Scopus list of publications for December 5, 2022
The circular RNA landscape in multiple sclerosis: Disease-specific associated variants and exon methylation shape circular RNA expression profile
(2023) Multiple Sclerosis and Related Disorders, 69, art. no. 104426, .
Cardamone, G.a , Paraboschi, E.M.a b , Soldà, G.a b , Liberatore, G.b , Rimoldi, V.a b , Cibella, J.b , Airi, F.b , Tisato, V.c , Cantoni, C.d , Gallia, F.b , Gemmati, D.c e , Piccio, L.d f , Duga, S.a b , Nobile-Orazio, E.b g , Asselta, R.a b
a Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Milan, Pieve Emanuele, 20072, Italy
b IRCCS Humanitas Research Hospital, Via Manzoni 56, Milan, Rozzano, 20089, Italy
c Department of Translational Medicine, University of Ferrara, Italy
d Department of Neurology, Washington University School of Medicine, St Louis, MO, United States
e Center Haemostasis & Thrombosis, University of Ferrara, Italy
f Brain and Mind Centre, University of Sydney, Sydney, NSW 2050, Australia
g Department of Medical Biotechnology and Translational Medicine, Milan University, Milan, Italy
Abstract
Background: Circular RNAs (circRNAs) are a class of non-coding RNAs increasingly emerging as crucial actors in the pathogenesis of human diseases, including autoimmune and neurological disorders as multiple sclerosis (MS). Despite several efforts, the mechanisms regulating circRNAs expression are still largely unknown and the circRNA profile and regulation in MS-relevant cell models has not been completely investigated. In this work, we aimed at exploring the global landscape of circRNA expression in MS patients, also evaluating a possible correlation with their genetic and epigenetic background. Methods: We performed RNA-seq experiments on circRNA-enriched samples, derived from peripheral blood mononuclear cells (PBMCs) of 10 MS patients and 10 matched controls and performed differential circRNA expression. The genetic background was evaluated using array genotyping, and an expression quantitative trait loci (eQTL) analysis was carried out. Results: Expression analysis revealed 166 differentially expressed circRNAs in MS patients, 125 of which are downregulated. One of the top dysregulated circRNAs, hsa_circ_0007990, derives from the PGAP3 gene, encoding a protein relevant for the control of autoimmune responses. The downregulation of this circRNA was confirmed in two independent replication cohorts, suggesting its implementation as a possible RNA-based biomarker. The eQTL analysis evidenced a significant association between 89 MS-associated loci and the expression of at least one circRNA, suggesting that MS-associated variants could impact on disease pathogenesis by altering circRNA profiles. Finally, we found a significant correlation between exon methylation and circRNA expression levels, supporting the hypothesis that epigenetic features may play an important role in the definition of the cell circRNA pool. Conclusion: We described the circRNA expression profile of PBMCs in MS patients, suggesting that MS-associated variants may tune the expression levels of circRNAs acting as “circ-QTLs”, and proposing a role for exon-based DNA methylation in regulating circRNA expression. © 2022
Author Keywords
Biomarkers; Circular RNAs; DNA methylation; eQTL; Multiple sclerosis
Funding details
Fondazione per la Ricerca BiomedicaFORB1734478
Document Type: Article
Publication Stage: Final
Source: Scopus
APOE ε4 genotype, amyloid-β, and sex interact to predict tau in regions of high APOE mRNA expression
(2022) Science Translational Medicine, 14 (671), p. eabl7646.
