A twitter thread by the main author:
[
[Link to So...
**Somatic Mutation:** a type of mutation that occurs in any of the ...
Endogenous mutational processes refer to mutations that result from...
### Peto's paradox
**Peto's paradox refers to the lack of correl...
If somatic mutations contribute to ageing, theory predicts that som...
> ***"First, they are histologically identifiable units that line t...
Observable anti-correlation between the number of mutations per yea...
Despite uncertainty in the estimates of both somatic mutation rates...
> ***"To investigate the relationship between somatic mutation rate...
> ***"Even if clear causal links between somatic mutations and agei...
Nature | Vol 604 | 21 April 2022 | 517
Article
Somatic mutation rates scale with lifespan
across mammals
Alex Cagan
1,15
, Adrian Baez-Ortega
1,15
, Natalia Brzozowska
1
, Federico Abascal
1
,
Tim H. H. Coorens
1
, Mathijs A. Sanders
1,2
, Andrew R. J. Lawson
1
, Luke M. R. Harvey
1
,
Shriram Bhosle
1
, David Jones
1
, Raul E. Alcantara
1
, Timothy M. Butler
1
, Yvette Hooks
1
,
Kirsty Roberts
1
, Elizabeth Anderson
1
, Sharna Lunn
1
, Edmund Flach
3
, Simon Spiro
3
,
Inez Januszczak
3,4
, Ethan Wrigglesworth
3
, Hannah Jenkins
3
, Tilly Dallas
3
, Nic Masters
3
,
Matthew W. Perkins
5
, Robert Deaville
5
, Megan Druce
6,7
, Ruzhica Bogeska
6,7
,
Michael D. Milsom
6,7
, Björn Neumann
8,9
, Frank Gorman
10
, Fernando Constantino-Casas
10
,
Laura Peachey
10,11
, Diana Bochynska
10,12
, Ewan St. John Smith
13
, Moritz Gerstung
14
,
Peter J. Campbell
1
, Elizabeth P. Murchison
10
, Michael R. Stratton
1
& Iñigo Martincorena
1
The rates and patterns of somatic mutation in normal tissues are largely unknown
outside of humans
1–7
. Comparative analyses can shed light on the diversity of
mutagenesis across species, and on long-standing hypotheses about the evolution of
somatic mutation rates and their role in cancer and ageing. Here we performed
whole-genome sequencing of 208 intestinal crypts from 56 individuals to study the
landscape of somatic mutation across 16 mammalian species. We found that somatic
mutagenesis was dominated by seemingly endogenous mutational processes in all
species, including 5-methylcytosine deamination and oxidative damage. With some
dierences, mutational signatures in other species resembled those described in
humans
8
, although the relative contribution of each signature varied across species.
Notably, the somatic mutation rate per year varied greatly across species and
exhibited a strong inverse relationship with species lifespan, with no other life-history
trait studied showing a comparable association. Despite widely dierent life histories
among the species we examined—including variation of around 30-fold in lifespan
and around 40,000-fold in body mass—the somatic mutation burden at the end of
lifespan varied only by a factor of around 3. These data unveil common mutational
processes across mammals, and suggest that somatic mutation rates are
evolutionarily constrained and may be a contributing factor in ageing.
Somatic mutations accumulate in healthy cells throughout life. They
underpin the development of cancer
9
and, for decades, have been
speculated to contribute to ageing
10–12
. Directly studying somatic muta-
tions in normal tissues has been challenging owing to the difficulty of
detecting mutations present in single cells or small clones in a tissue.
Only recent technological developments, such as invitro expansion of
single cells into colonies
13,14
, microdissection of histological units
8,15
,
single-cell sequencing
16,17
or single-molecule sequencing
18
, are begin-
ning to enable the study of somatic mutation in normal tissues.
Over the last few years, studies in humans have started to provide
a detailed understanding of somatic mutation rates and the contri-
bution of endogenous and exogenous mutational processes across
normal tissues
8,13,14,19,20
. These studies are also revealing how, as we
age, some human tissues are colonized by mutant cells that contain
cancer-driving mutations, and how this clonal composition changes
with age and disease. With the exception of some initial studies, far less
is known about somatic mutation in other species
1–7
. Yet, comparative
analyses of somatic mutagenesis would shed light on the diversity of
mutagenic processes across species, and on long-standing questions
regarding the evolution of somatic mutation rates and their role in
cancer and ageing.
A decades-long hypothesis on the evolution of somatic mutation
rates pertains to the relationship between body mass and cancer risk.
Some models predict that the risk of cancer should increase propor-
tionally to the number of cells at risk of transformation. However, there
appears to be no correlation between body mass and cancer risk across
https://doi.org/10.1038/s41586-022-04618-z
Received: 17 August 2021
Accepted: 7 March 2022
Published online: 13 April 2022
Open access
Check for updates
1
Cancer, Ageing and Somatic Mutation (CASM), Wellcome Sanger Institute, Hinxton, UK.
2
Department of Hematology, Erasmus MC Cancer Institute, Rotterdam, the Netherlands.
3
Wildlife
Health Services, Zoological Society of London, London, UK.
4
The Natural History Museum, London, UK.
5
Institute of Zoology, Zoological Society of London, London, UK.
6
Division of
Experimental Hematology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
7
Heidelberg Institute for Stem Cell Technology and Experimental Medicine GmbH (HI-STEM),
Heidelberg, Germany.
8
Wellcome Trust–Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK.
9
Department of Clinical Neurosciences, University
of Cambridge, Cambridge, UK.
10
Department of Veterinary Medicine, University of Cambridge, Cambridge, UK.
11
Bristol Veterinary School, Faculty of Health Sciences, University of Bristol,
Langford, UK.
12
Department of Pathology, Faculty of Veterinary Medicine, Universitatea de Stiinte Agricole si Medicina Veterinara, Cluj-Napoca, Romania.
13
Department of Pharmacology,
University of Cambridge, Cambridge, UK.
14
European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK.
15
These authors contributed equally: Alex Cagan,
Adrian Baez-Ortega.
e-mail: ac36@sanger.ac.uk; im3@sanger.ac.uk
518 | Nature | Vol 604 | 21 April 2022
Article
species
21,22
. This observation, known as Peto’s paradox, suggests that
the evolution of larger body sizes is likely to require the evolution of
stronger cancer suppression mechanisms
23,24
. Whether evolutionary
reduction of cancer risk across species is partly achieved by a reduction
of somatic mutation rates remains unknown.
A second long-standing hypothesis on the evolution of somatic muta-
tion rates relates to the proposed role of somatic mutations in ageing.
