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Rapid Identification of Methylase Specificity (RIMS-seq) jointly identifies
methylated motifs and generates shotgun sequencing of bacterial genomes
Chloé Baum1,2, Yu-Cheng-Lin1, Alexey Fomenkov1, Brian P. Anton1, Lixin Chen1, Thomas C. Evans Jr,1
Richard J Roberts1, Andrew C Tolonen2, Laurence Ettwiller1*
1. New England Biolabs, Inc. 240 County Road Ipswich, MA 01938, USA
2. Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université
Paris-Saclay, 91000 Évry, France
* Corresponding author : ettwiller@neb.com
Abstract
DNA methylation is widespread amongst eukaryotes and prokaryotes to modulate gene
expression and confer viral resistance. 5-methylcytosine (m5C) methylation has been
described in genomes of a large fraction of bacterial species as part of restriction-modification
systems, each composed of a methyltransferase and cognate restriction enzyme. Methylases
are site-specific and target sequences vary across organisms. High-throughput methods, such
as bisulfite-sequencing can identify m5C at base resolution but require specialized library
preparations and Single Molecule, Real-Time (SMRT) Sequencing usually misses m5C. Here,
we present a new method called RIMS-seq (Rapid Identification of Methylase Specificity) to
simultaneously sequence bacterial genomes and determine m5C methylase specificities using
a simple experimental protocol that closely resembles the DNA-seq protocol for Illumina.
Importantly, the resulting sequencing quality is identical to DNA-seq, enabling RIMS-seq to
substitute standard sequencing of bacterial genomes. Applied to bacteria and synthetic mixed
communities, RIMS-seq reveals new methylase specificities, supporting routine study of m5C
methylation while sequencing new genomes.
Introduction
DNA modifications catalysed by DNA methyltransferases are considered to be the most
abundant form of epigenetic modification in genomes of both prokaryotes and eukaryotes. In
prokaryotes, DNA methylation has been mainly described as part of the sequence-specific
restriction modification system (RM), a bacterial immune system to resist invasion of foreign
DNA (Loenen et al., 2014). As such, profiling methylation patterns gives insight into the selective
pressures driving evolution of their genomes.
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Around 90% of bacterial genomes contain at least one of the three common forms of DNA
methylation: 5-methylcytosine (m5C), N4-methylcytosine (m4C), and N6-methyladenine
(m6A))(Beaulaurier et al., 2019; Blow et al., 2016). Contrary to eukaryotes where the position of
the m5C methylation is variable and subject to epigenetic states, bacterial methylations tend to
be constitutively present at specific sites across the genome. These sites are defined by the
methylase specificity and, in the case of RM systems, tend to be fully methylated to avoid cuts
by the cognate restriction enzyme. The methylase recognition specificities typically vary from 4
to 8 nucleotides and are often, but not always, palindromic (Roberts et al., 2015).
PacBio Single Molecule, Real-Time (SMRT) sequencing has been instrumental in the
identification of methylase specificity largely because, in addition to providing long read
sequencing of bacterial genomes, m6A and m4C can easily be detected using the characteristic
interpulse duration (IPD) of those modified bases (Flusberg et al., 2010). Thus, a single run on
PacBio allows for both the sequencing and assembly of unknown bacterial genomes and the
determination of m6A and m4C methylase specificities. However, because the signal associated
with m5C bases is weaker than for m6A or m4C, the IPD ratio of m5C is very similar to the IPD of
unmodified cytosine. Thus, PacBio sequencing usually misses the m5C methylases activities
(Blow et al., 2016). Instead, the identification of m5C requires specialized methods such as
bisulfite sequencing or enzyme-based techniques such as EM-seq (Sun et al., 2021). Recently,
MFRE-Seq has been developed to identify m5C methylase specificities in bacteria (Anton et al.,
2021). MFRE-Seq uses a modification-dependent endonuclease that generates a
double-stranded DNA break at methylated m5CNNR sites, allowing the identification of m5C
methylase specificities at a subset of sites. m5C in the CpG context has been identified (Simpson
et al., 2017) and a signal for methylation can be observed at known methylated sites in bacteria
using Nanopore sequencing (Rand et al., 2017) but so far no technique has allowed for the dual
sequencing of genomes and the de novo determination of m5C methylase specificity for the
non-CpG contexts typically found in bacteria.
Herein we describe a novel approach called RIMS-seq to simultaneously sequence bacterial
genomes and globally profile m5C methylase specificity using a protocol that closely resembles
the standard Illumina DNA-seq with a single, additional step. RIMS-seq shows comparable
sequencing quality as DNA-seq and accurately identifies methylase specificities. Applied to
characterized strains or novel isolates, RIMS-seq de novo identifies novel activities without the
need for a reference genome and also permits the assembly of the bacterial genome at metrics
comparable to standard shotgun sequencing.
Results
Principle of RIMS-seq
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Spontaneous deamination of cytosine (C) leading to uracil (U) and of m5C leading to thymine (T)
are examples of common damage found in DNA. In vitro, this damage is often undesirable as it
can interfere with sequencing. The type of interference during sequencing depends on whether
the deamination occurs on C or m5C. Uracil blocks the passage of high-fidelity polymerases
typically used in library preparation protocols, preventing the amplification and sequencing of
U-containing DNA fragments. Conversely, DNA harboring T derived from m5C deamination can
be normally amplified, but results in C to T errors (Duncan and Miller, 1980; Fogg et al., 2002).
This distinction between blocking and mutagenic damage forms the basis of the RIMS-seq
method, allowing the identification of methylase specificity based on an elevated number of
reads containing C to T transitions specifically at methylated sites (Figure 1A). For this, the DNA
is subjected to a heat-alkaline treatment, inducing a level of deamination large enough to detect
the m5C methylase specificity without affecting the sequencing quality.
