Brain-derived neurotrophic factor (Bdnf) transcription is controlled by several promoters, which drive expression of multiple transcripts encoding an identical protein. We previously reported that BDNF derived from promoters I and II is highly expressed in hypothalamus and is critical for regulating aggression in male mice. Here we report that BDNF loss from these promoters causes reduced sexual receptivity and impaired maternal care in female mice, which is concomitant with decreased oxytocin (Oxt) expression during development. We identify a novel link between BDNF signaling, oxytocin, and maternal behavior by demonstrating that ablation of TrkB selectively in OXT neurons partially recapitulates maternal care impairments observed in BDNF-deficient females. Using translating ribosome affinity purification and RNA-sequencing we define a molecular profile for OXT neurons and delineate how BDNF signaling impacts gene pathways critical for structural and functional plasticity. Our findings highlight BDNF as a modulator of sexually-dimorphic hypothalamic circuits that govern female-typical behaviors.

Although transposable element (TE) derived DNA accounts for more than half of mammalian genomes and initiates a significant proportion of RNA transcripts, high throughput methods are rarely leveraged specifically to detect expression from interspersed repeats.

Differential expression analysis of RNA sequencing (RNA-seq) data typically relies on reconstructing transcripts or counting reads that overlap known gene structures. We previously introduced an intermediate statistical approach called differentially expressed region (DER) finder that seeks to identify contiguous regions of the genome showing differential expression signal at single base resolution without relying on existing annotation or potentially inaccurate transcript assembly.We present the derfinder software that improves our annotation-agnostic approach to RNA-seq analysis by: (i) implementing a computationally efficient bump-hunting approach to identify DERs that permits genome-scale analyses in a large number of samples, (ii) introducing a flexible statistical modeling framework, including multi-group and time-course analyses and (iii) introducing a new set of data visualizations for expressed region analysis. We apply this approach to public RNA-seq data from the Genotype-Tissue Expression (GTEx) project and BrainSpan project to show that derfinder permits the analysis of hundreds of samples at base resolution in R, identifies expression outside of known gene boundaries and can be used to visualize expressed regions at base-resolution. In simulations, our base resolution approaches enable discovery in the presence of incomplete annotation and is nearly as powerful as feature-level methods when the annotation is complete.derfinder analysis using expressed region-level and single base-level approaches provides a compromise between full transcript reconstruction and feature-level analysis. The package is available from Bioconductor at www.bioconductor.org/packages/derfinder.

Brain-derived neurotrophic factor (BDNF) signals through its cognate receptor tropomyosin receptor kinase B (TrkB) to promote the function of several classes of inhibitory interneurons. We previously reported that loss of BDNF-TrkB signaling in cortistatin (Cort)-expressing interneurons leads to behavioral hyperactivity and spontaneous seizures in mice. We performed bulk RNA sequencing (RNA-seq) from the cortex of mice with disruption of BDNF-TrkB signaling in cortistatin interneurons, and identified differential expression of genes important for excitatory neuron function. Using translating ribosome affinity purification and RNA-seq, we define a molecular profile for Cort-expressing inhibitory neurons and subsequently compare the translatome of normal and TrkB-depleted Cort neurons, revealing alterations in calcium signaling and axon development. Several of the genes enriched in Cort neurons and differentially expressed in TrkB-depleted neurons are also implicated in autism and epilepsy. Our findings highlight TrkB-dependent molecular pathways as critical for the maturation of inhibitory interneurons and support the hypothesis that loss of BDNF signaling in Cort interneurons leads to altered excitatory/inhibitory balance.

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 due to sarcolipin overexpression in the skeletal muscle, resulting in futile sarco(endo)plasmic reticulum Ca 2+ 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. 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.

Gene co-expression networks capture biological relationships between genes and are important tools in predicting gene function and understanding disease mechanisms. We show that technical and biological artifacts in gene expression data confound commonly used network reconstruction algorithms. We demonstrate theoretically, in simulation, and empirically, that principal component correction of gene expression measurements prior to network inference can reduce false discoveries. Using data from the GTEx project in multiple tissues, we show that this approach reduces false discoveries beyond correcting only for known confounders.

