Mechanistic insights into the role of amyloid-ß in innate immunity.

Colocalization of microbial pathogens and the ß-amyloid peptide (Aß) in the brain of Alzheimer's disease (AD) patients suggests that microbial infection may play a role in sporadic AD. Aß exhibits antimicrobial activity against numerous pathogens, supporting a potential role for Aß in the innate immune response. While mammalian amyloid is associated with disease, many bacteria form amyloid fibrils to fortify the biofilm that protects the cells from the surrounding environment. In the microbial AD hypothesis, Aß aggregates in response to infection to combat the pathogen. We hypothesize that this occurs through toxic Aß oligomers that contain α-sheet structure and form prior to fibrillization. De novo designed α-sheet peptides specifically bind to the α-sheet structure present in the oligomers of both bacterial and mammalian amyloidogenic proteins to neutralize toxicity and inhibit aggregation. Here, we measure the effect of E. coli on Aß, including upregulation, aggregation, and toxicity. Additionally, we determined the effect of Aß structure on E. coli amyloid fibrils, or curli comprised of the CsgA protein, and biofilm formation. We found that curli formation by E. coli increased Aß oligomer production, and Aß oligomers inhibited curli biogenesis and reduced biofilm cell density. Further, curli and biofilm inhibition by Aß oligomers increased E. coli susceptibility to gentamicin. Toxic oligomers of Aß and CsgA interact via α-sheet interactions, neutralizing their toxicity. These results suggest that exposure to toxic oligomers formed by microbial pathogens triggers Aß oligomer upregulation and aggregation to combat infection via selective interactions between α-sheet oligomers to neutralize toxicity of both species with subsequent inhibition of fibrillization.

Performance assessment and economic analysis of a human Liver-Chip for predictive toxicology.

BACKGROUND: Conventional preclinical models often miss drug toxicities, meaning the harm these drugs pose to humans is only realized in clinical trials or when they make it to market. This has caused the pharmaceutical industry to waste considerable time and resources developing drugs destined to fail. Organ-on-a-Chip technology has the potential improve success in drug development pipelines, as it can recapitulate organ-level pathophysiology and clinical responses; however, systematic and quantitative evaluations of Organ-Chips' predictive value have not yet been reported. METHODS: 870 Liver-Chips were analyzed to determine their ability to predict drug-induced liver injury caused by small molecules identified as benchmarks by the Innovation and Quality consortium, who has published guidelines defining criteria for qualifying preclinical models. An economic analysis was also performed to measure the value Liver-Chips could offer if they were broadly adopted in supporting toxicity-related decisions as part of preclinical development workflows. RESULTS: Here, we show that the Liver-Chip met the qualification guidelines across a blinded set of 27 known hepatotoxic and non-toxic drugs with a sensitivity of 87% and a specificity of 100%. We also show that this level of performance could generate over $3 billion annually for the pharmaceutical industry through increased small-molecule R&D productivity. CONCLUSIONS: The results of this study show how incorporating predictive Organ-Chips into drug development workflows could substantially improve drug discovery and development, allowing manufacturers to bring safer, more effective medicines to market in less time and at lower costs.

Drug development is lengthy and costly, as it relies on laboratory models that fail to predict human reactions to potential drugs. Because of this, toxic drugs sometimes go on to harm humans when they reach clinical trials or once they are in the marketplace. Organ-on-a-Chip technology involves growing cells on small devices to mimic organs of the body, such as the liver. Organ-Chips could potentially help identify toxicities earlier, but there is limited research into how well they predict these effects compared to conventional models. In this study, we analyzed 870 Liver-Chips to determine how well they predict drug-induced liver injury, a common cause of drug failure, and found that Liver-Chips outperformed conventional models. These results suggest that widespread acceptance of Organ-Chips could decrease drug attrition, help minimize harm to patients, and generate billions in revenue for the pharmaceutical industry.