A quantitative model of human neurodegenerative diseases involving protein aggregation
Abstract
Human neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis involve protein aggregation and share many other similarities. It is widely assumed that the protein aggregates exhibit a specific molecular mode of toxic action that propagates by molecular contact (seeding). This article presents a simple mathematical model arguing that these diseases are caused by reduced energy available after subtracting cell maintenance because of general turnover of the misfolded proteins rather than a specific toxic molecular action of the protein. Proteomic cost minimization can explain why highly expressed proteins changed less during evolution, leaving more energy for reproducing microorganisms on longer evolutionary timescales. In higher organisms, the excess energy instead defines cognitive capability, and the same equations remarkably apply. Proteomic cost minimization can explain why late-onset neurodegenerative diseases involve protein aggregation. The model rationalizes clinical ages of symptom onset for patients carrying pathogenic protein mutations: Unstable or aggregation-prone mutations confer a direct energy cost of turnover, but other risk modifiers also change the available cellular energy as ultimately defining clinical outcome. Proteomic cost minimization is consistent with current views on biomarker histories, explains conflicting data on overexpression models, is consistent with energy cost tradeoffs causing compensatory hyperconnectivity during early disease, and is supported by specific experiments showing that proteasome activity is required to confer toxicity to pathogenic mutants. The model lends promise to a quantitative personalized medicine of neurodegenerative disease.
Info
Journal Article, 2019
UN SDG Classification
DK Main Research Area
Science/Technology
