A Systems Biology Approach to Transcription Factor Binding Site Prediction

The elucidation of mammalian transcriptional regulatory networks holds great promise for both basic and translational research and remains one the greatest challenges to systems biology. Recent reverse engineering methods deduce regulatory interactions from large-scale mRNA expression profiles and cross-species conserved regulatory regions in DNA. Technical challenges faced by these methods include distinguishing between direct and indirect interactions, associating transcription regulators with predicted transcription factor binding sites (TFBSs), identifying non-linearly conserved binding sites across species, and providing realistic accuracy estimates.We address these challenges by closely integrating proven methods for regulatory network reverse engineering from mRNA expression data, linearly and non-linearly conserved regulatory region discovery, and TFBS evaluation and discovery. Using an extensive test set of high-likelihood interactions, which we collected in order to provide realistic prediction-accuracy estimates, we show that a careful integration of these methods leads to significant improvements in prediction accuracy. To verify our methods, we biochemically validated TFBS predictions made for both transcription factors (TFs) and co-factors; we validated binding site predictions made using a known E2F1 DNA-binding motif on E2F1 predicted promoter targets, known E2F1 and JUND motifs on JUND predicted promoter targets, and a de novo discovered motif for BCL6 on BCL6 predicted promoter targets. Finally, to demonstrate accuracy of prediction using an external dataset, we showed that sites matching predicted motifs for ZNF263 are significantly enriched in recent ZNF263 ChIP-seq data.Using an integrative framework, we were able to address technical challenges faced by state of the art network reverse engineering methods, leading to significant improvement in direct-interaction detection and TFBS-discovery accuracy. We estimated the accuracy of our framework on a human B-cell specific test set, which may help guide future methodological development

Tissue-specific regulatory elements in mammalian promoters

Transcription factor-binding sites and the cis-regulatory modules they compose are central determinants of gene expression. We previously showed that binding site motifs and modules in proximal promoters can be used to predict a significant portion of mammalian tissue-specific transcription. Here, we report on a systematic analysis of promoters controlling tissue-specific expression in heart, kidney, liver, pancreas, skeletal muscle, testis and CD4 T cells, for both human and mouse. We integrated multiple sources of expression data to compile sets of transcripts with strong evidence for tissue-specific regulation. The analysis of the promoters corresponding to these sets produced a catalog of predicted tissue-specific motifs and modules, and cis-regulatory elements. Predicted regulatory interactions are supported by statistical evidence, and provide a foundation for targeted experiments that will improve our understanding of tissue-specific regulatory networks. In a broader context, methods used to construct the catalog provide a model for the analysis of genomic regions that regulate differentially expressed genes

ChIP-on-chip significance analysis reveals large-scale binding and regulation by human transcription factor oncogenes

ChIP-on-chip has emerged as a powerful tool to dissect the complex network of regulatory interactions between transcription factors and their targets. However, most ChIP-on-chip analysis methods use conservative approaches aimed to minimize false-positive transcription factor targets. We present a model with improved sensitivity in detecting binding events from ChIP-on-chip data. Biochemically validated analysis in human T-cells reveals that three transcription factor oncogenes, NOTCH1, MYC, and HES1, bind one order of magnitude more promoters than previously thought. Gene expression profiling upon NOTCH1 inhibition shows broad-scale functional regulation across the entire range of predicted target genes, establishing a closer link between occupancy and regulation. Finally, the resolution of a more complete map of transcriptional targets reveals that MYC binds nearly all promoters bound by NOTCH1. Overall, these results suggest an unappreciated complexity of transcriptional regulatory networks and highlight the fundamental importance of genome-scale analysis to represent transcriptional programs

Correlating measurements across samples improves accuracy of large-scale expression profile experiments

Gene expression profiling technologies suffer from poor reproducibility across replicate experiments. However, when analyzing large datasets, probe-level expression profile correlation can help identify flawed probes and lead to the construction of truer probe sets with improved reproducibility. We describe methods to eliminate uninformative and flawed probes, account for dependence between probes, and address variability due to transcript-isoform mixtures. We test and validate our approach on Affymetrix microarrays and outline their future adaptation to other technologies