Illumina paired-end sequencing allows both ends of a DNA fragment to be sequenced,
generating a forward read (R1) and reverse read (R2). Resulting from m5C deamination, R1 has
the C to T read variants while R2 has the reverse-complement G to A variant. This difference
leads to an overall imbalance of C to T variants between R1 and R2 (Chen et al., 2017) (see also
Supplementary Figure 1 for explanation). Thus, sequence contexts for which the C to T read
variants are imbalanced in R1 compared to R2 correspond to m5C methylase specificity(ies).
Because of the limited deamination rate, RIMS-seq takes advantage of the collective signal at all
sites to define methylase specificity. Because C to T imbalance can be observed at nucleotide
resolution, RIMS-seq identifies at base resolution which of the cytosine within the motif is
methylated.
The experimental steps for RIMS-seq essentially follows the standard library preparation for
Illumina sequencing with an extra deamination step. Briefly, the bacterial genomic DNA is
fragmented and adaptors are added to the ends of DNA fragments using ligation (Figure 1B and
Methods). Between the ligation step and the amplification step, an alkaline heat treatment step
is added to increase the rate of deamination. Only un-deaminated DNA or DNA containing
deaminated m5C can be amplified and sequenced.
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Figure 1 : Principle of RIMS-seq. Deamination of cytidine leads to a blocking damage while
deamination of m5C leads to a mutagenic C to T damage only present on the first read (R1) of
paired-end reads in standard Illumina sequencing. Thus, an increase of C to T errors in R1 in
specific contexts is indicative of m5C. B. The workflow of RIMS-seq is equivalent to a regular
library preparation for Illumina DNA-seq with an extra step of limited alkaline deamination at
60°C. This step can be done immediately after adaptor ligation and does not require additional
cleaning steps. C. Fraction of C to T variants in XP12 (m5C) at all positions in the reads for R1
and R2 after 0min (DNA-seq), 10min, 30min, 60min, 2h, 3h, 5h and 14h of heat-alkaline
treatment. The C to T imbalance between R1 and R2 is indicative of deamination of m5C and
increases with heat-alkaline treatment time. D. Correlation between the C to T fold increase in
R1 compared to R2 according to time (r2=0.998).
Validation of RIMS-seq
●Optimization of the heat alkaline deamination step
We first evaluated the conditions to maximize the deamination of m5C while minimizing other
DNA damage. For this we used bacteriophage Xp12 genomic DNA that contains exclusively m5C
instead of C (Kuo et al., 1968) to measure the m5C deamination rates in various contexts.
To estimate the overall deamination rate of m5C, we quantified the imbalance of C to T read
variants between R1 and R2 for 0, 10 and 30 minutes, 1h, 2h, 3h, 5h and 14h of heat alkaline
treatment (Figure 1C). We observed an imbalance as early as 10 minutes with a 3.7 fold
increase of C to T read variants in R1 compared to R2. The increase is linear with time with a
maximum of 212 fold increase of C to T read variants in R1 compared to R2 after 14 hours of
heat alkaline treatment (Figure 1D). Next, we quantified the deamination rate at all Nm5CN
sequence contexts with N being A,T,C or G and show an increase of C to T variants in R1 in all
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contexts (Supplementary Figure 2A). Together, these results show that a measurable
deamination rate can be achieved in as soon as 10 minutes of heat alkaline deamination and
that deamination efficiency is similar in all sequence contexts.
To estimate the non-specific damage to the DNA leading to unwanted sequencing errors, we
quantified possible imbalances for other variant types (Supplementary Figure 2B). We found
that G to T variants show imbalance that is likely the result of oxidative damage resulting from
sonication, a common step in library preparation between RIMS-seq and DNA-seq (Chen et al.,
2017). Slight elevation of G to C and T to C read variants can be observed in RIMS-seq compared
to DNA-seq, but this damage is of low frequency and therefore is not expected to notably affect
the sequencing performance QC of RIMS-seq. We performed QC metrics and assemblies of
Xp12 for all the alkaline-heat treatment conditions, including a control DNA-seq. The overall
sequencing performances were assessed in terms of insert size, GC bias, and genome coverage.
Similar results were observed between RIMS-seq and the DNA-seq control at all treatment
times, indicating that the RIMS-seq heat-alkaline treatment does not affect the quality of the
libraries. (Supplementary Figure 3).
We also evaluated the quality of the assemblies compared to the Xp12 reference genome and
found that all conditions lead to a single contig corresponding to essentially the entire genome
with very few mismatches (Supplementary Table 1). These results suggest that the heat-alkaline
treatment does not affect the assembly quality, raising the possibility of using RIMS-seq for
simultaneous de novo genome assembly and methylase specificity identification. We found that
a 3 hour treatment provides a good compromise between the deamination rate (resulting in
about ~ 0.3 % of m5C showing C to T transition) and duration of the experiment. We found that
longer incubation times (up to 14h) increased the deamination rate by up to 1%, and decided
this is a slight sensitivity increase compared to the additional experimental time required.
●RIMS-seq is able to distinguish methylated versus unmethylated motifs in E. coli
To validate the application of RIMS-seq to bacterial genomes, we sequenced dcm+ (K12) and
dcm- (BL21) E. coli strains. In K12, the DNA cytosine methyltransferase dcm methylates cytosine
at CCWGG sites (C = m5C, W = A or T) and is responsible for all m5C methylation in this strain
(Marinus and Morris, 1973). E. coli BL21 has no known m5C methylation. Heat/alkaline
treatments were performed at three time points (10min, 1h, and 3h). In addition, we performed
a control experiment corresponding to the standard DNA-seq. Resulting libraries were
paired-end sequenced using Illumina and mapped to their corresponding genomes (Methods).
