Integrating transcription factor binding site information with gene expression datasets

Gene Set Correlation Analysis and Visualization Using Gene Expression Data

Background:

Gene set enrichment analyses (GSEA) provide a useful and powerful approach to identify differentially expressed gene sets with prior biological knowledge. Several GSEA algorithms have been proposed to perform enrichment analyses on groups of genes. However, many of these algorithms have focused on the identification of differentially expressed gene sets in a given phenotype.

Objective:

In this paper, we propose a gene set analytic framework, Gene Set Correlation Analysis (GSCoA), that simultaneously measures within and between gene sets variation to identify sets of genes enriched for differential expression and highly co-related pathways.

Methods:

We apply co-inertia analysis to the comparisons of cross-gene sets in gene expression data to measure the co-structure of expression profiles in pairs of gene sets. Co-inertia analysis (CIA) is one multivariate method to identify trends or co-relationships in multiple datasets, which contain the same samples. The objective of CIA is to seek ordinations (dimension reduction diagrams) of two gene sets such that the square covariance between the projections of the gene sets on successive axes is maximized. Simulation studies illustrate that CIA offers superior performance in identifying corelationships between gene sets in all simulation settings when compared to correlation-based gene set methods.

Result and Conclusion:

We also combine between-gene set CIA and GSEA to discover the relationships between gene sets significantly associated with phenotypes. In addition, we provide a graphical technique for visualizing and simultaneously exploring the associations of between and within gene sets and their interaction and network. We then demonstrate integration of within and between gene sets variation using CIA and GSEA, applied to the p53 gene expression data using the c2 curated gene sets. Ultimately, the GSCoA approach provides an attractive tool for identification and visualization of novel associations between pairs of gene sets by integrating co-relationships between gene sets into gene set analysis.

Interspersed 5'cis-regulatory elements ascertain the spatio-temporal transcription of cytoskeletal profilin gene family in Arabidopsis

Spatio-temporal expression patterns of cytoskeleton-associated profilin (PRF) family proteins in response to varied environmental stimuli are tightly regulated. Functional analyses of PRFs have revealed their crucial roles in varied developmental and stress related traits, but very little is implicit pertaining to cis-acting regulatory elements that regulate such intricate expression patterns. Here, we identified cis-elements with their varying distribution frequencies by scanning 1.5kbp upstream sequences of 5'regulatory regions of PRFs of dicot and monocot plant species. Predicted cis-elements in the regulatory sub-regions of Arabidopsis PRFs (AtPRFs) were predominantly associated with development-responsive motifs (DREs), light responsive elements (LREs), hormonal responsive elements (HREs), core motifs and stress-responsive elements (SREs). Interestingly, DREs, LREs and core promoter motifs, were extensively distributed up to the distal end of 5'regulatory regions on contrary to HREs present closer to the translational start site in Arabidopsis. The evolutionary footprints of predicted orthologous cis-elements were conserved, and preferably located in the proximal regions of 5'regulatory regions of evolutionarily diverged plant species. We also explored comprehensive tissue-specific global gene expression levels of PRFs under diverse hormonal and abiotic stress regimes. In response, the PRFs exhibited large transcriptional biases in a time- and organ-dependent manner. Further, the methodical elucidation of spatial expression analysis of predicted cis-elements binding transcription factors and relevant PRFs showed notable correlation. Results indicate that binding transcription factors' expression data is largely informative for envisaging their precise roles in the spatial regulation of target PRFs. These results highlight the importance of PRFs during plant development; and establish a relationship between their spatial expression patterns and presence of respective regulatory motifs in their promoter sequences. This information could be employed in future studies and field-utilization of cell wall structural genes.

Prediction of Gene Expression Patterns With Generalized Linear Regression Model

Cell reprogramming has played important roles in medical science, such as tissue repair, organ reconstruction, disease treatment, new drug development, and new species breeding. Oct4, a core pluripotency factor, has especially played a key role in somatic cell reprogramming through transcriptional control and affects the expression level of genes by its combination intensity. However, the quantitative relationship between Oct4 combination intensity and target gene expression is still not clear. Therefore, firstly, a generalized linear regression method was constructed to predict gene expression values in promoter regions affected by Oct4 combination intensity. Training data, including Oct4 combination intensity and target gene expression, were from promoter regions of genes with different cell development stages. Additionally, the quantitative relationship between gene expression and Oct4 combination intensity was analyzed with the proposed model. Then, the quantitative relationship between gene expression and Oct4 combination intensity at each stage of cell development was classified into high and low levels. Experimental analysis showed that the combination height of Oct4-inhibited gene expression decremented by a temporal exponential value, whereas the combination width of Oct4-promoted gene expression incremented by a temporal logarithmic value. Experimental results showed that the proposed method can achieve goodness of fit with high confidence.

