Genetic regulation of human brain proteome reveals proteins implicated in psychiatric disorders.

Psychiatric disorders are highly heritable yet polygenic, potentially involving hundreds of risk genes. Genome-wide association studies have identified hundreds of genomic susceptibility loci with susceptibility to psychiatric disorders; however, the contribution of these loci to the underlying psychopathology and etiology remains elusive. Here we generated deep human brain proteomics data by quantifying 11,608 proteins across 268 subjects using 11-plex tandem mass tag coupled with two-dimensional liquid chromatography-tandem mass spectrometry. Our analysis revealed 788 cis-acting protein quantitative trait loci associated with the expression of 883 proteins at a genome-wide false discovery rate <5%. In contrast to expression at the transcript level and complex diseases that are found to be mainly influenced by noncoding variants, we found protein expression level tends to be regulated by non-synonymous variants. We also provided evidence of 76 shared regulatory signals between gene expression and protein abundance. Mediation analysis revealed that for most (88%) of the colocalized genes, the expression levels of their corresponding proteins are regulated by cis-pQTLs via gene transcription. Using summary data-based Mendelian randomization analysis, we identified 4 proteins and 19 genes that are causally associated with schizophrenia. We further integrated multiple omics data with network analysis to prioritize candidate genes for schizophrenia risk loci. Collectively, our findings underscore the potential of proteome-wide linkage analysis in gaining mechanistic insights into the pathogenesis of psychiatric disorders.

Unraveling the epigenomic and transcriptomic interplay during alcohol-induced anxiolysis.

Positive effects of alcohol drinking such as anxiolysis and euphoria appear to be a crucial factor in the initiation and maintenance of alcohol use disorder (AUD). However, the mechanisms that lead from chromatin reorganization to transcriptomic changes after acute ethanol exposure remain unknown. Here, we used Assay for Transposase-Accessible Chromatin followed by high throughput sequencing (ATAC-seq) and RNA-seq to investigate epigenomic and transcriptomic changes that underlie anxiolytic effects of acute ethanol using an animal model. Analysis of ATAC-seq data revealed an overall open or permissive chromatin state that was associated with transcriptomic changes in the amygdala after acute ethanol exposure. We identified a candidate gene, Hif3a (Hypoxia-inducible factor 3, alpha subunit), that had 'open' chromatin regions (ATAC-seq peaks), associated with significantly increased active epigenetic histone acetylation marks and decreased DNA methylation at these regions. The mRNA levels of Hif3a were increased by acute ethanol exposure, but decreased in the amygdala during withdrawal after chronic ethanol exposure. Knockdown of Hif3a expression in the central nucleus of amygdala attenuated acute ethanol-induced increases in Hif3a mRNA levels and blocked anxiolysis in rats. These data indicate that chromatin accessibility and transcriptomic signatures in the amygdala after acute ethanol exposure underlie anxiolysis and possibly prime the chromatin for the development of AUD.

DRAMS: A tool to detect and re-align mixed-up samples for integrative studies of multi-omics data.

Studies of complex disorders benefit from integrative analyses of multiple omics data. Yet, sample mix-ups frequently occur in multi-omics studies, weakening statistical power and risking false findings. Accurately aligning sample information, genotype, and corresponding omics data is critical for integrative analyses. We developed DRAMS (https://github.com/Yi-Jiang/DRAMS) to Detect and Re-Align Mixed-up Samples to address the sample mix-up problem. It uses a logistic regression model followed by a modified topological sorting algorithm to identify the potential true IDs based on data relationships of multi-omics. According to tests using simulated data, the more types of omics data used or the smaller the proportion of mix-ups, the better that DRAMS performs. Applying DRAMS to real data from the PsychENCODE BrainGVEX project, we detected and corrected 201 (12.5% of total data generated) mix-ups. Of the 21 mix-ups involving errors of racial identity, DRAMS re-assigned all data to the correct racial group in the 1000 Genomes project. In doing so, quantitative trait loci (QTL) (FDR<0.01) increased by an average of 1.62-fold. The use of DRAMS in multi-omics studies will strengthen statistical power of the study and improve quality of the results. Even though very limited studies have multi-omics data in place, we expect such data will increase quickly with the needs of DRAMS.

