JUN 08, 2017 10:00 AM PDT
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WEBINAR: Analysis of the transcriptome of carriers of pathological variants in PSEN1, PSEN2 and APP that cause Alzheimer's Disease
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  • Assistant Professor in the Department of Psychiatry in Washington University in St Louis
      My research is focused in the identification of omics factors implicated in complex neurodegenerative traits; in particular Alzheimer Disease (AD). The identification of these genomic, trascriptomic and proteomic factors, and their interplay will help us to understand the etiology of the disease; and to develop new drugs and their respective companion diagnostics. I have employed traditional biomarkers of AD (i.e. Cerebrospinal fluid Aβ and tau) to identify novel CSF biomarker. The discovery of biomarkers for AD is a key to identify individuals in the presymptomatic stage (pathology present with cognition intact) of the disease. Biomarkers can potentially be used clinically for diagnosis and to track the effectiveness of novel therapies but can also be used as endophenotypes for genetic studies leading to the identification of novel variants/genes involved in AD, and novel therapeutic targets. Although genetic studies have identified many variants involved in the aetiology of complex traits, there is a considerable fraction of the genetic variance that remains unexplained. I have incorporated independent biological information into the GWAS dataset, allowing further interpretation of sub-threshold p-values in genetic studies, and thus enabling the detection of additional signals that would otherwise be missed in statistical analyses of genome wide studies. By shifting from the evaluation of individual variants toward groups of genes related by pathway -under the commonly accepted hypothesis that causal variants are not randomly distributed across the genome, but are instead clustered among functionally-related genes- we can prioritize for further investigation those variants that do not reach the genome-wide significance threshold. This is achieved by the development, employment and amalgamation of different statistical, bioinformatics and machine learning methods to extract more biological information from genetic studies.


    DATE: June 8, 2017
    TIME: 10:00AM PDT, 1:00PM EDT, 7:00PM CEST

    Alzheimer’s Disease (AD) is the result of complex interactions between risk factors that cause pleiotropic changes in molecular networks linking a host of biological processes. A variety of genetic factors have been shown to contribute to risk with varying degrees of penetrance: the identification of mutations in the amyloid-beta precursor protein (APP), presenilin (PSEN1 and PSEN2) genes that cause Mendelian forms of AD represented key milestones for understanding the initial mechanisms and pathways involved in AD pathogenesis. Remarkably, variants in these genes confer a different transcriptomic profiles, and mutation carriers clustered separately from their non-carrier siblings. New evidences provide support for both neuronal and glial specific pathways contributing to pathogenesis. However, little is understood about how the genetic loci and molecular changes are organized into common networks. We combined transcriptomic cell-type profiling and network co-expression analyses to study a unique collection of human postmortem brain tissue ascertained to represent the AD Mendelian mutations.

    Using novel digital deconvolution approaches, we derived cell-type specific expression. We ascertain the distribution of neuros, microglia, oligodendrocytes and astrocytes in a collection of more than 1500 AD and non-demented subjects. We derived gene regulatory networks employing the expression corrected for the distinct cell-type distributions, and identified modules that cluster genes that harbor variants usually associated with both early-onset autosomal dominant (PSEN1) and late-onset sporadic classifications of AD (SOD1, BACE1, PICALM, SLC4A2).

    Understanding variant-specific effects is of an immense importance for the elucidation of the underlying biology of the Alzheimer Disease. Our initial analysis reveals a transcriptional regulation module that link that early-onset autosomal dominant and late-onset sporadic genes.

    Learning Objectives:

    • Identify confounding factors that can affect transcriptomic analyses and learn how to address them
    • Familiarize with machine learning techniques that allow to validate results when analyzing dataset with a reduced number of samples
    • Learn digital deconvolution approaches to infer cell composition from RNA-seq data
    • Learn how transcriptomic profiles can reveal gene co-expression networks


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