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IDENTIFICATION OF A MOLNUPIRAVIR-ASSOCIATED MUTATIONAL SIGNATURE IN SARS-COV-2
SEQUENCING DATABASES
View ORCID ProfileTheo Sanderson, View ORCID ProfileRyan Hisner, View ORCID
ProfileI’ah Donovan-Banfield, View ORCID ProfileThomas Peacock, View ORCID
ProfileChristopher Ruis
doi: https://doi.org/10.1101/2023.01.26.23284998
This article is a preprint and has not been peer-reviewed [what does this
mean?]. It reports new medical research that has yet to be evaluated and so
should not be used to guide clinical practice.
Theo Sanderson
1Francis Crick Institute, London, UK
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* For correspondence: theo.sanderson@crick.ac.uk cr628@cam.ac.uk
Ryan Hisner
2Independent researcher, USA
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I’ah Donovan-Banfield
3Department of Infection Biology and Microbiomes, Institute of Infection,
Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK
4NIHR Health Protection Research Unit in Emerging and Zoonotic Infections,
Liverpool, UK
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Thomas Peacock
5Department of Infectious Disease, Imperial College London, London, UK
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Christopher Ruis
6Molecular Immunity Unit, University of Cambridge Department of Medicine,
MRC-Laboratory of Molecular Biology, Cambridge, UK
7Department of Veterinary Medicine, University of Cambridge, Cambridge, UK
8Cambridge Centre for AI in Medicine, University of Cambridge, Cambridge, UK
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ABSTRACT
Molnupiravir, an antiviral medication that has been widely used against
SARS-CoV-2, acts by inducing mutations in the virus genome during replication.
Most random mutations are likely to be deleterious to the virus, and many will
be lethal. Molnupiravir-induced elevated mutation rates have been shown to
decrease viral load in animal models. However, it is possible that some patients
treated with molnupiravir might not fully clear SARS-CoV-2 infections, with the
potential for onward transmission of molnupiravir-mutated viruses. We set out to
systematically investigate global sequencing databases for a signature of
molnupiravir mutagenesis. We find that a specific class of long phylogenetic
branches appear almost exclusively in sequences from 2022, after the
introduction of molnupiravir treatment, and in countries and age-groups with
widespread usage of the drug. We calculate a mutational spectrum from the AGILE
placebo-controlled clinical trial of molnupiravir and show that its signature,
with elevated G-to-A and C-to-T rates, largely corresponds to the mutational
spectrum seen in these long branches. Our data suggest a signature of
molnupiravir mutagenesis can be seen in global sequencing databases, in some
cases with onwards transmission.
INTRODUCTION
Molnupiravir is an antiviral drug, licensed in some countries for the treatment
of COVID-19. In the body, molnupiravir is ultimately converted into a
nucleotide-analog, molnupiravir triphosphate (MTP)1. MTP is capable of being
incorporated into RNA during strand synthesis, particularly by viral
RNA-dependent RNA polymerases, where it can result in errors of sequence
fidelity during viral genome replication. These errors in RNA replication result
in many viral progeny that are non-viable, and so reduce the virus’s effective
rate of growth – molnupiravir was shown to reduce viral replication in 24 hours
by 880-fold in vitro, and to reduce viral load in animal models (Rosenke et al.,
2021). Molnupiravir initially showed some limited efficacy as a treatment for
COVID-19 (Jayk Bernal et al., 2022; Extance, 2022), but subsequent larger
clinical trials found that molnupiravir did not reduce hospitalisation or death
rates in high risk groups (Butler, 2022). As one of the first orally
bioavailable antivirals on the market, molnupiravir has been widely adopted by
many countries, most recently China (Reuters, 2022). However, recent trial
results and the approval of more efficacious antivirals have since led to
several countries recommending against molnupiravir usage on the basis of
limited effectiveness (NICE Guidance ; NC19CET, 2022).
MTP appears to be incorporated into nascent RNA primarily by acting as an
analogue of cytosine (C), pairing opposite guanine (G) bases (Fig. 1). However,
once incorporated, the molnupiravir (M)-base can transition into an alternative
tautomeric form which resembles uracil (U) instead. This means that in the next
round of replication, to give the positive-sense SARS-CoV-2 genome, the M base
can pair with adenine (A), resulting in a G-to-A mutation, as shown in Fig. 2.
Incorporation of MTP can also occur during the second step synthesis of the
positive-sense genome. In this case, an initial positive-sense C correctly pairs
with a G in the first round of replication, but this G then pairs with an M base
during positive-sense synthesis. In the next round of replication this M can
then pair with A, which will result in a U in the final positive sense genome,
with the overall process producing a C-to-U mutation (Fig. S1).
