Skip to main content
NCBI home page
Molecular Genetics & Genomic Medicine logoLink to Molecular Genetics & Genomic Medicine
. 2013 Jun 26;1(3):174–186. doi: 10.1002/mgg3.20

Twin mitochondrial sequence analysis

Yosr Bouhlal 1, Selena Martinez 1, Henry Gong 1, Kevin Dumas 1, Joseph T C Shieh 1,2
PMCID: PMC3768015  NIHMSID: NIHMS484237  PMID: 24040623

Abstract

When applying genome-wide sequencing technologies to disease investigation, it is increasingly important to resolve sequence variation in regions of the genome that may have homologous sequences. The human mitochondrial genome challenges interpretation given the potential for heteroplasmy, somatic variation, and homologous nuclear mitochondrial sequences (numts). Identical twins share the same mitochondrial DNA (mtDNA) from early life, but whether the mitochondrial sequence remains similar is unclear. We compared an adult monozygotic twin pair using high-throughput sequencing and evaluated variants with primer extension and mitochondrial preenrichment. Thirty-seven variants were shared between the twin individuals, and the variants were verified on the original genomic DNA. These studies support highly identical genetic sequence in this case. Certain low-level variant calls were of high quality and homology to the mtDNA, and they were further evaluated. When we assessed calls in preenriched mtDNA templates, we found that these may represent numts, which can be differentiated from mtDNA variation. We conclude that twin identity extends to mtDNA, and it is critical to differentiate between numts and mtDNA in genome sequencing, particularly as significant heteroplasmy could influence genome interpretation. Further studies on mtDNA and numts will aid in understanding how variation occurs and persists.

Keywords: Genome, heteroplasmy, mitochondrial, primer extension, sequencing, twins

Introduction

Mitochondria play important roles in cellular function and human disease (Poulton et al. 2010; Koopman et al. 2012) and harbor DNA that encodes for tRNAs, rRNAs, and for proteins that function in energy production. Unlike the nuclear genome, the mitochondrial DNA (mtDNA) is present in many copies within the same cell. The mtDNA sequence reflects the maternally inherited mitochondrial genome and could harbor variation that arises somatically (Park and Larsson 2011). mtDNA may be subject to a higher mutation rate due to apparent decreased replication fidelity (Song et al. 2003; Lee and Johnson 2006); however, it is unclear whether mutations accumulate (Kujoth et al. 2007; Ameur et al. 2011) particularly in humans or whether variation is preexisting. Variation within the mtDNA sequence in an individual's cells can be homoplasmic (the same sequence) or heteroplasmic (coexisting different mtDNA sequences), and heteroplasmy levels can be related to disease (Lightowlers et al. 1997). Due to the segregation of the mtDNA during cell division, mtDNA variation could differ between cells as they divide, potentially due to selection. Such variation has been assessed in maternal transmission but the variation has not been examined in depth in adults, where multiple influences over time could affect sequence.

Deep sequencing is increasingly being utilized to detect genomic variation, and as more human genome sequencing is performed, it is clearly important to accurately detect mitochondrial variation and annotate variant function (Ruiz-Pesini et al. 2007; Calvo et al. 2012). However, mitochondrial variation can be misinterpreted given the presence of nuclear mitochondrial sequences (numts), which are highly similar nuclear fragments of the mitochondrial genome located on different chromosomes (Hazkani-Covo et al. 2010). Indeed, some studies report the difficulties in estimation of variation due to coamplification of numts with the mtDNA (Hirano et al. 1997; Parfait et al. 1998; Parr et al. 2006); however, a combination of high-throughput sequencing and validation could aid in thoroughly characterizing mitochondrial variation.

In our study, we evaluate and compare the presence of variants in mtDNA of an identical twin pair. Resulting from an early split of the developing embryo, twins harbor genomes that would be identical initially. Such twins represent a model for the analysis of mtDNA variation using a variety of methods, as any genetic difference observed between twins derived from the same zygote could represent somatic variation (Dumanski and Piotrowski 2012), whereas concordant variations found in twins would support genetic similarity (Bruder et al. 2008; Baranzini et al. 2010; Hallmayer et al. 2011; Jakobsen et al. 2011). Low levels of variation between twins could be detected in mitochondrial sequence. Here, we analyzed an adult twin pair with complementary methods to investigate variation in the mitochondrial genome sequence, and we determine the potential origin of ambiguous sequence variants from deep sequencing.

Materials and Methods

DNA isolation and molecular analysis

Informed consent was obtained under the guidelines of the institutional review board. Genomic DNA was isolated from the peripheral blood of a self-reported 21-year-old female adult identical twin pair, using standard methods (Qiagen, Valencia, CA). The twins grew up together and lived in a similar environment.

Zygosity of the twins was determined by genotyping highly polymorphic DNA loci using the PowerPlex® short tandem repeat kit (Promega, Sunnyvale, CA). Amplified fragments were detected using the ABI (Life Technologies, Grand Island, NY) 3730xl DNA Analyzer. Data were analyzed with GeneMapper software (Life Technologies).

Paired-end Illumina sequencing

DNA libraries were generated using Illumina Paired-End (Illumina Inc., San Diego, CA) library preparation kit according to the manufacturer's instructions. Libraries were quantified by qPCR using a KAPA (Kapa Biosystems Inc., Woburn, MA) library quantification kit and assessed on an Agilent Technologies (Santa Clara, CA) 2100 Bioanalyzer using a High Sensitivity DNA chip. Following an automatic cluster generation, paired-end sequencing was performed on Illumina HiSeq 2000 using the V1 flow cell Hiseq system version (Illumina Inc.). The libraries were subject to a second sequencing run using the V3 flow cell HiSeq system version.

