Time-course Gene Expression Profiles Research Articles

Recursive regularization for inferring gene networks from time-course gene expression profiles

BackgroundInferring gene networks from time-course microarray experiments with vector autoregressive (VAR) model is the process of identifying functional associations between genes through multivariate time series. This problem can be cast as a variable selection problem in Statistics. One of the promising methods for variable selection is the elastic net proposed by Zou and Hastie (2005). However, VAR modeling with the elastic net succeeds in increasing the number of true positives while it also results in increasing the number of false positives.ResultsBy incorporating relative importance of the VAR coefficients into the elastic net, we propose a new class of regularization, called recursive elastic net, to increase the capability of the elastic net and estimate gene networks based on the VAR model. The recursive elastic net can reduce the number of false positives gradually by updating the importance. Numerical simulations and comparisons demonstrate that the proposed method succeeds in reducing the number of false positives drastically while keeping the high number of true positives in the network inference and achieves two or more times higher true discovery rate (the proportion of true positives among the selected edges) than the competing methods even when the number of time points is small. We also compared our method with various reverse-engineering algorithms on experimental data of MCF-7 breast cancer cells stimulated with two ErbB ligands, EGF and HRG.ConclusionThe recursive elastic net is a powerful tool for inferring gene networks from time-course gene expression profiles.

Optimal classification for time-course gene expression data using functional data analysis

Classification problems have received considerable attention in biological and medical applications. In particular, classification methods combining to microarray technology play an important role in diagnosing and predicting disease, such as cancer, in medical research. Primary objective in classification is to build an optimal classifier based on the training sample in order to predict unknown class in the test sample. In this paper, we propose a unified approach for optimal gene classification with conjunction with functional principal component analysis (FPCA) in functional data analysis (FNDA) framework to classify time-course gene expression profiles based on information from the patterns. To derive an optimal classifier in FNDA, we also propose to find optimal number of bases in the smoothing step and functional principal components in FPCA using a cross-validation technique, and compare the performance of some popular classification techniques in the proposed setting. We illustrate the propose method with a simulation study and a real world data analysis.

Direct targets of the TRP63 transcription factor revealed by a combination of gene expression profiling and reverse engineering

Genome-wide identification of bona-fide targets of transcription factors in mammalian cells is still a challenge. We present a novel integrated computational and experimental approach to identify direct targets of a transcription factor. This consists of measuring time-course (dynamic) gene expression profiles upon perturbation of the transcription factor under study, and in applying a novel "reverse-engineering" algorithm (TSNI) to rank genes according to their probability of being direct targets. Using primary keratinocytes as a model system, we identified novel transcriptional target genes of TRP63, a crucial regulator of skin development. TSNI-predicted TRP63 target genes were validated by Trp63 knockdown and by ChIP-chip to identify TRP63-bound regions in vivo. Our study revealed that short sampling times, in the order of minutes, are needed to capture the dynamics of gene expression in mammalian cells. We show that TRP63 transiently regulates a subset of its direct targets, thus highlighting the importance of considering temporal dynamics when identifying transcriptional targets. Using this approach, we uncovered a previously unsuspected transient regulation of the AP-1 complex by TRP63 through direct regulation of a subset of AP-1 components. The integrated experimental and computational approach described here is readily applicable to other transcription factors in mammalian systems and is complementary to genome-wide identification of transcription-factor binding sites.

Statistical inference of transcriptional module-based gene networks from time course gene expression profiles by using state space models

Statistical inference of gene networks by using time-course microarray gene expression profiles is an essential step towards understanding the temporal structure of gene regulatory mechanisms. Unfortunately, most of the current studies have been limited to analysing a small number of genes because the length of time-course gene expression profiles is fairly short. One promising approach to overcome such a limitation is to infer gene networks by exploring the potential transcriptional modules which are sets of genes sharing a common function or involved in the same pathway. In this article, we present a novel approach based on the state space model to identify the transcriptional modules and module-based gene networks simultaneously. The state space model has the potential to infer large-scale gene networks, e.g. of order 10(3), from time-course gene expression profiles. Particularly, we succeeded in the identification of a cell cycle system by using the gene expression profiles of Saccharomyces cerevisiae in which the length of the time-course and number of genes were 24 and 4382, respectively. However, when analysing shorter time-course data, e.g. of length 10 or less, the parameter estimations of the state space model often fail due to overfitting. To extend the applicability of the state space model, we provide an approach to use the technical replicates of gene expression profiles, which are often measured in duplicate or triplicate. The use of technical replicates is important for achieving highly-efficient inferences of gene networks with short time-course data. The potential of the proposed method has been demonstrated through the time-course analysis of the gene expression profiles of human umbilical vein endothelial cells (HUVECs) undergoing growth factor deprivation-induced apoptosis. Supplementary Information and the software (TRANS-MNET) are available at http://daweb.ism.ac.jp/~yoshidar/software/ssm/.

