ORIGINAL RESEARCH
published: 17 July 2020
doi: 10.3389/fmicb.2020.01628
Edited by:
Yoshio Yamaoka,
Oita University, Japan
Reviewed by:
Jaunius Urbonavičius,
Vilnius Gediminas Technical
University, Lithuania
Juan F. Martin,
Universidad de León, Spain
*Correspondence:
Hirokazu Yano
yano.hirokazu@ige.tohoku.ac.jp
Ichizo Kobayashi
zat14430@nifty.com
† Present
address:
Hirokazu Yano,
Graduate School of Life Sciences,
Tohoku University, Sendai, Japan
Md. Zobaidul Alam,
Department of Microbiology,
University of Chittagong, Chittagong,
Bangladesh
Tomoko F. Shibata,
Kobe Reformed Theological
Seminary, Kobe, Japan
Yoshikazu Furuta,
Division of Infection and Immunity,
Research Center for Zoonosis
Control, Hokkaido University,
Sapporo, Japan
Sumio Sugano,
Future Medicine Education and
Research Organization at Chiba
University, Chiba, Japan
Specialty section:
This article was submitted to
Evolutionary and Genomic
Microbiology,
a section of the journal
Frontiers in Microbiology
Received: 30 March 2020
Accepted: 22 June 2020
Published: 17 July 2020
Networking and
Specificity-Changing DNA
Methyltransferases in Helicobacter
pylori
Hirokazu Yano 1,2* † , Md. Zobaidul Alam 1† , Emiko Rimbara 3 , Tomoko F. Shibata 4† ,
Masaki Fukuyo 5 , Yoshikazu Furuta 1,2† , Tomoaki Nishiyama 6 , Shuji Shigenobu 4 ,
Mitsuyasu Hasebe 4,7 , Atsushi Toyoda 8 , Yutaka Suzuki 1 , Sumio Sugano 1,2† ,
Keigo Shibayama 3 and Ichizo Kobayashi 1,2,9,10,11*
1
Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University
of Tokyo, Tokyo, Japan, 2 Institute of Medical Science, The University of Tokyo, Tokyo, Japan, 3 Department of Bacteriology II,
National Institute of Infectious Diseases (NIID), Musashimurayama, Japan, 4 National Institute for Basic Biology (NIBB),
Okazaki, Japan, 5 School of Medicine, Chiba University, Chiba, Japan, 6 Advanced Science Research Center, Kanazawa
University, Kanazawa, Japan, 7 Department of Basic Biology, School of Life Sciences, SOKENDAI (The Graduate University
for Advanced Studies), Okazaki, Japan, 8 Advanced Genomics Center, National Institute of Genetics, Mishima, Japan,
9
Department of Infectious Diseases, School of Medicine, Kyorin University, Mitaka, Japan, 10 Institut de Biologie Intégrative
de la Cellule (I2BC), Université Paris-Saclay, Gif-sur-Yvette, France, 11 Research Center for Micro-Nano Technology, Hosei
University, Koganei, Japan
Epigenetic DNA base methylation plays important roles in gene expression regulation.
We here describe a gene expression regulation network consisting of many DNA
methyltransferases each frequently changing its target sequence-specificity. Our object
Helicobacter pylori, a bacterium responsible for most incidence of stomach cancer,
carries a large and variable repertoire of sequence-specific DNA methyltransferases.
By creating a dozen of single-gene knockout strains for the methyltransferases, we
revealed that they form a network controlling methylome, transcriptome and adaptive
phenotype sets. The methyltransferases interact with each other in a hierarchical way,
sometimes regulated positively by one methyltransferase but negatively with another.
Motility, oxidative stress tolerance and DNA damage repair are likewise regulated
by multiple methyltransferases. Their regulation sometimes involves translation start
and stop codons suggesting coupling of methylation, transcription and translation.
The methyltransferases frequently change their sequence-specificity through gene
conversion of their target recognition domain and switch their target sets to remodel
the network. The emerging picture of a metamorphosing gene regulation network,
or firework, consisting of epigenetic systems ever-changing their specificity in search
for adaptation, provides a new paradigm in understanding global gene regulation and
adaptive evolution.
Keywords: epigenetics, methylome, DNA methylation, gastric cancer, epigenome, DNA methyltransferase, SMRT,
Pacbio
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INTRODUCTION
Furuta et al., 2014; Sánchez-Romero et al., 2015; Zhang et al.,
2017; Nye et al., 2019).
In order to examine the above epigenetics-based adaptive
evolution model, we here knocked out ten of specificitydeterminant genes of DNA methylation systems, examined the
resulting methylomes and transcriptomes, and predicted and
tested adaptation-related phenotype changes. We focused on
Type III and Type I RM systems, which frequently change
sequence specificity by domain movement (Furuta et al., 2011,
2014; Furuta and Kobayashi, 2012b), and several uncharacterized
Type II RM systems.
Our results revealed a huge gene regulation network that
involves many, interacting and ever-changing DNA methylation
systems as hub transcription factors (Figure 1C). It controls gene
expression and adaptive phenotype sets in a unique way.
The currently dominating model for adaptive evolution assuming
selection from diverse genome sequences is derived from genetics
and molecular biology of microorganisms and other forms of
life, but its general validity became thoroughly testable only
recently with the availability of many genome sequences within a
species. Meanwhile studies of cell differentiation in multicellular
organisms have revealed critical roles of epigenetics, here defined
as information added to genome nucleotide sequences and
heritable through genome replication. The diverse epigenomes,
as opposed to the diverse genomes, may be regarded as the
units of evolution (Figure 1A). There are indeed increasing
lines of evidence for trans-generation epigenetic inheritance
in plants and animals (Miska and Ferguson-Smith, 2016;
Quadrana and Colot, 2016). In unicellular and equally dividing
bacteria, a somatic cell can be regarded as a germ line cell, to
the first approximation, which simplifies the issue. Sequencespecific DNA base methylation there affects gene expression
(Sánchez-Romero et al., 2015) and its reversible changes can
lead to different phenotype sets (De Ste Croix et al., 2017;
Gorrell and Kwok, 2017). A DNA methyltransferase (MTase)
is often paired with a restriction enzyme to form a restrictionmodification (RM) system (Roberts et al., 2003). They form
a prokaryotic immune system attacking non-self DNA lacking
proper epigenetic DNA methylation and also behave as selfish
mobile elements (Vasu and Nagaraja, 2013).
Prokaryotes possess, on average, five DNA methyltransferase
genes and three methylated sequence motifs (Blow et al., 2016;
REBASE PacBio statistics, 2020). A large repertoire of DNA
methylation systems, with twenty to thirty genes per genome,
has been found in Helicobacter pylori (Furuta et al., 2014; Krebes
et al., 2014). They colonize on children’s stomach cells using their
motility, persist there for decades, tolerating attacks with ROS
(reactive oxygen species) by immune systems and repairing DNA
damages, and may eventually cause ulcer and cancer (Backert and
Yamaoka, 2016). Some of their RM systems are conserved, while
some others are present only in few lineages (Figure 1B; Vale
et al., 2009; Uchiyama et al., 2016). Many are obtained through
horizontal transfer, within the species or from a different species,
and decay by mutation (Lin et al., 2001; Furuta et al., 2011; Furuta
and Kobayashi, 2012b). In some RM types (Type I, Type III, Type
IIG), target sequence recognition domains (TRDs) move within a
gene and between genes, sometimes from various (eu)bacterial
species beyond phylogenetic barriers (Furuta et al., 2011;
Furuta and Kobayashi, 2012b). Because of these processes, the
repertoire of methylation systems changes rapidly and apparently
irreversibly during lineage diversification of H. pylori (Kojima
et al., 2016). Single-molecule real-time sequencing technology
indeed revealed highly diverged methylomes even for closely
related genomes (Furuta et al., 2014). Each of the methylomes
may have a unique gene expression pattern and phenotype
set and might be regarded as the units of adaptive evolution
(Figure 1A; Furuta and Kobayashi, 2012a). Previous experiments
have indeed found effects of individual DNA methylation genes
on the transcriptome and phenotype in many bacterial species
(Srikhanta et al., 2009, 2011; Fang et al., 2012; Kumar et al., 2012;
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MATERIALS AND METHODS
Bacterial Strains, Culture Media and
Plasmids
The strains, plasmids and their relevant characteristics are shown
in Supplementary Table S3 of the supporting information.
E. coli strain was cultured using Luria-Bertini (LB, Lennox)
broth (10 g tryptone, 5 g Yeast extract, 5 g NaCl per 1L).
Solid medium (LB-agar) was prepared by addition of 1.5% agar
[Nakalai tesque, Kyoto Japan]. If necessary, kanamycin (Km)
and chloramphenicol (Cm) [FUJIFILM Wako Pure Chemical
Corporation, Osaka, Japan] was added to LB or LB-agar at 50
and 25 µg/ml, respectively. Brucella Broth (BB) [BD Bioscience,
San Jose, CA, United States] supplemented with 10% fetal bovine
serum (BB-FBS) [Cell Culture Laboratories, Cincinnati, OH,
United States] was used for liquid culture of H. pylori. The solid
media used were BB-FBS agar (BB-FBS, 1.5% agar), BB-HB agar
(BB supplemented with 5% horse blood, 1.5% agar), or BD BBL
Trypticase Soy Agar with 5% Sheep Blood (TSA) [BD Bioscience].
