A genomic snapshot of Salmonella enterica serovar Typhi in Colombia.

Little is known about the genetic diversity of Salmonella enterica serovar Typhi (S. Typhi) circulating in Latin America. It has been observed that typhoid fever is still endemic in this part of the world; however, a lack of standardized blood culture surveillance across Latin American makes estimating the true disease burden problematic. The Colombian National Health Service established a surveillance system for tracking bacterial pathogens, including S. Typhi, in 2006. Here, we characterized 77 representative Colombian S. Typhi isolates collected between 1997 and 2018 using pulse field gel electrophoresis (PFGE; the accepted genotyping method in Latin America) and whole genome sequencing (WGS). We found that the main S. Typhi clades circulating in Colombia were clades 2.5 and 3.5. Notably, the sequenced S. Typhi isolates from Colombia were closely related in a global phylogeny. Consequently, these data suggest that these are endemic clades circulating in Colombia. We found that AMR in S. Typhi in Colombia was uncommon, with a small subset of organisms exhibiting mutations associated with reduced susceptibility to fluoroquinolones. This is the first time that S. Typhi isolated from Colombia have been characterized by WGS, and after comparing these data with those generated using PFGE, we conclude that PFGE is unsuitable for tracking S. Typhi clones and mapping transmission. The genetic diversity of pathogens such as S. Typhi is limited in Latin America and should be targeted for future surveillance studies incorporating WGS.

Introduction

Salmonella enterica serovar Typhi (S. Typhi) is the bacterial agent of typhoid fever. With between 9–13 million cases and 116,800 associated deaths annually, typhoid is still a public health problem in many low and middle-income countries (LMICs), particularly in South Asia and parts of sub-Saharan Africa[1,2]. Antimicrobial resistance (AMR) is a major issue, with multi-drug resistance (MDR; resistance to chloramphenicol, ampicillin, and trimethoprim-sulfamethoxazole) and fluoroquinolone resistance in genotype 4.3.1 (H58) organisms dominating the global genetic landscape [3,4]. The emergence of extensively-drug resistant (XDR; MDR and resistant to fluoroquinolones and third generation cephalosporins) in Pakistan and more recent reports of resistance to azithromycin in South Asia compound the problem [5,6]

Several international studies have aimed to fill data gaps regarding the global distribution of typhoid [7-10]. However, there have not been large multicenter population-based surveillance studies conducted in Latin America as there have been in sub-Saharan Africa and South Asia, nor is there routine blood culture surveillance, so this region represents a major data gap in global disease burden estimations [11-13]. The modelled incidence of typhoid in Latin America varies enormously, and estimates range from 1.0 (0.2–3.9) cases and 169 (32–642) cases per 100,000 person-years [8,14]. A lack of systematic surveillance also means that there are limited contemporary data on the circulating bacterial population, AMR profiles, and potential transmission dynamics within South America. However, a recent study revealed a large number of S. Typhi isolates with a high prevalence of decreased fluoroquinolone susceptibility in Colombia and El Salvador [15].

Pulsed Field Gel Electrophoresis (PFGE) is the conventional method for studying the genetic relationship between S. Typhi isolates in Latin America [16]. Using this method, we recently found that some S. Typhi isolates from Colombia shared indistinguishable PFGE patterns with organisms from Argentina, Chile, Perú, Venezuela, Brazil, and Guatemala, indicative of the circulation of common “continental” genotypes [17,18]. However, PFGE has limited discriminatory power to support subtyping and cannot identify genotype 4.3.1, other emerging genotypes, or AMR genes. Whole genome sequencing (WGS) is the gold standard for the investigating population structures, transmission dynamics, and molecular mechanisms of AMR in S. Typhi. In 2015, a landmark global S. Typhi genotyping scheme was published, but comprised only 20 genome sequences originating from Latin America (Argentina, El Salvador, French Guiana, and Peru)[3]. Since then, there have been only three additional publications describing S. Typhi isolated in Latin America and characterized using WGS, generating a further 36 genome sequences [4,19,20]

In response to local concern regarding the increase of typhoid fever and the international threat of AMR, the National Surveillance System Public Health (SIVIGILA) of Colombia made typhoid fever a notifiable disease in 2006, requiring laboratory follow-up [21]. Here, we aimed to generate the first insights into the molecular epidemiology of typhoid in Colombia by performing AMR profiling and comparative genotyping using both PFGE and WGS on a cross-sectional collection of S. Typhi isolated in Colombia between 1997 and 2018.

Methods

Ethics statement

This study was conducted in accordance with the principles expressed in the Declaration of Helsinki. The clinical bacterial isolates were collected through the Colombian Laboratory National Surveillance System under the scientific, technical and administrative standard for health research established in Colombian resolution 8430 of 1993 of the Ministry of Health. Patient data were analysed anonymously; consequently, formal ethical approval for the study was not necessary.

