RNA Biology
- Systems‐based analysis of Arabidopsis leaf growth reveals adaptation to water deficit
- Katja Baerenfaller*,1,†,
- Catherine Massonnet2,†,‡,
- Sean Walsh1,†,
- Sacha Baginsky1,§,
- Peter Bühlmann3,
- Lars Hennig1,4,
- Matthias Hirsch‐Hoffmann1,
- Katharine A Howell5,
- Sabine Kahlau5,6,
- Amandine Radziejwoski7,||,
- Doris Russenberger1,
- Dorothea Rutishauser8,¶,
- Ian Small5,
- Daniel Stekhoven3,9,
- Ronan Sulpice6,
- Julia Svozil1,
- Nathalie Wuyts2,††,
- Mark Stitt6,
- Pierre Hilson*,7,10,
- Christine Granier*,2 and
- Wilhelm Gruissem*,1,8
- 1 Department of Biology, ETH Zurich, Zurich, Switzerland
- 2 Laboratoire d'Ecophysiologie des Plantes sous Stress Environnementaux (LEPSE), INRA‐AGRO‐M, Montpellier Cedex, France
- 3 Seminar for Statistics, ETH Zurich, Zurich, Switzerland
- 4 Department of Plant Biology and Forest Genetics, Swedish University of Agricultural Sciences and Linnean Center for Plant Biology, Uppsala, Sweden
- 5 Plant Energy Biology, ARC Centre of Excellence, The University of Western Australia, Crawley, Western Australia, Australia
- 6 Max Planck Institute of Molecular Plant Physiology, Golm, Germany
- 7 Department of Plant Systems Biology, VIB, Gent, Belgium
- 8 Functional Genomics Center Zurich, Zurich, Switzerland
- 9 Competence Center for Systems Physiology and Metabolic Diseases, Zurich, Switzerland
- 10 Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium
- ↵*Corresponding authors. Department of Biology, ETH Zurich, CH‐8092 Zurich, Switzerland. Tel.:+ 41 446323866; Fax:+41 446321044; E-mail: bkatja{at}ethz.chDepartment of Biology, ETH Zurich, CH‐8092 Zurich, Switzerland. Tel.:+ 41 446320857; Fax:+41 446321079; E-mail: wgruissem{at}ethz.chDepartment of Plant Systems Biology, VIB, B‐9051 Gent, Belgium. Tel.:+ 32 93313830; Fax:+32 93313809; E-mail: pihil{at}psb.vib-ugent.beLaboratoire d'Ecophysiologie des Plantes sous Stress Environnementaux (LEPSE), INRA‐AGRO‐M, F‐34060 Montpellier Cedex 1, France. Tel.:+ 33 499612950; Fax:+33 467522116; E-mail: granier{at}supagro.inra.fr
↵† These authors contributed equally to this work
Deep profiling of the transcriptome and proteome during leaf development reveals unexpected responses to water deficit, as well as a surprising lack of protein‐level fluctuations during the day–night cycle, despite clear changes at the transcript level.
Synopsis
Deep profiling of the transcriptome and proteome during leaf development reveals unexpected responses to water deficit, as well as a surprising lack of protein‐level fluctuations during the day–night cycle, despite clear changes at the transcript level.
Transcript and protein variation patterns reflect the functional stages of the leaf.
Protein and transcript levels correlate well during leaf development, with some notable exceptions.
Diurnal transcript‐level fluctuations are not matched by corresponding diurnal fluctuations in the detected proteome.
Continuous reduced soil water content results in reduced leaf growth, but the plant adapts at molecular levels without showing a typical drought response.
Mol Syst Biol. 8: 606
- Received April 11, 2012.
- Accepted July 25, 2012.
- Copyright © 2012 EMBO and Macmillan Publishers Limited
This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.
