Integration of genome and chromatin structure with gene expression profiles to predict c-MYC recognition site binding and function

MicroRNA-27a-5p regulation by promoter methylation and MYC signaling in prostate carcinogenesis

Upregulation of MYC and miRNAs deregulation are common in prostate cancer (PCa). Overactive MYC may cause miRNAs’ expression deregulation through transcriptional and post-transcriptional mechanisms and epigenetic alterations are also involved in miRNAs dysregulation. Herein, we aimed to elucidate the role of regulatory network between MYC and miRNAs in prostate carcinogenesis. MYC expression was found upregulated in PCa cases and matched precursor lesions. MicroRNA’s microarray analysis of PCa samples with opposed MYC levels identified miRNAs significantly overexpressed in high-MYC PCa. However, validation of miR-27a-5p in primary prostate tissues disclosed downregulation in PCa, instead, correlating with aberrant promoter methylation. In a series of castration-resistant PCa (CRPC) cases, miR-27a-5p was upregulated, along with promoter hypomethylation. MYC and miR-27a-5p expression levels in LNCaP and PC3 cells mirrored those observed in hormone-naíve PCa and CRPC, respectively. ChIP analysis showed that miR-27a-5p expression is only regulated by c-Myc in the absence of aberrant promoter methylation. MiR-27a-5p knockdown in PC3 cells promoted cell growth, whereas miRNA forced expression in LNCaP and stable MYC-knockdown PC3 cells attenuated the malignant phenotype, suggesting a tumor suppressive role for miR-27a-5p. Furthermore, miR-27a-5p upregulation decreased EGFR/Akt1/mTOR signaling. We concluded that miR-27a-5p is positively regulated by MYC, and its silencing due to aberrant promoter methylation occurs early in prostate carcinogenesis, concomitantly with loss of MYC regulatory activity. Our results further suggest that along PCa progression, miR-27a-5p promoter becomes hypomethylated, allowing for MYC to resume its regulatory activity. However, the altered cellular context averts miR-27a-5p from successfully accomplishing its tumor suppressive function at this stage of disease.

A DNA shape-based regulatory score improves position-weight matrix-based recognition of transcription factor binding sites

MOTIVATION

The position-weight matrix (PWM) is a useful representation of a transcription factor binding site (TFBS) sequence pattern because the PWM can be estimated from a small number of representative TFBS sequences. However, because the PWM probability model assumes independence between individual nucleotide positions, the PWMs for some TFs poorly discriminate binding sites from non-binding-sites that have similar sequence content. Since the local three-dimensional DNA structure ('shape') is a determinant of TF binding specificity and since DNA shape has a significant sequence-dependence, we combined DNA shape-derived features into a TF-generalized regulatory score and tested whether the score could improve PWM-based discrimination of TFBS from non-binding-sites.

RESULTS

We compared a traditional PWM model to a model that combines the PWM with a DNA shape feature-based regulatory potential score, for accuracy in detecting binding sites for 75 vertebrate transcription factors. The PWM+shape model was more accurate than the PWM-only model, for 45% of TFs tested, with no significant loss of accuracy for the remaining TFs.

AVAILABILITY AND IMPLEMENTATION

The shape-based model is available as an open-source R package at that is archived on the GitHub software repository at https://github.com/ramseylab/regshape/.

CONTACT

stephen.ramsey@oregonstate.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Applications of alignment-free methods in epigenomics

Epigenetic mechanisms play an important role in the regulation of cell type-specific gene activities, yet how epigenetic patterns are established and maintained remains poorly understood. Recent studies have supported a role of DNA sequences in recruitment of epigenetic regulators. Alignment-free methods have been applied to identify distinct sequence features that are associated with epigenetic patterns and to predict epigenomic profiles. Here, we review recent advances in such applications, including the methods to map DNA sequence to feature space, sequence comparison and prediction models. Computational studies using these methods have provided important insights into the epigenetic regulatory mechanisms.

