Publications by authors named "Panos Roussos"

126 Publications

Single-nucleus transcriptome analysis of human brain immune response in patients with severe COVID-19.

Genome Med 2021 07 19;13(1):118. Epub 2021 Jul 19.

Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, New York, NY, 10029, USA.

Background: Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, has been associated with neurological and neuropsychiatric illness in many individuals. We sought to further our understanding of the relationship between brain tropism, neuro-inflammation, and host immune response in acute COVID-19 cases.

Methods: Three brain regions (dorsolateral prefrontal cortex, medulla oblongata, and choroid plexus) from 5 patients with severe COVID-19 and 4 controls were examined. The presence of the virus was assessed by western blot against viral spike protein, as well as viral transcriptome analysis covering > 99% of SARS-CoV-2 genome and all potential serotypes. Droplet-based single-nucleus RNA sequencing (snRNA-seq) was performed in the same samples to examine the impact of COVID-19 on transcription in individual cells of the brain.

Results: Quantification of viral spike S1 protein and viral transcripts did not detect SARS-CoV-2 in the postmortem brain tissue. However, analysis of 68,557 single-nucleus transcriptomes from three distinct regions of the brain identified an increased proportion of stromal cells, monocytes, and macrophages in the choroid plexus of COVID-19 patients. Furthermore, differential gene expression, pseudo-temporal trajectory, and gene regulatory network analyses revealed transcriptional changes in the cortical microglia associated with a range of biological processes, including cellular activation, mobility, and phagocytosis.

Conclusions: Despite the absence of detectable SARS-CoV-2 in the brain at the time of death, the findings suggest significant and persistent neuroinflammation in patients with acute COVID-19.
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http://dx.doi.org/10.1186/s13073-021-00933-8DOI Listing
July 2021

Profiling Basal Forebrain Cholinergic Neurons Reveals a Molecular Basis for Vulnerability Within the Ts65Dn Model of Down Syndrome and Alzheimer's Disease.

Mol Neurobiol 2021 Jul 14. Epub 2021 Jul 14.

Center for Dementia Research, Nathan Kline Institute, 140 Old Orangeburg Road, Orangeburg, NY, 10962, USA.

Basal forebrain cholinergic neuron (BFCN) degeneration is a hallmark of Down syndrome (DS) and Alzheimer's disease (AD). Current therapeutics have been unsuccessful in slowing disease progression, likely due to complex pathological interactions and dysregulated pathways that are poorly understood. The Ts65Dn trisomic mouse model recapitulates both cognitive and morphological deficits of DS and AD, including BFCN degeneration. We utilized Ts65Dn mice to understand mechanisms underlying BFCN degeneration to identify novel targets for therapeutic intervention. We performed high-throughput, single population RNA sequencing (RNA-seq) to interrogate transcriptomic changes within medial septal nucleus (MSN) BFCNs, using laser capture microdissection to individually isolate ~500 choline acetyltransferase-immunopositive neurons in Ts65Dn and normal disomic (2N) mice at 6 months of age (MO). Ts65Dn mice had unique MSN BFCN transcriptomic profiles at ~6 MO clearly differentiating them from 2N mice. Leveraging Ingenuity Pathway Analysis and KEGG analysis, we linked differentially expressed gene (DEG) changes within MSN BFCNs to several canonical pathways and aberrant physiological functions. The dysregulated transcriptomic profile of trisomic BFCNs provides key information underscoring selective vulnerability within the septohippocampal circuit. We propose both expected and novel therapeutic targets for DS and AD, including specific DEGs within cholinergic, glutamatergic, GABAergic, and neurotrophin pathways, as well as select targets for repairing oxidative phosphorylation status in neurons. We demonstrate and validate this interrogative quantitative bioinformatic analysis of a key dysregulated neuronal population linking single population transcript changes to an established pathological hallmark associated with cognitive decline for therapeutic development in human DS and AD.
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http://dx.doi.org/10.1007/s12035-021-02453-3DOI Listing
July 2021

Sex Differences in the Human Brain Transcriptome of Cases With Schizophrenia.

Biol Psychiatry 2021 Mar 25. Epub 2021 Mar 25.

Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York; Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York; Mental Illness Research, Education and Clinical Centers, James J. Peters VA Medical Center, Bronx, New York. Electronic address:

Background: While schizophrenia differs between males and females in the age of onset, symptomatology, and disease course, the molecular mechanisms underlying these differences remain uncharacterized.

Methods: To address questions about the sex-specific effects of schizophrenia, we performed a large-scale transcriptome analysis of RNA sequencing data from 437 controls and 341 cases from two distinct cohorts from the CommonMind Consortium.

Results: Analysis across the cohorts identified a reproducible gene expression signature of schizophrenia that was highly concordant with previous work. Differential expression across sex was reproducible across cohorts and identified X- and Y-linked genes, as well as those involved in dosage compensation. Intriguingly, the sex expression signature was also enriched for genes involved in neurexin family protein binding and synaptic organization. Differential expression analysis testing a sex-by-diagnosis interaction effect did not identify any genome-wide signature after multiple testing corrections. Gene coexpression network analysis was performed to reduce dimensionality from thousands of genes to dozens of modules and elucidate interactions among genes. We found enrichment of coexpression modules for sex-by-diagnosis differential expression signatures, which were highly reproducible across the two cohorts and involved a number of diverse pathways, including neural nucleus development, neuron projection morphogenesis, and regulation of neural precursor cell proliferation.

