Hope Center Member Publications

Conformational plasticity of NaK2K and TREK2 potassium channel selectivity filters
(2023) Nature Communications, 14 (1), art. no. 89, . 

Matamoros, M.^a b , Ng, X.W.^a b , Brettmann, J.B.^a c , Piston, D.W.^a b , Nichols, C.G.^a b

^a Center for Investigation of Membrane Excitability Diseases, Washington University School of Medicine, St. Louis, MO, United States
^b Department of Cell Biology and Physiology, Washington University School of Medicine, St. Louis, MO, United States
^c Millipore-Sigma Inc., St. Louis, MO, United States

Abstract
The K+ channel selectivity filter (SF) is defined by TxGYG amino acid sequences that generate four identical K+ binding sites (S1-S4). Only two sites (S3, S4) are present in the non-selective bacterial NaK channel, but a four-site K+-selective SF is obtained by mutating the wild-type TVGDGN SF sequence to a canonical K+ channel TVGYGD sequence (NaK2K mutant). Using single molecule FRET (smFRET), we show that the SF of NaK2K, but not of non-selective NaK, is ion-dependent, with the constricted SF configuration stabilized in high K+ conditions. Patch-clamp electrophysiology and non-canonical fluorescent amino acid incorporation show that NaK2K selectivity is reduced by crosslinking to limit SF conformational movement. Finally, the eukaryotic K+ channel TREK2 SF exhibits essentially identical smFRET-reported ion-dependent conformations as in prokaryotic K+ channels. Our results establish the generality of K+-induced SF conformational stability across the K+ channel superfamily, and introduce an approach to study manipulation of channel selectivity. © 2023, The Author(s).

Funding details
National Institutes of HealthNIHR35 HL140024
Washington University in St. LouisWUSTL
McDonnell Center for Cellular and Molecular Neurobiology, Washington University in St. Louis

Document Type: Article
Publication Stage: Final
Source: Scopus

Mitochondrial haplogroups and cognitive progression in Parkinson’s disease
(2023) Brain: A Journal of Neurology, 146 (1), pp. 42-49. 

Liu, G.^a , Ni, C.^a , Zhan, J.^a , Li, W.^a , Luo, J.^a , Liao, Z.^b c , Locascio, J.J.^b c d , Xian, W.^e , Chen, L.^e , Pei, Z.^e , Corvol, J.-C.^f , Maple-Grødem, J.^g h , Campbell, M.C.ⁱ , Elbaz, A.^j , Lesage, S.^f , Brice, A.^f , Hung, A.Y.^d , Schwarzschild, M.A.^d , Hayes, M.T.^k , Wills, A.-M.^d , Ravina, B.^l , Shoulson, I.^m , Taba, P.^n o , Kõks, S.^p q , Beach, T.G.^r , Cormier-Dequaire, F.^f , Alves, G.^g h s , Tysnes, O.-B.^t u , Perlmutter, J.S.^i v w , Heutink, P.^x , van Hilten, J.J.^y , Barker, R.A.^z aa , Williams-Gray, C.H.^z , Scherzer, C.R.^b c d k , International Genetics of Parkinson Disease Progression (IGPP) Consortium^ab

^a Neurobiology Research Center, School of Medicine, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong 518107, China
^b Brigham and Women’s Hospital and Harvard Medical School, APDA Center for Advanced Parkinson Research, Boston, MA 02115, United States
^c Neurogenomics Lab, Harvard Medical School, Brigham and Women’s Hospital, Boston, MA 02115, United States
^d Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, United States
^e Department of Neurology, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
^f Sorbonne Université, Institut du Cerveau – Paris Brain Institute – ICM, Institut National de la Santé et de la Recherche Médicale, Centre National de la Recherche Scientifique, Département de Neurologie et de Génétique, Hôpital Pitié-Salpêtrière, Assistance Publique Hôpitaux de Paris, Paris, F-75013, France
^g Norwegian Centre for Movement Disorders, Stavanger University Hospital, Stavanger, 4068, Norway
^h Department of Chemistry, Bioscience and Environmental Engineering, University of Stavanger, Stavanger, 4021, Norway
ⁱ Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States
^j Paris-Saclay University, UVSQ, Inserm, Gustave Roussy, CESP, Villejuif, F94805, France
^k Department of Neurology, Brigham and Women’s Hospital, Boston, MA 02115, United States
^l Praxis Precision Medicines, Cambridge, MA 02142, United States
^m Department of Neurology, Center for Health and Technology, University of Rochester, Rochester, NY 14642, USA
ⁿ Department of Neurology and Neurosurgery, Institute of Clinical Medicine, University of TartuTartu 50406, Estonia
^o Neurology Clinic, Tartu University HospitalTartu 50406, Estonia
^p Centre for Molecular Medicine and Innovative Therapeutics, Murdoch University, Murdoch, Perth, WA 6150, Australia
^q Perron Institute for Neurological and Translational Science, Nedlands, WA 6009, Australia
^r Banner Sun Health Research Institute, Sun City, United States
^s Department of Neurology, Stavanger University Hospital, Stavanger, 4068, Norway
^t Department of Neurology, Haukeland University Hospital, Bergen, 5020, Norway
^u Department of Clinical Medicine, University of Bergen, Bergen, 5020, Norway
^v Departments of Radiology and Neuroscience, Washington University School of Medicine, St. Louis, MO 63110, United States
^w Program of Physical Therapy and Program of Occupational Therapy, Washington University School of Medicine, St. Louis, MO 63110, United States
^x German Center for Neurodegenerative diseases (DZNE), 72076 Tübingen, Germany
^y Department of Neurology, Leiden University Medical Center, ZA Leiden, 2333, Netherlands
^z John Van Geest Centre for Brain Repair, Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0PY, United Kingdom
^aa Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge CB2 0AW, United Kingdom

