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Hum Mol Genet. Oct 1, 2012; 21(19): 4214–4224.
Published online Jun 26, 2012. doi:  10.1093/hmg/dds245
PMCID: PMC3441121
Mitochondrial DNA heteroplasmy in diabetes and normal adults: role of acquired and inherited mutational patterns in twins
Gal Avital,1 Mor Buchshtav,1 Ilia Zhidkov,1,2 Jeanette Tuval (Feder),1 Sarah Dadon,1 Eitan Rubin,2,3 Dan Glass,4 Timothy D. Spector,4 and Dan Mishmar1*
1Department of Life Sciences, Faculty of Natural Sciences,
2National institute of Biotechnology in the Negev (NIBN), and
3Department of Microbiology and Immunology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel,
4Department of Twin Research and Genetic Epidemiology, Division of Genetics and Molecular Medicine, Kings College, London SE1 7EH, UK
*To whom correspondence should be addressed at: Department of Life Sciences, Building 40, Room 005, Ben-Gurion University of the Negev, Beer-Sheba 84105, Israel. Tel: +972 86461355; Fax: +972 86461356; Email: dmishmar/at/
Received February 15, 2012; Revised June 3, 2012; Accepted June 19, 2012.
Heteroplasmy, the mixture of mitochondrial genomes (mtDNA), varies among individuals and cells. Heteroplasmy levels alter the penetrance of pathological mtDNA mutations, and the susceptibility to age-related diseases such as Parkinson's disease. Although mitochondrial dysfunction occurs in age-related type 2 diabetes mellitus (T2DM), the involvement of heteroplasmy in diabetes is unclear. We hypothesized that the heteroplasmic mutational (HM) pattern may change in T2DM. To test this, we used next-generation sequencing, i.e. massive parallel sequencing (MPS), along with PCR–cloning–Sanger sequencing to analyze HM in blood and skeletal muscle DNA samples from monozygotic (MZ) twins either concordant or discordant for T2DM. Great variability was identified in the repertoires and amounts of HMs among individuals, with a tendency towards more mutations in skeletal muscle than in blood. Whereas many HMs were unique, many were either shared among twin pairs or among tissues of the same individual, regardless of their prevalence. This suggested a heritable influence on even low abundance HMs. We found no clear differences between T2DM and controls. However, we found ~5-fold increase of HMs in non-coding sequences implying the influence of negative selection (P < 0.001). This negative selection was evident both in moderate to highly abundant heteroplasmy (>5% of the molecules per sample) and in low abundance heteroplasmy (<5% of the molecules). Although our study found no evidence supporting the involvement of HMs in the etiology of T2DM, the twin study found clear evidence of a heritable influence on the accumulation of HMs as well as the signatures of selection in heteroplasmic mutations.
Heteroplasmy, i.e. the mixed population of mitochondrial DNA (mtDNA) sequences in tissues or cells, is a common feature of mitochondrial genetics (1). In humans, the percentage of heteroplasmic mutations in a given sample correlates with penetrance of disease phenotypes (2). Patterns of heteroplasmic mutations are known to vary among different tissues within each individual, with the highest level recorded in post-mitotic tissues such as muscle and brain. Additionally, variability in heteroplasmic mutational (HM) patterns among tissue samples has been reported in forensic cases (3). The origin of heteroplasmic mutations can be either age-related accumulation or heritable variation (4,5). However, the proportion of heteroplasmic mtDNA mutations that are heritable rather than accumulated in an organism is unknown.
Unlike nuclear DNA, the cellular cytoplasm is assumed to divide randomly. Therefore, it has been suggested that HM patterns are likely to differ among daughter cells due to replicative segregation, which is the cellular equivalent of genetic drift in the population (1). Nevertheless, it has been recently argued that non-random forces play a role in shaping the repertoire of heteroplasmic mutations in human cells (6,7). Moreover, we showed that recurrent de novo mutations in cancer which have reached homoplasmy preferentially recapitulate the ancestral mtDNA state, further supporting the role natural selection plays in shaping the repertoire of heteroplasmic mutations (8). Therefore, both random and non-random forces are likely to shape the repertoire of heteroplasmic mutations in the cell.
The role of heteroplasmic mutations in rare mitochondrial disorders has been established but their involvement in many common complex disorders has not been thoroughly assessed. Over-representation of heteroplasmic mtDNA mutations was reported in Parkinson's disease (9). However, despite the major role of mitochondria in metabolism, patterns of heteroplasmy have not received much attention in metabolic disorders. This is despite mitochondrial dysfunction being well known to play a central role in metabolic disorders such as type 2 diabetes mellitus (T2DM) (10). First, expression levels of genes involved in the energy-producing system of the mitochondria, i.e. oxidative phosphorylation (OXPHOS), were reduced in T2DM when compared with normal controls (11). Second, offspring of T2DM patients who had not developed the disease showed reduced ATP production (12). Third, certain populations studied demonstrate an association of T2DM with mtDNA common variants (1315). Finally, differential levels of heteroplasmy of the A3243G mtDNA mutation have been shown to cause diabetes (16). These studies drew our attention to the possibility that patterns of heteroplasmy could be altered in T2DM and may even play a role in the disease etiology.
