Lineage calling can identify antibiotic resistan t clones ...An example is given by the pneumococcus...

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Lineage calling can identify antibiotic resistant clones within minutes

Karel Břinda1,2, Alanna Callendrello1, Lauren Cowley1, Themoula Charalampous3, Robyn S Lee1,

Derek R MacFadden1,4, Gregory Kucherov5,6, Justin O’Grady7,3, Michael Baym2,8, and William P

Hanage1

1 Center for Communicable Disease Dynamic, Department of Epidemiology, Harvard T.H. Chan School of Public

Health, Boston, USA

2 Department of Biomedical Informatics, Harvard Medical School, Boston, USA

3 University of East Anglia, Norwich Research Park, Norwich, UK

4 Division of Infectious Diseases, Department of Medicine, University of Toronto, Canada

5 CNRS/LIGM Université Paris-Est, Marne-la-Vallée, France

6 Skolkovo Institute of Science and Technology, Moscow, Russia

7 Quadram Institute Bioscience, Norwich Research Park, Norwich, UK

8 Laboratory of Systems Pharmacology, Harvard Medical School, Boston, USA

Correspondence to [email protected]

.CC-BY-NC 4.0 International licenseacertified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under

The copyright holder for this preprint (which was notthis version posted August 29, 2018. ; https://doi.org/10.1101/403204doi: bioRxiv preprint

https://doi.org/10.1101/403204

http://creativecommons.org/licenses/by-nc/4.0/

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Introductory Paragraph

Surveillance of circulating drug resistant bacteria is essential for healthcare providers to deliver

effective empiric antibiotic therapy. However, the results of surveillance may not be available

on a timescale that is optimal for guiding patient treatment. Here we present a method for

inferring characteristics of an unknown bacterial sample by identifying the presence of

sequence variation across the genome that is linked to a phenotype of interest, in this case drug

resistance. We demonstrate an implementation of this principle using sequence k-mer content,

matched to a database of known genomes. We show this technique can be applied to data

from an Oxford Nanopore device in real time and is capable of identifying the presence of a

known resistant strain in 5 minutes, even from a complex metagenomic sample. This flexible

approach has wide application to pathogen surveillance and may be used to greatly accelerate

diagnoses of resistant infections.



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Introduction

Antibiotic-resistant infections pose multiple challenges to healthcare systems, contributing to

higher mortality, morbidity, and escalating cost. Clinicians must regularly make rapid decisions

on empiric antibiotic treatment without knowing if a patient’s clinical syndrome is due to a drug

resistant organism. In some cases, this is directly linked to poor outcomes; in the case of septic

shock, the risk of death increases by an estimated 10% with every 60 minutes delay in initiating

effective treatment1.

Hence, there is interest in developing rapid, point-of-care techniques to detect the presence of

a resistant strain in a sample, for diagnostics and surveillance purposes. The continuing

development of sequencing technologies suggests that genomic data are particularly promising

for this purpose2. In principle, if a resistance gene or mutation can be detected in a sample, this

could be sufficient to inform treatment decisions. However for this to be applicable in practice,

several conditions must be satisfied: foremost, the resistance determinant must be already

identified, it must be sufficiently different from susceptible variants, and the genomic context

must be known, as loci with homology to known resistance determinants are also found in non-

pathogens3. Furthermore, to make diagnosis truly point-of-care, one must sequence as directly

as possible from clinical samples, without time-consuming culture steps. This implies a

metagenomic sample containing sequences from many different taxa, and so the genomic

context of the resistance locus may be obscured if we use short read technologies for

sequencing. An ideal approach would not depend on access to expensive, sophisticated

sequencing equipment, making it deployable close to the point of care and in resource-poor

settings.

The clinical question of whether an antibiotic is likely to work, i.e. the pathogen is susceptible, is

not equivalent to identifying whether a pathogen carries those mutations or genes that are

known to confer resistance. Prescription has long been informed by correlative features when



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causative ones are difficult to measure, for example whether the same syndrome (or ideally

pathogen) occurring in other patients from the same clinical environment have responded (or

were susceptible to) to a particular antibiotic. This also has been observed at the genetic level

as well, as a result of genetic linkage between resistance elements and the rest of the genome.

An example is given by the pneumococcus (Streptococcus pneumoniae), a major pathogen,

responsible for approximately 1.6 million deaths per year. The Centers for Disease Control have

rated the threat level of drug resistant pneumococcus as ‘serious’ 4. While resistance arises in

pneumococci through a variety of mechanisms and genes, approximately 90% of the variance in

the minimal inhibitory concentration (MIC) for multiple antibiotics of different classes, could be

explained by the loci determining the strain type alone5. This is particularly interesting, as none

of the loci used for strain classification themselves causes resistance. Thus, in the overwhelming

majority of cases, resistance can be accurately predicted from coarse strain typing based on

population structure.

This population structure can be leveraged to offer an alternative approach to detecting

resistance in which rather than detecting high-risk genes, we identify high-risk lineages. The

additional information available from genomic data allows a better definition of those closely

related parts of the population associated with resistance or susceptibility, which we call

‘phylogroups’. High-risk phylogroups can be readily determined by analysis of existing high-

quality draft genome assemblies, together with suitable metadata on MICs. Thus, given

sufficient correlation between the phylogroup and phenotype of interest (for example drug

resistance), rapid identification of the phylogroup alone can be sufficient for diagnostic

purposes.

An attractive option for this approach is to use long-read sequencing, such as nanopore

technology (Oxford Nanopore Technology (ONT)), given its additional correlative structure.

Although the ONT MinION device has a high (~10%) per base error rate6, it is also highly

portable and deployable in field conditions7. Furthermore, sequencing reads are streamed the



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computer as they are produced, so the results can be analyzed and reported in real time.

Recently, nanopore sequencing has been shown to provide rapid re-identification of human

samples within minutes8, predict antibiotic resistance of pathogens within hours9–14, or predict

sequence types of bacterial isolates within an hour15.

Here we present a method to match data from bacterial isolate sequencing and clinical

metagenomics against a genomic database of known isolates for which resistance has already

been determined, and predict antibiotic resistance based on the resistance profiles of the

matches. We demonstrate, using the example of pneumococcus and five antibiotics

(benzylpenicillin, ceftriaxone, trimethoprim-sulfamethoxazole, erythromycin, and tetracycline),

that we can identify known resistant clones, and their serotype, on a standard laptop within 5

minutes even from metagenomic data. Moreover, our solution is suitable for applications in

resource-poor contexts, making it not only useful for diagnosing infections, but also for

enhancing surveillance.



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Results

A database of resistance-associated sequence elements

To predict resistance in isolates and clinical samples we built a database of Resistance

Associated Sequence Elements (RASE). We generated a k-mer-based representation of lineages

for use to predict resistance by approximate matching. Following an analysis of the S.

pneumoniae genome and characteristics of nanopore reads, we set k-mer length to 18 (see

Methods). Our method depends on the initial availability of good quality data, and so we used

genomes of pneumococci sampled from a carriage study in Massachusetts children16,17 as the

main reference dataset; it consists of 616 carriage samples isolated from Massachusetts

children and comprises resistance data together with high quality draft genome assemblies

from Illumina HiSeq reads. These isolates have already been classified using Multi-Locus

Sequence Typing18,19 (MLST), which is the current ‘gold standard’ for defining clones and clonal

complexes used by the Pneumococcal Molecular Epidemiology Network20 (PMEN).

Based on the measured MICs, we assigned each isolate to an antibiotic-specific resistance

category using standard breakpoints (see Methods). Where data on MICs were not available,

we estimated the likely resistance phenotype of an isolate using ancestral state reconstruction

(see Methods). This was the case for a total of 494 records, concentrated in the data for

tetracycline (291 records) and ceftriaxone (176 records) susceptibility. A further advantage of

the dataset we chose was that we had access to the original isolates, and so additional

resistance testing was possible; in our subsequent experiments, if original MIC data were not

available for the best match in the RASE database, the relevant isolate was tested to confirm

resistance phenotype (see Methods). In all of 8 cases tested, ancestral state reconstruction

provided the correct resistance phenotype (shown in bold in Table 1). Out of all 616 isolates,

341 were associated with susceptibility to benzylpenicillin, 485 to ceftriaxone, 480 to



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trimethoprim-sulfamethoxazole, 484 to erythromycin, and 551 to tetracycline (Supplementary

Tables 1 and 2).

The constructed database occupies 320 MB RAM (4.3× compression rate) and can be further

compressed to 47 MB (29× compression rate) (Supplementary Figure 1). The RASE database

can be therefore used on portable devices and easily transmitted to the point of care over links

with a limited bandwidth.

Lineage calling using inexact matching

We developed an approach that we term ‘lineage calling’ (Figure 1) to match a nanopore read

to the phylogroup from which it came – where, as described above, phylogroup is a clade

associated with either resistance or susceptibility. We then used a modified version of

ProPhyle21, an accurate, resource-frugal and deterministic phylogeny-based DNA classification

tool based on the Burrows-Wheeler Transform22, to assign nanopore reads to positions on

phylogenetic trees and identify the closest match. Reads were assigned scores based on their

similarity to known sequences in the database. Generally speaking, longer reads, such as those

covering multiple accessory genes, tend to be specific and have high scores; whereas short

reads from the core genome, tend to be non-specific and have low scores, being found in many

genomes. Cumulative scores, which we call weights, are then used to measure how similar a

sample is to known genomes associated with resistance, already in the database. We compute

two metrics: the ‘phylogroup score’ and the ‘susceptibility score’ (described in more detail in

methods). These are ratios comparing the weights of the best match in the database, with the

weight of the next best match of a different phylogroup or susceptibility category respectively.

Intuitively the scores measure the confidence with which a sample is assigned to a given

phylogroup and quantify the risk of resistance based on the matching samples in the RASE

database.



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Results of prediction are reported in real time as the best matching genomes in the database,

together with the phylogroup score and the susceptibility scores to the antibiotics being tested

(examples shown in Figures 2 and 3). As the run progresses, these scores fluctuate and

eventually stabilize.



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Testing isolates present in the RASE database

We examined two isolates that were used to build the RASE database (SP01 and SP02 in Table

1A). They were selected to test whether we can correctly assign phylogroup even under the

best circumstances, given the relatively high error rate of nanopore sequencing6. The profile

obtained from the fully susceptible isolate is shown in Figure 2. Due to errors in the nanopore

sequence, only 20% of the bases matched k-mers in the RASE database – yet despite this, the

correct phylogroup was assigned within 1 minute. The best match stabilized within 7 minutes,

and this matched the isolate used in the test. The second tested isolate was predicted even

faster, with phylogroup and best match correctly detected and stabilized within 1 minute.

These experiments provide a proof of principle that lineage calling can be accurate and fast

even using sequence data with a relatively high per base error rate.

We also evaluated how long it took for resistance genes to be reliably detected in nanopore

reads. For SP02 we observed that at least 15 minutes was needed to detect resistance,

assuming that the genes in question can be unambiguously identified in nanopore data despite

the high per base error rate, and that the presence of the loci is directly linked to the resistance

phenotype (Supplementary Figure 2). If this is not the case, further delays would be expected.

Thus, lineage calling can offer a time advantage compared to methods based on identifying the

presence of resistance genes even in a sample of DNA from a purified isolate as opposed to a

metagenome, potentially allowing for more rapid changes to antimicrobial therapy.



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Testing isolates not present in the RASE database

We then examined four additional isolates (SP03–SP06 in Table 1A) for which the serotype and

limited antibiogram data were known, but the lineage was unknown. To identify the lineages of

these isolates we sequenced them by Illumina Miniseq, and confirmed the antibiogram of the

antibiotics being tested in this study. We compared three characteristics of the sample to

assess our performance: the serotype, the sequence type (ST) and the antibiograms

(benzylpenicillin, ceftriaxone, trimethoprim-sulfamethoxazole, erythromycin, and tetracycline

resistance according to EUCAST breakpoints23). Multi-locus Sequence Typing18 (MLST) is the

gold standard for strain assignment and divides the pathogen population into clonal complexes

(equivalent to lineages).

In all cases, the correct clonal complex was identified within five minutes, even if the correct ST

was absent from the RASE database, indicating the strength of the lineage calling method in

rapidly detecting similarity. However, this also illustrates the importance of a high quality and

suitable database for comparison, which contains the clones that are likely to be encountered

in disease. The two 23F samples (SP03 and SP06) were correctly called as being closely related

to the Tennessee 23F-4 clone identified by PMEN, a clone strongly associated with macrolide

resistance20. Consistent with this, the two samples were indeed resistant to erythromycin, as

was the closest match in the RASE database constructed from the Massachusetts sample. In the

case of SP05, the phylogroup score was borderline, reflecting divergence of the sample

undertest from the database, even though the susceptibility scores were accurate for the

antibiotics tested.



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Metagenomic sample testing

Because culture introduces significant delays, direct metagenomic sequencing of clinical

samples would be preferable. We therefore analyzed nanopore metagenomics data from

sputum samples obtained from patients suffering from lower respiratory tract infections2,

selecting 6 samples from the study that were already known to contain Streptococcus

pneumoniae (Table 1B, sorted by the estimated proportion of S. pneumoniae reads).

The sample displayed in Figure 3 (SP10) contains DNA from multiple bacterial species, and as a

result, few of the reads match to the k-mers in the RASE database (7% in contrast with 20% for

the sample used for proof of principle above). However, the sample was still inferred, again

within 5 minutes, to contain DNA identified as belonging to the Swedish 15A-25 clone (ST63)

which is also known to be associated with resistance phenotypes including macrolides and

tetracyclines24. This sample was confirmed to be resistant to the erythromycin, as well as

clindamycin, tetracycline and oxacillin2 according to EUCAST23. The result for oxacillin is

especially noteworthy, as the initial report of this clone did not report resistance to penicillin

antibiotics24. However, resistance to this class has subsequently emerged in this lineage, and so

the database used in this work correctly identified the risk of penicillin resistance in this sample.

