Major role for mRNA stability in shaping the kinetics of gene induction
© Elkon et al; licensee BioMed Central Ltd. 2010
Received: 11 January 2010
Accepted: 21 April 2010
Published: 21 April 2010
mRNA levels in cells are determined by the relative rates of RNA production and degradation. Yet, to date, most analyses of gene expression profiles were focused on mechanisms which regulate transcription, while the role of mRNA stability in modulating transcriptional networks was to a large extent overlooked. In particular, kinetic waves in transcriptional responses are usually interpreted as resulting from sequential activation of transcription factors.
In this study, we examined on a global scale the role of mRNA stability in shaping the kinetics of gene response. Analyzing numerous expression datasets we revealed a striking global anti-correlation between rapidity of induction and mRNA stability, fitting the prediction of a kinetic mathematical model. In contrast, the relationship between kinetics and stability was less significant when gene suppression was analyzed. Frequently, mRNAs that are stable under standard conditions were very rapidly down-regulated following stimulation. Such effect cannot be explained even by a complete shut-off of transcription, and therefore indicates intense modulation of RNA stability.
Taken together, our results demonstrate the key role of mRNA stability in determining induction kinetics in mammalian transcriptional networks.
mRNA levels in cells are determined by the relative rates of RNA production and degradation. Transcript levels at steady state therefore reflect equilibrium of RNA synthesis and decay. Gene expression microarrays, and more recently RNA deep-sequencing, are valuable means for genome-wide profiling of the cellular transcriptome and its modulation during normal development and in response to external perturbations. Standard microarray analyses on total cellular RNA provide a measure of mRNA abundance but cannot discriminate whether changes are due to alterations in RNA transcription or decay. New techniques have been developed to allow the measurement of actual rates of transcript production. When these are carried out in parallel to recording overall RNA abundance, they can reveal the relative contribution of alterations in gene transcription and mRNA stability to the observed net change in RNA abundance [1, 2].
Despite the above, to date, the contribution of RNA degradation to global changes in cellular transcriptome was largely overlooked. This was mostly due to the fact that most microarray studies, explicitly or implicitly, ascribed alterations in RNA levels to correlated changes in gene transcription. However, several pioneering studies have shed light on the critical role that modulation of mRNA stability plays in the regulation of cellular transcriptome in response to various stresses [3–7].
Recently, the critical role for mRNA stability in the induction kinetics of genes encoding inflammatory proteins was demonstrated at single-genes level . Here, we set out to examine this role on a large-scale utilizing a global atlas of mRNA stability recently generated in mammalian cells and analyzing numerous gene expression time-course datasets that collectively cover many different aspects of the cellular physiology.
Relationship between mRNA stability and induction kinetics in response to IL2
Next, we tested if there is a relationship between the observed kinetics of gene induction and mRNA stability. Namely, we tested whether fast responding genes were characterized by high instability (low T half-life), while late responders were more stable (high T half-life). In these tests we utilized a global atlas of mRNA stability in mammalian cells that was recently generated in murine fibroblast and human B cells [2, 12]. Importantly, these reports noted that mRNA half-life times were generally well conserved between the examined cell types and species (the median T1/2 in human and mouse cells was 315 min and 274 min, respectively). Using this data source, we found a striking correlation between response time and transcript stability: T half-lives of early-induced genes were significantly shorter than those of late-induced genes (Figure 2B, C), as predicted by the kinetic model.
Global relationship between mRNA stability and induction kinetics
mRNA stability is regulated mainly by cis-regulatory elements embedded in the transcript 3'-UTR [5, 15]. Seeking for major mechanisms that control stability in the datasets that we analyzed, we searched for enriched sequence patterns in the 3'-UTRs of the core set of early-induced genes. In agreement with previous reports [15, 16], we found that these highly unstable mRNAs are significantly enriched for the AU-rich element (ARE); the most highly enriched 7-mer in the 3'-UTRs of these genes was UAUUUAU, which appeared in 52% of the 3'-UTRs in this set compared to background frequency of 18% in all 3'-UTRs of mouse genes (p-value = 7.6*10-9, hypergeometric tail).