The prognostic utility of the transcription factor SRF in docetaxel-resistant prostate cancer: in-vitro discovery and in-vivo validation

Background

Docetaxel based therapy is one of the first line chemotherapeutic agents for the treatment of metastatic castrate-resistant prostate cancer. However, one of the major obstacles in the treatment of these patients is docetaxel-resistance. Defining the mechanisms of resistance so as to inform subsequent treatment options and combinations represents a challenge for clinicians and scientists.

Previous work by our group has shown complex changes in pro and anti-apoptotic proteins in the development of resistance to docetaxel. Targeting these changes individually does not significantly impact on the resistant phenotype but understanding the central signalling pathways and transcription factors (TFs) which control these could represent a more appropriate therapeutic targeting approach.

Methods

Using a number of docetaxel-resistant sublines of PC-3 cells, we have undertaken a transcriptomic analysis by expression microarray using the Affymetrix Human Gene 1.0 ST Array and in conjunction with bioinformatic analyses undertook to predict dysregulated TFs in docetaxel resistant prostate cancer. The clinical significance of this prediction was ascertained by performing immunohistochemical (IHC) analysis of an identified TF (SRF) in the metastatic sites from men who died of advanced CRPC. Investigation of the functional role of SRF was examined by manipulating SRF using SiRNA in a docetaxel-resistant PC-3 cell line model.

Results

The transcription factors identified include serum response factor (SRF), nuclear factor kappa-B (NFκB), heat shock factor protein 1 (HSF1), testicular receptor 2 & 4 (TR2 &4), vitamin-D and retinoid x receptor (VDR-RXR) and oestrogen-receptor 1 (ESR1), which are predicted to be responsible for the differential gene expression observed in docetaxel-resistance. IHC analysis to quantify nuclear expression of the identified TF SRF correlates with both survival from date of bone metastasis (p = 0.003), survival from androgen independence (p = 0.00002), and overall survival from prostate cancer (p = 0.0044). Functional knockdown of SRF by siRNA demonstrated a reversal of apoptotic resistance to docetaxel treatment in the docetaxel-resistant PC-3 cell line model.

Conclusions

Our results suggest that SRF could aid in treatment stratification of prostate cancer, and may also represent a therapeutic target in the treatment of men afflicted with advanced prostate cancer.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-017-3100-4) contains supplementary material, which is available to authorized users.

jNMFMA: a joint non-negative matrix factorization meta-analysis of transcriptomics data

MOTIVATION

Tremendous amount of omics data being accumulated poses a pressing challenge of meta-analyzing the heterogeneous data for mining new biological knowledge. Most existing methods deal with each gene independently, thus often resulting in high false positive rates in detecting differentially expressed genes (DEG). To our knowledge, no or little effort has been devoted to methods that consider dependence structures underlying transcriptomics data for DEG identification in meta-analysis context.

RESULTS

This article proposes a new meta-analysis method for identification of DEGs based on joint non-negative matrix factorization (jNMFMA). We mathematically extend non-negative matrix factorization (NMF) to a joint version (jNMF), which is used to simultaneously decompose multiple transcriptomics data matrices into one common submatrix plus multiple individual submatrices. By the jNMF, the dependence structures underlying transcriptomics data can be interrogated and utilized, while the high-dimensional transcriptomics data are mapped into a low-dimensional space spanned by metagenes that represent hidden biological signals. jNMFMA finally identifies DEGs as genes that are associated with differentially expressed metagenes. The ability of extracting dependence structures makes jNMFMA more efficient and robust to identify DEGs in meta-analysis context. Furthermore, jNMFMA is also flexible to identify DEGs that are consistent among various types of omics data, e.g. gene expression and DNA methylation. Experimental results on both simulation data and real-world cancer data demonstrate the effectiveness of jNMFMA and its superior performance over other popular approaches.

AVAILABILITY AND IMPLEMENTATION

R code for jNMFMA is available for non-commercial use via http://micblab.iim.ac.cn/Download/.

