Alzheimer's disease

The Lancet, Journal Year: 2021, Volume and Issue: 397(10284), P. 1577 - 1590

Published: March 2, 2021

Language: Английский

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The Amyloid-β Pathway in Alzheimer’s Disease

Molecular Psychiatry, Journal Year: 2021, Volume and Issue: 26(10), P. 5481 - 5503

Published: Aug. 30, 2021

Abstract Breakthroughs in molecular medicine have positioned the amyloid-β (Aβ) pathway at center of Alzheimer’s disease (AD) pathophysiology. While detailed mechanisms and spatial-temporal dynamics leading to synaptic failure, neurodegeneration, clinical onset are still under intense investigation, established biochemical alterations Aβ cycle remain core biological hallmark AD promising targets for development disease-modifying therapies. Here, we systematically review update vast state-of-the-art literature science with evidence from basic research studies human genetic multi-modal biomarker investigations, which supports a crucial role dyshomeostasis pathophysiological dynamics. We discuss highlighting differentiated interaction distinct species other AD-related mechanisms, such as tau-mediated, neuroimmune inflammatory changes, well neurochemical imbalance. Through lens latest multimodal vivo biomarkers AD, this cross-disciplinary examines compelling hypothesis- data-driven rationale Aβ-targeting therapeutic strategies early treatment AD.

Language: Английский

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Synergy between amyloid-β and tau in Alzheimer’s disease

Nature Neuroscience, Journal Year: 2020, Volume and Issue: 23(10), P. 1183 - 1193

Published: Aug. 10, 2020

Language: Английский

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Spatial Transcriptomics and In Situ Sequencing to Study Alzheimer’s Disease

Cell, Journal Year: 2020, Volume and Issue: 182(4), P. 976 - 991.e19

Published: July 22, 2020

Language: Английский

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Distinct amyloid-β and tau-associated microglia profiles in Alzheimer’s disease

Acta Neuropathologica, Journal Year: 2021, Volume and Issue: 141(5), P. 681 - 696

Published: Feb. 20, 2021

Abstract Alzheimer’s disease (AD) is the most prevalent form of dementia and characterized by abnormal extracellular aggregates amyloid-β intraneuronal hyperphosphorylated tau tangles neuropil threads. Microglia, tissue-resident macrophages central nervous system (CNS), are important for CNS homeostasis implicated in AD pathology. In amyloid mouse models, a phagocytic/activated microglia phenotype has been identified. How increasing levels pathology affect human transcriptional profiles unknown. Here, we performed snRNAseq on 482,472 nuclei from non-demented control brains containing only plaques or both Within population, distinct expression were identified which two pathology-associated. The AD1-microglia population abundance strongly correlated with tissue load localized to plaques. AD2-microglia phospho-tau these more abundant samples overt This full characterization disease-associated phenotypes provides new insights pathophysiological role offers targets microglia-state-specific therapeutic strategies.

Language: Английский

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Novel Alzheimer risk genes determine the microglia response to amyloid‐β but not to TAU pathology

EMBO Molecular Medicine, Journal Year: 2020, Volume and Issue: 12(3)

