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Girach Z, Sarian A, Maldonado-García C, Ravikumar N, Sergouniotis PI, Rothwell PM, Frangi AF, Julian TH. Retinal imaging for the assessment of stroke risk: a systematic review. J Neurol 2024; 271:2285-2297. [PMID: 38430271 PMCID: PMC11055692 DOI: 10.1007/s00415-023-12171-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 12/21/2023] [Accepted: 12/23/2023] [Indexed: 03/03/2024]
Abstract
BACKGROUND Stroke is a leading cause of morbidity and mortality. Retinal imaging allows non-invasive assessment of the microvasculature. Consequently, retinal imaging is a technology which is garnering increasing attention as a means of assessing cardiovascular health and stroke risk. METHODS A biomedical literature search was performed to identify prospective studies that assess the role of retinal imaging derived biomarkers as indicators of stroke risk. RESULTS Twenty-four studies were included in this systematic review. The available evidence suggests that wider retinal venules, lower fractal dimension, increased arteriolar tortuosity, presence of retinopathy, and presence of retinal emboli are associated with increased likelihood of stroke. There is weaker evidence to suggest that narrower arterioles and the presence of individual retinopathy traits such as microaneurysms and arteriovenous nicking indicate increased stroke risk. Our review identified three models utilizing artificial intelligence algorithms for the analysis of retinal images to predict stroke. Two of these focused on fundus photographs, whilst one also utilized optical coherence tomography (OCT) technology images. The constructed models performed similarly to conventional risk scores but did not significantly exceed their performance. Only two studies identified in this review used OCT imaging, despite the higher dimensionality of this data. CONCLUSION Whilst there is strong evidence that retinal imaging features can be used to indicate stroke risk, there is currently no predictive model which significantly outperforms conventional risk scores. To develop clinically useful tools, future research should focus on utilization of deep learning algorithms, validation in external cohorts, and analysis of OCT images.
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Affiliation(s)
- Zain Girach
- Sheffield Medical School, University of Sheffield, Beech Hill Rd, Broomhall, Sheffield, UK
| | - Arni Sarian
- Manchester Royal Infirmary, Manchester University NHS Foundation Trust, Oxford Rd, Manchester, UK
| | - Cynthia Maldonado-García
- Centre for Computational Imaging and Simulation Technologies in Biomedicine, School of Computing, University of Leeds, Leeds, UK
| | - Nishant Ravikumar
- Centre for Computational Imaging and Simulation Technologies in Biomedicine, School of Computing, University of Leeds, Leeds, UK
| | - Panagiotis I Sergouniotis
- Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, UK
- Manchester Centre for Genomic Medicine, Saint Mary's Hospital, Manchester University NHS Foundation Trust, Manchester, UK
- Manchester Royal Eye Hospital, Manchester University NHS Foundation Trust, Oxford Rd, Manchester, UK
| | - Peter M Rothwell
- Wolfson Centre for the Prevention of Stroke and Dementia, University of Oxford, Oxford, UK
| | - Alejandro F Frangi
- Division of Informatics, Imaging and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
- School of Computer Science, Faculty of Science and Engineering, University of Manchester, Kilburn Building, Manchester, UK
- Christabel Pankhurst Institute, The University of Manchester, Manchester, UK
| | - Thomas H Julian
- Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, UK.
- Manchester Centre for Genomic Medicine, Saint Mary's Hospital, Manchester University NHS Foundation Trust, Manchester, UK.
- Manchester Royal Eye Hospital, Manchester University NHS Foundation Trust, Oxford Rd, Manchester, UK.
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Harvey C, Weinreich M, Lee JA, Shaw AC, Ferraiuolo L, Mortiboys H, Zhang S, Hop PJ, Zwamborn RA, van Eijk K, Julian TH, Moll T, Iacoangeli A, Al Khleifat A, Quinn JP, Pfaff AL, Kõks S, Poulton J, Battle SL, Arking DE, Snyder MP, Veldink JH, Kenna KP, Shaw PJ, Cooper-Knock J. Rare and common genetic determinants of mitochondrial function determine severity but not risk of amyotrophic lateral sclerosis. Heliyon 2024; 10:e24975. [PMID: 38317984 PMCID: PMC10839612 DOI: 10.1016/j.heliyon.2024.e24975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 01/12/2024] [Accepted: 01/17/2024] [Indexed: 02/07/2024] Open
Abstract
Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease involving selective vulnerability of energy-intensive motor neurons (MNs). It has been unclear whether mitochondrial function is an upstream driver or a downstream modifier of neurotoxicity. We separated upstream genetic determinants of mitochondrial function, including genetic variation within the mitochondrial genome or autosomes; from downstream changeable factors including mitochondrial DNA copy number (mtCN). Across three cohorts including 6,437 ALS patients, we discovered that a set of mitochondrial haplotypes, chosen because they are linked to measurements of mitochondrial function, are a determinant of ALS survival following disease onset, but do not modify ALS risk. One particular haplotype appeared to be neuroprotective and was significantly over-represented in two cohorts of long-surviving ALS patients. Causal inference for mitochondrial function was achievable using mitochondrial haplotypes, but not autosomal SNPs in traditional Mendelian randomization (MR). Furthermore, rare loss-of-function genetic variants within, and reduced MN expression of, ACADM and DNA2 lead to ∼50 % shorter ALS survival; both proteins are implicated in mitochondrial function. Both mtCN and cellular vulnerability are linked to DNA2 function in ALS patient-derived neurons. Finally, MtCN responds dynamically to the onset of ALS independently of mitochondrial haplotype, and is correlated with disease severity. We conclude that, based on the genetic measures we have employed, mitochondrial function is a therapeutic target for amelioration of disease severity but not prevention of ALS.