Multiple forms of molecular damage, including somatic mutations,
telomere attrition, epigenetic drift and loss of proteostasis, have been
proposed to contribute to ageing, but their causal roles and relative con-
tributions remain debated
25,26
. Evolutionary theory predicts that species
will evolve protection or repair mechanisms against life-threatening
damage to minimize death from intrinsic causes, but that selection is
too weak to delay ageing far beyond the typical life expectancy of an
organism in the wild (Supplementary Note1). If somatic mutations
contribute to ageing, theory predicts that somatic mutation rates may
inversely correlate with lifespan across species
27,28
. This prediction has
remained largely untested owing to the difficulty of measuring somatic
mutation rates across species.
Detection of somatic mutations across species
The study of somatic mutations with standard whole-genome sequenc
-
ing requires isolating clonal groups of cells recently derived from a
single cell
8,13,14
. To study somatic mutations across a diverse set of
mammals, we isolated 208 individual intestinal crypts from 56 indi-
viduals across 16 species with a wide range of lifespans and body sizes:
black-and-white colobus monkey, cat, cow, dog, ferret, giraffe, har-
bour porpoise, horse, human, lion, mouse, naked mole-rat, rabbit, rat,
ring-tailed lemur and tiger (Supplementary Table1). We chose intestinal
crypts for several reasons. First, they are histologically identifiable units
that line the epithelium of the colon and small intestine and are amena-
ble to laser microdissection. Second, human studies have confirmed
that individual crypts become clonally derived from a single stem cell
and show a linear accumulation of mutations with age, which enables
the estimation of somatic mutation rates through genome sequencing
of single crypts
8
. Third, in most human crypts, most somatic mutations
are caused by endogenous mutational processes common to other
tissues, rather than by environmental mutagens
8,18
.
A colon sample was collected from each individual, with the excep-
tion of a ferret from which only a small intestine sample was available.
This sample was included because results in humans have shown that
the mutation rates of colorectal and small intestine epithelial stem cells
are similar
14,20
(Extended Data Fig.1). We then used laser microdissection
on histological sections to isolate individual crypts for whole-genome
sequencing with a low-input library preparation method
29
(Fig.1a,
Extended Data Fig.2, Supplementary Table2), with the exception of
human crypts, for which sequencing data were obtained from a pre-
vious study
8
. A bioinformatic pipeline was developed to call somatic
mutations robustly in all these species despite the variable quality of
their genome assemblies (Methods). The distribution of variant allele
fractions of the mutations detected in each crypt confirmed that crypts
are clonal units in all species, enabling the study of somatic mutation
rates and signatures (Extended Data Fig.3).
We found substantial variation in the number of somatic single-base
substitutions across species and across individuals within each species
(Fig.1b). For five species with samples from multiple individuals (dog,
human, mouse, naked mole-rat and rat), linear regression confirmed
a clear accumulation of somatic mutations with age (Fig.1c, Extended
Data Fig.4, Supplementary Table 3). All linear regressions were also con-
sistent with a non-significant intercept. This resembles observations
in humans
20
and suggests that the time required for a single stem cell
to drift to fixation within a crypt is a small fraction of the lifespan of a
species. This facilitates the estimation of somatic mutation rates across
species by dividing the number of mutations in a crypt by the age of the
individual (Supplementary Table4). The number of somatic insertions
and deletions (indels) was consistently lower than that of substitutions
in all crypts (Fig.1b), in agreement with previous findings in humans
8
.
Mutational signatures across mammals
Somatic mutations can be caused by multiple mutational processes, involv-
ing different forms of DNA damage and repair. Different processes cause
characteristic frequencies of base substitution types and indels at different
sequence contexts, often referred to as mutational signatures, which can
be inferred from mutation data
30
. Across species, the mutational spectra
showed clear similarities, with a dominance of cytosine-to-thymine (C>T)
substitutions at CpG sites, as observed in human colon, but with consid-
erable variation in the frequency of other substitution types (Fig.2a).
To quantify the contribution of different mutational processes to the
observed spectra, we applied mutational signature decomposition
8,30
.
We used a Bayesian model to infer mutational signatures denovo, while
accounting for differences in genome sequence composition across
species, and using the COSMIC human signature SBS1 (C>T substitu
-
tions at CpG sites) as a fixed prior to ensure its complete deconvolution
31
(Methods). This approach identified two signatures beyond SBS1, labelled
SBSB and SBSC, which resemble COSMIC human signatures SBS5 and
SBS18, respectively (cosine similarities 0.93 and 0.91) (Fig.2b).
This analysis suggests that the same three signatures that dominate
somatic mutagenesis in the human colon are dominant in other mam-
mals: SBS1, which is believed to result from the spontaneous deamina-
tion of 5-methylcytosine
8,32
; SBSB (SBS5), a common signature across
human tissues that may result from endogenous damage and repair
18,33
;
and SBSC (SBS18), which is dominated by C>A substitutions and attrib-
uted to oxidative DNA damage
30
. Signature SBSC contains a minor
component of T>A substitutions (resembling COSMIC SBS34), which
appear to be the result of DNA polymerase slippage at the boundaries
between adjacent adenine and thymine homopolymer tracts, but could
also reflect assembly errors at those sites
33
. Although all of the spe-
cies that we examined shared the three mutational signatures, their
contributions varied substantially across species (Fig.2c). SBSC was
particularly prominent in mouse and ferret, and the ratio of SBS1 to
SBSB/5 varied from approximately 1.2 in rat or rabbit to 6.4 in tiger.
In several species with data from multiple individuals, separate linear
regressions for each signature confirmed that mutations from all three
signatures accumulate with age (Fig.2d, Extended Data Fig.5).
Although signature deconvolution identified three signatures that
are active across species, we noticed some differences in the muta-
tional profile of signature SBSB among species. To investigate this
further, we inferred independent versions of SBSB from each species,
while accounting for differences in genome sequence composition
(Methods). This revealed inter-species variability in the mutational
profile of this signature, particularly in the C>T component (Extended
Data Fig.6). Species-specific versions of SBSB showed different simi-
larities to the related human signatures SBS5 and SBS40. For example,
SBSB inferred from the human data showed a stronger similarity with
the reference human signatureSBS5 (cosine similarities with SBS5
and SBS40: 0.93 and 0.84), whereas SBSB from rabbit more closely
resembled the reference human signatureSBS40 (0.87 and 0.91). These
observations are consistent with the hypothesis that SBS5 and SBS40
result from a combination of correlated mutational processes, with
some variation across human tissues
18,33
and across species.
Analysis of the indel mutational spectra revealed a dominance of
the human indel signatures ID1 and ID2, which are characterized by
single-nucleotide indels at A/T homopolymers, and probably caused
by strand slippage during DNA replication
30
(Extended Data Fig.7a).
