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Synergistic effects of common schizophrenia risk variants

Abstract

The mechanisms by which common risk variants of small effect interact to contribute to complex genetic disorders are unclear. Here, we apply a genetic approach, using isogenic human induced pluripotent stem cells, to evaluate the effects of schizophrenia (SZ)-associated common variants predicted to function as SZ expression quantitative trait loci (eQTLs). By integrating CRISPR-mediated gene editing, activation and repression technologies to study one putative SZ eQTL (FURIN rs4702) and four top-ranked SZ eQTL genes (FURIN, SNAP91, TSNARE1 and CLCN3), our platform resolves pre- and postsynaptic neuronal deficits, recapitulates genotype-dependent gene expression differences and identifies convergence downstream of SZ eQTL gene perturbations. Our observations highlight the cell-type-specific effects of common variants and demonstrate a synergistic effect between SZ eQTL genes that converges on synaptic function. We propose that the links between rare and common variants implicated in psychiatric disease risk constitute a potentially generalizable phenomenon occurring more widely in complex genetic disorders.

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Fig. 1: Prioritization of SZ SNPs and SZ genes for functional validation.
Fig. 2: CRISPR editing demonstrates the cell-type-specific impact of rs4702 on FURIN expression and resulting neural phenotypes.
Fig. 3: CRISPRa/i perturbations of the synaptic genes SNAP91 and TSNARE1 lead to transcriptomic changes and synaptic phenotypes.
Fig. 4: Transcriptomic analysis of combinatorial perturbation of SNAP91, TSNARE1, CLCN3 and FURIN in the direction predicted for their SZ eQTLs.
Fig. 5: The synergistic effects of SZ eQTL genes converge on synaptic function and common and rare variant signatures.

Data availability

All source donor hiPSCs have already been deposited at the Rutgers University Cell and DNA Repository (study 160; http://www.nimhstemcells.org/); CRISPR-edited hiPSCs are in the process of being submitted in advance of publication. The RNA-seq data are available at www.synapse.org/#!Synapse:syn20502314. Additionally, we make available the following resource by which neural cell-type-specific expression of any gene can be easily cross-referenced in our hiPSC datasets as well as case–control postmortem, hiPSC NPC and hiPSC-neuron RNA-seq datasets (https://schroden.shinyapps.io/BrennandLab-ExpressionApp-limited/). Owing to constraints reflecting the original consents, which are restricted to the study of neuropsychiatric disease only, the raw RNA-seq data will be made available by the authors upon reasonable request and institutional review board approval.

Code availability

Code is available at https://github.com/nadschro/SZvariant-synergy.

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Acknowledgements

This manuscript is dedicated to Pamela Sklar, a scientist, mentor and friend who advanced the field of psychiatric genetics. This work was partially supported by NIH grants (nos. R56 MH101454 to K.J.B., E.S. and H.M., R01 MH106056 to K.J.B., U19 MH104172 and U19 MH107367 to B.A., and R01 MH109897 to P.S. and K.J.B.), the New York Stem Cell Foundation (to K.J.B.) and project ALS (grant no. 2017-03 to J.G. and H.P.). We thank the Neuroscience and Stem Cell Cores at ISMMS. This work was supported in part through the computational resources and staff expertise provided by the Scientific Computing unit at ISMMS. P. O’Reilly provided thoughtful feedback on the manuscript. The GTEx Project was supported by the Common Fund of the Office of the Director of the NIH and by National Cancer Institute, National Human Genome Research Institute, National Heart, Lung, and Blood Institute, National Institute on Drug Abuse, National Institute of Mental Health and National Institute of Neurological Disorders and Stroke. The data used for the analyses described in this manuscript were obtained from the GTEx portal on 4 January 2019.

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Authors

Contributions

N.S., S.-M.H., P.S. and K.J.B. contributed to the experimental design. N.S. conducted all the CRISPR editing experiments, assisted by M.R.-M., A.T. and S.A. S.-M.H. completed all the CRISPRa/i experiments. K.Y. and H.M. conducted and analyzed all the CRISPRa/i electrophysiological experiments. N.S., S.-M.H. and M.R.-M. conducted all the MEA experiments. E.C. generated the CRISPR-edited organoids. P.J.M.D. generated the CRISPR-edited NPCs. J.G., E.H. and H.P. provided the LV-NFIA vectors. A.D., L.H. and E.A.S. conducted all the genomic analyses. N.S. conducted all the transcriptomic analyses, with critical advice from G.H. and E.F. All confocal imaging and semiautomated synaptic analyses were conducted by S.-M.H., M.R.-M. and N.B. V.S., D.G. and B.A. conducted all the automated high-content imaging analyses. K.A. and R.M. conducted the kinome analysis. K.J.B., N.S. and S.-M.H. wrote the manuscript.

Corresponding author

Correspondence to Kristen J. Brennand.

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Integrated supplementary information

Supplementary Figure 1 Gene and SNP prioritization.