Analysis of high-throughput sequencing data

To check read quality, raw sequencing data were analyzed using the next generation tool Fastqc (http://www.bioinformatics.bbsrc.ac.uk/projects/fastqc/) executed in an internal Galaxy server (Giardine et al. 2005) allowing large data file upload and processing. Sequencing reads were mapped to the hg19 reference genome (NCBI GRCh37; AF347015.1 [16571 bp]/NC_012920.1 [16569 bp]) using the Bowtie alignment package (0.12.7). Alignment was performed using normal and high stringency parameters for each of the two runs. Mapped reads were made available in BAM file format using SAM tools. All BAM files were visualized and analyzed using the Integrative Genomic Viewer IGV 2.1 (Robinson et al. 2011). Mitochondrial sequence reads, produced from the second high stringency run, were filtered from the whole genome data and converted to small BAM files and analyzed with MITO-BAM annotator online tool (Zhidkov et al. 2011). Using “Generate pileup” from SAM tools, we created from the BAM files pileup files for the twins. The generated pileup files were used to call Single Nucleotide Polymorphisms (SNPs) via the “Filter pileup” tool. For each potential variant, we set a quality threshold Q ≥ 30 where Q is the Phred quality score, and asked the program to call any SNP present at least in one read. Using IGV tool, we manually verified the presence and the Q value of all the called SNPs. In addition, we used the MITOBAM annotator tool to detect and annotate any variant also present in at least one read and with a Q ≥ 30. We also verified all SNPs manually applying more stringent filters by not considering potential variants surrounded by more than 10 mismatches within the same read, and mismatches localized within the five first or last bases of the same read. Potential variants were identified as having RefSNP (rs) numbers, while some variants were only found in Mitomap mtDNA Sequence Polymorphism database (http://www.mitomap.org/MITOMAP), and others were previously not reported.

Low-level variant evaluation

Variants detected at low frequency (<0.01%) were examined using the fluorescent primer extension assay SnapShot (Applied Biosystems, Life Technologies). We designed custom multiplex reactions according to the manufacturer's instruction and pooled up to five templates per reaction for scalability. As a first step we designed template primers and one extension primer for each variant and performed primer extension on 33 candidates. In order to verify if the detected variant nucleotides were from the mitochondrial or the nuclear genome, we amplified the entire mtDNA in two large fragments of more than 8 kb with an overlap of 183 bp (Voets et al. 2011). These fragments were used as templates in primer extension reactions using the same extension primers used in the first step. Postextension treatment was performed using 1 unit of calf intestinal phosphatase. Electrophoresis was run on an ABI 3730xl DNA Analyzer. Data were analyzed using PeakScanner software (Life Technologies).

Results

Molecular analysis of zygosity

Before assessing mitochondrial variation using high-throughput sequencing, we first verified zygosity of the self-reported identical adult twin pair studied. Short tandem repeat analysis was performed on genomic DNA from the blood of the twins. They shared identical alleles at 15 highly variable loci on their chromosomes, indicating monozygosity with high confidence (Table 1).

Table 1.

Short Tandem Repeat (STR) marker analysis for the twin pair and a DNA control, showing that twin individuals share the same alleles at 16 different loci across the genome confirming their monozygosity at >0.99999 confidence

Alleles
Locus Twin A Twin B Control
AMEL XX XX XX
CSF1PO 12 12 12 12 10 12
D13S317 10 11 10 11 11 11
D16S539 10 10 10 10 11 12
D18S51 14 21 14 21 15 19
D21S11 31 31 31 31 30 30
D3S1358 17 17 17 17 14 15
D5S818 11 11 11 11 11 11
D7S820 10 11 10 11 10 11
D8S1179 13 13 13 13 13 13
FGA 25 27 25 27 23 24
Penta_D 9 10 9 10 12 12
Penta_E 12 14 12 14 12 13
TH01 9.3 9.3 9.3 9.3 8 9.3
TPOX 11 11 11 11 8 8
vWA 17 17 17 17 17 18
Open in a new tab

Mitochondrial sequence analysis by high-throughput sequencing

To assess twin mitochondrial genome sequencing performance in the context of genome sequencing without prior sequence capture, we prepared paired-end whole genome sequencing libraries and used the Illumina HiSeq platform, which yielded over 100 million reads for each twin individual (Run 1, Table 2). A second sequencing run was performed on a newer version of flow cell (Run 2, Table 2), which greatly increased the number of reads by producing 275 million reads for twin A and over 314 million reads for twin B. We aligned these reads to the hg19 reference genome using Bowtie. Initial alignment using default parameters resulted in alignment of ∼47% of the reads (Alignment 1, Table 2), and this was improved to over 90% of reads aligned (Alignment 2, Table 2) using the “-y/–tryhard” mode, although the alignment performed more slowly. On the mitochondrial genome the mean depth of coverage was 1151 for the twin A and 1279 for the twin B (Fig. 1). We verified variants using the Integrated Genomics Viewer manually and detected 37 high-confidence variants (>99% of the reads), and all these were common to both twin A and B (Fig. 1). These variants included 34 homoplasmic variants and three nearly homoplasmic variants (Fig. 2A). Among these 37 variants, 27 were distributed on 12 genes throughout the mitochondrial genome (Fig. 2C), and 10 were localized at the hypervariable segments HV1 (16024–16383) and HV2 (57–372), which had variable coverage even after remapping to account for the circular mitochondrial genome. Although six variants were nonsynonymous, all were at positions of previously reported mtDNA polymorphism (Table 3).

Table 2.

Sequencing reads and mapping efficiency

Twin A Twin B
Sequencing run 1: V1 flow cell HiSeq system
 Total number of reads 125,989,576 101,559,160
 Stringency Alignment 1 Alignment 2 Alignment 1 Alignment 2
 Number of mapped reads 59,402,944 113,836,436 47,883,234 91,638,246
 Percentage of mapped reads 47.15 90.35 47.15 90.23
Sequencing run 2: V3 flow cell HiSeq system
 Total number of reads 275,033,292 314,499,024
 Stringency Alignment 1 Alignment 2 Alignment 1 Alignment 2
 Number of mapped reads 130,156,770 249,089,720 148,617,794 284,273,752
 Percentage of mapped reads 47.32 90.57 47.26 90.39
Open in a new tab

Figure 1.