Here, we investigate several basic features of heteroplasmy under healthy and disease conditions. These features include the contribution of inherited versus acquired somatic mutations to HM patterns, the effect of natural selection on heteroplasmic mutations and the contribution of heteroplasmy to T2DM. We use next-generation sequencing supplemented by a cloning–Sanger sequencing approach to investigate the HM pattern utilizing a twin design of monozygotic (MZ) twins either concordant or discordant to T2DM.
Assessing the pattern of heteroplasmic mutations in T2DM twins by cloning and sequencing
We assessed the HM pattern in MZ twins, either concordant or discordant for T2DM. First, we utilized a previously described approach (17) combining the PCR amplification of a D-loop mtDNA fragment, bacterial cloning and Sanger sequencing of ~20 clones from each of the tested samples. These included blood DNA samples (representing mitotic cells) of 9 T2DM discordant and 11 T2DM concordant twins, and available skeletal muscle DNA (representing post-mitotic cells) from a subset of 3 concordant and 4 discordant T2DM twins. To assess the error rate of this approach, we have PCR amplified the insert of one of the clones, re-transformed into E-coli and isolated 20 clones. After repeating this process three independent times, only one mutation-length variation of the cytosine repeat in position 309 was identified, suggesting a very low polymerase error rate. While analyzing the clones from our twins' samples, excluding indels from the analysis, higher proportions and a more diverse landscape of heteroplasmic mutations were observed in skeletal muscle compared with the corresponding blood samples. The repertoire and frequency of the heteroplasmic mutations were different between twin pairs. Shared heteroplasmic mutations were found among some of the twin pairs (Supplementary Material, Table S1). Careful analysis of the mutational pattern in the T2DM concordant and discordant twins considering the quantity of mutations, mutational distribution in the DNA fragment and percentage of heteroplasmic clones in the samples did not reveal any notable difference between any of the T2DM discordant twin members. Thus, the mutational landscape and frequency identified using this technique did not reveal any significant correlation between any tested attribute of the heteroplasmic mutations and the disease phenotype.
Since the PCR–cloning–sequencing approach did not reveal any clear difference between the T2DM discordant twins several possibilities emerged: (i) HM pattern is not clearly correlated with T2DM. (ii) The resolution of the PCR–cloning–sequencing is limited to the number of analyzed clones per sample (up to 29 clones in certain samples of our analysis) and a higher resolution technique should be sought. (iii) The analysis using the PCR–cloning–sequencing approach is limited to a relatively short mtDNA fragment (in our case, part of the non-coding region), and T2DM-associated mutations could inhabit other parts of the mitochondrial genome. Considering these caveats we sought a technique that will increase the resolution for the detection of heteroplasmic mutations throughout the mtDNA. Next-generation sequencing or massive parallel sequencing (MPS, SOLiD ABI) technology enabled the detection of heteroplasmic mutations as low as 1.6% of the analyzed sample (18, 19) using multiple sequence reads per mtDNA nucleotide position (20). Thus, MPS was utilized to sequence the PCR-amplified whole mtDNA from blood and skeletal muscle DNA samples available from four T2DM discordant and three T2DM concordant twin pairs.
MPS of the whole mtDNA in the tested twins' samples
Analysis of the MPS data revealed full coverage for the whole mitochondrial genome in each of the samples analyzed from blood and skeletal muscles. Since we aimed at analyzing heteroplasmic mutations as rare as 1.6% we focused our analysis only on nucleotide positions having >1000 × read coverage. A mean of 12 625.64 mtDNA positions had such coverage (Supplementary Material, Table S2, Fig. S1A and B), i.e. only a subset of the nucleotide positions were eventually analyzed in each of the samples. In general, the DNA quality from skeletal muscle biopsies was lower than that obtained from blood and indeed the sequence coverage in blood samples was higher than that of the skeletal muscle (Supplementary Material, Fig. S1A and B). Despite this, we maintained the minimal coverage threshold in order to avoid the detection of false heteroplasmic mutations.
After applying our stringent bioinformatic filters (see Materials and Methods), a total of 64 heteroplasmic mutations were identified, of which 52 mutations were found in skeletal muscle and 12 in blood samples. Of the 64 heteroplasmic mutations, 55 were previously described in the public databases (,, including 2 mutations that were previously shown to define the known mtDNA haplogroups H and UK (at positions 7028 and 9055, respectively). While comparing the two cell types, the mean number of heteroplasmic mutations per individual in skeletal muscle was 3.5 ± 1.28 (SE) and in blood it was 1.0 ± 0.23 (SE) (Fig. 1).
Figure 1.
Figure 1.
Distribution of the number of heteroplasmic mutations per individual in blood and skeletal muscles. The size of each dot is proportional to the number of subjects it refers to. The black line represents the median.
The heteroplasmy levels per individual ranged from 1.67 to 48.71% per mutation (Supplementary Material, Table S3). When dividing the overall mutations according to their prevalence in the samples, 24 mutations (37.5%) encompassed each 1.6–5% of the reads, 25 mutations (39.1%) encompassed each 5–10% of the reads, 7 mutations (10.9%) encompassed each 10–15% of the reads and 8 mutations (12.5%) encompassed each >15% of the reads. While comparing the D-loop fragment analyzed by PCR–cloning–Sanger sequencing with the MPS results, we found that the vast majority of the heteroplasmic mutations found in the cloning–sequencing approach were also present in the MPS raw data. Nevertheless, most of these mutations were removed by our stringent filters (Supplementary Material, Tables S1 and S3), consistent with similar analyses of heteroplasmy (6,18), thus leaving three heteroplasmic mutations confirmed by both the techniques: A73G in the skeletal muscle of samples 12 662 and 12 661, A385G in the skeletal muscle of sample 51 402 and C64A in the skeletal muscle of samples 68 441 and 70 251.
