A comparative structural bioinformatics analysis of inherited mutations in b-D-Mannosidase across multiple species reveals a genotype-phenotype correlation

Thi Huynh, Javed Mohammed Khan, Shoba Ranganathan*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

9 Citations (Scopus)

Abstract

Background: Lysosomal b-D-mannosidase is a glycosyl hydrolase that breaks down the glycosidic bonds at the non-reducing end of N-linked glycoproteins. Hence, it is a crucial enzyme in polysaccharide degradation pathway. Mutations in the MANBA gene that codes for lysosomal b-mannosidase, result in improper coding and malfunctioning of protein, leading to b-mannosidosis. Studying the location of mutations on the enzyme structure is a rational approach in order to understand the functional consequences of these mutations. Accordingly, the pathology and clinical manifestations of the disease could be correlated to the genotypic modifications. Results: The wild-type and inherited mutations of b-mannosidase were studied across four different species, human, cow, goat and mouse employing a previously demonstrated comprehensive homology modeling and mutational mapping technique, which reveals a correlation between the variation of genotype and the severity of phenotype in b-mannosidosis. X-ray crystallographic structure of b-mannosidase from Bacteroides thetaiotaomicron was used as template for 3D structural modeling of the wild-type enzymes containing all the associated ligands. These wild-type models subsequently served as templates for building mutational structures. Truncations account for approximately 70% of the mutational cases. In general, the proximity of mutations to the active site determines the severity of phenotypic expressions. Mapping mutations to the MANBA gene sequence has identified five mutational hot-spots. Conclusion: Although restrained by a limited dataset, our comprehensive study suggests a genotype-phenotype correlation in b-mannosidosis. A predictive approach for detecting likely b-mannosidosis is also demonstrated where we have extrapolated observed mutations from one species to homologous positions in other organisms based on the proximity of the mutations to the enzyme active site and their co-location from different organisms. Apart from aiding the detection of mutational hotspots in the gene, where novel mutations could be diseaseimplicated, this approach also provides a way to predict new disease mutations. Higher expression of the exoglycosidase chitobiase is said to play a vital role in determining disease phenotypes in human and mouse. A bigger dataset of inherited mutations as well as a parallel study of b-mannosidase and chitobiase activities in prospective patients would be interesting to better understand the underlying reasons for b-mannosidosis.

Original languageEnglish
Title of host publication10th Int. Conference on Bioinformatics - 1st ISCB Asia Joint Conference 2011, InCoB 2011/ISCB-Asia 2011: Computational Biology - Proceedings from Asia Pacific Bioinformatics Network (APBioNet)
EditorsChristian Schoenbach, Sheila Nathan, Tin Wee Tan, Shoba Ranganathan
Place of PublicationUnited Kingdom
PublisherSpringer, Springer Nature
Pages1-13
Number of pages13
Volume12, Suppl. 3
DOIs
Publication statusPublished - 2011
Event10th International Conference on Bioinformatics and 1st ISCB Asia Joint Conference 2011: Computational Biology, InCoB 2011/ISCB-Asia 2011 - Kuala Lumpur, Malaysia
Duration: 30 Nov 20112 Dec 2011

Other

Other10th International Conference on Bioinformatics and 1st ISCB Asia Joint Conference 2011: Computational Biology, InCoB 2011/ISCB-Asia 2011
CountryMalaysia
CityKuala Lumpur
Period30/11/112/12/11

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Huynh, T., Khan, J. M., & Ranganathan, S. (2011). A comparative structural bioinformatics analysis of inherited mutations in b-D-Mannosidase across multiple species reveals a genotype-phenotype correlation. In C. Schoenbach, S. Nathan, T. W. Tan, & S. Ranganathan (Eds.), 10th Int. Conference on Bioinformatics - 1st ISCB Asia Joint Conference 2011, InCoB 2011/ISCB-Asia 2011: Computational Biology - Proceedings from Asia Pacific Bioinformatics Network (APBioNet) (Vol. 12, Suppl. 3, pp. 1-13). [S22] United Kingdom: Springer, Springer Nature. https://doi.org/10.1186/1471-2164-12-S3-S22