Quantitative assessment of mitochondrial DNA copies from whole genome sequencing

  • Hsueh-Ting Chu1, 2,

    Affiliated with

    • William WL Hsiao3, 4,

      Affiliated with

      • Theresa TH Tsao5,

        Affiliated with

        • Ching-Mao Chang6,

          Affiliated with

          • Yen-Wenn Liu8,

            Affiliated with

            • Chen-Chieh Fan5,

              Affiliated with

              • Han Lin5,

                Affiliated with

                • Hen-Hong Chang6, 7,

                  Affiliated with

                  • Tze-Jung Yeh9,

                    Affiliated with

                    • Jen-Chih Chen9,

                      Affiliated with

                      • Dun-Ming Huang2,

                        Affiliated with

                        • Chaur-Chin Chen10 and

                          Affiliated with

                          • Cheng-Yan Kao5Email author

                            Affiliated with

                            BMC Genomics201213(Suppl 7):S5

                            DOI: 10.1186/1471-2164-13-S7-S5

                            Published: 13 December 2012

                            Abstract

                            Background

                            Mitochondrial dysfunction is associated with various aging diseases. The copy number of mtDNA in human cells may therefore be a potential biomarker for diagnostics of aging. Here we propose a new computational method for the accurate assessment of mtDNA copies from whole genome sequencing data.

                            Results

                            Two families of the human whole genome sequencing datasets from the HapMap and the 1000 Genomes projects were used for the accurate counting of mitochondrial DNA copy numbers. The results revealed the parental mitochondrial DNA copy numbers are significantly lower than that of their children in these samples. There are 8%~21% more copies of mtDNA in samples from the children than from their parents. The experiment demonstrated the possible correlations between the quantity of mitochondrial DNA and aging-related diseases.

                            Conclusions

                            Since the next-generation sequencing technology strives to deliver affordable and non-biased sequencing results, accurate assessment of mtDNA copy numbers can be achieved effectively from the output of whole genome sequencing. We implemented the method as a software package MitoCounter with the source code and user's guide available to the public at http://​sourceforge.​net/​projects/​mitocounter/​.

                            Background

                            Human mitochondria contain multiple copies of a 16.5 k bp, double-stranded, circular DNA molecule (Figure 1a). Since mitochondria are the organelles that generate chemical energy for cellular functions, many disease symptoms are linked to mitochondrial dysfunction, including poor growth, muscle weakness, hearing problems, visual problems, heart diseases, and liver diseases. There were many recent studies which showed significantly reduced mitochondrial DNA (mtDNA) copy numbers in cell samples of aging-related diseases [13]. A recent study also reported that mtDNA copy number is associated with cancer risk [4]. Therefore, quantitative assessment of mtDNA in human cells can elucidate the relationship between mitochondrial diseases and mitochondrial dysfunction.
                            http://static-content.springer.com/image/art%3A10.1186%2F1471-2164-13-S7-S5/MediaObjects/12864_2012_4415_Fig1_HTML.jpg
                            Figure 1

                            Overview of human whole genome sequencing. A) The human genome is composed of nuclear DNA and mitochondrial DNA. The nuclear DNA is stored on 23 chromosome pairs and there are multiple copies of small DNA located in mitochondria. B) The reads from the sequencing of human whole genome are mixed with both nuclear DNA and mitochondrial DNA.

                            In the past decade, quantitative real-time PCR assays were developed to estimate relative levels of mtDNA copy numbers in samples [2, 5, 6]. This approach measures the mtDNA copy number by determining the ratio of PCR amplicons to that of a single nuclear gene in experimental samples. The recent development of next-generation sequencing technology (NGS) revolutionized genomic studies and produced accurate whole genome sequencing (WGS) datasets [7]. As shown in Figure 1b, the output from human whole genome sequencing consists of both nuclear DNA (nuDNA) and mitochondrial DNA (mtDNA) molecules, thus it is convenient to assess mtDNA copy number from WGS dataset and can be an alternative to real-time PCR assays.

                            Here we demonstrate a computational method for counting mtDNA copy number using WGS datasets. The three steps in the process are (1) typing of mtDNA, (2) separation of mtDNA reads, and (3) calculation of mtDNA count. We developed a freely available software package called MitoCounter for this purpose. MitoCounter can be used to calculate the average copy numbers of mtDNA molecules in the sequenced samples. Besides, the separated mtDNA reads provide further analysis of mtDNA heteroplasmy. The mtDNA heteroplasmy represents the mixture of individual mtDNA mutations. Heteroplasmy levels can alter the clinical penetrance of primary mtDNA diseases [8, 9].

