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. 2013 Mar;5(3):192-208.
doi: 10.18632/aging.100546.

A Novel Diagnostic Tool Reveals Mitochondrial Pathology in Human Diseases and Aging

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Free PMC article

A Novel Diagnostic Tool Reveals Mitochondrial Pathology in Human Diseases and Aging

Morten Scheibye-Knudsen et al. Aging (Albany NY). .
Free PMC article

Abstract

The inherent complex and pleiotropic phenotype of mitochondrial diseases poses a significant diagnostic challenge for clinicians as well as an analytical barrier for scientists. To overcome these obstacles we compiled a novel database, www.mitodb.com, containing the clinical features of primary mitochondrial diseases. Based on this we developed a number of qualitative and quantitative measures, enabling us to determine whether a disorder can be characterized as mitochondrial. These included a clustering algorithm, a disease network, a mitochondrial barcode and two scoring algorithms. Using these tools we detected mitochondrial involvement in a number of diseases not previously recorded as mitochondrial. As a proof of principle Cockayne syndrome, ataxia with oculomotor apraxia 1 (AOA1), spinocerebellar ataxia with axonal neuropathy 1 (SCAN1) and ataxia-telangiectasia have recently been shown to have mitochondrial dysfunction and those diseases showed strong association with mitochondrial disorders. We next evaluated mitochondrial involvement in aging and detected two distinct categories of accelerated aging disorders, one of them being associated with mitochondrial dysfunction. Normal aging seemed to associate stronger with the mitochondrial diseases than the non-mitochondrial partially supporting a mitochondrial theory of aging.

Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1. Mitochondrial diseases have a defined clinical spectrum
(A) The top-20 clinical parameters seen in all mitochondrial diseases. (B) The top-20 clinical parameters seen in mitochondrial diseases with a mean age of onset before 20. (C) The top-20 clinical parameters seen in mitochondrial diseases with a mean age of onset after 20.
Figure 2
Figure 2. Putative mitochondrial diseases are identified using www.mitodb.com
(A) The clustering of several diseases of unknown pathogenesis with recently recognized mitochondrial dysfunction (blue) and mitochondrial (red) and non-mitochondrial diseases (green). AOA1: Ataxia with oculomotor apraxia 1; SCAN1: Spinocerebellar ataxia with axonal neuropathy 1. (B) A representation of how the putative mitochondrial diseases (blue) associate within the disease network. Each dot represents a disease and the closer two diseases are connected the shorter the distance between them. Mitochondrial diseases: red; non-mitochondrial diseases: green. (C) The mitochondrial barcode of a number of diseases. Each bar represents a clinical parameter that is shared with another disease in the database. Red is mitochondrial diseases, green is non-mitochondrial and blue is diseases of unknown pathogenesis. The tint is given by the percentage of patients that are affected in the disease tested multiplied by the percentage of patients that are affected in the disease in the database that shares the parameter. IOSCA: infantile onset spinocerebellar ataxia (D) The mito score of the putative mitochondrial diseases and two bona fide mitochondrial (MERFF and MELAS) and two non-mitochondrial (cystic fibrosis and Crouzon syndrome) diseases. (E) The SVM score of the tested diseases and two mitochondrial (MERFF and MELAS) and two non-mitochondrial (cystic fibrosis and Crouzon syndrome) diseases.
Figure 3
Figure 3. Normal aging and some accelerated aging disorders display phenotypical similarities to mitochondrial diseases
(A) Clustermap using uncentered similarity and average linkage of all the diseases in the database. Blue represents aging and the accelerated aging disorders. (B) A representation of how aging and the progerias (blue dots) associate within the disease network. (C) Mitochondrial barcode of some accelerated aging disorders. (D) The mito score of the tested diseases. (E) The SVM score of the tested diseases.
Figure 4
Figure 4. The phenotype of normal human aging based on published studies
Values represent the prevalence (%) of a given parameter.

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