If necessary, Km and Cm were added to BB-FBS agar at 15 and
5 µg/ml, respectively. Both liquid cultures and agar plates were
incubated at 37◦ C in the presence of 10% CO2 and 5% O2 .
DNA Manipulation and Mutant
Construction
The oligonucleotides used in this study are listed in
Supplementary Table S4. KOD FX neo (Toyobo, Ohtsu,
Japan) was used as the DNA polymerase for PCR. E. coli HST08
chemical competent cells (Takara Bio, Kusatsu, Japan) and
In-Fusion HD cloning kit (Takara Bio, Kusatsu, Japan) were
used for conventional cloning. Gene-EluteTM plasmid DNA
kit (Sigma-Aldrich, St. Louis, MO, United States) was used for
plasmid purification.
For gene knockout experiments, a region of approximately
800 bp flanking an MTase gene or the specificity subunit gene
was cloned into pUC119 or pUC19 together with a fragment
containing a Km resistance gene (aphA) with a promoter
derived from pHel3 (Heuermann and Haas, 1998). The resulting
constructs (Supplementary Table S3) were transferred to
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FIGURE 1 | Gene regulation network involving multiple DNA methyltransferases. (A) Two models for adaptive evolution. (i) Genome-based. (ii) Epigenome-based
model with a part of the methylomes (rpoB, top strand) of closely related H. pylori strains [modified from Furuta et al. (2014)]. One bar indicates one methylation and
(Continued)
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Networking DNA Methyltransferases
FIGURE 1 | Continued
one color indicates one methylation sequence motif. (B) Homology grouping of Target Recognition Domains in the analyzed restriction-modification loci in some
global strains (Lin et al., 2001; Furuta et al., 2011; Furuta and Kobayashi, 2012b; Kojima et al., 2016). (C) (i) Gene regulation network in H. pylori deduced from this
work. It shows hub DNA methyltransferases of various types, other transcription factors and their target genes. Interaction of DNA methyltransferases (ii) in strain
26695 and (iii) in strain P12. An arrow indicates a positive effect while a T shape indicates a negative effect. Type II RM systems including solitary DNA
methyltransferases are shown in a light blue box. Type I RM systems and Type III RM systems, in which TRD movement causes allelic variation, are shown in a dark
blue box. Two transcription factors (sigma factors) are shown in a circle. (iv) Five elementary patterns of interaction between DNA methyltransferases identified in the
network. (D) Opposite roles of two methylation systems in regulation. (i) Swimming motility. (ii) ROS metabolism. (iii) Methyltransferase expression. A DNA sequence
indicates a methylation motif. (E) Regulation of virulence-related genes. (i) Genes associated with urease synthesis and maturation. (ii) Genes in the cag
pathogenicity island (brown) and vacA gene. Light blue, Type II methyltransferase; dark blue, Type III methyltransferase.
Transcriptome Analysis
competent cells prepared in 300 µM sucrose by electroporation,
and the transformants were selected on BB-FBS agar containing
Km. After two rounds of single colony isolation, the clones were
stocked. The absence of the original locus from these mutants
was confirmed using PCR. To disrupt methyltransferase gene of a
Type II RM system, R gene alone was replaced with the Cm gene
first. Then, in the second recombination experiment, the Cm gene
and the M gene were replaced together by the Km gene.
In
gene
restoration
experiments,
the
original
methyltransferase gene (hpyAXM, hpyPVIIM) was PCRamplified from genomic DNA and joined with Cm resistance
gene on pUC19 with their respective c.a. 800-bp upstream and
downstream fragments. In parallel, a construct without the
MTase gene was also made. These constructs were transferred
into strain PIK14 (1hpyAXM) or PIK64 (1hpyPVIIM)
(Supplementary Figure S4). The absence of the initially
integrated Km allele from these mutants was confirmed by
PCR and sequencing.
Total RNA was extracted from H. pylori liquid culture at
exponential phase (OD600 nm = 0.40–0.45 in a 1-cm path cuvette)
using a Pure-Link RNA mini kit (Lifetechnologies, Carlsbad,
CA, United States). The rRNA was depleted from the total RNA
using a RiboZero gram-negative kit (Illumina, San Diego, CA,
United States) prior to cDNA synthesis. cDNA libraries of 200
to 400-bp fragment size were constructed using a TruSeq mRNA
kit (Illumina, San Diego, CA, United States) for strain 26695
and its derivatives, or SureSelect Strand Specific RNA library
kit (Agilent Technologies, Santa Clara, CA, United States) for
P12 and its derivatives. Indexed cDNA libraries were pooled and
sequenced in paired-end mode under a HiSeq 2500 platform with
unrelated samples.
Reads of 100 bp were mapped on to the strain 26695 genome
(RefseqID: NC_000915.1) or P12 genome (our re-sequenced
data) using the BWA program, allowing 4% mismatches. This
gave rise to 17 to 31 million mapped reads per sample for 26695
and its derivative samples, and 6.5 to 27 million mapped reads
for P12 and its derivative samples. The counts of mapped reads
per gene were obtained using HTSeq 0.6 (Anders et al., 2015).
The read depth was calculated using BEDtools (Quinlan and Hall,
2010). Differential gene expression analysis was conducted for
two sets of biological replicate data using Bioconductor package
TCC (Sun et al., 2013). In TCC, we used a TMM normalizationedgeR iteration protocol. In this work, the genes with q.value
(false discovery rate) < 0.01 and a.value (mean expression
value in log2 ) > 0.75 were considered to be differentially
expressed genes (DEGs).
Pathway activity analysis (Lee et al., 2008) was performed
using Bioconductor package GSVA (Hänzelmann et al., 2013).
TMM-normalized read count data was used as input. 71 KEGG
pathway categories that have at least five gene members in
H. pylori genome were considered. Gene expression changes
in members of representative KEGG categories were visualized
using Pathview (Luo and Brouwer, 2013).
For stain P12, transcription unit data was not available.
Therefore, we tentatively, defined regulatory regions of each
coding sequence as start codon plus 149 bp upstream region
(152 bp in total) for each coding sequence. Sequence motifs
search for the genomic segments and sequence retrieval were
conducted using standard functions implemented in R package
Biostrings (Pages et al., 2003). We used Marlov Maximal order
model as the number of expected motif count (Rocha et al., 1998).
Motif frequency (Mr) was defined as the ratio of observed counts
to expected counts.
Methylome Decoding
Total DNA was extracted from a stationary-phase liquid
culture of each H. pylori strain, PIK38, PIK39, PIK40, by
lysozyme treatment and phenol-chloroform extraction. The
DNA was further purified using Qiagen Genomic-tip 100-G
column (Qiagen, Hilden, Germany) following the manufacture’s
protocol. The DNA was sheared to ∼20 kb using a g-Tube
(Covaris, Woburn, MA, United States) and the libraries for
sequencing were constructed with SMRTbell Template Prep
Kit 1.0 (Pacific Biosciences of California, Menlo Park, CA,
United States) following the standard instruction for 20-kb
template preparation. In the process of size selection using
BluePippin (Sage Science, Beverly, MA, United States), 10–50 kb
molecules were collected with high-pass v3 program. Sequencing
was performed using PacBio RS II (Pacific Biosciences of
California, Menlo Park, CA, United States) with P5-C3
chemistry and 3-h movie. Data assembly was performed using
the program HGAP (Chin et al., 2013) packaged in SMRT
Analysis v2.2.0.
The re-sequenced strain P12 genome was annotated by
MiGAP pipeline in National Institute of Genetics, Japan
(Sugawara et al., 2009), and then the updated annotation was used
for RNA-seq data analysis.
Methylomes of strain 26695, PIK14, PIK16, and PIK17 were
decoded to confirm the loss of methylation at the expected
sites. For this analysis, we used stationary phase cell culture and
Genomic-tip 100-G column.
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qPCR
was placed on the center of the plate. To assay sensitivity against
DNA damaging agent, 7.5 µl of mitomycin C (10 µg/ml) (SigmaAldrich, St. Louis, MO, United States) was placed on the center of
the disk. After 3 days of incubation at 37◦ C in the CO2 incubator,
the diameter of the zone of inhibition (mm) was measured as
described above. The diameter of the disk was subtracted. The
significance of the difference was evaluated using t-test as above.
The qPCR was performed for validation of the transcriptome
data (Supplementary Table S5). This was performed for total
RNA before rRNA depletion, using a StepOnePlus real-time PCR
system (Life Technologies), ReveTra Ace RT-PCR (Toyobo), and
KOD SYBR qPCR kit (Toyobo) by the absolute quantitation
method described in the manufacturers’ protocols. Total RNA
was first converted to cDNA, and then used as a qPCR template.
A reference gene, which showed similar levels of expression in all
the strains in the RNA-seq experiments, was used as a reference
copy number for cDNA. This was HP1035 for 26695 and its
derivatives’ samples, lepB (HPP12_0582) for strain P12 and its
mod gene KO samples, purD (HPP12_1183) for hpyPVIIM KO
and paired P12 samples, and horF (HPP12_0684) for hpyPIM KO
and paired P12 samples, respectively. The quantity of cDNA was
determined on the basis of standard curves with R2 > 0.99. The
qPCR primers used are listed in Supplementary Table S4.
