PSMC3 proteasome subunit variants are associated with neurodevelopmental delay and type I interferon production

Abstract

A critical step in preserving protein homeostasis is the recognition, binding, unfolding, and translocation of protein substrates by six AAA-ATPase proteasome subunits (ATPase-associated with various cellular activities) termed PSMC1-6, which are required for degradation of proteins by 26 S proteasomes. Here, we identified 15 de novo missense variants in the PSMC3 gene encoding the AAA-ATPase proteasome subunit PSMC3/Rpt5 in 23 unrelated heterozygous patients with an autosomal dominant form of neurodevelopmental delay and intellectual disability. Expression of PSMC3 variants in mouse neuronal cultures led to altered dendrite development, and deletion of the PSMC3 fly ortholog Rpt5 impaired reversal learning capabilities in fruit flies. Structural modeling as well as proteomic and transcriptomic analyses of T cells derived from patients with PSMC3 variants implicated the PSMC3 variants in proteasome dysfunction through disruption of substrate translocation, induction of proteotoxic stress, and alterations in proteins controlling developmental and innate immune programs. The proteostatic perturbations in T cells from patients with PSMC3 variants correlated with a dysregulation in type I interferon (IFN) signaling in these T cells, which could be blocked by inhibition of the intracellular stress sensor protein kinase R (PKR). These results suggest that proteotoxic stress activated PKR in patient-derived T cells, resulting in a type I IFN response. The potential relationship among proteosome dysfunction, type I IFN production, and neurodevelopment suggests new directions in our understanding of pathogenesis in some neurodevelopmental disorders.

Unveiling the crucial neuronal role of the proteasomal ATPase subunit genePSMC5in neurodevelopmental proteasomopathies

“…3B ). Importantly, this is similar to what we previously reported with another proteasome subunit gene, PSMC3 11 .…”

“…Whole-blood samples collected from healthy individuals as well as Subjects S1, S6, S11, S12, S21, S32 and S33 and their respective parents and/or siblings =whenever possible= were processed using spin medium gradient centrifugations (pluriSelect) to separate and isolate peripheral blood mononuclear cells (PBMC) for cryopreservation using a standard freezing medium consisting of 90% FBS and 10 % DMSO. At a later point in time, PBMC were plated on 96-well plates together with irradiated allogeneic PBMC in the presence of IL-2 and PHA-L for T cell expansion as in 11 . Human T cells were maintained in RPMI1640 supplemented with 10 % human AB serum and 1% penicillin/streptomycin and analyzed at a resting state after 3-4 weeks of expansion.…”

“…Target gene expression was calculated relative to Ct values for the GAPDH control housekeeping genes. In some experiments, interferon (IFN) scores were calculated as the median of the RQ of the seven ISG over a single calibrator control following the procedure of Rice et al as in 11 . Semi-quantitative PCR was conducted on SH-SY5Y cDNA to amplify overexpressed HA- PSMC5 using a forward (5’-ATGGCGCTTGACGGACCAGAGCAGA-3’) primer binding to PSMC5 and a reverse primer (5’-GACAGTGGGAGTGGCACCTTCCAGGGTCAAGG-3’) binding to the polyadenylation signal of the pcDNA3.1/Zeo(+) expression vector.…”

“…Genetic investigations have further unveiled that loss-of-function variants in proteasome genes contribute to the onset and progression of a unique class of NDDs, specifically referred to as the neurodevelopmental proteasomopathies 8 . The inheritance pattern for these disorders can either be dominant, as seen with PSMD12 variants associated with Stankiewicz-Isidor syndrome [MIM: 617516] 9,10 and PSMC3 11 , or recessive, as observed in cases involving PSMB1 [MIM: 620038] 12 and PSMC3 [MIM:619354] 13 variants.…”

Facilitating the Molecular Diagnosis of Rare Genetic Disorders Through Facial Phenotypic Scores

“…With the recent advances in computer vision, many AI-based applications have begun to be used to facilitate diagnosis through facial image analysis (Dingemans et al, 2022;Dudding-Byth et al, 2017;Gurovich et al, 2019;Hsieh et al, 2022;Hustinx et al, 2023;Porras et al, 2021;Sümer et al, 2023) and to further delineate the phenotypes of novel diseases (Aerden et al, 2023;Asif et al, 2022;Averdunk et al, 2023;Ebstein et al, 2023;Guo et al, 2022;Kampmeier et al, 2022;Knaus et al, 2018;Lyon et al, 2023;Marbach et al, 2019;Oppermann et al, 2023;Pantel et al, 2018).…”