Roberto Mosca

Background: Previous research has implicated de novo and inherited truncating mutations in autism-spectrum disorder. We aim to investigate whether the load of inherited truncating mutations contributes similarly to high-functioning autism, and to characterize genes that harbour de novo variants in high-functioning autism. Methods: We performed whole-exome sequencing in 20 high-functioning autism families (average IQ = 100). Results: We observed no difference in the number of transmitted versus nontransmitted truncating alleles for high-functioning autism (117 v. 130, p = 0.78). Transmitted truncating and de novo variants in high-functioning autism were not enriched in gene ontology (GO) or Kyoto Encyclopedia of Genes and Genomes (KEGG) categories, or in autism-related gene sets. However, in a patient with high-functioning autism we identified a de novo variant in a canonical splice site of LRP1, a postsynaptic density gene that is a target for fragile X mental retardation protein (FRMP). This de novo variant leads to in-frame skipping of exon 29, removing 2 of 6 blades of the β-propeller domain 4 of LRP1, with putative functional consequences. Large data sets implicate LRP1 across a number of psychiatric disorders: de novo variants are associated with autism-spectrum disorder ( p = 0.039) and schizophrenia ( p = 0.008) from combined sequencing projects; common variants using genome-wide association study data sets from the Psychiatric Genomics Consortium show gene-based association in schizophrenia ( p = 6.6 × E⁻⁰⁷) and in a meta-analysis across 7 psychiatric disorders ( p = 2.3 × E⁻⁰³); and the burden of ultra-rare pathogenic variants has been shown to be higher in autism-spectrum disorder ( p = 1.2 × E⁻⁰⁵), using whole-exome sequencing from 6135 patients with schizophrenia, 1778 patients with autism-spectrum disorder and 7875 controls. Limitations: We had a limited sample of patients with high-functioning autism, related to difficulty in recruiting probands with high cognitive performance and no family history of psychiatric disorders. Conclusion: Previous studies and ours suggest an effect of truncating mutations restricted to severe autism-spectrum disorder phenotypes that are associated with intellectual disability. We provide evidence for pleiotropic effects of common and rare variants in the LRP1 gene across psychiatric phenotypes.

Previous research has implicated de novo (DN) and inherited truncating mutations in autism spectrum disorder (ASD). We aim to investigate whether the load of inherited truncating mutations contribute similarly to high functioning autism (HFA), and to characterise genes harbouring DN variants in HFA. We performed whole-exome sequencing (WES) in 20 HFA families (average IQ = 100). No difference was observed in the number of transmitted versus non-transmitted truncating alleles to HFA (117 vs 130, P = 0.32). Transmitted truncating and DN variants in HFA were not enriched in GO or KEGG categories, nor autism-related gene sets. However, in a HFA patient we identified a DN variant in a canonical splice site of LRP1 , a post-synaptic density gene that is a target for the FMRP. This DN leads to in-frame skipping of exon-29, removing 2 of 6 blades of the β-propeller domain-4 of LRP1, with putative functional consequences. Results using large datasets implicate LRP1 across psychiatric diseases: i) DN are associated with ASD ( P = 0.039) and schizophrenia ( P = 0.008) from combined sequencing projects; ii) Common variants using Psychiatric Genomics Consortium GWAS datasets show gene-based association in schizophrenia ( P = 6.6E-07) and across six psychiatric diseases (meta-analysis P = 8.1E-05); and iii) burden of ultra-rare pathogenic variants is higher in ASD ( P = 1.2E-05), using WES from 6,135 schizophrenia patients, 1,778 ASD patients and 6,245 controls. Previous and current studies suggest an impact of truncating mutations restricted to severe ASD phenotypes associated with intellectual disability. We provide evidence for pleiotropic effects of common and rare variants in the LRP1 gene across psychiatric phenotypes.

Yeast-two hybrid screening of E. coli proteins and integration with protein structure and genetic interaction data provides an extensive interactome resource. Efforts to map the Escherichia coli interactome have identified several hundred macromolecular complexes, but direct binary protein-protein interactions (PPIs) have not been surveyed on a large scale. Here we performed yeast two-hybrid screens of 3,305 baits against 3,606 preys (∼70% of the E. coli proteome) in duplicate to generate a map of 2,234 interactions, which approximately doubles the number of known binary PPIs in E. coli. Integration of binary PPI and genetic-interaction data revealed functional dependencies among components involved in cellular processes, including envelope integrity, flagellum assembly and protein quality control. Many of the binary interactions that we could map in multiprotein complexes were informative regarding internal topology of complexes and indicated that interactions in complexes are substantially more conserved than those interactions connecting different complexes. This resource will be useful for inferring bacterial gene function and provides a draft reference of the basic physical wiring network of this evolutionarily important model microbe.

The database of 3D interacting domains (3did, available online for browsing and bulk download at http://3did.irbbarcelona.org) is a catalog of protein–protein interactions for which a high-resolution 3D structure is known. 3did collects and classifies all structural templates of domain–domain interactions in the Protein Data Bank, providing molecular details for such interactions. The current version also includes a pipeline for the discovery and annotation of novel domain–motif interactions. For every interaction, 3did identifies and groups different binding modes by clustering similar interfaces into ‘interaction topologies’. By maintaining a constantly updated collection of domain-based structural interaction templates, 3did is a reference source of information for the structural characterization of protein interaction networks. 3did is updated every 6 months.