Classification of gene functions using support vector machine for time-course gene expression data

Since most biological systems are developmental and dynamic, time-course gene expression profiles provide an important characterization of gene functions. Assigning functions for genes with unknown functions based on time-course gene expressions is an important task in functional genomics. Recently, various methods have been proposed for the classification of gene functions based on time-course gene expression data. In this paper, we consider the classification of gene functions from functional data analysis viewpoint, where a functional support vector machine is adopted. The functional support vector machine can model temporal effects of time-course gene expression data by incorporating the coefficients as well as the basis matrix obtained from a finite expansion of gene expressions on a set of basis functions. We apply the functional support vector machine to both real microarray and simulated data. Our results indicate that the functional support vector machine is effective in discriminating gene functions of time-course gene expressions with predefined functions. The method also provides valuable functional information about interactions between genes and allows the assignment of new functions to genes with unknown functions.

Variability of DNA Microarray Gene Expression Profiles in Cultured Rat Primary Hepatocytes

DNA microarray is a powerful tool in biomedical research. However, transcriptomic profiling using DNA microarray is subject to many variations including biological variability. To evaluate the different sources of variation in mRNA gene expression profiles, gene expression profiles were monitored using the Affymetrix RatTox U34 arrays in cultured primary hepatocytes derived from six rats over a 26 hour period at 6 time points (0h, 2h, 5h, 8h, 14h and 26h) with two replicate arrays at each time point for each animal. In addition, the impact of sample size on the variability of differentially expressed gene lists and the consistency of biological responses were also investigated. Excellent intra-animal reproducibility was obtained at all time points with 0 out of 370 present probe sets across all time points showing significant difference between the 2 replicate arrays (3-way ANOVA, p ≤ 0.0001). However, large inter-animal biological variation in mRNA expression profiles was observed with 337 out of 370 present probe sets showing significant differences among 6 animals (3-way ANOVA, p ≤ 0.05). Principal Component Analysis (PCA) revealed that time effect (PC1) in this data set accounted for 47.4% of total variance indicating the dynamics of transcriptomics. The second and third largest effects came from animal difference, which accounted for 16.9% (PC2 and PC3) of the total variance. The reproducibility of gene lists and their functional classification was declined considerably when the sample size was decreased. Overall, our results strongly support that there is significant inter-animal variability in the time-course gene expression profiles, which is a confounding factor that must be carefully evaluated to correctly interpret microarray gene expression studies. The consistency of the gene lists and their biological functional classification are also sensitive to sample size with the reproducibility decreasing considerably under small sample size.

Identification of hair cycle-associated genes from time-course gene expression profile using fractal analysis

Microarray technology permits one to monitor thousands of processes going on inside the cell. This tool has been used to study gene expression profiles associated with the hair-growth cycle. We provide a novel method called the fractal analysis method to identify hair-growth cycle associated genes from time course gene expression profiles. Fractal analysis is a much better method than the computational method used by Lin et al. (2004). The fractal dimension obtained by fractal analysis process also indicates the irregularity in hair-growth pattern. The computational method used by Lin et al. (2004) was unable to make any inference about the hair-growth pattern.

Gene expression changes by amyloid β peptide-stimulated human postmortem brain microglia identify activation of multiple inflammatory processes

A central feature of the inflammatory pathology in Alzheimer's disease is activated microglia clustered around aggregated amyloid beta (Abeta) peptide-containing plaques. In vitro-cultured microglia can be activated to an inflammatory state by aggregated Abeta with the induction of a range of different neurotoxic factors and provide a model system for studying microglia Abeta interactions. Gene expression responses of human postmortem brain-derived microglia to aggregated Abeta were measured using whole genome microarrays to address the hypothesis that Abeta interactions with human microglia primarily induce proinflammatory genes and not activation of genes involved in Abeta phagocytosis and removal. The results demonstrated that Abeta activation of microglia induced a large alteration in gene transcription including activation of many proinflammatory cytokines and chemokines, most notably, interleukin (IL)-1beta, IL-8, and matrix metalloproteinases (MMP), including MMP1, MMP3, MMP9, MMP10, and MMP12. All of these genes could amplify ongoing inflammation, resulting in further neuronal loss. Changes in expression of receptors associated with Abeta phagocytosis did not match the changes in proinflammatory gene expression. Time-course gene expression profiling, along with real-time polymerase chain reaction validation of expression changes, demonstrated an acute phase of gene induction for many proinflammatory genes but also chronic activation for many other potentially toxic products. These chronically activated genes included indoleamine 2,3-dioxygenase and kynureninase, which are involved in formation of the neurotoxin quinolinic acid, and S100A8, a potential proinflammatory chemokine. These studies show that activation of microglia by Abeta induces multiple genes that could be involved in inflammatory responses contributing to neurodegenerative processes.