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Affiliation(s)
- Calum Harvey
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Marcel Weinreich
- Clinical Neurobiology, German Cancer Research Center and University Hospital Heidelberg, Germany
| | - James A.K. Lee
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Allan C. Shaw
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Laura Ferraiuolo
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Heather Mortiboys
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Sai Zhang
- Department of Epidemiology, University of Florida, Gainesville, FL, USA
| | - Paul J. Hop
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Ramona A.J. Zwamborn
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Kristel van Eijk
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Thomas H. Julian
- Division of Evolution, Infection and Genomics, School of Biological Sciences, The University of Manchester, Manchester, UK
| | - Tobias Moll
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Alfredo Iacoangeli
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, Department of Basic and Clinical Neuroscience, London, UK
| | - Ahmad Al Khleifat
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, Department of Basic and Clinical Neuroscience, London, UK
| | - John P. Quinn
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular & Integrative Biology, Liverpool, UK
| | - Abigail L. Pfaff
- Perron Institute for Neurological and Translational Science, Perth, Australia
- Centre for Molecular Medicine and Innovative Therapeutics, Murdoch University, Perth, Australia
| | - Sulev Kõks
- Perron Institute for Neurological and Translational Science, Perth, Australia
- Centre for Molecular Medicine and Innovative Therapeutics, Murdoch University, Perth, Australia
| | - Joanna Poulton
- Nuffield Department of Obstetrics and Gynaecology, University of Oxford, Oxford, UK
| | - Stephanie L. Battle
- McKusick-Nathans Institute, Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Dan E. Arking
- McKusick-Nathans Institute, Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Michael P. Snyder
- Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Project MinE ALS Sequencing Consortium
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
- Clinical Neurobiology, German Cancer Research Center and University Hospital Heidelberg, Germany
- Department of Epidemiology, University of Florida, Gainesville, FL, USA
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
- Division of Evolution, Infection and Genomics, School of Biological Sciences, The University of Manchester, Manchester, UK
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, Department of Basic and Clinical Neuroscience, London, UK
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular & Integrative Biology, Liverpool, UK
- Perron Institute for Neurological and Translational Science, Perth, Australia
- Centre for Molecular Medicine and Innovative Therapeutics, Murdoch University, Perth, Australia
- Nuffield Department of Obstetrics and Gynaecology, University of Oxford, Oxford, UK
- McKusick-Nathans Institute, Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Jan H. Veldink
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Kevin P. Kenna
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Pamela J. Shaw
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Johnathan Cooper-Knock
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
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Julian TH, Girach Z, Sanderson E, Guo H, Yu J, Cooper-Knock J, Black GC, Sergouniotis PI. Causal factors in primary open angle glaucoma: a phenome-wide Mendelian randomisation study. Sci Rep 2023; 13:9984. [PMID: 37340071 DOI: 10.1038/s41598-023-37144-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Accepted: 06/16/2023] [Indexed: 06/22/2023] Open
Abstract
Primary open angle glaucoma (POAG) is a chronic, adult-onset optic neuropathy associated with characteristic optic disc and/or visual field changes. With a view to identifying modifiable risk factors for this common neurodegenerative condition, we performed a 'phenome-wide' univariable Mendelian randomisation (MR) study that involved analysing the relationship between 9661 traits and POAG. Utilised analytical approaches included weighted mode based estimation, the weighted median method, the MR Egger method and the inverse variance weighted (IVW) approach. Eleven traits related to POAG risk were identified including: serum levels of the angiopoietin-1 receptor (OR [odds ratio] = 1.11, IVW p = 2.34E-06) and the cadherin 5 protein (OR = 1.06, IVW p = 1.31E-06); intraocular pressure (OR = 2.46-3.79, IVW p = 8.94E-44-3.00E-27); diabetes (OR = 5.17, beta = 1.64, IVW p = 9.68E-04); and waist circumference (OR = 0.79, IVW p = 1.66E-05). Future research focussing on the effects of adiposity, cadherin 5 and angiopoietin-1 receptor on POAG development and progression is expected to provide key insights that might inform the provision of lifestyle modification advice and/or the development of novel therapies.