For comparison, all datasets were down-sampled to 5 million reads corresponding to 200X
coverage of the E. coli genome and instances of high confidence C to T variants (Q score > 35)
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on either R1 or R2 were identified. As expected, control DNA-seq experiments show comparable
numbers of C to T read variants between R1 and R2, indicating true C to T variants or errors
during amplification and sequencing (Figure 2A). On the other hand, the overall number of C to
T read variants in R1 is progressively elevated for 10 min, 1 hour and 3 hours of heat-alkaline
treatment of the E. coli K12 samples with an overall 4 fold increase after 3 hours treatment
compared to no treatment; heat-alkaline treatments did not increase the rate of C to T read
variants in R2 (Figure 2A). We anticipate that the elevation of the E.coli K12 C to T read variants
in R1 is due to deamination of m5C. In this case, the elevation should be specifically found in Cs
in the context of CCWGG (with the underlined C corresponding to the base under
consideration). To demonstrate this, we calculated the fraction of C to T read variants in CCWGG
compared to other contexts. We observed a large elevation of the C to T read variants in the
CCAGG and CCTGG contexts for K12 (Figure 2B). As expected, the C to T read variants show no
elevation at CCAGG and CCTGG contexts for the E.coli BL21 strain that is missing the dcm
methylase gene (Figure 2B). Thus, this C to T read variant elevation is specific to the E.coli K12
strain subjected to heat-alkaline treatments, consistent with deamination detectable only on
methylated sites. Taken together, these results indicate that the elevated rate of C to T variants
observed in R1 from E.coli K12 is the result of m5C deamination in the CCWGG context.
Next, we assessed whether the difference in the C to T read variant context between R1 and R2
at the CCWGG motif provides a strong enough signal to be discernible over the background
noise. For this, we calculated the fraction of C to T read variants in CCWGG and CCWGG
compared to all the other NCNNN and CNNNN contexts, respectively. After 3 hours of
heat-alkaline treatment, the fraction of C to T read variants in a CCWGG context increased,
rising from only 1.9 % in regular DNA-seq to ~25% of all the C to T variants. This increase is only
observable in R1 of the K12 strain (Figure 2C). Conversely, no increase can be observed in
CCWGG context for which the C to T variant rate at the first C is assessed (Figure 2C). Thus,
RIMS-seq identified the second C as the one bearing the methylation, consistent with the well
described dcm methylation of E.coli K12 (Palmer and Marinus, 1994) (Marinus and Morris,
1973), highlighting the ability of RIMS-seq to identify m5C methylation at base resolution
within the methylated motif.
Next, we calculated significant (p.value < 0.01) differences in position-specific nucleotide
compositions around C to T variants in R1 compared to R2 using Two Sample Logo (Crooks et al.,
2004). We found a signal consistent with the dcm methylase specificity in K12 RIMS-seq samples
at one and three hours of heat alkaline treatment (Figure 2D) demonstrating that it is possible
to identify methylase specificities in genomic sequence subject to as little as 1h of alkaline
treatment. These results support the application of RIMS-seq for the de novo identification of
methylase specificity at base resolution.
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Figure 2. A. Barplots representing the number of C to T read variants for K12 in R1 and R2 after
different heat/alkaline treatment times. Colors represent duplicate experiments. B. Circular
barplot representing the rate of C to T read variants in all NCNNN contexts (with N = A,T C or G)
for R1 (green) and R2 (orange) for DNA-seq (left) and for 3 hours heat/alkaline treatment
(RIMS-seq) for K12 (center) and BL21 strains (right). C. Proportion of C to T read variants in
CCWGG (red) or CCWGG (green) contexts compared to other NCNNN or CNNNN contexts for R1
and R2 in K12 and BL21. The C to T read variants in CCWGG and CCWGG motifs represent less
than 2% of all variants except in K12 (R1 only) after 10 minutes, 1 and 3 hours treatments where
the CCWGG motifs represent 4.1%, 22.5% and 32.6% of all C to T read variants respectively. The
increase of C to T read variants in the CCWGG context is therefore specific to R1 in K12 strain.
D. Visualization of the statistically significant differences in position-specific nucleotide
compositions around C to T variants in R1 compared to R2 using Two Sample Logo (Crooks et al.,
2004) for the K12 sample subjected to (from top to bottom) 3H, 1H, 10 min and 0 min heat
alkaline treatment.
●RIMS-seq identifies the correct methylase specificity de novo in E. coli K12
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In order to identify methylase specificities de novo in RIMS-seq sequencing data, we devised an
analysis pipeline based on MoSDi (Marschall and Rahmann, 2009) to extract sequence motif(s)
with an over-representation of C to T transitions in R1 reads (Figure 3A, analysis pipeline
available at https://github.com/Ettwiller/RIMS-seq). In brief, the pipeline extracts the sequence
context at each C to T read variant in R1 (foreground) and R2 (background). MoSDi identifies the
highest over-represented motif in the foreground sequences compared to the background
sequences. To accommodate the presence of multiple methylases in the same host, the first
motif is subsequently masked in both the foreground and background sequences and the
pipeline is run again to find the second highest over-represented motif and so on until no
significant motifs can be found (see Methods for details). Running the pipeline on strain K12
identifies one significant over-represented motif corresponding to the CCWGG motif (p-value =
4.49e-96 and 1.10e-1575 for 10 min and 60 min of alkaline treatment respectively) with the
cytosine at position 2 being m5C.
