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Aging and disease    2017, Vol. 8 Issue (1) : 44-56     DOI: 10.14336/AD.2016.0629
Original Article |
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.
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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.

Keywords Aging      Alzheimer’s disease      Biocomplexity      Chaos Game Representation      Fractal lacunarity      mtDNA     
Corresponding Authors: Zaia Annamaria   
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These authors contributed equally to this work

Issue Date: 01 February 2017
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Zaia Annamaria
Maponi Pierluigi
Di Stefano Giuseppina
Casoli Tiziana
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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.
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AD patientsControlsp Value
Number (M/F)2/121/13
Age75.4 ± 5.173.1 ± 5.10.117
MMSE17.3 ± 3.428.2 ± 0.8<0.001
ADL5.2 ± 1.36.0 ± 0.00.022
IADL2.0 ± 1.48.0 ± 0.0<0.001
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).
NumberrCRSAD patientsControlsp Value
Adenosine51175178.8 ± 9.45174.9 ± 7.40.115
Cytosine51754937.3 ± 28.94948.6 ± 11.90.095
Guanine21632266.1 ± 15.02264.3 ± 13.60.371
Thymine40894161.9 ± 12.44156.3 ± 15.00.076
Total mutations18 ± 1023 ± 120.094
Homoplasmic14 ± 818 ± 90.090
Heteroplasmic3 ± 35 ± 40.119
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 assessmentProvides 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.
Brain imagingCT 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.
Spinal testsThe 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
[1] Piantanelli L, Rossolini G, Basso A, Piantanelli A, Malavolta M, Zaia A (2001). Use of mathematical models of survivorship in the study of biomarkers of aging: the role of heterogeneity. Mech Ageing Dev, 122: 1461-1475.
[2] Zaia A (2009). Osteoporosis and fracture risk: new perspectives for early diagnosis and treatment assessment. In: Mattingly BE, Pillare AC, editors. Osteoporosis: Etiology, Diagnosis and Treatment. Hauppauge NY: Nova Science Publishers, 267-290.
[3] Pettersson M, editor. Complexity and evolution. Cambridge: Cambridge University Press; 1996.
[4] Grassberger A, Procaccia I (1987). Measuring the strangeness of strange attractors. Physica D, 9: 189-208.
[5] Goldberger AL, Rigney DR, West BJ (1990) Chaos and fractals in human physiology. Sci Am, 262: 42-49.
[6] Goldberger AL (1996). Non-linear dynamics for clinicians: chaos theory, fractals, and complexity at the bedside. Lancet, 347: 1312-1314.
[7] Nonnenmacher TF, Baumann G, Losa GA (1990). Self-organization and fractal scaling patterns in biological systems. In: Menon J, editor. Trends in Biological Cybernetics. Trivandrum India: Publication Manager, Research Trends, Council of Scientific Research Integration, 65-73.
[8] Weibel ER (1991). Fractal geometry: a design principle for living organisms. Am J Physiol, 261: L361-L369.
[9] Querfurth HW, LaFerla FM (2010). Alzheimer’s disease. N Engl J Med, 362: 329-344.
[10] Ballard C, Gauthier S, Corbett A, Brayne C, Aarsland D, Jones E (2011). Alzheimer’s disease. Lancet, 377: 1019-1031.
[11] Brookmeyer R, Johnson E, Ziegler-Graham K, Arrighi HM (2007). Forecasting the global burden of Alzheimer’s disease. Alzheimers Dement, 3: 186-191.
[12] Hardy JA, Higgins GA (1992). Alzheimer’s disease: the amyloid cascade hypothesis. Science, 256: 184-185.
[13] Hardy J, Selkoe DJ (2002). The amyloid hypothesis of Alzheimer’s disease: progress and problems on the road to therapeutics. Science, 297: 353-356.
[14] Smigrodzki RM, Khan SM (2005). Mitochondrial microheteroplasmy and a theory of aging and age-related disease. Rejuvenation Res, 8: 172-198.
[15] Coskun P, Wyrembak J, Schriner SE, Chen H-W, Marciniack C, LaFerla F, Wallace DC (2012). A mitochondrial etiology of Alzheimer and Parkinson disease. Biochim Biophys Acta, 1820: 553-564.