Dincer, A.a b , Chen, C.D.a b , McKay, N.S.a b , Koenig, L.N.a b , McCullough, A.a b , Flores, S.a b , Keefe, S.J.a b , Schultz, S.A.c , Feldman, R.L.a b , Joseph-Mathurin, N.a b , Hornbeck, R.C.a b , Cruchaga, C.b d , Schindler, S.E.b e , Holtzman, D.M.b e f , Morris, J.C.b e , Fagan, A.M.b e , Benzinger, T.L.S.a b , Gordon, B.A.a b f g
a Mallinckrodt Institute of Radiology, Washington University School of MedicineSaint Louis MO 63110, United States
b Knight Alzheimer Disease Research Center, Washington University School of MedicineSaint Louis MO 63110, United States
c Massachusetts General Hospital, Boston, MA 02114, United States
d Department of Psychiatry, Washington University School of MedicineSaint Louis MO 63110, United States
e Department of Neurology, Washington University School of MedicineSaint Louis MO 63110, United States
f Hope Center for Neurological Disorders, Washington University School of MedicineSaint Louis MO 63110, United States
g Department of Psychological and Brain Sciences, Washington UniversitySaint Louis MO 63110, United States
Abstract
The apolipoprotein E (APOE) ε4 allele is strongly linked with cerebral β-amyloidosis, but its relationship with tauopathy is less established. We investigated the relationship between APOE ε4 carrier status, regional amyloid-β (Aβ), magnetic resonance imaging (MRI) volumetrics, tau positron emission tomography (PET), APOE messenger RNA (mRNA) expression maps, and cerebrospinal fluid phosphorylated tau (CSF ptau181). Three hundred fifty participants underwent imaging, and 270 had ptau181. We used computational models to evaluate the main effect of APOE ε4 carrier status on regional neuroimaging values and then the interaction of ε4 status and global Aβ on regional tau PET and brain volumes as well as CSF ptau181. Separately, we also examined the additional interactive influence of sex. We found that, for the same degree of Aβ burden, APOE ε4 carriers showed greater tau PET signal relative to noncarriers in temporal regions, but no interaction was present for MRI volumes or CSF ptau181. This potentiation of tau aggregation irrespective of sex occurred in brain regions with high APOE mRNA expression, suggesting local vulnerabilities to tauopathy. There were greater effects of APOE genotype in females, although the interactive sex effects did not strongly mirror mRNA expression. Pathology is not homogeneously expressed throughout the brain but mirrors underlying biological patterns such as gene expression.
Document Type: Article
Publication Stage: Final
Source: Scopus
Mindfulness Training for Depressed Older Adults Using Smartphone Technology: Protocol for a Fully Remote Precision Clinical Trial
(2022) JMIR Research Protocols, 11 (10), art. no. e39233, .
Schweiger, A.a b , Rodebaugh, T.L.c , Lenze, E.J.a d , Keenoy, K.d e , Hassenstab, J.c f , Kloeckner, J.a , Gettinger, T.R.a b , Nicol, G.E.a d g
a Healthy Mind Lab, Department of Psychiatry, Washington University, School of Medicine, Saint Louis, MO, United States
b School of Social Work, Saint Louis University, Saint Louis, MO, United States
c Department of Psychological and Brain Sciences, Washington University in Saint Louis, Saint Louis, MO, United States
d mHealth Research Core, Washington University, School of Medicine, Saint Louis, MO, United States
e Trial Care Unit, Center for Clinical Studies, Washington University, School of Medicine, Saint Louis, MO, United States
f Department of Neurology, Washington University, School of Medicine, Saint Louis, MO, United States
g Division of Child and Adolescent Psychiatry, Washington University, School of Medicine, Saint Louis, MO, United States
Abstract
Background: Precision medicine, optimized interventions, and access to care are catchphrases for the future of behavioral treatments. Progress has been slow due to the dearth of clinical trials that optimize interventions’ benefits, individually tailor interventions to meet individual needs and preferences, and lead to rapid implementation after effectiveness is demonstrated. Two innovations have emerged to meet these challenges: fully remote trials and precision clinical trials. Objective: This paper provides a detailed description of Mindful MyWay, a study designed to test online mindfulness training in older adults with depression. Consistent with the concept of fully remote trials using a smartphone app, the study requires no in-person contact and can be conducted with participants anywhere in the United States. Based upon the precision medicine framework, the study assesses participants using high-frequency assessments of symptoms, cognitive performance, and patient preferences to both understand the individualized nature of treatment response and help individually tailor the intervention. Methods: Mindful MyWay is an open-label early-phase clinical trial for individuals 65 years and older with current depression. A smartphone app was developed to help coordinate the study, deliver the intervention, and evaluate the acceptability of the intervention, as well as predictors and outcomes of it. The curriculum for the fully remote intervention parallels the mindfulness-based stress reduction curriculum, a protocolized group-based mindfulness training that is typically provided in person. After consent and screening, participants download The Healthy Mind Lab mobile health smartphone app from the Apple App Store, allowing them to complete brief smartphone-based assessments of depressive symptoms and cognitive performance 4 times each day for 4 weeks prior to and after completing the intervention. The intervention consists of an introduction video and 10 weekly mindfulness training sessions, with the expectation to practice mindfulness at home daily. The app collects participant preference data throughout the 10-week intervention period; these high-frequency assessments identify participants’ individually dynamic preferences toward the goal of optimizing the intervention in future iterations. Results: Participant recruitment and data collection began in March 2019. Final end point assessments will be collected in May 2022. The paper describes lessons learned regarding the critical role of early-phase testing prior to moving to a randomized trial. Conclusions: The Mindful MyWay study is an exemplar of innovative clinical trial designs that use smartphone technology in behavioral and neuropsychiatric conditions. These include fully remote studies that can recruit throughout the United States, including hard-to-access areas, and collect high-frequency data, which is ideal for idiographic assessment and individualized intervention optimization. Our findings will be used to modify our methods and inform future randomized controlled trials within a precision medicine framework. © 2022 Abigail Schweiger.