Since the D-loop contains multiple low complexity repeat regions that are not easily sequenced by MPS and hence removed by our analysis filters we sought to validate non-D-loop heteroplasmic mutations identified by the MPS. Two such heteroplasmic mutations (at positions 9077 and 15 132) were present in a relatively high fractions of the MPS sequence reads (>35%), thus facilitating their identification by traditional Sanger sequencing of PCR products. Direct Sanger sequencing of PCR products encompassing these positions in samples harboring these mutations as well as in a sample that was negative for these mutations confirmed the existence of the mutations as reported by the MPS (Supplementary Material, Fig. S2). Notably, the mutation at position 15 132 was detected by MPS in the blood and skeletal muscles of sample 68 842 as well as in the blood of sample 68 841 but not in the skeletal muscle of the latter individual. While carefully inspecting the MPS raw data, we have identified heteroplasmy at this position in the skeletal muscle of sample 68 841, but filter D (see Materials and Methods) removed it from the final results.
In order to assess the possible association of the HM pattern with T2DM we compared the pattern of heteroplasmy focusing on the discordant twins. Similar to the cloning–sequencing approach, while considering heteroplasmic mutation levels, repertoire and distribution throughout the mtDNA, no clear disease-associated pattern emerged. In summary, we did not find any clear association of any of the tested attributes of heteroplasmy with T2DM.
Previous studies have reported decreased mtDNA copy number in T2DM samples (21). We compared the mtDNA copy number between discordant and concordant twins (Table 1). One twin pair concordant for diabetes (70251-2) had a large difference in mtDNA copy number, but being T2DM concordant this difference could not be related to the disease state. When comparing all the discordant twins, the ratio of the mtDNA copy number between the healthy twin and the diabetic twin was close to one, i.e. there was no difference in the mtDNA copy number between the healthy and diabetic twins.
Table 1.
Table 1.
mtDNA copy number analysis in twin samples subjected to whole mtDNA next-generation sequencing.
Taken together, the cloning–sequencing analysis of the mtDNA control region, MPS analysis of whole mtDNA sequences and mtDNA copy number analysis did not reveal any clear T2DM-associated pattern in our twins.
Assessment of MPS errors of PCR products
To assess the level of MPS errors resulting from PCR amplification, we first cloned one of the amplified mtDNA fragments (Fragment 1, mtDNA nucleotide positions 16 387 to 6001) and used it as a template for PCR amplification and sequenced the PCR product using SOLiD ABI. Our analysis revealed a single mutation in a heteroplasmic state (nucleotide position 2924). Since this mutation was identified in a heteroplasmic state in eight unrelated samples (from the same sequencing run) we excluded this mutation from further analysis. As a result our control clone did not harbor any heteroplasmic mutations >1.6% of the reads, which is the accepted cut-off (18).
Additionally, the quality of heteroplasmy detection could also be assessed by analyzing recurrent heteroplasmy in twins. The majority of the heteroplasmic mutations (37 of 64 instances) are observed in paired samples (different tissues from the same individual or samples of twin pairs). Considering the low rate of heteroplasmy in general (~0.4 mutations/kbp), the probability of observing >17 common mutations shared between twins is highly unlikely (P < 1E−4, based on 10 000 permutations). These results are in agreement with previous reports (6,18) that demonstrate accurate and sensitive detection of heteroplasmy with MPS, provided that careful filtering of erroneous reads and bases is applied.
Heritable heteroplasmic mutations in MZ twins
The analysis of heteroplasmic mutations in the MZ twins (SOLiD ABI data) revealed three types of mutations: those that were shared among lymphocytes and skeletal muscle of the same individuals (n = 14), those shared among twins (n = 23) and the unique mutations (n = 27). Although only using additional dizygotic twins can fully quantify shared environmental effects—we assumed that the shared mutations, either among twins or between tissues of the same individual (a total of 15 mutations), were predominantly heritable changes. In contrast, the unique mutations (singletons), i.e. those appearing only once within a certain individual (a total of 27 changes), are likely to be enriched for changes that have accumulated over the lifetime of the individual.
Inspection of the repertoire and distribution of the heteroplasmic mutations revealed that it was very diverse among unrelated individuals (especially in skeletal muscle), but similar within a twin pair (Supplementary Material, Table S3, Fig. 2A). Notably, 8 out of 12 of the heteroplasmic mutations that were identified in blood were also found in skeletal muscle of the same individual. Since mutations that are shared between twin pairs, rather than between unrelated individuals, and mutations shared between tissues of the same individuals are likely to have arisen once during development we interpret both these mutations as reflecting shared germline mutations rather than independent somatic mutational events. Accordingly, 57.8% of the total set of heteroplasmic mutations were shared between twin pairs or tissues of the same individual (Fig. 2B); 37.5% of the shared mutations had low heteroplasmic levels (1.6–5.0%) which indicate that inherited heteroplasmic mutations can be found as rare somatic variants.
Figure 2.
Figure 2.
(A) Distribution of shared versus unique heteroplasmic mutations in skeletal muscle and blood samples. Y-axis—the number of heteroplasmic mutations, color divided in each sample into unique and shared. X-axis—Sample IDs. P, patient; M, (more ...)