The ratio of insertions (ID1) to deletions (ID2) appears to vary across
species, possibly reflecting a differential propensity for slippage of the
template and nascent DNA strands
30
. In addition, the indel spectra sug-
gest a potential contribution of signature ID9 (the aetiology of which
Nature | Vol 604 | 21 April 2022 | 519
remains unknown) to human, colobus, cow, giraffe and rabbit. Analysis
of indels longer than one base pair also suggested the presence of a
signature of four-base-pair insertions at tetrameric repeats, which was
particularly prevalent in mouse and tiger; a pattern of insertions of five
or more base pairs at repeats in colobus; and a pattern of deletions of
five or more base pairs at repeats, which was prominent in rabbit and
resembles ID8 (a signature possibly caused by double-strand break
repair through non-homologous end joining
30
) (Extended Data Fig.7a).
Other mutational processes and selection
The apparent lack of additional mutational signatures is noteworthy.
A previous study of 445 colorectal crypts from 42 human donors found
that many crypts were affected by a signature that was later attrib-
uted to colibactin, a genotoxin produced by pks
+
strains of Escheri-
chia coli
8,34,35
. Analysing the original human data and our non-human
data with the same methodology, we found evidence of colibactin
mutagenesis in 21% of human crypts, but only uncertain evidence
of colibactin in one non-human crypt (0.6%) (Extended Data Fig.7b,
Methods). This revealed a significant depletion of colibactin mutagen-
esis in the non-human crypts studied (Fisher’s exact test, P = 7 × 10
–14
).
The apparent difference in colibactin mutagenesis observed between
species, or between the cohorts studied, might result from a different
prevalence of pks
+
E. coli strains
36
or a different expression of colibactin
by pks
+
E. coli across species
37
. Finally, we also searched for evidence of
APOBEC signatures (SBS2 and SBS13), which have been reported in a
small number of human crypts and are believed to be caused by APOBEC
DNA-editing cytidine deaminases. We detected APOBEC signatures in
2% (n = 9) of human crypts and found only uncertain evidence in one
non-human crypt (P = 0.30).
Beyond substitutions and indels, crypts from the eight species with
chromosome-level genome assemblies were inspected for large-scale
copy number changes (at least 1 Mb) (Methods). Studies in humans
have found that large-scale copy number changes are relatively rare in
Mous
e
Rat
Ferret
Rabbit
Dog
Cat
Lion
Co
w
Tige
r
Giraffe
Ring-tailed lemur
Black-and-white colobus
Hors
e
Human
Naked mole-ratMouseHuman
Substitutions
Indels
Harbour porpoise
Dog
02468101214
0
1,000
2,000
3,000
4,000
020406080
0
500
1,000
1,500
2,000
0 0.5 1.0 1.5 2.0
0
200
400
600
800
02468
a
b
c
Horse Lion Naked mole-rat Rat
Mutations per genome
0
1,000
2,000
3,000
4,000
Naked mole-rat
Mutations
0
1,000
2,000
3,000
Age (years) Age (years) Age (years) Age (years)
Mutations
Mutations
Mutations
Fig. 1 | Somatic mutation burden in mammalian colorectal crypts. a,
Histology images of colon samples from horse, lion, naked mole-rat and rat,
with one colorectal crypt marked in each. Scale bars, 250 µm. b, Burden of
somatic substitutions and indels per diploid genome in each colorectal crypt
sample (corrected for the size of the analysable genome). Samples are grouped
by individual, with samples from the same individual coloured in the same
shade. Species, and individuals within each species, are sorted by mean
mutation burden. c, Linear regression of somatic substitution burden
(corrected for analysable genome size) on individual age for dog, human,
mouse and naked mole-rat samples. Samples from the same individual are
shown in the same colour. Regression was performed using mean mutation
burdens per individual. Shaded areas indicate 95% confidence intervals of the
regression line.
520 | Nature | Vol 604 | 21 April 2 022
Article
normal tissues, including colorectal epithelium
8
. Consistent with these
results, we only identified 4 large copy number changes across the 162
crypts included in this analysis: 2 megabase-scale deletions in 2 crypts
from the same cow; the loss of an X chromosome in a female mouse
crypt; and a 52-Mb segment with copy-neutral loss of heterozygosity
in a human crypt (Extended Data Fig.8, Methods). These results sug-
gest that large-scale somatic copy number changes in normal tissues
are also rare in other mammalian species.
Previous analyses in humans have shown that most somatic muta-
tions in colorectal crypts accumulate neutrally, without clear evidence
of negative selection against non-synonymous mutations and with a
low frequency of positively selected cancer-driver mutations
8
. To study
somatic selection in our data, we calculated the exome-wide ratio of
non-synonymous to synonymous substitution rates (dN/dS) in each of the
12 species with available genome annotation. To do so and to detect genes
under positive selection, while accounting for the effects of trinucleotide
Exposure
0
0.2
0.4
0.6
0.8
1.0
SBS1 SBSB SBSC
Mutation fraction
Human
C>A C>G C>T
T>A
T> CT>G
Cow
Dog Ferret Giraffe
Harbour porpoise Horse Lion
Mouse Naked mole-rat Rabbit
Rat Ring-tailed lemur Tiger
Cat
Black-and-white colobus
Mutation fraction
a
b
SBS1 SBSB SBSC
c
d
Age (years)
SBS1
SBSB
SBSC
2,000
3,000
Mutations
020406080 0 0.5 1.0 1.5 2.0 02468
Mouse
Naked mole-rat
Human
Mouse
Ra
t
Ferret
Rabbit
Do
g
Ca
t
Lion
Co
w
Tiger
Giraffe
Ring-tailed lemu
r
Black-and-white colobus
Hors
e
Naked mole-rat
Huma
n
Harbour porpoise
Mutations
Mutations
Mutation fraction
Mutation fraction
Mutation
probability
Mutation
probability
Mutation
probability
0
1,000
0
400
800
200
600
1,000
200
100
300
500
400
0
0.3
0
0.05
0
0.05
0
0.05
0
0.1
0
0.1
0
0.1
0
0.1
0
0.05
0
0.1
0
0.1
0
0.1
0
0.1
0
0.1
0
0.05
0
0.1
0
0.1
0
0.05
0
0.10
0
0.05
0.15
Age (years) Age (years)
Fig. 2 | Mutational processes in the mammalian colon. a, Mutational spectra
of somatic substitutions in each species. The x axis shows 96 mutation types on
a trinucleotide context, coloured by base substitution type. b, Mutational
signatures inferred from (SBSB, SBSC) or fitted to (SBS1) the species
mutational spectra shown in a, and normalized to the human genome
trinucleotide frequencies. The y axis shows mutation probability. c, Estimated
contribution of each signature to each sample. Samples are arranged
horizontally as in Fig.1b. d, Linear regression of signature-specific mutation
burdens (corrected for analysable genome size) on individual age for human,
mouse and naked mole-rat samples. Regression was performed using mean
mutation burdens per individual. Shaded areas indicate 95% confidence
intervals of the regression line.
