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Original research
Multimodal bioinformatic analyses of the neurodegenerative disease-associated TECPR2 gene reveal its diverse roles

Abstract

Background Loss of tectonin β-propeller repeat-containing 2 (TECPR2) function has been implicated in an array of neurodegenerative disorders, yet its physiological function remains largely unknown. Understanding TECPR2 function is essential for developing much needed precision therapeutics for TECPR2-related diseases.

Methods We leveraged considerable amounts of functional data to obtain a comprehensive perspective of the role of TECPR2 in health and disease. We integrated expression patterns, population variation, phylogenetic profiling, protein-protein interactions and regulatory network data for a minimally biased multimodal functional analysis. Genes and proteins linked to TECPR2 via multiple lines of evidence were subject to functional enrichment analyses to identify molecular mechanisms involving TECPR2.

Results TECPR2 was found to be part of a tight neurodevelopmental gene expression programme that includes KIF1A, ATXN1, TOM1L2 and FA2H, all implicated in neurological diseases. Functional enrichment analyses of TECPR2-related genes converged on a role in late autophagy and ribosomal processes. Large-scale population variation data demonstrated that this role is non-redundant.

Conclusions TECPR2 might serve as an indicator for the energy balance between protein synthesis and autophagy, and a marker for diseases associated with their imbalance, such as Alzheimer’s disease and Huntington’s disease. Specifically, we speculate that TECPR2 plays an important role as a proteostasis regulator during synaptogenesis, highlighting its importance in developing neurons. By advancing our understanding of TECPR2 function, this work provides an essential stepping stone towards the development of precision diagnostics and targeted treatment options for TECPR2-related disorders.

  • neurodegenerative diseases
  • genomics

Data availability statement

Data are available in a public, open access repository. All datasets used in this study are publicly available.

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