Conclusions: Overall, our results indicate that the effect size of sex differences in schizophrenia gene expression signatures is small and underscore the challenge of identifying robust sex-by-diagnosis signatures, which will require future analyses in larger cohorts.
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http://dx.doi.org/10.1016/j.biopsych.2021.03.020DOI Listing
March 2021

IL10RB as a key regulator of COVID-19 host susceptibility and severity.

medRxiv 2021 Jun 2. Epub 2021 Jun 2.

Background: Recent efforts have identified genetic loci that are associated with coronavirus disease 2019 (COVID-19) infection rates and disease outcome severity. Translating these genetic findings into druggable genes and readily available compounds that reduce COVID-19 host susceptibility is a critical next step.

Methods: We integrate COVID-19 genetic susceptibility variants, multi-tissue genetically regulated gene expression (GReX) and perturbargen signatures to identify candidate genes and compounds that reverse the predicted gene expression dysregulation associated with COVID-19 susceptibility. The top candidate gene is validated by testing both its GReX and observed blood transcriptome association with COVID-19 severity, as well as by perturbation to quantify effects on viral load and molecular pathway dysregulation. We validate the drug repositioning analysis by examining whether the top candidate compounds decrease COVID-19 incidence based on epidemiological evidence.

Results: We identify as the top key regulator of COVID-19 host susceptibility. Predicted GReX up-regulation of and higher expression in COVID-19 patient blood is associated with worse COVID-19 outcomes. IL10RB overexpression is associated with increased viral load and activation of immune-related molecular pathways. Azathioprine and retinol are prioritized as candidate compounds to reduce the likelihood of testing positive for COVID-19.

Conclusions: We establish an integrative data-driven approach for gene target prioritization. We identify and validate as a suitable molecular target for modulation of COVID-19 host susceptibility. Finally, we provide evidence for a few readily available medications that would warrant further investigation as drug repositioning candidates.
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http://dx.doi.org/10.1101/2021.05.31.21254851DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8183086PMC
June 2021

Identifying nootropic drug targets via large-scale cognitive GWAS and transcriptomics.

Neuropsychopharmacology 2021 May 25. Epub 2021 May 25.

Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

Broad-based cognitive deficits are an enduring and disabling symptom for many patients with severe mental illness, and these impairments are inadequately addressed by current medications. While novel drug targets for schizophrenia and depression have emerged from recent large-scale genome-wide association studies (GWAS) of these psychiatric disorders, GWAS of general cognitive ability can suggest potential targets for nootropic drug repurposing. Here, we (1) meta-analyze results from two recent cognitive GWAS to further enhance power for locus discovery; (2) employ several complementary transcriptomic methods to identify genes in these loci that are credibly associated with cognition; and (3) further annotate the resulting genes using multiple chemoinformatic databases to identify "druggable" targets. Using our meta-analytic data set (N = 373,617), we identified 241 independent cognition-associated loci (29 novel), and 76 genes were identified by 2 or more methods of gene identification. Actin and chromatin binding gene sets were identified as novel pathways that could be targeted via drug repurposing. Leveraging our transcriptomic and chemoinformatic databases, we identified 16 putative genes targeted by existing drugs potentially available for cognitive repurposing.
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http://dx.doi.org/10.1038/s41386-021-01023-4DOI Listing
May 2021

Genome-wide association study of more than 40,000 bipolar disorder cases provides new insights into the underlying biology.

Nat Genet 2021 06 17;53(6):817-829. Epub 2021 May 17.

Department of Neuroscience, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy.

Bipolar disorder is a heritable mental illness with complex etiology. We performed a genome-wide association study of 41,917 bipolar disorder cases and 371,549 controls of European ancestry, which identified 64 associated genomic loci. Bipolar disorder risk alleles were enriched in genes in synaptic signaling pathways and brain-expressed genes, particularly those with high specificity of expression in neurons of the prefrontal cortex and hippocampus. Significant signal enrichment was found in genes encoding targets of antipsychotics, calcium channel blockers, antiepileptics and anesthetics. Integrating expression quantitative trait locus data implicated 15 genes robustly linked to bipolar disorder via gene expression, encoding druggable targets such as HTR6, MCHR1, DCLK3 and FURIN. Analyses of bipolar disorder subtypes indicated high but imperfect genetic correlation between bipolar disorder type I and II and identified additional associated loci. Together, these results advance our understanding of the biological etiology of bipolar disorder, identify novel therapeutic leads and prioritize genes for functional follow-up studies.
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http://dx.doi.org/10.1038/s41588-021-00857-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8192451PMC
June 2021

Sex Differences in Molecular Rhythms in the Human Cortex.

Biol Psychiatry 2021 Mar 8. Epub 2021 Mar 8.

Department of Psychiatry, University of Pittsburgh Medical School, Pittsburgh, Pennsylvania; Translational Neuroscience Program, University of Pittsburgh Medical School, Pittsburgh, Pennsylvania. Electronic address:

Background: Diurnal rhythms in gene expression have been detected in the human brain. Previous studies found that males and females exhibit 24-hour rhythms in known circadian genes, with earlier peak expression in females. Whether there are sex differences in large-scale transcriptional rhythms in the cortex that align with observed sex differences in physiological and behavioral rhythms is currently unknown.

