Widespread changes in mRNA stability contribute to quiescence-specific gene expression patterns in a fibroblast model of quiescence
© The Author(s). 2017
Received: 23 August 2016
Accepted: 26 January 2017
Published: 1 February 2017
Quiescence, reversible exit from the cell division cycle, is characterized by large-scale changes in steady-state gene expression, yet mechanisms controlling these changes are in need of further elucidation. In order to characterize the effects of post-transcriptional control on the quiescent transcriptome in human fibroblasts, we determined mRNA decay rates for over 10,000 genes using a transcription shut-off time-course.
We found that ~500 of the genes monitored exhibited significant changes in decay rate upon quiescence induction. Genes involved in RNA processing and ribosome biogenesis were destabilized with quiescence, while genes involved in the developmental process were stabilized with quiescence. Moreover, extracellular matrix genes demonstrated an upregulation of gene expression that corresponded with a stabilization of these transcripts. Additionally, targets of a quiescence-associated microRNA (miR-29) were significantly enriched in the fraction of transcripts that were stabilized during quiescence.
Coordinated stability changes in clusters of genes with important functions in fibroblast quiescence maintenance are highly correlated with quiescence gene expression patterns. Analysis of miR-29 target decay rates suggests that microRNA-induced changes in RNA stability are important contributors to the quiescence gene expression program in fibroblasts. The identification of multiple stability-related gene clusters suggests that other posttranscriptional regulators of transcript stability may contribute to the coordination of quiescence gene expression. Such regulators may ultimately prove to be valuable targets for therapeutics that target proliferative cells, for instance, in cancer or fibrosis.
KeywordsQuiescence mRNA stability miR-29 Gene expression Post-transcriptional regulation Cell-cycle
Cellular quiescence is a state of cell cycle arrest that is characterized by the unique ability of cells to exit and re-enter the cell division cycle upon presentation of the appropriate stimulus. The proliferation of B-cells during an immune response, hepatic stellate cells in response to liver injury, and skin fibroblasts during wound healing all rely on the ability of cells to properly re-enter the cell cycle from a quiescent state [1–3]. Upon induction to the quiescent state, there are well-documented changes in gene expression, but the regulation and coordination of gene expression changes with quiescence is not fully understood [4–6]. Transcriptional repressors such as HES1 and FOXC1 that can control the expression of clusters of genes involved in maintaining the reversibility of quiescence have been shown to account for some of these observed expression changes [5, 7, 8]. In addition to transcriptional control, post-transcriptional contributions to quiescence gene regulation have the potential to have large effects on the quiescence gene expression signature. During quiescence, downregulation of cell cycle progression-promoting genes such as the transcription factor MYC is important for sustaining cell cycle exit. A transcript-stabilizing truncation containing an AU-rich element (ARE) in the MYC 3’UTR can lead to cell cycle misregulation and oncogenic transformation . Moreover, RNA binding proteins that elicit decay through interactions with AREs have been shown to be necessary in maintaining lymphocyte quiescence . Additionally, upregulation of cell cycle progression inhibitors like the cyclin-dependent kinase inhibitor CDKNIB (p27 (Kip1)) is required for cell cycle exit and CDKN1B has been shown to be extensively post-transcriptionally regulated [11, 12].
Post-transcriptional regulatory mechanisms are emerging as major contributors to the biological functions essential to quiescence biology. Directly determining the stability of mRNA transcripts has multiple advantages over steady-state determinations of gene expression for understanding contributions of post-transcriptional control to gene regulation. One advantage is that RNA stability measurements increase the ability to identify clusters of genes that are controlled by a common post-transcriptional regulator [13, 14]. In a lymphocyte model of quiescence, it was shown that around 50% of the gene expression changes observed upon activation to proliferation were controlled at the level of RNA stability [15, 16]. Additionally, microRNAs, which can act as posttranscriptional negative regulators of gene expression, have been shown to have effects on transcript stability . Thus, clustering transcripts by changes in stability can give greater resolution to the identification of miRNAs important for stability-based changes in gene expression rather than using steady state gene expression measurements for these inferences. Coupling stability measurements with steady-state gene expression measurements gives more information on how the steady state was reached and how future perturbations may affect the potential to reach a new steady state. This information is lost when solely analyzing gene expression data .