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Figure 1. Molnupiravir triphosphate can assume multiple tautomeric forms.
The N-hydroxylamine form resembles cytosine, while the oxime form more closely
resembles uracil. They therefore pair with guanine and adenine respectively.
(Figure adapted in part from Malone and Campbell (2021).)
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Figure 2. Molnupiravir drives G-to-A and C-to-U (C-to-T) mutations, and to a
lesser extent A-to-G and T-to-U (T-to-C) mutations
In the most common scenario, shown on the left-hand side, M is incorporated
opposite G nucleotides. It can then pair to A in subsequent replication,
creating a G-to-A mutation. If the original G resulted from negative strand
synthesis from a coding-strand C, then the G-to-A change ultimately creates a
C-to-U coding change (Fig. S1). In the second, less common, scenario shown at
the right hand side M is initially incorporated by pairing with A, which can
result in an A-to-G mutation or, if the original A came from a coding U, a
U-to-C mutation.
The free nucleotide MTP is less prone to tautomerisation than when incorporated
into RNA, and so this directionality of mutations is the most likely (Gordon et
al., 2021). However it is also possible for some MTP to bind, in place of U, to
A bases and undergo the above processes in reverse, causing A-to-G and U-to-C
mutations (Scenario 2 in Fig. 2, also Fig. S1).
It has been proposed that many major SARS-CoV-2 variants emerged from long-term
chronic infections. This model explains several peculiarities of variants such
as a general lack of genetic intermediates, rooting with much older sequences,
long phylogenetic branch lengths, and the level of convergent evolution with
known chronic infections (Rambaut et al., 2020; Viana et al., 2022; Hill et al.,
2022; Harari et al., 2022).
During analysis of long phylogenetic branches in the SARS-CoV-2 tree, recent
branches exhibiting potential hallmarks of molnupiravir-driven mutagenesis have
been noted, including clusters of sequences indicating onward transmission. We
therefore aimed to systematically identify sequences that might have been
influenced by molnupiravir and characterise their mutational profile to examine
the extent to which these signatures appear in global sequencing databases.
RESULTS
During examination of SARS-CoV-2 phylogenetic branches with large numbers of
mutations, a subset that contained skewed ratios of mutation classes and very
few transversion substitutions were identified (Hisner, 2022). These patterns
appeared very different to typical SARS-CoV-2 mutational spectra (Ruis et al.,
2022; Bloom et al., 2022; Masone et al., 2022; Tonkin-Hill et al., 2021).
To investigate this pattern more systematically we analysed a mutation-annotated
tree, derived from McB-roome et al. (2021), containing >13 million SARS-CoV-2
sequences from GISAID (Elbe and Buckland-Merrett, 2017) and the INSDC databases
(Cochrane et al., 2011). For each branch of the tree we counted the number of
each substitution class (A-to-T, A-to-G, etc. – we use T instead of U for the
remainder of this manuscript as in sequencing databases). Filtering this tree to
branches involving at least 20 substitutions, and plotting the proportion of
substitution types revealed a region of this space with higher G-to-A and almost
exclusively transition substitutions2, that only contained branches sampled in
2022 (Fig. 3A), suggesting some change (either biological or technical) had
resulted in a new mutational signature.
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Figure 3. A new mutational signature emerged with high G-to-A and high
transition ratio emerged in 2022 in some countries in global sequencing
databases
(A) Each point in this scatterplot represents a branch from the
mutation-annotated tree with >20 substitutions. Points are positioned according
the proportion of the branch’s mutations that are G-to-A (x) or any transition
mutation (y), and coloured by the year in which they occurred. A boxed region
with higher G-to-A and almost exclusively transition mutations occurs only in
2022. (B) A count of the number of branches which satisfy a specific criterion
(G-to-A ratio >= 25%, C-to-T ratio >= 20% transition ratio > 95%, total
mutations > 10). (C) A comparison of number of total genomes with number of
identified high G-to-A clusters (using the same criterion as B). Note
logarithmic axes (semi-log, with the truncated line representing zero). For
example, Australia has 97 clusters from a total of 119,194 genomes, whereas
France has 0 clusters from 313,680 genomes. (D) A comparison of the age
distributions for clusters with >10 mutations from the USA, partitioned by
whether or not they satisfy the high G-to-A criterion. High G-to-A clusters
correspond to older individuals.
