The three-dimensional landscape of cortical chromatin accessibility in Alzheimer's disease.

Alzheimer Disease / genetics Chromatin Humans Promoter Regions, Genetic Transcription Factors / genetics

Journal

Nature neuroscience
ISSN: 1546-1726
Titre abrégé: Nat Neurosci
Pays: United States
ID NLM: 9809671

Informations de publication

Date de publication:
10 2022
Historique:
received: 16 03 2021
accepted: 16 08 2022
pubmed: 29 9 2022
medline: 12 10 2022
entrez: 28 9 2022
Statut: ppublish

Résumé

To characterize the dysregulation of chromatin accessibility in Alzheimer's disease (AD), we generated 636 ATAC-seq libraries from neuronal and nonneuronal nuclei isolated from the superior temporal gyrus and entorhinal cortex of 153 AD cases and 56 controls. By analyzing a total of ~20 billion read pairs, we expanded the repertoire of known open chromatin regions (OCRs) in the human brain and identified cell-type-specific enhancer-promoter interactions. We show that interindividual variability in OCRs can be leveraged to identify cis-regulatory domains (CRDs) that capture the three-dimensional structure of the genome (3D genome). We identified AD-associated effects on chromatin accessibility, the 3D genome and transcription factor (TF) regulatory networks. For one of the most AD-perturbed TFs, USF2, we validated its regulatory effect on lysosomal genes. Overall, we applied a systematic approach to understanding the role of the 3D genome in AD. We provide all data as an online resource for widespread community-based analysis.

Identifiants

DOI: 10.1038/s41593-022-01166-7 PMID: 36171428 PMC: PMC9581463
pubmed: 36171428
doi: 10.1038/s41593-022-01166-7
pii: 10.1038/s41593-022-01166-7
pmc: PMC9581463
mid: NIHMS1836630
doi:

Substances chimiques

Chromatin 0
Transcription Factors 0

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

1366-1378

Subventions

Organisme : NIMH NIH HHS
ID : R01 MH109897
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG050986
Pays : United States
Organisme : NIGMS NIH HHS
ID : T32 GM062754
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH106056
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG067025
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG065582
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH121074
Pays : United States
Organisme : NIMH NIH HHS
ID : R56 MH101454
Pays : United States
Organisme : NIH HHS
ID : S10 OD018522
Pays : United States
Organisme : NIH HHS
ID : S10 OD026880
Pays : United States

Informations de copyright

© 2022. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.