Salmonella Typhi isolates

A total of 1,478 S. Typhi isolates were submitted to the surveillance program at the National Health Institute of Colombia between 1997 and 2018. These organisms were all associated with a reported typhoid and paratyphoid fever event and came from 22 of 32 Colombian departments and the Capital District of Colombia [21]. 1,077 (72.9%) of these isolates were successfully genotyped using the standard routine PFGE pipeline (S1 Fig) and 77 (5.2%) S. Typhi isolates were selected cross-sectionally for WGS (Fig 1 and S1 Table). Our aim was to generate a broad overview of circulating genotypes in Colombia and to identify genotype H58. Therefore, we included isolates from all years, from sampled departments, and a broad range of PFGE patterns, including at least one isolate of each mayor PFGE pattern and including the various AMR phenotypes. These isolates were both from outbreaks defined by the health authorities (n = 12) and sporadic cases (n = 65); 61 isolates originated from blood, 10 from stool, and six from other sources (3 bone marrow, 1 splenic abscess, 1 gluteus abscess, and 1 from a skin swab).

Fig 1

Colombian Salmonella Typhi isolates selected for whole genome sequencing.

PFGE-XbaI dendrogram generated with Dice coefficient and UPGMA clustering method (tolerance and optimization 1.5%) of the 77 selected S. Typhi isolates with isolate identification and PFGE pattern code. The red boxes indicate isolates from epidemiological confirmed outbreaks (A-H).

Bacterial identification and antimicrobial susceptibility testing

All isolates were identified using standard biochemical tests (Triple Sugar Iron Agar (TSI), Citrate, Urea, motility), the automated MicroScan, VITEK II system and the Kauffmann-White-Le Minor scheme to identify organisms suspected to be S. Typhi (Difco, United States) [22]

Antimicrobial susceptibility testing was performed using the Kirby-Bauer disk diffusion method against amoxicillin-clavulanic acid (AMC), chloramphenicol (CHL), nalidixic acid (NAL), tetracycline (TET), ampicillin (AMP), cefotaxime (CTX), ceftazidime (CAZ), trimethoprim-sulfamethoxazole (SXT), and Meropenem in combination with the MIC-based methods using the MicroScansystems according to manufactures recommendations. Ciprofloxacin (CIP) susceptibility was determined by agar dilution assays according to the CLSI standards of 2019 [23] Extended-Spectrum Beta-Lactamase (ESBLs) activity mediated by blaSHV, blaTEM, and blaCTX-M genes was confirmed by PCR amplification [24].

Molecular subtyping by PFGE

All organisms were subtyped by PFGE following standardized PulseNet protocols. [16]. Briefly, genomic DNA were digested with XbaI (Promega, USA) and subjected to gel electrophoresis. PFGE patterns from the different runs were normalized by aligning the reference digestion pattern of S. Braenderup H9812. Bands were assessed visually and by a computerized program (Gelcompare 4.0 software (Applied Maths, Belgium). Parameters of tolerance and optimization were set to 1.5% and similarities calculated according to Dice coefficient. The Clustering dendrogram was based on the unweighted pair-group method using arithmetic averages (UPGMA). The resulting XbaI patterns were compared with the local database and if indistinguishable (within this 1.5% tolerance) from an existing pattern the isolates was given the same PFGE code; if a unique pattern was deteremined a new PFGE code was assigned. All PFGE pattern codes were assigned following the PulseNet International guidelines for nomenclature, which includes 2 letters for the country or region, 3 letters for the serovar, 3 characters for the enzyme and 4 digits for the profile number (e.g. COINJPPX01.0001 for Colombia) [17]

Genome sequencing and SNP analysis

DNA was extracted using a Qiacube in combination with the Qiagen QIAamp DNA Mini Kit (Qiagen) at the Colombian National Health Institute (INS), following the manufacturer guidelines. DNA was quantified using a Qubit 2.0 fluorometer (Invitrogen) and 2μg of genomic DNA was subjected to indexed WGS by Illumina MiSeq platform to generate 100 bp paired end reads and 30x genome coverage. Genomic libraries were prepared with Nextera XT library prep Kit FC 121–1031. Raw Illumina reads were assembled using (Velvet v1.2) via an automated pipeline at the Wellcome Sanger Institute[25]. For preliminary analysis and global contextualization and for the detection of non-synonymous mutations in the Quinolone Resistance Determining Region (QRDR) of genes gyrA, gyrB, parC, and are, the assembled genomes were uploaded to PathogenWatch v3.2.2 (https://pathogen.watch/). Genotypes were assigned using GenoTyphi (https://github.com/katholt/genotyphi)

