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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.

Here we re-analyze RNA-sequencing data from the anterior cingulate cortex (ACC) of SZ patients using recent methods to improve accuracy and sensitivity of results, such as the quality surrogate variable analysis (qSVA) method and the derfinder R package. We found that genes significantly down-regulated in SZ demonstrated an enrichment for parvalbumin-positive interneurons (FDR 0.0001). Down-regulated genes were also enriched in oxidative phosphorylation functions (FDR 0.05). We also addressed whether lifetime exposure to antipsychotics might influence gene expression, highlighting DUSP6, LBH, and NR1D1. Our results support the role of redox imbalance/mitochondrial dysfunction and implicate interneuron subtypes in SZ pathophysiology.

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.SummaryWe examine the impact of genetic ancestry on gene expression and DNA methylation of admixed African/Black Americans, highlighting how genetic and environmental background affect risk for brain illness.

The public health crisis created by the COVID-19 pandemic has spurred a deluge of scientific research aimed at informing the public health and medical response to the pandemic. However, early in the pandemic, those working in frontline public health and clinical care had insufficient time to parse the rapidly evolving evidence and use it for decision-making. Academics in public health and medicine were well-placed to translate the evidence for use by frontline clinicians and public health practitioners. The Novel Coronavirus Research Compendium (NCRC), a group of >60 faculty and trainees across the United States, formed in March 2020 with the goal to quickly triage and review the large volume of preprints and peer-reviewed publications on SARS-CoV-2 and COVID-19 and summarize the most important, novel evidence to inform pandemic response. From April 6 through December 31, 2020, NCRC teams screened 54 192 peer-reviewed articles and preprints, of which 527 were selected for review and uploaded to the NCRC website for public consumption. Most articles were peer-reviewed publications (n = 395, 75.0%), published in 102 journals; 25.1% (n = 132) of articles reviewed were preprints. The NCRC is a successful model of how academics translate scientific knowledge for practitioners and help build capacity for this work among students. This approach could be used for health problems beyond COVID-19, but the effort is resource intensive and may not be sustainable in the long term.

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 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. 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.

Background Multispectral fluorescence imaging coupled with linear unmixing is a form of image data collection and analysis that allows for measuring multiple molecular signals in a single biological sample. Multiple fluorescent dyes, each measuring a unique molecule, are simultaneously measured and subsequently “unmixed” to provide a read-out for each molecular signal. This strategy allows for measuring highly multiplexed signals in a single data capture session, such as multiple proteins or RNAs in tissue slices or cultured cells, but can often result in mixed signals and bleed-through problems across dyes. Existing spectral unmixing algorithms are not optimized for challenging biological specimens such as post-mortem human brain tissue, and often require manual intervention to extract spectral signatures. We therefore developed an intuitive, automated, and flexible package called SUFI: spectral unmixing of fluorescent images. Results This package unmixes multispectral fluorescence images by automating the extraction of spectral signatures using vertex component analysis, and then performs one of three unmixing algorithms derived from remote sensing. We evaluate these remote sensing algorithms’ performances on four unique biological datasets and compare the results to unmixing results obtained using ZEN Black software (Zeiss). We lastly integrate our unmixing pipeline into the computational tool dotdotdot, which is used to quantify individual RNA transcripts at single cell resolution in intact tissues and perform differential expression analysis, and thereby provide an end-to-end solution for multispectral fluorescence image analysis and quantification. Conclusions In summary, we provide a robust, automated pipeline to assist biologists with improved spectral unmixing of multispectral fluorescence images.

Genetic variation in the transcription factor 4 (TCF4)gene is associated with risk for a variety of developmental and psychiatric conditions, which includes a syndromic form of ASD called Pitt Hopkins Syndrome (PTHS).TCF4encodes an activity-dependent transcription factor that is highly expressed during cortical development and in animal models is shown to regulate various aspects of neuronal development and function. However, our understanding of how disease-causing mutations in TCF4 confer pathophysiology in a human context is lacking. Here we show that cortical neurons derived from patients withTCF4mutations have deficits in spontaneous synaptic transmission, network excitability and homeostatic plasticity. Transcriptomic analysis indicates these phenotypes result from altered expression of genes involved in presynaptic neurotransmission and identifies the presynaptic binding protein, RIMBP2 as the most differentially expressed gene in PTHS neurons. Remarkably, TCF4-dependent deficits in spontaneous synaptic transmission and network excitability were rescued by increasing RIMBP2 expression in presynaptic neurons. Together, these results identify TCF4 as a critical transcriptional regulator of human synaptic development and plasticity and specifically identifies dysregulation of presynaptic function as an early pathophysiology in PTHS.

