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Sanoguera-Miralles L, Llinares-Burguet I, Bueno-Martínez E, Ramadane-Morchadi L, Stuani C, Valenzuela-Palomo A, García-Álvarez A, Pérez-Segura P, Buratti E, de la Hoya M, Velasco-Sampedro EA. Comprehensive splicing analysis of the alternatively spliced CHEK2 exons 8 and 10 reveals three enhancer/silencer-rich regions and 38 spliceogenic variants. J Pathol 2024; 262:395-409. [PMID: 38332730 DOI: 10.1002/path.6243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 10/26/2023] [Accepted: 11/28/2023] [Indexed: 02/10/2024]
Abstract
Splicing is controlled by a large set of regulatory elements (SREs) including splicing enhancers and silencers, which are involved in exon recognition. Variants at these motifs may dysregulate splicing and trigger loss-of-function transcripts associated with disease. Our goal here was to study the alternatively spliced exons 8 and 10 of the breast cancer susceptibility gene CHEK2. For this purpose, we used a previously published minigene with exons 6-10 that produced the expected minigene full-length transcript and replicated the naturally occurring events of exon 8 [Δ(E8)] and exon 10 [Δ(E10)] skipping. We then introduced 12 internal microdeletions of exons 8 and 10 by mutagenesis in order to map SRE-rich intervals by splicing assays in MCF-7 cells. We identified three minimal (10-, 11-, 15-nt) regions essential for exon recognition: c.863_877del [ex8, Δ(E8): 75%] and c.1073_1083del and c.1083_1092del [ex10, Δ(E10): 97% and 62%, respectively]. Then 87 variants found within these intervals were introduced into the wild-type minigene and tested functionally. Thirty-eight of them (44%) impaired splicing, four of which (c.883G>A, c.883G>T, c.884A>T, and c.1080G>T) induced negligible amounts (<5%) of the minigene full-length transcript. Another six variants (c.886G>A, c.886G>T, c.1075G>A, c.1075G>T, c.1076A>T, and c.1078G>T) showed significantly strong impacts (20-50% of the minigene full-length transcript). Thirty-three of the 38 spliceogenic variants were annotated as missense, three as nonsense, and two as synonymous, underlying the fact that any exonic change is capable of disrupting splicing. Moreover, c.883G>A, c.883G>T, and c.884A>T were classified as pathogenic/likely pathogenic variants according to ACMG/AMP (American College of Medical Genetics and Genomics/Association for Molecular Pathology)-based criteria. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
- Lara Sanoguera-Miralles
- Splicing and Genetic Susceptibility to Cancer, Unidad de Excelencia Instituto de Biomedicina y Genética Molecular de Valladolid (IBGM), Consejo Superior de Investigaciones Científicas - Universidad de Valladolid (CSIC-UVa), Valladolid, Spain
| | - Inés Llinares-Burguet
- Splicing and Genetic Susceptibility to Cancer, Unidad de Excelencia Instituto de Biomedicina y Genética Molecular de Valladolid (IBGM), Consejo Superior de Investigaciones Científicas - Universidad de Valladolid (CSIC-UVa), Valladolid, Spain
| | - Elena Bueno-Martínez
- Splicing and Genetic Susceptibility to Cancer, Unidad de Excelencia Instituto de Biomedicina y Genética Molecular de Valladolid (IBGM), Consejo Superior de Investigaciones Científicas - Universidad de Valladolid (CSIC-UVa), Valladolid, Spain
| | - Lobna Ramadane-Morchadi
- Molecular Oncology Laboratory CIBERONC, Hospital Clínico San Carlos, IdISSC (Instituto de Investigación Sanitaria del Hospital Clínico San Carlos), Madrid, Spain
| | - Cristiana Stuani
- Molecular Pathology Lab. International Centre of Genetic Engineering and Biotechnology, Trieste, Italy
| | - Alberto Valenzuela-Palomo
- Splicing and Genetic Susceptibility to Cancer, Unidad de Excelencia Instituto de Biomedicina y Genética Molecular de Valladolid (IBGM), Consejo Superior de Investigaciones Científicas - Universidad de Valladolid (CSIC-UVa), Valladolid, Spain
| | - Alicia García-Álvarez
- Splicing and Genetic Susceptibility to Cancer, Unidad de Excelencia Instituto de Biomedicina y Genética Molecular de Valladolid (IBGM), Consejo Superior de Investigaciones Científicas - Universidad de Valladolid (CSIC-UVa), Valladolid, Spain
| | - Pedro Pérez-Segura
- Molecular Oncology Laboratory CIBERONC, Hospital Clínico San Carlos, IdISSC (Instituto de Investigación Sanitaria del Hospital Clínico San Carlos), Madrid, Spain
| | - Emanuele Buratti
- Molecular Pathology Lab. International Centre of Genetic Engineering and Biotechnology, Trieste, Italy
| | - Miguel de la Hoya
- Molecular Oncology Laboratory CIBERONC, Hospital Clínico San Carlos, IdISSC (Instituto de Investigación Sanitaria del Hospital Clínico San Carlos), Madrid, Spain
| | - Eladio A Velasco-Sampedro
- Splicing and Genetic Susceptibility to Cancer, Unidad de Excelencia Instituto de Biomedicina y Genética Molecular de Valladolid (IBGM), Consejo Superior de Investigaciones Científicas - Universidad de Valladolid (CSIC-UVa), Valladolid, Spain
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Barbosa P, Savisaar R, Carmo-Fonseca M, Fonseca A. Computational prediction of human deep intronic variation. Gigascience 2022; 12:giad085. [PMID: 37878682 PMCID: PMC10599398 DOI: 10.1093/gigascience/giad085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 06/07/2023] [Accepted: 09/20/2023] [Indexed: 10/27/2023] Open
Abstract
BACKGROUND The adoption of whole-genome sequencing in genetic screens has facilitated the detection of genetic variation in the intronic regions of genes, far from annotated splice sites. However, selecting an appropriate computational tool to discriminate functionally relevant genetic variants from those with no effect is challenging, particularly for deep intronic regions where independent benchmarks are scarce. RESULTS In this study, we have provided an overview of the computational methods available and the extent to which they can be used to analyze deep intronic variation. We leveraged diverse datasets to extensively evaluate tool performance across different intronic regions, distinguishing between variants that are expected to disrupt splicing through different molecular mechanisms. Notably, we compared the performance of SpliceAI, a widely used sequence-based deep learning model, with that of more recent methods that extend its original implementation. We observed considerable differences in tool performance depending on the region considered, with variants generating cryptic splice sites being better predicted than those that potentially affect splicing regulatory elements. Finally, we devised a novel quantitative assessment of tool interpretability and found that tools providing mechanistic explanations of their predictions are often correct with respect to the ground - information, but the use of these tools results in decreased predictive power when compared to black box methods. CONCLUSIONS Our findings translate into practical recommendations for tool usage and provide a reference framework for applying prediction tools in deep intronic regions, enabling more informed decision-making by practitioners.
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Affiliation(s)
- Pedro Barbosa
- LASIGE, Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, 1749-016,, Lisboa, Portugal
- Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, 1649-028, Lisboa, Portugal
| | | | - Maria Carmo-Fonseca
- Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, 1649-028, Lisboa, Portugal
| | - Alcides Fonseca
- LASIGE, Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, 1749-016,, Lisboa, Portugal
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