Selection against Pathogenic mtDNA Mutations in a Stem Cell Population Leads to the Loss of the 3243A→G Mutation in Blood

Article (PDF Available)inThe American Journal of Human Genetics 82(2):333-43 · February 2008with60 Reads
DOI: 10.1016/j.ajhg.2007.10.007 · Source: PubMed
The mutation 3243A-->G is the most common heteroplasmic pathogenic mitochondrial DNA (mtDNA) mutation in humans, but it is not understood why the proportion of this mutation decreases in blood during life. Changing levels of mtDNA heteroplasmy are fundamentally related to the pathophysiology of the mitochondrial disease and correlate with clinical progression. To understand this process, we simulated the segregation of mtDNA in hematopoietic stem cells and leukocyte precursors. Our observations show that the percentage of mutant mtDNA in blood decreases exponentially over time. This is consistent with the existence of a selective process acting at the stem cell level and explains why the level of mutant mtDNA in blood is almost invariably lower than in nondividing (postmitotic) tissues such as skeletal muscle. By using this approach, we derived a formula from human data to correct for the change in heteroplasmy over time. A comparison of age-corrected blood heteroplasmy levels with skeletal muscle, an embryologically distinct postmitotic tissue, provides independent confirmation of the model. These findings indicate that selection against pathogenic mtDNA mutations occurs in a stem cell population.

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Selection against Pathogenic mtDNA Mutations
in a Stem Cell Population Leads to the Loss
of the 3243A/G Mutation in Blood
Harsha Karur Rajasimha,
Patrick F. Chinnery,
and David C. Samuels
The mutation 3243A/G is the most common heteroplasmic pathogenic mitochondrial DNA (mtDNA) mutation in humans, but it is
not understood why the proportion of this mution decreases in blood during life. Changing levels of mtDNA heteroplasmy are funda-
mentally related to the pathophysiology of the mitochondrial disease and correlate with clinical progression. To understand this process,
we simulated the segregation of mtDNA in hematopoietic stem cells and leukocyte precursors. Our observations show that the percent-
age of mutant mtDNA in blood decreases exponentially over time. This is consistent with the existence of a selective process acting at the
stem cell level and explains why the level of mutant mtDNA in blood is almost invariably lower than in nondividing (postmitotic) tissues
such as skeletal muscle. By using this approach, we derived a formula from human data to correct for the change in heteroplasmy over
time. A comparison of age-corrected blood heteroplasmy levels with skeletal muscle, an embryologically distinct postmitotic tissue,
provides independent confirmation of the model. These findings indicate that selection against pathogenic mtDNA mutations occurs
in a stem cell population.
Individuals with pathogenic mutations in their mitochon-
drial DNA (mtDNA) typically have a mixture of wild-type
and mutant mtDNA in each cell, a condition called heter-
A heteroplasmic cell can compensate for the
presence of mutant mtDNA until a certain threshold in
the mutation level is reached, beyond which cell function
is compromised.
The level of mtDNA heteroplasmy can
vary across the tissues in an individual
and can even
vary across the cells within a single tissue.
For the most
common heteroplasmic pathogenic mutation, 3243A/G
(MIM #590050.0001),
the general pattern to the tissue
variation is that the level of this pathogenic mtDNA muta-
tion is lower in dividing cell types, and cells recently
derived from dividing cells, than it is in long-lived post-
mitotic cells in the same individual. Specifically, mtDNA
3243A/G mutation levels in blood samples are almost
always lower than the mutation level determined from
a muscle biopsy.
The tissue variation and the variation
with time of mtDNA heteroplasmy are dependent on the
particular mutation. Most of the available clinical data is
for the 3243A/G mutation heteroplasmy, and we focus
primarily on this mutation in this analysis. Longitudinal
clinical studies have shown that 3243A/G blood hetero-
plasmy levels tend to slowly decrease over time.
is generally thought to occur through selection at the cel-
lular level, but it is not clear whether this occurs in the
stem cell population or the proliferating and differentiat-
ing blood cell precursors. The pathogenesis of diseases
caused by mtDNA mutations is driven by the loss of func-
tion of a cell type or the actual loss of the cells themselves.
The specific phenotype depends on the specific cell type
most strongly affected in that subject, due to intrinsic var-
iability in the heteroplasmy levels across tissues, sensitivity
of the cell (and the subject) to cofactors, or high energy re-
quirements of the cell type. Understanding the long-time-
scale dynamics of mtDNA heteroplasmy in different cell
populations in the body is a basic step in understanding
the pathology and the progression of these diseases.
In this paper, we analyze clinical data from two sets of
patients: those with the 3243A/G mutation and those
with the 8344A/G mutation (MIM #590060.0001). The
3243A/G mutation occurs in one of the mtDNA leucine
transfer RNA (tRNA) genes (MTTL1). Although this muta-
tion was first described
in mitochondrial encephalomy-
opathy with lactic acidosis and stroke-like episodes (MELAS
[MIM 540000]), the spectrum of phenotypes caused by
this mutation is extremely broad, extending from diabetes
and deafness to hypertrophic cardiomyopathy and retini-
tis pigmentosa. This high phenotypic variability might
in part be due to tissue-specific differences within each
individual in the heteroplasmy level of the mutation.