Relationship between genomic transcribed length and induction kinetics
Relationship between mRNA stability and kinetics of gene suppression
The mathematical kinetic model also predicts a similar relationship between RNA stability and kinetics of gene suppression. Genes whose expression is down-regulated rapidly are expected to encode RNAs with lower stability than genes whose expression is down-regulated at slower rate (Figure 1). Interestingly, while in several datasets we observed a very good agreement with this expectation, deviations from it were frequent. In five out of the ten datasets that we analyzed, we observed no relationship between mRNA stability and kinetics of gene suppression, while this relationship was highly significant for the induced genes (Additional file 1, Additional file 7). In those cases, the expression of many mRNAs which have high T half-life under standard conditions (above 5 hrs) was already down-regulated by at least a factor of 2.0 after only 1-2 hrs post treatment. Such a rapid down-regulation of stable RNAs cannot be explained even by a complete shut-off of transcription, and therefore suggests intense modulation of mRNA stability of the rapidly suppressed genes. We could not detect statistically enriched sequence motifs in the 3'-UTR of these genes that might point to the mechanism (e.g., microRNA, RNA-binding protein) which controls this stability modulation.
Two mechanisms which underlie the temporal order of gene induction are sequential activation of primary and secondary TFs and mRNA stability. These mechanisms operate in parallel, and therefore the observed induction kinetics is the result of their superposition. The kinetic model we used in our study is oversimplified as it assumes constant rates of transcription and degradation over time. In practice, however, most genes are regulated by modules of transcriptional activators and repressors rather than by single TF, the activity of TFs themselves is modulated over time and they often form interlocked feedback loops. Therefore, the kinetic patterns exhibited by responding genes are much more complicated than the simple exponential pattern predicted by the model. Yet, much insight can be gained on the dynamics of transcriptional networks using the simplified description. While many studies delineated the way various transcriptional networks are propagated by sequential cascades of TFs ([9, 17, 18], much less attention was given to the role of mRNA stability in shaping the induction dynamics. Recently, Hao and Baltimore  showed that mRNA stability significantly influences the induction kinetics of genes encoding inflammatory proteins. Here, by analyzing numerous gene expression datasets that collectively cover many different aspects of cellular physiology, we demonstrated on a global scale, the critical role that mRNA stability plays in shaping the dynamics of transcriptional networks. This global relationship agrees with the simple kinetic model for mRNA concentration which predicts that unstable transcripts respond faster than stable ones.
Of note, the striking anti-correlation that we detected in a diverse panel of cell types between mRNA stability and induction kinetics was derived using an atlas of mRNA half-lives measured in murine fibroblasts and human B-cells. This result indicates a broad conservation of RNA stability under different cell types and conditions, which therefore reflects, to a large extent, an intrinsic property of the mRNA molecules. This observation supports and strengthens Friedel et al. conclusion that mRNA stability is largely conserved between cell types and species .
Another factor which limits gene induction time is the genomic transcribed length. In accord, we observed that genes that were induced very rapidly were significantly short. Therefore, to achieve very rapid gene induction, substantial increase in transcriptional rate is coupled with rapid degradation rate and short genomic transcribed length, as exhibited by the core set of early-induced genes.
Interestingly, while we observed very good agreement with the kinetic model when analyzed gene induction, we found major deviations from the model predictions when analyzed gene suppression. In response to many stimuli, we observed very fast down-regulation of mRNAs whose half-life time is very high under normal conditions (as measured in fibroblasts (mouse) and B-cells (human)). Even a complete turn-off of transcription is not enough to achieve such a rapid reduction in the concentration of stable mRNAs. Therefore, those mRNAs that need to be down-regulated at a very high speed require regulatory mechanisms that decrease their stability. Key regulators of mRNA stability are RNA binding proteins (RBPs) and microRNAs (miRs), and ample information on the activation of these regulators in response to various stresses has been already accumulated. We speculate that activation of stimulus-specific RBPs and miRs is a major factor that underlies the deviation from model's prediction in the case of gene suppression.
