Hope Center member publications

CSF Tau phosphorylation at Thr205 is associated with loss of white matter integrity in autosomal dominant Alzheimer disease
(2022) Neurobiology of Disease, 168, art. no. 105714, . 

Strain, J.F.^a , Barthelemy, N.^a , Horie, K.^a , Gordon, B.A.^a c d , Kilgore, C.^a , Aschenbrenner, A.^a , Cruchaga, C.^a , Xiong, C.^c h , Joseph-Mathurin, N.^b c , Hassenstab, J.^a c h , Fagan, A.M.^a c , Li, Y.^a , Karch, C.M.^b , Perrin, R.J.^a , Berman, S.B.^e , Chhatwal, J.P.^f , Graff-Radford, N.R.^g , Mori, H.^h , Levin, J.ⁱ , Noble, J.M.^m , Allegri, R.^j , Schofield, P.R.^k l , Marcus, D.S.^c , Holtzman, D.M.^a c , Morris, J.C.^a c , Benzinger, T.L.S.^b c , McDade, E.M.^a , Bateman, R.J.^a c , Ances, B.M.^a b c

^a Department of Neurology, Washington University, St. Louis, MO 63110, United States
^b Department of Radiology, Washington University, St. Louis, MO 63110, United States
^c Knight Alzheimer’s Disease Research Center, Washington University, St. Louis, MO 63110, United States
^d Department of Psychological & Brain Sciences, Washington University, St. Louis, MO 63110, United States
^e Department of Neurology, University of Pittsburgh, Pittsburgh, PA 15213, United States
^f Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, United States
^g Department of Neurology, Mayo Clinic, Jacksonville, FL 32224, United States
^h Osaka City University School of Medicine Asahimachi, Abenoku, Osaka, 545-8585, Japan
ⁱ German Center for Neurodegenerative Disease (DZNE) Munich, Munich, Germany
^j School of Medicine, Universidad de Buenos Aires, Viamonte 430, CABA, C1053, Argentina
^k Neuroscience Research Australia, Sydney, NSW, Australia
^l Hope Center for Neurological Disorders, Washington University, St. Louis, MO 63100, United States
^m Department of Neurology, Columbia University, New York, NY 100310, United States

Abstract
Background: Hyperphosphorylation of tau leads to conformational changes that destabilize microtubules and hinder axonal transport in Alzheimer’s disease (AD). However, it remains unknown whether white matter (WM) decline due to AD is associated with specific Tau phosphorylation site(s). Methods: In autosomal dominant AD (ADAD) mutation carriers (MC) and non-carriers (NC) we compared cerebrospinal fluid (CSF) phosphorylation at tau sites (pT217, pT181, pS202, and pT205) and total tau with WM measures, as derived from diffusion tensor imaging (DTI), and cognition. A WM composite metric, derived from a principal component analysis, was used to identify spatial decline seen in ADAD. Results: The WM composite explained over 70% of the variance in MC. WM regions that strongly contributed to the spatial topography were located in callosal and cingulate regions. Loss of integrity within the WM composite was strongly associated with AD progression in MC as defined by the estimated years to onset (EYO) and cognitive decline. A linear regression demonstrated that amyloid, gray matter atrophy and phosphorylation at CSF tau site pT205 each uniquely explained a reduction in the WM composite within MC that was independent of vascular changes (white matter hyperintensities), and age. Hyperphosphorylation of CSF tau at other sites and total tau did not significantly predict WM composite loss. Conclusions: We identified a site-specific relationship between CSF phosphorylated tau and WM decline within MC. The presence of both amyloid deposition and Tau phosphorylation at pT205 were associated with WM composite loss. These findings highlight a primary AD-specific mechanism for WM dysfunction that is tightly coupled to symptom manifestation and cognitive decline. © 2022

Author Keywords
ADAD;  CSF;  PCA;  Phosphorylated tau;  White matter

Funding details
1P30NS098577, R01 EB009352
National Science FoundationNSFDMS1300280
National Institutes of HealthNIHP01AG003991, P01AG026276, P30NS048056, P30NS098577, P50AG05681, R01AG04343404, R01AG052550, R01EB009352, R01NR012657, R01NR012907, R01NR014449, UFAG032438, UL1TR000448
National Institute on AgingNIA
Alzheimer’s AssociationAAAARFD-20-681815
BrightFocus FoundationBFFA2018817F
Foundation for Barnes-Jewish HospitalFBJH
University of WashingtonUWUF1AG032438
Japan Agency for Medical Research and DevelopmentAMED
Hope Center for Neurological Disorders
Medical Research CouncilMRCMR/009076/1, MR/L023784/1
National Institute for Health ResearchNIHR
Korea Health Industry Development InstituteKHIDI
Deutsches Zentrum für Neurodegenerative ErkrankungenDZNE
Fleni

Document Type: Article
Publication Stage: Final
Source: Scopus

Assessing the Relationship of Patient Reported Outcome Measures With Functional Status in Dysferlinopathy: A Rasch Analysis Approach
(2022) Frontiers in Neurology, 13, art. no. 828525, . 