Summing up, we devised a novel sequencing strategy called RIMS-seq and its analysis pipeline
to identify m5C methylase specificity de novo. When applied to E. coli K12, RIMS-seq identifies
the dcm methylase specificity as CCWGG with the methylated site located on the second C,
consistent with the reported dcm methylase specificity (Table 1).
●RIMS-seq identifies multiple methylase specificities de novo within a single
microorganism
To assess whether RIMS-seq is able to identify methylase specificity in strains expressing
multiple methylases, we repeated the same procedure on a strain of Acinetobacter
calcoaceticus ATCC 49823 expressing two m5C methylases with known specificities (Roberts et
al., 2015). RIMS-seq identifies CGCG (p-value =2.33e-174 ) and GATC (p-value =3.02e-1308 )
(Table 1) both motifs have been confirmed by MFRE-seq (Anton et al., 2021). Thus, RIMS-seq is
able to de novo identify methylase specificities in bacteria expressing multiple methylases.
●RIMS-seq can be applied for genome sequencing and m5C profiling in bacteria without a
reference genome
We investigated whether RIMS-seq can be used to identify methylase specificities of
uncharacterized bacteria for which a reference genome is unavailable. More specifically we
evaluated if the reads generated using RIMS-seq can be used for both identifying methylase
specificities and generating an assembly of comparable quality to DNA-seq.
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For this, we performed RIMS-seq on A. calcoaceticus ATCC 49823 genomic DNA as described
above as well as a control DNA-seq experiment for which the alkaline treatment was replaced
by 3 hours incubation in TE (DNA-seq(+3H)). We compared the de novo assembly obtained from
the reads generated by the DNA-seq(+3H) and the de novo assembly obtained from the reads
generated by RIMS-seq (see Material and Methods). In brief, the alkaline treatment did not
alter the important metrics for assembly quality such as the rate of mismatches and N50
demonstrating that the elevated C to T variant rate at methylated sites is not high enough to
cause assembly errors (Figure 3C).
We then proceeded to map the RIMS-seq reads to the assembly and motifs were identified
using the RIMS-seq de novo motif discovery pipeline. As expected, the same motifs found when
mapping to the reference genome are also found in the A. calcoaceticus de novo assembly with
similar significance (GATC (p-value=1.44e-1255) and CGCG (p-value=8.6e-228) (Figure 3B).
These motifs correspond to the methylase specificities expected in this strain indicating that
RIMS-seq can be applied for genome sequencing and assembly of any bacterium without the
need for a reference genome.
●RIMS-seq can be complemented with SMRT sequencing to obtain a comprehensive
overview of methylase specificities
RIMS-seq performed in parallel with SMRT sequencing has the advantage of comprehensively
identifying all methylase specificities (m5C, m4C and m6A methylations) and results in an
assembly of higher quality than with short reads illumina data. We applied this hybrid approach
to Acinetobacter calcoaceticus ATCC 49823 for which a SMRT sequencing and assembly had
been done previously (Roberts et al., 2015). RIMS-seq was performed as described above and
the reads were mapped to the genome assembly obtained from SMRT-sequencing. We again
found the two m5C motifs : CGCG (p-value = 1.84e-1535) and GATC (p-value = 4.93e-6856) from
the RIMS-seq data in addition to the 13 m6A motifs described previously using SMRT
sequencing (Roberts et al., 2015). This result demonstrates the advantage of such a hybrid
approach in obtaining closed genomes with comprehensive epigenetic information.
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Figure 3 :De novo discovery of methylase specificity using RIMS-seq. A. Description of the
RIMS-seq motif analysis pipeline. First, C to T read variants are identified in both Read 1 and
Read 2 separately. Then, the MosDI program searches for overrepresented motifs. Once a motif
is found, the pipeline is repeated until no more motifs are found, enabling identification of
multiple methylase specificities in an organism. B. Fractions of C to T read variants in CGCG
(yellow) or GATC (green) contexts compared to other contexts for R1 and R2 in Acinetobacter
calcoaceticus ATCC 49823 using the assembled or the reference genome. The increase of C to T
read variants in these contexts is similar when using either the assembled or reference genomes
C. Assembly statistics obtained using the sequence from the standard DNA-seq (+3H, left) and
RIMS-seq (right). Visualization using assembly-stats program
(https://github.com/rjchallis/assembly-stats). The corresponding table with the statistical values
is available in the supplementary material (Supplementary Table 2).
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RIMS-seq can be applied to a variety of RM systems
Methylases targets are usually palindromic sequences between 4-8 nt, and a single bacterium
often possesses several, distinct MTase activities (Vasu and Nagaraja, 2013). Next, we tested the
general applicability of RIMS-seq and the de novo motif discovery pipeline using bacterial
genomic DNA from our in-house collections of strains.
For some bacterial strains, the methylase recognition specificities have been previously
experimentally characterized. In all of those strains, RIMS-seq confirms the specificities and
identifies the methylated cytosine at base resolution (Table 1). We have tested the
identification of 4-mers motifs such as GATC, CGCG (Acinetobacter calcoaceticus) and GCGC
(Haemophilus parahaemolyticus) up to 8-mers motifs such as ACCGCACT and AGTGCGGT
(Haemophilus influenzae). Motifs can be palindromic or non-palindromic (Table 1 and
Supplementary Table 3). In the latter case, RIMS-seq defines non-palindromic motifs at strand
resolution. For example, RIMS-seq identifies methylation at two non-palindromic motifs
ACCTGC as well as its reverse complement GCAGGT in the Bacillus fusiformis strain (Table 1).
