ChIP-chip Data Research Articles

MultiChIPmixHMM: an R package for ChIP-chip data analysis modeling spatial dependencies and multiple replicates

BackgroundChromatin immunoprecipitation coupled with hybridization to a tiling array (ChIP-chip) is a cost-effective and routinely used method to identify protein-DNA interactions or chromatin/histone modifications. The robust identification of ChIP-enriched regions is frequently complicated by noisy measurements. This identification can be improved by accounting for dependencies between adjacent probes on chromosomes and by modeling of biological replicates.ResultsMultiChIPmixHMM is a user-friendly R package to analyse ChIP-chip data modeling spatial dependencies between directly adjacent probes on a chromosome and enabling a simultaneous analysis of replicates. It is based on a linear regression mixture model, designed to perform a joint modeling of immunoprecipitated and input measurements.ConclusionWe show the utility of MultiChIPmixHMM by analyzing histone modifications of Arabidopsis thaliana. MultiChIPmixHMM is implemented in R and including functions in C, freely available from the CRAN web site: http://cran.r-project.org.

HARACNe: improving the accuracy of regulatory model reverse engineering via higher-order data processing inequality tests

A key goal of systems biology is to elucidate molecular mechanisms associated with physiologic and pathologic phenotypes based on the systematic and genome-wide understanding of cell context-specific molecular interaction models. To this end, reverse engineering approaches have been used to systematically dissect regulatory interactions in a specific tissue, based on the availability of large molecular profile datasets, thus improving our mechanistic understanding of complex diseases, such as cancer. In this paper, we introduce high-order Algorithm for the Reconstruction of Accurate Cellular Network (hARACNe), an extension of the ARACNe algorithm for the dissection of transcriptional regulatory networks. ARACNe uses the data processing inequality (DPI), from information theory, to detect and prune indirect interactions that are unlikely to be mediated by an actual physical interaction. Whereas ARACNe considers only first-order indirect interactions, i.e. those mediated by only one extra regulator, hARACNe considers a generalized form of indirect interactions via two, three or more other regulators. We show that use of higher-order DPI resulted in significantly improved performance, based on transcription factor (TF)-specific ChIP-chip data, as well as on gene expression profile following RNAi-mediated TF silencing.

Non-canonical protein-DNA interactions identified by ChIP are not artifacts

BackgroundChIP-chip and ChIP-seq are widely used methods to map protein-DNA interactions on a genomic scale in vivo. Waldminghaus and Skarstad recently reported, in this journal, a modified method for ChIP-chip. Based on a comparison of our previously-published ChIP-chip data for Escherichia coli σ32 with their own data, Waldminghaus and Skarstad concluded that many of the σ32 targets identified in our earlier work are false positives. In particular, we identified many non-canonical σ32 targets that are located inside genes or are associated with genes that show no detectable regulation by σ32. Waldminghaus and Skarstad propose that such non-canonical sites are artifacts, identified due to flaws in the standard ChIP methodology. Waldminghaus and Skarstad suggest specific changes to the standard ChIP procedure that reportedly eliminate the claimed artifacts.ResultsWe reanalyzed our published ChIP-chip datasets for σ32 and the datasets generated by Waldminghaus and Skarstad to assess data quality and reproducibility. We also performed targeted ChIP/qPCR for σ32 and an unrelated transcription factor, AraC, using the standard ChIP method and the modified ChIP method proposed by Waldminghaus and Skarstad. Furthermore, we determined the association of core RNA polymerase with disputed σ32 promoters, with and without overexpression of σ32. We show that (i) our published σ32 ChIP-chip datasets have a consistently higher dynamic range than those of Waldminghaus and Skarstad, (ii) our published σ32 ChIP-chip datasets are highly reproducible, whereas those of Waldminghaus and Skarstad are not, (iii) non-canonical σ32 target regions are enriched in a σ32 ChIP in a heat shock-dependent manner, regardless of the ChIP method used, (iv) association of core RNA polymerase with some disputed σ32 target genes is induced by overexpression of σ32, (v) σ32 targets disputed by Waldminghaus and Skarstad are predominantly those that are most weakly bound, and (vi) the modifications to the ChIP method proposed by Waldminghaus and Skarstad reduce enrichment of all protein-bound genomic regions.ConclusionsThe modifications to the ChIP-chip method suggested by Waldminghaus and Skarstad reduce rather than increase the quality of ChIP data. Hence, the non-canonical σ32 targets identified in our previous study are likely to be genuine. We propose that the failure of Waldminghaus and Skarstad to identify many of these σ32 targets is due predominantly to the lower data quality in their study. We conclude that surprising ChIP-chip results are not artifacts to be ignored, but rather indications that our understanding of DNA-binding proteins is incomplete.