Mayhew, A.G.^a , James, M.K.^a , Moore, U.^a , Sutherland, H.^a , Jacobs, M.^b c , Feng, J.^b , Lowes, L.P.^d , Alfano, L.N.^d , Muni Lofra, R.^a , Rufibach, L.E.^e , Rose, K.^f , Duong, T.^g h , Bello, L.ⁱ , Pedrosa-Hernández, I.^j , Holsten, S.^k , Sakamoto, C.^l , Canal, A.^m , Sánchez-Aguilera Práxedes, N.ⁿ , Thiele, S.^o , Siener, C.^p , Vandevelde, B.^q , DeWolf, B.^g , Maron, E.^r , Gordish-Dressman, H.^b c , Hilsden, H.^a , Guglieri, M.^a , Hogrel, J.-Y.^m , Blamire, A.M.^s , Carlier, P.G.^t , Spuler, S.^u , Day, J.W.^v , Jones, K.J.^f , Bharucha-Goebel, D.X.^w x , Salort-Campana, E.^q , Pestronk, A.^p , Walter, M.C.^o , Paradas, C.^y , Stojkovic, T.^m , Mori-Yoshimura, M.^z , Bravver, E.^k , Díaz-Manera, J.^aa ab , Pegoraro, E.ⁱ , Mendell, J.R.^d , Jain COS Consortium^d , Straub, V.^a

^a The John Walton Muscular Dystrophy Research Centre, Translational and Clinical Research Institute, Newcastle University and Newcastle Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
^b Center for Translational Science, Division of Biostatistics and Study Methodology, Children’s National Health System, Washington, DC, United States
^c Pediatrics, Epidemiology and Biostatistics, George Washington University, Washington, DC, United States
^d The Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, OH, United States
^e The Jain Foundation, Seattle, WA, United States
^f The Children’s Hospital at Westmead, The University of Sydney, Sydney, NSW, Australia
^g Cooperative International Neuromuscular Research Group, Children’s National Health System, Washington, DC, United States
^h Lucile Salter Packard Children’s Hospital at Stanford, Neurology, Palo Alto, CA, United States
ⁱ Department of Neuroscience, University of Padova, Padua, Italy
^j Physical Medicine and Rehabilitation, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
^k Neuroscience Institute, Carolinas Neuromuscular/ALS-MDA Center, Carolinas HealthCare System, Charlotte, NC, United States
^l Department of Physical Rehabilitation, National Center Hospital, National Center of Neurology and Psychiatry Tokyo, Tokyo, Japan
^m Institut de Myologie, AP-HP, GH Pitié-Salpêtrière, Paris, France
ⁿ Rehabilitation Hospital Universitario Virgen del Rocío Sevilla, Seville, Spain
^o Department of Neurology, Friedrich-Baur-Institute, Ludwig-Maximilians-University of Munich, Munich, Germany
^p Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
^q Service des Maladies Neuromusculaire et de la SLA, Hôpital de La Timone, Marseille, France
^r ELAN-PHYSIO, Praxis für Physiotherapie Maron, Berlin, Germany
^s Magnetic Resonance Centre, Institute for Cellular Medicine, Newcastle University, Newcastle upon Tyne, United Kingdom
^t AIM CEA NMR Laboratory, Institute of Myology, Pitié-Salpêtrière University Hospital, Paris, France
^u Charite Muscle Research Unit, Experimental and Clinical Research Center, A Joint Cooperation of the Charité Medical Faculty and the Max Delbrück Center for Molecular Medicine, Berlin, Germany
^v Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
^w Department of Neurology Children’s National Health System, Washington, DC, United States
^x National Institutes of Health, Bethesda, MD, United States
^y Neuromuscular Unit, Department of Neurology, Hospital U. Virgen del Rocío/Instituto de Biomedicina de Sevilla, Sevilla, Spain
^z Department of Neurology, National Center Hospital, National Center of Neurology and Psychiatry Tokyo, Tokyo, Japan
^aa Centro de Investigación Biomédica en Red en Enfermedades Raras (CIBERER, Barcelona, Spain
^ab Neuromuscular Disorders Unit, Neurology Department, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain

Abstract
Dysferlinopathy is a muscular dystrophy with a highly variable functional disease progression in which the relationship of function to some patient reported outcome measures (PROMs) has not been previously reported. This analysis aims to identify the suitability of PROMs and their association with motor performance.Two-hundred and four patients with dysferlinopathy were identified in the Jain Foundation’s Clinical Outcome Study in Dysferlinopathy from 14 sites in 8 countries. All patients completed the following PROMs: Individualized Neuromuscular Quality of Life Questionnaire (INQoL), International Physical Activity Questionnaire (IPAQ), and activity limitations for patients with upper and/or lower limb impairments (ACTIVLIMs). In addition, nonambulant patients completed the Egen Klassifikation Scale (EK). Assessments were conducted annually at baseline, years 1, 2, 3, and 4. Data were also collected on the North Star Assessment for Limb Girdle Type Muscular Dystrophies (NSAD) and Performance of Upper Limb (PUL) at these time points from year 2. Data were analyzed using descriptive statistics and Rasch analysis was conducted on ACTIVLIM, EK, INQoL. For associations, graphs (NSAD with ACTIVLIM, IPAQ and INQoL and EK with PUL) were generated from generalized estimating equations (GEE). The ACTIVLIM appeared robust psychometrically and was strongly associated with the NSAD total score (Pseudo R2 0.68). The INQoL performed less well and was poorly associated with the NSAD total score (Pseudo R2 0.18). EK scores were strongly associated with PUL (Pseudo R2 0.69). IPAQ was poorly associated with NSAD scores (Pseudo R2 0.09). This study showed that several of the chosen PROMs demonstrated change over time and a good association with functional outcomes. An alternative quality of life measure and method of collecting data on physical activity may need to be selected for assessing dysferlinopathy. Copyright © 2022 Mayhew, James, Moore, Sutherland, Jacobs, Feng, Lowes, Alfano, Muni Lofra, Rufibach, Rose, Duong, Bello, Pedrosa-Hernández, Holsten, Sakamoto, Canal, Sánchez-Aguilera Práxedes, Thiele, Siener, Vandevelde, DeWolf, Maron, Gordish-Dressman, Hilsden, Guglieri, Hogrel, Blamire, Carlier, Spuler, Day, Jones, Bharucha-Goebel, Salort-Campana, Pestronk, Walter, Paradas, Stojkovic, Mori-Yoshimura, Bravver, Díaz-Manera, Pegoraro, Mendell and Straub.

Author Keywords
clinical outcome assessments;  dysferlinopathy;  limb girdle muscular dystrophy;  PROMs;  quality of life

Funding details
MR/K000608/1
Jain Foundation

Document Type: Article
Publication Stage: Final
Source: Scopus

Wolframin is a novel regulator of tau pathology and neurodegeneration
(2022) Acta Neuropathologica, . 

Chen, S.^a b , Acosta, D.^a , Li, L.^a , Liang, J.^a , Chang, Y.^b c , Wang, C.^c , Fitzgerald, J.^a , Morrison, C.^a , Goulbourne, C.N.^d , Nakano, Y.^e , Villegas, N.C.H.^e f , Venkataraman, L.^a g , Brown, C.^h , Serrano, G.E.ⁱ , Bell, E.^j , Wemlinger, T.^k , Wu, M.^a , Kokiko-Cochran, O.N.^a , Popovich, P.^a , Flowers, X.E.^l , Honig, L.S.^l , Vonsattel, J.P.^l , Scharre, D.W.^j , Beach, T.G.ⁱ , Ma, Q.^c , Kuret, J.^m , Kõks, S.^n o , Urano, F.^h , Duff, K.E.^e p , Fu, H.^a q

