Divergent transcription from bidirectional promoters is frequently observed in eukaryotic genomes, but the biological relevance of divergent RNA transcripts (DT) is unknown. We identified and characterized BDNF-DT, a novel DT gene, and BDNF-AS-DT, a novel readthrough gene, in the locus containing BDNF, a gene with key roles in neuronal development, differentiation, and synaptic plasticity. BDNF-DT is independent from the known BDNF antisense (BDNF-AS), and its expression is developmentally regulated and positively correlated with BDNF in human postmortem dorsolateral prefrontal cortex (DLPFC). BDNF-DT and BDNF-AS-DT expression increase after induced depolarization, but the temporal dynamics follow expression of BDNF, suggesting a regulatory role. Moreover, CRISPR-mediated upregulation of BDNF in human neural progenitor cells drives BDNF-DT expression. Finally, BDNF-DT shows higher expression in DLPFC from patients diagnosed with schizophrenia compared to neurotypical controls, and genetically predicted lower expression of the BDNF-AS-DT readthrough transcript is associated with schizophrenia and with the schizophrenia-associated C allele of the rs6265 single-nucleotide polymorphism. These findings identify BDNF-DT and BDNF-AS-DT as novel, low-abundance genes that show coordinated expression with BDNF and association with schizophrenia risk, though their biological significance requires further validation given detection limitations and the need to establish causal roles.

Transcriptome and proteome sequencing of brain tissue homogenate has helped unravel processes underlying schizophrenia (SCZ). However, most studies have lacked granularity at the cell type level and have focused on individual brain regions, rather than examining expression dynamics across multiple regions or illness-relevant circuitries. We used laser capture microdissection to collect excitatory neuron-enriched samples from hippocampal subregions CA1 and presubiculum (SUB), and from dorsolateral prefrontal cortex (DLPFC), a circuit prominently implicated in schizophrenia. Using RNA sequencing and quantitative proteomics, we show significantly superior discrimination of brain regional identity in the transcriptomic (>90% accuracy) and proteomic data (>97% accuracy) compared with gene-level expression data (<70% in bulk). Patients with SCZ show hippocampal-specific differential protein phosphorylation. SCZ risk co-expression gene-sets that replicate across transcript and protein networks are enriched for transmembrane transporters in the DLPFC and CA1 and postsynaptic processes in the SUB. We demonstrate a strong directional connectivity effect of SCZ risk in that excitatory synaptic genes in CA1 unidirectionally predict gene expression in SUB. Finally, parallel CA1 snRNA-seq results suggest that in SCZ excitatory efferents in CA1 are affected by interactions with glia and by downregulation of inhibitory neuropeptide inputs. Our study proposes molecular mechanisms by which hippocampal communication, previously associated with SCZ at the macroscopic level, may be altered at the inter-field and interregional circuit level.

The human hippocampal trisynaptic circuit activity is essential for learning and memory. This canonical circuit has spatially distinct populations of neurons, but their unique contributions to neurodevelopment, as well as to dysfunction in neurodegenerative disorders, are missed when analyzing bulk tissue homogenates. Using matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) to guide laser capture microdissection (LCMD) of regions of interest for spatial multimodal analyses is a relatively new approach to study topographically distinct neuronal cell populations in heterogenous tissues. However, MALDI-MSI may not identify region-defining molecular mass-to-charge ions. Here, we apply a multimodal approach of MALDI-MSI-LCMD-lipidomic and proteomic analysis to the trisynaptic circuit. Our MALDI-MSI of the hippocampus revealed that the of mass-to-charge ions of the cornu ammonis 1 (CA1) and cornu ammonis 3 (CA3) did not segment from the surrounding tissue. Thus, we developed a novel histology-guided MALDI-MS imaging-LCMD-spatial lipidomic/proteomic pipeline with four steps which does not rely on segmentation analysis to determine and co-register regions of interest in tissue sections. Our pipeline allows MALDI imaging, LCMD, lipidomic and proteomic analysis from the same tissue section and does not require co-registration across serial sections. In addition, poly-l-lysine coating for improving tissue/cell adherence on indium-tin-oxide microscopy slides did not impact MALDI-MSI or spatial proteomics. We show that the human trisynaptic circuit proteomes of CA1 and CA3 pyramidal neurons are more similar to each other than those of the dentate gyrus (DG), which is consistent with previously reported transcriptomics studies. The spatial distributions of several phospholipids and proteins, however, were significantly different in cell bodies from the CA1, CA3 and DG regions, and these lipids correlated with some lipid metabolizing enzymes in those regions. As little is known about lipid metabolism in the hippocampus, our pipeline provides an initial step in studying the combined and differential spatial regulation of the lipids and proteins within the trisynaptic circuit that will provide insights into the development and disease-related molecular changes in these important hippocampal regions.