Sequenced reads and publicly available sequences were mapped and SNP called against the reference genome S. Typhi CT18 using the Sanger institute pipelines and following quality metrics as previously described [26]. Known recombinant regions such as prophage[4], were manually excluded, and any remaining recombinant regions were filtered using Gubbins (v1.4.10)[27]. The resultant core SNP alignment of 40,998 bp was used to infer Maximum Likelihood (ML) phylogenies using RAxML (v8.2.8)[28], specifying a generalized time-reversible model and a Gamma distribution to model site-specific rate variation (GTR+ Γ substitution model; GTRGAMMA in RAxML) with 100 bootstrap pseudoreplicates used to assess branch support. SNP distances for the core genome alignment of all the novel genome sequences were calculated from this alignment using snp-dists package (https://github.com/tseemann/snp-dists). SRST2 v0.2.0 [29] was used with the ARGannot [30] and PlasmidFinder [31] databases to detect the molecular determinants associated with AMR; standard cut-offs of >90% gene coverage and a minimum read dept of 5 were used. Maps drawn in inkscape v1.0.1 an open source scalable graphics editor.

Results

PFGE genotyping and isolate selection

PFGE is performed routinely for S. Typhi in Latin America; results are consolidated into the PulseNet Latin America and Caribbean Network database [16,17]. Organisms are given a unique PFGE code according to their genomic digestion pattern; 1,478 Colombian isolates were present in the national surveillance database at the initiation of this project. We selected 77 S. Typhi isolated between 1997 and 2018 to represent the broadest possible diversity (by PFGE; S1 Fig) for WGS. This collection comprised 60 unique PFGE profiles (Fig 1 and S1 Table), including the most commonly circulating restriction patterns in Colombia (e.g., COINXX.JPPX01. 0008-0083-0115)[18]. Twelve isolates also originated from eight outbreaks confirmed by the health authorities (A-H; 8, 4, 24, 9, 5, 2, 6, and 8 patients per outbreak respectively) (Figs 1 and S1); more than one isolate were included from two of these outbreaks (D and G). The selection was skewed towards more recent years based on number of available isolates and for AMR isolates [32].

AMR and population structure of Colombian S. Typhi

The 77 Colombian S. Typhi isolates were subjected to WGS and a phylogenetic tree was constructed from core genome SNPs (Fig 2). We found that genotypic variation in the population of Colombian S. Typhi was generally limited, with the majority of isolates restricted to two groups: major cluster 2 and 3. These clades could be further segregated into clades 2.5 (51/77; 66.2%), 3.5 (20/77; 24.9%), and 2 (4/77; 5.2%). In addition, we identified two isolates in major cluster 1; these organisms belonged to genotypes 0.1.3 and 1.1.

Fig 2

The phylogenetic structure of Salmonella Typhi in Colombia.

SNP based RAxML generated Maximum Likelihood phylogenetic tree of 77 selected Salmonella Typhi isolates. Branches are coloured by genotype (numbers shown). Column 1 indicates year of isolation, column 2 indicates department (state) of isolation, column 3 indicates the presence (brown) of SNPs in the QNDR and column D indicates acquired AMR genes (see legend). The map was drawn by inkscape v1.0.1 an Open Source Scalable Vector Graphics Editor.

Notably, unlike a recent observation from Chile, we did not identify genotype 4.3.1 (H58) isolates in this set of Colombian sequences, despite being specifically enriched for organisms that exhibited resistance to antimicrobials. However, we did identify 14 organisms in genotypes 1.1, 2.5 and 3.5 that contained a single SNP in the QRDR region (Fig 2 and Table 1), resulting in reduced susceptibility to fluoroquinolones. Overall, and unlike contemporaneous S. Typhi collections from Africa and Asia, this collection contained a limited accumulation of acquired AMR genes. We identified one isolate carrying the sul2 and tetA genes associated with resistance to tetracycline and sulphonamides, respectively. We additionally detected one organism from Bogota, isolated in 2012, which carried blaCTX-M-12, blaTEM-1, blaOXA-15, and Sul1, rendering it resistant to ampicillin, cephalosporins, and sulphamethoxazole (Fig 2 and Table 1).

Table 1

Colombian Salmonella Typhi isolates with antimicrobial resistance phenotypes and their associated genes and plasmids.