We examined unique mutations for recurrence among unrelated individuals and identified four such mutations (mtDNA positions 64, 73, 750, 7028). As the mtDNA is regarded as a single locus, only a linked combination of mutations could be regarded as sample contamination. None of the recurrent mutations were in combination with another recurrent mutation between two tested unrelated individual. Two of the four recurrent mutations were non-coding (D-loop), one was at an rRNA-coding region and one was a synonymous mutation at a coding gene (MT-CO1) region. Three of the four recurrent mutations were transversions, much more than expected by chance (P = 2E−4, binomial distribution). We cannot exclude mutational hotspots as the reason for recurrence of these mutations. It is noteworthy that mutations at positions 414 and 408, previously argued to be over-represented in aged individuals, were found to be recurrent in the PCR–cloning but were removed from the MPS data as they reside within the low complexity repeat regions.
Signatures of negative selection in heteroplasmic mutations
While inspecting the positions of heteroplasmic mutations (Table 2), we found that ~40% of the mutations occurred within the mtDNA non-coding regions. The rest of the heteroplasmic mutations (60%) mapped to the mtDNA-coding sequences (rRNA, tRNA and protein-coding genes). Similar non-coding/coding mutation ratios were obtained even when the mutations were divided into unique or shared or whether they occurred in healthy individuals or patients (Fig. 3). Since the non-coding sequences encompass only ~7% of the human mtDNA, this finding indicates clear over-representation of heteroplasmic mutations in non-coding sequences (P < 0.001). Even while dividing the heteroplasmic mutations by their levels (1.6–5% and >5%) ,the over-representation of non-coding sequences was observed regardless whether the mutations were ‘shared’ or unique. This ratio implies non-random characteristics in the appearance of both low and high level heteroplasmic mutations. While considering only mutations occurring in protein-coding genes, we observed under representation (though not statistically significant) of non-synonymous mutations (≤40%) when compared with synonymous mutations (≥60%). We also found an over-representation of transitions versus transversions. Such transition bias was previously observed in population genetics studies in humans and other species (2224). This suggests similar selective constraints acting on the mtDNA both within the cells and at the population level.
Table 2.
Table 2.
Distribution of the heteroplasmic mutations throughout the mtDNA found in the twins' whole mtDNA MPS.
Figure 3.
Figure 3.
Over-representation of heteroplasmic mutations in non-coding regions. In black—coding mtDNA coding sequences (rRNA, tRNA, and protein-coding genes); in grey—all non-coding mtDNA regions, mainly the D-loop.
We noticed that most of the recorded heteroplasmic mutations were identified in a single twin pair (i.e. 68 841.2, Fig. 2A). In order to avoid sampling bias, we repeated our analysis excluding these twins and still observed highly significant over-representation of non-coding mutations (chi square, P < 0.001, Supplementary Material, Table S4). We thus conclude that negative selection is likely to act on the accumulation of heteroplasmic mutations in our entire twin sample.
Our sequence analysis of mtDNA control region (cloning–sequencing) and whole mtDNA (MPS) of MZ twins revealed several novel insights into one of the most fundamental characteristics of mitochondrial genetics—heteroplasmy. First, the higher level of heteroplasmy in skeletal muscle versus blood samples supported previous suggestions that the level of heteroplasmy is higher in post-mitotic tissues (1). Second, the repertoire and level of heteroplasmy varied widely among different individuals. Third, a large portion of the HM pattern was inherited rather than de novo among twins. This was more evident in twins with many heteroplasmic mutations. Finally, a clear pattern of negative selection was observed among both highly abundant and low prevalence heteroplasmic mutations. All these observations underline both the usage of MPS and the choice of identical twins as a successful approach to investigate the nature of heteroplasmic mutations.
T2DM concordant and discordant twins were utilized to compare the etiological role of acquired HM pattern differences within a constant genetic background. Although our study enabled high resolution and identification of multiple heteroplasmic mutations, no clear T2DM-associated pattern was identified. Two obvious reasons could underlie these findings: (i) T2DM is an extremely complex disorder and is frequently referred to as a family of disorders. The disease is caused by the interplay between multiple genetic and environmental factors resulting in an estimated heritability of ~50% (25). Thus, although MZ twins notably reduce the genetic variability between the twin members, the genetic and epigenetic variability across individuals is still likely to be great. (ii) Although multiple sources point to the role that mitochondrial dysfunction plays in the etiology of T2DM, it is possible that any effect is dependent on small effect sizes and the sample size should be increased to exclude these and possible clinical subgroups (26).
That many heteroplasmic changes were either common in both twins within a pair or shared across both blood or skeletal muscle samples from the same individual suggest that many of the heteroplasmic mutations are inherited rather than accumulated over the lifetime of the individual. Although previous reports suggested the inheritance of certain discovered heteroplasmic mutations (reviewed in 27 and 28), especially disease-causing mutations, the extent of this phenomenon was not thoroughly addressed. In mice, apparently neutral heteroplasmic variants could persist for at least 14 generations (29). Recent MPS analysis (SOLiD ABI) of whole mtDNA sequences from several generations of mice either carrying a mutated DNA PolG or wild-type revealed among other findings a notable proportion of heteroplasmic mutations sharing among sibs, suggesting their inheritance from the maternal egg cytoplasm (20). Similarly, MPS analysis (Illumina) of whole mtDNA sequences from a single individual revealed mutations shared among different tissues (18). These findings, along with our observation of notable sharing of HM repertoires even among twin members having multiple heteroplasmic mutations, raise the possibility that transmission of multiple heteroplasmic mutations could constitute a common phenomenon, despite the possible action of the mitochondrial bottleneck during the development of the female germline (30, 31).