DNA methylation (DNAm) is essential for brain development and function and potentially mediates the effects of genetic risk variants underlying brain disorders. We present INTERACT, a transformer-based deep learning model to predict regulatory variants affecting DNAm levels in specific brain cell types, leveraging existing single-nucleus DNAm data from the human brain. We show that INTERACT accurately predicts cell type-specific DNAm profiles, achieving an average area under the receiver operating characteristic curve of 0.99 across cell types. Furthermore, INTERACT predicts cell type-specific DNAm regulatory variants, which reflect cellular context and enrich the heritability of brain-related traits in relevant cell types. We demonstrate that incorporating predicted variant effects and DNAm levels of CpG sites enhances the fine mapping for three brain disorders-schizophrenia, depression, and Alzheimer's disease-and facilitates mapping causal genes to particular cell types. Our study highlights the power of deep learning in identifying cell type-specific regulatory variants, which will enhance our understanding of the genetics of complex traits.

Alterations in cognitive and neuroimaging measures in psychosis may reflect altered brain-behavior interactions patterns accompanying the symptomatic manifestation of the disease. Using graph connectivity-based approaches, we tested the brain-behavior association between cognitive functioning and functional connectivity at different stages of psychosis. We collected resting-state fMRI of 204 neurotypical controls (NC) in two independent cohorts, 43 patients with chronic psychosis (PSY), and 22 subjects with subthreshold psychotic symptoms (STPS). In NC, we calculated graph connectivity metrics and tested their associations with neuropsychological scores. Replicable associations were tested in PSY and STPS and externally validated in three cohorts of 331, 371, and 232 individuals, respectively. NC showed a positive correlation between the degree centrality of a right prefrontal-cingulum-striatal circuit and total errors on Wisconsin Card Sorting Test. Conversely, PSY and STPS showed negative correlations. External replications confirmed both associations while highlighting the heterogeneity of STPS. Group differences in either centrality or cognition alone were not equally replicable. In four independent cohorts totaling 1,203 participants, we identified a replicable alteration of the brain-behavior association in different stages of psychosis. These results highlight the high replicability of multimodal markers and suggest the opportunity for longitudinal investigations that may test this marker for early risk identification.

Many psychiatric disorders share genetic liabilities, but whether these shared liabilities can be utilized to classify and differentiate psychiatric disorders remains unclear. In this study, we use polygenic risk scores (PRSs) of 42 traits comorbid with schizophrenia (SCZ), bipolar disorder (BIP), and major depressive disorder (MDD) to evaluate their utilities. We found that combining target specific PRS with PRSs of comorbid traits can improve the classification of the target disorders. Importantly, without inclusion of PRSs from targeted disorders, we can still classify SCZ (accuracy 0.710 ± 0.008, AUC 0.789 ± 0.011), BIP (accuracy 0.782 ± 0.006, AUC 0.852 ± 0.004), and MDD (accuracy 0.753 ± 0.019, AUC 0.822 ± 0.010). Furthermore, PRSs from comorbid traits alone can effectively differentiate unaffected controls and patients with SCZ, BIP, and MDD (accuracy 0.861 ± 0.003, AUC 0.961 ± 0.041). Our results demonstrate that shared liabilities can be used effectively to improve the classification and differentiation of these disorders. The finding that PRSs from comorbid traits alone can classify and differentiate SCZ, BIP and MDD reasonably well implies that a majority of the risk variants composing target PRSs are shared with comorbid traits. Overall, our results suggest that a data-driven approach may be feasible to classify and differentiate these disorders.