Methods: Diurnal rhythmicity of gene expression was determined for males and females using RNA sequencing data from human postmortem dorsolateral prefrontal cortex (DLPFC) and anterior cingulate cortex (ACC). Sex differences among rhythmic genes were determined using significance cutoffs, threshold-free analyses, and R difference. Phase concordance was assessed across the DLPFC and ACC for males and females. Pathway and transcription factor analyses were also conducted on significantly rhythmic genes.

Results: Canonical circadian genes had diurnal rhythms in both sexes with similar amplitude and phase. When analyses were expanded to the entire transcriptome, significant sex differences in transcriptional rhythms emerged. There were nearly twice as many rhythmic transcripts in the DLPFC in males and nearly 4 times as many rhythmic transcripts in the ACC in females. Results suggest a diurnal rhythm in synaptic transmission specific to the ACC in females (e.g., GABAergic [gamma-aminobutyric acidergic] and cholinergic neurotransmission). For males, there was phase concordance between the DLPFC and ACC, while phase asynchrony was found in females.

Conclusions: There are robust sex differences in molecular rhythms of genes in the DLPFC and ACC, providing potential mechanistic insights into how neurotransmission and synaptic function are modulated in a circadian-dependent and sex-specific manner.
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http://dx.doi.org/10.1016/j.biopsych.2021.03.005DOI Listing
March 2021

Integration of Alzheimer's disease genetics and myeloid genomics identifies disease risk regulatory elements and genes.

Nat Commun 2021 03 12;12(1):1610. Epub 2021 Mar 12.

Ronald M. Loeb Center for Alzheimer's Disease, Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

Genome-wide association studies (GWAS) have identified more than 40 loci associated with Alzheimer's disease (AD), but the causal variants, regulatory elements, genes and pathways remain largely unknown, impeding a mechanistic understanding of AD pathogenesis. Previously, we showed that AD risk alleles are enriched in myeloid-specific epigenomic annotations. Here, we show that they are specifically enriched in active enhancers of monocytes, macrophages and microglia. We integrated AD GWAS with myeloid epigenomic and transcriptomic datasets using analytical approaches to link myeloid enhancer activity to target gene expression regulation and AD risk modification. We identify AD risk enhancers and nominate candidate causal genes among their likely targets (including AP4E1, AP4M1, APBB3, BIN1, MS4A4A, MS4A6A, PILRA, RABEP1, SPI1, TP53INP1, and ZYX) in twenty loci. Fine-mapping of these enhancers nominates candidate functional variants that likely modify AD risk by regulating gene expression in myeloid cells. In the MS4A locus we identified a single candidate functional variant and validated it in human induced pluripotent stem cell (hiPSC)-derived microglia and brain. Taken together, this study integrates AD GWAS with multiple myeloid genomic datasets to investigate the mechanisms of AD risk alleles and nominates candidate functional variants, regulatory elements and genes that likely modulate disease susceptibility.
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http://dx.doi.org/10.1038/s41467-021-21823-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7955030PMC
March 2021

Disruption of nuclear architecture as a cause of COVID-19 induced anosmia.

bioRxiv 2021 Feb 9. Epub 2021 Feb 9.

Olfaction relies on a coordinated partnership between odorant flow and neuronal communication. Disruption in our ability to detect odors, or anosmia, has emerged as a hallmark symptom of infection with SARS-CoV-2, yet the mechanism behind this abrupt sensory deficit remains elusive. Here, using molecular evaluation of human olfactory epithelium (OE) from subjects succumbing to COVID-19 and a hamster model of SARS-CoV-2 infection, we discovered widespread downregulation of olfactory receptors (ORs) as well as key components of their signaling pathway. OR downregulation likely represents a non-cell autonomous effect, since SARS-CoV-2 detection in OSNs is extremely rare both in human and hamster OEs. A likely explanation for the reduction of OR transcription is the striking reorganization of nuclear architecture observed in the OSN lineage, which disrupts multi-chromosomal compartments regulating OR expression in humans and hamsters. Our experiments uncover a novel molecular mechanism by which a virus with a very selective tropism can elicit persistent transcriptional changes in cells that evade it, contributing to the severity of COVID-19.
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http://dx.doi.org/10.1101/2021.02.09.430314DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7885920PMC
February 2021

Common schizophrenia risk variants are enriched in open chromatin regions of human glutamatergic neurons.

Nat Commun 2020 11 4;11(1):5581. Epub 2020 Nov 4.

Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.

The chromatin landscape of human brain cells encompasses key information to understanding brain function. Here we use ATAC-seq to profile the chromatin structure in four distinct populations of cells (glutamatergic neurons, GABAergic neurons, oligodendrocytes, and microglia/astrocytes) from three different brain regions (anterior cingulate cortex, dorsolateral prefrontal cortex, and primary visual cortex) in human postmortem brain samples. We find that chromatin accessibility varies greatly by cell type and, more moderately, by brain region, with glutamatergic neurons showing the largest regional variability. Transcription factor footprinting implicates cell-specific transcriptional regulators and infers cell-specific regulation of protein-coding genes, long intergenic noncoding RNAs and microRNAs. In vivo transgenic mouse experiments validate the cell type specificity of several of these human-derived regulatory sequences. We find that open chromatin regions in glutamatergic neurons are enriched for neuropsychiatric risk variants, particularly those associated with schizophrenia. Integration of cell-specific chromatin data with a bulk tissue study of schizophrenia brains increases statistical power and confirms that glutamatergic neurons are most affected. These findings illustrate the utility of studying the cell-type-specific epigenome in complex tissues like the human brain, and the potential of such approaches to better understand the genetic basis of human brain function.
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http://dx.doi.org/10.1038/s41467-020-19319-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7643171PMC
November 2020

Differential gene regulatory pattern in the human brain from schizophrenia using transcriptomic-causal network.