Organism id	Genotype	Resistance Kirby-Bauer (mm)*	MIC	Resistance profile	AMR Genes/QRDR mutations	Plasmid content
INS-S.Ty1062/1087-15	3.5	NAL(28),AMP(33), S(14)	AMP >16CIP 0.008	AMP
INS-S.Ty0330/256-08	2.5	NAL(27), AMP(16),S(12)	CIP 0.008	AMP(I)
INS-S.Ty0115/36-03	2.0	NAL(28),SXT(6), TET(6), S(14)	CIP 0.008	SXT-TET
INS-S.Ty551/52-12	3.5	NAL(24),AMP(6), CTX(8), TET(6), S(6)	CIP 0.008	AMP-CTX	blaCTX-M-12, blaOXA-15, Sul, blaTEM-1	IncL/M(pOXA-48) IncFIB(pHCM2)
INS-S.Ty980/524-15	2.5	NAL(22), TET(6), S(6)CTX(6), CTX-CLA(10)CAZ(17), CAZ-CLA(19)	AMP >16 CTX >32CIP 0.016	AMP-CTX-TET-S	TetA(A), Sul2	ColRNAI
INS-S.Ty0130/69-04	1.1	NAL(6), S(14)	CIP 0.25	NAL-CIP(I)	gyrA_S83F
INS-GMR-S-331-18	3.5	NAL(6),CIP(26)(I)		NAL-CIP(I)	gyrA_D87Y
INS-S.Ty1100/1425-15	3.5	NAL(18)(I)	CIP 0.25	NAL(I)-CIP(I)	gyrA_S83F
INS-S.Ty772/314-14	2.5	NAL(10), CIP(25), S(10)	CIP 0.032	NAL	gyrA_D87V
INS-S.Ty423/316-10	2.5	NAL(6), S(14)	CIP 0.064	NAL	gyrA_S83Y
INS-S.Ty666/329-13	3.5	NAL(6), S(11)	CIP 0.032	NAL	gyrA_D87G
INS-S.Ty1013/755-15	2.5	NAL(6), S(13)	CIP 0.064	NAL	gyrA_D87G
INS-GMR-S-752-17	2.5	NAL(6), CIP(25), S(12)	CIP 0.064	NAL	gyrA_D87G
INS-GMR-S-91-18	3.5	NAL(6), CIP(24), S(14)	CIP 0.064	NAL	gyrA_S83F
INS-GMR-S-480-18	2.5	NAL(6), CIP(26), S(13)	CIP 0.064	NAL	gyrA_D87G
INS-GMR-S-432-18	3.5	NAL(6), CIP(23)	ND	NAL	gyrA_S83F
INS-S.Ty0253/397-07	2.5	NAL(6), S(12)	CIP 0.064	NAL	gyrA_D87N
INS-GMR-S-114-17	2.5	NAL(15),CIP(26), S(13)	CIP 0.064	NAL	gyrB_S464Y
INS-S.Ty563/214-12	2.5	NAL(18), CIP(28)	CIP 0.032	NAL(I)	gyrA_S83F
INS-S.Ty538/7-12	3.5	NAL(24), CIP(29), S(12)	CIP 0.008	Susceptible		IncFIB(pHCM2)

*Interpretation criteria according CLSI 2020

Associations between PFGE and WGS

We next aimed to compare the PFGE patterns of the 77 Colombian S. Typhi with that of phylogenetic structure created by WGS. First, we found that the paired isolates from the outbreaks (D and G) were indistinguishable; these organisms had identical PFGEs patterns and displayed no SNP differences in the WGS data (Figs 1 and 3). However, more generally, the PFGE restriction patterns and position in the dendrogram showed minimal concordance with their corresponding phylogenetic location from the WGS data (Fig 3). For example, three isolates from an outbreak (G) shared an identical PFGE restriction pattern (COINXX.JPPX01.0235). This association was encouraging, but on further investigation, an additional three S. Typhi isolates exhibited this same restriction profile. These three further isolates had no apparent epidemiological association with the specific outbreak, were from different geographical locations across Colombia, and were isolated several years after the outbreak (Fig 3). These isolates were determined to be >40 SNPs away in the phylogenetic tree from the isolates causing the outbreak. Lastly, we found a number of occasions where isolates within differing major WGS clades shared an identical PFGE digestion pattern. For example, isolates exhibiting the 0006, 0083, and 0250 PFGE patterns could be found in both clade 2.5 and clade 3.5 of the WGS-based phylogeny (Fig 3). As predicted, these data show that PGFE has limited discriminatory power to identify organisms that may or may not be closely genetically related, further supporting the transition to WGS for routine surveillance.

Fig 3

WGS and PFGE exhibit a limited correlation for Salmonella Typhi genotyping.

The branches of the SNP based RAxML phylogenetic tree and the first column are coloured by genotype. The sequential columns highlight the PFGE patterns that were present more than one occasion. i.e., the bottom two isolates of clade 2.5 with PFGE code COINXX.JPPX01.0004 and the top two isolates of clade 3.5 with PFGE code COINXX.JPPX01.0249. All PFGE patterns are additionally listed under PFGE code (the PFGE pattern correspond to the last 4 digits of the PFGE code) for enhanced visibility only unique PFGE are listed by code and not highlighted. The year of isolation and department are coloured as in Fig 2. Letter D and G indicate the two outbreaks from which we sequenced more than one isolate, these correspond with outbreak strains D and G in Fig 1.