Nature | Vol 604 | 21 April 2022 | 521
c f
Mutation rate per year
0
300
600
900
End-of-lifespan burden
e
2,000
4,000
6,000
Fraction of inter-species variance explained
0
0.2
0.4
0.6
0.8
1/lifespan
log
10
(BMR)
log
10
(adult mass)
Litter size
8,000
Individually
Paired with 1/lifespan
Mean mutation rate per year
0
200
400
600
800
0123456
Intercept = 678.94
Slope = –108.54
FVE = 0.44
Cat
Black-and-white colobus
Cow
Dog
Ferret
Giraffe
Horse
Human
Lion
Mouse
Rabbit
Rat
Ring-tailed lemur
Tiger
0
Mouse
Ra
t
Ferre
t
Rabbit
Dog
Ca
t
Lion
Co
w
Tige
r
Giraffe
Ri
n
g
-tailed lemur
Black-and-white colobus
Hors
e
Naked mole-rat
Huma
n
1.0
BMR residuals
log
10
(lifespan)
log
10
(adult mass)
FVE = 0.21
P = 0.085
1
23456
1.5
2.0
2.5
3.0
d
–3 –2 –1 01
2
–0.2
–0.1
0
0.1
0.2
Adult mass residual (allometric)
FVE = 0.06
P = 0.39
log
10
(mutation rate per year)
b
Lifespan residual (allometric)
–0.4
0
0.4
–0.4
0
0.4
Mutation rate residual (allometric)
Substitutions
Indels
log
10
(mutation rate per year)
Mutation rate residual (allometric)
FVE = 0.85
P = 1.0 × 10
–
6
0.5 1.0 1.5 2.0
1.5
2.0
2.5
3.0
Slope = –0.86
95% CI: (–1.08, –0.65)
1,200
a
0
200
400
600
800
Lifespan (years)
k = 3,206.4
95% CI: 2,683.9–3,728.9
FVE = 0.82
ELB range: 1,828.1–5,378.7
020406080
Cat
Black-and-white colobus
Cow
Dog
Ferret
Giraffe
Horse
Human
Lion
Mouse
Naked mole-rat
Rabbit
Rat
Ring-tailed lemur
Tiger
Mean mutation rate per year
FVE = 0.82
P = 3.2 × 10
–6
log
10
(adult mass)
Naked mole-rat
Fig. 3 | Associations between somatic mutation rates and life-history traits.
a, Somatic mutation rate per year and expected end-of-lifespan mutation
burden (ELB) per crypt. Samples are arranged horizontally as in Fig.1b; harbour
porpoise samples were excluded owing to the age of the sampled individual
being unknown. b, Left, allometric regression of somatic mutation rate on
lifespan. Right, regression of body-mass-adjusted residuals for somatic
mutation rate and lifespan (partial correlation; Methods). Regression was
performed using mean mutation rates per species. Shaded areas represent 95%
confidence intervals (CI) of regression lines. FVE and P values (by F-test) are
indicated (note that, for simple linear regression, FVE = R
2
). The dashed line
denotes a reference slope of –1. c, Zero-intercept LME regression of somatic
mutation rate on inverse lifespan (1/lifespan), presented on the scale of
untransformed lifespan (x axis). For simplicity, the y axis shows mean mutation
rates per species, although rates per crypt were used in the regression. The
darker shaded area indicates 95% CI of the regression line, and the lighter
shaded area marks a twofold deviation from the line. Point estimate and 95% CI
of the regression slope (k), FVE and range of end-of-lifespan burden are
indicated. d, Allometric regression and linear regression of lifespan-adjusted
residuals, for somatic mutation rate and body mass (elements as described in b).
e, Free-intercept LME regression of somatic mutation rate on log-transformed
body mass. The y axis shows mean mutation rates per species, although rates
per crypt were used in the regression. Shaded area indicates 95% bootstrap
interval of the regression line (n = 10,000 replicates). Point estimates of the
regression intercept and slope, and FVE, are indicated. f, FVE values for
free-intercept LME models using 1/lifespan or other life-history variables
(alone or combined with 1/lifespan) as explanatory variables. Error bars
indicate 95% bootstrap intervals (n = 10,000).
522 | Nature | Vol 604 | 21 April 2022
Article
sequence context and mutation rate variation across genes, we used
the dNdScv model
38
(Methods). Although the limited number of coding
somatic mutations observed in most species precluded an in-depth analy-
sis of selection, exome-wide dN/dS ratios for somatic substitutions were
not significantly different from unity in any species, in line with previous
findings in humans
8
(Extended Data Fig.9). Gene-level analysis did not
find genes under significant positive selection in any species, although
larger studies are likely to identify rare cancer-driver mutations
8
.
Correlation with life-history traits
Whereas similar mutational processes operate across the species sur-
veyed, the mutation rate per genome per year varied widely. Across
the 15 species with age information, we found that substitution rates
per genome ranged from 47 substitutions per year in humans to 796
substitutions per year in mice, and indel rates from 2.5 to 158 indels per
year, respectively (Fig.3a, Supplementary Table4, Methods).
To i nve st ig at e t h e r el at i on s h ip b et we en s o ma t i c mu ta t io n ra t es ,
lifespan and other life-history traits, we first estimated the lifespan
of each species using survival curves. We used a large collection of
mortality data from animals in zoos to minimize the effect of extrin-
sic mortality (Extended Data Fig.10). We defined lifespan as the age
at which 80% of individuals reaching adulthood have died, to reduce
the effects of outliers and variable cohort sizes that affect maximum
lifespan estimates
39
(Methods). Notably, we found a tight anticor-
relation between somatic mutation rates per year and lifespan across
species (Fig.3b). A log-log allometric regression yielded a strong
linear anticorrelation between mutation rate per year and lifespan
(fraction of inter-species variance explained (FVE) = 0.85, P = 1 × 10
–6
),
with a slope close to and not significantly different from –1. This sup-
ports a simple model in which somatic mutation rates per year are
inversely proportional to the lifespan of a species (rate
∝
1/lifespan),
such that the number of somatic mutations per cell at the end of the
lifespan (the end-of-lifespan burden; ELB) is similar in all species.
