Cigarette smoking during pregnancy is a major public health concern. While there are well-described consequences in early child development, there is very little known about the effects of maternal smoking on human cortical biology during prenatal life. We therefore performed a genome-wide differential gene expression analysis using RNA sequencing (RNA-seq) on prenatal (N = 33; 16 smoking-exposed) as well as adult (N = 207; 57 active smokers) human postmortem prefrontal cortices. Smoking exposure during the prenatal period was directly associated with differential expression of 14 genes; in contrast, during adulthood, despite a much larger sample size, only two genes showed significant differential expression (FDR < 10%). Moreover, 1,315 genes showed significantly different exposure effects between maternal smoking during pregnancy and direct exposure in adulthood (FDR < 10%)-these differences were largely driven by prenatal differences that were enriched for pathways previously implicated in addiction and synaptic function. Furthermore, prenatal and age-dependent differentially expressed genes were enriched for genes implicated in non-syndromic autism spectrum disorder (ASD) and were differentially expressed as a set between patients with ASD and controls in postmortem cortical regions. These results underscore the enhanced sensitivity to the biological effect of smoking exposure in the developing brain and offer insight into how maternal smoking during pregnancy affects gene expression in the prenatal human cortex. They also begin to address the relationship between in utero exposure to smoking and the heightened risks for the subsequent development of neuropsychiatric disorders.

Genome-wide association studies have identified 108 schizophrenia risk loci, but biological mechanisms for individual loci are largely unknown. Using developmental, genetic and illness-based RNA sequencing expression analysis in human brain, we characterized the human brain transcriptome around these loci and found enrichment for developmentally regulated genes with novel examples of shifting isoform usage across pre- and postnatal life. We found widespread expression quantitative trait loci (eQTLs), including many with transcript specificity and previously unannotated sequence that were independently replicated. We leveraged this general eQTL database to show that 48.1% of risk variants for schizophrenia associate with nearby expression. We lastly found 237 genes significantly differentially expressed between patients and controls, which replicated in an independent dataset, implicated synaptic processes, and were strongly regulated in early development. These findings together offer genetics- and diagnosis-related targets for better modeling of schizophrenia risk. This resource is publicly available at http://eqtl.brainseq.org/phase1 .

Most transcriptome-wide association studies (TWASs) so far focus on European ancestry and lack diversity. To overcome this limitation, we aggregated genome-wide association study (GWAS) summary statistics, whole-genome sequences and expression quantitative trait locus (eQTL) data from diverse ancestries. We developed a new approach, TESLA (multi-ancestry integrative study using an optimal linear combination of association statistics), to integrate an eQTL dataset with a multi-ancestry GWAS. By exploiting shared phenotypic effects between ancestries and accommodating potential effect heterogeneities, TESLA improves power over other TWAS methods. When applied to tobacco use phenotypes, TESLA identified 273 new genes, up to 55% more compared with alternative TWAS methods. These hits and subsequent fine mapping using TESLA point to target genes with biological relevance. In silico drug-repurposing analyses highlight several drugs with known efficacy, including dextromethorphan and galantamine, and new drugs such as muscle relaxants that may be repurposed for treating nicotine addiction.

DNA methylation (DNAm), a crucial epigenetic mark, plays a key role in gene regulation, mammalian development, and various human diseases. Single-cell technologies enable the profiling of DNAm states at cytosines within the DNA sequence of individual cells, but they often suffer from limited coverage of CpG sites. In this study, we introduce scMeFormer, a transformer-based deep learning model designed to impute DNAm states for each CpG site in single cells. Through comprehensive evaluations, we demonstrate the superior performance of scMeFormer compared to alternative models across four single-nucleus DNAm datasets generated by distinct technologies. Remarkably, scMeFormer exhibits high-fidelity imputation, even when dealing with significantly reduced coverage, as low as 10% of the original CpG sites. Furthermore, we applied scMeFormer to a single-nucleus DNAm dataset generated from the prefrontal cortex of four schizophrenia patients and four neurotypical controls. This enabled the identification of thousands of differentially methylated regions associated with schizophrenia that would have remained undetectable without imputation and added granularity to our understanding of epigenetic alterations in schizophrenia within specific cell types. Our study highlights the power of deep learning in imputing DNAm states in single cells, and we expect scMeFormer to be a valuable tool for single-cell DNAm studies.

