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Bayesian error analysis model for reconstructing transcriptional regulatory networks.

ABSTRACT: Transcription regulation is a fundamental biological process, and extensive efforts have been made to dissect its mechanisms through direct biological experiments and regulation modeling based on physical-chemical principles and mathematical formulations. Despite these efforts, transcription regulation is yet not well understood because of its complexity and limitations in biological experiments. Recent advances in high throughput technologies have provided substantial amounts and diverse types of genomic data that reveal valuable information on transcription regulation, including DNA sequence data, protein-DNA binding data, microarray gene expression data, and others. In this article, we propose a Bayesian error analysis model to integrate protein-DNA binding data and gene expression data to reconstruct transcriptional regulatory networks. There are two unique aspects to this proposed model. First, transcription is modeled as a set of biochemical reactions, and a linear system model with clear biological interpretation is developed. Second, measurement errors in both protein-DNA binding data and gene expression data are explicitly considered in a Bayesian hierarchical model framework. Model parameters are inferred through Markov chain Monte Carlo. The usefulness of this approach is demonstrated through its application to infer transcriptional regulatory networks in the yeast cell cycle.

SUBMITTER: Sun N

PROVIDER: S-EPMC1472417 | biostudies-literature | 2006 May

REPOSITORIES: biostudies-literature

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Bayesian error analysis model for reconstructing transcriptional regulatory networks.

Sun Ning N Carroll Raymond J RJ Zhao Hongyu H

Proceedings of the National Academy of Sciences of the United States of America 20060515 21

Transcription regulation is a fundamental biological process, and extensive efforts have been made to dissect its mechanisms through direct biological experiments and regulation modeling based on physical-chemical principles and mathematical formulations. Despite these efforts, transcription regulation is yet not well understood because of its complexity and limitations in biological experiments. Recent advances in high throughput technologies have provided substantial amounts and diverse types ...[more]

PMID: 16702552

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Project description:Transcriptional regulatory networks (TRNs) program cells to dynamically alter their gene expression in response to changing internal or environmental conditions. In this study, we develop a novel workflow for generating large-scale TRN models that integrates comparative genomics data, global gene expression analyses, and intrinsic properties of transcription factors (TFs). An assessment of this workflow using benchmark datasets for the well-studied γ-proteobacterium Escherichia coli showed that it outperforms expression-based inference approaches, having a significantly larger area under the precision-recall curve. Further analysis indicated that this integrated workflow captures different aspects of the E. coli TRN than expression-based approaches, potentially making them highly complementary. We leveraged this new workflow and observations to build a large-scale TRN model for the α-Proteobacterium Rhodobacter sphaeroides that comprises 120 gene clusters, 1211 genes (including 93 TFs), 1858 predicted protein-DNA interactions and 76 DNA binding motifs. We found that ~67% of the predicted gene clusters in this TRN are enriched for functions ranging from photosynthesis or central carbon metabolism to environmental stress responses. We also found that members of many of the predicted gene clusters were consistent with prior knowledge in R. sphaeroides and/or other bacteria. Experimental validation of predictions from this R. sphaeroides TRN model showed that high precision and recall was also obtained for TFs involved in photosynthesis (PpsR), carbon metabolism (RSP_0489) and iron homeostasis (RSP_3341). In addition, this integrative approach enabled generation of TRNs with increased information content relative to R. sphaeroides TRN models built via other approaches. We also show how this approach can be used to simultaneously produce TRN models for each related organism used in the comparative genomics analysis. Our results highlight the advantages of integrating comparative genomics of closely related organisms with gene expression data to assemble large-scale TRN models with high-quality predictions.

Project description:BackgroundMetagenomics enables the analysis of bacterial population composition and the study of emergent population features, such as shared metabolic pathways. Recently, we have shown that metagenomics datasets can be leveraged to characterize population-wide transcriptional regulatory networks, or meta-regulons, providing insights into how bacterial populations respond collectively to specific triggers. Here we formalize a Bayesian inference framework to analyze the composition of transcriptional regulatory networks in metagenomes by determining the probability of regulation of orthologous gene sequences. We assess the performance of this approach on synthetic datasets and we validate it by analyzing the copper-homeostasis network of Firmicutes species in the human gut microbiome.ResultsAssessment on synthetic datasets shows that our method provides a robust and interpretable metric for assessing putative regulation by a transcription factor on sets of promoter sequences mapping to an orthologous gene cluster. The inference framework integrates the regulatory contribution of secondary sites and can discern false positives arising from multiple instances of a clonal sequence. Posterior probabilities for orthologous gene clusters decline sharply when less than 20 % of mapped promoters have binding sites, but we introduce a sensitivity adjustment procedure to speed up computation that enhances regulation assessment in heterogeneous ortholog clusters. Analysis of the copper-homeostasis regulon governed by CsoR in the human gut microbiome Firmicutes reveals that CsoR controls itself and copper-translocating P-type ATPases, but not CopZ-type copper chaperones. Our analysis also indicates that CsoR frequently targets promoters with dual CsoR-binding sites, suggesting that it exploits higher-order binding conformations to fine-tune its activity.ConclusionsWe introduce and validate a method for the analysis of transcriptional regulatory networks from metagenomic data that enables inference of meta-regulons in a systematic and interpretable way. Validation of this method on the CsoR meta-regulon of gut microbiome Firmicutes illustrates the usefulness of the approach, revealing novel properties of the copper-homeostasis network in poorly characterized bacterial species and putting forward evidence of new mechanisms of DNA binding for this transcriptional regulator. Our approach will enable the comparative analysis of regulatory networks across metagenomes, yielding novel insights into the evolution of transcriptional regulatory networks.

Dataset Information

Bayesian error analysis model for reconstructing transcriptional regulatory networks.

Publications

Bayesian error analysis model for reconstructing transcriptional regulatory networks.

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