                            Methods

                            A computational assay for counting mtDNA copies from a WGS dataset

                            Since the library construction bias is minimized with the next-generation sequencing platform [10], both mitochondrial DNA (mtDNA) and nuclear DNA (nuDNA) are sequenced together with equal opportunities. The output dataset comprises a mixture of mtDNA reads and nuDNA reads. Let the total number of nucleotide bases in the nuclear genome be 2N (for diploid chromosomes) and the number of bases in a mitochondrial DNA is M. Then the summation of nucleotide bases in the entire human genome is 2N+kM, where k is the number of mtDNA copies. The numbers of reads from nuDNA and the number of reads from mtDNA should reflect the ratio of 2N:kM.

                            That is,
                            http://static-content.springer.com/image/art%3A10.1186%2F1471-2164-13-S7-S5/MediaObjects/12864_2012_4415_Equ1_HTML.gif
                            (1)

                            where mtBases is the total bases of sequenced reads from mtDNA and allBases is total bases of all sequenced reads from the output of a WGS procedure.

                            From an entire dataset of human whole genome sequencing, we separate the mtDNA reads from the others. Then the number of mtDNA copies can be approximated as
                            http://static-content.springer.com/image/art%3A10.1186%2F1471-2164-13-S7-S5/MediaObjects/12864_2012_4415_Equ2_HTML.gif
                            (2)

                            The equation for counting mtDNA copies is not suitable for plants (e.g. Arabidopsis) since their mtDNA sequences may contain segments of nuclear DNA. Besides, there are usually other DNA molecules in their cells, such as chloroplast genome and plasmid genome.

                            Software implementation

                            In order to precisely separate mtDNA reads from a WGS dataset, it is necessary to determine the genotype of the mitochondrial genome first. We designed a program WgsMitoAssembler to identify the homoplasmic sequences, which present the inherent mutations in most of mtDNA molecules. The program WgsMitoAssembler is a guided assembler, and it requires a reference mitochondrial sequence which is used to choose a beginning read and an ending read from the entire WGS dataset. We use the reference mtDNA sequence (GenBank: NC_001807.4) for the purpose. We then search for best candidate reads which can extend the beginning read from the 3' end to the 5' end until the ending read is met.

                            After the typing of the target mitochondrial genome, the homoplasmy sequence is used in the second program WgsMitoCounter. The program performs the job of separating mitochondrial reads from the entire WGS dataset. Considering that some of sequenced reads may contain erroneous bases, we design an error-tolerant mapping algorithm shown in Figure 2. We search for sub-sequences of paired reads which are indexed as mtDNA fragments and the accuracy of mapping is determined by the pairing distances. WgsMitoCounter will output a CSV file which records the number of mitochondrial reads in each run of the entire dataset. The template of final calculation for mtDNA copy number is provided in Additional file 1.
                            http://static-content.springer.com/image/art%3A10.1186%2F1471-2164-13-S7-S5/MediaObjects/12864_2012_4415_Fig2_HTML.jpg
                            Figure 2

                            An error-tolerant mapping algorithm for filtering mitochondrial reads.

                            Results and discussion

                            Parental mtDNA samples have less copy numbers

                            We apply the analysis to public WGS datasets from the HapMap [11] and 1000 Genomes [12] projects. We chose six high-coverage WGS datasets for two pedigree trios: YOR009 and CEPH146 and two low-coverage WGS datasets for individual elders (Sample ID: NA11831 and NA06985), listed in Table 1. YOR009 is an African family. CEPH1463 is a family from Utah with Northern and Western European ancestry. The two individuals are also from the CEPH population and were recorded as the grandparents in the 1000 Genomes project. These DNA samples were isolated from B-lymphocyte cells derived from blood. Table 2 lists the results of counting mitochondrial DNA on the selected datasets. The mtDNA counts for the YOR009 family are between 645~752 and for CEPH1463 family are between 734~950. Besides, the mtDNA counts for the two individual elders are 662 and 755.
                            Table 1

                            Typing of mtDNA from whole genome sequencing samples

                            HapMap Family

                            Sample

                            Sex

                            Relation

                            mtDNA

                            Length

                            Haplogroup*

                            Reference

                            Haplogroup

                            YOR009

                            NA18507

                            male

                            father

                            16567 bp

                            AF346986

                            L1b

                             

                            NA18508

                            female

                            mother

                            16567 bp

                            DQ341073

                            L3b

                             

                            NA18506

                            male

                            child

                            16567 bp

                            DQ341073

                            L3b

                            CEPH146

                            NA12891

                            male

                            father

                            16572 bp

                            EU715237

                            H1

                             

                            NA12892

                            female

                            mother

                            16570 bp

                            GU945543

                            H13a1a1

                             

                            NA12878

                            female

                            child

                            16570 bp

                            GU945543

                            H13a1a1

                            CEPH1350

                            NA11831

                            male

                            grandfather

                            16569 bp

                            AY495174

                            H5

                            CEPH1341

                            NA06985

                            female

                            grandmother

                            16569 bp

                            AY882388

                            U4b

                            * The references for haplogroup are selected from top BLAST results.