Author Keywords
adult; aging; clinical trial; death; depressed; depression; fully remote trial; intervention; medicine; mHealth; mind; mindfulness; needs; older; online; precision medicine; preferences; remote; session; smartphone; technology; training; treatment
Funding details
National Institutes of HealthNIHP50MH122351
Boehringer IngelheimBI
Merck
Patient-Centered Outcomes Research InstitutePCORI
National Center for Advancing Translational SciencesNCATS
Foundation for Barnes-Jewish HospitalFBJH
Institute of Clinical and Translational SciencesICTSUL1TR002345
McDonnell Center for Systems Neuroscience
Institute for Public Health, Washington University in St. Louis
Skoll Foundation
Document Type: Article
Publication Stage: Final
Source: Scopus
Automated Quantification of Compartmental Blood Volumes Enables Prediction of Delayed Cerebral Ischemia and Outcomes After Aneurysmal Subarachnoid Hemorrhage
(2022) World Neurosurgery, .
Yuan, J.Y.a , Chen, Y.b , Jayaraman, K.a , Kumar, A.b , Zlepper, Z.a , Allen, M.L.a , Athiraman, U.c , Osbun, J.a , Zipfel, G.a , Dhar, R.b
a Department of Neurosurgery, Washington University in St. Louis School of Medicine, St. Louis, MO, United States
b Department of Neurology, Washington University in St. Louis School of Medicine, St. Louis, MO, United States
c Department of Anesthesiology, Washington University in St. Louis School of Medicine, St. Louis, MO, United States
Abstract
Objective: The role of hemorrhage volume in risk of vasospasm, delayed cerebral ischemia (DCI), and poor outcomes after aneurysmal subarachnoid hemorrhage (SAH) is well established. However, the relative contribution of blood within individual compartments is unclear. We present an automated technique for measuring not only total but also volumes of blood in each major compartment after SAH. Methods: We trained convolutional neural networks to identify compartmental blood (cisterns, sulci, and ventricles) from baseline computed tomography scans of patients with SAH. We compared automated blood volumes against traditional markers of bleeding (modified Fisher score [mFS], Hijdra sum score [HSS]) in 190 SAH patients for prediction of vasospasm, DCI, and functional status (modified Rankin Scale) at hospital discharge. Results: Combined cisternal and sulcal volume was better correlated with mFS and HSS than cisternal volume alone (ρ = 0.63 vs. 0.58 and 0.75 vs. 0.70, P < 0.001). Only blood volume in combined cisternal plus sulcal compartments was independently associated with DCI (OR 1.023 per mL, 95% CI 1.002–1.048), after adjusting for clinical factors while ventricular blood volume was not. Total and specifically sulcal blood volume was strongly associated with poor outcome (OR 1.03 per mL, 1.01–1.06, P = 0.006 and OR 1.04, 1.00–1.08 for sulcal) as was HSS (OR 1.06 per point, 1.00–1.12, P = 0.04), while mFS was not (P = 0.24). Conclusions: An automated imaging algorithm can measure the volume of bleeding after SAH within individual compartments, demonstrating cisternal plus sulcal (and not ventricular) blood contributes to risk of DCI/vasospasm. Automated blood volume was independently associated with outcome, while qualitative grading was not. © 2022 Elsevier Inc.
Author Keywords
Cerebral vasospasm; Deep learning; Image segmentation; Intracranial aneurysm; Subarachnoid hemorrhage
Funding details
National Institutes of HealthNIHK23NS099440, R01NS121218
Document Type: Article
Publication Stage: Article in Press
Source: Scopus
LGMDD1 natural history and phenotypic spectrum: Implications for clinical trials
(2022) Annals of Clinical and Translational Neurology, .