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.

Obesity and type 2 diabetes are rapidly increasing in the adolescent population. We sought to determine whether adipokines, specifically leptin, C1q/TNF-related proteins 1 (CTRP1) and CTRP9, and the hepatokine fibroblast growth factor 21 (FGF21), are associated with obesity and hyperglycemia in a cohort of lean and obese adolescents, across the spectrum of glycemia. In an observational, longitudinal study of lean and obese adolescents, we measured fasting laboratory tests, oral glucose tolerance tests, and adipokines including leptin, CTRP1, CTRP9, and FGF21. Participants completed baseline and 2-year follow-up study visits and were categorized as lean (LC, lean control; n = 30), obese normoglycemic (ONG; n = 61), and obese hyperglycemic (OHG; n = 31) adolescents at baseline and lean (n = 8), ONG (n = 18), and OHG (n = 4) at follow-up. Groups were compared using ANOVA and regression analysis, and linear mixed effects modeling was used to test for differences in adipokine levels across baseline and follow-up visits. Results showed that at baseline, leptin was higher in all obese groups (P < 0.001) compared with LC. FGF21 was higher in OHG participants compared with LC (P < 0.001) and ONG (P < 0.001) and positively associated with fasting glucose (P < 0.001), fasting insulin (P < 0.001), Homeostasis Model Assessment-Insulin Resistance Index (HOMA-IR; P < 0.001), and hemoglobin A1c (HbA1c; P = 0.01). CTRP1 was higher in OHG compared with ONG (P = 0.03). CTRP9 was not associated with obesity or hyperglycemia in this pediatric cohort. At 2 years, leptin decreased in ONG (P = 0.003) and FGF21 increased in OHG (P = 0.02), relative to lean controls. Altered adipokine levels are associated with the inflammatory milieu in obese youth with and without hyperglycemia. In adolescence, the novel adipokine CTRP1 was elevated with hyperglycemia, whereas CTRP9 was unchanged in this cohort. NEW & NOTEWORTHY Leptin is higher in obese adolescents and FGF21 is higher in obese hyperglycemic adolescents. The novel adipokine CTRP1 is higher in obese hyperglycemic adolescents, whereas CTRP9 was unchanged in this adolescent cohort.

Genome-wide association studies have identified single nucleotide polymorphisms (SNPs) associated with schizophrenia risk. Integration of RNA-sequencing data from postmortem human brains with these risk SNPs identified transcripts associated with increased schizophrenia susceptibility, including a class of exon 9-spliced isoforms of Sorting nexin-19 (SNX19d9) and an isoform of Arsenic methyltransferase (AS3MT) splicing out exons 2 and 3 (AS3MTd2d3). However, the biological function of these transcript variants is unclear. Defining the cell types where these risk transcripts are dominantly expressed is an important step to understand function, in prioritizing specific cell types and/or neural pathways in subsequent studies. To identify the cell type-specific localization of SNX19 and AS3MT in the human dorsolateral prefrontal cortex (DLPFC), we used single-molecule in situ hybridization techniques combined with automated quantification and machine learning approaches to analyze 10 postmortem brains of neurotypical individuals. These analyses revealed that both pan-SNX19 and pan-AS3MT were more highly expressed in neurons than non-neurons in layers II/III and VI of DLPFC. Furthermore, pan-SNX19 was preferentially expressed in glutamatergic neurons, while pan-AS3MT was preferentially expressed in GABAergic neurons. Finally, we utilized duplex BaseScope technology, to delineate the localization of SNX19d9 and AS3MTd2d3 splice variants, revealing consistent trends in spatial gene expression among pan-transcripts and schizophrenia risk-related transcript variants. These findings demonstrate that schizophrenia risk transcripts have distinct localization patterns in the healthy human brains, and suggest that SNX19 transcripts might disrupt the normal function of glutamatergic neurons, while AS3MT may lead to disturbances in the GABAergic system in the pathophysiology of schizophrenia.

We used the 10x Genomics Visium platform to define the spatial topography of gene expression in the six-layered human dorsolateral prefrontal cortex (DLPFC). We identified extensive layer-enriched expression signatures, and refined associations to previous laminar markers. We overlaid our laminar expression signatures onto large-scale single nuclei 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 where morphological architecture is not as well-defined as cortical laminae. We lastly 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).

