XB-ART-57694Genome Biol January 5, 2021; 22 (1): 14.
Crosstalk between codon optimality and cis-regulatory elements dictates mRNA stability.
BACKGROUND: The regulation of messenger RNA (mRNA) stability has a profound impact on gene expression dynamics during embryogenesis. For example, in animals, maternally deposited mRNAs are degraded after fertilization to enable new developmental trajectories. Regulatory sequences in 3'' untranslated regions (3''UTRs) have long been considered the central determinants of mRNA stability. However, recent work indicates that the coding sequence also possesses regulatory information. Specifically, translation in cis impacts mRNA stability in a codon-dependent manner. However, the strength of this mechanism during embryogenesis, as well as its relationship with other known regulatory elements, such as microRNA, remains unclear. RESULTS: Here, we show that codon composition is a major predictor of mRNA stability in the early embryo. We show that this mechanism works in combination with other cis-regulatory elements to dictate mRNA stability in zebrafish and Xenopus embryos as well as in mouse and human cells. Furthermore, we show that microRNA targeting efficacy can be affected by substantial enrichment of optimal (stabilizing) or non-optimal (destabilizing) codons. Lastly, we find that one microRNA, miR-430, antagonizes the stabilizing effect of optimal codons during early embryogenesis in zebrafish. CONCLUSIONS: By integrating the contributions of different regulatory mechanisms, our work provides a framework for understanding how combinatorial control of mRNA stability shapes the gene expression landscape.
PubMed ID: 33402205
PMC ID: PMC7783504
Article link: Genome Biol
Species referenced: Xenopus laevis
Genes referenced: dicer1
GO keywords: regulation of mRNA stability
Article Images: [+] show captions
|Fig. 1. Codon composition predicts mRNA stability in vertebrates. a Scheme of the procedure to train a predictive model of mRNA stability. For each endogenous mRNA, the codon frequencies and the 3′UTR length are used as predictors to train a lasso regression model . The scatter plot shows the point density of predicted and observed mRNA stability (test set genes n = 7576, Pearson correlation test). b Scheme of the 1 nucleotide out of frame reporters (optimal and non-optimal): two mRNAs that differ in the codon composition due to a single nucleotide deletion (G in red, highlight with green) which creates a frameshift. The encoding mCherry fluorescent protein was followed by a cis-acting hydrolase element (P2A) and then by a coding region enriched in optimal or non-optimal codons due to the frameshift. P2A causes ribosome skipping; therefore, the mCherry is not fused to the optimal or non-optimal encoded proteins. The mRNA reporter pairs were co-injected with mRNA encoding for GFP as an internal control . c Fluorescence microscopy images of representative embryos at 8 h post-injection (hpi) with the indicated 1 nt out of frame reporter and GFP. Box plot displays fluorescence quantification at 8 hpi with each reporter. The mCherry fluorescence intensity was normalized to GFP intensity in each embryo (p = 0.041, paired t test). d mRNA stability predictions for 1 nucleotide out of frame reporters in fish and human cells. In all cases, the prediction for the optimal reporter is higher than that for the non-optimal (p = 0.007, binomial test)|
|Fig. 2. Codon optimality is the major determinant of mRNA stability. a Diagram depicting the maternal to zygotic transition in zebrafish. b Scatter plots of predicted and observed mRNA stability, during MZT, for maternal mRNAs in zebrafish and Xenopus. The gradient of color represents the content of optimal codons . The predicted mRNA stability correlates with the proportion of optimal codons (p < 2 × 10−16, Pearson correlation). The mRNA stability median of mRNAs enriched in optimal (red) or non-optimal (blue) codons, as well as mRNAs with miR-430/-427 target sites in the 3′UTR (green) are shown. c Codon content explains most of the mRNA decay during MZT. The x-axis shows the fraction of coding genes that can be regulated by different mRNA stability pathways. For microRNAs, this fraction corresponds to the number of seed sites (GCACTT) in the 3′UTR, and for m6A, the fraction is the number of target genes reported . The y-axis shows the Bayesian model comparison weights . These weights represent which model is more likely to predict unobserved data better, higher values indicate stronger regulatory effects|
|Fig. 3. Dissecting cis-regulatory elements after accounting for codon-mediated regulation. a Diagram describing the residual score (observed − predicted). mRNAs that decay more than expected by the model show negative residuals and potentially contain destabilizing cis-regulatory elements. The mRNAs with positive residual scores might have stabilizing cis-regulatory elements. b, c The model overestimates the mRNA stability of miR-430 targets. Sinaplot showing the distribution of the residual scores for targets and not targets of miR-430. In this type of plot, each dot represents an individual mRNA. The targets of miR-430 are grouped by the type of miR-430 seed (b) (p < 2 × 10−16, ANOVA test) or by the number of target seeds (c) (p < 2 × 10−16, ANOVA test) present in the 3′UTR during the MZT in zebrafish (Additional file 1: Fig. S1a-b) and Xenopus . d The model also overestimates the stability of mRNAs that contain m6A methylation mark. Sinaplot showing that m6A targets  display lower residual score distribution than not targets during the MZT in zebrafish (p < 2 × 10−16, t test). e Sylamer landscape plot that tracks occurrence biases of 6-nucleotide words in the 3′UTRs using hypergeometric p values for all words across the mRNAs ranking based on residual values . The highlighted 6-mers are significantly associated with mRNA stabilization (TATCTA, CTATCT, and TCTATC) and destabilization (GCACTT, TAGGAC, and GGACTT). The color shows the putative regulatory pathway that recognizes these 6-mers. The dotted line shows a hypergeometric p = 0.01|
