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Network Discovery Pipeline Elucidates Conserved Time-of-Day–Specific cis-Regulatory Modules

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      Abstract

      Correct daily phasing of transcription confers an adaptive advantage to almost all organisms, including higher plants. In this study, we describe a hypothesis-driven network discovery pipeline that identifies biologically relevant patterns in genome-scale data. To demonstrate its utility, we analyzed a comprehensive matrix of time courses interrogating the nuclear transcriptome of Arabidopsis thaliana plants grown under different thermocycles, photocycles, and circadian conditions. We show that 89% of Arabidopsis transcripts cycle in at least one condition and that most genes have peak expression at a particular time of day, which shifts depending on the environment. Thermocycles alone can drive at least half of all transcripts critical for synchronizing internal processes such as cell cycle and protein synthesis. We identified at least three distinct transcription modules controlling phase-specific expression, including a new midnight specific module, PBX/TBX/SBX. We validated the network discovery pipeline, as well as the midnight specific module, by demonstrating that the PBX element was sufficient to drive diurnal and circadian condition-dependent expression. Moreover, we show that the three transcription modules are conserved across Arabidopsis, poplar, and rice. These results confirm the complex interplay between thermocycles, photocycles, and the circadian clock on the daily transcription program, and provide a comprehensive view of the conserved genomic targets for a transcriptional network key to successful adaptation.

      Author Summary

      As the earth rotates, environmental conditions oscillate between illuminated warm days and dark cool nights. Plants have adapted to these changes by timing physiological processes to specific times of the day or night. Light and temperature signaling and the circadian clock regulate this adaptive response. To determine the contributions of each of these factors on gene regulation, we analyzed microarray time course experiments interrogating light, temperature, and circadian conditions. We discovered that almost all Arabidopsis genes cycle in at least one condition. From a signaling perspective, this suggests that light, temperature, and circadian clock play an important role in modulating many physiological pathways. To clarify the contribution of transcriptional regulation on this process, we mined the promoters of cycling genes to identify DNA elements associated with expression at specific times of day. This confirmed the importance of several DNA motifs such as the G-box and the evening element in the regulation of gene expression by light and the circadian clock, but also facilitated the discovery of new elements linked to a novel midnight regulatory module. Identification of orthologous promoter elements in rice and poplar revealed a conserved transcriptional regulatory network that allows global adaptation to the ever-changing daily environment.

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      Statistical significance for genomewide studies.

      With the increase in genomewide experiments and the sequencing of multiple genomes, the analysis of large data sets has become commonplace in biology. It is often the case that thousands of features in a genomewide data set are tested against some null hypothesis, where a number of features are expected to be significant. Here we propose an approach to measuring statistical significance in these genomewide studies based on the concept of the false discovery rate. This approach offers a sensible balance between the number of true and false positives that is automatically calibrated and easily interpreted. In doing so, a measure of statistical significance called the q value is associated with each tested feature. The q value is similar to the well known p value, except it is a measure of significance in terms of the false discovery rate rather than the false positive rate. Our approach avoids a flood of false positive results, while offering a more liberal criterion than what has been used in genome scans for linkage.
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        A genomic perspective on protein families.

        In order to extract the maximum amount of information from the rapidly accumulating genome sequences, all conserved genes need to be classified according to their homologous relationships. Comparison of proteins encoded in seven complete genomes from five major phylogenetic lineages and elucidation of consistent patterns of sequence similarities allowed the delineation of 720 clusters of orthologous groups (COGs). Each COG consists of individual orthologous proteins or orthologous sets of paralogs from at least three lineages. Orthologs typically have the same function, allowing transfer of functional information from one member to an entire COG. This relation automatically yields a number of functional predictions for poorly characterized genomes. The COGs comprise a framework for functional and evolutionary genome analysis.
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          Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data.

          Much of a cell's activity is organized as a network of interacting modules: sets of genes coregulated to respond to different conditions. We present a probabilistic method for identifying regulatory modules from gene expression data. Our procedure identifies modules of coregulated genes, their regulators and the conditions under which regulation occurs, generating testable hypotheses in the form 'regulator X regulates module Y under conditions W'. We applied the method to a Saccharomyces cerevisiae expression data set, showing its ability to identify functionally coherent modules and their correct regulators. We present microarray experiments supporting three novel predictions, suggesting regulatory roles for previously uncharacterized proteins.
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            Author and article information

            Affiliations
            [1 ] Plant Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, California, United States of America
            [2 ] Department of Botany and Plant Pathology, Oregon State University, Corvallis, Oregon, United States of America
            [3 ] Center for Genome Research and Biocomputing, Oregon State University, Corvallis, Oregon, United States of America
            [4 ] Section of Cell and Developmental Biology, University of California San Diego, La Jolla, California, United States of America
            [5 ] Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, California, United States of America
            Howard Hughes Medical Institute, Northwestern University, United States of America
            Author notes
            * To whom correspondence should be addressed. E-mail: chory@ 123456salk.edu
            Contributors
            Role: Editor
            Journal
            PLoS Genet
            pgen
            plge
            plosgen
            PLoS Genetics
            Public Library of Science (San Francisco, USA )
            1553-7390
            1553-7404
            February 2008
            1 February 2008
            13 December 2007
            : 4
            : 2
            2222925
            18248097
            10.1371/journal.pgen.0040014
            07-PLGE-RA-0600R2 plge-04-02-02
            (Editor)
            Copyright: © 2008 Michael 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.
            Counts
            Pages: 17
            Categories
            Research Article
            Computational Biology
            Developmental Biology
            Evolutionary Biology
            Molecular Biology
            Plant Biology
            Arabidopsis (Thale Cress)
            Oryza
            Custom metadata
            Michael TP, Mockler TC, Breton G, McEntee C, Byer A, et al. (2008) Network discovery pipeline elucidates conserved time-of-day–specific cis-regulatory modules. PLoS Genet 4(2): e14. doi: 10.1371/journal.pgen.0040014

            Genetics

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