Colombian S. Typhi in a global context

To determine if the detected Colombian genotypes were more likely to be of Colombian origin or introduced from other continents, we placed these contemporaneous Colombian isolates into a global context with an international collection of S. Typhi genome sequences. We constructed a phylogenetic tree of 3,382 publicly available S. Typhi genome sequences with the 77 contemporaneous Colombian isolates; genotype 4.3.1 (H58) sequences were excluded as they were not identified in this collection (Fig 4). The Colombian organisms in clades 2.5 and 3.5 clustered alongside other Colombian organisms within their respective genotypes. The nearest neighbours to these organisms were isolated in India (10592_2_45, genotype 2.5) and Vietnam (10425_1_60, genotype 3.5) in 1997 and 1993, respectively. In the absence of further sampling, these data suggest that clade 2.5 and clade 3.5 are locally circulating genotypes in Colombia. Similarly, the presence of genotypes 1.1 and 0.1.3 in Colombia is indicative of limited circulation of overseas genotypes. Organisms belonging to genotypes 1.1 and 0.1.3 are considered ancient and presently uncommon on the international S. Typhi genotypic landscape and are historically associated with typhoid in Africa [3].

Fig 4

The phylogenetic location of Colombian Salmonella Typhi in a global context.

SNP based RAxML based phylogenetic tree of the 77 Colombian isolates among 3,382 publicly available non-H58 S. Typhi genome sequences. Branches are coloured by genotype, inner ring depicts country of isolation, Asia in red shades, South East Asia in blue shades, Africa in green shades, Colombia in bright red and other Latin American countries in grey. The middle ring indicates AMR profile, any AMR is shown in light brown and MDR isolates are shown in dark brown. Finally, the outer ring shows number of QRDR SNPs with the colour intensity increases with increasing number of SNPs.

Discussion

Here, in this primary study of WGS data from S. Typhi circulating in Colombia, we show that the majority of organisms in the selected 20-year time span displayed limited genetic diversity, belonging mainly to two major clades: 3.5 and 2.5. The limited number of sequenced isolates from Latin America to date mainly belonged to primary cluster 2 and primary cluster 4, with genotype 2.0 in Argentina, Mexico, El Salvador, and Peru, genotype 2.3.2 in Argentina, El Salvador, and Mexico, genotype 4.1.0 in Brazil [19], and Argentina [3]. In the restricted number of isolates screened here, no genotype 4.3.1 (H58) isolates were found. Although samples were selected to present a maximal variation based on diverse PFGE patterns, included specifically outbreak and AMR related isolates, we cannot be certain H58 is not present in Colombia; however, we can surmise that this genotype is not as broadly distributed as in Asia and Africa. Before 2020, genotype 4.3.1 S. Typhi had not been detected in Latin America, but a recent study identified three independent introductions of H58 into Chile [20]. In Chile, the spread of these isolates appears to have been contained; however, this observation highlights the need for sustained genomic surveillance to detect any additional introductions and potential increased circulation of genotype 4.3.1 [33].

A key observation is that the prevalence of AMR in Colombian S. Typhi appears to be significantly lower than that observed in South Asia or Africa. This study, despite being enriched for AMR isolates, indicates an exceptionally low background of AMR in S. Typhi, with only one isolate carrying a plasmid containing AMR genes (IncL/M; pOXA-48), with an additional cryptic no-AMR plasmid (IncFIB; pHCM2) also detected. The precise reason(s) for a lower prevalence of AMR in S. Typhi, in Colombia are unknown and requires additional investigation. We hypothesise that a lower prevalence of AMR S. Typhi, in comparison to Asia and Africa, may be related to antimicrobial access and global pathogen dynamics. Generally, antimicrobial is not better regulated in this region than other locations with a high density of LMICs in the past [34]. However, in the last decade many Latin American countries developed their own National Action Plans to combat AMR under the guidance of PAHO [35]. AMR in S. Typhi is not static, and the global trajectory of AMR is increasing; consequently, there is a constant threat of the importation of AMR organisms and sustained surveillance in Colombia remains crucial. These factors highlight the importance of global typhoid surveillance and not purely restricting observations to Africa and Southeast Asia.

