Document Type

Article

Publication Date

January 2004

ISSN

00278424

Abstract

Cells adjust gene expression profiles in response to environmental and physiological changes through a series of signal transduction pathways. Upon activation or deactivation, the terminal regulators bind to or dissociate from DNA, respectively, and modulate transcriptional activities on particular promoters. Traditionally, individual reporter genes have been used to detect the activity of the transcription factors. This approach works well for simple, non-overlapping transcription pathways. For complex transcriptional networks, more sophisticated tools are required to deconvolute the contribution of each regulator. Here, we demonstrate the utility of network component analysis in determining multiple transcription factor activities based on transcriptome profiles and available connectivity information regarding network connectivity. We used Escherichia coli carbon source transition from glucose to acetate as a model system. Key results from this analysis were either consistent with physiology or verified by using independent measurements.Bacteria respond to environmental changes through a variety of sensor proteins, which eventually relay the signals to corresponding DNA binding proteins to modulate transcription. The DNA binding transcription regulators, or transcription factors (TFs), typically require posttranscriptional modification or ligand binding to assume an active conformation, which may bind to DNA and either positively or negatively regulate transcription. Here, the activity of a TF is defined as the concentration of its subpopulation capable of DNA binding. The collective activities of TFs can thus be regarded as the physiological state of the cell. Determining these TF activities (TFAs) allows better understanding of how cells respond to changes in the environment. Careful experimental studies in the past few decades have identified conditions that perturb each individual TF independent of others. Although such ideal conditions allowed useful characterization of molecular mechanisms, most environmental perturbations are complex and are likely to provoke multiple regulatory systems simultaneously. Without a proper method of decomposing the regulatory signals, it is difficult to investigate how microorganisms coordinate various regulatory pathways upon an environmental challenge.Here, we report the use of network component analysis (NCA) recently developed in our group (1) to determine the dynamics of the activities of various TFs during a physiological process. This approach uses both DNA microarray data and partial information regarding the membership of regulons as defined by each TF in question. It contrasts with other approaches, such as singular value decomposition (2) or independent component analysis (3), in that it does not depend on orthogonality and statistical independence. Rather, it uses biological information regarding regulatory network topology, even when the topology is incompletely defined. Furthermore, NCA differs from model-based parameter estimation (4) because it allows deconvolution of multiple regulatory pathways.We use the Escherichia coli transition from glucose to acetate media as an example. When switching from a glycolytic condition to a gluconeogenic condition with acetate as the sole carbon source, E. coli is known to induce a significant change in metabolic genes (5). In particular, the glyoxylate shunt (ace-BAK), the tricarboxylic acid (TCA) cycle, and the acetate uptake gene (acs) are up-regulated along with other genes under catabolite repression (6). The gene expression pattern has been studied by comparing the balanced growth culture in glucose and acetate media (6). However, how the cell coordinates the transition from one condition to the other during the adaptation phase has not been characterized. This adaptation phase provides an excellent case to demonstrate the utility of NCA. In addition, we validate the results of NCA by comparing the predicted TFA of a regulator, catabolite repressor protein (CRP), with the measured cAMP concentrations.

Comments

SJSU users: Use the following link to login and access the article via SJSU databases.This article was published in the Proceedings of the National Academy of Sciences, volume 101, issue 2, 2004, and can also be found online here.Copyright © The Authors

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