Dataset Information

U-rich elements drive pervasive cryptic splicing in 3' UTR massively parallel reporter assays.

ABSTRACT: Non-coding RNA sequences play essential roles in orchestrating gene expression. However, the sequence codes and mechanisms underpinning post-transcriptional regulation remain incompletely understood. Here, we revisit the finding from a prior massively parallel reporter assay (MPRA) that AU-rich (U-rich) elements in 3' untranslated regions (3' UTRs) can drive upregulation or downregulation of mRNA expression depending on 3' UTR context. We unexpectedly discover that this variable regulation arises from widespread cryptic splicing, predominately from an unannotated splice donor in the coding sequence of GFP to diverse acceptor sites in reporter 3' UTRs. Splicing is activated by U-rich sequences, which function as potent position-dependent regulators of 5' and 3' splice site choice and overall splicing efficiency. Splicing has diverse impacts on reporter expression, causing both increases and decreases in reporter expression via multiple mechanisms. We further provide evidence that cryptic splicing impacts between 10 to 50% of measurements made by other published 3' UTR MPRAs. Overall, our work emphasizes U-rich sequences as principal drivers of splicing and provides strategies to minimize cryptic splicing artifacts in reporter assays.

SUBMITTER: Dao K

PROVIDER: S-EPMC11326173 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

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U-rich elements drive pervasive cryptic splicing in 3' UTR massively parallel reporter assays.

Dao Khoa K Jungers Courtney F CF Djuranovic Sergej S Mustoe Anthony M AM

bioRxiv : the preprint server for biology 20240805

Non-coding RNA sequences play essential roles in orchestrating gene expression. However, the sequence codes and mechanisms underpinning post-transcriptional regulation remain incompletely understood. Here, we revisit the finding from a prior massively parallel reporter assay (MPRA) that AU-rich (U-rich) elements in 3' untranslated regions (3' UTRs) can drive upregulation or downregulation of mRNA expression depending on 3' UTR context. We unexpectedly discover that this variable regulation arise ...[more]

PMID: 39149310

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Project description:MotivationThe majority of the human genome is composed of non-coding regions containing regulatory elements such as enhancers, which are crucial for controlling gene expression. Many variants associated with complex traits are in these regions, and may disrupt gene regulatory sequences. Consequently, it is important to not only identify true enhancers but also to test if a variant within an enhancer affects gene regulation. Recently, allele-specific analysis in high-throughput reporter assays, such as massively parallel reporter assays (MPRAs), have been used to functionally validate non-coding variants. However, we are still missing high-quality and robust data analysis tools for these datasets.ResultsWe have further developed our method for allele-specific analysis QuASAR (quantitative allele-specific analysis of reads) to analyze allele-specific signals in barcoded read counts data from MPRA. Using this approach, we can take into account the uncertainty on the original plasmid proportions, over-dispersion, and sequencing errors. The provided allelic skew estimate and its standard error also simplifies meta-analysis of replicate experiments. Additionally, we show that a beta-binomial distribution better models the variability present in the allelic imbalance of these synthetic reporters and results in a test that is statistically well calibrated under the null. Applying this approach to the MPRA data, we found 602 SNPs with significant (false discovery rate 10%) allele-specific regulatory function in LCLs. We also show that we can combine MPRA with QuASAR estimates to validate existing experimental and computational annotations of regulatory variants. Our study shows that with appropriate data analysis tools, we can improve the power to detect allelic effects in high-throughput reporter assays.Availability and implementationhttp://github.com/piquelab/QuASAR/tree/master/mpra.Contactfluca@wayne.edu or rpique@wayne.edu.Supplementary informationSupplementary data are available online at Bioinformatics.

Dataset Information

U-rich elements drive pervasive cryptic splicing in 3' UTR massively parallel reporter assays.

Publications

U-rich elements drive pervasive cryptic splicing in 3' UTR massively parallel reporter assays.

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