The metagenomes SP11 and SP12 contain an estimated >20% reads that matched to S.

pneumoniae, and their serotypes were identified to be 15A and 3, respectively. The

susceptibility scores of the best matches were fully consistent with the susceptibility profiles

found in the samples, with the exception of tetracycline resistance of SP12. Further analysis of

the reads from SP12 using Krocus15 suggested that the pneumococcal DNA present was from

the ST180 clonal complex, and matched specifically either to the sequence type ST180 or

ST3798. This is consistent with identification as serotype 3, because this clonal complex

contains the great majority of isolates with this capsule type, which historically has not been

associated with resistance25. However, improved sampling and study of this lineage has

recently found highly divergent subclades that are associated with resistance. These lineages



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were previously rare, and thus were less likely to be included in our database, but now are

increasing in frequency26. In this case, ST 3798 is found to be in clade 1B, which is notable for

exhibiting sporadic tetracycline resistance. Again, the failure to match to this is a result of the

original database not containing a suitable example for comparison.

The last remaining samples, SP07–SP09, contained less than 5% unambiguously pneumococcal

reads, and as a result the phylogroup was not securely identified in these. Nevertheless, all

predicted phenotypes were concordant with phenotypic tests, with the exception of SP07

which matches the same isolate as SP12 (discussed above).



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Discussion

Effective methods for detecting resistance, or susceptibility from gene sequences do not need

to perform GWAS in reverse – using lineage calling, there is no requirement to detect the

variation that causes the phenotype, only that it be sufficiently strongly associated with the

phenotype to make reliable predictions. The results presented here show that if an identical

genome is present in the database, ProPhyle accurately matches it in 5 minutes and accurately

predicts resistance/susceptibility, and if the genome is not present the closest relative is

identified within a similar time span. Moreover, ProPhyle can be used successfully with

metagenomic data, here identifying the presence of the Sweden 15A-23 clone in a sputum

sample taken from a patient with lower respiratory tract infection in the UK. Together, these

results suggest that we can achieve robust lineage calling, even from complex data, within

minutes of nanopore sequencing.

A key advantage of this approach is that it is not limited by the relatively high error rate of

nanopore sequencing; it is not attempting to define the exact genome sequence of the sample

being tested, but merely which lineage it comes from. As a result, even when a small fraction of

k-mers in the read are informative in matching to the RASE database, this is sufficient to call the

lineage. This has the benefit of being faster than gene detection by virtue of the informative k-

mers being distributed throughout the genome, and so more likely to appear in the first few

reads sequenced by the nanopore. Therefore, the approach we present here can be seen as an

application of compressed sensing: by measuring a sparse signal distributed broadly across our

data we can identify it with comparatively few error-tolerant measurements.

Lineage calling has several advantages over methods that aim to detect the presence of the

specific sequences that confer resistance. Most importantly, we can identify clones that are

associated with susceptibility as well as resistance. The relevant loci need not be known in

advance, and because we are seeking to identify the lineage rather than the loci, it is much



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quicker. In our experiments it consistently took longer for a single copy of a resistance gene of

interest to pass through the pore and be identified than to identify the lineage. This is

particularly important when detecting mutational resistance that requires high genome

coverage (>30x). Finally, when resistance is plasmid-borne, identifying the lineage may be more

reliable at predicting susceptibility/resistance by lineage calling in metagenomic data, as the

source organism of plasmids in a metagenome is hard to identify.

These results suggest a two-step model for resistance diagnostics, in which the first is to

characterize the important pathogens in the population with highly accurate, high quality draft

genomes together with metadata on resistance or other phenotypes of interest, and then to

analyze clinical samples directly using nanopore-based metagenomics and the RASE software.

The importance of a high quality and representative database is shown by the failure to

accurately call erythromycin resistance for SP03 and SP06; the closest match to these two in the

RASE database was relatively distantly related to them and had diverged in its antibiogram.

Given the value and importance of an appropriate database, which is evident from our results,

it is notable that health laboratories are increasingly collecting datasets suitable for use with

RASE. The US Centers for Disease Control and Prevention have started using WGS to

characterize samples from their Active Bacterial Core Surveillance system, which obtains

isolates and MIC data from all isolates of S. pneumoniae causing invasive disease in a

population of more than 23 million. As a result of this initiative, raw reads and resistance data

for 1781 isolates collected from 2015 already exists27,28. While it is unlikely that a random

patient presenting with disease would be infected by a lineage not present in this sample, it is

possible. In the event that the sequenced isolate belongs to a clade that is absent from the

database or the confidence in cluster assignment to the studied species is not sufficiently

strong, RASE reports comparable similarity for multiple different phylogroups and the

phylogroup score drops accordingly (see experiments SP05 and SP07–SP09 in supplementary

online material). This will allow attention to rapidly be concentrated on any examples of

bacteria that are not present in the database. If we are to move away from culture towards



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metagenomic-based infection diagnosis in future, this feature of RASE will be extremely

valuable, pointing us toward clinical samples containing unusual lineages that can be cultured

and characterized.

A more serious issue, which we have not encountered in this study, but which may limit the

application of our approach to other pathogen-drug combinations, is the degree of linkage

between resistance and a specific lineage. If this is low, such that there is very weak association

between lineage and resistance phenotype, then we would not expect our approach to work.

This is particularly the case if resistance can arise from a single mutation during the course of

treatment (e.g., porin mutations which confer diminished susceptibility to carbapenems27).

Such an eventuality would not be detectable by any sequence-based method, but we note this

would also mislead conventional gold standard susceptibility testing if the mutation has not

already arisen at time of sample collection. In the case of the pneumococcus the degree of

linkage between resistance and the rest of the genome is high, as shown by the success of

ancestral state reconstruction in inferring the resistance status of isolates for which MIC data

were not originally reported. This suggests that perfect resistance data for all isolates may not

be necessary in all circumstances, however this will require further work to fully define, as will

how the RASE approach scales with increasing database size.

Another limitation of this approach for point-of-care use is the complexity and time required for

sample preparation, which currently includes human DNA depletion, DNA isolation and library

preparation, taking a total of 4 hours. However, we note that ONT Voltrax technology can be

used for automated library preparation and, potentially in the future, host depletion and DNA

extraction. Automation will simplify and speed up the sample preparation turnaround time. It

should be noted that this has been further reduced, with a Rapid Sequencing Kit offering library

preparation in 10 minutes29. Further advances in this space, including reduced costs, will be

required to bring the method closer to the bedside. For instance, the ONT Flongle flowcell

($100 as of August 2018) may help to address this issue.



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The benefits of lineage calling are in identifying high-risk clones earlier. It is easy to see how our

approach may be extended to include calling specific resistance loci, where they are known, but

a key advantage of our approach is that it is not limited by the requirement to know them in

advance. Lineage calling can be used to detect any phenotype that is sufficiently tightly linked

to a phylogeny, for instance to identify highly virulent strains that might merit closer attention.

Further applications may include rapid outbreak investigations, as the closely related isolates

involved in the outbreak will all be predicted to match to the same strain in the RASE database.

The approach also lends itself to enhanced surveillance, including field work situations; the

recent Ebola outbreak in West Africa, for example, saw MinION devices used in remote

locations without centralized and advance healthcare facilities. Finally, this approach is not at

present intended to supplant empiric therapies. Given the urgency of instituting appropriate

therapies, prescriptions should be made as early as possible. However, we may be able,

through lineage calling of samples taken when the tentative diagnosis is made, to institute

effective therapy at the second dose when the initial therapy is inadequate, long before it

would become clinically apparent the patient is not responding. The combination of high

quality RASE databases with lineage calling hence offers an alternative model for diagnostics

and surveillance, with wide applications for the management of infectious disease.



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Methods

Overview

RASE uses rapid approximate k-mer-based matching of long sequencing reads against a

database of genomes to predict resistance via lineage calling, using two key components: a

database containing genomic data and associated antibiograms, and a prediction pipeline. The

database contains a highly compressed lossless k-mer index, a representation of the tree

population structure, and metadata such as a phylogroup, serotype, sequence type and

resistance profiles (see ‘Resistance profiles’). The pipeline iterates over reads from the

nanopore sequencer and provides real-time predictions of phylogroup and resistance

(Figure 1).

Resistance profiles

For all antibiotics, RASE associates individual isolates with a resistance category, susceptible or

non-susceptible. First, MIC values are mined using regular expressions from the available

textual antibiograms, i.e., strings describing an interval of possible MIC values. Second, the

acquired intervals are compared to the antibiotic-specific breakpoints (Supplementary

Figure 3). If a given breakpoint is above or below the interval, susceptibility or non-

susceptibility is reported, respectively. However, no category can be assigned at this step if the

breakpoint lies within the extracted interval, an antibiogram is entirely missing, or an

antibiogram is present, but parsing failed. Third, missing categories are inferred using ancestral

state reconstruction on the associated phylogenetic tree while maximizing parsimony (i.e.,

minimizing the number of nodes switching its resistance category) breakpoints (Supplementary

Figure 4). When the solution is not unique, non-susceptibility is assigned.



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The RASE database was constructed with the standard EUCAST breakpoints23 ([g/ml]):

benzylpenicillin (PEN): 0.06, ceftriaxone (CRO): 0.25, trimethoprim-sulfamethoxazole (TMP):

1.00, Erythromycin (ERY): 0.25, and Tetracycline (TET): 1.00. While we have used the above

values in the present work, others may be readily defined and the database rapidly updated.

This is especially useful in the case where breakpoints may vary depending on the site of

infection (as is the case with pneumococcal meningitis and otitis media, where lower MICs are

considered to be resistant23).

K-mer-based matching

RASE uses the ProPhyle classifier21 (version 0.3.1.0) and its ProPhex component30 to identify the

most similar genomes in the database for every sequencing read. Its index stores k-mers of all

isolates’ assemblies in a highly compressed form, reducing the required memory footprint. The

database k-mers are first propagated along the phylogenetic tree and then greedily assembled

to contigs. The obtained contigs are then placed into a single text file, for which a BWT-index31

is constructed. The index can be searched for individual k-mers, retrieving a list of nodes whose

descending leaves correspond to isolates containing that k-mers.

In course of sequencing, every read is matched against the index and matches for all read’s k-

mers retrieved. These matches are then propagated to the level of leaves and isolates with the

highest number of shared k-mers identified.

Predicting resistance from phylogroups

All isolates in the database are associated with similarity weights that are set to zero at the start

of the run. Each time a new read is matched against the DB, the weights for the best match are

increased according to the read’s ‘information content’, calculated as the number of shared k-

mers between a genome and the read, divided by the number of best hits.



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Predictions are calculated based on the current state of the weights and the lineage or

phylogroup in which the best-matched isolate is found. First, a phylogroup is predicted as the

phylogroup of the best matching isolate. Then, a phylogroup score is calculated PGS=2f/(f+t)-1,

where f and t denote the scores of the best matches in the first (‘predicted’) and second best

(‘alternative’) phylogroup respectively. If PGS is higher than a specified threshold (0.6 in default

settings), the call is considered successful. If the score is lower than this, the read cannot be

securely assigned to a phylogroup, and this counts as a failure. Reads that do not match are not

used in subsequent analysis to predict resistance.

Resistance is predicted for individual antibiotics independently, using weights within the

predicted phylogroup. While certain phylogroups are certainly associated with susceptibility,

some others are not. For the latter, we propose the use of the susceptibility scores that

combine the resistance characteristics of the most similar strains in the RASE database. A

susceptibility score is calculated as SUS=s/(s+r), where s and r denote the score of the best

susceptible and non-susceptible strains within the predicted phylogroup. If SUS is greater than a

specified threshold (0.6 in default settings), susceptibility to the antibiotic is reported, non-

susceptibility otherwise. In most of cases, this algorithm predicts non-susceptibility or

susceptibility as the one of the best match. Nevertheless, when two genomes with different

resistance categories are of similar weights, non-susceptibility may be reported even though

the best match is susceptible.

To determine how RASE works with nanopore data generated in real time, the timestamps of

individual reads were first extracted and then used for sorting the base-called nanopore reads.

When the RASE pipeline was applied, the timestamps were used for expressing the predictions

as a function of time. The times of ProPhyle assignments were also compared to the original

timestamps to ensure that the prediction pipeline was not slower than sequencing.



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Optimizing k-mer length

First, the subword complexity function32 of pneumococcus was calculated using JellyFish33

(version 2.2.10) (Supplementary Figure 5). Then, based on the characteristics of the function

and technical limitations of ProPhyle, the possible range of k was determined as [17, 32]. For

these k-mer lengths, RASE indexes were constructed and their performance evaluated using the

RASE prediction pipeline and selected experiments. All these lengths k-mer lengths led to

similar predictions, but different prediction delays (Supplementary Figure 6). Based on the

obtained timing data, we set k to 18.

Lower time bounds on resistance gene detection

A complete genome assembly of the multidrug resistant SP02 isolate was computed from the

Nanopore reads using the CANU34 (version 1.5, with default parameters). Prior to the assembly

step, reads were filtered using SAMsift35 based on the matching quality with the RASE

database: only reads at least 1000bp long with at least 10% 18-mers shared with some of the

reference draft assemblies were used. The obtained assembly was further corrected by Pilon36

(version 1.2, default parameters) using Illumina reads from the same isolate (taxid ‘1QJAP’ in

the SPARC dataset17) mapped to the nanopore assembly using BWA-MEM37 (version 0.7.17,

with the default parameters) and sorted using SAMtools38.

The obtained assembly was searched for resistance-causing genes using the online CARD tool39

(as of 2018/08/01). All of the original nanopore reads were then mapped using Minimap240

(version 2.11, with ‘-x map-ont’) to the corrected assembly and resistance genes in the reads

identified using BEDtools–intersect41 (version 2.27.1, with ‘-F 95’). Timestamps of the

resistance-informative reads were extracted and associated with the genes. Only reads longer

than 2kbp were used in the analysis.



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Library preparation

For experiments SP01-SP06, cultures were grown in Todd–Hewitt medium with 0.5% yeast

extract (THY; Becton Dickinson and Company, Sparks, MD) at 37°C in 5% CO2 for 24 hrs. High

molecular weight (>1ug) genomic DNA was extracted and purified from cultures using DNeasy

Blood and Tissue kit (QIAGEN, Valencia CA). DNA concentration was measured using Qubit

fluorometer (Invitrogen, Grand Island NY). Library preparation was performed using the Oxford

Nanopore Technologies 1D ligation sequencing kit SQK LSK108.