We additionally aimed to assess the potential correlations and utility of PFGE for S. Typhi tracking across Latin America. We found that PFGE and SNP based phylogenetic do not correlate especially well. We found the same PFGE patterns in completely distinct primary clusters of the SNP based phylogeny. These observations again indicate that PFGE results in false clustering and is not appropriately sensitive for surveillance requiring high resolution delineation of local/regional population structure and dynamics of S. Typhi or for outbreak detection in Colombia. WGS is a more appropriate method and is therefore slowly being adopted as the gold standard for these purposes internationally. Lastly, we compared the Colombian isolates to publicly available non-H58 global isolates to determine whether Colombian organisms were imported. This global tree highlighted a lack of genomic information from Latin America. It was therefore impossible to determine whether observed cases are the result of introductions into Colombia from other Latin American countries or local endemic transmission. However, we found that even though the Colombian isolates were collected over 20-years, they formed their own clusters and were not closely related organisms from other locations. These observations suggest that the S. Typhi population structure in Colombia is likely driven by sustained endemic circulation of local genotypes.

This study has limitations, the main one being the small sample size of sequenced isolates. The need to select only a subset of samples meant we could have overlooked genotypes and the proportion of the detected genotypes may not be an accurate overview of the distribution. However, this study was aimed to assess S. Typhi genetic diversity in Colombia and we show that in spite of our diverse selection of organisms that 90% of the isolates belonged to two predominate clades. More thorough sequencing strategies are required to more accurately determine the distribution of genotypes.

This study provides an enhanced insight into the molecular epidemiology of S. Typhi in Colombia, constructing the pathogen population structure and identifying the predominant circulating genotypes. Our work demonstrates that routine surveillance with the integration of WGS is necessary not only to improve disease burden estimates, but also to track the national and regional transmission dynamics of S. Typhi and determine AMR profiles. These data will be pivotal to better estimate the burden of typhoid in the region, improve antimicrobial treatment practices and help policymakers to assess the need for typhoid conjugate vaccine introduction. While the population of S. Typhi in Colombia appears isolated, the emergence and spread of AMR variants have been observed internationally [5,6,33]. Consequently, it is critical for improved control and prevention measures that we establish routine WGS surveillance in Colombia and other Latin American countries to strengthen surveillance and monitoring the continental spread of S. Typhi.

Profile of the organisms selected for sequencing.

(XLSX)

Click here for additional data file.

PFGE clustering of Colombian Salmonella Typhi isolates.

PFGE-XbaI dendrogram generated with Dice coefficient and UPGMA clustering method (tolerance and optimization 1,5%) of 1,077 isolates. The isolates showed 51.45% genetic similarity and represent 211 unique XbaI digestion patterns (as of June 2021). The grey dots indicate the isolates selected for WGS.

(PPTX)

Click here for additional data file.

8 Jun 2021

Dear Professor Baker,

Thank you very much for submitting your manuscript "A genomic snapshot of Salmonella enterica serovar Typhi in Colombia" for consideration at PLOS Neglected Tropical Diseases. As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. In light of the reviews (below this email), we would like to invite the resubmission of a significantly-revised version that takes into account the reviewers' comments.

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Reviewer's Responses to Questions

Key Review Criteria Required for Acceptance?

As you describe the new analyses required for acceptance, please consider the following:

Methods

-Are the objectives of the study clearly articulated with a clear testable hypothesis stated?

-Is the study design appropriate to address the stated objectives?

-Is the population clearly described and appropriate for the hypothesis being tested?

-Is the sample size sufficient to ensure adequate power to address the hypothesis being tested?

-Were correct statistical analysis used to support conclusions?

-Are there concerns about ethical or regulatory requirements being met?

Reviewer #1: yes, yes, yes, yes, and yes. No concerns about ethical requirements

Reviewer #2: See below

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Results

-Does the analysis presented match the analysis plan?

-Are the results clearly and completely presented?

-Are the figures (Tables, Images) of sufficient quality for clarity?

Reviewer #1: Yes analysis and results are clearly and completely presented

Figures, in my version of the manuscript, are somewhat blurred.

Reviewer #2: See below

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Conclusions

-Are the conclusions supported by the data presented?

-Are the limitations of analysis clearly described?

-Do the authors discuss how these data can be helpful to advance our understanding of the topic under study?

-Is public health relevance addressed?

Reviewer #1: Yes, yes, yes and yes

Reviewer #2: See below

--------------------

Editorial and Data Presentation Modifications?

Use this section for editorial suggestions as well as relatively minor modifications of existing data that would enhance clarity. If the only modifications needed are minor and/or editorial, you may wish to recommend “Minor Revision” or “Accept”.

Reviewer #1: (No Response)

Reviewer #2: See below

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Summary and General Comments

Use this section to provide overall comments, discuss strengths/weaknesses of the study, novelty, significance, general execution and scholarship. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. If requesting major revision, please articulate the new experiments that are needed.

