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Social Processes Initiative in Neurobiology of the Schizophrenia(s) Traveling Human Phantoms
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Sex affects transcriptional associations with schizophrenia across the dorsolateral prefrontal cortex, hippocampus, and caudate nucleus
<p>This is supplementary data and source data for the manuscript, <em>"Sex affects transcriptional associations with schizophrenia across the dorsolateral prefrontal cortex, hippocampus, and caudate nucleus"</em>.</p> <p><strong>Abstract</strong>: Schizophrenia is a complex neuropsychiatric disorder with sexually dimorphic features, including differential symptomatology, drug responsiveness, and male incidence rate. Prior large-scale transcriptome analyses for sex differences in schizophrenia have focused on the prefrontal cortex. Analyzing BrainSeq Consortium data (caudate nucleus: n=399, dorsolateral prefrontal cortex: n=377, and hippocampus: n=394), we identified 831 unique genes that exhibit sex differences across brain regions, enriched for immune-related pathways. We observed X-chromosome dosage reduction in the hippocampus of male individuals with schizophrenia. Our sex interaction model revealed 148 junctions dysregulated in a sex-specific manner in schizophrenia. Sex-specific schizophrenia analysis identified dozens of differentially expressed genes, notably enriched in immune-related pathways. Finally, our sex-interacting expression quantitative trait loci analysis revealed 704 unique genes, nine associated with schizophrenia risk. These findings emphasize the importance of sex-informed analysis of sexually dimorphic traits, inform personalized therapeutic strategies in schizophrenia, and highlight the need for increased female samples for schizophrenia analyses.</p>
Improving causality perception judgments in schizophrenia spectrum disorder via transcranial direct current stimulation - Dataset
<p>Raw data related to the publication:</p> <p>Schülke, R., Schmitter, C. V., & Straube, B. (2023). Improving causality perception judgments in schizophrenia spectrum disorder via transcranial direct current stimulation. <em>Journal of Psychiatry and Neuroscience</em>, <em>48</em>(4), E245–E254. <a href="https://doi.org/10.1503/jpn.220184">https://doi.org/10.1503/jpn.220184</a></p> <p>Variables:</p> <ul> <li>Subject</li> <li>Condition – Stimulation condition; parietal (left parietal cathodal, right parietal anodal [LPC-RPA]), frontoparietal (left frontal cathodal, right parietal anodal [LFC-­RPA]), frontal (left frontal cathodal, right frontal anodal [LFC­-RFA])</li> <li>Timepoint – Before/After (stimulation)</li> <li>Angle – in degrees</li> <li>Angle_scaled – mean-centered and scaled Angle</li> <li>Delay_ms – in milliseconds</li> <li>Delay_ms_scaled – mean-centered and scaled Delayed_ms</li> <li>Causality – causal/non-causal (judgment)</li> <li>RT – reaction time in milliseconds</li> </ul> <p>In the original version of the data, the data had been incorrectly labelled: The data actually corresponding to the LFC-RPA condition had been incorrectly labelled as LPC-RPA, and the data actually corresponding to the LPC-RPA condition had been incorrectly labelled as LFC-RPA. This has been corrected with the 04/2024 version of the dataset.</p>
Consensus molecular environment of schizophrenia risk genes in co-expression networks shifting across age and brain regions