Abstract
Mitochondria are a culprit in the onset of Parkinson’s disease, but their role during disease progression is unclear. Here we used Cox proportional hazards models to exam the effect of variation in the mitochondrial genome on longitudinal cognitive and motor progression over time in 4064 patients with Parkinson’s disease. Mitochondrial macro-haplogroup was associated with reduced risk of cognitive disease progression in the discovery and replication population. In the combined analysis, patients with the super macro-haplogroup J, T, U# had a 41% lower risk of cognitive progression with P = 2.42 × 10-6 compared to those with macro-haplogroup H. Exploratory analysis indicated that the common mitochondrial DNA variant, m.2706A>G, was associated with slower cognitive decline with a hazard ratio of 0.68 (95% confidence interval 0.56-0.81) and P = 2.46 × 10-5. Mitochondrial haplogroups were not appreciably linked to motor progression. This initial genetic survival study of the mitochondrial genome suggests that mitochondrial haplogroups may be associated with the pace of cognitive progression in Parkinson’s disease over time. © The Author(s) 2022. Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Author Keywords
cognitive progression;  mitochondrial haplogroups;  Parkinson’s disease

Document Type: Article
Publication Stage: Final
Source: Scopus

Diffusion basis spectrum imaging predicts long-term clinical outcomes following surgery in cervical spondylotic myelopathy
(2023) Spine Journal, . 

Zhang, J.K.^a , Jayasekera, D.^b , Javeed, S.^a , Greenberg, J.K.^a , Blum, J.^c , Dibble, C.F.^a , Sun, P.^d , Song, S.-K.^c , Ray, W.Z.^a

^a Department of Neurological Surgery, Washington University School of Medicine, Saint Louis, MO 63110, United States
^b Department of Biomedical Engineering, Washington University McKelvey School of Engineering, Saint Louis, MO 63130, United States
^c Department of Radiology, Washington University School of Medicine, St. Louis, MO, United States
^d Department of Imaging Physics, UT MD Anderson Cancer Center, Houston, TX 77030, United States

Abstract
BACKGROUND CONTEXT: A major shortcoming in improving care for cervical spondylotic myelopathy (CSM) patients is the lack of robust quantitative imaging tools to guide surgical decision-making. Diffusion basis spectrum imaging (DBSI), an advanced diffusion-weighted MRI technique, provides objective assessments of white matter tract integrity that may help prognosticate outcomes in patients undergoing surgery for CSM. PURPOSE: To examine the ability of DBSI to predict clinically important CSM outcome measures at 2-years follow-up. STUDY DESIGN/SETTING: Prospective cohort study. PATIENT SAMPLE: Patients undergoing decompressive cervical surgery for CSM. OUTCOME MEASURES: Neurofunctional status was assessed by the mJOA, MDI, and DASH. Quality-of-life was measured by the SF-36 PCS and SF-36 MCS. The NDI evaluated self-reported neck pain, and patient satisfaction was assessed by the NASS satisfaction index. METHODS: Fifty CSM patients who underwent cervical decompressive surgery were enrolled. Preoperative DBSI metrics assessed white matter tract integrity through fractional anisotropy, fiber fraction, axial diffusivity, and radial diffusivity. To evaluate extra-axonal diffusion, DBSI measures restricted and nonrestricted fractions. Patient-reported outcome measures were evaluated preoperatively and up to 2-years follow-up. Support vector machine classification algorithms were used to predict surgical outcomes at 2-years follow-up. Specifically, three feature sets were built for each of the seven clinical outcome measures (eg, mJOA), including clinical only, DBSI only, and combined feature sets. RESULTS: Twenty-seven mild (mJOA 15–17), 12 moderate (12–14) and 11 severe (0–11) CSM patients were enrolled. Twenty-four (60%) patients underwent anterior decompressive surgery compared with 16 (40%) posterior approaches. The mean (SD) follow-up was 23.2 (5.6, range 6.1–32.8) months. Feature sets built on combined data (ie, clinical+DBSI metrics) performed significantly better for all outcome measures compared with those only including clinical or DBSI data. When predicting improvement in the mJOA, the clinically driven feature set had an accuracy of 61.9 [61.6, 62.5], compared with 78.6 [78.4, 79.2] in the DBSI feature set, and 90.5 [90.2, 90.8] in the combined feature set. CONCLUSIONS: When combined with key clinical covariates, preoperative DBSI metrics predicted improvement after surgical decompression for CSM with high accuracy for multiple outcome measures. These results suggest that DBSI may serve as a noninvasive imaging biomarker for CSM valuable in guiding patient selection and informing preoperative counseling. LEVEL OF EVIDENCE: II © 2022 Elsevier Inc.