Références

Andrews, S. J., Fulton-Howard, B. & Goate, A. Interpretation of risk loci from genome-wide association studies of Alzheimer’s disease. Lancet Neurol. 19, 326–335 (2020).
pubmed: 31986256 pmcid: 8176461 doi: 10.1016/S1474-4422(19)30435-1
Marzi, S. J. et al. A histone acetylome-wide association study of Alzheimer’s disease identifies disease-associated H3K27ac differences in the entorhinal cortex. Nat. Neurosci. 21, 1618–1627 (2018).
pubmed: 30349106 doi: 10.1038/s41593-018-0253-7
Klein, H.-U. et al. Epigenome-wide study uncovers large-scale changes in histone acetylation driven by tau pathology in aging and Alzheimer’s human brains. Nat. Neurosci. 22, 37–46 (2019).
pubmed: 30559478 doi: 10.1038/s41593-018-0291-1
Gasparoni, G. et al. DNA methylation analysis on purified neurons and glia dissects age and Alzheimer’s disease-specific changes in the human cortex. Epigenet. Chromatin 11, 41 (2018).
doi: 10.1186/s13072-018-0211-3
Li, P. et al. Epigenetic dysregulation of enhancers in neurons is associated with Alzheimer’s disease pathology and cognitive symptoms. Nat. Commun. 10, 2246 (2019).
pubmed: 31113950 pmcid: 6529540 doi: 10.1038/s41467-019-10101-7
Raj, T. et al. Integrative transcriptome analyses of the aging brain implicate altered splicing in Alzheimer’s disease susceptibility. Nat. Genet. 50, 1584–1592 (2018).
pubmed: 30297968 pmcid: 6354244 doi: 10.1038/s41588-018-0238-1
Nott, A. et al. Brain cell type-specific enhancer–promoter interactome maps and disease-risk association. Science 366, 1134–1139 (2019).
pubmed: 31727856 pmcid: 7028213 doi: 10.1126/science.aay0793
Kozlenkov, A. et al. Substantial DNA methylation differences between two major neuronal subtypes in human brain. Nucleic Acids Res. 44, 2593–2612 (2016).
pubmed: 26612861 doi: 10.1093/nar/gkv1304
Wang, M. et al. The Mount Sinai cohort of large-scale genomic, transcriptomic and proteomic data in Alzheimer’s disease. Sci. Data 5, 180185 (2018).
pubmed: 30204156 pmcid: 6132187 doi: 10.1038/sdata.2018.185
Fillenbaum, G. G. et al. Consortium to Establish a Registry for Alzheimer’s Disease (CERAD): the first twenty years. Alzheimers Dement. 4, 96–109 (2008).
pubmed: 18631955 pmcid: 2808763 doi: 10.1016/j.jalz.2007.08.005
Braak, H. & Braak, E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol. 82, 239–259 (1991).
pubmed: 1759558 doi: 10.1007/BF00308809
Braak, H., Alafuzoff, I., Arzberger, T., Kretzschmar, H. & Del Tredici, K. Staging of Alzheimer disease-associated neurofibrillary pathology using paraffin sections and immunocytochemistry. Acta Neuropathol. 112, 389–404 (2006).
pubmed: 16906426 pmcid: 3906709 doi: 10.1007/s00401-006-0127-z
Morris, J. C. The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology 43, 2412–2414 (1993).
pubmed: 8232972 doi: 10.1212/WNL.43.11.2412-a
Nelson, P. T. et al. Correlation of Alzheimer disease neuropathologic changes with cognitive status: a review of the literature. J. Neuropathol. Exp. Neurol. 71, 362–381 (2012).
pubmed: 22487856 doi: 10.1097/NEN.0b013e31825018f7
Fullard, J. F. et al. An atlas of chromatin accessibility in the adult human brain. Genome Res. 28, 1243–1252 (2018).
pubmed: 29945882 pmcid: 6071637 doi: 10.1101/gr.232488.117