^a Department of Neuroscience, The Ohio State University, Columbus, OH, United States
^b Biomedical Sciences Graduate Program, The Ohio State University, Columbus, OH, United States
^c Department of Biomedical Informatics, The Ohio State University, Columbus, OH, United States
^d Center for Dementia Research, The Nathan S. Kline Institute for Psychiatric Research, New York, NY, United States
^e Department of Pathology and Cell Biology, Columbia University Medical Center, New York, NY, United States
^f Current address: Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States
^g Center for Gene Therapy, Nationwide Children’s Hospital, Columbus, OH, United States
^h Department of Medicine, Washington University School of Medicine, St. Louis, MO, United States
ⁱ Banner Sun Health Research Institute, Sun City, AZ, United States
^j Department of Neurology, Center for Cognitive and Memory Disorders, Center for Neuromodulation, The Ohio State University, Columbus, OH, United States
^k Clinical Research Center, Clinical Trials Management Organization, The Ohio State University, Columbus, OH, United States
^l Department of Neurology, Columbia University Irving Medical Center, New York, NY, United States
^m Department of Biological Chemistry & Pharmacology, The Ohio State University, Columbus, OH, United States
ⁿ Centre for Molecular Medicine and Innovative Therapeutics, Murdoch University, Perth, WA, Australia
^o Perron Institute for Neurological and Translational Science, Perth, WA, Australia
^p UK Dementia Research Institute, UCL Queen Square Institute of Neurology, London, United Kingdom
^q Discovery Theme on Chronic Brain Injury, The Ohio State University, Columbus, OH, United States

Abstract
Selective neuronal vulnerability to protein aggregation is found in many neurodegenerative diseases including Alzheimer’s disease (AD). Understanding the molecular origins of this selective vulnerability is, therefore, of fundamental importance. Tau protein aggregates have been found in Wolframin (WFS1)-expressing excitatory neurons in the entorhinal cortex, one of the earliest affected regions in AD. The role of WFS1 in Tauopathies and its levels in tau pathology-associated neurodegeneration, however, is largely unknown. Here we report that WFS1 deficiency is associated with increased tau pathology and neurodegeneration, whereas overexpression of WFS1 reduces those changes. We also find that WFS1 interacts with tau protein and controls the susceptibility to tau pathology. Furthermore, chronic ER stress and autophagy-lysosome pathway (ALP)-associated genes are enriched in WFS1-high excitatory neurons in human AD at early Braak stages. The protein levels of ER stress and autophagy-lysosome pathway (ALP)-associated proteins are changed in tau transgenic mice with WFS1 deficiency, while overexpression of WFS1 reverses those changes. This work demonstrates a possible role for WFS1 in the regulation of tau pathology and neurodegeneration via chronic ER stress and the downstream ALP. Our findings provide insights into mechanisms that underpin selective neuronal vulnerability, and for developing new therapeutics to protect vulnerable neurons in AD. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Author Keywords
Alzheimer’s disease;  Autophagy-lysosome pathway;  Entorhinal cortex;  ER stress;  Neurodegeneration;  Neuronal vulnerability;  Tau pathology;  WFS1;  Wolframin

Funding details
P30 CA016058
P30AG066462, P50AG008702
National Institutes of HealthNIHP30-AG19610, R01-GM131399, U24-NS072026
U.S. Department of DefenseDOD
National Institute on AgingNIA
National Cancer InstituteNCI
National Institute of General Medical SciencesNIGMSAARF-17-505009
National Institute of Neurological Disorders and StrokeNINDSP30 NS104177, P30NS04578, UL1TR002733
Michael J. Fox Foundation for Parkinson’s ResearchMJFF
Alzheimer’s AssociationAAW81XWH1910309
National Center for Advancing Translational SciencesNCATS
Ohio State UniversityOSU
Arizona Department of Health ServicesADHS211002
Arizona Biomedical Research CommissionABRC0011, 05-901, 1001, 4001

Document Type: Article
Publication Stage: Article in Press
Source: Scopus

Impaired neurogenesis alters brain biomechanics in a neuroprogenitor-based genetic subtype of congenital hydrocephalus
(2022) Nature Neuroscience, . 

