Cited by 452Fungal and yeast genetics researchDNA Repair MechanismsBioinformatics and Genomic Networks

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Rewiring of Genetic Networks in Response to DNA Damage

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Bandyopadhyay, Sourav, Mehta, Monika, Kuo, Dwight, Sung, Min-Kyung, Chuang, Ryan, Jaehnig, Eric J., Bodenmiller, Bernd, Licon, Katherine, Copeland, Wilbert, Shales, Michael, Fiedler, Dorothea, Dutkowski, Janusz, Guénolé, Aude, van Attikum, Haico, Shokat, Kevan M., Kolodner, Richard D., Huh, Won-Ki, Aebersold, Ruedi, Keogh, Michael-Christopher, Krogan, Nevan J., Ideker, Trey·2010·Science

DOI 10.1126/science.1195618PMC PMC3006187

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Abstract

DNA Damage Pathways Revealed: Despite the dynamic nature of cellular responses, the genetic networks that govern these responses have been mapped primarily as static snapshots. Bandyopadhyay et al. (p. 1385 ; see the Perspective by Friedman and Schuldiner ) report a comparison of large genetic interactomes measured among all yeast kinases, phosphatases, and transcription factors, as the cell responded to DNA damage. The interactomes revealed were highly dynamic structures that changed dramatically with changing conditions. These dynamic interactions reveal genetic relationships that can be more effective than classical “static” interactions (for example, synthetic lethals and epistasis maps) in identifying pathways of interest.

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Jeong, H., Mason, S., Barabási, A., & Oltvai, Z. (2001) · Nature · doi:10.1038/35075138 · Cited by 5,327
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Costanzo, M., Baryshnikova, A., Bellay, J., Kim, Y., Spear, E. D., Sevier, C. S., Ding, H., Koh, J. L. Y., Toufighi, K., Mostafavi, S., et al. (2010) · Science · doi:10.1126/science.1180823 · Cited by 2,162
Multiple Pathways of Recombination Induced by Double-Strand Breaks in Saccharomyces cerevisiae
Pâques, F., & Haber, J. E. (1999) · Microbiology and Molecular Biology Reviews · doi:10.1128/mmbr.63.2.349-404.1999 · Cited by 2,120
Functional dissection of protein complexes involved in yeast chromosome biology using a genetic interaction map
Collins, S. R., Miller, K. M., Maas, N. L., Roguev, A., Fillingham, J., Chu, C. S., Schuldiner, M., Gebbia, M., Recht, J., Shales, M., et al. (2007) · Nature · doi:10.1038/nature05649 · Cited by 869
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Exploring genetic interactions and networks with yeast
Boone, C., Bussey, H., & Andrews, B. J. (2007) · Nature Reviews Genetics · doi:10.1038/nrg2085 · Cited by 661
A Snf2 Family ATPase Complex Required for Recruitment of the Histone H2A Variant Htz1
Krogan, N. J., Keogh, M., Datta, N., Sawa, C., Ryan, O. W., Ding, H., Haw, R., Pootoolal, J., Tong, A. H. Y., Canadien, V., et al. (2003) · Molecular Cell · doi:10.1016/s1097-2765(03)00497-0 · Cited by 588
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A DNA Integrity Network in the Yeast Saccharomyces cerevisiae
Pan, X., Ye, P., Yuan, D., Wang, X., Bader, J. S., & Boeke, J. D. (2006) · Cell · doi:10.1016/j.cell.2005.12.036 · Cited by 540
Systematic mapping of genetic interactions in Caenorhabditis elegans identifies common modifiers of diverse signaling pathways
Lehner, B., Crombie, C., Tischler, J., Fortunato, A., & Fraser, A. G. (2006) · Nature Genetics · doi:10.1038/ng1844 · Cited by 502
A genetic study of X-ray sensitive mutants in yeast
Game, J. C., & Mortimer, R. (1974) · Mutation research. Fundamental and molecular mechanisms of mutagenesis · doi:10.1016/0027-5107(74)90176-6 · Cited by 477
Proteome-wide identification of in vivo targets of DNA damage checkpoint kinases
Smolka, M. B., Albuquerque, C. P., Chen, S.-H., & Zhou, H. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0701622104 · Cited by 404