Motility Assay
From the stock, each strain was streaked on three TSA plates
and incubated Luria for 2 overnights Luria After incubation,
1 ml BB Luria was spread on the colonies and then they were
scraped off with a sterile loop. Then 200 µl of the liquid culture
was mixed with 10 ml BB-FBS Luria and incubated overnight at
70 rpm. Next day, the culture was diluted to OD600 of 0.8 and
5 µl of the culture was spotted on the center of BB-FBS soft agar
[0.35% agar (w/v)]. Optionally, 50 µg ml−1 triphenyl tetrazolium
chloride (Sigma-Aldrich, St. Louis, MO, United Kingdom) was
added to visualize pH shift around the cells. After 5 days of
incubation, the diameter of bacterial swimming zone with central
inoculation spot was measured. The significance of the difference
was evaluated using t-test as described above.
Growth
From the 50% glycerol stock, each strain was streaked on four
TSA plates and incubated for 2 days. The colonies of one plate was
pooled in 1 ml BB and then transferred to a Petri dish containing
10 ml BB-FBS and incubated at 37◦ C in the presence of 10% CO2
and 5% O2 in a CO2 incubator for 24 h. After incubation, the
liquid culture was diluted to OD600 nm = 0.1 in 30 ml fresh BB
medium with 10% FBS and incubated in the CO2 incubator with
agitation (rpm-90) for 4 days, and OD600 nm was recorded at 6,
12, 24, 30, 48, and 72 h (or 6, 12, 24, 36, and 48 h for strain 26695
data set) after inoculation. At the same time, serial dilution of
each strain was made and spread on BB-HB agar. After 3 days
of incubation, colonies were counted and cfu (colony forming
unit) was calculated.
RESULTS
Diverse DNA Methyltransferases and
Their Target Motifs Shaping the
Methylome
We knocked out each of the specificity-determinant genes
of a total of 10 known/putative DNA methylation systems
(Figure 2A) in two laboratory strains, P12 and 26695. Although
both belong to hpEurope population (Backert and Yamaoka,
2016), they differ in the repertoire of RM systems (Figure 1B).
RM systems are classified into three (Roberts et al., 2003). Type
I RM systems are composed of three subunits: a restriction
(R) enzyme subunit, a methyltransferase (M) subunit, and
a specificity (S) subunit usually with two target recognition
domains (TRDs). In Type II RM systems, R enzyme and
M enzyme separately recognize a sequence and M enzyme
methylates both strands. In Type IIP RM systems among them,
the recognition sequence is a palindrome. Type III RM systems
are likewise composed of R and M proteins, but their TRD is
present within the M protein and methylation takes place on one
strand of a non-palindromic target sequence.
By decoding methylome of P12 and the knockouts, we
detected modification of A or C in 15 motifs (Supplementary
Table S1) [see also (Furuta et al., 2014)]. A methylation occurred
at 2,578/2,579 sites of the GACC motif in the chromosome,
but its methylation was very much decreased in 1HPP12_1497
(Supplementary Table S1). We concluded that GACC is the
recognition motif of the HPP12_1497 product for A methylation
and propose to designate this Type III RM system as HpyPX. The
other two Type III RM loci in P12 examined (Figure 2A) are
not related to any methylation activity detected (Supplementary
Table S1). Methylation activity of locus 3 was not detected in
Oxidative Stress Resistance Test
Liquid culture was prepared in triplicate for each strain described
above. They were then diluted in BB to OD600 nm = 0.8. After
dilution, 100 µl of the cell suspension was spread onto BBHB agar with a cotton swab. A sterile paper disk (5 mm in
diameter) was placed on the center of the plate. To assay peroxide
sensitivity, 7.5 µl of a reactive oxygen species (ROS)-inducing
agent [30% hydrogen peroxide (HP) (Wako Pure Chemical Ind.
Ltd.) or 5% tert-butyl hydroperoxide (Wako Pure Chemical Ind.
Ltd.) was placed on the center of the disk. After 3 days of
incubation at 37◦ C in the CO2 incubator as described above,
the diameter of the zone of inhibition (mm) was measured. The
diameter of the disk was subtracted. To evaluate the significance
for the difference between the control and the mutant, we used
two-tailed Student’s t-test or Welch t-test after testing equality of
variance by F-test assuming normal distribution as we could have
only small sample size in each experiment.
Mitomycin C Sensitivity Test
Liquid cultures in triplicate were prepared for each strain as
described above and then diluted in BB to OD600 nm = 0.8. After
dilution, 100 µl of the cell suspension was spread onto BB-HB
agar using a cotton swab. A sterile paper disk (5 mm in diameter)
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FIGURE 2 | Methyltransferases, their target distribution, knockouts’ effects on transcriptome and growth. (A) Methlyltransferase name, target motif with methylated
base underlined, and its number per genome (mSites). Parental strain (P12 or 26695), locus tag, genetic maps around the methylation specificity gene and its
(Continued)
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FIGURE 2 | Continued
knockout. (B) Different distribution of relative abundance of methylation motifs along the genome. The four major genomic islands are highlighted in blue. Motif
frequency was determined for a 10-kb overlapping sliding window. Motif frequency (Mr) was defined as the ratio of the observed count to the count expected from a
Markov model. Red horizontal bar indicates the median. (C) Transcriptome response to DNA methyltransferase gene knockout, shown as a MA plot with X-axis for
average read count across samples (a.value) and Y -axis for fold change in log2 (knockout mutant/parent [m.value)]. Red dot indicates differentially expressed genes
(DEGs) (q.value < 0.01, m.value ≥ 0.75). The transcriptome data of HpyPX (GTAC) knockout of P12 (C (iv)) was published earlier (Zhang et al., 2017). (D) Culture
OD during 4-day incubation period. A point and an error bar indicate the mean and the SEM, respectively.
the other strains examined (Furuta et al., 2014; Krebes et al.,
2014; Lee et al., 2015). As this locus is conserved and fosters
allelic TRD variation (Figure 1B; Furuta and Kobayashi, 2012b;
Kojima et al., 2016), it may have some unknown function. We
further created P12 mutants knocked out for three Type II MTase
genes (HpyPI, HpyPVII, HpyPVIII) and 26695 mutants for a
Type II MTase gene (HpyAX), a Type III MTase gene (HpyAXI),
and a Type I specificity gene (HpyAXIII) (Figures 1B, 2A). The
26695 knockout mutants lost methylation at the expected motifs
(Krebes et al., 2014): HpyAX, TCGA; HpyAXIII, CTAN8 TGT
and ACAN8 TAG; HpyAXI, GCAG (Supplementary Table S1).
An earlier work examined the distribution of methylation
motifs along the genome (Furuta et al., 2014). The genomic
islands referred to as plasticity zones (Kersulyte et al., 2009),
or Integrative Conjugative Elements (Fischer et al., 2010),
apparently avoid occurrence of m4C, and m6A methylation
(Furuta et al., 2014). We now analyzed the distribution of each
of the above methylation motifs in comparison with the expected
frequency (Figure 2B). Their average frequency turned out to
vary greatly with GTAC and TCGA strongly avoided (Humbert
and Salama, 2008; Zhang et al., 2017). The CATG motif of a
conserved Type IIP RM system turned out to occur less in
the genomic islands including cagPAI (cag pathogenicity island)
(Supplementary Figure S1, left), but this is not the case for
the other motifs (Figure 2B). For GACC motif in particular,
the sliding windows overlapping with PZ1 and PZ3 showed
the highest abundance and those overlapping with PZ2 and the
neighboring 23 rRNA gene showed a relatively high abundance
(Supplementary Figure S1 right, Supplementary Table S2).
We could not detect any significant influence of the Type IIP RM
system HpyPVIII (TCNNGA).
We found that the knockout of one MTase gene often affects
expression of other MTases [Figures 1C(ii–iv)]. We extracted
several elementary patterns in their interaction [Figure 1C(iv)].
One MTase’s expression may be stimulated positively by multiple
(up to three) MTases. One MTase may have a positive regulator
MTase and a negative regulator MTase [see also Figure 1D(iii)].
When one MTase positively regulates another MTase, a 3rd
MTase may positively regulate both the MTases, negatively
regulate both the MTases, or regulate the two MTases in the
opposite directions. A few Type II MTase genes (5mC MTase
targeting GCGC; 6mA MTase targeting GAAGG) were repeatedly
detected as a regulatory target of other MTases. Presence of these
interactions likely modify action of one MTase on the methylome,
transcriptome and phenotype.
Expression of Type II systems, HpyPI, HpyPVII, or HpyAX,
was not affected by knockouts of Type I or Type III methylation
systems, but instead these Type II MTases affected expression of
Type I, Type IIG, or Type III RM systems [Figures 1C(ii,iii)].
Because Type I, Type IIG and Type III RM systems change
their TRDs over time (Figure 1B, “Introduction”), this apparent
hierarchy between two groups of RM systems could be of some
biological significance.
Some of the target MTase genes carry motif sequence copies
of the controlling MTase in their coding or upstream regions
(Figures 3ABD–F, Supplementary Figures S6A–D), which
suggests gene expression control through local methylation.
