Time Series Gene Expression Research Articles

Table with results of the time series gene expression analysis for liver samples.

Table with results of the time series gene expression analysis for liver samples.

Reconstruction of gene regulatory networks using S-system with a genetic algorithm and flower pollination algorithm hybrid

Accurate reconstruction of gene regulatory networks from time-series gene expression data is a significant challenge for computer scientists. In this paper, we have proposed a genetic algorithm and flower pollination algorithm hybrid for the reverse engineering of gene regulatory network based on decoupled S-systems. Here, genetic algorithm has been used to select the best combination of genes, which act as regulators in the network. Flower pollination algorithm has been used to calculate the best possible S-system parameters for which the training error is minimal for those regulators. The proposed method has been tested on small-scale and medium-scale; synthetic benchmark networks and in-slico benchmark networks extracted from the GeneNetWeaver database, as well as the real-world experimental datasets of the yeast IMRA and DNA SOS repair network of Escherichia coli. The experiments reveal that the proposed hybrid methodology is capable of inferring gene regulatory networks more accurately with lesser training data and in lesser computational time compared to other existing methods.

New algorithms for inferring gene regulatory networks from time-series expression data on Apache Spark

Gene regulatory networks (GRNs) are crucial to understand the inner workings of the cell and the complexity of gene interactions. Numerous algorithms have been developed to infer GRNs from gene expression data. As the number of identified genes increases and the complexity of their interactions is uncovered, gene networks become cumbersome to test. Furthermore, prodding through experimental results requires an enormous amount of computation, resulting in slow data processing. Therefore, new approaches are needed to analyse copious amounts of experimental data from cellular GRNs. To meet this need, cloud computing is promising as reported in the literature. Here we present two new algorithms for reverse engineering GRNs in a cloud environment. The algorithms, implemented in Spark, employ an information-theoretic approach to infer GRNs from time-series gene expression data. Experimental results show that one of our new algorithms is faster than, yet as accurate as, two existing cloud-based GRN inference methods.

Time series analysis of gene changes and processes after burn with human gene expression profiles

Severe burns might be followed by severe infection associated with high mortality. In this study, we aimed to identify changes in genes and processes across time points after burn via analyzing time series gene expression profiles in burn patients and control from the Gene Expression Omnibus (GEO). Patients were classified into four groups according to time after burn and weighted gene co-expression network analysis (WGCNA) obtained three gene modules including magenta, yellow and greenyellow modules that significantly correlated positively with time after burn. We also identified four groups of differentially expressed genes (DEGs) in samples at 0–1d, 1–2d, 2–4d, and 4–7d after burn compared with controls. Functional enrichment analysis of those DEGs indicated significant enrichment of inflammatory/immune related processes throughout time points after burn, while, samples at later time points were also closely associated with cell activation regulation related processes. Short time series expression analysis of overlapping genes among the four lists of DEGs screened out two temporal gene expression profiles that exhibited decreasing and increasing expression trend across times after burn, and genes contained in those two profiles might be related to pathologic changes after severe burn.

Gene expression profiling of the early pathogenesis of wooden breast disease in commercial broiler chickens using RNA-sequencing.

Wooden Breast Disease (WBD), a myopathy in commercial broiler chickens characterized by abnormally firm consistency of the pectoral muscle, impacts the poultry industry negatively due to severe reduction in meat quality traits. To unravel the molecular profile associated with the onset and early development of WBD in broiler chickens, we compared time-series gene expression profiles of Pectoralis (P.) major muscles between unaffected and affected birds from a high-breast-muscle-yield, purebred broiler line. P. major biopsy samples were collected from the cranial and caudal aspects of the muscle belly in birds that were raised up to 7 weeks of age (i.e. market age). Three subsets of biopsy samples comprising 6 unaffected (U) and 10 affected (A) from week 2 (cranial) and 4 (caudal), and 4U and 11A from week 3 (cranial) were processed for RNA-sequencing analysis. Sequence reads generated were processed using a suite of bioinformatics programs producing differentially expressed (DE) genes for each dataset at fold-change (A/U or U/A) >1.3 and False Discovery Ratio (FDR) <0.05 (week 2: 41 genes; week 3: 618 genes and week 4: 39 genes). Functional analysis of DE genes using literature mining, BioDBnet and IPA revealed several biological processes and pathways associated with onset and progress of WBD. Top among them were dysregulation of energy metabolism, response to inflammation, vascular disease and remodeling of extracellular matrix. This study reveals that presence of molecular perturbations involving the vasculature, extracellular matrix and metabolism are pertinent to the onset and early pathogenesis of WBD in commercial meat-type chickens.

