Timing of Gene Expression Responses to Environmental Changes

Cells respond to environmental perturbations with changes in their gene expression that are coordinated in magnitude and time. Timing information about individual genes, rather than clusters, provides a refined way to view and analyze responses, but it is hard to estimate accurately. To analyze response timing of individual genes, we developed a parametric model that captures the typical temporal responses: an abrupt early response followed by a second transition to a steady state. This impulse model explicitly represents natural temporal properties such as the onset and the offset time, and can be estimated robustly, as demonstrated by its superior ability to impute missing values in gene expression data. Using response time of individual genes, we identify relations between gene function and their response timing, showing, for example, how cytosolic ribosomal genes are only repressed after the mitochondrial ribosome is activated. We further demonstrate a strong relation between the binding affinity of a transcription factor and the activation timing of its targets, suggesting that graded binding affinities could be a widely used mechanism for controlling expression timing. See online Supplementary Material at (www.liebertonline.com).

[1]  J. Keene RNA regulons: coordination of post-transcriptional events , 2007, Nature Reviews Genetics.

[2]  D. Botstein,et al.  Genomic expression responses to DNA-damaging agents and the regulatory role of the yeast ATR homolog Mec1p. , 2001, Molecular biology of the cell.

[3]  Michael Ruogu Zhang,et al.  Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. , 1998, Molecular biology of the cell.

[4]  U. Alon,et al.  Ordering Genes in a Flagella Pathway by Analysis of Expression Kinetics from Living Bacteria , 2001, Science.

[5]  U. Alon,et al.  Just-in-time transcription program in metabolic pathways , 2004, Nature Genetics.

[6]  J. Barker,et al.  Large-scale temporal gene expression mapping of central nervous system development. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[7]  Eyke Hüllermeier,et al.  Clustering of gene expression data using a local shape-based similarity measure , 2005, Bioinform..

[8]  Russ B. Altman,et al.  Missing value estimation methods for DNA microarrays , 2001, Bioinform..

[9]  I. Androulakis,et al.  Analysis of time-series gene expression data: methods, challenges, and opportunities. , 2007, Annual review of biomedical engineering.

[10]  D. Botstein,et al.  Singular value decomposition for genome-wide expression data processing and modeling. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[11]  Aurélien Mazurie,et al.  Gene networks inference using dynamic Bayesian networks , 2003, ECCB.

[12]  Hitoshi Iwahashi,et al.  Effects of Iodine on Global Gene Expression in Saccharomyces cerevisiae , 2005, Bioscience, biotechnology, and biochemistry.

[13]  I. Simon,et al.  Combined static and dynamic analysis for determining the quality of time-series expression profiles , 2005, Nature Biotechnology.

[14]  P. Brown,et al.  Exploring the metabolic and genetic control of gene expression on a genomic scale. , 1997, Science.

[15]  Wenxuan Zhong,et al.  A data-driven clustering method for time course gene expression data , 2006, Nucleic acids research.

[16]  John D. Storey,et al.  Significance analysis of time course microarray experiments. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[17]  E. O’Shea,et al.  Chromatin decouples promoter threshold from dynamic range , 2008, Nature.

[18]  D. Botstein,et al.  Genomic expression programs in the response of yeast cells to environmental changes. , 2000, Molecular biology of the cell.

[19]  Amos Tanay,et al.  Extensive low-affinity transcriptional interactions in the yeast genome. , 2006, Genome research.

[20]  William H. Offenhauser,et al.  Wild Boars as Hosts of Human-Pathogenic Anaplasma phagocytophilum Variants , 2012, Emerging infectious diseases.

[21]  E. Lander,et al.  Remodeling of yeast genome expression in response to environmental changes. , 2001, Molecular biology of the cell.

[22]  Ziv Bar-Joseph,et al.  Clustering short time series gene expression data , 2005, ISMB.

[23]  Andre Boorsma,et al.  Transcriptional Response of Saccharomyces cerevisiae to the Plasma Membrane-Perturbing Compound Chitosan , 2005, Eukaryotic Cell.

[24]  Neal S. Holter,et al.  Dynamic modeling of gene expression data. , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[25]  D. Koller,et al.  Activity motifs reveal principles of timing in transcriptional control of the yeast metabolic network , 2008, Nature Biotechnology.

[26]  L. P. Zhao,et al.  Statistical modeling of large microarray data sets to identify stimulus-response profiles , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[27]  Min Zou,et al.  A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data , 2005, Bioinform..

[28]  E. Gilson,et al.  A haploid-specific transcriptional response to irradiation in Saccharomyces cerevisiae , 2005, Nucleic acids research.

[29]  Paola Sebastiani,et al.  Cluster analysis of gene expression dynamics , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[30]  Neal S. Holter,et al.  Fundamental patterns underlying gene expression profiles: simplicity from complexity. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[31]  Nicola J. Rinaldi,et al.  Transcriptional regulatory code of a eukaryotic genome , 2004, Nature.

[32]  Alexander Schliep,et al.  Using hidden Markov models to analyze gene expression time course data , 2003, ISMB.

[33]  P. Bork,et al.  Dynamic Complex Formation During the Yeast Cell Cycle , 2005, Science.

[34]  K. Kwast,et al.  Dynamical Remodeling of the Transcriptome during Short-Term Anaerobiosis in Saccharomyces cerevisiae: Differential Response and Role of Msn2 and/or Msn4 and Other Factors in Galactose and Glucose Media , 2005, Molecular and Cellular Biology.

[35]  Hongzhe Li,et al.  Clustering of time-course gene expression data using a mixed-effects model with B-splines , 2003, Bioinform..

[36]  Tommi S. Jaakkola,et al.  Continuous Representations of Time-Series Gene Expression Data , 2003, J. Comput. Biol..

[37]  M. Gerstein,et al.  Beyond synexpression relationships: local clustering of time-shifted and inverted gene expression profiles identifies new, biologically relevant interactions. , 2001, Journal of molecular biology.

[38]  K. Shedden,et al.  Analysis of cell-cycle-specific gene expression in human cells as determined by microarrays and double-thymidine block synchronization , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[39]  Korbinian Strimmer,et al.  Identifying periodically expressed transcripts in microarray time series data , 2008, Bioinform..