A number of RM systems have been expressed in other hosts such as E. coli for biotechnological
applications. For the methylase M.HhaI recognizing GCGC (Roberts et al., 2015), we performed
RIMS-seq and a control DNA-seq(+3H) on both the native strain (Haemophilus parahaemolyticus
ATCC 10014) and in E. coli K12 expressing the recombinant version of M.HhaI. Interestingly, we
found that the de novo RIMS-seq analysis algorithm identifies RCGC (with R being either A or G)
for the recombinant strain and GCGC for the native strain (Figure 4A). Conversely, no notable
elevation of C to T read variants are observed for the native strain (Figure 4B), confirming the de
novo motif discovery results from the analysis pipeline. Collectively, these results suggest that
the recombinant methylase shows star activity, notably in the context of ACGC, that is not found
in the native strain. We hypothesize that the star activity is the result of the over-expression of
the methylase in E. coli K12. Interestingly, ACGC is not a palindrome motif and consequently the
star activity results in hemi-methylation of the ACGC sites and not the GCGT motif.
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Figure 4 : C to T error profile in GCGC (canonical recognition site), ACGC, TCGC, CCGC and GCGT.
in R1 reads (orange) and R2 reads (red) for RIMS-seq (upper panel) and DNA-seq(+3H) (lower
panel) A. Recombinant HhaI methylase expressed in E.coli B. Native HhaI methylase expressed
in Haemophilus parahaemolyticus. Elevation of C to T in the R1 read variant can be observed in
the context of GCGC for both the recombinant and native HhaI genomic DNA and in the context
of ACGC only for DNA from the recombinant but not the native HhaI.
RIMS-seq can be applied to microbial communities
We assessed whether RIMS-seq can be applied to mixed microbial communities using synthetic
gut and skin microbiomes from ATCC containing 12 and 6 bacterial species, respectively. We also
complemented the RIMS-seq experiment with the control experiment DNA-seq(+3H) and a
bisulfite treatment to validate the RIMS-seq findings. Reads were mapped to their respective
microbiome reference genomes (Methods). For the gut microbiome we found a mapping rate
(properly paired only) of 95.79%, 95.77% and 66.2% for RIMS-seq, DNA-seq and bisulfite-seq
respectively. Concerning the skin microbiome, 85.89%, 85.35% and 54.9% of reads were
mapped for RIMS-seq, DNA-seq and bisulfite-seq respectively. The low mapping rate for
bisulfite-seq is a known challenge as the reduction of the alphabet to A, G, T generates
ambiguous mapping (Grehl et al., 2020).
To use RIMS-seq as an equivalent to DNA-seq for mixed community applications, RIMS-seq
should produce sequencing quality metrics that are similar to standard DNA-seq, especially on
the estimation of species relative abundances. We therefore compared RIMS-seq sequencing
performances with DNA-seq(+3H) and bisulfite sequencing. We found that bisulfite sequencing
elevates abundances of AT-rich species such as Clostridioides difficile (71% AT), Enterococcus
faecalis (63% AT) and Fusobacterium nucleatum (73% AT) (Figure 5A, Supplementary Figure 4).
For example, bisulfite sequencing over-estimated the presence of Clostridioides difficile by a
factor of 2.65 and Staphylococcus epidermidis by a factor of 3.9 relative to DNA-seq. This
over-estimation of an AT rich genome by bisulfite is a known bias of bisulfite sequencing and
relates to damage at cytosine bases (Olova et al., 2018). Conversely, we found that the species
abundances are similar between DNA-seq(+3H) and RIMS-seq (abundance ratios between 0.8
and 1.2) indicating that RIMS-seq can be used to quantitatively estimate microbial composition.
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Figure 5 :A. Bacterial abundance in the ATCC gut microbiome calculated from bisulfite-seq data
(left) and RIMS-seq (Right) normalized to DNAseq(+3H). The normalized abundance is plotted
relative to the GC content of each bacterium. B. Methylation level in Helicobacter pylori (ATCC
gut microbiome) calculated from bisulfite-seq data. The methylation level was calculated for
cytosine positions in the context of GCGC (yellow) and randomly selected positions in other
contexts (blue). C. Methylation levels in Acinetobacter johnsonii (left) and Streptococcus mitis
(right) (ATCC skin microbiome). The methylation level was calculated for cytosine positions in
the context of ACGT (yellow) and randomly selected positions in other contexts (blue). These
bisulfite-seq data suggest some sites are methylated in the context of ACGT, but they are not
fully methylated.
●RIMS-seq identifies known and novel methylase specificities in synthetic microbial
communities
Overall, we found motifs for 6 out of the 12 gut microbiome species and 5 out of the 6 skin
microbiome species (Supplementary Table 3). The motifs range from 4 to 8 nucleotides long
and 70% are palindromic. Interestingly, we found an unknown palindromic motif GGCSGCC
(with S being either C or G) from Micrococcus luteus (NC_012803.1) in the skin community. To
our knowledge, this is the first time this 7nt motif is identified, showing the potential of
RIMS-seq to identify new methylase specificities.
