Training Signaling Pathway Maps to Biochemical Data with Constrained Fuzzy Logic: Quantitative Analysis of Liver Cell Responses to Inflammatory Stimuli

Predictive understanding of cell signaling network operation based on general prior knowledge but consistent with empirical data in a specific environmental context is a current challenge in computational biology. Recent work has demonstrated that Boolean logic can be used to create context-specific network models by training proteomic pathway maps to dedicated biochemical data; however, the Boolean formalism is restricted to characterizing protein species as either fully active or inactive. To advance beyond this limitation, we propose a novel form of fuzzy logic sufficiently flexible to model quantitative data but also sufficiently simple to efficiently construct models by training pathway maps on dedicated experimental measurements. Our new approach, termed constrained fuzzy logic (cFL), converts a prior knowledge network (obtained from literature or interactome databases) into a computable model that describes graded values of protein activation across multiple pathways. We train a cFL-converted network to experimental data describing hepatocytic protein activation by inflammatory cytokines and demonstrate the application of the resultant trained models for three important purposes: (a) generating experimentally testable biological hypotheses concerning pathway crosstalk, (b) establishing capability for quantitative prediction of protein activity, and (c) prediction and understanding of the cytokine release phenotypic response. Our methodology systematically and quantitatively trains a protein pathway map summarizing curated literature to context-specific biochemical data. This process generates a computable model yielding successful prediction of new test data and offering biological insight into complex datasets that are difficult to fully analyze by intuition alone.National Institutes of Health (U.S.) (NIH grant P50-GM68762)National Institutes of Health (U.S.) (Grant U54-CA112967)United States. Dept. of Defense (Institute for Collaborative Biotechnologies

TFM-Explorer: mining cis-regulatory regions in genomes

DNA-binding transcription factors (TFs) play a central role in transcription regulation, and computational approaches that help in elucidating complex mechanisms governing this basic biological process are of great use. In this perspective, we present the TFM-Explorer web server that is a toolbox to identify putative TF binding sites within a set of upstream regulatory sequences of genes sharing some regulatory mechanisms. TFM-Explorer finds local regions showing overrepresentation of binding sites. Accepted organisms are human, mouse, rat, chicken and drosophila. The server employs a number of features to help users to analyze their data: visualization of selected binding sites on genomic sequences, and selection of cis-regulatory modules. TFM-Explorer is available at http://bioinfo.lifl.fr/TFM

Altered translation of GATA1 in Diamond-Blackfan anemia

Ribosomal protein haploinsufficiency occurs in diverse human diseases including Diamond-Blackfan anemia (DBA)[superscript 1, 2], congenital asplenia[superscript 3] and T cell leukemia[superscript 4]. Yet, how mutations in genes encoding ubiquitously expressed proteins such as these result in cell-type– and tissue-specific defects remains unknown[superscript 5]. Here, we identify mutations in GATA1, encoding the critical hematopoietic transcription factor GATA-binding protein-1, that reduce levels of full-length GATA1 protein and cause DBA in rare instances. We show that ribosomal protein haploinsufficiency, the more common cause of DBA, can lead to decreased GATA1 mRNA translation, possibly resulting from a higher threshold for initiation of translation of this mRNA in comparison with other mRNAs. In primary hematopoietic cells from patients with mutations in RPS19, encoding ribosomal protein S19, the amplitude of a transcriptional signature of GATA1 target genes was globally and specifically reduced, indicating that the activity, but not the mRNA level, of GATA1 is decreased in patients with DBA associated with mutations affecting ribosomal proteins. Moreover, the defective hematopoiesis observed in patients with DBA associated with ribosomal protein haploinsufficiency could be partially overcome by increasing GATA1 protein levels. Our results provide a paradigm by which selective defects in translation due to mutations affecting ubiquitous ribosomal proteins can result in human disease.National Institutes of Health (U.S.) (Grant P01 HL32262)National Institutes of Health (U.S.) (Grant U54 HG003067-09

Wnt addiction of genetically defined cancers reversed by PORCN inhibition

Enhanced sensitivity to Wnts is an emerging hallmark of a subset of cancers, defined in part by mutations regulating the abundance of their receptors. Whether these mutations identify a clinical opportunity is an important question. Inhibition of Wnt secretion by blocking an essential post-translational modification, palmitoleation, provides a useful therapeutic intervention. We developed a novel potent, orally available PORCN inhibitor, ETC-1922159 (henceforth called ETC-159) that blocks the secretion and activity of all Wnts. ETC-159 is remarkably effective in treating RSPO-translocation bearing colorectal cancer (CRC) patient-derived xenografts. This is the first example of effective targeted therapy for this subset of CRC. Consistent with a central role of Wnt signaling in regulation of gene expression, inhibition of PORCN in RSPO3-translocated cancers causes a marked remodeling of the transcriptome, with loss of cell cycle, stem cell and proliferation genes, and an increase in differentiation markers. Inhibition of Wnt signaling by PORCN inhibition holds promise as differentiation therapy in genetically defined human cancers

Assembly PCR synthesis of optimally designed, compact, multi-responsive promoters suited to gene therapy application

