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      Evaluation of mitochondrial DNA copy number estimation techniques

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          Abstract

          Mitochondrial DNA copy number (mtDNA-CN), a measure of the number of mitochondrial genomes per cell, is a minimally invasive proxy measure for mitochondrial function and has been associated with several aging-related diseases. Although quantitative real-time PCR (qPCR) is the current gold standard method for measuring mtDNA-CN, mtDNA-CN can also be measured from genotyping microarray probe intensities and DNA sequencing read counts. To conduct a comprehensive examination on the performance of these methods, we use known mtDNA-CN correlates (age, sex, white blood cell count, Duffy locus genotype, incident cardiovascular disease) to evaluate mtDNA-CN calculated from qPCR, two microarray platforms, as well as whole genome (WGS) and whole exome sequence (WES) data across 1,085 participants from the Atherosclerosis Risk in Communities (ARIC) study and 3,489 participants from the Multi-Ethnic Study of Atherosclerosis (MESA). We observe mtDNA-CN derived from WGS data is significantly more associated with known correlates compared to all other methods ( p < 0.001). Additionally, mtDNA-CN measured from WGS is on average more significantly associated with traits by 5.6 orders of magnitude and has effect size estimates 5.8 times more extreme than the current gold standard of qPCR. We further investigated the role of DNA extraction method on mtDNA-CN estimate reproducibility and found mtDNA-CN estimated from cell lysate is significantly less variable than traditional phenol-chloroform-isoamyl alcohol ( p = 5.44x10 -4) and silica-based column selection ( p = 2.82x10 -7). In conclusion, we recommend the field moves towards more accurate methods for mtDNA-CN, as well as re-analyze trait associations as more WGS data becomes available from larger initiatives such as TOPMed.

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          Rapid amplification of plasmid and phage DNA using Phi 29 DNA polymerase and multiply-primed rolling circle amplification.

          We describe a simple method of using rolling circle amplification to amplify vector DNA such as M13 or plasmid DNA from single colonies or plaques. Using random primers and phi29 DNA polymerase, circular DNA templates can be amplified 10,000-fold in a few hours. This procedure removes the need for lengthy growth periods and traditional DNA isolation methods. Reaction products can be used directly for DNA sequencing after phosphatase treatment to inactivate unincorporated nucleotides. Amplified products can also be used for in vitro cloning, library construction, and other molecular biology applications.
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            Epigenetic regulation of insulin resistance in nonalcoholic fatty liver disease: impact of liver methylation of the peroxisome proliferator-activated receptor γ coactivator 1α promoter.

            Insulin resistance (IR) and mitochondrial dysfunction play a central role in the pathophysiology of nonalcoholic fatty liver disease (NAFLD). We hypothesized that genetic factors and epigenetic modifications occurring in the liver contribute to the IR phenotype. We specifically examined whether fatty liver and IR are modified by hepatic DNA methylation of the peroxisome proliferator-activated receptor γ coactivator 1α (PPARGC1A) and mitochondrial transcription factor A (TFAM) promoters, and also evaluated whether liver mitochondrial DNA (mtDNA) content is associated with NAFLD and IR. We studied liver biopsies obtained from NAFLD patients in a case-control design. After bisulfite treatment of DNA, we used methylation-specific polymerase chain reaction (PCR) to assess the putative methylation of three CpG in the PPARGC1A and TFAM promoters. Liver mtDNA quantification using nuclear DNA (nDNA) as a reference was evaluated by way of real-time PCR. Liver PPARGC1A methylated DNA/unmethylated DNA ratio correlated with plasma fasting insulin levels and homeostasis model assessment of insulin resistance (HOMA-IR); TFAM methylated DNA/unmethylated DNA ratio was inversely correlated with insulin levels. PPARGC1A promoter methylation was inversely correlated with the abundance of liver PPARGC1A messenger RNA. The liver mtDNA/nDNA ratio was significantly higher in control livers compared with NAFLD livers. mtDNA/nDNA ratio was inversely correlated with HOMA-IR, fasting glucose, and insulin and was inversely correlated with PPARGC1A promoter methylation. Our data suggest that the IR phenotype and the liver transcriptional activity of PPARGC1A show a tight interaction, probably through epigenetic modifications. Decreased liver mtDNA content concomitantly contributes to peripheral IR. Copyright © 2010 American Association for the Study of Liver Diseases.
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              Reduced mitochondrial DNA copy number is a biomarker of Parkinson's disease

