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The toxicity of an "artificial" amyloid is related to how it interacts with membranes.

ABSTRACT: Despite intensive research into how amyloid structures can impair cellular viability, the molecular nature of these toxic species and the cellular mechanisms involved are not clearly defined and may differ from one disease to another. We systematically analyzed, in Saccharomyces cerevisiae, genes that increase the toxicity of an amyloid (M8), previously selected in yeast on the sole basis of its cellular toxicity (and consequently qualified as "artificial"). This genomic screening identified the Vps-C HOPS (homotypic vacuole fusion and protein sorting) complex as a key-player in amyloid toxicity. This finding led us to analyze further the phenotype induced by M8 expression. M8-expressing cells displayed an identical phenotype to vps mutants in terms of endocytosis, vacuolar morphology and salt sensitivity. The direct and specific interaction between M8 and lipids reinforces the role of membrane formation in toxicity due to M8. Together these findings suggest a model in which amyloid toxicity results from membrane fission.

SUBMITTER: Couthouis J

PROVIDER: S-EPMC3268961 | biostudies-literature | 2010 Oct-Dec

REPOSITORIES: biostudies-literature

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The toxicity of an "artificial" amyloid is related to how it interacts with membranes.

Couthouis Julien J Marchal Christelle C D'Angelo Fabien F Berthelot Karine K Cullin Christophe C

Prion 20101023 4

Despite intensive research into how amyloid structures can impair cellular viability, the molecular nature of these toxic species and the cellular mechanisms involved are not clearly defined and may differ from one disease to another. We systematically analyzed, in Saccharomyces cerevisiae, genes that increase the toxicity of an amyloid (M8), previously selected in yeast on the sole basis of its cellular toxicity (and consequently qualified as "artificial"). This genomic screening identified the ...[more]

PMID: 21057225

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Project description:ImportanceAmyloid-related imaging abnormalities (ARIA) are brain magnetic resonance imaging (MRI) findings associated with the use of amyloid-β-directed monoclonal antibody therapies in Alzheimer disease (AD). ARIA monitoring is important to inform treatment dosing decisions and might be improved through assistive software.ObjectiveTo assess the clinical performance of an artificial intelligence (AI)-based software tool for assisting radiological interpretation of brain MRI scans in patients monitored for ARIA.Design, setting, and participantsThis diagnostic study used a multiple-reader multiple-case design to evaluate the diagnostic performance of radiologists assisted by the software vs unassisted. The study enrolled 16 US Board of Radiology-certified radiologists to perform radiological reading with (assisted) and without the software (unassisted). The study encompassed 199 retrospective cases, where each case consisted of a predosing baseline and a postdosing follow-up MRI of patients from aducanumab clinical trials PRIME, EMERGE, and ENGAGE. Statistical analysis was performed from April to July 2023.ExposuresUse of icobrain aria, an AI-based assistive software for ARIA detection and quantification.Main outcomes and measuresCoprimary end points were the difference in diagnostic accuracy between assisted and unassisted detection of ARIA-E (edema and/or sulcal effusion) and ARIA-H (microhemorrhage and/or superficial siderosis) independently, assessed with the area under the receiver operating characteristic curve (AUC).ResultsAmong the 199 participants included in this study of radiological reading performance, mean (SD) age was 70.4 (7.2) years; 105 (52.8%) were female; 23 (11.6%) were Asian, 1 (0.5%) was Black, 157 (78.9%) were White, and 18 (9.0%) were other or unreported race and ethnicity. Among the 16 radiological readers included, 2 were specialized neuroradiologists (12.5%), 11 were male individuals (68.8%), 7 were individuals working in academic hospitals (43.8%), and they had a mean (SD) of 9.5 (5.1) years of experience. Radiologists assisted by the software were significantly superior in detecting ARIA than unassisted radiologists, with a mean assisted AUC of 0.87 (95% CI, 0.84-0.91) for ARIA-E detection (AUC improvement of 0.05 [95% CI, 0.02-0.08]; P = .001]) and 0.83 (95% CI, 0.78-0.87) for ARIA-H detection (AUC improvement of 0.04 [95% CI, 0.02-0.07]; P = .001). Sensitivity was significantly higher in assisted reading compared with unassisted reading (87% vs 71% for ARIA-E detection; 79% vs 69% for ARIA-H detection), while specificity remained above 80% for the detection of both ARIA types.Conclusions and relevanceThis diagnostic study found that radiological reading performance for ARIA detection and diagnosis was significantly better when using the AI-based assistive software. Hence, the software has the potential to be a clinically important tool to improve safety monitoring and management of patients with AD treated with amyloid-β-directed monoclonal antibody therapies.

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The toxicity of an "artificial" amyloid is related to how it interacts with membranes.

Publications

The toxicity of an "artificial" amyloid is related to how it interacts with membranes.

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