Noticing that this signature also involved a high proportion of C-to-T
mutations, we created a criterion for branches of interest, which we refer to as
“high G-to-A” branches: we selected branches involving at least 10
substitutions, of which at least 25% were G-to-A, at least 20% were C-to-T and
at most 5% were transversions. Again, these branches were almost all sampled in
2022 (Fig. 3B). The branches were predominantly sampled from a small number of
countries, which could not be explained by differences in sequencing efforts
(Fig. 3C, Table 1). Many countries which exhibited a high proportion of high
G-to-A branches use molnupiravir: >380,000 prescriptions had occurred in
Australia by the end of 2022 (Department of Aged Care Webinar, 2022), >30,000 in
the UK (NHS, 2023; Butler, 2022), and >240,000 in the US within the early months
of 2022 (Gold et al., 2022). Countries with high levels of total sequencing but
a low number of G-to-A branches (Canada, France) have not authorised the use of
molnupiravir (Goverment of Canada, 2022; Spencer et al., 2021). Age metadata
from the US showed a significant bias towards patients with older ages for these
high G-to-A branches, compared to control branches with similar numbers of
mutations but without selection on substitution-type (Fig. 3D). Where age data
was available in Australia it also identified long branches primarily in an aged
population. This is consistent with the prioritised use of molnupiravir to treat
older individuals, who are at greater risk from severe infection, in these
countries. In Australia, molnupiravir was pre-placed in aged-care facilities,
and it was recommended that it be considered for all patients aged 70 or older,
with or without symptoms (Australian Department of Health and Aged Care, 2022).
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Table 1.
Number of high G-to-A (G-to-A ratio >= 25%, C-to-T ratio >= 20% transition ratio
> 95%, total mutations > 10) identified by country in 2022, set against total
number of genomes. Only countries with >10,000 genomes are included.
We found that high G-to-A branches had a different distribution of branch
lengths from other types of branches, with an enrichment for longer branch
lengths (Fig. 4). We next sought to see whether these high G-to-A branches were
associated with a different mutation rate to other branches of similar length.
We assigned a date to each node of the tree using Chronumental (Sanderson, 2021)
and observed that the branch length measured in time was shorter for branches
with a high G-to-A signature than for branches of matched length without this
signature (Fig. S2), suggesting an increased mutation rate.
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Figure 4. Branch length distribution for high G-to-A branches compared to other
branches
Plot is limited to branch lengths greater than 10, in order to be confident in
the branch type, and to branch lengths less than 20 due to the exclusion of long
branch samples from the UShER MAT. Branches are filtered to those from 2022.
Most of the long branches sampled have just a single descendant tip sequence in
sequencing databases, but in some cases branches have given rise to clusters
with a significant number of descendant sequences. For example, a cluster in
Australia in August 2022 involves 20 tip sequences, with distinct age metadata
indicating that they truly derive from multiple individuals (Fig. 5). This
cluster involves 25 substitutions in the main branch of which all are
transitions, 44% are C-to-T and 36% are G-to-A. Closely related outgroups date
from July 2022, suggesting that these mutations emerged in a period of 1-2
months. There are many other examples of high G-to-A branches with multiple
descendant sequences, including from the United Kingdom (Fig. 6A,B).
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Figure 5. A cluster of 20 individuals emerging from a high G-to-A mutation event
This cluster involves a saltation of 25 mutations occuring within a period of
perhaps under a month, all of which are transition substitutions, with an
elevated G-to-A rate. Sequences are annotated with age metadata suggestive of an
outbreak in an aged-care facility.
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Figure 6. Phylogenetic trees for three high G-to-A events
(A) A cluster of four sequences from the UK from Feb-March 2022 with 13 shared
muations with the high G-to-A signature. (B) A cluster of four sequences from
the UK from Feb 2022 with 31 shared muations with the high G-to-A signature. (C)
A singleton sequence from Australia with a high G-to-A signature and a total of
133 mutations. Just 2 of the 133 mutations observed are transversions and
transitions include numerous G-to-A events.
During the construction of the daily-updated mutation-annotated tree (McBroome
et al., 2021), samples highly divergent from the existing tree are excluded.
This is a necessary step given the technical errors in some SARS-CoV-2
sequencing data, but it also means that highly divergent molnupiravir-induced
sequences might be excluded. To examine this effect, we created a comprehensive
mutation-annotated-tree for Australia, allowing identification of even the most
divergent sequences in this subset. This analysis allowed the identification of
mutational events involving up to 130 substitutions (Fig. 6C), with the same
signature of elevated G-to-A mutation rates and almost exclusively transition
substitutions. The cases we identified with these very high numbers of mutations
involved single sequences, and could represent sequences resulting from
chronically infected individuals who have been treated with multiple courses of
molnupiravir.
