modelo matemático de processos celulares

modelo matemático de processos celulares

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Global effects of DNA replication and DNA replication origin activity on eukaryotic gene expression

Larsson Omberg1,5, Joel R Meyerson1,6, Kayta Kobayashi2,7, Lucy S Drury3, John FX Diffley3,* and Orly Alter1,4,*

1 Department of BiomedicalEngineering,Universityof Texas,Austin, TX, USA,2 Collegeof Pharmacy,Universityof Texas,Austin, TX, USA, 3 Cancer ResearchUK London ResearchInstitute, ClareHall Laboratories, South Mimms,Hertfordshire, UK and 4 Institutes for Cellular and MolecularBiology, and ComputationalEngineering and Sciences, Universityof Texas, Austin, TX, USA 5 Present address: Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853, USA 6 Present address: Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA 7 Present address: Pharmacy Department, Intermountain Medical Center, Murray, UT 84157, USA * Corresponding authors. O Alter, Department of Biomedical Engineeringand Institutes for Cellular and MolecularBiologyand Computational Engineering and Sciences, Universityof Texas,Austin,TX 78712,USA. Tel.: þ 1 512 471 7939;Fax: þ 1 512 471 2149; and JFX Diffley,CancerResearchUK London ResearchInstitute, ClareHall Laboratories,South Mimms,HertfordshireEN6 3LD,UK. Tel.: þ 4 1707625 869;Fax: þ 4 1707 625 801;

Received 4.3.09; accepted 19.8.09

This report provides a global view of how gene expression is affected by DNA replication. We analyzed synchronized cultures of Saccharomyces cerevisiae under conditions that prevent DNA replication initiation without delaying cell cycle progression. We use a higher-order singular value decomposition to integrate the global mRNA expression measured in the multiple time courses, detect and remove experimental artifacts and identify significant combinations of patterns of expression variation across the genes, time points and conditions. We find that, first, B88% of the global mRNA expression is independent of DNA replication. Second, the requirement of DNA replication forefficient histone gene expression is independent of conditions that elicit DNAdamage checkpoint responses. Third, origin licensing decreases the expression of genes with origins near their30 ends,revealingthatdownstreamoriginscanregulatetheexpressionofupstreamgenes.This confirms previouspredictions frommathematical modeling of a global causal coordination between DNA replication origin activity and mRNA expression, and shows that mathematical modeling of DNA microarray data can be used to correctly predict previously unknown biological modes of regulation. Molecular Systems Biology 5: 312; published online 13 October 2009; doi:10.1038/msb.2009.70 Subject Categories: functional genomics; cell cycle Keywords: a higher-order singular value decomposition (HOSVD); cell cycle; DNA replication origin licensing and firing; DNA microarrays; mRNA expression

This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits distribution and reproduction in any medium, provided the original author and source are credited.Creationofderivativeworksispermitted but the resultingwork may bedistributedonlyunder the same or similar licence to this one. This licence does not permit commercial exploitation without specific permission.


DNA replication and transcription occur on a common template, and there are many ways in which these activities may influence each other. First,the passage of DNA replication forks offers an opportunity to change gene expression patterns. The transcription of capsid genes generally occurs late in most viral infections and is often dependent upon prior replication of the viral genome (Rosenthal and Brown, 1977; Thomas and Mathews, 1980; Toth et al, 1992). In bacteriophage T4, there is a direct coupling of replication and transcription because the sliding clamp processivity factor gp45 acts as a mobile transcriptional enhancer (Herendeen et al, 1989). Such coupling may serve a regulatory role: DNA replication differentially affects the transcription of the embryonic and somatic 5S rRNA genes in the frog Xenopus laevis (Wolffe and Brown, 1986). Second, juxtaposed genes and replication origins can influence each other’s activity. The origin of the virus SV40 replication overlaps promoter and enhancer elements for both early and late gene expression (Cowan et al, 1973; Alwine et al, 1977), and there are many examples of transcription factors influencing replication origin function (DePamphilis, 1988). Moreover, induced transcription into a yeast Saccharomyces cerevisiae replication origin can inactivate it (Snyder et al, 1988). Finally, clashes between replication and transcription machinery are

& 2009 EMBO and Macmillan Publishers Limited Molecular Systems Biology 2009 1

Molecular Systems Biology 5; Article number 312; doi:10.1038/msb.2009.70 Citation: Molecular Systems Biology 5:312

& 2009 EMBO and Macmillan Publishers Limited All rights reserved 1744-4292/09


1 The structure of the data in this study of an order higher than that of a matrix.

Genes, time points and conditions of Mcm2–7 origin binding, each represent a degree of freedom a cuboid, that is, a third-order tensor. Unfolded into a matrix, these degrees of freedom are lost and much of the information in the data tensor might also be lost.

We integrate these data by using a tensor

HOSVD (Supplementary information


2 and

3, and



This HOSVD uncovers in the data tensor

(Supplementary information


3) patterns of mRNA expression variation across the genes, time points and conditions, depicted in the raster display of Supplementary information


) with overexpression

(red), no change in expression

(black) and underexpression


This HOSVD was recently reformulated in analogy with the matrix singular value decomposition

(SVD) (Golub and

Van Loan,

Alter et al ,

Nielsen et al ,

Alter and Golub,

Alter, 2006;

Li and

Klevecz, such that it separates the data tensor into a weighted sum of combinations of three patterns each, that is, ‘subtensors’, as depicted in the raster display of Supplementary information


), where the second and third combinations and their corresponding weights are shown explicitly.

In these raster displays, each expression pattern across the time points is centered at its time-invariant level.

The genes are sorted by their

‘angular distances’ between the second and third combinations

(Supplementary information


2.6), which represent the unperturbed cell cycle expression oscillations


2 and


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