Computational Reconstruction of Transcriptional Relationships from ChIP-Chip Data

Eukaryotic gene transcription is a complex process, which requires the orchestrated recruitment of a large number of proteins, such as sequence-specific DNA binding factors, chromatin remodelers and modifiers, and general transcription machinery, to regulatory regions. Previous works have shown that these regulatory proteins favor specific organizational theme along promoters. Details about how they cooperatively regulate transcriptional process, however, remain unclear. We developed a method to reconstruct a Bayesian network (BN) model representing functional relationships among various transcriptional components. The positive/negative influence between these components was measured from protein binding and nucleosome occupancy data and embedded into the model. Application on S.cerevisiae ChIP-Chip data showed that the proposed method can recover confirmed relationships, such as Isw1-Pol II, TFIIH-Pol II, TFIIB-TBP, Pol II-H3K36Me3, H3K4Me3-H3K14Ac, etc. Moreover, it can distinguish colocating components from functionally related ones. Novel relationships, e.g., ones between Mediator and chromatin remodeling complexes (CRCs), and the combinatorial regulation of Pol II recruitment and activity by CRCs and general transcription factors (GTFs), were also suggested. protein binding events during transcription positively influence each other. Among contributing components, GTFs and CRCs play pivotal roles in transcriptional regulation. These findings provide insights into the regulatory mechanism.

DISPOM: A DISCRIMINATIVE DE-NOVO MOTIF DISCOVERY TOOL BASED ON THE JSTACS LIBRARY

DNA-binding proteins are a main component of gene regulation as they activate or repress gene expression by binding to specific binding sites in target regions of genomic DNA. However, de-novo discovery of these binding sites in target regions obtained by wet-lab experiments is a challenging problem in computational biology, which has not yet been solved satisfactorily. Here, we present a detailed description and analysis of the de-novo motif discovery tool Dispom, which has been developed for finding binding sites of DNA-binding proteins that are differentially abundant in a set of target regions compared to a set of control regions. Two additional features of Dispom are its capability of modeling positional preferences of binding sites and adjusting the length of the motif in the learning process. Dispom yields an increased prediction accuracy compared to existing tools for de-novo motif discovery, suggesting that the combination of searching for differentially abundant motifs, inferring their positional distributions, and adjusting the motif lengths is beneficial for de-novo motif discovery. When applying Dispom to promoters of auxin-responsive genes and those of ABI3 target genes from Arabidopsis thaliana, we identify relevant binding motifs with pronounced positional distributions. These results suggest that learning motifs, their positional distributions, and their lengths by a discriminative learning principle may aid motif discovery from ChIP-chip and gene expression data. We make Dispom freely available as part of Jstacs, an open-source Java library that is tailored to statistical sequence analysis. To facilitate extensions of Dispom, we describe its implementation using Jstacs in this manuscript. In addition, we provide a stand-alone application of Dispom at http://www.jstacs.de/index.php/Dispom for instant use.

Inferring functional transcription factor-gene binding pairs by integrating transcription factor binding data with transcription factor knockout data

BackgroundChromatin immunoprecipitation (ChIP) experiments are now the most comprehensive experimental approaches for mapping the binding of transcription factors (TFs) to their target genes. However, ChIP data alone is insufficient for identifying functional binding target genes of TFs for two reasons. First, there is an inherent high false positive/negative rate in ChIP-chip or ChIP-seq experiments. Second, binding signals in the ChIP data do not necessarily imply functionality.MethodsIt is known that ChIP-chip data and TF knockout (TFKO) data reveal complementary information on gene regulation. While ChIP-chip data can provide TF-gene binding pairs, TFKO data can provide TF-gene regulation pairs. Therefore, we propose a novel network approach for identifying functional TF-gene binding pairs by integrating the ChIP-chip data with the TFKO data. In our method, a TF-gene binding pair from the ChIP-chip data is regarded to be functional if it also has high confident curated TFKO TF-gene regulatory relation or deduced hypostatic TF-gene regulatory relation.Results and conclusionsWe first validated our method on a gathered ground truth set. Then we applied our method to the ChIP-chip data to identify functional TF-gene binding pairs. The biological significance of our identified functional TF-gene binding pairs was shown by assessing their functional enrichment, the prevalence of protein-protein interaction, and expression coherence. Our results outperformed the results of three existing methods across all measures. And our identified functional targets of TFs also showed statistical significance over the randomly assigned TF-gene pairs. We also showed that our method is dataset independent and can apply to ChIP-seq data and the E. coli genome. Finally, we provided an example showing the biological applicability of our notion.