Duy, P.Q.^a b c , Weise, S.C.^d , Marini, C.^e , Li, X.-J.^f g , Liang, D.^a , Dahl, P.J.^h i , Ma, S.^a , Spajic, A.^a , Dong, W.^j , Juusola, J.^k , Kiziltug, E.^b , Kundishora, A.J.^b , Koundal, S.^l , Pedram, M.Z.^l , Torres-Fernández, L.A.^d , Händler, K.^m n o , De Domenico, E.^m n o , Becker, M.^m n o , Ulas, T.^m n o , Juranek, S.A.^p , Cuevas, E.^q , Hao, L.T.^b , Jux, B.^d , Sousa, A.M.M.^a , Liu, F.^a , Kim, S.-K.^a , Li, M.^a , Yang, Y.^r , Takeo, Y.^b , Duque, A.^a , Nelson-Williams, C.^s , Ha, Y.^t , Selvaganesan, K.^t , Robert, S.M.^b , Singh, A.K.^b , Allington, G.^b , Furey, C.G.^b , Timberlake, A.T.^s , Reeves, B.C.^b , Smith, H.^b , Dunbar, A.^b , DeSpenza, T., Jr.^b , Goto, J.^u , Marlier, A.^b , Moreno-De-Luca, A.^v , Yu, X.^w , Butler, W.E.^w , Carter, B.S.^w , Lake, E.M.R.^t , Constable, R.T.^t , Rakic, P.^a , Lin, H.^r , Deniz, E.^x , Benveniste, H.^l , Malvankar, N.S.^h i , Estrada-Veras, J.I.^y z aa , Walsh, C.A.^ab ac ad , Alper, S.L.^ad ae , Schultze, J.L.^m n o , Paeschke, K.^p , Doetzlhofer, A.^f g , Wulczyn, F.G.^e , Jin, S.C.^af , Lifton, R.P.^j , Sestan, N.^a , Kolanus, W.^d , Kahle, K.T.^{w ab ad ag}

^a Department of Neuroscience and Kavli Institute for Neuroscience, Yale University School of Medicine, New Haven, CT, United States
^b Department of Neurosurgery, Yale University School of Medicine, New Haven, CT, United States
^c Medical Scientist Training Program, Yale University School of Medicine, New Haven, CT, United States
^d Molecular Immunology and Cell Biology, Life & Medical Sciences Institute (LIMES), University of Bonn, Bonn, Germany
^e Institute for Cell Biology and Neurobiology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
^f Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, United States
^g Center for Hearing and Balance, Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, United States
^h Microbial Sciences Institute, Yale University, West Haven, CT, United States
ⁱ Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, United States
^j Laboratory of Human Genetics and Genomics, The Rockefeller University, New York, NY, United States
^k GeneDx, Gaithersburg, MD, United States
^l Department of Anesthesiology, Yale University School of Medicine, New Haven, CT, United States
^m Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany
ⁿ Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
^o Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE). PRECISE Platform for Genomics and Epigenomics at DZNE and University of Bonn, Bonn, Germany
^p Department of Oncology, Hematology and Rheumatology, University Hospital Bonn, Bonn, Germany
^q Stem Cells and Regenerative Medicine Section, University College London Great Ormond Street Institute of Child Health, London, United Kingdom
^r Yale Stem Cell Center, Yale University School of Medicine, New Haven, CT, United States
^s Department of Genetics, Yale University School of Medicine, New Haven, CT, United States
^t Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, United States
^u Division of Pediatric Neurosurgery, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States
^v Department of Radiology, Autism & Developmental Medicine Institute, Genomic Medicine Institute, Geisinger, Danville, PA, United States
^w Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
^x Department of Pediatrics, Yale University School of Medicine, New Haven, CT, United States
^y Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, United States
^z Pediatric Subspecialty Genetics Walter Reed National Military Medical Center, Bethesda, MD, United States
^aa Murtha Cancer Center/Research Program, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD, United States
^ab Division of Genetics and Genomics, Manton Center for Orphan Disease Research, Department of Pediatrics, and Howard Hughes Medical Institute, Boston Children’s Hospital, Boston, MA, United States
^ac Departments of Pediatrics and Neurology, Harvard Medical School, Boston, MA, United States
^ad Broad Institute of MIT and Harvard, Cambridge, MA, United States
^ae Division of Nephrology and Vascular Biology Research Center, Beth Israel Deaconess Medical Center and Department of Medicine, Harvard Medical School, Boston, MA, United States
^af Department of Genetics, Washington University School of Medicine, St. Louis, MO, United States
^ag Harvard Center for Hydrocephalus and Neurodevelopmental Disorders, Massachusetts General Hospital, Boston, MA, United States

Abstract
Hydrocephalus, characterized by cerebral ventricular dilatation, is routinely attributed to primary defects in cerebrospinal fluid (CSF) homeostasis. This fosters CSF shunting as the leading reason for brain surgery in children despite considerable disease heterogeneity. In this study, by integrating human brain transcriptomics with whole-exome sequencing of 483 patients with congenital hydrocephalus (CH), we found convergence of CH risk genes in embryonic neuroepithelial stem cells. Of all CH risk genes, TRIM71/lin-41 harbors the most de novo mutations and is most specifically expressed in neuroepithelial cells. Mice harboring neuroepithelial cell-specific Trim71 deletion or CH-specific Trim71 mutation exhibit prenatal hydrocephalus. CH mutations disrupt TRIM71 binding to its RNA targets, causing premature neuroepithelial cell differentiation and reduced neurogenesis. Cortical hypoplasia leads to a hypercompliant cortex and secondary ventricular enlargement without primary defects in CSF circulation. These data highlight the importance of precisely regulated neuroepithelial cell fate for normal brain–CSF biomechanics and support a clinically relevant neuroprogenitor-based paradigm of CH. © 2022, The Author(s), under exclusive licence to Springer Nature America, Inc.