Hauberg, M. E. et al. Common schizophrenia risk variants are enriched in open chromatin regions of human glutamatergic neurons. Nat. Commun. 11, 5581 (2020).
pubmed: 33149216 pmcid: 7643171 doi: 10.1038/s41467-020-19319-2
Hu, B. et al. Neuronal and glial 3D chromatin architecture informs the cellular etiology of brain disorders. Nat. Commun. 12, 3968 (2021).
pubmed: 34172755 pmcid: 8233376 doi: 10.1038/s41467-021-24243-0
Hoffman, G. E. & Schadt, E. E. variancePartition: interpreting drivers of variation in complex gene expression studies. BMC Bioinformatics 17, 483 (2016).
pubmed: 27884101 pmcid: 5123296 doi: 10.1186/s12859-016-1323-z
Fulco, C. P. et al. Activity-by-contact model of enhancer–promoter regulation from thousands of CRISPR perturbations. Nat. Genet. 51, 1664–1669 (2019).
pubmed: 31784727 pmcid: 6886585 doi: 10.1038/s41588-019-0538-0
Mifsud, B. et al. Mapping long-range promoter contacts in human cells with high-resolution capture Hi-C. Nat. Genet. 47, 598–606 (2015).
pubmed: 25938943 doi: 10.1038/ng.3286
Corces, M. R. et al. Single-cell epigenomic analyses implicate candidate causal variants at inherited risk loci for Alzheimer’s and Parkinson’s diseases. Nat. Genet. 52, 1158–1168 (2020).
pubmed: 33106633 pmcid: 7606627 doi: 10.1038/s41588-020-00721-x
Hormozdiari, F. et al. Leveraging molecular quantitative trait loci to understand the genetic architecture of diseases and complex traits. Nat. Genet. 50, 1041–1047 (2018).
pubmed: 29942083 pmcid: 6030458 doi: 10.1038/s41588-018-0148-2
Pickar-Oliver, A. & Gersbach, C. A. The next generation of CRISPR–Cas technologies and applications. Nat. Rev. Mol. Cell Biol. 20, 490–507 (2019).
pubmed: 31147612 pmcid: 7079207 doi: 10.1038/s41580-019-0131-5
Wang, M. et al. Integrative network analysis of nineteen brain regions identifies molecular signatures and networks underlying selective regional vulnerability to Alzheimer’s disease. Genome Med. 8, 104 (2016).
pubmed: 27799057 pmcid: 5088659 doi: 10.1186/s13073-016-0355-3
Khan, U. A. et al. Molecular drivers and cortical spread of lateral entorhinal cortex dysfunction in preclinical Alzheimer’s disease. Nat. Neurosci. 17, 304–311 (2014).
pubmed: 24362760 doi: 10.1038/nn.3606
Barrera, J. et al. Sex dependent glial-specific changes in the chromatin accessibility landscape in late-onset Alzheimer’s disease brains. Mol. Neurodegener. 16, 58 (2021).
pubmed: 34429139 pmcid: 8383438 doi: 10.1186/s13024-021-00481-0
Bowles, K. et al. 17q21.31 sub-haplotypes underlying H1-associated risk for Parkinson’s disease are associated with LRRC37A/2 expression in astrocytes. Mol. Neurodegener. 16, 1–21 (2022).
Jansen, I. E. et al. Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nat. Genet. 51, 404–413 (2019).
pubmed: 30617256 pmcid: 6836675 doi: 10.1038/s41588-018-0311-9
Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).
pubmed: 26414678 pmcid: 4626285 doi: 10.1038/ng.3404
Kunkle, B. W. et al. Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing. Nat. Genet. 51, 414–430 (2019).
pubmed: 30820047 pmcid: 6463297 doi: 10.1038/s41588-019-0358-2
Yokoyama, K. et al. NYAP: a phosphoprotein family that links PI3K to WAVE1 signalling in neurons. EMBO J. 30, 4739–4754 (2011).
pubmed: 21946561 pmcid: 3243607 doi: 10.1038/emboj.2011.348