              Like any organ, the brain is susceptible to the march of time and a reduction in mitochondrial biogenesis is a hallmark of the aging process. In the largest investigation of mitochondrial copy number in Parkinson's disease (PD) to date and by using multiple tissues, we demonstrate that reduced Parkinson DNA (mitochondrial DNA mtDNA) copy number is a biomarker for the etiology of PD. We used established methods of mtDNA quantification to assess the copy number of mtDNA in n = 363 peripheral blood samples, n = 151 substantia nigra pars compacta tissue samples and n = 120 frontal cortex tissue samples from community-based PD cases fulfilling UK-PD Society brain bank criteria for the diagnosis of PD. Accepting technical limitations, our data show that PD patients suffer a significant reduction in mtDNA copy number in both peripheral blood and the vulnerable substantia nigra pars compacta when compared to matched controls. Our study indicates that reduced mtDNA copy number is restricted to the affected brain tissue, but is also reflected in the peripheral blood, suggesting that mtDNA copy number may be a viable diagnostic predictor of PD.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: Writing – review & editing
                Role: Formal analysisRole: SoftwareRole: Writing – review & editing
                Role: Data curationRole: Project administrationRole: ResourcesRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: SupervisionRole: Writing – review & editing
                Role: Data curationRole: Funding acquisitionRole: Writing – review & editing
                Role: Data curationRole: Funding acquisitionRole: SupervisionRole: Writing – review & editing
                Role: Data curationRole: Funding acquisitionRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                31 January 2020
                2020
                : 15
                : 1
                : e0228166
                Affiliations
                [1 ] Department of Genetic Medicine, McKusick-Nathans Institute, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America
                [2 ] Department of Laboratory Medicine and Pathology, University of Minnesota Medical School, Minneapolis, MN, United States of America
                [3 ] Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United States of America
                [4 ] Department of Epidemiology and the Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
                [5 ] LABioMed and Department of Pediatrics, at Harbor-UCLA Medical Center, Institute for Translational Genomics and Population Sciences, Torrance, CA, United States of America
                [6 ] Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, United States of America
                Vanderbilt University Medical Center, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0003-2737-0549
                http://orcid.org/0000-0002-2646-7288
                http://orcid.org/0000-0002-2756-4370
                http://orcid.org/0000-0001-7191-1723
                http://orcid.org/0000-0001-8980-8695
                Article
                PONE-D-19-28415
                10.1371/journal.pone.0228166
                6994099
                32004343
                94ffd930-5a24-4d6c-8eec-1f45fe218cc1
                © 2020 Longchamps et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 10 October 2019
                : 8 January 2020
                Page count
                Figures: 3, Tables: 2, Pages: 14
                Funding
                This research was supported by grant R01HL131573 from the US National Institutes of Health (Longchamps, Castellani, Guallar, and Arking) and by grant P30AG021334 from the Johns Hopkins University Claude D. Pepper Older Americans Independence Center National Institute on Aging (Dr Arking). The Atherosclerosis Risk in Communities study has been funded in whole or in part with Federal funds from the National Heart, Lung, and Blood Institute, National Institutes of Health, Department of Health and Human Services, under Contract nos. (HHSN268201700001I, HHSN268201700002I, HHSN268201700003I, HHSN268201700005I, HHSN268201700004I). The Multi-Ethnic Study of Atherosclerosis is supported by contracts HHSN268201500003I, N01-HC 95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168 and N01-HC-95169 from the National Heart, Lung, and Blood Institute, and by grants UL1-TR-000040, UL1-TR-001079, and UL1-TR-001420 from the National Center for Advancing Translational Sciences (NCATS). Dr. Rotter and Dr. Taylor’s efforts were supported in part by the National Center for Advancing Translational Sciences, CTSI grant UL1TR001881, and the National Institute of Diabetes and Digestive and Kidney Disease Diabetes Research Center (DRC) grant DK063491 to the Southern California Diabetes Endocrinology Research Center. Funding for SHARe genotyping was provided by National Heart, Lung, and Blood Institute contract N02-HL-64278. The funding organizations had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
                Categories
                Research Article
                Research and analysis methods
                Extraction techniques
                DNA extraction
                Medicine and Health Sciences
                Cardiovascular Medicine
                Cardiovascular Diseases
                Biology and Life Sciences
                Biochemistry
                Bioenergetics
                Energy-Producing Organelles
                Mitochondria
                Biology and Life Sciences
                Cell Biology
                Cellular Structures and Organelles
                Energy-Producing Organelles
                Mitochondria
                Research and Analysis Methods
                Bioassays and Physiological Analysis
                Microarrays
                Biology and Life Sciences
                Molecular Biology
                Molecular Biology Techniques
                Genotyping
                Research and Analysis Methods
                Molecular Biology Techniques
                Genotyping
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                Cell Biology
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                Genetics
                Molecular Genetics
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                Research and Analysis Methods
                Database and Informatics Methods
                Bioinformatics
                Sequence Analysis
                Sequence Alignment
                Custom metadata
                Data from this study are available upon request as these data contain potentially identifying and sensitive patient information. Individuals who wish to access these data are welcome to contact the ARIC coordinating center at UNC ( aricpub@ 123456unc.edu ) or the MESA coordinating center ( chsccweb@ 123456u.washington.edu ).

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