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Decoupling of degradation from deadenylation reshapes poly(A) tail length in yeast meiosis

Nature Structural & Molecular Biology volume 28, pages 1038–1049 (2021)Cite this article

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Abstract

Nascent messenger RNA is endowed with a poly(A) tail that is subject to gradual deadenylation and subsequent degradation in the cytoplasm. Deadenylation and degradation rates are typically correlated, rendering it difficult to dissect the determinants governing each of these processes and the mechanistic basis of their coupling. Here we developed an approach that allows systematic, robust and multiplexed quantification of poly(A) tails in Saccharomyces cerevisiae. Our results suggest that mRNA deadenylation and degradation rates are decoupled during meiosis, and that transcript length is a major determinant of deadenylation rates and a key contributor to reshaping of poly(A) tail lengths. Meiosis-specific decoupling also leads to unique positive associations between poly(A) tail length and gene expression. The decoupling is associated with a focal localization pattern of the RNA degradation factor Xrn1, and can be phenocopied by Xrn1 deletion under nonmeiotic conditions. Importantly, the association of transcript length with deadenylation rates is conserved across eukaryotes. Our study uncovers a factor that shapes deadenylation rate and reveals a unique context in which degradation is decoupled from deadenylation.

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Fig. 1: Meiotic poly(A) tail reshaping is associated with transcript length.
Fig. 2: Model for poly(A) tail length dynamics.
Fig. 3: Combination of modeling and perturbational assays shows decoupling of degradation from deadenylation in meiosis.
Fig. 4: Decoupling of degradation from deadenylation during meiosis triggers an association between gene expression and tail length.
Fig. 5: Xrn1 foci formation is connected to poly(A) tail–transcript length interaction.
Fig. 6: Transcript length impact in deadenylation is conserved in mammals.
Fig. 7: Poly(A) tail regulation in coupled and uncoupled regimes.

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Data availability

The datasets generated in this study for analyzing poly(A) tail length and RNA expression are available on Gene Expression Omnibus (GEO) under accession number GSE171329. Tail-seq and PAL-seq data were obtained from: GEO database (accession numbers GSE116355 and GSE134660). Meiotic RNA-seq and mass spectrometry data were obtained from GEO database (accession number GSE108778) and MassIVE (MSV000081874).

Code availability

Code for the analyses described in this paper is available from the corresponding author upon request.

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Acknowledgements

S.S. received funding from the Israel Science Foundation (grant no. 543165), the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant no. 714023 and ERC-POC 899122) and the Estate of Emile Mimran. S.S. is the incumbent of the Robert Edward and Roselyn Rich Manson Career Development Chair in Perpetuity. Y.A. is supported by the Israel Science Foundation (grant no. 1105/20). Y.A. is the incumbent of the Sygnet Career Development Chair for Bioinformatics.

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Authors and Affiliations

  1. Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel

    David Wiener, Yaron Antebi & Schraga Schwartz

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  1. David Wiener
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Contributions

D.W. and S.S. conceived the project. D.W. conducted all experiments. D.W. and S.S. performed the data analysis. Y.A. developed the mathematical model with the input of D.W. and S.S. S.S. acquired funding. D.W., Y.A. and S.S. wrote the paper.

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Correspondence to Yaron Antebi or Schraga Schwartz.

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Extended data

Extended Data Fig. 1 mPAT-seq accurately measures poly(A) tail length.

A) Cumulative fraction plot showing quantification of the spike-ins adenosines homopolymeric region length in NextSeq (top) and MiSeq (bottom) platforms. B) Barplot of Spearman correlation between mPAT-seq replicas with different depth requirements across all of the samples obtained in 1 hr interval after meiosis induction. C) Boxplot of genes mean poly(A) tail length in Wt and deadenylases deletion strains. Two sided Wilcoxon test was performed over n = 1 biologically independent samples. The boxplot was generated like in Fig. 2f. D) Barplot of Spearman correlation between PC1 loadings and different mRNA properties. E) Scatterplot of the PC1 loadings and the log2 of transcript length per gene. The figure was generated like in Fig. 1d (p < 2.2e-16). F) Histogram showing FACS DNA zstaining quantification across the meiotic time course. G) Kinetic for meiotic nuclear divisions as assayed by DAPI DNA staining. H) Percentage of spores obtained in deadenylases and xrn1 deletions meiotic time course measured after 24Hrs in SPO.