Chen, X. et al. Cholecystokinin release triggered by NMDA receptors produces LTP and sound–sound associative memory. Proc. Natl. Acad. Sci. U.S.A. 116, 6397–6406 (2019).
pubmed: 30850520 pmcid: 6442640 doi: 10.1073/pnas.1816833116
Li, X., Long, J., He, T., Belshaw, R. & Scott, J. Integrated genomic approaches identify major pathways and upstream regulators in late onset Alzheimer’s disease. Sci. Rep. 5, 12393 (2015).
pubmed: 26202100 pmcid: 4511863 doi: 10.1038/srep12393
Castillo, E. et al. Comparative profiling of cortical gene expression in Alzheimer’s disease patients and mouse models demonstrates a link between amyloidosis and neuroinflammation. Sci. Rep. 7, 17762 (2017).
pubmed: 29259249 pmcid: 5736730 doi: 10.1038/s41598-017-17999-3
Hokama, M. et al. Altered expression of diabetes-related genes in Alzheimer’s disease brains: the Hisayama study. Cereb. Cortex 24, 2476–2488 (2014).
pubmed: 23595620 doi: 10.1093/cercor/bht101
Chan, P. M. & Manser, E. PAKs in human disease. Prog. Mol. Biol. Transl. Sci. 106, 171–187 (2012).
pubmed: 22340718 doi: 10.1016/B978-0-12-396456-4.00011-0
Bell, R. D. & Zlokovic, B. V. Neurovascular mechanisms and blood–brain barrier disorder in Alzheimer’s disease. Acta Neuropathol. 118, 103–113 (2009).
pubmed: 19319544 pmcid: 2853006 doi: 10.1007/s00401-009-0522-3
Yuan, Z. et al. Regulation of neuronal cell death by MST1–FOXO1 signaling. J. Biol. Chem. 284, 11285–11292 (2009).
pubmed: 19221179 pmcid: 2670133 doi: 10.1074/jbc.M900461200
Greer, E. L. & Brunet, A. FOXO transcription factors at the interface between longevity and tumor suppression. Oncogene 24, 7410–7425 (2005).
pubmed: 16288288 doi: 10.1038/sj.onc.1209086
Webb, A. E., Kundaje, A. & Brunet, A. Characterization of the direct targets of FOXO transcription factors throughout evolution. Aging Cell 15, 673–685 (2016).
pubmed: 27061590 pmcid: 4933671 doi: 10.1111/acel.12479
Koopmans, F. et al. SynGO: an evidence-based, expert-curated knowledge base for the synapse. Neuron 103, 217–234 (2019).
pubmed: 31171447 pmcid: 6764089 doi: 10.1016/j.neuron.2019.05.002
Bentsen, M. et al. ATAC-seq footprinting unravels kinetics of transcription factor binding during zygotic genome activation. Nat. Commun. 11, 4267 (2020).
pubmed: 32848148 pmcid: 7449963 doi: 10.1038/s41467-020-18035-1
Rahman, M. R. et al. Network-based approach to identify molecular signatures and therapeutic agents in Alzheimer’s disease. Comput. Biol. Chem. 78, 431–439 (2019).
pubmed: 30606694 doi: 10.1016/j.compbiolchem.2018.12.011
Acquaah-Mensah, G. K. & Taylor, R. C. Brain in situ hybridization maps as a source for reverse-engineering transcriptional regulatory networks: Alzheimer’s disease insights. Gene 586, 77–86 (2016).
pubmed: 27050105 doi: 10.1016/j.gene.2016.03.045
Qin, L. et al. Ethnicity-specific and overlapping alterations of brain hydroxymethylome in Alzheimer’s disease. Hum. Mol. Genet. 29, 149–158 (2020).
pubmed: 31814020 doi: 10.1093/hmg/ddz273
Malik, B. R., Maddison, D. C., Smith, G. A. & Peters, O. M. Autophagic and endo-lysosomal dysfunction in neurodegenerative disease. Mol. Brain 12, 100 (2019).
pubmed: 31783880 pmcid: 6884906 doi: 10.1186/s13041-019-0504-x
Yamanaka, T. et al. Genome-wide analyses in neuronal cells reveal that upstream transcription factors regulate lysosomal gene expression. FEBS J. 283, 1077–1087 (2016).
