Analysis of the caudate nucleus transcriptome in individuals with schizophrenia highlights effects of antipsychotics and new risk genes

Keeler, Benjamin; Chen, Qiang; Jaffe, Andrew E.; Stolz, Joshua M.; Collado‐Torres, Leonardo; Huuki-Myers, Louise A.; Burke, Emily E.; Arora, Raman; Feltrin, Arthur Sant’Anna; Barbosa, André Rocha; Radulescu, Eugenia; Pergola, Giulio; Ulrich, William; Deep-Soboslay, Amy; Tao, Ran; Matsumoto, Mitsuyuki; Saito, Takeshi; Tajinda, Katsunori; Hoeppner, Daniel J.; Collier, David; Malki, Karim; Miller, Bradley B.; Furey, Maura L.; Hibar, Derrek P.; Kolb, Hartmuth C.; Didriksen, Michael; Folkersen, Lasse; Kam‐Thong, Tony; Malhotra, Dheeraj; Shin, Joo Heon; Jaffe, Andrew E.; Narurkar, Rujuta; Straub, Richard E.; Hyde, Thomas M.; Kleinman, Joel E.; Erwin, Jennifer A.; Weinberger, Daniel R.; Paquola, Apuã C.M.

doi:10.1038/s41593-022-01182-7

Cited by 40 publications

(57 citation statements)

References 81 publications

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“…Our replication of a psychosis-related increase in NM-MRI contrast in schizophrenia adds to the evidence for presynaptic alterations in SN dopamine neurons from PET and more recent stem-cell and genetics work. Our findings are also generally consistent with a preferential involvement of the nigrostriatal pathway in psychosis, since the primate ventral-SN area, where psychosis effects predominated here (Figure 2) and in work by Cassidy et al, with remarkable overlap between the 2 studies, projects preferentially to dorsal associative striatum .…”

Section: Discussionsupporting

confidence: 91%

Generalizability and Out-of-Sample Predictive Ability of Associations Between Neuromelanin-Sensitive Magnetic Resonance Imaging and Psychosis in Antipsychotic-Free Individuals

Wengler,

Baker,

Velikovskaya

et al. 2024

JAMA Psychiatry

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ImportanceThe link between psychosis and dopaminergic dysfunction is established, but no generalizable biomarkers with clear potential for clinical adoption exist.ObjectiveTo replicate previous findings relating neuromelanin-sensitive magnetic resonance imaging (NM-MRI), a proxy measure of dopamine function, to psychosis severity in antipsychotic-free individuals in the psychosis spectrum and to evaluate the out-of-sample predictive ability of NM-MRI for psychosis severity.Design, Setting, and ParticipantsThis cross-sectional study recruited participants from 2019 to 2023 in the New York City area (main samples) and Mexico City area (external validation sample). The main samples consisted of 42 antipsychotic-free patients with schizophrenia, 53 antipsychotic-free individuals at clinical high risk for psychosis (CHR), and 52 matched healthy controls. An external validation sample consisted of 16 antipsychotic-naive patients with schizophrenia.Main Outcomes and MeasuresNM-MRI contrast within a subregion of the substantia nigra previously linked to psychosis severity (a priori psychosis region of interest [ROI]) and psychosis severity measured using the Positive and Negative Syndrome Scale (PANSS) in schizophrenia and the Structured Interview for Psychosis-Risk Syndromes (SIPS) in CHR. The cross-validated performance of linear support vector regression to predict psychosis severity across schizophrenia and CHR was assessed, and a final trained model was tested on the external validation sample.ResultsOf the 163 included participants, 76 (46.6%) were female, and the mean (SD) age was 29.2 (10.4) years. In the schizophrenia sample, higher PANSS positive total scores correlated with higher mean NM-MRI contrast in the psychosis ROI (t37 = 2.24, P = .03; partial r = 0.35; 95% CI, 0.05 to 0.55). In the CHR sample, no significant association was found between higher SIPS positive total score and NM-MRI contrast in the psychosis ROI (t48 = −0.55, P = .68; partial r = −0.08; 95% CI, −0.36 to 0.23). The 10-fold cross-validated prediction accuracy of psychosis severity was above chance in held-out test data (mean r = 0.305, P = .01; mean root-mean-square error [RMSE] = 1.001, P = .005). External validation prediction accuracy was also above chance (r = 0.422, P = .046; RMSE = 0.882, P = .047).Conclusions and RelevanceThis study provided a direct ROI-based replication of the in-sample association between NM-MRI contrast and psychosis severity in antipsychotic-free patients with schizophrenia. In turn, it failed to replicate such association in CHR individuals. Most critically, cross-validated machine-learning analyses provided a proof-of-concept demonstration that NM-MRI patterns can be used to predict psychosis severity in new data, suggesting potential for developing clinically useful tools.

