Blockade of Hsp90 by 17AAG antagonizes MDMX and synergizes with Nutlin to induce p53-mediated apoptosis in solid tumors

Strategies to induce p53 activation in wtp53-retaining tumors carry high potential in cancer therapy. Nutlin, a potent highly selective MDM2 inhibitor, induces non-genotoxic p53 activation. Although Nutlin shows promise in promoting cell death in hematopoietic malignancies, a major roadblock is that most solid cancers do not undergo apoptosis but merely reversible growth arrest. p53 inhibition by unopposed MDMX is one major cause for apoptosis resistance to Nutlin. The Hsp90 chaperone is ubiquitously activated in cancer cells and supports oncogenic survival pathways, many of which antagonize p53. The Hsp90 inhibitor 17-allylamino-17-demethoxygeldanamycin (17AAG) is known to induce p53-dependent apoptosis. We show here that in multiple difficult-to-kill solid tumor cells 17AAG modulates several critical components that synergize with Nutlin-activated p53 signaling to convert Nutlin's transient cytostatic response into a cytotoxic killing response in vitro and in xenografts. Combined with Nutlin, 17AAG destabilizes MDMX, reduces MDM2, induces PUMA and inhibits oncogenic survival pathways, such as PI3K/AKT, which counteract p53 signaling at multiple levels. Mechanistically, 17AAG interferes with the repressive MDMX–p53 axis by inducing robust MDMX degradation, thereby markedly increasing p53 transcription compared with Nutlin alone. To our knowledge Nutlin+17AAG represents the first effective pharmacologic knockdown of MDMX. Our study identifies 17AAG as a promising synthetic lethal partner for a more efficient Nutlin-based therapy

Extracting the abstraction pyramid from complex networks

<p>Abstract</p> <p>Background</p> <p>At present, the organization of system modules is typically limited to either a multilevel hierarchy that describes the "vertical" relationships between modules at different levels (e.g., module A at level two is included in module B at level one), or a single-level graph that represents the "horizontal" relationships among modules (e.g., genetic interactions between module A and module B). Both types of organizations fail to provide a broader and deeper view of the complex systems that arise from an integration of vertical and horizontal relationships.</p> <p>Results</p> <p>We propose a complex network analysis tool, Pyramabs, which was developed to integrate vertical and horizontal relationships and extract information at various granularities to create a pyramid from a complex system of interacting objects. The pyramid depicts the nested structure implied in a complex system, and shows the vertical relationships between abstract networks at different levels. In addition, at each level the abstract network of modules, which are connected by weighted links, represents the modules' horizontal relationships. We first tested Pyramabs on hierarchical random networks to verify its ability to find the module organization pre-embedded in the networks. We later tested it on a protein-protein interaction (PPI) network and a metabolic network. According to Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG), the vertical relationships identified from the PPI and metabolic pathways correctly characterized the <it>inclusion </it>(i.e., <it>part-of</it>) relationship, and the horizontal relationships provided a good indication of the functional closeness between modules. Our experiments with Pyramabs demonstrated its ability to perform knowledge mining in complex systems.</p> <p>Conclusions</p> <p>Networks are a flexible and convenient method of representing interactions in a complex system, and an increasing amount of information in real-world situations is described by complex networks. We considered the analysis of a complex network as an iterative process for extracting meaningful information at multiple granularities from a system of interacting objects. The quality of the interpretation of the networks depends on the completeness and expressiveness of the extracted knowledge representations. Pyramabs was designed to interpret a complex network through a disclosure of a pyramid of abstractions. The abstraction pyramid is a new knowledge representation that combines vertical and horizontal viewpoints at different degrees of abstraction. Interpretations in this form are more accurate and more meaningful than multilevel dendrograms or single-level graphs. Pyramabs can be accessed at <url>http://140.113.166.165/pyramabs.php/</url>.</p

Gene Amplification, ABC Transporters and Cytochrome P450s: Unraveling the Molecular Basis of Pyrethroid Resistance in the Dengue Vector, Aedes aegypti

