1. Combinatorial impact of SNPs & regulatory RNAs in the aetiology of late onset Alzheimer’s disease
The 3’ un‐translated regions (UTRs) of mRNAs being the major node of microRNAs (miRNAs) mediated post‐transcriptional regulation, we investigated the impact of SNPs (single nucleotide polymorphisms) residing within 3’ UTRs of genes associated with late‐onset AD (LOAD) on miRNA targeting to elucidate disease aetiology. We adopted an in‐house prediction pipeline to study the impact of SNPs reported to be associated with LOAD obtained from various GWAS studies on targeting sites for a set of 142 differentially expressed up‐regulated miRNAs in various parts of the brain (CSF, cortex, hippocampus and cerebellum). We obtained 10 potential miRNA‐target SNP interactions that significantly contribute to either “gain‐of‐function” or “loss‐of‐function” of the miRNA‐mediated regulatory network and influence LOAD susceptibility. Further, pathway analysis using Metacore revealed that the aberrant expression of some key genes due to presence of SNP in miRNA target sites lead to neuro fibrillary tangle formation, plaque formation and ultimately  neuronal cell death, that are known to be the hallmarks of  AD pathogenesis.
Introduction
Recent times have seen growing concerns on conspicuous disease affecting the brain of aged people, Alzheimer’s disease (AD), the neurodegenerative disease. The high death occurrence in the late-onset affected individuals (LOAD) has made it the most crucial type which is governed by an array of risk alleles/polymorphisms in different genes as evident from several genome wide association studies (GWAS). SNPs associated with LOAD located within 3’UTR which are the putative binding sites for miRNAs interfere with miRNA‐mediated regulation of corresponding target expression and can contribute to the risk of disease. Therefore, we investigated to understand the role of miRSNPs altering target pool expression which will help us to achieve a deeper insight into unexplored variables of gene regulations linked to development and progression of this neurodegenerative disease.
Methodology
Jyoti Roy , Sagar Udaseen , Parinita Agrawal , Bedanta Ballav Mohanty , Garima Singh , Devyani Samantarrai , Mousumi Sahu , Chandra Bhushan and Bibekanand Mallick*
RNAi and Functional Genomics Laboratory, Department of Life Science,
National Institute of Technology Rourkela, Odisha, , India
* s: ,
Abstract
Hierarchical Clustering of DE mRNA in LOAD
SNP-gene
ncRNA
miRNA targeting
rs4646_CYP19A1
hsa-miR-9-5p
Site destruction
rs5848_GRN
hsa-miR-92a-1-5p
rs _TFCP2
hsa-miR-197-5p
rs6857_PVRL2
hsa-miR-517-5p
rs _ESR2
hsa-miR-34a-5p
Site creation
rs _LDLR
hsa-miR-575
rs _IDE
rs _CDKN2A
hsa-miR-34c-5p
rs _SORL1
hsa-miR-92b-3p
LOAD pathway (Metacore)
Putative miRNA-miRSNP interactome A B C
hsa-miR-34a-5p IDE B. hsa-miR-197-5p TFCP influencing LOAD pathogenesis C. TFCP2, SORL1, IDE interacting with the biomarkers of the disease
Results
Key Findings
A common pool of 142 significantly up regulated miRNAs from various parts of the brain tissues of LOAD patients and SNPs associated with LOAD located within 3’ UTR (74 nos.) were considered to evaluate the impact of these SNPs in this disease through miRNA mediated regulation.
Our in-house prediction pipeline yielded 672 miRSNP sites from which 207 sites are screened based on significant MFE change and seed types between ancestral and mutated target sites.
Further, 10 miRSNPs were screened with maximum of 2 wobble base pairs within seed site, most significant seed topology, presence of Supplementary and compensatory sites influencing binding efficiency for further analysis. To strengthen our prediction, we further checked for the cooperativity by any other up regulated miRNAs from our set confirming exclusive one gene-one miRNA relationship.
The functional implication of selected miRSNPs using Metacore revealed involvement of these deregulated transcripts in cellular processes like neuronal apoptosis, tau hyper phosphorylation (NFT formation), secretase enzyme activity (plaque formation), protein trafficking (APP transport and processing), transcriptional regulation of other genes etc. affecting LOAD progression.
Heat map of miRNAs in LOAD
Acknowledgements
The authors acknowledge National Institute of Technology, Rourkela for the financial assistance and facilities received in support for carrying out this research work . The authors also acknowledge DST, DBT and CSIR for their financial assistance.
Presented at: International Conference on Mathematical and Computational Biology , I.I.T. Kanpur (India), 28 Feb. – 3 Mar. 2015