Integrating time-course microarray gene expression profiles with cytotoxicity for identification of biomarkers in primary rat hepatocytes exposed to cadmium

DNA microarrays can provide information about the expression levels of thousands of genes simultaneously at the transcriptomic level, while conventional cell viability and cytotoxicity measurement methods provide information about the biological functions at the cellular level. Integrating these data at different levels provides a promising approach for evaluating or predicting how cells respond to chemical exposure. It is important to investigate the multi-scale biological system in a systematic way to better understand the gene regulation networks and signal transduction pathways involved in the cellular responses to environmental factors. Primary rat hepatocytes were exposed to cadmium acetate at 0, 1.25 and 2 microM. mRNA expression profiles at 0, 3, 6, 12 and 24 h were measured using the Affymetrix RatTox U34 GeneChip arrays. Simultaneously, cytotoxicity was assessed by lactase dehydrogenase leakage assay. Gene expression profiles at different time points were used to evaluate cytotoxicity at subsequent time points using partial least squares, and it was found that gene expression profiles at 0 h had the best prediction accuracy for the cytotoxicity observed at 12 h. Some biomarkers whose expression profiles showed strong relationship with cytotoxicity were identified and the underlying pathways were reconstructed to illustrate how hepatocytes respond to cadmium exposure. Permutation studies were also applied to assess the reliability of the predictive models. Matlab source code is available upon request and DNA microarray data are available at GEO (http://www.ncbi.nlm.nih.gov/geo).

Expression Profiling and Cellular Localization of Genes Associated with the Hair Cycle Induced by Wax Depilation

The hair cycle is a highly regulated process controlled by multiple factors. Systematic analysis of gene expression patterns in each stage of the hair cycle would provide information useful for understanding this complicated process. To identify genes associated with the hair cycle, we used DNA microarray hybridization to analyze sequential gene expression patterns in mouse skin following hair cycle synchronization by wax depilation. Messenger RNA levels in mouse skin at various times after depilation were compared with those prior to depilation (resting phase). According to their expression patterns, upregulated genes were categorized into four groups: early anagen, middle anagen, late anagen/early catagen, and middle/late catagen, and processes that take place in each stage were evaluated. We identified 12 new components that are specifically expressed in the hair follicle, 11 genes in anagen including carbonic anhydrase 6, cytokeratin 12, and matrix metalloproteinase-11 in catagen that were confirmed using in situ hybridization. The strategy used here allowed us to identify unknown genes or process previously not suspected to have a role in hair biology. These analyses will contribute to elucidating the mechanisms of hair cycle regulation and should lead to the identification of novel molecular targets for hair growth and/or depilation agents.

MSin3A corepressor regulates diverse transcriptional networks governing normal and neoplastic growth and survival

mSin3A is a core component of a large multiprotein corepressor complex with associated histone deacetylase (HDAC) enzymatic activity. Physical interactions of mSin3A with many sequence-specific transcription factors has linked the mSin3A corepressor complex to the regulation of diverse signaling pathways and associated biological processes. To dissect the complex nature of mSin3A's actions, we monitored the impact of conditional mSin3A deletion on the developmental, cell biological, and transcriptional levels. mSin3A was shown to play an essential role in early embryonic development and in the proliferation and survival of primary, immortalized, and transformed cells. Genetic and biochemical analyses established a role for mSin3A/HDAC in p53 deacetylation and activation, although genetic deletion of p53 was not sufficient to attenuate the mSin3A null cell lethal phenotype. Consistent with mSin3A's broad biological activities beyond regulation of the p53 pathway, time-course gene expression profiling following mSin3A deletion revealed deregulation of genes involved in cell cycle regulation, DNA replication, DNA repair, apoptosis, chromatin modifications, and mitochondrial metabolism. Computational analysis of the mSin3A transcriptome using a knowledge-based database revealed several nodal points through which mSin3A influences gene expression, including the Myc-Mad, E2F, and p53 transcriptional networks. Further validation of these nodes derived from in silico promoter analysis showing enrichment for Myc-Mad, E2F, and p53 cis-regulatory elements in regulatory regions of up-regulated genes following mSin3A depletion. Significantly, in silico promoter analyses also revealed specific cis-regulatory elements binding the transcriptional activator Stat and the ISWI ATP-dependent nucleosome remodeling factor Falz, thereby expanding further the mSin3A network of regulatory factors. Together, these integrated genetic, biochemical, and computational studies demonstrate the involvement of mSin3A in the regulation of diverse pathways governing many aspects of normal and neoplastic growth and survival and provide an experimental framework for the analysis of essential genes with diverse biological functions.