To further study the relationship between somatic mutation rates
and life-history variables, we used linear mixed-effects (LME) regres-
sion models. These models account for the hierarchical structure of
the data (with multiple crypts per individual and multiple individuals
per species), as well as the heteroscedasticity of somatic mutation rate
estimates across species (Methods). Using these models, we estimated
that the inverse of lifespan explained 82% of the inter-species variance
in somatic substitution rates (rate = k/lifespan) (P = 2.9 × 10
–9
; Fig.3c),
with the slope of this regression (k) representing the mean estimated
ELB across species (3,206.4 substitutions per genome per crypt, 95%
confidence interval 2,683.9–3,728.9). Of note, despite uncertainty
in the estimates of both somatic mutation rates and lifespans, and
despite the diverse life histories of the species surveyed—including
around 30-fold variation in lifespan and around 40,000-fold variation in
body mass—the estimated mutation load per cell at the end of lifespan
varied by only around threefold across species (Table1). Analogous
results were obtained when repeating the analysis with estimates of
the protein-coding mutation rate, which may be a better proxy for the
functional effect of somatic mutations (85% of variance explained; ELB:
31 coding substitutions per crypt) (Extended Data Fig.11, Methods).
We next examined the association between somatic mutation rates
and adult body mass, which is known to be a common confounder in cor-
relations that involve lifespan
40,41
. An anticorrelation between somatic
mutation rates and body mass may be expected if the modulation of
cancer risk across species of vastly different sizes has been a major
factor in the evolution of somatic mutation rates. We observed that
log-transformed adult body mass was less strongly associated with
somatic substitution ratesthan the inverse of lifespan (allometric regres-
sion FVE = 0.21, Fig.3d; LME regression FVE = 0.44, Fig.3e). Given that
lifespan is correlated with body mass, we performed two tests to assess
whether body mass explained any variation in somatic mutation rates
that was not explained by lifespan. First, including both the inverse of
lifespan and log-transformed adult body mass in the regression model
suggested that body mass does not explain a significant amount of
variance in somatic mutation rates across species after accounting for
the effect of lifespan (likelihood ratio tests: P = 0.16 for body mass on
a model with lifespan; P < 10
–4
for lifespan on a model with body mass;
Fig.3f, Methods). Second, partial correlation analyses using allometric
regressions further confirmed that the association between somatic
mutation rates and lifespan is unlikely to be mediated by the effect of
body mass on both variables (lifespan residuals: P = 3.2 × 10
–6
, FVE = 0.82,
Fig.3b; body mass residuals: P = 0.39, FVE = 0.06, Fig.3d; Methods).
The fact that the variation in somatic mutation rates across species
appears to be dominated by lifespan rather than body size is also appar-
ent when looking at particularly informative species. Giraffe and naked
mole-rat, for instance, have similar somatic mutation rates (99 and 93
substitutions per year, respectively), in line with their similar lifespans
(80th percentiles: 24 and 25 years, respectively), despite a difference
of around 23,000-fold in adult body mass (Fig.3c, e). Similarly, cows,
giraffes and horses weigh much more than an average human, and yet
have somatic mutation rates that are several fold higher, in line with
expectation from their lifespan but not their body mass. Altogether, the
weak correlation between body mass and somatic mutation rates after
correction for lifespan suggests that the evolution of larger body sizes
may have relied on alternative or additional strategies to limit cancer
risk, as has been speculated
24,42
(Supplementary Note2). Of note, the
low somatic mutation rate of naked mole-rats, which is unusual for their
body mass but in line with their long lifespan (Fig.3c, e), might contrib-
ute to the exceptionally low incidence rates of cancer in this species
43
.
We found similar results for other life-history variables that have been
proposed to correlate with lifespan, namely basal metabolic rate (BMR)
and litter size
44
(Fig.3f). With the caveat that estimates for these variables
vary in quality, they showed weaker correlations with the somatic mutation
rate as single predictors, and small non-significant increases in explana-
tory power when considered together with lifespan (likelihood ratio tests:
P = 0.92 for litter size; P = 0.083 for log-BMR; P = 0.79 for allometric BMR
residuals; Fig.3f, Methods). We note that the results above are robust to
the use of alternative measures of the somatic mutation rate, including
the rate per exome or mutations per Mb (Extended Data Fig.11, Methods);
alternative estimates of lifespan, including maximum lifespan (Extended
Data Fig.12, Methods); alternative regression models, including a Bayesian
hierarchical model and a phylogenetic generalised least-squares regres-
sion, which accounts for the effect of phylogenetic relationships (Extended
Data Fig. 13a, b, Methods); and bootstrapping analyses at the level of indi-
viduals or species (Extended Data Fig.13c, Methods).
Mutational processes and lifespan
To i n ve s t i g a te wh e t h e r a s i n g l e b i o l o g i ca l p r o c e s s co u l d d r i v e t h e a s s o -
ciation between somatic mutation rates and lifespan, we analysed each
mutational signature separately. SBS1, SBSB/5 and SBSC/18 are believed
to result from different forms of DNA damage and are expected to be
Table 1 | Variation in adult body mass, lifespan, somatic
mutation rate and end-of-lifespan mutation burden across
the 16 mammalian species surveyed
Variable Minimum Maximum Fold variation
Adult mass (g) 20.50 800,000.00 39,024.39
Lifespan (years) 2.75 83.67 30.44
Mutation rate per year
(substitutions per genome)
47.12 796.42 16.90
End-of-lifespan burden
(substitutions per genome)
1,828.08 5,378.73 2.94
Species-level estimates are provided in Supplementary Tables3 and 6.
Nature | Vol 604 | 21 April 2022 | 523
subject to different DNA repair pathways
18,33
. They also appear to differ
in their association with the rate of cell division in humans, with SBS1
being more common in fast-proliferating tissues, such as colon and
embryonic or foetal tissues, and SBS5 dominating in post-mitotic cells
in the absence of cell division
14,18,20
. Overall, we found clear anticor-
relations between mutation rates per year and lifespan for the three
substitution signatures and for indels, suggesting that a single biologi-
cal process or DNA repair pathway is unlikely to be responsible for this
association (Fig.4). The total mutation burden also appears to show a
closer fit with lifespan than individual mutational processes, as meas-
ured by the range of end-of-lifespan burden for each process across
species (Fig.4). This might be expected if the observed anticorrelation
were the result of evolutionary pressure on somatic mutation rates.
DNA damage and somatic mutations in the mitochondrial genome
have also attracted considerable interest in the ageing field
45
. Our
whole-genome sequencing of individual crypts provided high coverage
of the mitochondrial genome, ranging from 2,188- to 29,691-fold. Nor-
malized against the nuclear coverage, these data suggest that colorectal
crypts contain on the order of around 100–2,000 mitochondrial genomes
per cell (Extended Data Fig.14a). Using a mutation-calling algorithm that
is sensitive to low-frequency variants, we found a total of 261 mitochon-
drial mutations across 199 crypts (Extended Data Fig.14a, Methods). The
mutational spectra across species appeared broadly consistent with that
observed in humans, with a dominance of C>T and A>G substitutions
that are believed to result from mitochondrial DNA (mtDNA) replication
errors rather than DNA damage
46
(Extended Data Fig.14b). Although the
low number of mitochondrial mutations detected per species precludes
a detailed analysis, the estimated number of somatic mutations per copy
of mtDNA also appears to show an anticorrelation with lifespan. Across
species, we obtained an average of 0.23 detectable mutations per copy
of the mitochondrial genome by the end of lifespan (Fig.4, Methods)—a
considerable burden given the coding-sequence density and the func-
tional relevance of the mitochondrial genome.