For experiments SP07-SP12, library preparation was performed using the ONT Rapid Low-Input

Barcoding kit SQK-RLB001, with saponin-based host DNA depletion used for reducing the

proportion of human reads. More details can be found in the original manuscript2.

MinION sequencing

Sequencing was performed on the MinION MK1 device using R9.4/FLO-MIN106 flowcells,

according to the manufacturer’s instructions. For experiments SP01-SP06, base-calling was

performed using ONT Metrichor (versions 1.6.11 (SP01), 1.7.3 (SP02), 1.7.14 (SP03–SP06))

simultaneously with sequencing and all reads passing Metrichor quality check were used in the

further analysis. For experiments SP07-SP09, ONT MinKNOW software (versions 1.4-1.13.1) was

used to collect raw sequencing data and ONT Albacore (versions 1.2.2-2.1.10) was used for local

base-calling of the raw data after sequencing runs were completed.

Testing resistance phenotype

Additional retesting of SPARC isolates was done using microdilution. Organism suspensions

were prepared from overnight growth on blood agar plates to the density of a 0.5 McFarland

standard. This organism suspension was then diluted to provide a final inoculum of 105 to 106



https://doi.org/10.1101/403204


22

CFU/ml. Microdilution trays were prepared according to the NCCLS methodology with cation-

adjusted Mueller-Hinton broth (Sigma-Aldrich) supplemented with 5% lysed horse blood

(Hemostat Laboratories)42,43. Penicillin (TRC Canada) and chloramphenicol (USB) concentrations

ranged from 0.016 to 16 μg/ml. Erythromycin (Enzo Life Sciences), tetracycline (Sigma-Aldrich),

and trimethoprim-sulfamethoxazole (MP Biomedicals) concentrations ranged from 0.0625 to 64

μg/ml. Ceftriaxone (Sigma-Aldrich) concentrations ranged from 0.007 to 8 μg/ml. The

microdilution trays were incubated in ambient air at 35°C for 24 h. The MICs were then visually

read and breakpoints applied. A list of individual microdilution measurements and the obtained

resistance categories is provided in Supplementary Table 3.

Resistance of streptococcus in the metagenomic samples (SP07–SP12) was determined by agar

diffusion using the EUCAST methodology23. First, the inoculated agar plates were incubated at

37 °C overnight and then examined for growth with the potential for re-incubation up to 48

hours. Then, the samples were screened to oxacillin: if the zone diameter r was >20mm, the

isolate was considered sensitive to benzylpenicillin, otherwise a full MIC measurement to

benzylpenicillin was done. Finally, the isolate was screened for resistance to tetracycline

(r≥25mm for sensitive, r<22mm for resistant) and erythromycin (r≥22mm for sensitive, r<19mm

for resistant); when the isolate showed intermediate resistance, a full MIC measurement was

done.

Results for all tested samples – isolates and metagenomes – are summarized in Supplementary

Table 4.

Data, implementation and availability

RASE was developed using Python, GNU Make, GNU Parallel44, Snakemake45, and the ETE346

and PySam libraries. Bioconda47 was used to ensure reproducibility of the software



https://doi.org/10.1101/403204


23

environments. The code for constructing databases, together with the default RASE DB, is

available from http://github.com/c2-d2/rase-db; the RASE prediction pipeline is located at

http://github.com/c2-d2/rase-pipeline; and additional material to this paper can be found on

http://github.com/c2-d2/rase-supplement. All code is available under the MIT license.

Sequencing data for all experiments from this study can be downloaded from

http://doi.org/10.5281/zenodo.1405173; for the metagenomic experiments, only the filtered

datasets (i.e., after removing the remaining human reads in silico) were made publicly available.

Acknowledgements

This work was supported by the Bill & Melinda Gates Foundation (GCGH GCE OPP1151010, KB

and WPH), NIH – National Institute of Allergy and Infectious Diseases (R01 AI106786-05, KB),

the Canadian Institutes of Health Research (FRN 152448, RSL), and the Canadian Institutes for

Health Research (a fellowship grant, DRM). This paper presents independent research funded

by the National Institute for Health Research (NIHR) under its Programme Grants for Applied

Research Programme (Reference Number RP-PG-0514-20018, JOG), the UK Antimicrobial

Resistance Cross Council Initiative (MR/N013956/1, JOG), Rosetrees Trust (A749, JOG), the

University of East Anglia (JOG, TC), and Oxford Nanopore Technologies (JOG, TC). Portions of

this research were conducted on the O2 and Odyssey high performance compute clusters,

supported by the Research Computing Groups at Harvard Medical School and at the Harvard

Faculty of Arts and Sciences, respectively. The authors thank Joshua Metlay for providing the

test isolates for experiments SP03–SP06, which were collected as part of a population-wide

surveillance study done in the Philadelphia region, supported by NIH (R01 AI46645). The

authors also thank Yonatan H Grad, Brian J Arnold, Taj Azarian, and Cristina M Herren for useful

comments in various stages of this project.



https://doi.org/10.1101/403204


24

Transparency declarations

JOG received financial support for attending ONT and other conferences and an honorarium for

speaking at ONT headquarters. JOG received funding and consumable support from ONT for

TC’s PhD studentship.



https://doi.org/10.1101/403204


25

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Sample PG detected Serotype Antibiogram - PEN

Antibiogram - CRO

Antibiogram - TMP

Antibiogram - ERY

Antibiogram - TET

ST match

CC match

Actual Best match

Actual Best match

Actual Best match

Actual Best match

Actual Best match

Actual Best match

sp01 yes 11D 11D S S S S S S S S S(1) S(1) Yes Yes sp02 yes 19A 19A R R R R R R R R R(2) R(2) Yes Yes sp03 yes 23F 23F R R R R R R R S(3) S S OoD Yes sp04 yes 19A 19A R R R R R R R R R R(4) OoD Yes sp05 no (borderline) 19F 19F R R R R R R R R R R OoD Yes sp06 yes 23F 23F R R R R R R R S(3) S S OoD Yes

Sample PG detected

SP Antibiogram - PEN

Antibiogram - ERY

Antibiogram - TET

sp07 no 2.3% S S NA S R S(5) sp08 no|yes 2.5% S S(!) S S S S(6) sp09 no 4.0% S S NA S S S(7) sp10 yes 21% R R R R R R(8) sp11 yes 70% R R R R R R(8) sp12 yes 86% S S S S R S(5)

A)

Legend

S Susceptible S(!) Best match S, but high

risk of R reported R Non-susceptible NA Not available OoD Out-of-database (…) ID of a retested sample …|… Original | filtered sample

(when the results differ) SP Fraction of S. pneumoniae

reads

Correct prediction

Incorrect prediction

Cannot be evaluated

B)

Table 1: Predicted phenotypes for (A) isolates and (B) metagenomes. The table displays actual and predicted

resistance phenotypes (S = susceptible, R = non-susceptible) for individual experiments, as well as information

on match of the predicted sequence type and clonal complex. Resistance categories in bold were inferred using

ancestral reconstruction and were also confirmed using phenotypic testing (seeMethods andSupplementary Table

3).



https://doi.org/10.1101/403204


Match againstthe database

Read nextnanoporeread

Predict and report

Updateweights

Load the RASEdatabase

time

Predict strain and phylogroup

Best PG

2ndbest PG

f

t

PGS = 2f/(f+t)-1

Predict susceptibility

Non-susceptibleSusceptible

s

rSUS = s/(s+r)

3An

tibio

tics

Strains

Phylogroups

Susceptible

21

A B C D E F G H I J K L M N O P Q R

Non-susceptible

RASE database

Match to all strains

K-mers

Nanopore read

Similarityscores

A B C D E F GH I J K L MNO P QR




ProPhyle

Figure 1: Overview of the RASE approach. The RASE approach uses three components: the RASE database,

an approximate k-mer-based matching component based on ProPhyle, and a prediction component interpreting

the risk based on the resistance of strains of the assigned phylogroup. In the load step, the precomputed RASE

database is loaded into memory. The RASE pipeline iterates over reads streamed from the nanopore sequencer.

Each read is matched against the database using ProPhyle. Retrieved assignments are propagated to the leaves

and similarity scores computed. These are used to identify best-matching strains (possibly many) and to update

weights associated with these strains. Indeed, a single read is rarely specific, it typically matches equally scored

multiple nodes. The best phylogroup is identified and a phylogroup score calculated (PGS). Based on the resistance

profiles of strains in this phylogroup, susceptibility to each of the antibiotics is predicted from the best match and

reported together with a susceptibility score quantifying the risk of resistance.



https://doi.org/10.1101/403204


TETERYTMPCROPEN

0.00

0.01

0.02

0.03

0.04

0.05

Rel

ative

sim

ilarit

y to

sam

ple

R34−3203 263137 R34−3080 E4NSI NKLJL J1LX6 9ABKR R34−3149 R34−3071 F1ODP RQ6NH 303656 R34−3102 F7IXH 386329 5J97G STPDE R34−3170 3YFA4 459747 QHXZZ R34−3095 R52DJ 6ZCAZ DFS2Q 28I7H XCJ97 R34−3208 242588 389109 R34−3136 F1G5R RAV85 6CO3C R34−3228 FP9DM H3Q9M 8G3XE R34−3214 R34−3054 8AH8E HWJVH 335574 K9N0O R34−3156 T14BF R34−3173 7NA98 OZJ9F ZPN7P KYDZM R34−3147 074124 R34−3043 102720 R34−3052 R34−3081 R34−3155 X703J 302649 187406 R34−3121 132571 14YE5 R34−3118 R34−3151 R34−3181 R34−3031 R34−3075 144370

Isolate

Reads: 263 Bps: 491,264 Useful bps: 18%

Susceptibilitynon−susceptiblesusceptible

Phylogroup210Others

2017−06−06 17:17:17

TETERYTMPCROPEN

0.00

0.01

0.02

0.03

0.04

Rel

ative

sim

ilarit

y to

sam

ple

263137 R34−3203 E4NSI K9N0O 5J97G R34−3080 QHXZZ 242588 F1ODP 6ZCAZ R34−3208 R34−3149 9ABKR FP9DM 459747 J1LX6 RQ6NH H3Q9M F7IXH 8G3XE 28I7H F1G5R 3YFA4 HWJVH R34−3170 8AH8E RAV85 R34−3156 335574 R34−3095 DFS2Q XCJ97 R52DJ R34−3228 6CO3C R34−3054 303656 R34−3214 386329 R34−3081 R34−3071 389109 T14BF R34−3102 R34−3155 R34−3052 R34−3147 KYDZM 074124 102720 3XUC4 ZPN7P OZJ9F R34−3121 R34−3173 NKLJL 187406 ODNWT STPDE R34−3206 GJ6MQ 436915 G2Z0B R34−3136 R34−3196 SHTPB DALR8 103453 8QTW4 R34−3118

Isolate

Reads: 1,270 Bps: 2,267,820 Useful bps: 19%


Phylogroup216Others

2017−06−06 17:21:17

TETERYTMPCROPEN

0.00

0.01

0.02

0.03

0.04

0.05

0.06

Rel

ative

sim

ilarit

y to

sam

ple

E4NSI 459747 H3Q9M R34−3203 QHXZZ R34−3095 R34−3149 389109 FP9DM 8AH8E T14BF XCJ97 3YFA4 6CO3C R34−3208 RAV85 F1ODP 8G3XE R34−3214 F7IXH R34−3054 28I7H R52DJ 6ZCAZ J1LX6 263137 HWJVH R34−3071 5J97G 335574 F1G5R RQ6NH K9N0O R34−3228 DFS2Q 9ABKR R34−3080 242588 R34−3156 R34−3170 R34−3081 KYDZM R34−3155 R34−3052 102720 386329 R34−3147 074124 R34−3102 187406 303656 R34−3121 R34−3118 OZJ9F R34−3173 ZPN7P VODW3 NKLJL SLXY1 8QTW4 110093 119571 R34−3176 18FDW R34−3199 BL0SN 397079 3XUC4 HB7S5 W5HGR

Isolate



Phylogroup216Others

2017−06−07 08:58:17

0

100

200

300

400

#rea

ds (t

hous

ands

) Predicted PG stabilizedAlternative PG stabilizedIsolate stabilized

0.0

0.2

0.4

0.6

0.8

1.0

PG s

core

fail

pass

0.0

0.2

0.4

0.6

0.8

1.0

PEN

sus

c sc

ore

non−

susc

susc

0.0

0.2

0.4

0.6

0.8

1.0

CRO

sus

c sc

ore

non−

susc

susc

0.0

0.2

0.4

0.6

0.8

1.0

TMP

susc

sco

re

non−

susc

susc

0.0

0.2

0.4

0.6

0.8

1.0

ERY

susc

sco

re

non−

susc

susc

0.0

0.2

0.4

0.6

0.8

1.0

TET

susc

sco

re

non−

susc

susc

0 5 10 15minutes

14.0 15.5hours

b) t=1

min

c) t=5

min

d) t=1

5.5h

...

...

...

...

...

...

