Abstract
Alzheimer’s disease (AD) represents one major health concern for our growing elderly population. It accounts for increasing impairment of cognitive capacity followed by loss of executive function in late stage. AD pathogenesis is multifaceted and difficult to pinpoint, and understanding AD etiology will be critical to effectively diagnose and treat the disease. An interesting hypothesis concerning AD development postulates a cause-effect relationship between accumulation of mitochondrial DNA (mtDNA) mutations and neurodegenerative changes associated with this pathology. Here we propose a computerized method for an easy and fast mtDNA mutations-based characterization of AD. The method has been built taking into account the complexity of living being and fractal properties of many anatomic and physiologic structures, including mtDNA. Dealing with mtDNA mutations as gaps in the nucleotide sequence, fractal lacunarity appears a suitable tool to differentiate between aging and AD. Therefore, Chaos Game Representation method has been used to display DNA fractal properties after adapting the algorithm to visualize also heteroplasmic mutations. Parameter β from our fractal lacunarity method, based on hyperbola model function, has been measured to quantitatively characterize AD on the basis of mtDNA mutations. Results from this pilot study to develop the method show that fractal lacunarity parameter β of mtDNA is statistically different in AD patients when compared to age-matched controls. Fractal lacunarity analysis represents a useful tool to analyze mtDNA mutations. Lacunarity parameter β is able to characterize individual mutation profile of mitochondrial genome and appears a promising index to discriminate between AD and aging.
Highlights
The introduction in gerontologic field of paradigms, such as theory of complexity, chaos, and fractals, provide new tools and suggest new approaches to the study of aging processes
Our method of fractal lacunarity analysis based on hyperbola model function was systematically applied to Chaos Game Representation (CGR) images generated by revised Cambridge Reference Sequence (rCRS), mitochondrial DNA (mtDNA) sequences from 5 Alzheimer’s disease (AD) patients, and mtDNA sequences from 5 agematched controls to set up the method and test its robustness
For any set of matrices 2Lx2L generated with CGR method, we obtained an identical set of reports for each mtDNA sequence processed for six times
Summary
The introduction in gerontologic field of paradigms, such as theory of complexity, chaos, and fractals, provide new tools and suggest new approaches to the study of aging processes. Lacunarity of mtDNA in Alzeimer’s Disease individual phenotype In this view, by considering living systems as complex systems [3], life trajectories of subjects in a population, whatever close at birth, will evolve fluctuating with time, progressively enlarging the variance of their phenotype characteristics, among which aging. The concept that a complex system with a chaotic behavior often generates fractal structures, the so-called strange attractors [4], that can be observed at critical points, highlights the usefulness of fractal analysis as a suitable tool to measure biocomplexity and its changes with aging at both functional and structural levels [5,6,7,8]. Alzheimer’s disease (AD) represents one major health concern for our growing elderly population It accounts for increasing impairment of cognitive capacity followed by loss of executive function in late stage [9,10]. The most widely accepted hypothesis is the amyloid cascade hypothesis [12]: it posits amyloid-β (Aβ) plays an early and vital role in AD, as it triggers a cascade of events responsible for synaptic dysfunction, tau pathology, and neural loss [13]
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