Discussion
Using whole-genome sequencing of 208 colorectal crypts from 56
individuals, we provide insights into the somatic mutational landscape
of 16 mammalian species. Despite their different diets and life histo-
ries, we found considerable similarities in their mutational spectra.
Three main mutational signatures explain the spectra across species,
albeit with varying contributions and subtle variations in the profile
of signature SBSB. These results suggest that, at least in the colorectal
epithelium, a conserved set of mutational processes dominate somatic
mutagenesis across mammals.
The most notable finding of this study is the inverse scaling of somatic
mutation rates with lifespan—a long-standing prediction of the somatic
mutation theory of ageing
11,27
. Considering evolutionary and mechanis-
tic models of ageing together provides a framework for discussing the
possible implications of these results for ageing (seeSupplementary
Note1). Jointly, these models predict ageing to be multifactorial, with
multiple forms of molecular and cellular damage contributing to organ-
ismal ageing owing to evolutionary limits to selection acting on the rates
of these processes. The inverse scaling of somatic mutation rates and
lifespan is consistent with somatic mutations contributing to ageing
and with somatic mutation rates being evolutionarily constrained,
although we discuss alternative explanations below. This interpreta-
tion is also supported by studies reporting more efficient DNA repair
in longer-lived species
47,48
. Somatic mutations could contribute to
ageing in different ways. Traditionally, they have been proposed to
contribute to ageing through deleterious effects on cellular fitness
11,49
,
but recent findings question this assumption (Supplementary Note1).
Instead, the discovery of widespread clonal expansions in ageing human
tissues
19,50–52
raises the possibility that some somatic mutations con
-
tribute to ageing by driving clonal expansions of functionally altered
cells at a cost to the organism
49,53,54
. Examples include the possible links
between clonal haematopoiesis and cardiovascular disease
54
; between
mutations in liver disease and insulin resistance
55
; and between driver
mutations in cavernomas and brain haemorrhages
49,53,56
. Detailed stud-
ies on the extent and effect of somatic mutations and clonal expansions
on age-related diseases and ageingphenotypes may help to clarify
the precise role—if any—of somatic mutations in ageing. Even if clear
causal links between somatic mutations and ageing are established,
ageing is likely to be multifactorial. Other forms of molecular damage
involved in ageing could be expected to show similar anticorrelations
with lifespan and, indeed, such anticorrelations have been reported
for telomere shortening and protein turnover
57,58
.
Alternative non-causal explanations for the observed anticorrelation
between somatic mutation rates and lifespan need to be considered.
One alternative explanation is that cell division rates couldscale with
lifespan and explain the observed somatic mutation rates. Available
estimates of cell division rates, although imperfect and limited to a few
species, do not readily support this argument (Methods). More impor-
tantly, studies in humans have shown that cell division rates are not a
major determinant of somatic mutation rates across human tissues
14,18
.
Another alternative explanation for the observed anticorrelation might
be that selection acts to reduce germline mutation rates in species with
longer reproductive spans, which in turn causes an anticorrelation of
somatic mutation rates and lifespan. Although selective pressure on
SBS1 mutations per genome per year
k = 2,129.3
95% CI: 1,687.2–2,571.4
FVE = 0.69
ELB: 870.9–3,423.9
SBSC mutations per genome per year
k = 393.7
95% CI: 224.1–563.2
FVE = 0.30
ELB: 113.4–1,171.3
k = 374.1
95% CI: 267.4–480.8
FVE = 0.78
ELB: 171.8–801.9
0
0.08
mtDNA mutations per mtDNA copy per year
k = 0.23
95% CI: 0.21–0.26
FVE = 0.94
ELB: 0.00–0.85
0.06
0.04
0.02
Lifespan (years)
k = 673.0
95% CI: 530.8–815.1
FVE = 0.85
ELB: 397.6–1,612.5
020406
08
0
Lifespan (years)
020406080
0
50
100
150
200
250
300
0
100
200
300
400
0
50
100
150
200
250
0
50
100
150
SBSB mutations per genome per yearIndels per genome per year
Fig. 4 | Association between mutation rate subtypes and species lifespan.
Zero-intercept LME regression of somatic rates of signature-specific
substitutions, indels and mtDNA mutations on inverse lifespan (1/lifespan),
presented on the scale of untransformed lifespan (x axis). For simplicity, y axes
present mean mutation rates per species, although mutation rates per crypt
were used in the regressions. The darker shaded areas indicate 95% confidence
intervals (CI) of the regression lines, and the lighter shaded areas mark a
twofold deviation from the regression lines. Point estimates and 95% CI of the
regression slope (k), fraction of inter-species variance explained by for each
model (FVE) and ranges of end-of-lifespan burden (ELB) are indicated.
524 | Nature | Vol 604 | 21 April 2022
Article
germline mutation rates could influence somatic mutation rates, it is
unlikely that germline mutation rates tightly determine somatic muta-
tion rates: somatic mutation rates in humans are 10–20 times higher
than germline mutation rates, show variability across cell types and
are influenced by additional mutational processes
18,20
. Overall, the
strong scaling of somatic mutation rates with lifespan across mammals,
despite the different rates between germline and soma and thevariable
contributions of different mutational processesacross species, sug-
gests that somatic mutation rates themselves have been evolutionarily
constrained, possibly through selection on multiple DNA repair path-
ways. Alternative explanations need to be able to explain the strength
of the scaling despite these differences.
Altogether, this study provides a detailed description of somatic
mutation across mammals, identifying common and variable features
and shedding light on long-standing hypotheses. Scaled across the tree
of life and across tissues, in species with markedly different physiolo-
gies, life histories, genome compositions and mutagenic exposures,
similar studies promise to transform our understanding of somatic
mutation and its effects on evolution, ageing and disease.
Online content
Any methods, additional references, Nature Research reporting sum-
maries, source data, extended data, supplementary information,
acknowledgements, peer review information; details of author contri-
butions and competing interests; and statements of data and code avail-
ability are available at https://doi.org/10.1038/s41586-022-04618-z.
1. Garcia, A. M. etal. Age- and temperature-dependent somatic mutation accumulation in
Drosophila melanogaster. PLoS Genet. 6, e1000950 (2010).
2. Behjati, S. etal. Genome sequencing of normal cells reveals developmental lineages and
mutational processes. Nature 513, 422–425 (2014).