Published: Jan. 17, 2020

Article17 January 2020Open Access Transparent process Novel Alzheimer risk genes determine the microglia response to amyloid-β but not TAU pathology Annerieke Sierksma orcid.org/0000-0001-9233-972X VIB Center for Brain & Disease Research, Leuven, Belgium Laboratory Research of Neurodegenerative Diseases, Department Neurosciences, Leuven Institute (LBI), KU (University Leuven), Search more papers by this author Ashley Lu Renzo Mancuso orcid.org/0000-0002-7046-3348 Nicola Fattorelli orcid.org/0000-0001-5564-8179 Thrupp Evgenia Salta Jesus Zoco orcid.org/0000-0003-1164-4306 David Blum orcid.org/0000-0001-5691-431X INSERM, CHU Lille, LabEx DISTALZ, UMR-S 1172, Tauopathies, Université France Luc Buée Bart De Strooper Corresponding Author [email protected] orcid.org/0000-0001-5455-5819 UK Dementia Institute, University College London, Mark Fiers orcid.org/0000-0001-5694-2409 Information Sierksma1,2,‡, Lu1,2,‡, Mancuso1,2, Fattorelli1,2, Thrupp1,2, Salta1,2, Zoco1,2, Blum3, Buée3, *,1,2,4,‡ and *,1,2,‡ 1VIB 2Laboratory 3INSERM, 4UK ‡These authors contributed equally work as first senior *Corresponding author. Tel: +32 4957 71044; E-mail: 4944 95150; EMBO Mol Med (2020)12:e10606https://doi.org/10.15252/emmm.201910606 PDFDownload PDF article text main figures. Peer ReviewDownload a summary editorial decision including letters, reviewer comments responses feedback. ToolsAdd favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures Info Abstract Polygenic scores have identified that genetic variants without genome-wide significance still add developing Alzheimer's disease (AD). Whether how subthreshold loci translate into relevant pathways is unknown. We investigate here involvement AD in transcriptional two mouse models: APPswe/PS1L166P Thy-TAU22. A unique gene expression module, highly enriched genes, specifically responsive Aβ pathology. identify module 7 established (APOE, CLU, INPP5D, CD33, PLCG2, SPI1, FCER1G) 11 GWAS below threshold (GPC2, TREML2, SYK, GRN, SLC2A5, SAMSN1, PYDC1, HEXB, RRBP1, LYN, BLNK), become significantly upregulated when exposed Aβ. Single sequencing confirms Aβ, TAU, induces marked changes microglia, increased proportions activated microglia. conclude functionally translates different pathway pathology, placing downstream amyloid upstream Synopsis It unknown (AD) manifests itself at molecular cellular level brain. Analysis TAUtg an APPtg models show mainly reflected are among transcriptomic APPtg/Amyloid plaques forming mice massive deregulation with aging, increasing neuroinflammatory while TAUtg/tangle display downregulation neuronal genes. Many above genome wide co-regulated large involved neuroinflammation. 18 prioritized, which all expressed may regulate their function (in red synopsis figure). 15 many adopt phenotype facing than Introduction Genetic background strongly determines sporadic (Gatz et al, 2006). Unlike APOE4 polymorphism 42 other loci, thousands SNPs associated do reach (Efthymiou Goate, 2017; Marioni 2018; Verheijen Sleegers, 2018). (PRSs) incorporate contributions these variations relate (Purcell 2009). PRSs currently prediction accuracy 84%, albeit major proportion can be attributed APOE status alone (Escott-Price 2017). Two crucial questions arise from myriad studies: (i) Are linked (Aβ) or (ii) they converge within single pathway, define parallel lead AD? Although it remains challenging causally link affected we expect least part implicated association