BMC Bioinformatics 2020 Oct 21;21(1):469. Epub 2020 Oct 21.

Department of Psychiatry, Pamela Sklar Division of Psychiatric Genomics and Friedman Brain Institute, Icahn School of Medicine At Mount Sinai, Hess CSM Building Floor 9 Room 107, 1470 Madison Ave, New York, NY, 10029, USA.

Background: Common and complex traits are the consequence of the interaction and regulation of multiple genes simultaneously, therefore characterizing the interconnectivity of genes is essential to unravel the underlying biological networks. However, the focus of many studies is on the differential expression of individual genes or on co-expression analysis.

Methods: Going beyond analysis of one gene at a time, we systematically integrated transcriptomics, genotypes and Hi-C data to identify interconnectivities among individual genes as a causal network. We utilized different machine learning techniques to extract information from the network and identify differential regulatory pattern between cases and controls. We used data from the Allen Brain Atlas for replication.

Results: Employing the integrative systems approach on the data from CommonMind Consortium showed that gene transcription is controlled by genetic variants proximal to the gene (cis-regulatory factors), and transcribed distal genes (trans-regulatory factors). We identified differential gene regulatory patterns in SCZ-cases versus controls and novel SCZ-associated genes that may play roles in the disorder since some of them are primary expressed in human brain. In addition, we observed genes known associated with SCZ are not likely (OR = 0.59) to have high impacts (degree > 3) on the network.

Conclusions: Causal networks could reveal underlying patterns and the role of genes individually and as a group. Establishing principles that govern relationships between genes provides a mechanistic understanding of the dysregulated gene transcription patterns in SCZ and creates more efficient experimental designs for further studies. This information cannot be obtained by studying a single gene at the time.
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http://dx.doi.org/10.1186/s12859-020-03753-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7579819PMC
October 2020

Chromatin accessibility mapping of the striatum identifies tyrosine kinase FYN as a therapeutic target for heroin use disorder.

Nat Commun 2020 09 14;11(1):4634. Epub 2020 Sep 14.

Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.

The current opioid epidemic necessitates a better understanding of human addiction neurobiology to develop efficacious treatment approaches. Here, we perform genome-wide assessment of chromatin accessibility of the human striatum in heroin users and matched controls. Our study reveals distinct neuronal and non-neuronal epigenetic signatures, and identifies a locus in the proximity of the gene encoding tyrosine kinase FYN as the most affected region in neurons. FYN expression, kinase activity and the phosphorylation of its target Tau are increased by heroin use in the post-mortem human striatum, as well as in rats trained to self-administer heroin and primary striatal neurons treated with chronic morphine in vitro. Pharmacological or genetic manipulation of FYN activity significantly attenuates heroin self-administration and responding for drug-paired cues in rodents. Our findings suggest that striatal FYN is an important driver of heroin-related neurodegenerative-like pathology and drug-taking behavior, making FYN a promising therapeutic target for heroin use disorder.
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http://dx.doi.org/10.1038/s41467-020-18114-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7490718PMC
September 2020

Dream: powerful differential expression analysis for repeated measures designs.

Bioinformatics 2021 04;37(2):192-201

Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.

Summary: Large-scale transcriptome studies with multiple samples per individual are widely used to study disease biology. Yet, current methods for differential expression are inadequate for cross-individual testing for these repeated measures designs. Most problematic, we observe across multiple datasets that current methods can give reproducible false-positive findings that are driven by genetic regulation of gene expression, yet are unrelated to the trait of interest. Here, we introduce a statistical software package, dream, that increases power, controls the false positive rate, enables multiple types of hypothesis tests, and integrates with standard workflows. In 12 analyses in 6 independent datasets, dream yields biological insight not found with existing software while addressing the issue of reproducible false-positive findings.

Availability And Implementation: Dream is available within the variancePartition Bioconductor package at http://bioconductor.org/packages/variancePartition.

Contact: [email protected]

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btaa687DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8055218PMC
April 2021

Functional annotation of rare structural variation in the human brain.

Nat Commun 2020 06 12;11(1):2990. Epub 2020 Jun 12.

Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.

Structural variants (SVs) contribute to many disorders, yet, functionally annotating them remains a major challenge. Here, we integrate SVs with RNA-sequencing from human post-mortem brains to quantify their dosage and regulatory effects. We show that genic and regulatory SVs exist at significantly lower frequencies than intergenic SVs. Functional impact of copy number variants (CNVs) stems from both the proportion of genic and regulatory content altered and loss-of-function intolerance of the gene. We train a linear model to predict expression effects of rare CNVs and use it to annotate regulatory disruption of CNVs from 14,891 independent genome-sequenced individuals. Pathogenic deletions implicated in neurodevelopmental disorders show significantly more extreme regulatory disruption scores and if rank ordered would be prioritized higher than using frequency or length alone. This work shows the deleteriousness of regulatory SVs, particularly those altering CTCF sites and provides a simple approach for functionally annotating the regulatory consequences of CNVs.
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http://dx.doi.org/10.1038/s41467-020-16736-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7293301PMC
June 2020

Comparison of brain connectomes by MRI and genomics and its implication in Alzheimer's disease.

BMC Med 2020 02 6;18(1):23. Epub 2020 Feb 6.

Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.

Background: The human brain is complex and interconnected structurally. Brain connectome change is associated with Alzheimer's disease (AD) and other neurodegenerative diseases. Genetics and genomics studies have identified molecular changes in AD; however, the results are often limited to isolated brain regions and are difficult to interpret its findings in respect to brain connectome. The mechanisms of how one brain region impacts the molecular pathways in other regions have not been systematically studied. And how the brain regions susceptible to AD pathology interact with each other at the transcriptome level and how these interactions relate to brain connectome change are unclear.

Methods: Here, we compared structural brain connectomes defined by probabilistic tracts using diffusion magnetic resonance imaging data in Alzheimer's Disease Neuroimaging Initiative database and a brain transcriptome dataset covering 17 brain regions.

Results: We observed that the changes in diffusion measures associated with AD diagnosis status and the associations were replicated in an independent cohort. The result suggests that disease associated white matter changes are focal. Analysis of the brain connectome by genomic data, tissue-tissue transcriptional synchronization between 17 brain regions, indicates that the regions connected by AD-associated tracts were likely connected at the transcriptome level with high number of tissue-to-tissue correlated (TTC) gene pairs (P = 0.03). And genes involved in TTC gene pairs between white matter tract connected brain regions were enriched in signaling pathways (P = 6.08 × 10). Further pathway interaction analysis identified ionotropic glutamate receptor pathway and Toll receptor signaling pathways to be important for tissue-tissue synchronization at the transcriptome level. Transcript profile entailing Toll receptor signaling in the blood was significantly associated with diffusion properties of white matter tracts, notable association between fractional anisotropy and bilateral cingulum angular bundles (P = 1.0 × 10 and 4.9 × 10 for left and right respectively).

Conclusions: In summary, our study suggests that brain connectomes defined by MRI and transcriptome data overlap with each other.
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http://dx.doi.org/10.1186/s12916-019-1488-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7003435PMC
February 2020

decorate: differential epigenetic correlation test.

Bioinformatics 2020 05;36(9):2856-2861

Pamela Sklar Division of Psychiatric Genomics.

Motivation: Identifying correlated epigenetic features and finding differences in correlation between individuals with disease compared to controls can give novel insight into disease biology. This framework has been successful in analysis of gene expression data, but application to epigenetic data has been limited by the computational cost, lack of scalable software and lack of robust statistical tests.

Results: Decorate, differential epigenetic correlation test, identifies correlated epigenetic features and finds clusters of features that are differentially correlated between two or more subsets of the data. The software scales to genome-wide datasets of epigenetic assays on hundreds of individuals. We apply decorate to four large-scale datasets of DNA methylation, ATAC-seq and histone modification ChIP-seq.

Availability And Implementation: decorate R package is available from https://github.com/GabrielHoffman/decorate.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btaa067DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7203736PMC
May 2020

CommonMind Consortium provides transcriptomic and epigenomic data for Schizophrenia and Bipolar Disorder.

Sci Data 2019 09 24;6(1):180. Epub 2019 Sep 24.

Pamela Sklar Division of Psychiatric Genomics, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA.

Schizophrenia and bipolar disorder are serious mental illnesses that affect more than 2% of adults. While large-scale genetics studies have identified genomic regions associated with disease risk, less is known about the molecular mechanisms by which risk alleles with small effects lead to schizophrenia and bipolar disorder. In order to fill this gap between genetics and disease phenotype, we have undertaken a multi-cohort genomics study of postmortem brains from controls, individuals with schizophrenia and bipolar disorder. Here we present a public resource of functional genomic data from the dorsolateral prefrontal cortex (DLPFC; Brodmann areas 9 and 46) of 986 individuals from 4 separate brain banks, including 353 diagnosed with schizophrenia and 120 with bipolar disorder. The genomic data include RNA-seq and SNP genotypes on 980 individuals, and ATAC-seq on 269 individuals, of which 264 are a subset of individuals with RNA-seq. We have performed extensive preprocessing and quality control on these data so that the research community can take advantage of this public resource available on the Synapse platform at http://CommonMind.org .
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http://dx.doi.org/10.1038/s41597-019-0183-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6760149PMC
September 2019

Functional interpretation of genetic variants using deep learning predicts impact on chromatin accessibility and histone modification.

Nucleic Acids Res 2019 11;47(20):10597-10611

Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

Identifying functional variants underlying disease risk and adoption of personalized medicine are currently limited by the challenge of interpreting the functional consequences of genetic variants. Predicting the functional effects of disease-associated protein-coding variants is increasingly routine. Yet, the vast majority of risk variants are non-coding, and predicting the functional consequence and prioritizing variants for functional validation remains a major challenge. Here, we develop a deep learning model to accurately predict locus-specific signals from four epigenetic assays using only DNA sequence as input. Given the predicted epigenetic signal from DNA sequence for the reference and alternative alleles at a given locus, we generate a score of the predicted epigenetic consequences for 438 million variants observed in previous sequencing projects. These impact scores are assay-specific, are predictive of allele-specific transcription factor binding and are enriched for variants associated with gene expression and disease risk. Nucleotide-level functional consequence scores for non-coding variants can refine the mechanism of known functional variants, identify novel risk variants and prioritize downstream experiments.
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http://dx.doi.org/10.1093/nar/gkz808DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6847046PMC
November 2019

Global landscape and genetic regulation of RNA editing in cortical samples from individuals with schizophrenia.