Posttraumatic stress disorder (PTSD) is a debilitating neuropsychiatric disease that is highly comorbid with major depressive disorder (MDD) and bipolar disorder. The overlap in symptoms is hypothesized to stem from partially shared genetics and underlying neurobiological mechanisms. To delineate conservation between transcriptional patterns across PTSD and MDD, the authors examined gene expression in the human cortex and amygdala in these disorders.RNA sequencing was performed in the postmortem brain of two prefrontal cortex regions and two amygdala regions from donors diagnosed with PTSD (N=107) or MDD (N=109) as well as from neurotypical donors (N=109).The authors identified a limited number of differentially expressed genes (DEGs) specific to PTSD, with nearly all mapping to cortical versus amygdala regions. PTSD-specific DEGs were enriched in gene sets associated with downregulated immune-related pathways and microglia as well as with subpopulations of GABAergic inhibitory neurons. While a greater number of DEGs associated with MDD were identified, most overlapped with PTSD, and only a few were MDD specific. The authors used weighted gene coexpression network analysis as an orthogonal approach to confirm the observed cellular and molecular associations.These findings provide supporting evidence for involvement of decreased immune signaling and neuroinflammation in MDD and PTSD pathophysiology, and extend evidence that GABAergic neurons have functional significance in PTSD.

Background Spatially-resolved transcriptomics has now enabled the quantification of high-throughput and transcriptome-wide gene expression in intact tissue while also retaining the spatial coordinates. Incorporating the precise spatial mapping of gene activity advances our understanding of intact tissue-specific biological processes. In order to interpret these novel spatial data types, interactive visualization tools are necessary. Results We describe spatialLIBD, an R/Bioconductor package to interactively explore spatially-resolved transcriptomics data generated with the 10x Genomics Visium platform. The package contains functions to interactively access, visualize, and inspect the observed spatial gene expression data and data-driven clusters identified with supervised or unsupervised analyses, either on the user’s computer or through a web application. Conclusions spatialLIBD is available at https://bioconductor.org/packages/spatialLIBD. It is fully compatible with SpatialExperiment and the Bioconductor ecosystem. Its functionality facilitates analyzing and interactively exploring spatially-resolved data from the Visium platform.

MicroRNAs (miRNAs) are small noncoding RNAs (sncRNAs) that function in post-transcriptional gene regulation through imperfect base pairing with mRNA targets, which results in inhibition of translation and typically destabilization of bound transcripts. Sequence-based algorithms historically used to predict miRNA targets face inherent challenges in reliably reflecting in vivo interactions. Recent strategies have directly profiled miRNA–target interactions by crosslinking and ligation of sncRNAs to their targets within the RNA-induced silencing complex (RISC), followed by high-throughput sequencing of the chimeric sncRNA:target RNAs. Despite the strength of these direct profiling approaches, standardized pipelines for effectively analyzing the resulting chimeric sncRNA:target RNA sequencing data are not readily available. Here we present SCRAP, a robustsmallchimeric RNAanalysispipeline for the bioinformatic processing of chimeric sncRNA:target RNA sequencing data. SCRAP consists of two parts, each of which is specifically optimized for the distinctive characteristics of chimeric small RNA sequencing reads: first, read processing and alignment and second, peak calling and annotation. We apply SCRAP to benchmark chimeric sncRNA:target RNA sequencing data sets generated by distinct molecular approaches, and compare SCRAP to existing chimeric RNA analysis pipelines. SCRAP has minimal hardware requirements, is cross-platform, and contains extensive annotations to broaden accessibility for processing small chimeric RNA sequencing data and enable insights into the targets of small noncoding RNAs in regulating diverse biological systems.