- Dynamics of protein noise can distinguish between alternate sources of gene‐expression variability
- 1 Department of Chemistry and Biochemistry, University of California, San Diego, CA, USA
- 2 Department of Electrical and Computer Engineering, University of Delaware, Newark, DE, USA
- 3 Biophysics Graduate Group, University of California, San Francisco, CA, USA
- 4 The Gladstone Institute of Virology and Immunology, San Francisco CA, USA
- 5 Center for Systems and Synthetic Biology, University of California, San Francisco, CA, USA
- 6 Department of Biochemistry and Biophysics, University of California, San Francisco, CA, USA
- ↵*Corresponding author. The Gladstone Institutes, University of California, San Francisco, 1650 Owens Street, San Francisco, CA 94158, USA. Tel.:+1 415 734 4946; Fax:+1 415 355 0855; E-mail: leor.weinberger{at}gladstone.ucsf.edu
↵† These authors contributed equally to this work
Intrinsic noise in gene expression stems from two major sources: promoter fluctuations and lowcopy mRNA fluctuations. A new method allows these sources to be distinguished using transient changes in protein variance without the need for involved single molecule mRNA assays, and reveals that promoter fluctuations drive variability in HIV‐1 gene expression.
Synopsis
Intrinsic noise in gene expression stems from two major sources: promoter fluctuations and lowcopy mRNA fluctuations. A new method allows these sources to be distinguished using transient changes in protein variance without the need for involved single molecule mRNA assays, and reveals that promoter fluctuations drive variability in HIV‐1 gene expression.
This work presents a method that discriminates between two alternate sources of intrinsic noise in gene expression (RNA birth/death processes and promoter fluctuations) even when expression levels are high, thereby complementing mRNA FISH.
The method exploits the fact that transcriptional blockage results in more rapid increases in intercellular protein variability when mRNA birth/death processes dominates over promoter fluctuations.
Application of the method to the HIV‐1 expression system implicates promoter fluctuations, not mRNA birth/death processes, as the primary source of noise in HIV‐1 expression.
Mol Syst Biol. 8: 607
- Received January 26, 2012.
- Accepted July 30, 2012.
- Copyright © 2012 EMBO and Macmillan Publishers Limited
This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.
- Chromatin measurements reveal contributions of synthesis and decay to steady‐state mRNA levels
- Sylvia C Tippmann1,2,3,
- Robert Ivanek1,
- Dimos Gaidatzis1,3,
- Anne Schöler1,2,3,
- Leslie Hoerner1,
- Erik van Nimwegen4,
- Peter F Stadler5,6,7,8,9,10,11,
- Michael B Stadler*,1,3 and
- Dirk Schübeler*,1,2
- 1 Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
- 2 University of Basel, Basel, Switzerland
- 3 Swiss Institute of Bioinformatics, Basel, Switzerland
- 4 Biozentrum, University of Basel, and Swiss Institute of Bioinformatics, Basel, Switzerland
- 5 Department of Theoretical Chemistry, University of Vienna, Wien, Austria
- 6 Interdisciplinary Center for Bioinformatics, University of Leipzig, Leipzig, Germany
- 7 Bioinformatics Group, Department of Computer Science, University of Leipzig, Leipzig, Germany
- 8 Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany
- 9 Fraunhofer Institut für Zelltherapie und Immunologie—IZI, Leipzig, Germany
- 10 Center For Non‐Coding RNA in Technology and Health, University of Copenhagen, Frederiksberg C, Denmark
- 11 Santa Fe Institute, Santa Fe, NM, USA
- ↵*Corresponding authors. Friedrich Miescher Institute for Biomedical Research, Maulbeerstrasse 66, Basel, Baselstadt 4058, Switzerland. Tel.: +41 61 697 6492; Fax: +41 61 697 3976; E-mail: michael.stadler{at}fmi.ch or Tel.: +41 61 697 8269; Fax: +41 61 697 3976; E-mail: dirk{at}fmi.ch
Histone modification, polymerase binding, mRNA half‐life, and miRNA abundance measurements in mouse cells are used to dissect the relative contribution of each to mRNA levels, revealing control primarily at the level of transcription, with minor contributions from post‐transcriptional processes.
Synopsis
Histone modification, polymerase binding, mRNA half‐life, and miRNA abundance measurements in mouse cells are used to dissect the relative contribution of each to mRNA levels, revealing control primarily at the level of transcription, with minor contributions from post‐transcriptional processes.
A linear model of three histone modifications and RNAP II occupancy can predict >80% of the variance in mRNA levels.
mRNA half‐life explains an additional 1.4% variance in mRNA levels.
miRNA‐mediated silencing does not explain any variance on a genome‐wide scale.
H3K36me3 has different predictive power in dividing and non‐dividingcells.
Mol Syst Biol. 8: 593
- Received November 2, 2011.
- Accepted May 22, 2012.