We next assessed the mutational spectrum (the patterns of contextual nucleotide
substitutions) on these branches. The spectrum we identified (Fig. 7A) is
dominated by G-to-A and C-to-T transition mutations with smaller contributions
from A-to-G and T-to-C transitions. This pattern is consistent with the known
mechanisms of action for molnupiravir (Fig. 2). These transitions exhibit
preference for particular surrounding nucleotide contexts, for example G-to-A
mutations occur most commonly in TGT and TGC contexts. This may represent a
preference for molnupiravir binding adjacent to particular surrounding
nucleotides, a preference of the viral RdRp to incorporate molnupiravir adjacent
to specific nucleotides, or a preference of the viral proof-reading exonuclease
to remove molnupiravir in specific contextual surroundings.
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Figure 7. Mutational spectrum from long phylogenetic branches in global
sequencing databases
(A) Single base substitution (SBS) spectrum of long phylogenetic branches. (B)
Comparison of contextual mutation preferences in C-to-T and G-to-A mutations on
long phylogenetic branches. The proportion of C-to-T or G-to-A mutations in the
equivalent (reverse) contexts are plotted. There is a significant correlation,
showing similar contextual preferences for C-to-T and G-to-A mutations.
Proportions are normalised for the number of times the context occurs in the
genome.
The dominance of both C-to-T and G-to-A mutations in the spectrum is likely due
to molnupiravir-induced G-to-A mutations during synthesis of different strands
during virus replication. Incorporation of molnupiravir during negative strand
synthesis will result in G-to-A mutations in the virus consensus sequence while
incorporation during positive strand synthesis will be read as C-to-T mutations
(Fig. S1). Consistent with this, we observe a strong positive correlation
between the mutational biases in equivalent contextual patterns within C-to-T
and G-to-A mutations (Pearson’s r = 0.88, 95% CI 0.68-0.96, p < 0.001, Fig. 7B)
(e.g. a C-to-T mutation in the ACG context on one strand is the equivalent of a
G-to-A mutation in the CGT context on the other strand).
To compare the observed signatures to mutations in known molnupiravir-exposed
individuals, we re-examined a genomic dataset from the AGILE Phase IIa clinical
trial (Donovan-Banfield et al. (2022), NCT04746183). For the first time we
analysed the spectrum of likely molnupiravir-induced mutations by comparing day
1 samples (taken just before treatment initiation) to day 5 samples from the
same patient. This dataset had the advantage that it also included individuals
treated with placebo, providing a control for mutational spectra in the absence
of molnupiravir. We found that molnupiravir-treated patients exhibited
significantly higher mutational burdens than patients treated with placebo
(ANOVA p < 0.001, Fig. S5). The spectrum of mutations was highly different
between placebo and molnupiravir (Fig. 8A, cosine similarity between spectra =
0.68). Assuming that the mutational processes within the placebo patients also
occurred in the molnupiravir treatment, we subtracted the placebo spectrum to
obtain the mutations specifically induced by molnupiravir (Fig. 8A). Again, we
found a significant enrichment of transition mutations (Fig. 8B-C).
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Figure 8. Molnupiravir mutational spectra from the AGILE clinical trial
(A) SBS spectra of mutations in patients treated with placebo or molnupiravir in
the AGILE trial. The right hand panel shows the mutations induced by
molnupiravir, calculated by subtracting the placebo spectrum (left panel) from
the spectrum of all mutations in molnupiravir treated patients (centre panel).
Spectra are shown as the number of mutations per available context per patient.
(B) Comparison of the number of each mutation type (summed across all contexts)
between placebo and molnupiravir treatments. Error bars show confidence
intervals from mutation bootstrapping (see methods). (C) The ratio of each
mutation type between treatments was calculated by dividing the mutational
burden in molnupiravir by that in placebo. The red dashed line shows an equal
burden.
We used the calculated spectrum to examine whether the long phylogenetic
branches identified above are likely to be molnupiravir-driven. The overall
patterns of mutations in the long phylogenetic branches are qualitatively
similar to the AGILE trial patients treated with molnupiravir, with a
preponderance of transition mutations, most commonly C-to-T and G-to-A (Fig. 7,
Fig. 8).
The contextual patterns within each transition mutation are highly similar
between the known molnupiravir spectrum and the long phylogenetic branches (Fig.
S6). This suggests a shared driver of transition mutations and therefore
supports the long phylogenetic branches being driven by treatment with
molnupiravir.
