Genomic convergence and network analysis approach to identify candidate genes in Alzheimer's disease

BACKGROUND: Alzheimer’s disease (AD) is one of the leading genetically complex and heterogeneous disorder that is influenced by both genetic and environmental factors. The underlying risk factors remain largely unclear for this heterogeneous disorder. In recent years, high throughput methodologies, such as genome-wide linkage analysis (GWL), genome-wide association (GWA) studies, and genome-wide expression profiling (GWE), have led to the identification of several candidate genes associated with AD. However, due to lack of consistency within their findings, an integrative approach is warranted. Here, we have designed a rank based gene prioritization approach involving convergent analysis of multi-dimensional data and protein-protein interaction (PPI) network modelling. RESULTS: Our approach employs integration of three different AD datasets- GWL,GWA and GWE to identify overlapping candidate genes ranked using a novel cumulative rank score (S(R)) based method followed by prioritization using clusters derived from PPI network. S(R) for each gene is calculated by addition of rank assigned to individual gene based on either p value or score in three datasets. This analysis yielded 108 plausible AD genes. Network modelling by creating PPI using proteins encoded by these genes and their direct interactors resulted in a layered network of 640 proteins. Clustering of these proteins further helped us in identifying 6 significant clusters with 7 proteins (EGFR, ACTB, CDC2, IRAK1, APOE, ABCA1 and AMPH) forming the central hub nodes. Functional annotation of 108 genes revealed their role in several biological activities such as neurogenesis, regulation of MAP kinase activity, response to calcium ion, endocytosis paralleling the AD specific attributes. Finally, 3 potential biochemical biomarkers were found from the overlap of 108 AD proteins with proteins from CSF and plasma proteome. EGFR and ACTB were found to be the two most significant AD risk genes. CONCLUSIONS: With the assumption that common genetic signals obtained from different methodological platforms might serve as robust AD risk markers than candidates identified using single dimension approach, here we demonstrated an integrated genomic convergence approach for disease candidate gene prioritization from heterogeneous data sources linked to AD. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2164-15-199) contains supplementary material, which is available to authorized users

A single nucleotide polymorphism in transcobalamin II (I5V) induces structural changes in the protein as revealed by molecular modeling studies

Cobalamin is an essential micronutrient in mammals. Deficiencies of this micronutrient have been implicated as risk factors for various complex diseases. Cobalamin is transported to the cells by the transport protein transcobalamin II (TCII), and hence genetic variations (like single nucleotide polymorphisms) in TCII could be perceived to affect the binding of cobalamin to TCII, thereby modulating the intracellular concentrations of cobalamin. To understand whether three nonsynonymous mutations in TCII (I5V, P241R, and R381Q) alter the structure of the protein which could potentially affect cobalamin binding, we performed molecular dynamics simulation in silico. Superimposition of active sites of the four simulated models (wild type and three variants) with the human TCII crystal structure revealed that the distance between the Nε nitrogen atom of His-173 and the cobalt ion of cobalamin deviated considerably in the I5V model as compared to wild type and other variants. His-173 directly coordinates with the cobalt ion of cobalamin. Further, from our dynamic cross-correlation and principal component analysis it appears that in the I5V model the β-domain moves apart from the α-domain creating a wide gap between the two domains. This might facilitate the initial binding of cobalamin in the I5V model as cobalamin enters the binding site through the gap between the two domains. These observations were not found in the other variants. We thus speculate that binding of cobalamin will be more facile in the I5V variant

Inhibition of Mycobacterium tuberculosis dihydrodipicolinate synthase by alpha-ketopimelic acid and its other structural analogues

The Mycobacterium tuberculosis dihydrodipicolinate synthase (Mtb-dapA) is an essential gene. Mtb-DapA catalyzes the aldol condensation between pyruvate and L-aspartate-beta-semialdehyde (ASA) to yield dihydrodipicolinate. In this work we tested the inhibitory effects of structural analogues of pyruvate on recombinant Mtb-DapA (Mtb-rDapA) using a coupled assay with recombinant dihydrodipicolinate reductase (Mtb-rDapB). Alpha-ketopimelic acid (alpha-KPA) showed maximum inhibition of 88% and IC50 of 21 mu M in the presence of pyruvate (500 mu M) and ASA (400 mu M). Competition experiments with pyruvate and ASA revealed competition of alpha-KPA with pyruvate. Liquid chromatography-mass spectrometry (LC-MS) data with multiple reaction monitoring (MRM) showed that the relative abundance peak of final product, 2,3,4,5-tetrahydrodipicolinate, was decreased by 50%. Thermal shift assays showed 1 degrees C Tm shift of Mtb-rDapA upon binding alpha-KPA. The 2.4 angstrom crystal structure of Mtb-rDapA-alpha-KPA complex showed the interaction of critical residues at the active site with alpha-KPA. Molecular dynamics simulations over 500 ns of pyruvate docked to Mtb-DapA and of alpha-KPA-bound Mtb-rDapA revealed formation of hydrogen bonds with pyruvate throughout in contrast to alpha-KPA. Molecular descriptors analysis showed that ligands with polar surface area of 91.7 angstrom(2) are likely inhibitors. In summary, alpha-hydroxypimelic acid and other analogues could be explored further as inhibitors of Mtb-DapA