Comparing Transcription Rate and mRNA Abundance as Parameters for Biochemical Pathway and Network Analysis Brewster Hayles, Sailu Yellaboina¤, Degeng Wang* Department of Cell Biology, Microbiology, and Molecular Biology (CMMB), University of South Florida, Tampa, Florida, United States of America Abstract The cells adapt to extra- and intra-cellular signals by dynamic orchestration of activities of pathways in the biochemical networks. Dynamic control of the gene expression process represents a major mechanism for pathway activity regulation. Gene expression has thus been routinely measured, most frequently at steady-state mRNA abundance level using micro- array technology. The results are widely used in statistical inference of the structures of underlying biochemical networks, with the assumption that functionally related genes exhibit similar dynamic profiles. Steady-state mRNA abundance, however, is a composite of two factors: transcription rate and mRNA degradation rate. The question being asked here is therefore whether steady-state mRNA abundance or any of two factors is a more informative measurement target for studying network dynamics. The yeast S. cerevisiae was used as model organism and transcription rate was chosen out of the two factors in this study, because genome-wide determination of transcription rates has been reported for several physiological processes in this species. Our strategy is to test which one is a better measurement of functional relatedness between genes. The analysis was performed on those S. cerevisiae genes that have bacterial orthologs as identified by reciprocal BLAST analysis, so that functional relatedness of a gene pair can be measured by the frequency at which their bacterial orthologs co-occur in the same operon in the collection of bacterial genomes. It is found that transcription rate data is generally a better parameter for functional relatedness than steady state mRNA abundance, suggesting transcription rate data is more informative to use in deciphering the logics used by the cells in dynamic regulation of biochemical network behaviors. The significance of this finding for network and systems biology, as well as biomedical research in general, is discussed. Citation: Hayles B, Yellaboina S, Wang D (2010) Comparing Transcription Rate and mRNA Abundance as Parameters for Biochemical Pathway and Network Analysis. PLoS ONE 5(3): e9908. doi:10.1371/journal.pone.0009908 Editor: Timothy Ravasi, King Abdullah University of Science and Technology, Saudi Arabia Received January 5, 2010; Accepted February 19, 2010; Published March 26, 2010 Copyright: 2010 Hayles et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This work was supported by the University of South Florida (USF) Foundation and a USF New Researcher Grant to D.W. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. * E-mail: dwang1@cas.usf.edu ¤ Current address: Biostatistics Branch, National Institute of Environmental Health Sciences (NIEHS), National Institutes of Health (NIH), Research Triangle Park, North Carolina, United States of America Introduction Biochemical networks underlie essentially all cellular functions. Proteins encoded in the genomic sequences form biochemical pathways and pathways join together to form networks. Cellular biochemical networks are highly modular in that they consist of a hierarchical organization of functional modules [1]. For instance, metabolic enzymes form the metabolic network; protein kinases are backbones of the signaling network; and transcription factors are major components of the transcription regulation network. The cells constantly adapt to changes in their environment and internal conditions. Consequently, biochemical networks are never static. The cells respond to extra- and intra-cellular signals by dynamic orchestration of activities of pathways in the networks; activate pathways that are needed and inactivate the others [2]. For example, during carbon source shift from glucose to galactose, the yeast cells activate galactose utilization pathways, while inactivating pathways required for glucose metabolism [3,4]. Roughly two factors determine how active a pathway is: the abundance and the activity of its components. The activity of many proteins, the latter of the two factors, is under tight post- translational control. There are many forms of post-translational control: covalent chemical modification such as phosphorylation [5] and glycosylation [6], allosteric interaction with regulatory partners [7], etc. The other determining factor of biochemical pathway activity, the abundance of proteins, is also under tight cellular regulation. Gene expression regulation is one of the major regulatory mechanisms. Hence, gene expression has been, and will continue to be, extensively studied. Perhaps due to the availability of reliable and economical measurement methods such as the SAGE [8], the micro-array and the upcoming deep sequencing technologies [9], mRNA transcript levels have been the most frequently studied, even though gene expression process is a multi-stepped process. Measurement targets are usually steady state mRNA abundance. The results are widely used in statistical inference of the structures of underlying biochemical networks [10], as well as in regulatory sequence analysis to detect common sequence motifs that drive transcription of functionally related genes [11,12]. The underlying assumption is that functionally related genes should exhibit similar PLoS ONE | www.plosone.org 1 March 2010 | Volume 5 | Issue 3 | e9908