Figure 1

Open in a new tab

Mitochondrial DNA sequencing coverage and variant map for twin A (upper plot) and twin B (lower plot). The x-axis represents the nucleotide position on the mitochondrial genome and the y-axis shows the number of reads (depth of coverage) for each nucleotide position (maximum of 1084 reads for twin A and 1395 reads for twin B). Homoplasmic variants (colored vertical lines at specific genomic locations) in both twin A and twin B are concordant. Each variant is colored according to the base type (red for T, green for A, blue for C, and brown for G) compared to the hg19 reference base.

Figure 2.

Figure 2

Open in a new tab

High-throughput called variants common to both twins. (A) Homoplasmic/nearly homoplasmic variants detected for twin A and twin B are concordant. The y-axis represents the ratio of variant to reference base. The x-axis represents the alignment position of the variant detected. (B) Low-level heteroplasmic variant calls detected in both twin A and twin B. (C) Distribution of novel and reported mitochondrial sequence variants detected in both twins A and B. The y-axis represents the number of variants. The x-axis represents the mitochondrial genes. Bars represent reported (light) and unreported variants (dark).

Table 3.

Positions and predicted effect of homoplasmic and nearly homoplasmic variants common to both twins A and B (n = 37 variants)

mtDNA position Gene Reference allele Variant Mutation type First AA Second AA Variant identifier Coverage twin A Variant ratio twin A (%) Coverage twin B Variant ratio twin B (%)
150 MT-DLOOP T C Transversion NA NA rs62581312 30 100 28 100
153 MT-DLOOP A G Transition NA NA RP 34 100 28 100
195 MT-DLOOP C T Transversion NA NA rs2857291 8 100 9 100
408 MT-DLOOP A T Transversion NA NA rs28412942 372 100 382 100
489 MT-DLOOP T C Transversion NA NA rs28625645 440 100 570 100
1888 MT-RNR2 G A Transversion NA NA rs28358577 860 100 1070 100
2353 MT-RNR2 C T Transversion NA NA rs28358579 854 100 1035 100
2483 MT-RNR2 C T Transversion NA NA rs80056772 881 100 1036 100
3552 MT-ND1 T A Transversion A A rs28358587 713 100 907 100
4715 MT-ND2 A G Transition G G RP 854 100 898 99.9
7196 MT-CO1 C A Transversion L L RP 900 99.5 1127 100
8584 MT-ATP6 G A Transversion A T rs55728079 733 100 963 100
9377 MT-CO3 G A Transition W W rs28380140 684 100 855 100
9545 MT-CO3 A G Transition G G RP 803 100 944 100
9962 MT-CO3 G A Transition L L RP 929 100 1086 100
10400 MT-ND3 C T Transversion T T rs28358278 822 100 1064 100
10819 MT-ND4 G A Transition K K rs28358283 952 100 1090 100
11017 MT-ND4 C T Transversion S S rs28594904 858 100 975 100
11722 MT-ND4 C T Transversion H Y rs28471078 795 100 972 100
11914 MT-ND4 G A Transversion T T rs2853496 951 100 1169 100
12092 MT-ND4 C T Transversion L F RP 912 100 1097 100
12414 MT-ND5 T C Transition P P RP 879 100 965 99.9
12850 MT-ND5 G A Transversion V I rs28705385 881 100 1251 100
13263 MT-ND5 A G Transition Q Q rs28359175 852 100 1098 100
14212 MT-ND6 C T Transversion V V rs28357672 819 100 975 100
14318 MT-ND6 T C Transversion N S rs28357675 847 100 1042 100
14783 MT-CYB T C Transversion L1 L2 rs28357680 804 100 981 100
14905 MT-CYB A G Transversion M M rs28357682 770 100 1084 100
15043 MT-CYB G A Transversion G G rs56038008 873 100 1128 100
15487 MT-CYB A T Transversion P P rs28357370 870 100 1007 100
15930 MT-TT G A Transversion NA NA rs41441949 836 100 947 100
15932 MT-TT C C Transition NA NA rs28601282 859 100 1013 100
16153 MT-DLOOP G A Transversion NA NA rs2853512 189 100 273 100
16172 MT-DLOOP C T Transversion NA NA rs2853817 80 100 100 100
16190 MT-DLOOP C T Transversion NA NA RP 79 100 82 100
16299 MT-DLOOP T C Transversion NA NA RP 251 100 221 100
16520 MT-DLOOP C T Transversion NA NA rs3937033 47 100 101 100
Open in a new tab

RP, reported polymorphism; AA, First amino acid is reference while Second amino acid is the effect of the variant.

Low-level variation analysis

We noted the presence of potential single-base sequence differences present at very low level, one to three reads per locus, excluding duplicate reads. As these differences could represent a number of possibilities including sequencing errors, read mapping ambiguity due to numts, or true mitochondrial variation, we investigated these further. In analyzing potential low-level variants in the mitochondrial genome, we used stringent filters: a strict nucleotide quality score (Phred score Q ≥ 30), the presence of the variant in at least one sequencing read, and variant surrounded by nonvariant sequence (potential variant is at least five bases from a read end, and lack of other sequence differences in same read).