We noticed that the vast majority of the mutations identified by the PCR–cloning–sequencing technique were present in the MPS data, but were removed from our final analysis by our stringent filters. Notably, the purpose of our filters was to reduce the effect of false positive results. Therefore, we decided against retaining these particular mutations and removing others just because these mutations were also observed in the cloning–sequencing technique. We are aware that by taking this approach, many true heteroplasmic mutations will be overlooked, thus increasing the rate of false negative results. An excellent example for this outcome is the mutation at position 15 132 which was identified by the MPS and confirmed by Sanger sequencing in several samples but was removed from the MPS analysis of the skeletal muscle of sample 68 841 by our filter D. Hence, utilizing our filters could reduce false positive results but may lead to overlooking true heteroplasmic mutations. This is one of the reasons why PCR–cloning–Sanger sequencing was used in addition to MPS, as these techniques are complementary: the drawbacks of one are relieved by the other. As an example, low complexity repeat regions could be analyzed by the cloning–sequencing approach but currently cannot be analyzed by MPS, at least not with high certainty. Second, the short reads generated by SOLiD interfere with identifying mutational combinations, which could be partially identified by the cloning–sequencing approach. Third, mutations in the MPS are discovered at much higher resolution than by the cloning–sequencing approach and as a result many mutations are seen in the MPS but not by cloning. Thus, until long high quality reads are generated by any of the MPS platforms there is benefit in using the current MPS platforms in combination with complementary techniques, such as the cloning–sequencing approach.
Inheritance of HM patterns may have serious implications on evolution, since these could set the basis for recombination, thus resulting in new genetic backgrounds (27). Nevertheless, recombination events among mtDNA molecules which differ in a single mutation, which constitute most events of heteroplasmy (3), are likely to have relatively little influence on evolution. Alternatively, recombination among mixed populations of mtDNA molecules that differ in their type (different haplogroups) would likely influence population genetic variation. Such a mixture of two different mtDNA types could occur upon rare paternal leakage of mtDNA (32,33). Our results, both using the cloning–sequencing and MPS techniques, did not reveal clear evidence for such a pattern. As SOLiD analysis produces short reads, combinations of mtDNA mutations within a single molecule could not be easily detected. This may be overcome by introduction of MPS techniques generating long reads of single molecules, such as the recently developed technique by Pacific biosciences (
While investigating the distribution of heteroplasmic mutations we found significant over-representation of mutations in the control region, suggesting the action of negative selection on the mtDNA-coding region. Recent studies in mice used experimental designs that could adequately differentiate between drift and selection (20, 34, 35). These studies generated maternal mice heteroplasmic for pathogenic mutations, and then tracked their progression into future generations. All three studies presented evidence for purifying selection during oogenesis within few generations. Two plausible mechanisms of selection, working at the organelle level, have been put forward by Shoubridge and Wai (36): (i) pathogenic mtDNA mutations may make the mitochondria prone to autophagy, or (ii) non-silent mutations will simply make these mitochondria less efficient at replication, leading to them being outnumbered by healthy organelles. A recent study suggested that mitophagy factors actively select against mutated mtDNA molecule in human cells, thus supporting the autophagy mechanism (37). Additionally, we found that the effect of negative selection was evident both when analyzing low abundance (1.6–5%) or high abundance (>5%) mutations, which argues for the existence of an active selective mechanism that sifts through the repertoire of heteroplasmic mutations. This is further supported by the fact that 85.9% (55/64) out of the identified heteroplasmic mutations were already present in the public databases, thus reflecting selection against rarer variants which are expected to be enriched with mutations with deleterious potential.
In summary we performed deep sequence analysis of HM patterns throughout the human mtDNA in MZ twins either discordant or concordant for the T2DM phenotype. Although no clear correlation between heteroplasmic mutations and T2DM could be observed, we identified evidence both for the inheritance of heteroplasmic mutations and for the action of negative natural selection even over low abundance heteroplasmic mutations.
Blood samples were collected from 20 Caucasian twin pairs (Supplementary Material, Table S5), 11 concordant and 9 discordant to T2DM, and skeletal muscle samples from a subgroup of 3 concordant and 4 discordant twins ( from the TwinsUK registry (38). The samples were taken from individuals at age ≥50. DNA from blood was extracted utilizing Nucleon BACC 3 DNA extraction kit (www.GEN-PROBE.COM). DNA from skeletal muscle was extracted using a QIAGEN DNeasy blood & tissue kit.
PCR amplification of an mtDNA D-loop fragment and bacterial cloning to detect heteroplasmic mutations
PCR amplification of a 600 bp fragment from the mtDNA D-loop (nucleotide positions 1–600 in the revised Cambridge Reference Sequence (rCRS), GenBankaccession number NC_012920) was performed using all blood and skeletal muscle samples. PCR amplification was performed in a 20 μl volume containing 5 × GC buffer (FINNZYMES), 1 mm dNTPs mix (Bio-Lab Ltd), 2.5 mm MgSO4 (FINNZYMES), 0.5 μm of each primer (IDT, Integrated DNA Technologies), 1 U of Phusion Taq polymerase (FINNZYMES) and 50 ng of the DNA samples as templates. Primers pairs used for PCR amplification are either (i) 16526 F: CCTACTTCAGGGTCATAAAG and 626 R: TTTATGGGGTGATGTGAGCC or (ii) 1-F: CCGGCCGGATCCGATCACAGGTCTATCACCC and 722-R: GGCCGGAAGCTTTCGTGGTGATTTAGAGGGT. Numbers in the primer names designate the nucleotide position of their 5′ end.