The 8344A/G mutation occurs in the mtDNA lysine
tRNA gene (MTTK). This mutation is associated with Myo-
clonic Epilepsy with Ragged-Red Fibers Syndrome (MERRF
[MIM #545000]) and can also cause multiple lipomata,
cardiomyopathy, optic atrophy, myopathy, ataxia, and
dementia. The 8344A/G mutation is also one of many
mutations associated with Leigh Syndrome (MIM
#256000), a relapsing encephalopathy that usually pres-
ents in childhood and is characterized by lesions in the
basal ganglia.
Although the presence of a mtDNA mutation in blood
rarely causes a clinical problem, understanding why het-
eroplasmy changes in blood has important implications.
Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061 USA;
Mitochondrial Research Group and
Institute of Human Genetics, Newcastle University, The Medical School, Newcastle-upon-Tyne NE2 4HH, UK
DOI 10.1016/j.ajhg.2007.10.007. ª2008 by The American Society of Human Genetics. All rights reserved.
The American Journal of Human Genetics 82, 333–343, February 2008 333
First, this tissue type can be affected by mtDNA mutations
because large-scale deletions of mtDNA can lead to pancy-
topenia due to bone marrow aplasia (Pearson syndrome
[MIM #557000]
). Second, the decreasing level of mutant
mtDNA in blood presents a diagnostic challenge because
the mutation might not be detectable in blood samples
from affected individuals with standard techniques.
Third, changing heteroplasmy levels with time introduces
a major confounding variable when one is studying the
transmission of mtDNA heteroplasmy, compromising stud-
ies of the mtDNA genetic bottleneck in humans
and lim-
iting the development of genetic-counseling guidelines.
Finally, understanding the mechanisms behind changing
heteroplasmy levels in blood is likely to have broader im-
plications for other stem cell populations and the role of
age-related diseases.
A few serial measurements of 3243A/G heteroplasmy
have been made in blood samples from the same patient
at different time points. These studies reported a decrease
in the proportion of mutant mtDNA by approximately
0.5% to 2% per year,
on the basis of pairs of measure-
ments separated by several years. With this limited infor-
mation, only a single value for the rate of decrease of
mtDNA blood heteroplasmy can be calculated for each
subject. It has not been known whether the variability in
the measured rate of heteroplasmy decrease is due to vari-
ation across subjects or variation in the rate over time.
However, on the basis of the simulation results reported
here, we have reanalyzed published data to show that
the rate of decrease in 3243A/G blood heteroplasmy is
not constant, but is instead exponentially decreasing with
time in a consistent manner across many subjects.
We have separated the problem of changes in blood het-
eroplasmy level into two questions. First, how does the
heteroplasmy level in the hematopoietic stem cell popula-
tion change over time? Second, how well does the hetero-
plasmy level in peripheral blood represent the hetero-
plasmy levels in the hematopoietic stem cell population?
We have developed two separate simulations to address
these two questions.
Material and Methods
Clinical Data
The clinical data were identified from a systematic search of the lit-
erature. Papers were identified that contained both the hetero-
plasmy level for an mtDNA point mutation measured in blood
and the subject’s age at sample collection. The focus was on iden-
tifying papers reporting data for multiple subjects. We concen-
trated on the 3243A/G and 8344A/G mutations because of
the relatively large amount of data available for these two mu-
tations. A total of 275 unique data pairs were identified for
and 48 unique data pairs were identified
for 8344A/G.
For the 3243A/G mutation, a subset of these
studies was identified that also reported mtDNA heteroplasmy in
muscle tissue.
In cases where the data were
only given in graphical form, the approximate numerical values
were determined from these published graphs with the software
Engauge Digitizer. For the analysis of changes in blood hetero-
plasmy levels over long time scales, one study was identified
that had two measurements of 3243A/G in blood separated by
9 to 19 years, for six subjects.
To extend this dataset, our collab-
orators gathered this data for a further 11 subjects.
We used two related simulations: a stem cell model (Figure 1A) and
a model of the rapid expansion of blood progenitor cells (Fig-
ure 1B). The simulation parameters are summarized in Table 1.
In the stem cell model, we simulated populations of 20,000 to
2,000,000 stem cells with up to approximately 1000 to 2000
mtDNA molecules per cell for a maximum duration of 100 years.
The actual number of mtDNA per hematopoietic stem cell is
unknown, so we used a range of values and used the simulation
results to determine the behavior for an arbitrary mtDNA copy
number per cell. For simplicity, the initial heteroplasmy level in
all stem cells was set to a uniform value, P
. In the progenitor
cell model, we began with a single cell and modeled a series of ap-
proximately 20 cell divisions
to form the mature blood cells.