CONTACT

hqwang@ustc.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The co-inertia approach in identification of specific microRNA in early and advanced atherosclerosis plaque

MicroRNAs (miRs) are short, non-coding RNAs that regulate gene expression by absolute or partial binding to mRNA, which results in transcript degradation and translation blocking. Atherosclerosis, as a complex and progressive disease, represents one of the main causes of cardiovascular clinical complications and even death. We applied co-inertia analysis (CIA) as a novel computation method, to determine which miRs are potentially associated with differences in gene expression levels originating from microarray data of early and advanced atherosclerotic plaque. As the CIA has not been applied in the field of atherosclerosis yet, we hypothesized that using CIA we can get novel information about the miRs that have significant role in early phase of disease or in severe phase of disease. The characteristic split in the data along the axes of performed CIA showed the difference in the gene expression pattern between early atherosclerosis and advanced atherosclerotic plaque. The advanced atherosclerotic plaques showed more homogenous gene expression pattern than early atherosclerosis samples. In early carotid lesions five out of five algorithms predicted miR-24, four out of five predicted miR-155, miR-145, and miR-100 as early active miRs. These miRs could be "protective" in plaque evolution context because they were not active in advanced plaques according to our results. They were reported previously as atheroprotective, which in a way represents confirmation of CIA application in atherosclerosis. We detected 13 new miRs which could be active in early plaque phenotype according to CIA prediction. In the advanced plaques we predicted miR-221, miR-222, miR-127 and miR-146 which were previously revealed to have atherogenic properties. In addition to miRs that have literature support, we also found new 8 miRs that, with described function so far, could present a novelty in research of atherosclerotic plaque evolution. All of these examples show that CIA results have a great potential to be of interest in future research in atherosclerotic plaque progression. We validated the applicability of CIA in the field of atherosclerosis, but we also found new interesting miR competitors that have strong potential to serve as markers and plaque development factors. These results should be experimentally confirmed in further research with ultimate goal to discover new mediators and blood markers, which could improve the prevention and therapy of this complex disease.

TGFβ and CCN2/CTGF mediate actin related gene expression by differential E2F1/CREB activation

BACKGROUND

CCN2/CTGF is an established effector of TGFβ driven responses in diabetic nephropathy. We have identified an interaction between CCN2 and TGFβ leading to altered phenotypic differentiation and inhibited cellular migration. Here we determine the gene expression profile associated with this phenotype and define a transcriptional basis for differential actin related gene expression and cytoskeletal function.

RESULTS

From a panel of genes regulated by TGFβ and CCN2, we used co-inertia analysis to identify and then experimentally verify a subset of transcription factors, E2F1 and CREB, that regulate an expression fingerprint implicated in altered actin dynamics and cell hypertrophy. Importantly, actin related genes containing E2F1 and CREB binding sites, stratified by expression profile within the dataset. Further analysis of actin and cytoskeletal related genes from patients with diabetic nephropathy suggests recapitulation of this programme during the development of renal disease. The Rho family member Cdc42 was also found uniquely to be activated in cells treated with TGFβ and CCN2; Cdc42 interacting genes were differentially regulated in diabetic nephropathy.

CONCLUSIONS

TGFβ and CCN2 attenuate CREB and augment E2F1 transcriptional activation with the likely effect of altering actin cytoskeletal and cell growth/hypertrophic gene activity with implications for cell dysfunction in diabetic kidney disease. The cytoskeletal regulator Cdc42 may play a role in this signalling response.

A gene expression profile indicative of early stage HER2 targeted therapy response

Background

Efficacious application of HER2-targetting agents requires the identification of novel predictive biomarkers. Lapatinib, afatinib and neratinib are tyrosine kinase inhibitors (TKIs) of HER2 and EGFR growth factor receptors. A panel of breast cancer cell lines was treated with these agents, trastuzumab, gefitinib and cytotoxic therapies and the expression pattern of a specific panel of genes using RT-PCR was investigated as a potential marker of early drug response to HER2-targeting therapies.

Results

Treatment of HER2 TKI-sensitive SKBR3 and BT474 cell lines with lapatinib, afatinib and neratinib induced an increase in the expression of RB1CC1, ERBB3, FOXO3a and NR3C1. The response directly correlated with the degree of sensitivity. This expression pattern switched from up-regulated to down-regulated in the HER2 expressing, HER2-TKI insensitive cell line MDAMB453. Expression of the CCND1 gene demonstrated an inversely proportional response to drug exposure. A similar expression pattern was observed following the treatment with both neratinib and afatinib. These patterns were retained following exposure to traztuzumab and lapatinib plus capecitabine. In contrast, gefitinib, dasatinib and epirubicin treatment resulted in a completely different expression pattern change.