pubmed: 26756308 doi: 10.1111/febs.13650
Johnson, D. E., Ostrowski, P., Jaumouillé, V. & Grinstein, S. The position of lysosomes within the cell determines their luminal pH. J. Cell Biol. 212, 677–692 (2016).
pubmed: 26975849 pmcid: 4792074 doi: 10.1083/jcb.201507112
Hayek, S. R., Rane, H. S. & Parra, K. J. Reciprocal regulation of v-ATPase and glycolytic pathway elements in health and disease. Front. Physiol. 10, 127 (2019).
pubmed: 30828305 pmcid: 6384264 doi: 10.3389/fphys.2019.00127
Couoh-Cardel, S., Milgrom, E. & Wilkens, S. Affinity purification and structural features of the yeast vacuolar ATPase V0 membrane sector. J. Biol. Chem. 290, 27959–27971 (2015).
pubmed: 26416888 pmcid: 4646912 doi: 10.1074/jbc.M115.662494
Delaneau, O. et al. Chromatin three-dimensional interactions mediate genetic effects on gene expression. Science 364, eaat8266 (2019).
pubmed: 31048460 doi: 10.1126/science.aat8266
Ambroise, C., Dehman, A., Neuvial, P., Rigaill, G. & Vialaneix, N. Adjacency-constrained hierarchical clustering of a band similarity matrix with application to genomics. Algorithms Mol. Biol. 14, 22 (2019).
pubmed: 31807137 pmcid: 6857244 doi: 10.1186/s13015-019-0157-4
Hoffman, G. E., Bendl, J., Girdhar, K. & Roussos, P. decorate: differential epigenetic correlation test. Bioinformatics 36, 2856–2861 (2020).
pubmed: 32003784 pmcid: 7203736 doi: 10.1093/bioinformatics/btaa067
Rao, S. S. P. et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 159, 1665–1680 (2014).
pubmed: 25497547 pmcid: 5635824 doi: 10.1016/j.cell.2014.11.021
Lieberman-Aiden, E. et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326, 289–293 (2009).
pubmed: 19815776 pmcid: 2858594 doi: 10.1126/science.1181369
De Jager, P. L. et al. A multi-omic atlas of the human frontal cortex for aging and Alzheimer’s disease research. Sci. Data 5, 180142 (2018).
pubmed: 30084846 pmcid: 6080491 doi: 10.1038/sdata.2018.142
Meuleman, W. et al. Constitutive nuclear lamina–genome interactions are highly conserved and associated with A/T-rich sequence. Genome Res. 23, 270–280 (2013).
pubmed: 23124521 pmcid: 3561868 doi: 10.1101/gr.141028.112
Roadmap Epigenomics Consortiumet al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).
pmcid: 4530010 doi: 10.1038/nature14248
Corces, M.R. et al. The chromatin accessibility landscape of primary human cancers. Science 362, eaav1898 (2018).
pubmed: 30361341 pmcid: 6408149 doi: 10.1126/science.aav1898
Ishizuka, Y. & Hanamura, K. Drebrin in Alzheimer’s disease. Adv. Exp. Med. Biol. 1006, 203–223 (2017).
pubmed: 28865022 doi: 10.1007/978-4-431-56550-5_12
Turi, Z., Lacey, M., Mistrik, M. & Moudry, P. Impaired ribosome biogenesis: mechanisms and relevance to cancer and aging. Aging 11, 2512–2540 (2019).
pubmed: 31026227 pmcid: 6520011 doi: 10.18632/aging.101922
Chambers, D. M., Peters, J. & Abbott, C. M. The lethal mutation of the mouse wasted (wst) is a deletion that abolishes expression of a tissue-specific isoform of translation elongation factor 1α, encoded by the Eef1a2 gene. Proc. Natl. Acad. Sci. U.S.A. 95, 4463–4468 (1998).
pubmed: 9539760 pmcid: 22512 doi: 10.1073/pnas.95.8.4463
Zeisel, A. et al. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142 (2015).
pubmed: 25700174 doi: 10.1126/science.aaa1934
