Dataset Information

A computational pipeline to infer alternative poly-adenylation from 3' sequencing data.

ABSTRACT: An increasing number of investigations have established alternative polyadenylation (APA) as a key mechanism of gene regulation through altering the length of 3' untranslated region (UTR) and generating distinct mRNA termini. Further, appreciation for the significance of APA in disease contexts propelled the development of several 3' sequencing techniques. While these RNA sequencing technologies have advanced APA analysis, the intrinsic limitation of 3' read coverage and lack of appropriate computational tools constrain precise mapping and quantification of polyadenylation sites. Notably, Poly(A)-ClickSeq (PAC-seq) overcomes limiting factors such as poly(A) enrichment and 3' linker ligation steps using click-chemistry. Here we provide an updated PolyA-miner protocol, a computational approach to analyze PAC-seq or other 3'-Seq datasets. As a key practical constraint, we also provide a detailed account on the impact of sequencing depth on the number of detected polyadenylation sites and APA changes. This protocol is also updated to handle unique molecular identifiers used to address PCR duplication potentially observed in PAC-seq.

SUBMITTER: Yalamanchili HK

PROVIDER: S-EPMC10866047 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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A computational pipeline to infer alternative poly-adenylation from 3' sequencing data.

Yalamanchili Hari Krishna HK Elrod Nathan D ND Jensen Madeline K MK Ji Ping P Lin Ai A Wagner Eric J EJ Liu Zhandong Z

Methods in enzymology 20210605

An increasing number of investigations have established alternative polyadenylation (APA) as a key mechanism of gene regulation through altering the length of 3' untranslated region (UTR) and generating distinct mRNA termini. Further, appreciation for the significance of APA in disease contexts propelled the development of several 3' sequencing techniques. While these RNA sequencing technologies have advanced APA analysis, the intrinsic limitation of 3' read coverage and lack of appropriate comp ...[more]

PMID: 34183121

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Project description:α1-anti-trypsin (A1AT), encoded by SERPINA1, is a neutrophil elastase inhibitor that controls the inflammatory response in the lung. Severe A1AT deficiency increases risk for Chronic Obstructive Pulmonary Disease (COPD), however, the role of A1AT in COPD in non-deficient individuals is not well known. We identify a 2.1-fold increase (p = 2.5x10-6) in the use of a distal poly-adenylation site in primary lung tissue RNA-seq in 82 COPD cases when compared to 64 controls and replicate this in an independent study of 376 COPD and 267 controls. This alternative polyadenylation event involves two sites, a proximal and distal site, 61 and 1683 nucleotides downstream of the A1AT stop codon. To characterize this event, we measured the distal ratio in human primary tissue short read RNA-seq data and corroborated our results with long read RNA-seq data. Integrating these results with 3' end RNA-seq and nanoluciferase reporter assay experiments we show that use of the distal site yields mRNA transcripts with over 50-fold decreased translation efficiency and A1AT expression. We identified seven RNA binding proteins using enhanced CrossLinking and ImmunoPrecipitation precipitation (eCLIP) with one or more binding sites in the SERPINA1 3' UTR. We combined these data with measurements of the distal ratio in shRNA knockdown experiments, nuclear and cytoplasmic fractionation, and chemical RNA structure probing. We identify Quaking Homolog (QKI) as a modulator of SERPINA1 mRNA translation and confirm the role of QKI in SERPINA1 translation with luciferase reporter assays. Analysis of single-cell RNA-seq showed differences in the distribution of the SERPINA1 distal ratio among hepatocytes, macrophages, αβ-Tcells and plasma cells in the liver. Alveolar Type 1,2, dendritic cells and macrophages also vary in their distal ratio in the lung. Our work reveals a complex post-transcriptional mechanism that regulates alternative polyadenylation and A1AT expression in COPD.

Project description:Almost 70% of human genes undergo alternative polyadenylation (APA) and generate mRNA transcripts with varying lengths, typically of the 3' untranslated regions (UTR). APA plays an important role in development and cellular differentiation, and its dysregulation can cause neuropsychiatric diseases and increase cancer severity. Increasing awareness of APA's role in human health and disease has propelled the development of several 3' sequencing (3'Seq) techniques that allow for precise identification of APA sites. However, despite the recent data explosion, there are no robust computational tools that are precisely designed to analyze 3'Seq data. Analytical approaches that have been used to analyze these data predominantly use proximal to distal usage. With about 50% of human genes having more than two APA isoforms, current methods fail to capture the entirety of APA changes and do not account for non-proximal to non-distal changes. Addressing these key challenges, this study demonstrates PolyA-miner, an algorithm to accurately detect and assess differential alternative polyadenylation specifically from 3'Seq data. Genes are abstracted as APA matrices, and differential APA usage is inferred using iterative consensus non-negative matrix factorization (NMF) based clustering. PolyA-miner accounts for all non-proximal to non-distal APA switches using vector projections and reflects precise gene-level 3'UTR changes. It can also effectively identify novel APA sites that are otherwise undetected when using reference-based approaches. Evaluation on multiple datasets-first-generation MicroArray Quality Control (MAQC) brain and Universal Human Reference (UHR) PolyA-seq data, recent glioblastoma cell line NUDT21 knockdown Poly(A)-ClickSeq (PAC-seq) data, and our own mouse hippocampal and human stem cell-derived neuron PAC-seq data-strongly supports the value and protocol-independent applicability of PolyA-miner. Strikingly, in the glioblastoma cell line data, PolyA-miner identified more than twice the number of genes with APA changes than initially reported. With the emerging importance of APA in human development and disease, PolyA-miner can significantly improve data analysis and help decode the underlying APA dynamics.

Dataset Information

A computational pipeline to infer alternative poly-adenylation from 3' sequencing data.

Publications

A computational pipeline to infer alternative poly-adenylation from 3' sequencing data.

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