Conclusions

In these HER2-expressing cell line models, lapatinib, neratinib, afatinib and trastuzumab treatment generated a characteristic and specific gene expression response, proportionate to the sensitivity of the cell lines to the HER2 inhibitor.

Characterisation of the induced changes in expression levels of these genes may therefore give a valuable, very early predictor of the likely extent and specificity of tumour HER2 inhibitor response in patients, potentially guiding more specific use of these agents.

Exploring the transcription factor activity in high-throughput gene expression data using RLQ analysis

Background

Interpretation of gene expression microarray data in the light of external information on both columns and rows (experimental variables and gene annotations) facilitates the extraction of pertinent information hidden in these complex data. Biologists classically interpret genes of interest after retrieving functional information from a subset of genes of interest. Transcription factors play an important role in orchestrating the regulation of gene expression. Their activity can be deduced by examining the presence of putative transcription factors binding sites in the gene promoter regions.

Results

In this paper we present the multivariate statistical method RLQ which aims to analyze microarray data where additional information is available on both genes and samples. As an illustrative example, we applied RLQ methodology to analyze transcription factor activity associated with the time-course effect of steroids on the growth of primary human lung fibroblasts. RLQ could successfully predict transcription factor activity, and could integrate various other sources of external information in the main frame of the analysis. The approach was validated by means of alternative statistical methods and biological validation.

Conclusions

RLQ provides an efficient way of extracting and visualizing structures present in a gene expression dataset by directly modeling the link between experimental variables and gene annotations.

Identification of transcription factors associated with castration-resistance: is the serum responsive factor a potential therapeutic target?

BACKGROUND

Advanced prostate cancer is treated by hormone ablation therapy. However, despite an initial response, the majority of men relapse to develop castration-resistant disease for which there are no effective treatments. We have previously shown that manipulating individual proteins has only minor alterations on the resistant phenotype so we hypothesize that targeting the central transcription factors (TFs) would represent a better therapeutic approach.

METHODS

We have undertaken a transcriptomic analysis of gene expression differences between the androgen-dependent LNCaP parental cells and its castration-resistant Abl and Hof sublines, revealing 1,660 genes associated with castration-resistance. Using effective bioinformatic techniques, these transcriptomic data were integrated with TF binding sites resulting in a list of TFs associated with the differential gene expression observed.

RESULTS

Following validation of the gene-chip results, the serum response factor (SRF) was chosen for clinical validation and functional analysis due to its recent association with prostate cancer progression. SRF immunoreactivity in prostate tumor samples was shown for the first time to be associated with castration-resistance. SRF inhibition by siRNA and the small molecule inhibitor CCG-1423 resulted in decreased proliferation.

CONCLUSION

SRF is a key TF by which resistant cells survive with depleted levels of androgens representing a target for therapeutic manipulation.

Gene expression analysis in prostate cancer: the importance of the endogenous control

BACKGROUND

Aberrant gene expression is a hallmark of cancer. Quantitative reverse-transcription PCR (qRT-PCR) is the gold-standard for quantifying gene expression, and commonly employs a house-keeping gene (HKG) as an endogenous control to normalize results; the choice of which is critical for accurate data interpretation. Many factors, including sample type, pathological state, and oxygen levels influence gene expression including putative HKGs. The aim of this study was to determine the suitability of commonly used HKGs for qRT-PCR in prostate cancer.

METHODS

Prostate cancer (LNCaP, 22Rv1, PC3, and DU145) and normal (PWR1E and RWPE1) cell lines were cultured in air and hypoxia. The performance of 16 HKGs was assessed using Normfinder and coefficient of variation. In silico promoter analysis was performed to identify putative hypoxia response elements (HREs). The impact of the endogenous control on expression levels of HIF1A and GSTP1 was investigated by qRT-PCR in cell lines and tissue specimens respectively.

RESULTS

Hypoxia altered expression of several HKGs: IPO8, B2M, and PGK1. The most stably expressed HKGs were ACTB, PPIA, and UBC. Both UBC and ACTB showed constitutive expression of HIF1A in air and hypoxia, while PGK1 falsely implied a sixfold hypoxia-induced down-regulation. In prostate tumors, UBC and PGK1 both revealed down-regulation of GSTP1 relative to matched benign, whereas ACTB showed variability.