[16] Payne BA, Wilson IJ, Yu-Wai-Man P, Coxhead J, Deehan D, Horvath R, et al (2013). Universal heteroplasmy of human mitochondrial DNA. Hum Mol Genet, 22: 384-390.
[17] Dhillon VS, Fenech M (2014). Mutations that affect mitochondrial functions and their association with neurodegenerative diseases. Mutat Res, 759: 1-13.
[18] Mandelbrot BB. The Fractal Geometry of Nature. New York: WH Freeman; 1982.
[19] Lipsitz LA, Goldberger AL (1992). Loss of ‘complexity’ and aging: Potential applications of fractals and chaos theory to senescence. JAMA, 267: 1806-1809.
[20] Losa GA, Nonnenmacher TF (1996). Self-similarity and fractal irregularity in pathologic tissues. Mod Pathol, 9: 174-182.
[21] Cross SS (1997). Fractals in pathology. J Pathol, 182: 1-8.
[22] Oiwa NN, Glazier JA (2004). Self-similar mitochondrial DNA. Cell Biochem Biophys 41: 41-62.
[23] Goldberger LA, Peng CK, Lipsitz LA (2002). What is physiologic complexity and how does it change with aging and disease? Neurobiol Aging, 23: 23-26.
[24] Piantanelli A, Serresi S, Ricotti G, Rossolini G, Zaia A, Basso A, Piantanelli L (2002). Color-based method for fractal dimension estimation of pigmented skin lesion contour. In: Losa GA, editor. Fractals in Biology and Medicine. Basel: Birkhauser Press, 127-136.
[25] Vaillancourt DE, Newell KM (2002). Changing complexity in human behaviour and physiology through aging and disease. Neurobiol Aging, 23: 1-11.
[26] Lipsitz LA (2004). Physiological complexity, aging, and the path to frailty. Sci Aging Knowledge Environ, 16: pe16.
[27] Doubal FN, MacGillivray TJ, Patton N, Dhillon B, Dennis MS, Wardlaw JM (2010). Fractal analysis of retinal vessels suggests that a distinct vasculopathy causes lacunar stroke. Neurology, 74: pe11027.
[28] Fiz JA, Monte-Moreno E, Andreo F, Auteri SJ, Sanz-Santos J, Serra P, et al (2014). Fractal dimension analysis of malignant and benign endobronchial ultrasound nodes. BMC Med Imaging, 14: pe22.
[29] Captur G, Karperien AL, Li C, Zemrak F, Tobon-Gomez C, Gao X, Bluemke DA, et al (2015). Fractal frontiers in cardiovascular magnetic resonance: towards clinical implementation. J Cardiovasc Magn R, 17: pe80.
[30] Hao B-L (2000). Fractals from genomes - exact solutions of a biology-inspired problem. Physica A, 282: 225-246.
[31] Kirilyuk AP (2004). Complex-Dynamical Extension of the Fractal Paradigm and its Applications in Life Sciences. In: Losa GA, Merlini D, Nonnenmacher TF, Weibel E, editors. Fractals in Biology and Medicine. Basel: Birkhauser Press, 233-244.
[32] Zhou LQ, Yu ZG, Deng JQ, Anh V, Long SC (2005). A fractal method to distinguish coding and non-coding sequences in a complete genome based on a number sequence representation. J Theor Biol, 232: 559-567.
[33] Aldrich PR, Horsley RK, Turcic SM (2011). Symmetry in the Language of Gene Expression: A Survey of Gene Promoter Networks in Multiple Bacterial Species and Non-σ Regulons. Symmetry, 3: 750-766.
[34] Cattani C, Pierro G (2013). On the fractal geometry of DNA by the binary image analysis. Bull Math Biol, 75: 1544-1570.
[35] Mandelbrot BB (1993). A Fractal’s Lacunarity, and how it can be Tuned and Measured. In: Nonnenmacher TF, Losa GA, Weibel ER, editors. Fractals in Biology and Medicine. Basel: Birkhauser Press, 8-21.
[36] Mandelbrot BB (1977). The Fractal Geometry of Nature. In: Trees and the Diameter Exponent. New York: WH Freeman, 156-165.