Dengue is the most rapidly spreading arboviral infection of humans and each year there are 50–100 million cases of dengue fever. There is no vaccine or drug to prevent dengue infection so control of the mosquitoes that transmit this virus is the only option to reduce transmission. Removing mosquito habitats close to human homes can be effective but in reality most dengue control programmes rely on a small number of chemical insecticides. Therefore, when the mosquito vectors develop resistance to the available insecticides, dengue control is jeopardized. In this study we examined the causes of resistance to the insecticide class most commonly used in mosquito control, the pyrethroids. We found that a group of genes, which have been implicated in detoxifying these insecticides in other populations of dengue vectors, were highly over expressed in both these Caribbean populations and we investigated the molecular basis of this increased expression. The next steps, which will be a considerable challenge, are to utilize this information to develop effective means of restoring insecticide susceptibility in dengue vectors

Discriminative motif discovery in DNA and protein sequences using the DEME algorithm

<p>Abstract</p> <p>Background</p> <p>Motif discovery aims to detect short, highly conserved patterns in a collection of unaligned DNA or protein sequences. Discriminative motif finding algorithms aim to increase the sensitivity and selectivity of motif discovery by utilizing a second set of sequences, and searching only for patterns that can differentiate the two sets of sequences. Potential applications of discriminative motif discovery include discovering transcription factor binding site motifs in ChIP-chip data and finding protein motifs involved in thermal stability using sets of orthologous proteins from thermophilic and mesophilic organisms.</p> <p>Results</p> <p>We describe DEME, a discriminative motif discovery algorithm for use with protein and DNA sequences. Input to DEME is two sets of sequences; a "positive" set and a "negative" set. DEME represents motifs using a probabilistic model, and uses a novel combination of global and local search to find the motif that optimally discriminates between the two sets of sequences. DEME is unique among discriminative motif finders in that it uses an informative Bayesian prior on protein motif columns, allowing it to incorporate prior knowledge of residue characteristics. We also introduce four, synthetic, discriminative motif discovery problems that are designed for evaluating discriminative motif finders in various biologically motivated contexts. We test DEME using these synthetic problems and on two biological problems: finding yeast transcription factor binding motifs in ChIP-chip data, and finding motifs that discriminate between groups of thermophilic and mesophilic orthologous proteins.</p> <p>Conclusion</p> <p>Using artificial data, we show that DEME is more effective than a non-discriminative approach when there are "decoy" motifs or when a variant of the motif is present in the "negative" sequences. With real data, we show that DEME is as good, but not better than non-discriminative algorithms at discovering yeast transcription factor binding motifs. We also show that DEME can find highly informative thermal-stability protein motifs. Binaries for the stand-alone program DEME is free for academic use and is available at <url>http://bioinformatics.org.au/deme/</url></p

Metabolic regulation by p53

We are increasingly aware that cellular metabolism plays a vital role in diseases such as cancer, and that p53 is an important regulator of metabolic pathways. By transcriptional activation and other means, p53 is able to contribute to the regulation of glycolysis, oxidative phosphorylation, glutaminolysis, insulin sensitivity, nucleotide biosynthesis, mitochondrial integrity, fatty acid oxidation, antioxidant response, autophagy and mTOR signalling. The ability to positively and negatively regulate many of these pathways, combined with feedback signalling from these pathways to p53, demonstrates the reciprocal and flexible nature of the regulation, facilitating a diverse range of responses to metabolic stress. Intriguingly, metabolic stress triggers primarily an adaptive (rather than pro-apoptotic) p53 response, and p53 is emerging as an important regulator of metabolic homeostasis. A better understanding of how p53 coordinates metabolic adaptation will facilitate the identification of novel therapeutic targets and will also illuminate the wider role of p53 in human biology

Defining Global Gene Expression Changes of the Hypothalamic-Pituitary-Gonadal Axis in Female sGnRH-Antisense Transgenic Common Carp (Cyprinus carpio)