Major Depressive Disorder (MDD) is a common, complex disorder that is a leading cause of disability worldwide and a significant risk factor for suicide. In this study, we have performed the largest molecular analysis of MDD in postmortem human brains (846 samples across 458 individuals) in the subgenual Anterior Cingulate Cortex (sACC) and the Amygdala, two regions central to mood regulation and the pathophysiology of MDD. We found extensive expression differences, particularly at the level of specific transcripts, with prominent enrichment for genes associated with the vesicular functioning, the postsynaptic density, GTPase signaling, and gene splicing. We find associated transcriptional features in 107 of 243 genome-wide significant loci for MDD and, through integrative analyses, highlight convergence of genetic risk, gene expression, and network-based analyses on dysregulated glutamatergic signaling and synaptic vesicular functioning. Together, these results provide an initial mechanistic understanding of MDD and highlight potential targets for novel drug discovery.

OBJECTIVE:The objective of this study was to define the molecular neuroanatomy of the human habenula (Hb) and identify transcriptomic differences between brains of individuals with schizophrenia and nonpsychiatric control brains. METHODS:This study utilized Hb-enriched postmortem human brain tissue. Single-nucleus RNA sequencing (snRNA-seq) was conducted to identify molecularly defined Hb cell types (N=7 donors), and single-molecule fluorescent in situ hybridization (smFISH) was performed to validate cell types and map their spatial locations (N=5 independent donors). Bulk RNA sequencing (RNA-seq) (schizophrenia, N=35; nonpsychiatric control, N=33) and cell type deconvolution were used to identify differentially expressed genes (DEGs), which were then compared to dorsolateral prefrontal cortex, hippocampus, and caudate schizophrenia DEGs. Expression quantitative trait loci (eQTLs) and schizophrenia risk colocalization analyses were performed. RESULTS:snRNA-seq identified 17 cell type clusters across 16,437 nuclei, including three medial and seven lateral Hb populations, several of which were conserved in rodents. smFISH validated snRNA-seq Hb cell types and depicted their spatial organization. Bulk RNA-seq analyses yielded 173 schizophrenia-associated DEGs (false discovery rate<0.1), of which 129 (75%) were unique to Hb-enriched tissue. eQTL analysis identified 717 independent single-nucleotide polymorphism (SNP)-gene pairs (false discovery rate<0.05). Of these, 16 pairs included a SNP that is a schizophrenia risk variant, and seven different pairs included a schizophrenia DEG. eQTL and schizophrenia risk colocalization analysis identified 16 colocalized genes, nine of which have not been previously identified. CONCLUSIONS:These results identify topographically organized cell types with distinct molecular signatures in the human habenula and demonstrate unique genetic differences associated with schizophrenia, thereby providing novel molecular insights into the role of the habenula in neuropsychiatric disorders.