Multilayered chromatin analysis reveals E2f, Smad and Zfx as transcriptional regulators of histones

Histones, the building blocks of eukaryotic chromatin, are essential for genome packaging, function and regulation. However, little is known about their transcriptional regulation. Here we conducted a comprehensive computational analysis, based on chromatin immunoprecipitation-sequencing and -microarray analysis (ChIP-seq and ChIP-chip) data of over 50 transcription factors and histone modifications in mouse embryonic stem cells. Enrichment scores supported by gene expression data from gene knockout studies identified E2f1 and E2f4 as master regulators of histone genes, CTCF and Zfx as repressors of core and linker histones, respectively, and Smad1, Smad2, YY1 and Ep300 as restricted or cell type-specific regulators. We propose that histone gene regulation is substantially more complex than previously thought, and that a combination of factors orchestrate histone gene regulation, from strict synchronization with S phase to targeted regulation of specific histone subtypes.

AnnotateGenomicRegions: a web application

AnnotateGenomicRegions: a web application

Improved predictions of transcription factor binding sites using physicochemical features of DNA.

Typical approaches for predicting transcription factor binding sites (TFBSs) involve use of a position-specific weight matrix (PWM) to statistically characterize the sequences of the known sites. Recently, an alternative physicochemical approach, called SiteSleuth, was proposed. In this approach, a linear support vector machine (SVM) classifier is trained to distinguish TFBSs from background sequences based on local chemical and structural features of DNA. SiteSleuth appears to generally perform better than PWM-based methods. Here, we improve the SiteSleuth approach by considering both new physicochemical features and algorithmic modifications. New features are derived from Gibbs energies of amino acid–DNA interactions and hydroxyl radical cleavage profiles of DNA. Algorithmic modifications consist of inclusion of a feature selection step, use of a nonlinear kernel in the SVM classifier, and use of a consensus-based post-processing step for predictions. We also considered SVM classification based on letter features alone to distinguish performance gains from use of SVM-based models versus use of physicochemical features. The accuracy of each of the variant methods considered was assessed by cross validation using data available in the RegulonDB database for 54 Escherichia coli TFs, as well as by experimental validation using published ChIP-chip data available for Fis and Lrp.

Histone H3 lysine 4 monomethylation (H3K4me1) and H3 lysine 9 monomethylation (H3K9me1): Distribution and their association in regulating gene expression under hyperglycaemic/hyperinsulinemic conditions in 3T3 cells

Hyperglycemia/hyperinsulinemia are leading cause for the induction type 2 diabetes and the role of post-translational histone modifications in dysregulating the expression of genes has emerged as potential important contributor in the progression of disease. The paradoxical nature of histone H3-Lysine 4 and Lysine 9 mono-methylation (H3K4me1 and H3K9me1) in both gene activation and repression motivated us to elucidate the functional relationship of these histone modifications in regulating expression of genes under hyperglycaemic/hyperinsulinemic condition. Chromatin immunoprecipitation-microarray analysis (ChIP-chip) was performed with H3 acetylation, H3K4me1 and H3K9me1 antibody. CLUSTER analysis of ChIP-chip (Chromatin immunoprecipitation-microarray analysis) data showed that mRNA expression and H3 acetylation/H3K4me1 levels on genes were inversely correlated with H3K9me1 levels on the transcribed regions, after 30 min of insulin stimulation under hyperglycaemic condition. Interestingly, we provide first evidence regarding regulation of histone de/acetylases and de/methylases; Myst4, Jmjd2b, Aof1 and Set by H3Ac, H3K4me1 and H3K9me1 under hyperinsulinemic/hyperglycaemic condition. ChIP-qPCR analysis shows association of increased H3Ac/H3K4me1 and decreased levels of H3K9me1 in up regulation of Myst4, Jmjd2, Set and Aof1 genes. We further analyse promoter occupancy of histone modifications by ChIP walking and observed increased occupancy of H3Ac/H3K4me1 on promoter region (-1000 to -1) of active genes and H3K9me1 on inactive genes under hyperglycemic/hyperinsulinemic condition. To best of our knowledge this is the first report that shows regulation of chromatin remodelling genes by alteration in the occupancy of histone H3Ac/H3K4/K9me on both promoter and transcribed regions.

