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Multiple in silico tools predict phenotypic manifestations in congenital thrombotic thrombocytopenic purpura
Author(s) -
Hing Zachary A.,
Schiller Tal,
Wu Andrew,
HamasakiKatagiri Nobuko,
Struble Evi Budo,
RussekCohen Estelle,
KimchiSarfaty Chava
Publication year - 2013
Publication title -
british journal of haematology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.907
H-Index - 186
eISSN - 1365-2141
pISSN - 0007-1048
DOI - 10.1111/bjh.12214
Subject(s) - in silico , mutation , phenotype , adamts13 , thrombotic thrombocytopenic purpura , genetics , biology , computational biology , medicine , bioinformatics , gene , immunology , platelet
Summary Congenital thrombotic thrombocytopenic purpura ( cTTP ) is a rare, recessively inherited genetic disorder with varying clinical presentation that is caused by ADAMTS 13 mutations. Several studies have found limited associations between ADAMTS 13 mutations and cTTP phenotype. The use of in silico tools that examine multiple mutation characteristics may better predict phenotype. We analysed 118 ADAMTS 13 mutations found in 144 cTTP patients reported in the literature and examined associations of several mutation characteristics, including N ‐terminal proximity, the evolutionary conservation of the affected amino acid position, as well as amino acid charge/phosphorylation and genetic codon usage to disease phenotype. Structure‐altering mutations were examined for their impact on ADAMTS 13 function based on existing ADAMTS 13 crystallographic data ( AA 77‐685). Our in silico data indicate that: (i) The position of the mutation in the N ‐ or C ‐terminus, (ii) evolutionary conservation and (iii) codon usage of the affected mutation position are associated with disease parameters, such as age of onset, organ damage and fresh frozen plasma prophylaxis. In conclusion, the usage of multiple in silico tools presents a promising strategy in refining predictions for the diverse presentation of cTTP . Enhancing our utilization of in silico tools to find genotype‐phenotype associations will create better‐tailored approaches for individual patient treatment.