While the transition patterns are highly similar, the AGILE trial molnupiravir
spectrum contains a high rate of G-to-T mutations that is not present in the
long phylogenetic branches (Fig. 7, Fig. 8). This high rate is also present in
the placebo-treated patients, although the rate appears higher in molnupiravir
treatment (Fig. 8A). The rate of G-to-T mutations in both placebo and
molnupiravir groups appears higher than that calculated from within patient
mutations from untreated individuals (Tonkin-Hill et al. (2021), Fig. S7), for
reasons that are not clear.
DISCUSSION
We have shown a variety of lines of evidence that together suggest that a
signature of molnupiravir treatment is visible in global sequencing databases.
We identified a set of long phylogenetic branches that exhibit a high number of
transition mutations. The number of these branches increased dramatically in
2022 and they are enriched for countries and age groups known to be exposed to
molnupiravir. Mutation rates on these branches, were elevated, consistent with a
recent study from Fountain-Jones et al. (2022) in immuno-compromised patients.
The branches exhibited a mutational spectrum that is highly similar to that in
patients known to be treated with molnupiravir. The sequencing data suggest that
in at least some cases, viruses with a large number of molnupiravir-induced
substitutions have been transmitted to other individuals, at least in a limited
manner.
Molnupiravir’s mode of action is often described using the term “error
catastrophe” – the concept that there is an upper limit on the mutation rate of
a virus beyond which it is unable to maintain self-identity (Eigen, 1971). This
model has been criticised on its own terms (Summers and Litwin, 2006), but is
particularly problematic in the case of molnupiravir treatment. The model
assumes a steady-state condition, with the mutation rate fixed at a particular
level. The threshold for error catastrophe is the mutation rate at which,
according to the model, the starting sequence will ultimately be lost after an
infinite time at this steady-state. However molnupiravir treatment does not
involve a steady state, but a temporarily elevated mutation rate over a short
treatment period. Therefore there isn’t any particular threshold point, or
well-defined “catastrophe” condition. We suggest that the use of this term in
the context of mutagenic antiviral drugs is unhelpful. It is enough to think of
these drugs as acting through mutagenesis to reduce the number of viable progeny
that each virion is able to produce – particularly given that much of the
reduction in fitness will be due to “single-hit” lethal events (Summers and
Litwin, 2006).
New variants of SARS-CoV-2 are generated through acquisition of mutations that
enhance properties including immune evasion and intrinsic transmissibility
(Telenti et al., 2022; Carabelli et al., 2023). The impact of molnupiravir
treatment on the trajectory of variant generation and transmission is difficult
to predict. On the one hand, molnupiravir increases the amount of sequence
diversity in the surviving viral population in the host and this might be
expected to provide more material for selection to act on during intra-host
evolution towards these properties that increase fitness. However, a high
proportion of induced mutations are likely to be deleterious or neutral, and it
is necessary to consider the counterfactual to molnupiravir treatment. As
molnupiravir results in a modest reduction in viral load in treated patients
(Khoo et al., 2022), it is possible that in the absence of treatment the total
viral load would be higher and chronic infections might persist for longer.
Variants generated through chronic infections might be fitter than those that
have accumulated mutations during molnupiravir treatment, albeit taking a much
longer period of time to accumulate the same number of mutations and therefore
usually being derived from older, rather than contemporary lineages. At the time
of writing we have not identified a molnupiravir-implicated cluster that had
spread to more than 21 individuals.
There are some limitations of our work. Detecting a particular branch as
involving a molnupiravir-like signature is a probabilistic rather than absolute
judgement: where molnupiravir creates just a handful of mutations (which trial
data suggests is often the case), branch lengths will be too small to assign the
cause of the mutations with confidence. We therefore limited our analyses here
to long branches. This approach may also fail to detect branches which feature a
substantial number of molnupiravir-induced mutations along-side a considerable
number of mutations from other causes (which might occur in chronic infections).
We discovered drastically different rates of molnupiravir-associated sequences
by country, and suggest that this reflects in part whether, and how,
molnupiravir is used in different geographical regions – however there will also
be contributions from the rate at which genomes are sequenced in settings where
molnupiravir is used. For example, if molnupiravir is used primarily in
aged-care facilities and viruses in these facilities are significantly more
likely to be sequenced than those in the general community this will elevate the
ascertainment rate of such sequences. Furthermore, it is likely that some
included sequences were specifically analysed due to representing continued test
positivity after molnupiravir treatment as part of specific studies. Such
effects are likely to differ based on sequencing priorities in different
locations.