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We validated the results obtained with RIMS-seq using bisulfite sequencing. RIMS-seq identified
2 motifs in Helicobacter pylori from the ATCC synthetic gut microbiome: GCGC as well as an
additional non-palindromic motif CCTC that was identified by the bisulfite analysis pipeline as
CYTC with Y being either C or T. The CCTC motif is very common in Helicobacter pyloris species,
it has been described to be modified at m5C on one strand, while modified at m6A on the other
strand (Roberts et al., 2015). In order to confirm the RIMS-seq motif, we investigated the
bisulfite-seq data and compared the methylation level in cytosines present either in the CCTC
context versus cytosines in any other context. We see a methylation level above 90% at the
cytosines in the CCTC context confirming the existence of this methylated motif in Helicobacter
pylori (Figure 5B). Interestingly, m4C methylation in Helicobacter pylori has been shown to also
occur at TCTTC (Vitkute et al., 2001), resulting in the composite motif CYTC (TCTTC and NCCTC)
found in the bisulfite data. Contrary to bisulfite, RIMS-seq does not identify m4C methylation
(Vilkaitis and Klimasauskas, 1999).
Also interestingly, bisulfite-seq results indicate that the ACGT motif in Acinetobacter johnsonii
and Streptococcus mitis from the ATCC synthetic skin microbiome are not fully methylated
(Figure 5C). Most of the sites in Acinetobacter johnsonii show a methylation of about 10% while
in Streptococcus mitis, the average methylation per site is 23%. These results highlight that
despite the low methylation levels, RIMS-seq is able to detect the ACGT motif at high
significance (p-value < 1e-100).
Organism
Accession numbers
(biosample)
RIMS-seq motif(s)
Validated motif(s)
Escherichia coli K12
SAMN02604091
CCWGG
CCWGG (1,2,4)
Acinetobacter
Calcoaceticus ATCC
49823
SAMN14530202
GATC
CGCG
GATC (4)
CGCG (2,4)
Bacillus fusiformis
1083
SAMN17843035
ACCTGC
GCAGGT
ACCTGC (2,3)
GCAGGT (2,3)
Bacillus
amyloliquefaciens H
ATCC 49763
SAMN12284742
GCWGC
GCWGC (3)
Clostridium
acetobutilicum ABKn8
SAMN17843114
GCNNGC
GCNNGC (3)
Aeromonas
SAMN14533640
GCCGGC
GCCGGC (3)
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hydrophila NEB724
Haemophilus
influenzae Rd ATCC
51907
SAMN02603991
ACCGCACT
AGTGCGGT
No validation
performed
Haemophilus
parahaemoltyicus
ATCC 10014
SAMN11345835
GCGC
GCGC (2)
M.HhaI clone (E. coli)
NA
RCGC
CCWGG(a)
GCGC (4)
CCWGG (1,2,4)(a)
Table 1 : Methylases specificity obtained using RIMS-seq and validated using different methods.
The method is indicated by a number next to the motif. : Evidence for the validated motifs are
(1) Bisulfite-seq (material and Methods), (2) REBASE (Roberts et al., 2015) (3) EM-seq (material
and method) (4) MFRE-seq (Anton et al., 2021).
(a) The E. coli strain used is Dcm+, resulting in the discovery of both the Dcm (CCWGG) and
M.HhaI motifs (GCGC). RIMS-seq discovered RCGC instead of GCGC motif (see text for
explanation)
Discussion
In this study, we developed RIMS-seq, a sequencing method to simultaneously obtain high
quality genomic sequence and discover m5C methylase specificity(ies) in bacteria using a single
library preparation. The simplicity of the procedure makes RIMS-seq a cost effective and time
saving method with only an additional 3h sodium hydroxide incubation and an additional
column-based cleaning step. Theoretically, the cleaning step can be avoided if a small volume of
the library is used for the amplification step, but we have not tested this procedure.
Due to the limited deamination rate, RIMS-seq is equivalent to short read DNA-seq in terms of
sequencing quality. Sequencing QC metrics such as coverage, GC content and mapping rate are
similar for RIMS-seq and DNA-seq. Thus, RIMS-seq can be used for applications such as, but not
limited to, shotgun sequencing, genome assembly and estimation of species composition of
complex microbial communities. This dual aspect of RIMS-seq is analogous to SMRT sequencing
for which methylation is inferred from the IPD ratio. We showed that both PacBio and RIMS-seq
can be complementary with the ability to obtain a complete methylome: m6A and m4C
methylase specificities can be obtained from SMRT sequencing while m5C methylase specificity
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can be obtained from RIMS-seq. Combining both sequencing technologies also allows for a
hybrid assembly strategy resulting in closed reference genomes of high sequencing accuracy.
We applied RIMS-seq to several bacteria and identified a variety of methylation motifs, ranging
from 4 to 8nt long, palindromic and non-palindromic. Some of these motifs were identified for
the first time, demonstrating the potential of the technology to discover new methylase
specificities, from known as well as from unknown genomes. We also validated that RIMS-seq
can identify multiple methylase specificities from a synthetic microbial community and estimate
species abundances. However, RIMS-seq has caveats similar to metagenomics sequencing when
applied to study natural microbial communities. Closely related species are likely to co-exist and
assigning the motif to the correct species can be challenging. Furthermore, single nucleotide
polymorphisms may confound the identification of the C to T deamination increasing the
background noise for the detection of motifs. Finally, species in microbiomes are unevenly
represented which can cause RIMS-seq to identify motifs only in the most abundant species.
Because RIMS-seq is based on a limited deamination, it requires the combined signal over many
reads to be large enough to effectively identify methylase specificity. For the vast majority of
the methylases in RM systems, methylation is present at a sufficient number of sites across the
genome for RIMS-seq to determine their specificities. Nonetheless, bacterial methylases can be
involved in other processes such as, but not limited to, DNA mismatch repair (Modrich and
Lahue, 1996), gene regulation (Casadesús and Low, 2006) and sporulation (Oliveira et al., 2020)
and the recognition sites may not necessary be fully methylated. Partially methylated sites can
be found using RIMS-seq but more analysis needs to be done to evaluate how pervasive
methylation needs to be to provide a RIMS-seq signal. In other cases, methylated motifs are too
specific or under purifying selection, resulting in just a handful of sites in the genome. In these
cases, RIMS-seq signals can only be obtained with enough read coverage to compensate for the
scarcity of those sites. While the methylase specificities are of great interest in bacteria due to
their diversity in recognition sequences, applying RIMS-seq to humans would lead to the
identification of the already well-described CpG context. In this case, other technologies such as
EM-seq or bisulfite-seq are more appropriate as they enable the precise genomic location to be
obtained.
