Unveiling genetics of non-syndromic albinism using whole exome sequencing: A comprehensive study of TYR, TYRP1, OCA2 and MC1R genes in 17 families.

BACKGROUND: Oculocutaneous albinism (OCA) is a group of skin depigmentation disorders. Clinical presentation of OCA includes defects in melanocyte differentiation, melanin biosynthesis, and melanosome maturation and transport. OBJECTIVES: A molecular diagnostics study of families presenting oculocutaneous albinism. METHODS: In this study, 17 consanguineous OCA families consisting of 93 patients were investigated. Whole Exome Sequencing (WES) of the index patient in each family were performed. Short listed variants of WES were Sanger validated for Mendelian segregation in obligate carriers and other available family members. Variant prioritization and pathogenicity were classified as per the criteria of American College Medical Genetics and Genomics (ACMG). Comparative computational modelling was performed to predict the potential damaging effect of the altered proteins. RESULTS: 15 pathogenic variations: c.132 T > A, c.346C > T, c.488C > G, c.1037G > A in TYR, c.1211C > T, c.1441G > A, c.1706_1707insT, c.2020C > G, c.2402G > C, c.2430del, in OCA2, c.1067G > A in TYRP1 and c.451C > T, c.515G > T, c.766C > T, c.917G > A in MC1R genes were identified. Three variants in OCA2 gene were characterized: c.1706_1707insT, c.2430del, and c.2402G > C, all of which were not reported before in OCA families. CONCLUSION: A few studies focusing on mutation screening of OCA patients have been reported before; however, this study has uniquely presents the Pakhtun ethnic population residing on the North-Western boarder. It explains that TYR, OCA2, TYRP1, and MC1R variations lead to non-syndromic OCA phenotype The overlapping phenotypes of OCA can precisely be diagnosed for its molecular pathogenicity using WES. This study recommends WES as a first-line molecular diagnostic tool, and provides a basis for developing customized genetic tests i.e. pre-marital screening to reduce the disease burden in the future generations.

In-Silico Analysis of Amotosalen Hydrochloride Binding to CD-61 of Platelets.

OBJECTIVE: To determine the docking of Amotosalen hydrochloride (AH) at CD-61 of platelets, and to suggest the cause of bleeding in AH treated platelets transfusion. STUDY DESIGN: Descriptive study. PLACE AND DURATION OF STUDY: Medical College, Taif University, Taif, Saudi Arabia, from October 2014 to May 2015. METHODOLOGY: The study was carried out in-silico. PDB (protein data bank) code of Tirofiban bound to CD-61 was 2vdm. CD-61 was docked with Tirofiban using online docking tools, i.e. Patchdock and Firedock. Then, Amotosalen hydrochloride and CD-61 were also docked. Best docking poses to active sites of 2vdm were found. Ligplot of interactions of ligands and CD-61 were obtained. Then comparison of hydrogen bonds, hydrogen bond lengths, and hydrophobic bonds of 2vdm molecule and best poses of docking results were done. Patchdock and Firedock results of best poses were also analysed using SPSS version 16. RESULTS: More amino acids were involved in hydrogen and hydrophobic bonds in Patchdock and Firedock docking of Amotosalen hydrochloride with CD-61 than Patchdock and Firedock docking of CD-61 with Tirofiban. The binding energy was more in latter than former. CONCLUSION: Amotosalen hydrochloride binds to the active site of CD-61 with weaker binding force. Haemorrhage seen in Amotosalen hydrochloride-treated platelets might be due to binding of Amotosalen hydrochloride to CD-61.

Can dipstick method help in bacterial detection in platelet bags? A tertiary care hospitals blood bank review.

Objective: To find the bacterial and biochemical details of bags used in platelet transfusion. Methods: This cross-sectional study was carried out at a tertiary care hospital of Saudi Arabia (King Khalid Hospital, Najran) from January to June 2012, and comprised platelet bags. Samples for bacterial detection and biochemical testing of platelet bags were taken from blood bags on Day 6 of donation. Bacterial detection was done by using aerobic culture bottle, different gram stain, cultures and analytical profile index strips. Glucose, pH and protein measurements were done by Multistix dipstick method. SPSS 16 was used for data analysis. RESULTS: Of the 352 platelet bags, 1(0.28%) showed bacterial growth on Day 6 of collection. That bacterium was Staphylococcus epidermidis. Glucose content and pH of that platelet bag was 144.14mg/dl and 5, respectively. The overall mean pH of platelet bags was 6.69±0.55 (range: 3-7). Moreover, 255(72.4%) bags showed pH of 7, 90(25.5%) of 6, 5(1.4%) of 5 and 2(0.57%) showed pH of 3 on Day 6. The overall mean protein level was 6.162±0.204g/dl (range: 5.8-6.6). Pearson bivariate correlation between platelet bag's pH and glucose content was 0.707 (p=0.001). CONCLUSIONS: Positive correlation was found between platelet bag's glucose and pH levels.