Both simulation models contained many common features.
Each mtDNA molecule in the simulation can be of either a wild-
type mtDNA or a mutant-type mtDNA. We only considered point
substitution mutations in this study because the dynamics of de-
letions might be different. Each simulated cell had integer values
W and M representing the number of wild-type and mutant
mtDNA in that cell. These numbers changed over time because
of the processes of mtDNA replication (Figure 2A), degradation
(Figure 2B), and cell division
(Figure 2C). Note that although
the total number of mtDNA molecules in the simulated cell is
N ¼ W þ M, the total number N was not held constant over
time and varied as W and M varied independently. The variables
W and M were the basic variables of this model.
Cell Division
At each cell division, we assumed that the mtDNA molecules in the
original cell were distributed randomly to each daughter cell with
equal probability (Figure 2C). We modeled this with a Poisson dis-
tribution separately for the values W and M, with a mean value of
W/2 and M/2 molecules distributed to each daughter cell. The sum
Figure 1. Schematic Diagram of the Two Cell-Division Models
Open ovals represent stem cells. Shaded ovals represent progenitor
334 The American Journal of Human Genetics 82, 333–343, February 2008
of the mtDNA copy numbers in the two daughter cells equaled the
copy number in the original cell. The timing of the cell division was
stochastic, with mean value D for the time between cell divisions.
For the hematopoietic stem cell model, we set D ¼ 1 year,
and for
the blood progenitor model, we set D ¼ 1 day.
In the stem cell
model (Figure 1A), the two daughter cells were randomly assigned
identities as stem cells or progenitor cells, with equal probabil-
(because we were modeling the consequences of stem cell
division, we did not incorporate the complicated feedback mecha-
nisms controlling the stem cell self-renewal
because these de-
tailed mechanisms were not considered relevant to this problem).
The daughter cells labeled as ‘stem cells’ were followed through
subsequent cell divisions, and the daughter cells labeled ‘progeni-
tor cells’ were removed from this simulation, which was designed
to only model the hematopoietic stem cell population. In the
blood progenitor model (Figure 1B) all daughter cells were labeled
as progenitor cells, and were retained in that simulation.
Mitochondrial DNA molecules were removed from the simulation
(Figure 2B) with a half-life, T
, which we typically set in the
range 10 to 20 days.
Both wild-type and mutant mtDNA were de-
graded with the same half-life. In one time step of length Dt, the
number of wild-type mtDNA molecules lost to random degra-
dation (W
) was calculated from a Poisson distribution with
mean value of (ln(2) W Dt/T
), where W was the number of
wild-type mtDNA in the cell. A similar calculation determined
the number of mutant mtDNA molecules degraded.
Mitochondrial DNA Replication
The simulated cells copied their mtDNA (Figure 2A) at a rate R to
compensate for the loss of mtDNA by degradation and to repopu-
late the cell with mtDNA after cell divisions. Let N
be the target
total number of mtDNA in the cell. Note that N
was merely a
parameter used for the calculation of the mtDNA replication rate
and that it might be slightly different than the total number of
mtDNA molecules actually present in the simulation at a given time,
N ¼ W þ M. In steady state, the actual copy number N fluctuated
about the target number N
. The mtDNA replication rate R was mod-
eled as a sum of two terms:
R ¼
=DÞ: (1)
The first term was the replication needed to replace degraded
mtDNA. The second term was the replication rate needed to com-
pensate for the decrease in mtDNA copy number from cell divi-
sions. This replication rate was distributed proportionally over
the mutant and wild-type mtDNA in the cell. The total number
of wild-type mtDNA copied (W
) and mutant mtDNA copied
) in one time step of length Dt was R Dt ¼ W
þ M
(Figure 2A). The separation into W
and M
was made
through a binomial probability distribution.
Cell Loss
For the modeling of the pathogenic effect of the mutant mtDNA,
any cell with a mutation level above a set threshold m
was re-
moved from the simulation. Values in the range 60% < m
100% were used in the simulation. This range covers the range
of reported values for the threshold of 3243A/G.
This model
of cell loss was used in both the hematopoietic stem cell simula-
tion and in the blood precursor simulation.
Measurements of the Rate of Heteroplasmy Decrease
To remove the effect of an initial transient (the shoulder in the ini-
tial short period of simulation in Figure 3 A) on the measurement
of the heteroplasmy decay rate, we only used simulation data after
the mutation level had dropped to below 90% of its initial value.
Because the average mutation level in the simulation decreased
Table 1. Simulation Parameter Values
Parameter Meaning Values
D Mean time between cell
1 year (stem cell model) or
1 day (progenitor model)
mtDNA half-life 10 to 20 days
Cells with mutation level
greater than m
removed from the
60% to 100%
Target number of mtDNA
molecules in simulated
cell (used to set
replication rate). Also
used to set the initial
copy number in the cell.
