Dataset Information

Gene regulatory logic of dopamine neuron differentiation.

ABSTRACT: Dopamine signalling regulates a variety of complex behaviours, and defects in dopamine neuron function or survival result in severe human pathologies, such as Parkinson's disease. The common denominator of all dopamine neurons is the expression of dopamine pathway genes, which code for a set of phylogenetically conserved proteins involved in dopamine synthesis and transport. Gene regulatory mechanisms that result in the direct activation of dopamine pathway genes and thereby ultimately determine the identity of dopamine neurons are poorly understood in all systems studied so far. Here we show that a simple cis-regulatory element, the dopamine (DA) motif, controls the expression of all dopamine pathway genes in all dopaminergic cell types in Caenorhabditis elegans. The DA motif is activated by the ETS transcription factor AST-1. Loss of ast-1 results in the failure of all distinct dopaminergic neuronal subtypes to terminally differentiate. Ectopic expression of ast-1 is sufficient to activate the dopamine pathway in some cellular contexts. Vertebrate dopamine pathway genes also contain phylogenetically conserved DA motifs that can be activated by the mouse ETS transcription factor Etv1 (also known as ER81), and a specific class of dopamine neurons fails to differentiate in mice lacking Etv1. Moreover, ectopic Etv1 expression induces dopaminergic fate marker expression in neuronal primary cultures. Mouse Etv1 can also functionally substitute for ast-1 in C. elegans. Our studies reveal a simple and apparently conserved regulatory logic of dopamine neuron terminal differentiation and may provide new entry points into the diagnosis or therapy of conditions in which dopamine neurons are defective.

SUBMITTER: Flames N

PROVIDER: S-EPMC2671564 | biostudies-literature | 2009 Apr

REPOSITORIES: biostudies-literature

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Gene regulatory logic of dopamine neuron differentiation.

Flames Nuria N Hobert Oliver O

Nature 20090315 7240

Dopamine signalling regulates a variety of complex behaviours, and defects in dopamine neuron function or survival result in severe human pathologies, such as Parkinson's disease. The common denominator of all dopamine neurons is the expression of dopamine pathway genes, which code for a set of phylogenetically conserved proteins involved in dopamine synthesis and transport. Gene regulatory mechanisms that result in the direct activation of dopamine pathway genes and thereby ultimately determine ...[more]

PMID: 19287374

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Project description:BackgroundGene regulatory network inference remains a challenging problem in systems biology despite the numerous approaches that have been proposed. When substantial knowledge on a gene regulatory network is already available, supervised network inference is appropriate. Such a method builds a binary classifier able to assign a class (Regulation/No regulation) to an ordered pair of genes. Once learnt, the pairwise classifier can be used to predict new regulations. In this work, we explore the framework of Markov Logic Networks (MLN) that combine features of probabilistic graphical models with the expressivity of first-order logic rules.ResultsWe propose to learn a Markov Logic network, e.g. a set of weighted rules that conclude on the predicate "regulates", starting from a known gene regulatory network involved in the switch proliferation/differentiation of keratinocyte cells, a set of experimental transcriptomic data and various descriptions of genes all encoded into first-order logic. As training data are unbalanced, we use asymmetric bagging to learn a set of MLNs. The prediction of a new regulation can then be obtained by averaging predictions of individual MLNs. As a side contribution, we propose three in silico tests to assess the performance of any pairwise classifier in various network inference tasks on real datasets. A first test consists of measuring the average performance on balanced edge prediction problem; a second one deals with the ability of the classifier, once enhanced by asymmetric bagging, to update a given network. Finally our main result concerns a third test that measures the ability of the method to predict regulations with a new set of genes. As expected, MLN, when provided with only numerical discretized gene expression data, does not perform as well as a pairwise SVM in terms of AUPR. However, when a more complete description of gene properties is provided by heterogeneous sources, MLN achieves the same performance as a black-box model such as a pairwise SVM while providing relevant insights on the predictions.ConclusionsThe numerical studies show that MLN achieves very good predictive performance while opening the door to some interpretability of the decisions. Besides the ability to suggest new regulations, such an approach allows to cross-validate experimental data with existing knowledge.

Dataset Information

Gene regulatory logic of dopamine neuron differentiation.

Publications

Gene regulatory logic of dopamine neuron differentiation.

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