MICRAT: a novel algorithm for inferring gene regulatory networks using time series gene expression data

BackgroundReconstruction of gene regulatory networks (GRNs), also known as reverse engineering of GRNs, aims to infer the potential regulation relationships between genes. With the development of biotechnology, such as gene chip microarray and RNA-sequencing, the high-throughput data generated provide us with more opportunities to infer the gene-gene interaction relationships using gene expression data and hence understand the underlying mechanism of biological processes. Gene regulatory networks are known to exhibit a multiplicity of interaction mechanisms which include functional and non-functional, and linear and non-linear relationships. Meanwhile, the regulatory interactions between genes and gene products are not spontaneous since various processes involved in producing fully functional and measurable concentrations of transcriptional factors/proteins lead to a delay in gene regulation. Many different approaches for reconstructing GRNs have been proposed, but the existing GRN inference approaches such as probabilistic Boolean networks and dynamic Bayesian networks have various limitations and relatively low accuracy. Inferring GRNs from time series microarray data or RNA-sequencing data remains a very challenging inverse problem due to its nonlinearity, high dimensionality, sparse and noisy data, and significant computational cost, which motivates us to develop more effective inference methods.ResultsWe developed a novel algorithm, MICRAT (Maximal Information coefficient with Conditional Relative Average entropy and Time-series mutual information), for inferring GRNs from time series gene expression data. Maximal information coefficient (MIC) is an effective measure of dependence for two-variable relationships. It captures a wide range of associations, both functional and non-functional, and thus has good performance on measuring the dependence between two genes. Our approach mainly includes two procedures. Firstly, it employs maximal information coefficient for constructing an undirected graph to represent the underlying relationships between genes. Secondly, it directs the edges in the undirected graph for inferring regulators and their targets. In this procedure, the conditional relative average entropies of each pair of nodes (or genes) are employed to indicate the directions of edges. Since the time delay might exist in the expression of regulators and target genes, time series mutual information is combined to cooperatively direct the edges for inferring the potential regulators and their targets. We evaluated the performance of MICRAT by applying it to synthetic datasets as well as real gene expression data and compare with other GRN inference methods. We inferred five 10-gene and five 100-gene networks from the DREAM4 challenge that were generated using the gene expression simulator GeneNetWeaver (GNW). MICRAT was also used to reconstruct GRNs on real gene expression data including part of the DNA-damaged response pathway (SOS DNA repair network) and experimental dataset in E. Coli. The results showed that MICRAT significantly improved the inference accuracy, compared to other inference methods, such as TDBN, etc.ConclusionIn this work, a novel algorithm, MICRAT, for inferring GRNs from time series gene expression data was proposed by taking into account dependence and time delay of expressions of a regulator and its target genes. This approach employed maximal information coefficients for reconstructing an undirected graph to represent the underlying relationships between genes. The edges were directed by combining conditional relative average entropy with time course mutual information of pairs of genes. The proposed algorithm was evaluated on the benchmark GRNs provided by the DREAM4 challenge and part of the real SOS DNA repair network in E. Coli. The experimental study showed that our approach was comparable to other methods on 10-gene datasets and outperformed other methods on 100-gene datasets in GRN inference from time series datasets.

Optimized time-lag differential method for constructing gene regulatory network

One major molecular genetics control mechanism is transcriptional gene regulation where regulatory genes control other genes over time. The biological approaches to determine the Gene Regulatory Networks (GRN), require costly experimental setup and time for generating voluminous time series gene expression data. Computational model has been developed to generate GRN by analyzing time series gene expression data. Sometimes gene to gene interaction not performing in order due to change of activity in the cell, resulting regulatory error, creating difficulty to identify the target genes. Searching of regulatory genes with permissible error, producing maximum number of target genes is the real challenge for predicting the most important and instant regulating pathway of the human GRNs. The aim of the paper is to establish a computational methodology to reconstruct the GRN from short time series gene expression data with an objective to explain the observed behavior of biological systems. We employ nondominated sorting genetic algorithm (NSGA-II) to develop the proposed Optimized time-lag differential (OTD) algorithm, which identifies maximum number of target genes for the selected regulatory genes within minimum regulating time. Experiments have been conducted using eight different time series microarray data set showing better performance compare to others.

BiXGBoost: a scalable, flexible boosting-based method for reconstructing gene regulatory networks.

Reconstructing gene regulatory networks (GRNs) based on gene expression profiles is still an enormous challenge in systems biology. Random forest-based methods have been proved a kind of efficient methods to evaluate the importance of gene regulations. Nevertheless, the accuracy of traditional methods can be further improved. With time-series gene expression data, exploiting inherent time information and high order time lag are promising strategies to improve the power and accuracy of GRNs inference. In this study, we propose a scalable, flexible approach called BiXGBoost to reconstruct GRNs. BiXGBoost is a bidirectional-based method by considering both candidate regulatory genes and target genes for a specific gene. Moreover, BiXGBoost utilizes time information efficiently and integrates XGBoost to evaluate the feature importance. Randomization and regularization are also applied in BiXGBoost to address the over-fitting problem. The results on DREAM4 and Escherichia coli datasets show the good performance of BiXGBoost on different scale of networks. Our Python implementation of BiXGBoost is available at https://github.com/zrq0123/BiXGBoost. Supplementary data are available at Bioinformatics online.

DNA mapping and kinetic modeling of the HrdB regulon in Streptomyces coelicolor.

HrdB in streptomycetes is a principal sigma factor whose deletion is lethal. This is also the reason why its regulon has not been investigated so far. To overcome experimental obstacles, for investigating the HrdB regulon, we constructed a strain whose HrdB protein was tagged by an HA epitope. ChIP-seq experiment, done in 3 repeats, identified 2137 protein-coding genes organized in 337 operons, 75 small RNAs, 62 tRNAs, 6 rRNAs and 3 miscellaneous RNAs. Subsequent kinetic modeling of regulation of protein-coding genes with HrdB alone and with a complex of HrdB and a transcriptional cofactor RbpA, using gene expression time series, identified 1694 genes that were under their direct control. When using the HrdB–RbpA complex in the model, an increase of the model fidelity was found for 322 genes. Functional analysis revealed that HrdB controls the majority of gene groups essential for the primary metabolism and the vegetative growth. Particularly, almost all ribosomal protein-coding genes were found in the HrdB regulon. Analysis of promoter binding sites revealed binding motif at the −10 region and suggested the possible role of mono- or di-nucleotides upstream of the −10 element.

Profiling Cellular Processes in Adipose Tissue during Weight Loss Using Time Series Gene Expression.

Obesity is a global epidemic identified as a major risk factor for multiple chronic diseases and, consequently, diet-induced weight loss is used to counter obesity. The adipose tissue is the primary tissue affected in diet-induced weight loss, yet the underlying molecular mechanisms and changes are not completely deciphered. In this study, we present a network biology analysis workflow which enables the profiling of the cellular processes affected by weight loss in the subcutaneous adipose tissue. Time series gene expression data from a dietary intervention dataset with two diets was analysed. Differentially expressed genes were used to generate co-expression networks using a method that capitalises on the repeat measurements in the data and finds correlations between gene expression changes over time. Using the network analysis tool Cytoscape, an overlap network of conserved components in the co-expression networks was constructed, clustered on topology to find densely correlated genes, and analysed using Gene Ontology enrichment analysis. We found five clusters involved in key metabolic processes, but also adipose tissue development and tissue remodelling processes were enriched. In conclusion, we present a flexible network biology workflow for finding important processes and relevant genes associated with weight loss, using a time series co-expression network approach that is robust towards the high inter-individual variation in humans.

Gene Expression Is Not Random: Scaling, Long-Range Cross-Dependence, and Fractal Characteristics of Gene Regulatory Networks.