An increasingly compelling body of literature indicates the glucocorticoid receptor cochaperone FK506-binding protein 51 (FKBP51) is a promising target for novel psychiatric therapeutics. However, the mechanisms regulating the corresponding FKBP5 gene directly in the human brain remain largely unknown yet are needed to facilitate the development of precise mechanism-based treatment approaches. Here, we examined FKBP5 DNA methylation patterns in postmortem human brain samples from the dorsolateral prefrontal cortex of individuals who lived with a major psychiatric disorder (schizophrenia, major depression, or bipolar disorder; n=329) and controls n=231. We identified that cytosine-phosphate-guanine-dinucleotide (CpG) specific FKBP5 DNA methylation is altered in psychiatric disorders across the FKBP5 locus, and that these changes are differentially associated with age and genotype (rs1360780 CC vs CT/TT). Individuals with schizophrenia had significantly lower levels of DNA methylation in the proximal enhancer of FKBP5 , which also negatively correlated with FKBP5 gene expression. These changes were also associated with predicted glucocorticoid response elements (GREs) in the proximal enhancer, but not other transcription factor binding sites. This evidence supports that in the human cortex, FKBP5 DNA methylation is associated with both genetic and ageing effects, and that the associations between these factors vary at a diagnosis-specific level in psychopathology. This may have implications for developing FKBP5 -targeted therapeutics and defining a subgroup of patients who will benefit from such treatments. ### Competing Interest Statement Dr Binder is a co-inventor of the following patent applications: FKBP5: a novel target for antidepressant therapy. European Patent # EP1687443 B1: Polymorphisms in ABCB1 associated with a lack of clinical response to medicaments. United States Patent # 8030033; Means and methods for diagnosing predisposition for treatment emergent suicidal ideation (TESI). European application number: 08016477.5, international application number: PCT/EP2009/061575. The remaining authors declare no competing interests or conflicts of interest.

DNA methylation (DNAm) is a key epigenetic mark with essential roles in gene regulation, mammalian development, and human diseases. Single-cell technologies enable profiling DNAm at cytosines in individual cells, but they often suffer from low coverage for CpG sites. We introduce scMeFormer, a transformer-based deep learning model for imputing DNAm states at each CpG site in single cells. Comprehensive evaluations across five single-nucleus DNAm datasets from human and mouse demonstrate scMeFormer's superior performance over alternative models, achieving high-fidelity imputation even with coverage reduced to 10% of original CpG sites. Applying scMeFormer to a single-nucleus DNAm dataset from the prefrontal cortex of patients with schizophrenia and controls identified thousands of schizophrenia-associated differentially methylated regions that would have remained undetectable without imputation and added granularity to our understanding of epigenetic alterations in schizophrenia. We anticipate that scMeFormer will be a valuable tool for advancing single-cell DNAm studies.

Immature dentate granule cells (imGCs) arising from adult hippocampal neurogenesis contribute to plasticity, learning and memory, but their evolutionary changes across species and specialized features in humans remain poorly understood. Here we performed machine-learning-augmented analysis of published single-nucleus RNA-sequencing datasets and identified macaque imGCs with transcriptome-wide immature neuronal characteristics. Our cross-species comparisons among humans, monkeys, pigs and mice showed few shared (such as DPYSL5), but mostly species-specific gene expression in imGCs that converged onto common biological processes regulating neuronal development. We further identified human-specific transcriptomic features of imGCs and demonstrated the functional roles of human imGC-enriched expression of a family of proton-transporting vacuolar-type ATPase subtypes in the development of imGCs derived from human pluripotent stem cells. Our study reveals divergent gene expression patterns but convergent biological processes in the molecular characteristics of imGCs across species, highlighting the importance of conducting independent molecular and functional analyses for adult neurogenesis in different species.

Variability between human pluripotent stem cell (hPSC) lines remains a challenge and opportunity in biomedicine. In this study, hPSC lines from multiple donors were differentiated toward neuroectoderm and mesendoderm lineages. We revealed dynamic transcriptomic patterns that delineate the emergence of these lineages, which were conserved across lines, along with individual line-specific transcriptional signatures that were invariant throughout differentiation. These transcriptomic signatures predicted an antagonism between SOX21-driven forebrain fates and retinoic acid-induced hindbrain fates. Replicate lines and paired adult tissue demonstrated the stability of these line-specific transcriptomic traits. We show that this transcriptomic variation in lineage bias had both genetic and epigenetic origins, aligned with the anterior-to-posterior structure of early mammalian development, and was present across a large collection of hPSC lines. These findings contribute to developing systematic analyses of PSCs to define the origin and consequences of variation in the early events orchestrating individual human development.

