadian-influenced changes in gene expression, and it can be extended to apply to any organism with regular oscillations in gene expression patterns across a large portion of the genome.

Nonparametric Bayesian inference for perturbed and orthologous gene regulatory networks

Motivation: The generation of time series transcriptomic datasets collected under multiple experimental conditions has proven to be a powerful approach for disentangling complex biological processes, allowing for the reverse engineering of gene regulatory networks (GRNs). Most methods for reverse engineering GRNs from multiple datasets assume that each of the time series were generated from networks with identical topology. In this study, we outline a hierarchical, non-parametric Bayesian approach for reverse engineering GRNs using multiple time series that can be applied in a number of novel situations including: (i) where different, but overlapping sets of transcription factors are expected to bind in the different experimental conditions; that is, where switching events could potentially arise under the different treatments and (ii) for inference in evolutionary related species in which orthologous GRNs exist. More generally, the method can be used to identify context-specific regulation by leveraging time series gene expression data alongside methods that can identify putative lists of transcription factors or transcription factor targets.Results: The hierarchical inference outperforms related (but non-hierarchical) approaches when the networks used to generate the data were identical, and performs comparably even when the networks used to generate data were independent. The method was subsequently used alongside yeast one hybrid and microarray time series data to infer potential transcriptional switches in Arabidopsis thaliana response to stress. The results confirm previous biological studies and allow for additional insights into gene regulation under various abiotic stresses.Availability: The methods outlined in this article have been implemented in Matlab and are available on request.Contact: d.l.wild@warwick.ac.ukSupplementary Information: Supplementary data is available for this article.

Visualizing dimensionality reduction of systems biology data

One of the challenges in analyzing high-dimensional expression data is the detection of important biological signals. A common approach is to apply a dimension reduction method, such as principal component analysis. Typically, after application of such a method the data is projected and visualized in the new coordinate system, using scatter plots or profile plots. These methods provide good results if the data have certain properties which become visible in the new coordinate system and which were hard to detect in the original coordinate system. Often however, the application of only one method does not suffice to capture all important signals. Therefore several methods addressing different aspects of the data need to be applied. We have developed a framework for linear and non-linear dimension reduction methods within our visual analytics pipeline SpRay. This includes measures that assist the interpretation of the factorization result. Different visualizations of these measures can be combined with functional annotations that support the interpretation of the results. We show an application to high-resolution time series microarray data in the antibiotic-producing organism Streptomyces coelicolor as well as to microarray data measuring expression of cells with normal karyotype and cells with trisomies of human chromosomes 13 and 21.

Data simulation and regulatory network reconstruction from time-series microarray data using stepwise multiple linear regression

Time-series microarray data can capture dynamic genomic behavior not available in steady-state expression data, which has made time-series analysis especially useful in the study of dynamic cellular processes such as the circadian rhythm, disease progression, drug response, and the cell cycle. Using the information available in the time-series data, we address three related computational problems: the prediction of gene expression levels from previous time steps, the simulation of an entire time-series microarray dataset, and the reconstruction of gene regulatory networks. We model the gene expression levels using a linear model, due to its simplicity and the ability to interpret the coefficients as interactions in the underlying regulatory network. A stepwise multiple linear regression method is applied to fit the parameters of the linear model to a given training dataset. The learned model is utilized in predicting and replicating the time course of the expression levels and in identifying the regulatory interactions. Each predicted interaction is also associated with a statistical significance to provide a confidence measure that can guide prioritization in further costly manual or experimental verification. We demonstrate the performance of our approach on several yeast cell-cycle datasets and show that it provides comparable or greater accuracy than existing methods and provides additional quantitative information about the interactions not available from the other methods.

Estimation of nonlinear gene regulatory networks via L1 regularized NVAR from time series gene expression data.

Recently, nonlinear vector autoregressive (NVAR) model based on Granger causality was proposed to infer nonlinear gene regulatory networks from time series gene expression data. Since NVAR requires a large number of parameters due to the basis expansion, the length of time series microarray data is insufficient for accurate parameter estimation and we need to limit the size of the gene set strongly. To address this limitation, we employ L1 regularization technique to estimate NVAR. Under L1 regularization, direct parents of each gene can be selected efficiently even when the number of parameters exceeds the number of data samples. We can thus estimate larger gene regulatory networks more accurately than those from existing methods. Through the simulation study, we verify the effectiveness of the proposed method by comparing its limitation in the number of genes to that of the existing NVAR. The proposed method is also applied to time series microarray data of Human hela cell cycle.

Analysis and practical guideline of constraint-based boolean method in genetic network inference.