We also found that some MTases control expression of
known transcription factors [Figures 1C(I,iii)]. Inactivation
of HPP12_1365 reduced expression of its downstream genes
encoding a two-component signal transduction system, which
likely explains the transcriptome change. GACC motif is
abundant in the windows covering PZ1, PZ3, and a 23S
rRNA gene along the genome (Supplementary Table S2,
Supplementary Figure S1) as we mentioned above. Upon
inactivation of hpyPXM, reduced expression was observed
downstream of a 23S rRNA gene including the adjacent PZ2
region (Figure 4G). Parts of genomic islands (PZ2, PZ3)
with high GACC frequency reduced expression in hpyPXM
knockout (Supplementary Figure S1). Activation of mobile DNA
transcription by methylation is the opposite to what is commonly
observed in eukaryotes (Slotkin and Martienssen, 2007).
To address whether the MTases contribute to adaptive
phenotype, we first determined the effect of the knockouts
on the growth (Figure 2D). We detected a difference for
HpyPI (CATG), HpyPVII (ATTAAT), HpyPX (GACC), HpyAX
(TCGA), for which we also detected a large transcriptome change
Gene Regulation Network Involving
Many Sequence-Specific MTases as Hub
Regulators
Gene regulation network of H. pylori was proposed to be built
shallow with only few transcription factors (Danielli et al., 2010).
However, our transcriptome analysis of the MTase knockouts
(Figure 2C, Supplementary Data Set S1) revealed a large
network involving many MTases as hub regulators (Figure 1C).
The MTases affect expression of virulence factors such as
urease-related genes, cagPAI genes and vacA among others
(Figure 1E). The MTases varied widely in their effects on
the transcriptome (Figure 2C). MTases for CATG (M.HpyPI),
ATTAAT (M.HpyPVII), GACC (M.HpyPX), TCGA (M.HpyAX)
turned out to influence a large number of genes. Except for
Type III MTase, M.HpyPX, these are solitary Type II MTases.
In contrast, the Type I system HpyAXIII (CTAN8 TGT) and the
Type III system HpyAXI (GCAG) have only minor influence.
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FIGURE 3 | Effects of methyltransferase knockouts on transcript level of several methyltransferase loci. (A) HpyAI locus. (B) HpyAIII locus. (C) HP1403 locus.
(D) HpyAXVII locus. (E) Hpy99XX homolog locus. (F) HpyAXVIII locus. The parental strain is 26695. Except for (E,F), read coverage is shown in logarithmic scale.
A blue arrow indicates a differentially expressed gene. A vertical line indicates a copy of methylation motif. A broken arrow indicates a pseudogene. The transcripts
are from both strands as RNA-seq for strain 26695 was carried-out in “non-stranded” protocol.
(Figure 2C). Among these, HpyPX (GACC) knockout increased
growth, whereas the other knockouts decreased (Figure 2D and
Supplementary Figure S2).
level using KEGG onthology. Top 33 categories changed are
shown in Figure 4A. Among those, more than half categories
decreased activity. These include pathways associated with
DNA replication and repair (ko03410, base excision repair
(Supplementary Figure S3A); ko03420, nucleotide excision
repair; ko03440, homologous recombination) and cell motility
[ko02030, bacterial chemotaxis; ko02040, flagellar assembly
(Supplementary Figure S3D)].
The knockout showed a larger clear zone around mitomycin
C, a DNA damaging agent, on blood agar (Figure 4F) (t = −4.8,
df = 2.1, P = 0.035 in two-tailed Welch’s t-test). In the swimming
Gm6ACC MTase Has Positive Effect on
Motility and DNA Damage Tolerance
HpyPX (GACC, Type III) was previously shown to switch
ON/OFF by simple-repeat length changes and affect expression
of 6 genes (Srikhanta et al., 2011). From our transcriptome
data, we evaluated its gene regulation activities at the pathway
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FIGURE 4 | Transcriptome and phenotype changes in methyltransferase knockouts. (A) Transcriptome changes at the pathway level in 1hpyPXM. Top 33
KEGG pathways with P < 0.1 (n = 2) in Welch’s t-test are shown. A pathway category with P < 0.05 is indicated by a red side bar. (B) Shut down of motility regulation in
(Continued)
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FIGURE 4 | Continued
1hpyPXM revealed by transcriptome changes at the gene level. Read count was scaled to Z-score. A category of enrichment (P < 0.05) is indicated by a red side
bar. (C) Read coverages in the parent and the knockout (upper panel) and GACC motif distribution (lower panels) around fliA (for sigma28). (D) Read coverages and
GACC distribution around rpoN (for sigma54). A blue box indicates a differentially expressed gene. A vertical line indicates a copy of the methylation motif.
(E) Swimming motility. Significance of the difference was evaluated by two-tailed Welch’s t-test. (F) Sensitivity to mitomycin C (MMC). (G) Transcriptome changes at
the pathway level in 1hpyPIM (CATG), in 1hpyPVIIM (ATTAAT), and in 1hpyAXM (TCGA). (H) Increased oxidative stress resistance in 1hpyPVIIM (ATTAAT).
(I) Increased swimming motility in 1hpyPVIIM (ATTAAT). (J) Reduced oxidative stress resistance in 1hpyAXM (TCGA). (K) (i) Hydrogen peroxide. (ii) tert-butyl
hydrogen peroxide (TBHP). Statistical significance of the difference was evaluated by two-tailed Student’s t-test.
(Bereswill et al., 1998), is also activated. The knockout formed
a smaller inhibition zone than the parent strain P12 around a
filter with hydrogen-peroxide, which generates oxidative stress
(t = 8.9, df = 4, P = 9.0 × 10−4 in two-tailed Student’s t-test)
(Figure 4H). These results indicate that M.HpyPVII has a
negative effect on ROS tolerance [Figure 1D(ii)].
In 1hpyAXM (TCGA), genes for antioxidant proteins,
catalase (HP0875), thioredoxin (HP0824), NAD (P) H-flavin
oxidoreductase (FrxA), and cysteine synthesis-associated
proteins (MetB, CysK), were repressed (Supplementary Data
Set S1). Both catalase and cysteine can confer tolerance to
oxidative stress. Furthermore, iron (Fe2+ ion) transporter gene
ceuE (HP1162) was found moderately activated [q.value = 7.5E04, m.value = 0.59 in differential gene expression analysis (see
“Materials and Methods”)]. As expected, a larger inhibition zone
was observed for two ROS inducing agents, hydrogenperoxide
and tert-butyl hydrogen peroxide (TBHP), in the 1hpyAXM
strain [Figures 4K(i,ii)]. When the intact hpyAXM gene was
restored, the inhibition zone was increased (Supplementary
Figures S4A,B). These results indicate that M.HpyAX (TCGA)
has a positive role in oxidative stress resistance [see also
Figure 1D(ii)].
Expression of flaA, the flagellin gene, and several chemotaxis
genes were increased in the M.HpyPVII (ATTAAT) knockout
[Supplementary Data Set S1, Figure 4H(ii)]. The migration
zone was larger (Figure 4I) (t = −9.034, df = 4, P = 8.3 × 10−4
in two-tailed Student’s t-test). When the intact hpyPVIIM
gene was restored, a smaller migration zone was observed
(t = −7.4, df = 4, P = 1.8 × 10−3 in two-tailed Student’s
t-test) (Supplementary Figures S4C,D). These results indicate
that M.HpyPVII negatively regulates motility [Figure 1D(i)].
motility assay on soft agar, the migration zone was significantly
smaller in the knockout (Figure 4E) (t = 13.5, df = 4.9,
P = 4.3 × 10−5 , in two-tailed Welch’s t-test). These indicate
that M.HpyPX has a positive role in DNA damage resistance
and in motility.
Eighty-one genes suggested to be involved in motility of
H. pylori (Rust et al., 2008) are classified into four according
to the sigma factor involved in their transcription (Figure 4B).
Most genes constituting this network reduced expression in the
knockout except for 6 chemotaxis-related proteins and others
(Figure 4B). 11/12 class 2 genes and 4/4 class 3 were repressed.
Differentially expressed genes were significantly overrepresented
in these classes (P = 5.6 × 10−3 for class 3, P = 5.4 × 10−6
for class 2, and P = 1.5 × 10−4 for intermediate in one-tailed
Fisher’s exact test). In the intermediate class, 15/23 genes were
repressed with differentially expressed genes overrepresented
(odds ratio = 5.2, P = 1.5 × 10−4 in one-tailed Fisher’s exact
test). These responses are consistent with the strongly reduced
expression of rpoN (σ54) for class 2 (1/3.5) and fliA (σ28) for
class 3 (1/2.6). GACC motifs are present in the operon-like
gene clusters containing fliA and rpoN (Figures 4C,D), so that
their methylation may directly influence the expression. These
results suggest that repression of fliA (for σ28) and rpoN (for
σ54) expression is a major cause of the shutdown of the motility
network by the M.HpyPX knockout.
Eleven pathway categories increased transcripts in the
M.HpyPX knockout (Figure 4A; Supplementary Data Set S1).
These belong to carbohydrate metabolism (ko00020, TCA
cycle, 10 genes; ko00620, pyruvate metabolism), energy
metabolism (ko00190, oxidative phosphorylation), and lipid
metabolism (ko00061, fatty acid biosynthesis; ko00071, fatty acid
degradation), and translation (ko03010, ribosome). Expression
levels of most members in the TCA cycle and ribosome
categories were increased (Supplementary Figures S3B,C).