- Copyright © 2012 EMBO and Macmillan Publishers Limited
This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
- Transcription start site associated RNAs in bacteria
- Eva Yus1,†,
- Marc Güell1,†,
- Ana P Vivancos2,
- Wei‐Hua Chen3,
- María Lluch‐Senar1,
- Javier Delgado1,
- Anne‐Claude Gavin3,
- Peer Bork3 and
- Luis Serrano*,1,4
- 1 Center for Genomic Regulation (CRG), UPF, Barcelona, Spain
- 2 Translational Research Program, Vall d'Hebron Institute of Oncology, Barcelona, Spain
- 3 European Molecular Biology Laboratory, Heidelberg, Germany
- 4 Institució Catalana de Recerca i estudis Avançats (ICREA), Barcelona, Spain
- ↵*Corresponding author. EMBL‐CRG Systems Biology Unit, Center for Genomic Regulation (CRG‐UPF), c. Dr Aiguader 88, Barcelona 08003, Spain. Tel.:+34 933160247; Fax:+34 933160099; E-mail: luis.serrano{at}crg.es
↵† These authors contributed equally to this work
A new class of small RNA (~45 bases long) is identified in gram positive and negative bacteria. These tssRNAs are associated with RNA polymerase pausing some 45 bases downstream of the transcription start site and show global changes in expression during the growth cycle.
Synopsis
A new class of small RNA (~45 bases long) is identified in gram positive and negative bacteria. These tssRNAs are associated with RNA polymerase pausing some 45 bases downstream of the transcription start site and show global changes in expression during the growth cycle.
A new class of bacterial small RNAs have been identified. They are related to eukaryotic tiRNAs in their localization (transcription start sites, TSS) but not in their biogenesis.
tssRNAs are generated at the same positions as long transcripts, as well as at independent positions, but both seem to have promoter‐like characteristics (Pribnow box).
We provide compelling evidence that tssRNAs are not mRNA degradation products and neither abortive transcripts; rather, they are newly synthesized transcripts and require more factors than the basal transcription machinery (i.e., RNA polymerase subunits)
tssRNAs show dynamic behavior dependent on the growth phase.
We show that RNA polymerase is halted at tssRNAs positions, both in bona fide genes and in positions where no long transcript is produced. This indicates that tssRNAs could be generated by RNA polymerase pausing to ensure that no spurious long RNA is generated by random appearance of Pribnow sequences in the genome.
Mol Syst Biol. 8: 585
- Received March 22, 2011.
- Accepted April 24, 2012.
- Copyright © 2012 EMBO and Macmillan Publishers Limited
This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.
- Comparative transcriptomics of pathogenic and non‐pathogenic Listeria species
- Omri Wurtzel1,†,
- Nina Sesto2,3,4,†,
- J R Mellin2,3,4,
- Iris Karunker1,
- Sarit Edelheit1,
- Christophe Bécavin2,3,4,
- Cristel Archambaud2,3,4,
- Pascale Cossart*,2,3,4 and
- Rotem Sorek*,1
- 1 Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel
- 2 Unité des Interactions Bactéries‐Cellules, Institut Pasteur, Paris, France
- 3 Institut National de la Santé et de la Recherche Médicale U604, Paris, France
- 4 Institut National de la Recherche Agronomique USC2020, Paris, France
- ↵*Corresponding authors. Unité des Interactions Bactéries‐Cellules, Institut Pasteur, 25 rue du Dr Roux, 75015 Paris, France. Tel.:+33 1 45 68 88 41; Fax:+33 1 45 68 87 06; E-mail: pcossart{at}pasteur.frDepartment of Molecular Genetics, Weizmann Institute of Science, PO 26, Rehovot 76100, Israel. Tel.:+972 8 934 6342; Fax:+972 8 934 4108; E-mail: rotem.sorek{at}weizmann.ac.il
↵† These authors contributed equally to this work
Comparative RNA‐seq analysis of two related pathogenic and non‐pathogenic bacterial strains reveals a hidden layer of divergence in the non‐coding genome as well as conserved, widespread regulatory structures called ‘Excludons’, which mediate regulation through long non‐coding antisense RNAs.
Synopsis
Comparative RNA‐seq analysis of two related pathogenic and non‐pathogenic bacterial strains reveals a hidden layer of divergence in the non‐coding genome as well as conserved, widespread regulatory structures called ‘Excludons’, which mediate regulation through long non‐coding antisense RNAs.
