The gastrin and cholecystokinin receptors mediated signaling network : a scaffold for data analysis and new hypotheses on regulatory mechanisms

Abstract Background The gastrointestinal peptide hormones cholecystokinin and gastrin exert their biological functions via cholecystokinin receptors CCK1R and CCK2R respectively. Gastrin, a central regulator of gastric acid secretion, is involved in growth and differentiation of gastric and colonic mucosa, and there is evidence that it is pro-carcinogenic. Cholecystokinin is implicated in digestion, appetite control and body weight regulation, and may play a role in several digestive disorders. Results We performed a detailed analysis of the literature reporting experimental evidence on signaling pathways triggered by CCK1R and CCK2R, in order to create a comprehensive map of gastrin and cholecystokinin-mediated intracellular signaling cascades. The resulting signaling map captures 413 reactions involving 530 molecular species, and incorporates the currently available knowledge into one integrated signaling network. The decomposition of the signaling map into sub-networks revealed 18 modules that represent higher-level structures of the signaling map. These modules allow a more compact mapping of intracellular signaling reactions to known cell behavioral outcomes such as proliferation, migration and apoptosis. The integration of large-scale protein-protein interaction data to this literature-based signaling map in combination with topological analyses allowed us to identify 70 proteins able to increase the compactness of the map. These proteins represent experimentally testable hypotheses for gaining new knowledge on gastrin- and cholecystokinin receptor signaling. The CCKR map is freely available both in a downloadable, machine-readable SBML-compatible format and as a web resource through PAYAO ( http://sblab.celldesigner.org:18080/Payao11/bin/ ). Conclusion We have demonstrated how a literature-based CCKR signaling map together with its protein interaction extensions can be analyzed to generate new hypotheses on molecular mechanisms involved in gastrin- and cholecystokinin-mediated regulation of cellular processes

Functional studies on transfected cell microarray analysed by linear regression modelling

Transfected cell microarray is a promising method for accelerating the functional exploration of the genome, giving information about protein function in the living cell. The microarrays consist of clusters of cells (spots) overexpressing or silencing a particular gene product. The subsequent analysis of the phenotypic consequences of such perturbations can then be detected using cell-based assays. The focus in the present study was to establish an experimental design and a robust analysis approach for fluorescence intensity data, and to address the use of replicates for studying regulation of gene expression with varying complexity and effect size. Our analysis pipeline includes measurement of fluorescence intensities, normalization strategies using negative control spots and internal control plasmids, and linear regression (ANOVA) modelling for estimating biological effects and calculating P-values for comparisons of interests. Our results show the potential of transfected cell microarrays in studying complex regulation of gene expression by enabling measurement of biological responses in cells with overexpression and downregulation of specific gene products, combined with the possibility of assaying the effects of external stimuli. Simulation experiments show that transfected cell microarrays can be used to reliably detect even quantitatively minor biological effects by including several technical and experimental replicates

Different skeletal effects of the peroxisome proliferator activated receptor (PPAR)α agonist fenofibrate and the PPARγ agonist pioglitazone

<p>Abstract</p> <p>Background</p> <p>All the peroxisome proliferator activated receptors (PPARs) are found to be expressed in bone cells. The PPARγ agonist rosiglitazone has been shown to decrease bone mass in mice and thiazolidinediones (TZDs) have recently been found to increase bone loss and fracture risk in humans treated for type 2 diabetes mellitus. The aim of the study was to examine the effect of the PPARα agonist fenofibrate (FENO) and the PPARγ agonist pioglitazone (PIO) on bone in intact female rats.</p> <p>Methods</p> <p>Rats were given methylcellulose (vehicle), fenofibrate or pioglitazone (35 mg/kg body weight/day) by gavage for 4 months. BMC, BMD, and body composition were measured by DXA. Histomorphometry and biomechanical testing of excised femurs were performed. Effects of the compounds on bone cells were studied.</p> <p>Results</p> <p>The FENO group had higher femoral BMD and smaller medullary area at the distal femur; while trabecular bone volume was similar to controls. Whole body BMD, BMC, and trabecular bone volume were lower, while medullary area was increased in PIO rats compared to controls. Ultimate bending moment and energy absorption of the femoral shafts were reduced in the PIO group, while similar to controls in the FENO group. Plasma osteocalcin was higher in the FENO group than in the other groups. FENO stimulated proliferation and differentiation of, and OPG release from, the preosteoblast cell line MC3T3-E1.</p> <p>Conclusion</p> <p>We show opposite skeletal effects of PPARα and γ agonists in intact female rats. FENO resulted in significantly higher femoral BMD and lower medullary area, while PIO induced bone loss and impairment of the mechanical strength. This represents a novel effect of PPARα activation.</p