This may explain increased growth in the M.HpyPX knockout
[Figure 2D(vii)].
Cm6ATG at Translation Start Codon
Affects Gene Expression
To address whether methylation locally affects transcription
as known for several solitary DNA MTases (Sánchez-Romero
et al., 2015) and a Type I MTase (Furuta et al., 2014), we
analyzed relation between occurrence of the methylation site
and the transcript changes in detail for Cm6ATG (1hpyPIM),
which includes a potential translation start codon ATG
(Figure 5). When the group of genes with a CATG motif
including start codon ATG and the group without one were
compared, higher level of transcript changes by the knockout
of Cm6ATG methyltransferase gene was detected in the former
(P = 0.023, in one-tailed Wilcoxon’s rank sum test) (Figure 5A,
right). Therefore, such start codon methylation somehow
promotes transcription.
ROS Tolerance Is Negatively Regulated
by ATTAm6AT MTase but Positively by
TCGm6A MTase
In the M.HpyPVII (ATTAAT) gene knockout, genes
encoding antioxidant proteins, thioredoxin-dependent alkyl
hydroperoxide reductase (HPP12_1554), NAD(P)H-flavin
oxidoreductase (FrxA), and iron-dependent superoxide
dismutase (HPP12_1031), were activated (Supplementary Data
Set S1). The pfr (= ftnA) gene for ferritin, an iron (Fe2+ ion)
storage protein, tolerating cation-mediated radical generation
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FIGURE 5 | Effects of M.HpyPI (CATG) knockout on transcriptome. (A) Genes with high CATG motif abundance in the regulatory region or genes starting with CATG
motif tend to be affected. Transcript abundance change was compared between two groups. The upstream 149 bp from the coding region was defined as the
regulatory region. Statistical significance of the difference was evaluated by one-tailed Wilcoxon’s rank sum test; n.s.: P > 0.05. CDS, coding sequence.
(B) Sequence conservation in the upstream region of CATG-starting genes. Conservation level was presented using Seqlogo (Schneider and Stephens, 1990).
(C) Large transcript changes in hopM. (D) Large transcript changes in cytochrome c peroxidase gene. (E) (i) Large transcript changes in mccB and mccC genes for
microcin on a plasmid. (ii) nucleotide sequence around mccB upstream region. CATG was highlighted in yellow. SD sequences are underlined. Putative terminator
sequence (Zukher et al., 2014) were indicated by arrows. (F) Transcript abundance changes, upon M.HpyPVII knockout, in the bioB gene ending with three copies of
ATTAAT. A blue box indicates a differentially expressed gene. A vertical line indicates a copy of a methylation motif.
other without “AAGG” (N = 176), the level of gene expression
change (absolute value of m.value) was greater in the former
group (p-value = 0.01429, one-tailed Wilcoxon rank sum test.
Further upstream, the differentially expressed genes with
CATG start show more of the features of transcriptional
promoters for housekeeping σ80 , consisting of an extended
Pribnow box and a periodic AT-rich signal (Sharma et al.,
2010), than those not differentially expressed genes with
CATG start (Figure 5B). These observations suggest that
some association between start codon methylation and nearby
upstream transcription initiation underlies the transcript changes
in the knockout.
The transcript fold change was also significantly higher in
genes with higher motif frequency in the upstream regulatory
region (P = 0.043, one-tailed Wilcoxon’s rank sum test)
(Figure 5A, left). We do not know whether this effect is due to
the presence of an initiator peptide starting with CATG. There
was no such effect detected for the CATG frequency within the
coding sequence (Figure 5A, middle).
Upstream of the gene-starting CATG there are sequences
characteristic of SD (Figure 5B, top panel and the middle panel
as opposed to the bottom panel). When CDSs starting with
CATG were separated into two groups: one carrying typical SD
sequence “AAGG” within 15 bp from ATG (n = 207) and the
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The largest transcript abundance difference by the knockout
among the genes with CATG start were found for hopM,
encoding an outer membrane protein, and for cytochrome c
peroxidase, an electron transfer protein (Figure 5A right, CD).
They both carry a cluster of CATG sites around the start
codon (some in dnaE, for DNA polymerase III, neighboring the
cytochrome c peroxidase gene).
Among the genes not starting with CATG, the largest
transcript differences by the knockout was observed for microcin
(a bacteriocin) genes, mccB and mccC [Figures 5A,E(i,ii)]. The
mccABC operon carries a copy of CATG in the unstraslated
region, the second CATG at the start codon for mccA, encoding
a small peptide and not counted as a gene in our analysis, and
the third CATG in the 5′ side within mccB gene. Palindromic
DNA sequences potentially forming a stem-loop structure on
RNA leading transcription termination is found in this region
[Figure 5E(ii)]. There was no CATG in the corresponding region
in the E. coli mccABC operon. Ribosome-controlled transcription
termination is involved in translation of these genes (Zukher
et al., 2014). These again suggest some association between
methylation, transcription, and translation.
For 1hpyPAX (TCGA), 1hpyPVIIM (ATTAAT) and
1hpyPXM (GACC), no such difference between the groups
was detected (Supplementary Figure S5). The highest
transcript fold changes were observed in ‘inactivated’
MTase genes: GANTC-methylating MTase for 1hpyPVIIM
(Supplementary Figure S5B) and a Type I M gene (HP0463)
for 1hpyPAX (Supplementary Figure S5). This again indicates
MTase-mediated control of expression of another MTase. Autorepression activity may be present in these target MTases in their
intact forms as in some MTases (Karyagina et al., 1997; Mruk
et al., 2011) and the gene inactivation may have led to loss of
such auto-repression and to the large effects.
Because ATTAAT, the methylation motif of HpyPVIIM,
contains TAA, a translation stop codon, we sought for genes with
this stop codon among the differentially expressed genes between
1hpyPVIIM (ATTAAT) and its parent. We noticed one such case
involving bioB (Figure 5F, Supplementary Data Set S1). This
gene carries three copies of ATTAAT toward its 3′ end and the
last copy was annotated as the stop codon.
changes or site-specific recombination (Sánchez-Romero et al.,
2015). The network may be worth the name of Firework.
Genome comparison revealed various ways in which the DNA
methyltransferases change their sequence specificity in H. pylori:
(1) amino-acid substitution (Furuta et al., 2014), (2) homologous
recombination moving target recognition domains (Furuta and
Kobayashi, 2012a, 2013; Furuta et al., 2014), and (3) changes
in the number of central repeats in the S subunit of Type I
system (Price et al., 1989; Andres et al., 2010). Although (2)
and (3) appear more rapid than (1), there has been no attempt
to measure these rates in the microevolution process or in the
laboratory setting.
The Firework metamorphoses likely in search for adaptation.
The phenotype affected by MTases includes motility, which is
central to H. pylori’s establishment and persistence of infection.
Two MTases have opposing effects on motility, an accelerator
and a brake [Figure 1D(i)], likely for fine tuning. ROS from the
host immune system damages DNA and proteins of H. pylori
but also acts as a signal controlling redox status of enzymes
and regulatory proteins (Ortiz de Orué Lucana et al., 2012).
Two MTases are involved in ROS metabolism in opposite ways
[Figure 1D(ii)]. Their overlap in some physiological functions
may allow loss of one of the MTase genes during long-term
H. pylori evolution. Even in a small H. pylori population in
Japan, we observed sporadic loss of hpyAXM-equivalent gene
(Kojima et al., 2016). Genes/apparent operons detected as
differentially express genes in more than three MTase knockouts
include groEL chaperone operon (4 knockouts, 3 activation, 1
repression), dnaK for a chaperon, vacA, HPP12_1177 (F0F1 ATP
synthase subunit), HPP12_0834 (RNA-binding protein), mdaB
(antioxidant protein), flaA (flagellin), ureB (urease), HPP12_0571
(membrane protein), babB (outer membrane protein), and mcc
(microcin) operon on the plasmid pHPP12 (Supplementary
Data Set S1). Some of these genes are involved in host interaction
and virulence (vacA, flaA, ureB, babB, ureB) [Rust et al., 2008;
Figures 1E(i,ii)] and in bacterial interaction (mcc) (Zukher et al.,
2014). Effect of MTase on chaperon expression was reported
earlier (Srikhanta et al., 2005; Vitoriano et al., 2013). The baseexcision repair category (KEGG id 03410) is repressed in three
MTase knockouts (Figures 4A,H; Supplementary Figure S3A).
We do not know whether this is related to the presence of a baseexcision type restriction enzyme in Helicobacter (Miyazono et al.,
2014; Kojima and Kobayashi, 2015). These observations imply
that the chaperones, the DNA repair systems, the motility system,
the redox system, and the host interaction systems can sense the
activity of MTases.
Reversible changes of gene expression, phase variation, may
result from ON/OFF of DNA methyltransferase genes by simplerepeat length changes (Srikhanta et al., 2009) and from generation
of a specificity subunit by site-specific recombination events in
a restriction-modification system (Manso et al., 2014; Li et al.,
2016). The unique features of the present system are that (1)
DNA methyltransferases form a hierarchical network, (2) the
specificity changes can take place in many of the hub DNA
methyltransferases by movement of the domains (in addition to
ON/OFF of the DNA methyltransferase genes) (3) and the overall
change in the gene regulation network appears irreversible.