1000 to 2000
Initial mutation level in
0% to 100%
Dt Simulation time step 1 hr
No symbol Total simulation duration 100 years (stem cell
model) or 20 to 25 days
(progenitor model)
No symbol Total number of cells 20,000 to 2,000,000
(stem cell model) or
1 to 1,000,000
(progenitor model)
Figure 2. Schematic Diagrams of the mtDNA Dynamics in the
(A) In relaxed replication, the number of wild-type (W) and mutant
(M) mtDNA molecules was increased over one time step by the
amounts W
and M
, respectively.
(B) In the random-degradation model, the numbers W and M were
decreased over one time step by W
and M
(C) At a cell-division event, the values for W and M were randomly
distributed to the two daughter cells.
The American Journal of Human Genetics 82, 333–343, February 2008 335
over time, we only used the mutation-level values down to 10%
mutant, to remove the effects of noise due to discreteness at low
levels of mutant mtDNA copy number per cell.
Hematopoietic Stem Cell Simulation Results
The processes of relaxed replication of mtDNA,
tion of mtDNA, and random segregation between daugh-
ter cells all caused the heteroplasmy levels in this popula-
tion of stem cells to spread out. As the distribution of
heteroplasmy widened through random genetic drift,
some cells developed a high mutation heteroplasmy level
and were removed from the simulation, modeling the
pathogenic effect of the mtDNA mutation. Over time,
this loss of cells with high mtDNA mutation level gradu-
ally decreased the average mutation level of the simulated
hematopoietic stem cell population. Decreasing hetero-
plasmy levels in blood have been reported for the
3243A/G mutation,
based on two measurements
spaced over a few years. These measurements are usually
interpreted as a simple linear rate of decrease in the mutant
mtDNA levels in blood samples. Our simulation results
suggest that the rate of loss of heteroplasmy from blood
stem cells is actually exponential (Figure 3A), not a con-
stant linear rate.
In the model, the average mutation level as a function of
time was described by a decaying exponential, after an
initial transient period (Figure 3A). By varying the model
parameters [mtDNA half-life T
and initial copy number
per cell N
¼ W(0) þ M(0)], we found that the average mu-
tation level m(t) as a function of time, t, had the functional
, (2)
where m
was a constant approximately equal to the initial
mutation level and C was a dimensionless constant, with
the value of C ¼ 1.45 5 0.07 determined from the simula-
tions (Table 2).
Sets of simulations where the mutation-level threshold
for cell removal, m
, was varied showed that the decay
constant C is a function of the mutation threshold (Fig-
ure 3B), with lower thresholds causing faster loss of the
mutation from the stem cell population, an intuitively
reasonable behavior. In the range 60% % m
% 100%,
this dependence was well described by function
C ¼ C
Þ, (3)
where the constant values determined by a linear fit were
¼1.1 5 0.1 and A ¼ (230 5 8)%. Even at a threshold
of 100% (Figure 3B), requiring the simulated cell to fix on
the mutation before the cell is removed, the simulation
still showed an exponential decrease in heteroplasmy level
in the hematopoietic stem cell population.
Blood Precursor Simulation Results
The hematopoietic stem cell simulation modeled the
changes in the population of blood stem cells over time.
To relate this to measurements taken from samples of pe-
ripheral blood, we needed to know how mtDNA hetero-
plasmy levels can change through the process of forming
the mature blood cells from the stem cell population. We
modeled this very simply as a series of rapid cell divisions
with a mean cell division period of 1 day.
For the purpose
of this model, we ignored all of the complications of the
different types of blood cells, and we focused just on the
basic question of how a rapid expansion in the cell popu-
lation could affect the mean mtDNA heteroplasmy level
Figure 3. Simulation Results for the Blood Stem Cell Model
(A) An exponential decrease in mean mutation level in blood stem
cell model. Results are shown for two simulations with different
initial mutation levels, 70% and 30%. Other simulation parameters
were T
¼ 10 days, N ¼ 1000, and m
¼ 90%.
(B) Dependence of the decay constant C, defined in Equation 2,on
the mutation threshold for loss of the stem cell, m
. The line is
a fit to the data with Equation 3 (with R
¼ 0.968 and p < 0.0001).
Table 2. The Value of the Exponential-Decay Constant C in
Equation 2 Computed for Various Simulation Parameters
Number of
Stem Cells
mtDNA Copy
Number, N
Level, m (%) C
20,000 1,000 10 30 1.4251
20,000 1,000 10 70 1.4212
20,000 1,000 20 30 1.5234
20,000 1,000 20 70 1.5459
20,000 2,000 10 30 1.3870
20,000 2,000 10 70 1.3662
20,000 2,000 20 30 1.5530
20,000 2,000 20 70 1.4423
2,000,000 1,000 10 30 1.4075
The parameters N
, and the initial heteroplasmy levels were varied. The
mean decay constant in these simulations was C ¼ 1.45, with standard
deviation ¼ 0.07.