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Affiliation(s)
- Thomas H Julian
- Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
- Manchester Royal Eye Hospital, Manchester University NHS Foundation Trust, Manchester, UK
| | - Zain Girach
- Sheffield Medical School, University of Sheffield, Sheffield, UK
| | - Eleanor Sanderson
- MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Hui Guo
- Centre for Biostatistics, Division of Population Health, Health Services Research and Primary Care, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Jonathan Yu
- Manchester Royal Eye Hospital, Manchester University NHS Foundation Trust, Manchester, UK
| | - Johnathan Cooper-Knock
- Department of Neuroscience, Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Graeme C Black
- Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
- Manchester Centre for Genomic Medicine, Saint Mary's Hospital, Manchester University NHS Foundation Trust, Manchester, UK
| | - Panagiotis I Sergouniotis
- Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.
- Manchester Royal Eye Hospital, Manchester University NHS Foundation Trust, Manchester, UK.
- Manchester Centre for Genomic Medicine, Saint Mary's Hospital, Manchester University NHS Foundation Trust, Manchester, UK.
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge, UK.
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Julian TH, Cooper-Knock J, MacGregor S, Guo H, Aslam T, Sanderson E, Black GCM, Sergouniotis PI. Phenome-wide Mendelian randomisation analysis identifies causal factors for age-related macular degeneration. eLife 2023; 12:82546. [PMID: 36705323 PMCID: PMC9883012 DOI: 10.7554/elife.82546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 12/18/2022] [Indexed: 01/28/2023] Open
Abstract
Background Age-related macular degeneration (AMD) is a leading cause of blindness in the industrialised world and is projected to affect >280 million people worldwide by 2040. Aiming to identify causal factors and potential therapeutic targets for this common condition, we designed and undertook a phenome-wide Mendelian randomisation (MR) study. Methods We evaluated the effect of 4591 exposure traits on early AMD using univariable MR. Statistically significant results were explored further using: validation in an advanced AMD cohort; MR Bayesian model averaging (MR-BMA); and multivariable MR. Results Overall, 44 traits were found to be putatively causal for early AMD in univariable analysis. Serum proteins that were found to have significant relationships with AMD included S100-A5 (odds ratio [OR] = 1.07, p-value = 6.80E-06), cathepsin F (OR = 1.10, p-value = 7.16E-05), and serine palmitoyltransferase 2 (OR = 0.86, p-value = 1.00E-03). Univariable MR analysis also supported roles for complement and immune cell traits. Although numerous lipid traits were found to be significantly related to AMD, MR-BMA suggested a driving causal role for serum sphingomyelin (marginal inclusion probability [MIP] = 0.76; model-averaged causal estimate [MACE] = 0.29). Conclusions The results of this MR study support several putative causal factors for AMD and highlight avenues for future translational research. Funding This project was funded by the Wellcome Trust (224643/Z/21/Z; 200990/Z/16/Z); the University of Manchester's Wellcome Institutional Strategic Support Fund (Wellcome ISSF) grant (204796/Z/16/Z); the UK National Institute for Health Research (NIHR) Academic Clinical Fellow and Clinical Lecturer Programmes; Retina UK and Fight for Sight (GR586); the Australian National Health and Medical Research Council (NHMRC) (1150144).