In summary, RIMS-seq is a new technology allowing the simultaneous investigation of both the
genomic sequence and the methylation in prokaryotes. Because this technique is easy to
implement and shows similar sequencing metrics to DNA-seq, RIMS-seq has the potential to
substitute DNA-seq for microbial studies.
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Material and Methods
Samples and genomic DNA collection
Skin microbiome genomic DNA (ATCC®MSA-1005) and gut microbiome genomic DNA (ATCC®
MSA-1006) were obtained from ATCC. E. coli BL21 genomic DNA was extracted from a culture of
E. coli BL21 DE3 cells (C2527, New England Biolabs) using the DNEasy Blood and Tissue kit
(69504, Qiagen). E. coli K12 MG1655 genomic DNA was extracted from a cell culture using the
DNEasy Blood and Tissue kit (69504, Qiagen). All the other gDNA from the bacteria presented in
Table 1 were isolated using the Monarch genomic DNA purification kit (T3010S, New England
Biolabs). Xp12 phage genomic DNA was obtained from Peter Weigele and Yian-Jiun Lee at New
England Biolabs.
RIMS-seq library preparation
100ng of gDNA was sonicated in 1X TE buffer using the Covaris S2 (Covaris) with the standard
protocol for 50µL and 200bp insert size.
The subsequent fragmented gDNA was used as the starting input for the NEBNext Ultra II library
prep kit for Illumina (E7645, New England Biolabs) following the manufacturer’s
recommendations until the USER treatment step. The regular unmethylated loop-shaped
adapter was used for ligation. After the USER treatment (step included), the samples were
subjected to heat alkaline deamination: 1M NaOH pH 13 was added to a final concentration of
0.1M and the reactions were placed in a thermocycler at 60˚C for 3h. Then, the samples were
immediately cooled down on ice and 1M of acetic acid was added to a final concentration of
0.1M in order to neutralize the reactions.
The neutralized reactions were cleaned up using the Zymo oligo clean and concentrator kit
(D4060 Zymo Research) and the DNA was eluted in 20µL of 0.1X TE.
PCR amplification of the samples was done following NEBNext Ultra II library prep kit for
Illumina protocol (ER7645, New England Biolabs) and the NEBNext®Multiplex Oligos for
Illumina®(E7337A, New England Biolabs). The number of PCR cycles was tested and optimized
for each sample following the standard procedure for library preparation. PCR reactions were
cleaned up using 0.9X NEBNext Sample purification beads (E7137AA, New England Biolabs) and
eluted in 25µL of 0.1X TE. All the libraries were evaluated on a TapeStation High sensitivity
DNA1000 (Agilent Technologies) and paired-end sequenced on Illumina.
Bisulfite-seq library preparation
1% of lambda phage gDNA (D1221, Promega) was spiked-into 300ng gDNA to use as an
unmethylated internal control. The samples were sonicated in 1X TE buffer using the Covaris S2
(Covaris) with the standard protocol for 50µL and 200bp insert size.
The subsequent fragmented gDNA was used as the starting input for the NEBNext Ultra II library
prep kit for Illumina (E7645, New England Biolabs) following the manufacturer’s
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recommendations until the USER treatment step. The methylated loop-shaped adapter was
used for ligation. After USER, a 0.6X clean-up was performed using the NEBNext Sample
purification beads (E7137AA, New England Biolabs) and eluted in 20µL of 0.1X TE. A TapeStation
High Sensitivity DNA1000 was used to assess the quality of the library before subsequent
bisulfite treatment. The Zymo EZ DNA Methylation-Gold Kit (D5005, Zymo Research) was used
for bisulfite treatment, following the manufacturer’s suggestions.
PCR amplification of the samples was done following the suggestions from NEBNext Ultra II
library prep kit for Illumina (ER7645, New England Biolabs), using the NEBNext®Multiplex Oligos
for Illumina®(E7337A, New England Biolabs) and NEBNext®Q5U®Master Mix (M0597, New
England Biolabs).
The number of PCR cycles was tested and optimized for each sample. The PCR reactions were
cleaned up using 0.9X NEBNext Sample purification beads (E7137AA, New England Biolabs) and
eluted in 25µL of 0.1X TE. All the libraries were screened on a TapeStation High sensitivity
DNA1000 (Agilent Technologies) and paired-end sequenced on Illumina.
RIMS-seq data analysis
Paired-end reads were trimmed using Trim Galore 0.6.3 (option --trim1). The Acinetobacter
calcoaceticus ATCC 49823 data have been trimmed using Trim Galore version 0.6.3 instead and
downsampled to 1 million reads. Reads were mapped to the appropriate genome using BWA
mem with the paired-end mode (version 0.7.5a-r418 and version 0.7.17-r1188 for the
Acinetobacter calcoaceticus). When using an assembled genome directly from RIMS-seq data,
trimmed RIMS-seq reads were assembled using SPAdes (SPAdes-3.13.0 (Nurk et al.,
2013)default parameters). Reads were split according to the read origin (Read 1 or Read 2) using
samtools (version 1.8) with -f 64 (for Read 1) and -f 128 (for Read 2) and samtools mpileup
(version 1.8) was run on the split read files with the following parameters : -O -s -q 10 -Q 0. For
Acinetobacter calcoaceticus, the unmapped reads, reads without a mapped mate and the non
primary alignments were filtered out using the flags -F 12 and -F 256.