We detected many single-base candidate low-level variants in the twins (Tables S1, S2, and S3); the presence of more low-level potential variants was correlated with higher coverage. Importantly, we performed additional validation on the original genomic DNA to rule out error introduced by library preparation and complementary studies to evaluate their origin. We focused on 76 low-level variants that were shared between twins (Fig. 2B) given their potentially inherited nature. The low-level variant calls were distributed over the whole mitochondrial genome including the three genes coding for the respiratory chain complex IV and the six genes coding for the respiratory chain complex I (Fig. 2C). Twenty of these variants were previously reported (Mitomap database). From the 56 possibly novel variants, 17 mapped to rRNA or tRNA genes (10 on the RNR2 gene, one on the RNR1 gene, and six distributed on five tRNA genes). The other 39 variants were in coding regions of mitochondrial genes, and 30 were nonsynonymous (Table 4). We designed primers (primer sequences available upon request) to amplify the surrounding regions and primers for fluorescent primer extension for detection of these 30 as they could have functional consequence if confirmed. We also examined two low-level variants called in two different tRNA genes that have been implicated in potential disease, m.12258C>A in diabetes mellitus and Deafness syndrome, and m.3275C>A in Leber's hereditary optic neuropathy (Lynn et al. 1998; Garcia-Lozano et al. 2000). We also tested a variant that was detected in only one twin (twin B, m.4456C>T) for comparison.

Table 4.

Novel and nonsynonymous variants common to both twins A and B (n = 30 variants)

mtDNA position Gene Major allele Twin A Twin B
Minor allele Minor allele ratio (%) First AA Second AA Mutation type Minor allele Minor allele ratio (%) First AA Second AA Mutation type
3455 MT-ND1 C T 0.11 A V Transition A 0.13 A D Transversion
3668 MT-ND1 G A 0.12 W STOP Transition A 0.16 W STOP Transition
3805 MT-ND1 A G 0.10 T A Transition T 0.12 T S Transversion
4971 MT-ND2 G T 0.10 G C Transversion T 0.12 G C Transversion
4998 MT-ND2 A T 0.09 K STOP Transversion G 0.12 K E Transition
5014 MT-ND2 C G 0.09 S C Transversion A 0.12 S Y Transversion
5055 MT-ND2 T C 0.35 Y H Transition C 0.13 Y H Transition
5107 MT-ND2 C T 0.12 T I Transition A 0.13 T N Transversion
5470 MT-ND2 C T 0.11 H M Transition A 0.12 T K Transversion
5481 MT-ND2 C A 0.10 P T Transversion A 0.12 P T Transversion
5906 MT-CO1 G A 0.11 M M Transition T 0.14 M M Transition
6569 MT-CO1 C A 0.22 P P Transversion A 0.14 R S Transversion
6849 MT-CO1 A C 0.08 T P Transversion T 0.14 T S Transversion
6935 MT-CO1 C T 0.08 H Y Transition T/A 0.10 H N Transversion
6998 MT-CO1 C A 0.08 I M Transversion A 0.10 I M Transversion
7131 MT-CO1 G T 0.09 A S Transition T 0.11 A S Transition
7219 MT-CO1 G T 0.18 R L Transversion T 0.11 R L Transversion
7610 MT-CO2 C A 0.13 L M Transversion A 0.14 L M Transversion
7978 MT-CO2 G T 0.09 G V Transition T 0.12 G V Transition
8048 MT-CO2 A T 0.09 T S Transversion G 0.12 T A Transversion
8085 MT-CO2 C G 0.10 T STOP Transversion A 0.12 T K Transversion
8156 MT-CO2 G C 0.28 V L Transversion C 0.33 V L Transversion
8243 MT-CO2 G T 0.10 E STOP Transversion A/T 0.12 E K/STOP Transition
9744 MT-CO3 G T 0.09 E STOP Transversion T 0.22 E STOP Transversion
10867 MT-ND4 C A 0.09 I M Transversion G 0.11 I M Transversion
11351 MT-ND4 G T 0.09 A S Transversion T 0.11 A S Transversion
11638 MT-ND4 C T 0.11 H Y Transition A 0.11 T N Transversion
11941 MT-ND4 T A 0.08 L Q Transversion G 0.10 L Q Transversion
13725 MT-ND5 C A 0.14 F L Transversion A 0.15 F L Transversion
15266 MT-CYB A C 0.10 T P Transversion G 0.17 T A Transversion
Open in a new tab

AA, amino acid effect of variant.

Using the original genomic DNA, we found the major reference allele in 33/33 positions, which confirms the results from the high-throughput sequencing, and the low-level variant was seen in 9/33 (27.3%) (Table 5). An example is shown in Figure 3. In all cases, primer extension revealed concordant results between the twin individuals with or without variant sequence. Where low-level variants were not confirmed, these could represent sequencing false positives or variant detection could be limited by sensitivity even though the seven primer extension assays detected variants seen in <1% of high-throughput sequencing reads.

Table 5.

Comparison of SNP detection using the HiSeq and the primer extension techniques

mtDNA position Gene Primer extension HiSeq HiSeq minor allele twin A HiSeq minor allele twin B
Major allele Minor allele Major allele Run2 HS Run2 MS Run1 HS Run1 MS Run2 HS Run2 MS Run1 HS Run1 MS
3275 MT-TL1 C – C T A – – G G – –
3455 MT-ND1 C A C A – – – T T – –
3668 MT-ND1 G – G A A – – A A – –
3805 MT-ND1 A – A T T – – G G – –
4456 MT-TM C T C C T – – T – – –
4971 MT-ND2 G – G T T – – T T – –
4998 MT-ND2 A C A G – C – T T C –
5014 MT-ND2 C A C A A A – G A – –
5055 MT-ND2 T – T C C – – C C – –
5107 MT-ND2 C – C A A – – T A – –
5470 MT-ND2 C – C A – – – T – – –
5481 MT-ND2 C – C A – – – – A – A
5906 MT-CO1 G – G T T – – A – T T
6569 MT-CO2 C A C A A – – A A – –
6849 MT-CO1 A – A T T/C C C C T C C
6935 MT-CO1 C A C T/A T T – T T T –
6998 MT-CO1 C T C A A – T A – – –
7131 MT-CO1 G – G T T – – T T – –
7219 MT-CO1 G – G T T A A T T T T
7610 MT-CO2 C – C A – T T A – – –
7978 MT-CO2 G C G T C – – T C – –
8048 MT-CO2 A – A G T – – T T – –
8085 MT-CO2 C – C A A – – G – – –
8156 MT-CO2 G – G C C C C C C C C
8243 MT-CO2 G – G A/T T – – T T T/C T/C
9744 MT-CO3 G – G T T T T T T – –
10867 MT-ND4 C C G – A – A – – –
12258 MT-TS2 C – C A – A A A A – –
11351 MT-ND4 G – G T T C – T T – T
11638 MT-ND4 C – C A A A – T T T T
11941 MT-ND4 T A T G – G G A A – –
13725 MT-ND5 C – C A – – – A – A –
15266 MT-CYB A – A G G G G C C C C
Open in a new tab

HiSeq, high-throughput sequencing; MS, aligned with moderate stringency; HS, aligned with high stringency. Dash indicates minor allele was not detected.

