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Targeted sequencing reveals complex, phenotype-correlated genotypes in cystic fibrosis

BACKGROUND: Cystic fibrosis (CF) is one of the most common life-threatening genetic disorders. Around 2000 variants in the CFTR gene have been identified, with some proportion known to be pathogenic and 300 disease-causing mutations have been characterized in detail by CFTR2 database, which complica...

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Autores principales: Ivanov, Maxim, Matsvay, Alina, Glazova, Olga, Krasovskiy, Stanislav, Usacheva, Mariya, Amelina, Elena, Chernyak, Aleksandr, Ivanov, Mikhail, Musienko, Sergey, Prodanov, Timofey, Kovalenko, Sergey, Baranova, Ancha, Khafizov, Kamil
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5836842/
https://www.ncbi.nlm.nih.gov/pubmed/29504914
http://dx.doi.org/10.1186/s12920-018-0328-z
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author Ivanov, Maxim
Matsvay, Alina
Glazova, Olga
Krasovskiy, Stanislav
Usacheva, Mariya
Amelina, Elena
Chernyak, Aleksandr
Ivanov, Mikhail
Musienko, Sergey
Prodanov, Timofey
Kovalenko, Sergey
Baranova, Ancha
Khafizov, Kamil
author_facet Ivanov, Maxim
Matsvay, Alina
Glazova, Olga
Krasovskiy, Stanislav
Usacheva, Mariya
Amelina, Elena
Chernyak, Aleksandr
Ivanov, Mikhail
Musienko, Sergey
Prodanov, Timofey
Kovalenko, Sergey
Baranova, Ancha
Khafizov, Kamil
author_sort Ivanov, Maxim
collection PubMed
description BACKGROUND: Cystic fibrosis (CF) is one of the most common life-threatening genetic disorders. Around 2000 variants in the CFTR gene have been identified, with some proportion known to be pathogenic and 300 disease-causing mutations have been characterized in detail by CFTR2 database, which complicates its analysis with conventional methods. METHODS: We conducted next-generation sequencing (NGS) in a cohort of 89 adult patients negative for p.Phe508del homozygosity. Complete clinical and demographic information were available for 84 patients. RESULTS: By combining MLPA with NGS, we identified disease-causing alleles in all the CF patients. Importantly, in 10% of cases, standard bioinformatics pipelines were inefficient in identifying causative mutations. Class IV-V mutations were observed in 38 (45%) cases, predominantly ones with pancreatic sufficient CF disease; rest of the patients had Class I-III mutations. Diabetes was seen only in patients homozygous for class I-III mutations. We found that 12% of the patients were heterozygous for more than two pathogenic CFTR mutations. Two patients were observed with p.[Arg1070Gln, Ser466*] complex allele which was associated with milder pulmonary obstructions (FVC 107 and 109% versus 67%, CI 95%: 63-72%; FEV 90 and 111% versus 47%, CI 95%: 37-48%). For the first time p.[Phe508del, Leu467Phe] complex allele was reported, observed in four patients (5%). CONCLUSION: NGS can be a more information-gaining technology compared to standard methods. Combined with its equivalent diagnostic performance, it can therefore be implemented in the clinical practice, although careful validation is still required. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12920-018-0328-z) contains supplementary material, which is available to authorized users.
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spelling pubmed-58368422018-03-07 Targeted sequencing reveals complex, phenotype-correlated genotypes in cystic fibrosis Ivanov, Maxim Matsvay, Alina Glazova, Olga Krasovskiy, Stanislav Usacheva, Mariya Amelina, Elena Chernyak, Aleksandr Ivanov, Mikhail Musienko, Sergey Prodanov, Timofey Kovalenko, Sergey Baranova, Ancha Khafizov, Kamil BMC Med Genomics Research BACKGROUND: Cystic fibrosis (CF) is one of the most common life-threatening genetic disorders. Around 2000 variants in the CFTR gene have been identified, with some proportion known to be pathogenic and 300 disease-causing mutations have been characterized in detail by CFTR2 database, which complicates its analysis with conventional methods. METHODS: We conducted next-generation sequencing (NGS) in a cohort of 89 adult patients negative for p.Phe508del homozygosity. Complete clinical and demographic information were available for 84 patients. RESULTS: By combining MLPA with NGS, we identified disease-causing alleles in all the CF patients. Importantly, in 10% of cases, standard bioinformatics pipelines were inefficient in identifying causative mutations. Class IV-V mutations were observed in 38 (45%) cases, predominantly ones with pancreatic sufficient CF disease; rest of the patients had Class I-III mutations. Diabetes was seen only in patients homozygous for class I-III mutations. We found that 12% of the patients were heterozygous for more than two pathogenic CFTR mutations. Two patients were observed with p.[Arg1070Gln, Ser466*] complex allele which was associated with milder pulmonary obstructions (FVC 107 and 109% versus 67%, CI 95%: 63-72%; FEV 90 and 111% versus 47%, CI 95%: 37-48%). For the first time p.[Phe508del, Leu467Phe] complex allele was reported, observed in four patients (5%). CONCLUSION: NGS can be a more information-gaining technology compared to standard methods. Combined with its equivalent diagnostic performance, it can therefore be implemented in the clinical practice, although careful validation is still required. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12920-018-0328-z) contains supplementary material, which is available to authorized users. BioMed Central 2018-02-13 /pmc/articles/PMC5836842/ /pubmed/29504914 http://dx.doi.org/10.1186/s12920-018-0328-z Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Ivanov, Maxim
Matsvay, Alina
Glazova, Olga
Krasovskiy, Stanislav
Usacheva, Mariya
Amelina, Elena
Chernyak, Aleksandr
Ivanov, Mikhail
Musienko, Sergey
Prodanov, Timofey
Kovalenko, Sergey
Baranova, Ancha
Khafizov, Kamil
Targeted sequencing reveals complex, phenotype-correlated genotypes in cystic fibrosis
title Targeted sequencing reveals complex, phenotype-correlated genotypes in cystic fibrosis
title_full Targeted sequencing reveals complex, phenotype-correlated genotypes in cystic fibrosis
title_fullStr Targeted sequencing reveals complex, phenotype-correlated genotypes in cystic fibrosis
title_full_unstemmed Targeted sequencing reveals complex, phenotype-correlated genotypes in cystic fibrosis
title_short Targeted sequencing reveals complex, phenotype-correlated genotypes in cystic fibrosis
title_sort targeted sequencing reveals complex, phenotype-correlated genotypes in cystic fibrosis
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5836842/
https://www.ncbi.nlm.nih.gov/pubmed/29504914
http://dx.doi.org/10.1186/s12920-018-0328-z
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