De-novo identification of motifs using RIMS-seq
Programs and a detailed manual for the de-novo identification of motifs in RIMS-seq are
available on github (https://github.com/Ettwiller/RIMS-seq/). Using the mpileup files, positions
and 14bp flanking regions in the genome for which a high quality (base quality score >= 35) C to
T in R1 or a G to A in R2 was observed were extracted for the foreground. Positions and 14bp
flanking regions for which a high quality (base quality score >= 35) G to A in R1 or a C to T in R2
was observed were extracted for the background. C to T or G to A in the first position of reads
were ignored. If the percentage of C to T or G to A are above 5% for at least 5 reads at any given
position, the position was ignored (to avoid considering positions containing true variants).
Motifs that are found significantly enriched (p-value < 1e-100) in the foreground sequences
compared to background sequences were found using mosdi pipeline mosdi-discovery with the
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following parameters : ‘mosdi-discovery -v discovery -q x -i -T 1e-100 -M 8,1,0,4 8 occ-count’
using the foreground sequences with x being the output of the following command :
‘mosdi-utils count-qgrams -A "dna" ‘ using the background sequences.
To identify additional motifs, the most significant motif found using mosdi-discovery is removed
from the foreground and background sequences using the following parameter : ‘mosdi-utils
cut-out-motif -M X’ and the motif discovery process is repeated until no motif can be found.
Sequence logo generation
Using the mpileup files, positions in the genome for which a high quality (base quality score >=
35) C to T in R1 or a G to A in R2 was observed were extracted for the foreground using the
get_motif_step1.pl program. Positions for which a high quality (base quality score >= 35) G to A
in R1 or a C to T in R2 was observed were extracted for the background. The +/- 7bp regions
flanking those positions were used to run Two sample logo (Vacic et al., 2006). Parameters were
set as t-test, p.value < 0.01.
Bisulfite-seq data analysis
Reads were trimmed using Trim Galore 0.6.3 and mapped to the bisulfite-converted
concatenated reference genomes of each respective synthetic microbiome using bismark 0.22.2
with default parameters. PCR duplicates were removed using deduplicate_bismark and
methylation information extracted using bismark_methylation_extractor using default
parameters. For the microbiome, the bismark_methylation_extractor with
--split_by_chromosome option was used to output one methylation report per bacterium. The
motif identification was done as previously described in (Anton et al. 2021).
EM-seq
EM-seq was performed according to the standard protocol (NEB E7120S)
Motif identification was done as previously described in (Anton et al., 2021).
Analysis and abundance estimation in synthetic microbiomes
RIMS-seq, DNA-seq and Bisulfite-seq were performed on the synthetic gut and skin microbiome
as described. Reads derived from RIMS-seq, DNA-seq and Bisulfite-seq were mapped as
described to a ‘meta-genome’ composed of the reference genomes of all the bacteria included
in the corresponding synthetic community (see Supplementary Table 3 for detailed
compositions). Mapped reads were split according to each bacterium and RIMS-seq or bisulfite
analysis pipelines were run on individual genomes as described above. Abundance was
estimated using the number of mapped reads per bacteria and normalized to the total number
of mapped reads. Normalized species abundances in RIMS-seq and Bisulfite-seq were compared
to the normalized species abundances in DNA-seq.
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Quality control of the data
The insert size for each downsampled filtered bam file was calculated using Picard version
2.20.8 using the default parameters and the option CollectInsertSizeMetrics (“Picard Toolkit.”
2019. Broad Institute, GitHub Repository. http://broadinstitute.github.io/picard/; Broad
Institute) .
The GC bias for each downsampled filtered bam file was calculated and plotted using Picard
version 2.20.8 using the default parameters and the option CollectGcBiasMetrics.
Xp12 genome assembly
Reads were downsampled to a 30X coverage using seqtk 1.3.106, trimmed using trimgalore
0.6.5 and assembled using Spades 3.14.1 with the --isolate option. Assembly quality was
assessed using Quast 5.0.2. Reads used for assembly were then mapped back to the assembly
using BWA mem 0.7.17 and mapping statistics were generated using samtools flagstat 1.10.2
Xp12 sequencing performance assessment
Reads were trimmed using trimgalore 0.6.5 and mapped to the Xp12 reference genome using
BWA mem 0.7.17. Insert size and GC bias were assessed using Picard Toolkit and genome
coverage using Qualimap 2.1.1.
Code and data availability
The data have been deposited with links to BioProject accession number PRJNA706563 in the
NCBI BioProject database (https://www.ncbi.nlm.nih.gov/bioproject/).
Custom-built bioinformatics pipelines to analyse sequencing reads from RIMS-seq are available
at https://github.com/Ettwiller/RIMS-seq/
Acknowledgments
We thank Peter Weigele and Yian-Jiun Lee from New England Biolabs for the Xp12 genomic DNA
and genomic sequence. We thank Ivan Correa and Nan Dai for their assistance with LC-MS and
Ira Schildkraut for his help with methylase specificities.
Competing interests
CB, YCL, AF, BPA, LC, TCE, RR and LE are or were employees of New England Biolabs Inc. a
manufacturer of restriction enzymes and molecular reagents.
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The copyright holder for this preprintthis version posted March 8, 2021. ; https://doi.org/10.1101/2021.03.08.434449doi: bioRxiv preprint