Gene expression is a vital process through which cells react to the environment and express functional behavior. Understanding the dynamics of gene expression could prove crucial in unraveling the physical complexities involved in this process. Specifically, understanding the coherent complex structure of transcriptional dynamics is the goal of numerous computational studies aiming to study and finally control cellular processes. Here, we report the scaling properties of gene expression time series in Escherichia coli and Saccharomyces cerevisiae. Unlike previous studies, which report the fractal and long-range dependency of DNA structure, we investigate the individual gene expression dynamics as well as the cross-dependency between them in the context of gene regulatory network. Our results demonstrate that the gene expression time series display fractal and long-range dependence characteristics. In addition, the dynamics between genes and linked transcription factors in gene regulatory networks are also fractal and long-range cross-correlated. The cross-correlation exponents in gene regulatory networks are not unique. The distribution of the cross-correlation exponents of gene regulatory networks for several types of cells can be interpreted as a measure of the complexity of their functional behavior.

Analysis of Time-Series Gene Expression Data to Explore Mechanisms of Chemical-Induced Hepatic Steatosis Toxicity.

Non-alcoholic fatty liver disease (NAFLD) represents a wide spectrum of disease, ranging from simple fatty liver through steatosis with inflammation and necrosis to cirrhosis. One of the most challenging problems in biomedical research and within the chemical industry is to understand the underlying mechanisms of complex disease, and complex adverse outcome pathways (AOPs). Based on a set of 28 steatotic chemicals with gene expression data measured on primary hepatocytes at three times (2, 8, and 24 h) and three doses (low, medium, and high), we identified genes and pathways, defined as molecular initiating events (MIEs) and key events (KEs) of steatosis using a combination of a time series and pathway analyses. Among the genes deregulated by these compounds, the study highlighted OSBPL9, ALDH7A1, MYADM, SLC51B, PRDX6, GPAT3, TMEM135, DLGDA5, BCO2, APO10LA, TSPAN6, NEURL1B, and DUSP1. Furthermore, pathway analysis indicated deregulation of pathways related to lipid accumulation, such as fat digestion and absorption, linoleic and linolenic acid metabolism, calcium signaling pathway, fatty acid metabolism, peroxisome, retinol metabolism, and steroid metabolic pathways in a time dependent manner. Such transcription profile analysis can help in the understanding of the steatosis evolution over time generated by chemical exposure.

Bayesian Data Fusion of Gene Expression and Histone Modification Profiles for Inference of Gene Regulatory Network.

Accurately reconstructing gene regulatory networks (GRNs) from high-throughput gene expression data has been a major challenge in systems biology for decades. Many approaches have been proposed to solve this problem. However, there is still much room for the improvement of GRN inference. Integrating data from different sources is a promising strategy. Epigenetic modifications have a close relationship with gene regulation. Hence, epigenetic data such as histone modification profiles can provide useful information for uncovering regulatory interactions between genes. In this paper, we propose a method to integrate epigenetic data into the inference of GRNs. In particular, a dynamic Bayesian network (DBN) is employed to infer gene regulations from time-series gene expression data. Epigenetic data (histone modification profiles here) are integrated into the prior probability distribution of the Bayesian model. Our method has been validated on both synthetic and real datasets. Experimental results show that the integration of epigenetic data can significantly improve the performance of GRN inference. As more epigenetic datasets become available, our method would be useful for elucidating the gene regulatory mechanisms driving various cellular activities. The source code and testing datasets are available at https://github.com/Zheng-Lab/MetaGRN/tree/master/histonePrior.

A Boolean network inference from time-series gene expression data using a genetic algorithm.

Inferring a gene regulatory network from time-series gene expression data is a fundamental problem in systems biology, and many methods have been proposed. However, most of them were not efficient in inferring regulatory relations involved by a large number of genes because they limited the number of regulatory genes or computed an approximated reliability of multivariate relations. Therefore, an improved method is needed to efficiently search more generalized and scalable regulatory relations. In this study, we propose a genetic algorithm-based Boolean network inference (GABNI) method which can search an optimal Boolean regulatory function of a large number of regulatory genes. For an efficient search, it solves the problem in two stages. GABNI first exploits an existing method, a mutual information-based Boolean network inference (MIBNI), because it can quickly find an optimal solution in a small-scale inference problem. When MIBNI fails to find an optimal solution, a genetic algorithm (GA) is applied to search an optimal set of regulatory genes in a wider solution space. In particular, we modified a typical GA framework to efficiently reduce a search space. We compared GABNI with four well-known inference methods through extensive simulations on both the artificial and the real gene expression datasets. Our results demonstrated that GABNI significantly outperformed them in both structural and dynamics accuracies. The proposed method is an efficient and scalable tool to infer a Boolean network from time-series gene expression data. Supplementary data are available at Bioinformatics online.