Boolean-based method, despite of its simplicity, would be a more attractive approach for inferring a network from high-throughput expression data if its effectiveness has not been limited by high false positive prediction. In this study, we explored factors that could simply be adjusted to improve the accuracy of inferring networks. Our work focused on the analysis of the effects of discretisation methods, biological constraints, and stringency of Boolean function assignment on the performance of Boolean network, including accuracy, precision, specificity and sensitivity, using three sets of microarray time-series data. The study showed that biological constraints have pivotal influence on the network performance over the other factors. It can reduce the variation in network performance resulting from the arbitrary selection of discretisation methods and stringency settings. We also presented the master Boolean network as an approach to establish the unique solution for Boolean analysis. The information acquired from the analysis was summarised and deployed as a general guideline for an efficient use of Boolean-based method in the network inference. In the end, we provided an example of the use of such a guideline in the study of Arabidopsis circadian clock genetic network from which much interesting biological information can be inferred.

Detection of Perturbation Phases and Developmental Stages in Organisms from DNA Microarray Time Series Data

Available DNA microarray time series that record gene expression along the developmental stages of multicellular eukaryotes, or in unicellular organisms subject to external perturbations such as stress and diauxie, are analyzed. By pairwise comparison of the gene expression profiles on the basis of a translation-invariant and scale-invariant distance measure corresponding to least-rectangle regression, it is shown that peaks in the average distance values are noticeable and are localized around specific time points. These points systematically coincide with the transition points between developmental phases or just follow the external perturbations. This approach can thus be used to identify automatically, from microarray time series alone, the presence of external perturbations or the succession of developmental stages in arbitrary cell systems. Moreover, our results show that there is a striking similarity between the gene expression responses to these a priori very different phenomena. In contrast, the cell cycle does not involve a perturbation-like phase, but rather continuous gene expression remodeling. Similar analyses were conducted using three other standard distance measures, showing that the one we introduced was superior. Based on these findings, we set up an adapted clustering method that uses this distance measure and classifies the genes on the basis of their expression profiles within each developmental stage or between perturbation phases.

Learning the structure of gene regulatory networks from time series gene expression data

BackgroundDynamic Bayesian Network (DBN) is an approach widely used for reconstruction of gene regulatory networks from time-series microarray data. Its performance in network reconstruction depends on a structure learning algorithm. REVEAL (REVerse Engineering ALgorithm) is one of the algorithms implemented for learning DBN structure and used to reconstruct gene regulatory networks (GRN). However, the two-stage temporal Bayes network (2TBN) structure of DBN that specifies correlation between time slices cannot be obtained by score metrics used in REVEAL.MethodsIn this paper, we study a more sophisticated score function for DBN first proposed by Nir Friedman for stationary DBNs structure learning of both initial and transition networks but has not yet been used for reconstruction of GRNs. We implemented Friedman's Bayesian Information Criterion (BIC) score function, modified K2 algorithm to learn Dynamic Bayesian Network structure with the score function and tested the performance of the algorithm for GRN reconstruction with synthetic time series gene expression data generated by GeneNetWeaver and real yeast benchmark experiment data.ResultsWe implemented an algorithm for DBN structure learning with Friedman's score function, tested it on reconstruction of both synthetic networks and real yeast networks and compared it with REVEAL in the absence or presence of preprocessed network generated by Zou&Conzen's algorithm. By introducing a stationary correlation between two consecutive time slices, Friedman's score function showed a higher precision and recall than the naive REVEAL algorithm.ConclusionsFriedman's score metrics for DBN can be used to reconstruct transition networks and has a great potential to improve the accuracy of gene regulatory network structure prediction with time series gene expression datasets.

Bayesian hierarchical clustering for microarray time series data with replicates and outlier measurements

BackgroundPost-genomic molecular biology has resulted in an explosion of data, providing measurements for large numbers of genes, proteins and metabolites. Time series experiments have become increasingly common, necessitating the development of novel analysis tools that capture the resulting data structure. Outlier measurements at one or more time points present a significant challenge, while potentially valuable replicate information is often ignored by existing techniques.ResultsWe present a generative model-based Bayesian hierarchical clustering algorithm for microarray time series that employs Gaussian process regression to capture the structure of the data. By using a mixture model likelihood, our method permits a small proportion of the data to be modelled as outlier measurements, and adopts an empirical Bayes approach which uses replicate observations to inform a prior distribution of the noise variance. The method automatically learns the optimum number of clusters and can incorporate non-uniformly sampled time points. Using a wide variety of experimental data sets, we show that our algorithm consistently yields higher quality and more biologically meaningful clusters than current state-of-the-art methodologies. We highlight the importance of modelling outlier values by demonstrating that noisy genes can be grouped with other genes of similar biological function. We demonstrate the importance of including replicate information, which we find enables the discrimination of additional distinct expression profiles.ConclusionsBy incorporating outlier measurements and replicate values, this clustering algorithm for time series microarray data provides a step towards a better treatment of the noise inherent in measurements from high-throughput genomic technologies. Timeseries BHC is available as part of the R package 'BHC' (version 1.5), which is available for download from Bioconductor (version 2.9 and above) via http://www.bioconductor.org/packages/release/bioc/html/BHC.html?pagewanted=all.