CONCLUSIONS

This study demonstrates that no universal endogenous control exists for gene expression studies, even within one disease type. It highlights the importance of validating expression of intended HKGs between different sample types and environmental exposures.

Gene expression changes as markers of early lapatinib response in a panel of breast cancer cell lines

Background

Lapatinib, a tyrosine kinase inhibitor of HER2 and EGFR and is approved, in combination with capecitabine, for the treatment of trastuzumab-refractory metastatic breast cancer. In order to establish a possible gene expression response to lapatinib, a panel of breast cancer cell lines with varying sensitivity to lapatinib were analysed using a combination of microarray and qPCR profiling.

Methods

Co-inertia analysis (CIA), a data integration technique, was used to identify transcription factors associated with the lapatinib response on a previously published dataset of 96 microarrays. RNA was extracted from BT474, SKBR3, EFM192A, HCC1954, MDAMB453 and MDAMB231 breast cancer cell lines displaying a range of lapatinib sensitivities and HER2 expression treated with 1 μM of lapatinib for 12 hours and quantified using Taqman RT-PCR. A fold change ≥ ± 2 was considered significant.

Results

A list of 421 differentially-expressed genes and 8 transcription factors (TFs) whose potential regulatory impact was inferred in silico, were identified as associated with lapatinib response. From this group, a panel of 27 genes (including the 8 TFs) were selected for qPCR validation. 5 genes were determined to be significantly differentially expressed following the 12 hr treatment of 1 μM lapatinib across all six cell lines. Furthermore, the expression of 4 of these genes (RB1CC1, FOXO3A, NR3C1 and ERBB3) was directly correlated with the degree of sensitivity of the cell line to lapatinib and their expression was observed to “switch” from up-regulated to down-regulated when the cell lines were arranged in a lapatinib-sensitive to insensitive order. These included the novel lapatinib response-associated genes RB1CC1 and NR3C1. Additionally, Cyclin D1 (CCND1), a common regulator of the other four proteins, was also demonstrated to observe a proportional response to lapatinib exposure.

Conclusions

A panel of 5 genes were determined to be differentially expressed in response to lapatinib at the 12 hour time point examined. The expression of these 5 genes correlated directly with lapatinib sensitivity. We propose that the gene expression profile may represent both an early measure of the likelihood of sensitivity and the level of response to lapatinib and may therefore have application in early response detection.

pcaGoPromoter--an R package for biological and regulatory interpretation of principal components in genome-wide gene expression data

Analyzing data obtained from genome-wide gene expression experiments is challenging due to the quantity of variables, the need for multivariate analyses, and the demands of managing large amounts of data. Here we present the R package pcaGoPromoter, which facilitates the interpretation of genome-wide expression data and overcomes the aforementioned problems. In the first step, principal component analysis (PCA) is applied to survey any differences between experiments and possible groupings. The next step is the interpretation of the principal components with respect to both biological function and regulation by predicted transcription factor binding sites. The robustness of the results is evaluated using cross-validation, and illustrative plots of PCA scores and gene ontology terms are available. pcaGoPromoter works with any platform that uses gene symbols or Entrez IDs as probe identifiers. In addition, support for several popular Affymetrix GeneChip platforms is provided. To illustrate the features of the pcaGoPromoter package a serum stimulation experiment was performed and the genome-wide gene expression in the resulting samples was profiled using the Affymetrix Human Genome U133 Plus 2.0 chip. Array data were analyzed using pcaGoPromoter package tools, resulting in a clear separation of the experiments into three groups: controls, serum only and serum with inhibitor. Functional annotation of the axes in the PCA score plot showed the expected serum-promoted biological processes, e.g., cell cycle progression and the predicted involvement of expected transcription factors, including E2F. In addition, unexpected results, e.g., cholesterol synthesis in serum-depleted cells and NF-κB activation in inhibitor treated cells, were noted. In summary, the pcaGoPromoter R package provides a collection of tools for analyzing gene expression data. These tools give an overview of the input data via PCA, functional interpretation by gene ontology terms (biological processes), and an indication of the involvement of possible transcription factors.

