Schizophrenia-Linked Protein tSNARE1 Regulates Endosomal Trafficking in Cortical Neurons.

TSNARE1, which encodes the protein tSNARE1, is a high-confidence gene candidate for schizophrenia risk, but nothing is known about its cellular or physiological function. We identified the major gene products of TSNARE1 and their cytoplasmic localization and function in endosomal trafficking in cortical neurons. We validated three primary isoforms of TSNARE1 expressed in human brain, all of which encode a syntaxin-like Qa SNARE domain. RNA-sequencing data from adult and fetal human brain suggested that the majority of tSNARE1 lacks a transmembrane domain that is thought to be necessary for membrane fusion. Biochemical data demonstrate that tSNARE1 can compete with Stx12 for incorporation into an endosomal SNARE complex, supporting its possible role as an inhibitory SNARE. Live-cell imaging in cortical neurons from mice of both sexes demonstrated that brain tSNARE1 isoforms localized to the endosomal network. The most abundant brain isoform, tSNARE1c, localized most frequently to Rab7+ late endosomes, and endogenous tSNARE1 displayed a similar localization in human neural progenitor cells and neuroblastoma cells. In mature rat neurons from both sexes, tSNARE1 localized to the dendritic shaft and dendritic spines, supporting a role for tSNARE1 at the postsynapse. Expression of either tSNARE1b or tSNARE1c, which differ only in their inclusion or exclusion of an Myb-like domain, delayed the trafficking of the dendritic endosomal cargo Nsg1 into late endosomal and lysosomal compartments. These data suggest that tSNARE1 regulates endosomal trafficking in cortical neurons, likely by negatively regulating early endosomal to late endosomal trafficking.SIGNIFICANCE STATEMENT Schizophrenia is a severe and polygenic neuropsychiatric disorder. Understanding the functions of high-confidence candidate genes is critical toward understanding how their dysfunction contributes to schizophrenia pathogenesis. TSNARE1 is one of the high-confidence candidate genes for schizophrenia risk, yet nothing was known about its cellular or physiological function. Here we describe the major isoforms of TSNARE1 and their cytoplasmic localization and function in the endosomal network in cortical neurons. Our results are consistent with the hypothesis that the majority of brain tSNARE1 acts as a negative regulator to endolysosomal trafficking.

Liver regulatory mechanisms of noncoding variants at lipid and metabolic trait loci.

Genome-wide association studies (GWASs) have identified hundreds of risk loci for liver disease and lipid-related metabolic traits, although identifying their target genes and molecular mechanisms remains challenging. We predicted target genes at GWAS signals by integrating them with molecular quantitative trait loci for liver gene expression (eQTL) and liver chromatin accessibility QTL (caQTL). We predicted specific regulatory caQTL variants at four GWAS signals located near EFHD1, LITAF, ZNF329, and GPR180. Using transcriptional reporter assays, we determined that caQTL variants rs13395911, rs11644920, rs34003091, and rs9556404 exhibit allelic differences in regulatory activity. We also performed a protein binding assay for rs13395911 and found that FOXA2 differentially interacts with the alleles of rs13395911. For variants rs13395911 and rs11644920 in putative enhancer regulatory elements, we used CRISPRi to demonstrate that repression of the enhancers altered the expression of the predicted target and/or nearby genes. Repression of the element at rs13395911 reduced the expression of EFHD1, and repression of the element at rs11644920 reduced the expression of LITAF, SNN, and TXNDC11. Finally, we showed that EFHD1 is a metabolically active gene in HepG2 cells. Together, these results provide key steps to connect genetic variants with cellular mechanisms and help elucidate the causes of liver disease.

Systematic investigation of allelic regulatory activity of schizophrenia-associated common variants.

Genome-wide association studies (GWASs) have successfully identified 145 genomic regions that contribute to schizophrenia risk, but linkage disequilibrium makes it challenging to discern causal variants. We performed a massively parallel reporter assay (MPRA) on 5,173 fine-mapped schizophrenia GWAS variants in primary human neural progenitors and identified 439 variants with allelic regulatory effects (MPRA-positive variants). Transcription factor binding had modest predictive power, while fine-map posterior probability, enhancer overlap, and evolutionary conservation failed to predict MPRA-positive variants. Furthermore, 64% of MPRA-positive variants did not exhibit expressive quantitative trait loci signature, suggesting that MPRA could identify yet unexplored variants with regulatory potentials. To predict the combinatorial effect of MPRA-positive variants on gene regulation, we propose an accessibility-by-contact model that combines MPRA-measured allelic activity with neuronal chromatin architecture.

Focus on your locus with a massively parallel reporter assay.

A growing number of variants associated with risk for neurodevelopmental disorders have been identified by genome-wide association and whole genome sequencing studies. As common risk variants often fall within large haplotype blocks covering long stretches of the noncoding genome, the causal variants within an associated locus are often unknown. Similarly, the effect of rare noncoding risk variants identified by whole genome sequencing on molecular traits is seldom known without functional assays. A massively parallel reporter assay (MPRA) is an assay that can functionally validate thousands of regulatory elements simultaneously using high-throughput sequencing and barcode technology. MPRA has been adapted to various experimental designs that measure gene regulatory effects of genetic variants within cis- and trans-regulatory elements as well as posttranscriptional processes. This review discusses different MPRA designs that have been or could be used in the future to experimentally validate genetic variants associated with neurodevelopmental disorders. Though MPRA has limitations such as it does not model genomic context, this assay can help narrow down the underlying genetic causes of neurodevelopmental disorders by screening thousands of sequences in one experiment. We conclude by describing future directions of this technique such as applications of MPRA for gene-by-environment interactions and pharmacogenetics.

Common genetic risk variants identified in the SPARK cohort support DDHD2 as a candidate risk gene for autism.

Autism spectrum disorder (ASD) is a highly heritable neurodevelopmental disorder. Large genetically informative cohorts of individuals with ASD have led to the identification of a limited number of common genome-wide significant (GWS) risk loci to date. However, many more common genetic variants are expected to contribute to ASD risk given the high heritability. Here, we performed a genome-wide association study (GWAS) on 6222 case-pseudocontrol pairs from the Simons Foundation Powering Autism Research for Knowledge (SPARK) dataset to identify additional common genetic risk factors and molecular mechanisms underlying risk for ASD. We identified one novel GWS locus from the SPARK GWAS and four significant loci, including an additional novel locus from meta-analysis with a previous GWAS. We replicated the previous observation of significant enrichment of ASD heritability within regulatory regions of the developing cortex, indicating that disruption of gene regulation during neurodevelopment is critical for ASD risk. We further employed a massively parallel reporter assay (MPRA) and identified a putative causal variant at the novel locus from SPARK GWAS with strong impacts on gene regulation (rs7001340). Expression quantitative trait loci data demonstrated an association between the risk allele and decreased expression of DDHD2 (DDHD domain containing 2) in both adult and prenatal brains. In conclusion, by integrating genetic association data with multi-omic gene regulatory annotations and experimental validation, we fine-mapped a causal risk variant and demonstrated that DDHD2 is a novel gene associated with ASD risk.