Exercise tames the wild side of the Myc network: a hypothesis

We propose that the well-documented therapeutic actions of repeated physical activities over human lifespan are mediated by the rapidly turning over proto-oncogenic Myc (myelocytomatosis) network of transcription factors. This transcription factor network is unique in utilizing promoter and epigenomic (acetylation/deacetylation, methylation/demethylation) mechanisms for controlling genes that include those encoding intermediary metabolism (the primary source of acetyl groups), mitochondrial functions and biogenesis, and coupling their expression with regulation of cell growth and proliferation. We further propose that remote functioning of the network occurs because there are two arms of this network, which consists of driver cells (e.g., working myocytes) that metabolize carbohydrates, fats, proteins, and oxygen and produce redox-modulating metabolites such as H₂O₂, NAD⁺, and lactate. The exercise-induced products represent autocrine, paracrine, or endocrine signals for target recipient cells (e.g., aortic endothelium, hepatocytes, and pancreatic β-cells) in which the metabolic signals are coupled with genomic networks and interorgan signaling is activated. And finally, we propose that lactate, the major metabolite released from working muscles and transported into recipient cells, links the two arms of the signaling pathway. Recently discovered contributions of the Myc network in stem cell development and maintenance further suggest that regular physical activity may prevent age-related diseases such as cardiovascular pathologies, cancers, diabetes, and neurological functions through prevention of stem cell dysfunctions and depletion with aging. Hence, regular physical activities may attenuate the various deleterious effects of the Myc network on health, the wild side of the Myc-network, through modulating transcription of genes associated with glucose and energy metabolism and maintain a healthy human status.

Regulation of gene transcription by the oncoprotein MYC

The proteins of the MYC/MAX/MAD network are central regulators of many key processes associated with basic cell physiology. These include the regulation of protein biosynthesis, energy metabolism, proliferation, and apoptosis. Molecularly the MYC/MAX/MAD network achieves these broad activities by controlling the expression of many target genes, which are primarily responsible for the diverse physiological consequences elicited by the network. The MYC proteins of the network possess oncogenic activity and their functional deregulation is associated with the majority of human tumors. Over the last years we have witnessed the accumulation of a considerable number of molecular observations that suggest many different biochemical means and tools by which MYC controls gene expression. We will summarize the more recent findings and discuss how these different building blocks might come together to explain how MYC regulates gene transcription. We note that despite the many molecular details known, we do not have an integrated view of how MYC uses the different tools, neither in a spatial nor in a temporal order.

Occupancy classification of position weight matrix-inferred transcription factor binding sites

BACKGROUND

Computational prediction of Transcription Factor Binding Sites (TFBS) from sequence data alone is difficult and error-prone. Machine learning techniques utilizing additional environmental information about a predicted binding site (such as distances from the site to particular chromatin features) to determine its occupancy/functionality class show promise as methods to achieve more accurate prediction of true TFBS in silico. We evaluate the Bayesian Network (BN) and Support Vector Machine (SVM) machine learning techniques on four distinct TFBS data sets and analyze their performance. We describe the features that are most useful for classification and contrast and compare these feature sets between the factors.

RESULTS

Our results demonstrate good performance of classifiers both on TFBS for transcription factors used for initial training and for TFBS for other factors in cross-classification experiments. We find that distances to chromatin modifications (specifically, histone modification islands) as well as distances between such modifications to be effective predictors of TFBS occupancy, though the impact of individual predictors is largely TF specific. In our experiments, Bayesian network classifiers outperform SVM classifiers.

CONCLUSIONS

Our results demonstrate good performance of machine learning techniques on the problem of occupancy classification, and demonstrate that effective classification can be achieved using distances to chromatin features. We additionally demonstrate that cross-classification of TFBS is possible, suggesting the possibility of constructing a generalizable occupancy classifier capable of handling TFBS for many different transcription factors.

Bayesian analysis of high-throughput quantitative measurement of protein-DNA interactions

Transcriptional regulation depends upon the binding of transcription factor (TF) proteins to DNA in a sequence-dependent manner. Although many experimental methods address the interaction between DNA and proteins, they generally do not comprehensively and accurately assess the full binding repertoire (the complete set of sequences that might be bound with at least moderate strength). Here, we develop and evaluate through simulation an experimental approach that allows simultaneous high-throughput quantitative analysis of TF binding affinity to thousands of potential DNA ligands. Tens of thousands of putative binding targets can be mixed with a TF, and both the pre-bound and bound target pools sequenced. A hierarchical Bayesian Markov chain Monte Carlo approach determines posterior estimates for the dissociation constants, sequence-specific binding energies, and free TF concentrations. A unique feature of our approach is that dissociation constants are jointly estimated from their inferred degree of binding and from a model of binding energetics, depending on how many sequence reads are available and the explanatory power of the energy model. Careful experimental design is necessary to obtain accurate results over a wide range of dissociation constants. This approach, which we call Simultaneous Ultra high-throughput Ligand Dissociation EXperiment (SULDEX), is theoretically capable of rapid and accurate elucidation of an entire TF-binding repertoire.