Inference of gene networks from gene expression time series using recurrent neural networks and sparse MAP estimation.

The inference of genetic regulatory networks (GRNs) provides insight into the cellular responses to signals. A class of recurrent neural networks (RNNs) capturing the dynamics of GRN has been used as a basis for inferring small-scale GRNs from gene expression time series. The Bayesian framework facilitates incorporating the hypothesis of GRN into the model estimation to improve the accuracy of GRN inference. We present new methods for inferring small-scale GRNs based on RNNs. The weights of wires of RNN represent the strengths of gene-to-gene regulatory interactions. We use a class of automatic relevance determination (ARD) priors to enforce the sparsity in the maximum a posteriori (MAP) estimates of wire weights of RNN. A particle swarm optimization (PSO) is integrated as an optimization engine into the MAP estimation process. Likely networks of genes generated based on estimated wire weights are combined using the majority rule to determine a final estimated GRN. As an alternative, a class of [Formula: see text] priors is used for attaining the sparse MAP estimates of wire weights of RNN. We also infer the GRN using the maximum likelihood (ML) estimates of wire weights of RNN. The RNN-based GRN inference algorithms, ARD-RNN, [Formula: see text] -RNN, and ML-RNN are tested on simulated and experimental E. coli and yeast time series containing 6-11 genes and 7-19 data points. Published GRN inference algorithms based on regressions and mutual information networks are performed on the benchmark datasets to compare performances. ARD and [Formula: see text] -norm priors are used for the estimation of wire weights of RNN. Results of GRN inference experiments show that ARD-RNN, [Formula: see text] -RNN have similar best accuracies on the simulated time series. The ARD-RNN is more accurate than [Formula: see text] -RNN, ML-RNN, and mostly more accurate than the reference algorithms on the experimental time series. The effectiveness of ARD-RNN for inferring small-scale GRNs using gene expression time series of limited length is empirically verified.

Potential Genes and Pathways of Neonatal Sepsis Based on Functional Gene Set Enrichment Analyses.

Background Neonatal sepsis (NS) is considered as the most common cause of neonatal deaths that newborns suffer from. Although numerous studies focus on gene biomarkers of NS, the predictive value of the gene biomarkers is low. NS pathogenesis is still needed to be investigated. Methods After data preprocessing, we used KEGG enrichment method to identify the differentially expressed pathways between NS and normal controls. Then, functional principal component analysis (FPCA) was adopted to calculate gene values in NS. In order to further study the key signaling pathway of the NS, elastic-net regression model, Mann–Whitney U test, and coexpression network were used to estimate the weights of signaling pathway and hub genes. Results A total of 115 different pathways between NS and controls were first identified. FPCA made full use of time-series gene expression information and estimated F values of genes in the different pathways. The top 1000 genes were considered as the different genes and were further analyzed by elastic-net regression and MWU test. There were 7 key signaling pathways between the NS and controls, according to different sources. Among those genes involved in key pathways, 7 hub genes, PIK3CA, TGFBR2, CDKN1B, KRAS, E2F3, TRAF6, and CHUK, were determined based on the coexpression network. Most of them were cancer-related genes. PIK3CA was considered as the common marker, which is highly expressed in the lymphocyte group. Little was known about the correlation of PIK3CA with NS, which gives us a new enlightenment for NS study. Conclusion This research might provide the perspective information to explore the potential novel genes and pathways as NS therapy targets.

Consistency of cybernetic variables with gene expression profiles: A more rigorous test.

