1
|
Al-Chalabi A, Andrews J, Farhan S. Recent advances in the genetics of familial and sporadic ALS. INTERNATIONAL REVIEW OF NEUROBIOLOGY 2024; 176:49-74. [PMID: 38802182 DOI: 10.1016/bs.irn.2024.04.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
ALS shows complex genetic inheritance patterns. In about 5% to 10% of cases, there is a family history of ALS or a related condition such as frontotemporal dementia in a first or second degree relative, and for about 80% of such people a pathogenic gene variant can be identified. Such variants are also seen in people with no family history because of factor influencing the expression of genes, such as age. Genetic susceptibility factors also contribute to risk, and the heritability of ALS is between 40% and 60%. The genetic variants influencing ALS risk include single base changes, repeat expansions, copy number variants, and others. Here we review what is known of the genetic landscape and architecture of ALS.
Collapse
Affiliation(s)
- Ammar Al-Chalabi
- Department of Basic and Clinical Neuroscience, King's College London, London, United Kingdom.
| | - Jinsy Andrews
- Department of Neurology, Columbia University, New York, NY, United States
| | - Sali Farhan
- Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital, Montreal, QC, Canada; Department of Human Genetics, Montreal Neurological Institute-Hospital, Montreal, QC, Canada
| |
Collapse
|
2
|
Jagaraj CJ, Shadfar S, Kashani SA, Saravanabavan S, Farzana F, Atkin JD. Molecular hallmarks of ageing in amyotrophic lateral sclerosis. Cell Mol Life Sci 2024; 81:111. [PMID: 38430277 PMCID: PMC10908642 DOI: 10.1007/s00018-024-05164-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 01/21/2024] [Accepted: 02/06/2024] [Indexed: 03/03/2024]
Abstract
Amyotrophic lateral sclerosis (ALS) is a fatal, severely debilitating and rapidly progressing disorder affecting motor neurons in the brain, brainstem, and spinal cord. Unfortunately, there are few effective treatments, thus there remains a critical need to find novel interventions that can mitigate against its effects. Whilst the aetiology of ALS remains unclear, ageing is the major risk factor. Ageing is a slowly progressive process marked by functional decline of an organism over its lifespan. However, it remains unclear how ageing promotes the risk of ALS. At the molecular and cellular level there are specific hallmarks characteristic of normal ageing. These hallmarks are highly inter-related and overlap significantly with each other. Moreover, whilst ageing is a normal process, there are striking similarities at the molecular level between these factors and neurodegeneration in ALS. Nine ageing hallmarks were originally proposed: genomic instability, loss of telomeres, senescence, epigenetic modifications, dysregulated nutrient sensing, loss of proteostasis, mitochondrial dysfunction, stem cell exhaustion, and altered inter-cellular communication. However, these were recently (2023) expanded to include dysregulation of autophagy, inflammation and dysbiosis. Hence, given the latest updates to these hallmarks, and their close association to disease processes in ALS, a new examination of their relationship to pathophysiology is warranted. In this review, we describe possible mechanisms by which normal ageing impacts on neurodegenerative mechanisms implicated in ALS, and new therapeutic interventions that may arise from this.
Collapse
Affiliation(s)
- Cyril Jones Jagaraj
- MND Research Centre, Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, 75 Talavera Road, Sydney, NSW, 2109, Australia
| | - Sina Shadfar
- MND Research Centre, Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, 75 Talavera Road, Sydney, NSW, 2109, Australia
| | - Sara Assar Kashani
- MND Research Centre, Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, 75 Talavera Road, Sydney, NSW, 2109, Australia
| | - Sayanthooran Saravanabavan
- MND Research Centre, Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, 75 Talavera Road, Sydney, NSW, 2109, Australia
| | - Fabiha Farzana
- MND Research Centre, Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, 75 Talavera Road, Sydney, NSW, 2109, Australia
| | - Julie D Atkin
- MND Research Centre, Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, 75 Talavera Road, Sydney, NSW, 2109, Australia.