Zhang, W. et al. Integrative transcriptome imputation reveals tissue-specific and shared biological mechanisms mediating susceptibility to complex traits. Nat. Commun. 10, 3834 (2019).
pubmed: 31444360 pmcid: 6707297 doi: 10.1038/s41467-019-11874-7
Ernst, J. & Kellis, M. Chromatin-state discovery and genome annotation with ChromHMM. Nat. Protoc. 12, 2478–2492 (2017).
pubmed: 29120462 pmcid: 5945550 doi: 10.1038/nprot.2017.124
Meuleman, W. et al. Index and biological spectrum of human DNase I hypersensitive sites. Nature 584, 244–251 (2020).
pubmed: 32728217 pmcid: 7422677 doi: 10.1038/s41586-020-2559-3
Dong, P. et al. Population-level variation of enhancer expression identifies novel disease mechanisms in the human brain. Nat. Genet. https://doi.org/10.1038/s41588-022-01170-4 (2022).
Ernst, J. & Kellis, M. Large-scale imputation of epigenomic datasets for systematic annotation of diverse human tissues. Nat. Biotechnol. 33, 364–376 (2015).
pubmed: 25690853 pmcid: 4512306 doi: 10.1038/nbt.3157
Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).
pubmed: 25605792 pmcid: 4402510 doi: 10.1093/nar/gkv007
Hoffman, G. E. & Roussos, P. Dream: powerful differential expression analysis for repeated measures designs. Bioinformatics 37, 192–201 (2021).
pubmed: 32730587 doi: 10.1093/bioinformatics/btaa687
de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015).
pubmed: 25885710 pmcid: 4401657 doi: 10.1371/journal.pcbi.1004219
Haroutunian, V., Katsel, P. & Schmeidler, J. Transcriptional vulnerability of brain regions in Alzheimer’s disease and dementia. Neurobiol. Aging 30, 561–573 (2009).
pubmed: 17845826 doi: 10.1016/j.neurobiolaging.2007.07.021
Morris, J. C. et al. The Consortium to Establish a Registry for Alzheimer’s Disease (CERAD). Part I. Clinical and neuropsychological assessment of Alzheimer’s disease. Neurology 39, 1159–1165 (1989).
pubmed: 2771064 doi: 10.1212/WNL.39.9.1159
Buenrostro, J. D., Wu, B., Chang, H. Y. & Greenleaf, W. J. ATAC-seq: a method for assaying chromatin accessibility genome-wide. Curr. Protoc. Mol. Biol. 109, 21.29.1–21.29.9 (2015).
doi: 10.1002/0471142727.mb2129s109
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
pubmed: 23104886 doi: 10.1093/bioinformatics/bts635
Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).
pubmed: 18798982 pmcid: 2592715 doi: 10.1186/gb-2008-9-9-r137
McKenna, A. et al. The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).
pubmed: 20644199 pmcid: 2928508 doi: 10.1101/gr.107524.110
Manichaikul, A. et al. Robust relationship inference in genome-wide association studies. Bioinformatics 26, 2867–2873 (2010).
pubmed: 20926424 pmcid: 3025716 doi: 10.1093/bioinformatics/btq559
Servant, N. et al. HiC-Pro: an optimized and flexible pipeline for Hi-C data processing. Genome Biol. 16, 259 (2015).
pubmed: 26619908 pmcid: 4665391 doi: 10.1186/s13059-015-0831-x
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
pubmed: 22388286 pmcid: 3322381 doi: 10.1038/nmeth.1923
Durand, N. C. et al. Juicer provides a one-click system for analyzing loop-resolution Hi-C experiments. Cell Syst. 3, 95–98 (2016).
pubmed: 27467249 pmcid: 5846465 doi: 10.1016/j.cels.2016.07.002
Shin, H. et al. TopDom: an efficient and deterministic method for identifying topological domains in genomes. Nucleic Acids Res. 44, e70 (2016).