|Fig. 4. Codon optimality affects mRNA stability and gene expression of microRNA targets and m6A targets in vertebrates. mRNAs were divided into targets (microRNA or m6A) and nontargets. Each group was divided into four equal groups with decreasing levels of optimal codons. All p values were computed with a linear model. a Sinaplot showing the distribution of mRNA stability during MZT (log2-fold change 6 h post-fertilization (hpf)/2 hpf) for targets and not target genes of miR-430/-427 in zebrafish (Additional file 1: Fig. S1a-b) and Xenopus  embryos. b Sinaplot showing the distribution of mRNA stability during MZT in zebrafish for methylated (m6A) and non-methylated mRNAs . c Sinaplot showing the mRNA stability distribution of m6A and miR-291a targets  and not targets in mouse embryonic stem cells. d Sinaplot showing the RNA-level distribution of miR-1 and miR-155 targets  after microRNA transfection in human cells. e Diagram depicting reporter genes containing almost identical nucleotide sequence but different codon composition (enriched in optimal or non-optimal codons) due to a single insertion changing the frame. For each coding region, a 3′UTR sequence containing miR-17 seed sites (7-mer AGCACTT) or a mutant version with two nucleotides mutated disrupting the miR-17 seed sites were cloned. The boxplot shows the distribution of scaled mCherry/GFP intensity for reporters with and without miR-17 seed site in transfected 293T human cells. Both coding sequence and miR-17 affect the reporter expression (p values computed with paired t test)|
|Fig. 5. Targeting efficacy of miR-430/-427 can be affected by the coding sequence. a Scatter plot comparing codon optimality level and change in expression between wildtype zebrafish embryos vs maternal/zygotic Dicer mutant embryos at 6 h post-fertilization . Only mRNAs with miR-430 seed in the 3′UTR (GCACTT) are shown. The line represents the average change in expression (log2-fold WT/Dicer at 6 hpf) as a function of the codon optimality level (Additional file 1: Table S5). The effect of miR-430 is not constant across different levels of codon optimality (p value was obtained using an F test comparing a model with a non-linear effect on codon optimality vs constant effect). The confidence interval was determined with bootstrap replicates (n = 100) . b Line plot showing the expected decrease in gene expression due to miR-430 during zebrafish MZT (log2-fold change 6 vs 2 hpf) (Additional file 1: Fig. S1a-b). The y-axis represents a measure, estimated from the data, of the miR-430 repressive strength with respect to codon optimality (x-axis). For example, for an mRNA that is very repressed by miR-430, the miR-430 component will be larger (higher negative value in the y-axis). However, for another gene, which has a weak miR-430, the miR-430 component is smaller (closer to 0 in the y-axis). The p value denotes the statistical significance of the non-linear interaction between codon optimality and miR-430 presence (F test) obtained with a generalized additive model . The confidence interval was determined with bootstrap replicates (n = 100) . c Scheme of the reporter library which includes random fragments of the zebrafish transcriptome . Transcripts share the same 5′ and 3′UTR but some sequences contain a stop codon in the coding region. These stop codons create a random and longer 3′UTR sequence. mRNAs were injected at the one‐cell stage in zebrafish , and the reporter library is analyzed at 2 and 8 hpi using high-throughput sequencing. To analyze the depletion of miR-430 with respect to the codon content of the transcripts, we filtered those sequences that contain a coding region of at least 350 nucleotides and a random 3′UTR length of at least 75 nucleotides. d, e Analysis of miR-430/427 depletion in the reporter library. The reporters were grouped into 7-tiles, equal size, with increasing levels of codon optimality (Additional file 1: Fig. S5c). For each tile, we computed the depletion of the miR-430/427 seed (GCACTT) in the 3′UTR (log2-fold 8 h/2 h for zebrafish and 9h/1 h for Xenopus). Each boxplot is formed by bootstrap replicates (n = 100) |
|Fig. 6. MicroRNAs antagonize codon optimality effect on mRNA stability during the MZT. a Sinaplot showing the codon optimality distribution (Table S5) for the top 1000 most unstable maternal genes during zebrafish and Xenopus MZT. The stability was defined based on the log2-fold change of early (2 h) vs late time points (fish = 6 h, Xenopus = 9 h) in fish (Additional file 1: Fig. S1a-b) and Xenopus . The genes were divided into groups based on the numbers or seed type of miR-430/427. The content of optimal codons increases with the miR-430 regulation strength (p = 2 × 10−4 zebrafish, p = 2 × 10−3 Xenopus, linear regression). The p value was obtained by comparing the difference in the mean level of optimal codons (PLS1) between genes with and without miR-430/427 sites using a linear model. b Heatmap of miR-430 enrichment in the 3′UTR as a function of mRNA stability and codon optimality level. The miR-430 enrichment was estimated with a generalized linear model. Unstable mRNAs enriched in optimal codons temp to contain miR-430 sites (e.g., smarca2). c Scatter plot comparing the RNA stability, during MZT, of ortholog genes in zebrafish (Additional file 1: Fig. S1a-b) and Xenopus . d Scatter plot comparing the content of optimal codons in zebrafish and Xenopus for ortholog genes . e Sinaplot showing the codon optimality distribution for unstable mRNAs in panel a that are orthologs (n = 280). Messenger RNAs were divided into four categories according to the presence or absence of miR-430/-427 seeds in both species. The orthologous mRNA with miR-430/-427 seeds in both species are the most enriched in optimal codons (p = 0.0868, one-way ANOVA test)|
|Fig. 7. Model showing that the mRNA stability depends on the regulatory elements of the coding and the 3′UTR, suggesting that to fully understand mRNA stability, the regulatory information across the entire mRNA sequence needs to be integrated, rather than focusing solely on the 3′UTR or in the coding sequence|
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