The action mechanism of the Myc inhibitor termed Omomyc may give clues on how to target Myc for cancer therapy

Recent evidence points to Myc--a multifaceted bHLHZip transcription factor deregulated in the majority of human cancers--as a priority target for therapy. How to target Myc is less clear, given its involvement in a variety of key functions in healthy cells. Here we report on the action mechanism of the Myc interfering molecule termed Omomyc, which demonstrated astounding therapeutic efficacy in transgenic mouse cancer models in vivo. Omomyc action is different from the one that can be obtained by gene knockout or RNA interference, approaches designed to block all functions of a gene product. This molecule--instead--appears to cause an edge-specific perturbation that destroys some protein interactions of the Myc node and keeps others intact, with the result of reshaping the Myc transcriptome. Omomyc selectively targets Myc protein interactions: it binds c- and N-Myc, Max and Miz-1, but does not bind Mad or select HLH proteins. Specifically, it prevents Myc binding to promoter E-boxes and transactivation of target genes while retaining Miz-1 dependent binding to promoters and transrepression. This is accompanied by broad epigenetic changes such as decreased acetylation and increased methylation at H3 lysine 9. In the presence of Omomyc, the Myc interactome is channeled to repression and its activity appears to switch from a pro-oncogenic to a tumor suppressive one. Given the extraordinary therapeutic impact of Omomyc in animal models, these data suggest that successfully targeting Myc for cancer therapy might require a similar twofold action, in order to prevent Myc/Max binding to E-boxes and, at the same time, keep repressing genes that would be repressed by Myc.

DHODH modulates transcriptional elongation in the neural crest and melanoma

Melanoma is a tumour of transformed melanocytes, which are originally derived from the embryonic neural crest. It is unknown to what extent the programs that regulate neural crest development interact with mutations in the BRAF oncogene, which is the most commonly mutated gene in human melanoma. We have used zebrafish embryos to identify the initiating transcriptional events that occur on activation of human BRAF(V600E) (which encodes an amino acid substitution mutant of BRAF) in the neural crest lineage. Zebrafish embryos that are transgenic for mitfa:BRAF(V600E) and lack p53 (also known as tp53) have a gene signature that is enriched for markers of multipotent neural crest cells, and neural crest progenitors from these embryos fail to terminally differentiate. To determine whether these early transcriptional events are important for melanoma pathogenesis, we performed a chemical genetic screen to identify small-molecule suppressors of the neural crest lineage, which were then tested for their effects on melanoma. One class of compound, inhibitors of dihydroorotate dehydrogenase (DHODH), for example leflunomide, led to an almost complete abrogation of neural crest development in zebrafish and to a reduction in the self-renewal of mammalian neural crest stem cells. Leflunomide exerts these effects by inhibiting the transcriptional elongation of genes that are required for neural crest development and melanoma growth. When used alone or in combination with a specific inhibitor of the BRAF(V600E) oncogene, DHODH inhibition led to a marked decrease in melanoma growth both in vitro and in mouse xenograft studies. Taken together, these studies highlight developmental pathways in neural crest cells that have a direct bearing on melanoma formation.

Regulation of Bach2 by the aryl hydrocarbon receptor as a mechanism for suppression of B-cell differentiation by 2,3,7,8-tetrachlorodibenzo-p-dioxin

Exposure to the aryl hydrocarbon receptor (AHR) agonist, 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) alters B-cell differentiation and suppresses antibody production. Previous genomic studies in mouse B cells identified Bach2 as a direct target of the AHR. Bach2 is known to repress expression of Prdm1, a key transcription factor involved in B-cell differentiation, by binding to Maf elements (MAREs) in the regulatory regions of the gene. Chromatin immunoprecipitation followed by quantitative PCR in TCDD-treated lipopolysaccharide (LPS)-activated B cells showed increased binding of the AHR within the first intron in the Bach2 gene. The binding was further confirmed by electrophoretic mobility shift assay (EMSA). TCDD also induced expression of Bach2 in activated as well as resting B cells from 2 to 24h post-treatment in a time- and concentration-dependent manner. Expression of Prdm1 was decreased by TCDD at 24h and was consistent with repression by Bach2. Increased DNA binding activity to the intron 5 MARE with increasing TCDD concentrations was observed by EMSA. Supershifts identified the presence of Bach2 in the DNA binding complex associated with the intron 5 MARE of Prdm1. Functional validation of the role of Bach2 in the suppression of B-cell differentiation by TCDD was performed using RNA interference (RNAi). Knockdown of Bach2 showed approximately 40% reversal in the TCDD-induced suppression of IgM secretion when compared to controls. The results suggest that the transcriptional regulation of Bach2 by the AHR is one of the mechanisms involved in the suppression of B-cell differentiation by TCDD.