Extended Data Fig. 2 Decoupling of metabolic conditions from the meiotic gene program.

A) Barplot of Spearman correlation between gene mean poly(A) tail length and log2 of transcript length in the A/A background. B) Schematic representation of the return to growth (RtG) experiment, circles indicate when each of the samples was taken. C) Percentage of spores obtained in RtG experiments measured after 24Hrs in SPO. D) Barplot showing the correlation between poly(A) tail length and transcript length after transfer to YPD following 1Hr or 7Hrs of SPO incubation. E) Heatmap of the correlations of the RtG poly(A) tail profiles. Color code corresponds to the strength of Spearman correlation between each sample.

Extended Data Fig. 3 Poly(A) tail model is robust to deviation in the two key assumptions.

A) Simulated tail distribution of.genes with increasing variance in Amax and Amin between genes. B) Pooled distribution of the different simulations. Color code corresponds to the standard deviation of Amax and Amin distributions. C) Scatterplot showing the correlation between mean poly(A) tail and the fraction of short tail of the simulated distributions. D) Equation describing the linear interaction between poly(A) tail length and deadenylation rate (β) (top). Tail distributions under different rates of tail impact on deadenylation rate represented as b. Color code corresponds to this value (middle). Scatterplot showing the correlation between mean poly(A) tail and the fraction of short tail of the simulated distributions (bottom). E) Similar to D but for an exponential relationship between poly(A) tail length and the deadenylation rate.

Extended Data Fig. 4 Regulatory modules for poly(A) tail length dynamics.

A) A heatmap of calculated poly(A) tail lengths across 3-orders of magnitude variations in deadenylation and degradation rates, demonstrating the dependency of mean poly(A) tail lengths on the ratio between degradation and deadenylation rates. B) Scatterplot of the short tails fraction vs the mean poly(A) tail length, both in vegetative growth (left) and after 5Hrs in SPO (right). The number of genes in the analysis is specified in the figure. Spearman correlation and significance of the correlation coefficient p-value (Vegetative growth, p < 2.2e-16; Meiosis, p < 2.2e-16) using a t-distribution with n – 2 degrees of freedom were calculated. C) Transcript length (Ltx) potential regulatory modules over deadenylation (β) and degradation rates (γ). The first row consists of a simple linear relationship of transcript length over one of the rates (Or none in 1). The second row consists of the modules where transcript length affects both of the rates independently. The third and fourth rows contain modules where transcript length impacts one of the rates and in a dependent manner affects the other.

Extended Data Fig. 5 Disruption of production and degradation rates.

A) Scatterplot of the short tail fraction of xrn1Δ/Δ in vegetative growth condition vs the Wt, both in vegetative growth (left) and after 5Hrs in SPO (right). Genes were color coded by transcript length. Spearman correlation and significance of the correlation coefficient p-value (Vegetative growth, p = 5.6e-07; Meiosis, p < 2.2e-19) using a t-distribution with n – 2 degrees of freedom were calculated, and the number of analyzed transcripts specified. B) Xrn1 downregulation using the degron system. Left: boxplot of genes mean poly(A) tail length in Wt, before and after 30 min auxin addition. Middle: Spearman correlation between genes mean poly(A) tail length and log2 of transcript length. Right: Boxplot of the fraction of tails under 20nt per genes and plotted by transcript length groups indicating the number of genes per box. The boxplot was generated like in Fig. 2f. C) Boxplot showing the fraction of short tail per gene in the Wt and deadenylases deletion strains over n = 1 biologically independent samples. The boxplot was generated like in Fig. 2f. D) Tail length distributions of mRNA molecules of the Wt and pop2Δ/Δ in vegetative growth. A dashed line was drawn at 20nt. E) Barplot showing the relative induction of Ccr4 after induction measured by RT-qPCR.F) Barplot showing Spearman correlation between genes mean poly(A) tail length and log2 of transcript length of the Wt and Ccr4 inducible strain before and after induction. G) Boxplot of the fraction of tails under 20nt per gene and plotted by transcript length groups. The number of genes per group is indicated with the corresponding specific color. The boxplot was generated like in Fig. 2f.