The Linker Histone Plays a Dual Role during Gametogenesis in Saccharomyces cerevisiae

The differentiation of gametes involves dramatic changes to chromatin, affecting transcription, meiosis, and cell morphology. Sporulation in Saccharomyces cerevisiae shares many chromatin features with spermatogenesis, including a 10-fold compaction of the nucleus. To identify new proteins involved in spore nuclear organization, we purified chromatin from mature spores and discovered a significant enrichment of the linker histone (Hho1). The function of Hho1 has proven to be elusive during vegetative growth, but here we demonstrate its requirement for efficient sporulation and full compaction of the spore genome. Hho1 chromatin immunoprecipitation followed by sequencing (ChIP-seq) revealed increased genome-wide binding in mature spores and provides novel in vivo evidence of the linker histone binding to nucleosomal linker DNA. We also link Hho1 function to the transcription factor Ume6, the master repressor of early meiotic genes. Hho1 and Ume6 are depleted during meiosis, and analysis of published ChIP-chip data obtained during vegetative growth reveals a high binding correlation of both proteins at promoters of early meiotic genes. Moreover, Ume6 promotes binding of Hho1 to meiotic gene promoters. Thus, Hho1 may play a dual role during sporulation: Hho1 and Ume6 depletion facilitates the onset of meiosis via activation of Ume6-repressed early meiotic genes, whereas Hho1 enrichment in mature spores contributes to spore genome compaction.

Journal of Cellular Biochemistry: Volume 113, Number 8, August, 2012

Journal of Cellular BiochemistryVolume 113, Issue 8 p. C1-C1 CoverFree Access Journal of Cellular Biochemistry: Volume 113, Number 8, August, 2012 First published: 18 June 2012 https://doi.org/10.1002/jcb.24282AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract Cover: Validation of the H3ac ChIP-chip data by ChIP-qPCR and identification of sites that are consistently enriched in H3ac when CFTR localizes in the nuclear interior. Sixty-three qPCR primer pairs (numbered 1-63) were used. Regions A-E are indicated. See article by Muck et al., page 2607–2621 this issue. Volume113, Issue8August 2012Pages C1-C1 RelatedInformation

Research Resource: A Genome-Wide Study Identifies Potential New Target Genes for POU1F1

The pituitary transcription factor POU1F1 is required for the differentiation of lactotrope, thyrotrope, and somatotrope cells. Its expression is maintained in the adult and is crucial for the expression of prolactin, GH, and TSHβ-subunit. Different studies indicated that POU1F1 could also have other functions in these cells. The identification of new targets of this factor could be useful to obtain a better understanding of these functions. To address this question we combined data obtained from expression microarrays and from chromatin immunoprecipitation (ChIP)-chips. Gene expression microarray assays were used to detect genes that have their expression modified in somatolactotrope GH4C1 cells by the expression of a dominant-negative form of POU1F1, POU1F1(R271W), and led to the identification of 1346 such genes. ChIP-chip experiments were performed from mouse pituitaries and identified 1671 POU1F1-binding sites in gene-promoter regions. Intersecting the gene expression and the ChIP-chip data yielded 121 potential new direct targets. The initial set of 1346 genes identified using the microarrays, as well as the 121 potential new direct targets, were analyzed with DAVID bioinformatics resource for gene ontology term enrichment and cluster. This analysis revealed enrichment in different terms related to protein synthesis and transport, to apoptosis, and to cell division. The present study represents an integrative genome-wide approach to identify new target genes of POU1F1 and downstream networks controlled by this factor.

De novo motif discovery facilitates identification of interactions between transcription factors in Saccharomyces cerevisiae

Gene regulation involves complicated mechanisms such as cooperativity between a set of transcription factors (TFs). Previous studies have used target genes shared by two TFs as a clue to infer TF-TF interactions. However, this task remains challenging because the target genes with low binding affinity are frequently omitted by experimental data, especially when a single strict threshold is employed. This article aims at improving the accuracy of inferring TF-TF interactions by incorporating motif discovery as a fundamental step when detecting overlapping targets of TFs based on ChIP-chip data. The proposed method, simTFBS, outperforms three naïve methods that adopt fixed thresholds when inferring TF-TF interactions based on ChIP-chip data. In addition, simTFBS is compared with two advanced methods and demonstrates its advantages in predicting TF-TF interactions. By comparing simTFBS with predictions based on the set of available annotated yeast TF binding motifs, we demonstrate that the good performance of simTFBS is indeed coming from the additional motifs found by the proposed procedures. Supplementary data are available at Bioinformatics online.