A genomic strategy for the functional validation of colorectal cancer genes identifies potential therapeutic targets

Genes that are highly overexpressed in tumor cells can be required for tumor cell survival and have the potential to be selective therapeutic targets. In an attempt to identify such targets, we combined a functional genomics and a systems biology approach to assess the consequences of RNAi-mediated silencing of overexpressed genes that were selected from 140 gene expression profiles from colorectal cancers (CRCs) and matched normal mucosa. In order to identify credible models for in-depth functional analysis, we first confirmed the overexpression of these genes in 25 different CRC cell lines. We then identified five candidate genes that profoundly reduced the viability of CRC cell lines when silenced with either siRNAs or short-hairpin RNAs (shRNAs), i.e., HMGA1, TACSTD2, RRM2, RPS2 and NOL5A. These genes were further studied by systematic analysis of comprehensive gene expression profiles generated following siRNA-mediated silencing. Exploration of these RNAi-specific gene expression signatures allowed the identification of the functional space in which the five genes operate and showed enrichment for cancer-specific signaling pathways, some known to be involved in CRC. By comparing the expression of the RNAi signature genes with their respective expression levels in an independent set of primary rectal carcinomas, we could recapitulate these defined RNAi signatures, therefore, establishing the biological relevance of our observations. This strategy identified the signaling pathways that are affected by the prominent oncogenes HMGA1 and TACSTD2, established a yet unknown link between RRM2 and PLK1 and identified RPS2 and NOL5A as promising potential therapeutic targets in CRC.

A genomic strategy for the functional validation of colorectal cancer genes identifies potential therapeutic targets

Genes that are highly overexpressed in tumor cells can be required for tumor cell survival and have the potential to be selective therapeutic targets. In an attempt to identify such targets, we combined a functional genomics and a systems biology approach to assess the consequences of RNAi-mediated silencing of overexpressed genes that were selected from 140 gene expression profiles from colorectal cancers (CRCs) and matched normal mucosa. In order to identify credible models for in-depth functional analysis, we first confirmed the overexpression of these genes in 25 different CRC cell lines. We then identified five candidate genes that profoundly reduced the viability of CRC cell lines when silenced with either siRNAs or short-hairpin RNAs (shRNAs), i.e., HMGA1, TACSTD2, RRM2, RPS2 and NOL5A. These genes were further studied by systematic analysis of comprehensive gene expression profiles generated following siRNA-mediated silencing. Exploration of these RNAi-specific gene expression signatures allowed the identification of the functional space in which the five genes operate and showed enrichment for cancer-specific signaling pathways, some known to be involved in CRC. By comparing the expression of the RNAi signature genes with their respective expression levels in an independent set of primary rectal carcinomas, we could recapitulate these defined RNAi signatures, therefore, establishing the biological relevance of our observations. This strategy identified the signaling pathways that are affected by the prominent oncogenes HMGA1 and TACSTD2, established a yet unknown link between RRM2 and PLK1 and identified RPS2 and NOL5A as promising potential therapeutic targets in CRC.

An integrated genomic analysis of aryl hydrocarbon receptor-mediated inhibition of B-cell differentiation

The aryl hydrocarbon receptor (AHR) agonist 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) alters differentiation of B cells and suppresses antibody production. A combination of whole-genome, microarray-based chromatin immunoprecipitation (ChIP-on-chip), and time course gene expression microarray analysis was performed on the mouse B-cell line CH12.LX following exposure to lipopolysaccharide (LPS) or LPS and TCDD to identify the primary and downstream transcriptional elements of B-cell differentiation that are altered by the AHR. ChIP-on-chip analysis identified 1893 regions with a significant increase in AHR binding with TCDD treatment. Transcription factor binding site analysis on the ChIP-on-chip data showed enrichment in AHR response elements. Other transcription factors showed significant coenrichment with AHR response elements. When ChIP-on-chip regions were compared with gene expression changes at the early time points, 78 genes were identified as potential direct targets of the AHR. AHR binding and expression changes were confirmed for a subset of genes in primary mouse B cells. Network analysis examining connections between the 78 potential AHR target genes and three transcription factors known to regulate B-cell differentiation indicated multiple paths for potential regulation by the AHR. Enrichment analysis on the differentially expressed genes at each time point evaluated the downstream impact of AHR-regulated gene expression changes on B-cell-related processes. AHR-mediated impairment of B-cell differentiation occurred at multiple nodes of the B-cell differentiation network and potentially through multiple mechanisms including direct cis-acting effects on key regulators of B-cell differentiation, indirect regulation of B-cell differentiation-related pathways, and transcriptional coregulation of target genes by AHR and other transcription factors.

p53 binds preferentially to genomic regions with high DNA-encoded nucleosome occupancy