336 The American Journal of Human Genetics 82, 333–343, February 2008
in these cells. Figure 4 shows a set of ten typical simulation
results. Figure 4A shows the increase in the cell number as
a function of time. The variation in this plot across re-
peated simulations shows the sensitivity of the total cell
number to the stochastic timing of the first few cell divi-
sions. This variation is not critical for the simulation re-
sults on mtDNA heteroplasmy levels. The primary result
is in Figure 4B, where the mean mutant mtDNA level,
m(t), is shown. Throughout this expansion from a single
initial cell, the mean mutant mtDNA level remained rela-
tively constant, varying only a few percent. In this model
of an exponentially expanding population of cells, there
was no consistent shift in heteroplasmy level, as there
was in the stem cell model, even though both simulations
included the threshold mechanism for removing cells with
high mutation heteroplasmy. Our conclusion is that the
mean mtDNA mutation level measured in peripheral
blood is a good measure of the mean mutation level in
the stem cell population from which those cells were de-
rived. Moreover, the lack of any significant effect of selec-
tion in the differentiating (non-stem cell) blood precursors
(Figure 4B) indicates that the selective mechanism must be
acting at the stem cell level and not as the committed
blood cell precursors mature. This is consistent with popu-
lation genetic models and observations, which show that
selection does not act significantly on an exponentially
expanding population.
Analysis of Experimental Data
The primary results of our modeling were qualitative ones:
first, that the mean mtDNA mutation levels in hematopoi-
etic stem cells should decrease exponentially over long
time scales, and second, that measurements of mutation
levels in peripheral blood samples should be a good proxy
for the mutation level in the stem cell population. To apply
these concepts to the published clinical data on mtDNA
mutation levels in blood, we first needed a way to deter-
mine whether the observed mtDNA heteroplasmy de-
creases exponentially, as predicted by the simulations.
The existing clinical data consists only of pairs of measure-
ments spaced over a few years at most. With just pairs of
measurements, not a long time series, we can only calcu-
late the rate of change of heteroplasmy. For an exponential
decay, like Equation 2, the rate of change of heteroplasmy
is not constant, but is instead proportional to the hetero-
plasmy level. Therefore, a plot of the rate of mtDNA heter-
oplasmy change versus initial heteroplasmy level should
be a linear plot with zero intercept and negative slope. If
the decay is constant instead of exponential, then data
on this plot will lie in a horizontal line with zero slope.
Because the decrease of blood mtDNA heteroplasmy in
the simulation was very slow (Figure 3A), we need clinical
data with measurements separated in time by approxi-
mately a decade. The best existing clinical data available
was from a study by Rahman et al.
where measurements
of 3243A/G heteroplasmy in blood were separated by 9
to 19 years. However, that study only reported values for
six individuals. To extend the dataset, we and our collabo-
rators carried out repeat measurements of blood hetero-
plasmy on 11 individuals (all with the 3243A/ G muta-
tion) to compare to measurements that had been made 5
or more years previously. Those data have been published
The new data from the Pyle et al. study
together well with the data from the Rahman et al. study
(Figure 5, note that this is plotted so that an exponential de-
cay in mtDNA heteroplasmy with time shows as a straight
line). Taken together, these two clinical datasets give a con-
sistent picture of an exponential decrease in blood mtDNA
heteroplasmy levels over time in a consistent manner in
different individuals from different studies.
Serial measurements from the same patient are rare, par-
ticularly over a long time period. The vast majority of the
available clinical data consist of heteroplasmy measure-
ments at a single time or at relatively closely spaced times.
Even those data can be used to test the predictions of this
model. One intuitively obvious consequence of this expo-
nential decay model is that we should not observe high
3243A/G blood heteroplasmy levels in older individuals.
Figure 4. Simulation Results for the Progenitor Cell Model
Results are shown for five repetitions of the simulation starting at
70% mutant and five repetitions starting at 30% mutant.
(A) The exponential increase in the number of simulated cells,
starting from a single progenitor cell and dividing on average
once per day.
(B) The mean heteroplasmy of the cells over the course of the
expansion in the number of cells.
The American Journal of Human Genetics 82, 333–343, February 2008 337
We can quantify this limitation by defining a maximum
blood heteroplasmy level as
max½mðtÞ ¼ ð100%ÞexpðStÞ, (4)
where S ¼ 0.020 5 0.003 (1/years) was the slope measured
from the data in Figure 5. Blood heteroplasmy measure-
ments for 3243A/G mtDNA mutations should fall under
this maximum limit. We identified 23 published stud-
reporting 3243A/G blood mtDNA hetero-
plasmy levels together with the subjects’ age, with a total
of 275 unique data points. The data lie below the predicted
maximum heteroplasmy (Figure 6A) except for five mea-
surements that lie just above the line, and the data extend
from zero mutation up to the predicted maximum. There is
a region of the plot, at young age and low heteroplasmy,
with very few data points. This is likely to be just an ascer-
tainment effect, because most patients with 3243A/G
present with symptoms in their teenage years or older.