Findlay, A.R., Robinson, S.E., Poelker, S., Seiffert, M., Bengoechea, R., Weihl, C.C.
Neuromuscular Division, Department of Neurology, Washington University Saint Louis, Saint Louis, MO, United States
Abstract
Objective: To delineate the full phenotypic spectrum and characterize the natural history of limb girdle muscular dystrophy type D1 (LGMDD1). Methods: We extracted age at clinical events of interest contributing to LGMDD1 disease burden via a systematic literature and chart review. Manual muscle testing and quantitative dynamometry data were used to estimate annualized rates of change. We also conducted a cross-sectional observational study using previously validated patient-reported outcome assessments (ACTIVLIM, PROMIS-57) and a new LGMDD1 questionnaire. Some individuals underwent repeat ACTIVLIM and LGMDD1 questionnaire assessments at 1.5 and 2.5 years. Results: A total of 122 LGMDD1 patients were included from 14 different countries. We identified two new variants (p.E54K, p.V99A). In vitro assays and segregation support their pathogenicity. The mean onset age was 29.7 years. Genotype appears to impact onset age, weakness pattern, and median time to loss of ambulation (34 years). Dysphagia was the most frequent abnormality (51.4%). Deltoids, biceps, grip, iliopsoas, and hamstrings strength decreased by (0.5-1 lb/year). Cross-sectional ACTIVLIM and LGMDD1 questionnaire scores correlated with years from disease onset. Longitudinally, only the LGMDD1 questionnaire detected significant progression at both 1.5 and 2.5 years. Treatment trials would require 62 (1.5 years) or 30 (2.5 years) patients to detect a 70% reduction in the progression of the LGMDD1 questionnaire. Interpretation: This study is the largest description of LGMDD1 patients to date and highlights potential genotype-dependent differences that need to be verified prospectively. Future clinical trials will need to account for variability in these key phenotypic features when selecting outcome measures and enrolling patients. © 2022 The Authors. Annals of Clinical and Translational Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.
Funding details
National Institutes of HealthNIHK08AR075894, K24AR073317, R01AR068797, R03AR081395
St. Louis Children’s HospitalSLCH
Document Type: Article
Publication Stage: Article in Press
Source: Scopus
Hyperammonaemia disrupts daily rhythms reversibly by elevating glutamate in the central circadian pacemaker
(2022) Liver International, .
Granados-Fuentes, D.a , Cho, K.b , Patti, G.J.b , Costa, R.c d e , Herzog, E.D.a , Montagnese, S.e f
a Biology Department, Washington University in St. Louis, St. Louis, MO, United States
b Center for Metabolomics and Isotope Tracing, Washington University in St. Louis, St. Louis, MO, United States
c Department of Biology, University of Padova, Padova, Italy
d Institute of Neuroscience, National Research Council of Italy (CNR), Padova, Italy
e Chronobiology Section, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom
f Department of Medicine, University of Padova, Padova, Italy
Abstract
Patients with cirrhosis exhibit features of circadian disruption. Hyperammonaemia has been suggested to impair both homeostatic and circadian sleep regulation. Here, we tested if hyperammonaemia directly disrupts circadian rhythm generation in the central pacemaker, the suprachiasmatic nuclei (SCN) of the hypothalamus. Wheel-running activity was recorded from mice fed with a hyperammonaemic or normal diet for ~35 days in a 12:12 light–dark (LD) cycle followed by ~15 days in constant darkness (DD). The expression of the clock protein PERIOD2 (PER2) was recorded from SCN explants before, during and after ammonia exposure, ±glutamate receptor antagonists. In LD, hyperammonaemic mice advanced their daily activity onset time by ~1 h (16.8 ± 0.3 vs. 18.1 ± 0.04 h, p =.009) and decreased their total activity, concentrating it during the first half of the night. In DD, hyperammonaemia reduced the amplitude of daily activity (551.5 ± 27.7 vs. 724.9 ± 59 counts, p =.007), with no changes in circadian period. Ammonia (≥0.01 mM) rapidly and significantly reduced PER2 amplitude, and slightly increased circadian period. The decrease in PER2 amplitude correlated with decreased synchrony among circadian cells in the SCN and increased extracellular glutamate, which was rescued by AMPA glutamate receptor antagonists. These data suggest that hyperammonaemia affects circadian regulation of rest-activity behaviour by increasing extracellular glutamate in the SCN. © 2022 The Authors. Liver International published by John Wiley & Sons Ltd.