BackgroundRecent breakthroughs in psychiatric genetics have implicated biological pathways onto which genetic risk for psychiatric disorders converges. However, these studies do not reveal the developmental time point(s) at which these pathways are relevant.MethodsWe aimed to determine the relationship between psychiatric risk and developmental gene expression relating to discrete biological pathways. We used post-mortem RNA sequencing data (BrainSeq and BrainSpan) from brain tissue at multiple pre- and post-natal timepoints and summary statistics from recent genome-wide association studies of schizophrenia, bipolar disorder and major depressive disorder. We prioritised gene sets for overall enrichment of association with each disorder, and then tested the relationship between the association of each of their constituent genes with their relative expression at each developmental stage.ResultsWe observed relationships between the expression of genes involved involtage-gated cation channel activityduring Early Midfetal, Adolescence and Early Adulthood timepoints and association with schizophrenia and bipolar disorder, such that genes more strongly associated with these disorders had relatively low expression during Early Midfetal development and higher expression during Adolescence and Early Adulthood. The relationship with schizophrenia was strongest for the subset of genes related to calcium channel activity, whilst for bipolar disorder the relationship was distributed between calcium and potassium channel activity genes.ConclusionsOur results indicate periods during development when biological pathways related to the activity of calcium and potassium channels may be most vulnerable to the effects of genetic variants conferring risk to psychiatric disorders. Furthermore, they indicate key time points and potential targets for disorder-specific therapeutic interventions.

Aim: To investigate the role of epigenetics in HIV pathophysiology. Materials & methods: We conducted an epigenome-wide association scan on HIV infection status among people who inject drugs in the AIDS Linked to the IntraVenous Experience study with primary (n = 397) and validation samples (n = 390). DNA methylation from blood was measured by the Illumina EPIC BeadChip. We controlled for cell type heterogeneity by HIV status. Results: HIV infection status was associated (p -8) with DNA methylation at 49 CpG sites. Sites were enriched in response to virus, interferon signaling pathway, etc. Among these sites, discovery and validation t-statistics were highly correlated (r = 0.96). Conclusion: In a cohort of people who inject drugs, HIV status was associated with differential DNA methylation at biologically meaningful sites.

Background 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. Methods Spatial and genetic targeting of HC-PrL circuitry was used for RNA sequencing (n = 31), chemogenetic stimulation (n = 44), in vivo calcium imaging (n = 20), ex vivo electrophysiology (n = 8), and molecular regulation of plasticity cascades during fear behavior (context fear retrieval) (n = 16). Results We showed that ventral HC (vHC) neurons with projections to the PrL cortex (vHC-PrL projectors) are a transcriptomically distinct subpopulation compared with adjacent nonprojecting neurons, and we showed complementary enrichment for diverse neuronal processes and central nervous system–related clinical gene sets. We further showed that stimulation of this population of vHC-PrL projectors suppresses context fear memory retrieval and impairs the ability of PrL neurons to dynamically distinguish between distinct phases of fear learning. Using transgenic and circuit-specific molecular targeting approaches, we demonstrated that unique patterns of activity-dependent gene transcription associated with brain-derived neurotrophic factor signaling within vHC-PrL projectors causally regulated activity in excitatory and inhibitory PrL neurons during context fear memory retrieval. Conclusions Together, our data show that activity-dependent brain-derived neurotrophic factor release from molecularly distinct vHC-PrL projection neurons modulates postsynaptic signaling in both inhibitory and excitatory PrL neurons, modifying activity in discrete populations of PrL neurons to suppress freezing during context fear memory retrieval.

DNA methylation (DNAm) is a key epigenetic regulator of gene expression across development. The developing prenatal brain is a highly dynamic tissue, but our understanding of key drivers of epigenetic variability across development is limited. We therefore assessed genomic methylation at over 39 million sites in the prenatal cortex using whole genome bisulfite sequencing and found loci and regions in which methylation levels are dynamic across development. We saw that DNAm at these loci was associated with nearby gene expression and enriched for enhancer chromatin states in prenatal brain tissue. Additionally, these loci were enriched for genes associated with psychiatric disorders and genes involved with neurogenesis. We also found autosomal differences in DNAm between the sexes during prenatal development, though these have less clear functional consequences. We lastly confirmed that the dynamic methylation at this critical period is specifically CpG methylation, with very low levels of CpH methylation. Our findings provide detailed insight into prenatal brain development as well as clues to the pathogenesis of psychiatric traits seen later in life.