[37] de Melo RHC, Conci A (2013). How succolarity could be used as another fractal measure in image analysis. Telecom Syst, 52: 1643-1655.
[38] N’Diaye M, Terranova L, Mallet R, Mabilleau G, Chappard D (2015). Three-dimensional arrangement of β-tricalcium phosphate granules evaluated by microcomputed tomography and fractal analysis. Acta Biomater, 11: 404-411.
[39] Plotnick RE, Gardner RH, Hargrove WW, Prestegard K, Perlmutter M (1996). Lacunarity analysis: A general technique for the analysis of spatial patterns. Phys Rev E, 53: 5461-5468.
[40] Allain C, Cloitre M (1991). Characterizing the lacunarity of random and deterministic fractal sets. Phy Rev A, 44: 3552-3558.
[41] Jeffrey HJ (1990). Chaos game representation of gene structure. Nucleic Acids Res, 18: 2163-2170.
[42] Deschavanne PJ, Giron A, Vilain J, Fagot G, Fertil B (1999). Genomic signature: characterization and classification of species assessed by chaos game representation of sequences. Mol Biol Evol, 16:1391-1399.
[43] Fu W, Wang Y, Lu D (2005). Multifractal Analysis of Genomic Sequences CGR Images. Conf Proc IEEE Eng Med Biol Soc, 5: 4783-4786.
[44] Stan C, Cristescu CP, Scarlat EI (2010). Similarity analysis for DNA sequences based on chaos game representation. Case study: the albumin. J Theor Biol, 267: 513-518.
[45] Zaia A, Eleonori R, Maponi P, Rossi R, Murri R (2005). Medical Imaging and Osteoporosis: Fractal’s Lacunarity Analysis of Trabecular Bone in MR Images. In: Tsymbal A, Cunningham P, editors. Proceedings - Eighteenth IEEE Symposium on Computer-Based Medical Systems - CBMS 2005, Dublin, Ireland. Los Alamitos CA: IEEE Computer Society Press, 3-8.
[46] Zaia A, Eleonori R, Maponi P, Rossi R, Murri R (2006). MR Imaging and Osteoporosis: Fractal Lacunarity Analysis of Trabecular Bone. IEEE Trans Inf Technol Biomed, 10: 484-489.
[47] Zaia A, Rossi R, Egidi N, Maponi P (2010). Fractal’s lacunarity analysis of trabecular bone in MR images. In: Tavares J, Jorge N, editors. Computational Vision and Medical Image Processing VipIMAGE 2009. Balkema: CRC Press, 421-426.
[48] Zaia A (2015). Fractal lacunarity of trabecular bone and magnetic resonance imaging: New perspectives for osteoporotic fracture risk assessment. World J Orthop, 6: 221-235.
[49] Casoli T, Di Stefano G, Spazzafumo L, Balietti M, Giorgetti B, Giuli C, et al (2014). Contribution of non-reference alleles in mtDNA of Alzheimer’s disease patients. Ann Clin Transl Neurol, 1: 284-289.
[50] Maitra A, Cohen Y, Gillespie SE, Mambo E, Fukushima N, Hoque MO, et al (2004). The Human MitoChip: a high-throughput sequencing microarray for mitochondrial mutation detection. Genome Res, 14: 812-819.
[51] Xie HM, Perin JC, Schurr TG, Dulik MC, Zhadanov SI, Baur JA, et al (2011). Mitochondrial genome sequence analysis: a custom bioinformatics pipeline substantially improves Affymetrix MitoChip v2.0 call rate and accuracy. BMC Bioinformatics, 12: pe 402.
[52] Edgar R., Domrachev M, Lash AE (2002). Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res, 30: 207-210.
[53] Vinga S, Carvalho AM, Francisco AP, Russo LM, Almeida JS (2012). Pattern matching through Chaos Game Representation: bridging numerical and discrete data structures for biological sequence analysis. Algorithms Mol Biol, 7: pe10.
[54] Wang Y, Hill K, Singh S, Kari L (2005). The spectrum of genomic signatures: from dinucleotides to chaos game representation. Gene, 346: 173-185.