...

b

a

c

d

Figure 2: Timeline and rank plots for an isolate. a) Number of reads, phylogroup score, and susceptibility scores

for individual antibiotics as a function of time from the start of sequencing. The point markers depict the times of

stabilization for the predicted phylogroup, the alternative phylogroup and the most similar isolate, respectively. b)-d)

Similarity rank plots for selected time points (1 minute, 5 minutes, and the end of sequencing). The bars correspond

to 70 best matching isolates in the database and display the predicted level of sample-to-strain relative similarity (i.e.,

normalized weights). They are arranged by rank and colored according to the presence in the predicted, alternative

or another phylogroup. The bottom panels display the susceptibility profiles of the isolates.



https://doi.org/10.1101/403204


TETERYTMPCROPEN

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.10

Rel

ative

sim

ilarit

y to

sam

ple

8QTW4 R34−3064 R34−3199 HB7S5 R34−3126 R34−3179 XVMDP Z4952 R34−3130 XCB66 R34−3040 SLXY1 CSI7H R34−3225 R34−3226 IB8MW R34−3036 R34−3150 UJECB KRHYA R34−3138 W5HGR R34−3077 R34−3158 T8K26 R34−3016 BA5TC 333793 M66VO 28PCJ FU8OL 397079 STPDE 5Z52R P0N8K LOOXY R34−3197 Z60YP 044744 103453 323485 AFVC5 160986 R34−3098 R34−3075 R34−3189 R34−3026 W9GKO 187406 R34−3121 277394 5MZ1D NJ41D R34−3109 132571 14YE5 R34−3198 304793 F5UY4 0NWX9 NIPQY R34−3081 R34−3088 072782 217475 483391 R34−3160 R34−3212 303656 R34−3102

Isolate

Reads: 370 Bps: 867,246 Useful bps: 6%


Phylogroup35Others

2017−05−26 17:30:52

TETERYTMPCROPEN

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

Rel

ative

sim

ilarit

y to

sam

ple

8QTW4 HB7S5 IB8MW XVMDP R34−3199 R34−3064 XCB66 Z4952 R34−3138 W5HGR T8K26 R34−3126 R34−3225 R34−3179 R34−3150 R34−3226 KRHYA UJECB R34−3130 CSI7H R34−3036 SLXY1 R34−3040 R34−3158 R34−3077 R34−3088 18FDW 9LUM5 018044 397079 277394 2W7ME 34YLE WG60C 28PCJ R34−3026 F5UY4 R34−3170 R34−3027 044744 333793 160986 304793 342672 R34−3108 VODW3 R34−3183 403790 VPPXV 255210 CM917 218229 334847 173015 388483 1TR6C R34−3023 R34−3029 427937 R34−3016 STPDE GJ6MQ 436915 R34−3037 KYDZM 6GU7V N5O68 7NA98 AFVC5 R34−3098

Isolate



Phylogroup312Others

2017−05−26 17:34:52

TETERYTMPCROPEN

0.00

0.01

0.02

0.03

0.04

0.05

0.06

Rel

ative

sim

ilarit

y to

sam

ple

8QTW4 T8K26 HB7S5 SLXY1 R34−3199 XCB66 W5HGR CSI7H UJECB XVMDP Z4952 R34−3138 IB8MW KRHYA R34−3158 R34−3225 R34−3179 R34−3040 R34−3130 R34−3126 R34−3226 R34−3064 R34−3036 R34−3150 R34−3077 R34−3088 WG60C 34YLE 18FDW 277394 403790 397079 255210 R34−3023 160986 R34−3026 R34−3108 R34−3136 VODW3 R34−3194 044744 R34−3183 388483 F5UY4 304793 R34−3021 28PCJ M66VO 2W7ME 342672 R34−3029 R34−3012 110093 R34−3176 018044 R34−3037 H3Q9M R34−3027 R34−3028 427937 436154 333793 334847 218229 R34−3013 173015 103453 9LUM5 R34−3014 1LQKE

Isolate



Phylogroup312Others

2017−05−27 11:01:52

010203040506070

#rea

ds (t

hous

ands

) Predicted PG stabilizedAlternative PG stabilizedIsolate stabilized

0.0

0.2

0.4

0.6

0.8

1.0

PG s

core

fail

pass

0.0

0.2

0.4

0.6

0.8

1.0

PEN

sus

c sc

ore

non−

susc

susc

0.0

0.2

0.4

0.6

0.8

1.0

CRO

sus

c sc

ore

non−

susc

susc

0.0

0.2

0.4

0.6

0.8

1.0

TMP

susc

sco

re

non−

susc

susc

0.0

0.2

0.4

0.6

0.8

1.0

ERY

susc

sco

re

non−

susc

susc

0.0

0.2

0.4

0.6

0.8

1.0

TET

susc

sco

re

non−

susc

susc

0 5 10 15minutes

13.5 15.0hours

b) t=1

min

c) t=5

min

d) t=1

7.5h

...

...

...

...

...

...

...

b

a

c

d

Figure 3: Timeline and rank plots for a metagenome. The figure is of the same format as Figure 2.



https://doi.org/10.1101/403204


seq−fa seq−fagz ind−mem ind−transm

Size of the RASE database

Siz

e [M

B]

050

010

0015

00

1380

384320

47

Supplementary Figure 1: Size and memory footprint of the RASE database and index. The graph compares

the size of the ProPhyle RASE index to the size of the original sequences: original draft assemblies (seq−fa), original

draft assemblies compressed using gzip (seq−fagz), memory footprint of ProPhyle with the RASE index (ind−mem),

and size of the ProPhyle RASE index compressed for transmission (ind−transm).



https://doi.org/10.1101/403204


0 10 20 30 40 50 60

05

1015

20

Time (minutes)

# ge

ne o

ccur

renc

es

tetmelmefAermB

Supplementary Figure 2: Timeline of resistance genes. Number of occurrences of individual resistance genes

in reads of SP02, as a function of time for the first hour of nanopore sequencing.



https://doi.org/10.1101/403204


100 200 300 400 500 600

−8

−6

−4

−2

02

46

Isolate

log

2 P

EN

_M

IC

PG 8 PG 16 PG 7 PG 16 PG 15 PG 16 PG 11 PG 16 PG 6 PG 16 PG 3 PG 16 PG 2 PG 14 PG 16 PG 4 PG 9 PG 12 PG 16 PG 1 PG 16 PG 10 PG 5 PG 13 PG 16 PG 8

non−susc.

susceptible

inferred non−susc.

inferred susc.