Funding details
National Institutes of HealthNIH
National Heart, Lung, and Blood InstituteNHLBICTSA1405, R00HL143036-02
Burroughs Wellcome FundBWF
Hartwell FoundationDA023999, MH113257
Children’s Discovery InstituteCDI1DP2AI138259-01, CDI-FR-2021-926
Yale Cancer Center
Hydrocephalus AssociationHA5R21NS116484-02
Deutsche ForschungsgemeinschaftDFGEXC2151–390873048, WU 563/3-1

Document Type: Article
Publication Stage: Article in Press
Source: Scopus

Biomarker clustering in autosomal dominant Alzheimer’s disease
(2022) Alzheimer’s and Dementia, . 

Luckett, P.H.^w , Chen, C.^w , Gordon, B.A.^w , Wisch, J.^w , Berman, S.B.^a , Chhatwal, J.P.^b , Cruchaga, C.^w , Fagan, A.M.^w , Farlow, M.R.^c , Fox, N.C.^d , Jucker, M.^e f , Levin, J.^g h i , Masters, C.L.^j , Mori, H.^k , Noble, J.M.^l , Salloway, S.^m , Schofield, P.R.^n o , Brickman, A.M.^p q , Brooks, W.S.^n o , Cash, D.M.^d , Fulham, M.J.^p q , Ghetti, B.^c , Jack, C.R., Jr.^r , Vöglein, J.^g , Klunk, W.E.^a , Koeppe, R.^s , Su, Y.^t , Weiner, M.^u v , Wang, Q.^w , Marcus, D.^w , Koudelis, D.^w , Joseph-Mathurin, N.^w , Cash, L.^w , Hornbeck, R.^w , Xiong, C.^w , Perrin, R.J.^w , Karch, C.M.^w , Hassenstab, J.^w , McDade, E.^w , Morris, J.C.^w , Benzinger, T.L.S.^w , Bateman, R.J.^w , Ances, B.M.^w , for the Dominantly Inherited Alzheimer Network (DIAN)^w

^a University of Pittsburgh, Pittsburgh, PA, United States
^b Brigham and Women’s Hospital, Massachusetts General Hospital, Boston, MA, United States
^c Indiana University, Bloomington, IN, United States
^d Dementia Research Centre, UCL Queen Square Institute of Neurology, London, United Kingdom
^e German Center for Neurodegenerative Disease, Tübingen, Germany
^f Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
^g Department of Neurology, Ludwig-Maximilians-Universität München, Munich, Germany
^h German Center for Neurodegenerative Diseases, Munich, Germany
ⁱ Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
^j Florey Institute, The University of Melbourne, Parkville, VIC, Australia
^k Osaka City University Medical School, Nagaoka Sutoku University, Osaka, Abenoku, Japan
^l Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, G.H. Sergievsky Center, and Department of Neurology, Columbia University Irving Medical Center, New York, NY, United States
^m Butler Hospital and Warren Alpert Medical School of Brown University, Providence, RI, United States
ⁿ Neuroscience Research Australia, Randwick, NSW, Australia
^o School of Medical Sciences, University of New South Wales, Sydney, NSW, Australia
^p Department of Molecular Imaging, Royal Prince Alfred Hospital, Camperdown, NSW, Australia
^q The University of Sydney, SydneyNSW, Australia
^r Mayo Clinic, Rochester, MN, United States
^s University of Michigan, Michigan, Ann Arbor, United States
^t Banner Alzheimer Institute, Phoenix, AZ, United States
^u University of California San Francisco, San Francisco, CA, United States
^v San Francisco Veterans Affairs Medical Center, San Francisco, CA, United States
^w Washington University in St. Louis, St. Louis, MO, United States

Abstract
INTRODUCTION: As the number of biomarkers used to study Alzheimer’s disease (AD) continues to increase, it is important to understand the utility of any given biomarker, as well as what additional information a biomarker provides when compared to others. METHODS: We used hierarchical clustering to group 19 cross-sectional biomarkers in autosomal dominant AD. Feature selection identified biomarkers that were the strongest predictors of mutation status and estimated years from symptom onset (EYO). Biomarkers identified included clinical assessments, neuroimaging, cerebrospinal fluid amyloid, and tau, and emerging biomarkers of neuronal integrity and inflammation. RESULTS: Three primary clusters were identified: neurodegeneration, amyloid/tau, and emerging biomarkers. Feature selection identified amyloid and tau measures as the primary predictors of mutation status and EYO. Emerging biomarkers of neuronal integrity and inflammation were relatively weak predictors. DISCUSSION: These results provide novel insight into our understanding of the relationships among biomarkers and the staging of biomarkers based on disease progression. © 2022 the Alzheimer’s Association.