Figure 3.

Figure 3

Open in a new tab

Electropherograms showing primer extension assay for two positions from either twin A (left) or twin B (right). The blue and black peaks correspond to the major alleles (G at position 9606, C at 6998), while the small red peaks indicate the presence of a T allele at position 6998 at a very low level.

Given these findings, for the seven variants confirmed by primer extension, we considered the possibility that nuclear mtDNA fragments (numts) could be generating apparent mitochondrial sequence variants. To test this possibility, we generated hemigenomes of mtDNA using high-fidelity long PCR and used these as templates for the primer extension assays, as this would eliminate numts (Li et al. 2012). Accordingly, we did not find low-level variants from the preamplified mitochondrial hemigenomes (Fig. 4). The absence of low-level variant detection from the hemigenomes could suggest that they are not present in the mtDNA, but we cannot exclude the possibility that these variants are present in the mtDNA at such a low level that they were not amplified in the hemigenome reaction.

Figure 4.

Figure 4

Open in a new tab

Electropherograms showing the genotype detected in each twin by primer extension on genomic DNA (A) or mitochondrial hemigenome templates (B). The black peak corresponds to the reference allele “C” at position 6998 while the red peak indicates the presence of a “T” allele at a low level. The low-level variant detected using primer extension from genomic DNA (B) was absent in assay with the mitochondrial hemigenome (B) suggesting the T allele signal may result from an extramitochondrial source (numt).

In order to determine if variants reflected numt sequences (from other regions of the genome), we directly compared variant-containing reads to the genome (Data S1). We found homologies to regions of chromosomes 1 and 17, where numts are known to exist. Reads containing position 4457 or 6569, for example, have complete read homology to corresponding regions on chromosome 1 and the primer extension could coamplify mitochondrial and nuclear DNA, explaining the apparent variation in those positions, whereas primer extension on mitochondrial hemigenome only detected the mitochondrial genome. There were, however, several low-level variants, 3455 for example, where sequencing read and primer extension assay precise homology to the nuclear genome were both not present, suggesting these variants may be present in mtDNA albeit at low levels that may not be detectable by analysis of long mitochondrial fragments.

To detect numts that could be mapping to the mitochondrial genome and producing low-level apparent variants, we performed read mapping using whole genome reads from the twins to the mtDNA only as reference. Depth of coverage increased up to 600 reads higher than the initial mapping using the whole hg19 genomic reference, and almost all of these increased the only allele or major allele call. We examined the low-level variants previously analyzed using the initial mapping data and the primer extension assay in table 5. Many of low-level variants did not show any increase in reads and these could be due to errors. However, the number of variant reads increased in 7 of 33 positions in twin A and B (Fig. 5). This increase is likely due to the presence of numts that now were solely mapped to the mtDNA. Some of these variants were present in reads that show high similarity with regions in the assembled nuclear genome. For example, reads with the variant at position 6569 are highly similar to chr1:567077-567211 (Data S1). The number of reads containing the variant allele at this specific position increased for both twins after read mapping to mtDNA alone, suggesting that this variant is due to a numt.

Figure 5.

Figure 5

Open in a new tab

Mapping reads to test for nuclear mitochondrial sequences. Resulting low-level variant reads for twins when reads are mapped to the hg 19 reference genome (red dots) or to the mtDNA genome only (blue circles). The y-axis represents the number of reads with the variant allele for each position on the mitochondrial sequence, x-axis.

These findings support mitochondrial similarity in twins and demonstrate the importance of distinguishing mitochondrial genome from homologous nuclear DNA using a combination of methods.

Discussion

In this study, we analyzed mitochondrial sequence variants in a pair of adult twins using high-throughput sequencing and validation. These data also allowed us to ask whether these two independent individuals harbored mitochondrial somatic variation in their blood. Our results revealed identical homoplasmic variants in mtDNA sequence from blood-derived genomic DNA, which lend support to the idea that monozygotic twins may be highly similar in their mitochondrial genomic sequence, as they seem to be in nuclear genomic sequence (Baranzini et al. 2010; Jakobsen et al. 2011). The presence of shared variants in twins suggests that the variants were likely present since conception (Avital et al. 2012). As mtDNA variation impacts many biological processes and can affect disease, it presents important challenges to diagnostic capabilities and could inform us about mutation and selection. Any intertwin differences in variant accumulation may depend on individuals studied, tissues examined or on age of subjects.

Heteroplasmy may change with age due to the accumulation of mtDNA variation Wallace 2001 Krishnan et al. 2007; Larsson 2010), Lee and Wei 2012; (Bratic and Larsson 2013, but this may depend on several factors. Mitochondrial heteroplasmy in blood samples was increased in older aged individuals, with no obvious mutation type pattern (Sondheimer et al. 2011). Targeted studies of the mtDNA have demonstrated variation in grandmothers (do Rosário Marinho et al. 2011). Interestingly, studies that examined murine liver did not detect differences in mutation load with age (Ameur et al. 2011). In our analysis of two adult twin blood samples, we did not find accumulated mtDNA mutation above the 1% level. In addition, as our twin pair were young adults, it will be interesting to examine variants at different ages and environments. If numt and sequencing errors can be excluded, these low-level variants could possibly result from replication errors.