R3

4−

30

63

36

51

52

R3

4−

31

88

R3

4−

30

34

PC

KM

OIM

AP

Y5

09

36

5E

SB

KM

PN

3IH

8P

Y5

X4

92

70

6K

51

SW

TG

9A

8A

YF

PY

IRE

4I

P4

GIC

R3

4−

31

75

09

53

44

YQ

UA

QR

34

−3

07

9R

34

−3

12

4F

UO

YQ

08

01

04

2E

AK

R3

66

29

3W

LTN

82

10

16

21

48

05

6D

PG

ZI

31

20

34

R3

4−

31

03

05

71

44

RC

BG

R0

OH

1I

AT

BL

M6

EO

69

RM

EO

XR

L3

6E

6U

UP

J6

JS

Y8

R3

4−

30

68

RE

QG

JV

QC

7K

RZ

PN

F1

35

77

11

60

44

91

V4

ME

R3

4−

31

16

Z6

0Y

PL

OO

XY

R3

4−

31

85

WN

YE

LK

MM

CT

R3

4−

32

18

21

81

86

R3

4−

30

73 2

66

03

5R

34

−3

19

7B

HC

QB

6F

XS

QR

34

−3

04

9R

34

−3

11

0R

34

−3

06

1IZ

QW

IL

IW2

81

03

45

3R

34

−3

12

2R

34

−3

22

3R

34

−3

02

31

10

09

3W

9G

4K

2V

BT

KR

34

−3

10

8R

34

−3

11

3Q

IKY

SQ

04

13

TJR

UQ

DA

LR

80

27

99

9R

34

−3

04

3R

34

−3

07

04

39

69

9S

QA

DW

27

73

94

R3

4−

31

93 R

34

−3

22

4R

34

−3

13

7R

34

−3

02

82

09

46

42

84

96

8R

34

−3

19

8G

WT

K7

R3

4−

30

56

E3

GX

Y 3X

UC

4R

34

−3

16

3B

4R

32

D8

RP

Y5

5N

O4

R3

4−

32

19

UF

G1

AY

89

K0

7R

CF

0 3B

DE

MR

JB

FJ

30

47

93

F5

UY

4R

34

−3

08

4R

34

−3

21

7R

34

−3

10

10

72

51

1R

34

−3

20

2P

21

3M

06

56

45

R3

4−

31

90

LD

D8

7R

34

−3

07

83

69

82

06

GU

7V

R3

4−

31

41

R3

4−

31

42

R3

4−

30

85

41

61

85

50

35

74

9S

KO

TC

HP

0M

LA

BT

O0

6U

RQ

NF

PT

SD

H8

X5

−1 1

QJA

PO

QT

RJ

R3

4−

32

21

R3

4−

32

07

10

55

42

R3

4−

30

94

R3

4−

32

00

R3

4−

30

98

40

44

63

20

88

66

20

99

30

R3

4−

30

58

WM

K3

TR

34

−3

02

6 04

47

44

R3

4−

30

37

06

70

94

R3

4−

30

25

4K

4C

9R

34

−3

09

78

1L

MX

O0

RH

BO

61

U7

0U

64

IB

1K

MB

1V

DX

86

89

3Z

R3

4−

32

29

CC

V1

HL

S3

OB

4P

YM

00

NW

X9

R3

4−

30

87 R

34

−3

08

3U

TE

DZ

R3

4−

31

64

R3

4−

31

39

AF

VC

5F

77

ZH

QW

SZ

TR

34

−3

13

1R

34

−3

20

4W

AM

FH

R3

4−

32

27

BZ

2I7 R

34

−3

17

2P

1N

M2

R3

4−

30

74

T8

Z8

OO

8I1

E0

07

64

9R

34

−3

16

5W

9G

KO JB

YF

YR

34

−3

04

4 RE

AO

U3

79

67

8R

34

−3

11

9R

34

−3

06

9R

S9

D2

9D

25

HW

8IH

X3

4Y

LE

WG

60

CR

34

−3

04

84

38

18

0R

34

−3

11

5N

A0

3L

DR

1P

D 6JT

2I

R3

4−

30

86

14

YE

51

32

57

1U

3E

O1

R3

4−

31

36

17

47

02

R3

4−

31

94

48

16

43

1T

R6

C1

53

43

8U

B6

XH

R3

4−

31

86

48

78

27

22

38

32

01

ZW

MB

JF

60

HR

2T

30

FQ

8K

H8

YK

WA

12

WS

R3

4−

30

47

R3

4−

32

09

0G

B6

KR

34

−3

12

32

8P

CJ

O3

45

21

LQ

KE

5X

CD

5U

NU

OJ

DN

B9

0O

SE

Y6

TV

66

E3

97

07

9 18

FD

W8

QT

W4

XV

MD

PZ

49

52

R3

4−

31

79

HB

7S

5R

34

−3

19

9C

SI7

HR

34

−3

04

0R

34

−3

15

0R

34

−3

03

6IB

8M

WR

34

−3

07

7X

CB

66

SL

XY

1R

34

−3

12

6R

34

−3

13

0R

34

−3

15

8R

34

−3

06

4 R3

4−

31

38

KR

HY

AW

5H

GR

UJE

CB

T8

K2

6R

34

−3

22

5R

34

−3

22

6R

34

−3

10

23

03

65

6Z

PN

7P

OZ

J9

FR

34

−3

17

3R

34

−3

12

11

87

40

6R

34

−3

14

9R

34

−3

17

0H

3Q

9M

10

27

20

R3

4−

30

52

R3

4−

30

81

KY

DZ

M0

74

12

43

86

32

9R

34

−3

14

7R

34

−3

15

5R

34

−3

07

1R

34

−3

20

3R

34

−3

20

82

8I7

HF

1O

DP

F1

G5

RE

4N

SI

45

97

47

QH

XZ

ZR

34

−3

21

4T

14

BF

R3

4−

30

54

RA

V8

56

CO

3C

8G

3X

E8

AH

8E

33

55

74

J1

LX

6D

FS

2Q

R5

2D

J3

YFA

42

63

13

7F

P9

DM

XC

J9

72

42

58

8H

WJV

HR

34

−3

09

5 38

91

09

6Z

CA

ZR

34

−3

15

69

AB

KR

RQ

6N

HR

34

−3

22

8R

34

−3

08

0F

7IX

H5

J9

7G

K9

N0

O3

42

67

2C

XQ

2T

R3

4−

32

01

06

54

35

17

97

43

10

84

80

05

17

15

44

86

43

R3

4−

31

71 R

34

−3

16

8A

I3G

Z2

RL

C4

R3

4−

31

76

43

07

72

34

13

65

R3

4−

32

31

R3

4−

32

30

26

86

60

30

64

35

14

25

34

R3

4−

30

12

R3

4−

30

90

R3

4−

30

82

R3

4−

31

87

FJZ

ZW

38

53

85

FB

PN

SR

WZ

JE

16

32

86

12

05

81

R3

4−

31

67

DQ

KY

E0

35

10

0Q

3H

1U

12

0T

71

53

40

8R

34

−3

01

7 R3

4−

31

43

R3

4−

30

91

I0U

ZQ

R3

4−

31

89

ZX

PK

HR

34

−3

17

7C

JIS

L 94

OR

HY

01

60

9T

42

WR

34

−3

05

1R

34

−3

05

72

AH

BE

ON

38

ST

EF

7S

R3

4−

30

50

DP

07

VR

34

−3

07

64

62

95

60

25

31

02

03

69

22

39

43

4R

34

−3

02

9F

OR

95

T7

IZ1

8A

4V

RQ

RP

PW

R3

4−

30

13

15

16

84

42

79

37

R3

4−

31

81

C3

W4

1R

34

−3

02

27

LL

CL

BD

GW

M0

4W

B3

LF

B1

5W

CU

LS

DY

JG

LR

34

−3

19

5R

34

−3

11

4R

34

−3

16

92

48

73

4R

34

−3

01

9R

34

−3

02

03

MB

4Z

WY

MQ

IL

IT4

QJ9

GM

MR

34

−3

12

7R

34

−3

05

5R

34

−3

17

4R

34

−3

06

7Y

HP

D0

27

64

30

36

55

02

04

94

70

35

45

57

15

43

89

5Z

S1

X4

03

79

0R

34

−3

08

82

55

21

0W

VC

E6

R3

4−

30

32

R3

4−

30

39

R3

4−

30

15

68

G2

D4

62

74

65

08

90

7E

KR

1O

P0

N8

KR

34

−3

14

6K

81

21

J2

9X

81

44

37

0R

34

−3

04

6 14

75

87

R3

4−

30

65

HU

4A

A8

PN

F1

2W

7M

EM

66

VO

X7

03

J SH

TP

BR

D7

KX

BA

5T

C2

Y1

CQ

R3

4−

30

62

00

19

96

R3

4−

31

66

33

68

50

06

20

31

R3

4−

30

75 4

72

69

90

89

84

80

42

39

7C

V6

JA

13

56

31

38

37

39

R3

4−

32

22

R3

4−

30

18

R3

4−

30

35 4

46

37

61

18

03

9R

34

−3

22

02

99

80

1P

KM

M4

27

93

88

N6

I98

00

13

34

R3

4−

30

24

R3

4−

30

21

R3

4−

31

57

G2

Z0

BN

5O

68

5Z

52

RR

34

−3

18

4R

N3

X8

R3

4−

31

82

R3

4−

31

11

RU

J9

0 29

OR

IQ

7F

26 UV

VW

AB

L0

SN

7C

7G

1R

34

−3

19

1 R3

4−

32

13

4H

UA

7 B5

0V

6N

IPQ

YR

34

−3

11

8 12

75

30

R3

4−

32

16

47

39

37

0C

NY

CR

34

−3

17

84

D0

HZ

R3

4−

31

12

R3

4−

31

20

MF

II0

X7

1H

5 Y6

BS

G6

U8

ZJ

3U

TO

8F

RIZ

42

6IE

T3

GW

V1

R3

4−

30

59

04

02

41

R3

4−

31

96

7N

A9

83

33

79

3Y

SO

FP

R3

4−

30

33

N8

YA

4E

0Z

KK

VO

DW

31

19

57

1N

B8

E4

R3

4−

30

72

PH

Y0

3Q

D0

ZV

8C

RC

EV

5V

F1

29

18

80

6P

U3

9R

34

−3

08

9R

34

−3

09

23

JK

7Y

R3

4−

31

25

05

7G

3R

34

−3

04

1Q

U2

WM

LY2

88

OD

NW

T4

XIL

AQ

QK

9Y

NK

LJL

09

32

09

R3

4−

30

60

R3

4−

30

53

SE

OT

TP

WG

X0

ST

PD

EN

J4

1D

3F

BX

JR

34

−3

10

95

MZ

1D

R3

4−

30

93

F8

P2

5R

34

−3

21

5X

QR

0B

VP

PX

V2

34

32

33

12

94

2R

34

−3

04

54

22

26

4R

34

−3

01

65

W9

08

04

28

61

R3

4−

31

80

10

10

58

R3

4−

31

00

CM

91

7F

U8

OL

R3

4−

30

27

16

66

37

R3

4−

30

99

42

08

81

R3

4−

32

11 R

34

−3

21

00

15

44

50

TN

5H

R3

4−

30

96

R3

4−

31

51 3

59

37

01

81

27

21

60

98

64

36

15

40

18

04

43

88

48

3R

34

−3

03

83

02

64

93

23

48

51

73

01

52

18

22

93

34

84

74

36

91

5G

J6

MQ

9L

UM

5R

34

−3

18

3R

34

−3

21

2R

34

−3

16

04

83

39

10

72

78

2R

34

−3

19

22

17

47

5D

H8

X5

−2

XZ

HZ

PR

34

−3

03

1 R3

4−

31

40

WQ

9G

VR

34

−3

15

9F

QP

LS

PX

B8

VR

34

−3

20

6R

34

−3

20

53

62

41

23

72

29

71

46

06

6R

34

−3

14

4 R3

4−

30

14

EH

2W

NR

34

−3

01

1Q

LC

SW

IZC

EG

LJ6

JL

WW

C1

TR

34

−3

15

3M

6A

JV

5Q

BR

PR

34

−3

15

4R

34

−3

03

0

100 200 300 400 500 600

−6

−4

−2

02

46

Isolate

log

2 C

RO

_M

IC


non−susc.

susceptible


inferred susc.

R3

4−

30

63

36

51

52

R3

4−

31

88

R3

4−

30

34

PC

KM

OIM

AP

Y5

09

36

5E

SB

KM PN

3IH

8P

Y5

X4

92

70

6 K5

1S

WT

G9

A8

AY

FP

YIR

E4

IP

4G

ICR

34

−3

17

50

95

34

4Y

QU

AQ

R3

4−

30

79

R3

4−

31

24

FU

OY

Q0

80

10

4 2E

AK

R3

66

29

3W

LTN

82

10

16

21

48

05

6D

PG

ZI

31

20

34

R3

4−

31

03

05

71

44

RC

BG

R0

OH

1I

AT

BL

M 6E

O6

9R

ME

OX

RL

36

E6

UU

PJ

6JS

Y8

R3

4−

30

68

RE

QG

JV

QC

7K

RZ

PN

F1

35

77

11

60

44

9 1V

4M

ER

34

−3

11

6Z

60

YP

LO

OX

YR

34

−3

18

5W

NY

EL

KM

MC

TR

34

−3

21

82

18

18

6R

34

−3

07

32

66

03

5R

34

−3

19

7B

HC

QB

6F

XS

QR

34

−3

04

9R

34

−3

11

0R

34

−3

06

1IZ

QW

IL

IW2

81

03

45

3R

34

−3

12

2R

34

−3

22

3R

34

−3

02

31

10

09

3W

9G

4K

2V

BT

KR

34

−3

10

8R

34

−3

11

3Q

IKY

SQ

04

13

TJR

UQ

DA

LR

80

27

99

9R

34

−3

04

3R

34

−3

07

04

39

69

9S

QA

DW

27

73

94

R3

4−

31

93

R3

4−

32

24

R3

4−

31

37

R3

4−

30

28

20

94

64

28

49

68

R3

4−

31

98

GW

TK

7R

34

−3

05

6E

3G

XY 3X

UC

4R

34

−3

16

3B

4R

32

D8

RP

Y5

5N

O4

R3

4−

32

19

UF

G1

AY

89

K0

7R

CF

03

BD

EM

RJB

FJ

30

47

93

F5

UY

4R

34

−3

08

4R

34

−3

21

7R

34

−3

10

10

72

51

1R

34

−3

20

2P

21

3M

06

56

45

R3

4−

31

90

LD

D8

7R

34

−3

07

83

69

82

06

GU

7V

R3

4−

31

41

R3

4−

31

42

R3

4−

30

85

41

61

85

50

35

74

9S

KO

TC

HP

0M

LA

BT

O0

6U

RQ

NF

PT

SD

H8

X5

−1

1Q

JA

PO

QT

RJ

R3

4−

32

21

R3

4−

32

07

10

55

42

R3

4−

30

94

R3

4−

32

00

R3

4−

30

98

40

44

63

20

88

66

20

99

30

R3

4−

30

58

WM

K3

TR

34

−3

02

60

44

74

4R

34

−3

03

70

67

09

4R

34

−3

02

54

K4

C9

R3

4−

30

97 8

1L

MX

O0

RH

BO

61

U7

0U

64

IB

1K

MB

1V

DX

86

89

3Z

R3

4−

32

29

CC

V1

HL

S3

OB

4P

YM

00

NW

X9

R3

4−

30

87 R

34

−3

08

3U

TE

DZ

R3

4−

31

64

R3

4−

31

39

AF

VC

5F

77

ZH

QW

SZ

TR

34

−3

13

1R

34

−3

20

4W

AM

FH

R3

4−

32

27

BZ

2I7

R3

4−

31

72

P1

NM

2R

34

−3

07

4 T8

Z8

OO

8I1

E0

07

64

9R

34

−3

16

5W

9G

KO

JB

YF

YR

34

−3

04

4R

EA

OU

37

96

78

R3

4−

31

19

R3

4−

30

69 R

S9

D2

9D

25

HW

8IH

X3

4Y

LE

WG

60

CR

34

−3

04

84

38

18

0R

34

−3

11

5N

A0

3L

DR

1P

D 6JT

2I

R3

4−

30

86

14

YE

51

32

57

1U

3E

O1

R3

4−

31

36

17

47

02

R3

4−

31

94

48

16

43

1T

R6

C1

53

43

8U

B6

XH

R3

4−

31

86

48

78

27

22

38

32

01

ZW

MB

JF

60 H

R2

T3

0F

Q8

KH

8Y

KW

A1

2W

SR

34

−3

04

7R

34

−3

20

90

GB

6K

R3

4−

31

23

28

PC

JO

34

52

1L

QK

E5

XC

D5

UN

UO

JD

NB

90

OS

EY

6T

V6

6E

39

70

79

18

FD

W8

QT

W4

XV

MD

PZ

49

52

R3

4−

31

79

HB

7S

5R

34

−3

19

9C

SI7

HR

34

−3

04

0R

34

−3

15

0R

34

−3

03

6IB

8M

WR

34

−3

07

7X

CB

66

SL

XY

1R

34

−3

12

6R

34

−3

13

0R

34

−3

15

8R

34

−3

06

4R

34

−3

13

8K

RH

YA

W5

HG

RU

JE

CB T

8K

26

R3

4−

32

25

R3

4−

32

26

R3

4−

31

02

30

36

56

ZP

N7

PO

ZJ9

FR

34

−3

17

3R

34

−3

12

11

87

40

6R

34

−3

14

9R

34

−3

17

0H

3Q

9M

10

27

20

R3

4−

30

52

R3

4−

30

81

KY

DZ

M0

74

12

43

86

32

9R

34

−3

14

7R

34

−3

15

5R

34

−3

07

1R

34

−3

20

3R

34

−3

20

82

8I7

HF

1O

DP

F1

G5

RE

4N

SI

45

97

47

QH

XZ

ZR

34

−3

21

4T

14

BF

R3

4−

30

54

RA

V8

56

CO

3C

8G

3X

E8

AH

8E

33

55

74

J1

LX

6D

FS

2Q

R5

2D

J3

YFA

42

63

13

7F

P9

DM

XC

J9

72

42

58

8H

WJV

HR

34

−3

09

53

89

10

96

ZC

AZ

R3

4−

31

56

9A

BK

RR

Q6

NH

R3

4−

32

28

R3

4−

30

80

F7

IXH

5J9

7G

K9

N0

O3

42

67

2C

XQ

2T

R3

4−

32

01

06

54

35

17

97

43

10

84

80

05

17

15

44

86

43

R3

4−

31

71

R3

4−

31

68

AI3

GZ

2R

LC

4R

34

−3

17

64

30

77

23

41

36

5R

34

−3

23

1R

34

−3

23

02

68

66

03

06

43

51

42

53

4R

34

−3

01

2R

34

−3

09

0R

34

−3

08

2R

34

−3

18

7F

JZ

ZW

38

53

85

FB

PN

SR

WZ

JE

16

32

86

12

05

81

R3

4−

31

67

DQ

KY

E0

35

10

0Q

3H

1U

12

0T

71

53

40

8R

34

−3

01

7R

34

−3

14

3R

34

−3

09

1I0

UZ

QR

34

−3

18

9Z

XP

KH

R3

4−

31

77

CJIS

L9

4O

RH Y0

16

09

T4

2W

R3

4−

30

51

R3

4−

30

57

2A

HB

EO

N3

8S

TE

F7

SR

34

−3

05

0D

P0

7V

R3

4−

30

76

46

29

56

02

53

10

20

36

92

23

94

34

R3

4−

30

29

FO

R9

5T

7IZ

18

A4

VR

QR

PP

WR

34

−3

01

31

51

68

44

27

93

7R

34

−3

18

1C

3W

41

R3

4−

30

22

7L

LC

LB

DG

WM

04

WB

3L

FB

15

WC

UL

SD

YJG

LR

34

−3

19

5R

34

−3

11

4R

34

−3

16

92

48

73

4R

34

−3

01

9R

34

−3

02

03

MB

4Z

WY

MQ

IL

IT4

QJ9

GM

MR

34

−3

12

7R

34

−3

05

5R

34

−3

17

4R

34

−3

06

7Y

HP

D0

27

64

30

36

55

02

04

94

70

35

45

57

15

43

89

5Z

S1

X4

03

79

0R

34

−3

08

82

55

21

0W

VC

E6

R3

4−

30

32

R3

4−

30

39

R3

4−

30

15

68

G2

D4

62

74

65

08

90

7E

KR

1O

P0

N8

KR

34

−3

14

6K

81

21

J2

9X

81

44

37

0R

34

−3

04

61

47

58

7R

34

−3

06

5H

U4

AA 8P

NF

12

W7

ME

M6

6V

OX

70

3J S

HT

PB

RD

7K

XB

A5

TC

2Y

1C

QR

34

−3

06

20

01

99

6R

34

−3

16

63

36

85

00

62

03

1R

34

−3

07

54

72

69

90

89

84

80

42

39

7 CV

6JA

13

56

31

38

37

39

R3

4−

32

22

R3

4−

30

18

R3

4−

30

35

44

63

76

11

80

39

R3

4−

32

20

29

98

01

PK

MM

42

79

38

8N

6I9

8 00

13

34

R3

4−

30

24

R3

4−

30

21

R3

4−

31

57

G2

Z0

BN

5O

68

5Z

52

RR

34

−3

18

4R

N3

X8

R3

4−

31

82

R3

4−

31

11

RU

J9

0 29

OR

IQ

7F

26

UV

VW

AB

L0

SN 7C

7G

1R

34

−3

19

1R

34

−3

21

34

HU

A7

B5

0V

6N

IPQ

YR

34

−3

11

81

27

53

0R

34

−3

21

64

73

93

70

CN

YC

R3

4−

31

78

4D

0H

ZR

34

−3

11

2R

34

−3

12

0M

FII

0 X7

1H

5Y

6B

SG

6U

8Z

J3

UT

O8

FR

IZ4

26

IET

3G

WV

1R

34

−3

05

90

40

24

1R

34

−3

19

67

NA

98

33

37

93

YS

OF

PR

34

−3

03

3N

8Y

A4

E0

ZK

KV

OD

W3

11

95

71

NB

8E

4R

34

−3

07

2P

HY

03

QD

0Z

V8

CR

CE

V5

VF

12

91

88

06

PU

39

R3

4−

30

89

R3

4−

30

92

3JK

7Y

R3

4−

31

25

05

7G

3R

34

−3

04

1Q

U2

WM

LY2

88

OD

NW

T4

XIL

AQ

QK

9Y

NK

LJL

09

32

09

R3

4−

30

60

R3

4−

30

53

SE

OT

TP

WG

X0

ST

PD

EN

J4

1D 3F

BX

JR

34

−3

10

95

MZ

1D

R3

4−

30

93

F8

P2

5R

34

−3

21

5X

QR

0B

VP

PX

V2

34

32

33

12

94

2R

34

−3

04

54

22

26

4R

34

−3

01

65

W9

08

04

28

61

R3

4−

31

80

10

10

58

R3

4−

31

00

CM

91

7F

U8

OL

R3

4−

30

27

16

66

37

R3

4−

30

99

42

08

81

R3

4−

32

11

R3

4−

32

10

01

54

45

0T

N5

HR

34

−3

09

6R

34

−3

15

13

59

37

01

81

27

21

60

98

64

36

15

40

18

04

43

88

48

3R

34

−3

03

83

02

64

93

23

48

51

73

01

52

18

22

93

34

84

74

36

91

5G

J6

MQ

9L

UM

5R

34

−3

18

3R

34

−3

21

2R

34

−3

16

04

83

39

10

72

78

2R

34

−3

19

22

17

47

5 DH

8X

5−

2X

ZH

ZP

R3

4−

30

31

R3

4−

31

40

WQ

9G

VR

34

−3

15

9F

QP

LS

PX

B8

VR

34

−3

20

6R

34

−3

20

53

62

41

23

72

29

71

46

06

6R

34

−3

14

4R

34

−3

01

4E

H2

WN

R3

4−

30

11

QL

CS

WIZ

CE

GL

J6

JL

WW

C1

TR

34

−3

15

3M

6A

JV 5Q

BR

PR

34

−3

15

4R

34

−3

03

0

100 200 300 400 500 600

−4

−2

02

46

Isolate

log

2 T

MP

_M

IC


non−susc.

susceptible


inferred susc.

