Alzheimer's disease

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

Published: March 2, 2021

Language: Английский

---

Rodent models for Alzheimer disease

Nature reviews. Neuroscience, Journal Year: 2018, Volume and Issue: 19(10), P. 583 - 598

Published: Sept. 7, 2018

Language: Английский

---

An atlas of cortical circular RNA expression in Alzheimer disease brains demonstrates clinical and pathological associations

Nature Neuroscience, Journal Year: 2019, Volume and Issue: 22(11), P. 1903 - 1912

Published: Oct. 7, 2019

Language: Английский

---

Microglia in Alzheimer Disease: Well-Known Targets and New Opportunities

Frontiers in Aging Neuroscience, Journal Year: 2019, Volume and Issue: 11

Published: Aug. 30, 2019

Microglia are the resident macrophages of central nervous system. They play key roles in brain development and physiology during life aging. Equipped with a variety molecular sensors through various functions they can fulfil, critically involved maintaining brain's homeostasis. In Alzheimer disease (AD), microglia reaction was initially thought to be incidental triggered by amyloid deposits dystrophic neurites. However, recent genome-wide association studies have established that majority AD risk loci found or near genes highly sometimes uniquely expressed microglia. This leads concept being early steps identified them as important potential therapeutic targets. Whether is beneficial, detrimental both progression still unclear subject intense debate. this review, we presenting state-of-knowledge report intended highlight microglial pathways shown progression. We first address acquisition new alteration their homeostatic reactive Second, propose summary parameters currently emerging field need considered identify relevant Finally, discuss many obstacles designing efficient strategies for present innovative technologies may foster our understanding pathology. Ultimately, work aims fly over make general reliable current knowledge regarding microglia's involvement research opportunities field.

Language: Английский

---

The role of astroglia in Alzheimer's disease: pathophysiology and clinical implications

The Lancet Neurology, Journal Year: 2019, Volume and Issue: 18(4), P. 406 - 414

Published: Feb. 19, 2019

Language: Английский

---

Interpretation of risk loci from genome-wide association studies of Alzheimer's disease

The Lancet Neurology, Journal Year: 2020, Volume and Issue: 19(4), P. 326 - 335

Published: Jan. 24, 2020

Language: Английский

---

Advances in developing novel therapeutic strategies for Alzheimer’s disease

Molecular Neurodegeneration, Journal Year: 2018, Volume and Issue: 13(1)

Published: Dec. 1, 2018

Alzheimer’s Disease (AD), the most prevalent neurodegenerative disease of aging, affects one in eight older Americans. Nearly all drug treatments tested for AD today have failed to show any efficacy. There is a great need therapies prevent and/or slow progression AD. The major challenge development lack clarity about mechanisms underlying pathogenesis and pathophysiology. Several studies support notion that multifactorial disease. While there abundant evidence amyloid plays role pathogenesis, other been implicated such as tangle formation spread, dysregulated protein degradation pathways, neuroinflammation, loss by neurotrophic factors. Therefore, current paradigms design shifted from single target approach (primarily amyloid-centric) developing drugs targeted at multiple aspects, treating later stages focusing on preventive strategies early development. Here, we summarize new trends development, including pre-clinical clinical trials different aspects (mechanism-based versus non-mechanism based, e.g. symptomatic treatments, lifestyle modifications risk factor management).

Language: Английский

---

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

---

Questions concerning the role of amyloid-β in the definition, aetiology and diagnosis of Alzheimer’s disease

Acta Neuropathologica, Journal Year: 2018, Volume and Issue: 136(5), P. 663 - 689

Published: Oct. 22, 2018

The dominant hypothesis of Alzheimer's disease (AD) aetiology, the neuropathological guidelines for diagnosing AD and majority high-profile therapeutic efforts, in both research clinical practice, have been built around one possible causal factor, amyloid-β (Aβ). However, link between Aβ remains unproven. Here, context a detailed assessment historical contemporary studies, we raise critical questions regarding role definition, diagnosis aetiology AD. We illustrate that holistic view available data does not support an unequivocal conclusion has central or unique Instead, suggest alternative views are potentially valid, at this time. propose unbiased way forward field, beyond current Aβ-centric approach, without excluding Aβ, is required to come accurate understanding dementia and, ultimately, effective treatment.

Language: Английский

---

Brain lipidomics: From functional landscape to clinical significance

Science Advances, Journal Year: 2022, Volume and Issue: 8(37)

Published: Sept. 16, 2022

Lipids are crucial components of cellular function owing to their role in membrane formation, intercellular signaling, energy storage, and homeostasis maintenance. In the brain, lipid dysregulations have been associated with etiology progression neurodegeneration other neurological pathologies. Hence, brain lipids emerging as important potential targets for early diagnosis prognosis diseases. This review aims highlight significance usefulness lipidomics diagnosing treating We explored alterations diseases, paying attention organ-specific characteristics functions lipids. As recent advances would impossible without analytical techniques, we provide up-to-date information on mass spectrometric approaches integrative analysis omic approaches. Last, present applications combined artificial intelligence techniques interdisciplinary collaborative research diseases clinical heterogeneities.

Language: Английский

---