                            Table 2

                            Counts of mtDNA from whole genome sequencing samples

                            Sample

                            Relation

                            SRA ID

                            of dataset

                            Runs

                            in dataset

                            Total

                            bases

                            mtDNA

                            bases

                            mtDNA

                            Ratio

                            mtDNA

                            count

                            NA18507

                            father

                            ERX009609

                            24

                            135.2G

                            249.8M

                            0.185%

                            646.84

                            NA18508

                            mother

                            ERX009610

                            24

                            133.2G

                            239.7M

                            0.180%

                            629.69

                            NA18506

                            child

                            ERX009608

                            24

                            132.3G

                            273.0M

                            0.206%

                            722.10

                            NA12891

                            father

                            ERX000172

                            35

                            1.538G

                            36.78M

                            0.239%

                            837.11

                            NA12892

                            mother

                            ERX000174

                            42

                            1.543G

                            31.58M

                            0.205%

                            716.25

                            NA12878

                            child

                            ERX000170

                            55

                            2.762G

                            71.92M

                            0.260%

                            911.38

                            NA11831

                            grandfather

                            SRX116265

                            1

                            4.15G

                            7.54M

                            0.182%

                            662.74

                            NA06985

                            grandmother

                            SRX116266

                            1

                            11.97G

                            24.80M

                            0.207%

                            755.92

                            For the counting results of these WGS samples (Additional file 2 and 3), ANOVA analysis revealed significant differences among the mtDNA counts within each family group: for YOR009, F(2,69) = 916.01, p = 2.06E-50 and for CEPH1463, F(2,169) = 58.75, p = 7.26632E-19. It showed that the offspring had 8%~23% more mtDNA than their parents in these samples. Although we did not investigate the possible artefacts caused by sequencing procedures, the results consistently demonstrated that there are more mtDNA sequences within younger persons' lymphocyte cells.

                            Conclusions

                            Many studies suggested that mitochondrial functions become defective as we age. Recent findings suggests that structural changes in mitochondria, including increased mitochondrial fragmentation and decreased mitochondrial fusion, are critical factors associated with mitochondrial dysfunction and cell death in aging and neurodegenerative diseases [13, 14]. Therefore, the proposed quantitative analysis of mtDNA can help to further elucidate the dynamics of mitochondrial diseases. It is expected that cost for sequencing personal whole genome will be less than $1000 in the near future. For the purpose of counting mitochondrial DNA, it only requires a low coverage of the whole genome and the cost may be further reduced to $50. The cost-effectiveness of the procedure makes the proposed method of counting mitochondrial DNA as a useful diagnostic tool to study aging and aging-related diseases for individuals.

                            Availability and requirements

                            In the MitoCounter software package, both the programs WgsMitoAssembler and WgsMitoCounter were implemented in C# with the .NET Framework which can be run on 64-bit Windows. The program WgsMitoCounter requires paired-end WGS datasets from Illumina sequencing platform. The MitoCounter software with a user manual is available at the Web site: http://​sourceforge.​net/​projects/​mitocounter/​

                            Declarations

                            Acknowledgements

                            We thank the members in the Bioinformatics Lab, NTU, for valuable discussions and useful insights. We thank C. S. Chiou, C. H. Chan and C. F. Chang for comments and discussion. We thank Flora Kao for editing and proofreading.

                            This article has been published as part of BMC Genomics Volume 13 Supplement 7, 2012: Eleventh International Conference on Bioinformatics (InCoB2012): Computational Biology. The full contents of the supplement are available online at http://​www.​biomedcentral.​com/​bmcgenomics/​supplements/​13/​S7.

                            Authors’ Affiliations

                            (1)
                            Department of Biomedical informatics, Asia University
                            (2)
                            Department of Computer Science and Information Engineering, Asia University
                            (3)
                            BCCDC Public Health Microbiology & Reference Laboratory
                            (4)
                            Department of Pathology and Laboratory Medicine
                            (5)
                            Department of Computer Science and Information Engineering, National Taiwan University
                            (6)
                            Graduate Institute of Clinical Medical Science, Chang Gung University
                            (7)
                            Center for Traditional Chinese Medicine, Chang Gung Memorial Hospital at Taoyuan, Chang Gung Medical Foundation
                            (8)
                            Institute of Food Science and Technology, National Taiwan University
                            (9)
                            Institute of Biotechnology, National Taiwan University
                            (10)
                            Department of Computer Science, National Tsing Hua University

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                            Copyright

                            © Chu et al.; licensee BioMed Central Ltd. 2012

                            This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://​creativecommons.​org/​licenses/​by/​2.​0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.