Protein network analysis reveals selectively vulnerable regions and biological processes in FTD

Objective: The neuroanatomical profile of behavioral variant frontotemporal dementia (bvFTD) suggests a common biological etiology of disease despite disparate pathologic causes; we investigated the genetic underpinnings of this selective regional vulnerability to identify new risk factors for bvFTD. Methods: We used recently developed analytical techniques designed to address the limitations of genome-wide association studies to generate a protein interaction network of 63 bvFTD risk genes. We characterized this network using gene expression data from healthy and diseased human brain tissue, evaluating regional network expression patterns across the lifespan as well as the cell types and biological processes most affected in bvFTD. Results: We found that bvFTD network genes show enriched expression across the human lifespan in vulnerable neuronal populations, are implicated in cell signaling, cell cycle, immune function, and development, and are differentially expressed in pathologically confirmed frontotemporal lobar degeneration cases. Five of the genes highlighted by our differential expression analyses, BAIAP2, ERBB3, POU2F2, SMARCA2, and CDC37, appear to be novel bvFTD risk loci. Conclusions: Our findings suggest that the cumulative burden of common genetic variation in an interacting protein network expressed in specific brain regions across the lifespan may influence susceptibility to bvFTD

Genetic variation across RNA metabolism and cell death gene networks is implicated in the semantic variant of primary progressive aphasia

The semantic variant of primary progressive aphasia (svPPA) is a clinical syndrome characterized by neurodegeneration and progressive loss of semantic knowledge. Unlike many other forms of frontotemporal lobar degeneration (FTLD), svPPA has a highly consistent underlying pathology composed of TDP-43 (a regulator of RNA and DNA transcription metabolism). Previous genetic studies of svPPA are limited by small sample sizes and a paucity of common risk variants. Despite this, svPPA's relatively homogenous clinicopathologic phenotype makes it an ideal investigative model to examine genetic processes that may drive neurodegenerative disease. In this study, we used GWAS metadata, tissue samples from pathologically confirmed frontotemporal lobar degeneration, and in silico techniques to identify and characterize protein interaction networks associated with svPPA risk. We identified 64 svPPA risk genes that interact at the protein level. The protein pathways represented in this svPPA gene network are critical regulators of RNA metabolism and cell death, such as SMAD proteins and NOTCH1. Many of the genes in this network are involved in TDP-43 metabolism. Contrary to the conventional notion that svPPA is a clinical syndrome with few genetic risk factors, our analyses show that svPPA risk is complex and polygenic in nature. Risk for svPPA is likely driven by multiple common variants in genes interacting with TDP-43, along with cell death,x` working in combination to promote neurodegeneration