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Transcription factors regulate gene expression by binding regulatory DNA. Understanding the rules governing such binding is an essential step in describing the network of regulatory interactions, and its pathological alterations. We show that describing regulatory regions in terms of their profile of total binding affinities for transcription factors leads to increased predictive power compared to methods based on the identification of discrete binding sites. This applies both to the prediction of transcription factor binding as revealed by ChIP-seq experiments and to the prediction of gene expression through RNA-seq. Further significant improvements in predictive power are obtained when regulatory regions are defined based on chromatin states inferred from histone modification data. [ABSTRACT FROM AUTHOR]

Formalin fixed paraffin-embedded (FFPE) tumor specimens are the conventionally archived material in clinical practice, representing an invaluable tissue source for biomarkers development, validation and routine implementation. For many prospective clinical trials, this material has been collected allowing for a prospective-retrospective study design which represents a successful strategy to define clinical utility for candidate markers. Gene expression data can be obtained even from FFPE specimens with the broadly used Affymetrix HG-U133 Plus 2.0 microarray platform. Nevertheless, important major discrepancies remain in expression data obtained from FFPE compared to fresh-frozen samples, prompting the need for appropriate data processing which could help to obtain more consistent results in downstream analyses. In a publicly available dataset of matched frozen and FFPE expression data, the performances of different normalization methods and specifically designed Chip Description Files (CDFs) were compared. The use of an alternative CDFs together with fRMA normalization significantly improved frozen-FFPE sample correlations, frozen-FFPE probeset correlations and agreement of differential analysis between different tumor subtypes. The relevance of our optimized data processing was assessed and validated using two independent datasets. In this study we demonstrated that an appropriate data processing can significantly improve the reliability of gene expression data derived from FFPE tissues using the standard Affymetrix platform. Tools for the implementation of our data processing algorithm are made publicly available at http://www.biocut.unito.it/cdf-ffpe/. [ABSTRACT FROM AUTHOR]

MicroRNAs are single-stranded non-coding RNAs that simultaneously down-modulate the expression of multiple genes post-transcriptionally by binding to the 3′UTRs of target mRNAs. Here we used computational methods to predict microRNAs relevant in breast cancer progression. Specifically, we applied different microRNA target prediction algorithms to various groups of differentially expressed protein-coding genes obtained from four breast cancer datasets. Six potential candidates were identified, among them miR-223, previously described to be highly expressed in the tumor microenvironment and known to be actively transferred into breast cancer cells. To investigate the function of miR-223 in tumorigenesis and to define its molecular mechanism, we overexpressed miR-223 in breast cancer cells in a transient or stable manner. Alternatively we overexpressed miR-223 in mouse embryonic fibroblasts or HEK293 cells and used their conditioned medium to treat tumor cells. With both approaches, we obtained elevated levels of miR-223 in tumor cells and observed decreased migration, increased cell death in anoikis conditions and augmented sensitivity to chemotherapy but no effect on adhesion and proliferation. The analysis of miR-223 predicted targets revealed enrichment in cell death and survival-related genes and in pathways frequently altered in breast cancer. Among these genes, we showed that protein levels for STAT5A, ITGA3 and NRAS were modulated by miR-223. In addition, we proved that STAT5A is a direct miR-223 target and highlighted a possible correlation between miR-223 and STAT5A in migration and chemotherapy response. Our investigation revealed that a computational analysis of cancer gene expression datasets can be a relevant tool to identify microRNAs involved in cancer progression and that miR-223 has a prominent role in breast malignancy that could potentially be exploited therapeutically. [ABSTRACT FROM AUTHOR]

In pluripotent stem cells, there is increasing evidence for crosstalk between post-transcriptional and transcriptional networks, offering multifold steps at which pluripotency can be controlled. In addition to well-studied transcription factors, chromatin modifiers and miRNAs, RNA-binding proteins are emerging as fundamental players in pluripotency regulation. Here, we report a new role for the RNA-binding protein ESRP1 in the control of pluripotency. Knockdown of Esrp1 in mouse embryonic stem cells induces, other than the well-documented epithelial to mesenchymal-like state, also an increase in expression of the core transcription factors Oct4, Nanog and Sox2, thereby enhancing self-renewal of these cells. Esrp1-depleted embryonic stem cells displayed impaired early differentiation in vitro and formed larger teratomas in vivo when compared to control embryonic stem cells. We also show that ESRP1 binds to Oct4 and Sox2 mRNAs and decreases their polysomal loading. ESRP1 thus acts as a physiological regulator of the finely-tuned balance between self-renewal and commitment to a restricted developmental fate. Importantly, both mouse and human epithelial stem cells highly express ESRP1, pinpointing the importance of this RNA-binding protein in stem cell biology. [ABSTRACT FROM AUTHOR]

