The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Antisense expression increases gene expression variability and locus interdependency
Open accessAbstract
Genome-wide transcription profiling has revealed extensive expression of non-coding RNAs antisense to genes, yet their functions, if any, remain to be understood. In this study, we perform a systematic analysis of sense-antisense expression in response to genetic and environmental changes in yeast. We find that antisense expression is associated with genes of larger expression variability. This is characterized by more 'switching off' at low levels of expression for genes with antisense compared to genes without, yet similar expression at maximal induction. By disrupting antisense transcription, we demonstrate that antisense expression confers an on-off switch on gene regulation for the SUR7 gene. Consistent with this, genes that must respond in a switch-like manner, such as stress-response and environment-specific genes, are enriched for antisense expression. In addition, our data provide evidence that antisense expression initiated from bidirectional promoters enables the spreading of regulatory signals from one locus to neighbouring genes. These results indicate a general regulatory effect of antisense expression on sense genes and emphasize the importance of antisense-initiating regions downstream of genes in models of gene regulation.
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Cites
Statistical significance for genomewide studies
Storey, J. D., & Tibshirani, R. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1530509100 · Cited by 9,692
The ENCODE Project Consortium (2007) · Nature · doi:10.1038/nature05874 · Cited by 5,223
Genomic Expression Programs in the Response of Yeast Cells to Environmental Changes
Gasch, A. P., Spellman, P. T., Kao, C. M., Carmel-Harel, O., Eisen, M. B., Storz, G., Botstein, D., & Brown, P. O. (2000) · Molecular Biology of the Cell · doi:10.1091/mbc.11.12.4241 · Cited by 4,815
Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise
Newman, J. R. S., Ghaemmaghami, S., Ihmels, J., Breslow, D. K., Noble, M., DeRisi, J. L., & Weissman, J. S. (2006) · Nature · doi:10.1038/nature04785 · Cited by 1,483
Transcriptome complexity in a genome-reduced bacterium
Güell, M., van Noort, V., Yus, E., Chen, W.-H., Leigh-Bell, J., Michalodimitrakis, K., Yamada, T., Arumugam, M., Doerks, T., Kühner, S., et al. (2009) · Science · doi:10.1126/science.1176951 · Cited by 418
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Extensive transcriptional heterogeneity revealed by isoform profiling
Pelechano, V., Wei, W., & Steinmetz, L. M. (2013) · Nature · doi:10.1038/nature12121 · Cited by 451
Self-citationDivergent lncRNAs Regulate Gene Expression and Lineage Differentiation in Pluripotent Cells
Luo, S., Lu, J. Y., Liu, L., Yin, Y., Chen, C., Han, X., Wu, B., Xu, R., Liu, W., Yan, P., et al. (2016) · Cell Stem Cell · doi:10.1016/j.stem.2016.01.024 · Cited by 409
Extensive Degradation of RNA Precursors by the Exosome in Wild-Type Cells
Gudipati, R. K., Xu, Z., Lebreton, A., Séraphin, B., Steinmetz, L. M., Jacquier, A., & Libri, D. (2012) · Molecular Cell · doi:10.1016/j.molcel.2012.08.018 · Cited by 236
Self-citationHendrickson, D. G., Soifer, I., Wranik, B. J., Kim, G., Robles, M., Gibney, P. A., & McIsaac, R. S. (2018) · eLife · doi:10.7554/elife.39911 · Cited by 65
Koster, M. J. E., & Timmers, H. T. M. (2015) · Nucleic Acids Research · doi:10.1093/nar/gku1263 · Cited by 55
“They might regulate and fine-tune mRNA expression, act as buffers to prevent cells from responding to weak signals, allow increased expression variability, play roles in condition-specific and environmental stress responses and function as proto-genes that could give rise to protein-coding genes (6,49,50).”
Background
Metadata
- Authors
- Xu, Z., Wei, W., Gagneur, J., Clauder‐Münster, S., Smolik, M., Huber, W., & Steinmetz, L. M.
- Venue
- Molecular Systems Biology
- Published
- 2011-01
- Volume
- 7
- Issue
- 1
- Pages
- 468-468
- ISSN
- 1744-4292 (ISSN-L)
- OpenAlex
- W2150900589
- Cited by
- 177
- References
- 5
- FWCI
- 6.48
- Type
- journal article
- Language
- en
- Open access
- gold
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