studies (GWAS) affect brain (De Karran, 2016; Efthymiou Such model integrates parts hypothesis complex genetics AD, will coherent view on pathogenesis AD. Profiling postmortem tissue only provides insights advanced stages cannot delineate cause–consequence relationships, required develop mechanistic (Zhang 2013). Transgenic hand partially recapitulate frontotemporal dementia (FTD) phenotypes, provide detailed functional initial steps disease, high relevance preventative therapeutic interventions (Zahs Ashe, 2010). What lacking until now, however, integration information data obtained human. Doing so help whether sub-significant This would increase confidence truly indicate play role. Here, perform profiling hippocampus after exposure early (4 months age; 4M) mature (10-11M). use (APPtg) Thy-TAU22 (TAUtg) mice, both expressing transgene Thy1.2 promotor (Radde 2006; Schindowski The biochemical insults mimicked animals reflect morphological hallmarks (SAD) familial cases (FAD). Therefore, FAD useful assess demonstrate despite similar robust cognitive severe age-dependent deregulation, milder over time stable phenotype. uniquely coordinated deregulated multicellular network functions Tau observe alterations biology. strong neuroinflammation demonstrating (ARM; 57%) homeostatic (20%) APPtg-11M whereas phenotypic shifts were much less pronounced TAUtg-11M mice. Our evidence microglial promotes candidate future research. Results At 4M age, cognitively intact mild levels 10M overlapping profiles hippocampus-dependent mnemonic deficits substantial (see Fig 1A; Radde Lo RNA-seq was performed (TG) respective wild-type (WT) littermates, n = 12 per group 96 total, yielding average 7.7 million reads sample 1B). Bulk good alteration upon onset progression allows us uncover co-regulation beyond individual cell types. Figure 1. Enrichment A. Visualization load TAUwt, TAUtg, 9M age. Immunofluorescent staining X34 (a fluorescent derivative Congo Red; magenta), Iba1 (microglia; green), TO-PRO-3 (nuclei; blue) has been pseudocolored. Scale bar 100 μm. B. Experimental design mRNA using experimental group. C. Explanation 2 × linear model, where those cells labeled 1 compared 0. In age comparison, 10-month-old (10M) 4-month-old (4M) genotype transgenic age*genotype transcripts differentially TG groups. D. Based al (2018), various sets created cut-off P-values indicated x-axis (number each set written gray). assessed statistical comparisons (A*G: age*genotype, Gen: genotype), enrichment analysis (GSEA, Subramanian 2005). Colors represent Benjamini–Yekutieli-adjusted P-value enrichment; blank means no significant enrichment. Download figure PowerPoint found (Fig 1C) employed effects genotype, interaction. comparison identifies change between strain (i.e., WT 10M, analyzing strains separately; see 2A). shows differences 2B). interaction, finally, assesses aging 2C). study thus reflects manifesting critical points: initially signs occur, later on, manifest accompanying deficits. 