Cellular deconvolution of bulk RNA-sequencing (RNA-seq) data using single cell or nuclei RNA-seq (sc/snRNA-seq) reference data is an important strategy for estimating cell type composition in heterogeneous tissues, such as human brain. Several deconvolution methods have been developed and they have been previously benchmarked against simulated data, pseudobulked sc/snRNA-seq data, or cell type proportions derived from immunohistochemistry reference data. A major limitation preventing the improvement of deconvolution algorithms has been the lack of highly integrated datasets with orthogonal measurements of gene expression and estimates of cell type proportions on the same tissue block. The performance of existing deconvolution algorithms has not yet been explored across different RNA extraction methods (e.g. cytosolic, nuclear, or whole cell RNA), different library preparation types (e.g. mRNA enrichment vs. ribosomal RNA depletion), or with matched single cell reference datasets.

regionReport is an R package for generating detailed interactive reports from region-level genomic analyses as well as feature-level RNA-seq. The report includes quality-control checks, an overview of the results, an interactive table of the genomic regions or features of interest and reproducibility information. regionReport provides specialised reports for exploring DESeq2, edgeR, or derfinder differential expression analyses results. regionReport is also flexible and can easily be expanded with report templates for other analysis pipelines.

Using a functional approach to investigate the epigenetics of type 2 diabetes (T2D), we combine three lines of evidence-diet-induced epigenetic dysregulation in mouse, epigenetic conservation in humans, and T2D clinical risk evidence-to identify genes implicated in T2D pathogenesis through epigenetic mechanisms related to obesity. Beginning with dietary manipulation of genetically homogeneous mice, we identify differentially DNA-methylated genomic regions. We then replicate these results in adipose samples from lean and obese patients pre- and post-Roux-en-Y gastric bypass, identifying regions where both the location and direction of methylation change are conserved. These regions overlap with 27 genetic T2D risk loci, only one of which was deemed significant by GWAS alone. Functional analysis of genes associated with these regions revealed four genes with roles in insulin resistance, demonstrating the potential general utility of this approach for complementing conventional human genetic studies by integrating cross-species epigenomics and clinical genetic risk.

The human prefrontal cortex (PFC), a mastermind of the brain, is one of the last brain regions to mature. To investigate the role of epigenetics in the development of PFC, we examined DNA methylation in ∼14,500 genes at ∼27,000 CpG loci focused on 5' promoter regions in 108 subjects range in age from fetal to elderly. DNA methylation in the PFC shows unique temporal patterns across life. The fastest changes occur during the prenatal period, slow down markedly after birth and continue to slow further with aging. At the genome level, the transition from fetal to postnatal life is typified by a reversal of direction, from demethylation prenatally to increased methylation postnatally. DNA methylation is strongly associated with genotypic variants and correlates with expression of a subset of genes, including genes involved in brain development and in de novo DNA methylation. Our results indicate that promoter DNA methylation in the human PFC is a highly dynamic process modified by genetic variance and regulating gene transcription. Additional discovery is made possible with a stand-alone application, BrainCloudMethyl.

DNA methylation (DNAm) is important in brain development and is potentially important in schizophrenia. We characterized DNAm in prefrontal cortex from 335 non-psychiatric controls across the lifespan and 191 patients with schizophrenia and identified widespread changes in the transition from prenatal to postnatal life. These DNAm changes manifest in the transcriptome, correlate strongly with a shifting cellular landscape and overlap regions of genetic risk for schizophrenia. A quarter of published genome-wide association studies (GWAS)-suggestive loci (4,208 of 15,930, P < 10(-100)) manifest as significant methylation quantitative trait loci (meQTLs), including 59.6% of GWAS-positive schizophrenia loci. We identified 2,104 CpGs that differ between schizophrenia patients and controls that were enriched for genes related to development and neurodifferentiation. The schizophrenia-associated CpGs strongly correlate with changes related to the prenatal-postnatal transition and show slight enrichment for GWAS risk loci while not corresponding to CpGs differentiating adolescence from later adult life. These data implicate an epigenetic component to the developmental origins of this disorder.

Brain-derived neurotrophic factor (BDNF) plays a key role in energy balance. In population studies, SNPs of the BDNF locus have been linked to obesity, but the mechanism by which these variants cause weight gain is unknown. Here, we examined human hypothalamic BDNF expression in association with 44 BDNF SNPs. We observed that the minor C allele of rs12291063 is associated with lower human ventromedial hypothalamic BDNF expression (p < 0.001) and greater adiposity in both adult and pediatric cohorts (p values < 0.05). We further demonstrated that the major T allele for rs12291063 possesses a binding capacity for the transcriptional regulator, heterogeneous nuclear ribonucleoprotein D0B, knockdown of which disrupts transactivation by the T allele. Binding and transactivation functions are both disrupted by substituting C for T. These findings provide a rationale for BDNF augmentation as a targeted treatment for obesity in individuals who have the rs12291063 CC genotype.