pubmed: 26704975 doi: 10.1093/nar/gkv1505
Yu, G., Wang, L.-G. & He, Q.-Y. ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization. Bioinformatics 31, 2382–2383 (2015).
pubmed: 25765347 doi: 10.1093/bioinformatics/btv145
Lawrence, M. et al. Software for computing and annotating genomic ranges. PLoS Comput. Biol. 9, e1003118 (2013).
pubmed: 23950696 pmcid: 3738458 doi: 10.1371/journal.pcbi.1003118
Zhang, Y. et al. An RNA-sequencing transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex. J. Neurosci. 34, 11929–11947 (2014).
pubmed: 25186741 pmcid: 4152602 doi: 10.1523/JNEUROSCI.1860-14.2014
Liberzon, A. et al. Molecular Signatures Database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011).
pubmed: 21546393 pmcid: 3106198 doi: 10.1093/bioinformatics/btr260
McLean, C. Y. et al. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 28, 495–501 (2010).
pubmed: 20436461 pmcid: 4840234 doi: 10.1038/nbt.1630
Law, C.W. et al. RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR. F1000Res 5, 1408 (2016).
doi: 10.12688/f1000research.9005.1
Yoshida, H. et al. The cis-regulatory atlas of the mouse immune system. Cell 176, 897–912 (2019).
pubmed: 30686579 pmcid: 6785993 doi: 10.1016/j.cell.2018.12.036
Anscombe, F. J. The transformation of Poisson, binomial and negative-binomial data. Biometrika 35, 246–254 (1948).
doi: 10.1093/biomet/35.3-4.246
Francis Harrison, P. Varistran: Anscombe’s variance stabilizing transformation for RNA-seq gene expression data. JOSS 2, 257 (2017).
doi: 10.21105/joss.00257
Gandal, M. J. et al. Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder. Science 362, eaat8127 (2018).
pubmed: 30545856 pmcid: 6443102 doi: 10.1126/science.aat8127
Nagpal, S. et al. TIGAR: an improved Bayesian tool for transcriptomic data imputation enhances gene mapping of complex traits. Am. J. Hum. Genet. 105, 258–266 (2019).
pubmed: 31230719 pmcid: 6698804 doi: 10.1016/j.ajhg.2019.05.018
Becker, M., Kuhse, J. & Kirsch, J. Effects of two elongation factor 1A isoforms on the formation of gephyrin clusters at inhibitory synapses in hippocampal neurons. Histochem. Cell Biol. 140, 603–609 (2013).
pubmed: 23839781 doi: 10.1007/s00418-013-1122-9
Shirao, T. et al. The role of drebrin in neurons. J. Neurochem. 141, 819–834 (2017).
pubmed: 28199019 doi: 10.1111/jnc.13988
Hetman, M. & Slomnicki, L. P. Ribosomal biogenesis as an emerging target of neurodevelopmental pathologies. J. Neurochem. 148, 325–347 (2019).
pubmed: 30144322 doi: 10.1111/jnc.14576
Schrode, N. et al. Synergistic effects of common schizophrenia risk variants. Nat. Genet. 51, 1475–1485 (2019).
pubmed: 31548722 pmcid: 6778520 doi: 10.1038/s41588-019-0497-5
Weirauch, M. T. et al. Determination and inference of eukaryotic transcription factor sequence specificity. Cell 158, 1431–1443 (2014).
pubmed: 25215497 pmcid: 4163041 doi: 10.1016/j.cell.2014.08.009
Gupta, S., Stamatoyannopoulos, J. A., Bailey, T. L. & Noble, W. S. Quantifying similarity between motifs. Genome Biol. 8, R24 (2007).
pubmed: 17324271 pmcid: 1852410 doi: 10.1186/gb-2007-8-2-r24
Reyna, M. A., Leiserson, M. D. M. & Raphael, B. J. Hierarchical HotNet: identifying hierarchies of altered subnetworks. Bioinformatics 34, i972–i980 (2018).
pubmed: 30423088 pmcid: 6129270 doi: 10.1093/bioinformatics/bty613