Extended Data Fig. 6 The negative correlation between poly(A) tail and gene expression in vegetative growth conditions is driven by a subset of highly expressed genes.

Scatterplots of the poly(A) tail length vs log2 of normalized gene expression in the Wt in vegetative growth (Left) meiosis (Center) and xrn1Δ/Δ in vegetative growth (Right). Highly expressed genes were excluded from the analysis retaining the following percentages of genes: 100% (up), 90% (middle), and 75% (bottom). The figure was generated like in Fig. 1d (100%:Vegetative growth, p < 2.2e-16; Meiosis, p < 2.2e-16; xrn1Δ/Δ, p < 2.2e-16–90%:Vegetative growth, p < 2.2e-16; Meiosis, p < 2.2e-16; xrn1Δ/Δ, p < 2.2e-16–75%:Vegetative growth, p < 2.5e-07; Meiosis, p < 2.2e-16; xrn1Δ/Δ, p < 2.2e-16).

Extended Data Fig. 7 Expression levels of the degradation and deadenylation machinery related genes do not undergo major changes in meiosis.

Mass spectrometry (blue) and RNA-seq (red) results from Cheng et25 were normalized to vegetative control and plotted. The dashed line is drawn at a 2-fold change. Meiosis related genes were selected as control.

Extended Data Fig. 8 Initial poly(A) tail length and decapping rate are not associated with transcript length.

A) Scatterplot of log2 of the initial poly(A) tail (nt) and log2 of transcript length.Spearman correlation and significance of the correlation coefficient p-value(p = 1.2e-05) using a t-distribution with n – 2 degrees of freedom were calculated, and the number of analysed transcripts specified. B) Scatterplot of log2 of the degradation rate and log2 of transcript length. Spearman correlation and significance of the correlation coefficient p-value (p = 9.1e-12) using a t-distribution with n – 2 degrees of freedom were calculated, and the number of analysed transcripts specified.

Extended Data Fig. 9 Biological implications behind decoupling.

A) Scatterplot of the poly(A) tail differences between the ccr4Δ/Δ (up)/pop2Δ/Δ(down) and the Wt vs the log2 of transcript length, both in vegetative growth (left) and after 5Hrs in SPO (right). The figure was generated like in Fig. 1d (ccr4Δ/Δ:Vegetative growth, p < 2.2e-16; Meiosis, p < 2.2e-16-pop2Δ/Δ:Vegetative growth, p < 2.2e-16; Meiosis, p < 2.2e-16). B) Barplot showing the standard deviation of the log2 normalized gene expression of the Wt (Vegetative growth and meiosis) and the xrn1Δ/Δ strain in vegetative growth conditions. B) Similar to A but for cells uncommitted (1Hr in SPO) and committed (7Hrs in SPO) to meiosis, before and after transfer to YPD. C) Relevant groups of genes for mitosis and meiosis undergo tail length changes across meiosis. Gene ontology terms associated with elongated and shortened tails across the meiotic time course.

Extended Data Fig. 10 Partially degraded mRNA molecules are not detectable with RiboZero RNA-seq.

A) Sample normalized coverage across normalized transcript length generated with Picard tools60 in the Wt (Vegetative growth and meiosis) and xrn1Δ/Δ strains. B) Violin plots showing the gene specific 3′ Bias calculated either as the ratio of the last 100nt coverage divided by the first 100nt coverage (Left) or as the ratio of the last 10%nt coverage divided by the first 10%nt coverage (Right). C) Scatterplots of both 3′bias metrics vs the log2 of transcript length. Spearman correlation was calculated and significance of the correlation coefficient p-value (100nt-Vegetative growth, p = 0.44; Meiosis, p = 0.54; xrn1Δ/Δ, p = 0.8–10%:Vegetative growth, p = 0.81; Meiosis, p = 0.93; xrn1Δ/Δ, p = 0.39) using a t-distribution with n – 2 degrees of freedom were calculated, and the number of analysed transcripts specified.

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Wiener, D., Antebi, Y. & Schwartz, S. Decoupling of degradation from deadenylation reshapes poly(A) tail length in yeast meiosis. Nat Struct Mol Biol 28, 1038–1049 (2021). https://doi.org/10.1038/s41594-021-00694-3

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