Reviewer #1: No weak points in this manuscript. An important paper concerning the molecular epidemiology of S. Thypi. The paper concludes that the S. Typhi population structure in Colombia is mainly driven by endemic circulation of local genotypes. Interesting are the Colombian organisms in clades 2.5 and 3.5. These have their nearest neighbors isolated in India and Vietnam. International trade in agricultural, aquacultural and manufactured food products has facilitated the introduction of new Salmonella serovars within the geographical boundaries of importing countries. Is there perhaps a trade relation between these countries that explains the presence of these genotypes in Colombia? Colombia and Vietnam are the two major coffee export-oriented countries in the world. India and Colombia, as far as I know, have no trade agreements?

Reviewer #2: The manuscript titled “A genomic snapshot of Salmonella enterica serovar Typhi in Colombia” describes the inclusion and analysis of isolates of S. Typhi from Columbia. This manuscript adds new S. Typhi isolates from Colombia, a country on a continent where genome sequences of S. Typhi is largely absent, to the growing global collection of genome data. A technically-sound standardized characterization pipeline was followed using whole genome sequencing, SNP analysis, genotyping, and antimicrobial susceptibility testing. PFGE, in which the study team has extensive expertise, is used as a non-WGS comparator method and determined to be not adequate for the tracking of fine level analysis such as outbreaks and transmission events. While the study makes use of established pipelines and is well done, the primary hypothesis of this work is to address the lack of genomic information from Latin America and Columbia specifically. The data in this study is limited to Columbia, and there is no evidence that would suggest that the trends observed here are generally applicable to all Latin American countries. In fact, the first paragraph of the discussion would suggest that in fact the isolates from Columbia do not represent the rest of the Latin American isolates, even the small subset included there. The manuscript should be rewritten to address Columbia only and not attempt to extrapolate to all of Latin America. The finding that PFGE is not adequate for tracking transmission and outbreaks is not a new finding, but rather one of the reasons many have moved to WGS. This discussion can be removed from the study as it provides very little. Overall, the study is well done but the results and discussion should be reframed to focus on what can be said from this data. Additional comments on focusing the manuscript are included below.

Major comments:

The choice to perform a cross-sectional analysis of 77 (5.2%), rather than a comprehensive analysis of all or a majority of the 1,478 isolates from 1997 to 2018, is not well justified in light of the primary motivations of this manuscript, i.e., to fill in the gap/blind spot of S. Typhi genomics in South America, specifically in Colombia. In a previous publication by the authors [ref 18, doi:10.1371/journal.pntd.0008040], 402 isolates out of 846 reported typhoid cases from 2012-2015 were available for microbiological characterization and genotyping analyzed using PFGE, suggesting many available isolates were either not sequenced or not included. It is unclear why these isolates were not included. Under results (starting with line 184), the authors explain that isolate selections across the 22 years were made to maximize PFGE diversity, but later the authors conclude that PFGE correlated poorly with PFGE. Indeed, WGS of all available isolates could add an interesting investigation into the clonality of the eight presumably distinct outbreaks from which single distinct PFGE isolates were selection (Fig. 1A), if it is fair to assume the outbreaks were designated as such due to a concordance of epidemiological linkage of persons, place, and time with a matching PFGE type.

In another example where a lack of PFGE and genotype correlation is problematic for the study design, line 211 asserts a lack of genotype 4.3.1 (H58) in Colombia and line 2015 asserts limited accumulation of AMR genes,” but these statements may be false if one or several of the isolates excluded on the basis of PFGE pattern was in fact the H58 clone. Overall, this suggests to me that the inclusion criteria are flawed and should be reconsidered, or the implications of sampling bias should be further discussed. In its present form, the paper is an interim analysis that could be proven wrong by the same isolate collection on which it is based. Ideally, all available isolates would be sequenced and submitted to the public collection via this paper.

If diverse PFGE patterns is of importance to the authors, a subsequent sub-analysis beginning with a cross-section of the maximal dataset could be suitable in order to reach the conclusion that PFGE does not correlate well with WGS genotype, which is one of the current genotyping standards in the field. Additional methodological detail on how the isolates were determined as the same or different should be included. While many readers will be familiar with this methodology, it is not clear in the current version of the manuscript (i.e. how many bands different were required to determine that two isolates were the same or different).

The authors suggest there are 60 unique PFGE patterns, but only 8 (A-H) are labeled in Figure 1A. The inclusion of the PFGE pattern in Figure 3, where most of the isolates appear to lack these designations is also confusing. Why are there no PFGE designations included. Additionally Figure 1B does not add significantly to the submission and can be removed.

While the PFGE to WGS associations are interesting, they are not surprising considering the difference in the methodology. However, with this dataset it would be good to interrogate the associations between the actual phenotypes and genotypes for AMR.

Minor comments:

Line 74: missing the word “cases” after “13 million”

Lines 161-162 – what quality metrics were used to include or exclude any of the assembled genomes? Any coverage, quality or contig size cutoffs were used in this study.