Our finding also revealed that low-level variation detected in the mitochondrial sequence from whole genome high-throughput sequencing can reflect homologous mitochondrial sequences. Low-level variants could represent sequencing errors, low frequency variation, numts, or a combination of these possibilities. The low-read calls considered in our studies only represented the top ∼0.5% of nucleotide quality score. Only with secondary sequencing assays do we see that the variants may not present in mitochondrial genome-enriched templates. Although we verified some of these nucleotides using analysis of original genomic DNA samples, some of these low-level called variants in mitochondrial sequence could not be detected by a secondary method. These variants could represent sequencing error or could reflect differences in sensitivity of primer extension. High-throughput sequencing errors that can be due to the sequencing signal dephasing that occur during the sequencing cycling (Metzker 2010) and might be due to DNA back folding (Allhoff et al. 2013). Additional studies are needed to develop algorithms to filter these out prior to variant call.

The detection of possible numts in our sequencing studies provides insights into the dynamic nature of the mitochondrial sequence over evolutionary time; however, it also poses challenges in mitochondrial genome interpretation. Our findings support that initial mtDNA enrichment can identify mtDNA signals separately from potential numts, and techniques such as primer extension may be considered as an adjunct to high-throughput sequencing.

In our analyses, we found that many of the low-level variants detected by sequencing, even those mapped with high homology to the mitochondrial sequence, were not detected by primer extension using mtDNA hemigenomes. In addition, the mapping parameters could affect the low-level variant calls, and these factors could potentially influence the rate of false positives. If low-level mtDNA variants exist, they may be present at very low levels of heteroplasmy (Payne et al. 2012). Although low-level variation may not substantially impact the interpretation of clinical laboratory results, they may be helpful for understanding variation origin and could exist at different frequencies in different tissues (Payne et al. 2012). Given selective pressure on a specific gene, these low-level variants could potentially accumulate over time. While low-level variants resulting from sequencing may represent mapping ambiguity or result from presequence processing of DNA, identification of the exact same variants in two adult monozygotic twins by primer extension from the original DNA samples suggests these variants could exist. If true variants arise that differ between monozygotic twins, these could be somatic mutations that accumulate due to the low replication fidelity of the mtDNA polymerase (Lee and Johnson 2006). While the probability of the human mtDNA polymerase to misincorporate bases is estimated to be ∼5.6 × 10−7 to 2.8 × 10−8 errors per incorporated base (Lee and Johnson 2006), overall somatic mutation rates in mitochondria are not well defined. Mitochondrial mutation rates using population and phylogenetic data have found ∼1.66 × 10−8 ± 1.48 × 10−9 substitutions per nucleotide per year (Soares et al. 2009). In our studies of adult twins, we found that the mitochondrial sequence was largely identical. For the variants that appeared different between twins, we calculated 1.2–1.6 × 10−6 events observed per site per year. If 1–1.4% of these calls were somatic events, the observed rate per individual would approximate the predicted error rate. A recent study reported an intratwin pair difference in monozygotic twins suggesting a posttwinning de novo copy number variant event (Ehli et al. 2012). Several other studies have reported CNVs in twin studies (Bruder et al. 2008; Maiti et al. 2011; Sasaki et al. 2011; Breckpot et al. 2012), and so there may be differences in somatic Copy Number Variant and SNP occurrence. Further studies should address the possibility of somatic events and selection.

Current read mapping to the genome insufficiently accounts for the homology between numts and the mtDNA. Variants from whole genome sequencing may be found in the mitochondrial genome or the corresponding numt, which could alter estimates of heteroplasmy. This is an important consideration in assays that measure mtDNA heteroplasmy especially for mitochondrial disorder diagnosis, where a threshold of variation may need to be surpassed to express disease. Our findings are salient given the increased application of genome sequencing and efforts to identify mosaic variation and to distinguish regions of genome homology. We suggest that combined methods of high-throughput sequencing, techniques like primer extension, and mtDNA enrichment may be useful in assessing mtDNA variants and numts.

Finally, we conclude the following: (1) these adult twins had highly similar mtDNA sequence from blood. (2) We did not find differential somatic heteroplasmy >1% to suggest accumulating mutation over time, although more twin pairs and tissues should be tested in the future. (3) Low-level variants, only some of which are numts, are detected by high-throughput sequencing and can be confirmed by primer extension.

Acknowledgments

This work was supported by the National Institutes of Health (Grant Number HL092970 to J. S.) and the National Center for Research Resources, the National Center for Advancing Translational Sciences, and the Office of the Director, National Institutes of Health (UCSF-CTSI Grant Number KL2 RR024130). We thank the contributions of the UCSF genomics core in the Institute for Human Genetics.

Conflict of Interest

None declared.

Supporting Information

Additional Supporting Information may be found in the online version of this article:

Data S1. Summary of Blast, BLAT, and nuclear mitochondrial sequences (numts) analysis applied to the reads and extension PCR template fragment of eight variants.

Table S1. List of annotated Low level variants for Twin A.

Table S2. List of annotated Low level variants for Twin B.

