Literature DB >> 22289124

Dynamics of oscillatory phenotypes in Saccharomyces cerevisiae reveal a network of genome-wide transcriptional oscillators.

Shwe L Chin1, Ian M Marcus, Robert R Klevecz, Caroline M Li.   

Abstract

Genetic and environmental factors are well-studied influences on phenotype; however, time is a variable that is rarely considered when studying changes in cellular phenotype. Time-resolved microarray data revealed genome-wide transcriptional oscillation in a yeast continuous culture system with ∼ 2 and ∼ 4 h periods. We mapped the global patterns of transcriptional oscillations into a 3D map to represent different cellular phenotypes of redox cycles. This map shows the dynamic nature of gene expression in that transcripts are ordered and coupled to each other through time and concentration space. Although cells differed in oscillation periods, transcripts involved in certain processes were conserved in a deterministic way. When oscillation period lengthened, the peak to trough ratio of transcripts increased and the fraction of cells in the unbudded (G0/G1) phase of the cell division cycle increased. Decreasing the glucose level in the culture medium was one way to increase the redox cycle, possibly from changes in metabolic flux. The period may be responding to lower glucose levels by increasing the fraction of cells in G1 and reducing S-phase gating so that cells can spend more time in catabolic processes. Our results support that gene transcripts are coordinated with metabolic functions and the cell division cycle.
© 2012 The Authors Journal compilation © 2012 FEBS.

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Year:  2012        PMID: 22289124      PMCID: PMC3368069          DOI: 10.1111/j.1742-4658.2012.08508.x

Source DB:  PubMed          Journal:  FEBS J        ISSN: 1742-464X            Impact factor:   5.542


  38 in total

1.  Acute glucose starvation activates the nuclear localization signal of a stress-specific yeast transcription factor.

Authors:  Wolfram Görner; Erich Durchschlag; Julia Wolf; Elizabeth L Brown; Gustav Ammerer; Helmut Ruis; Christoph Schüller
Journal:  EMBO J       Date:  2002-01-15       Impact factor: 11.598

2.  A genomewide oscillation in transcription gates DNA replication and cell cycle.

Authors:  Robert R Klevecz; James Bolen; Gerald Forrest; Douglas B Murray
Journal:  Proc Natl Acad Sci U S A       Date:  2004-01-20       Impact factor: 11.205

Review 3.  True or false: all genes are rhythmic.

Authors:  Andrey A Ptitsyn; Jeffrey M Gimble
Journal:  Ann Med       Date:  2010-12-08       Impact factor: 4.709

4.  Logic of the yeast metabolic cycle: temporal compartmentalization of cellular processes.

Authors:  Benjamin P Tu; Andrzej Kudlicki; Maga Rowicka; Steven L McKnight
Journal:  Science       Date:  2005-10-27       Impact factor: 47.728

5.  Restriction of DNA replication to the reductive phase of the metabolic cycle protects genome integrity.

Authors:  Zheng Chen; Elizabeth A Odstrcil; Benjamin P Tu; Steven L McKnight
Journal:  Science       Date:  2007-06-29       Impact factor: 47.728

Review 6.  Powering through ribosome assembly.

Authors:  Bethany S Strunk; Katrin Karbstein
Journal:  RNA       Date:  2009-10-22       Impact factor: 4.942

7.  A tuneable attractor underlies yeast respiratory dynamics.

Authors:  Douglas B Murray; David Lloyd
Journal:  Biosystems       Date:  2006-09-16       Impact factor: 1.973

8.  Oscillations in continuous cultures of budding yeast: a segregated parameter analysis.

Authors:  D Porro; E Martegani; B M Ranzi; L Alberghina
Journal:  Biotechnol Bioeng       Date:  1988-08-05       Impact factor: 4.530

9.  A role for the transcription factors Mbp1 and Swi4 in progression from G1 to S phase.

Authors:  C Koch; T Moll; M Neuberg; H Ahorn; K Nasmyth
Journal:  Science       Date:  1993-09-17       Impact factor: 47.728

Review 10.  Signal processing and the design of microarray time-series experiments.

Authors:  Robert R Klevecz; Caroline M Li; James L Bolen
Journal:  Methods Mol Biol       Date:  2007
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  11 in total

1.  Semi-supervised prediction of gene regulatory networks using machine learning algorithms.

Authors:  Nihir Patel; Jason T L Wang
Journal:  J Biosci       Date:  2015-10       Impact factor: 1.826

Review 2.  Homeostasis of redox status derived from glucose metabolic pathway could be the key to understanding the Warburg effect.

Authors:  Shiwu Zhang; Chuanwei Yang; Zhenduo Yang; Dan Zhang; Xiaoping Ma; Gordon Mills; Zesheng Liu
Journal:  Am J Cancer Res       Date:  2015-02-15       Impact factor: 6.166

Review 3.  The molecular basis of metabolic cycles and their relationship to circadian rhythms.

Authors:  Jane Mellor
Journal:  Nat Struct Mol Biol       Date:  2016-12-06       Impact factor: 15.369

Review 4.  Homeostasis of redox status derived from glucose metabolic pathway could be the key to understanding the Warburg effect.

Authors:  Shiwu Zhang; Chuanwei Yang; Zhenduo Yang; Dan Zhang; Xiaoping Ma; Gordon Mills; Zesheng Liu
Journal:  Am J Cancer Res       Date:  2015-03-15       Impact factor: 6.166

5.  Comprehensive analysis of forty yeast microarray datasets reveals a novel subset of genes (APha-RiB) consistently negatively associated with ribosome biogenesis.

Authors:  Basel Abu-Jamous; Rui Fa; David J Roberts; Asoke K Nandi
Journal:  BMC Bioinformatics       Date:  2014-09-29       Impact factor: 3.169

6.  Free energy rhythms in Saccharomyces cerevisiae: a dynamic perspective with implications for ribosomal biogenesis.

Authors:  A Gross; Caroline M Li; F Remacle; R D Levine
Journal:  Biochemistry       Date:  2013-02-20       Impact factor: 3.162

7.  MapReduce Algorithms for Inferring Gene Regulatory Networks from Time-Series Microarray Data Using an Information-Theoretic Approach.

Authors:  Yasser Abduallah; Turki Turki; Kevin Byron; Zongxuan Du; Miguel Cervantes-Cervantes; Jason T L Wang
Journal:  Biomed Res Int       Date:  2017-01-22       Impact factor: 3.411

8.  Eukaryotic cell biology is temporally coordinated to support the energetic demands of protein homeostasis.

Authors:  John S O'Neill; Nathaniel P Hoyle; J Brian Robertson; Rachel S Edgar; Andrew D Beale; Sew Y Peak-Chew; Jason Day; Ana S H Costa; Christian Frezza; Helen C Causton
Journal:  Nat Commun       Date:  2020-09-17       Impact factor: 14.919

9.  Inference of Large-scale Time-delayed Gene Regulatory Network with Parallel MapReduce Cloud Platform.

Authors:  Bin Yang; Wenzheng Bao; De-Shuang Huang; Yuehui Chen
Journal:  Sci Rep       Date:  2018-12-12       Impact factor: 4.379

10.  Identifying transcription factor complexes and their roles.

Authors:  Thorsten Will; Volkhard Helms
Journal:  Bioinformatics       Date:  2014-09-01       Impact factor: 6.937

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