A quantitative model of human neurodegenerative diseases involving protein aggregation

Abstract

Human neurodegenerative diseases such as Alzheimers disease, Parkinsons 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. This paper presents a simple mathematical model arguing that clinical cognitive status relates to the energy available after subtracting cell maintenance due to general turnover of the misfolded proteins, rather than a specific toxic molecular action per se. 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, and is supported by specific experiments showing that proteasome activity is required to confer toxicity to pathogenic mutants. The mechanism and model lend promise to a quantitative personalized medicine of neurodegenerative disease.