In this study, we characterized changes in mRNA stability between proliferating and quiescent fibroblasts using genome-wide measurements of mRNA stability. Analysis of these measurements revealed stability-regulated gene clusters with shared biological functions. Concurrent measurements of steady-state gene expression allowed more insight into how up and down regulated gene clusters were affected by changes in RNA stability. Notably, a stability-regulated cluster was defined by targets of the quiescence-associated miRNA, miR-29, giving further insight into its mechanism of action.
Genome-wide changes in RNA stability with quiescence induction
To understand the global role of post-transcriptional control in the regulation of the quiescent transcriptome, we used microarrays to determine half-lives of over 10,000 transcripts in human foreskin fibroblasts. Transcription was inhibited in either proliferating (P) cells or cells made quiescent by 7 days of contact inhibition of growth (CI7). Samples were collected over an 8 h time period and analyzed by microarray. Decay constants were calculated by fitting the log decrease in transcript abundance to a linear model and genes with a poor fit to the linear model were filtered from further analysis (see Methods). Correlations of decay constants with publically available data sets were in good concordance with previous calculations in other cell culture models (Additional file 1).
K-means clustering of decay constants across time course replicates resulted in two main profiles of stability changes (Fig. 1a). Genes in cluster 1 exhibited faster decay during quiescence and were enriched for genes involved in RNA processing and ribosome biogenesis. Genes in cluster 2 exhibited faster decay during proliferation and were enriched in genes involved in developmental processes and anatomical structure development.
Genes with no significant change in RNA stability measurements between P and CI7 fibroblasts were also characterized by enrichments in Gene Ontology (GO) terms. The GO terms identified shared similarity to those reported in previous studies of transcript decay rates in human cells [19, 20]. Fast-decaying transcripts in both states were enriched for GO terms such as “regulation of gene expression”, while slow-decaying transcripts in both states were enriched for ontology terms related to “cellular respiration”. A full list of clustered decay rate distributions and corresponding enriched GO terms is included in Additional file 2, Additional file 3: Figure S1, and Additional file 4. Gene set enrichment analysis of genes with significant changes in RNA stability provided further resolution of stability-regulated quiescence gene sets. Top sets with faster decay during quiescence included mitochondrial translation, DNA damage response, and RNA-related processes (Additional file 3: Figure S2A). In contrast, top sets with slower decay during quiescence included genes involved in differentiation and extracellular matrix disassembly (Additional file 3: Figure S2B).
Changes in RNA stability correlate with a subset of gene categories that have large changes in gene expression during quiescence
miR-29 targets involving extracellular matrix are stabilized during quiescence
In a fibroblast model of quiescence, we observed that RNA stability, an important mechanism for post-transcriptional regulation, changes on a genome-wide scale during quiescence. Defining whether the expression levels of a transcript are controlled by transcriptional or post-transcriptional mechanisms can provide further insight into the mechanisms underlying coordinated changes in gene expression. Our findings are consistent with other reports that transcript stability can be an important contributor to changes in cell fate [14, 27, 28]. For instance, in differentiating C2C12 cells, there were changes in transcript decay during differentiation to a myoblast phenotype that were closely associated with changes in gene expression . Additionally, in lymphocytes, transcript decay rates were shown to be critical for the sequential waves of gene expression events that occur upon activation . In our study of proliferating versus quiescent fibroblasts, there was no overall correlation between mRNA abundance and decay rate. With a more focused analysis on genes with the most significant increase or decrease in expression, we were able to determine that extracellular matrix genes were characterized by slower decay rates and a higher abundance in quiescent compared with proliferating cells.
Extracellular matrix metabolism genes, which are highly upregulated during quiescence, exhibit increased transcript stability in the quiescent state compared to the stability of these same genes when fibroblasts are proliferating. Such findings would be consistent with an important role for skin fibroblasts in synthesizing collagen that forms the connective tissue in skin. These data suggest that the changes in collagen and extracellular matrix production are controlled, at least in part, post-transcriptionally at the level of mRNA stability. This insight into gene regulation is masked when profiling steady state levels of gene expression, but when RNA stability is also profiled, more insight into the mechanistic coordination of related gene sets is gained. Moreover, miR-29, a negative regulator of collagen gene expression, is downregulated during quiescence induction. Upregulation of miR-29 during proliferation leads to negative regulation of these same transcripts, which share a common biological function, by decreasing their stability.