Transcriptome-wide profiling of acute stress induced changes in ribosome occupancy level using external standards.

Acute cellular stress is known to induce a global reduction in mRNA translation through suppression of cap dependent translation. Selective translation in response to acute stress has been shown to play important roles in regulating the stress response. However, accurately profiling translational changes transcriptome-wide in response to acute cellular stress has been challenging. Commonly used data normalization methods operate on the assumption that any systematic shifts are experimental artifacts. Consequently, if applied to profiling acute cellular stress-induced mRNA translation changes, these methods are expected to produce biased estimates. To address this issue, we designed, produced, and evaluated a panel of 16 oligomers to serve as external standards for ribosome profiling studies. Using Sodium Arsenite treatment-induced oxidative stress in lymphoblastoid cell lines as a model system, we applied spike-in oligomers as external standards. We found our spike-in oligomers to display a strong linear correlation between the observed and the expected quantification, with small ratio compression at the lower concentration range. Using the expected fold changes constructed from spike-in controls, we found in our dataset that TMM normalization, a popular global scaling normalization approach, produced 87.5% false positives at a significant cutoff that is expected to produce only 10% false positive discoveries. In addition, TMM normalization produced a systematic shift of fold change by 3.25 fold. These results highlight the consequences of applying global scaling approaches to conditions that clearly violate their key assumptions. In contrast, we found RUVg normalization using spike-in oligomers as control genes recapitulated the expected stress induced global reduction of translation and resulted in little, if any, systematic shifts in the expected fold change. Our results clearly demonstrated the utility of our spike-in oligomers, both for constructing expected results as controls and for data normalization.

Disease modeling and gene correction of LGMDR21 iPSCs elucidates the role of POGLUT1 in skeletal muscle maintenance, regeneration, and the satellite cell niche.

Autosomal recessive limb-girdle muscular dystrophy 21 (LGMDR21) is caused by pathogenic variants in protein O-glucosyltransferase 1 (POGLUT1), which is responsible for O-glucosylation of specific epidermal growth factor (EGF) repeats found in ∼50 mammalian proteins, including Notch receptors. Previous data from patient biopsies indicated that impaired Notch signaling, reduction of muscle stem cells, and accelerated differentiation are probably involved in disease etiopathology. Using patient induced pluripotent stem cells (iPSCs), their corrected isotypes, and control iPSCs, gene expression profiling indicated dysregulation of POGLUT1, NOTCH, muscle development, extracellular matrix (ECM), cell adhesion, and migration as involved pathways. They also exhibited reduced in vitro POGLUT1 enzymatic activity and NOTCH signaling as well as defective myogenesis, proliferation, migration and differentiation. Furthermore, in vivo studies demonstrated significant reductions in engraftment, muscle stem cell formation, PAX7 expression, and maintenance, along with an increased percentage of mislocalized PAX7+ cells in the interstitial space. Gene correction in patient iPSCs using CRISPR-Cas9 nickase led to the rescue of the main in vitro and in vivo phenotypes. These results demonstrate the efficacy of iPSCs and gene correction in disease modeling and rescue of the phenotypes and provide evidence of the involvement of muscle stem cell niche localization, PAX7 expression, and cell migration as possible mechanisms in LGMDR21.

The impact of common variants on gene expression in the human brain: from RNA to protein to schizophrenia risk.

