More than 80 loss-of-function (LOF) mutations in the SLC6A8 creatine transporter (hCRT1) are responsible for cerebral creatine deficiency syndrome (CCDS), which gives rise to a spectrum of neurological defects, including intellectual disability, epilepsy, and autism spectrum disorder. To gain insight into the nature of the molecular defects caused by these mutations, we quantitatively profiled the cellular processing, trafficking, expression, and function of eight pathogenic CCDS variants in relation to the wild type (WT) and one neutral isoform. All eight CCDS variants exhibit measurable proteostatic deficiencies that likely contribute to the observed LOF. However, the magnitudes of their specific effects on the expression and trafficking of hCRT1 vary considerably, and we find that the LOF associated with two of these variants primarily arises from the disruption of the substrate-binding pocket. In conjunction with an analysis of structural models of the transporter, we use these data to suggest mechanistic classifications for these variants. To evaluate potential avenues for therapeutic intervention, we assessed the sensitivity of these variants to temperature and measured their response to the proteostasis regulator 4-phenylbutyrate (4-PBA). Only one of the tested variants (G132V) is sensitive to temperature, though its response to 4-PBA is negligible. Nevertheless, 4-PBA significantly enhances the activity of WT hCRT1 in HEK293T cells, which suggests it may be worth evaluating as a therapeutic for female intellectual disability patients carrying a single CCDS mutation. Together, these findings reveal that pathogenic SLC6A8 mutations cause a spectrum of molecular defects that should be taken into consideration in future efforts to develop CCDS therapeutics.
The cotranslational misfolding of the cystic fibrosis transmembrane conductance regulator (CFTR) plays a central role in the molecular basis of cystic fibrosis (CF). The misfolding of the most common CF variant (ΔF508) remodels both the translational regulation and quality control of CFTR. Nevertheless, it is unclear how the misassembly of the nascent polypeptide influences the activity of the translation machinery. In this work, we identify a structural motif within the CFTR transcript that stimulates efficient -1 ribosomal frameshifting and triggers the premature termination of translation. Though this motif does not appear to impact the wild-type CFTR interactome, silent mutations that disrupt this RNA structure alter how ΔF508 CFTR interacts with numerous translation and quality control proteins. Moreover, disrupting this RNA structure enhances both the expression and function of ΔF508 CFTR with no impact on wild-type. Finally, we show that disrupting this motif enhances the pharmacological rescue of ΔF508 by Trikafta, which implies ribosomal frameshifting antagonizes the effects of leading CF therapeutics. Together, our results reveal that ribosomal frameshifting selectively reduces the expression and assembly of a misfolded CFTR variant. These findings suggest cotranslational misfolding alters the processivity of translation and potentially the stability of the mRNA transcript through the dynamic modulation of ribosomal frameshifting.
Missense mutations that compromise the plasma membrane expression (PME) of integral membrane proteins (MPs) are the root cause of numerous genetic diseases. Differentiation of this class of mutations from those that specifically modify the activity of the folded protein has proven useful for the development and targeting of precision therapeutics. Nevertheless, it remains challenging to predict the effects of mutations on the stability and/ or expression of MPs. In this work, we utilize deep mutational scanning data to train a series of artificial neural networks to predict the effects of mutations on the PME of the G-protein coupled receptor (GPCR) rhodopsin from structural and/ or evolutionary features. We show that our best performing network, which we term PMEpred, can differentiate pathogenic rhodopsin variants that induce misfolding from those that primarily compromise signaling. This network also generates statistically significant predictions for the effects of mutations on the PME of another GPCR (Beta-2 adrenergic receptor) but not for an unrelated voltage-gated potassium channel (KCNQ1). Notably, our analyses of these networks suggest structural features alone are generally sufficient to recapitulate the observed mutagenic trends. Moreover, our findings imply that networks trained in this manner may be generalizable to proteins that share a common fold. Implications of our findings for the design of mechanistically specific genetic predictors are discussed.
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