Global changes in miRNA abundance have been documented in multiple mammalian quiescence models [26, 29, 30] and contribute to regulatory networks necessary for defining and maintaining cell cycle phenotypes [31, 32]. Differences in corresponding RNA binding protein complexes  and transcript architecture  during quiescence can lead to different mechanisms of miRNA-dependent transcript regulation. Cell-cell contact was reported to enhance miRNA processing, resulting in increased miRNA biogenesis and more efficient formation of RNA-induced silencing complexes . When considering miRNA-dependent effects on RNA stability, this model suggests that there might be rapid decay of miRNA targets in quiescent cells. In another study, miRNAs have been reported to be stored in inactive low molecular weight Argonaute protein complexes that lack GW182 in a quiescence model . These findings suggest that miRNA target genes might be more stable in the quiescent state. Our data identified examples of both cases where a portion of miRNA targets were more stable during quiescence (including miR-29 targets), and a portion of targets were less stable during quiescence. These results are consistent with our own analysis in which the levels of miRNAs were monitored by microarray and miR-29 levels were discovered to decline relative to other miRNAs in quiescent cells . The results are also consistent with our findings that miR-29 hastens cell cycle re-entry from quiescence .
miRNA-dependent alterations of transcript stability [17, 35, 36] and translational efficiency  have both been identified as contributors to gene regulatory changes and this work adds to our understanding of the role of miRNAs in the regulation of transcript stability changes between cell cycle states. Our analysis of miRNA targets enriched for differential decay between P and CI7 fibroblasts highlight a potential role for the miR-17-92 cluster, and miR-200 in promoting transcript decay in quiescent cells, and miR-130 in promoting transcript decay in proliferating cells. Moreover, the miR-17-92 cluster in particular has been previously implicated in cell cycle regulation [38, 39]. These microRNAs are candidates for further study as potential regulators of the proliferation-quiescence transition.
Our work further defines a role for specific changes in transcript stability as contributors to gene regulation in a fibroblast quiescence model. Regulators of gene expression such as miR-29 serve as candidate targets for affecting the expression of extracellular matrix expression, for instance, in fibrotic disease.
This study provides a rich set of data to add to the growing knowledge about how mRNA stability contributes to gene expression changes as a cell responds to various stimuli. We were able to integrate data on mRNA transcript stability, mRNA transcript abundance and miRNA expression to better understand how genes involved in extracellular matrix metabolism are regulated during fibroblast quiescence. Further studies elucidating the role of RNA biology in fibroblast quiescence will improve our understanding of complex proliferative and secretory diseases such as fibrosis.
Cell culture and transcription shutoff time course
Human fibroblasts were isolated from the dermal layer of neonatal foreskin tissue as previously described . Proliferating and contact-inhibited fibroblast were maintained in DMEM supplemented with 10% fetal bovine serum (FBS). Proliferating cells were seeded at 5 × 105 cells per 10 cm plate and split every 48 h. Contact-inhibited cells were seeded at 5 × 105 cells per 10 cm plate with medium changes every 48 h until the end of a 7-day time period.
To inhibit transcription in proliferating and 7-day contact-inhibited fibroblasts, actinomycin D was added to the culture media at a concentration of 15 μg/mL. Cells were washed with PBS and cell lysates were collected using Trizol (Life Technologies) at 0, 120, 240, and 480 min after addition of actinomycin D.
Decay rate constant calculations
Based on the first order nature of mRNA decay kinetics, fluorescence intensities were log-transformed and fit to a linear decay model, using time, cell cycle condition (P or CI7), and biological replicate as predictors. Genes with a poor fit to the log linear decay model (6297 of 14212 total transcripts, ANOVA F-test, p > 0.05) were filtered from subsequent analysis. Transcripts with a significant interaction term between condition and time, according to an ANOVA F-test (FDR < 0.05), were considered to have different decay rates between P and CI7 fibroblasts. To aide in the interpretation of changes in decay between states, a metric (decay metric) to compare P to CI7 decay constants was calculated by taking the difference between the P and CI7 decay constants and using the local False Discovery Rate  to bring the value of constant comparisons without a significant change between conditions towards 0. All original intensities and calculated values are available in Additional file 4.