The human transcription factor TP53 is a pivotal roadblock against cancer. A key unresolved question is how the p53 protein selects its genomic binding sites in vivo out of a large pool of potential consensus sites. We hypothesized that chromatin may play a significant role in this site-selection process. To test this, we used a custom DNA microarray to measure p53 binding at approximately 2000 sites predicted to possess high-sequence specificity, and identified both strongly bound and weakly bound sites. When placed within a plasmid, weakly bound sites become p53 responsive and regain p53 binding when stably integrated into random genomic locations. Notably, strongly bound sites reside preferentially within genomic regions whose DNA sequence is predicted to encode relatively high intrinsic nucleosome occupancy. Using in vivo nucleosome occupancy measurements under conditions where p53 is inactive, we experimentally confirmed this prediction. Furthermore, upon p53 activation, nucleosomes are partially displaced from a relatively broad region surrounding the bound p53 sites, and this displacement is rapidly reversed upon inactivation of p53. Thus, in contrast to the general assumption that transcription-factor binding is preferred in sites that have low nucleosome occupancy prior to factor activation, we find that p53 binding occurs preferentially within a chromatin context of high intrinsic nucleosome occupancy.

Integrating multiple evidence sources to predict transcription factor binding in the human genome

Information about the binding preferences of many transcription factors is known and characterized by a sequence binding motif. However, determining regions of the genome in which a transcription factor binds based on its motif is a challenging problem, particularly in species with large genomes, since there are often many sequences containing matches to the motif but are not bound. Several rules based on sequence conservation or location, relative to a transcription start site, have been proposed to help differentiate true binding sites from random ones. Other evidence sources may also be informative for this task. We developed a method for integrating multiple evidence sources using logistic regression classifiers. Our method works in two steps. First, we infer a score quantifying the general binding preferences of transcription factor binding at all locations based on a large set of evidence features, without using any motif specific information. Then, we combined this general binding preference score with motif information for specific transcription factors to improve prediction of regions bound by the factor. Using cross-validation and new experimental data we show that, surprisingly, the general binding preference can be highly predictive of true locations of transcription factor binding even when no binding motif is used. When combined with motif information our method outperforms previous methods for predicting locations of true binding.

Bayesian network expansion identifies new ROS and biofilm regulators

Signaling and regulatory pathways that guide gene expression have only been partially defined for most organisms. However, given the increasing number of microarray measurements, it may be possible to reconstruct such pathways and uncover missing connections directly from experimental data. Using a compendium of microarray gene expression data obtained from Escherichia coli, we constructed a series of Bayesian network models for the reactive oxygen species (ROS) pathway as defined by EcoCyc. A consensus Bayesian network model was generated using those networks sharing the top recovered score. This microarray-based network only partially agreed with the known ROS pathway curated from the literature and databases. A top network was then expanded to predict genes that could enhance the Bayesian network model using an algorithm we termed ‘BN+1’. This expansion procedure predicted many stress-related genes (e.g., dusB and uspE), and their possible interactions with other ROS pathway genes. A term enrichment method discovered that biofilm-associated microarray data usually contained high expression levels of both uspE and gadX. The predicted involvement of gene uspE in the ROS pathway and interactions between uspE and gadX were confirmed experimentally using E. coli reporter strains. Genes gadX and uspE showed a feedback relationship in regulating each other's expression. Both genes were verified to regulate biofilm formation through gene knockout experiments. These data suggest that the BN+1 expansion method can faithfully uncover hidden or unknown genes for a selected pathway with significant biological roles. The presently reported BN+1 expansion method is a generalized approach applicable to the characterization and expansion of other biological pathways and living systems.

The ups and downs of Myc biology

The basic helix-loop-helix protein Myc is a renowned transcription factor controlling disparate aspects of cell physiology that, together, allow efficient proliferation of somatic cells. This ability, together with the observation that its deregulated expression occurs in the majority of human cancers, suggests that Myc could be a good therapeutic target. However, several aspects of Myc biology remain elusive: what is the major difference between oncogenic and physiological Myc? How does oncogenic Myc evade the intrinsic tumor surveillance pathways provided by evolution? If Myc inhibition were even possible, what would be the consequences for the homeostasis of normal proliferating tissues versus the fate of cancer cells? Here we summarize the latest works addressing these issues.

Quantitative nanoproteomics for protein complexes (QNanoPX) related to estrogen transcriptional action

