Abstract

The past decade has seen a rapid acceleration in the discovery of new genetic causes of ALS, with more than 20 putative ALS-causing genes now cited. These genes encode proteins that cover a diverse range of molecular functions, including free radical scavenging (e.g., SOD1), regulation of RNA homeostasis (e.g., TDP-43 and FUS), and protein degradation through the ubiquitin-proteasome system (e.g., ubiquilin-2 and cyclin F) and autophagy (TBK1 and sequestosome-1/p62). It is likely that the various initial triggers of disease (either genetic, environmental and/or gene-environment interaction) must converge upon a common set of molecular pathways that underlie ALS pathogenesis. Given the complexity, it is not surprising that a catalog of molecular pathways and proteostasis dysfunctions have been linked to ALS. One of the challenges in ALS research is determining, at the early stage of discovery, whether a new gene mutation is indeed disease-specific, and if it is linked to signaling pathways that trigger neuronal cell death. We have established a proof-of-concept proteogenomic workflow to assess new gene mutations, using CCNF (cyclin F) as an example, in cell culture models to screen whether potential gene candidates fit the criteria of activating apoptosis. This can provide an informative and time-efficient output that can be extended further for validation in a variety of in vitro and in vivo models and/or for mechanistic studies. As a proof-of-concept, we expressed cyclin F mutations (K97R, S195R, S509P, R574Q, S621G) in HEK293 cells for label-free quantitative proteomics that bioinformatically predicted activation of the neuronal cell death pathways, which was validated by immunoblot analysis. Proteomic analysis of induced pluripotent stem cells (iPSCs) derived from patient fibroblasts bearing the S621G mutation showed the same activation of these pathways providing compelling evidence for these candidate gene mutations to be strong candidates for further validation and mechanistic studies (such as E3 enzymatic activity assays, protein–protein and protein–substrate studies, and neuronal apoptosis and aberrant branching measurements in zebrafish). Our proteogenomics approach has great utility and provides a relatively high-throughput screening platform to explore candidate gene mutations for their propensity to cause neuronal cell death, which will guide a researcher for further experimental studies.

Highlights

  • Proteogenomics is an integrated area of research that blends genomics and proteomics, which was originally described to use proteomics data to improve genome annotations and subsequent protein characterization (Jaffe et al, 2004; Nesvizhskii, 2014)

  • With the emergence of whole genome sequencing that have facilitated the rapid discovery of new genes for basic scientific research and in medicine, more than 20 putative genes are cited as causing ALS (Mejzini et al, 2019). These genes encode proteins that cover a diverse range of molecular functions, including free radical scavenging, regulation of RNA homeostasis [TAR DNA-binding protein 43 (TDP-43) and fused in sarcoma (FUS)], and protein degradation through the ubiquitin-proteasome system and autophagy

  • It is well established that familial origins represent about 10% of all ALS cases, and while more than 20 genes are identified as causing ALS, only few have been fully validated to determine the full extent of the perturbed molecular mechanisms and pathways

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Summary

Introduction

Proteogenomics is an integrated area of research that blends genomics and proteomics, which was originally described to use proteomics data to improve genome annotations and subsequent protein characterization (Jaffe et al, 2004; Nesvizhskii, 2014). Unlike cancer research where the biology involves uncontrolled proliferation [and cells can be immortalized and grown in culture (Mitra et al, 2013) or xenografts (Yada et al, 2018)]; neurodegenerative research is underpinned by finding the causative mechanisms that lead to cell death in specific cell types (e.g., neurons), by which a large population of cells have already perished before a patient even presents with symptoms (Sabatelli et al, 2011; Branch et al, 2016; Woollacott and Rohrer, 2016) This makes sourcing diseaseaffected neuronal cells from patients extremely challenging, and generating in vitro and in vivo models to recapitulate the multistep processes for the purposes of biomedical and therapeutic research even more difficult (Al-Chalabi et al, 2014; Morrice et al, 2018; Vucic et al, 2020). None are considered the ‘classic’ or ‘benchmark’ models given the numerous and converging signaling pathways that all affect the biology differently

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