Alternative polyadenylation (APA) is emerging as an important layer of gene regulation because the majority of mammalian protein-coding genes contain multiple polyadenylation (pA) sites in their 3′ UTR. By alteration of 3′ UTR length, APA can considerably affect post-transcriptional gene regulation. Yet, our understanding of APA remains rudimentary. Novel single-cell RNA sequencing (scRNA-seq) techniques allow molecular characterization of different cell types to an unprecedented degree. Notably, the most popular scRNA-seq protocols specifically sequence the 3′ end of transcripts. Building on this property, we implemented a method for analysing patterns of APA regulation from such data. Analyzing multiple datasets from diverse tissues, we identified widespread modulation of APA in different cell types resulting in global 3′ UTR shortening/lengthening and enhanced cleavage at intronic pA sites. Our results provide a proof-of-concept demonstration that the huge volume of scRNA-seq data that accumulates in the public domain offers a unique resource for the exploration of APA based on a very broad collection of cell types and biological conditions.

Highlights d A cell-type-specific transcriptomic map of the cochlear response to noise d Noise-resilient type 1A auditory neurons upregulate the ATF3/4 pathway d Monocytes significantly alter their gene expression in response to noise exposure d STAT3/IRF7 are probable regulators of a general cochlear transcriptomic response to noise

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Alternative polyadenylation (APA) is emerging as a widespread regulatory layer since the majority of human protein-coding genes contain several polyadenylation (p(A)) sites in their 3'UTRs. By generating isoforms with different 3'UTR length, APA potentially affects mRNA stability, translation efficiency, nuclear export, and cellular localization. Polyadenylation sites are regulated by adjacent RNA cis-regulatory elements, the principals among them are the polyadenylation signal (PAS) AAUAAA and its main variant AUUAAA, typically located 20-nt upstream of the p(A) site. Mutations in PAS and other auxiliary poly(A) cis-elements in the 3'UTR of several genes have been shown to cause human Mendelian diseases, and to date, only a few common SNPs that regulate APA were associated with complex diseases. Here, we systematically searched for SNPs that affect gene expression and human traits by modulation of 3'UTR APA. First, focusing on the variants most likely to exert the strongest effect, we identified 2,305 SNPs that interrupt the canonical PAS or its main variant. Implementing pA-QTL tests using GTEx RNA-seq data, we identified 330 PAS SNPs (called PAS pA-QTLs) that were significantly associated with the usage of their p(A) site. As expected, PAS-interrupting alleles were mostly linked with decreased cleavage at their p(A) site and the consequential 3'UTR lengthening. However, interestingly, in~10% of the cases, the PAS-interrupting allele was associated with increased usage of an upstream p(A) site and 3'UTR shortening. As an indication of the functional effects of these PAS pA-QTLs on gene expression and complex human traits, we observed for few dozens of them marked colocalization with eQTL and/or GWAS signals. The PAS-interrupting alleles linked with 3'UTR lengthening were also strongly associated with decreased gene expression, indicating that shorter isoforms generated by APA are generally more stable than longer ones. Last, we carried out an extended, genome-wide analysis of 3'UTR variants and detected thousands of additional pA-QTLs having weaker effects compared to the PAS pA-QTLs.

Precision oncology is gradually advancing into mainstream clinical practice, demonstrating significant survival benefits. However, eligibility and response rates remain limited in many cases, calling for better predictive biomarkers. Here we present ENLIGHT, a transcriptomics-based computational approach that identifies clinically relevant genomic interactions and uses them to predict patients' response to a variety of therapies in multiple cancer types without training on previous response data. We study ENLIGHT in two translationally oriented scenarios, Personalized Medicine (PM), aimed at prioritizing treatments for a single patient, and Clinical Trial Design (CTD), selecting the most likely responders in a patient cohort. Evaluating ENLIGHT's PM performance on 21 blinded clinical trial datasets, we show that it can effectively predict treatment response across multiple therapies and cancer types (obtaining an odds ratio of 2.59), substantially improving upon SELECT, a previously published transcriptomics-based approach, and performing as well as supervised predictors developed for specific indications and drugs, but on a much broader array of therapies and indications. In the CTD scenario, ENLIGHT can markedly enhance the success of clinical trials by excluding non-responders while achieving more than 90% of the response rate attainable under an optimal exclusion strategy. In sum, ENLIGHT is one of the first approaches to demonstrably predict therapeutic response across multiple cancer types by leveraging the transcriptome.