A-C. Endogenous expression profiling of five SZ-eQTL genes (SNAP91, TSNARE1, CLCN3, FURIN and CNTN4) in (A) brain tissue from the Genotype-Tissue Expression (GTEx) project, (B) single-cell RNA-Seq of post-mortem DLPFC from Psychencode and (C) hiPSC-derived neural cells (NPCs, forebrain (FB)-neurons, NGN2-excitatory neurons) and primary astrocytes. FPKM (Fragments Per Kilobase of transcript per Million mapped reads) of each gene were quantified from RNA-Seq. Three biological replicates represent data from three individuals. All boxplots show first to third quartile and the median. Whiskers indicate largest/smallest observation (no larger/smaller than hinge +/- 1.5 * inter-quartile range). D-F. Relative fold changes of mRNA levels for SNAP91, TSNARE1, CLCN3 and FURIN altered by CRISPRa (purple) or CRISPRi (orange) in (D) NPCs, n=52 independent samples per gene, (E) D8 NGN2-excitatory neurons, n=36 independent samples per gene, and (F) D21 NGN2-excitatory neurons, n=36 independent samples per gene. Numbers under data points indicate their fold changes. Fold changes were calculated by normalization of targeted CRISPRa/i altered gene mRNA level to that of CRISPRa/i scramble controls. C1 (circle), C2 (rectangle) and C3 (triangle) indicate hiPSC-NPC lines from three independent male controls (see Supplementary Table 1 for more information). Data are presented as means ± SEM. P-values were calculated using two-tailed unpaired T-test. G. Neuronal (blue) and non-neuronal (green) active chromatin mark H3K27ac ChIP-seq peaks over five SZ-eQTL genes (FURIN, SNAP91, TSNARE1, CLCN3 and CNTN4) (modified from UCSC Genome browser tracks68).

Supplementary Figure 2 CRISPR-editing of FURIN rs4702.

A. Results of FURIN rs4702 G allele enrichment through two rounds of Taqman ddPCR screening. B. Sanger sequencing confirming seamless allelic conversion of AA to GG at FURIN rs4702. ddPCR experiments were performed three times with similar results. C. Off-target validation of FURIN rs4702 G allele knock-in. The top three predicted off-target regions of SNP editing gRNA-rs4702 were amplified and Sanger sequenced. Traces for all clones show no indels or mismatches in a 700-1000 bp region around the off-target region in two separate experiments. D. Log2 (relative expression) of four genes predicted to be rs4702 eQTL genes, in rs4702 AA and GG D7 NGN2-excitatory neurons (n=2 cell culture samples ea). P-values were calculated using two-sided t-tests. E. Average radial migration distance of rs4702 AA and GG neurospheres, derived from NPCs, after 48h in culture (n=28, left) and representative images (right). Scale bar = 200 μm. P-values were calculated using two-sided t-tests. F. Representative confocal images of day 43 rs4702 AA (left panel) and GG (right panel) human cortical spheroids (hCS, top) and human subpallium spheroids (hSS, bottom). hCS were immunostained against cortical markers CTIP2 (green) and SATB2 (red) along with the neuronal marker MAP2 (cyan). hSS were immunostained against inhibitory markers GABA (red) and GAD67 (green), along with MAP2. White squares on 10x images represent close-up area shown in 40x images. Scale bar = 150 μm in 10x images and 50 μm in 40x images. 3 separate organoids each showed similar results. G. Log2 (FURIN expression) in rs4702 AA and GG D43 and D77 hiPSC-derived human cortical spheroids (hCS, left) and subpallium spheroids (hSS, right) through qPCR H. Altered kinase activity. Waterfall plots showing changes in degree of phosphorylation at reporter peptides for rs4702 AA vs GG neurons. Peptides with increased (FC > 1.30) or decreased phosphorylation (FC < 0.70) are highlighted in black and red, respectively (left panel). A kinase network was obtained by growing the kinome array hits with kinase interacting partners as identified using STRING. Circle size corresponds to the number of interactions, with larger circles having more interactions. Thick lines represent interactions with a kinome array direct hit, while dashed lines represent interactions made between kinome array indirect hits (right panel). All boxplots show first to third quartile and the median. Whiskers indicate largest/smallest observation (no larger/smaller than hinge +/- 1.5 * inter-quartile range).

Supplementary Figure 3 SNAP91 and TSNARE1 CRISPRa/i perturbations.