The serotonin 2A receptor is widely implicated in genetic association studies and remains an important drug target for psychiatric, neurological, and cardiovascular conditions. RNA sequencing redefined the architecture of the serotonin 2A receptor gene (HTR2A), revealing novel mRNA transcript isoforms utilizing unannotated untranslated regions of the gene. Expression of these untranslated regions is modulated by common single nucleotide polymorphisms (SNPs), namely rs6311. Previous studies did not fully capture the complexity of the sense- and antisense-encoded transcripts with respect to novel exons in the HTR2A gene locus. Here, we comprehensively catalogued exons and RNA isoforms for both HTR2A and HTR2A-AS1 using RNA-Seq from human prefrontal cortex and multiple mouse tissues. We subsequently tested associations between expression of newfound gene features and common SNPs in humans.

Mounting evidence supports that LINE-1 (L1) retrotransposition can occur postzygotically in healthy and diseased human tissues, contributing to genomic mosaicism in the brain and other somatic tissues of an individual. However, the genomic distribution of somatic human-specific LINE-1 (L1Hs) insertions and their potential impact on carrier cells remain unclear. Here, using a PCR-based targeted bulk sequencing approach, we profiled 9,181 somatic insertions from 20 postmortem tissues from five Rett patients and their matched healthy controls. We identified and validated somatic L1Hs insertions in both cortical neurons and non-brain tissues. In Rett patients, somatic insertions were significantly depleted in exons-mainly contributed by long genes-than healthy controls, implying that cells carrying MECP2 mutations might be defenseless against a second exonic L1Hs insertion. We observed a significant increase of somatic L1Hs insertions in the brain compared with non-brain tissues from the same individual. Compared to germline insertions, somatic insertions were less sense-depleted to transcripts, indicating that they underwent weaker selective pressure on the orientation of insertion. Our observations demonstrate that somatic L1Hs insertions contribute to genomic diversity and MeCP2 dysfunction alters their genomic patterns in Rett patients.

Context fear memory dysregulation is a hallmark symptom of several neuropsychiatric disorders, including generalized anxiety disorder and posttraumatic stress disorder. The hippocampus (HC) and prelimbic (PrL) subregion of the medial prefrontal cortex have been linked with context fear memory retrieval in rodents, but the mechanisms by which HC-PrL circuitry regulates this process remain poorly understood.

Of the 108 Schizophrenia (SZ) risk-loci discovered through genome-wide association studies (GWAS), 96 are not altering the sequence of any protein. Evidence linking non-coding risk-SNPs and genes may be established using expression quantitative trait loci (eQTL). However, other approaches such allelic expression quantitative trait loci (aeQTL) also may be of use.

Transcriptome compartmentalization by the nuclear membrane provides both stochastic and functional buffering of transcript activity in the cytoplasm, and has recently been implicated in neurodegenerative disease processes. Although many mechanisms regulating transcript compartmentalization are also prevalent in brain development, the extent to which subcellular localization differs as the brain matures has yet to be addressed. To characterize the nuclear and cytoplasmic transcriptomes during brain development, we sequenced both RNA fractions from homogenate prenatal and adult human postmortem cortex using poly(A)+ and Ribo-Zero library preparation methods. We find that while many genes are differentially expressed by fraction and developmental expression changes are similarly detectable in nuclear and cytoplasmic RNA, the compartmented transcriptomes become more distinct as the brain matures, perhaps reflecting increased utilization of nuclear retention as a regulatory strategy in adult brain. We examined potential mechanisms of this developmental divergence including alternative splicing, RNA editing, nuclear pore composition, RNA-binding protein motif enrichment, and RNA secondary structure. Intron retention is associated with greater nuclear abundance in a subset of transcripts, as is enrichment for several splicing factor binding motifs. Finally, we examined disease association with fraction-regulated gene sets and found nuclear-enriched genes were also preferentially enriched in gene sets associated with neurodevelopmental psychiatric disorders. These results suggest that although gene-level expression is globally comparable between fractions, nuclear retention of transcripts may play an underappreciated role in developmental regulation of gene expression in brain, particularly in genes whose dysregulation is related to neuropsychiatric disorders.