The Dlx and Msx homeodomain transcription factors play important roles in the control of limb development. The combined disruption of Msx1 and Msx2, as well as that of D lx5 and Dlx6, lead to limb patterning defects with anomalies in digit number and shape. Msx1;Msx2 double mutants are characterized by the loss of derivatives of the anterior limb mesoderm which is not observed in either of the simple mutants. Dlx5;Dlx6 double mutants exhibit hindlimb ectrodactyly. While the morphogenetic action of Msx genes seems to involve the BMP molecules, the mode of action of Dlx genes still remains elusive. Here, examining the limb phenotypes of combined Dlx and Msx mutants we reveal a new Dlx-Msx regulatory loop directly involving BMPs. In Msx1;Dlx5;Dlx6 triple mutant mice (TKO), beside the expected ectrodactyly, we also observe the hallmark morphological anomalies of Msx1;Msx2 double mutants suggesting an epistatic role of Dlx5 and Dlx6 over Msx2. In Msx2;Dlx5;Dlx6 TKO mice we only observe an aggravation of the ectrodactyly defect without changes in the number of the individual components of the limb. Using a combination of qPCR, ChIP and bioinformatic analyses, we identify two Dlx/Msx regulatory pathways: 1) in the anterior limb mesoderm a non-cell autonomous Msx-Dlx regulatory loop involves BMP molecules through the AER and 2) in AER cells and, at later stages, in the limb mesoderm the regulation of Msx2 by Dlx5 and Dlx6 occurs also cell autonomously. These data bring new elements to decipher the complex AER-mesoderm dialogue that takes place during limb development and provide clues to understanding the etiology of congenital limb malformations. [ABSTRACT FROM AUTHOR]

We sought exonic transcriptional regulatory elements by shotgun cloning human cDNA fragments into luciferase reporter vectors and measuring the resulting expression levels in liver cells. We uncovered seven regulatory elements within coding regions and three within 3' untranslated regions (UTRs). Two of the putative regulatory elements were enhancers and eight were silencers. The regulatory elements were generally but not consistently evolutionarily conserved and also showed a trend toward decreased population diversity. Furthermore, the exonic regulatory elements were enriched in known transcription factor binding sites (TFBSs) and were associated with several histone modifications and transcriptionally relevant chromatin. Evidence was obtained for bidirectional cis-regulation of a coding region element within a tubulin gene, TUBA1B, by the transcription factors PPARA and RORA. We estimate that hundreds of exonic transcriptional regulatory elements exist, an unexpected finding that highlights a surprising multi-functionality of sequences in the human genome. [ABSTRACT FROM AUTHOR]

Among thousands of long non-coding RNAs (lncRNAs) only a small subset is functionally characterized and the functional annotation of lncRNAs on the genomic scale remains inadequate. In this study we computationally characterized two functionally different parts of human lncRNAs transcriptome based on their ability to bind the polycomb repressive complex, PRC2. This classification is enabled by the fact that while all lncRNAs constitute a diverse set of sequences, the classes of PRC2-binding and PRC2 non-binding lncRNAs possess characteristic combinations of sequence-structure patterns and, therefore, can be separated within the feature space. Based on the specific combination of features, we built several machine-learning classifiers and identified the SVM-based classifier as the best performing. We further showed that the SVM-based classifier is able to generalize on the independent data sets. We observed that this classifier, trained on the human lncRNAs, can predict up to 59.4% of PRC2-binding lncRNAs in mice. This suggests that, despite the low degree of sequence conservation, many lncRNAs play functionally conserved biological roles. [ABSTRACT FROM AUTHOR]

Data normalization is a crucial preliminary step in analyzing genomic datasets. The goal of normalization is to remove global variation to make readings across different experiments comparable. In addition, most genomic loci have non-uniform sensitivity to any given assay because of variation in local sequence properties. In microarray experiments, this non-uniform sensitivity is due to different DNA hybridization and cross-hybridization efficiencies, known as the probe effect. In this paper we introduce a new scheme, called Group Normalization (GN), to remove both global and local biases in one integrated step, whereby we determine the normalized probe signal by finding a set of reference probes with similar responses. Compared to conventional normalization methods such as Quantile normalization and physically motivated probe effect models, our proposed method is general in the sense that it does not require the assumption that the underlying signal distribution be identical for the treatment and control, and is flexible enough to correct for nonlinear and higher order probe effects. The Group Normalization algorithm is computationally efficient and easy to implement. We also describe a variant of the Group Normalization algorithm, called Cross Normalization, which efficiently amplifies biologically relevant differences between any two genomic datasets. [ABSTRACT FROM AUTHOR]

