Integrative Omics approaches for understanding molecular pathogenesis of Dementia with Lewy bodies

Objectives:
This project aims to enhance our understanding of molecular pathogenesis of Dementia with Lewy bodies (DLB). Its objectives are to:,
1. Develop a polygenic risk score (PRS) for DLB and to evaluate its ability to differentiate DLB from older people without cognitive impairment and from people with other dementias.
2. Investigate post-mortem cortical transcriptomics of people with DLB, and to identify differentially expressed genes (DEGs) in DLB brains.
3. Identify dysfunctional molecular pathways and networks in DLB brains and to compare them with dysfunctional molecular networks in brains of people with Alzheimer's disease (AD).
4. Evaluate the relationship between DLB genomic and transcriptomic data
5. Integrate DLB genomic and transcriptomic data for gaining biological insights regarding its pathogenesis and potential therapeutic targets.

Research plan:
I. Study I (0-6 months): PRS of DLB and its ability to differentiate DLB from older people without cognitive impairment and other dementias
1. DLB specific GWAS data and genome sequencing data will be used to generate a PRS on DLB samples from the Brains for Dementia Research (BDR) to identify the best single nucleotides polymorphism (SNP) model to discriminate DLB from older people without cognitive impairment.
2. This PRS will then be used to see if it can differentiate DLB from other types of Dementia (AD, vascular dementia, Frontotemporal dementia, and Parkinson's Disease Dementia).
3. We can then use the PRS SNP model for AD from the BDR, and compare it with the DLB model to identify general dementia SNPs and DLB specific SNPs.

II. Study II (7-18 months): Investigating post-mortem cortical transcriptomics of people with DLB
1. We will obtain BDR post-mortem cortical samples from people with DLB and older people without cognitive impairment (n=40 of each). Total RNA will be extracted, and next-generation RNA-sequencing (RNA-Seq) will be completed by following our previous reported methodology (https://pubmed.ncbi.nlm.nih.gov/31327631/).
2. DEGs will be identified by an experimentally validated edgeR algorithm. This will allow us to match up of BDR transcriptomic and genomic data, which have already been generated.
3. Functional pathway analyses of identified DLB DEGs and to compare them with the post-mortem cortical transcriptomic data from people with AD. Comparing DLB and AD DEGs and their downstream dysfunctional molecular pathways and networks may identify potential gene expression biomarkers specific to DLB.

III. Study III (19-24 months): Evaluate the relationship between DLB genomic and transcriptomic data
1. eQTL Exploration for general dementia and DLB specific factors using our genomic and transcriptomic data.
2. Our genomic and transcriptomic data will be linked to find eQTLs for general dementia genes/SNPs and specific DLB gene/SNPs and correlating phenotypes with PRS.

IV. Study IV (25-36 months): Integrate DLB genomic and transcriptomic data for gaining further biological insights
1. Genome scale metabolic modelling for understanding metabolic reprogramming in DLB brains
2. In-silico gene silencing analyses for identifying potential therapeutic targets
3. Exploring machine learning/ AI's ability to gain biological insights from our DLB genomic and transcriptomic data.