R3

4−

30

63

36

51

52

R3

4−

31

88

R3

4−

30

34

PC

KM

OIM

AP

Y5

09

36

5E

SB

KM

PN

3IH

8P

Y5

X4

92

70

6 K5

1S

WT

G9

A8

AY

FP

YIR

E4

IP

4G

ICR

34

−3

17

50

95

34

4 YQ

UA

QR

34

−3

07

9R

34

−3

12

4F

UO

YQ

08

01

04

2E

AK

R3

66

29

3W

LTN

82

10

16

21

48

05

6D

PG

ZI

31

20

34

R3

4−

31

03

05

71

44

RC

BG

R0

OH

1I

AT

BL

M6

EO

69

RM

EO

X RL

36

E6

UU

PJ

6JS

Y8

R3

4−

30

68

RE

QG

JV

QC

7K

RZ

PN

F1

35

77

11

60

44

91

V4

ME

R3

4−

31

16

Z6

0Y

PL

OO

XY

R3

4−

31

85

WN

YE

LK

MM

CT

R3

4−

32

18

21

81

86

R3

4−

30

73

26

60

35

R3

4−

31

97

BH

CQ

B6

FX

SQ

R3

4−

30

49

R3

4−

31

10

R3

4−

30

61

IZQ

WI

LIW

28

10

34

53

R3

4−

31

22

R3

4−

32

23

R3

4−

30

23

11

00

93

W9

G4

K2

VB

TK

R3

4−

31

08

R3

4−

31

13

QIK

YS

Q0

41

3T

JR

UQ

DA

LR

80

27

99

9R

34

−3

04

3R

34

−3

07

04

39

69

9S

QA

DW

27

73

94

R3

4−

31

93

R3

4−

32

24

R3

4−

31

37

R3

4−

30

28

20

94

64

28

49

68

R3

4−

31

98

GW

TK

7R

34

−3

05

6E

3G

XY

3X

UC

4R

34

−3

16

3B

4R

32

D8

RP

Y5

5N

O4

R3

4−

32

19

UF

G1

AY

89

K0

7R

CF

03

BD

EM R

JB

FJ

30

47

93

F5

UY

4R

34

−3

08

4R

34

−3

21

7R

34

−3

10

10

72

51

1R

34

−3

20

2P

21

3M

06

56

45

R3

4−

31

90

LD

D8

7R

34

−3

07

83

69

82

06

GU

7V

R3

4−

31

41

R3

4−

31

42

R3

4−

30

85

41

61

85

50

35

74

9S

KO

TC

HP

0M L

AB

TO

06

UR

QN

FP

TS

DH

8X

5−

11

QJA

PO

QT

RJ

R3

4−

32

21

R3

4−

32

07

10

55

42

R3

4−

30

94

R3

4−

32

00

R3

4−

30

98

40

44

63

20

88

66

20

99

30

R3

4−

30

58

WM

K3

TR

34

−3

02

60

44

74

4R

34

−3

03

70

67

09

4R

34

−3

02

54

K4

C9

R3

4−

30

97

81

LM

XO

0R

HB

O6

1U

70

U6

4I

B1

KM

B1

VD

X8

68

93

ZR

34

−3

22

9C

CV

1H

LS

3O

B 4P

YM

00

NW

X9

R3

4−

30

87

R3

4−

30

83

UT

ED

ZR

34

−3

16

4R

34

−3

13

9A

FV

C5

F7

7Z

HQ

WS

ZT

R3

4−

31

31

R3

4−

32

04

WA

MF

HR

34

−3

22

7B

Z2

I7R

34

−3

17

2P

1N

M2

R3

4−

30

74

T8

Z8

OO

8I1

E0

07

64

9R

34

−3

16

5W

9G

KO

JB

YF

YR

34

−3

04

4R

EA

OU

37

96

78

R3

4−

31

19

R3

4−

30

69

RS

9D

2 9D

25

HW

8IH

X3

4Y

LE

WG

60

CR

34

−3

04

84

38

18

0R

34

−3

11

5N

A0

3L

DR

1P

D6

JT

2I

R3

4−

30

86

14

YE

51

32

57

1U

3E

O1

R3

4−

31

36

17

47

02

R3

4−

31

94

48

16

43

1T

R6

C1

53

43

8U

B6

XH

R3

4−

31

86

48

78

27

22

38

32

01

ZW

MB

JF

60

HR

2T

30

FQ

8K

H8

YK

WA

12

WS

R3

4−

30

47

R3

4−

32

09

0G

B6

KR

34

−3

12

32

8P

CJ

O3

45

21

LQ

KE

5X

CD

5 UN

UO

JD

NB

90

OS

EY

6T

V6

6E

39

70

79

18

FD

W 8Q

TW

4X

VM

DP

Z4

95

2R

34

−3

17

9H

B7

S5

R3

4−

31

99

CS

I7H

R3

4−

30

40

R3

4−

31

50

R3

4−

30

36

IB8

MW

R3

4−

30

77

XC

B6

6S

LX

Y1

R3

4−

31

26

R3

4−

31

30

R3

4−

31

58

R3

4−

30

64

R3

4−

31

38

KR

HY

AW

5H

GR

UJE

CB

T8

K2

6R

34

−3

22

5R

34

−3

22

6R

34

−3

10

23

03

65

6Z

PN

7P

OZ

J9

FR

34

−3

17

3R

34

−3

12

11

87

40

6R

34

−3

14

9R

34

−3

17

0H

3Q

9M

10

27

20

R3

4−

30

52

R3

4−

30

81

KY

DZ

M0

74

12

43

86

32

9R

34

−3

14

7R

34

−3

15

5R

34

−3

07

1R

34

−3

20

3R

34

−3

20

82

8I7

HF

1O

DP

F1

G5

RE

4N

SI

45

97

47

QH

XZ

ZR

34

−3

21

4T

14

BF

R3

4−

30

54

RA

V8

56

CO

3C 8

G3

XE

8A

H8

E3

35

57

4J1

LX

6D

FS

2Q

R5

2D

J3

YFA

42

63

13

7F

P9

DM

XC

J9

72

42

58

8H

WJV

HR

34

−3

09

53

89

10

96

ZC

AZ

R3

4−

31

56

9A

BK

RR

Q6

NH

R3

4−

32

28

R3

4−

30

80

F7

IXH 5J9

7G

K9

N0

O3

42

67

2C

XQ

2T

R3

4−

32

01

06

54

35

17

97

43

10

84

80

05

17

15

44

86

43

R3

4−

31

71

R3

4−

31

68

AI3

GZ

2R

LC

4R

34

−3

17

64

30

77

23

41

36

5R

34

−3

23

1R

34

−3

23

02

68

66

03

06

43

51

42

53

4R

34

−3

01

2R

34

−3

09

0R

34

−3

08

2R

34

−3

18

7F

JZ

ZW

38

53

85

FB

PN

SR

WZ

JE

16

32

86

12

05

81

R3

4−

31

67

DQ

KY

E0

35

10

0Q

3H

1U

12

0T

71

53

40

8R

34

−3

01

7R

34

−3

14

3R

34

−3

09

1I0

UZ

QR

34

−3

18

9Z

XP

KH

R3

4−

31

77

CJIS

L9

4O

RH

Y0

16

09

T4

2W

R3

4−

30

51

R3

4−

30

57

2A

HB

EO

N3

8S

TE

F7

SR

34

−3

05

0D

P0

7V

R3

4−

30

76

46

29

56

02

53

10

20

36

92

23

94

34

R3

4−

30

29

FO

R9

5T

7IZ

18

A4

VR

QR

PP

WR

34

−3

01

31

51

68

44

27

93

7R

34

−3

18

1C

3W

41

R3

4−

30

22

7L

LC

LB

DG

WM

04

WB

3L

FB

15

WC

UL

SD

YJG

LR

34

−3

19

5R

34

−3

11

4R

34

−3

16

92

48

73

4R

34

−3

01

9R

34

−3

02

03

MB

4Z

WY

MQ

IL

IT4

QJ9

GM

MR

34

−3

12

7R

34

−3

05

5R

34

−3

17

4R

34

−3

06

7Y

HP

D0

27

64

30

36

55

02

04

94

70

35

45

57

15

43

89

5Z

S1

X4

03

79

0R

34

−3

08

82

55

21

0W

VC

E6

R3

4−

30

32

R3

4−

30

39

R3

4−

30

15

68

G2

D4

62

74

65

08

90

7E

KR

1O

P0

N8

KR

34

−3

14

6K

81

21

J2

9X

81

44

37

0R

34

−3

04

61

47

58

7R

34

−3

06

5H

U4

AA

8P

NF

12

W7

ME

M6

6V

OX

70

3J

SH

TP

BR

D7

KX

BA

5T

C2

Y1

CQ

R3

4−

30

62

00

19

96

R3

4−

31

66

33

68

50

06

20

31

R3

4−

30

75

47

26

99

08

98

48

04

23

97

CV

6JA

13

56

31

38

37

39

R3

4−

32

22

R3

4−

30

18

R3

4−

30

35

44

63

76

11

80

39

R3

4−

32

20

29

98

01

PK

MM

42

79

38

8N

6I9

80

01

33

4R

34

−3

02

4R

34

−3

02

1R

34

−3

15

7G

2Z

0B

N5

O6

85

Z5

2R

R3

4−

31

84

RN

3X

8R

34

−3

18

2R

34

−3

11

1R

UJ9

02

9O

RI

Q7

F2

6 UV

VW

AB

L0

SN

7C

7G

1R

34

−3

19

1R

34

−3

21

34

HU

A7

B5

0V

6 NIP

QY

R3

4−

31

18

12

75

30

R3

4−

32

16

47

39

37

0C

NY

CR

34

−3

17

84

D0

HZ

R3

4−

31

12

R3

4−

31

20

MF

II0

X7

1H

5Y

6B

SG

6U

8Z

J3

UT

O8 F

RIZ

42

6IE

T3

GW

V1

R3

4−

30

590

40

24

1R

34

−3

19

67

NA

98

33

37

93 Y

SO

FP

R3

4−

30

33

N8

YA

4E

0Z

KK

VO

DW

31

19

57

1N

B8

E4

R3

4−

30

72

PH

Y0

3Q

D0

ZV 8

CR

CE

V5

VF

12

91

88

06

PU

39

R3

4−

30

89

R3

4−

30

92

3JK

7Y

R3

4−

31

25

05

7G

3R

34

−3

04

1Q

U2

WM

LY2

88

OD

NW

T4

XIL

AQ

QK

9Y

NK

LJL

09

32

09

R3

4−

30

60

R3

4−

30

53

SE

OT

TP

WG

X0

ST

PD

EN

J4

1D

3F

BX

JR

34

−3

10

95

MZ

1D

R3

4−

30

93

F8

P2

5R

34

−3

21

5X

QR

0B

VP

PX

V2

34

32

33

12

94

2R

34

−3

04

54

22

26

4R

34

−3

01

65

W9

08

04

28

61

R3

4−

31

80

10

10

58

R3

4−

31

00

CM

91

7 FU

8O

LR

34

−3

02

71

66

63

7R

34

−3

09

94

20

88

1R

34

−3

21

1R

34

−3

21

00

15

44

50

TN

5H

R3

4−

30

96

R3

4−

31

51

35

93

70

18

12

72

16

09

86

43

61

54

01

80

44

38

84

83

R3

4−

30

38

30

26

49

32

34

85

17

30

15

21

82

29

33

48

47

43

69

15 G

J6

MQ

9L

UM

5R

34

−3

18

3R

34

−3

21

2R

34

−3

16

04

83

39

10

72

78

2R

34

−3

19

22

17

47

5 DH

8X

5−

2X

ZH

ZP

R3

4−

30

31

R3

4−

31

40

WQ

9G

VR

34

−3

15

9F

QP

LS

PX

B8

VR

34

−3

20

6R

34

−3

20

53

62

41

23

72

29

71

46

06

6R

34

−3

14

4R

34

−3

01

4E

H2

WN

R3

4−

30

11

QL

CS

W IZC

EG

LJ6

JL W

WC

1T

R3

4−

31

53

M6

AJV

5Q

BR

PR

34

−3

15

4R

34

−3

03

0

100 200 300 400 500 600

−5

05

Isolate

log

2 E

RY

_M

IC


non−susc.

susceptible


inferred susc.