Psychiatric disorders are associated with accelerated aging and enhanced risk for neurodegenerative disorders. Brain aging is associated with molecular, cellular, and structural changes that are robust on the group level, yet show substantial inter-individual variability. Here we assessed deviations in gene expression from normal age-dependent trajectories, and tested their validity as predictors of risk for major mental illnesses and neurodegenerative disorders. We performed large-scale gene expression and genotype analyses in postmortem samples of two frontal cortical brain regions from 214 control subjects aged 20–90 years. Individual estimates of “molecular age” were derived from age-dependent genes, identified by robust regression analysis. Deviation from chronological age was defined as “delta age”. Genetic variants associated with deviations from normal gene expression patterns were identified by expression quantitative trait loci (cis-eQTL) of age-dependent genes or genome-wide association study (GWAS) on delta age, combined into distinct polygenic risk scores (PRScis-eQTL and PRSGWAS), and tested for predicting brain disorders or pathology in independent postmortem expression datasets and clinical cohorts. In these validation datasets, molecular ages, defined by 68 and 76 age-related genes for two brain regions respectively, were positively correlated with chronological ages (r = 0.88/0.91), elevated in bipolar disorder (BP) and schizophrenia (SCZ), and unchanged in major depressive disorder (MDD). Exploratory analyses in independent clinical datasets show that PRSs were associated with SCZ and MDD diagnostics, and with cognition in SCZ and pathology in Alzheimer’s disease (AD). These results suggest that older molecular brain aging is a common feature of severe mental illnesses and neurodegeneration.

Gestational exposure to air pollution increases the risk of autism spectrum disorder and cognitive impairments with unresolved molecular mechanisms. This study exposed C57BL/6J mice throughout gestation to urban-derived nanosized particulate matter (nPM). Young adult male and female offspring were studied for behavioral and metabolic changes using forced swim test, fat gain, glucose tolerance, and hippocampal transcriptome. Gestational nPM exposure caused increased depressive behaviors, decreased neurogenesis in the dentate gyrus, and increased glucose tolerance in adult male offspring. Both sexes gained fat and body weight. Gestational nPM exposure induced 29 differentially expressed genes (DEGs) in adult hippocampus related to cytokine production, IL17a signaling, and dopamine degradation in both sexes. Stratification by sex showed twofold more DEGs in males than females (69 vs 37), as well as male-specific enrichment of DEGs mediating serotonin signaling, endocytosis, Gαi, and cAMP signaling. Gene co-expression analysis (WCGNA) identified a module of 43 genes with divergent responses to nPM between the sexes. Chronic changes in 14 DEGs (e.g., microRNA9-1) were associated with depressive behaviors, adiposity and glucose intolerance. These genes enriched neuroimmune pathways such as HMGB1 and TLR4. Based on cerebral cortex transcriptome data of neonates, we traced the initial nPM responses of HMGB1 pathway. In vitro, mixed glia responded to 24 h nPM with lower HMGB1 protein and increased proinflammatory cytokines. This response was ameliorated by TLR4 knockdown. In sum, we identified transcriptional changes that could be associated with air pollution-mediated behavioral and phenotypic changes. These identified genes merit further mechanistic studies for therapeutic intervention development.

While a definitive understanding of schizophrenia etiology is far from current reality, an increasing body of evidence implicates perturbations in early development that alter the trajectory of brain maturation in this disorder, leading to abnormal function in early childhood and adulthood. This atypical development likely arises from an interaction of many brain cell types that each follow distinct developmental paths. Because both cellular identity and development are governed by the transcriptome and epigenome, two levels of gene regulation that have the potential to reflect both genetic and environmental influences, mapping ‘omic changes over development in diverse cells is a fruitful avenue for schizophrenia research. In this review, we provide a survey of human brain cellular composition and development, levels of genomic regulation that determine cellular identity and developmental trajectories, and what is known about how genomic regulation is dysregulated in specific cell types in schizophrenia. We also outline technical challenges and solutions to conducting cell type-specific functional genomic studies in human postmortem brain.

BackgroundBisulfite 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.ResultsWe present BiocMAP, a Bioconductor-friendly Methylation Analysis Pipeline consisting of two modules, to address the above concerns. The first module performs computationally-intensive read alignment using Arioc, a GPU-accelerated short-read aligner. The extraction module extracts and merges DNA methylation proportions - the fractions of methylated cytosines across all cells in a sample at a given genomic site. Since GPUs are not always available on the same computing environments where traditional CPU-based analyses are convenient, BiocMAP is split into two modules, with just the alignment module requiring an available GPU. Bioconductor-based output objects in R utilize an on-disk data representation to drastically reduce required main memory and make WGBS projects computationally feasible to more researchers.ConclusionsBiocMAP is implemented using Nextflow and available at http://research.libd.org/BiocMAP/. To enable reproducible analysis across a variety of typical computing environments, BiocMAP can be containerized with Docker or Singularity, and executed locally or with the SLURM or SGE scheduling engines. By providing Bioconductor objects, BiocMAP’s output can be integrated with powerful analytical open source software for analyzing methylation data.