Nat Neurosci 2019 09 27;22(9):1402-1412. Epub 2019 Aug 27.

Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

RNA editing critically regulates neurodevelopment and normal neuronal function. The global landscape of RNA editing was surveyed across 364 schizophrenia cases and 383 control postmortem brain samples from the CommonMind Consortium, comprising two regions: dorsolateral prefrontal cortex and anterior cingulate cortex. In schizophrenia, RNA editing sites in genes encoding AMPA-type glutamate receptors and postsynaptic density proteins were less edited, whereas those encoding translation initiation machinery were edited more. These sites replicate between brain regions, map to 3'-untranslated regions and intronic regions, share common sequence motifs and overlap with binding sites for RNA-binding proteins crucial for neurodevelopment. These findings cross-validate in hundreds of non-overlapping dorsolateral prefrontal cortex samples. Furthermore, ~30% of RNA editing sites associate with cis-regulatory variants (editing quantitative trait loci or edQTLs). Fine-mapping edQTLs with schizophrenia risk loci revealed co-localization of eleven edQTLs with six loci. The findings demonstrate widespread altered RNA editing in schizophrenia and its genetic regulation, and suggest a causal and mechanistic role of RNA editing in schizophrenia neuropathology.
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http://dx.doi.org/10.1038/s41593-019-0463-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6791127PMC
September 2019

Integrative transcriptome imputation reveals tissue-specific and shared biological mechanisms mediating susceptibility to complex traits.

Nat Commun 2019 08 23;10(1):3834. Epub 2019 Aug 23.

Department of Psychiatry, Pamela Sklar Division of Psychiatric Genomics and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.

Transcriptome-wide association studies integrate gene expression data with common risk variation to identify gene-trait associations. By incorporating epigenome data to estimate the functional importance of genetic variation on gene expression, we generate a small but significant improvement in the accuracy of transcriptome prediction and increase the power to detect significant expression-trait associations. Joint analysis of 14 large-scale transcriptome datasets and 58 traits identify 13,724 significant expression-trait associations that converge on biological processes and relevant phenotypes in human and mouse phenotype databases. We perform drug repurposing analysis and identify compounds that mimic, or reverse, trait-specific changes. We identify genes that exhibit agonistic pleiotropy for genetically correlated traits that converge on shared biological pathways and elucidate distinct processes in disease etiopathogenesis. Overall, this comprehensive analysis provides insight into the specificity and convergence of gene expression on susceptibility to complex traits.
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http://dx.doi.org/10.1038/s41467-019-11874-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6707297PMC
August 2019

Genetic Variation in Long-Range Enhancers.

Curr Top Behav Neurosci 2019 ;42:35-50

Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

Cis-regulatory elements (CREs), including insulators, promoters, and enhancers, play critical roles in the establishment and maintenance of normal cellular function. Within each cell, the 3D structure of chromatin is arranged in specific patterns to expose the CREs required for optimal spatiotemporal regulation of gene expression. CREs can act over large distances along the linear genome, facilitated by looping of the intervening chromatin to allow direct interaction between distal regulatory elements and their target genes. A number of pathologies are associated with dysregulation of CRE function, including developmental disorders, cancers, and neuropsychiatric disease. A majority of known neuropsychiatric disease risk loci are noncoding, and increasing evidence suggests that they contribute to disease through disruption of CREs. As such, rather than directly altering the amino acid content of proteins, these variants are instead thought to affect where, when, and to what extent a given gene is expressed. The distances over which CREs can operate often render their target genes difficult to identify. Furthermore, as many risk loci contain multiple variants in high linkage disequilibrium, identification of the causative single nucleotide polymorphism(s) therein is not straightforward. Thus, deciphering the genetic etiology of complex neuropsychiatric disorders presents a significant challenge.
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http://dx.doi.org/10.1007/7854_2019_110DOI Listing
January 2020

Pleiotropic Meta-Analysis of Cognition, Education, and Schizophrenia Differentiates Roles of Early Neurodevelopmental and Adult Synaptic Pathways.

Am J Hum Genet 2019 08;105(2):334-350

Centre for Epidemiology, Division of Population Health, Health Services Research and Primary Care, University of Manchester, Manchester M139PL, United Kingdom; School of Healthcare Sciences, Manchester Metropolitan University, Manchester M15 6BH, United Kingdom.

Susceptibility to schizophrenia is inversely correlated with general cognitive ability at both the phenotypic and the genetic level. Paradoxically, a modest but consistent positive genetic correlation has been reported between schizophrenia and educational attainment, despite the strong positive genetic correlation between cognitive ability and educational attainment. Here we leverage published genome-wide association studies (GWASs) in cognitive ability, education, and schizophrenia to parse biological mechanisms underlying these results. Association analysis based on subsets (ASSET), a pleiotropic meta-analytic technique, allowed jointly associated loci to be identified and characterized. Specifically, we identified subsets of variants associated in the expected ("concordant") direction across all three phenotypes (i.e., greater risk for schizophrenia, lower cognitive ability, and lower educational attainment); these were contrasted with variants that demonstrated the counterintuitive ("discordant") relationship between education and schizophrenia (i.e., greater risk for schizophrenia and higher educational attainment). ASSET analysis revealed 235 independent loci associated with cognitive ability, education, and/or schizophrenia at p < 5 × 10. Pleiotropic analysis successfully identified more than 100 loci that were not significant in the input GWASs. Many of these have been validated by larger, more recent single-phenotype GWASs. Leveraging the joint genetic correlations of cognitive ability, education, and schizophrenia, we were able to dissociate two distinct biological mechanisms-early neurodevelopmental pathways that characterize concordant allelic variation and adulthood synaptic pruning pathways-that were linked to the paradoxical positive genetic association between education and schizophrenia. Furthermore, genetic correlation analyses revealed that these mechanisms contribute not only to the etiopathogenesis of schizophrenia but also to the broader biological dimensions implicated in both general health outcomes and psychiatric illness.
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http://dx.doi.org/10.1016/j.ajhg.2019.06.012DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6699140PMC
August 2019

Publisher Correction: Gene expression imputation across multiple brain regions provides insights into schizophrenia risk.

