Abstract: BACKGROUND & AIMS: Neurotensin promotes inflammation and colon cancer via the receptor NTR1. MicroRNAs (miRs) regulate protein synthesis by degrading or preventing translation of mRNAs. We analyzed expression of 365 different microRNAs by human colonic epithelial cells (NCM460) following activation of NTR1. METHODS: We performed microarray analysis of mRNA expression by neurotensin-stimulated NCM460 cells that overexpressed NTR1. NF-κB binding sites were identified and tumorigenesis was assessed using soft agar assays and xenograft analysis of SCID mice. Targets of neurotensin-regulated microRNAs were identified via bioinformatic, real-time PCR, and immunoblot analyses. We analyzed RNA samples from human normal colon and tumor samples. RESULTS: Neurotensin stimulated differential expression of 38 microRNAs, including miR-21 and miR-155, which have been associated with tumor growth and contain NF-κB binding sites. Neurotensin expression increased colony formation by HCT-116 cells. Blocking miR-21 and/or miR-155 prevented colony formation (P<.001). In mice, intraperitoneal administration of neurotensin increased the growth rate of HCT-116 xenograft tumors; blocking miR-21 and/or miR-155 slowed this tumor growth. Neurotensin activated Akt in HCT-116 cells; this effect was inhibited by blocking miR-21 and/or miR-155 (P<.001). Neurotensin activated AKT through miR-155-mediated suppression of the phosphatase PPP2CA. Levels PTEN and SOCS1 mRNA, potential targets of miR-21 and miR-155, respectively, were downregulated by these miRs. Levels of NTR1, miR-21, and miR-155 increased significantly in human colon tumor samples, compared with normal tissues, whereas PPP2CA, SOCS1, and PTEN mRNAs were significantly reduced. CONCLUSIONS: NTR1 activation stimulates expression of miR-21 and miR-155 in colonocytes, via Akt and NF-κB, to downregulate PTEN and SOCS1 and promote growth of tumors in mice. Levels of NTR1, miR-21, and miR-155 increase in human colon tumor samples and correlate with tumor stage.
Abstract: Hepatocyte nuclear factor 4α (HNF4α) is essential for liver development and hepatocyte function. Here, we show that transient inhibition of HNF4α initiates hepatocellular transformation through a microRNA-inflammatory feedback loop circuit consisting of miR-124, IL6R, STAT3, miR-24, and miR-629. Moreover, we show that, once this circuit is activated, it maintains suppression of HNF4α and sustains oncogenesis. Systemic administration of miR-124, which modulates inflammatory signaling, prevents and suppresses hepatocellular carcinogenesis by inducing tumor-specific apoptosis without toxic side effects. As we also show that this HNF4α circuit is perturbed in human hepatocellular carcinomas, our data raise the possibility that manipulation of this microRNA feedback-inflammatory loop has therapeutic potential for treating liver cancer.
Abstract: Sequence-specific binding by transcription factors (TFs) interprets regulatory information encoded in the genome. Using recently published universal protein binding microarray (PBM) data on the in vitro DNA binding preferences of these proteins for all possible 8-base-pair sequences, we examined the evolutionary conservation and enrichment within putative regulatory regions of the binding sequences of a diverse library of 104 nonredundant mouse TFs spanning 22 different DNA-binding domain structural classes. We found that not only high affinity binding sites, but also numerous moderate and low affinity binding sites, are under negative selection in the mouse genome. These 8-mers occur preferentially in putative regulatory regions of the mouse genome, including CpG islands and non-exonic ultraconserved elements (UCEs). Of TFs whose PBM "bound" 8-mers are enriched within sets of tissue-specific UCEs, many are expressed in the same tissue(s) as the UCE-driven gene expression. Phylogenetically conserved motif occurrences of various TFs were also enriched in the noncoding sequence surrounding numerous gene sets corresponding to Gene Ontology categories and tissue-specific gene expression clusters, suggesting involvement in transcriptional regulation of those genes. Altogether, our results indicate that many of the sequences bound by these proteins in vitro, including lower affinity DNA sequences, are likely to be functionally important in vivo. This study not only provides an initial analysis of the potential regulatory associations of 104 mouse TFs, but also presents an approach for the functional analysis of TFs from any other metazoan genome as their DNA binding preferences are determined by PBMs or other technologies.
Abstract: A transient inflammatory signal can initiate an epigenetic switch from nontransformed to cancer cells via a positive feedback loop involving NF-kappaB, Lin28, let-7, and IL-6. We identify differentially regulated microRNAs important for this switch and putative transcription factor binding sites in their promoters. STAT3, a transcription factor activated by IL-6, directly activates miR-21 and miR-181b-1. Remarkably, transient expression of either microRNA induces the epigenetic switch. MiR-21 and miR-181b-1, respectively, inhibit PTEN and CYLD tumor suppressors, leading to increased NF-kappaB activity required to maintain the transformed state. These STAT3-mediated regulatory circuits are required for the transformed state in diverse cell lines and tumor growth in xenografts, and their transcriptional signatures are observed in colon adenocarcinomas. Thus, STAT3 is not only a downstream target of IL-6 but, with miR-21, miR-181b-1, PTEN, and CYLD, is part of the positive feedback loop that underlies the epigenetic switch that links inflammation to cancer.