R3

4−

30

63

36

51

52

R3

4−

31

88

R3

4−

30

34

PC

KM

OIM

AP

Y5

09

36

5E

SB

KM

PN

3IH

8P

Y5

X4

92

70

6K

51

SW

TG

9A

8A

YF

PY

IRE

4I

P4

GIC

R3

4−

31

75

09

53

44

YQ

UA

QR

34

−3

07

9R

34

−3

12

4F

UO

YQ

08

01

04

2E

AK

R3

66

29

3W

LTN

82

10

16

21

48

05

6D

PG

ZI

31

20

34

R3

4−

31

03

05

71

44

RC

BG

R0

OH

1I

AT

BL

M6

EO

69

RM

EO

XR

L3

6E 6U

UP

J6

JS

Y8

R3

4−

30

68

RE

QG

JV

QC

7K

RZ

PN

F1

35

77

11

60

44

91

V4

ME

R3

4−

31

16

Z6

0Y

P LO

OX

YR

34

−3

18

5W

NY

EL

KM

MC

TR

34

−3

21

82

18

18

6R

34

−3

07

32

66

03

5R

34

−3

19

7B

HC

QB

6F

XS

QR

34

−3

04

9R

34

−3

11

0R

34

−3

06

1IZ

QW

IL

IW2

81

03

45

3R

34

−3

12

2R

34

−3

22

3R

34

−3

02

31

10

09

3W

9G

4K

2V

BT

KR

34

−3

10

8R

34

−3

11

3Q

IKY

SQ

04

13

TJR

UQ

DA

LR

80

27

99

9R

34

−3

04

3R

34

−3

07

04

39

69

9S

QA

DW

27

73

94

R3

4−

31

93

R3

4−

32

24

R3

4−

31

37

R3

4−

30

28

20

94

64

28

49

68

R3

4−

31

98 G

WT

K7

R3

4−

30

56

E3

GX

Y3

XU

C4

R3

4−

31

63

B4

R3

2 D8

RP

Y5

5N

O4

R3

4−

32

19

UF

G1

AY

89

K0

7R

CF

03

BD

EM

RJB

FJ

30

47

93

F5

UY

4R

34

−3

08

4R

34

−3

21

7R

34

−3

10

10

72

51

1R

34

−3

20

2P

21

3M

06

56

45

R3

4−

31

90

LD

D8

7R

34

−3

07

83

69

82

06

GU

7V

R3

4−

31

41

R3

4−

31

42

R3

4−

30

85

41

61

85

50

35

74

9S

KO

TC

HP

0M

LA

BT

O0

6U

RQ

NF

PT

SD

H8

X5

−1

1Q

JA

PO

QT

RJ

R3

4−

32

21

R3

4−

32

07

10

55

42

R3

4−

30

94

R3

4−

32

00

R3

4−

30

98

40

44

63

20

88

66

20

99

30

R3

4−

30

58

WM

K3

TR

34

−3

02

60

44

74

4R

34

−3

03

70

67

09

4R

34

−3

02

54

K4

C9

R3

4−

30

97

81

LM

XO

0R

HB

O6

1U

70

U6

4I

B1

KM

B1

VD

X8

68

93

ZR

34

−3

22

9C

CV

1H

LS

3O

B4

PY

M0

0N

WX

9R

34

−3

08

7R

34

−3

08

3U

TE

DZ

R3

4−

31

64

R3

4−

31

39

AF

VC

5F

77

ZH

QW

SZ

TR

34

−3

13

1R

34

−3

20

4W

AM

FH

R3

4−

32

27

BZ

2I7

R3

4−

31

72

P1

NM

2R

34

−3

07

4T

8Z

8O O8

I1E

00

76

49

R3

4−

31

65

W9

GK

OJB

YF

YR

34

−3

04

4R

EA

OU

37

96

78

R3

4−

31

19

R3

4−

30

69

RS

9D

29

D2

5H

W8

IHX

34

YL

EW

G6

0C

R3

4−

30

48

43

81

80

R3

4−

31

15

NA

03

LD

R1

PD

6JT

2I

R3

4−

30

86

14

YE

51

32

57

1 U3

EO

1R

34

−3

13

61

74

70

2R

34

−3

19

44

81

64

31

TR

6C

15

34

38 UB

6X

HR

34

−3

18

64

87

82

72

23

83

20

1Z

WM

BJF

60

HR

2T

30

FQ

8K H8

YK

WA

12

WS

R3

4−

30

47

R3

4−

32

09

0G

B6

KR

34

−3

12

32

8P

CJ

O3

45

21

LQ

KE

5X

CD

5U

NU

OJ

DN

B9

0O

SE

Y6

TV

66

E3

97

07

91

8F

DW

8Q

TW

4X

VM

DP

Z4

95

2R

34

−3

17

9H

B7

S5

R3

4−

31

99

CS

I7H

R3

4−

30

40

R3

4−

31

50

R3

4−

30

36

IB8

MW

R3

4−

30

77

XC

B6

6S

LX

Y1

R3

4−

31

26

R3

4−

31

30

R3

4−

31

58

R3

4−

30

64

R3

4−

31

38

KR

HY

AW

5H

GR

UJE

CB

T8

K2

6R

34

−3

22

5R

34

−3

22

6R

34

−3

10

23

03

65

6Z

PN

7P

OZ

J9

FR

34

−3

17

3R

34

−3

12

11

87

40

6R

34

−3

14

9R

34

−3

17

0H

3Q

9M

10

27

20

R3

4−

30

52

R3

4−

30

81

KY

DZ

M0

74

12

43

86

32

9R

34

−3

14

7R

34

−3

15

5R

34

−3

07

1R

34

−3

20

3R

34

−3

20

82

8I7

HF

1O

DP

F1

G5

RE

4N

SI

45

97

47

QH

XZ

ZR

34

−3

21

4T

14

BF

R3

4−

30

54

RA

V8

56

CO

3C

8G

3X

E8

AH

8E

33

55

74

J1

LX

6D

FS

2Q

R5

2D

J3

YFA

42

63

13

7 FP

9D

MX

CJ9

72

42

58

8H

WJV

HR

34

−3

09

53

89

10

96

ZC

AZ

R3

4−

31

56

9A

BK

RR

Q6

NH

R3

4−

32

28

R3

4−

30

80

F7

IXH

5J9

7G

K9

N0

O3

42

67

2C

XQ

2T

R3

4−

32

01

06

54

35

17

97

43

10

84

80

05

17

15

44

86

43

R3

4−

31

71

R3

4−

31

68

AI3

GZ

2R

LC

4R

34

−3

17

64

30

77

23

41

36

5R

34

−3

23

1R

34

−3

23

02

68

66

03

06

43

51

42

53

4R

34

−3

01

2R

34

−3

09

0R

34

−3

08

2R

34

−3

18

7F

JZ

ZW

38

53

85

FB

PN

SR

WZ

JE

16

32

86

12

05

81

R3

4−

31

67

DQ

KY

E0

35

10

0Q

3H

1U

12

0T

71

53

40

8R

34

−3

01

7R

34

−3

14

3R

34

−3

09

1I0

UZ

QR

34

−3

18

9Z

XP

KH

R3

4−

31

77

CJIS

L9

4O

RH

Y0

16

09

T4

2W

R3

4−

30

51

R3

4−

30

57

2A

HB

EO

N3

8S

TE

F7

SR

34

−3

05

0D

P0

7V

R3

4−

30

76

46

29

56

02

53

10

20

36

92

23

94

34

R3

4−

30

29

FO

R9

5T

7IZ

1 8A

4V

RQ

RP

PW

R3

4−

30

13

15

16

84

42

79

37

R3

4−

31

81

C3

W4

1R

34

−3

02

27

LL

CL

BD

GW

M0

4W

B3

LF

B1

5W

CU

LS

DY

JG

LR

34

−3

19

5R

34

−3

11

4R

34

−3

16

92

48

73

4R

34

−3

01

9R

34

−3

02

03

MB

4Z

WY

MQ

IL

IT4

QJ9

GM

MR

34

−3

12

7R

34

−3

05

5R

34

−3

17

4R

34

−3

06

7Y

HP

D0

27

64

30

36

55

02

04

94

70

35

45

57

15

43

89 5Z

S1

X4

03

79

0R

34

−3

08

82

55

21

0W

VC

E6

R3

4−

30

32

R3

4−

30

39

R3

4−

30

15

68

G2

D4

62

74

65

08

90

7E

KR

1O

P0

N8

KR

34

−3

14

6K

81

21

J2

9X

81

44

37

0R

34

−3

04

61

47

58

7R

34

−3

06

5H

U4

AA

8P

NF

12

W7

ME

M6

6V

OX

70

3J

SH

TP

BR

D7

KX

BA

5T

C2

Y1

CQ

R3

4−

30

62

00

19

96

R3

4−

31

66

33

68

50

06

20

31

R3

4−

30

75

47

26

99

08

98

48

04

23

97

CV

6JA

13

56

31

38

37

39

R3

4−

32

22

R3

4−

30

18

R3

4−

30

35

44

63

76

11

80

39

R3

4−

32

20

29

98

01

PK

MM

42

79

38

8N

6I9

80

01

33

4R

34

−3

02

4R

34

−3

02

1R

34

−3

15

7G

2Z

0B

N5

O6

85

Z5

2R

R3

4−

31

84

RN

3X

8R

34

−3

18

2R

34

−3

11

1R

UJ9

02

9O

RI

Q7

F2

6U

VV

WA

BL

0S

N7

C7

G1

R3

4−

31

91

R3

4−

32

13

4H

UA

7B

50

V6

NIP

QY

R3

4−

31

18

12

75

30

R3

4−

32

16

47

39

37 0C

NY

CR

34

−3

17

84

D0

HZ

R3

4−

31

12

R3

4−

31

20

MF

II0

X7

1H

5 Y6

BS

G6

U8

ZJ

3U

TO

8F

RIZ

4 26

IET

3G

WV

1R

34

−3

05

90

40

24

1R

34

−3

19

67

NA

98

33

37

93 YS

OF

PR

34

−3

03

3N

8Y

A4

E0

ZK

KV

OD

W3

11

95

71

NB

8E

4R

34

−3

07

2P

HY

03

QD

0Z

V8

CR

CE

V5

VF

12

91

88

06

PU

39

R3

4−

30

89

R3

4−

30

92

3JK

7Y

R3

4−

31

25

05

7G

3R

34

−3

04

1Q

U2

WM

LY2

88

OD

NW

T4

XIL

AQ

QK

9Y

NK

LJL

09

32

09

R3

4−

30

60

R3

4−

30

53

SE

OT

TP

WG

X0

ST

PD

EN

J4

1D

3F

BX

JR

34

−3

10

95

MZ

1D

R3

4−

30

93

F8

P2

5R

34

−3

21

5X

QR

0B

VP

PX

V2

34

32

33

12

94

2R

34

−3

04

54

22

26

4R

34

−3

01

65

W9

08

04

28

61

R3

4−

31

80

10

10

58

R3

4−

31

00

CM

91

7F

U8

OL

R3

4−

30

27

16

66

37

R3

4−

30

99

42

08

81

R3

4−

32

11

R3

4−

32

10

01

54

45

0T

N5

HR

34

−3

09

6R

34

−3

15

13

59

37

01

81

27

21

60

98

64

36

15

40

18

04

43

88

48

3R

34

−3

03

83

02

64

93

23

48

51

73

01

52

18

22

93

34

84

74

36

91

5G

J6

MQ

9L

UM

5R

34

−3

18

3R

34

−3

21

2R

34

−3

16

04

83

39

10

72

78

2R

34

−3

19

22

17

47

5D

H8

X5

−2

XZ

HZ

PR

34

−3

03

1R

34

−3

14

0W

Q9

GV

R3

4−

31

59

FQ

PL

SP

XB

8V

R3

4−

32

06

R3

4−

32

05

36

24

12

37

22

97

14

60

66

R3

4−

31

44

R3

4−

30

14

EH

2W

NR

34

−3

01

1Q

LC

SW

IZC

EG

LJ6

JL

WW

C1

TR

34

−3

15

3M

6A

JV

5Q

BR

PR

34

−3

15

4R

34

−3

03

0

100 200 300 400 500 600

−4

−2

02

4

Isolate

log

2 T

ET

_M

IC


non−susc.

susceptible


inferred susc.