3. Milholland, B. etal. Differences between germline and somatic mutation rates in humans
and mice. Nat. Commun. 8, 15183 (2017).
4. Schmid-Siegert, E. etal. Low number of fixed somatic mutations in a long-lived oak tree.
Nat. Plants 3, 926–929 (2017).
5. Jager, M. etal. Deficiency of nucleotide excision repair is associated with mutational
signature observed in cancer. Genome Res. 29, 1067–1077 (2019).
6. Orr, A. J. etal. A phylogenomic approach reveals a low somatic mutation rate in a
long-lived plant. Proc. R. Soc. B 287, 20192364 (2020).
7. López, E. H. & Palumbi, S. R. Somatic mutations and genome stability maintenance in
clonal coral colonies. Mol. Biol. Evol. 37, 828–838 (2020).
8. Lee-Six, H. etal. The landscape of somatic mutation in normal colorectal epithelial cells.
Nature 574, 532–537 (2019).
9. Stratton, M. R., Campbell, P. J. & Futreal, P. A. The cancer genome. Nature 458, 719–724
(2009).
10. Szilard, L. On the nature of the aging process. Proc. Natl Acad. Sci. USA 45, 30–45
(1959).
11. Morley, A. A. The somatic mutation theory of ageing. Mutat. Res. 338, 19–23 (1995).
12. Vijg, J. & Dong, X. Pathogenic mechanisms of somatic mutation and genome mosaicism
in aging. Cell 182, 12–23 (2020).
13. Welch, J. S. etal. The origin and evolution of mutations in acute myeloid leukemia. Cell
150, 264–278 (2012).
14. Blokzijl, F. etal. Tissue-specific mutation accumulation in human adult stem cells during
life. Nature 538, 260–264 (2016).
15. Moore, L. etal. The mutational landscape of normal human endometrial epithelium.
Nature 580, 640–646 (2020).
16. Lodato, M. A. etal. Aging and neurodegeneration are associated with increased
mutations in single human neurons. Science 359, 555–559 (2018).
17. Zhang, L. etal. Single-cell whole-genome sequencing reveals the functional landscape
of somatic mutations in B lymphocytes across the human lifespan. Proc. Natl Acad. Sci.
USA 116, 9014–9019 (2019).
18. Abascal, F. etal. Somatic mutation landscapes at single-molecule resolution. Nature 593,
405–410 (2021).
19. Martincorena, I. etal. Tumor evolution. High burden and pervasive positive selection of
somatic mutations in normal human skin. Science 348, 880–886 (2015).
20. Moore, L. etal. The mutational landscape of human somatic and germline cells. Nature
597, 381–386 (2021).
21. Peto, R., Roe, F. J., Lee, P. N., Levy, L. & Clack, J. Cancer and ageing in mice and men.
Br. J. Cancer 32, 411–426 (1975).
22. Vincze, O. etal. Cancer risk across mammals. Nature 601, 263–267 (2022).
23. Peto, R. Epidemiology, multistage models, and short-term mutagenicity tests.
Int. J. Epidemiol. 45, 621–637 (2016).
24. Tollis, M., Boddy, A. M. & Maley, C. C. Peto’s paradox: how has evolution solved the
problem of cancer prevention? BMC Biol. 15, 60 (2017).
25. López-Otín, C., Blasco, M. A., Partridge, L., Serrano, M. & Kroemer, G. The hallmarks of
aging. Cell 153, 1194–1217 (2013).
26. Schumacher, B., Pothof, J., Vijg, J. & Hoeijmakers, J. H. J. The central role of DNA damage
in the ageing process. Nature 592, 695–703 (2021).
27. Burnet, M. Intrinsic Mutagenesis: a Genetic Approach to Ageing (Springer, 1974).
28. Kirkwood, T. B. & Holliday, R. The evolution of ageing and longevity. Proc. R. Soc. B 205,
531–546 (1979).
29. Ellis, P. etal. Reliable detection of somatic mutations in solid tissues by laser-capture
microdissection and low-input DNA sequencing. Nat. Protoc. 16, 841–871 (2021).
30. Alexandrov, L. B. etal. The repertoire of mutational signatures in human cancer. Nature
578, 94–101 (2020).
31. Gori, K. & Baez-Ortega, A. sigfit: flexible Bayesian inference of mutational signatures.
Preprint at bioRxiv https://doi.org/10.1101/372896 (2020).
32. Lindahl, T. & Nyberg, B. Heat-induced deamination of cytosine residues in
deoxyribonucleic acid. Biochemistry 13, 3405–3410 (1974).
33. Zou, X. etal. A systematic CRISPR screen defines mutational mechanisms underpinning
signatures caused by replication errors and endogenous DNA damage. Nat. Cancer 2,
643–657 (2021).
34. Wilson, M. R. etal. The human gut bacterial genotoxin colibactin alkylates DNA. Science
363, eaar7785 (2019).
35. Pleguezuelos-Manzano, C. etal. Mutational signature in colorectal cancer caused by
genotoxic pks
+
E. coli. Nature 580, 269–273 (2020).
36. Smati, M. etal. Quantitative analysis of commensal Escherichia coli populations reveals
host-specific enterotypes at the intra-species level. MicrobiologyOpen 4, 604–615 (2015).
37. Oliero, M. etal. Oligosaccharides increase the genotoxic effect of colibactin produced by
pks
+
Escherichia coli strains. BMC Cancer 21, 172 (2021).
38. Martincorena, I. etal. Universal patterns of selection in cancer and somatic tissues. Cell
171, 1029–1041 (2017).
39. Moorad, J. A., Promislow, D. E. L., Flesness, N. & Miller, R. A. A comparative assessment of
univariate longevity measures using zoological animal records. Aging Cell 11, 940–948
(2012).
40. Speakman, J. R. Correlations between physiology and lifespan—two widely ignored
problems with comparative studies. Aging Cell 4, 167–175 (2005).
41. de Magalhães, J. P., Costa, J. & Church, G. M. An analysis of the relationship between
metabolism, developmental schedules, and longevity using phylogenetic independent
contrasts. J. Gerontol. A 62, 149–160 (2007).
42. Vazquez, J. M. & Lynch, V. J. Pervasive duplication of tumor suppressors in Afrotherians
during the evolution of large bodies and reduced cancer risk. eLife 10, e65041 (2021).
43. Smith, E. S. J., Schuhmacher, L.-N. & Husson, Z. The naked mole-rat as an animal
model in biomedical research: current perspectives. Open Access Anim. Physiol. 7,
137–148 (2015).
44. Millar, J. S. & Zammuto, R. M. Life histories of mammals: an analysis of life tables. Ecology
64, 631–635 (1983).
45. Kauppila, T. E. S., Kauppila, J. H. K. & Larsson, N.-G. Mammalian mitochondria and aging:
an update. Cell Metab. 25, 57–71 (2017).