Author Keywords
Autosomal dominant Alzheimer’s disease;  biomarkers;  machine learning

Funding details
156243
National Institutes of HealthNIHK01AG053474, K23AG046363, P01AG003991, P01AG026276, P30NS098577, P50AG005681, P50AG05131, R01AG052550, R01EB009352, U01AG042791‐S1, UFAG 032438, UL1TR000448
U.S. Department of DefenseDODPPRN‐1501‐26817, W81XWH‐13‐1‐0259, W81XWH‐15‐2‐0070
Foundation for the National Institutes of HealthFNIHR1AG046179
National Institute on AgingNIA
Mayo Clinic1R01AG053798‐01A1, 1R01AG058676‐01A1, 1RF1AG059009‐01, 1U2CA060426‐01, 5U19AG024904‐14, R01 MH098062
Alzheimer’s AssociationAA18–109929, BHR‐16‐459161
California Department of Public HealthCDPH16–10054, 174552, 18‐PAF01312, 2015‐A‐011‐NET, 444951–54249
Biogen
Foundation for Barnes-Jewish HospitalFBJH
Japan Agency for Medical Research and DevelopmentAMED
Hope Center for Neurological Disorders
Medical Research CouncilMRC20‐681815, MR/009076/1, MR/L023784/1
National Institute for Health ResearchNIHR
Deutsches Zentrum für Neurodegenerative ErkrankungenDZNE

Document Type: Article
Publication Stage: Article in Press
Source: Scopus

Germline mosaicism of a missense variant in KCNC2 in a multiplex family with autism and epilepsy characterized by long-read sequencing
(2022) American Journal of Medical Genetics, Part A, . 

Mehinovic, E.^a , Gray, T.^b , Campbell, M.^b , Ekholm, J.^c , Wenger, A.^c , Rowell, W.^c , Grudo, A.^c , Grimwood, J.^d , Korlach, J.^c , Gurnett, C.^e , Constantino, J.N.^b , Turner, T.N.^a

^a Department of Genetics, Washington University School of Medicine, St. Louis, MO, United States
^b Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
^c Pacific Biosciences, Menlo Park, CA, United States
^d HudsonAlpha Institute for Biotechnology, Huntsville, AL, United States
^e Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States

Abstract
Currently, protein-coding de novo variants and large copy number variants have been identified as important for ~30% of individuals with autism. One approach to identify relevant variation in individuals who lack these types of events is by utilizing newer genomic technologies. In this study, highly accurate PacBio HiFi long-read sequencing was applied to a family with autism, epileptic encephalopathy, cognitive impairment, and mild dysmorphic features (two affected female siblings, unaffected parents, and one unaffected male sibling) with no known clinical variant. From our long-read sequencing data, a de novo missense variant in the KCNC2 gene (encodes Kv3.2) was identified in both affected children. This variant was phased to the paternal chromosome of origin and is likely a germline mosaic. In silico assessment revealed the variant was not in controls, highly conserved, and predicted damaging. This specific missense variant (Val473Ala) has been shown in both an ortholog and paralog of Kv3.2 to accelerate current decay, shift the voltage dependence of activation, and prevent the channel from entering a long-lasting open state. Seven additional missense variants have been identified in other individuals with neurodevelopmental disorders (p = 1.03 × 10−5). KCNC2 is most highly expressed in the brain; in particular, in the thalamus and is enriched in GABAergic neurons. Long-read sequencing was useful in discovering the relevant variant in this family with autism that had remained a mystery for several years and will potentially have great benefits in the clinic once it is widely available. © 2022 The Authors. American Journal of Medical Genetics Part A published by Wiley Periodicals LLC.