- La Trobe Institute for Molecular Science, La Trobe University, Bundoora, Melbourne, VIC, 3086, Australia.
| |
Collapse
|
3
|
Marriott H, Kabiljo R, Hunt GP, Khleifat AA, Jones A, Troakes C, Pfaff AL, Quinn JP, Koks S, Dobson RJ, Schwab P, Al-Chalabi A, Iacoangeli A. Unsupervised machine learning identifies distinct ALS molecular subtypes in post-mortem motor cortex and blood expression data. Acta Neuropathol Commun 2023; 11:208. [PMID: 38129934 PMCID: PMC10734072 DOI: 10.1186/s40478-023-01686-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 11/10/2023] [Indexed: 12/23/2023] Open
Abstract
Amyotrophic lateral sclerosis (ALS) displays considerable clinical and genetic heterogeneity. Machine learning approaches have previously been utilised for patient stratification in ALS as they can disentangle complex disease landscapes. However, lack of independent validation in different populations and tissue samples have greatly limited their use in clinical and research settings. We overcame these issues by performing hierarchical clustering on the 5000 most variably expressed autosomal genes from motor cortex expression data of people with sporadic ALS from the KCL BrainBank (N = 112). Three molecular phenotypes linked to ALS pathogenesis were identified: synaptic and neuropeptide signalling, oxidative stress and apoptosis, and neuroinflammation. Cluster validation was achieved by applying linear discriminant analysis models to cases from TargetALS US motor cortex (N = 93), as well as Italian (N = 15) and Dutch (N = 397) blood expression datasets, for which there was a high assignment probability (80-90%) for each molecular subtype. The ALS and motor cortex specificity of the expression signatures were tested by mapping KCL BrainBank controls (N = 59), and occipital cortex (N = 45) and cerebellum (N = 123) samples from TargetALS to each cluster, before constructing case-control and motor cortex-region logistic regression classifiers. We found that the signatures were not only able to distinguish people with ALS from controls (AUC 0.88 ± 0.10), but also reflect the motor cortex-based disease process, as there was perfect discrimination between motor cortex and the other brain regions. Cell types known to be involved in the biological processes of each molecular phenotype were found in higher proportions, reinforcing their biological interpretation. Phenotype analysis revealed distinct cluster-related outcomes in both motor cortex datasets, relating to disease onset and progression-related measures. Our results support the hypothesis that different mechanisms underpin ALS pathogenesis in subgroups of patients and demonstrate potential for the development of personalised treatment approaches. Our method is available for the scientific and clinical community at https://alsgeclustering.er.kcl.ac.uk .
Collapse
Affiliation(s)
- Heather Marriott
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology and Neuroscience, King?s College London, London, SE5 9NU, UK
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Renata Kabiljo
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Guy P Hunt
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology and Neuroscience, King?s College London, London, SE5 9NU, UK
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Perron Institute for Neurological and Translational Science, Nedlands, WA, 6009, Australia
- Centre for Molecular Medicine and Innovative Therapeutics, Murdoch University, Murdoch, WA, 6150, Australia
| | - Ahmad Al Khleifat
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology and Neuroscience, King?s College London, London, SE5 9NU, UK
| | - Ashley Jones
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology and Neuroscience, King?s College London, London, SE5 9NU, UK
| | - Claire Troakes
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology and Neuroscience, King?s College London, London, SE5 9NU, UK
- MRC London Neurodegenerative Diseases Brain Bank, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Abigail L Pfaff
- Perron Institute for Neurological and Translational Science, Nedlands, WA, 6009, Australia
- Centre for Molecular Medicine and Innovative Therapeutics, Murdoch University, Murdoch, WA, 6150, Australia
| | - John P Quinn
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, L69 3BX, UK
| | - Sulev Koks
- Perron Institute for Neurological and Translational Science, Nedlands, WA, 6009, Australia
- Centre for Molecular Medicine and Innovative Therapeutics, Murdoch University, Murdoch, WA, 6150, Australia
| | - Richard J Dobson
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- NIHR Maudsley Biomedical Research Centre (BRC), South London and Maudsley NHS Foundation Trust and King's College London, London, UK
- Institute of Health Informatics, University College London, London, UK
- NIHR Biomedical Research Centre, University College London Hospitals NHS Foundation Trust, London, UK
| | - Patrick Schwab
- GlaxoSmithKline, Artificial Intelligence and Machine Learning, Durham, NC, USA
| | - Ammar Al-Chalabi
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology and Neuroscience, King?s College London, London, SE5 9NU, UK
- King's College Hospital, London, SE5 9RS, UK
| | - Alfredo Iacoangeli
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology and Neuroscience, King?s College London, London, SE5 9NU, UK.
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
- NIHR Maudsley Biomedical Research Centre (BRC), South London and Maudsley NHS Foundation Trust and King's College London, London, UK.
| |
Collapse
|