2. Changes exacerbate miceLog2 fold (LFC) (x-axis) (y-axis) differential analysis. Upregulated right (TAUtg mice) upper (APPtg graph; downregulated left lower graph. Colored dots (Benjamini–Yekutieli-adjusted (Padj) < 0.05) (green dots), (yellow (red dots). Spearman correlation ranking either most up- combined score LFC Padj signed log10(P-value), sign determined LFC). Genes i.e., versus independently genotype. Thus, positive due TG, interaction comparing TG-10M groups 1C). Depicts 314 Marioni-based P 0.001 onto LFC/LFC plot (panel C). Green changed. wondered GWAS-based included variants, 5 10e-8 studies, well contribute predictions through polygenic inheritance 2015, examined multiple such taken combines Biobank AD-by-proxy IGAP database confers based proximity (thus referred noticing assumptions). Using arbitrary Bonferroni-adjusted (Pmar) cut-offs decreasing association, size 1D Appendix Table S1). PRS demonstrated up 0.5 improve predictive power decided limit our Pmar 0.05, already 1,799 (GSEA; 1D) size, ranging 92 (Pmar 5e-6) 5e-2), consistently, (Padj 1e-250) changing ("APPtg A*G" 1D), smallest (n 5e-06) contains microglia-expressed e.g., Treml2, Inpp5d, Gal3st4, 2D Dataset EV1). enhance clustering To effect detail 2A–C caused independent transgene) practically identical (Spearman R +0.95, 1.3e-29, 95% interval (CI) +0.91 +0.97; When only, similarity becomes rather moderate (R +0.50, 1.1e-19, CI +0.41 +0.58; 2B) slightly enhanced +0.67, 1e-106, +0.63 +0.71; ways, very pathologies causing divergent reactions. APP/PSEN1 causes prominent (287 total) dots, 219, 76%) [log2 (LFC): +0.07 +5.00, 0.05]. component added age*genotype), even [623 mRNAs 78%), LFC: +0.12 +2.98, 0.05], 175 downregulate (LFC: −0.67 −0.08, 0.05; often responses, Tyrobp (LFC (G): +1.19, (A*G): +1.53), Cst7 G: A*G: +2.62), Itgax +3.22, +2.24). These strong, 32-fold. Indeed, specific types (Zeisel 2015), verify 80% microglia-specific predominantly 2D). appears likely source, persistent 2C) follow response. As discuss below, origin being explained microgliosis 2006), also consequence states (Keren-Shaul Krasemann Sala Frigerio 2019). Click expand figure. EV1. glial loadZ-score distribution type-specific defined Zeisel (2015) SynaptomeDB (Pirooznia 2012). Boxplots: center line, median; box limits, 25th–75th quartiles; whiskers, 1.5× interquartile range. Empirical (Pbonf) shift z-score Materials Methods). ***Pbonf 0.001, **Pbonf 0.01, *Pbonf 0.05. markedly fewer little aggravation time. (TAUwt TAUtg), 47 +0.06 +1.52; 77 −1.30 −0.05, 2B, yellow Only 9 +0.25 +0.60, 2C, dots) model. majority (62%) TAUwt decreased overlap (C1qa, C1qc, Tyrobp, Ctss, Irf8, Mpeg1, Cst7, Rab3il1) astroglial (Gfap) origin. (much milder) TAUtg. With exception 2.08), upregulation indeed modest (average 8 others: 0.38) (max 2.98; 0.70). Similarly, demonstrates astrocytic older ages, loss synaptic Overall, molecular, pathobiological, fundamentally exhibiting phenotypes drives exacerbating inflammatory response, related functions. Most importantly, transcriptionally active accumulating Next, unbiased weighted co-expression (WGCNA; Zhang Horvath, 2005; Langfelder 2008) separately cluster modules. total 63 modules (Appendix Figs S2 S3). GSEA generated 3A S1), largest (e.g., enrich 4 APPtg- TAUtg-based (Turquoise, Blue, However, taking smaller), persistently APPtg-Blue 3A). 4,236) 62% [age*genotype, expected chance (log2 odds ratio (LOR): 2.90, 1.54e-158)]. assume integrated important stress large, APPTg-Blue (1e-250 0.01). generally module. 3. represents present Gene Fisher's exact test (2018) WGCNA-derived −log10 P-value) Numbers x-axis: black cut-off; gray set. Log2 analysis, assessing Color code dots/numbers: 15,824); 4,236); green 493); 9); 9). Z-score (***Pbonf 0.001) Ontology (GO) depicts −log10(FDR-adjusted multiplied (ES), line −log10(0.049)*(ES 1). E. "top 18" prioritized finding intersection 4,236), 314), 798), SuperExactTest (Wang 2015). italics