Reaction conditions were as follows: denaturation at 98°C for 30 s followed by 35 cycles each including denaturation at 98°C for 10 s, annealing at 60°C for 60 s and elongation at 72°C for 30 s. The cycles were followed by an extension step at 72°C for 5 min. The reaction was concluded at 15°C and the reaction mixture was stored at −20°C until usage. PCR products were visualized on a standard 2% Agarose gel with EtBr to ensure specificity and were then purified using Wizard SV Gel and PCR Clean-up system (Promega), following manufacturer's protocol. The purified PCR products were ligated into pGEMT-Easy vector (Promega). The ligation reaction was performed using T4 ligase (Promega) with vector/insert ratio of 1:3 at 4°C overnight according to the protocol of a DNA ligation kit (Promega). Then, 5 μl of the ligation reaction was mixed with 50 μl of competent Escherichia coli cells (DH5-α E. coli strain) and was subjected to electric shock using GenePulserXcell (BIO-RAD). Following electroporation, 500 μl of luria broth (LB) were added, and the cells were shaken gently at 37°C for 1 h. The bacteria were plated onto LB Petri dishes containing 50 μg/ml of ampicillin, 40 μg of 0.1 m isopropyl-β-D-thio-galactoside (IPTG) and 40 μl of 2% X-gal, and grown overnight at 37°C. Following 'blue/white colony selection at least 20 colonies (white colonies contain the plasmid) from each individual were isolated and grown in 5 ml of liquid LB with 100 mg/ml of ampicillin at 37°C for 12 h, by shaking. The plasmid was purified using the Wizard plus SV minipreps DNA purification system (Promega), according to the manufacturer's protocol. We used restriction enzyme Eco521 (Fermentas) to confirm the existence of the insert in the plasmids. In order to detect sequence variation within each sample, 20–24 plasmid clones from each of the tested samples (i.e. blood DNA samples from 22 twin pairs and skeletal muscle DNA from 9 twin pairs, a total of ~1120 plasmid clones) were sequenced by an ABI 3100 sequencing machine using SP6 and T7 standard primers. The analysis of samples from two T2DM discordant twin pairs was omitted because of cross contamination (see below), thus leaving 1080 clones for further analysis. Sequences generated from clones of each given sample were aligned, consensus sequence was generated and heteroplasmic mutations were identified by comparing each of the aligned clones' sequences with the rCRS and with the consensus sequence of the individual using Sequencher 4.10.1. The heteroplasmic mutations were defined as those differing from the consensus sequence of the individual and were documented for each sample.
PCR amplification of the whole mtDNA
PCR amplification of the whole mtDNA was performed using skeletal muscle samples, and their counterparts blood DNAs available from seven twin pairs including three T2DM concordant and four discordant twin pairs (14 samples total). The amplification was performed in three overlapping DNA fragments using three pairs of primers (listed below). Since the whole mtDNA amplification was designed for next-generation sequence analysis, which enables deep sequence coverage, we anticipated that such high resolution would be more prone to detect even low abundance contaminants. Therefore, while preparing the samples for PCR amplification we applied extra care for sterility. In brief, PCR was performed in a laminar flow hood using a dedicated set of reagents. PCR amplification was performed in a 25 μl volume containing 5× GC buffer (FINNZYMES) or 5× HF buffer, 1 mm dNTPs (Bio-Lab Ltd), 2.5 mm MgSO4 (FINNZYMES), 0.5 μm of each primer (IDT), 1 U of Phusion Taq polymerase (FINNZYMES) and 50 ng of the DNA samples as templates. Primers used for PCR amplification (nucleotide positions are indicated according to rCRS GenBank accession number NC_012920):
Fragment 1 (mtDNA positions 16 387 to 6001): Fragment 1 (forward)—5′-ATAGGGGTCCCTTGACCACCATCCTCCGT-3′; Fragment 1 (reverse)—5′-GAGCTGTGCCTAGGAC TCCAGCTCATGCGCCG-3′ Fragment 2 (mtDNA positions 4848 to 11 926): Fragment 2 (forward)—5′-CGGCCTGC TTCTTCTCACATGACAAAAAC-3′; Fragment 2 (reverse) — 5′-GATCAGGAGAACGTGGTTACTAGCACAGAGAG- 3′ Fragment 3 (mtDNA positions 11 699 to 162): Fragment 3 (forward)—5′ CATTCTCATAATCGCCCACGGGCTTAC ATCC-3′; Fragment 3(reverse)—5′-GTTCGCCTGTAA TATTGAACGTAGGTGCG-3′. We also used an alternative set of primers for fragment 3 in some cases, encompassing mtDNA positions 11 574 to 635: Fragment 3-new (forward)—5′-CAAGCTCCATCTGCCTACGACAAACAG ACC-3′ Fragment 3-new (reverse)—5′-GATGTGAGCCC GTCTAAACATTTTCAGTG-3′.