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Affiliation(s)
- Thomas H Julian
- Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of ManchesterManchesterUnited Kingdom
- Manchester Royal Eye Hospital, Manchester University NHS Foundation TrustManchesterUnited Kingdom
| | - Johnathan Cooper-Knock
- Department of Neuroscience, Sheffield Institute for Translational Neuroscience (SITraN), University of SheffieldSheffieldUnited Kingdom
| | - Stuart MacGregor
- Statistical Genetics, QIMR Berghofer Medical Research InstituteBrisbaneAustralia
| | - Hui Guo
- Centre for Biostatistics, Division of Population Health, Health Services Research and Primary Care, School of Health Sciences, Faculty of Biology, Medicine and Health, University of ManchesterManchesterUnited Kingdom
| | - Tariq Aslam
- Manchester Royal Eye Hospital, Manchester University NHS Foundation TrustManchesterUnited Kingdom
- Division of Pharmacy and Optometry, Faculty of Biology, Medicine and Health, School of Health Sciences, University of ManchesterManchesterUnited Kingdom
| | - Eleanor Sanderson
- MRC Integrative Epidemiology Unit, University of BristolBristolUnited Kingdom
| | - Graeme CM Black
- Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of ManchesterManchesterUnited Kingdom
- Manchester Centre for Genomic Medicine, Saint Mary’s Hospital, Manchester University NHS Foundation TrustManchesterUnited Kingdom
| | - Panagiotis I Sergouniotis
- Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of ManchesterManchesterUnited Kingdom
- Manchester Royal Eye Hospital, Manchester University NHS Foundation TrustManchesterUnited Kingdom
- Manchester Centre for Genomic Medicine, Saint Mary’s Hospital, Manchester University NHS Foundation TrustManchesterUnited Kingdom
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome CampusCambridgeUnited Kingdom
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Cooper‐Knock J, Julian TH, Feneberg E, Highley JR, Sidra M, Turner MR, Talbot K, Ansorge O, Allen SP, Moll T, Shelkovnikova T, Castelli L, Hautbergue GM, Hewitt C, Kirby J, Wharton SB, Mead RJ, Shaw PJ. Atypical TDP-43 protein expression in an ALS pedigree carrying a p.Y374X truncation mutation in TARDBP. Brain Pathol 2023; 33:e13104. [PMID: 35871544 PMCID: PMC9836368 DOI: 10.1111/bpa.13104] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 05/30/2022] [Indexed: 01/25/2023] Open
Abstract
We describe an autosomal dominant, multi-generational, amyotrophic lateral sclerosis (ALS) pedigree in which disease co-segregates with a heterozygous p.Y374X nonsense mutation within TDP-43. Mislocalization of TDP-43 and formation of insoluble TDP-43-positive neuronal cytoplasmic inclusions is the hallmark pathology in >95% of ALS patients. Neuropathological examination of the single case for which CNS tissue was available indicated typical TDP-43 pathology within lower motor neurons, but classical TDP-43-positive inclusions were absent from motor cortex. The mutated allele is transcribed and translated in patient fibroblasts and motor cortex tissue, but overall TDP-43 protein expression is reduced compared to wild-type controls. Despite absence of TDP-43-positive inclusions we confirmed deficient TDP-43 splicing function within motor cortex tissue. Furthermore, urea fractionation and mass spectrometry of motor cortex tissue carrying the mutation revealed atypical TDP-43 protein species but not typical C-terminal fragments. We conclude that the p.Y374X mutation underpins a monogenic, fully penetrant form of ALS. Reduced expression of TDP-43 combined with atypical TDP-43 protein species and absent C-terminal fragments extends the molecular phenotypes associated with TDP-43 mutations and with ALS more broadly. Future work will need to include the findings from this pedigree in dissecting the mechanisms of TDP-43-mediated toxicity.
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Affiliation(s)
- Johnathan Cooper‐Knock
- Sheffield Institute for Translational Neuroscience (SITraN)University of SheffieldSheffieldUK
| | - Thomas H. Julian
- Sheffield Institute for Translational Neuroscience (SITraN)University of SheffieldSheffieldUK
| | - Emily Feneberg
- Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK
- Neurology Department, Klinikum Rechts der IsarTechnical University of MunichMunichGermany
| | - J. Robin Highley
- Sheffield Institute for Translational Neuroscience (SITraN)University of SheffieldSheffieldUK
| | - Maurice Sidra
- Sheffield Institute for Translational Neuroscience (SITraN)University of SheffieldSheffieldUK
| | - Martin R. Turner
- Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK
| | - Kevin Talbot
- Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK
| | - Olaf Ansorge
- Academic Unit of NeuropathologyUniversity of OxfordOxfordUK
| | - Scott P. Allen
- Sheffield Institute for Translational Neuroscience (SITraN)University of SheffieldSheffieldUK
| | - Tobias Moll
- Sheffield Institute for Translational Neuroscience (SITraN)University of SheffieldSheffieldUK
| | - Tatyana Shelkovnikova
- Sheffield Institute for Translational Neuroscience (SITraN)University of SheffieldSheffieldUK
| | - Lydia Castelli
- Sheffield Institute for Translational Neuroscience (SITraN)University of SheffieldSheffieldUK
| | - Guillaume M. Hautbergue