We would recommend public health authorities in countries showing these patterns
perform investigations to determine if these sequences or clusters can indeed be
directly linked back to use of molnupiravir. These data will be useful for
ongoing assessments of the risks and benefits of this treatment, and may guide
the future development of mutagenic agents as antivirals, particularly for
viruses with high mutational tolerances such as coronaviruses.
METHODS
PROCESSING OF MUTATION-ANNOTATED TREE
To identify clusters in global sequence databases with a molnupiravir associated
signature we analysed a regularly updated mutation-annotated tree built using
UShER (Turakhia et al., 2021) with almost all global data – a version of the
McBroome et al. (2021) tree. We parsed the tree using a custom script adapted
from Taxonium Tools (Sanderson, 2022). The script added metadata from sequencing
databases to each node, then passed these metadata to parent nodes using simple
heuristics: (1) a parent node was annotated with a year if all of its
descendants were annotated with that year, (2) a parent node was annotated with
a particular country if all of its descendants were annotated with that country,
(3) a parent node was annotated with the mean age of its (age-annotated)
descendants.
MUTATION RATE ANALYSIS
We used Chronumental (Sanderson, 2021) to assign dates to each node in the
mutation-annotated tree. We ran Chronumental for 300 steps, then extracted
length of time in days from the output Newick tree, and compared against the
number of mutations on the branch, splitting by whether the branch satisfied our
criteria to be a high G-to-A branch.
GENERATION OF CLUSTER TREES
For the cluster of 20 individuals, we observed small imperfections in UShER’s
representation of the mutation-annoated tree within the cluster resulting from
missing coverage at some positions. We therefore recalculated the tree that we
display here. We took the 20 sequences in the cluster, and the three closest
outgroup sequences, we aligned using Nextclade (Aksamentov et al., 2021),
calculated a tree using iqtree (Minh et al., 2020) and reconstructed the
mutation-annotated tree using TreeTime (Sagulenko et al., 2018). We visualised
the tree using FigTree (Rambaut, 2018).
ADDING BACK EXCLUDED DIVERGENT SEQUENCES TO THE MUTATION-ANNOTATED TREE
We used GISAIDR (Wirth and Duchene, 2022) to download all Australian sequences
in 2022 from the GISAID database. We then filtered those based on sequence ID to
those absent from the existing mutation-anotated tree (MAT). We aligned these
sequences to the Hu-1 reference using flowalign. We pruned the existing MAT to
of each mutation was identified using the Wuhan-Hu-1 genome (accession
NC_045512.2), incorporating mutations acquired earlier in the path. Mutation
counts retain only Australian sequences, and then added all sequences missing
from the tree using UShER (Turakhia et al., 2021), without filtering on
parsimonious placements or path length, to achieve a complete MAT for Australia.
CALCULATION OF MUTATIONAL SPECTRUM OF LONG PHYLOGENETIC BRANCHES
To calculate the SBS mutational spectrum of the long phylogenetic branches, we
extracted the path of mutations leading to each branch from the 2022-12-18 UShER
phylogenetic tree. Internal and tip branches in these paths containing at least
ten mutations were identified and their mutations extracted. The context the
Wuhan-Hu-1 genome. MutTui (https://github.com/chrisruis/MutTui) was used to
rescale and plot mutational spectra.
CALCULATION OF MOLNUPIRAVIR AND PLACEBO MUTATIONAL SPECTRA USING AGILE TRIAL
DATA
We calculated molnupiravir and placebo SBS spectra using previously published
variant data (Donovan-Banfield et al., 2022). We used deep sequencing data from
samples collected on day one (pre-treatment) and day five (post-treatment) from
65 patients treated with placebo and 58 patients treated with molnupiravir. For
each patient, we used the consensus sequence of the day one sample as the
reference sequence and identified mutations as variants in the day five sample
away from the patient reference sequence in at least 5% of reads at genome sites
with at least 100-fold coverage. The surrounding nucleotide context of each
mutation was identified from the patient reference sequence.
We converted mutation counts to mutational burdens by dividing each mutation
count by the number of the starting triplet in the Wuhan-Hu-1 genome (accession
NC_-045512.2) and by the number of patients in the treatment group (65 for
placebo and 58 for molnupiravir). This therefore rescales by the number of
opportunities for each mutation to occur across the genome and the number of
patients in each group.
To ensure that any spectrum differences between placebo and molnupiravir
treatments are not due to previously observed differences in spectrum between
SARS-CoV-2 variants (Ruis et al., 2022; Bloom et al., 2022), we compared the
distribution of variants between the treatments (Fig. S3). The distributions
were highly similar.