Identifying biologically interpretable transcription factor knockout targets by jointly analyzing the transcription factor knockout microarray and the ChIP-chip data

BackgroundTranscription factor knockout microarrays (TFKMs) provide useful information about gene regulation. By using statistical methods for detecting differentially expressed genes between the gene expression microarray data of the mutant and wild type strains, the TF knockout targets of the knocked-out TF can be identified. However, the identified TF knockout targets may contain a certain amount of false positives due to the experimental noises inherent in the high-throughput microarray technology. Even if the identified TF knockout targets are true, the molecular mechanisms of how a TF regulates its TF knockout targets remain unknown by this kind of statistical approaches.ResultsTo solve these two problems, we developed a method to filter out the false positives in the original TF knockout targets (identified by statistical approaches) so that the biologically interpretable TF knockout targets can be extracted. Our method can further generate experimentally testable hypotheses of the molecular mechanisms of how a TF regulates its biologically interpretable TF knockout targets. The details of our method are as follows. First, a TF binding network was constructed using the ChIP-chip data deposited in the YEASTRACT database. Then for each original TF knockout target, it is said to be biologically interpretable if a path (in the TF binding network) from the knocked-out TF to this target could be identified by our path search algorithm. The identified path explains how the TF may regulate this target either directly by binding to its promoter or indirectly through intermediate TFs. After checking all the original TF knockout targets, the biologically interpretable ones could be extracted and the false positives could be filtered out. We validated the biological significance of our refined (i.e., biologically interpretable) TF knockout targets by assessing their functional enrichment, expression coherence, and the prevalence of protein-protein interactions. Our refined TF knockout targets outperform the original TF knockout targets across all measures.ConclusionsBy jointly analyzing the TFKM and ChIP-chip data, our method can extract the biologically interpretable TF knockout targets by identifying paths (in the TF binding network) from the knocked-out TF to these targets. The identified paths form experimentally testable hypotheses regarding the molecular mechanisms of how a TF may regulate its knockout targets. About seven hundred hypotheses generated by our methods have been experimentally validated in the literature. Our work demonstrates that integrating different data sources is a powerful approach to study complex biological systems.

Normalization of High Dimensional Genomics Data Where the Distribution of the Altered Variables Is Skewed

Genome-wide analysis of gene expression or protein binding patterns using different array or sequencing based technologies is now routinely performed to compare different populations, such as treatment and reference groups. It is often necessary to normalize the data obtained to remove technical variation introduced in the course of conducting experimental work, but standard normalization techniques are not capable of eliminating technical bias in cases where the distribution of the truly altered variables is skewed, i.e. when a large fraction of the variables are either positively or negatively affected by the treatment. However, several experiments are likely to generate such skewed distributions, including ChIP-chip experiments for the study of chromatin, gene expression experiments for the study of apoptosis, and SNP-studies of copy number variation in normal and tumour tissues. A preliminary study using spike-in array data established that the capacity of an experiment to identify altered variables and generate unbiased estimates of the fold change decreases as the fraction of altered variables and the skewness increases. We propose the following work-flow for analyzing high-dimensional experiments with regions of altered variables: (1) Pre-process raw data using one of the standard normalization techniques. (2) Investigate if the distribution of the altered variables is skewed. (3) If the distribution is not believed to be skewed, no additional normalization is needed. Otherwise, re-normalize the data using a novel HMM-assisted normalization procedure. (4) Perform downstream analysis. Here, ChIP-chip data and simulated data were used to evaluate the performance of the work-flow. It was found that skewed distributions can be detected by using the novel DSE-test (Detection of Skewed Experiments). Furthermore, applying the HMM-assisted normalization to experiments where the distribution of the truly altered variables is skewed results in considerably higher sensitivity and lower bias than can be attained using standard and invariant normalization methods.