Author Keywords
astrocytes; central circadian clock; cirrhosis; hyperammonaemia; sleep-wake inversion; suprachiasmatic nuclei
Funding details
National Institutes of HealthNIHGM131403, R35ES028365
Document Type: Article
Publication Stage: Article in Press
Source: Scopus
CSF tau microtubule-binding region identifies pathological changes in primary tauopathies
(2022) Nature Medicine, .
Horie, K.a b , Barthélemy, N.R.a b , Spina, S.c , VandeVrede, L.c , He, Y.a b , Paterson, R.W.d , Wright, B.A.e , Day, G.S.f , Davis, A.A.a g , Karch, C.M.g h i , Seeley, W.W.c , Perrin, R.J.a g i j , Koppisetti, R.K.a h , Shaikh, F.a , Lago, A.L.c , Heuer, H.W.c , Ghoshal, N.a h , Gabelle, A.k , Miller, B.L.c , Boxer, A.L.c , Bateman, R.J.a b g i , Sato, C.a b
a Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
b The Tracy Family Stable Isotope Labeling Quantitation Center, Washington University School of Medicine, St. Louis, MO, United States
c Department of Neurology, University of California San Francisco, San Francisco, CA, United States
d Department of Neurology, University College London Queen Square Institute of Neurology, University College London, London, United Kingdom
e Department of Neurosciences, University of California San Diego School of Medicine, La JollaCA, United States
f Department of Neurology, Mayo Clinic Florida, Jacksonville, FL, United States
g Hope Center for Neurological Disorders, St. Louis, MO, United States
h Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
i Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, United States
j Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, United States
k Memory Research and Resources Center, Department of Neurology, University Hospital of Montpellier, Neurosciences Institute of Montpellier, University of Montpellier, Montpellier, France
Abstract
Despite recent advances in fluid biomarker research in Alzheimer’s disease (AD), there are no fluid biomarkers or imaging tracers with utility for diagnosis and/or theragnosis available for other tauopathies. Using immunoprecipitation and mass spectrometry, we show that 4 repeat (4R) isoform-specific tau species from microtubule-binding region (MTBR-tau275 and MTBR-tau282) increase in the brains of corticobasal degeneration (CBD), progressive supranuclear palsy (PSP), frontotemporal lobar degeneration (FTLD)-MAPT and AD but decrease inversely in the cerebrospinal fluid (CSF) of CBD, FTLD-MAPT and AD compared to control and other FTLD-tau (for example, Pick’s disease). CSF MTBR-tau measures are reproducible in repeated lumbar punctures and can be used to distinguish CBD from control (receiver operating characteristic area under the curve (AUC) = 0.889) and other FTLD-tau, such as PSP (AUC = 0.886). CSF MTBR-tau275 and MTBR-tau282 may represent the first affirmative biomarkers to aid in the diagnosis of primary tauopathies and facilitate clinical trial designs. © 2022, The Author(s).
Document Type: Article
Publication Stage: Article in Press
Source: Scopus
Neocortical Lewy Body Pathology Parallels Parkinson’s Dementia, but Not Always
(2022) Annals of Neurology, .