To calculate the total mutational burden of each mutational class, we summed the
mutational burden of the 16 contextual mutations within the class. Confidence
intervals were calculated by bootstrapping mutations. Here, the original set of
mutations within the treatment was resampled with replacement before rescaling
by triplet availability and number of patients. 1000 bootstraps were run and 95%
confidence intervals calculated.
To enable comparison with an additional SARS-CoV-2 dataset containing mutations
acquired during within host infections, we obtained variants from a previous
deep sequencing study Tonkin-Hill et al. (2021). We identified the surrounding
nucleotide context of each mutation using the Wuhan-Hu-1 genome and rescaled by
context availability.
COMPARISON OF MUTATIONAL SPECTRA
The cosine similarity between placebo and molnupiravir spectra was calculated
using MutTui (https://github.com/chrisruis/MutTui).
We compared contextual patterns within each transition mutation between the long
phylogenetic branch and AGILE molnupiravir spectra through regression of the
proportion of mutations within the mutational class that are in each context. To
assess significance of the correlation, we randomised the proportions within the
mutational class within each spectrum and recalculated the correlation. 1000
randomisations were carried out and the p-value calculated as the proportion of
randomisations with a correlation at least as large as in the real data.
DATA AVAILABILITY
No new primary data was generated for this study. We used data from
international sequencing databases (GISAID and INSDC), and from the AGILE
clinical trial, where genomic data were obtained from BioProject PRJNA854613 at
the SRA. Our GitHub repository is available at
https://github.com/theosanderson/molnupiravir.
https://github.com/theosanderson/molnupiravir
DATA AVAILABILITY
No new primary data was generated for this study. We used data from
international sequencing databases (Elbe and Buckland-Merrett, 2017; Cochrane et
al., 2011), and from the AGILE clinical trial (Donovan-Banfield et al., 2022),
where genomic data were obtained from BioProject PRJNA854613 at the SRA. Our
GitHub repository is available at https://github.com/theosanderson/molnupiravir.
The findings of this study are based on metadata associated with 14,449,737
sequences available on GISAID up to December 2022, and accessible at
10.55876/gis8.230110wz and 10.55876/gis8.230110db (see also, Supplemental
Tables). The findings of this study are also based on 6,594,478 sequences from
INSDC – authors, metadata, and sequences are available here. Data present in
both databases are deduplicated before the construction of the
mutation-annotated tree.
AUTHOR CONTRIBUTIONS
RH identified initial branches, and their likely connection to molnupiravir. TS
performed analyses of mutation-annotated tree and global metadata. CR performed
all mutational spectra analyses. ID-B created bioinformatic pipelines for the
AGILE trial data. All authors participated in mansuscript writing.
FUNDING
TS was supported by the Wellcome Trust (210918/Z/18/Z) and the Francis Crick
Institute which receives its core funding from Cancer Research UK (FC001043),
the UK Medical Research Council (FC001043), and the Wellcome Trust (FC001043).
This research was funded in whole, or in part, by the Wellcome Trust
[210918/Z/18/Z, FC001043]. For the purpose of Open Access, the authors have
applied a CC-BY public copyright licence to any Author Accepted Manuscript
resulting from this preprint.
ID-B is supported by PhD funding from the National Institute for Health and Care
Research (NIHR) Health Protection Research Unit (HPRU) in Emerging and Zoonotic
Infections at University of Liverpool in partnership with Public Health England
(PHE) (now UKHSA), in collaboration with Liverpool School of Tropical Medicine
and the University of Oxford (award 200907). The views expressed are those of
the authors and not necessarily those of the Department of Health and Social
Care or NIHR. Neither the funders or trial sponsor were involved in the study
design, data collection, analysis, interpretation, nor the preparation of the
manuscript.
TP was funded by the G2P-UK National Virology Consortium funded by the MRC
(MR/W005611/1).
CR was supported by a Fondation Botnar Research Award (Programme grant 6063) and
UK Cystic Fibrosis Trust (Innovation Hub Award 001).
SUPPLEMENTARY INFORMATION
* Download figure
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Figure S1. Possible outcomes from MTP incorporation
This figure depicts some of the mutational pathways related to MTP incorporation
into MTP. The first column shows what may be a common event, but is not
detectable by sequencing. MTP can be incorporated into RNA (pairing with G) and
then pair with G again in the next round of synthesis, which will result in no
mutation in the final sequence. However if the MTP takes on an alternative
tautomeric form after incorporation it can bind to A, creating a G-to-A
mutation. The third column shows that if the positive-sense base is C, then this
will bind to a G in the formation of the negative-sense genome. In subsequent
replication this negative sense genome can undergo the same G-to-A mutation seen
in the second column, which ultimately results in a positive sense C-to-T
mutation. Although the biases of tautomeric forms for the free and incorporated
MTP nucleotides appear to favour these directionalities of mutations, the
reverse is also possible, resulting in A-to-G and T-to-C mutations.