Microarray labeling, hybridization, and raw data processing
RNA was isolated from Trizol lysates according to the manufacturer’s protocol . RNA quality was verified on a Bioanalyzer 2100 using reagents from the RNA nano 6000 kit (Agilent Technology). RNA that passed quality and yield cutoffs were reverse transcribed into cDNA and subsequently labelled with the dye Cyanine-3 through the transcription of cRNA using the Quick Amp Microarray Labelling Kit (Agilent Technology). RNA spike-in control A (Agilent Technology) was added to each RNA sample in the time course at equal amounts as a control for labelling and hybridization efficiency. Labeled cRNA was hybridized to Human 4 × 44 K gene expression microarrays using the one-color format for 17 h at 65 °C (Agilent Technology). Fluorescence intensities were detected using a microarray scanner (Agilent Technology) and assigned to the appropriate gene using Feature Extractor 6.1 (Agilent Technology) software. Probes with fluorescent intensities above background and above Feature Extractor quality control thresholds were used in decay rate constant determinations.
Microarray cRNA transcription and hybridization
Total RNA samples from P, CI7, and CI14 cells were reverse transcribed into cDNA and fluorescently labeled with Cyanine 3-CTP (CY3) (quiescent samples) or Cyanine 5-CTP (CY5) (proliferating samples) to make cRNA according to the manufacturer’s protocol for the Quick Amp Labeling Kit for Microarray Analysis (Agilent). cRNA samples that passed yield and labeling standards were fragmented and proliferating and quiescent samples were hybridized to Human gene expression 4 × 44 K arrays (Agilent) for 17 h at 65 °C in an oven rotating the arrays at 10 rpm.
Fluorescence intensities were detected using the Genepix scanner (Agilent) and probe identities were determined using Agilent’s feature extractor version 11.5. Probes detected over background fluorescence thresholds were used in subsequent gene expression analysis. Microarray data was uploaded to the Princeton University MicroArray database (PUMAdb) and is accessible for download with the proper permissions. Log2 ratios of quiescent versus proliferating gene expression are available in Additional file 5.
Gene ontology analysis and miRNA enrichment analysis
Overrepresentation of gene ontology terms in gene clusters defined by k-means clustering of decay constants was determined using the Lewis-Sigler Gene Ontology tool (http://go.princeton.edu/). miRNA enrichment was calculated by collecting the predicted TargetScan  targets of each miRNA, then using a Wilcoxon rank-sum test to compare differences in decay constants Pconstant – CI7constant within the set to genes outside the set . Enrichment tests and violin plots displaying the differences in decay constants Pconstant – CI7constant for targets of each miRNA were performed using the GSEAMA package (David Robinson – Princeton University) implemented in R. The difference in decay constants between P and CI7 fibroblasts from miR-29 targets were compared to non-targets using a Chi-squared test for independence to test whether miR-29 targets versus non-targets had a higher proportion of genes that decayed faster during proliferation as compared to quiescence.
We would like to thank Eric Suh and Josh Bloom for their initial help with data analysis, Alison Gammie for helpful comments on the written manuscript, and all members of the Coller lab for their thoughtful contributions.
HAC was the Milton E. Cassel scholar of the Rita Allen Foundation (http://www.ritaallenfoundation.org/). ELJ was supported in part by a National Science Foundation Graduate Research Fellowship DGE-0646086. This work was funded by grants to HAC from the National Institute of General Medical Sciences Center of Excellence grant P50 GM071508 (P.I. David Botstein), PhRMA Foundation grant 2007RSGl9572, National Science Foundation Grant OCI-1047879 to David August, National Institute of General Medical Sciences R01 GM081686, National Institute of General Medical Sciences, the Eli & Edythe Broad Center of Regenerative Medicine & Stem Cell Research, the Iris Cantor Women’s Health Center/UCLA CTSI NIH Grant UL1TR000124, and the Leukemia Lymphoma Society. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. HAC is a member of the Eli & Edythe Broad Center of Regenerative Medicine & Stem Cell Research, the UCLA Molecular Biology Institute, and the UCLA Bioinformatics Interdepartmental Program.
Availability of data and materials
The datasets supporting the conclusions of this article are included within the article and its Additional files.
ELJ performed the experiments. ELJ and DGR analyzed the data. ELJ and HAC conceived the study. ELJ and HAC wrote the manuscript. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Consent for publication
Ethics approval and consent to participate
All experiments were approved by the Princeton Institutional Review Board IRB #3134 and written consent for the use of foreskin tissue was provided by the parents/legal guardians of all tissue donors in this study.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
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