Dataset Information

In vivo analysis of influenza A mRNA secondary structures identifies critical regulatory motifs.

ABSTRACT: The influenza A virus (IAV) is a continuous health threat to humans as well as animals due to its recurring epidemics and pandemics. The IAV genome is segmented and the eight negative-sense viral RNAs (vRNAs) are transcribed into positive sense complementary RNAs (cRNAs) and viral messenger RNAs (mRNAs) inside infected host cells. A role for the secondary structure of IAV mRNAs has been hypothesized and debated for many years, but knowledge on the structure mRNAs adopt in vivo is currently missing. Here we solve, for the first time, the in vivo secondary structure of IAV mRNAs in living infected cells. We demonstrate that, compared to the in vitro refolded structure, in vivo IAV mRNAs are less structured but exhibit specific locally stable elements. Moreover, we show that the targeted disruption of these high-confidence structured domains results in an extraordinary attenuation of IAV replicative capacity. Collectively, our data provide the first comprehensive map of the in vivo structural landscape of IAV mRNAs, hence providing the means for the development of new RNA-targeted antivirals.

SUBMITTER: Simon LM

PROVIDER: S-EPMC6648356 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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In vivo analysis of influenza A mRNA secondary structures identifies critical regulatory motifs.

Simon Lisa Marie LM Morandi Edoardo E Luganini Anna A Gribaudo Giorgio G Martinez-Sobrido Luis L Turner Douglas H DH Oliviero Salvatore S Incarnato Danny D

Nucleic acids research 20190701 13

The influenza A virus (IAV) is a continuous health threat to humans as well as animals due to its recurring epidemics and pandemics. The IAV genome is segmented and the eight negative-sense viral RNAs (vRNAs) are transcribed into positive sense complementary RNAs (cRNAs) and viral messenger RNAs (mRNAs) inside infected host cells. A role for the secondary structure of IAV mRNAs has been hypothesized and debated for many years, but knowledge on the structure mRNAs adopt in vivo is currently missi ...[more]

PMID: 31053845

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Project description:BACKGROUND: RNA-binding proteins interact with specific RNA molecules to regulate important cellular processes. It is therefore necessary to identify the RNA interaction partners in order to understand the precise functions of such proteins. Protein-RNA interactions are typically characterized using in vivo and in vitro experiments but these may not detect all binding partners. Therefore, computational methods that capture the protein-dependent nature of such binding interactions could help to predict potential binding partners in silico. RESULTS: We have developed three methods to predict whether an RNA can interact with a particular RNA-binding protein using support vector machines and different features based on the sequence (the Oli method), the motif score (the OliMo method) and the secondary structure (the OliMoSS method). We applied these approaches to different experimentally-derived datasets and compared the predictions with RNAcontext and RPISeq. Oli outperformed OliMoSS and RPISeq, confirming our protein-specific predictions and suggesting that tetranucleotide frequencies are appropriate discriminative features. Oli and RNAcontext were the most competitive methods in terms of the area under curve. A precision-recall curve analysis achieved higher precision values for Oli. On a second experimental dataset including real negative binding information, Oli outperformed RNAcontext with a precision of 0.73 vs. 0.59. CONCLUSIONS: Our experiments showed that features based on primary sequence information are sufficiently discriminating to predict specific RNA-protein interactions. Sequence motifs and secondary structure information were not necessary to improve these predictions. Finally we confirmed that protein-specific experimental data concerning RNA-protein interactions are valuable sources of information that can be used for the efficient training of models for in silico predictions. The scripts are available upon request to the corresponding author.

Dataset Information

In vivo analysis of influenza A mRNA secondary structures identifies critical regulatory motifs.

Publications

In vivo analysis of influenza A mRNA secondary structures identifies critical regulatory motifs.

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