We developed an integrated proteomics approach using a chemically functionalized gold nanoparticle (AuNP) as a novel probe for affinity purification to analyze a large protein complex in vivo. We then applied this approach to globally map the transcriptional activation complex of the estrogen response element (ERE). This approach was designated as quantitative nanoproteomics for protein complexes (QNanoPX). In this approach, the positive AuNP-ERE probes were functionalized with polyethylene glycol (PEG), and the consensus sequence of ERE and negative AuNP-PEG probes were functionalized with PEG without the ERE via a thiolated self-assembly monolayer technique. The AuNP-ERE probe had substantially low nonspecific binding and high solubility, which resulted in a 20-fold enrichment of the factor compared with gel beads. In addition, the surface-only binding allows the probe to capture a large protein complex without any restrictions due to pore size. The affinity purification method was combined with MS-based quantitative proteomics and statistical methods to reveal the components of the ERE complex in MCF-7 cells and to identify those components within the complex that were altered by the presence of 17beta-estradiol (E2). Results indicated that a majority of proteins pulled down by the positive probe exhibited significant binding, and approximately one-half of the proteins, including estrogen receptor alpha (ERalpha), were slightly but significantly affected by a 24-h treatment with E2. Based on a combination of bioinformatics and pathway analysis, most of the affected proteins, however, appeared to be related to the transcriptional regulation of not only ERalpha but also c-Myc. Further confirmation indicated that E2 enhanced the ERE binding of c-Myc by 14-fold, indicating that c-Myc may play a major role, along with ERalpha, in E2-mediated transcription. Taken together, our results demonstrated a successful QNanoPX approach toward new pathway discovery and further revealed the importance of cross-interactions among transcription factors.

Incorporating existing network information into gene network inference

One methodology that has met success to infer gene networks from gene expression data is based upon ordinary differential equations (ODE). However new types of data continue to be produced, so it is worthwhile to investigate how to integrate these new data types into the inference procedure. One such data is physical interactions between transcription factors and the genes they regulate as measured by ChIP-chip or ChIP-seq experiments. These interactions can be incorporated into the gene network inference procedure as a priori network information. In this article, we extend the ODE methodology into a general optimization framework that incorporates existing network information in combination with regularization parameters that encourage network sparsity. We provide theoretical results proving convergence of the estimator for our method and show the corresponding probabilistic interpretation also converges. We demonstrate our method on simulated network data and show that existing network information improves performance, overcomes the lack of observations, and performs well even when some of the existing network information is incorrect. We further apply our method to the core regulatory network of embryonic stem cells utilizing predicted interactions from two studies as existing network information. We show that including the prior network information constructs a more closely representative regulatory network versus when no information is provided.

A core MYC gene expression signature is prominent in basal-like breast cancer but only partially overlaps the core serum response

BACKGROUND

The MYC oncogene contributes to induction and growth of many cancers but the full spectrum of the MYC transcriptional response remains unclear.

METHODOLOGY/PRINCIPAL FINDINGS

Using microarrays, we conducted a detailed kinetic study of genes that respond to MYCN or MYCNDeltaMBII induction in primary human fibroblasts. In parallel, we determined the response to steady state overexpression of MYCN and MYCNDeltaMBII in the same cell type. An overlapping set of 398 genes from the two protocols was designated a 'Core MYC Signature' and used for further analysis. Comparison of the Core MYC Signature to a published study of the genes induced by serum stimulation revealed that only 7.4% of the Core MYC Signature genes are in the Core Serum Response and display similar expression changes to both MYC and serum. Furthermore, more than 50% of the Core MYC Signature genes were not influenced by serum stimulation. In contrast, comparison to a panel of breast cancers revealed a strong concordance in gene expression between the Core MYC Signature and the basal-like breast tumor subtype, which is a subtype with poor prognosis. This concordance was supported by the higher average level of MYC expression in the same tumor samples.

CONCLUSIONS/SIGNIFICANCE

The Core MYC Signature has clinical relevance as this profile can be used to deduce an underlying genetic program that is likely to contribute to a clinical phenotype. Therefore, the presence of the Core MYC Signature may predict clinical responsiveness to therapeutics that are designed to disrupt MYC-mediated phenotypes.

Gene expression profiling differentiates autism case-controls and phenotypic variants of autism spectrum disorders: evidence for circadian rhythm dysfunction in severe autism

Autism spectrum disorders (ASD) are neurodevelopmental disorders characterized by delayed/abnormal language development, deficits in social interaction, repetitive behaviors and restricted interests. The heterogeneity in clinical presentation of ASD, likely due to different etiologies, complicates genetic/biological analyses of these disorders. DNA microarray analyses were conducted on 116 lymphoblastoid cell lines (LCL) from individuals with idiopathic autism who are divided into three phenotypic subgroups according to severity scores from the commonly used Autism Diagnostic Interview-Revised questionnaire and age-matched, nonautistic controls. Statistical analyses of gene expression data from control LCL against that of LCL from ASD probands identify genes for which expression levels are either quantitatively or qualitatively associated with phenotypic severity. Comparison of the significant differentially expressed genes from each subgroup relative to the control group reveals differentially expressed genes unique to each subgroup as well as genes in common across subgroups. Among the findings unique to the most severely affected ASD group are 15 genes that regulate circadian rhythm, which has been shown to have multiple effects on neurological as well as metabolic functions commonly dysregulated in autism. Among the genes common to all three subgroups of ASD are 20 novel genes mostly in putative noncoding regions, which appear to associate with androgen sensitivity and which may underlie the strong 4:1 bias toward affected males.