BACKGROUND: The hypothalamic-pituitary-gonadal (HPG) axis is critical in the development and regulation of reproduction in fish. The inhibition of neuropeptide gonadotropin-releasing hormone (GnRH) expression may diminish or severely hamper gonadal development due to it being the key regulator of the axis, and then provide a model for the comprehensive study of the expression patterns of genes with respect to the fish reproductive system. METHODOLOGY/PRINCIPAL FINDINGS: In a previous study we injected 342 fertilized eggs from the common carp (Cyprinus carpio) with a gene construct that expressed antisense sGnRH. Four years later, we found a total of 38 transgenic fish with abnormal or missing gonads. From this group we selected the 12 sterile females with abnormal ovaries in which we combined suppression subtractive hybridization (SSH) and cDNA microarray analysis to define changes in gene expression of the HPG axis in the present study. As a result, nine, 28, and 212 genes were separately identified as being differentially expressed in hypothalamus, pituitary, and ovary, of which 87 genes were novel. The number of down- and up-regulated genes was five and four (hypothalamus), 16 and 12 (pituitary), 119 and 93 (ovary), respectively. Functional analyses showed that these genes involved in several biological processes, such as biosynthesis, organogenesis, metabolism pathways, immune systems, transport links, and apoptosis. Within these categories, significant genes for neuropeptides, gonadotropins, metabolic, oogenesis and inflammatory factors were identified. CONCLUSIONS/SIGNIFICANCE: This study indicated the progressive scaling-up effect of hypothalamic sGnRH antisense on the pituitary and ovary receptors of female carp and provided comprehensive data with respect to global changes in gene expression throughout the HPG signaling pathway, contributing towards improving our understanding of the molecular mechanisms and regulative pathways in the reproductive system of teleost fish

Genetic association analysis of the RTK/ERK pathway with aggressive prostate cancer highlights the potential role of CCND2 in disease progression

The RTK/ERK signaling pathway has been implicated in prostate cancer progression. However, the genetic relevance of this pathway to aggressive prostate cancer at the SNP level remains undefined. Here we performed a SNP and gene-based association analysis of the RTK/ERK pathway with aggressive prostate cancer in a cohort comprising 956 aggressive and 347 non-aggressive cases. We identified several loci including rs3217869/CCND2 within the pathway shown to be significantly associated with aggressive prostate cancer. Our functional analysis revealed a statistically significant relationship between rs3217869 risk genotype and decreased CCND2 expression levels in a collection of 119 prostate cancer patient samples. Reduced expression of CCND2 promoted cell proliferation and its overexpression inhibited cell growth of prostate cancer. Strikingly, CCND2 downregulation was consistently observed in the advanced prostate cancer in 18 available clinical data sets with a total amount of 1,095 prostate samples. Furthermore, the lower expression levels of CCND2 markedly correlated with prostate tumor progression to high Gleason score and elevated PSA levels, and served as an independent predictor of biochemical relapse and overall survival in a large cohort of prostate cancer patients. Together, we have identified an association of genetic variants and genes in the RTK/ERK pathway with prostate cancer aggressiveness, and highlighted the potential importance of CCND2 in prostate cancer susceptibility and tumor progression to metastasis

Restriction site polymorphism-based candidate gene mapping for seedling drought tolerance in cowpea [Vigna unguiculata (L.) Walp.]

Quantitative trait loci (QTL) studies provide insight into the complexity of drought tolerance mechanisms. Molecular markers used in these studies also allow for marker-assisted selection (MAS) in breeding programs, enabling transfer of genetic factors between breeding lines without complete knowledge of their exact nature. However, potential for recombination between markers and target genes limit the utility of MAS-based strategies. Candidate gene mapping offers an alternative solution to identify trait determinants underlying QTL of interest. Here, we used restriction site polymorphisms to investigate co-location of candidate genes with QTL for seedling drought stress-induced premature senescence identified previously in cowpea. Genomic DNA isolated from 113 F2:8 RILs of drought-tolerant IT93K503-1 and drought susceptible CB46 genotypes was digested with combinations of EcoR1 and HpaII, Mse1, or Msp1 restriction enzymes and amplified with primers designed from 13 drought-responsive cDNAs. JoinMap 3.0 and MapQTL 4.0 software were used to incorporate polymorphic markers onto the AFLP map and to analyze their association with the drought response QTL. Seven markers co-located with peaks of previously identified QTL. Isolation, sequencing, and blast analysis of these markers confirmed their significant homology with drought or other abiotic stress-induced expressed sequence tags (EST) from cowpea and other plant systems. Further, homology with coding sequences for a multidrug resistance protein 3 and a photosystem I assembly protein ycf3 was revealed in two of these candidates. These results provide a platform for the identification and characterization of genetic trait determinants underlying seedling drought tolerance in cowpea