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Deciphering disordered regions controlling mRNA decay in high-throughput

Nature volume 642, pages 805–813 (2025)Cite this article

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Abstract

Intrinsically disordered regions within proteins drive specific molecular functions despite lacking a defined structure1,2. Although disordered regions are integral to controlling mRNA stability and translation, the mechanisms underlying these regulatory effects remain unclear3. Here we reveal the molecular determinants of this activity using high-throughput functional profiling. Systematic mutagenesis across hundreds of regulatory disordered elements, combined with machine learning, reveals a complex pattern of molecular features important for their activity. The presence and arrangement of aromatic residues strongly predicts the ability of seemingly diverse protein sequences to influence mRNA stability and translation. We further show how many of these regulatory elements exert their effects by engaging core mRNA decay machinery. Our results define molecular features and biochemical pathways that explain how disordered regions control mRNA expression and shed light on broader principles within functional, unstructured proteins.

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Fig. 1: High-throughput functional profiling of post-transcriptional regulatory activity in disordered regions.
Fig. 2: Disordered post-transcriptional regulators function through 5′–3′ mRNA decay.
Fig. 3: Mutational scanning of repressive disordered regions reveals functional motifs.
Fig. 4: Repressive motifs identified by mutational scanning are required for post-transcriptional repressor function.
Fig. 5: Aromatic amino acids are enriched in repressive disordered regions.

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Data availability

Raw sequencing data and processed reads generated from this study have been deposited in the NCBI Gene Expression Omnibus (accession number GSE254492). All raw data analysed in this study are available on Zenodo63 (https://doi.org/10.5281/zenodo.14708299).

Code availability

Code and raw data used for analysis are available on GitHub (https://github.com/ingolia-lab/post-transcriptional-idrs) and Zenodo63 (https://doi.org/10.5281/zenodo.14708299).

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Acknowledgements

We thank H. Nolla for help with the flow cytometry; Twist Biosciences for assistance with the oligonucleotide library design; A. Martin, A. S. Lyon and M. V. Staller for helpful discussion and comments on the manuscript; I. Ornelas for assistance with cloning; and members of the Ingolia and L. Lareau laboratories for suggestions throughout the study. This work was supported by NIH Ruth L. Kirschstein Postdoctoral Fellowship F32 GM148044 (to J.H.L.) and R01 GM130996 (to N.T.I.). Sequencing was supported by an NIH S10 OD018174 instrumentation grant to the QB3 Genomics facility at UC Berkeley.

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Authors and Affiliations

  1. Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA

    Joseph H. Lobel & Nicholas T. Ingolia

  2. Center for Computational Biology and California Institute for Quantitative Biosciences, University of California, Berkeley, Berkeley, CA, USA

    Nicholas T. Ingolia

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  1. Joseph H. Lobel
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  2. Nicholas T. Ingolia
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Contributions

J.H.L. and N.T.I. designed the study. J.H.L. performed all experiments and analysis, with help from N.T.I. for genomics and computational approaches. J.H.L. wrote the manuscript with input and editing from N.T.I., who supervised the project.

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Correspondence to Nicholas T. Ingolia.

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N.T.I. holds equity in and serves as a scientific advisor to Tevard Biosciences, and holds equity in Velia Therapeutics. J.H.L. declares no competing interests.

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Extended data figures and tables

Extended Data Fig. 1 Validation of high-throughput tethering approach.

a,b, Gating schemes used for flow cytometry analysis. These criteria were applied across all subsequent analyses. c, Representative raw YFP and RFP distributions for tethering controls. d, Mean iRFP/RFP ratios from flow cytometry for tethering fragments. Individual values from each replicate and standard deviation are displayed (n = 3). e, Normalized read distribution of two fragments across bins. Colour shades show two independent biological replicates. f, Gating scheme for FACS on the iRFP/RFP distribution. Bins divide the cells into four roughly equal-sized populations. g,h, Correlation between replicates for g, activity scores and h, stability scores, with Pearson correlation coefficient displayed in figure panels. i, Distribution of RFP signal from mScarlet normalizer for each replicate measured in Fig. 1f. j, Plasmid shuffling assay of Nab3 variants in media lacking uracil (Left) or containing 5-FOA (right).

Extended Data Fig. 2 Additional analysis of disordered fragments within full-length proteins.

a,b, Waterfall plot of a, activity scores and b, stability scores for fragments within each ORF. Threshold scores used to identify repressors or unstable fragments are shown in grey. c, Full gene ontology analysis of statistically significant terms for genes with repressive fragments with the Benjamini-Hochberg corrected p-values from Fisher’s exact test. FDR, false discovery rate. d, Cumulative distribution function of genes with repressive peptides found across different RNA-protein interaction datasets from yeast51.