- Sheffield Institute for Translational Neuroscience (SITraN)University of SheffieldSheffieldUK
| | - Christopher Hewitt
- Sheffield Institute for Translational Neuroscience (SITraN)University of SheffieldSheffieldUK
- Amarin UK LimitedAmarin CorporationLondonUK
| | - Janine Kirby
- Sheffield Institute for Translational Neuroscience (SITraN)University of SheffieldSheffieldUK
| | - Stephen B. Wharton
- Sheffield Institute for Translational Neuroscience (SITraN)University of SheffieldSheffieldUK
| | - Richard J. Mead
- Sheffield Institute for Translational Neuroscience (SITraN)University of SheffieldSheffieldUK
| | - Pamela J. Shaw
- Sheffield Institute for Translational Neuroscience (SITraN)University of SheffieldSheffieldUK
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Şanlı BA, Whittaker KJ, Motsi GK, Shen E, Julian TH, Cooper-Knock J. Unbiased metabolome screen links serum urate to risk of Alzheimer's disease. Neurobiol Aging 2022; 120:167-176. [DOI: 10.1016/j.neurobiolaging.2022.09.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Revised: 09/07/2022] [Accepted: 09/08/2022] [Indexed: 10/14/2022]
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Zhang S, Cooper-Knock J, Weimer AK, Shi M, Kozhaya L, Unutmaz D, Harvey C, Julian TH, Furini S, Frullanti E, Fava F, Renieri A, Gao P, Shen X, Timpanaro IS, Kenna KP, Baillie JK, Davis MM, Tsao PS, Snyder MP. Multiomic analysis reveals cell-type-specific molecular determinants of COVID-19 severity. Cell Syst 2022; 13:598-614.e6. [PMID: 35690068 PMCID: PMC9163145 DOI: 10.1016/j.cels.2022.05.007] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 04/02/2022] [Accepted: 05/18/2022] [Indexed: 01/26/2023]
Abstract
The determinants of severe COVID-19 in healthy adults are poorly understood, which limits the opportunity for early intervention. We present a multiomic analysis using machine learning to characterize the genomic basis of COVID-19 severity. We use single-cell multiome profiling of human lungs to link genetic signals to cell-type-specific functions. We discover >1,000 risk genes across 19 cell types, which account for 77% of the SNP-based heritability for severe disease. Genetic risk is particularly focused within natural killer (NK) cells and T cells, placing the dysfunction of these cells upstream of severe disease. Mendelian randomization and single-cell profiling of human NK cells support the role of NK cells and further localize genetic risk to CD56bright NK cells, which are key cytokine producers during the innate immune response. Rare variant analysis confirms the enrichment of severe-disease-associated genetic variation within NK-cell risk genes. Our study provides insights into the pathogenesis of severe COVID-19 with potential therapeutic targets.
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Affiliation(s)
- Sai Zhang
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; VA Palo Alto Epidemiology Research and Information Center for Genomics, VA Palo Alto Health Care System, Palo Alto, CA 94304, USA; Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Johnathan Cooper-Knock
- Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield S10 2HQ, UK
| | - Annika K Weimer
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Minyi Shi
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Lina Kozhaya
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Derya Unutmaz
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Calum Harvey
- Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield S10 2HQ, UK
| | - Thomas H Julian
- Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield S10 2HQ, UK
| | - Simone Furini
- Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy
| | - Elisa Frullanti
- Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy; Medical Genetics, Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy
| | - Francesca Fava
- Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy; Medical Genetics, Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy; Genetica Medica, Azienda Ospedaliero-Universitaria Senese, 53100 Siena, Italy
| | - Alessandra Renieri
- Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy; Medical Genetics, Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy; Genetica Medica, Azienda Ospedaliero-Universitaria Senese, 53100 Siena, Italy
| | - Peng Gao
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Xiaotao Shen
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Ilia Sarah Timpanaro
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, 3584 CX Utrecht, the Netherlands
| | - Kevin P Kenna
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, 3584 CX Utrecht, the Netherlands
| | - J Kenneth Baillie
- Roslin Institute, University of Edinburgh, Easter Bush, Midlothian EH25 9RG, UK; MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh EH4 2XU, UK; Intensive Care Unit, Royal Infirmary of Edinburgh, Edinburgh EH16 4SA, UK
| | - Mark M Davis
- Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA; Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Philip S Tsao
- VA Palo Alto Epidemiology Research and Information Center for Genomics, VA Palo Alto Health Care System, Palo Alto, CA 94304, USA; Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.
| | - Michael P Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA 94305, USA.