Martin, W.R.W.a , Younce, J.R.b , Campbell, M.C.c d , Racette, B.A.c e f , Norris, S.A.c , Ushe, M.c , Criswell, S.c , Davis, A.A.c , Alfradique-Dunham, I.c , Maiti, B.c , Cairns, N.J.g , Perrin, R.J.c h , Kotzbauer, P.T.c , Perlmutter, J.S.c d i
a Department of Medicine (Neurology), University of Alberta, Edmonton, Canada
b Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
c Department of Neurology, Washington University in St. Louis, St. Louis, MO, United States
d Department of Radiology, Washington University in St. Louis, St. Louis, MO, United States
e Department of Neurology, Barrow Neurological Institute, Phoenix, Azerbaijan
f School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
g College of Medicine and Health, University of Exeter, Exeter, United Kingdom
h Department of Pathology and Immunology, Washington University in St. Louis, St. Louis, MO, United States
i Departments of Neuroscience, Physical Therapy and Occupational Therapy, Washington University in St. Louis, St. Louis, MO, United States
Abstract
Objective: The objective of this study was to evaluate the relationship between Parkinson’s disease (PD) with dementia and cortical proteinopathies in a large population of pathologically confirmed patients with PD. Methods: We reviewed clinical data from all patients with autopsy data seen in the Movement Disorders Center at Washington University, St. Louis, between 1996 and 2019. All patients with a diagnosis of PD based on neuropathology were included. We used logistic regression and multivariate analysis of covariance (MANCOVA) to investigate the relationship between neuropathology and dementia. Results: A total of 165 patients with PD met inclusion criteria. Among these, 128 had clinical dementia. Those with dementia had greater mean ages of motor onset and death but equivalent mean disease duration. The delay between motor symptom onset and dementia was 1 year or less in 14 individuals, meeting research diagnostic criteria for possible or probable dementia with Lewy bodies (DLB). Braak Lewy body stage was associated with diagnosis of dementia, whereas severities of Alzheimer’s disease neuropathologic change (ADNC) and small vessel pathology did not. Pathology of individuals diagnosed with DLB did not differ significantly from that of other patients with PD with dementia. Six percent of individuals with PD and dementia did not have neocortical Lewy bodies; and 68% of the individuals with PD but without dementia did have neocortical Lewy bodies. Interpretation: Neocortical Lewy bodies almost always accompany dementia in PD; however, they also appear in most PD patients without dementia. In some cases, dementia may occur in patients with PD without neocortical Lewy bodies, ADNC, or small vessel disease. Thus, other factors not directly related to these classic neuropathologic features may contribute to PD dementia. ANN NEUROL 2022. © 2022 American Neurological Association.
Funding details
National Institutes of HealthNIH
National Institute on AgingNIAK23NS125107, NS075321, NS097437, NS097799, U10NS077384
National Institute of Neurological Disorders and StrokeNINDS
American Parkinson Disease AssociationAPDA
Foundation for Barnes-Jewish HospitalFBJH
Document Type: Article
Publication Stage: Article in Press
Source: Scopus
Exome sequencing identifies rare damaging variants in ATP8B4 and ABCA1 as risk factors for Alzheimer’s disease
(2022) Nature Genetics, .
Holstege, H.a b c d , Hulsman, M.a b c d , Charbonnier, C.e , Grenier-Boley, B.f , Quenez, O.e , Grozeva, D.g , van Rooij, J.G.J.h i , Sims, R.g , Ahmad, S.j k , Amin, N.j l , Norsworthy, P.J.m , Dols-Icardo, O.n o , Hummerich, H.m , Kawalia, A.p , Amouyel, P.f , Beecham, G.W.q , Berr, C.r , Bis, J.C.s , Boland, A.t , Bossù, P.u , Bouwman, F.b c , Bras, J.v w , Campion, D.e , Cochran, J.N.x , Daniele, A.y , Dartigues, J.-F.z , Debette, S.z aa , Deleuze, J.-F.t , Denning, N.ab , DeStefano, A.L.ac ad ae , Farrer, L.A.ac ae af ag , Fernández, M.V.ah ai aj , Fox, N.C.ak , Galimberti, D.al am , Genin, E.an , Gille, J.J.P.ao , Le Guen, Y.ap , Guerreiro, R.v w , Haines, J.L.aq , Holmes, C.ar , Ikram, M.A.j , Ikram, M.K.j , Jansen, I.E.b c as , Kraaij, R.i , Lathrop, M.at , Lemstra, A.W.b c , Lleó, A.n o , Luckcuck, L.g , Mannens, M.M.A.M.au , Marshall, R.g , Martin, E.R.q av , Masullo, C.aw , Mayeux, R.ax ay , Mecocci, P.az , Meggy, A.ab , Mol, M.O.h , Morgan, K.ba , Myers, R.M.x , Nacmias, B.bb bc , Naj, A.C.bd be , Napolioni, V.ap bf , Pasquier, F.bg , Pastor, P.bh bi , Pericak-Vance, M.A.q av , Raybould, R.ab , Redon, R.bj , Reinders, M.J.T.d , Richard, A.-C.e , Riedel-Heller, S.G.bk , Rivadeneira, F.i , Rousseau, S.e , Ryan, N.S.ak , Saad, S.g , Sanchez-Juan, P.o bl , Schellenberg, G.D.be , Scheltens, P.b c , Schott, J.M.ak , Seripa, D.bm , Seshadri, S.ad ae bn , Sie, D.ao , Sistermans, E.A.ao , Sorbi, S.bb bc , van Spaendonk, R.ao , Spalletta, G.bo , Tesi, N.a b c d , Tijms, B.b , Uitterlinden, A.G.i , van der Lee, S.J.a b c d , Visser, P.J.b , Wagner, M.bp bq , Wallon, D.br , Wang, L.-S.be , Zarea, A.br , Clarimon, J.n o , van Swieten, J.C.h , Greicius, M.D.ap , Yokoyama, J.S.bs , Cruchaga, C.ah ai aj , Hardy, J.bt , Ramirez, A.p bn bp bq bu , Mead, S.m , van der Flier, W.M.b c , van Duijn, C.M.j l , Williams, J.g , Nicolas, G.e , Bellenguez, C.f , Lambert, J.-C.f
