The multidimensional perturbation value: a single metric to measure similarity and activity of treatments in high-throughput multidimensional screens.

Screens using high-throughput, information-rich technologies such as microarrays, high-content screening (HCS), and next-generation sequencing (NGS) have become increasingly widespread. Compared with single-readout assays, these methods produce a more comprehensive picture of the effects of screened treatments. However, interpreting such multidimensional readouts is challenging. Univariate statistics such as t-tests and Z-factors cannot easily be applied to multidimensional profiles, leaving no obvious way to answer common screening questions such as "Is treatment X active in this assay?" and "Is treatment X different from (or equivalent to) treatment Y?" We have developed a simple, straightforward metric, the multidimensional perturbation value (mp-value), which can be used to answer these questions. Here, we demonstrate application of the mp-value to three data sets: a multiplexed gene expression screen of compounds and genomic reagents, a microarray-based gene expression screen of compounds, and an HCS compound screen. In all data sets, active treatments were successfully identified using the mp-value, and simulations and follow-up analyses supported the mp-value's statistical and biological validity. We believe the mp-value represents a promising way to simplify the analysis of multidimensional data while taking full advantage of its richness.

Molecular mechanism underlying the regulatory specificity of a Drosophila homeodomain protein that specifies myoblast identity.

A subfamily of Drosophila homeodomain (HD) transcription factors (TFs) controls the identities of individual muscle founder cells (FCs). However, the molecular mechanisms by which these TFs generate unique FC genetic programs remain unknown. To investigate this problem, we first applied genome-wide mRNA expression profiling to identify genes that are activated or repressed by the muscle HD TFs Slouch (Slou) and Muscle segment homeobox (Msh). Next, we used protein-binding microarrays to define the sequences that are bound by Slou, Msh and other HD TFs that have mesodermal expression. These studies revealed that a large class of HDs, including Slou and Msh, predominantly recognize TAAT core sequences but that each HD also binds to unique sites that deviate from this canonical motif. To understand better the regulatory specificity of an individual FC identity HD, we evaluated the functions of atypical binding sites that are preferentially bound by Slou relative to other HDs within muscle enhancers that are either activated or repressed by this TF. These studies showed that Slou regulates the activities of particular myoblast enhancers through Slou-preferred sequences, whereas swapping these sequences for sites that are capable of binding to multiple HD family members does not support the normal regulatory functions of Slou. Moreover, atypical Slou-binding sites are overrepresented in putative enhancers associated with additional Slou-responsive FC genes. Collectively, these studies provide new insights into the roles of individual HD TFs in determining cellular identity, and suggest that the diversity of HD binding preferences can confer regulatory specificity.

An HNF4α-miRNA inflammatory feedback circuit regulates hepatocellular oncogenesis.

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.

STAT3 activation of miR-21 and miR-181b-1 via PTEN and CYLD are part of the epigenetic switch linking inflammation to cancer.

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.

A transcriptional signature and common gene networks link cancer with lipid metabolism and diverse human diseases.

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.

Conservation and regulatory associations of a wide affinity range of mouse transcription factor binding sites.

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.

Diversity and complexity in DNA recognition by transcription factors.

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.

Variation in homeodomain DNA binding revealed by high-resolution analysis of sequence preferences.

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.

Systematic identification of mammalian regulatory motifs' target genes and functions.

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.

Smoking and suicidal behaviors in the National Comorbidity Survey: Replication.

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.