Auteurs

Jaroslav Bendl (J)

Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
ORCID: 0000-0001-9989-2720

Mads E Hauberg (ME)

Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Department of Biomedicine, Aarhus University, Aarhus, Denmark.
The Lundbeck Foundation Initiative of Integrative Psychiatric Research (iPSYCH), Aarhus University, Aarhus, Denmark.
Centre for Integrative Sequencing (iSEQ), Aarhus University, Aarhus, Denmark.
ORCID: 0000-0003-4873-2764

Kiran Girdhar (K)

Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
ORCID: 0000-0002-5622-042X

Eunju Im (E)

Center for Dementia Research, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA.
Department of Psychiatry, New York University Langone Health, New York, NY, USA.
ORCID: 0000-0002-7839-8878

James M Vicari (JM)

Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Graduate School of Biomedical Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
ORCID: 0000-0002-4633-9961

Samir Rahman (S)

Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

Michael B Fernando (MB)

Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Graduate School of Biomedical Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
ORCID: 0000-0002-4620-8048

Kayla G Townsley (KG)

Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Graduate School of Biomedical Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
ORCID: 0000-0003-4706-5752

Pengfei Dong (P)

Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
ORCID: 0000-0003-3735-5158

Ruth Misir (R)

Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

Steven P Kleopoulos (SP)

Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

Sarah M Reach (SM)

Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

Pasha Apontes (P)

Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

Biao Zeng (B)

Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

Wen Zhang (W)

Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

Georgios Voloudakis (G)

Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
ORCID: 0000-0002-5729-632X

Kristen J Brennand (KJ)

Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Department of Psychiatry, Yale University, New Haven, CT, USA.
ORCID: 0000-0003-0993-5956

Ralph A Nixon (RA)

Center for Dementia Research, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA.
Department of Psychiatry, New York University Langone Health, New York, NY, USA.
Department of Cell Biology, New York University Langone Health, New York, NY, USA.
New York University Neuroscience Institute, New York, NY, USA.
ORCID: 0000-0001-5124-1021

Vahram Haroutunian (V)

Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, NY, USA.
ORCID: 0000-0001-5860-2512

Gabriel E Hoffman (GE)

Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
ORCID: 0000-0002-0957-0224

John F Fullard (JF)

Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
ORCID: 0000-0001-9874-2907

Panos Roussos (P)

Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA. panagiotis.roussos@mssm.edu.
Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. panagiotis.roussos@mssm.edu.
Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA. panagiotis.roussos@mssm.edu.
Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA. panagiotis.roussos@mssm.edu.
Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA. panagiotis.roussos@mssm.edu.
Center for Dementia Research, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA. panagiotis.roussos@mssm.edu.
Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, NY, USA. panagiotis.roussos@mssm.edu.
ORCID: 0000-0002-4640-6239

Articles similaires

[Redispensing of expensive oral anticancer medicines: a practical application].

Lisanne N van Merendonk, Kübra Akgöl, Bastiaan Nuijen
1.00
Humans Antineoplastic Agents Administration, Oral Drug Costs Counterfeit Drugs

Smoking Cessation and Incident Cardiovascular Disease.

Jun Hwan Cho, Seung Yong Shin, Hoseob Kim et al.
1.00
Humans Male Smoking Cessation Cardiovascular Diseases Female

Evaluation of Low-Value Services Across Major Medicare Advantage Insurers and Traditional Medicare.

Ciara Duggan, Adam L Beckman, Ishani Ganguli et al.
1.00
Humans United States Aged Cross-Sectional Studies Medicare Part C

Effectiveness of Virtual Yoga for Chronic Low Back Pain: A Randomized Clinical Trial.

Hallie Tankha, Devyn Gaskins, Amanda Shallcross et al.
1.00
Humans Yoga Low Back Pain Female Male

Classifications MeSH

questionsmedicales.fr › Troubles mentaux › Troubles neurocognitifs › Démence › Maladie d'Alzheimer › The three-dimensional landscape of cortical...
questionsmedicales.fr › Phénomènes génétiques › Structures génétiques › Structures de chromosome › Chromatine › The three-dimensional landscape of cortical...
questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › The three-dimensional landscape of cortical...
questionsmedicales.fr › Phénomènes génétiques › Structures génétiques › Génome › Composants de génome › Gènes › Composants de gène › Éléments de régulation transcriptionnelle › Régions promotrices (génétique) › The three-dimensional landscape of cortical...
questionsmedicales.fr › Acides aminés, peptides et protéines › Protéines › Facteurs de transcription › The three-dimensional landscape of cortical...