DISCUSSION
These results revealed a complex gene expression regulation
network involving many DNA methyltransferases, as well as
known transcription regulators, as hub regulators (Figure 1C).
The MTases regulate each other in several patterns so that
methylation activity of one MTase influences various aspects
of cell phenotypes likely increasing phenotypic variation,
consistent with the epigenome-base adaptive evolution
model. Many of these MTase hubs are present in only
limited lineages (Figure 1B) and/or change their sequence
specificity (see section “Introduction”), frequently remodeling
the network. The system may evolve irreversibly through these
changes. Such irreversibility distinguishes the system from the
known reversible phenomena involving simple-repeat length
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MTases regulate its expression and activity in various ways
(Mruk and Kobayashi, 2014). One is auto-repression, which may
explain increased expression of MTase genes (above). One MTase
of H. pylori, M.HpyAXI (GCAG), changes its biochemical activity
depending on pH (Banerjee and Rao, 2011). M.EcoRI transcript
overlaps with an antisense RNA forming a bi-stable switch (Mruk
et al., 2011). If an MTase can sense environmental signal, the
Firework can be a simple system to generate phenotypic but
heritable variations in a short time in vivo. To fully explore the
potential for such “Lamarckian” evolution, further investigation
is necessary on changes of genes and expression of MTases in
response to environmental signals.
To our surprise, association of DNA methylation and
translation initiation/termination in determining transcript
abundance was suggested from multiple lines of observation with
CATG, including a potential start codon ATG, and ATTAAT,
including a stop codon TAA.
Our following observations suggest relation of DNA
methylation at CATG at start codon and transcript abundance.
4. The CATG-starting genes with the largest transcript
differences by the knockout (hopM and cytochrome c
peroxidase) also carried CATGs around the start codon
ATG (Figures 5C,D).
5. A large transcript differences by the knockout was observed
for mccBC operon (Figures 5A,E), which carries a CATG in
the upstream untranslated region and another CATG at the
start codon for mccA. Ribosome-controlled transcription
termination is involved in translation of these genes
(Zukher et al., 2014).
These observations suggest that methylation at or around start
codon affects transcription. We also found a case (Figures 5F,
Supplementary Data Set S1), where methylation at three copies
of ATTAAT at 3′ end (with the last copy annotated as the
stop codon) decreases transcript. This suggests methylation at a
potential translation stop codon may affect transcription.
These findings may be explained by the coupling of
translation and transcription in bacteria (Figure 6; McGary and
Nudler, 2013). When coupled to RNA polymerase (RNAP),
the translating ribosome ensures transcriptional processivity by
preventing RNAP backtracking. The trailing ribosome “pushes”
RNAP, thereby modulating the rate of transcription by creating
a synchrony of mRNA production and protein synthesis. The
first 50 nucleotides of transcribed mRNA are particularly prone
to stalling and backtracking. The rate of translation depends
on the mRNA secondary structures. The ribosome behaves as
an mRNA helicase, disrupting mRNA duplexes. In a Cryo-EM
structure of RNA polymerase – ribosome 30S subunit complex
1. The genes with CATG start showed a higher level
of transcript changes by the knockout of Cm6ATG
methyltransferase gene (Figure 5A, right).
2. When CDSs starting with CATG were separated into two
groups: one with SD and the other without one, the level of
gene expression change was greater in the former.
3. The differentially expressed genes with CATG
start show more of the features of transcriptional
promoters (Figure 5B).
FIGURE 6 | A hypothesis for the role of DNA base methylation in transcription-translation coupling. Translation by ribosome (A) prevents mRNA release by Rho (B).
A hypothetical protein binding the methylated base, (C) RNA polymerase and ribosome affects the coupling. M, a methylated base in DNA.
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foundation of promotion of medical sciences and medical care
(HY), and in part by the MEXT KAKENHI 221S0002 (YF and
IK), 24113506, and 26113704 (IK), and, a Grant in Promotion
of Basic Research Activities for Innovative Biosciences (grant
no. 121205003001002100019) from Bio-oriented Technology
Research Advance Institution, and the Science and technology
research promotion program for agriculture, forestry, fisheries
and food industry (grant no. 26025A) from MAFF to IK.
This work was supported by the NIBB Collaborative Research
Program (19-454) to IK. Data analysis was supported by the
supercomputer system of the Human Genome Center (HGC) of
Institute of Medical Science at University of Tokyo.
(Demo et al., 2017), the RNA exit tunnel of RNA polymerase
aligns with the SD-binding site of 30S subunit. Ribosomal protein
S1 forms a wall of the tunnel between RNAP and the 30S subunit,
consistent with its role in directing mRNAs onto the ribosome.
NusG, present in E. coli and H. pylori, can bind both RNA
polymerase and ribosome to mediate the coupling (Krupp et al.,
2019; Washburn et al., 2019). Some of these elements are close to
the template DNA (Krupp et al., 2019) and may well be affected
by DNA base methylation and proteins bound to the methylated
DNA. Indeed, a palindromic DNA sequence generating a stemloop structure on mRNA that might lead to transcript release by
Rho are found between mccA and mccB. Type I methylation on a
long palindrome decreases transcript in an operon-like structure
(Furuta et al., 2014). Further informatic and experimental works
are necessary to test this hypothesis.
While we were preparing this manuscript, one paper appeared
(Srikhanta et al., 2017), which knocked out M.HpyPX in P12
and confirmed its methylation motif as GACC and reported
its effects on transcription and motility. One recent paper
(Kumar et al., 2018) demonstrated effects of Tm4CTTC MTase
on transcription, natural transformation and host interaction.
Another paper (Estibariz et al., 2019) demonstrated that
Gm5CGC methylation in the promoter affects transcription.
ACKNOWLEDGMENTS
We thank Professor R. Haas at the Ludwig-MaximiliansUniversity of Munich for providing us strain P12. We also thank
K. Abe and T. Horiuchi at the University of Tokyo for cDNA
library preparation and NGS data collection and Miho Kiyooka
and Chen Wei at the National Institute of Genetics (Mishima,
Japan) for Pacbio sequencing.
SUPPLEMENTARY MATERIAL
DATA AVAILABILITY STATEMENT
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fmicb.
2020.01628/full#supplementary-material
The datasets generated for this study can be found in the DDBJ
Sequence Read Archive (https://www.ddbj.nig.ac.jp/dra/indexe.html) under accession numbers DRA005953, DRA004356,
DRA003551, and DRA003688.
FIGURE S1 | Gene expression changes in high GACC frequency regions in the
hypoPXM knockout.
FIGURE S2 | Growth pattern of P12 methyltransferase gene knockouts.
AUTHOR CONTRIBUTIONS
FIGURE S3 | Expression changes of genes in specific KEGG categories.
FIGURE S4 | Gene restoration for 1hpyAXM and 1hpyPVIIM.
HY and IK conceptualized the study. HY, MF, YF, TS, TN, and
SSh contributed to the formal analysis. HY, ER, MA, and TS
investigated the study. YS, SSu, MH, AT, KS, and IK provided
the resources. HY, MA, TS, and IK wrote the original draft. HY,
MF, and IK reviewed, and edited the manuscript. IK supervised
the study. All authors contributed to the article and approved the
submitted version.
FIGURE S5 | Effect of methylation in the regulatory and CDS regions on
the transcriptome.
FIGURE S6 | Transcript levels of the representative R-M systems under the
control of multiple R-M systems in strain P12.
TABLE S1 | Methylomes of methyltransferase knockout mutants.
TABLE S2 | Genomic segments with high and low GACC abundance.
TABLE S3 | Bacterial strains and plasmids used.
FUNDING
TABLE S4 | Oligonucleotides used.
This research was supported by the JSPS KAKENHI 25850049
(HY), 25291080 (IK), 22128001 (MH), Ichiro Kanehara
TABLE S5 | qPCR for RNA-seq validation.
REFERENCES
Backert, S., and Yamaoka, Y. (2016). Helicobacter pylori Research - From Bench to
Bedside. Tokyo: Springer.
Banerjee, A., and Rao, D. N. (2011). Functional analysis of an acid adaptive DNA
adenine methyltransferase from Helicobacter pylori 26695. PLoS One 6:e16810.
doi: 10.1371/journal.pone.0016810
Bereswill, S., Waidner, U., Odenbreit, S., Lichte, F., Fassbinder, F., Bode, G., et al.
(1998). Structural, functional and mutational analysis of the pfr gene encoding
a ferritin from Helicobacter pylori. Microbiology 144(Pt 9), 2505–2516. doi:
10.1099/00221287-144-9-2505
DATASET S1 | Differential gene expression analysis.
Anders, S., Pyl, P. T., and Huber, W. (2015). HTSeq–a Python framework to
work with high-throughput sequencing data. Bioinformatics 31, 166–169. doi:
10.1093/bioinformatics/btu638
Andres, S., Skoglund, A., Nilsson, C., Krabbe, M., Björkholm, B., and Engstrand,
L. (2010). Type I restriction-modification loci reveal high allelic diversity in
clinical Helicobacter pylori isolates. Helicobacter 15, 114–125. doi: 10.1111/j.
1523-5378.2010.00745.x
Frontiers in Microbiology | www.frontiersin.org
14
July 2020 | Volume 11 | Article 1628
Yano et al.