Proteomic profiling reveals the prognostic value of adenomatous polyposis coli-end-binding protein 1 in hepatocellular carcinoma

Histological differentiation is a major pathological parameter associated with poor prognosis in patients with hepatocellular carcinoma (HCC) and the molecular signature underlying HCC differentiation may involve key proteins potentially affecting the malignant characters of HCC. To develop prognostic biomarkers for HCC, we examined the global protein expression profiles of 45 surgically resected tissues, including 27 HCCs with different degree of histological differentiation, 11 adjacent nontumor tissues, and seven normal liver tissues. Unsupervised classification grouped the 45 samples according to their histological classification based on the protein expression profiles created by laser microdissection and two-dimensional difference gel electrophoresis (2D-DIGE). Statistical analysis and mass spectrometry identified 26 proteins with differential expression, of which 14 were functionally linked to c-Myc, AP-1, HIF1A, hepatocyte nuclear factor 4 alpha, or the Ras superfamily (RhoA, CDC42, and Rac1). Among the proteins identified, we focused on APC-binding protein EB1 (EB1) because it was dominantly expressed in poorly differentiated HCCs, which generally correlate with the poor prognosis in patients with HCC. In addition, EB1 is controlled by c-Myc, RhoA, and CDC42, which have all been linked to HCC malignancy. Immunohistochemistry in a further 145 HCC cases revealed that EB1 significantly correlated with the degree of histological differentiation (P < 0.001), and univariate and multivariate analyses indicated that EB1 is an independent prognostic factor for recurrence (hazard ratio, 2.740; 95% confidence interval, 1.771-4.239; P < 0.001) and survival (hazard ratio, 2.256; 95% confidence interval, 1.337-3.807; P = 0.002) of patients with HCC after curative surgery.

CONCLUSION

Proteomic profiling revealed the molecular signature behind the progression of HCC, and the prognostic value of EB1 in HCC.

High-throughput chromatin information enables accurate tissue-specific prediction of transcription factor binding sites

In silico prediction of transcription factor binding sites (TFBSs) is central to the task of gene regulatory network elucidation. Genomic DNA sequence information provides a basis for these predictions, due to the sequence specificity of TF-binding events. However, DNA sequence alone is an impoverished source of information for the task of TFBS prediction in eukaryotes, as additional factors, such as chromatin structure regulate binding events. We show that incorporating high-throughput chromatin modification estimates can greatly improve the accuracy of in silico prediction of in vivo binding for a wide range of TFs in human and mouse. This improvement is superior to the improvement gained by equivalent use of either transcription start site proximity or phylogenetic conservation information. Importantly, predictions made with the use of chromatin structure information are tissue specific. This result supports the biological hypothesis that chromatin modulates TF binding to produce tissue-specific binding profiles in higher eukaryotes, and suggests that the use of chromatin modification information can lead to accurate tissue-specific transcriptional regulatory network elucidation.

Combined analysis of murine and human microarrays and ChIP analysis reveals genes associated with the ability of MYC to maintain tumorigenesis

The MYC oncogene has been implicated in the regulation of up to thousands of genes involved in many cellular programs including proliferation, growth, differentiation, self-renewal, and apoptosis. MYC is thought to induce cancer through an exaggerated effect on these physiologic programs. Which of these genes are responsible for the ability of MYC to initiate and/or maintain tumorigenesis is not clear. Previously, we have shown that upon brief MYC inactivation, some tumors undergo sustained regression. Here we demonstrate that upon MYC inactivation there are global permanent changes in gene expression detected by microarray analysis. By applying StepMiner analysis, we identified genes whose expression most strongly correlated with the ability of MYC to induce a neoplastic state. Notably, genes were identified that exhibited permanent changes in mRNA expression upon MYC inactivation. Importantly, permanent changes in gene expression could be shown by chromatin immunoprecipitation (ChIP) to be associated with permanent changes in the ability of MYC to bind to the promoter regions. Our list of candidate genes associated with tumor maintenance was further refined by comparing our analysis with other published results to generate a gene signature associated with MYC-induced tumorigenesis in mice. To validate the role of gene signatures associated with MYC in human tumorigenesis, we examined the expression of human homologs in 273 published human lymphoma microarray datasets in Affymetrix U133A format. One large functional group of these genes included the ribosomal structural proteins. In addition, we identified a group of genes involved in a diverse array of cellular functions including: BZW2, H2AFY, SFRS3, NAP1L1, NOLA2, UBE2D2, CCNG1, LIFR, FABP3, and EDG1. Hence, through our analysis of gene expression in murine tumor models and human lymphomas, we have identified a novel gene signature correlated with the ability of MYC to maintain tumorigenesis.

