Biocomplexity and Fractality in the Search of Biomarkers of Aging and Pathology: Focus on Mitochondrial DNA and Alzheimer’s Disease
Zaia Annamaria1,*, Maponi Pierluigi2, Di Stefano Giuseppina3, Casoli Tiziana4
1Laboratory of Bioinformatics, Bioengineering and Domotics, Italian National Research Center on Aging - INRCA, via Birarelli 8, 60121 Ancona, Italy 2School of Science and Technology, University of Camerino, via Madonna delle Carceri 9, 62032 Camerino (MC), Italy 3Research, Innovation and Technology Transfer Office, Italian National Research Center on Aging - INRCA, via Birarelli 8, 60121 Ancona, Italy 4Scientific and Technological Area, Italian National Research Center on Aging - INRCA, via Birarelli 8, 60121 Ancona, Italy.
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.
Zaia Annamaria,Maponi Pierluigi,Di Stefano Giuseppina, et al. Biocomplexity and Fractality in the Search of Biomarkers of Aging and Pathology: Focus on Mitochondrial DNA and Alzheimer’s Disease[J]. Aging and disease,
2017, 8(1): 44-56.
Table 1 Characteristics of subjects included in the study
Figure 1. Chaos Game Representation method. CGR organization for L=1,2,3 in the case of mtDNA four-symbol alphabet sequence.
Figure 2. Chaos Game Representation of human mtDNA. Whole revised Cambridge Reference Sequence processed by CGR method generates matrices 2Lx2L. Matrices for L=1 to L=6 are reported (a). CGR representation of human mtDNA resembles self-similarity of the triangle of Sierpinski, an ideal fractal built through repeated iterations starting from a square (b).
5178.8 ± 9.4
5174.9 ± 7.4
4937.3 ± 28.9
4948.6 ± 11.9
2266.1 ± 15.0
2264.3 ± 13.6
4161.9 ± 12.4
4156.3 ± 15.0
18 ± 10
23 ± 12
14 ± 8
18 ± 9
3 ± 3
5 ± 4
Table 2 Characteristics of mtDNA sequences processed by the proposed method
Figure 3. Schematic representation of fractal lacunarity analysis. (Top left) rCRS mtDNA image generated by CGR matrix for L=5 is a 32x32 square. The plot (bottom) represents the result of GBA application (dotted line), for bmin=3, as fitted by hyperbola function (solid line) used to calculate the triplet of parameters a, b, γ. rCRS: revised Cambridge Reference Sequence; Chaos Game Representation; GBA: Gliding Box Algorithm.
Figure 4. Examples of CGR images of mtDNA sequences from different subjects. Matrices for L=5 (top) and L=6 (bottom) generated from mtDNA of rCRS (left), AD patient (middle), and Control (Ctr, right). Lacunarity parameter b value for each representation is reported. CGR: Chaos Game Representation; rCRS: revised Cambridge Reference Sequence; AD: Alzheimer’s Disease.
Figure 5. Correlation of lacunarity parameter β with classical diagnostic indices of Alzheimer’s disease (AD). MMSE-Mini Mental State Examination (left); ADL-Activities of Daily Living (middle); IADL-Instrumental Activities of Daily Living (right). Triangles and circles represent AD patients and age-matched controls respectively.
Cognitive and neuropsychological assessment
Provides a detailed picture of cognitive status. Thinking skills that are explored include memory, language, visual-spatial perception, attention, motor function, and executive function (e. g. MMSE, NPI, ADL, IADL)
Identifies very early subtle cognitive changes and which areas of mental functioning are affected. It can help distinguish AD from other forms of dementia. The cost is low and the tests are not invasive.
An abnormal result can have many explanations other than AD. It can miss cognitive impairment in those who are highly educated. It can be tiring and stressful for patients being tested.
CT scans and MRI examine structural changes of the brain. PET scans can show metabolic changes and amyloid deposition.
Allows finding possible other causes of dementia symptoms (brain trauma, tumor, or stroke). PET scans can help distinguish AD from frontotemporal dementia.
Brain imaging may require the use of intravenous "tracing" agents, that can cause side effects. MRI scanners can induce claustrophobia and may not be compatible with pacemakers or other devices. The cost is notably high.
The amounts of three AD biomarkers, amyloid-β 42, total tau, phosphorylated tau, are determined in CSF through a lumbar spine puncture.
CSF biomarkers can identify patients without clinical or preclinical signs of AD. A low level of amyloid-β 42 in patients with mild cognitive impairment seems to predict with 80-90 % accuracy who will not develop AD.
It is an invasive test to be performed by an expert high qualified specialist. Risk exists for infection, ble eding, and pain .
Table 4 Different approaches to AD diagnosis used in clinical practice
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