The molecular pathology of stress-related disorders remains elusive. Our brain multiregion, multiomic study of posttraumatic stress disorder (PTSD) and major depressive disorder (MDD) included the central nucleus of the amygdala, hippocampal dentate gyrus, and medial prefrontal cortex (mPFC). Genes and exons within the mPFC carried most disease signals replicated across two independent cohorts. Pathways pointed to immune function, neuronal and synaptic regulation, and stress hormones. Multiomic factor and gene network analyses provided the underlying genomic structure. Single nucleus RNA sequencing in dorsolateral PFC revealed dysregulated (stress-related) signals in neuronal and non-neuronal cell types. Analyses of brain-blood intersections in >50,000 UK Biobank participants were conducted along with fine-mapping of the results of PTSD and MDD genome-wide association studies to distinguish risk from disease processes. Our data suggest shared and distinct molecular pathology in both disorders and propose potential therapeutic targets and biomarkers.

The polygenic architecture of schizophrenia implicates several molecular pathways involved in synaptic function. However, it is unclear how polygenic risk funnels through these pathways to translate into syndromic illness. Using tensor decomposition, we analyze gene co-expression in the caudate nucleus, hippocampus, and dorsolateral prefrontal cortex of post-mortem brain samples from 358 individuals. We identify a set of genes predominantly expressed in the caudate nucleus and associated with both clinical state and genetic risk for schizophrenia that shows dopaminergic selectivity. A higher polygenic risk score for schizophrenia parsed by this set of genes predicts greater dopamine synthesis in the striatum and greater striatal activation during reward anticipation. These results translate dopamine-linked genetic risk variation into in vivo neurochemical and hemodynamic phenotypes in the striatum that have long been implicated in the pathophysiology of schizophrenia. Here, the authors report that schizophrenia risk variants mapping to a striatal dopamine-related gene set are associated with increased striatal dopamine synthesis capacity and increased striatal activity during reward anticipation in humans.

By using genetic admixture in the multi-omic analysis of postmortem brains from Black Americans, we show that genetic ancestry influences gene expression in the brain. Notably, we find enrichment of ancestry-associated genes for immune response and vascular function, but not neuronal function. Our findings have potential implications for stroke, Parkinson's disease and Alzheimer's disease.

DNA repetitive sequences (or repeats) comprise over 50% of the human genome and have a crucial regulatory role, specifically regulating transcription machinery. The human brain is the tissue with the highest detectable repeat expression and dysregulations on the repeat activity are related to several neurological and neurodegenerative disorders, as repeat-derived products can stimulate a pro-inflammatory response. Even so, it is unclear how repeat expression acts on the aging neurotypical brain. Here, we leverage a large postmortem transcriptome cohort spanning the human lifespan to assess global repeat expression in the neurotypical brain. We identified 21,696 differentially expressed repeats (DERs) that varied across seven age bins (Prenatal; 0-15; 16-29; 30-39; 40-49; 50-59; 60+) across the caudate nucleus (n=271), dorsolateral prefrontal cortex (n=304), and hippocampus (n=310). Interestingly, we found that long interspersed nuclear elements and long terminal repeats (LTRs) DERs were the most abundant repeat families when comparing infants to early adolescence (0-15) with older adults (60+). Of these differentially regulated LTRs, we identified 17 shared across all brain regions, including increased expression of HERV-K-int in older adult brains (60+). Co-expression analysis from each of the three brain regions also showed repeats from the HERV subfamily were intramodular hubs in its subnetworks. While we do not observe a strong global relationship between repeat expression and age, we identified HERV-K as a repeat signature associated with the aging neurotypical brain. Our study is the first global assessment of repeat expression in the neurotypical brain.