Language: Английский

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The Impact of Systemic Inflammation on Alzheimer’s Disease Pathology

Frontiers in Immunology, Journal Year: 2022, Volume and Issue: 12

Published: Jan. 6, 2022

Alzheimer’s disease (AD) is a devastating age-related neurodegenerative disorder with an alarming increasing prevalence. Except for the recently FDA-approved Aducanumab of which therapeutic effect not yet conclusively proven, only symptomatic medication that effective some AD patients available. In order to be able design more rational and treatments, our understanding mechanisms behind pathogenesis progression urgently needs improved. Over last years, it became increasingly clear peripheral inflammation one detrimental factors can contribute disease. Here, we discuss current how systemic intestinal (referred as gut-brain axis) inflammatory processes may affect brain pathology, specific focus on AD. Moreover, give comprehensive overview different preclinical well clinical studies link Inflammation initiation progression. Altogether, this review broadens pathology help in further research aiming identify novel targets.

Language: Английский

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Cellular senescence and Alzheimer disease: the egg and the chicken scenario

Nature reviews. Neuroscience, Journal Year: 2020, Volume and Issue: 21(8), P. 433 - 444

Published: June 29, 2020

Language: Английский

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Microglia facilitate loss of perineuronal nets in the Alzheimer's disease brain

EBioMedicine, Journal Year: 2020, Volume and Issue: 58, P. 102919 - 102919

Published: July 31, 2020

BackgroundMicroglia, the brain's principal immune cell, are increasingly implicated in Alzheimer's disease (AD), but molecular interfaces through which these cells contribute to amyloid beta (Aβ)-related neurodegeneration unclear. We recently identified microglial contributions homeostatic and disease-associated modulation of perineuronal nets (PNNs), extracellular matrix structures that enwrap stabilize neuronal synapses, whether PNNs altered AD remains controversial.MethodsExtensive histological analysis was performed on male female 5xFAD mice at 4, 8, 12, 18 months age assess plaque burden, microgliosis, PNNs. Findings were validated postmortem tissue. The role neuroinflammation PNN loss investigated via LPS treatment, ability prevent or rescue disease-related reductions assessed by treating 3xTg-AD model with colony-stimulating factor 1 receptor (CSF1R) inhibitor PLX5622 deplete microglia.FindingsUtilizing mouse human cortical tissue, we report extensively lost proportion burden. Activated microglia closely associate engulf damaged brain, inclusions material evident microglia, while aggrecan, a critical component, deposits within dense-core plaques. Disease-associated parvalbumin (PV)+ interneurons, frequently coated PNNs, preceded coverage integrity impairments, similar phenotypes elicited wild-type following activation LPS. Chronic pharmacological depletion prevents loss, results observed aged mice, this occurs despite persistence.InterpretationWe conclude phenotypically facilitate plaque-dependent brain.FundingThe NIH (NIA, NINDS) Association.

Language: Английский

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Understanding Alzheimer Disease at the Interface between Genetics and Transcriptomics