Comprehensive understanding of gene expression networks can be gained only once we obtain a global delineation of the orchestrated modulation of transcription and degradation rates carried out by cells in normal development and in response to perturbations. In this study, we elucidated the key role of mRNA stability in shaping the kinetics of gene induction in intricate gene networks in mammalian cells.
Gene expression data analysis
Expression data were downloaded from public repositories (GEO and ArrayExpress). All datasets used Affymetrix arrays. Expression levels were calculated using the rma method  (implemented in Affymetirx Expression Console tool). For each dataset, presence flags were calculated using MAS5, and only probe-sets that were flagged as 'Present' in at least two chips were retained for subsequent analysis. For genes represented by multiple probe-sets, we chose the one probe-set with the highest median intensity in the dataset.
Kinetic clustering and all statistical analyses of T half-life and genomic transcribed length distributions were done in R.
Transcript sequences and genomic transcribed length
Transcript sequences and lengths for all human and mouse genes were obtained using Biomart (Ensembl v54) . For genes encoding multiple transcripts, the transcript with the longest genomic transcribed length was used in length distribution tests.
Enrichment test for k-mers in 3'-UTR of murine genes was carried out using the AMADEUS tool . For genes with multiple forms of 3'-UTR, the longest one was used. Enrichment test for GO functional categories was carried out using DAVID web-service . In both, the core set of early-induced genes was compared to a background set of all murine genes.
We thank Chaim Linhart for critical reading of the manuscript. RE is supported by an EMBO long-term fellowship.
- Garcia-Martinez J, Aranda A, Perez-Ortin JE: Genomic run-on evaluates transcription rates for all yeast genes and identifies gene regulatory mechanisms. Molecular cell. 2004, 15 (2): 303-313. 10.1016/j.molcel.2004.06.004.PubMedView ArticleGoogle Scholar
- Dolken L, Ruzsics Z, Radle B, Friedel CC, Zimmer R, Mages J, Hoffmann R, Dickinson P, Forster T, Ghazal P: High-resolution gene expression profiling for simultaneous kinetic parameter analysis of RNA synthesis and decay. RNA (New York, NY). 2008, 14 (9): 1959-1972.View ArticleGoogle Scholar
- Cheadle C, Fan J, Cho-Chung YS, Werner T, Ray J, Do L, Gorospe M, Becker KG: Control of gene expression during T cell activation: alternate regulation of mRNA transcription and mRNA stability. BMC genomics. 2005, 6 (1): 75-10.1186/1471-2164-6-75.PubMed CentralPubMedView ArticleGoogle Scholar
- Keene JD: RNA regulons: coordination of post-transcriptional events. Nature reviews. 2007, 8 (7): 533-543. 10.1038/nrg2111.PubMedView ArticleGoogle Scholar
- Narsai R, Howell KA, Millar AH, O'Toole N, Small I, Whelan J: Genome-wide analysis of mRNA decay rates and their determinants in Arabidopsis thaliana. The Plant cell. 2007, 19 (11): 3418-3436. 10.1105/tpc.107.055046.PubMed CentralPubMedView ArticleGoogle Scholar
- Romero-Santacreu L, Moreno J, Perez-Ortin JE, Alepuz P: Specific and global regulation of mRNA stability during osmotic stress in Saccharomyces cerevisiae. RNA (New York, NY). 2009, 15 (6): 1110-1120.View ArticleGoogle Scholar
- Shalem O, Dahan O, Levo M, Martinez MR, Furman I, Segal E, Pilpel Y: Transient transcriptional responses to stress are generated by opposing effects of mRNA production and degradation. Molecular systems biology. 2008, 4: 223-10.1038/msb.2008.59.PubMed CentralPubMedView ArticleGoogle Scholar