YeTFaSCo: a database of evaluated yeast transcription factor sequence specificities

The yeast Saccharomyces cerevisiae is a prevalent system for the analysis of transcriptional networks. As a result, multiple DNA-binding sequence specificities (motifs) have been derived for most yeast transcription factors (TFs). However, motifs from different studies are often inconsistent with each other, making subsequent analyses complicated and confusing. Here, we have created YeTFaSCo (The Yeast Transcription Factor Specificity Compendium, http://yetfasco.ccbr.utoronto.ca/), an extensive collection of S. cerevisiae TF specificities. YeTFaSCo differs from related databases by being more comprehensive (including 1709 motifs for 256 proteins or protein complexes), and by evaluating the motifs using multiple objective quality metrics. The metrics include correlation between motif matches and ChIP-chip data, gene expression patterns, and GO terms, as well as motif agreement between different studies. YeTFaSCo also features an index of ‘expert-curated’ motifs, each associated with a confidence assessment. In addition, the database website features tools for motif analysis, including a sequence scanning function and precomputed genome-browser tracks of motif occurrences across the entire yeast genome. Users can also search the database for motifs that are similar to a query motif.

Deciphering the transcriptional regulatory logic of amino acid metabolism

Although metabolic networks have been reconstructed on a genome scale, the corresponding reconstruction and integration of governing transcriptional regulatory networks has not been fully achieved. Here we reconstruct such an integrated network for amino acid metabolism in Escherichia coli. Analysis of ChIP-chip and gene expression data for the transcription factors ArgR, Lrp and TrpR showed that 19 out of 20 amino acid biosynthetic pathways are either directly or indirectly controlled by these regulators. Classifying the regulated genes into three functional categories of transport, biosynthesis and metabolism leads to the elucidation of regulatory motifs that constitute the integrated network's basic building blocks. The regulatory logic of these motifs was determined on the basis of relationships between transcription factor binding and changes in the amount of transcript in response to exogenous amino acids. Remarkably, the resulting logic shows how amino acids are differentiated as signaling and nutrient molecules, revealing the overarching regulatory principles of the amino acid stimulon.

Global and Threshold-Free Transcriptional Regulatory Networks Reconstruction Through Integrating ChIP-Chip and Expression Data

Inferring transcriptional regulatory networks from high-throughput biological data is a major challenge to bioinformatics today. To address this challenge, we developed TReNGO (Transcriptional Regulatory Networks reconstruction based on Global Optimization), a global and threshold-free algorithm with simulated annealing for inferring regulatory networks by the integration of ChIP-chip and expression data. Superior to existing methods, TReNGO was expected to find the optimal structure of transcriptional regulatory networks without any arbitrary thresholds or predetermined number of transcriptional modules (TMs). TReNGO was applied to both synthetic data and real yeast data in the rapamycin response. In these applications, we demonstrated an improved functional coherence of TMs and TF (transcription factor)- target predictions by TReNGO when compared to GRAM, COGRIM or to analyzing ChIP-chip data alone. We also demonstrated the ability of TReNGO to discover unexpected biological processes that TFs may be involved in and to also identify interesting novel combinations of TFs.

Attenuation of the beta-catenin/TCF4 complex in colorectal cancer cells induces several growth-suppressive microRNAs that target cancer promoting genes

Aberrant activation of the Wnt signaling pathway is causally involved in the formation of most colorectal cancers (CRCs). Although detailed knowledge exists regarding Wnt-regulated protein-coding genes, much less is known about the possible involvement of non-coding RNAs. Here we used TaqMan Array MicroRNA Cards, capable of detecting 664 unique human microRNAs (miRNAs), to describe changes of the miRNA transcriptome following disruption of beta-catenin/TCF4 activity in DLD1 CRC cells. Most miRNAs appeared to respond independent of host gene regulation and proximal TCF4 chromatin occupancy as inferred from expression microarray and ChIP-chip data. A module of miRNAs induced by abrogated Wnt signaling in vitro was downregulated in two independent series of human primary CRCs (n=76) relative to normal adjacent mucosa (n=34). Several of these miRNAs (miR-145, miR-126, miR-30e-3p and miR-139-5p) markedly inhibited CRC cell growth in vitro when ectopically expressed. By using an integrative approach of proteomics and expression microarrays, we found numerous mRNAs and proteins to be affected by ectopic miR-30e-3p levels. This included HELZ and PIK3C2A that were directly repressed by several miRNA binding sites as confirmed by luciferase reporter assays in combination with mutational analyses. Finally, small interfering RNA-mediated downregulation of PIK3C2A, but not HELZ, was sufficient on its own to restrict CRC cell growth. Collectively, our study demonstrates that multiple miRNAs are upregulated as a consequence of forced attenuation of Wnt signaling in CRC cells, and some of these miRNAs inhibit cell growth with concomitant suppression of several growth-stimulatory cancer-related genes.