Increased African-American research participation is critical to the applicability and generalizability of biomedical research, as population diversity continues to increase both domestically and abroad. Yet numerous studies document historical origins of mistrust, as well as other barriers that may contribute to resistance in the African-American community towards participation in biomedical research. However, a growing body of more recent scientific evidence suggests that African-Americans value research and are willing to participate when asked. In the present study, we set out to determine factors associated with research participation of African-American families in postmortem human brain tissue donation for neuropsychiatric disorders as compared with Caucasian families, from same-day medical examiner autopsy referrals. We retrospectively reviewed brain donation rates, as well as demographic and clinical factors associated with donation in 1,421 consecutive referrals to three medical examiner's offices from 2010-2015. Overall, 69.7% of all next-of-kin contacted agreed to brain donation. While Caucasian families consented to donate brain tissue at a significantly higher rate (74.1%) than African-American families (57.0%) (p<0.001), African-American brain donation rates were as high as 60.5% in referrals from Maryland. Neither African-American nor Caucasian donors differed significantly from non-donors on any demographic or clinical factors ascertained, including age, sex, diagnosis of the donor, or in the relationship of the next-of-kin being contacted (p>0.05). However, Caucasian donors were significantly older, had more years of education, were more likely to be referred for study due to a psychiatric diagnosis, more likely to have comorbid substance abuse, and more likely to have died via suicide, as compared with African-American donors (p<0.05). When African-American participants are identified and approached, African-American families as well as Caucasian families are indeed willing to donate brain tissue on the spot for neuropsychiatric research, which supports the belief that African-American attitudes towards biomedical research may be more favorable than previously thought.

There is growing evidence for the role of DNA methylation (DNAm) quantitative trait loci (mQTLs) in the genetics of complex traits, including psychiatric disorders. However, due to extensive linkage disequilibrium (LD) of the genome, it is challenging to identify causal genetic variations that drive DNAm levels by population-based genetic association studies. This limits the utility of mQTLs for fine-mapping risk loci underlying psychiatric disorders identified by genome-wide association studies (GWAS). Here we present INTERACT, a deep learning model that integrates convolutional neural networks with transformer, to predict effects of genetic variations on DNAm levels at CpG sites in the human brain. We show that INTERACT-derived DNAm regulatory variants are not confounded by LD, are concentrated in regulatory genomic regions in the human brain, and are convergent with mQTL evidence from genetic association analysis. We further demonstrate that predicted DNAm regulatory variants are enriched for heritability of brain-related traits and improve polygenic risk prediction for schizophrenia across diverse ancestry samples. Finally, we applied predicted DNAm regulatory variants for fine-mapping schizophrenia GWAS risk loci to identify potential novel risk genes. Our study shows the power of a deep learning approach to identify functional regulatory variants that may elucidate the genetic basis of complex traits.

RNA sequencing offers advantages over other quantification methods for microRNA (miRNA), yet numerous biases make reliable quantification challenging. Previous evaluations of these biases have focused on adapter ligation bias with limited evaluation of reverse transcription bias or amplification bias. Furthermore, evaluations of the quantification of isomiRs (miRNA isoforms) or the influence of starting amount on performance have been very limited. No study had yet evaluated the quantification of isomiRs of altered length or compared the consistency of results derived from multiple moderate starting inputs. We therefore evaluated quantifications of miRNA and isomiRs using four library preparation kits, with various starting amounts, as well as quantifications following removal of duplicate reads using unique molecular identifiers (UMIs) to mitigate reverse transcription and amplification biases.

Antibody class switch recombination (CSR) to IgG, IgA, or IgE is a hallmark of adaptive immunity, allowing antibody function diversification beyond IgM. CSR involves a deletion of the IgM/IgD constant region genes placing a new acceptor Constant gene, downstream of the VDJH exon. CSR depends on non-coding (CSRnc) transcription of donor Iμ and acceptor IH exons, located 5' upstream of each CH coding gene. Although, our knowledge of the role of CSRnc transcription has advanced greatly, its extension and importance in healthy and diseased humans is scarce. We analyzed CSRnc transcription in 70,603 publicly available RNA-seq samples, including GTEx, TCGA, and the Sequence Read Archive using recount2, an online resource consisting of normalized RNA-seq gene and exon counts, as well as, coverage BigWig files that can be programmatically accessed through R. CSRnc transcription was validated with a qRT-PCR assay for Iμ, Iγ3, and Iγ1 in humans in response to vaccination. We mapped IH transcription for the human IGH locus, including the less understood IGHD gene. CSRnc transcription was restricted to B cells and is widely distributed in normal adult tissues, but predominant in blood, spleen, MALT-containing tissues, visceral adipose tissue and some so-called "immune privileged" tissues. However, significant Iγ4 expression was found even in non-lymphoid fetal tissues. CSRnc expression in cancer tissues mimicked the expression of their normal counterparts, with notable pattern changes in some common cancer subsets. CSRnc transcription in tumors appears to result from tumor infiltration by B cells, since CSRnc transcription was not detected in corresponding tumor-derived immortal cell lines. Additionally, significantly increased Iδ transcription in ileal mucosa in Crohn's disease with ulceration was found. In conclusion, CSRnc transcription occurs in multiple anatomical locations beyond classical secondary lymphoid organs, representing a potentially useful marker of effector B cell responses in normal and pathological immune responses. The pattern of IH exon expression may reveal clues of the local immune response (i.e., cytokine milieu) in health and disease. This is a great example of how the public recount2 data can be used to further our understanding of transcription, including regions outside the known transcriptome.