Background: The impact of genetic variants on gene expression has been intensely studied at the transcription level, yielding in valuable insights into the association between genes and the risk of complex disorders, such as schizophrenia (SCZ). However, the downstream impact of these variants and the molecular mechanisms connecting transcription variation to disease risk are not well understood. Results: We quantitated ribosome occupancy in prefrontal cortex samples of the BrainGVEX cohort. Together with transcriptomics and proteomics data from the same cohort, we performed cis-Quantitative Trait Locus (QTL) mapping and identified 3,253 expression QTLs (eQTLs), 1,344 ribosome occupancy QTLs (rQTLs), and 657 protein QTLs (pQTLs) out of 7,458 genes quantitated in all three omics types from 185 samples. Of the eQTLs identified, only 34% have their effects propagated to the protein level. Further analysis on the effect size of prefrontal cortex eQTLs identified from an independent dataset showed clear post-transcriptional attenuation of eQTL effects. To investigate the biological relevance of the attenuated eQTLs, we identified 70 expression-specific QTLs (esQTLs), 51 ribosome-occupancy-specific QTLs (rsQTLs), and 107 protein-specific QTLs (psQTLs). Five of these omics-specific QTLs showed strong colocalization with SCZ GWAS signals, three of them are esQTLs. The limited number of GWAS colocalization discoveries from omics-specific QTLs and the apparent prevalence of eQTL attenuation prompted us to take a complementary approach to investigate the functional relevance of attenuated eQTLs. Using S-PrediXcan we identified 74 SCZ risk genes, 34% of which were novel, and 67% of these risk genes were replicated in a MR-Egger test. Notably, 52 out of 74 risk genes were identified using eQTL data and 70% of these SCZ-risk-gene-driving eQTLs show little to no evidence of driving corresponding variations at the protein level. Conclusion: The effect of eQTLs on gene expression in the prefrontal cortex is commonly attenuated post-transcriptionally. Many of the attenuated eQTLs still correlate with SCZ GWAS signal. Further investigation is needed to elucidate a mechanistic link between attenuated eQTLs and SCZ disease risk.

A Myogenic Double-Reporter Human Pluripotent Stem Cell Line Allows Prospective Isolation of Skeletal Muscle Progenitors.

Myogenic differentiation of human pluripotent stem cells (hPSCs) has been done by gene overexpression or directed differentiation. However, viral integration, long-term culture, and the presence of unwanted cells are the main obstacles. By using CRISPR/Cas9n, a double-reporter human embryonic stem cell (hESC) line was generated for PAX7/MYF5, allowing prospective readout. This strategy allowed pathway screen to define efficient myogenic induction in hPSCs. Next, surface marker screen allowed identification of CD10 and CD24 for purification of myogenic progenitors and exclusion of non-myogenic cells. CD10 expression was also identified on human satellite cells and skeletal muscle progenitors. In vitro and in vivo studies using transgene and/or reporter-free hPSCs further validated myogenic potential of the cells by formation of new fibers expressing human dystrophin as well as donor-derived satellite cells in NSG-mdx4Cv mice. This study provides biological insights for myogenic differentiation of hPSCs using a double-reporter cell resource and defines an improved myogenic differentiation and purification strategy.

Evaluation of chromatin accessibility in prefrontal cortex of individuals with schizophrenia.

Schizophrenia genome-wide association studies have identified >150 regions of the genome associated with disease risk, yet there is little evidence that coding mutations contribute to this disorder. To explore the mechanism of non-coding regulatory elements in schizophrenia, we performed ATAC-seq on adult prefrontal cortex brain samples from 135 individuals with schizophrenia and 137 controls, and identified 118,152 ATAC-seq peaks. These accessible chromatin regions in the brain are highly enriched for schizophrenia SNP heritability. Accessible chromatin regions that overlap evolutionarily conserved regions exhibit an even higher heritability enrichment, indicating that sequence conservation can further refine functional risk variants. We identify few differences in chromatin accessibility between cases and controls, in contrast to thousands of age-related differential accessible chromatin regions. Altogether, we characterize chromatin accessibility in the human prefrontal cortex, the effect of schizophrenia and age on chromatin accessibility, and provide evidence that our dataset will allow for fine mapping of risk variants.

Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder.

Most genetic risk for psychiatric disease lies in regulatory regions, implicating pathogenic dysregulation of gene expression and splicing. However, comprehensive assessments of transcriptomic organization in diseased brains are limited. In this work, we integrated genotypes and RNA sequencing in brain samples from 1695 individuals with autism spectrum disorder (ASD), schizophrenia, and bipolar disorder, as well as controls. More than 25% of the transcriptome exhibits differential splicing or expression, with isoform-level changes capturing the largest disease effects and genetic enrichments. Coexpression networks isolate disease-specific neuronal alterations, as well as microglial, astrocyte, and interferon-response modules defining previously unidentified neural-immune mechanisms. We integrated genetic and genomic data to perform a transcriptome-wide association study, prioritizing disease loci likely mediated by cis effects on brain expression. This transcriptome-wide characterization of the molecular pathology across three major psychiatric disorders provides a comprehensive resource for mechanistic insight and therapeutic development.