Influenza virus infection induces widespread alterations of host cell splicing

Abstract

Influenza A viruses (IAVs) use diverse mechanisms to interfere with cellular gene expression. Although many RNA-seq studies have documented IAV-induced changes in host mRNA abundance, few were designed to allow an accurate quantification of changes in host mRNA splicing. Here, we show that IAV infection of human lung cells induces widespread alterations of cellular splicing, with an overall increase in exon inclusion and decrease in intron retention. Over half of the mRNAs that show differential splicing undergo no significant changes in abundance or in their 3′ end termination site, suggesting that IAVs can specifically manipulate cellular splicing. Among a randomly selected subset of 21 IAV-sensitive alternative splicing events, most are specific to IAV infection as they are not observed upon infection with VSV, induction of interferon expression or induction of an osmotic stress. Finally, the analysis of splicing changes in RED-depleted cells reveals a limited but significant overlap with the splicing changes in IAV-infected cells. This observation suggests that hijacking of RED by IAVs to promote splicing of the abundant viral NS1 mRNAs could partially divert RED from its target mRNAs. All our RNA-seq datasets and analyses are made accessible for browsing through a user-friendly Shiny interface (http://virhostnet.prabi.fr:3838/shinyapps/flu-splicing or https://github.com/cbenoitp/flu-splicing).

The landscape of differential splicing and transcript alternations in severe COVID‐19 infection

“…Differential splicing is observed in several disease conditions, leading to alternations of different transcript variants that can switch cell functionality, leading to disease development [37]. Several viruses have been demonstrated to interact with the host cellular splicing machinery that can help the viruses replicate efficiently by disrupting a number of host cellular processes to prevent innate antiviral responses [16][17][18][19][20]. Recent studies have shown that modulation of splicing is one of the key mechanisms for suppressing host response to [27,28,51,52].…”

“…All these different disease-specific transcripts can generate the different phenotypes, which impact the normal protein interaction networks, switch pathways and change the regulatory landscape of the cells that can drive the disease development [11][12][13][14][15]. Viral infections can also lead to widespread alternations of cellular splicing and altered transcripts, which are significantly associated with viral pathogenesis [16][17][18][19][20].…”

“…[11][12][13][14][15]. Viral infections can also lead to widespread alternations of cellular splicing and altered transcripts, which are significantly associated with viral pathogenesis [16][17][18][19][20].…”

Integrative analysis in Pinus revealed long‐term heat stress splicing memory

“…Contrary to angiosperms, we found that IR may not be the most prevalent type of AS in conifers. Taking all the above into account, together with the limited capacity of KisSplice to assemble long introns, which requires their full coverage by reads to be correctly assembled (Ashraf et al., 2020 ), and earlier described accelerated RNA Polymerase II kinetics and nuclear DNA hypomethylation during high temperature treatment (Jonkers & Lis, 2015 ; Lamelas et al., 2020 ), we propose that simultaneous availability of weak and strong splice sites due to loss of transcription control (Luco et al., 2011 ) could result in favored retention of introns flanked by weak splice sites. However, short introns are significantly more retained because their flanking splice sites are recognized as a unit (Monteuuis et al., 2019 ).…”

IRFinder-S: a comprehensive suite to discover and explore intron retention

“…We previously published a method called IRFinder, an algorithm for detecting and quantifying IR events, that is frequently used as a benchmark for IR detection and quantification [8][9][10][11][12]. This software and its associated database have been critical in the detection and interpretation of IR events in numerous studies [13][14][15][16][17][18][19]. However, building on 4 years of user feedback, it is apparent that IRFinder is lacking features that would enable bench scientists to more reliably identify actionable IR events, share IR data, and dynamically analyze changes in IR levels between multiple samples.…”