Exon identity influences splicing induced by exonic variants and in silico prediction efficacy.


Journal

Journal of cystic fibrosis : official journal of the European Cystic Fibrosis Society
ISSN: 1873-5010
Titre abrégé: J Cyst Fibros
Pays: Netherlands
ID NLM: 101128966

Informations de publication

Date de publication:
05 2021
Historique:
received: 27 03 2020
revised: 22 09 2020
accepted: 03 12 2020
pubmed: 21 12 2020
medline: 27 1 2022
entrez: 20 12 2020
Statut: ppublish

Résumé

Minigenes and in silico prediction tools are commonly used to assess the impact on splicing of CFTR variants. Exon skipping is often neglected though it could impact the efficacy of targeted therapies. The aim of the study was to identify exon skipping associated with CFTR variants and to evaluate in silico predictions of seven freely available software. CFTR basal exon skipping was evaluated on endogenous mRNA extracted from non-CF nasal cells and on two CFTR minigene banks. In silico tools and minigene systems were used to evaluate the impact of CFTR exonic variants on exon skipping. Data showed that out of 65 CFTR variants tested, 26 enhanced exon skipping and that in silico prediction efficacy was of 50%-66%. Some in silico tools presented predictions with a bias towards the occurrence of splicing events while others presented a bias towards the absence of splicing events (non-detection including true negatives and false negatives). Classification of exons depending on their basal exon skipping level increased prediction rates up to 80%. This study indicates that taking basal exon skipping into account could orientate the choice of the in silico tools to improve prediction rates. It also highlights the need to validate effects using in vitro assays or mRNA studies in patients. Eventually, it shows that variant-guided therapy should also target exon skipping associated with variants.

Sections du résumé

BACKGROUND
Minigenes and in silico prediction tools are commonly used to assess the impact on splicing of CFTR variants. Exon skipping is often neglected though it could impact the efficacy of targeted therapies. The aim of the study was to identify exon skipping associated with CFTR variants and to evaluate in silico predictions of seven freely available software.
METHODS
CFTR basal exon skipping was evaluated on endogenous mRNA extracted from non-CF nasal cells and on two CFTR minigene banks. In silico tools and minigene systems were used to evaluate the impact of CFTR exonic variants on exon skipping.
RESULTS
Data showed that out of 65 CFTR variants tested, 26 enhanced exon skipping and that in silico prediction efficacy was of 50%-66%. Some in silico tools presented predictions with a bias towards the occurrence of splicing events while others presented a bias towards the absence of splicing events (non-detection including true negatives and false negatives). Classification of exons depending on their basal exon skipping level increased prediction rates up to 80%.
CONCLUSION
This study indicates that taking basal exon skipping into account could orientate the choice of the in silico tools to improve prediction rates. It also highlights the need to validate effects using in vitro assays or mRNA studies in patients. Eventually, it shows that variant-guided therapy should also target exon skipping associated with variants.

Identifiants

pubmed: 33341408
pii: S1569-1993(20)30935-8
doi: 10.1016/j.jcf.2020.12.003
pii:
doi:

Substances chimiques

RNA Splice Sites 0
Cystic Fibrosis Transmembrane Conductance Regulator 126880-72-6

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

464-472

Informations de copyright

Copyright © 2020. Published by Elsevier B.V.

Déclaration de conflit d'intérêts

Declaration of Competing Interest Authors declare no conflicts of interests

Auteurs

Natacha Martin (N)

INSERM, U955, Institut de Recherche Henri Mondor, IMRB, Créteil, France.

Anne Bergougnoux (A)

CHU de Montpellier, Laboratoire de Génétique Moléculaire, Montpellier, France; Université de Montpellier, Laboratoire de Génétique de Maladies Rares, EA7402 Montpellier, France.

Nesrine Baatallah (N)

INSERM U1151, Institut Necker Enfants Malades, INEM, Paris, France; Université Paris Descartes, Paris, France.

Benoit Chevalier (B)

INSERM U1151, Institut Necker Enfants Malades, INEM, Paris, France; Université Paris Descartes, Paris, France.

Jessica Varilh (J)

Université de Montpellier, Laboratoire de Génétique de Maladies Rares, EA7402 Montpellier, France.

David Baux (D)

CHU de Montpellier, Laboratoire de Génétique Moléculaire, Montpellier, France; Université de Montpellier, Laboratoire de Génétique de Maladies Rares, EA7402 Montpellier, France.

Bruno Costes (B)

INSERM, U955, Institut de Recherche Henri Mondor, IMRB, Créteil, France.

Pascale Fanen (P)

INSERM, U955, Institut de Recherche Henri Mondor, IMRB, Créteil, France; Department of Genetics, GH Henri Mondor, APHP, Créteil, France.

Caroline Raynal (C)

CHU de Montpellier, Laboratoire de Génétique Moléculaire, Montpellier, France; Université de Montpellier, Laboratoire de Génétique de Maladies Rares, EA7402 Montpellier, France.

Isabelle Sermet-Gaudelus (I)

INSERM U1151, Institut Necker Enfants Malades, INEM, Paris, France; Université Paris Descartes, Paris, France.

Emmanuelle Girodon (E)

INSERM U1151, Institut Necker Enfants Malades, INEM, Paris, France; Laboratoire de Génétique et Biologie Moléculaires, Hôpital Cochin, APHP Centre-Université de Paris, Paris, France.

Magali Taulan-Cadars (M)

Université de Montpellier, Laboratoire de Génétique de Maladies Rares, EA7402 Montpellier, France. Electronic address: magali.taulan@inserm.fr.

Alexandre Hinzpeter (A)

INSERM U1151, Institut Necker Enfants Malades, INEM, Paris, France; Université Paris Descartes, Paris, France. Electronic address: alexandre.hinzpeter@inserm.fr.

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Classifications MeSH