Comparative transcriptome sequencing of two closely related bacterial strains reveals a hidden layer of divergence in the non‐coding genome.
Pathogen‐specific non‐coding RNAs, which might contribute to virulence, are revealed.
The Listeria genome contains a class of unusually long antisense RNAs (lasRNAs) which spans divergent genes and repress expression of the genes located opposite to them while activating the other. The genetic organization of these lasRNAs and operon was named an excludon.
The exhaustive transcriptome information from this publication is provided as an open resource with a web‐accessible transcriptome browser.
Mol Syst Biol. 8: 583
- Received January 5, 2012.
- Accepted March 9, 2012.
- Copyright © 2012 EMBO and Macmillan Publishers Limited
This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.
- Genes adopt non‐optimal codon usage to generate cell cycle‐dependent oscillations in protein levels
- Milana Frenkel‐Morgenstern*,1,2,†,
- Tamar Danon1,
- Thomas Christian3,
- Takao Igarashi3,
- Lydia Cohen1,
- Ya‐Ming Hou3 and
- Lars Juhl Jensen4
- 1 Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
- 2 Department of Structural Biology, Weizmann Institute of Science, Rehovot, Israel
- 3 Department of Biochemistry and Molecular Biology, Thomas Jefferson University, Philadelphia, PA, USA
- 4 Disease Systems Biology, Novo Nordisk Foundation for Protein Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
- ↵*Corresponding author. Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 76100, Israel. Tel.: +34 601046898; Fax: +34 912945037; E-mail: milana.frenkel{at}weizmann.ac.il or E-mail: mmorgenstern{at}cnio.es
Most cell cycle‐regulated genes adopt non‐optimal codon usage, namely, their translation involves wobbly matching codons. Here, the authors show that tRNA expression is cyclic and that codon usage, therefore, can give rise to cell‐cycle regulation of proteins.
Synopsis
Most cell cycle‐regulated genes adopt non‐optimal codon usage, namely, their translation involves wobbly matching codons. Here, the authors show that tRNA expression is cyclic and that codon usage, therefore, can give rise to cell‐cycle regulation of proteins.
Most cell cycle‐regulated genes adopt non‐optimal codon usage.
Non‐optimal codon usage can give rise to cell‐cycle dynamics at the protein level.
The high expression of transfer RNAs (tRNAs) observed in G2 phase enables cell cycle‐regulated genes to adopt non‐optimal codon usage, and conversely the lower expression of tRNAs at the end of G1 phase is associated with optimal codon usage.
The protein levels of aminoacyl‐tRNA synthetases oscillate, peaking in G2/M phase, consistent with the observed cyclic expression of tRNAs.
Mol Syst Biol. 8: 572
- Received November 28, 2011.
- Accepted January 11, 2012.
- Copyright © 2012 EMBO and Macmillan Publishers Limited
This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.
- Transcriptional activity regulates alternative cleavage and polyadenylation
- Zhe Ji1,2,†,
- Wenting Luo1,2,†,
- Wencheng Li1,
- Mainul Hoque1,
- Zhenhua Pan1,
- Yun Zhao1 and
- Bin Tian*,1,2
- 1 Department of Biochemistry and Molecular Biology, New Jersey Medical School, University of Medicine and Dentistry of New Jersey, Newark, NJ, USA
- 2 Graduate School of Biomedical Sciences, University of Medicine and Dentistry of New Jersey, Newark, NJ, USA
- ↵*Corresponding author. Department of Biochemistry and Molecular Biology, New Jersey Medical School, University of Medicine and Dentistry of New Jersey, Newark, NJ 07103, USA. Tel.: +1 973 972 3615; Fax: +1 973 972 5594; E-mail: btian{at}umdnj.edu
↵† These authors contributed equally to this work
Transcriptomic and epigenomic data, as well as reporter and nuclear run‐on assays collectively show that transcriptional activity regulates the relative abundance of alternative polyadenylation isoforms, indicating general coupling of 3′ end processing to transcription.
Synopsis
Transcriptomic and epigenomic data, as well as reporter and nuclear run‐on assays collectively show that transcriptional activity regulates the relative abundance of alternative polyadenylation isoforms, indicating general coupling of 3′ end processing to transcription.