R3

4−

30

63

36

51

52

R3

4−

31

88

R3

4−

30

34

PC

KM

OIM

AP

Y5

09

36

5E

SB

KM

PN

3IH

8P

Y5

X4

92

70

6K

51

SW

TG

9A

8A

YF

PY

IRE

4I

P4

GIC

R3

4−

31

75

09

53

44

YQ

UA

QR

34

−3

07

9R

34

−3

12

4F

UO

YQ

08

01

04

2E

AK

R3

66

29

3W

LTN

82

10

16

21

48

05

6D

PG

ZI

31

20

34

R3

4−

31

03

05

71

44

RC

BG

R0

OH

1I

AT

BL

M6

EO

69

RM

EO

XR

L3

6E

6U

UP

J6

JS

Y8

R3

4−

30

68

RE

QG

JV

QC

7K

RZ

PN

F1

35

77

11

60

44

91

V4

ME

R3

4−

31

16

Z6

0Y

PL

OO

XY

R3

4−

31

85

WN

YE

LK

MM

CT

R3

4−

32

18

21

81

86

R3

4−

30

73

26

60

35

R3

4−

31

97

BH

CQ

B6

FX

SQ

R3

4−

30

49

R3

4−

31

10

R3

4−

30

61

IZQ

WI

LIW

28

10

34

53

R3

4−

31

22

R3

4−

32

23

R3

4−

30

23

11

00

93

W9

G4

K2

VB

TK

R3

4−

31

08

R3

4−

31

13

QIK

YS

Q0

41

3T

JR

UQ

DA

LR

80

27

99

9R

34

−3

04

3R

34

−3

07

04

39

69

9S

QA

DW

27

73

94

R3

4−

31

93

R3

4−

32

24

R3

4−

31

37

R3

4−

30

28

20

94

64

28

49

68

R3

4−

31

98

GW

TK

7R

34

−3

05

6E

3G

XY

3X

UC

4R

34

−3

16

3B

4R

32

D8

RP

Y5

5N

O4

R3

4−

32

19

UF

G1

AY

89

K0

7R

CF

03

BD

EM

RJB

FJ

30

47

93

F5

UY

4R

34

−3

08

4R

34

−3

21

7R

34

−3

10

10

72

51

1R

34

−3

20

2P

21

3M

06

56

45

R3

4−

31

90

LD

D8

7R

34

−3

07

83

69

82

06

GU

7V

R3

4−

31

41

R3

4−

31

42

R3

4−

30

85

41

61

85

50

35

74

9S

KO

TC

HP

0M

LA

BT

O0

6U

RQ

NF

PT

SD

H8

X5

−1

1Q

JA

PO

QT

RJ

R3

4−

32

21

R3

4−

32

07

10

55

42

R3

4−

30

94

R3

4−

32

00

R3

4−

30

98

40

44

63

20

88

66

20

99

30

R3

4−

30

58

WM

K3

TR

34

−3

02

60

44

74

4R

34

−3

03

70

67

09

4R

34

−3

02

54

K4

C9

R3

4−

30

97

81

LM

XO

0R

HB

O6

1U

70

U6

4I

B1

KM

B1

VD

X8

68

93

ZR

34

−3

22

9C

CV

1H

LS

3O

B4

PY

M0

0N

WX

9R

34

−3

08

7R

34

−3

08

3U

TE

DZ

R3

4−

31

64

R3

4−

31

39

AF

VC

5F

77

ZH

QW

SZ

TR

34

−3

13

1R

34

−3

20

4W

AM

FH

R3

4−

32

27

BZ

2I7

R3

4−

31

72

P1

NM

2R

34

−3

07

4T

8Z

8O

O8

I1E

00

76

49

R3

4−

31

65

W9

GK

OJB

YF

YR

34

−3

04

4R

EA

OU

37

96

78

R3

4−

31

19

R3

4−

30

69

RS

9D

29

D2

5H

W8

IHX

34

YL

EW

G6

0C

R3

4−

30

48

43

81

80

R3

4−

31

15

NA

03

LD

R1

PD

6JT

2I

R3

4−

30

86

14

YE

51

32

57

1U

3E

O1

R3

4−

31

36

17

47

02

R3

4−

31

94

48

16

43

1T

R6

C1

53

43

8U

B6

XH

R3

4−

31

86

48

78

27

22

38

32

01

ZW

MB

JF

60

HR

2T

30

FQ

8K

H8

YK

WA

12

WS

R3

4−

30

47

R3

4−

32

09

0G

B6

KR

34

−3

12

32

8P

CJ

O3

45

21

LQ

KE

5X

CD

5U

NU

OJ

DN

B9

0O

SE

Y6

TV

66

E3

97

07

91

8F

DW

8Q

TW

4X

VM

DP

Z4

95

2R

34

−3

17

9H

B7

S5

R3

4−

31

99

CS

I7H

R3

4−

30

40

R3

4−

31

50

R3

4−

30

36

IB8

MW

R3

4−

30

77

XC

B6

6S

LX

Y1

R3

4−

31

26

R3

4−

31

30

R3

4−

31

58

R3

4−

30

64

R3

4−

31

38

KR

HY

AW

5H

GR

UJE

CB

T8

K2

6R

34

−3

22

5R

34

−3

22

6R

34

−3

10

23

03

65

6Z

PN

7P

OZ

J9

FR

34

−3

17

3R

34

−3

12

11

87

40

6R

34

−3

14

9R

34

−3

17

0H

3Q

9M

10

27

20

R3

4−

30

52

R3

4−

30

81

KY

DZ

M0

74

12

43

86

32

9R

34

−3

14

7R

34

−3

15

5R

34

−3

07

1R

34

−3

20

3R

34

−3

20

82

8I7

HF

1O

DP

F1

G5

RE

4N

SI

45

97

47

QH

XZ

ZR

34

−3

21

4T

14

BF

R3

4−

30

54

RA

V8

56

CO

3C

8G

3X

E8

AH

8E

33

55

74

J1

LX

6D

FS

2Q

R5

2D

J3

YFA

42

63

13

7F

P9

DM

XC

J9

72

42

58

8H

WJV

HR

34

−3

09

53

89

10

96

ZC

AZ

R3

4−

31

56

9A

BK

RR

Q6

NH

R3

4−

32

28

R3

4−

30

80

F7

IXH

5J9

7G

K9

N0

O3

42

67

2C

XQ

2T

R3

4−

32

01

06

54

35

17

97

43

10

84

80

05

17

15

44

86

43

R3

4−

31

71

R3

4−

31

68

AI3

GZ

2R

LC

4R

34

−3

17

64

30

77

23

41

36

5R

34

−3

23

1R

34

−3

23

02

68

66

03

06

43

51

42

53

4R

34

−3

01

2R

34

−3

09

0R

34

−3

08

2R

34

−3

18

7F

JZ

ZW

38

53

85

FB

PN

SR

WZ

JE

16

32

86

12

05

81

R3

4−

31

67

DQ

KY

E0

35

10

0Q

3H

1U

12

0T

71

53

40

8R

34

−3

01

7R

34

−3

14

3R

34

−3

09

1I0

UZ

QR

34

−3

18

9Z

XP

KH

R3

4−

31

77

CJIS

L9

4O

RH

Y0

16

09

T4

2W

R3

4−

30

51

R3

4−

30

57

2A

HB

EO

N3

8S

TE

F7

SR

34

−3

05

0D

P0

7V

R3

4−

30

76

46

29

56

02

53

10

20

36

92

23

94

34

R3

4−

30

29

FO

R9

5T

7IZ

18

A4

VR

QR

PP

WR

34

−3

01

31

51

68

44

27

93

7R

34

−3

18

1C

3W

41

R3

4−

30

22

7L

LC

LB

DG

WM

04

WB

3L

FB

15

WC

UL

SD

YJG

LR

34

−3

19

5R

34

−3

11

4R

34

−3

16

92

48

73

4R

34

−3

01

9R

34

−3

02

03

MB

4Z

WY

MQ

IL

IT4

QJ9

GM

MR

34

−3

12

7R

34

−3

05

5R

34

−3

17

4R

34

−3

06

7Y

HP

D0

27

64

30

36

55

02

04

94

70

35

45

57

15

43

89

5Z

S1

X4

03

79

0R

34

−3

08

82

55

21

0W

VC

E6

R3

4−

30

32

R3

4−

30

39

R3

4−

30

15

68

G2

D4

62

74

65

08

90

7E

KR

1O

P0

N8

KR

34

−3

14

6K

81

21

J2

9X

81

44

37

0R

34

−3

04

61

47

58

7R

34

−3

06

5H

U4

AA

8P

NF

12

W7

ME

M6

6V

OX

70

3J

SH

TP

BR

D7

KX

BA

5T

C2

Y1

CQ

R3

4−

30

62

00

19

96

R3

4−

31

66

33

68

50

06

20

31

R3

4−

30

75

47

26

99

08

98

48

04

23

97

CV

6JA

13

56

31

38

37

39

R3

4−

32

22

R3

4−

30

18

R3

4−

30

35

44

63

76

11

80

39

R3

4−

32

20

29

98

01

PK

MM

42

79

38

8N

6I9

80

01

33

4R

34

−3

02

4R

34

−3

02

1R

34

−3

15

7G

2Z

0B

N5

O6

85

Z5

2R

R3

4−

31

84

RN

3X

8R

34

−3

18

2R

34

−3

11

1R

UJ9

02

9O

RI

Q7

F2

6U

VV

WA

BL

0S

N7

C7

G1

R3

4−

31

91

R3

4−

32

13

4H

UA

7B

50

V6

NIP

QY

R3

4−

31

18

12

75

30

R3

4−

32

16

47

39

37

0C

NY

CR

34

−3

17

84

D0

HZ

R3

4−

31

12

R3

4−

31

20

MF

II0

X7

1H

5Y

6B

SG

6U

8Z

J3

UT

O8

FR

IZ4

26

IET

3G

WV

1R

34

−3

05

90

40

24

1R

34

−3

19

67

NA

98

33

37

93

YS

OF

PR

34

−3

03

3N

8Y

A4

E0

ZK

KV

OD

W3

11

95

71

NB

8E

4R

34

−3

07

2P

HY

03

QD

0Z

V8

CR

CE

V5

VF

12

91

88

06

PU

39

R3

4−

30

89

R3

4−

30

92

3JK

7Y

R3

4−

31

25

05

7G

3R

34

−3

04

1Q

U2

WM

LY2

88

OD

NW

T4

XIL

AQ

QK

9Y

NK

LJL

09

32

09

R3

4−

30

60

R3

4−

30

53

SE

OT

TP

WG

X0

ST

PD

EN

J4

1D

3F

BX

JR

34

−3

10

95

MZ

1D

R3

4−

30

93

F8

P2

5R

34

−3

21

5X

QR

0B

VP

PX

V2

34

32

33

12

94

2R

34

−3

04

54

22

26

4R

34

−3

01

65

W9

08

04

28

61

R3

4−

31

80

10

10

58

R3

4−

31

00

CM

91

7F

U8

OL

R3

4−

30

27

16

66

37

R3

4−

30

99

42

08

81

R3

4−

32

11

R3

4−

32

10

01

54

45

0T

N5

HR

34

−3

09

6R

34

−3

15

13

59

37

01

81

27

21

60

98

64

36

15

40

18

04

43

88

48

3R

34

−3

03

83

02

64

93

23

48

51

73

01

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0

Supplementary Figure 3: MIC intervals for individual isolates in the RASE database. The plot illustrates MIC

intervals and point values extracted from. Each panel corresponds to a single antibiotic, while vertical lines and points

correspond to individual isolates. Their colors correspond to the resistance category after applying a breakpoint

(horizontal lines). When a resistance category could not be assigned directly (i.e., in case of an interval crossing the

breakpoint line), then it was inferred using ancestral state reconstruction.



https://doi.org/10.1101/403204


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R34-3130

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0.125585

Supplementary Figure 4: Ancestral state reconstruction of resistance categories in the RASE database.

Each panel corresponds to a single antibiotic and displays the database phylogenetic tree, colored according to the

reconstructed resistance categories for the antibiotic (blue, green, red, violet correspond to ‘susceptible’, ‘unknown

– inferred susceptible’, ‘non-susceptible’, ‘unknown – inferred non-susceptible’, respectively).



https://doi.org/10.1101/403204


10 15 20 25 30 35 40

050

010

0015

0020

0025

00

k

#k−

mer

s (x

1000

)

k

Subword complexity function of S. pneumoniae

S.pneumoniaeRandom DNAAsymptote

Supplementary Figure 5: Subword complexity of pneumococcus. The plot depicts the number of canonical

k-mers as a function of k for S.pneumoniae ATCC 700669 (NC_011900.1) and for a random DNA text containing all

possible k-mers. For k<10, the pneumococcus k-mer composition is similar to the one of random text. For k > 14,

the k-mer sets are almost saturated and the complexity grows very slowly. Since the genome has a finite length

and is circular, the function has an asymptote, which would be attained for k equal to the length of the genome

(2,221,315). The highlighted region corresponds to the range of k values, which are suitable for use in RASE. Note

that k-mers longer than 32 are not currently supported by ProPhyle.



https://doi.org/10.1101/403204


20 25 30

020

4060

Index

NA

PG

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● SP01SP02SP03SP04SP05SP06SP07SP08SP09

20 25 30

020

4060

Index

NA

PG

2

●● ●

●

●

●●

●●

●● ●

●● ● ● ●

20 25 30

020

4060

NA

Isol

ate

●

●● ● ● ● ● ● ● ● ● ● ●

● ● ● ●

Supplementary Figure 6: Delays in prediction based on the k-mer length. The plot displays delays in prediction

as a function of the used k-mer length, for all experiments and all possible k-mer lengths. Each horizontal panel

displays times required for stabilization of one of the three predictions: phylogroup (PG), alternative phylogroup

(PG2), and closest isolate (Isolate). Every column within a panel corresponds to a single k-mer length. When

the required time exceeded 1 hour, the point is displayed at the top. Experiments where phylogroup could not be

identified are plotted in red. The highlighted column corresponds to the k-mer length used for constructing RASE.



https://doi.org/10.1101/403204


Supplementary Table 1: Prevalence of resistance phenotypes across phylogroups. For all sequencing ex-

periments, the table displays the best matching isolates, the strain MIC and all measurements of database MICs

(the original reported values or categories inferred using ancestral state reconstruction when not available, retested

values, and the resulting resistance categories).

Supplementary Table 2: Metadata for all isolates included in the RASE database. Each record contains the

strain’s taxid, phylogroup, serotype, sequence type (ST), order in the phylogenetic tree, and three fields related to

resistance for every antibiotics: the ‘_mic’, ‘_int’, ‘_cat’ fields contain the original published MIC information (possibly

corrected after retesting), the extracted MIC interval, and the resulting category after ancestral state reconstruction

(S = susceptible, R = non-susceptible, s = unknown but reconstructed susceptible, r = unknown but reconstructed

non-susceptible), respectively.

Supplementary Table 3: Additional MIC measurements for selected strains. The table contains results from

strain retesting. Each record contains date when the retesting was done, the antibiotic, the measured MIC, and the

corresponding resistance category according the RASE breakpoints.

Supplementary Table 4: Overview of performed resistance tests. For all sequencing experiments, the table

displays the best matching isolates, the strain MIC and all measurements of database MICs (the original reported

values or categories inferred using ancestral state reconstruction when not available, retested values, and the re-

sulting resistance categories).



https://doi.org/10.1101/403204


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Lineage calling can identify antibiotic resistan t clones ...An example is given by the pneumococcus...

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