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Wen, Z., Liu, Z.-P., Yan, Y., Piao, G., Liu, Z., Wu, J., & Chen, L. (2012) · PLoS ONE · doi:10.1371/journal.pone.0041854 · Cited by 7
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Hart, T., Koh, C. W., & Moffat, J. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/134346 · Cited by 6
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Herken, B. W., Wong, G. T., Mäkiniemi, A., Lundberg, E. K., Norman, T. M., & Gilbert, L. A. (2026) · Molecular Cell · doi:10.1016/j.molcel.2026.01.025 · Cited by 8
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Chen, B., Gao, S., Ji, C., & Song, G. (2017) · Molecular Medicine Reports · doi:10.3892/mmr.2017.7656 · Cited by 6
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Authors: Bandyopadhyay, S., Mehta, M., Kuo, D., Sung, M.-K., Chuang, R., Jaehnig, E. J., Bodenmiller, B., Licon, K., Copeland, W., Shales, M., Fiedler, D., Dutkowski, J., Guénolé, A., van Attikum, H., Shokat, K. M., Kolodner, R. D., Huh, W.-K., Aebersold, R., Keogh, M.-C., Krogan, N. J., & Ideker, T.
Affiliations: University of California San Diego (US)
Albert Einstein College of Medicine (US)
Seoul National University (KR)
University of Zurich (CH)
University of California, San Francisco (US)
Howard Hughes Medical Institute (US)
Leiden University Medical Center (NL)
Department of Toxicogenetics, Leiden University Medical Center, Leiden, Netherlands.
Corresponding author: Keogh, Michael-Christopher
Krogan, Nevan J.
Ideker, Trey
ORCID: Bandyopadhyay, Sourav
Mehta, Monika
Kuo, Dwight
Sung, Min-Kyung
Chuang, Ryan
Jaehnig, Eric J.
Bodenmiller, Bernd
Copeland, Wilbert
Fiedler, Dorothea
Shokat, Kevan M.
Kolodner, Richard D.
Huh, Won-Ki
Aebersold, Ruedi
Keogh, Michael-Christopher
Krogan, Nevan J.
Ideker, Trey
Venue: Science
Publisher: American Association for the Advancement of Science (AAAS)
Published: 2010-12-03
Volume: 330
Issue: 6009
Pages: 1385-1389
ISSN: 0036-8075 (ISSN-L)
1095-9203
DOI: 10.1126/science.1195618
PMC: PMC3006187
OpenAlex: W1988263223
Cited by: 447
References: 23
Citations by year: 2026: 7 · 2025: 5 · 2024: 12 · 2023: 10 · 2022: 17 · 2021: 18 · 2020: 31 · 2019: 40 · 2018: 22 · 2017: 42 · 2016: 28 · 2015: 37 · 2014: 55 · 2013: 51 · 2012: 41
FWCI: 15.91
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Locations: Science (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Data Archiving and Networked Services (DANS) (submittedVersion)
Leiden Repository (Leiden University) (submittedVersion)
Zurich Open Repository and Archive (University of Zurich) (submittedVersion)
Funders: Howard Hughes Medical Institute
National Cancer Institute (P30CA013330)
National Institute of General Medical Sciences (R01GM084448, P50GM081879, R01GM084279, R37GM026017, R01GM026017)
National Institute of Environmental Health Sciences (R01ES014811)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Fungal and yeast genetics research
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › DNA Repair Mechanisms
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Keywords: DNA damage response, genetic interaction networks, phosphatase, transcription factors, DNA repair pathways, protein complex stability, network rewiring
MeSH terms: Chromatin, DNA Damage, DNA Repair, DNA, Fungal, Epistasis, Genetic, Genes, Fungal, Histones, Methyl Methanesulfonate, Mutagens, Mutation, Phosphoprotein Phosphatases, Saccharomyces cerevisiae, Transcription Factors, Signal Transduction, Protein Serine-Threonine Kinases, Mitogen-Activated Protein Kinases, Protein Interaction Mapping, Saccharomyces cerevisiae Proteins, Gene Regulatory Networks

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