A major part of the post-transcriptional regulation of gene expression is affected by trans-acting elements, such as microRNAs, binding the 3' untraslated region (UTR) of their target mRNAs. Proliferating cells partly escape this type of negative regulation by expressing shorter 3' UTRs, depleted of microRNA binding sites, compared to non-proliferating cells. Using large-scale gene expression datasets, we show that a similar phenomenon takes place in breast and lung cancer: tumors expressing shorter 3' UTRs tend to be more aggressive and to result in shorter patient survival. Moreover, we show that a gene expression signature based only on the expression ratio of alternative 3' UTRs is a strong predictor of survival in both tumors. Genes undergoing 3'UTR shortening in aggressive tumors of the two tissues significantly overlap, and several of them are known to be involved in tumor progression. However the pattern of 3' UTR shortening in aggressive tumors in vivo is clearly distinct from analogous patterns involved in proliferation and transformation. [ABSTRACT FROM AUTHOR]

Gene-on-gene regulations are key components of every living organism. Dynamical abstract models of genetic regulatory networks help explain the genome's evolvability and robustness. These properties can be attributed to the structural topology of the graph formed by genes, as vertices, and regulatory interactions, as edges. Moreover, the actual gene interaction of each gene is believed to play a key role in the stability of the structure. With advances in biology, some effort was deployed to develop update functions in Boolean models that include recent knowledge. We combine real-life gene interaction networks with novel update functions in a Boolean model. We use two sub-networks of biological organisms, the yeast cell-cycle and the mouse embryonic stem cell, as topological support for our system. On these structures, we substitute the original random update functions by a novel threshold-based dynamic function in which the promoting and repressing effect of each interaction is considered. We use a third real-life regulatory network, along with its inferred Boolean update functions to validate the proposed update function. Results of this validation hint to increased biological plausibility of the threshold-based function. To investigate the dynamical behavior of this new model, we visualized the phase transition between order and chaos into the critical regime using Derrida plots. We complement the qualitative nature of Derrida plots with an alternative measure, the criticality distance, that also allows to discriminate between regimes in a quantitative way. Simulation on both real-life genetic regulatory networks show that there exists a set of parameters that allows the systems to operate in the critical region. This new model includes experimentally derived biological information and recent discoveries, which makes it potentially useful to guide experimental research. The update function confers additional realism to the model, while reducing the complexity and solution space, thus making it easier to investigate. [ABSTRACT FROM AUTHOR]

Ewing's sarcoma family tumors (ESFT) are the second most common bone malignancy in children and young adults, characterized by unique chromosomal translocations that in 85% of cases lead to expression of the EWS-FLI-1 fusion protein. EWS-FLI-1 functions as an aberrant transcription factor that can both induce and suppress members of its target gene repertoire. We have recently demonstrated that EWS-FLI-1 can alter microRNA (miRNA) expression and that miRNA145 is a direct EWS-FLI-1 target whose suppression is implicated in ESFT development. Here, we use miRNA arrays to compare the global miRNA expression profile of human mesenchymal stem cells (MSC) and ESFT cell lines, and show that ESFT display a distinct miRNA signature that includes induction of the oncogenic miRNA 17-92 cluster and repression of the tumor suppressor let-7 family. We demonstrate that direct repression of let-7a by EWS-FLI-1 participates in the tumorigenic potential of ESFT cells in vivo. The mechanism whereby let-7a expression regulates ESFT growth is shown to be mediated by its target gene HMGA2, as let-7a overexpression and HMGA2 repression both block ESFT cell tumorigenicity. Consistent with these observations, systemic delivery of synthetic let-7a into ESFT-bearing mice restored its expression in tumor cells, decreased HMGA2 expression levels and resulted in ESFT growth inhibition in vivo. Our observations provide evidence that deregulation of let-7a target gene expression participates in ESFT development and identify let-7a as promising new therapeutic target for one of the most aggressive pediatric malignancies. [ABSTRACT FROM AUTHOR]