Using RNA‐seq and exon array data for a large number of human and mouse tissues and cells, we identified a general correlation between relative expression of alternative polyadenylation (APA) isoforms and gene expression level: short 3′UTR isoforms are relatively more abundant when genes are highly expressed whereas long 3′UTR isoforms are relatively more abundant when genes are lowly expressed.
Using reporter assays with different promoters, we found that induction of transcription leads to more usage of promoter‐proximal polyA sites, suggesting modulation of 3′ end processing efficiency by transcriptional activity. Global analysis and reporter‐based assays further revealed that regulation of polyA site choice by transcription takes place when genes are regulated under different cell conditions.
Using global and reporter‐based nuclear run‐on assays, we found that highly expressed genes tend to have more RNA polymerase II pausing at promoter‐proximal polyA sites, as compared with lowly expressed genes, supporting the notion that the efficiency of 3′ end processing is coupled to transcriptional activity.
Highly expressed genes have a lower nucleosome level but higher H3K4me3 and H3K36me3 levels at promoter‐proximal polyA sites relative to distal ones, as compared with lowly expressed genes, indicating that transcriptional activity impacts 3′ end processing and regulation of APA leaves epigenetic signatures.
Mol Syst Biol. 7: 534
- Received June 27, 2011.
- Accepted August 8, 2011.
- Copyright © 2011 EMBO and Macmillan Publishers Limited
This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.
- Coupled pre‐mRNA and mRNA dynamics unveil operational strategies underlying transcriptional responses to stimuli
- Amit Zeisel1,†,
- Wolfgang J Köstler2,†,‡,
- Natali Molotski3,
- Jonathan M Tsai2,
- Rita Krauthgamer4,
- Jasmine Jacob‐Hirsch5,
- Gideon Rechavi5,
- Yoav Soen1,3,
- Steffen Jung4,
- Yosef Yarden*,2 and
- Eytan Domany*,1
- 1 Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot, Israel
- 2 Department of Biological Regulation, Weizmann Institute of Science, Rehovot, Israel
- 3 Department of Biological Chemistry, Weizmann Institute of Science, Rehovot, Israel
- 4 Department of Immunology, Weizmann Institute of Science, Rehovot, Israel
- 5 Sheba Cancer Research Center, The Chaim Sheba Medical Center and Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
- ↵*Corresponding authors. Department of Biological Regulation, Weizmann Institute of Science, Rehovot 76100, Israel. Tel.:+972 8934 4502; Fax:+972 8934 2488 E-mail: yosef.yarden{at}weizmann.ac.ilDepartment of Physics of Complex Systems, Weizmann Institute of Science, Rehovot 76100, Israel. Tel.:+972 8934 3964; Fax: +972 8934 4109; E-mail: eytan.domany{at}weizmann.ac.il
↵† These authors contributed equally to this work
Genome‐wide simultaneous measurements of pre‐mRNA and mRNA expression reveal unexpected time‐dependent transcript production and degradation profiles in response to external stimulus, as well as a striking lack of concordance between mRNA abundance and transcript production profiles.
Synopsis
Genome‐wide simultaneous measurements of pre‐mRNA and mRNA expression reveal unexpected time‐dependent transcript production and degradation profiles in response to external stimulus, as well as a striking lack of concordance between mRNA abundance and transcript production profiles.
By analyzing the signals from intronic probes of exon arrays, we performed, for the first time, genome‐wide measurement of pre‐mRNA expression dynamics.
We discovered a striking lack of correspondence between mRNA and pre‐mRNA temporal expression profiles following stimulus, demonstrating that measurement of mRNA dynamics does not suffice to infer transcript production profiles.
By combining simultaneous measurement of pre‐mRNA and mRNA profiles with a simple new quantitative theoretical description of transcription, we are able to infer complex time dependence of both transcript production and mRNA degradation.
The production profiles of many transcripts reveal an operational strategy we termed Production Overshoot, which is used to accelerate mRNA response. The biological relevance of our findings was substantiated by observing similar results when studying the response of three different mammalian cell types to different stimuli.
Mol Syst Biol. 7: 529
- Received March 28, 2011.
- Accepted July 17, 2011.
- Copyright © 2011 EMBO and Macmillan Publishers Limited
This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.