Beyond being one the most widely used psychoactive drugs in the world, cannabis has been identified as an environmental risk factor for psychosis. Though the relationship between cannabis use and psychiatric disorders remains controversial, consistent association between early adolescent cannabis use and the subsequent risk of psychosis suggested adolescence may be a particularly vulnerable period. Previous findings on gene by environment interactions indicated that cannabis use may only increase the risk for psychosis in the subjects who have a specific genetic vulnerability. The type 1 cannabinoid receptor (CB1), encoded by the CNR1 gene, is a key component of the endocannabinoid system. As the primary endocannabinoid receptor in the brain, CB1 is the main molecular target of the endocannabinoid ligand, as well as tetrahydrocannabinol (THC), the principal psychoactive ingredient of cannabis. In this study, we have examined mRNA expression and DNA methylation of CNR1 in human prefrontal cortex (PFC), hippocampus, and caudate samples. The expression of CNR1 is higher in fetal PFC and hippocampus, then drops down dramatically after birth. The lifespan trajectory of CNR1 expression in the DLPFC differentially correlated with age by allelic variation at rs4680, a functional polymorphism in the COMT gene. Compared with COMT methionine158 carriers, Caucasian carriers of the COMT valine158 allele have a stronger negative correlation between the expression of CNR1 in DLPFC and age. In contrast, the methylation level of cg02498983, which is negatively correlated with the expression of CNR1 in PFC, showed the strongest positive correlation with age in PFC of Caucasian carriers of COMT valine158. Additionally, we have observed decreased mRNA expression of CNR1 in the DLPFC of patients with schizophrenia. Further analysis revealed a positive eQTL SNP, rs806368, which predicted the expression of a novel transcript of CNR1 in human DLPFC, hippocampus and caudate. This SNP has been associated with addiction and other psychiatric disorders. THC or ethanol are each significantly associated with dysregulated expression of CNR1 in the PFC of patients with affective disorder, and the expression of CNR1 is significantly upregulated in the PFC of schizophrenia patients who completed suicide. Our results support previous studies that have implicated the endocannabinoid system in the pathology of schizophrenia and provided additional insight into the mechanism of increasing risk for schizophrenia in the adolescent cannabis users.

We used the 10x Genomics Visium platform to define the spatial topography of gene expression in the six-layered human dorsolateral prefrontal cortex. We identified extensive layer-enriched expression signatures and refined associations to previous laminar markers. We overlaid our laminar expression signatures on large-scale single nucleus RNA-sequencing data, enhancing spatial annotation of expression-driven clusters. By integrating neuropsychiatric disorder gene sets, we showed differential layer-enriched expression of genes associated with schizophrenia and autism spectrum disorder, highlighting the clinical relevance of spatially defined expression. We then developed a data-driven framework to define unsupervised clusters in spatial transcriptomics data, which can be applied to other tissues or brain regions in which morphological architecture is not as well defined as cortical laminae. Last, we created a web application for the scientific community to explore these raw and summarized data to augment ongoing neuroscience and spatial transcriptomics research ( http://research.libd.org/spatialLIBD ).