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Section: Discussionsupporting

confidence: 91%

Generalizability and Out-of-Sample Predictive Ability of Associations Between Neuromelanin-Sensitive Magnetic Resonance Imaging and Psychosis in Antipsychotic-Free Individuals

Wengler,

Baker,

Velikovskaya

et al. 2024

JAMA Psychiatry

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“…As such, we used these continuous genetic ancestry estimates to identify differentially expressed features (genes, transcripts, exons, and junctions) that were linearly correlated with ancestry levels and adjusted for sex, age, and RNA quality. This RNA quality adjustment includes experiment-based RNA degradation metrics (obtained with the qSVA methodology) that account for batch effect and cell composition ( 12 , 20 ). To increase our power of detection and improve effect size estimates, we applied the multivariate adaptive shrinkage (“mash” ( 21 )) method, which leverages the correlation structure of genetic ancestry effects across brain regions (see Methods for details).…”

Section: Resultsmentioning

confidence: 99%

“…In comparison, the BrainSeq Consortium, a collaboration between seven pharmaceutical companies and the Lieber Institute for Brain Development (LIBD), has one of the largest postmortem brain collections of psychiatric disorders, including 784 Black American samples across 587 unique individuals, with a mean age of 44. While reports from this consortium and other large-scale analyses in the brainincluding from the hippocampus, caudate nucleus ("caudate"), dorsolateral prefrontal cortex (DLPFC), and granule cells of the dentate gyrus ("dentate gyrus")have samples of diverse genetic ancestry (11)(12)(13)(14)(15)(16)(17), they have typically been "adjusted" for ancestry status, which limits our understanding of ancestry-specific effects in the brain.…”

Section: Introductionmentioning

confidence: 99%

Genetic and environmental contributions to ancestry differences in gene expression in the human brain

Keeler

Chen

Eagles

et al. 2023

Preprint

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Ancestral differences in genomic variation are determining factors in gene regulation; however, most gene expression studies have been limited to European ancestry samples or adjusted for ancestry to identify ancestry-independent associations. We instead examined the impact of genetic ancestry on gene expression and DNA methylation (DNAm) in admixed African/Black American neurotypical individuals to untangle effects of genetic and environmental factors. Ancestry-associated differentially expressed genes (DEGs), transcripts, and gene networks, while notably not implicating neurons, are enriched for genes related to immune response and vascular tissue and explain up to 26% of heritability for ischemic stroke, 27% of heritability for Parkinson's disease, and 30% of heritability for Alzhemier's disease. Ancestry-associated DEGs also show general enrichment for heritability of diverse immune-related traits but depletion for psychiatric-related traits. The cell-type enrichments and direction of effects vary by brain region. These DEGs are less evolutionarily constrained and are largely explained by genetic variations; roughly 15% are predicted by DNAm variation implicating environmental exposures. We also compared Black and White Americans, confirming most of these ancestry-associated DEGs. Our results highlight how environment and genetic background affect genetic ancestry differences in gene expression in the human brain and affect risk for brain illness.

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“…In the sliding window analysis, patients with SCZ generally showed greater enrichment for SCZ risk genes in caudate nucleus networks, the brain region with maximum dopaminergic innervation. This finding may be associated with D 2 receptor–targeting antipsychotic treatment in patients with SCZ or with the impact of variants in genes relevant to dopaminergic transmission ( 52 ). Notably, our results based on a connectivity match between networks across brain regions and age failed to support previous findings of less connected coexpression networks in the DLPFC of patients with SCZ ( 5 ).…”

Section: Discussionmentioning

confidence: 99%

“…We characterized the modules of interest using the Gene Ontology Database (PANTHER) (86) and clusterProfiler package (annotation package: org.Hs.eg.db 3.16). We also computed enrichments for cell specificity (83); transcriptome-wide association study (TWAS) genes obtained from caudate nucleus (52), hippocampus (61), and DLPFC data (7,61,87); DEGs obtained from caudate nucleus (52), hippocampus, and DLPFC data (61); DEGs in each brain region (5,6,52,61,88), genes proximal to differentially methylated CpG islands (DMGs) in PFC, and blood (89)(90)(91)(92)(93)(94); DEGs in humans relative to great apes (88); SCZ drug target genes (95)(96)(97)(98); and loss-of-function intolerant genes (99) in the selected modules. For TWAS and DEGs, we performed a brain region-specific enrichment using the appropriate gene list of each tissue.…”

Section: Enrichment Methodsmentioning

confidence: 99%

Consensus molecular environment of schizophrenia risk genes in coexpression networks shifting across age and brain regions

et al. 2023

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Schizophrenia is a neurodevelopmental brain disorder whose genetic risk is associated with shifting clinical phenomena across the life span. We investigated the convergence of putative schizophrenia risk genes in brain coexpression networks in postmortem human prefrontal cortex (DLPFC), hippocampus, caudate nucleus, and dentate gyrus granule cells, parsed by specific age periods (total N = 833). The results support an early prefrontal involvement in the biology underlying schizophrenia and reveal a dynamic interplay of regions in which age parsing explains more variance in schizophrenia risk compared to lumping all age periods together. Across multiple data sources and publications, we identify 28 genes that are the most consistently found partners in modules enriched for schizophrenia risk genes in DLPFC; twenty-three are previously unidentified associations with schizophrenia. In iPSC-derived neurons, the relationship of these genes with schizophrenia risk genes is maintained. The genetic architecture of schizophrenia is embedded in shifting coexpression patterns across brain regions and time, potentially underwriting its shifting clinical presentation.

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Analysis of the caudate nucleus transcriptome in individuals with schizophrenia highlights effects of antipsychotics and new risk genes

Cited by 40 publications

References 81 publications

Generalizability and Out-of-Sample Predictive Ability of Associations Between Neuromelanin-Sensitive Magnetic Resonance Imaging and Psychosis in Antipsychotic-Free Individuals

Generalizability and Out-of-Sample Predictive Ability of Associations Between Neuromelanin-Sensitive Magnetic Resonance Imaging and Psychosis in Antipsychotic-Free Individuals

Genetic and environmental contributions to ancestry differences in gene expression in the human brain

Consensus molecular environment of schizophrenia risk genes in coexpression networks shifting across age and brain regions

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