a Genomics of Neurodegenerative Diseases and Aging, Human Genetics, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, Netherlands
b Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, Netherlands
c Amsterdam Neuroscience, Neurodegeneration, Amsterdam, Netherlands
d Delft Bioinformatics Lab, Delft University of Technology, Delft, Netherlands
e Université Rouen Normandie, INSERM U1245 and CHU Rouen, Department of Genetics and CNRMAJ, Rouen, France
f Université Lille, INSERM, Centre Hospitalier Universitaire Lille, Institut Pasteur de Lille, U1167-RID-AGE facteurs de risque et déterminants moléculaires des maladies liées au vieillissement, Lille, France
g Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neuroscience, School of Medicine, Cardiff University, Cardiff, United Kingdom
h Department of Neurology, Erasmus Medical Centre, Rotterdam, Netherlands
i Department of Internal Medicine, Erasmus Medical Centre, Rotterdam, Netherlands
j Department of Epidemiology, Erasmus Medical Centre, Rotterdam, Netherlands
k Leiden Academic Centre for Drug Research, Leiden, Netherlands
l Nuffield Department of Population Health Oxford University, Oxford, United Kingdom
m Medical Research Council Prion Unit at University College London, University College London Institute of Prion Diseases, London, United Kingdom
n Department of Neurology, II B Sant Pau, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
o Biomedical Research Networking Center on Neurodegenerative Diseases, National Institute of Health Carlos III, Madrid, Spain
p Division of Neurogenetics and Molecular Psychiatry, Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
q The John P. Hussman Institute for Human Genomics, University of Miami, Miami, FL, United States
r Université Montpellier, INSERM, Institute for Neurosciences of Montpellier, Montpellier, France
s Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, United States
t Université Paris-Saclay, Commissariat à l’Énergie Atomique et aux Énergies Alternatives, Centre National de Recherche en Génomique Humaine Evry, Gif-sur-Yvette, France
u Experimental Neuro-psychobiology Laboratory, Department of Clinical and Behavioral Neurology, Istituto di Ricovero e Cura a Carattere Scientifico Santa Lucia Foundation, Rome, Italy
v Department of Neurodegenerative Science, Van Andel Institute, Grand Rapids, MI, United States
w Division of Psychiatry and Behavioral Medicine, Michigan State University College of Human Medicine, Grand Rapids, MI, United States
x HudsonAlpha Institute for Biotechnology, Huntsville, AL, United States
y Department of Neuroscience, Catholic University of Sacred Heart, Fondazione Policlinico Universitario A. Gemelli Istituto di Ricovero e Cura a Carattere Scientifico, Rome, Italy
z Université Bordeaux, INSERM, Bordeaux Population Health Research Center, Bordeaux, France
aa Department of Neurology, Bordeaux University Hospital, Bordeaux, France
ab UKDRI Cardiff, School of Medicine, Cardiff University, Cardiff, United Kingdom
ac Department of Biostatistics, Boston University School of Public Health, Boston, MA, United States
ad Framingham Heart Study, Framingham, MA, United States
ae Department of Neurology, Boston University School of Medicine, Boston, MA, United States
af Department of Epidemiology, Boston University, Boston, MA, United States
ag Department of Medicine (Biomedical Genetics), Boston University, Boston, MA, United States
ah Neurogenomics and Informatics Center, Washington University School of Medicine, St Louis, MO, United States
ai Psychiatry Department, Washington University School of Medicine, St Louis, MO, United States
aj Hope Center for Neurological Disorders, Washington University School of Medicine, St Louis, MO, United States
ak Dementia Research Centre, University College London Queen Square Institute of Neurology, London, United Kingdom
al Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Ca’ Granda, Ospedale Policlinico, Milan, Italy
am University of Milan, Milan, Italy
an Université Brest, INSERM, Etablissement Français du Sang, Centre Hospitalier Universitaire Brest, Unité Mixte de Recherche 1078, GGB, Brest, France