- Bolouri H, Davidson EH: Transcriptional regulatory cascades in development: initial rates, not steady state, determine network kinetics. Proceedings of the National Academy of Sciences of the United States of America. 2003, 100 (16): 9371-9376. 10.1073/pnas.1533293100.PubMed CentralPubMedView ArticleGoogle Scholar
- Smith J, Theodoris C, Davidson EH: A gene regulatory network subcircuit drives a dynamic pattern of gene expression. Science (New York, NY). 2007, 318 (5851): 794-797.View ArticleGoogle Scholar
- Perez-Ortin JE, Alepuz PM, Moreno J: Genomics and gene transcription kinetics in yeast. Trends Genet. 2007, 23 (5): 250-257. 10.1016/j.tig.2007.03.006.PubMedView ArticleGoogle Scholar
- Barenco M, Tomescu D, Brewer D, Callard R, Stark J, Hubank M: Ranked prediction of p53 targets using hidden variable dynamic modeling. Genome biology. 2006, 7 (3): R25-10.1186/gb-2006-7-3-r25.PubMed CentralPubMedView ArticleGoogle Scholar
- Friedel CC, Dolken L, Ruzsics Z, Koszinowski UH, Zimmer R: Conserved principles of mammalian transcriptional regulation revealed by RNA half-life. Nucleic acids research. 2009, 37 (17): e115-10.1093/nar/gkp542.PubMed CentralPubMedView ArticleGoogle Scholar
- Hao S, Baltimore D: The stability of mRNA influences the temporal order of the induction of genes encoding inflammatory molecules. Nature immunology. 2009, 10 (3): 281-288. 10.1038/ni.1699.PubMed CentralPubMedView ArticleGoogle Scholar
- Zhang Z, Martino A, Faulon JL: Identification of expression patterns of IL-2-responsive genes in the murine T cell line CTLL-2. J Interferon Cytokine Res. 2007, 27 (12): 991-995. 10.1089/jir.2006.0169.PubMedView ArticleGoogle Scholar
- Yang E, van Nimwegen E, Zavolan M, Rajewsky N, Schroeder M, Magnasco M, Darnell JE: Decay rates of human mRNAs: correlation with functional characteristics and sequence attributes. Genome research. 2003, 13 (8): 1863-1872.PubMed CentralPubMedGoogle Scholar
- Khabar KS: The AU-rich transcriptome: more than interferons and cytokines, and its role in disease. J Interferon Cytokine Res. 2005, 25 (1): 1-10. 10.1089/jir.2005.25.1.PubMedView ArticleGoogle Scholar
- Litvak V, Ramsey SA, Rust AG, Zak DE, Kennedy KA, Lampano AE, Nykter M, Shmulevich I, Aderem A: Function of C/EBPdelta in a regulatory circuit that discriminates between transient and persistent TLR4-induced signals. Nature immunology. 2009, 10 (4): 437-443. 10.1038/ni.1721.PubMed CentralPubMedView ArticleGoogle Scholar
- Elkon R, Linhart C, Halperin Y, Shiloh Y, Shamir R: Functional genomic delineation of TLR-induced transcriptional networks. BMC genomics. 2007, 8: 394-10.1186/1471-2164-8-394.PubMed CentralPubMedView ArticleGoogle Scholar
- Irizarry RA, Bolstad BM, Collin F, Cope LM, Hobbs B, Speed TP: Summaries of Affymetrix GeneChip probe level data. Nucleic acids research. 2003, 31 (4): e15-10.1093/nar/gng015.PubMed CentralPubMedView ArticleGoogle Scholar
- Haider S, Ballester B, Smedley D, Zhang J, Rice P, Kasprzyk A: BioMart Central Portal--unified access to biological data. Nucleic acids research. 2009, W23-27. 10.1093/nar/gkp265. 37 Web Server
- Linhart C, Halperin Y, Shamir R: Transcription factor and microRNA motif discovery: the Amadeus platform and a compendium of metazoan target sets. Genome research. 2008, 18 (7): 1180-1189. 10.1101/gr.076117.108.PubMed CentralPubMedView ArticleGoogle Scholar
- Huang da W, Sherman BT, Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature protocols. 2009, 4 (1): 44-57. 10.1038/nprot.2008.211.PubMedView ArticleGoogle Scholar