The few data points in Figure 6A that lie just above the
predicted maximum heteroplasmy are not a concern.
These could easily be caused by very slight individual var-
iations in the rate of decrease of the 3243A/G mutation.
Only cases that lie far above the predicted maximum het-
eroplasmy limit would be of concern. Four such cases
were reported in Hammans et al.,
and they were recog-
nized by those authors as being very unusual. All four cases
came from a single small pedigree that also contained a ho-
moplasmic T to C transition at position 3290, in the same
tRNA gene as the pathogenic 3243A/G mutation. The
3290T/C sequence variant was not found in 140 controls
or 50 patients. Hammans et al. noted that this family had
a unique clinical phenotype and suggested that this
3290T/C sequence variant might have altered the pathol-
ogy of the 3243A/G mutation. El Meziane et al.
also shown experimentally that a second mutation, at
position 12300 in that case, can suppress the pathogenicity
of the 3243A/G mutation.
On the basis of this analysis of the published data, we
can suggest a simple method for correcting for the subject’s
age in blood heteroplasmy measurements. We defined an
age-corrected mutation level as follows:
¼ m
: (5)
These age-corrected blood heteroplasmy measurements
are plotted in Figure 6B.
For the 3243A/G mutation, the heteroplasmy levels
clearly decrease with age, and we have shown that the
loss of hematopoietic stem cells with high heteroplasmy
levels can lead to this exponential decrease. However, it is
possible that for some other pathogenic mtDNA mutations,
Figure 6. Comparison of Clinical Data to a Predicted Maximum
Mutation Heteroplasmy in Peripheral Blood
(A) The theoretical maximum blood mutation level (Equation 4)
and 275 measured values from 23 separate studies
on subjects with the 3243A/G mutation. The theoretical upper
limit is set with an initial heteroplasmy of 100%.
(B) The 3243A/G data modified by the age correction (Equation 5).
(C) Blood heteroplasmy data (48 measured values) for the
8344A/G mutation.
The clinical results plotted here show
no indication of a decrease in blood heteroplasmy with the sub-
jects’ age for this mutation.
Figure 5. Analysis of Clinical Data to Test for an Exponential
Decrease in Peripheral Blood Heteroplasmy
The data are taken from two independent experiments
on the
3243A/G mutation. The line is a linear fit to the data, with slope
0.020 5 0.003 (1/year) and intercept 0.14 5 0.14 (%/year)
¼ 0.68 and p < 10
). A linear plot of this data with negative
slope indicates an exponential decay of the blood heteroplasmy.
338 The American Journal of Human Genetics 82, 333–343, February 2008
these stem cells could remain viable even when homoplas-
mic for the mutation. In these cases, or in the case of neu-
tral mutations, we would not expect to see a loss of the
mtDNA mutation over time from the blood. These low-
penetrance pathogenic mutations were excluded from the
analysis of Figures 6A and 6B. One such mutation for which
a significant amount of heteroplasmy data exists is the
8344A/G mutation. Indeed, when blood heteroplasmy
measurements for subjects with this mutation
plotted as a function of age, an age-dependent upper limit
to heteroplasmy did not appear (Figure 6C), indicating that
this mutation is not significantly decreased in the blood
over time.
In the 3243A/G mutation, measurements of blood het-
eroplasmy levels are almost always less than the hetero-
plasmy levels measured in muscle biopsies.
For data sets
where we have the measured heteroplasmy in blood and
in muscle, along with the subjects’ age, we can test to see
whether the age-corrected blood heteroplasmy is consis-
tent with the muscle heteroplasmy.
Figure 7A, we plot blood heteroplasmy versus muscle het-
eroplasmy for the 3243A/G mutation. The line marks
equal heteroplasmy values. Although there is a significant
correlation between the blood and muscle heteroplasmy
values, all of the subjects had lower heteroplasmy in blood
compared to muscle. Also, there is a pronounced age effect
visible in this data, with older subjects generally having
a greater difference between muscle and blood hetero-
plasmy. Assuming that muscle heteroplasmy is relatively
constant over time, we applied the age correction of Equa-
tion 5 to the blood heteroplasmy levels (Figure 7B). Both
the blood measurements and the age-corrected blood
measurements are highly significantly correlated with the
muscle heteroplasmy measurements (p < 0.0001 in both
cases). The improvement is that the age-corrected data
clustered around the line of equal heteroplasmy, though
there was still some tendency for the age-corrected blood
heteroplasmy to be lower than the muscle heteroplasmy,
particularly for muscle heteroplasmy levels above 75%.
However, the age stratification disappeared in the age-cor-
rected data, with all three age groups overlapping.
Our analysis of the clinical data relating mtDNA hetero-
plasmy levels of the 3243A/G mutation in blood to
the subjects’ age (Figures 5 and 6) indicates that the mu-
tant heteroplasmy level does decrease exponentially with
time, as predicted by the simulation. Our simulations of
hematopoetic stem cells showed that the mechanism of
loss of stem cells with high mutation level was sufficient
to cause an exponential decrease in blood heteroplasmy.
