Dataset Information

Analysis of Drosophila segmentation network identifies a JNK pathway factor overexpressed in kidney cancer.

ABSTRACT: We constructed a large-scale functional network model in Drosophila melanogaster built around two key transcription factors involved in the process of embryonic segmentation. Analysis of the model allowed the identification of a new role for the ubiquitin E3 ligase complex factor SPOP. In Drosophila, the gene encoding SPOP is a target of segmentation transcription factors. Drosophila SPOP mediates degradation of the Jun kinase phosphatase Puckered, thereby inducing tumor necrosis factor (TNF)/Eiger-dependent apoptosis. In humans, we found that SPOP plays a conserved role in TNF-mediated JNK signaling and was highly expressed in 99% of clear cell renal cell carcinomas (RCCs), the most prevalent form of kidney cancer. SPOP expression distinguished histological subtypes of RCC and facilitated identification of clear cell RCC as the primary tumor for metastatic lesions.

SUBMITTER: Liu J

PROVIDER: S-EPMC2756524 | biostudies-literature | 2009 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Analysis of Drosophila segmentation network identifies a JNK pathway factor overexpressed in kidney cancer.

Liu Jiang J Ghanim Murad M Xue Lei L Brown Christopher D CD Iossifov Ivan I Angeletti Cesar C Hua Sujun S Nègre Nicolas N Ludwig Michael M Stricker Thomas T Al-Ahmadie Hikmat A HA Tretiakova Maria M Camp Robert L RL Perera-Alberto Montse M Rimm David L DL Xu Tian T Rzhetsky Andrey A White Kevin P KP

Science (New York, N.Y.) 20090122 5918

We constructed a large-scale functional network model in Drosophila melanogaster built around two key transcription factors involved in the process of embryonic segmentation. Analysis of the model allowed the identification of a new role for the ubiquitin E3 ligase complex factor SPOP. In Drosophila, the gene encoding SPOP is a target of segmentation transcription factors. Drosophila SPOP mediates degradation of the Jun kinase phosphatase Puckered, thereby inducing tumor necrosis factor (TNF)/Ei ...[more]

PMID: 19164706

Similar Datasets

Project description:The segmentation gene network of Drosophila consists of maternal and zygotic factors that generate, by transcriptional (cross-) regulation, expression patterns of increasing complexity along the anterior-posterior axis of the embryo. Using known binding site information for maternal and zygotic gap transcription factors, the computer algorithm Ahab recovers known segmentation control elements (modules) with excellent success and predicts many novel modules within the network and genome-wide. We show that novel module predictions are highly enriched in the network and typically clustered proximal to the promoter, not only upstream, but also in intronic space and downstream. When placed upstream of a reporter gene, they consistently drive patterned blastoderm expression, in most cases faithfully producing one or more pattern elements of the endogenous gene. Moreover, we demonstrate for the entire set of known and newly validated modules that Ahab's prediction of binding sites correlates well with the expression patterns produced by the modules, revealing basic rules governing their composition. Specifically, we show that maternal factors consistently act as activators and that gap factors act as repressors, except for the bimodal factor Hunchback. Our data suggest a simple context-dependent rule for its switch from repressive to activating function. Overall, the composition of modules appears well fitted to the spatiotemporal distribution of their positive and negative input factors. Finally, by comparing Ahab predictions with different categories of transcription factor input, we confirm the global regulatory structure of the segmentation gene network, but find odd skipped behaving like a primary pair-rule gene. The study expands our knowledge of the segmentation gene network by increasing the number of experimentally tested modules by 50%. For the first time, the entire set of validated modules is analyzed for binding site composition under a uniform set of criteria, permitting the definition of basic composition rules. The study demonstrates that computational methods are a powerful complement to experimental approaches in the analysis of transcription networks.

Project description:Cis-regulatory modules that drive precise spatial-temporal patterns of gene expression are central to the process of metazoan development. We describe a new computational strategy to annotate genomic sequences based on their "pattern generating potential" and to produce quantitative descriptions of transcriptional regulatory networks at the level of individual protein-module interactions. We use this approach to convert the qualitative understanding of interactions that regulate Drosophila segmentation into a network model in which a confidence value is associated with each transcription factor-module interaction. Sequence information from multiple Drosophila species is integrated with transcription factor binding specificities to determine conserved binding site frequencies across the genome. These binding site profiles are combined with transcription factor expression information to create a model to predict module activity patterns. This model is used to scan genomic sequences for the potential to generate all or part of the expression pattern of a nearby gene, obtained from available gene expression databases. Interactions between individual transcription factors and modules are inferred by a statistical method to quantify a factor's contribution to the module's pattern generating potential. We use these pattern generating potentials to systematically describe the location and function of known and novel cis-regulatory modules in the segmentation network, identifying many examples of modules predicted to have overlapping expression activities. Surprisingly, conserved transcription factor binding site frequencies were as effective as experimental measurements of occupancy in predicting module expression patterns or factor-module interactions. Thus, unlike previous module prediction methods, this method predicts not only the location of modules but also their spatial activity pattern and the factors that directly determine this pattern. As databases of transcription factor specificities and in vivo gene expression patterns grow, analysis of pattern generating potentials provides a general method to decode transcriptional regulatory sequences and networks.

Dataset Information

Analysis of Drosophila segmentation network identifies a JNK pathway factor overexpressed in kidney cancer.

Publications

Analysis of Drosophila segmentation network identifies a JNK pathway factor overexpressed in kidney cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets