www.nature.com/scientificreports

OPEN

Rapid profiling of drug‑resistantbacteria using DNA‑bindingdyes and a nanopore‑based DNA
sequencer
Ayumu Ohno1, Kazuo Umezawa2, Satomi Asai3,4, Kirill Kryukov1,5, So Nakagawa1,
Hayato Miyachi3,4 & Tadashi Imanishi1*
Spread of drug-resistant bacteria is a serious problem worldwide. We thus designed a new sequencebased protocol that can quickly identify bacterial compositions of clinical samples and their drugresistance profiles simultaneously. Here we utilized propidium monoazide (PMA) that prohibits DNA
amplifications from dead bacteria, and subjected the original and antibiotics-treated samples to 16SrRNA metagenome sequencing. We tested our protocol on bacterial mixtures, and observed thatsequencing reads derived from drug-resistant bacteria were significantly increased compared withthose from drug-sensitive bacteria when samples were treated by antibiotics. Our protocol is scalable
and will be useful for quickly profiling drug-resistant bacteria.
Since the discovery of penicillin by Alexander Fleming in 1928, numerous infectious diseases have been cured byvarious antibiotics. However, many drug-resistant bacteria, such as penicillin-resistant Streptococcus pneumoniae
and methicillin-resistant Staphylococcus aureus, appeared and prevailed ­worldwide1. Moreover, multidrug-resistant bacteria have been detected against several drugs such as Pseudomonas aeruginosa (MDRP)2, Acinetobacterbaumannii and super-multidrug-resistant tubercle bacillus (extensively drug-resistant tuberculosis)3–5, rendering
antimicrobial treatments difficult. Thus, bacterial detection and appropriate use of antibiotics are very importantto prevent the emergence and spread of drug-resistant bacteria.
Conventionally, for the diagnosis of bacterial infectious diseases, bacterial detection and antimicrobial susceptibility testing (AST) are performed using culture-based methods following the guidelines of the Clinical andLaboratory Standards Institute (CLSI)6,7. However, based on the CLSI, it takes at least a few days for bacterial
identification by AST, and has a limitation due to a low culture-positive rate. Therefore, treatment of bacterial
infections at initial diagnosis inevitably depends on empirical approach. Recently, molecular methods using
nucleic acids for the detection of pathogens have been developed; however, these methods often result in detection of non-target of interests due to contamination of indigenous or inviable bacteria, hence making meaningfulinterpretation of the results difficult.
Therefore, a rapid method for the identification of pathogens and their antimicrobial spectra is necessaryin addition to antimicrobial stewardship, in order to treat individual infected patients properly and to preventthe spread of drug-resistant bacteria, as a control measure of nosocomial infection. To achieve this purpose,
we incorporated two kinds of technologies. One is the propidium monoazide (PMA) that is effective for live/
dead cell d­ iscrimination8,9. PMA is a DNA-intercalating dye with an azide group that makes a covalent bondwith a DNA when exposed to bright visible light (absorbance at 465–475 nm)10. DNAs bound with PMA cannot be amplified by polymerase chain reaction (PCR). Since DNA is usually in the cells, PMA cannot interact
with them; however, for the dead cells, DNAs are exposed to the outside and can be bound with PMA and PCRamplification of PMA-bound dead cell’s DNA is i­ nhibited8–10. Therefore, in mixed samples of the live and deadcells, the proportion of dead cell’s DNA detected by DNA sequencing will be very low. In conventional culture
methods, more than 10 h are required to observe the effects of antibiotics on bacteria, so it takes long time to1

Department of Molecular Life Science, Tokai University School of Medicine, Isehara, Kanagawa 259‑1193,
Japan. 2Department of Emergency and Critical Care Medicine, Tokai University School of Medicine, Isehara,
Kanagawa 259‑1193, Japan. 3Department of Laboratory Medicine, Tokai University School of Medicine, Isehara,
Kanagawa 259‑1193, Japan. 4Infection Control Division, Tokai University Hospital, Isehara, Kanagawa 259‑1193,
Japan. 5Department of Genomics and Evolutionary Biology, National Institute of Genetics, Mishima,
Shizuoka 411‑8540, Japan. *email: imanishi@tokai.ac.jp
Scientific Reports |(2021) 11:3436| https://doi.org/10.1038/s41598-021-82903-z

1Vol.:(0123456789)

www.nature.com/scientificreports/

Figure 1.  Comparison of bacterial compositions by nanopore DNA sequencing. (A) Results of AST by batch
test. The gray lines represent the average OD values. (B) Identification of bacterial compositions by the nanopore
DNA sequencer MinION using bacterial mixtures of E. coli (black) and PAO1 (white) and (C) E. coli (black)and MDRP (gray). The number inside the parentheses is the number of reads. The gray line shows frequency of
E. coli in untreated bacterial mixture. The arrows show the VBD indeces. The VBD index is the estimated rateof decrease of the number of viable bacteria by the effect of antibiotics. ND untreated bacterial mixture; Ampampicillin treatment; GM gentamicin treatment.

find an appropriate antibacterial drug. In this study, the reaction time of antibiotics on bacterial suspension was
set to 1 h, and we examined whether the proportion of live cells will change or not by the effect of antibiotics.
The other technology is the rapid 16S rRNA metagenome sequencing. We previously developed a portablesystem for rapid 16S rRNA metagenome analysis using the nanopore DNA sequencer MinION and laptop­computers11–13. By combining these two technologies, we aimed at establishing a rapid and accurate diagnostic
technology that can detect drug-resistant bacteria.

Results

Antimicrobial sensitivity testing (AST).

It has been reported that Pseudomonas aeruginosa (PAO1)
exhibits drug resistance to ampicillin and MDRP exhibits drug resistance to ampicillin and ­gentamicin14, andEscherichia coli (E. coli) exhibits sensitivity to ampicillin and gentamicin, but we confirmed the reactivity ofantibiotics in actual bacteria. The bacterial suspension ­(107 CFU/mL) was dispensed onto the plate, and thenampicillin or gentamicin was added. After culturing, the quantity of Escherichia coli (E. coli) was decreased with
ampicillin and gentamicin treatment, and the quantity of PAO1 was decreased with ampicillin treatment. In
contrast, the quantity of MDRP did not change by antibiotic treatments (Fig. 1A).

Identification of bacterial compositions by 16S rRNA metagenome sequencing. A total of531,691 reads were obtained in two MinION runs of 16S metagenome analysis. By assigning each read to bacterial species, we calculated the bacterial compositions in each experiment and estimated the effects of antibiotics.
In the E. coli and PAO1 mixture, the proportion of reads from E. coli was drastically decreased when treated byampicillin (Fig. 1B). The frequency of reads for E. coli was 54.9% without antibiotic treatment, but it changed to
Scientific Reports |Vol:.(1234567890)(2021) 11:3436 |https://doi.org/10.1038/s41598-021-82903-z

2

www.nature.com/scientificreports/

Bacterial mixture

Antibiotics

Ct

Tm (℃)

E. coli and PAO1

No drug

18.6

81.0

E. coli and PAO1

No drug

19.6

80.2

E. coli and PAO1

Ampicillin

19.4

81.0

E. coli and PAO1

Ampicillin

18.4

81.0

E. coli and PAO1

Gentamicin

23.4

80.2

E. coli and PAO1

Gentamicin

24.3

80.6

E. coli and MDRP

No drug

17.9

81.3

E. coli and MDRP

No drug

18.2

81.0

E. coli and MDRP

Ampicillin

19.1

81.0

E. coli and MDRP

Ampicillin

20.3

81.0

E. coli and MDRP

Gentamicin

21.4

81.0

E. coli and MDRP

Gentamicin

20.5

80.6

Table 1.  Comparison of Ct values after antibiotic and propidium monoazide (PMA) treatment in bacterial
mixtures. The effect of PMA was confirmed by comparing the Ct values obtained by real-time PCR using
bacterial mixture. The bacerial genome of each sample was prepared to 3 ng and used for real-time PCR. The
results show the proportion of viable bacteria in the 3 ng.

Bacteria

Antibiotics

Ct

Tm (℃)

E. coli

No drug

20.6

80.6

E. coli

No drug

22.4

80.2

E. coli

Ampicillin

26.4

80.2

E. coli

Ampicillin

27.1

80.6

E. coli

Gentamicin

24.4

80.2

E. coli

Gentamicin

22.4

80.2

PAO1

No drug

17.6

80.6

PAO1

No drug

16.4

80.6

PAO1

Ampicillin

17.2

80.6

PAO1

Ampicillin

16.0

81.0

PAO1

Gentamicin

23.8

80.6

PAO1

Gentamicin

23.0

80.6

MDRP

No drug

19.2

80.6

MDRP

No drug

16.5

80.2

MDRP

Ampicillin

19.2

80.6

MDRP

Ampicillin

19.2

80.6

MDRP

Gentamicin

18.2

80.2

MDRP

Gentamicin

18.2

80.2

Table 2.  Comparison of Ct values after antibiotics and propidium monoazide (PMA) treatment. The effectof PMA was confirmed by comparing the Ct values obtained by real-time PCR. The bacterial genome of eachsample was prepared to 3 ng and used for real-time PCR. The results show the proportion of viable bacteria inthe 3 ng.

18% and 22% in two experiments. These correspond to 85.5% and 75.3% decrease of viable bacteria in terms of
viable bacteria decrease (VBD) index (see “Methods” for details). When treated by gentamicin, to which both E.
coli and PAO1 are sensitive, the proportion of reads from E. coli did not decrease drastically. However, from theresults of real-time PCR, it was confirmed that the total amount of E. coli and PAO1 decreased (Table 1). Next,
in the E. coli and MDRP mixture, the frequency of reads from E. coli dropped to 53.3% and 62.2%, compared
to 70.2% without antibiotics treatment (Fig. 1C). This corresponds to 38.0% and 10.6% decrease of VBD index.
When gentamicin was used, the frequency of E. coli reads dropped to 9.5% and 6.9%, corresponding to 94.3%
and 96.0% VBD index, showing drastic decrease.

Measuring concentrations of viable bacteria by real‑time PCR. In order to reinforce the evidence
from 16S metagenome sequencing, we conducted real-time PCR on viable bacterial genomes after antibiotics
and PMA treatments. Here, we used universal primers to amplify the V2 region of 16S rRNA g­ enes11. First, wemeasured concentrations of bacterial genomes from culture fluids of E. coli, PAO1, or MDRP (Table 2). The Ct
values of untreated E. coli were 20.6Ct and 22.4Ct, but those of ampicillin-treated E. coli were 26.4Ct and 27.1Ct,
showing that the amount of E. coli apparently decreased with ampicillin treatment. The Ct values of gentamicinScientific Reports |(2021) 11:3436 |https://doi.org/10.1038/s41598-021-82903-z

3Vol.:(0123456789)

www.nature.com/scientificreports/
treated E. coli were 24.4Ct and 22.4Ct, showing a slight decrease from untreated E. coli, suggesting a milder
effect of gentamicin on E. coli. On the other hand, the Ct values showed no difference between untreated andampicillin-treated PAO1, while the amount of the gentamicin-treated PAO1 apparently decreased. The Ct values
showed not much difference between untreated and treated MDRP by ampicillin or gentamicin. These results
are mostly consistent with the results of AST (Fig. 1A). Also, these results confirmed that PMA is effective inremoving dead bacteria and measuring only viable bacteria.
Next, we conducted real-time PCR on genomic DNA extracted from bacterial mixtures after antibiotics and
PMA treatments that have been examined by 16S metagenome sequencing (Table 1). In the E. coli and PAO1
mixture, the Ct values of the untreated mixture were 18.6Ct and 19.6Ct, those of the ampicillin-treated mixture were 19.4Ct and 18.4Ct, and those of the gentamicin-treated mixture were 23.4Ct and 24.3Ct. Although
there was no difference between untreated and ampicillin-treated mixture, the amount of total bacteria clearlydecreased by the gentamicin treatment. Additionally, in the E. coli and MDRP mixture, the amount of total
bacteria slightly decreased under ampicillin and gentamicin treatments, which is consistent with the fact that
only E. coli is sensitive to these drugs.

Detection limit.

A total of 14,636 reads were obtained in two MinION runs of 16S metagenome analysis(Supplementary Table S1). The read counts of each bacterium were 5,730 reads for E. coli, 5,053 reads for PAO1
and 3,343 reads for MDRP. In all bacteria samples, each bacteria were detected in the genome extracted from8 × 107 CFU/0.1 mL. Additionally, sequencing reads was detected from 8 × 104 CFU/0.1 mL (302.0 reads) for
PAO1 and 8 × 104 CFU/0.1 mL and 8 × 103 CFU/0.1 mL (35 reads and 27 reads respectively) for MDRP, with
detection limits of 8 × 104 CFU/0.1 mL for PAO1 and 8 × 103 CFU/0.1 mL for MDRP, respectively. Unfortunately,
E. coli could not detect reads at 8 × 104 CFU/0.1 mL and 8 × 102 CFU/0.1 mL (Supplementary Table S1).

Discussion

In this study, we proposed a new method for rapid identification of drug-resistant bacteria by a combination of
16S metagenome sequencing and the use of PMA and antibiotics, and examined its feasibility. First, we assess
the effect of PMA and antibiotics on PCR amplification using three bacterial species, and confirmed that only
viable bacteria could be amplified by PCR as expected. Next, by a 16S metagenome sequencing of bacterial
mixtures after treatment of antibiotics and PMA, we observed changes of bacterial compositions, from which
we estimated the existence of drug-resistant bacteria. Furthermore, we examined the total amount of bacterial
genomes by real-time PCR, and confirmed that the amount of drug-sensitive bacteria decreased, which was notapparent solely from the proportion of sequencing reads. From these results, we conclude that we can correctly
estimate the drug-resistant bacteria by predicting the bacterial compositions and drug-resistance profiles fromthe proportion of sequencing reads, with the help of real-time PCRs. It is not difficult to determine a drugresistant species from a bacterial mixture, if not more than one drug-resistant bacteria exist. Even when there
is no drug-resistant bacteria, it is possible to judge from the results of real-time PCR, because total amount ofbacterial genomes should be significantly decreased.
However, there may be difficult situations, depending on the bacterial species or kinds of antibiotics. For
example, metagenome sequencing results of ampicillin-treated mixture of E. coli and PAO1 showed significant
decrease of E. coli (Fig. 1B), but real-time PCR showed that total amount of bacterial genome did not change,
although one of the bacteria is sensitive to ampicillin (Table 2). In this case, PAO1 might have grown to someextent in one hour even under the ampicillin treatment. We may encounter such an unexpected result in actual
cases, so we need to examine effects of antibiotics on as many combinations of bacterial species and antibiotics
as possible before putting this method to practical use.
In general, bacterial detection and AST for the diagnosis of bacterial infectious diseases are carried out following the guidelines of the Clinical and Laboratory Standards Institute (CLSI)6,7. However, based on the CLSI, it
takes at least a few days for bacterial identification by AST, and has a limitation due to a low culture-positive rate.
In contrast, the method we report here can possibly provide bacterial detection and AST results from clinical samples in shorter time than conventional culture-based methods (Figs. 1 and 2A). We previously showedthat PCR and library preparation for 16S metagenome sequencing can be done in about 1 h from a given DNA­sample11, and another 1 h is required to determine bacterial species computationally for each of 5000 reads usingreference genomes of 5850 bacterial s­ pecies12. Therefore, considering the time for DNA extraction from bacteria(about 1 h) and irradiation of blue LED (15 min), detection of bacteria and drug-resistant bacteria in a sample
will be possible within 4 h by our method.
Of course, further validation of our method is necessary using other antibiotics and other drug-resistantbacteria. In addition, the detection limits were 8 × 104 CFU for PAO1 and 8 × 103 CFU for MDRP, indicating that
E. coli could be 8 × 105 CFU or higher. Therefore, it was suggested that it is necessary to reconsider the library
production method and lower the detection limits. However, our method is scalable, and the automation of theexperimental procedure is also possible. We hope that, in the future, our method will be useful as a rapid andefficient way to determine bacterial compositions and AST simultaneously.

Methods

Sample/culture preparation. We prepared three bacterial species of which antimicrobial spectra are
known: E. coli (ATCC 25922), PAO1, and MDRP. Those bacteria were cultured in heart infusion broth (HIB)(Thermo Fisher Scientific, Waltham, MA, USA) overnight, and were collected by centrifugation. Then, the bacteria pellet was resuspended with saline. The bacterial suspension was double serially diluted using saline on
96-well plate to measure the concentration of viable bacteria. After dilution, O­ D562 was measured in each wellusing a microplate reader and the quantity of the bacteria was calculated.
Scientific Reports |Vol:.(1234567890)(2021) 11:3436 |https://doi.org/10.1038/s41598-021-82903-z

4

www.nature.com/scientificreports/

Figure 2.  Study design and LED irradiator. (A) Study design. (B) a handmade blue LED irradiator with 60
SMD6060 chips. Top: setup; bottom: when turned on.

Batch culture experiments. For AST, a bacterial suspension was prepared at 1­ 07 colony-forming units(CFU)/mL by HIB. Ampicillin or gentamicin was added to the wells containing bacteria and cultured at 37 °C
overnight, and O­ D562 was measured in each well using a VersaMax microplate reader (Molecular Devices LLC).

Antibiotic treatment, PMA treatment and light‑emitting diode irradiation. To examine thedrug-sensitivity on the genome-base, each bacterial suspension was adjusted to ­107 CFU/mL. Mixtures of bacterial suspensions were also prepared. 100 μL of ampicillin (final concentration: 16 μg/mL) or gentamicin (final
concentration: 32 μg/mL) was added to 100 μL of each of bacterial suspensions and bacterial mixtures. After the
addition of the antibiotics, the bacterial suspensions were cultured for 1 h at 37℃ and mixed well by vortexing(Fig. 2A). A 2.5 mM PMAxx (Biotium, Inc., Hayward, CA, USA) was added to the culture medium, and the blue
light-emitting diode (LED) was irradiated for 15 min (Fig. 2B). Then, bacterial DNA was extracted from theculture medium using Bactozol (Molecular Research Center, Inc., Cincinnati, OH, USA).
Real‑time PCR.

To confirm the effect of PMAxx, we used real-time PCR targeting the V2 hypervariable
regions of the 16S rRNA gene (Fig. 2A), using Fast SYBR Green Master Mix and an ABI 7500 Fast Real-Time
PCR System (Applied Biosystems, Foster City, CA, USA).

Amplification and library preparation. To compare bacteria compositions between samples, a total of

10 ng of bacterial DNA was amplified with the 16S Barcoding Kit (SQK-RAB204; Oxford Nanopore Technologies, Oxford, UK) by PCR as described in the manufacturer’s protocol. Post-PCR clean-up was performed using
Agencourt AMPure XP beads (Beckman Coulter, Brea, CA, USA) and eluted in 10 μL of 10 mM Tris–HCl (pH8.0) with 50 mM NaCl.

DNA sequencing analysis using MinION. MinION sequencing was performed using the MinION
Mk1b sequencer and FLO-MIN106 flow cells. Nucleotides of each read were called by Albacore version 2.1.3(Oxford Nanopore Technologies), and the sequences were deposited in the DDBJ DRA database (https​://www.
ddbj.nig.ac.jp/dra/index​-e.html) under the accession numbers DRR187692 to DRR187701. Bacterial species
were assigned using the minimap2 s­ oftware15 with the bacterial genomes obtained from the GenomeSync database (http://genom​esync​.org) as we previously ­reported12,13.
We defined the VBD index as the proportion (%) of bacteria killed by the effect of antimicrobial drugs. Thiscan be estimated from the number of sequencing reads. VBD index is calculated by (1 − (S × R0)/(R × S0)) × 100,
where R and S are the numbers of reads from drug-resistant and drug-sensitive bacteria when antibiotics wereused, and R0 and S0 are the numbers of reads from drug-resistant and drug-sensitive bacteria when no drug
was used.

Scientific Reports |(2021) 11:3436 |https://doi.org/10.1038/s41598-021-82903-z

5Vol.:(0123456789)

www.nature.com/scientificreports/
Detection limit. After ­OD562 measurement, a tenfold serial dilution series of 8 × 101 to 8 × 104 CFU/0.1 mL
and 8 × 107 CFU/0.1 mL were prepared for each bacterial suspension. After bacterial DNA extraction, a library
was prepared for MinION sequencing. The detection limit was evaluated using the obtained sequencing reads.

Received: 24 August 2020; Accepted: 27 January 2021

Reference

1. Enright, M. C. et al. The evolutionary history of methicillin-resistant Staphylococcus aureus (MRSA). Proc. Natl. Acad. Sci. USA
99(11), 7687–7692. https​://doi.org/10.1073/pnas.12210​8599 (2002).
2. Saiman, L. et al. Antibiotic susceptibility of multiply resistant Pseudomonas aeruginosa isolated from patients with cystic fibrosis,
including candidates for transplantation. Clin. Infect. Dis. 23(3), 532–537. https​://doi.org/10.1093/clini​ds/23.3.532 (1996).
3. Asai, S. et al. An outbreak of blaOXA-51-like- and blaOXA-66- positive Acinetobacter baumannii ST208 in the emergency intensive
care unit. J. Med. Microbiol. 63, 1517–1523. https​://doi.org/10.1099/jmm.0.07750​3-0 (2014).
4. Umezawa, K. et al. Outbreak of drug-resistant Acinetobacter baumannii ST219 caused by oral care using tap water from contaminated hand hygiene sinks as a reservoir. Am. J. Infect. Control. 43, 1249–1251. https​://doi.org/10.1016/j.ajic.2015.06.016 (2015).
5. Gandhi, N. R. et al. Multidrug-resistant and extensively drug-resistant tuberculosis: A threat to global control of tuberculosis.
Lancet 375(9728), 1830–1843. https​://doi.org/10.1016/S0140​-6736(10)60410​-2 (2010).
6. European Committee on Antimicrobial Susceptibility Testing. Breakpoint Tables for Interpretation of MICs and Zone Diameters,
Version 1.3, http://www.eucas​t.org/clini​cal_break​point​s (7 November 2011, date last accessed). (2011)
7. Clinical and Laboratory Standards Institute. Performance Standards for Antimicrobial Susceptibility Testing: Twenty-first Informational Supplement M100–S21 (CLSI, Wayne, PA, 2011).
8. Nocker, A., Sossa-Fernandez, P., Burr, M. D. & Camper, A. K. Use of propidium monoazide for live/dead distinction in microbial
ecology. Appl. Environ. Microbiol. 73(16), 5111–5117. https​://doi.org/10.1128/AEM.02987​-06 (2011).
9. Pan, Y. & Breidt, F. Jr. Enumeration of viable Listeria monocytogenes cells by real-time PCR with propidium monoazide and ethidium monoazide in the presence of dead cells. Appl. Environ. Microbiol. 73(24), 8028–8031. https​://doi.org/10.1128/AEM.01198​-07 (2007).
10. Nocker, A., Mazza, A., Masson, L., Camper, A. K. & Brousseau, R. Selective detection of live bacteria combining propidium
monoazide sample treatment with microarray technology. J. Microbiol. Methods 76(3), 253–261. https​://doi.org/10.1016/j.mimet​.2008.11.004 (2009).
11. Mitsuhashi, S. et al. A portable system for rapid bacterial composition analysis using a nanopore-based sequencer and laptop
computer. Sci. Rep. 7(1), 5657. https​://doi.org/10.1038/s4159​8-017-13712​-6 (2017).
12. Nakagawa, S. et al. Rapid sequencing-based diagnosis of infectious bacterial species from meningitis patients in Zambia. Clin.
Transl. Immunol. 8(11), e01087. https​://doi.org/10.1002/cti2.1087 (2019).
13. Kai, S. et al. Rapid bacterial identification by direct PCR amplification of 16S rRNA genes using the MinION nanopore sequencer.
FEBS Open Biol. 9, 548–557. https​://doi.org/10.1002/2211-5463.12590​ (2019).
14. Hancock, R. E. & Speert, D. P. Antibiotic resistance in Pseudomonas aeruginosa: Mechanisms and impact on treatment. Drug Resist.
Update 3(4), 247–255. https​://doi.org/10.1054/drup.2000.0152 (2000).
15. Li, H. Minimap2: Pairwise alignment for nucleotide sequences. Bioinformatics 34(18), 3094–3100. https​://doi.org/10.1093/bioin​
forma​tics/bty19​1 (2018).

Acknowledgements

We thank the staff of the Department of Laboratory Medicine at the Tokai University School of Medicine fortechnical assistance.

Author contributions

A.O., K.U., S.A., K.K., S.N., H.M., and T.I. designed the study. S.A. and H.M. collected experimental materials.
A.O. and K.U. conducted experiments. A.O., K.K., and S.N. analyzed and interpreted the data. A.O., K.U., S.N.,
and T.I. wrote the main manuscript text. All authors reviewed the manuscript.

Funding

This work was supported by a grant from the Japan Initiative for Global Research Network on Infectious Diseases(J-GRID) of the Japan Agency for Medical Research and Development (AMED) (JP17fm0108023), a specific
grant-in-aid from Takeda Science Foundation, and the Kurata Grants of the Hitachi Global Foundation.

Competing interests

The authors declare no competing interests.

Additional information

Supplementary Information The online version contains supplementary material available at https​://doi.
org/10.1038/s4159​8-021-82903​-z.
Correspondence and requests for materials should be addressed to T.I.
Reprints and permissions information is available at www.nature.com/reprints.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps andinstitutional affiliations.

Scientific Reports |Vol:.(1234567890)(2021) 11:3436 |https://doi.org/10.1038/s41598-021-82903-z

6

www.nature.com/scientificreports/
Open Access This article is licensed under a Creative Commons Attribution 4.0 International
License, which permits use, sharing, adaptation, distribution and reproduction in any medium or
format, as long as you give appropriate credit to the original author(s) and the source, provide a link to theCreative Commons licence, and indicate if changes were made. The images or other third party material in thisarticle are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to thematerial. If material is not included in the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly fromthe copyright holder. To view a copy of this licence, visit http://creat​iveco​mmons​.org/licen​ses/by/4.0/.
© The Author(s) 2021

Scientific Reports |(2021) 11:3436 |https://doi.org/10.1038/s41598-021-82903-z

7Vol.:(0123456789)