Rare Disease Mechanisms Identified by Genealogical Proteomics of Copper Homeostasis Mutant Pedigrees

Abstract

Rare neurological diseases shed light onto universal neurobiological processes. However, molecular mechanisms connecting genetic defects to their disease phenotypes are elusive. Here, we obtain mechanistic information by comparing proteomes of cells from individuals with rare disorders with proteomes from their disease-free consanguineous relatives. We use triple-SILAC mass spectrometry to quantify proteomes from human pedigrees affected by mutations in ATP7A, which cause Menkes disease, a rare neurodegenerative and neurodevelopmental disorder stemming from systemic copper depletion. We identified 214 proteins whose expression was altered in ATP7A fibroblasts. Bioinformatic analysis of ATP7A-mutant proteomes identified known phenotypes and processes affected in rare genetic diseases causing copper dyshomeostasis, including altered mitochondrial function. We found connections between copper dyshomeostasis and the UCHL1/PARK5 pathway of Parkinson disease, which we validated with mitochondrial respiration and Drosophila genetics assays. We propose that our genealogical "omics" strategy can be broadly applied to identify mechanisms linking a genomic locus to its phenotypes.

Switching on Endogenous Metal Binding Proteins in Parkinson’s Disease

“…Likewise, genetic mutation to the ATP7B transporter, which removes the metal from cells and from the body via the bile, causes a disease of Cu accumulation (Wilson’s disease) [18,20,21]. Indeed, a recent proteomics study found increased expression of ubiquitin C-terminal hydrolase L1 (UCHL-1) in human ATP7A -/y fibroblasts providing a potential link between Cu dyshomeostasis and the familial PD PARK5 mutation [22]).…”

From fuzziness to precision medicine: on the rapidly evolving proteomics with implications in mitochondrial connectivity to rare human disease

“…However, such an approach does not fully resolve the puzzle, since it is difficult to sort out which SNP(s) drive(s) patient proteome alterations when compared with healthy subject maps, since many SNPs may exist in patient genomes and any or a combination of them may be responsible for the phenotype observed. Genealogical proteomics contribute to resolving this roadblock by comparing patient proteome profiles with consanguineous healthy subjects instead of non-relative healthy individuals as a reference point for data filtration ( Zlatic et al., 2018 ). This enhances refining disease-specific protein levels, since healthy and patient subjects belonging to the same family line may share common disease-irrelevant SNPs and also exhibit protein accumulation or deprivation commonalities that are not disease specific.…”

“…Within a family, patient fibroblast proteome profiles were compared with their healthy kin using triple-SILAC ( Figure 4 A). Wild-type cells were supplemented with “light” unlabeled 12 C- and 14 N-arginine and lysine amino acids, while ATP7A null cells were incubated with either “medium” 13 C-arginine and 2 H-lysine or “heavy” 13 C- and 15 N-tagged arginine and lysine-containing media for isotope amino acid incorporations ( Zlatic et al., 2018 ). A total of 214 non-redundant proteins exhibited increased or decreased production in patients versus healthy kin, including 15 mt proteins, along with enzymes that require ATP7A for copper loading into the Golgi complex, and a collection of other copper metabolism enzymes.…”

Molecular Systems Biology of Neurodevelopmental Disorders, Rett Syndrome as an Archetype

“…Proteomes and transcriptomes have the added advantage of being hereditable molecular phenotypes, allowing their use in family trait studies (Wu et al, 2013; Parts et al, 2014; Wright et al, 2014; Huang et al, 2015). In the case of cellular proteomes, we have demonstrated they follow genealogical relationships among subjects within a pedigree and segregate those with the disease from their non-diseased/unaffected family members (Gokhale et al, 2018; Zlatic et al, 2018). This strategy can be carried further with the pairing of classical twin studies, a number of which have been published in Rett families, and the novel techniques discussed here (van Dongen et al, 2012).…”