Table S3. Sequencing SNP calling by Phred quality score.

mgg30001-0174-SD1.xlsx (97.6KB, xlsx)
mgg30001-0174-SD2.xlsx (2.7MB, xlsx)
mgg30001-0174-SD3.docx (1.6MB, docx)
mgg30001-0174-SD4.docx (14.8KB, docx)

References

  1. Allhoff M, Schönhuth A, Martin M, Costa IG, Rahmann S, Marschall T. Discovering motifs that induce sequencing errors. BMC Bioinform. 2013;14:S1. doi: 10.1186/1471-2105-14-S5-S1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Ameur A, Stewart JB, Freyer C, Hagström E, Ingman M, Gyllensten U. Ultra-deep sequencing of mouse mitochondrial DNA: mutational patterns and their origins. PLoS Genet. 2011;7:e1002028. doi: 10.1371/journal.pgen.1002028. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Avital G, Buchshtav M, Zhidkov I, Tuval Feder J, Dadon S, Rubin E, et al. Mitochondrial DNA heteroplasmy in diabetes and normal adults: role of acquired and inherited mutational patterns in twins. Hum. Mol. Genet. 2012;21:4214–4224. doi: 10.1093/hmg/dds245. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Baranzini SE, Mudge J, Khankhanian JC, van Velkinburgh P, Khrebtukova I, Miller NA, et al. Genome, epigenome and RNA sequences of monozygotic twins discordant for multiple sclerosis. Nature. 2010;464:1351–1356. doi: 10.1038/nature08990. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Bratic A, Larsson NG. The role of mitochondria in aging. J Clin Invest. 2013;123:951–957. doi: 10.1172/JCI64125. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Breckpot J, Thienpont B, Gewillig M, Allegaert K, Vermeesch JR, Devriendt K. Differences in copy number variation between discordant monozygotic twins as a model for exploring chromosomal mosaicism in congenital heart defects. Mol. Syndromol. 2012;2:81–87. doi: 10.1159/000335284. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Bruder CE, Piotrowski A, Gijsbers AA, Andersson R, Erickson S, Diaz de Ståhl T, et al. Phenotypically concordant and discordant monozygotic twins display different DNA copy-number-variation profiles. Am. J. Hum. Genet. 2008;82:763–771. doi: 10.1016/j.ajhg.2007.12.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Calvo SE, Compton AG, Hershman SG, Lim SC, Lieber DS, Tucker EJ, et al. Molecular diagnosis of infantile mitochondrial disease with targeted next-generation sequencing. Sci. Transl. Med. 2012;4:118ra10. doi: 10.1126/scitranslmed.3003310. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Dumanski JP, Piotrowski A. Structural genetic variation in the context of somatic mosaicism. Methods Mol. Biol. 2012;838:249–272. doi: 10.1007/978-1-61779-507-7_12. [DOI] [PubMed] [Google Scholar]
  10. Ehli EA, Abdellaoui A, Hu Y, Hottenga JJ, Kattenberg M, van Beijsterveldt T, et al. De novo and inherited CNVs in MZ twin pairs selected for discordance and concordance on attention problems. Eur. J. Hum. Genet. 2012;20:1037–1043. doi: 10.1038/ejhg.2012.49. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Garcia-Lozano JR, Aguilera I, Bautista J, Nunez-Roldan A. A new mitochondrial DNA mutation in the tRNA leucine 1 gene (C3275A) in a patient with Leber's hereditary optic neuropathy. Hum. Mutat. 2000;15:120–121. doi: 10.1002/(SICI)1098-1004(200001)15:1<120::AID-HUMU33>3.0.CO;2-8. [DOI] [PubMed] [Google Scholar]
  12. Giardine B, Riemer C, Hardison RC, Burhans R, Elnitski L, Shah P, et al. Galaxy: a platform for interactive large-scale genome analysis. Genome Res. 2005;15:1451–1455. doi: 10.1101/gr.4086505. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Hallmayer J, Cleveland S, Torres A, Phillips J, Cohen B, Torigoe T, et al. Genetic heritability and shared environmental factors among twin pairs with autism. Arch. Gen. Psychiatry. 2011;68:1095–1102. doi: 10.1001/archgenpsychiatry.2011.76. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Hazkani-Covo E, Zeller RM, Martin W. Molecular poltergeists: mitochondrial DNA copies (numts) in sequenced nuclear genomes. PLoS Genet. 2010;6:e1000834. doi: 10.1371/journal.pgen.1000834. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Hirano M, Shtilbans A, Mayeux R, Davidson MM, DiMauro S, Knowles JA, et al. Apparent mtDNA heteroplasmy in Alzheimer's disease patients and in normals due to PCR amplification of nucleus-embedded mtDNA pseudogenes. Proc. Natl Acad. Sci. USA. 1997;94:14894–14899. doi: 10.1073/pnas.94.26.14894. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Jakobsen LP, Bugge M, Ullmann R, Schjerling CK, Borup R, Hansen L, et al. 500K SNP array analyses in blood and saliva showed no differences in a pair of monozygotic twins discordant for cleft lip. Am. J. Med. Genet. A. 2011;155A:652–655. doi: 10.1002/ajmg.a.33855. [DOI] [PubMed] [Google Scholar]
  17. Koopman WJ, Willems PH, Smeitink JA. Monogenic mitochondrial disorders. N. Engl. J. Med. 2012;366:1132–1141. doi: 10.1056/NEJMra1012478. [DOI] [PubMed] [Google Scholar]
  18. Krishnan KJ, Greaves LC, Reeve AK, Turnbull D. The ageing mitochondrial genome. Nucleic Acids Res. 2007;35:7399–7405. doi: 10.1093/nar/gkm635. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Kujoth GC, Bradshaw PC, Haroon S, Prolla TA. The role of mitochondrial DNA mutations in mammalian aging. PLoS Genet. 2007;3:e24. doi: 10.1371/journal.pgen.0030024. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Larsson NG. Somatic mitochondrial DNA mutations in mammalian aging. Annu. Rev. Biochem. 2010;79:683–706. doi: 10.1146/annurev-biochem-060408-093701. [DOI] [PubMed] [Google Scholar]
  21. Lee HR, Johnson KA. Fidelity of the human mitochondrial DNA polymerase. J. Biol. Chem. 2006;281:36236–36240. doi: 10.1074/jbc.M607964200. [DOI] [PubMed] [Google Scholar]
