Dataset Information

Nonparametric identification of regulatory interactions from spatial and temporal gene expression data.

ABSTRACT:

Background

The correlation between the expression levels of transcription factors and their target genes can be used to infer interactions within animal regulatory networks, but current methods are limited in their ability to make correct predictions.

Results

Here we describe a novel approach which uses nonparametric statistics to generate ordinary differential equation (ODE) models from expression data. Compared to other dynamical methods, our approach requires minimal information about the mathematical structure of the ODE; it does not use qualitative descriptions of interactions within the network; and it employs new statistics to protect against over-fitting. It generates spatio-temporal maps of factor activity, highlighting the times and spatial locations at which different regulators might affect target gene expression levels. We identify an ODE model for eve mRNA pattern formation in the Drosophila melanogaster blastoderm and show that this reproduces the experimental patterns well. Compared to a non-dynamic, spatial-correlation model, our ODE gives 59% better agreement to the experimentally measured pattern. Our model suggests that protein factors frequently have the potential to behave as both an activator and inhibitor for the same cis-regulatory module depending on the factors' concentration, and implies different modes of activation and repression.

Conclusions

Our method provides an objective quantification of the regulatory potential of transcription factors in a network, is suitable for both low- and moderate-dimensional gene expression datasets, and includes improvements over existing dynamic and static models.

SUBMITTER: Aswani A

PROVIDER: S-EPMC2933715 | biostudies-literature | 2010 Aug

REPOSITORIES: biostudies-literature

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Publications

Nonparametric identification of regulatory interactions from spatial and temporal gene expression data.

Aswani Anil A Keränen Soile V E SV Brown James J Fowlkes Charless C CC Knowles David W DW Biggin Mark D MD Bickel Peter P Tomlin Claire J CJ

BMC bioinformatics 20100804

<h4>Background</h4>The correlation between the expression levels of transcription factors and their target genes can be used to infer interactions within animal regulatory networks, but current methods are limited in their ability to make correct predictions.<h4>Results</h4>Here we describe a novel approach which uses nonparametric statistics to generate ordinary differential equation (ODE) models from expression data. Compared to other dynamical methods, our approach requires minimal informatio ...[more]

PMID: 20684787

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Project description:Gene regulatory interactions are of fundamental importance to various biological functions and processes. However, only a few previous computational studies have claimed success in revealing genome-wide regulatory landscapes from temporal gene expression data, especially for complex eukaryotes like human. Moreover, recent work suggests that these methods still suffer from the curse of dimensionality if a network size increases to 100 or higher.Here we present a novel scalable algorithm for identifying genome-wide gene regulatory network (GRN) structures, and we have verified the algorithm performances by extensive simulation studies based on the DREAM challenge benchmark data. The highlight of our method is that its superior performance does not degenerate even for a network size on the order of 104, and is thus readily applicable to large-scale complex networks. Such a breakthrough is achieved by considering both prior biological knowledge and multiple topological properties (i.e., sparsity and hub gene structure) of complex networks in the regularized formulation. We also validate and illustrate the application of our algorithm in practice using the time-course gene expression data from a study on human respiratory epithelial cells in response to influenza A virus (IAV) infection, as well as the CHIP-seq data from ENCODE on transcription factor (TF) and target gene interactions. An interesting finding, owing to the proposed algorithm, is that the biggest hub structures (e.g., top ten) in the GRN all center at some transcription factors in the context of epithelial cell infection by IAV.The proposed algorithm is the first scalable method for large complex network structure identification. The GRN structure identified by our algorithm could reveal possible biological links and help researchers to choose which gene functions to investigate in a biological event. The algorithm described in this article is implemented in MATLAB Ⓡ , and the source code is freely available from https://github.com/Hongyu-Miao/DMI.git .

Project description:Neuronal differentiation often requires target-derived signals from the cells they innervate. These signals typically activate neural subtype-specific genes, but the gene regulatory mechanisms remain largely unknown. Highly restricted expression of the FMRFa neuropeptide in Drosophila Tv4 neurons requires target-derived BMP signaling and a transcription factor code that includes Apterous. Using integrase transgenesis of enhancer reporters, we functionally dissected the Tv4-enhancer of FMRFa within its native cellular context. We identified two essential but discrete cis-elements, a BMP-response element (BMP-RE) that binds BMP-activated pMad, and a homeodomain-response element (HD-RE) that binds Apterous. These cis-elements have low activity and must be combined for Tv4-enhancer activity. Such combinatorial activity is often a mechanism for restricting expression to the intersection of cis-element spatiotemporal activities. However, concatemers of the HD-RE and BMP-RE cis-elements were found to independently generate the same spatiotemporal expression as the Tv4-enhancer. Thus, the Tv4-enhancer atypically combines two low-activity cis-elements that confer the same output from distinct inputs. The activation of target-dependent genes is assumed to 'wait' for target contact. We tested this directly, and unexpectedly found that premature BMP activity could not induce early FMRFa expression; also, we show that the BMP-insensitive HD-RE cis-element is activated at the time of target contact. This led us to uncover a role for the nuclear receptor, seven up (svp), as a repressor of FMRFa induction prior to target contact. Svp is normally downregulated immediately prior to target contact, and we found that maintaining Svp expression prevents cis-element activation, whereas reducing svp gene dosage prematurely activates cis-element activity. We conclude that the target-dependent FMRFa gene is repressed prior to target contact, and that target-derived BMP signaling directly activates FMRFa gene expression through an atypical gene regulatory mechanism.

Project description:BackgroundGenes work coordinately as gene modules or gene networks. Various computational approaches have been proposed to find gene modules based on gene expression data; for example, gene clustering is a popular method for grouping genes with similar gene expression patterns. However, traditional gene clustering often yields unsatisfactory results for regulatory module identification because the resulting gene clusters are co-expressed but not necessarily co-regulated.ResultsWe propose a novel approach, motif-guided sparse decomposition (mSD), to identify gene regulatory modules by integrating gene expression data and DNA sequence motif information. The mSD approach is implemented as a two-step algorithm comprising estimates of (1) transcription factor activity and (2) the strength of the predicted gene regulation event(s). Specifically, a motif-guided clustering method is first developed to estimate the transcription factor activity of a gene module; sparse component analysis is then applied to estimate the regulation strength, and so predict the target genes of the transcription factors. The mSD approach was first tested for its improved performance in finding regulatory modules using simulated and real yeast data, revealing functionally distinct gene modules enriched with biologically validated transcription factors. We then demonstrated the efficacy of the mSD approach on breast cancer cell line data and uncovered several important gene regulatory modules related to endocrine therapy of breast cancer.ConclusionWe have developed a new integrated strategy, namely motif-guided sparse decomposition (mSD) of gene expression data, for regulatory module identification. The mSD method features a novel motif-guided clustering method for transcription factor activity estimation by finding a balance between co-regulation and co-expression. The mSD method further utilizes a sparse decomposition method for regulation strength estimation. The experimental results show that such a motif-guided strategy can provide context-specific regulatory modules in both yeast and breast cancer studies.

Dataset Information

Nonparametric identification of regulatory interactions from spatial and temporal gene expression data.

Background

Results

Conclusions

Publications

Nonparametric identification of regulatory interactions from spatial and temporal gene expression data.

Similar Datasets

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