Trends in Genetics, Journal Year: 2018, Volume and Issue: 34(6), P. 434 - 447

Published: March 21, 2018

Due to risk gene pleiotropy, difficulty in finding functional variants, and poor reflection of physiological complexity genetic analysis, translation new findings for Alzheimer disease (AD) into mechanisms has been difficult. Transcriptomic analysis provided additional support previously identified genes while also identifying novel associated genes, helping elucidate disease. Refinement transcriptomics through 2nd 3rd generation sequencing, single-cell sequencing bioinformatics is revealing involved AD unattainable detail, including brain region- cell-type-specific expression changes molecular processes such as transcript rescue events, challenging the direct interpretation an association between variant phenotype. Transcriptome postmortem uncovered central biological pathways regulator 'hub' disease, example, SPI.1 TYROBP immune response. Over 25 are known affect developing (AD), most common neurodegenerative dementia. However, mechanistic insights improved management remains limited, due difficulties determining consequences associations. Transcriptomics increasingly being used corroborate or enhance discoveries. These approaches, which include second third bioinformatics, reveal allele-specific events connecting profiles, provide converging evidence pathophysiological underlying AD. Simultaneously, they highlight patterns, alternative splicing that straightforward relation a AD, re-emphasizing need integrated approach genetics understanding Alzheimer's genetically complex, multifactorial leads There no cure yet, high prevalence continuously increasing incidence it poses major threat personal health well care system. Patients display progressive decline cognitive capabilities, with characteristic early loss episodic memory, eventually resulting complete dependency death. The preceded by long prodromal phase [1Jack Jr, C.R. et al.Introduction recommendations from National Institute on Aging-Alzheimer's Association workgroups diagnostic guidelines disease.Alzheimers Dement. 2011; 7: 257-262Abstract Full Text PDF PubMed Scopus (1259) Google Scholar, 2McKhann G.M. al.The diagnosis dementia disease: 263-269Abstract (9313) Scholar]. Neuropathological hippocampal cortical atrophy, visible upon neuroimaging macroscopic examination. Characteristic microscopic features intracellular neurofibrillary tangles (NFTs) hyperphosphorylated tau protein extracellular depositions Amyloid-β (Aβ)1–42 peptide, accompanied neuronal synapse reactive gliosis [3Braak H. Braak E. stageing Alzheimer-related changes.Acta Neuropathol. 1991; 82: 239-259Crossref (11595) Initial etiology was presented observation families multiple generations affected rare onset form (EOAD, <65 years). Molecular investigation these pedigrees resulted identification amyloid precursor (APP), presenilin 1 (PSEN1) 2 (PSEN2) Pathogenic mutations converge general mechanism increased Aβ1-42 accumulation Aβ1–42/Aβ1–40 ratio. Hundreds dominantly inherited pathogenic have since described mostly EOAD patients, although only explaining up 10% (reviewed [4Cacace R. al.Molecular early-onset revisited.Alzheimers 2016; 12: 733-748Abstract (301) Scholar]). Most patients late-onset (LOAD). While mutation APP, PSEN1 PSEN2 infrequently LOAD typically considered multifactorial, strong polygenic component estimated heritability 80%. well-known factor APOE ε4 allele (see Glossary), approximately 25% liability [5Cuyvers al.Genetic variations genome-wide studies beyond.Lancet Neurol. 15: 857-868Abstract (171) past decade, complex research successful factors both low-penetrant (e.g., [6Lambert J.C. al.Meta-analysis 74,046 individuals identifies 11 susceptibility loci disease.Nat. Genet. 