[55] Linnane AW, Marzuki S, Ozawa T, Tanaka M (1989). Mitochondrial DNA mutations as an important contributor to ageing and degenerative diseases. Lancet, 1: 642-645.
[56] Lakatos A, Derbeneva O, Younes D, Keator D, Bakken T, Lvova M, et al (2010). Association between mitochondrial DNA variations and Alzheimer’s disease in the ADNI cohort. Neurobiol Aging, 31: 1355-1363.
[57] Filosto M, Scarpelli M, Cotelli MS, Vielmi V, Todeschini A, Gregorelli V, et al (2011). The role of mitochondria in neurodegenerative diseases. J Neurol, 258:1763-1774.
[58] Federico A, Cardaioli E, Da Pozzo P, Formichi P, Gallus GN, Radi E (2012). Mitochondria, oxidative stress and neurodegeneration. J Neurol Sci, 322: 254-262.
[59] Lagouge M, Larsson NG (2013). The role of mitochondrial DNA mutations and free radicals in disease and ageing. J Intern Med, 273: 529-543.
[60] Casoli T, Spazzafumo L, Di Stefano G, Conti F (2015). Role of diffuse low-level heteroplasmy of mitochondrial DNA in Alzheimer’s disease neurodegeneration. Front Aging Neurosci, 7: pe 142.
[61] Hauptmann S, Keil U, Scherping I, Bonert A, Eckert A, Muller WE (2006). Mitochondrial dysfunction in sporadic and genetic Alzheimer’s disease. Exp Gerontol, 41: 668-673.
[62] Lin MT, Beal MF (2006). Mitochondrial dysfunction and oxidative stress in neurodegenerative diseases. Nature, 443: 787-795.
[63] Reddy PH, Beal MF (2008). Amyloid beta, mitochondrial dysfunction and synaptic damage: implications for cognitive decline in aging and Alzheimer’s disease. Trends Mol Med, 14: 45-53.
[64] Hudson G, Sims R, Harold D, Chapman J, Hollingworth P, Gerrish A, et al (2012). No consistent evidence for association between mtDNA variants and Alzheimer disease. Neurology, 78: 1038-1042.
[65] Coskun PE, Beal MF, Wallace DC (2004). Alzheimer’s brains harbor somatic mtDNA control-region mutations that suppress mitochondrial transcription and replication. Proc Natl Acad Sci USA, 10: 10726-10731.
[66] Tanaka N, Goto YI, Akanuma J, Kato M, Kinoshita T, Yamashita F, et al (2010). Mitochondrial DNA variants in a Japanese population of patients with Alzheimer’s disease. Mitochondrion, 10: 32-37.
[67] Elson JL, Herrnsstadt C, Preston G, Thal L, Morris CM, Edwardson JA, et al (2006). Does the mitochondrial genome play a role in the etiology of Alzheimer’s disease? Hum Genet, 119: 241-254.
[68] Morris JK, Honea RA, Vidoni ED, Swerdlow RH, Burns JM (2014). Is Alzheimer’s disease a sistemic disease? Biochim Biophys Acta, 1842: 1340-1349.
[69] Taylor RW, Turnbull DM (2005). Mitochondrial DNA mutations in human disease. Nat Rev Genet, 6: 389-402.
[70] Swerdlow RH, Burns JM, Khan SM (2014). The Alzheimer’s disease cascade hypothesis: Progress and perspectives. Biochim Biophys Acta, 1842: 1219-1231.
[71] Gellerich FN, Deschauer M, Chen Y, Muller T, Neudecker S, Zierz S (2002). Mitochondrial respiratory rates and activities of respiratory chain complexes correlate linearly with heteroplasmy of deleted mtDNA without threshold and independently of deletion size. Biochem Biophys Acta, 1556: 41-52.
[72] Gopakumar G, Nair AS (2011). Lacunarity Analysis of Genomic Sequences: A Potential Bio-sequence Analysis Method. Bioinformatics and Biomedical Engineering (iCBBE) 5th International Conference on, 1-4.
[73] Kilic KI, Abiyev RH (2011). Exploiting the synergy between fractal dimension and lacunarity for improved texture recognition. Signal Processing, 91: 2332-2344.
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