<p>This is the online data repository accompanying the following manuscript:<br><strong>Consensus molecular environment of schizophrenia risk genes in coexpression networks shifting across age and brain regions</strong></p> <p><em>Giulio Pergola<sup>1,2,3,*</sup>, Madhur Parihar<sup>1</sup>, Leonardo Sportelli<sup>1,2</sup>, Rahul Bharadwaj<sup>1</sup>, Christopher Borcuk<sup>2</sup>, Eugenia Radulescu<sup>1</sup>, Loredana Bellantuono<sup>2,5</sup>, Giuseppe Blasi<sup>2,4</sup>, Qiang Chen<sup>1</sup>, Joel E. Kleinman<sup>1,3</sup>, Yanhong Wang<sup>1</sup>, Srinidhi Rao Sripathy<sup>1</sup>, Brady J. Maher<sup>1,3,7</sup>, Alfonso Monaco<sup>5,9</sup>, Fabiana Rossi<sup>1,2</sup>, Joo Heon Shin<sup>1</sup>, Thomas M. Hyde<sup>1,3,6</sup>, Alessandro Bertolino<sup>2,4,*</sup>, Daniel R. Weinberger<sup>1,7,8,*</sup></em></p> <p> </p> <p><strong>Affiliations:</strong></p> <p><em>1)Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD (USA)<br>2)Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy<br>3)Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland<br>4)Azienda Ospedaliero-Universitaria Consorziale Policlinico, Bari, Italy<br>5)Istituto Nazionale di Fisica Nucleare (INFN), Bari, Italy<br>6)Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland<br>7)Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland<br>8)Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland<br>9)Dipartimento Interateneo di fisica, Università degli Studi di Bari Aldo Moro, Bari, Italy</em></p> <p> </p> <p><strong>Abstract:</strong></p> <p><em>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.</em></p> <p> </p> <p><strong>Citation:</strong> <em>Giulio Pergola et al. ,Consensus molecular environment of schizophrenia risk genes in coexpression networks shifting across age and brain regions.Sci. Adv.9, eade2812(2023).DOI:10.1126/sciadv.ade2812</em></p> <p> </p> <p><strong>Data Files:<br>DLPFC hit.genes_kb_200__online.version.zip: </strong><br>Interactive Sankey plot for age-parsed DLPFC networks with SCZ genes (200 kbp list) only. For Sankey plots, hover mouse over the links to see the list of genes. Also supports zoom, drag and selection.<br><strong>DLPFC hit.genes_kb_200__paper.version.zip:</strong><br>Interactive Sankey plot for age-parsed DLPFC networks with SCZ genes (200 kbp list) only. For paper version of the figure, smaller modules are merged into a macro-module (lightgrey color)<br><strong>DLPFC all.genes_kb_200__online.version.zip:</strong><br>Interactive Sankey plot for age-parsed DLPFC networks with all genes<br><strong>DLPFC all.genes_kb_200__paper.version.zip:</strong><br>Interactive Sankey plot for age-parsed DLPFC networks with all genes. For paper version of the figure, smaller modules are merged into a macro-module (lightgrey color)<br><strong>HP hit.genes_kb_200__online.version.zip:</strong><br>Interactive Sankey plot for age-parsed Hippocampus networks with SCZ genes (200 kbp list) only<br><strong>HP hit.genes_kb_200__paper.version.zip:</strong><br>Interactive Sankey plot for age-parsed Hippocampus networks with SCZ genes (200 kbp list) only. For paper version of the figure, smaller modules are merged into a macro-module (lightgrey color)<br><strong>HP all.genes_kb_200__online.version.zip:</strong><br>Interactive Sankey plot for age-parsed Hippocampus networks with all genes<br><strong>HP all.genes_kb_200__paper.version.zip:</strong><br>Interactive Sankey plot for age-parsed Hippocampus networks with all genes. For paper version of the figure, smaller modules are merged into a macro-module (lightgrey color)<br><strong>Modulewise SCZ enrichment(1.0).xlsx:</strong><br>Excel file contains module level SCZ enrichment results for all networks<br><strong>wide_form_test_slidingwindow_NC_SchizoNew(v1.4)_final.xlsx:</strong><br>Excel file contains WGCNA output for sliding window networks<br><strong>wide_form_WGCNA(v3.7.1)_final.xlsx:</strong><br>Excel file contains WGCNA output for our generated networks and from previously published networks<br><strong>libdnetworks(NC).preprocessed.exp.RData: </strong><br>Preprocessed ranknormalised expression assay for age-parsed/nonparsed NC networks (DLPFC, HP, CAUDATE, DENTATE). For fixed window and sliding window study.<br><strong>libdnetworks(SCZ).preprocessed.exp.RData: </strong><br>Preprocessed ranknormalised expression assay for nonparsed SCZ networks (DLPFC, HP, CAUDATE, DENTATE). For the sliding window study.<br><strong>sample_matched_HP_DG_qsva(NC).preprocessed.exp.RData:</strong><br>Preprocessed ranknormalised expression assay for the sample-matched HP-DG. QSVA removed pipeline. For Cell population enrichment study.