A. Mean difference (MA) plots of SNAP91 and TSNARE1 CRISPRa/i differential expression in NGN2-excitatory neurons, with significantly differentially expressed genes highlighted (empirical Bayes method + multiple testing correction, FDR <5%). B. Expression variance is partitioned into fractions attributable to each experimental variable. Violin plots show the percentage of variance explained by each variable over all genes (n=19,255) and samples (n=28), with violin width indicating density estimate. Overlaid boxplots show first to third quartile and the median. Whiskers indicate largest/smallest observation (no larger/smaller than hinge +/- 1.5 * inter-quartile range). C. Hierarchical clustering of RNA-Seq samples from (A) with 2699 RNA-Seq samples, including brain tissue, hiPSC-derived neural cells, pluripotent cells as well as fibroblasts and blood. The current study’s NGN2-excitatory neurons show clustering by cell type. D. Principle component analysis of 2699 samples as in (C). E. Detailed schematic of assessing synaptic effect of increased SNAP91 expression in NGN2-excitatory neurons using a fluorescent labeling strategy (Fig. 3d). Lawns of either dCas9-VPR + scramble gRNA or dCas9-VPR + SNAP91 gRNA NGN2-excitatory neurons were prepared. Rare dCas9-VPR NGN2-excitatory neurons labeled by either dTomato (scramble gRNA) or eGFP (SNAP91 gRNA) were both seeded as ~1% of each neuronal lawn. The effect of increasing SNAP91 expression on postsynaptic function is measured by comparing synaptic activity between differently colored neurons within the same neuronal lawn. The effect of increasing SNAP91 expression on presynaptic function is measured by comparing electrical activity between same-colored neurons on different lawns. F. Representative traces of sEPSCs (spontaneous Excitatory Postsynaptic Currents) from day 28-31 NGN2-excitatory neurons with each condition described in the figure. G. Representative summary heatmap and clustering of mean values of synapse-associated features as measured in 612 samples (2 batches, two groups (CRISPRa/i), three gRNA treatments (scramble, SNAP91, TSNARE1), each of which were imaged using 3 channels as stained for nuclear, axon/dendritic, and synaptic markers using 5 Z levels images as obtained from confocal immunofluorescence imaging. H. Average burst duration (top) and mean firing rate (bottom) in SNAP91 and TSNARE1 CRISPRa/i D23 NGN2-excitatory neurons (left, n=24 independent cell culture wells ea) and at various time points from 15 to 35 days in vitro (DIV) (right, n=48 ea across 4 time points). Line fitted through locally weighted smoothing (loess). Shaded areas represent 95% confidence interval. All boxplots show first to third quartile and the median. Whiskers indicate largest/smallest observation (no larger/smaller than hinge +/- 1.5 * inter-quartile range); overlaid scatter plots show means of replicates from individual lines. P-values were calculated using a two-sided t-test. I. Analysis of concordance between differential expression results of SNAP91 CRISPRi and TSNARE1 CRISPRa in NGN2-excitatory neurons (n=4 independent samples). All genes are plotted. Shared significant DEGs are marked in red. Concordance of red genes was evaluated based on Spearman correlation between t-statistics from the two data sets (adj. R2 = 0.7, p= 7.3e−23). P-values were computed through a one-sided hypothesis test for the Spearman correlation coefficients being greater than zero.

Supplementary Figure 4 Combinatorial perturbation of SNAP91, TSNARE1, CLCN3 and FURIN.

A. Mean difference (MA) plots of differential expression in SNAP91, TSNARE1, CLCN3 CRISPRa, FURIN RNAi, the additive model of these perturbations, the combinatorial perturbation and the resulting synergistic effect in NGN2-excitatory neurons, with significantly differentially expressed genes highlighted (empirical Bayes method + multiple testing correction, FDR <5%). B. Heat map detailing the contrasts applied to the SZ-eQTL gene modified samples and controls to produce differential expression models as in (A). C. Expression variance is partitioned into fractions attributable to each experimental variable. Violin plots show the percentage of variance explained by each variable over all genes (n=19,469) and samples (n=34), with violin width indicating density estimate. Overlaid boxplots show first to third quartile and the median. Whiskers indicate largest/smallest observation (no larger/smaller than hinge +/- 1.5 * inter-quartile range). D. Pie chart showing percentages of genes that fall into different synergistic differential expression categories (n=19,469 genes total). E. Hierarchical clustering of the differential expression log2 (fold changes) of categories in (D), in the additive model vs. the combinatorial perturbation. CRMP1 and DLX1, as seen in Fig. 5b, are part of the ‘less up’ and ‘same up’ categories, respectively. F. Over-representation analysis (ORA)-derived significant module enrichment (FDR<5%) for ranked genes in the ‘more up’ and ‘more down’ synergistic effect categories.

Supplementary information

Supplementary Information

Supplementary Figs. 1–4, Tables 1–7 and Note

Reporting Summary

Supplementary Dataset 1

Differentially expressed genes following gene modifications in neurons (FDR < 10%). Empirical Bayes moderation was applied to obtain precise estimates of gene-wise variability. P-values were adjusted for multiple hypotheses testing using FDR estimation.

Supplementary Dataset 2

Curated neural gene sets and categories.

Supplementary Dataset 3

Synergistic effect categories.

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Schrode, N., Ho, SM., Yamamuro, K. et al. Synergistic effects of common schizophrenia risk variants. Nat Genet 51, 1475–1485 (2019). https://doi.org/10.1038/s41588-019-0497-5

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