Abstract: Transcriptional profiling of two isogenic models of transformation identifies a gene signature linking cancer with inflammatory and metabolic diseases. In accord with this common transcriptional program, many drugs used for treatment of diabetes and cardiovascular diseases inhibit transformation and tumor growth. Unexpectedly, lipid metabolism genes are important for transformation and are upregulated in cancer tissues. As in atherosclerosis, oxidized LDL and its receptor OLR1 activate the inflammatory pathway through NF-kappaB, leading to transformation. OLR1 is important for maintaining the transformed state in developmentally diverse cancer cell lines and for tumor growth, suggesting a molecular connection between cancer and atherosclerosis. We suggest that the interplay between this common transcriptional program and cell-type-specific factors gives rise to phenotypically disparate human diseases.
Abstract: Sequence preferences of DNA binding proteins are a primary mechanism by which cells interpret the genome. Despite the central importance of these proteins in physiology, development, and evolution, comprehensive DNA binding specificities have been determined experimentally for only a few proteins. Here, we used microarrays containing all 10-base pair sequences to examine the binding specificities of 104 distinct mouse DNA binding proteins representing 22 structural classes. Our results reveal a complex landscape of binding, with virtually every protein analyzed possessing unique preferences. Roughly half of the proteins each recognized multiple distinctly different sequence motifs, challenging our molecular understanding of how proteins interact with their DNA binding sites. This complexity in DNA recognition may be important in gene regulation and in the evolution of transcriptional regulatory networks.
Abstract: We developed an algorithm, Lever, that systematically maps metazoan DNA regulatory motifs or motif combinations to sets of genes. Lever assesses whether the motifs are enriched in cis-regulatory modules (CRMs), predicted by our PhylCRM algorithm, in the noncoding sequences surrounding the genes. Lever analysis allows unbiased inference of functional annotations to regulatory motifs and candidate CRMs. We used human myogenic differentiation as a model system to statistically assess greater than 25,000 pairings of gene sets and motifs or motif combinations. We assigned functional annotations to candidate regulatory motifs predicted previously and identified gene sets that are likely to be co-regulated via shared regulatory motifs. Lever allows moving beyond the identification of putative regulatory motifs in mammalian genomes, toward understanding their biological roles. This approach is general and can be applied readily to any cell type, gene expression pattern or organism of interest.
Abstract: Most homeodomains are unique within a genome, yet many are highly conserved across vast evolutionary distances, implying strong selection on their precise DNA-binding specificities. We determined the binding preferences of the majority (168) of mouse homeodomains to all possible 8-base sequences, revealing rich and complex patterns of sequence specificity and showing that there are at least 65 distinct homeodomain DNA-binding activities. We developed a computational system that successfully predicts binding sites for homeodomain proteins as distant from mouse as Drosophila and C. elegans, and we infer full 8-mer binding profiles for the majority of known animal homeodomains. Our results provide an unprecedented level of resolution in the analysis of this simple domain structure and suggest that variation in sequence recognition may be a factor in its functional diversity and evolutionary success.
Abstract: Controversy exists about the role of mental disorders in the consistently documented association between smoking and suicidal behavior. This controversy is addressed here with data from the nationally representative National Comorbidity Survey-Replication (NCS-R). Assessments were made of 12-month smoking, suicidal behaviors (ideation, plans, attempts), and DSM-IV disorders (anxiety, mood, impulse-control, and substance use disorders). Statistically significant odds ratios (2.9-3.1) were found between 12-month smoking and 12-month suicidal behaviors. However, the associations of smoking with the outcomes became insignificant with controls for DSM-IV mental disorders. Although clear adjudication among contending hypotheses about causal mechanisms cannot be made from the cross-sectional NCS-R data, the results make it clear that future research on smoking and suicidal behaviors should focus more centrally than previous research on mental disorders either as common causes, markers, or mediators.
Abstract: Little is known about the population prevalence of sleep problems or whether the associations of sleep problems with role impairment are due to comorbid mental disorders.
Abstract: Despite growing interest in adult attention-deficit/hyperactivity disorder (ADHD), little is known about predictors of persistence of childhood cases into adulthood.
Abstract: A unified approach is taken for deriving new generalization data dependent bounds for several classes of algorithms explored in the existing literature by different approaches. This unified approach is based on an extension of Vapnik's inequality for VC classes of sets to random classes of sets - that is, classes depending on the random data, invariant under permutation of the data and possessing the increasing property.Generalization bounds are derived for convex combinations of functions from random classes with certain properties. Algorithms, such as SVMs (support vector machines), boosting with decision stumps, radial basis function networks, some hierarchies of kernel machines or convex combinations of indicator functions over sets with finite VC dimension, generate classifier functions that fall into the above category. We also explore the individual complexities of the classifiers, such as sparsity of weights and weighted variance over clusters from the convex combination introduced by Koltchinskii and Panchenko (2004), and show sparsity-type and cluster-variance-type generalization bounds for random classes.
Abstract: We prove new margin type bounds on the generalization error of voting classifiers that take into account the sparsity of weights and certain measures of clustering of weak classifiers in the convex combination. We also present experimental results to illustrate the behavior of the parameters of interest for several data sets.