However, that does not mean that other mechanisms
could not also lead to an exponential decrease. For exam-
ple, Battersby et al.
have reported a very fast exponential
decrease in blood heteroplasmy in mice constructed to
have a mixture of two naturally occurring nonpathogenic
murine mtDNA strains, BALB/c and NZB. In these mice,
the NZB mtDNA strain was lost from the blood with a decay
constant S
¼ 1.387 year
(almost 70 times faster than
the value S
¼ 0.02 year
from Figure 5). These au-
thors test, and reject, the hypothesis that this selection
against NZB mtDNA is occurring through an immune
mechanism. They also did not find any functional differ-
ence in oxidative phosphorylation as a function of the cel-
lular heteroplasmy level, indicating that a threshold effect
and the cell-loss mechanism modeled in our simulation are
likely not playing a role in those mice. The mechanism
driving the rapid loss of NZB heteroplasmy in the blood
of those mice is still unknown.
The data presented in Figures 5 and 6A, illustrating the
exponential loss of pathogenic mtDNA from human blood,
came exclusively from measurements on the 3243A/G
mutation, the most commonly studied and the most
commonly occurring
heteroplasmic pathogenic mtDNA
point mutation. Determination of whether this exponen-
tial loss occurs for other pathogenic mtDNA mutants
will require either a number of pairs of longitudinal blood
Figure 7. An Application of the Age Correction for Blood
(A) Comparison of 3243A/G heteroplasmy levels in muscle and in
(B) Comparison with the blood heteroplasmy corrected for
age (Equation 5). The corrections were made with a value of
S ¼ 0.020 year
, the value determined from the data in Figure 5.
The solid lines are the lines of equal blood and muscle hetero-
The American Journal of Human Genetics 82, 333–343, February 2008 339
heteroplasmy measurements spaced by approximately
10 years (Figure 5) or a much larger number of single-
time-point measurements (Figure 6). In the latter case, it
is important that a wide range of subject ages are sampled,
so that the upper limit on blood heteroplasmy as a function
of age can be detected.
We have focused our analysis on the 3243A/G mtDNA
point mutations. However, this does not mean that other
mtDNA mutations causing low-penetrance diseases, such
as Leber’s Hereditary Optic Neuropathy (LHON [MIM
#535000]), cannot also show a decrease in blood hetero-
plasmy in some individuals. For those individuals who
have the currently unknown cofactors that lead to the dis-
ease state, it is possible that the loss of high-heteroplasmy
blood stem cells will occur (if this stem cell population is
affected by the cofactors). Indeed, sporadic cases of de-
creasing blood heteroplasmy of LHON mutations have
been reported.
We can speculate that the observance
of a decrease in blood heteroplasmy over time, or of a lower
heteroplasmy in blood compared to muscle, in carriers of
LHON mtDNA mutations could indicate the presence of
the pathogenic cofactors in those individuals, even in
cases where the overall heteroplasmy level of the LHON
mutation might not be high enough to cause the disease
state (along with the presence of the cofactor). In LHON,
an interacting nuclear genetic locus
or transient expo-
sure to environmental toxins (such as excess alcohol or to-
bacco, see
for a positive correlation and
for no corre-
lation) might act as cofactors for the development of the
disease state.
We have also focused our analysis on blood samples, for
obvious practical reasons. The same behavior of exponen-
tially decreasing mtDNA heteroplasmy would also occur in
any other tissue that would experience the loss of cells
with high heteroplasmy. Decreasing 3243A/G mutation
levels with age have been reported in epithelial cells
and in buccal mucosa.
In contrast, skeletal muscle is
a large multinucleate postmitotic cell, and there is minimal
muscle cell loss in patients even with high levels of
This fundamental difference probably ex-
plains why mutant genomes tend to accumulate in nondi-
viding tissues such as skeletal muscle,
as we have dis-
cussed before.
Despite the known variability of blood
heteroplasmy with age, blood samples are still very com-
monly used for the determination of heteroplasmy levels
in individuals, most commonly in asymptomatic individ-
uals. The use of blood samples for the determination of
heteroplasmy might lead to misleading artifacts, such as
the reported increase in heteroplasmy of a pathogenic
mtDNA mutation across generations,
a measurement
that naturally entails comparisons of individuals with rad-
ically different ages. The possibility of this artifact has been
raised before;
however, this is the first time that a viable
method of correcting for this artifact has been suggested.
Do the observations in blood have a broader relevance?
Recent observation in colonic
and gastric
show that pathogenic point mutations of mtDNA accumu-
late with age within the stem cell niche of healthy subjects,
causing mitochondrial dysfunction in the daughter cell
population. The work described here is directly applicable
to situations such as this if the mutation leads to cellular
dysfunction or cell death in any self-renewing population.