How were the AMR genes determined to be present or absent. The current manuscript just indicates that they were screened, without any indication of what is considered present or absent.

Fig 2. Many colors for department are indistinguishable and appear to repeat. Consider recoloring, using numbers instead of colors, or adding a supplementary table with each isolate listed followed by key epidemiological and genotypic variables.

Lines 206-9: commas to separate the list are missing, and capitalization of Column is inconsistent.

Line 254: The source (presumably pathogen.watch) and the genome representation (alignment against CT18 or another local reference, or de novo assembly) of these 3,381 publicly available isolates would be helpful to know, as it is important that genomes are processed in the same way – aligned to CT18 or assembled – before being input into RAxML.

Lines 288-299: The suggestion that there is less AMR in many pathogens in Latina America is not supported by references or additional data and seems to be based on conjecture at this point. These paragraph should be referenced appropriately or removed.

Line 418 – Data availability – it does not appear as though the data is publicly available with the included project ID. The authors must ensure that all data, both raw sequences and assembled data are publicly available prior to resubmission as both are used in the analyses described in this study.

The figure legends are lacking in any detail.

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Reviewer #1: No

Reviewer #2: No

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21 Jul 2021

Submitted filename: Cover letter20210720.docx

Click here for additional data file.

24 Aug 2021

Dear Dr. Baker,

We are pleased to inform you that your manuscript 'A genomic snapshot of Salmonella enterica serovar Typhi in Colombia' has been provisionally accepted for publication in PLOS Neglected Tropical Diseases.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.

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Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Neglected Tropical Diseases.

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Travis J Bourret

Associate Editor

PLOS Neglected Tropical Diseases

Sujay Chattopadhyay, PhD

Deputy Editor

PLOS Neglected Tropical Diseases

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Reviewer's Responses to Questions

Key Review Criteria Required for Acceptance?

As you describe the new analyses required for acceptance, please consider the following:

Methods

-Are the objectives of the study clearly articulated with a clear testable hypothesis stated?

-Is the study design appropriate to address the stated objectives?

-Is the population clearly described and appropriate for the hypothesis being tested?

-Is the sample size sufficient to ensure adequate power to address the hypothesis being tested?

-Were correct statistical analysis used to support conclusions?

-Are there concerns about ethical or regulatory requirements being met?

Reviewer #1: I approved the earlier version and as I see that no important changes have been made to this version I also approve this version. An important paper in the field.

Reviewer #2: (No Response)

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Results

-Does the analysis presented match the analysis plan?

-Are the results clearly and completely presented?

-Are the figures (Tables, Images) of sufficient quality for clarity?

Reviewer #1: Yes

Reviewer #2: (No Response)

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Conclusions

-Are the conclusions supported by the data presented?

-Are the limitations of analysis clearly described?

-Do the authors discuss how these data can be helpful to advance our understanding of the topic under study?

-Is public health relevance addressed?

Reviewer #1: yes

Reviewer #2: (No Response)

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Editorial and Data Presentation Modifications?

Use this section for editorial suggestions as well as relatively minor modifications of existing data that would enhance clarity. If the only modifications needed are minor and/or editorial, you may wish to recommend “Minor Revision” or “Accept”.

Reviewer #1: (No Response)

Reviewer #2: Minor Grammar issues:

Line 135: “mayor” should be “major”

Line 155: There is an aberrant period after “protocols.”

Line 289: “antimicrobial is” should be “antimicrobials are”

Like 534: “1,5%” should be “1.5%”

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Summary and General Comments

Use this section to provide overall comments, discuss strengths/weaknesses of the study, novelty, significance, general execution and scholarship. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. If requesting major revision, please articulate the new experiments that are needed.

Reviewer #1: (No Response)

Reviewer #2: Overall, the authors have accepted that these data will apply to Columbia, but not the rest of South America and have re-written this to reflect that suggestion. They have done a good job addressing the comments of the previous reviewers. The key point where I have an issue is with the lack of data release. While they have released the raw reads, which is great, the assemblies, upon which much of the results are based are not in the public domain. It is difficult, if not impossible to completely review the paper completely without this information in the public domain. There have been far too many retractions and loss of data based on the "promises" to release data upon acceptance of the work.

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Reviewer #1: No

Reviewer #2: No

8 Sep 2021

Dear Professor Baker,

We are delighted to inform you that your manuscript, "A genomic snapshot of Salmonella enterica serovar Typhi in Colombia," has been formally accepted for publication in PLOS Neglected Tropical Diseases.

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Thank you again for supporting open-access publishing; we are looking forward to publishing your work in PLOS Neglected Tropical Diseases.

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co-Editor-in-Chief

PLOS Neglected Tropical Diseases

Paul Brindley

co-Editor-in-Chief

PLOS Neglected Tropical Diseases