2013; 45: 1452-1458Crossref (2713) Scholar]) alleles intermediate penetrance [7Guerreiro al.TREM2 variants disease.N. Engl. J. Med. 368: 117-127Crossref (1896) 8Steinberg S. al.Loss-of-function ABCA7 confer 2015; 47: 445-447Crossref (221) 9Sims al.Rare coding PLCG2, ABI3, TREM2 implicate microglial-mediated innate immunity 2017; 49: 1373-1384Crossref (519) cascade hypothesis dominated efforts towards development diagnostics therapeutics discovery pathway-based shed light range contributing proposing targets therapy development. Translational impact still limited however, owing – amongst others pleiotropy actually how, tissues insufficiently represented analysis. field active pursuit (GWAS) next large cohorts, trend emerging simultaneous interrogation data study effect newly at level transcriptome [10Jones L. al.Convergent 11: 658-671Abstract (42) In parallel, refinement methodology, allows investigating detail. Here, we review state-of-the-art investigations emphasis their interface. search initially querying variation, successfully GWAS. At least 42 genes/loci significance one GWAS 11Lambert al.Genome-wide CLU CR1 2009; 41: 1094-1099Crossref (1156) 12Harold D. PICALM 1088-1093Crossref (2166) 13Seshadri disease.J. Am. Assoc. 2010; 303: 1832-1840Crossref (973) 14Naj A.C. al.Common MS4A4/MS4A6E, CD2AP, CD33 EPHA1 43: 436-441Crossref (1450) 15Hollingworth P. ABCA7, MS4A6A/MS4A4E, EPHA1, CD2AP 429-435Crossref (1465) 16Lee J.H. al.Identification replication CLU, PICALM, BIN1 Caribbean Hispanic individuals.Arch. 68: 320-328Crossref (151) 17Miyashita A. al.SORL1 Japanese, Koreans Caucasians.PLoS One. 8e58618Crossref (112) 18Bertram reveals putative addition APOE.Am. Hum. 2008; 83: 623-632Abstract (381) 19Jun G. al.PLXNA4 modulates phosphorylation.Ann. 2014; 76: 379-392Crossref (46) 20Wijsman E.M. familial replicates nominates CUGBP2 interaction APOE.PLoS 7e1001308Crossref (191) Scholar], BIN1, CASS4, CD33, CELF1, CR1, FERMT2, HLA-cluster, INPP5D, MEF2C, MS4A6A, NME8, PTK2B, SLC24A4/RIN3, SORL1, DGS2, ZCWPW1 confirmed meta-analysis, regarded established LOAD. DSG2 did not show largest meta-analysis published date Family-based approaches reported significant overlapping those case-control GWAS, APOE, CD33. addition, PLXNA4, CUGBP2, TRPC4AP, ATXN1, APOC1 uncharacterized chromosome 14 locus 14q31.2 were [18Bertram Scholar] but replicated approaches. Alternative analytical detected FRMD4A sliding window haplotype-based [21Lambert haplotype disease.Mol. Psychiatry. 18: 461-470Crossref (75) TP53INP1 IGHV1-67 gene-wide [22Escott-Price V. al.Gene-wide detects two disease.PLoS 9e94661Crossref (88) Expanding beyond single-variant single-gene revealed despite differences pathway definition studies. Common response, lipid metabolism, endocytosis, cell adhesion molecule (CAM) 23Jones implicates system cholesterol metabolism aetiology 5e13950Crossref (299) 24Lambert al.Implication analysis.J. Alzheimers Dis. 20: 1107-1118Crossref 25Hong M.G. transmembrane transport 55: 707-709Crossref 26Liu al.Cell molecules contribute analyses studies.J. Neurochem. 2012; 120: 190-198Crossref (69) 27Ramanan V.K. memory impairment Disease Neuroimaging Initiative (ADNI) cohort candidates, canonical pathways, networks.Brain. Imaging Behav. 6: 634-648Crossref (58) 28Perez-Palma al.Overrepresentation glutamate signaling network-based enrichment using studies.PLoS 9e95413Crossref (45) 29Xiang Z. al.Integrating highlights purine Neurobiol. 52: 514-521Crossref (23) (Table 1). A methodologically distinct partitioning adaptive response [30Gagliano S.A. al.Genomics Parkinson's diseases.Ann. Clin. Transl. 3: 924-933Crossref (51) Scholar].Table 1Significantly Enriched Pathways Meta-analysis Studies ADaP values indicate multiple-testing corrected according respective terms showing strongest within each pathway. Abbreviations: ADGC, Genetics Consortium; CHARGE, Cohorts Heart Aging Genomic Epidemiology; EADI, European Initiative; GERAD, Genetic Environmental Risk Disease; IGAP, International Genomics Project.GWAS sets analyzedConsulted databaseLipid metabolismImmune responseEndocytosisSynaptic transmissionCell moleculesMiscellaneousGERAD/EADI ScholarALIGATOR/GSA:KEGG, GO databasesSterol transport(P = 0.0079), 0.0079)Immunoglobulin mediated response(P 4 × 10−3), 3 10−3)Synaptic transmission, cholinergic(P 5.0 10−3)EADI ScholarKEGG, databasesRIG-I-like receptor signaling(P 10−2) Antigen processing presentation(P 2.0 10−2)Regulation autophagy(P 0.007)EADI ScholarGencodis/DAVID: databaseIntracellular 7.2 10−6)Discovery ADNI ScholarIGSEA: KEGG database. WebGestalt/DAVID: databaseRIG-I-like 7.00 10−4), Natural killer cytotoxicity(P 8.56 10−5), 3.50 10−7)Cell KEGG(P 1.84 10−6)Regulation 6.22 10−5)ADNI, composite measure phenotype ScholarGSA-SNP: BioCarta, KEGG, GO, Reactome databasesAllograft rejection(P 3.9 