Analysis of RNA decay for alternatively spliced transcripts by RNA tagging
Final Report Abstract
Metabolic labeling of RNA (=RNA tagging) using 4-thiouridine (4sU) provides a snapshot of RNA that is newly transcribed within a certain time interval. Before the start of this project, RNA tagging had only been applied to study transcription regulation and RNA turnover at whole gene level. In this project, we combined RNA tagging with RNA sequencing (denoted as 4sU-seq) to characterize processing of newly transcribed RNAs, including (alternative) splicing, processing of non-coding RNAs and transcription termination. We analyzed time-courses of increasing labeling durations (from 5 to 60 min) in steady-state conditions to study “normal” RNA splicing and processing as well as time-courses of RNA tagging to delineate changes in RNA processing in dynamical systems in response to a stimulus, e.g. infection by Herpes simplex virus 1 (HSV-1). Using progressive and ultra-short 4sU-seq, we identified classes of introns with different splicing kinetics, which were characterized by a distinct association with intron length, gene length, and splice site strength. Strikingly, one of these classes represented temporary intron retention events in the primary transcript that were later efficiently removed either by secondary (post-transcriptional) splicing or nonsensemediated decay. Furthermore, we showed that processing of most, but not all small nucleolar (sno)RNA-containing introns was remarkably inefficient with the majority of introns being spliced and degraded rather than processed into mature snoRNAs. For HSV-1 infection, we showed that HSV-1, unexpectedly, triggers the disruption of transcription termination of cellular, but not viral, genes. This results in extensive transcription for tens of thousands of nucleotides beyond poly(A) sites and into downstream genes. As a consequence, hundreds of cellular genes seem to be transcriptionally induced but are not translated. In contrast to previous reports, we showed that HSV-1 does not inhibit co-transcriptional splicing, but rather appears to induce aberrant splicing events that are either novel or associated with nonsensemediated decay. Furthermore, splicing appeared to be surprisingly robust as evidenced by novel intergenic splicing events between exons of neighboring cellular genes as well as splicing at some but generally not all introns of genes with read-in transcription. Analysis of all 4sU-seq data sets was supported by the development of a new RNA-seq mapping program, ContextMap 2, during this project. The approach of ContextMap 2 is unique as it considers all valid alignments for a read at the same time, including both unspliced and spliced alignments, and takes into account supporting evidence for an alignment provided by other reads. Furthermore, ContextMap 2 supports parallel mapping of reads against alternative read sources, such as rRNA and microbe genomes in addition to a host genome, allows integration of alternative short read alignment programs using a plug-in structure and allows mapping of reads containing part of the poly(A) tail. In summary, we showed that RNA tagging combined with RNA-seq allows unparalleled insights into the kinetics of RNA transcription, splicing and processing both in steady-state systems as well as dynamical systems such as virus-infected cells.
Publications
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A context-based approach to identify the most likely mapping for RNA-seq experiments. BMC Bioinformatics. 2012;13 Suppl 6:S9
Bonfert T, Csaba G, Zimmer R, Friedel CC
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Detection and correction of probelevel artefacts on microarrays. BMC Bioinformatics. 2012; 13:114
Petri T, Berchtold E, Zimmer R, Friedel CC
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Real-time transcriptional profiling of cellular and viral gene expression during lytic cytomegalovirus infection. PLoS Pathog. 2012; 8(9):e1002908
Marcinowski L, Lidschreiber M, Windhager L, Rieder M, Bosse JB, Rädle B, Bonfert T, Györy I, de Graaf M, Prazeres da Costa O, Rosenstiel P, Friedel CC, Zimmer R, Ruzsics Z, Dölken L
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Ultrashort and progressive 4sU-tagging reveals key characteristics of RNA processing at nucleotide resolution. Genome Res. 2012; 22(10):2031-42
Windhager L, Bonfert T, Burger K, Ruzsics Z, Krebs S, Kaufmann S, Malterer G, L'Hernault A, Schilhabel M, Schreiber S, Rosenstiel P, Zimmer R, Eick D, Friedel CC, Dölken L
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4-thiouridine inhibits rRNA synthesis and causes a nucleolar stress response. RNA Biol. 2013; 10(10):1623-30
Burger K, Mühl B, Kellner M, Rohrmoser M, Gruber-Eber A, Windhager L, Friedel CC, Dölken L, Eick D
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Deciphering the modulation of gene expression by type I and II interferons combining 4sU-tagging, translational arrest and in silico promoter analysis. Nucleic Acids Res. 2013; 41(17):8107-25
Trilling M, Bellora N, Rutkowski AJ, de Graaf M, Dickinson P, Robertson K, Prazeres da Costa O, Ghazal P, Friedel CC, Albà MM, Dölken L
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Mining RNA-seq data for infections and contaminations. PLoS One. 2013; 8(9):e73071
Bonfert T, Csaba G, Zimmer R, Friedel CC
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ContextMap 2: fast and accurate context-based RNA-seq mapping. BMC Bioinformatics. 2015; 16:122
Bonfert T, Kirner E, Csaba G, Zimmer R, Friedel CC
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Widespread disruption of host transcription termination in HSV-1 infection. Nat Commun. 2015; 6:7126
Rutkowski AJ, Erhard F, L'Hernault A, Bonfert T, Schilhabel M, Crump C, Rosenstiel P, Efstathiou S, Zimmer R, Friedel CC, Dölken L
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Prediction of Poly(A) Sites by Poly(A) Read Mapping. PLoS One. 2017; 12(1):e0170914
Bonfert T, Friedel CC