  22. Lee HC, Wei YH. Mitochondria and aging. Adv. Exp. Med. Biol. 2012;942:311–327. doi: 10.1007/978-94-007-2869-1_14. [DOI] [PubMed] [Google Scholar]
  23. Li M, Schroeder R, Ko A, Stoneking M. Fidelity of capture-enrichment for mtDNA genome sequencing: influence of NUMTs. Nucleic Acids Res. 2012;40:e137. doi: 10.1093/nar/gks499. doi: 10.1093/nar/gks499. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Lightowlers RN, Chinnery PF, Turnbull DM, Howell N. Mammalian mitochondrial genetics: heredity, heteroplasmy and disease. Trends Genet. 1997;13:450–455. doi: 10.1016/s0168-9525(97)01266-3. [DOI] [PubMed] [Google Scholar]
  25. Lynn S, Wardell T, Johnson MA, Chinnery PF, Daly ME, Walker M, et al. Mitochondrial diabetes: investigation and identification of a novel mutation. Diabetes. 1998;47:1800–1802. doi: 10.2337/diabetes.47.11.1800. [DOI] [PubMed] [Google Scholar]
  26. Maiti S, Kumar KH, Castellani CA, O'Reilly R, Singh SM. Ontogenetic de novo copy number variations (CNVs) as a source of genetic individuality: studies on two families with MZD twins for schizophrenia. PLoS ONE. 2011;6:e17125. doi: 10.1371/journal.pone.0017125. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Metzker ML. Sequencing technologies – the next generation. Nat. Rev. Genet. 2010;11:31–46. doi: 10.1038/nrg2626. [DOI] [PubMed] [Google Scholar]
  28. Parfait B, Rustin P, Munnich A, Rötig A. Co-amplification of nuclear pseudogenes and assessment of heteroplasmy of mitochondrial DNA mutations. Biochem. Biophys. Res. Commun. 1998;247:57–59. doi: 10.1006/bbrc.1998.8666. [DOI] [PubMed] [Google Scholar]
  29. Park CB, Larsson NG. Mitochondrial DNA mutations in disease and aging. J. Cell Biol. 2011;193:809–818. doi: 10.1083/jcb.201010024. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Parr RL, Maki J, Reguly B, Dakubo GD, Aguirre A, Wittock R, et al. The pseudo-mitochondrial genome influences mistakes in heteroplasmy interpretation. BMC Genomics. 2006;7:185. doi: 10.1186/1471-2164-7-185. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Payne BA, Wilson IJ, Yu-Wai-Man P, Coxhead J, Deehan D, Horvath R, et al. Universal heteroplasmy of human mitochondrial DNA. Vol. 22. Hum. Mol. Genet; 2012. pp. 384–390. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Poulton J, Chiaratti MR, Meirelles FV, Kennedy S, Wells D, Holt IJ. Transmission of mitochondrial DNA diseases and ways to prevent them. PLoS Genet. 2010;6:e1001066. doi: 10.1371/journal.pgen.1001066. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Robinson JT, Thorvaldsdóttir H, Winckler W, Guttman M, Lander ES, Getz G, et al. Integrative genomics viewer. Nat. Biotechnol. 2011;29:24–26. doi: 10.1038/nbt.1754. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. do Rosário Marinho AN, Santos MR, de Moraes S, Ribeiro-Dos-Santos A. Human aging and somatic point mutations in mtDNA: a comparative study of generational differences (grandparents and grandchildren) Genet. Mol. Biol. 2011;34:31–34. doi: 10.1590/S1415-47572010005000106. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Ruiz-Pesini E, Lott MT, Procaccio V, Poole JC, Brandon MC, Mishmar D, et al. An enhanced MITOMAP with a global mtDNA mutational phylogeny. Nucleic Acids Res. 2007;35:823–828. doi: 10.1093/nar/gkl927. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Sasaki H, Emi M, Iijima H, Ito N, Sato H, Yabe I, et al. Copy number loss of (src homology 2 domain containing)-transforming protein 2 (SHC2) gene: discordant loss in monozygotic twins and frequent loss in patients with multiple system atrophy. Mol. Brain. 2011;4:24. doi: 10.1186/1756-6606-4-24. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Soares P, Ermini L, Thomson N, Mormina M, Rito T, Rohl A, et al. Correcting for purifying selection: an improved human mitochondrial molecular clock. Am. J. Hum. Genet. 2009;84:740–759. doi: 10.1016/j.ajhg.2009.05.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Sondheimer N, Glatz CE, Tirone JE, Deardorff MA, Krieger AM, Hakonarson H. Neutral mitochondrial heteroplasmy and the influence of aging. Hum. Mol. Genet. 2011;20:1653–1659. doi: 10.1093/hmg/ddr043. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Song S, Wheeler LJ, Mathews CJ. Deoxyribonucleotide pool imbalance stimulates deletions in HeLa cell mitochondrial DNA. J. Biol. Chem. 2003;278:43893–43896. doi: 10.1074/jbc.C300401200. [DOI] [PubMed] [Google Scholar]
  40. Voets AM, Stassen BJ, van den Bosch AP, Hendrickx AT, Hellebrekers DM, Van Laer L, et al. Large scale mtDNA sequencing reveals sequence and functional conservation as major determinants of homoplasmic mtDNA variant distribution. Mitochondrion. 2011;11:964–972. doi: 10.1016/j.mito.2011.09.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Wallace DC. A mitochondrial paradigm for degenerative diseases and ageing. Novartis Found. Symp. 2001;235:247–263. doi: 10.1002/0470868694.ch20. [DOI] [PubMed] [Google Scholar]
  42. Zhidkov I, Nagar T, Mishmar D, Rubin E. MitoBam Annotator: a web-based tool for detecting and annotating heteroplasmy in human mitochondrial DNA sequences. Mitochondrion. 2011;11:924–928. doi: 10.1016/j.mito.2011.08.005. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

mgg30001-0174-SD1.xlsx (97.6KB, xlsx)
mgg30001-0174-SD2.xlsx (2.7MB, xlsx)
mgg30001-0174-SD3.docx (1.6MB, docx)
mgg30001-0174-SD4.docx (14.8KB, docx)

Articles from Molecular Genetics & Genomic Medicine are provided here courtesy of Blackwell Publishing

RESOURCES