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Julian TH, Boddy S, Islam M, Kurz J, Whittaker KJ, Moll T, Harvey C, Zhang S, Snyder MP, McDermott C, Cooper-Knock J, Shaw PJ. A review of Mendelian randomization in amyotrophic lateral sclerosis. Brain 2022; 145:832-842. [PMID: 34791088 PMCID: PMC9050546 DOI: 10.1093/brain/awab420] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 10/02/2021] [Accepted: 10/29/2021] [Indexed: 11/13/2022] Open
Abstract
Amyotrophic lateral sclerosis is a relatively common and rapidly progressive neurodegenerative disease that, in the majority of cases, is thought to be determined by a complex gene-environment interaction. Exponential growth in the number of performed genome-wide association studies combined with the advent of Mendelian randomization is opening significant new opportunities to identify environmental exposures that increase or decrease the risk of amyotrophic lateral sclerosis. Each of these discoveries has the potential to shape new therapeutic interventions. However, to do so, rigorous methodological standards must be applied in the performance of Mendelian randomization. We have reviewed Mendelian randomization studies performed in amyotrophic lateral sclerosis to date. We identified 20 Mendelian randomization studies, including evaluation of physical exercise, adiposity, cognitive performance, immune function, blood lipids, sleep behaviours, educational attainment, alcohol consumption, smoking and type 2 diabetes mellitus. We have evaluated each study using gold standard methodology supported by the Mendelian randomization literature and the STROBE-Mendelian randomization checklist. Where discrepancies exist between Mendelian randomization studies, we suggest the underlying reasons. A number of studies conclude that there is a causal link between blood lipids and risk of amyotrophic lateral sclerosis; replication across different datasets and even different populations adds confidence. For other putative risk factors, such as smoking and immune function, Mendelian randomization studies have provided cause for doubt. We highlight the use of positive control analyses in choosing exposure single nucleotide polymorphisms (SNPs) to make up the Mendelian randomization instrument, use of SNP clumping to avoid false positive results due to SNPs in linkage and the importance of multiple testing correction. We discuss the implications of survival bias for study of late age of onset diseases such as amyotrophic lateral sclerosis and make recommendations to mitigate this potentially important confounder. For Mendelian randomization to be useful to the amyotrophic lateral sclerosis field, high methodological standards must be applied to ensure reproducibility. Mendelian randomization is already an impactful tool, but poor-quality studies will lead to incorrect interpretations by a field that includes non-statisticians, wasted resources and missed opportunities.
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Affiliation(s)
- Thomas H Julian
- Department of Neuroscience, Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Sarah Boddy
- Department of Neuroscience, Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Mahjabin Islam
- Department of Neuroscience, Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Julian Kurz
- Department of Neuroscience, Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Katherine J Whittaker
- Department of Neuroscience, Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Tobias Moll
- Department of Neuroscience, Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Calum Harvey
- Department of Neuroscience, Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Sai Zhang
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Michael P Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Christopher McDermott
- Department of Neuroscience, Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Johnathan Cooper-Knock
- Department of Neuroscience, Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Pamela J Shaw
- Department of Neuroscience, Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
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Boddy S, Islam M, Moll T, Kurz J, Burrows D, McGown A, Bhargava A, Julian TH, Harvey C, Marshall JNG, Hall BPC, Allen SP, Kenna KP, Sanderson E, Zhang S, Ramesh T, Snyder MP, Shaw PJ, McDermott C, Cooper-Knock J. Unbiased metabolome screen leads to personalized medicine strategy for amyotrophic lateral sclerosis. Brain Commun 2022; 4:fcac069. [PMID: 35441136 PMCID: PMC9010771 DOI: 10.1093/braincomms/fcac069] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 11/29/2021] [Accepted: 03/15/2022] [Indexed: 11/17/2022] Open
Abstract
Amyotrophic lateral sclerosis is a rapidly progressive neurodegenerative disease that affects 1/350 individuals in the United Kingdom. The cause of amyotrophic lateral sclerosis is unknown in the majority of cases. Two-sample Mendelian randomization enables causal inference between an exposure, such as the serum concentration of a specific metabolite, and disease risk. We obtained genome-wide association study summary statistics for serum concentrations of 566 metabolites which were population matched with a genome-wide association study of amyotrophic lateral sclerosis. For each metabolite, we performed Mendelian randomization using an inverse variance weighted estimate for significance testing. After stringent Bonferroni multiple testing correction, our unbiased screen revealed three metabolites that were significantly linked to the risk of amyotrophic lateral sclerosis: Estrone-3-sulphate and bradykinin were protective, which is consistent with literature describing a male preponderance of amyotrophic lateral sclerosis and a preventive effect of angiotensin-converting enzyme inhibitors which inhibit the breakdown of bradykinin. Serum isoleucine was positively associated with amyotrophic lateral sclerosis risk. All three metabolites were supported by robust Mendelian randomization measures and sensitivity analyses; estrone-3-sulphate and isoleucine were confirmed in a validation amyotrophic lateral sclerosis genome-wide association study. Estrone-3-sulphate is metabolized to the more active estradiol by the enzyme 17β-hydroxysteroid dehydrogenase 1; further, Mendelian randomization demonstrated a protective effect of estradiol and rare variant analysis showed that missense variants within HSD17B1, the gene encoding 17β-hydroxysteroid dehydrogenase 1, modify risk for amyotrophic lateral sclerosis. Finally, in a zebrafish model of C9ORF72-amyotrophic lateral sclerosis, we present evidence that estradiol is neuroprotective. Isoleucine is metabolized via methylmalonyl-CoA mutase encoded by the gene MMUT in a reaction that consumes vitamin B12. Multivariable Mendelian randomization revealed that the toxic effect of isoleucine is dependent on the depletion of vitamin B12; consistent with this, rare variants which reduce the function of MMUT are protective against amyotrophic lateral sclerosis. We propose that amyotrophic lateral sclerosis patients and family members with high serum isoleucine levels should be offered supplementation with vitamin B12.