- Cell‐to‐cell variability of alternative RNA splicing
- 1 Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- 2 Cavendish Laboratory, Cambridge, UK
- 3 Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA
- ↵*Corresponding author. Department of Systems Biology, Harvard Medical School, 200 Longwood Ave. WAB 536, Boston, MA 02115, USA. Tel.: +1 617 432 6401; Fax: +1 617 432 5012; E-mail: pamela_silver{at}hms.harvard.edu
The role of mRNA processing in gene expression variability is poorly characterized. This study investigates the extent of cell‐to‐cell variability of alternative RNA splicing in mammalian cells using single‐molecule imaging of CAPRIN1 and MKNK2 splice isoforms.
Visual Overview
Synopsis
The role of mRNA processing in gene expression variability is poorly characterized. This study investigates the extent of cell‐to‐cell variability of alternative RNA splicing in mammalian cells using single‐molecule imaging of CAPRIN1 and MKNK2 splice isoforms.
Biological gene expression is a complex process which includes transcription, mRNA processing, and translation. As gene expression is a fundamental aspect of biological behavior, a central question within the fields of molecular and cellular biology is how effectively cells control the abundance of their gene expression products, mRNA and protein.
Previous experimental and theoretical studies have shown that there can be substantial cell‐to‐cell variation in gene expression, even between genetically identical cells grown in uniform conditions. This variation was shown to be important in a variety of biological contexts such as development, virology, immune system function, and cancer treatment. One major source of variability was shown to be transcriptional bursting, or the process in which genes are expressed sporadically separated by long durations of inexpression. Additionally, since the biochemical reactions that govern gene expression are often mediated by molecular species that are present in low numbers, variability can arise from stochastic effects owing to the random chance that an individual biochemical reaction will occur.
The role of mRNA processing in gene expression variability has not been examined thoroughly, particularly with respect to alternative splicing. Alternative RNA splicing is a form of mRNA processing which leads to the synthesis of multiple different mRNAs from a single gene. In this process, the nascent mRNA (pre‐mRNA) of a gene contains sequences known as introns that can be excised in different combinations to generate multiple gene products, known as isoforms. As alternative splicing occurs in the vast majority of human genes, it presents a potentially major source of cell‐to‐cell variability in gene expression.
In this study, we sought to characterize the extent of cell‐to‐cell variability that arises from alternative RNA splicing. To do so, we utilized a single‐molecule imaging approach based on fluorescent in situ hybridization to study the cell‐to‐cell variability in isoform ratios of two genes, CAPRIN1 and MKNK2, which each contain two splice isoforms (Figure 2 from the manuscript). Using a clonally derived, diploid, non‐transformed cell line (Rpe1 cells—retinal pigment epithelial cells), we found that variability is remarkably close to the minimum possible given the probabilistic chance of individual splicing events. In contrast, we found that isoform ratio variability was substantially larger in clonally derived HeLa cells, a cancerous cell line with an unstable karyotype. To explain the differences between the two cell lines, we further examined the potential origins of isoform ratio variability. We first studied several known sources of mRNA variability, such as transcriptional bursting, but found that they did not contribute significantly to the difference between cell lines. However, when we examined the role of splicing factors in controlling cell‐to‐cell variability, we found that lesser control over the regulation of alternative splicing is likely to be the primary source of this difference.
Cell‐to‐cell variability in gene expression owing to alternative splicing is an inevitable feature of biology. Since spliced isoforms can have different and even opposing cellular functions, it would be interesting to see if such variability can have phenotypic consequences in various biological settings. We anticipate that future work will shed light on the extent of cell‐to‐cell variability of alternative splicing for additional genes, and may identify splicing events where heterogeneity has an important functional role.
We applied a single‐molecule imaging approach to visualize the alternatively spliced isoforms of two genes, CAPRIN1 and MKNK2, in human cells.
We found that cell‐to‐cell variability in isoform ratios is close to the minimum possible in the absence of feedback in clonal Rpe1 cells, a diploid non‐transformed cell line. In contrast, clonal HeLa cells displayed much larger isoform ratio variability between cells.
Experimental and theoretical analysis suggests that variability in the regulatory splicing machinery contributes to this difference between cell lines.
Mol Syst Biol. 7: 506
- Received March 16, 2011.
- Accepted April 29, 2011.
- Copyright © 2011 EMBO and Macmillan Publishers Limited
This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
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