10−2)Transmission across chemical synapses(P 1.77 10−4)Focal adhesion(P 0.006), Cell (CAMs)(P 2.9 10−2)Calcium pathway(P 1.17 Viral myocarditis (P 0.039), Long-term depression/potentiation(P 8.0 10−3)/(P 1.9 10−2)Combined TGen1, NIA-LOAD/NCRAD, databasesLipid 8.12 10−9)Endocytosis(P 2.24 10−6)Glutamate pathway(P=1.86 10−11), axon guidance(P 1.20 10−9)Focal 2.63 10−10)Protein autophosphorylation(P 2.30 10−12), kinase activity(P 1.18 10−12),Calcium 1.27 10−11)IGAP 10Jones ScholarALIGATOR/GSEA: databasesCholesterol 2.96 10−9), sterol 3.91 10−9)Humoral circulating immunoglobulin(P 3.27 regulation 10−12)Regulation endocytosis(P 1.31 10−11)Clathrin adaptor complex(P folding(P 1.60 10−3)GERAD profiling temporal cortex Scholar.KEGGAxon 3.03 10−3)Cell 1.04 10−5)Calcium 2.08 4.00 Purine metabolism(P 10−4)a P Project. Open table tab gained momentum after combined whole genome (WGS) exome (WES) non-synonymous mutation, p.R47H, increases 31Jonsson T. al.Variant 107-116Crossref (1649) widely replicated. Numerous independent further among SORL1 already be implicated factors. heterozygous missense premature termination codon (PTC) found, notably and/or [32Pottier C. al.High frequency potentially autosomal dominant 17: 875-879Crossref (195) For found PTC mutations, varying age carriers proportion positive family history [8Steinberg 33Cuyvers al.Mutations Belgian patients: targeted resequencing study.Lancet 14: 814-822Abstract (105) Large-scale efforts, Sequencing Project (ADSP) includes ∼11 000 participants, providing [34Beecham, (2017) Whole-genome variation candidate Conference | July 16-20, 2017, & Dementia: Journal Association. London, EnglandGoogle likely insight near future. An exome-wide low-frequency chip genotyping proposed is, ABI3 PLGC2, latter protective [9Sims Both classical pathology. challenging, complexity. putatively interact networks different time points levels subcellular, cellular, tissue, organic level. Increasingly, address this combining This studying topology, cell-type-dependent manner, identify similarities networks, uncover interactors relate hub nodes pathways. commonly performed either microarray hybridization next-generation RNA sequencing. Although methods enable large-scale expression, principles differ fundamentally (Box 1).Box 1Common Methods ProfilingMicroarray provides cost-efficient quantification thousands transcripts parallel. Complementary DNA (cDNA) libraries reverse transcribed samples introduced array prior differential expression. necessity correcting nonbiological effects signal output represents drawback. Microarray confounded probe sensitivity platforms. Additionally, very low highly expressed proves problematic [77Shendure beginning end microarrays?.Nat. Methods. 5: 585-587Crossref (247) As probes cannot designed unknown sequences, unable transcripts. Probe design restricted knowledge design. Human reference builds updated times over last ten years [78Tyner UCSC Genome Browser database: 2017 update.Nucleic Acids Res. D626-D634PubMed should kept mind when interpreting meta-analyses several datasets.By contrast, (RNA-Seq) hybridization-free method allowing massive parallel cDNA Selection subset RNAs total sample transcription obtain library enriched RNAs, miRNA (poly-adenylated) mRNA. procedures involve removal abundant ribosomal depletion pull-down poly-adenylated (poly-A) oligo-dT beads. Of note, lacking poly-A tail small mRNAs non-coding (ncRNAs) retained pulldown, shown relevant ncRNA 51A maps intron antisense direction. upregulated frontal regulates [79Ciarlo al.An intronic ncRNA-dependent affecting Aβ formation post-mortem samples.Dis. Model Mech. 424-433Crossref (134) Known degradation sequences tissue presents limitation use selected RNA-Seq context can overcome 3′ mRNA-sequencing, where annealing UTR, circumventing non-polyadenylated disadvantage inability discriminate isoforms. Quantification generated involves read alignment quality control filtering, number reads aligned abundance. enables directional cDNA, generating spanning exons maintaining information reads. splice specific interest diseases [80Sutherland G.T. al.Understanding pathogenesis will realize promise transcriptomics?.J. 116: 937-946Crossref (52) datasets. By sequ

Language: Английский

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