* Download figure
* Open in new tab
Figure S2. High G-to-A branches involve the same number of mutations occurring
in a shorter period of time than other branch types
We used Chronumental to assign a date to all nodes in the global UShER mutation
annotated tree, then isolated branch lengths >10 and <=20, and plotted the mean
length of time and its 95% confidence interval by number of mutations, as well
as a fitted linear model, for Australia, Japan, the UK and the USA. This
analysis should be seen only as semi-quantitative due to the fact that the
algorithm will attempt to reconcile its chronology with the overall SARS-CoV-2
mutation rate and therefore be conservative, and that sequencing errors and
recombination can also drive a large apparent rate of mutations in a short time.
* Download figure
* Open in new tab
Figure S3. Distribution of major SARS-CoV-2 variants between placebo and
molnupiravir treatments in the AGILE trial dataset.
The proportion of patients infected with each variant is shown. The proportions
are similar suggesting that differences between placebo and molnupiravir spectra
will not be influenced by previously observed spectrum differences between
variants (Ruis et al., Bloom et al.). VOC = variant of concern.
* Download figure
* Open in new tab
Figure S4. Context locations within the mutational spectrum.
The RNA mutational spectrum contains 12 mutation types, for example C-to-T. The
spectrum also captures the nucleotides surrounding each mutation. There are four
potential upstream nucleotides and four potential downstream nucleotides. This
figure shows the location of each of the 16 contexts within an example mutation
type. For example, the leftmost bar represents C-to-T mutations in the ACA
context while the second leftmost bar represents C-to-T mutations in the ACC
context.
* Download figure
* Open in new tab
Figure S5. Comparison of the mutational burden in patients treated with placebo
and patients treated with molnupiravir.
The total number of mutations across all substitution classes is plotted in the
day 5 sample of each patient in the AGILE trial.
* Download figure
* Open in new tab
Figure S6. Comparison of contextual patterns within transition mutations between
the molnupiravir spectrum calculated from AGILE trial data and the spectrum of
long phylogenetic branches.
The proportion of mutations within the respective transition (for example the
proportion of C>T mutations that are in the ACA context) is shown. P-values
represent the proportion of context randomisations with a Pearson’s r
correlation at least as great as with the real data (see methods). Proportions
are normalised for the number of times the context occurs in the genome.
* Download figure
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Figure S7. Mutational spectrum of mutations acquired within untreated patients.
Mutations are from Tonkin-Hill et al. (2021).
ACKNOWLEDGEMENTS
We gratefully acknowledge all data contributors, i.e., the Authors and their
Originating laboratories responsible for obtaining the specimens, and their
Submitting laboratories for generating the genetic sequence and metadata and
sharing via the GISAID Initiative, on which this research is based. We are also
very grateful to everyone who has contributed to the generation of the genomes
that have been deposited in the INSDC databases, on which this research is also
based. We thank Angie Hinrichs and colleagues for access to an UShER
mutation-annotated tree built with all available genomic data. We thank Jesse
Bloom, Michael Lin, Richard Neher, and Kelley Harris for useful discussions.
This preprint uses a LaTeX template from Stephen Royle and Ricardo Henriques.
FOOTNOTES
* ↵1 MTP is also known as β-D-N4-hydroxycytidine triphosphate (NHCTP).
* ↵2 ”Transition” substitutions are: G-to-A, A-to-G, C-to-T, T-to-C. Other
substitutions are known as “transversions”.
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Identification of a molnupiravir-associated mutational signature in SARS-CoV-2
sequencing databases
Theo Sanderson, Ryan Hisner, I’ah Donovan-Banfield, Thomas Peacock, Christopher
Ruis
medRxiv 2023.01.26.23284998; doi: https://doi.org/10.1101/2023.01.26.23284998
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Identification of a molnupiravir-associated mutational signature in SARS-CoV-2
sequencing databases
Theo Sanderson, Ryan Hisner, I’ah Donovan-Banfield, Thomas Peacock, Christopher
Ruis
medRxiv 2023.01.26.23284998; doi: https://doi.org/10.1101/2023.01.26.23284998
This article is a preprint and has not been peer-reviewed [what does this
mean?]. It reports new medical research that has yet to be evaluated and so
should not be used to guide clinical practice.
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