Nat Genet 2019 Jun;51(6):1068

Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA.

In the HTML version of the article originally published, the author group 'The Schizophrenia Working Group of the Psychiatric Genomics Consortium' was displayed incorrectly. The error has been corrected in the HTML version of the article.
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http://dx.doi.org/10.1038/s41588-019-0435-6DOI Listing
June 2019

Genome-wide association study identifies 30 loci associated with bipolar disorder.

Nat Genet 2019 05 1;51(5):793-803. Epub 2019 May 1.

Department of Psychiatry, Weill Cornell Medical College, New York, NY, USA.

Bipolar disorder is a highly heritable psychiatric disorder. We performed a genome-wide association study (GWAS) including 20,352 cases and 31,358 controls of European descent, with follow-up analysis of 822 variants with P < 1 × 10 in an additional 9,412 cases and 137,760 controls. Eight of the 19 variants that were genome-wide significant (P < 5 × 10) in the discovery GWAS were not genome-wide significant in the combined analysis, consistent with small effect sizes and limited power but also with genetic heterogeneity. In the combined analysis, 30 loci were genome-wide significant, including 20 newly identified loci. The significant loci contain genes encoding ion channels, neurotransmitter transporters and synaptic components. Pathway analysis revealed nine significantly enriched gene sets, including regulation of insulin secretion and endocannabinoid signaling. Bipolar I disorder is strongly genetically correlated with schizophrenia, driven by psychosis, whereas bipolar II disorder is more strongly correlated with major depressive disorder. These findings address key clinical questions and provide potential biological mechanisms for bipolar disorder.
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http://dx.doi.org/10.1038/s41588-019-0397-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6956732PMC
May 2019

Author Correction: Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function.

Nat Commun 2019 May 1;10(1):2068. Epub 2019 May 1.

Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, 00014, Finland.

Christina M. Lill, who contributed to analysis of data, was inadvertently omitted from the author list in the originally published version of this article. This has now been corrected in both the PDF and HTML versions of the article.
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http://dx.doi.org/10.1038/s41467-019-10160-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6494826PMC
May 2019

Gene expression imputation across multiple brain regions provides insights into schizophrenia risk.

Nat Genet 2019 04 25;51(4):659-674. Epub 2019 Mar 25.

Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA.

Transcriptomic imputation approaches combine eQTL reference panels with large-scale genotype data in order to test associations between disease and gene expression. These genic associations could elucidate signals in complex genome-wide association study (GWAS) loci and may disentangle the role of different tissues in disease development. We used the largest eQTL reference panel for the dorso-lateral prefrontal cortex (DLPFC) to create a set of gene expression predictors and demonstrate their utility. We applied DLPFC and 12 GTEx-brain predictors to 40,299 schizophrenia cases and 65,264 matched controls for a large transcriptomic imputation study of schizophrenia. We identified 413 genic associations across 13 brain regions. Stepwise conditioning identified 67 non-MHC genes, of which 14 did not fall within previous GWAS loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple porphyric disorder pathways. We investigated developmental expression patterns among the 67 non-MHC genes and identified specific groups of pre- and postnatal expression.
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http://dx.doi.org/10.1038/s41588-019-0364-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7034316PMC
April 2019

Identification of common genetic risk variants for autism spectrum disorder.

Nat Genet 2019 03 25;51(3):431-444. Epub 2019 Feb 25.

The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark.

Autism spectrum disorder (ASD) is a highly heritable and heterogeneous group of neurodevelopmental phenotypes diagnosed in more than 1% of children. Common genetic variants contribute substantially to ASD susceptibility, but to date no individual variants have been robustly associated with ASD. With a marked sample-size increase from a unique Danish population resource, we report a genome-wide association meta-analysis of 18,381 individuals with ASD and 27,969 controls that identified five genome-wide-significant loci. Leveraging GWAS results from three phenotypes with significantly overlapping genetic architectures (schizophrenia, major depression, and educational attainment), we identified seven additional loci shared with other traits at equally strict significance levels. Dissecting the polygenic architecture, we found both quantitative and qualitative polygenic heterogeneity across ASD subtypes. These results highlight biological insights, particularly relating to neuronal function and corticogenesis, and establish that GWAS performed at scale will be much more productive in the near term in ASD.
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http://dx.doi.org/10.1038/s41588-019-0344-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6454898PMC
March 2019

The expression of long noncoding RNA NEAT1 is reduced in schizophrenia and modulates oligodendrocytes transcription.

NPJ Schizophr 2019 Jan 29;5(1). Epub 2019 Jan 29.

Department of Psychiatry, The Icahn School of Medicine at Mount Sinai, New York, NY, USA.