PCR was conducted in a two-step program, starting by a denaturation step at 94°C for 2 min followed by 30 cycles including 94°C for 15 s and 72°C for 7 min. After the cycles a final extension step was performed at 72°C for 12 min. The reaction was concluded at 15°C and stored at −20°C until usage. PCR products were then purified using the Wizard SV Gel and PCR Clean-up system (Promega), following the manufacture's protocol or using a HiYield Gel/PCR DNA fragment extraction kit (RCB Bioscience). In order to reach a concentration of 500–1000 ng of amplified whole mtDNA of each of the tested samples in 30 μl volume (the requirement of the next-generation sequencing facility), an equimolar mixture of the three segments was prepared for next-generation sequencing.
Next-generation sequencing (MPS)
Amplified whole mtDNA from blood DNA and skeletal muscle DNA of three T2DM concordant and six T2DM discordant twin pairs were subjected to SOLiD ABI sequencing in tagged libraries. Libraries were prepared from 500–600 ng DNA using the SOLiD™ fragment library construction kit according to manufacturer instructions. Emulsion-PCR was preformed according to the manufacturer's instructions. Sequencing of 50 bp read length was performed using the Applied Biosystems SOLiD 4 System, at the Center of Genomic Technologies in The Hebrew University of Jerusalem.
The MPS raw data are available at the sequence read archive (SRA), study accession number SRP010986.1.
Analysis of MPS data
The resultant sequence reads from the SOLiD ABI sequencing were aligned to the rCRS (GenBank accession number NC_012920) using the BFAST alignment tool (39). Each sample was then re-alligned to its own consensus mtDNA sequence. We used the 1000 genome sequence analysis protocol as default ( Specifically, we used –u parameter in ‘bfastlocalalign’ in order to exclude insertions/deletions from the analysis. At ‘bfastpostprocess’ we chose the –a3 parameter to use only the uniquely mapped reads, thus reducing the odds for contamination by nuclear mtDNA pseudogenes. We then used SAM tools (40) to convert the SAM sequence format to the BAM format. For heteroplasmic filtering and annotations, we used our recently developed MitoBamAnnotator (19) with the default parameters. Specifically, heteroplasmic mutations were not listed if they occurred within low complexity regions (filter A, Supplementary Material, Table S1), if they occurred within a region covered by <1000 high quality sequence reads (filter B, Supplementary Material, Table S1), if they occurred within the PCR amplification primer sequences (filter C, Supplementary Material, Table S1), if their minimal heteroplasmic fraction was <1.6% with a minimum of 0.8% fraction per strand (filter D, Supplementary Material, Table S1) and if their minimal sequence read count per strand was <5 (filter E, Supplementary Material, Table S1). To exclude samples that were suspected of cross contamination, we have created a secondary sequence based on the rCRS reference sequence with the second best base of the heteroplasmic mutations of each individual. This ‘secondary sequence’ generated from each of the analyzed samples was analyzed by Haplogrep (41) which classifies mtDNA into the known population genetic backgrounds in humans (haplogroups). This procedure enabled us to detect samples from 2 of the 9 twin pairs the secondary sequence of which resembled well-known human mtDNA haplogroup. Samples from these two twin pairs were excluded from further analysis, thus leaving 20 twin pairs in the bacterial cloning Sanger sequencing (11 T2DM concordant and 9 discordant) and 7 pairs (3 T2DM concordant and 4 discordant) for further analysis.
PCR amplification and Sanger sequencing of mtDNA fragments encompassing positions 9077 and 15 132 to validate non-D-loop heteroplasmic mutations observed by MPS
We PCR amplified the mtDNA nucleotide positions 8815–9819 and 14 838–15 912 (according to the revised Cambridge Reference Sequence, GenBank accession number NC_012920) to verify the identified high level MPS heteroplasmic mutations at positions 9077 (within 95 451 and 95 452 blood and skeletal muscle samples) and 15 132 (in 68 841, 688 412 blood and skeletal muscle samples) using 51 401 blood sample as a negative control for both amplified fragments. PCR amplification was performed in a 25 μl reaction volume containing 5× GC buffer (FINNZYMES) or 5× HF buffer, 1 mm dNTPs (Bio-Lab Ltd), 2.5 mm MgSO4 (FINNZYMES), 0.5 μm of each primer (IDT), 1 U of Phusion Taq polymerase (FINNZYMES) and 50 ng of the DNA samples as templates. Primers used for PCR amplification: position 8815 to 9819—forward 5′-CTCATTTACACCAACCACCC-3′ reverse GCC AATAATGACGTGAAGTCC. Position 14 838 to 15 912—forward 5′-TCCAACATCTCCGCATGATG-3′ reverse 5′-TC TCCGGTTTACAAGACTGG-3′.