Primary tumor growth induces host tissue responses that are believed to support and promote tumor progression. Identification of the molecular characteristics of the tumor microenvironment and elucidation of its crosstalk with tumor cells may therefore be crucial for improving our understanding of the processes implicated in cancer progression, identifying potential therapeutic targets, and uncovering stromal gene expression signatures that may predict clinical outcome. A key issue to resolve, therefore, is whether the stromal response to tumor growth is largely a generic phenomenon, irrespective of the tumor type or whether the response reflects tumor-specific properties. To address similarity or distinction of stromal gene expression changes during cancer progression, oligonucleotide-based Affymetrix microarray technology was used to compare the transcriptomes of laser-microdissected stromal cells derived from invasive human breast and prostate carcinoma. Invasive breast and prostate cancer-associated stroma was observed to display distinct transcriptomes, with a limited number of shared genes. Interestingly, both breast and prostate tumor-specific dysregulated stromal genes were observed to cluster breast and prostate cancer patients, respectively, into two distinct groups with statistically different clinical outcomes. By contrast, a gene signature that was common to the reactive stroma of both tumor types did not have survival predictive value. Univariate Cox analysis identified genes whose expression level was most strongly associated with patient survival. Taken together, these observations suggest that the tumor microenvironment displays distinct features according to the tumor type that provides survival-predictive value. [ABSTRACT FROM AUTHOR]

Introduction: The classification of breast cancer patients into risk groups provides a powerful tool for the identification of patients who will benefit from aggressive systemic therapy. The analysis of microarray data has generated several gene expression signatures that improve diagnosis and allow risk assessment. There is also evidence that cell proliferation-related genes have a high predictive power within these signatures. Methods: We thus constructed a gene expression signature (the DM signature) using the human orthologues of 108 Drosophila melanogaster genes required for either the maintenance of chromosome integrity (36 genes) or mitotic division (72 genes). Results: The DM signature has minimal overlap with the extant signatures and is highly predictive of survival in 5 large breast cancer datasets. In addition, we show that the DM signature outperforms many widely used breast cancer signatures in predictive power, and performs comparably to other proliferation-based signatures. For most genes of the DM signature, an increased expression is negatively correlated with patient survival. The genes that provide the highest contribution to the predictive power of the DM signature are those involved in cytokinesis. Conclusion: This finding highlights cytokinesis as an important marker in breast cancer prognosis and as a possible target for antimitotic therapies. [ABSTRACT FROM AUTHOR]

Background: Vector-borne diseases for which transmission occurs exclusively between vectors and hosts can be modeled as spreading on a bipartite network. Methodology/Principal Findings: In such models the spreading of the disease strongly depends on the degree distribution of the two classes of nodes. It is sufficient for one of the classes to have a scale-free degree distribution with a slow enough decay for the network to have asymptotically vanishing epidemic threshold. Data on the distribution of Ixodes ricinus ticks on mice and lizards from two independent studies are well described by a scale-free distribution compatible with an asymptotically vanishing epidemic threshold. The commonly used negative binomial, instead, cannot describe the right tail of the empirical distribution. Conclusions/Significance: The extreme aggregation of vectors on hosts, described by the power-law decay of the degree distribution, makes the epidemic threshold decrease with the size of the network and vanish asymptotically. [ABSTRACT FROM AUTHOR]

Background: The EWS-FLI-1 fusion protein is associated with 85-90% of Ewing's sarcoma family tumors (ESFT), the remaining 10-15% of cases expressing chimeric genes encoding EWS or FUS fused to one of several ets transcription factor family members, including ERG-1, FEV, ETV1 and ETV6. ESFT are dependent on insulin-like growth factor-1 (IGF-1) for growth and survival and recent evidence suggests that mesenchymal progenitor/stem cells constitute a candidate ESFT origin. Methodology/Principal Findings: To address the functional relatedness between ESFT-associated fusion proteins, we compared mouse progenitor cell (MPC) permissiveness for EWS-FLI-1, EWS-ERG and FUS-ERG expression and assessed the corresponding expression profile changes. Whereas all MPC isolates tested could stably express EWS-FLI-1, only some sustained stable EWS-ERG expression and none could express FUS-ERG for more than 3-5 days. Only 14% and 4% of the total number of genes that were respectively induced and repressed in MPCs by the three fusion proteins were shared. However, all three fusion proteins, but neither FLI-1 nor ERG-1 alone, activated the IGF1 promoter and induced IGF1 expression. Conclusion/Significance: Whereas expression of different ESFT-associated fusion proteins may require distinct cellular microenvironments and induce transcriptome changes of limited similarity, IGF1 induction may provide one common mechanism for their implication in ESFT pathogenesis. [ABSTRACT FROM AUTHOR]

