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      Novel pathogenic mutations and skin biopsy analysis in Knobloch syndrome

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          Abstract

          Purpose

          To facilitate future diagnosis of Knobloch syndrome (KS) and better understand its etiology, we sought to identify not yet described COL18A1 mutations in KS patients. In addition, we tested whether mutations in this gene lead to absence of the COL18A1 gene product and attempted to better characterize the functional effect of a previously reported missense mutation.

          Methods

          Direct sequencing of COL18A1 exons was performed in KS patients from four unrelated pedigrees. We used immunofluorescent histochemistry in skin biopsies to evaluate the presence of type XVIII collagen in four KS patients carrying two already described mutations: c.3277C>T, a nonsense mutation, and c.3601G>A, a missense mutation. Furthermore, we determined the binding properties of the mutated endostatin domain p.A1381T (c.3601G>A) to extracellular matrix proteins using ELISA and surface plasmon resonance assays.

          Results

          We identified four novel mutations in COL18A1, including a large deletion involving exon 41. Skin biopsies from KS patients revealed lack of type XVIII collagen in epithelial basement membranes and blood vessels. We also found a reduced affinity of p.A1381T endostatin to some extracellular matrix components.

          Conclusions

          COL18A1 mutations involved in Knobloch syndrome have a distribution bias toward the coding exons of the C-terminal end. Large deletions must also be considered when point mutations are not identified in patients with characteristic KS phenotype. We report, for the first time, lack of type XVIII collagen in KS patients by immunofluorescent histochemistry in skin biopsy samples. As a final point, we suggest the employment of this technique as a preliminary and complementary test for diagnosis of KS in cases when mutation screening either does not detect mutations or reveals mutations of uncertain effect, such as the p.A1381T change.

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          Most cited references34

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          Improved splice site detection in Genie.

          We present an improved splice site predictor for the genefinding program Genie. Genie is based on a generalized Hidden Markov Model (GHMM) that describes the grammar of a legal parse of a multi-exon gene in a DNA sequence. In Genie, probabilities are estimated for gene features by using dynamic programming to combine information from multiple content and signal sensors, including sensors that integrate matches to homologous sequences from a database. One of the hardest problems in genefinding is to determine the complete gene structure correctly. The splice site sensors are the key signal sensors that address this problem. We replaced the existing splice site sensors in Genie with two novel neural networks based on dinucleotide frequencies. Using these novel sensors, Genie shows significant improvements in the sensitivity and specificity of gene structure identification. Experimental results in tests using a standard set of annotated genes showed that Genie identified 86% of coding nucleotides correctly with a specificity of 85%, versus 80% and 84% in the older system. In further splice site experiments, we also looked at correlations between splice site scores and intron and exon lengths, as well as at the effect of distance to the nearest splice site on false positive rates.
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            Prediction of human mRNA donor and acceptor sites from the DNA sequence.

            Artificial neural networks have been applied to the prediction of splice site location in human pre-mRNA. A joint prediction scheme where prediction of transition regions between introns and exons regulates a cutoff level for splice site assignment was able to predict splice site locations with confidence levels far better than previously reported in the literature. The problem of predicting donor and acceptor sites in human genes is hampered by the presence of numerous amounts of false positives: here, the distribution of these false splice sites is examined and linked to a possible scenario for the splicing mechanism in vivo. When the presented method detects 95% of the true donor and acceptor sites, it makes less than 0.1% false donor site assignments and less than 0.4% false acceptor site assignments. For the large data set used in this study, this means that on average there are one and a half false donor sites per true donor site and six false acceptor sites per true acceptor site. With the joint assignment method, more than a fifth of the true donor sites and around one fourth of the true acceptor sites could be detected without accompaniment of any false positive predictions. Highly confident splice sites could not be isolated with a widely used weight matrix method or by separate splice site networks. A complementary relation between the confidence levels of the coding/non-coding and the separate splice site networks was observed, with many weak splice sites having sharp transitions in the coding/non-coding signal and many stronger splice sites having more ill-defined transitions between coding and non-coding.
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              Nomenclature for the description of human sequence variations.

              A nomenclature system has recently been suggested for the description of changes (mutations and polymorphisms) in DNA and protein sequences. These nomenclature recommendations have now been largely accepted. However, current rules do not yet cover all types of mutations, nor do they cover more complex mutations. This document lists the existing recommendations and summarizes suggestions for the description of additional, more complex changes. Another version of this paper has been published in Hum Mut 15:7-12, 2000.
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                Author and article information

                Journal
                Mol Vis
                MV
                Molecular Vision
                Molecular Vision
                1090-0535
                2009
                23 April 2009
                : 15
                : 801-809
                Affiliations
                [1 ]Centro de Estudos do Genoma Humano, Departamento de Genética e Biologia Evolutiva, Instituto de Biociências, Universidade de São Paulo, São Paulo, Brazil
                [2 ]Collagen Research Unit, Biocenter and Department of Medical Biochemistry and Molecular Biology, University of Oulu, Oulu, Finland
                [3 ]Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
                [4 ]Departamento de Física e Informática, Instituto de Física de São Carlos, Universidade de São Paulo, São Carlos, Brazil
                [5 ]Departamento de Físico-Química, Instituto de Química de São Carlos, Universidade de São Paulo, São Carlos, Brazil
                Author notes
                Correspondence to: Dr. Marita-Rita Passos-Bueno, Centro de Estudos do Genoma Humano, Instituto de Biociências, Universidade de São Paulo, Departamento de Genética e Biologia Evolutiva, Rua do Matão, 277, Sala 200, São Paulo, SP 05508-090, Brazil; Phone: 55-11-3091-9910; FAX: 55-11-3091-7419; email: passos@ 123456ib.usp.br
                Article
                82 2008MOLVIS363
                2671584
                19390655
                20fbfcd5-e46d-4716-9ad2-1420fce3cb07
                Copyright @ 2009

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 07 November 2008
                : 15 April 2009
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                Vision sciences
                Vision sciences

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