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Affiliation(s)
- Sarah Boddy
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Mahjabin Islam
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Tobias Moll
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Julian Kurz
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - David Burrows
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Alexander McGown
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Anushka Bhargava
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Thomas H Julian
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Calum Harvey
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Jack NG Marshall
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Benjamin PC Hall
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Scott P Allen
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Kevin P Kenna
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Eleanor Sanderson
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, UK
| | - Sai Zhang
- Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Tennore Ramesh
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Michael P Snyder
- Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Pamela J Shaw
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Christopher McDermott
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Johnathan Cooper-Knock
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
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Iqbal A, Greig M, Arshad MF, Julian TH, Ee Tan S, Elliott J. Higher admission activated partial thromboplastin time, neutrophil-lymphocyte ratio, serum sodium, and anticoagulant use predict in-hospital COVID-19 mortality in people with Diabetes: Findings from Two University Hospitals in the U.K. Diabetes Res Clin Pract 2021; 178:108955. [PMID: 34273452 PMCID: PMC8278840 DOI: 10.1016/j.diabres.2021.108955] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 06/30/2021] [Accepted: 07/08/2021] [Indexed: 12/16/2022]
Abstract
AIMS To create and compare survival models from admission laboratory indices in people hospitalized with coronavirus disease 2019 (COVID-19) with and without diabetes. METHODS Retrospective observational study of patients with COVID-19 with or without diabetes admitted to Sheffield Teaching Hospitals from 29 February to 01 May 2020. Predictive variables for in-hospital mortality from COVID-19 were explored using Cox proportional hazard models. RESULTS Out of 505 patients, 156 (30.8%) had diabetes mellitus (DM) of which 143 (91.7%) had type 2 diabetes. There were significantly higher in-hospital COVID-19 deaths in those with DM [DM COVID-19 deaths 54 (34.6%) vs. non-DM COVID-19 deaths 88 (25.2%): P < 0.05]. Activated partial thromboplastin time (APPT) > 24 s without anticoagulants (HR 6.38, 95% CI: 1.07-37.87: P = 0.04), APTT > 24 s with anticoagulants (HR 24.01, 95% CI: 3.63-159.01: P < 0.001), neutrophil-lymphocyte ratio > 8 (HR 6.18, 95% CI: 2.36-16.16: P < 0.001), and sodium > 136 mmol/L (HR 3.27, 95% CI: 1.12-9.56: P = 0.03) at admission, were only associated with in-hospital COVID-19 mortality for those with diabetes. CONCLUSIONS At admission, elevated APTT with or without anticoagulants, neutrophil-lymphocyte ratio and serum sodium are unique factors that predict in-hospital COVID-19 mortality in patients with diabetes compared to those without. This novel finding may lead to research into haematological and biochemical mechanisms to understand why those with diabetes are more susceptible to poor outcomes when infected with Covid-19, and contribute to identification of those most at risk when admitted to hospital.
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Affiliation(s)
- Ahmed Iqbal
- Department of Diabetes and Endocrinology, Sheffield Teaching Hospitals, Sheffield, UK; Department of Oncology and Metabolism, The Medical School, The University of Sheffield, Sheffield, UK
| | - Marni Greig
- Department of Diabetes and Endocrinology, Sheffield Teaching Hospitals, Sheffield, UK; Department of Infection, Immunity and Cardiovascular Disease, The Medical School, The University of Sheffield, Sheffield, UK
| | - Muhammad Fahad Arshad
- Department of Diabetes and Endocrinology, Sheffield Teaching Hospitals, Sheffield, UK; Department of Oncology and Metabolism, The Medical School, The University of Sheffield, Sheffield, UK
| | - Thomas H Julian
- Department of Diabetes and Endocrinology, Sheffield Teaching Hospitals, Sheffield, UK
| | - Sher Ee Tan
- Department of Diabetes and Endocrinology, Sheffield Teaching Hospitals, Sheffield, UK
| | - Jackie Elliott
- Department of Diabetes and Endocrinology, Sheffield Teaching Hospitals, Sheffield, UK; Department of Oncology and Metabolism, The Medical School, The University of Sheffield, Sheffield, UK.