Background: High-throughput gene expression data can predict gene function through the ''guilt by association'' principle: coexpressed genes are likely to be functionally associated. Methodology/Principal Findings: We analyzed publicly available expression data on normal human tissues. The analysis is based on the integration of data obtained with two experimental platforms (microarrays and SAGE) and of various measures of dissimilarity between expression profiles. The building blocks of the procedure are the Ranked Coexpression Groups (RCG), small sets of tightly coexpressed genes which are analyzed in terms of functional annotation. Functionally characterized RCGs are selected by means of the majority rule and used to predict new functional annotations. Functionally characterized RCGs are enriched in groups of genes associated to similar phenotypes. We exploit this fact to find new candidate disease genes for many OMIM phenotypes of unknown molecular origin. Conclusions/Significance: We predict new functional annotations for many human genes, showing that the integration of different data sets and coexpression measures significantly improves the scope of the results. Combining gene expression data, functional annotation and known phenotype-gene associations we provide candidate genes for several genetic diseases of unknown molecular basis. [ABSTRACT FROM AUTHOR]

Background: Even in the post-genomic era, the identification of candidate genes within loci associated with human genetic diseases is a very demanding task, because the critical region may typically contain hundreds of positional candidates. Since genes implicated in similar phenotypes tend to share very similar expression profiles, high throughput gene expression data may represent a very important resource to identify the best candidates for sequencing. However, so far, gene coexpression has not been used very successfully to prioritize positional candidates. Methodology/Principal Findings: We show that it is possible to reliably identify disease-relevant relationships among genes from massive microarray datasets by concentrating only on genes sharing similar expression profiles in both human and mouse. Moreover, we show systematically that the integration of human-mouse conserved coexpression with a phenotype similarity map allows the efficient identification of disease genes in large genomic regions. Finally, using this approach on 850 OMIM loci characterized by an unknown molecular basis, we propose high-probability candidates for 81 genetic diseases. Conclusion: Our results demonstrate that conserved coexpression, even at the human-mouse phylogenetic distance, represents a very strong criterion to predict disease-relevant relationships among human genes. [ABSTRACT FROM AUTHOR]

Background: A characteristic SYT-SSX fusion gene resulting from the chromosomal translocation t(X;18)(p11;q11) is detectable in almost all synovial sarcomas, a malignant soft tissue tumor widely believed to originate from as yet unidentified pluripotent stem cells. The resulting fusion protein has no DNA binding motifs but possesses protein-protein interaction domains that are believed to mediate association with chromatin remodeling complexes. Despite recent advances in the identification of molecules that interact with SYT-SSX and with the corresponding wild type SYT and SSX proteins, the mechanisms whereby the SYT-SSX might contribute to neoplastic transformation remain unclear. Epigenetic deregulation has been suggested to be one possible mechanism., Methodology/principal Findings: We addressed the effect of SYT/SSX expression on the transcriptome of four independent isolates of primary human bone marrow mesenchymal stem cells (hMSC). We observed transcriptional changes similar to the gene expression signature of synovial sarcoma, principally involving genes whose regulation is linked to epigenetic factors, including imprinted genes, genes with transcription start sites within a CpG island and chromatin related genes. Single population analysis revealed hMSC isolate-specific transcriptional changes involving genes that are important for biological functions of stem cells as well as genes that are considered to be molecular markers of synovial sarcoma including IGF2, EPHRINS, and BCL2. Methylation status analysis of sequences at the H19/IGF2 imprinted locus indicated that distinct epigenetic features characterize hMSC populations and condition the transcriptional effects of SYT-SSX expression., Conclusions/significance: Our observations suggest that epigenetic features may define the cellular microenvironment in which SYT-SSX displays its functional effects.

Primary and metastatic tumor growth induces host tissue responses that are believed to support tumor progression. Understanding the molecular changes within the tumor microenvironment during tumor progression may therefore be relevant not only for discovering potential therapeutic targets, but also for identifying putative molecular signatures that may improve tumor classification and predict clinical outcome. To selectively address stromal gene expression changes during cancer progression, we performed cDNA microarray analysis of laser-microdissected stromal cells derived from prostate intraepithelial neoplasia (PIN) and invasive cancer in a multistage model of prostate carcinogenesis. Human orthologs of genes identified in the stromal reaction to tumor progression in this mouse model were observed to be expressed in several human cancers, and to cluster prostate and breast cancer patients into groups with statistically different clinical outcomes. Univariate Cox analysis showed that overexpression of these genes is associated with shorter survival and recurrence-free periods. Taken together, our observations provide evidence that the expression signature of the stromal response to tumor invasion in a mouse tumor model can be used to probe human cancer, and to provide a powerful prognostic indicator for some of the most frequent human malignancies.