Toward a clinical diagnostic pipeline for SPINK1 intronic variants
Toward a clinical diagnostic pipeline for SPINK1 intronic variants
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建立 SPINK1 内含子变异的临床诊断流程
DOI：
10.1186/s40246-019-0193-7
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发表时间：
2019-02
期刊：
影响因子：
4.5
通讯作者：
Chen Jian Min
中科院分区：
文献类型：
--
作者：
Tang Xin Ying;Lin Jin Huan;Zou Wen Bin;Masson Emmanuelle;Boulling Arnaud;Deng Shun Jiang;Cooper David N;Liao Zhuan;Ferec Claude;Li Zhao Shen;Chen Jian Min
SPINK1内含子变异体在慢性胰腺炎中的临床意义以前已经通过各种方法进行了评估，包括基于细胞培养的全长基因测定。该测定的结果与计算机剪接预测之间的密切相关性是显而易见的。然而，到目前为止，还缺乏专门设计用于准确有效地分类SPINK1内含子变体的临床诊断管道。在这里，我们提出了这样一个管道，并探讨其有效性和潜在的效用，在加强新描述的SPINK1内含子变异的分类。我们证实了在最近报道的三种致病性SPINK1内含子变异体的背景下，计算机剪接预测和基于细胞培养的全长基因测定结果之间的密切相关性。然后，我们将计算机剪接预测和全长基因测定集成到逐步方法中，并测试其在SPINK1内含子变体的两个新数据集的分类中的实用性。第一个数据集包括通过对SPINK1基因的整个内含子序列进行测序在52名遗传原因不明的中国慢性胰腺炎患者中鉴定的16种深度内含子变体。第二个数据集包括五种新的罕见近端内含子变异，这些变异是通过对法国胰腺炎患者SPINK1基因的常规分析鉴定的。采用5%的次要等位基因频率作为群体频率过滤器，16个深内含子变体中的6个立即被分类为良性。在计算机预测的其余10个深内含子的变体和5个罕见的近端内含子的变体就其可能对剪接位点选择的影响表明，只有一个近端内含子的变体，c.194 + 5G A，可能是功能上的意义。采用基于细胞培养的全长基因测定，我们功能分析了c.194 + 5G A，连同7个预测的非功能性变体，从而验证了它们在所有情况下对剪接的预测影响。我们证明了计算机预测的准确性和有效性，结合基于细胞培养的全长基因测定，用于SPINK1内含子变体的分类。基于这些发现，我们提出了一个可操作的管道，用于在临床诊断环境中对SPINK1内含子变异进行分类。
The clinical significance of SPINK1 intronic variants in chronic pancreatitis has been previously assessed by various approaches including a cell culture-based full-length gene assay. A close correlation between the results of this assay and in silico splicing prediction was apparent. However, until now, a clinical diagnostic pipeline specifically designed to classify SPINK1 intronic variants accurately and efficiently has been lacking. Herein, we present just such a pipeline and explore its efficacy and potential utility in potentiating the classification of newly described SPINK1 intronic variants. We confirm a close correlation between in silico splicing prediction and results from the cell culture-based full-length gene assay in the context of three recently reported pathogenic SPINK1 intronic variants. We then integrated in silico splicing prediction and the full-length gene assay into a stepwise approach and tested its utility in the classification of two novel datasets of SPINK1 intronic variants. The first dataset comprised 16 deep intronic variants identified in 52 genetically unexplained Chinese chronic pancreatitis patients by sequencing the entire intronic sequence of the SPINK1 gene. The second dataset comprised five novel rare proximal intronic variants identified through the routine analysis of the SPINK1 gene in French pancreatitis patients. Employing a minor allele frequency of > 5% as a population frequency filter, 6 of the 16 deep intronic variants were immediately classified as benign. In silico prediction of the remaining ten deep intronic variants and the five rare proximal intronic variants with respect to their likely impact on splice site selection suggested that only one proximal intronic variant, c.194 + 5G > A, was likely to be of functional significance. Employing the cell culture-based full-length gene assay, we functionally analyzed c.194 + 5G > A, together with seven predicted non-functional variants, thereby validating their predicted effects on splicing in all cases. We demonstrated the accuracy and efficiency of in silico prediction in combination with the cell culture-based full-length gene assay for the classification of SPINK1 intronic variants. Based upon these findings, we propose an operational pipeline for classifying SPINK1 intronic variants in the clinical diagnostic setting.