The targeted inactivation of oncogenes may be a specific and effective treatment of cancer. However, how oncogene inactivation leads to tumor regression is not clear. Previously, we have shown that even the brief inactivation of the MYC oncogene can result in the sustained regression of at least some tumors. To understand the mechanism, we have utilized several novel genomic analyses to define a set of genes that strongly correlate with the ability of the MYC oncogene to maintain tumorigenesis. First, we generated a novel data set from microarray analyses of murine tumors that we analyzed by StepMiner to identify discrete step changes in gene expression after the inactivation or the reactivation of the MYC oncogene. Second, we utilized Boolean Network Analysis to further define the subset of genes highly correlated with MYC in human tumorigenesis. Third, we utilized ChIP analysis to demonstrate that in many cases the permanent changes of gene expression we uncovered were associated with changes in the ability of MYC to occupy the promoter locus. Our general strategy could be similarly utilized in other experimental model systems to understand how specific oncogenes contribute to the maintenance of tumorigenesis.

Transcription profiling of lung adenocarcinomas of c-myc-transgenic mice: identification of the c-myc regulatory gene network

Background

The transcriptional regulator c-Myc is the most frequently deregulated oncogene in human tumors. Targeted overexpression of this gene in mice results in distinct types of lung adenocarcinomas. By using microarray technology, alterations in the expression of genes were captured based on a female transgenic mouse model in which, indeed, c-Myc overexpression in alveolar epithelium results in the development of bronchiolo-alveolar carcinoma (BAC) and papillary adenocarcinoma (PLAC). In this study, we analyzed exclusively the promoters of induced genes by different in silico methods in order to elucidate the c-Myc transcriptional regulatory network.

Results

We analyzed the promoters of 361 transcriptionally induced genes with respect to c-Myc binding sites and found 110 putative binding sites in 94 promoters. Furthermore, we analyzed the flanking sequences (+/- 100 bp) around the 110 c-Myc binding sites and found Ap2, Zf5, Zic3, and E2f binding sites to be overrepresented in these regions. Then, we analyzed the promoters of 361 induced genes with respect to binding sites of other transcription factors (TFs) which were upregulated by c-Myc overexpression. We identified at least one binding site of at least one of these TFs in 220 promoters, thus elucidating a potential transcription factor network. The analysis correlated well with the significant overexpression of the TFs Atf2, Foxf1a, Smad4, Sox4, Sp3 and Stat5a. Finally, we analyzed promoters of regulated genes which where apparently not regulated by c-Myc or other c-Myc targeted TFs and identified overrepresented Oct1, Mzf1, Ppargamma, Plzf, Ets, and HmgIY binding sites when compared against control promoter background.

Conclusion

Our in silico data suggest a model of a transcriptional regulatory network in which different TFs act in concert upon c-Myc overexpression. We determined molecular rules for transcriptional regulation to explain, in part, the carcinogenic effect seen in mice overexpressing the c-Myc oncogene.

Genetic and genomic analysis modeling of germline c-MYC overexpression and cancer susceptibility

BACKGROUND

Germline genetic variation is associated with the differential expression of many human genes. The phenotypic effects of this type of variation may be important when considering susceptibility to common genetic diseases. Three regions at 8q24 have recently been identified to independently confer risk of prostate cancer. Variation at 8q24 has also recently been associated with risk of breast and colorectal cancer. However, none of the risk variants map at or relatively close to known genes, with c-MYC mapping a few hundred kilobases distally.

RESULTS

This study identifies cis-regulators of germline c-MYC expression in immortalized lymphocytes of HapMap individuals. Quantitative analysis of c-MYC expression in normal prostate tissues suggests an association between overexpression and variants in Region 1 of prostate cancer risk. Somatic c-MYC overexpression correlates with prostate cancer progression and more aggressive tumor forms, which was also a pathological variable associated with Region 1. Expression profiling analysis and modeling of transcriptional regulatory networks predicts a functional association between MYC and the prostate tumor suppressor KLF6. Analysis of MYC/Myc-driven cell transformation and tumorigenesis substantiates a model in which MYC overexpression promotes transformation by down-regulating KLF6. In this model, a feedback loop through E-cadherin down-regulation causes further transactivation of c-MYC.

CONCLUSION

This study proposes that variation at putative 8q24 cis-regulator(s) of transcription can significantly alter germline c-MYC expression levels and, thus, contribute to prostate cancer susceptibility by down-regulating the prostate tumor suppressor KLF6 gene.