In silico analysis of cis-acting regulatory elements in 5' regulatory regions of sucrose transporter gene families in rice (Oryza sativa Japonica) and Arabidopsis thaliana

The regulation of gene expression involves a multifarious regulatory system. Each gene contains a unique combination of cis-acting regulatory sequence elements in the 5' regulatory region that determines its temporal and spatial expression. Cis-acting regulatory elements are essential transcriptional gene regulatory units; they control many biological processes and stress responses. Thus a full understanding of the transcriptional gene regulation system will depend on successful functional analyses of cis-acting elements. Cis-acting regulatory elements present within the 5' regulatory region of the sucrose transporter gene families in rice (Oryza sativa Japonica cultivar-group) and Arabidopsis thaliana, were identified using a bioinformatics approach. The possible cis-acting regulatory elements were predicted by scanning 1.5kbp of 5' regulatory regions of the sucrose transporter genes translational start sites, using Plant CARE, PLACE and Genomatix Matinspector professional databases. Several cis-acting regulatory elements that are associated with plant development, plant hormonal regulation and stress response were identified, and were present in varying frequencies within the 1.5kbp of 5' regulatory region, among which are; A-box, RY, CAT, Pyrimidine-box, Sucrose-box, ABRE, ARF, ERE, GARE, Me-JA, ARE, DRE, GA-motif, GATA, GT-1, MYC, MYB, W-box, and I-box. This result reveals the probable cis-acting regulatory elements that possibly are involved in the expression and regulation of sucrose transporter gene families in rice and Arabidopsis thaliana during cellular development or environmental stress conditions.

Detecting microRNA activity from gene expression data

Background

MicroRNAs (miRNAs) are non-coding RNAs that regulate gene expression by binding to the messenger RNA (mRNA) of protein coding genes. They control gene expression by either inhibiting translation or inducing mRNA degradation. A number of computational techniques have been developed to identify the targets of miRNAs. In this study we used predicted miRNA-gene interactions to analyse mRNA gene expression microarray data to predict miRNAs associated with particular diseases or conditions.

Results

Here we combine correspondence analysis, between group analysis and co-inertia analysis (CIA) to determine which miRNAs are associated with differences in gene expression levels in microarray data sets. Using a database of miRNA target predictions from TargetScan, TargetScanS, PicTar4way PicTar5way, and miRanda and combining these data with gene expression levels from sets of microarrays, this method produces a ranked list of miRNAs associated with a specified split in samples. We applied this to three different microarray datasets, a papillary thyroid carcinoma dataset, an in-house dataset of lipopolysaccharide treated mouse macrophages, and a multi-tissue dataset. In each case we were able to identified miRNAs of biological importance.

Conclusions

We describe a technique to integrate gene expression data and miRNA target predictions from multiple sources.

Integrating gene expression and GO classification for PCA by preclustering

Background

Gene expression data can be analyzed by summarizing groups of individual gene expression profiles based on GO annotation information. The mean expression profile per group can then be used to identify interesting GO categories in relation to the experimental settings. However, the expression profiles present in GO classes are often heterogeneous, i.e., there are several different expression profiles within one class. As a result, important experimental findings can be obscured because the summarizing profile does not seem to be of interest. We propose to tackle this problem by finding homogeneous subclasses within GO categories: preclustering.

Results

Two microarray datasets are analyzed. First, a selection of genes from a well-known Saccharomyces cerevisiae dataset is used. The GO class "cell wall organization and biogenesis" is shown as a specific example. After preclustering, this term can be associated with different phases in the cell cycle, where it could not be associated with a specific phase previously. Second, a dataset of differentiation of human Mesenchymal Stem Cells (MSC) into osteoblasts is used. For this dataset results are shown in which the GO term "skeletal development" is a specific example of a heterogeneous GO class for which better associations can be made after preclustering. The Intra Cluster Correlation (ICC), a measure of cluster tightness, is applied to identify relevant clusters.

Conclusions

We show that this method leads to an improved interpretability of results in Principal Component Analysis.

Genome-wide identification of hypoxia-inducible factor binding sites and target genes by a probabilistic model integrating transcription-profiling data and in silico binding site prediction

The transcriptional response driven by Hypoxia-inducible factor (HIF) is central to the adaptation to oxygen restriction. Hence, the complete identification of HIF targets is essential for understanding the cellular responses to hypoxia. Herein we describe a computational strategy based on the combination of phylogenetic footprinting and transcription profiling meta-analysis for the identification of HIF-target genes. Comparison of the resulting candidates with published HIF1a genome-wide chromatin immunoprecipitation indicates a high sensitivity (78%) and specificity (97.8%). To validate our strategy, we performed HIF1a chromatin immunoprecipitation on a set of putative targets. Our results confirm the robustness of the computational strategy in predicting HIF-binding sites and reveal several novel HIF targets, including RE1-silencing transcription factor co-repressor (RCOR2). In addition, mapping of described polymorphisms to the predicted HIF-binding sites identified several single-nucleotide polymorphisms (SNPs) that could alter HIF binding. As a proof of principle, we demonstrate that SNP rs17004038, mapping to a functional hypoxia response element in the macrophage migration inhibitory factor (MIF) locus, prevents induction of this gene by hypoxia. Altogether, our results show that the proposed strategy is a powerful tool for the identification of HIF direct targets that expands our knowledge of the cellular adaptation to hypoxia and provides cues on the inter-individual variation in this response.

