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This article is part of the supplement: Eighth International Conference on Bioinformatics (InCoB2009): Computational Biology

Open Access Proceedings

A multi-species comparative structural bioinformatics analysis of inherited mutations in α-D-Mannosidase reveals strong genotype-phenotype correlation

Javed Mohammed Khan1 and Shoba Ranganathan12*

Author affiliations

1 Department of Chemistry and Biomolecular Sciences and ARC center of excellence in Bioinformatics, Macquarie University, NSW 2109, Australia

2 Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore 117597

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Citation and License

BMC Genomics 2009, 10(Suppl 3):S33  doi:10.1186/1471-2164-10-S3-S33

Published: 3 December 2009

Abstract

Background

Lysosomal α-mannosidase is an enzyme that acts to degrade N-linked oligosaccharides and hence plays an important role in mannose metabolism in humans and other mammalian species, especially livestock. Mutations in the gene (MAN2B1) encoding lysosomal α-D-mannosidase cause improper coding, resulting in dysfunctional or non-functional protein, causing the disease α-mannosidosis. Mapping disease mutations to the structure of the protein can help in understanding the functional consequences of these mutations and thus indirectly, the finer aspects of the pathology and clinical manifestations of the disease, including phenotypic severity as a function of the genotype.

Results

A comprehensive homology modeling study of all the wild-type and inherited mutations of lysosomal α-mannosidase in four different species, human, cow, cat and guinea pig, reveals a significant correlation between the severity of the genotype and the phenotype in α-mannosidosis. We used the X-ray crystallographic structure of bovine lysosomal α-mannosidase as template, containing only two disulphide bonds and some ligands, to build structural models of wild-type structures with four disulfide linkages and all bound ligands. These wild-type models were then used as templates for disease mutations. All the truncations and substitutions involving the residues in and around the active site and those that destabilize the fold led to severe genotypes resulting in lethal phenotypes, whereas the mutations lying away from the active site were milder in both their genotypic and phenotypic expression.

Conclusion

Based on the co-location of mutations from different organisms and their proximity to the enzyme active site, we have extrapolated observed mutations from one species to homologous positions in other organisms, as a predictive approach for detecting likely α-mannosidosis. Besides predicting new disease mutations, this approach also provides a way for detecting mutation hotspots in the gene, where novel mutations could be implicated in disease. The current study has identified five mutational hot-spot regions along the MAN2B1 gene. Structural mapping can thus provide a rational approach for predicting the phenotype of a disease, based on observed genotypic variations.