In silico and in vivo splicing analysis of <it>MLH1 </it>and <it>MSH2 </it>missense mutations shows exon- and tissue-specific effects
<p>Abstract</p> <p>Background</p> <p>Abnormalities of pre-mRNA splicing are increasingly recognized as an important mechanism through which gene mutations cause disease. However, apart from the mutations in the donor and acceptor sites, the effects on splicing of other...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2006-09-01
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| Series: | BMC Genomics |
| Online Access: | http://www.biomedcentral.com/1471-2164/7/243 |
| _version_ | 1818787687078821888 |
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| author | Guanti Ginevra Resta Nicoletta Surdo Nicoletta Lastella Patrizia Stella Alessandro |
| author_facet | Guanti Ginevra Resta Nicoletta Surdo Nicoletta Lastella Patrizia Stella Alessandro |
| author_sort | Guanti Ginevra |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>Abnormalities of pre-mRNA splicing are increasingly recognized as an important mechanism through which gene mutations cause disease. However, apart from the mutations in the donor and acceptor sites, the effects on splicing of other sequence variations are difficult to predict. Loosely defined exonic and intronic sequences have been shown to affect splicing efficiency by means of silencing and enhancement mechanisms. Thus, nucleotide substitutions in these sequences can induce aberrant splicing. Web-based resources have recently been developed to facilitate the identification of nucleotide changes that could alter splicing. However, computer predictions do not always correlate with in vivo splicing defects. The issue of unclassified variants in cancer predisposing genes is very important both for the correct ascertainment of cancer risk and for the understanding of the basic mechanisms of cancer gene function and regulation. Therefore we aimed to verify how predictions that can be drawn from in silico analysis correlate with results obtained in an in vivo splicing assay.</p> <p>Results</p> <p>We analysed 99 hMLH1 and hMSH2 missense mutations with six different algorithms. Transfection of three different cell lines with 20 missense mutations, showed that a minority of them lead to defective splicing. Moreover, we observed that some exons and some mutations show cell-specific differences in the frequency of exon inclusion.</p> <p>Conclusion</p> <p>Our results suggest that the available algorithms, while potentially helpful in identifying splicing modulators especially when they are located in weakly defined exons, do not always correspond to an obvious modification of the splicing pattern. Thus caution must be used in assessing the pathogenicity of a missense or silent mutation with prediction programs. The variations observed in the splicing proficiency in three different cell lines suggest that nucleotide changes may dictate alternative splice site selection in a tissue-specific manner contributing to the widely observed phenotypic variability in inherited cancers.</p> |
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| format | Article |
| id | doaj.art-f9bea276e22440afb58cc88a549c1f50 |
| institution | Directory Open Access Journal |
| issn | 1471-2164 |
| language | English |
| last_indexed | 2024-12-18T14:11:44Z |
| publishDate | 2006-09-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genomics |
| spelling | doaj.art-f9bea276e22440afb58cc88a549c1f502022-12-21T21:05:06ZengBMCBMC Genomics1471-21642006-09-017124310.1186/1471-2164-7-243In silico and in vivo splicing analysis of <it>MLH1 </it>and <it>MSH2 </it>missense mutations shows exon- and tissue-specific effectsGuanti GinevraResta NicolettaSurdo NicolettaLastella PatriziaStella Alessandro<p>Abstract</p> <p>Background</p> <p>Abnormalities of pre-mRNA splicing are increasingly recognized as an important mechanism through which gene mutations cause disease. However, apart from the mutations in the donor and acceptor sites, the effects on splicing of other sequence variations are difficult to predict. Loosely defined exonic and intronic sequences have been shown to affect splicing efficiency by means of silencing and enhancement mechanisms. Thus, nucleotide substitutions in these sequences can induce aberrant splicing. Web-based resources have recently been developed to facilitate the identification of nucleotide changes that could alter splicing. However, computer predictions do not always correlate with in vivo splicing defects. The issue of unclassified variants in cancer predisposing genes is very important both for the correct ascertainment of cancer risk and for the understanding of the basic mechanisms of cancer gene function and regulation. Therefore we aimed to verify how predictions that can be drawn from in silico analysis correlate with results obtained in an in vivo splicing assay.</p> <p>Results</p> <p>We analysed 99 hMLH1 and hMSH2 missense mutations with six different algorithms. Transfection of three different cell lines with 20 missense mutations, showed that a minority of them lead to defective splicing. Moreover, we observed that some exons and some mutations show cell-specific differences in the frequency of exon inclusion.</p> <p>Conclusion</p> <p>Our results suggest that the available algorithms, while potentially helpful in identifying splicing modulators especially when they are located in weakly defined exons, do not always correspond to an obvious modification of the splicing pattern. Thus caution must be used in assessing the pathogenicity of a missense or silent mutation with prediction programs. The variations observed in the splicing proficiency in three different cell lines suggest that nucleotide changes may dictate alternative splice site selection in a tissue-specific manner contributing to the widely observed phenotypic variability in inherited cancers.</p>http://www.biomedcentral.com/1471-2164/7/243 |
| spellingShingle | Guanti Ginevra Resta Nicoletta Surdo Nicoletta Lastella Patrizia Stella Alessandro In silico and in vivo splicing analysis of <it>MLH1 </it>and <it>MSH2 </it>missense mutations shows exon- and tissue-specific effects BMC Genomics |
| title | In silico and in vivo splicing analysis of <it>MLH1 </it>and <it>MSH2 </it>missense mutations shows exon- and tissue-specific effects |
| title_full | In silico and in vivo splicing analysis of <it>MLH1 </it>and <it>MSH2 </it>missense mutations shows exon- and tissue-specific effects |
| title_fullStr | In silico and in vivo splicing analysis of <it>MLH1 </it>and <it>MSH2 </it>missense mutations shows exon- and tissue-specific effects |
| title_full_unstemmed | In silico and in vivo splicing analysis of <it>MLH1 </it>and <it>MSH2 </it>missense mutations shows exon- and tissue-specific effects |
| title_short | In silico and in vivo splicing analysis of <it>MLH1 </it>and <it>MSH2 </it>missense mutations shows exon- and tissue-specific effects |
| title_sort | in silico and in vivo splicing analysis of it mlh1 it and it msh2 it missense mutations shows exon and tissue specific effects |
| url | http://www.biomedcentral.com/1471-2164/7/243 |
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