Autism spectrum disorder (ASD) is genetically heterogeneous with convergent symptomatology, suggesting common dysregulated pathways. In this study, we analyzed brain transcriptional changes in five mouse models of Pitt-Hopkins syndrome (PTHS), a syndromic form of ASD caused by mutations in the TCF4 gene, but not the TCF7L2 gene. Analyses of differentially expressed genes (DEGs) highlighted oligodendrocyte (OL) dysregulation, which we confirmed in two additional mouse models of syndromic ASD (Ptenm3m4/m3m4 and Mecp2tm1.1Bird). The PTHS mouse models showed cell-autonomous reductions in OL numbers and myelination, functionally confirming OL transcriptional signatures. We also integrated PTHS mouse model DEGs with human idiopathic ASD postmortem brain RNA-sequencing data and found significant enrichment of overlapping DEGs and common myelination-associated pathways. Notably, DEGs from syndromic ASD mouse models and reduced deconvoluted OL numbers distinguished human idiopathic ASD cases from controls across three postmortem brain data sets. These results implicate disruptions in OL biology as a cellular mechanism in ASD pathology.

Trisomy 21 is one of the most complex genetic perturbations compatible with postnatal survival. Dosage imbalance arising from the triplication of genes on human chromosome 21 (Hsa21) affects multiple organ systems. Much of Down syndrome (DS) research, however, has focused on addressing how aneuploidy dysregulates CNS function leading to cognitive deficit. Although obesity, diabetes, and associated sequelae such as fatty liver and dyslipidemia are well documented in the DS population, only limited studies have been conducted to determine how gene dosage imbalance affects whole-body metabolism. Here, we conduct a comprehensive and systematic analysis of key metabolic parameters across different physiological states in the Ts65Dn trisomic mouse model of DS.

The lateral septum (LS), a GABAergic structure located in the basal forebrain, is implicated in social behavior, learning and memory. We previously demonstrated that expression of tropomyosin kinase receptor B (TrkB) in LS neurons is required for social novelty recognition. To better understand molecular mechanisms by which TrkB signaling controls behavior, we locally knocked down TrkB in LS and used bulk RNA-sequencing to identify changes in gene expression downstream of TrkB. TrkB knockdown induces upregulation of genes associated with inflammation and immune responses, and downregulation of genes associated with synaptic signaling and plasticity. Next, we generated one of the first atlases of molecular profiles for LS cell types using single nucleus RNA-sequencing (snRNA-seq). We identified markers for the septum broadly, and the LS specifically, as well as for all neuronal cell types. We then investigated whether the differentially expressed genes (DEGs) induced by TrkB knockdown map to specific LS cell types. Enrichment testing identified that downregulated DEGs are broadly expressed across neuronal clusters. Enrichment analyses of these DEGs demonstrated that downregulated genes are uniquely expressed in the LS, and associated with either synaptic plasticity or neurodevelopmental disorders. Upregulated genes are enriched in LS microglia, associated with immune response and inflammation, and linked to both neurodegenerative disease and neuropsychiatric disorders. In addition, many of these genes are implicated in regulating social behaviors. In summary, the findings implicate TrkB signaling in the LS as a critical regulator of gene networks associated with psychiatric disorders that display social deficits, including schizophrenia and autism, and with neurodegenerative diseases, including Alzheimer's.

Bisulfite sequencing is a powerful tool for profiling genomic methylation, an epigenetic modification critical in the understanding of cancer, psychiatric disorders, and many other conditions. Raw data generated by whole genome bisulfite sequencing (WGBS) requires several computational steps before it is ready for statistical analysis, and particular care is required to process data in a timely and memory-efficient manner. Alignment to a reference genome is one of the most computationally demanding steps in a WGBS workflow, taking several hours or even days with commonly used WGBS-specific alignment software. This naturally motivates the creation of computational workflows that can utilize GPU-based alignment software to greatly speed up the bottleneck step. In addition, WGBS produces raw data that is large and often unwieldy; a lack of memory-efficient representation of data by existing pipelines renders WGBS impractical or impossible to many researchers.

The consequences of aneuploidy have traditionally been studied in cell and animal models in which the extrachromosomal DNA is from the same species. Here, we explore a fundamental question concerning the impact of aneuploidy on systemic metabolism using a non-mosaic transchromosomic mouse model (TcMAC21) carrying a near-complete human chromosome 21. Independent of diets and housing temperatures, TcMAC21 mice consume more calories, are hyperactive and hypermetabolic, remain consistently lean and profoundly insulin sensitive, and have a higher body temperature. The hypermetabolism and elevated thermogenesis are likely due to a combination of increased activity level and sarcolipin overexpression in the skeletal muscle, resulting in futile sarco(endo)plasmic reticulum Ca2+ ATPase (SERCA) activity and energy dissipation. Mitochondrial respiration is also markedly increased in skeletal muscle to meet the high ATP demand created by the futile cycle and hyperactivity. This serendipitous discovery provides proof-of-concept that sarcolipin-mediated thermogenesis via uncoupling of the SERCA pump can be harnessed to promote energy expenditure and metabolic health.