The Number Of Titrated Microrna Species Dictates Cerna Regulation

microRNAs (miRNAs) play key roles in cancer, but their propensity to couple their targets as competing endogenous RNAs (ceRNAs) has only recently emerged. Multiple models have studied ceRNA regulation, but these models did not account for the effects of co-regulation by miRNAs with many targets. We modeled ceRNA and simulated its effects using established parameters for miRNA/mRNA interaction kinetics while accounting for co-regulation by multiple miRNAs with many targets. Our simulations suggested that co-regulation by many miRNA species is more likely to produce physiologically relevant context-independent couplings. To test this, we studied the overlap of inferred ceRNA networks from four tumor contexts-our proposed pan-cancer ceRNA interactome (PCI). PCI was composed of interactions between genes that were coregulated by nearly three-times as many miRNAs as other inferred ceRNA interactions. Evidence from expression-profiling datasets suggested that PCI interactions are predictive of gene expression in 12 independent tumor-and non-tumor contexts. Biochemical assays confirmed ceRNA couplings for two PCI subnetworks, including oncogenes CCND1, HIF1A and HMGA2, and tumor suppressors PTEN, RB1 and TP53. Our results suggest that PCI is enriched for context-independent interactions that are coupled by many miRNA species and are more likely to be context independent

A human B-cell interactome identifies MYB and FOXM1 as master regulators of proliferation in germinal centers

Assembly of a mixed interaction network specific to human B cells.Identification and validation of master regulators of germinal center reaction.MYB and FOXM1 are synergistic master regulators of proliferation in germinal center B cells and control a new protein complex involving replication and mitotic-related genes

Kinetic modelling of competition and depletion of shared miRNAs by competing endogenous RNAs

Non-conding RNAs play a key role in the post-transcriptional regulation of mRNA translation and turnover in eukaryotes. miRNAs, in particular, interact with their target RNAs through protein-mediated, sequence-specific binding, giving rise to extended and highly heterogeneous miRNA-RNA interaction networks. Within such networks, competition to bind miRNAs can generate an effective positive coupling between their targets. Competing endogenous RNAs (ceRNAs) can in turn regulate each other through miRNA-mediated crosstalk. Albeit potentially weak, ceRNA interactions can occur both dynamically, affecting e.g. the regulatory clock, and at stationarity, in which case ceRNA networks as a whole can be implicated in the composition of the cell's proteome. Many features of ceRNA interactions, including the conditions under which they become significant, can be unraveled by mathematical and in silico models. We review the understanding of the ceRNA effect obtained within such frameworks, focusing on the methods employed to quantify it, its role in the processing of gene expression noise, and how network topology can determine its reach.Comment: review article, 29 pages, 7 figure

MYCN-driven fatty acid uptake is a metabolic vulnerability in neuroblastoma

Half of high-risk neuroblastoma patients have MYCN amplification. Here, the authors show that MYCN induces fatty acid uptake and synthesis to support neuroblastoma and inhibition of a fatty acid transporter impairs tumor progression in preclinical models.Neuroblastoma (NB) is a childhood cancer arising from sympatho-adrenal neural crest cells. MYCN amplification is found in half of high-risk NB patients; however, no available therapies directly target MYCN. Using multi-dimensional metabolic profiling in MYCN expression systems and primary patient tumors, we comprehensively characterized the metabolic landscape driven by MYCN in NB. MYCN amplification leads to glycerolipid accumulation by promoting fatty acid (FA) uptake and biosynthesis. We found that cells expressing amplified MYCN depend highly on FA uptake for survival. Mechanistically, MYCN directly upregulates FA transport protein 2 (FATP2), encoded by SLC27A2. Genetic depletion of SLC27A2 impairs NB survival, and pharmacological SLC27A2 inhibition selectively suppresses tumor growth, prolongs animal survival, and exerts synergistic anti-tumor effects when combined with conventional chemotherapies in multiple preclinical NB models. This study identifies FA uptake as a critical metabolic dependency for MYCN-amplified tumors. Inhibiting FA uptake is an effective approach for improving current treatment regimens