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影响因子：
30.8
作者：
Witt, Heiko;Beer, Sebastian;Rosendahl, Jonas;Chen, Jian-Min;Chandak, Giriraj Ratan;Masamune, Atsushi;Bence, Melinda;Szmola, Richard;Oracz, Grzegorz;Macek, Milan, Jr.;Bhatia, Eesh;Steigenberger, Sandra;Lasher, Denise;Buehler, Florence;Delaporte, Catherine;Tebbing, Johanna;Ludwig, Maren;Pilsak, Claudia;Saum, Karolin;Bugert, Peter;Masson, Emmanuelle;Paliwal, Sumit;Bhaskar, Seema;Sobczynska-Tomaszewska, Agnieszka;Bak, Daniel;Balascak, Ivan;Choudhuri, Gourdas;Reddy, D. Nageshwar;Rao, G. Venkat;Thomas, Varghese;Kume, Kiyoshi;Nakano, Eriko;Kakuta, Yoichi;Shimosegawa, Tooru;Durko, Lukasz;Szabo, Andras;Schnur, Andrea;Hegyi, Peter;Rakonczay, Zoltan, Jr.;Pfuetzer, Roland;Schneider, Alexander;Groneberg, David Alexander;Braun, Markus;Schmidt, Hartmut;Witt, Ulrike;Friess, Helmut;Alguel, Hana;Landt, Olfert;Schuelke, Markus;Krueger, Renate;Wiedenmann, Bertram;Schmidt, Frank;Zimmer, Klaus-Peter;Kovacs, Peter;Stumvoll, Michael;Blueher, Matthias;Mueller, Thomas;Janecke, Andreas;Teich, Niels;Gruetzmann, Robert;Schulz, Hans-Ulrich;Moessner, Joachim;Keim, Volker;Loehr, Matthias;Ferec, Claude;Sahin-Toth, Miklos
通讯作者：
Sahin-Toth, Miklos
影响因子：
24.5
作者：
Kereszturi E;Király O;Sahin-Tóth M
通讯作者：
Sahin-Tóth M
影响因子：
14.9
作者：
Leman R;Gaildrat P;Le Gac G;Ka C;Fichou Y;Audrezet MP;Caux-Moncoutier V;Caputo SM;Boutry-Kryza N;Léone M;Mazoyer S;Bonnet-Dorion F;Sevenet N;Guillaud-Bataille M;Rouleau E;Bressac-de Paillerets B;Wappenschmidt B;Rossing M;Muller D;Bourdon V;Revillon F;Parsons MT;Rousselin A;Davy G;Castelain G;Castéra L;Sokolowska J;Coulet F;Delnatte C;Férec C;Spurdle AB;Martins A;Krieger S;Houdayer C
通讯作者：
Houdayer C
DOI：
10.1101/479493
发表时间：
2018-11
期刊：
bioRxiv
影响因子：
--
作者：
Jin‐Huan Lin;Xin-Ying Tang;A. Boulling;Wen‐Bin Zou;E. Masson;Y. Fichou;L. Raud;M. Tertre;
通讯作者：
Jin‐Huan Lin;Xin-Ying Tang;A. Boulling;Wen‐Bin Zou;E. Masson;Y. Fichou;L. Raud;M. Tertre;
影响因子：
29.4
作者：
Zou, Wen-Bin;Boulling, Arnaud;Liao, Zhuan
通讯作者：
Liao, Zhuan