MRC-DTA studentship award British Pharmacological Society Integrative Awar

A systematic, large-scale comparison of transcription factor binding site models

Background The modelling of gene regulation is a major challenge in biomedical research. This process is dominated by transcription factors (TFs) and mutations in their binding sites (TFBSs) may cause the misregulation of genes, eventually leading to disease. The consequences of DNA variants on TF binding are modelled in silico using binding matrices, but it remains unclear whether these are capable of accurately representing in vivo binding. In this study, we present a systematic comparison of binding models for 82 human TFs from three freely available sources: JASPAR matrices, HT-SELEX-generated models and matrices derived from protein binding microarrays (PBMs). We determined their ability to detect experimentally verified “real” in vivo TFBSs derived from ENCODE ChIP-seq data. As negative controls we chose random downstream exonic sequences, which are unlikely to harbour TFBS. All models were assessed by receiver operating characteristics (ROC) analysis. Results While the area- under-curve was low for most of the tested models with only 47 % reaching a score of 0.7 or higher, we noticed strong differences between the various position-specific scoring matrices with JASPAR and HT-SELEX models showing higher success rates than PBM-derived models. In addition, we found that while TFBS sequences showed a higher degree of conservation than randomly chosen sequences, there was a high variability between individual TFBSs. Conclusions Our results show that only few of the matrix-based models used to predict potential TFBS are able to reliably detect experimentally confirmed TFBS. We compiled our findings in a freely accessible web application called ePOSSUM (http:/mutationtaster.charite.de/ePOSSUM/) which uses a Bayes classifier to assess the impact of genetic alterations on TF binding in user-defined sequences. Additionally, ePOSSUM provides information on the reliability of the prediction using our test set of experimentally confirmed binding sites

Cellular dissection of psoriasis for transcriptome analyses and the post-GWAS era

Abstract Background Genome-scale studies of psoriasis have been used to identify genes of potential relevance to disease mechanisms. For many identified genes, however, the cell type mediating disease activity is uncertain, which has limited our ability to design gene functional studies based on genomic findings. Methods We identified differentially expressed genes (DEGs) with altered expression in psoriasis lesions (n = 216 patients), as well as candidate genes near susceptibility loci from psoriasis GWAS studies. These gene sets were characterized based upon their expression across 10 cell types present in psoriasis lesions. Susceptibility-associated variation at intergenic (non-coding) loci was evaluated to identify sites of allele-specific transcription factor binding. Results Half of DEGs showed highest expression in skin cells, although the dominant cell type differed between psoriasis-increased DEGs (keratinocytes, 35%) and psoriasis-decreased DEGs (fibroblasts, 33%). In contrast, psoriasis GWAS candidates tended to have highest expression in immune cells (71%), with a significant fraction showing maximal expression in neutrophils (24%, P < 0.001). By identifying candidate cell types for genes near susceptibility loci, we could identify and prioritize SNPs at which susceptibility variants are predicted to influence transcription factor binding. This led to the identification of potentially causal (non-coding) SNPs for which susceptibility variants influence binding of AP-1, NF-κB, IRF1, STAT3 and STAT4. Conclusions These findings underscore the role of innate immunity in psoriasis and highlight neutrophils as a cell type linked with pathogenetic mechanisms. Assignment of candidate cell types to genes emerging from GWAS studies provides a first step towards functional analysis, and we have proposed an approach for generating hypotheses to explain GWAS hits at intergenic loci.http://deepblue.lib.umich.edu/bitstream/2027.42/109537/1/12920_2013_Article_485.pd

Common variation near CDKN1A, POLD3 and SHROOM2 influences colorectal cancer risk

We performed a meta-analysis of five genome-wide association studies to identify common variants influencing colorectal cancer (CRC) risk comprising 8,682 cases and 9,649 controls. Replication analysis was performed in case-control sets totaling 21,096 cases and 19,555 controls. We identified three new CRC risk loci at 6p21 (rs1321311, near CDKN1A; P = 1.14 × 10(-10)), 11q13.4 (rs3824999, intronic to POLD3; P = 3.65 × 10(-10)) and Xp22.2 (rs5934683, near SHROOM2; P = 7.30 × 10(-10)) This brings the number of independent loci associated with CRC risk to 20 and provides further insight into the genetic architecture of inherited susceptibility to CRC.Swedish Research Council et al.Manuscrip

Selected heterozygosity at cis-regulatory sequences increases the expression homogeneity of a cell population in humans

Background: Examples of heterozygote advantage in humans are scarce and limited to protein-coding sequences. Here, we attempt a genome-wide functional inference of advantageous heterozygosity at cis-regulatory regions. Results: The single-nucleotide polymorphisms bearing the signatures of balancing selection are enriched in active cis-regulatory regions of immune cells and epithelial cells, the latter of which provide barrier function and innate immunity. Examples associated with ancient trans-specific balancing selection are also discovered. Allelic imbalance in chromatin accessibility and divergence in transcription factor motif sequences indicate that these balanced polymorphisms cause distinct regulatory variation. However, a majority of these variants show no association with the expression level of the target gene. Instead, single-cell experimental data for gene expression and chromatin accessibility demonstrate that heterozygous sequences can lower cell-to-cell variability in proportion to selection strengths. This negative correlation is more pronounced for highly expressed genes and consistently observed when using different data and methods. Based on mathematical modeling, we hypothesize that extrinsic noise from fluctuations in transcription factor activity may be amplified in homozygotes, whereas it is buffered in heterozygotes. While high expression levels are coupled with intrinsic noise reduction, regulatory heterozygosity can contribute to the suppression of extrinsic noise. Conclusions: This mechanism may confer a selective advantage by increasing cell population homogeneity and thereby enhancing the collective action of the cells, especially of those involved in the defense systems in humansope