Gene Expression Analysis of Immune-Mediated Arrest of Tumorigenesis in a Transgenic Mouse Model of HER-2/neu-Positive Basal-Like Mammary Carcinoma

We previously showed that a vaccine combining interleukin 12 and allogeneic p185(neu)-positive mammary carcinoma cells completely prevented multifocal mammary carcinogenesis in HER-2/neu transgenic mice. To identify the molecular events responsible for effective tumor prevention and to define the tumor gene expression signature, we used microarrays to analyze the expression profile of mammary tissue of untreated transgenic mice and of vaccine-treated, tumor-free mice at different time points. Mammary tissue from vaccinated mice displayed a gene expression profile different from that of untreated, tumor-bearing mice but similar to that of normal/hyperplastic mammary gland. Comparison of treated and untreated mice at 15 weeks of age revealed up-regulation of genes encoding antibodies, chemokines, gamma-interferon-induced genes and inflammatory molecules, and down-regulation of early genes induced by tumor development. The gene expression signature of HER-2/neu-transformed tumor cells showed modulation of genes promoting proliferation, angiogenesis, migration, invasion, and metastasis and inhibiting apoptosis and immune response. Meta-analysis of microarray data on human breast cancer showed that the signature of tumors arising in murine HER-2/neu transgenic model correctly classified human HER-2/neu-expressing tumors and normal breast tissue. Moreover murine and human HER-2/neu-positive tumors share the signature of basal-like breast cancers. This gene expression analysis reveals the immune events associated with prevention of tumor development and shows that HER-2/neu transgenic mice represent a good model of a poor-prognosis group of human breast tumors.

Time-course data analysis of gene expression profiles reveals purR regulon concerns in organic solvent tolerance in Escherichia coli

A time-course gene-expression profile was generated for Escherichia coli TK31 when it was exposed to an organic solvent mixture, and classified by fuzzy adaptive resonance theory (Fuzzy ART). It was found that the purR regulon plays an important role in the organic solvent tolerance (OST) of E. coli.

Identification of hair cycle-associated genes from time-course gene expression profile data by using replicate variance

The hair-growth cycle is an example of a cyclic process that is well characterized morphologically but understood incompletely at the molecular level. As an initial step in discovering regulators in hair-follicle morphogenesis and cycling, we used DNA microarrays to profile mRNA expression in mouse back skin from eight representative time points. We developed a statistical algorithm to identify the set of genes expressed within skin that are associated specifically with the hair-growth cycle. The methodology takes advantage of higher replicate variance during asynchronous hair cycles in comparison with synchronous cycles. More than one-third of genes with detectable skin expression showed hair-cycle-related changes in expression, suggesting that many more genes may be associated with the hair-growth cycle than have been identified in the literature. By using a probabilistic clustering algorithm for replicated measurements, these genes were grouped into 30 time-course profile clusters, which fall into four major classes. Distinct genetic pathways were characteristic for the different time-course profile clusters, providing insights into the regulation of hair-follicle cycling and suggesting that this approach is useful for identifying hair follicle regulators. In addition to revealing known hair-related genes, we identified genes that were not previously known to be hair cycle-associated and confirmed their temporal and spatial expression patterns during the hair-growth cycle by quantitative real-time PCR and in situ hybridization. The same computational approach should be generally useful for identifying genes associated with cyclic processes from complex tissues.

Correspondence analysis of microarray time-course data in case–control design

Although different statistical approaches have been proposed for analyzing microarray time-course data, method for analyzing such data collected using the popular case–control design in clinical investigations has not been proposed perhaps due to the increased complexity for the existing parametric or non-parametric approaches. In this paper, we introduce a new multivariate data analyzing technique, the correspondence analysis, to analyze the high dimensional microarray time-course data in case–control design. We show, through an example on type 2 diabetes, how the nice features of the correspondence analysis can be use to explore the various time-course gene expression profiles that exist in the data. By coordinating and examining the projections on the reduced dimensions by both the genes and the time-course experiments, we are able to identify important genes and time-course patterns and make inferences on their biological relevance. Using the sample replicates, we propose a bootstrap procedure for inferring the significance of contributions on the leading dimensions by both the time-course experiments and the genes. Striking differences in the time-course patterns in the normal controls and diabetes patients have been revealed. In addition, the method also identifies genes that display similar or comparable time-course expression patterns shared by both the cases and the controls. We conclude that our correspondence analysis based approach can be a useful tool for analyzing high dimensional microarray data collected in clinical investigations.