Trends in Microbiology
ReviewAdvances in Analyzing Virus-Induced Alterations of Host Cell Splicing
Section snippets
Alteration of Cellular Splicing: A Complex Facet of Virus–Host Interactions
In higher eukaryotic cells most genes are transcribed as precursor messenger RNAs (pre-mRNAs) that undergo splicing, a maturation process through which RNA sequences (introns) are removed and the remaining sequences (exons) are ligated together. Splicing occurs in the nucleus and is catalyzed by the spliceosome, a large and highly dynamic ribonucleoprotein complex 1, 2. Most mammalian pre-mRNAs are subject to alternative splicing (AS), and human genes contain on average 8.8 exons and 7.8
Viral Manipulation of the Host Splicing Machinery
Viruses that replicate in the nucleus of infected cells and gain access to the splicing machinery (e.g., adenoviruses, herpesviruses, and influenza viruses) have evolved an expansion of their coding capacity by producing spliced viral mRNAs. However, manipulation of the host splicing machinery is not exclusive to nuclear viruses and has also been observed with viruses that replicate in the cytoplasm such as picornaviruses or flaviviruses (Table 1). This can be accounted for by the
Peering into Virus-Induced Alterations of Host AS Events
To date there are only a few studies that performed a transcriptome-wide microarray or RNAseq analysis of cellular AS events in virus-infected cells (Table 2). They were carried out with herpesviruses 26, 27, 28, reoviruses 24, 29, dengue viruses (DENV) 12, 30, Zika virus [31], and influenza viruses [32], and reveal several hundreds of host genes that show altered mRNA splicing upon infection. When examined, no correlation was found between changes in AS events and changes in mRNA expression
Toward a More Accurate View: Methodological Challenges
RNAseq transcriptomic analysis of AS is a challenging issue, and even more so when performed on a virus–host system. Accurate quantification of isoform abundance requires a high read number (about 50 million paired-end reads of at least 75 bp are recommended for the human transcriptome), which to date can only be provided by the Illumina technology. However, a serious limitation of Illumina RNAseq is that it relies on short reads, so that the resolution of exon connectivity and full-length
Integration of Isoform Data Sets in a Systems Biology Perspective
The rapid advancement of high-throughput technologies has led to the development of the systems biology field, which aims at modeling the properties of complex biological systems, and predicting their response to biological or chemical perturbations. The commonly used ‘top down’ systems biology approach turns RNAseq measurement of expression levels into a variable which can be included in a mathematical model, such as a generalized linear model or a multivariate analysis (principal component
Concluding Remarks
The importance of characterizing the transcriptome landscape of virus-infected cells at the splicing level is highlighted by recent studies which reveal significant AS alterations in response to viral infection. The observed changes in AS events, which are regulated through a very complex protein–RNA interaction network, can be either a direct consequence of viral manipulation of the host splicing machinery or result indirectly from virus-induced immune responses or cellular damages.
Acknowledgments
U.A. is part of the Pasteur–Paris University (PPU) International PhD Program, which has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 665807, and from the Institut Carnot Pasteur Microbes & Santé. C.B.P. has received funding from the Fondation pour la Recherche Médicale. This study was supported by the ANR ASTER (Grant No. ANR-16-CE23-0001) and the LabEx IBEID (Grant No. 10-LABX-0062).
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2022, Cell ReportsCitation Excerpt :We cannot exclude other mechanisms, such as alternative gene activation states in innate immune cells or many environmental and genetic factors that may affect functional diversity in gene expression patterns. The substantial overlap between lncRNAs and DS signals highlights pre-mRNA splicing as an essential mechanism for post-transcriptional regulation of gene expression, allowing immune cell functions to proceed more rapidly (Ashraf et al., 2019; Carpenter et al., 2014). Application of a modular repertoire framework to the analysis of transcriptional data reduces the bias of using predefined functional pathways and increases the correlation analysis information, assigning biological significance of the co-expressed genes to the observed trends (Gaucher et al., 2008; Querec et al., 2009).