Constructing gene regulatory networks for long term photosynthetic light acclimation in Arabidopsis thaliana

BackgroundPhotosynthetic light acclimation is an important process that allows plants to optimize the efficiency of photosynthesis, which is the core technology for green energy. However, currently little is known about the molecular mechanisms behind the regulation of the photosynthetic light acclimation response. In this study, a systematic method is proposed to investigate this mechanism by constructing gene regulatory networks from microarray data of Arabidopsis thaliana.MethodsThe potential TF-gene regulatory pairs of photosynthetic light acclimation have been obtained by data mining of literature and databases. Following the identification of these potential TF-gene pairs, they have been refined using Pearson's correlation, allowing the construction of a rough gene regulatory network. This rough gene regulatory network is then pruned using time series microarray data of Arabidopsis thaliana via the maximum likelihood system identification method and Akaike's system order detection method to approach the real gene regulatory network of photosynthetic light acclimation.ResultsBy comparing the gene regulatory networks under the PSI-to-PSII light shift and the PSII-to-PSI light shift, it is possible to identify important transcription factors for the different light shift conditions. Furthermore, the robustness of the gene network, in particular the hubs and weak linkage points, are also discussed under the different light conditions to gain further insight into the mechanisms of photosynthesis.ConclusionsThis study investigates the molecular mechanisms of photosynthetic light acclimation for Arabidopsis thaliana from the physiological level. This has been achieved through the construction of gene regulatory networks from the limited data sources and literature via an efficient computation method. If more experimental data for whole-genome ChIP-chip data and microarray data with multiple sampling points becomes available in the future, the proposed method will be improved on by constructing the whole-genome gene regulatory network. These advances will greatly improve our understanding of the mechanisms of the photosynthetic system.

A comparison of benchmark task and insight evaluation methods for information visualization

This study compares two different empirical research methods for evaluating information visualizations: the traditional benchmark-task method and the insight method. The methods are compared using criteria such as the conclusions about the visualization designs provided by each method, the time participants spent during the study, the time and effort required to analyse the resulting empirical data, and the effect of individual differences between participants on the results. The study compares three graph visualization alternatives that associate bioinformatics microarray time series data to pathway graph vertices in order to investigate the effect of different visual grouping structures in visualization designs that integrate multiple data types. It is confirmed that visual grouping should match task structure, but interactive grouping proves to be a well-rounded alternative. Overall, the results validate the insight method’s ability to confirm results of the task method, but also show advantages of the insight method to illuminate additional types of tasks. Efficiency and insight frequently correlate, but important distinctions are found. Categories: H.5.2 [Information Interfaces and Presentation]: User Interfaces – evaluation/methodology.

Functional Maturation of Drug Transporters in the Developing, Neonatal, and Postnatal Kidney

Because renal function in newborns is immature, the pharmacokinetics of drugs administered to neonates vary significantly from adult patients. The establishment of drug transport systems is a key process in the functional maturation of the nephron. However, a thorough examination of the expression of the main drug transporters in the kidney throughout all stages of development (embryonic, postnatal, and mature) has yet to be carried out, and the functional (physiological) impact is not well understood. Using time-series microarray data, we analyzed the temporal behavior of mRNA levels for a wide range of SLC and ABC transporters in the rodent kidney throughout a developmental time series. We find dynamic increases between the postnatal and mature stages of development for a number of transporters, including the proximal tubule-specific drug and organic anion transporters (OATs) OAT1 (SLC22a6) and OAT3 (SLC22a8). The OATs are the major multispecific basolateral drug, toxin, and metabolite transporters in the proximal tubule responsible for handling of many drugs, as well as the prototypical OAT substrate para-aminohippurate (PAH). We therefore performed specific in vivo pharmacokinetic analysis of the transport of PAH in postnatal and maturing rodent kidney. We show that there is a 4-fold increase in PAH clearance during this period. Clearance studies in Oat1 and Oat3 knockouts confirm that, as in the adult, Oat1 is the principle transporter of PAH in the postnatal kidney. The substantial differences observed supports the need for better understanding of pharmacokinetics in the newborn and juvenile kidney compared with the adult kidney at the basic and clinical level.

TEMPORAL GRAPHICAL MODELS FOR CROSS-SPECIES GENE REGULATORY NETWORK DISCOVERY

Many genes and biological processes function in similar ways across different species. Cross-species gene expression analysis, as a powerful tool to characterize the dynamical properties of the cell, has found a number of applications, such as identifying a conserved core set of cell cycle genes. However, to the best of our knowledge, there is limited effort on developing appropriate techniques to capture the causality relations between genes from time-series microarray data across species. In this paper, we present hidden Markov random field regression with L(1) penalty to uncover the regulatory network structure for different species. The algorithm provides a framework for sharing information across species via hidden component graphs and is able to incorporate domain knowledge across species easily. We demonstrate our method on two synthetic datasets and apply it to discover causal graphs from innate immune response data.