Reaction conditions were as follows: denaturation at 94°C for 2 min followed by 30 cycles each including denaturation at 94°C for 15 s, annealing at 58°C for 30 s and elongation at 72°C for 30 s. The cycles were followed by a final extension step at 72°C for 12 min. The reaction was concluded at 15°C and the reaction mixture was stored at −20°C until usage. PCR products were visualized on a standard 1% agarose gel with EtBr to ensure specificity and were then purified using the Wizard SV Gel and PCR Clean-up system (Promega), following the manufacture's protocol. Each of the tested samples were sequenced in the ABI 3100 sequencing machine using the above-mentioned amplification primers (BGU sequencing facility). Sequences generated from each given sample were aligned to the rCRS using Sequencher 4.10.1. (GeneCodes Inc.)
mtDNA copy number
Previously mtDNA copy number was shown to differ between type 2 diabetes individuals versus healthy people (21). Therefore, we assessed the mtDNA copy number in our tested samples by real-time PCR. Specifically, real-time PCR assessment of the mtDNA copy number was performed by comparing Ct of a nuclear DNA-encoded locus (NDUFC2), an mtDNA-encoded marker (the gene ND3) and a standard plasmid reference amplified using known concentration (see the detailed explanation below). To amplify ND3 (rCRS nucleotide position 10 166–10 355), an mtDNA-encoded gene, we used the following primers (numbers correspond to the nucleotide position of the 5w end of the primers in rCRS):
  • Forward—10166F: 5′-TTACGAGTGCGGCTTCGACC-3′
  • Reverse—10355R: 5′-ACTCATAGGCCAGACTTAGG-3′
The primers for our nuclear DNA locus were designed from exon II of NDUFC2 (GenBank accession number CR533448):
PCR amplification was performed in a 20 μl volume, 25 ng of the DNA samples as templates, 1X Absolute Blue SYBR Green ROX Mix (Thermo) and 50 mm of each primer. Thermal reaction conditions are preheating at 94°C for 15 s and 40 cycles including a denaturation step at 98°C for 10 s, annealing step at 60°C for 20 s and extension step at 72°C for 15 s. The final extension was performed at 72°C for 7 min. The reaction was concluded by cooling the samples to 15°C. The amplification was monitored using the MX3000P real-time PCR machine (Stratagene). The threshold cycle (Ct) values were related to the standard curve made using cloned PCR products of each of the amplified fragments, separately. The copy number of this calibrator was determined by dividing the total DNA concentration by the molecular weight of each plasmid molecule. The molecular weight of cloned plasmid molecules was estimated as follows: (bp × MWt)/A, where MWt denotes the molecular weight of double-stranded DNA (660 g/mol), A denotes Avogadro's number (6.02 × 1023 molecules/mol). Calibrators were prepared by serial 10× dilutions of the plasmid DNA samples which range from106 to 109 plasmid copies. A calibration curve was established using these calibrator samples.
Simulating concordant heteroplasmic mutations
The number of concordant heteroplasmies expected by chance was estimated using highly liberal simulations: (i) kM and kB heteroplasmic mutations were randomly drawn from a genome of length LM and LB (correspondingly) for every blood (B) and muscle (M) sample of every individual, where kX is the number of heteroplasmic variations observed in tissue X (i.e. B or M) of that individual and LX is the coverage of tissue X (i.e. B or M) for that individual; (ii) the simulated heteroplasmic mutations of both the samples (blood/muscle) of each individual were combined and (iii) the number of positions found to harbor heteroplasmic mutations in each pair of twins is counted as the number of positions common to the two lists of mutations generated in step (ii). The probability to observe 17 concordant mutations is taken as the fraction of simulations in which 17 or more positions were shared between the twins.
Control clone for MPS analysis
In order to control for mutations generated by our experimental procedure, we assessed the level of heteroplasmic mutations in one of our amplified segments of the whole human mtDNA (Fragment 1, see above). In order to achieve a template free of intracellular variation the purified PCR product was cloned, i.e. ligated into pGEMT-Easy vector (Promega). The ligation reaction was performed using T4 ligase (Promega) with a vector/insert ratio of 1:3 at 4°C overnight according to the protocol of DNA Ligation Kit (Promega). Then, 5 μl of the ligation reaction was mixed with 50μl of competent E. coli cells (DH5-α) and was subjected to electric shock using GenePulserXcell (BIO-RAD). Following electroporation, 500 μl of LB was added, and the cells were shaken gently at 37°C for 1 h. The bacteria were plated onto LB Petri dishes containing 50 μg/ml of ampicillin, 40 μg of 0.1 m IPTG and 40 μl of 2% X-gal, and grown overnight at 37°C. Following ‘blue/white’ colony selection at least 20 colonies (white colonies contain the plasmid) were isolated and grown in 5 ml of liquid LB with 100 mg/ml ampicillin at 37°C for 12 h, by shaking. The plasmid was purified using the Wizard plus SV miniprep DNA purification system (Promega), according to the manufacturer's protocol. We used restriction enzymes ScaI and HpaI (Fermentas) to confirm the existence of the insert in the plasmids. We sequenced the insert by an ABI 3100 sequencing machine using SP6 and T7 standard primers. After verifying the presence of mtDNA fragment 1 in the plasmid, we used the plasmid as a template for PCR amplification utilizing the original fragment 1 amplification primers (as mentioned above). The purified PCR product was sequenced using SOLiD and analyzed (as mentioned above).
This work was funded by an Israeli Science Foundation grant 387/08 and a grant from the National Institute of Biotechnology in the Negev (NIBN) (D.M.). TwinsUK was funded by the Wellcome Trust and the NIHR Biomedical Resource Centre Grant to Guys and St Thomas’ hospitals and KCL. D.G. was supported by the MRC.
Supplementary Material
Supplementary Data
The authors would like to thank Ms Anya Gorodetzky for her contribution during the very early stages of the work and to Gabriela Surdulescu and the staff at the Department of Twins Research Center, London, UK.
Conflict of Interest statement. T.S. is an NIHR Senior Investigator and an ERC Senior Researcher.
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