Oligodendrocyte (OLG)-related abnormalities have been broadly observed in schizophrenia (SZ); however, the etiology of these abnormalities remains unknown. As SZ is broadly believed to be a developmental disorder, the etiology of the myelin abnormalities in SZ may be related to OLG fate specification during development. Noncoding RNAs (ncRNAs) are an important part of multifaceted transcriptional complexes participating in neurogenic commitment and regulation of postmitotic cell function. The long ncRNA, NEAT1, is a structural component of paraspeckles (subnuclear bodies in interchromatin regions) that may control activity of developmental enhancers of OLG fate specification. Gene expression studies of multiple cortical regions from individuals with SZ showed strong downregulation of NEAT1 levels relative to controls. NEAT1-deficient mice show significant decreases in the numbers of OLG-lineage cells in the frontal cortex. To gain further insight into biological processes affected by NEAT1 deficiency, we analyzed RNA-seq data from frontal cortex of NEAT1 mice. Analyses of differentially expressed gene signature from NEAT1 mice revealed a significant impact on processes related to OLG differentiation and RNA posttranscriptional modification with the underlying mechanisms involving Wnt signaling, cell contact interactions, and regulation of cholesterol/lipid metabolism. Additional studies revealed evidence of co-expression of SOX10, an OLG transcription factor, and NEAT1, and showed enrichment of OLG-specific transcripts in NEAT1 purified chromatin isolates from human frontal cortex. Reduced nuclear retention of quaking isoform 5 in NEAT1 mice shed light on possible mechanism(s) responsible for reduced expression of OLG/myelin proteins and supported the involvement of NEAT1 in oligodendrocyte function.
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http://dx.doi.org/10.1038/s41537-019-0071-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6386752PMC
January 2019

Assessment of somatic single-nucleotide variation in brain tissue of cases with schizophrenia.

Transl Psychiatry 2019 01 17;9(1):21. Epub 2019 Jan 17.

Department of Psychiatry, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.

The genetic architecture of schizophrenia (SCZ) includes numerous risk loci across a range of frequencies and sizes, including common and rare single-nucleotide variants and insertions/deletions (indels), as well as rare copy number variants (CNVs). Despite the clear heritability of the disease, monozygotic twins are discordant for SCZ at a significant rate. Somatic variants-genetic changes that arise after fertilization rather than through germline inheritance-are widespread in the human brain and known to contribute to risk for both rare and common neuropsychiatric conditions. The contribution of somatic variants in the brain to risk of SCZ remains to be determined. In this study, we surveyed somatic single-nucleotide variants (sSNVs) in the brains of controls and individuals with SCZ (n = 10 and n = 9, respectively). From each individual, whole-exome sequencing (WES) was performed on DNA from neuronal and non-neuronal nuclei isolated by fluorescence activated nuclear sorting (FANS) from frozen postmortem prefrontal cortex (PFC) samples, as well as DNA extracted from temporal muscle as a reference. We identified an increased burden of sSNVs in cases compared to controls (SCZ rate = 2.78, control rate = 0.70; P = 0.0092, linear mixed effects model), that included a higher rate of non-synonymous and loss-of-function variants (SCZ rate = 1.33, control rate = 0.50; P = 0.047, linear mixed effects model). Our findings suggest sSNVs in the brain may constitute an additional component of the complex genetic architecture of SCZ. This perspective argues for the need to further investigate somatic variation in the brain as an explanation of the discordance in monozygotic twins and a potential guide to the identification of novel therapeutic targets.
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http://dx.doi.org/10.1038/s41398-018-0342-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6336839PMC
January 2019

Biopsy During Minimally Invasive Intracerebral Hemorrhage Clot Evacuation.

World Neurosurg 2018 Dec 24. Epub 2018 Dec 24.

Department of Neurosurgery, Mount Sinai Hospital, New York, New York, USA. Electronic address:

Background: The safety and efficacy of brain parenchyma biopsy during minimally invasive (MIS) intracerebral hemorrhage (ICH) clot evacuation has not been previously reported. The objective of this study was to establish the safety and diagnostic efficacy of brain biopsy during MIS ICH clot evacuation and to validate the modified Boston criteria as a predictor of cerebral amyloid angiopathy (CAA) in this cohort.

Methods: From October 2016 to March 2018, superficial and perihematomal biopsies were collected for 40 patients undergoing MIS ICH clot evacuation and analyzed by the pathology department to assess for various ICH etiologies. Additionally, the admission magnetic resonance imaging or computed tomography scan of each patient was analyzed and evaluated for the likelihood of a CAA etiology based on the modified Boston criteria. Student t test was used to analyze intergroup differences in continuous variables, and a 2-tailed Fisher exact test was used to determine intergroup differences of categorical variables, with significance set at P < 0.05.

Results: Two of the 40 patients (5%) experienced postoperative rebleed. Four of the 40 patients (10%) had evidence of CAA on biopsy. Patients with CAA on biopsy were older (P = 0.005) and had a higher prevalence of parietal lobe (P = 0.02) and occipital lobe (P = 0.001) hemorrhage. The modified Boston criteria had a sensitivity of 100% (95% confidence interval [CI], 39.6%-100%) and a specificity of 72.2% (95% CI, 54.6%-84.2%) for predicting CAA on biopsy.

Conclusions: Brain biopsy in MIS ICH clot evacuation is safe and allows for the diagnosis of various ICH etiologies.
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http://dx.doi.org/10.1016/j.wneu.2018.12.058DOI Listing
December 2018