Extended Data Fig. 3 Depletion of core mRNA decay nucleases and activity scores in depleted backgrounds.

a-d, Growth curves of reporter strains expressing a, the Tir1 F-box protein only, or with the mAID tag on b, Ccr4 c, Pop2 or d, Dcp2. All strains were treated with 500 µM IAA or 1% DMSO as indicated. Different shades correspond to individual biological replicates (n = 3). e, Fitted growth rates for the curves shown in a-d. Mean of fits for individual biological replicates and standard deviation are displayed (n = 3). f-h, Correlation between activity scores for biological replicates of tethering library in depletion strains after ~16 h of IAA treatment for f, Ccr4 depletion g, Pop2 depletion and h, Dcp2 depletion, with Pearson correlation coefficients shown in panel legends. i-j, Comparison of average activity scores in the wildtype strain and average activity scores in strains where nucleases have been depleted. i, Ccr4 j, Dcp2 (n = 2). Repressors with significant difference in activity are shown in red. Grey lines demarcate statistical significance (see Methods, padj ≤ 0.05).

Extended Data Fig. 4 Sort-seq of mutational scanning library and activity scores.

a, Distribution of YFP:RFP ratios of reporter yeast expressing the mutational scanning library. Vertical lines demarcate equal population bins used in sorting. The distribution is more uniform than the proteome-wide library because these tethering constructs are derived from highly repressive peptides. b,c, Correlation between b, stability scores and c, activity scores for biological replicates of the mutational scanning screen. The Pearson correlation coefficient is displayed in panel legend. d, Correlation between average activity scores for wildtype repressive fragments in the mutational scanning library screen and for these peptides in proteome-wide disordered library screen (n = 2). Repressors are shown in black and 20 inactive controls shown in red. e, Cumulative probability of the conservation scores of fragments or motifs. Conservation was calculated from multiple sequence alignments of each protein from the yeast genome order browser and per-residue conservation scores were determined (see Methods). Only residues within motifs are significantly different from the other distributions (Kolmogorov-Smirnov test p-value < 0.05).

Extended Data Fig. 5 Functional analysis of motifs within post-transcriptional repressors.

a, Western blot of wildtype and motif-mutated, V5-tagged Tis11 from log-phase cells grown in complete synthetic media. Representative result from n = 2. b, Growth curves for wildtype, motif mutant, and ∆tis11 in complete synthetic media alone (no ferrozine) and c, with 750 µM ferrozine and 300 µM ammonium iron(II) sulfate. Different shades correspond to biological replicates (n = 3). d, Fragments from Eap1 in wildtype disordered library screen, coloured as in Fig. 3c. Individual measurements are shown as points (n = 2). The Y(X4)Lφ and repressive motif regions are highlighted in purple. e, Sequence alignment from representative yeast species of conserved sequences within the repressive region of Eap1. Motif-1 and Motif-2 are displayed and correspond to naming in Fig. 4g,h. E. cym, Eremothecium cymbalariae; V. pol, Vanderwaltozyma polyspora; T. pha, Tetrapisispora phaffii; K. lac, Kluyveromyces lactis.

Extended Data Fig. 6 Features and predictions of repressive post-transcriptional disordered regions.

a, Correlation between number of negative changes and average activity scores (n = 2). b, Correlation between number of aromatic residues and average activity scores (n = 2). c, Prediction probabilities of the motif-dependent classifier to predict the composition-driven repressors (red) and the composition-driven classifier to predict motif-dependent repressors (blue). d, Correlation between activity score and Kyte-Doolittle hydrophobicity (n = 2). e, Coefficients for dipeptides in the logistic regression classifier for composition-driven repressors. The model includes both single-amino acid and dipeptides as variables. f, Correlation between average activity scores for composition-driven repressors and the spacing of aromatic amino acids as defined by the parameter Ωaro (n = 2)33. Activity score is shown for each wildtype and stable scrambled composition-driven peptide, determined from the mutational scanning screen. g-i, Light attention model predictions for three composition-driven repressors using their ESM-1b representations. Peptide examined is displayed in panel legend. j, Correlation between predicted and measured activity scores for Mrn1(61–110) variants generated from the light attention model (n = 3). k, Correlation between predicted score in the final linear layer of our light attention model and number of di-aromatic residues (bottom) with density shown (top). The parent peptide used for 10,000 random permutations is indicated above. l, Correlation between dipeptide content and measured activity of composition-driven repressors and their scrambles. The Pearson correlation coefficient is displayed in panels when appropriate.

Supplementary information

Supplementary Information

Supplementary Fig. 1 and Supplementary Table 2.

Reporting Summary

Supplementary Table 1

Activity and stability scores for all wildtype IDR peptides in wildtype and mRNA degradation defective yeast. Activity and stability scores for all IDR variants. Activity and stability scores for 900 repressive fragments. Annotated list of 395 yeast proteins containing repressive fragments.

Supplementary Table 3

Plasmids, guides, repair templates, and primers used in this study.

Supplementary Table 4

DNA and protein sequences for all wildtype IDRs.

Supplementary Table 5

DNA and protein sequences for all IDR variants.

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Lobel, J.H., Ingolia, N.T. Deciphering disordered regions controlling mRNA decay in high-throughput. Nature 642, 805–813 (2025). https://doi.org/10.1038/s41586-025-08919-x

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