Molecular mechanisms involved in TNF - and gastrin-mediated gene regulation

Cells in a multicellular organism depend on extensive communication with each other. Eukaryotic cells have developed elaborate signal transduction systems by which externally provided information (hormones, cytokines, growth factors, ions and other signaling molecules) is converted into intracellular information that regulates the internal workings of the cell, such as transcription of genes. This can be exemplified by gastric acid production in humans. Secretion of gastric acid is regulated by the peptide hormone gastrin, and involves an interplay between different cells in the stomach mucosa as well as induction of gene expression (Figure 1). The physiological response to food intake includes release of gastrin from G-cells. Gastrin binds to receptors on the ECL-cells and thereby induces histamine release from internal vesicles. Histamine in turn acts upon parietal cells that are stimulated to produce and secrete gastric acid. The regulatory functions of gastrin also include release of somatostatin from D-cells and induction of histidine decarboxylase (HDC) gene expression. HDC catalyses the ratelimiting step in histamine production in ECL-cells

A high-throughput drug combination screen of targeted small molecule inhibitors in cancer cell lines

While there is a high interest in drug combinations in cancer therapy, openly accessible datasets for drug combination responses are sparse. Here we present a dataset comprising 171 pairwise combinations of 19 individual drugs targeting signal transduction mechanisms across eight cancer cell lines, where the effect of each drug and drug combination is reported as cell viability assessed by metabolic activity. Drugs are chosen by their capacity to specifically interfere with well-known signal transduction mechanisms. Signalling processes targeted by the drugs include PI3K/AKT, NFkB, JAK/STAT, CTNNB1/TCF, and MAPK pathways. Drug combinations are classified as synergistic based on the Bliss independence synergy metrics. The data identifies combinations that synergistically reduce cancer cell viability and that can be of interest for further pre-clinical investigations.publishedVersio

High-throughput screening reveals higher synergistic effect of MEK inhibitor combinations in colon cancer spheroids

Drug combinations have been proposed to combat drug resistance, but putative treatments are challenged by low bench-to-bed translational efficiency. To explore the effect of cell culture format and readout methods on identification of synergistic drug combinations in vitro, we studied response to 21 clinically relevant drug combinations in standard planar (2D) layouts and physiologically more relevant spheroid (3D) cultures of HCT-116, HT-29 and SW-620 cells. By assessing changes in viability, confluency and spheroid size, we were able to identify readout- and culture format-independent synergies, as well as synergies specific to either culture format or readout method. In particular, we found that spheroids, compared to 2D cultures, were generally both more sensitive and showed greater synergistic response to combinations involving a MEK inhibitor. These results further shed light on the importance of including more complex culture models in order to increase the efficiency of drug discovery pipelines.publishedVersio

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

The effectiveness of most cancer targeted therapies is short-lived. Tumors often developresistance that might be overcome with drug combinations. However, the number of possiblecombinations is vast, necessitating data-driven approaches tofind optimal patient-specifictreatments. Here we report AstraZeneca’s large drug combination dataset, consisting of11,576 experiments from 910 combinations across 85 molecularly characterized cancer celllines, and results of a DREAM Challenge to evaluate computational strategies for predictingsynergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensivemethodological development and benchmarking. Winning methods incorporate priorknowledge of drug-target interactions. Synergy is predicted with an accuracy matching bio-logical replicates for >60% of combinations. However, 20% of drug combinations are poorlypredicted by all methods. Genomic rationale for synergy predictions are identified, includingADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting tosynergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells

Small RNA expression from viruses, bacteria and human miRNAs in colon cancer tissue and its association with microsatellite instability and tumor location

Abstract Background MicroRNAs (miRNA) and other small RNAs are frequently dysregulated in cancer and are promising biomarkers for colon cancer. Here we profile human, virus and bacteria small RNAs in normal and tumor tissue from early stage colon cancer and correlate the expression with clinical parameters. Methods Small RNAs from colon cancer tissue and adjacent normal mucosa of 48 patients were sequenced using Illumina high-throughput sequencing. Clinical parameters were correlated with the small RNA expression data using linear models. We performed a meta-analysis by comparing publicly available small RNA sequencing datasets with our original sequencing data to confirm the main findings. Results We identified 331 differentially expressed miRNAs between tumor and normal samples. We found that the major changes in miRNA expression between left and right colon are due to miRNAs located within the Hox-developmental genes, including miR-10b, miR-196b and miR-615. Further, we identified new miRNAs associated with microsatellite instability (MSI), including miR-335, miR-26 and miR-625. We performed a meta-analysis on all publicly available miRNA-seq datasets and identified 117 common miRNAs that were differentially expressed between tumor and normal tissue. The miRNAs miR-135b and miR-31 were the most significant upregulated miRNA in tumor across all datasets. The miRNA miR-133a was the most strongly downregulated miRNA in our dataset and also showed consistent downregulation in the other datasets. The miRNAs associated with MSI and tumor location in our data showed similar changes in the other datasets. Finally, we show that small RNAs from Epstein-Barr virus and Fusobacterium nucleatum are differentially expressed between tumor and normal adjacent tissue. Conclusions Small RNA profiling in colon cancer tissue revealed novel RNAs associated with MSI and tumor location. We show that Fusobacterium nucleatum are detectable at the RNA-level in colon tissue, and that both Fusobacterium nucleatum and Epstein-Barr virus separate tumor and normal tissue