Networking DNA Methyltransferases
Blow, M. J., Clark, T. A., Daum, C. G., Deutschbauer, A. M., Fomenkov, A.,
Fries, R., et al. (2016). The epigenomic landscape of prokaryotes. PLoS Genet.
12:e1005854. doi: 10.1371/journal.pgen.1005854
Chin, C. S., Alexander, D. H., Marks, P., Klammer, A. A., Drake, J., Heiner, C.,
et al. (2013). Nonhybrid, finished microbial genome assemblies from longread SMRT sequencing data. Nat. Methods 10, 563–569. doi: 10.1038/nmeth.
2474
Danielli, A., Amore, G., and Scarlato, V. (2010). Built shallow to maintain
homeostasis and persistent infection: insight into the transcriptional regulatory
network of the gastric human pathogen Helicobacter pylori. PLoS Pathog.
6:e1000938. doi: 10.1371/journal.ppat.1000938
De Ste Croix, M., Vacca, I., Kwun, M. J., Ralph, J. D., Bentley, S. D., Haigh, R.,
et al. (2017). Phase-variable methylation and epigenetic regulation by type I
restriction-modification systems. FEMS Microbiol. Rev. 41(Suppl._1), S3–S15.
doi: 10.1093/femsre/fux025
Demo, G., Rasouly, A., Vasilyev, N., Svetlov, V., Loveland, A. B., Diaz-Avalos, R.,
et al. (2017). Structure of RNA polymerase bound to ribosomal 30S subunit.
eLife 6:e28560. doi: 10.7554/eLife.28560
Estibariz, I., Overmann, A., Ailloud, F., Krebes, J., Josenhans, C., and Suerbaum,
S. (2019). The core genome m5C methyltransferase JHP1050 (M.Hpy99III)
plays an important role in orchestrating gene expression in Helicobacter pylori.
Nucleic Acids Res. 47, 2336–2348. doi: 10.1093/nar/gky1307
Fang, G., Munera, D., Friedman, D. I., Mandlik, A., Chao, M. C., Banerjee, O., et al.
(2012). Genome-wide mapping of methylated adenine residues in pathogenic
Escherichia coli using single-molecule real-time sequencing. Nat. Biotechnol. 30,
1232–1239. doi: 10.1038/nbt.2432
Fischer, W., Windhager, L., Rohrer, S., Zeiller, M., Karnholz, A., Hoffmann, R., et al.
(2010). Strain-specific genes of Helicobacter pylori: genome evolution driven by
a novel type IV secretion system and genomic island transfer. Nucleic Acids Res.
38, 6089–6101. doi: 10.1093/nar/gkq378
Furuta, Y., Kawai, M., Uchiyama, I., and Kobayashi, I. (2011). Domain movement
within a gene: a novel evolutionary mechanism for protein diversification. PLoS
One 6:e18819. doi: 10.1371/journal.pone.0018819
Furuta, Y., and Kobayashi, I. (2012a). Mobility of DNA sequence recognition
domains in DNA methyltransferases suggests epigenetics-driven adaptive
evolution. Mob. Genet. Elements 2, 292–296. doi: 10.4161/mge.23371
Furuta, Y., and Kobayashi, I. (2012b). Movement of DNA sequence recognition
domains between non-orthologous proteins. Nucleic Acids Res. 40, 9218–9232.
doi: 10.1093/nar/gks681
Furuta, Y., and Kobayashi, I. (2013). “Restriction-modification systems as mobile
epigenetic elements,” in Madame Curie Bioscience Database, eds A. P. Roberts
and P. Mullany (Austin, TX: Landes Bioscience), 85–103.
Furuta, Y., Namba-Fukuyo, H., Shibata, T. F., Nishiyama, T., Shigenobu, S.,
Suzuki, Y., et al. (2014). Methylome diversification through changes in DNA
methyltransferase sequence specificity. PLoS Genet. 10:e1004272. doi: 10.1371/
journal.pgen.1004272
Gorrell, R., and Kwok, T. (2017). The Helicobacter pylori methylome: roles in
gene regulation and virulence. Curr. Top. Microbiol. Immunol. 400, 105–127.
doi: 10.1007/978-3-319-50520-6_5
Hänzelmann, S., Castelo, R., and Guinney, J. (2013). GSVA: gene set variation
analysis for microarray and RNA-seq data. BMC Bioinform. 14:7. doi: 10.1186/
1471-2105-14-7
Heuermann, D., and Haas, R. (1998). A stable shuttle vector system for efficient
genetic complementation of Helicobacter pylori strains by transformation and
conjugation. Mol. Gen. Genet. 257, 519–528. doi: 10.1007/s004380050677
Humbert, O., and Salama, N. R. (2008). The Helicobacter pylori HpyAXII
restriction-modification system limits exogenous DNA uptake by targeting
GTAC sites but shows asymmetric conservation of the DNA methyltransferase
and restriction endonuclease components. Nucleic Acids Res. 36, 6893–6906.
doi: 10.1093/nar/gkn718
Karyagina, A., Shilov, I., Tashlitskii, V., Khodoun, M., Vasil’ev, S., Lau, P. C., et al.
(1997). Specific binding of sso II DNA methyltransferase to its promoter region
provides the regulation of sso II restriction-modification gene expression.
Nucleic Acids Res. 25, 2114–2120. doi: 10.1093/nar/25.11.2114
Kersulyte, D., Lee, W., Subramaniam, D., Anant, S., Herrera, P., Cabrera, L., et al.
(2009). Helicobacter pylori’s plasticity zones are novel transposable elements.
PLoS One 4:e6859. doi: 10.1371/journal.pone.0006859
Kojima, K. K., Furuta, Y., Yahara, K., Fukuyo, M., Shiwa, Y., Nishiumi, S., et al.
(2016). Population evolution of Helicobacter pylori through diversification in
Frontiers in Microbiology | www.frontiersin.org
DNA methylation and interstrain sequence homogenization. Mol. Biol. Evol.
33, 2848–2859. doi: 10.1093/molbev/msw162
Kojima, K. K., and Kobayashi, I. (2015). Transmission of the PabI family of
restriction DNA glycosylase genes: mobility and long-term inheritance. BMC
Genomics 16:817. doi: 10.1186/s12864-015-2021-3
Krebes, J., Morgan, R. D., Bunk, B., Spröer, C., Luong, K., Parusel, R., et al. (2014).
The complex methylome of the human gastric pathogen Helicobacter pylori.
Nucleic Acids Res. 42, 2415–2432. doi: 10.1093/nar/gkt1201
Krupp, F., Said, N., Huang, Y. H., Loll, B., Bürger, J., Mielke, T., et al.
(2019). Structural Basis for the Action of an All-Purpose Transcription Antitermination Factor. Mol. Cell. 74, 143.e–157.e. doi: 10.1016/j.molcel.2019.
01.016
Kumar, R., Mukhopadhyay, A. K., Ghosh, P., and Rao, D. N. (2012). Comparative
transcriptomics of H. pylori strains AM5, SS1 and their hpyAVIBM deletion
mutants: possible roles of cytosine methylation. PLoS One 7:e42303. doi: 10.
1371/journal.pone.0042303
Kumar, S., Karmakar, B. C., Nagarajan, D., Mukhopadhyay, A. K., Morgan, R. D.,
and Rao, D. N. (2018). N4-cytosine DNA methylation regulates transcription
and pathogenesis in Helicobacter pylori. Nucleic Acids Res. 46, 3429–3445. doi:
10.1093/nar/gky126
Lee, E., Chuang, H. Y., Kim, J. W., Ideker, T., and Lee, D. (2008). Inferring pathway
activity toward precise disease classification. PLoS Comput. Biol. 4:e1000217.
doi: 10.1371/journal.pcbi.1000217
Lee, W. C., Anton, B. P., Wang, S., Baybayan, P., Singh, S., Ashby, M., et al. (2015).
The complete methylome of Helicobacter pylori UM032. BMC Genomics 16:424.
doi: 10.1186/s12864-015-1585-2
Li, J., Li, J. W., Feng, Z., Wang, J., An, H., Liu, Y., et al. (2016). Epigenetic switch
driven by DNA inversions dictates phase variation in Streptococcus pneumoniae.
PLoS Pathog. 12:e1005762. doi: 10.1371/journal.ppat.1005762
Lin, L. F., Posfai, J., Roberts, R. J., and Kong, H. (2001). Comparative genomics of
the restriction-modification systems in Helicobacter pylori. Proc. Natl. Acad. Sci.
U.S.A. 98, 2740–2745. doi: 10.1073/pnas.051612298
Luo, W., and Brouwer, C. (2013). Pathview: an R/Bioconductor package for
pathway-based data integration and visualization. Bioinformatics 29, 1830–
1831. doi: 10.1093/bioinformatics/btt285
Manso, A. S., Chai, M. H., Atack, J. M., Furi, L., De Ste Croix, M., Haigh, R., et al.
(2014). A random six-phase switch regulates pneumococcal virulence via global
epigenetic changes. Nat Commun. 5:5055. doi: 10.1038/ncomms6055
McGary, K., and Nudler, E. (2013). RNA polymerase and the ribosome: the
close relationship. Curr. Opin. Microbiol. 16, 112–117. doi: 10.1016/j.mib.2013.