Diauxic growth of Escherichia coli is driven by a host of internal, complex regulatory actions. In this classic scenario of cellular control, the cell employs a rational algorithm to modulate its metabolism in a competitive fashion. Cybernetic models of metabolism, whose development now spans three decades, were first formulated to describe regulation of cells in complex, multi-substrate environments. They modeled this scenario using the hypothesis that the formation of the enzymatic machinery is regulated to maximize a return on investment. While this assumption is made on the basis of logical arguments rooted in evolutionary principles, little effort has been taken to validate if enzymes are truly synthesized in the same fashion that is predicted by cybernetic variables. This work revisits the original cybernetic models describing diauxic growth and compares their predictions of enzyme synthesis control with time series gene expression data in microarray and qRT-PCR formats. Three separate studies are made for two different strains of E. coli. The first is for the growth of E. coli BW25113 on a mixture of glucose and acetate, whose gene expression changes are metered by microarray. Another is also for the sequential consumption of glucose and acetate but involves strain MG1655 and employs qRT-PCR. The final is for E. coli MG1655 on glucose and lactose. By demonstrating how cybernetic variables for induced enzyme synthesis mimic the behavior of transcriptional data, a strong argument for using cybernetic models is made. © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 34:858-867, 2018.

Identification and prioritization of differentially expressed genes for time-series gene expression data

Identification of differentially expressed genes (DEGs) in time course studies is very useful for understanding gene function, and can help determine key genes during specific stages of plant development. A few existing methods focus on the detection of DEGs within a single biological group, enabling to study temporal changes in gene expression. To utilize a rapidly increasing amount of single-group time-series expression data, we propose a two-step method that integrates the temporal characteristics of time-series data to obtain a B-spline curve fit. Firstly, a flat gene filter based on the Ljung–Box test is used to filter out flat genes. Then, a B-spline model is used to identify DEGs. For use in biological experiments, these DEGs should be screened, to determine their biological importance. To identify high-confidence promising DEGs for specific biological processes, we propose a novel gene prioritization approach based on the partner evaluation principle. This novel gene prioritization approach utilizes existing co-expression information to rank DEGs that are likely to be involved in a specific biological process/condition. The proposed method is validated on the Arabidopsis thaliana seed germination dataset and on the rice anther development expression dataset.

GEsture: an online hand-drawing tool for gene expression pattern search.

Gene expression profiling data provide useful information for the investigation of biological function and process. However, identifying a specific expression pattern from extensive time series gene expression data is not an easy task. Clustering, a popular method, is often used to classify similar expression genes, however, genes with a ‘desirable’ or ‘user-defined’ pattern cannot be efficiently detected by clustering methods. To address these limitations, we developed an online tool called GEsture. Users can draw, or graph a curve using a mouse instead of inputting abstract parameters of clustering methods. GEsture explores genes showing similar, opposite and time-delay expression patterns with a gene expression curve as input from time series datasets. We presented three examples that illustrate the capacity of GEsture in gene hunting while following users’ requirements. GEsture also provides visualization tools (such as expression pattern figure, heat map and correlation network) to display the searching results. The result outputs may provide useful information for researchers to understand the targets, function and biological processes of the involved genes.

Automated transition analysis of activated gene regulation during diauxic nutrient shift in Escherichia coli and adipocyte differentiation in mouse cells

BackgroundComprehensively understanding the dynamics of biological systems is among the biggest current challenges in biology and medicine. To acquire this understanding, researchers have measured the time-series expression profiles of cell lines of various organisms. Biological technologies have also drastically improved, providing a huge amount of information with support from bioinformatics and systems biology. However, the transitions between the activation and inactivation of gene regulations, at the temporal resolution of single time points, are difficult to extract from time-course gene expression profiles.ResultsOur proposed method reports the activation period of each gene regulation from gene expression profiles and a gene regulatory network. The correctness and effectiveness of the method were validated by analyzing the diauxic shift from glucose to lactose in Escherichia coli. The method completely detected the three periods of the shift; 1) consumption of glucose as nutrient source, 2) the period of seeking another nutrient source and 3) consumption of lactose as nutrient source. We then applied the method to mouse adipocyte differentiation data. Cell differentiation into adipocytes is known to involve two waves of the gene regulation cascade, and sub-waves are predicted. From the gene expression profiles of the cell differentiation process from ES to adipose cells (62 time points), our method acquired four periods; three periods covering the two known waves of the cascade, and a final period of gene regulations when the differentiation to adipocytes was completed.ConclusionsOur proposed method identifies the transitions of gene regulations from time-series gene expression profiles. Dynamic analyses are essential for deep understanding of biological systems and for identifying the causes of the onset of diseases such as diabetes and osteoporosis. The proposed method can greatly contribute to the progress of biology and medicine.