We employed Illumina 450 K Infinium microarrays to profile DNA methylation (DNAm) in neuronal nuclei separated by fluorescence-activated sorting from the postmortem orbitofrontal cortex (OFC) of heroin users who died from heroin overdose (N = 37), suicide completers (N = 22) with no evidence of heroin use and from control subjects who did not abuse illicit drugs and died of non-suicide causes (N = 28). We identified 1298 differentially methylated CpG sites (DMSs) between heroin users and controls, and 454 DMSs between suicide completers and controls (p < 0.001). DMSs and corresponding genes (DMGs) in heroin users showed significant differences in the preferential context of hyper and hypo DM. HyperDMSs were enriched in gene bodies and exons but depleted in promoters, whereas hypoDMSs were enriched in promoters and enhancers. In addition, hyperDMGs showed preference for genes expressed specifically by glutamatergic as opposed to GABAergic neurons and enrichment for axonogenesis- and synaptic-related gene ontology categories, whereas hypoDMGs were enriched for transcription factor activity- and gene expression regulation-related terms. Finally, we found that the DNAm-based "epigenetic age" of neurons from heroin users was younger than that in controls. Suicide-related results were more difficult to interpret. Collectively, these findings suggest that the observed DNAm differences could represent functionally significant marks of heroin-associated plasticity in the OFC.

Smoking is a leading cause of preventable morbidity and mortality. Smoking is heritable, and genome-wide association studies (GWAS) of smoking behaviors have identified hundreds of significant loci. Most GWAS-identified variants are noncoding with unknown neurobiological effects. We used genome-wide genotype, DNA methylation, and RNA sequencing data in postmortem human nucleus accumbens (NAc) to identify cis-methylation/expression quantitative trait loci (meQTLs/eQTLs), investigate variant-by-cigarette smoking interactions across the genome, and overlay QTL evidence at smoking GWAS-identified loci to evaluate their regulatory potential. Active smokers (N=52) and nonsmokers (N=171) were defined based on cotinine biomarker levels and next-of-kin reporting. We simultaneously tested variant and variant-by-smoking interaction effects on methylation and expression, separately, adjusting for biological and technical covariates and using a two-stage multiple testing approach with eigenMT and Bonferroni corrections. We found >2 million significant meQTL variants (padj<0.05) corresponding to 41,695 unique CpGs. Results were largely driven by main effects; five meQTLs, mapping to NUDT12, FAM53B, RNF39, and ADRA1B, showed a significant interaction with smoking. We found 57,683 significant eQTLs for 958 unique eGenes (padj<0.05) and no smoking interactions. Colocalization analyses identified loci with smoking-associated GWAS variants that overlapped meQTLs/eQTLs, suggesting that these heritable factors may influence smoking behaviors through functional effects on methylation/expression. One locus containing MUSTIN1 and ITIH4 colocalized across all data types (GWAS + meQTL + eQTL). In this first genome-wide meQTL map in the human NAc, the enriched overlap with smoking GWAS-identified genetic loci provides evidence that gene regulation in the brain helps explain the neurobiology of smoking behaviors.

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.

Epigenome-wide association studies of human disease and other quantitative traits are becoming increasingly common. A series of papers reporting age-related changes in DNA methylation profiles in peripheral blood have already been published. However, blood is a heterogeneous collection of different cell types, each with a very different DNA methylation profile.

DNA methylation levels change with age. Recent studies have identified biomarkers of chronological age based on DNA methylation levels. It is not yet known whether DNA methylation age captures aspects of biological age.

The primate-specific brain voltage-gated potassium channel isoform Kv11.1-3.1 has been identified as a novel therapeutic target for the treatment of schizophrenia. While this ether-a-go-go related K(+)channel has shown clinical relevance, drug discovery efforts have been hampered due to low and inconsistent activity in cell-based assays. This poor activity is hypothesized to result from poor trafficking via the lack of an intact channel-stabilizing Per-Ant-Sim (PAS) domain. Here we characterize Kv11.1-3.1 cellular localization and show decreased channel expression and cell surface trafficking relative to the PAS-domain containing major isoform, Kv11.1-1A. Using small molecule inhibition of proteasome degradation, cellular expression and plasma membrane trafficking are rescued. These findings implicate the importance of the unfolded-protein response and endoplasmic reticulum associated degradation pathways in the expression and regulation of this schizophrenia risk factor. Utilizing this identified phenomenon, an electrophysiological and high throughput in-vitro fluorescent assay platform has been developed for drug discovery in order to explore a potentially new class of cognitive therapeutics.

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 .

Significance analysis plays a major role in identifying and ranking genes, transcription factor binding sites, DNA methylation regions, and other high-throughput features associated with illness. We propose a new approach, called gene set bagging, for measuring the probability that a gene set replicates in future studies. Gene set bagging involves resampling the original high-throughput data, performing gene-set analysis on the resampled data, and confirming that biological categories replicate in the bagged samples.