<br><strong>sample_matched_HP_DG_noqsva(NC).preprocessed.exp.RData:</strong><br>Preprocessed ranknormalised expression assay for the sample-matched HP-DG. No QSVA removed pipeline. For Cell population enrichment study.<br><strong>stemcell.preprocessed.exp.RData:</strong><br>Preprocessed ranknormalised expression assay for the iPSC network. For replication in human iPSC data study. Neuronal samples averaged for each “RealGenome”.<br><strong>SCZ.ref.list.sciadv.ade2812.rds</strong>: List of All Biotypes/ Protein Coding Schizophrenia reference genelist for following bins: PGC3, 0 kbp, 20 kbp, 50 kbp, 100 kbp, 150 kbp, 200 kbp, 250 kbp, 500 kbp.</p> <p> </p> <p>Accompanying code can be found at: <a href="https://github.com/LieberInstitute/Brain_WGCNA">https://github.com/LieberInstitute/Brain_WGCNA</a><br>Data from this repository is also available at: <a href="https://nets.libd.org/age_wgcna/">https://nets.libd.org/age_wgcna/</a></p> <p> </p> <p>For any data inquiries please contact:<br><strong>Giulio Pergola: </strong><a href="mailto:Giulio.Pergola@libd.org"><strong>Giulio.Pergola@libd.org</strong></a></p> <p> </p>
Data from: Optical coherence tomography reveals retinal thinning in schizophrenia spectrum disorders
<p>This dataset contains supporting data for the publication: Boudriot, E., Schworm, B., Slapakova, L. <em>et al.</em> Optical coherence tomography reveals retinal thinning in schizophrenia spectrum disorders. <em>Eur Arch Psychiatry Clin Neurosci</em> (2022). https://doi.org/10.1007/s00406-022-01455-z</p> <p> </p>
Cross-species analysis identifies mitochondrial dysregulation as a functional consequence of the schizophrenia-associated 3q29 deletion
<p>The 1.6Mb deletion at chromosome 3q29 (3q29Del) is the strongest identified genetic risk factor for schizophrenia, but the effects of this variant on neurodevelopment are not well understood. We interrogated the developing neural transcriptome in two experimental model systems with complementary advantages: isogenic human cortical organoids and isocortex from the 3q29Del mouse model. We profiled transcriptomes from isogenic cortical organoids that were aged for 2 months and 12 months, as well as perinatal mouse isocortex, all at single-cell resolution. Systematic pathway analysis implicated dysregulation of mitochondrial function and energy metabolism. These molecular signatures were supported by analysis of oxidative phosphorylation protein complex expression in mouse brains and assays of mitochondrial function in engineered cell lines. Together these data indicate that metabolic disruption is associated with 3q29Del and is conserved across species.</p>
Fig. 15 in Positive association between PTN polymorphisms and schizophrenia in Northeast Chinese Han population.
Fig. 15. Genitalia of Durgella pentata sp. nov. paratype CUMZ 14240. A: general view of the genital system and B: internal structure of the penis and epiphallus. White arrow indicates the end of the penis.
Fig. 14 in Positive association between PTN polymorphisms and schizophrenia in Northeast Chinese Han population.
Fig. 14. Genitalia of Durgella libas CUMZ 14236. A: general view of the genital system and B: internal structure of the penis and epiphallus. White arrow indicates the end of the penis.
Fig. 11 in Positive association between PTN polymorphisms and schizophrenia in Northeast Chinese Han population.
Fig. 11. Representative SEM images of the radula. A, B: D. birmanica CUMZ 14233. C, D: D. levicula CUMZ 14253. E, F: D. erratica CUMZ 14234. G, H: D. siamensis CUMZ 14232. Yellow arrow indicates the central tooth.
Fig. 10 in Positive association between PTN polymorphisms and schizophrenia in Northeast Chinese Han population.
Fig. 10. Genitalia of Durgella species. A: D. erratica CUMZ 14234. B, C: D. siamensis CUMZ 14232; B: general view of the genital system; C: internal structure of the penis and epiphallus. White arrow indicates the end of the penis.
Fig. 8 in Positive association between PTN polymorphisms and schizophrenia in Northeast Chinese Han population.
