In 2017, we have had a very exciting webinar, on how the analysis of the transcriptome of carriers of pathological variants that cause Alzheimer’s Disease was used to deepen the understanding of this disease.
Alzheimer’s disease is a neurodegenerative disease and the cause of 60–70% of cases of dementia. In its essence, it is a multifactorial disease, the result of complex interactions between risk factors that cause pleiotropic changes in molecular networks between various biological processes. The identification of mutations in the amyloid-beta precursor protein (APP), and PSEN1 and PSEN2 genes that cause Mendelian forms of Alzheimer’s disease, represented key milestones for understanding the initial mechanisms and pathways involved in the pathogenesis of this disease. Remarkably, variants in these genes confer different transcriptomic profiles, and mutation carriers clustered separately from their non-carrier siblings. New evidence provide support for both neuronal and glial specific pathways contributing to pathogenesis, still little is understood about how the genetic loci and molecular changes are organized into common networks.
Dr. Oscar Harari, PhD from the Department of Psychiatry at Washington University in St Louis, talked about his lab’s research, where they combined transcriptomic cell-type profiling and network co-expression analyses to study postmortem brains of carriers of Alzheimer’s disease Mendelian mutations in PSEN1, PSEN2 and APP genes. Using digital deconvolution approaches, researchers obtained cell-type specific expressions, and confirmed the distribution of neuros, microglia, oligodendrocytes, and astrocytes in a collection of more than 1500 Alzheimer’s disease and non-demented subjects. As a result, they mapped 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 Alzheimer’s disease (SOD1, BACE1, PICALM, SLC4A2).
QuantSeq was used to prepare 3′ mRNA-Seq libraries for gene expression profiling prior to applying correction for the distinct cell-type distributions.