Identification of cell cycle-related regulatory motifs using a kernel canonical correlation analysis

BACKGROUND

Gene regulation is a key mechanism in higher eukaryotic cellular processes. One of the major challenges in gene regulation studies is to identify regulators affecting the expression of their target genes in specific biological processes. Despite their importance, regulators involved in diverse biological processes still remain largely unrevealed. In the present study, we propose a kernel-based approach to efficiently identify core regulatory elements involved in specific biological processes using gene expression profiles.

RESULTS

We developed a framework that can detect correlations between gene expression profiles and the upstream sequences on the basis of the kernel canonical correlation analysis (kernel CCA). Using a yeast cell cycle dataset, we demonstrated that upstream sequence patterns were closely related to gene expression profiles based on the canonical correlation scores obtained by measuring the correlation between them. Our results showed that the cell cycle-specific regulatory motifs could be found successfully based on the motif weights derived through kernel CCA. Furthermore, we identified co-regulatory motif pairs using the same framework.

CONCLUSION

Given expression profiles, our method was able to identify regulatory motifs involved in specific biological processes. The method could be applied to the elucidation of the unknown regulatory mechanisms associated with complex gene regulatory processes.

Transcriptome profiling of human pre-implantation development

Background

Preimplantation development is a crucial step in early human development. However, the molecular basis of human preimplantation development is not well known.

Methodology

By applying microarray on 397 human oocytes and embryos at six developmental stages, we studied the transcription dynamics during human preimplantation development.

Principal Findings

We found that the preimplantation development consisted of two main transitions: from metaphase-II oocyte to 4-cell embryo where mainly the maternal genes were expressed, and from 8-cell embryo to blastocyst with down-regulation of the maternal genes and up-regulation of embryonic genes. Human preimplantation development proved relatively autonomous. Genes predominantly expressed in oocytes and embryos are well conserved during evolution.

Significance

Our database and findings provide fundamental resources for understanding the genetic network controlling early human development.

Tissue-specific regulatory network extractor (TS-REX): a database and software resource for the tissue and cell type-specific investigation of transcription factor-gene networks

The prediction of transcription factor binding sites in genomic sequences is in principle very useful to identify upstream regulatory factors. However, when applying this concept to genomes of multicellular organisms such as mammals, one has to deal with a large number of false positive predictions since many transcription factor genes are only expressed in specific tissues or cell types. We developed TS-REX, a database/software system that supports the analysis of tissue and cell type-specific transcription factor-gene networks based on expressed sequence tag abundance of transcription factor-encoding genes in UniGene EST libraries. The use of expression levels of transcription factor-encoding genes according to hierarchical anatomical classifications covering different tissues and cell types makes it possible to filter out irrelevant binding site predictions and to identify candidates of potential functional importance for further experimental testing. TS-REX covers ESTs from H. sapiens and M. musculus, and allows the characterization of both presence and specificity of transcription factors in user-specified tissues or cell types. The software allows users to interactively visualize transcription factor-gene networks, as well as to export data for further processing. TS-REX was applied to predict regulators of Polycomb group genes in six human tumor tissues and in human embryonic stem cells.

Gene network signaling in hormone responsiveness modifies apoptosis and autophagy in breast cancer cells