Fig. 8. Shells of Durgella species. A: D. rhaphiellus syntypes ZMB/MOLL 5033. B–D: D. siamensis; B: syntypes SMF 227168/1; C, D: CUMZ 14232.
Fig. 7 in Positive association between PTN polymorphisms and schizophrenia in Northeast Chinese Han population.
Fig. 7. Shells of Durgella species. A–C: D. erratica; A: syntypes NHMUK 1895.1.1.4–6; B, C: specimen CUMZ 14234. D: D. concinna syntype NHMUK 1865.9.3.15.
Fig. 5 in Positive association between PTN polymorphisms and schizophrenia in Northeast Chinese Han population.
Fig. 5. Shells of Durgella species. A: D. birmanica modified from Hanley and Theobald (1876). B, C: D. birmanica CUMZ 14233. D–F: D. levicula; D: NHMUK 1903.7.1.785; E: CUMZ 14251; F: CUMZ 14245.
Fig. 13 in Positive association between PTN polymorphisms and schizophrenia in Northeast Chinese Han population.
Fig. 13. Microsculpture of Durgella shells: protoconch and early teleoconch (first column), and close-up view of protoconch (second column). A, B: D. libas CUMZ 14236. C, D: D. pentata sp. nov. paratype CUMZ 14240. E, F: D. nulla sp. nov. paratype CUMZ 14228.
Fig. 4 in Positive association between PTN polymorphisms and schizophrenia in Northeast Chinese Han population.
Fig. 4. Eggs and mating behavior of Durgella species recorded in the field. A: eggs of D. libas from Wachirathan Waterfall, Chiang Mai Province. B: eggs of D. pentata sp. nov. from Tham Chiang Dao, Chiang Mai Province. C: eggs of D. nulla sp. nov. from Phu Pha Lom, Loei Province. D: mating pairs of D. nulla sp. nov. from Phu Pha Lom, Loei Province.
Fig. 2 in Positive association between PTN polymorphisms and schizophrenia in Northeast Chinese Han population.
Fig. 2. Synoptic illustration of mantle extensions of the genus Durgella. A, B: four mantle extensions with left dorsal lobe undivided. C, D: five mantle extensions with left dorsal lobe divided into anterior and posterior left dorsal lobes. White arrow indicates pneumostome or breathing pore.
Fig. 7 in Fig. 1 in Positive association between PTN polymorphisms and schizophrenia in Northeast Chinese Han population.
Fig. 7. Generalized Additive Models (GAMs) showing relationships between square rooted abundance of the species and explanatory variables. Only significant relationships with unimodal distributions are shown. Abbreviation: Dev. expl., Deviance explained, i.e., a variable showing variance explained by GAMs. Shaded stripes indicate 95% confidence interval.
Fig. 6 in Fig. 1 in Positive association between PTN polymorphisms and schizophrenia in Northeast Chinese Han population.
Fig. 6. Classification tree for the occurrence of a) V. lilljeborgi, b) V. genesii and c) V. geyeri at 327 sites. Numbers at each node indicate that the species was: absent/low population density/high population density, respectively. See Materials and Methods for details. The major splitter predictor and its split values are in bold whereas surrogates (i.e., predictors that distribute at least 90% of the cases to the same group as the primary splitter) are below the major splitter. Numbers in black circles indicate the number of splits. Photographs: Radovan Coufal (live individuals), Michal Horsák (shells).
Fig. 6 in Positive association between PTN polymorphisms and schizophrenia in Northeast Chinese Han population.
Fig. 6. Genitalia of Durgella species. A: D. birmanica CUMZ 14233. B, C: D. levicula CUMZ 14253; B: general view of the genital system; C internal structure of the penis and epiphallus. White arrow indicates the end of the penis.
Fig. 5 in Fig. 1 in Positive association between PTN polymorphisms and schizophrenia in Northeast Chinese Han population.
Fig. 5. Variation of analysed environmental predictors using box and whisker plots. Letters above boxplots indicate homogeneous/heterogeneous groups (Kruskal-Wallis test followed by Dunn post hoc texts). The central line of each boxplot refers to the median value, the box delineates the first and third quartiles, whiskers refer to the non-outlier values and asterisks indicate outliers.
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