Author Keywords
autism;  channel;  epilepsy;  genetics;  genomics;  long-read sequencing

Funding details
National Institutes of HealthNIHP50HD103525, R00MH117165
National Institute of Mental HealthNIMH
National Institute on Drug AbuseNIDA
National Heart, Lung, and Blood InstituteNHLBI
National Human Genome Research InstituteNHGRI
National Cancer InstituteNCI
National Institute of Neurological Disorders and StrokeNINDS

Document Type: Article
Publication Stage: Article in Press
Source: Scopus

APOE Antibody Inhibits Aβ-Associated Tau Seeding and Spreading in a Mouse Model
(2022) Annals of Neurology, . 

Gratuze, M., Jiang, H., Wang, C., Xiong, M., Bao, X., Holtzman, D.M.

Department of Neurology, Hope Center for Neurological Disorders, Knight Alzheimer’s Disease Research Center, Washington University School of Medicine, St. Louis, MO, United States

Abstract
APOE is the strongest genetic factor for late-onset Alzheimer’s disease (AD). A specific conformation of the ApoE protein is present in amyloid-β (Aβ) containing plaques. Immunotherapy targeting ApoE in plaques reduces brain Aβ deposits in mice. Here, we evaluated the effects of the anti-human APOE antibody HAE-4 on amyloid plaques, Aβ-mediated tau seeding and spreading, and neuritic dystrophy in the 5XFAD amyloid mice expressing human ApoE4. HAE-4 reduced Aβ plaques as well as Aβ-driven tau seeding/spreading and neuritic dystrophy. These results demonstrate that HAE-4 may provide therapeutic effects on amyloid removal and Aβ driven downstream consequences such as tauopathy. ANN NEUROL 2022. © 2022 American Neurological Association.

Funding details
National Institutes of HealthNIHAG047644

Document Type: Article
Publication Stage: Article in Press
Source: Scopus

Deep learning resting state functional magnetic resonance imaging lateralization of temporal lobe epilepsy
(2022) Epilepsia, . 

Luckett, P.H.^a , Maccotta, L.^b , Lee, J.J.^c , Park, K.Y.^a , U. F. Dosenbach, N.^b , Ances, B.M.^b , Hogan, R.E.^b , Shimony, J.S.^c , Leuthardt, E.C.^a

^a Department of Neurological Surgery, Washington University School of Medicine, St Louis, MO, United States
^b Department of Neurology, Washington University School of Medicine, St Louis, MO, United States
^c Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, United States

Abstract
Objective: Localization of focal epilepsy is critical for surgical treatment of refractory seizures. There remains a great need for noninvasive techniques to localize seizures for surgical decision-making. We investigate the use of deep learning using resting state functional magnetic resonance imaging (RS-fMRI) to identify the hemisphere of seizure onset in temporal lobe epilepsy (TLE) patients. Methods: A total of 2132 healthy controls and 32 preoperative TLE patients were studied. All participants underwent structural MRI and RS-fMRI. Healthy control data were used to generate training samples for a three-dimensional convolutional neural network (3DCNN). RS-fMRI was synthetically altered in randomly lateralized regions in the healthy control participants. The model was then trained to classify the hemisphere containing synthetic noise. Finally, the model was tested on TLE patients to assess its performance for detecting biological seizure onset zones, and gradient-weighted class activation mapping (Grad-CAM) identified the strongest predictive regions. Results: The 3DCNN classified healthy control hemispheres known to contain synthetic noise with 96% accuracy, and TLE hemispheres clinically identified to be seizure onset zones with 90.6% accuracy. Grad-CAM identified a range of temporal, frontal, parietal, and subcortical regions that were strong anatomical predictors of the seizure onset zone, and the resting state networks that colocalized with Grad-CAM results included default mode, medial temporal, and dorsal attention networks. Lastly, in an analysis of a subset of patients with postsurgical outcomes, the 3DCNN leveraged a more focal set of regions to achieve classification in patients with Engel Class >I compared to Engel Class I. Significance: Noninvasive techniques capable of localizing the seizure onset zone could improve presurgical planning in patients with intractable epilepsy. We have demonstrated the ability of deep learning to identify the correct hemisphere of the seizure onset zone in TLE patients using RS-fMRI with high accuracy. This approach represents a novel technique of seizure lateralization that could improve preoperative surgical planning. © 2022 International League Against Epilepsy.

Author Keywords
epilepsy;  machine learning;  resting state functional connectivity

Funding details
National Institutes of HealthNIHP01 AG003991, P01 AG026276, P50 HD103525, R01 AG057680, R01 CA203861, R01 DA054009, R01 MH118031, R01 NR014449, R01 NR015738

Document Type: Article
Publication Stage: Article in Press
Source: Scopus