Resistance to endocrine therapies, whether de novo or acquired, remains a major limitation in the ability to cure many tumors that express detectable levels of the estrogen receptor alpha protein (ER). While several resistance phenotypes have been described, endocrine unresponsiveness in the context of therapy-induced tumor growth appears to be the most prevalent. The signaling that regulates endocrine resistant phenotypes is poorly understood but it involves a complex signaling network with a topology that includes redundant and degenerative features. To be relevant to clinical outcomes, the most pertinent features of this network are those that ultimately affect the endocrine-regulated components of the cell fate and cell proliferation machineries. We show that autophagy, as supported by the endocrine regulation of monodansylcadaverine staining, increased LC3 cleavage, and reduced expression of p62/SQSTM1, plays an important role in breast cancer cells responding to endocrine therapy. We further show that the cell fate machinery includes both apoptotic and autophagic functions that are potentially regulated through integrated signaling that flows through key members of the BCL2 gene family and beclin-1 (BECN1). This signaling links cellular functions in mitochondria and endoplasmic reticulum, the latter as a consequence of induction of the unfolded protein response. We have taken a seed-gene approach to begin extracting critical nodes and edges that represent central signaling events in the endocrine regulation of apoptosis and autophagy. Three seed nodes were identified from global gene or protein expression analyses and supported by subsequent functional studies that established their abilities to affect cell fate. The seed nodes of nuclear factor kappa B (NFkappaB), interferon regulatory factor-1 (IRF1), and X-box binding protein-1 (XBP1)are linked by directional edges that support signal flow through a preliminary network that is grown to include key regulators of their individual function: NEMO/IKKgamma, nucleophosmin and ER respectively. Signaling proceeds through BCL2 gene family members and BECN1 ultimately to regulate cell fate.

Simultaneous analysis of distinct Omics data sets with integration of biological knowledge: Multiple Factor Analysis approach

Background

Genomic analysis will greatly benefit from considering in a global way various sources of molecular data with the related biological knowledge. It is thus of great importance to provide useful integrative approaches dedicated to ease the interpretation of microarray data.

Results

Here, we introduce a data-mining approach, Multiple Factor Analysis (MFA), to combine multiple data sets and to add formalized knowledge. MFA is used to jointly analyse the structure emerging from genomic and transcriptomic data sets. The common structures are underlined and graphical outputs are provided such that biological meaning becomes easily retrievable. Gene Ontology terms are used to build gene modules that are superimposed on the experimentally interpreted plots. Functional interpretations are then supported by a step-by-step sequence of graphical representations.

Conclusion

When applied to genomic and transcriptomic data and associated Gene Ontology annotations, our method prioritize the biological processes linked to the experimental settings. Furthermore, it reduces the time and effort to analyze large amounts of 'Omics' data.

Hypoxia selectively activates the CREB family of transcription factors in the in vivo lung

RATIONALE

Pulmonary hypertension is a common complication of chronic hypoxic lung diseases and is associated with increased morbidity and reduced survival. The pulmonary vascular changes in response to hypoxia, both structural and functional, are unique to this circulation.

OBJECTIVES

To identify transcription factor pathways uniquely activated in the lung in response to hypoxia.

METHODS

After exposure to environmental hypoxia (10% O(2)) for varying periods (3 h to 2 wk), lungs and systemic organs were isolated from groups of adult male mice. Bioinformatic examination of genes the expression of which changed in the hypoxic lung (assessed using microarray analysis) identified potential lung-selective transcription factors controlling these changes in gene expression. In separate further experiments, lung-selective activation of these candidate transcription factors was tested in hypoxic mice and by comparing hypoxic responses of primary human pulmonary and cardiac microvascular endothelial cells in vitro.

MEASUREMENTS AND MAIN RESULTS

Bioinformatic analysis identified cAMP response element binding (CREB) family members as candidate lung-selective hypoxia-responsive transcription factors. Further in vivo experiments demonstrated activation of CREB and activating transcription factor (ATF)1 and up-regulation of CREB family-responsive genes in the hypoxic lung, but not in other organs. Hypoxia-dependent CREB activation and CREB-responsive gene expression was observed in human primary lung, but not cardiac microvascular endothelial cells.

CONCLUSIONS

These findings suggest that activation of CREB and AFT1 plays a key role in the lung-specific responses to hypoxia, and that lung microvascular endothelial cells are important, proximal effector cells in the specific responses of the pulmonary circulation to hypoxia.

Computational methods to dissect cis-regulatory transcriptional networks

The formation of diverse cell types from an invariant set of genes is governed by biochemical and molecular processes that regulate gene activity. A complete understanding of the regulatory mechanisms of gene expression is the major function of genomics. Computational genomics is a rapidly emerging area for deciphering the regulation of metazoan genes as well as interpreting the results of high-throughput screening. The integration of computer science with biology has expedited molecular modelling and processing of large-scale data inputs such as microarrays, analysis of genomes, transcriptomes and proteomes. Many bioinformaticians have developed various algorithms for predicting transcriptional regulatory mechanisms from the sequence, gene expression and interaction data. This review contains compiled information of various computational methods adopted to dissect gene expression pathways.