ao Genome Diagnostics, Department of Human Genetics, VU University, AmsterdamUMC (location VUmc), Amsterdam, Netherlands
ap Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, United States
aq Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, United States
ar Clinical and Experimental Science, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
as Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije University, Amsterdam, Netherlands
at McGill University and Genome Quebec Innovation Centre, Montreal, QC, Canada
au Department of Human Genetics, Amsterdam UMC, University of Amsterdam, Amsterdam Reproduction and Development Research Institute, Amsterdam, Netherlands
av Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami, Miami, FL, United States
aw Institute of Neurology, Catholic University of the Sacred Heart, Rome, Italy
ax Taub Institute on Alzheimer’s Disease and the Aging Brain, Department of Neurology, Columbia University, New York, NY, United States
ay Gertrude H. Sergievsky Center, Columbia University, New York, NY, United States
az Institute of Gerontology and Geriatrics, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
ba Human Genetics, School of Life Sciences, University of Nottingham, Nottingham, United Kingdom
bb Department of Neuroscience, Psychology, Drug Research and Child Health University of Florence, Florence, Italy
bc IRCCS Fondazione Don Carlo Gnocchi, Florence, Italy
bd Penn Neurodegeneration Genomics Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
be Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
bf Genomic and Molecular Epidemiology Laboratory, School of Biosciences and Veterinary Medicine, University of Camerino, Camerino, Italy
bg Université Lille, INSERM, Centre Hospitalier Universitaire Lille, UMR1172, Resources and Research Memory Center (MRRC) of Distalz, Licend, Lille, France
bh Fundació Docència i Recerca MútuaTerrassa and Movement Disorders Unit, Department of Neurology, University Hospital MútuaTerrassa, Barcelona, Spain
bi Memory Disorders Unit, Department of Neurology, Hospital Universitari Mutua de Terrassa, Barcelona, Spain
bj Université de Nantes, Centre Hospitalier Universitaire Nantes, Centre National de la Recherche Scientifique, INSERM, l’institut du Thorax, Nantes, France
bk Institute of Social Medicine, Occupational Health and Public Health, University of Leipzig, Leipzig, Germany
bl Neurology Service, Marqués de Valdecilla University Hospital (University of Cantabria and IDIVAL), Santander, Spain
bm Laboratory for Advanced Hematological Diagnostics, Department of Hematology and Stem Cell Transplant, Lecce, Italy
bn Department of Psychiatry and Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, San Antonio, TX, United States
bo Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, Istituto di Ricovero e Cura a Carattere Scientifico Santa Lucia Foundation, Rome, Italy
bp Department of Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Medical Faculty, Bonn, Germany
bq German Center for Neurodegenerative Diseases, Bonn, Germany
br Université Rouen Normandie, INSERM U1245 and CHU Rouen, Department of Neurology and CNRMAJ, Rouen, France
bs Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, United States
bt Reta Lila Weston Research Laboratories, Department of Molecular Neuroscience, University College London Institute of Neurology, London, United Kingdom
bu Cluster of Excellence Cellular Stress Responses in Aging-Associated Diseases, University of Cologne, Cologne, Germany
Abstract
Alzheimer’s disease (AD), the leading cause of dementia, has an estimated heritability of approximately 70%1. The genetic component of AD has been mainly assessed using genome-wide association studies, which do not capture the risk contributed by rare variants2. Here, we compared the gene-based burden of rare damaging variants in exome sequencing data from 32,558 individuals—16,036 AD cases and 16,522 controls. Next to variants in TREM2, SORL1 and ABCA7, we observed a significant association of rare, predicted damaging variants in ATP8B4 and ABCA1 with AD risk, and a suggestive signal in ADAM10. Additionally, the rare-variant burden in RIN3, CLU, ZCWPW1 and ACE highlighted these genes as potential drivers of respective AD-genome-wide association study loci. Variants associated with the strongest effect on AD risk, in particular loss-of-function variants, are enriched in early-onset AD cases. Our results provide additional evidence for a major role for amyloid-β precursor protein processing, amyloid-β aggregation, lipid metabolism and microglial function in AD. © 2022, The Author(s).
Document Type: Article
Publication Stage: Article in Press
Source: Scopus