01.010
Miska, E. A., and Ferguson-Smith, A. C. (2016). Transgenerational inheritance:
models and mechanisms of non-DNA sequence-based inheritance. Science 354,
59–63. doi: 10.1126/science.aaf4945
Miyazono, K., Furuta, Y., Watanabe-Matsui, M., Miyakawa, T., Ito, T., Kobayashi,
I., et al. (2014). A sequence-specific DNA glycosylase mediates restrictionmodification in Pyrococcus abyssi. Nat Commun. 5:3178. doi: 10.1038/
ncomms4178
Mruk, I., and Kobayashi, I. (2014). To be or not to be: regulation of restrictionmodification systems and other toxin-antitoxin systems. Nucleic Acids Res. 42,
70–86. doi: 10.1093/nar/gkt711
Mruk, I., Liu, Y., Ge, L., and Kobayashi, I. (2011). Antisense RNA associated with
biological regulation of a restriction-modification system. Nucleic Acids Res. 39,
5622–5632. doi: 10.1093/nar/gkr166
Nye, T. M., Jacob, K. M., Holley, E. K., Nevarez, J. M., Dawid, S., Simmons,
L. A., et al. (2019). DNA methylation from a Type I restriction modification
system influences gene expression and virulence in Streptococcus pyogenes.
PLoS Pathog. 15:e1007841. doi: 10.1371/journal.ppat.1007841
Ortiz de Orué Lucana, D., Wedderhoff, I., and Groves, M. R. (2012). ROS-mediated
signalling in bacteria: zinc-containing Cys-X-X-Cys redox centres and ironbased oxidative Stress. J Signal Transduct. 2012:605905. doi: 10.1155/2012/
605905
Pages, H., Aboyoun, P., Gentleman, R., and DebRoy, S. (2003). Biostrings:
String Objects Representing Biological Sequences, and Matching Algorithms.
R package version 2381.
Price, C., Lingner, J., Bickle, T. A., Firman, K., and Glover, S. W. (1989). Basis
for changes in DNA recognition by the EcoR124 and EcoR124/3 type I DNA
restriction and modification enzymes. J. Mol. Biol. 205, 115–125. doi: 10.1016/
0022-2836(89)90369-0
15
July 2020 | Volume 11 | Article 1628
Yano et al.
Networking DNA Methyltransferases
Sun, J., Nishiyama, T., Shimizu, K., and Kadota, K. (2013). TCC: an R package
for comparing tag count data with robust normalization strategies. BMC
Bioinformatics 14:219. doi: 10.1186/1471-2105-14-219
Uchiyama, I., Albritton, J., Fukuyo, M., Kojima, K. K., Yahara, K., and Kobayashi,
I. (2016). A novel approach to Helicobacter pylori pan-genome analysis for
identification ofgenomic islands. PLoS One 11:e0159419. doi: 10.1371/journal.
pone.0159419
Vale, F. F., Mégraud, F., and Vítor, J. M. (2009). Geographic distribution of
methyltransferases of Helicobacter pylori: evidence of human host population
isolation and migration. BMC Microbiol. 9:193. doi: 10.1186/1471-21809-193
Vasu, K., and Nagaraja, V. (2013). Diverse functions of restriction-modification
systems in addition to cellular defense. Microbiol. Mol. Biol. Rev. 77, 53–72.
doi: 10.1128/MMBR.00044-12
Vitoriano, I., Vítor, J. M., Oleastro, M., Roxo-Rosa, M., and Vale, F. F. (2013).
Proteome variability among Helicobacter pylori isolates clustered according to
genomic methylation. J. Appl. Microbiol. 114, 1817–1832. doi: 10.1111/jam.
12187
Washburn, R. S., Zuber, P. K., Sun, M., Hashem, Y., Shen, B., Li, W., et al. (2019).
Escherichia coli NusG links the lead ribosome with the transcription elongation
complex. bioRxiv [Preprint] doi: 10.101101/871962
Zhang, Y., Matsuzaka, T., Yano, H., Furuta, Y., Nakano, T., Ishikawa, K., et al.
(2017). Restriction glycosylases: involvement of endonuclease activities in the
restriction process. Nucleic Acids Res. 45, 1392–1403. doi: 10.1093/nar/gkw1250
Zukher, I., Novikova, M., Tikhonov, A., Nesterchuk, M. V., Osterman, I. A.,
Djordjevic, M., et al. (2014). Ribosome-controlled transcription termination
is essential for the production of antibiotic microcin C. Nucleic Acids Res. 42,
11891–11902. doi: 10.1093/nar/gku880
Quadrana, L., and Colot, V. (2016). Plant transgenerational epigenetics. Annu. Rev.
Genet. 50, 467–491. doi: 10.1146/annurev-genet-120215-035254
Quinlan, A. R., and Hall, I. M. (2010). BEDTools: a flexible suite of utilities
for comparing genomic features. Bioinformatics 26, 841–842. doi: 10.1093/
bioinformatics/btq033
REBASE PacBio statistics (2020). Available online at: http://rebase.neb.com/rebase/
pbstatlist.html (accessed May 22 2020). doi: 10.1093/bioinformatics/btq033
Roberts, R. J., Belfort, M., Bestor, T., Bhagwat, A. S., Bickle, T. A., Bitinaite, J.,
et al. (2003). A nomenclature for restriction enzymes, DNA methyltransferases,
homing endonucleases and their genes. Nucleic Acids Res. 31, 1805–1812. doi:
10.1093/nar/gkg274
Rocha, E. P., Viari, A., and Danchin, A. (1998). Oligonucleotide bias in Bacillus
subtilis: general trends and taxonomic comparisons. Nucleic Acids Res. 26,
2971–2980. doi: 10.1093/nar/26.12.2971
Rust, M., Schweinitzer, T., and Josenhans, C. (2008). “Helicobacter flagella, motility
and chemotaxis,” in Helicobacter pylori: Molecular Genetics and Cellular Biology,
ed. Y. Yamaoka (Poole, UK: Caister Academic Press), 61–85.
Sánchez-Romero, M. A., Cota, I., and Casadesús, J. (2015). DNA methylation in
bacteria: from the methyl group to the methylome. Curr. Opin. Microbiol. 25,
9–16. doi: 10.1016/j.mib.2015.03.004
Schneider, T. D., and Stephens, R. M. (1990). Sequence logos: a new way to display
consensus sequences. Nucleic Acids Res. 18, 6097–6100. doi: 10.1093/nar/18.20.
6097
Sharma, C. M., Hoffmann, S., Darfeuille, F., Reignier, J., Findeiss, S., Sittka, A., et al.
(2010). The primary transcriptome of the major human pathogen Helicobacter
pylori. Nature 464, 250–255. doi: 10.1038/nature08756
Slotkin, R. K., and Martienssen, R. (2007). Transposable elements and the
epigenetic regulation of the genome. Nat. Rev. Genet. 8, 272–285. doi: 10.1038/
nrg2072
Srikhanta, Y. N., Dowideit, S. J., Edwards, J. L., Falsetta, M. L., Wu, H. J., Harrison,
O. B., et al. (2009). Phasevarions mediate random switching of gene expression
in pathogenic Neisseria. PLoS Pathog. 5:e1000400. doi: 10.1371/journal.ppat.
1000400
Srikhanta, Y. N., Gorrell, R. J., Power, P. M., Tsyganov, K., Boitano, M., Clark, T. A.,
et al. (2017). Methylomic and phenotypic analysis of the ModH5 phasevarion
of Helicobacter pylori. Sci. Rep. 7:16140. doi: 10.1038/s41598-017-15721-x
Srikhanta, Y. N., Gorrell, R. J., Steen, J. A., Gawthorne, J. A., Kwok, T., Grimmond,
S. M., et al. (2011). Phasevarion mediated epigenetic gene regulation in
Helicobacter pylori. PLoS One 6:e27569. doi: 10.1371/journal.pone.0027569
Srikhanta, Y. N., Maguire, T. L., Stacey, K. J., Grimmond, S. M., and Jennings, M. P.
(2005). The phasevarion: a genetic system controlling coordinated, random
switching of expression of multiple genes. Proc. Natl. Acad. Sci. U.S.A. 102,
5547–5551. doi: 10.1073/pnas.0501169102
Sugawara, H., Ohyama, A., Mori, H., and Kurokawa, K. (2009). “Microbial Genome
Annotation Pipeline (MiGAP) for diverse users,” in Proceedings of the The
20th International Conference on Genome Informatics (GIW2009) Poster and
Software Demonstrations, Yokohama.
Frontiers in Microbiology | www.frontiersin.org
Conflict of Interest: The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be construed as a
potential conflict of interest.
Citation: Yano H, Alam MZ, Rimbara E, Shibata TF, Fukuyo M, Furuta Y,
Nishiyama T, Shigenobu S, Hasebe M, Toyoda A, Suzuki Y, Sugano S,
Shibayama K and Kobayashi I (2020) Networking and Specificity-Changing
DNA Methyltransferases in Helicobacter pylori. Front. Microbiol. 11:1628.
doi: 10.3389/fmicb.2020.01628
Copyright © 2020 Yano, Alam, Rimbara, Shibata, Fukuyo, Furuta, Nishiyama,
Shigenobu, Hasebe, Toyoda, Suzuki, Sugano, Shibayama and Kobayashi. This is an
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