46. Ju, Y. S. etal. Origins and functional consequences of somatic mitochondrial DNA
mutations in human cancer. eLife 3, e02935 (2014).
47. Hall, K. Y., Hart, R. W., Benirschke, A. K. & Walford, R. L. Correlation between
ultraviolet-induced DNA repair in primate lymphocytes and fibroblasts and species
maximum achievable life span. Mech. Ageing Dev. 24, 163–173 (1984).
48. Zhang, L. etal. Maintenance of genome sequence integrity in long- and short-lived
rodent species. Sci. Adv. 7, eabj3284 (2021).
49. Smith, J. M. Review lectures on senescence—I. The causes of ageing. Proc. R. Soc. B 157,
115–127 (1962).
50. Jaiswal, S. et al. Age-related clonal hematopoiesis associated with adverse outcomes.
N. Engl. J. Med. 371, 2488–2498 (2014).
51. Martincorena, I. etal. Somatic mutant clones colonize the human esophagus with age.
Science 362, 911–917 (2018).
52. Yokoyama, A. etal. Age-related remodelling of oesophageal epithelia by mutated cancer
drivers. Nature 565, 312–317 (2019).
53. Ren, A. A. etal. PIK3CA and CCM mutations fuel cavernomas through a cancer-like
mechanism. Nature 594, 271–276 (2021).
54. Jaiswal, S. & Libby, P. Clonal haematopoiesis: connecting ageing and inflammation in
cardiovascular disease. Nat. Rev. Cardiol. 17, 137–144 (2020).
55. Ng, S. W. K. etal. Convergent somatic mutations in metabolism genes in chronic liver
disease. Nature 598, 473–478 (2021).
56. Jaiswal, S. & Ebert, B. L. Clonal hematopoiesis in human aging and disease. Science 366,
eaan4673 (2019).
57. Swovick, K. etal. Interspecies differences in proteome turnover kinetics are correlated
with life spans and energetic demands. Mol. Cell. Proteomics 20, 100041 (2021).
58. Whittemore, K., Vera, E., Martínez-Nevado, E., Sanpera, C. & Blasco, M. A. Telomere
shortening rate predicts species life span. Proc. Natl Acad. Sci. USA 116, 15122–15127
(2019).
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
Open Access This article is licensed under a Creative Commons Attribution
4.0 International License, which permits use, sharing, adaptation, distribution
and reproduction in any medium or format, as long as you give appropriate
credit to the original author(s) and the source, provide a link to the Creative Commons license,
and indicate if changes were made. The images or other third party material in this article are
included in the article’s Creative Commons license, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons license and your
intended use is not permitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a copy of this license,
visit http://creativecommons.org/licenses/by/4.0/.
© The Author(s) 2022
A twitter thread by the main author:
[](https://twitter.com/ATJCagan/status/1428653113167302657?s=20&t=eft1p7VZeSxHNU_VkmybFA)
> ***"First, they are histologically identifiable units that line the epithelium of the colon and small intestine and are amena- ble to laser microdissection. Second, human studies have confirmed that individual crypts become clonally derived from a single stem cell and show a linear accumulation of mutations with age, which enables the estimation of somatic mutation rates through genome sequencing of single crypts. Third, in most human crypts, most somatic mutations are caused by endogenous mutational processes common to other tissues, rather than by environmental mutagens."***
Endogenous mutational processes refer to mutations that result from normal cellular processes like errors in DNA replication, depurination of DNA, and damage to DNA by oxygen free radicals.
Exogenous mutational processes refer to mutations caused by all types of physical, chemical and biological agents.
### TL;DR
As we age we accumulate DNA damage caused by somatic mutations. For decades there's been speculation that somatic mutations may contribute to aging - if true the theory predicts that they would inversely correlate with lifespan across species.
Assuming that every cell has some probability of becoming cancerous, large organisms should have an increased risk of developing cancer compared to small organisms. We don't observe this in nature - as a matter of fact animals with 1,000 times more cells than humans do not exhibit an increased cancer risk - this is called Peto's paradox.
For this study the researchers studied 16 mammalian species with a variation of around 30-fold in lifespan and around 40,000-fold in body mass. Despite these differences this study found that the somatic mutation burden at the end of lifespan varied only by a factor of around 3. These data unveil common mutational processes across mammals, and suggest that somatic mutation rates are evolutionarily constrained and may be a contributing factor in aging.
This study provides a detailed description of somatic mutation across mammals, identifying common and variable features and shedding light on long-standing hypotheses. The findings presented here deepen our understanding of somatic mutation and its effects on evolution, aging and disease.
If somatic mutations contribute to ageing, theory predicts that somatic mutation rates may inversely correlate with lifespan across species. This prediction has remained largely untested owing to the difficulty of measuring somatic mutation rates across species.
### Peto's paradox
**Peto's paradox refers to the lack of correlation between body size and cancer risk.** Assuming that every cell has some probability of becoming cancerous, large organisms should have an increased risk of developing cancer compared to small organisms. We don't observe this in nature - as a matter of fact animals with 1,000 times more cells than humans do not exhibit an increased cancer risk.
Learn more here: [Peto’s Paradox: Evolution’s Prescription for Cancer Prevention](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3060950/)
Observable anti-correlation between the number of mutations per year and lifespan - supporting a simple model in which somatic mutation rates per year are inversely proportional to the lifespan of a species.
Visual TL;DR
[Link to Source](https://twitter.com/ATJCagan/status/1428670644745539589/photo/1)
> ***"To investigate the relationship between somatic mutation rates, lifespan and other life-history traits, we first estimated the lifespan of each species using survival curves. We used a large collection of mortality data from animals in zoos to minimize the effect of extrinsic mortality (Extended Data Fig. 10). We defined lifespan as the age at which 80% of individuals reaching adulthood have died, to reduce the effects of outliers and variable cohort sizes that affect maximum lifespan estimates. Notably, we found a tight anticorrelation between somatic mutation rates per year and lifespan across species."***
Despite uncertainty in the estimates of both somatic mutation rates and lifespans, and despite the diverse life histories of the species surveyed - including around 30-fold variation in lifespan and around 40,000-fold variation in body mass - **the estimated mutation load per cell at the end of lifespan varied by only around threefold across species.**
> ***"Even if clear causal links between somatic mutations and ageing are established, ageing is likely to be multifactorial. Other forms of molecular damage involved in ageing could be expected to show similar anticorrelations with lifespan and, indeed, such anticorrelations have been reported for telomere shortening and protein turnover."***
**Somatic Mutation:** a type of mutation that occurs in any of the cells of the body except the sperm and egg cells. Somatic mutations are not passed on to children. These alterations can (but do not always) cause cancer or other diseases.