For hematological stem cells, at least, our observations do
suggest that stem cell viability is partly dependent upon
an intact mitochondrial respiratory chain. Our observa-
tions also provide some hope for developing autologous
stem cell therapies for mtDNA diseases, where, paradoxi-
cally, disease progression is associated with the cleansing
of the inherited mtDNA mutation from stem cells (an ef-
fect that is explained by the results of this study). These
could be harnessed for the delivery of wild-type mtDNA
to diseased cells, as has been demonstrated in human
skeletal muscle.
P.F.C. is a Wellcome Trust Senior Clinical Research Fellow. We are
very grateful to Angela Pyle for allowing us advanced access to
her data before publication.
Received: July 5, 2007
Revised: September 18, 2007
Accepted: October 3, 2007
Published online: February 7, 2008
Web Resources
The URLs for data presented herein are as follows:
Engauge Digitizer,
Hematopoietic Stem Cell simulation codes,
Online Mendelian Inheritance in Man (OMIM), http://www.ncbi.
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    • "It is thus theoretically possible that paternal mtDNA was originally transmitted to the offspring and subsequently lost from buccal cells before the DNA samples were acquired. The loss of mutated mtDNA has been observed in patients with mtDNA diseases, most typically m.3243A>G, probably through selection against a deleterious allele at the stem cell level [26] . However, for most inherited heteroplasmic mtDNA mutations (eg m.8993T>C), the percentage level of the mutation is the same in a wide range of different tissues [27], including buccal swabs. "
    [Show abstract] [Hide abstract] ABSTRACT: Recent reports have questioned the accepted dogma that mammalian mitochondrial DNA (mtDNA) is strictly maternally inherited. In humans, the argument hinges on detecting a signature of inter-molecular recombination in mtDNA sequences sampled at the population level, inferring a paternal source for the mixed haplotypes. However, interpreting these data is fraught with difficulty, and direct experimental evidence is lacking. Using extreme-high depth mtDNA re-sequencing up to ~1.2 million-fold coverage, we find no evidence that paternal mtDNA haplotypes are transmitted to offspring in humans, thus excluding a simple dilution mechanism for uniparental transmission of mtDNA present in all healthy individuals. Our findings indicate that an active mechanism eliminates paternal mtDNA which likely acts at the molecular level.
    Full-text · Article · May 2015
    • "In the NZB mouse model, the rate of selection is currently held to be independent of initial heteroplasmy, and constant with time (Battersby et al., 2003). The human A3243G mtDNA mutation is lost continually from the blood of patients (Rahman et al., 2001; Rajasimha et al., 2008). To find out whether mtDNA dynamics change during organismal development or stay constant at all times, we applied an appropriate population genetic model for mtDNA segregation in our data. "
    [Show abstract] [Hide abstract] ABSTRACT: The dynamics by which mitochondrial DNA (mtDNA) evolves within organisms are still poorly understood, despite the fact that inheritance and proliferation of mutated mtDNA cause fatal and incurable diseases. When two mtDNA haplotypes are present in a cell, it is usually assumed that segregation (the proliferation of one haplotype over another) is negligible. We challenge this assumption by showing that segregation depends on the genetic distance between haplotypes. We provide evidence by creating four mouse models containing mtDNA haplotype pairs of varying diversity. We find tissue-specific segregation in all models over a wide range of tissues. Key findings are segregation in postmitotic tissues (important for disease models) and segregation covering all developmental stages from prenatal to old age. We identify four dynamic regimes of mtDNA segregation. Our findings suggest potential complications for therapies in human populations: we propose "haplotype matching" as an approach to avoid these issues.
    Full-text · Article · Jun 2014
    • "The model and the data indicate that the loss of heteroplasmy is an exponential function of the form: M age-corrected ¼ m(t)e St , where m(t) is the average mutational level as a function of time and S ¼ 0.020 + 0.003 (1/year) (Rajasimha et al. 2008). While the percentage of the mtDNA tRNA Leu(UUR) 3243A.G mutation heteroplasmy declines with age in blood, the heteroplasmy level of the MERRF tRNA Lys 8344A.G mutation does not (Rajasimha et al. 2008). Hence, different mtDNA mutations show different tissue-specific stabilities indicating that a variety of physiological processes must modulate the fate of heteroplasmic variants in tissues. "
    [Show abstract] [Hide abstract] ABSTRACT: The unorthodox genetics of the mtDNA is providing new perspectives on the etiology of the common "complex" diseases. The maternally inherited mtDNA codes for essential energy genes, is present in thousands of copies per cell, and has a very high mutation rate. New mtDNA mutations arise among thousands of other mtDNAs. The mechanisms by which these "heteroplasmic" mtDNA mutations come to predominate in the female germline and somatic tissues is poorly understood, but essential for understanding the clinical variability of a range of diseases. Maternal inheritance and heteroplasmy also pose major challengers for the diagnosis and prevention of mtDNA disease.
    Full-text · Article · Nov 2013
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