Author Keywords
Cervical spondylotic myelopathy;  Diffusion basis spectrum imaging;  Diffusion-weighted MRI;  Machine learning;  Support vector machine

Funding details
U01- EY025500
National Institutes of HealthNIHTL1TR002344
National Institute of Neurological Disorders and StrokeNINDSR01 – NS047592
National Center for Advancing Translational SciencesNCATS

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

Cognitive and brain reserve predict decline in adverse driving behaviors among cognitively normal older adults
(2022) Frontiers in Psychology, 13, art. no. 1076735, . 

Murphy, S.A.^a , Chen, L.^b , Doherty, J.M.^a , Acharyya, P.^a , Riley, N.^a , Johnson, A.M.^c , Walker, A.^a , Domash, H.^a , Jorgensen, M.^a , Bayat, S.^d e , Carr, D.B.^f , Ances, B.M.^a g h , Babulal, G.M.^a i j k

^a Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
^b Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, United States
^c Center for Clinical Studies, Washington University School of Medicine, St. Louis, MO, United States
^d Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada
^e Department of Geomatics Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada
^f Department of Medicine, Division of Geriatrics and Nutritional Science, Washington University School of Medicine, St. Louis, MO, United States
^g Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, United States
^h Washington University School of Medicine, Mallinckrodt Institute of Radiology, St. Louis, MO, United States
ⁱ Washington University School of Medicine, Institute for Public Health, St. Louis, MO, United States
^j Department of Psychology, Faculty of Humanities, University of Johannesburg, Johannesburg, South Africa
^k Department of Clinical Research and Leadership, The George Washington University School of Medicine and Health SciencesWashington, WA, United States

Abstract
Daily driving is a multi-faceted, real-world, behavioral measure of cognitive functioning requiring multiple cognitive domains working synergistically to complete this instrumental activity of daily living. As the global population of older adult continues to grow, motor vehicle crashes become more frequent among this demographic. Cognitive reserve (CR) is the brain’s adaptability or functional robustness despite damage, while brain reserve (BR) refers the structural, neuroanatomical resources. This study examined whether CR and BR predicted changes in adverse driving behaviors in cognitively normal older adults. Cognitively normal older adults (Clinical Dementia Rating 0) were enrolled from longitudinal studies at the Knight Alzheimer’s Disease Research Center at Washington University. Participants (n = 186) were ≥65 years of age, required to have Magnetic Resonance Imaging (MRI) data, neuropsychological testing data, and at least one full year of naturalistic driving data prior to the beginning of COVID-19 lockdown in the United States (March 2020) as measured by Driving Real World In-vehicle Evaluation System (DRIVES). Findings suggest numerous changes in driving behaviors over time were predicted by increased hippocampal and whole brain atrophy, as well as lower CR scores as proxied by the Wide Range Achievement Test 4. These changes indicate that those with lower BR and CR are more likely to reduce their driving exposure and limit trips as they age and may be more likely to avoid highways where speeding and aggressive maneuvers frequently occur. Copyright © 2022 Murphy, Chen, Doherty, Acharyya, Riley, Johnson, Walker, Domash, Jorgensen, Bayat, Carr, Ances and Babulal.

Author Keywords
aging;  brain reserve;  cognitive reserve;  driving (veh);  older (elderly) drivers

Funding details
National Institutes of HealthNIH
National Institute on AgingNIAAG056466, AG067428, AG068183
BrightFocus FoundationBFFA2021142S

Document Type: Article
Publication Stage: Final
Source: Scopus