Most of neurodegenerative cases present a complex genetic architecture, in which many genes and pathways are associated the etiology of diseases, and  downstream effects of genetic variants can affect a myriad of factors in biological networks, including transcriptomic, proteomic, epigenomic, among others, that could converge into disease.  The analysis of data sets with several omics assays can reveal novel mechanistic insights, otherwise missed in omic-specific analyses. Different correlation and network-based approaches can be used to integrate data across multiple omics layers, to generate data-driven hypothesis, that investigating the quantitative effect that a specific altered feature in one layer (e.g. genetic variant, over-expressed or silenced gene in a specific cell type) alters an entire subnetwork.

Currently, we are actively mining into genetic, transcriptomic and proteomic datasets from human dataset as well as IPSC-derived neurons, microglia and/or astrocytes. We are investigating how post-transcriptional effects modulate protein synthesis, and the effect these have in AD. To do so, we are proposing an analytical framework that will later be applied to interpretation of large number of brains and IPS-derived cells and isogenic controls. In parallel, we are employing network analysis and Bayesian causal modeling methodologies, to model and integrate transcript and protein co-expreesion levels, with genomic data. We are studying networks that are altered in affected brains, compared to control neuropathologically free brains, and also the networks altered by specific pathological mutations in APP, PSEN1 and PSEN2 as well coding variants with high effect on risk, in genes like APOE, TREM2, ABCA7, SORL1 and PLD3.

Manhattan plot and correlation curves for differentially expressed genes in Neurodegeneration


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