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Zhang S, Cooper-Knock J, Weimer AK, Harvey C, Julian TH, Wang C, Li J, Furini S, Frullanti E, Fava F, Renieri A, Pan C, Song J, Billing-Ross P, Gao P, Shen X, Timpanaro IS, Kenna KP, Davis MM, Tsao PS, Snyder MP. Common and rare variant analyses combined with single-cell multiomics reveal cell-type-specific molecular mechanisms of COVID-19 severity. medRxiv 2021:2021.06.15.21258703. [PMID: 34189540 PMCID: PMC8240695 DOI: 10.1101/2021.06.15.21258703] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
The determinants of severe COVID-19 in non-elderly adults are poorly understood, which limits opportunities for early intervention and treatment. Here we present novel machine learning frameworks for identifying common and rare disease-associated genetic variation, which outperform conventional approaches. By integrating single-cell multiomics profiling of human lungs to link genetic signals to cell-type-specific functions, we have discovered and validated over 1,000 risk genes underlying severe COVID-19 across 19 cell types. Identified risk genes are overexpressed in healthy lungs but relatively downregulated in severely diseased lungs. Genetic risk for severe COVID-19, within both common and rare variants, is particularly enriched in natural killer (NK) cells, which places these immune cells upstream in the pathogenesis of severe disease. Mendelian randomization indicates that failed NKG2D-mediated activation of NK cells leads to critical illness. Network analysis further links multiple pathways associated with NK cell activation, including type-I-interferon-mediated signalling, to severe COVID-19. Our rare variant model, PULSE, enables sensitive prediction of severe disease in non-elderly patients based on whole-exome sequencing; individualized predictions are accurate independent of age and sex, and are consistent across multiple populations and cohorts. Risk stratification based on exome sequencing has the potential to facilitate post-exposure prophylaxis in at-risk individuals, potentially based around augmentation of NK cell function. Overall, our study characterizes a comprehensive genetic landscape of COVID-19 severity and provides novel insights into the molecular mechanisms of severe disease, leading to new therapeutic targets and sensitive detection of at-risk individuals.
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Julian TH, Glascow N, Barry ADF, Moll T, Harvey C, Klimentidis YC, Newell M, Zhang S, Snyder MP, Cooper-Knock J, Shaw PJ. Physical exercise is a risk factor for amyotrophic lateral sclerosis: Convergent evidence from Mendelian randomisation, transcriptomics and risk genotypes. EBioMedicine 2021; 68:103397. [PMID: 34051439 PMCID: PMC8170114 DOI: 10.1016/j.ebiom.2021.103397] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 04/13/2021] [Accepted: 04/28/2021] [Indexed: 12/12/2022] Open
Abstract
Background Amyotrophic lateral sclerosis (ALS) is a universally fatal neurodegenerative disease. ALS is determined by gene-environment interactions and improved understanding of these interactions may lead to effective personalised medicine. The role of physical exercise in the development of ALS is currently controversial. Methods First, we dissected the exercise-ALS relationship in a series of two-sample Mendelian randomisation (MR) experiments. Next we tested for enrichment of ALS genetic risk within exercise-associated transcriptome changes. Finally, we applied a validated physical activity questionnaire in a small cohort of genetically selected ALS patients. Findings We present MR evidence supporting a causal relationship between genetic liability to frequent and strenuous leisure-time exercise and ALS using a liberal instrument (multiplicative random effects IVW, p=0.01). Transcriptomic analysis revealed that genes with altered expression in response to acute exercise are enriched with known ALS risk genes (permutation test, p=0.013) including C9ORF72, and with ALS-associated rare variants of uncertain significance. Questionnaire evidence revealed that age of onset is inversely proportional to historical physical activity for C9ORF72-ALS (Cox proportional hazards model, Wald test p=0.007, likelihood ratio test p=0.01, concordance=74%) but not for non-C9ORF72-ALS. Variability in average physical activity was lower in C9ORF72-ALS compared to both non-C9ORF72-ALS (F-test, p=0.002) and neurologically normal controls (F-test, p=0.049) which is consistent with a homogeneous effect of physical activity in all C9ORF72-ALS patients. Interpretation Our MR approach suggests a positive causal relationship between ALS and physical exercise. Exercise is likely to cause motor neuron injury only in patients with a risk-genotype. Consistent with this we have shown that ALS risk genes are activated in response to exercise. In particular, we propose that G4C2-repeat expansion of C9ORF72 predisposes to exercise-induced ALS. Funding We acknowledge support from the Wellcome Trust (JCK, 216596/Z/19/Z), NIHR (PJS, NF-SI-0617-10077; IS-BRC-1215-20017) and NIH (MPS, CEGS 5P50HG00773504, 1P50HL083800, 1R01HL101388, 1R01-HL122939, S10OD025212, P30DK116074, and UM1HG009442).
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Affiliation(s)
- Thomas H Julian
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Nicholas Glascow
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - A Dylan Fisher Barry
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Tobias Moll
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Calum Harvey
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Yann C Klimentidis
- Department of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ, USA
| | - Michelle Newell
- Department of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ, USA
| | - Sai Zhang
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA; Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Michael P Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA; Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Johnathan Cooper-Knock
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Pamela J Shaw
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK.
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