OBJECTIVE: The authors sought to study the transcriptomic and genomic features of completed suicide by parsing the method chosen, to capture molecular correlates of the distinctive frame of mind of individuals who die by suicide while reducing heterogeneity. METHOD: The authors analyzed gene expression (RNA sequencing) from postmortem dorsolateral prefrontal cortex of patients who died by suicide with violent versus non-violent means and other non-suicide patients with the same psychiatric disorders, and also of neurotypicals (total n=329). They then examined genomic risk-scores (GRS) for each psychiatric disorder included, and GRS for cognition (IQ) and for suicide attempt, testing how they predict diagnosis or traits (total n=888). RESULTS: Patients who died by suicide by violent means (vS-pt) showed a transcriptomic pattern remarkably divergent from each of the other patient groups but less from neurotypicals; consistently, their genomic profile of risk was relatively low for their diagnosed illness as well as for suicide attempt and relatively high for IQ, i.e., more similar to neurotypicals than other patients. Differentially expressed genes (DEGs) associated with vS-pt pointed to purinergic signaling in microglia, showing similarities to GWAS of Drosophila aggression. WGCNA revealed that these DEGs were co-expressed in a context of mitochondrial metabolic activation unique to vS-pt. CONCLUSIONS: These findings suggest that vS-pt are in part biologically separable from other patients with the same diagnoses, and their behavioral outcome may be less dependent on genetic risk for conventional psychiatric disorders while associated with an alteration of purinergic signaling and mitochondrial metabolism.

We present recount3, a resource consisting of over 750,000 publicly available human and mouse RNA sequencing (RNA-seq) samples uniformly processed by our new Monorail analysis pipeline. To facilitate access to the data, we provide the recount3 and snapcount R/Bioconductor packages as well as complementary web resources. Using these tools, data can be downloaded as study-level summaries or queried for specific exon-exon junctions, genes, samples, or other features. Monorail can be used to process local and/or private data, allowing results to be directly compared to any study in recount3. Taken together, our tools help biologists maximize the utility of publicly available RNA-seq data, especially to improve their understanding of newly collected data. recount3 is available from http://rna.recount.bio.

Background: The lateral septum (LS) is a critical social behavior node. The LS is activated by social novelty, and perturbing LS activity impairs social recognition. However, the neural circuits and cell signaling pathways that converge on the LS to mediate this behavior aren9t known. We identify brain-derived neurotrophic factor (BDNF) in projection neurons from the basolateral amgydala (BLA) to the LS, and tropomyosin kinase B receptor (TrkB) signaling in LS neurons, as required for social novelty recognition. Methods: We used single-molecule fluorescent in-situ hybridization to quantify Ntrk2 expression in LS. Viral transgenesis was used to induce cre-recombinase mediated TrkB knockdown in LS to evaluate impact on social recognition behavior, and social novelty-induced c-Fos expression in the LS. We used viral approaches for projection-specific ablation and BDNF-depletion in BLA-LS projection neurons to assess the role of this circuit in social recognition behavior. Results: The majority of GABAergic neurons in LS express TrkB. TrkB knockdown in LS abolishes social novelty recognition, and decreases LS activation in response to social novelty. Selectively ablating BLA-LS projection neurons abolishes social novelty recognition behavior, an effect that is phenocopied by depleting BDNF in this circuit. Conclusions: BLA-LS projection neurons depend on BDNF expression to regulate social recognition in the mouse. Within the LS, neurons require intact TrkB receptor signaling to mediate this behavior.

Antipsychotic drugs are the current first-line of treatment for schizophrenia and other psychotic conditions. However, their molecular effects on the human brain are poorly studied, due to difficulty of tissue access and confounders associated with disease status. Here we examine differences in gene expression and DNA methylation associated with positive antipsychotic drug toxicology status in the human caudate nucleus. We find no genome-wide significant differences in DNA methylation, but abundant differences in gene expression. These gene expression differences are overall quite similar to gene expression differences between schizophrenia cases and controls. Interestingly, gene expression differences based on antipsychotic toxicology are different between brain regions, potentially due to affected cell type differences. We finally assess similarities with effects in a mouse model, which finds some overlapping effects but many differences as well. As a first look at the molecular effects of antipsychotics in the human brain, the lack of epigenetic effects is unexpected, possibly because long term treatment effects may be relatively stable for extended periods.