GRID1/GluD1 homozygous variants linked to intellectual disability and spastic paraplegia impair mGlu1/5 receptor signaling and excitatory synapses.

The ionotropic glutamate delta receptor GluD1, encoded by the GRID1 gene, is involved in synapse formation, function, and plasticity. GluD1 does not bind glutamate, but instead cerebellin and D-serine, which allow the formation of trans-synaptic bridges, and trigger transmembrane signaling. Despite wide expression in the nervous system, pathogenic GRID1 variants have not been characterized in humans so far. We report homozygous missense GRID1 variants in five individuals from two unrelated consanguineous families presenting with intellectual disability and spastic paraplegia, without (p.Thr752Met) or with (p.Arg161His) diagnosis of glaucoma, a threefold phenotypic association whose genetic bases had not been elucidated previously. Molecular modeling and electrophysiological recordings indicated that Arg161His and Thr752Met mutations alter the hinge between GluD1 cerebellin and D-serine binding domains and the function of this latter domain, respectively. Expression, trafficking, physical interaction with metabotropic glutamate receptor mGlu1, and cerebellin binding of GluD1 mutants were not conspicuously altered. Conversely, upon expression in neurons of dissociated or organotypic slice cultures, we found that both GluD1 mutants hampered metabotropic glutamate receptor mGlu1/5 signaling via Ca2+ and the ERK pathway and impaired dendrite morphology and excitatory synapse density. These results show that the clinical phenotypes are distinct entities segregating in the families as an autosomal recessive trait, and caused by pathophysiological effects of GluD1 mutants involving metabotropic glutamate receptor signaling and neuronal connectivity. Our findings unravel the importance of GluD1 receptor signaling in sensory, cognitive and motor functions of the human nervous system.

Unbiased phenotype and genotype matching maximizes gene discovery and diagnostic yield.

PURPOSE: Widespread application of next-generation sequencing, combined with data exchange platforms, has provided molecular diagnoses for countless families. To maximize diagnostic yield, we implemented an unbiased semi-automated genematching algorithm based on genotype and phenotype matching. METHODS: Rare homozygous variants identified in 2 or more affected individuals, but not in healthy individuals, were extracted from our local database of ∼12,000 exomes. Phenotype similarity scores (PSS), based on human phenotype ontology terms, were assigned to each pair of individuals matched at the genotype level using HPOsim. RESULTS: 33,792 genotype-matched pairs were discovered, representing variants in 7567 unique genes. There was an enrichment of PSS ≥0.1 among pathogenic/likely pathogenic variant-level pairs (94.3% in pathogenic/likely pathogenic variant-level matches vs 34.75% in all matches). We highlighted founder or region-specific variants as an internal positive control and proceeded to identify candidate disease genes. Variant-level matches were particularly helpful in cases involving inframe indels and splice region variants beyond the canonical splice sites, which may otherwise have been disregarded, allowing for detection of candidate disease genes, such as KAT2A, RPAIN, and LAMP3. CONCLUSION: Semi-automated genotype matching combined with PSS is a powerful tool to resolve variants of uncertain significance and to identify candidate disease genes.