Literature DB >> 35143500

Multiple phylogenetically-diverse, differentially-virulent Burkholderia pseudomallei isolated from a single soil sample collected in Thailand.

Chandler Roe1, Adam J Vazquez1, Paul D Phillips1, Chris J Allender1, Richard A Bowen2, Roxanne D Nottingham1, Adina Doyle1, Gumphol Wongsuwan3, Vanaporn Wuthiekanun3, Direk Limmathurotsakul3, Sharon Peacock4, Paul Keim1, Apichai Tuanyok1, David M Wagner1, Jason W Sahl1.   

Abstract

Burkholderia pseudomallei is a soil-dwelling bacterium endemic to Southeast Asia and northern Australia that causes the disease, melioidosis. Although the global genomic diversity of clinical B. pseudomallei isolates has been investigated, there is limited understanding of its genomic diversity across small geographic scales, especially in soil. In this study, we obtained 288 B. pseudomallei isolates from a single soil sample (~100g; intensive site 2, INT2) collected at a depth of 30cm from a site in Ubon Ratchathani Province, Thailand. We sequenced the genomes of 169 of these isolates that represent 7 distinct sequence types (STs), including a new ST (ST1820), based on multi-locus sequence typing (MLST) analysis. A core genome SNP phylogeny demonstrated that all identified STs share a recent common ancestor that diverged an estimated 796-1260 years ago. A pan-genomics analysis demonstrated recombination between clades and intra-MLST phylogenetic and gene differences. To identify potential differential virulence between STs, groups of BALB/c mice (5 mice/isolate) were challenged via subcutaneous injection (500 CFUs) with 30 INT2 isolates representing 5 different STs; over the 21-day experiment, eight isolates killed all mice, 2 isolates killed an intermediate number of mice (1-2), and 20 isolates killed no mice. Although the virulence results were largely stratified by ST, one virulent isolate and six attenuated isolates were from the same ST (ST1005), suggesting that variably conserved genomic regions may contribute to virulence. Genomes from the animal-challenged isolates were subjected to a bacterial genome-wide association study to identify genomic regions associated with differential virulence. One associated region is a unique variant of Hcp1, a component of the type VI secretion system, which may result in attenuation. The results of this study have implications for comprehensive sampling strategies, environmental exposure risk assessment, and understanding recombination and differential virulence in B. pseudomallei.

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Year:  2022        PMID： 35143500      PMCID： PMC8865643          DOI： 10.1371/journal.pntd.0010172

Source DB:  PubMed          Journal:  PLoS Negl Trop Dis        ISSN： 1935-2727


Introduction

Burkholderia pseudomallei is a soil-dwelling bacterium endemic to Southeast Asia and northern Australia where it is the causative agent of melioidosis, a potentially fatal disease in humans [1]. In Thailand, B. pseudomallei causes 19% of community-acquired bacteremia [2] and in northern Australia, melioidosis is the most common cause of fatal bacteremic pneumonia [3]. Agricultural workers are among the highest at-risk population to develop infection due to repeated exposure to B. pseudomallei, primarily due to direct contact with compromised skin [4]. Melioidosis can be difficult to treat with antibiotics [5] and has been associated with a mortality rate as high as 50% in symptomatic individuals [6]. The most probable route of infection occurs through percutaneous inoculation [7], although inhalation and ingestion are also important [8,9]. Most genome sequencing of B. pseudomallei has focused on clinical isolates, although sequencing and analysis of environmental isolates has been performed, including recent studies focused on novel diversity in the Caribbean [10,11]. Other studies have investigated the microdiversity of B. pseudomallei in the environment using sub-genomic methods, such as pulsed-field gel electrophoresis (PFGE) [12] and multi-locus sequence typing (MLST) [13,14]. Although one study found 4 distinct sequence types (STs) within a single soil sample in Ubon, Thailand [12], the resulting data did not identify genomic and phylogenetic differences within each ST. Another study surveyed genotypic diversity with sub-genomic methods in Northeastern Thailand and found 7 distinct STs across 11 soil samples [13]. A study on MLST types in Australia identified that the diversity of B. pseudomallei populations increased with sampling area, suggesting localized adaptation [15]. A recent study used whole genome sequencing to identify genomic signatures that differ between clinical and environmental B. pseudomallei isolates in Northeastern Thailand [16]; however, the focus of this study was the overlap between clinical and environmental isolates and not the within-ST diversity of B. pseudomallei. The B. pseudomallei genome is highly plastic [17], owing largely to the acquisition of genomic islands (GIs) [18], which are often acquired from other species via horizontal gene transfer [18]. These GIs generally consist of several contiguous genes and account for an average 5.8% of an individual B. pseudomallei genome [19]. Previous studies have demonstrated high levels of homologous recombination throughout the B. pseudomallei genome, including within housekeeping genes [20]; research has shown that recombination is the major driver of evolution in B. pseudomallei [19] and a diverse and adaptable genome is crucial for its environmental adaptation and success within soil [14,21,22]. Understanding the accessory genome of B. pseudomallei isolates collected from a limited geographic region may provide a better understanding of its ability to adapt to local ecological conditions. Multiple mechanisms associated with pathogenesis and virulence have been described in B. pseudomallei. One of the most characterized virulence factors is the cluster 1 type VI secretion system (T6SS) [23], although the type III secretion system cluster 3 also has been associated with virulence [9]. Recent studies associated a filamentous hemagglutinin (FhaB3) with septic shock and mortality in Australia [24] and the oxidoreductase membrane protein DsbB with virulence based on a knock-out study and subsequent animal challenge [25]. Although the characterization of these factors has improved our understanding of B. pseudomallei pathogenesis, most mechanisms remain unidentified and virulence in this species remains poorly understood [26,27]. These types of analyses are further confounded by host factors associated with melioidosis [28], suggesting that an interplay of pathogen and host factors are associated with disease severity and patient outcome [8]. The purpose of this study was to examine the evolutionary dynamics of environmental B. pseudomallei and investigate differential virulence from a single soil sample from Thailand. We sequenced 169 B. pseudomallei genomes from the soil sample out of a set of 288 isolates. A total of 30 isolates were selected for a murine infection study and comparative genomics and machine learning were used to identify differences between differentially-virulent groups. The results have implications for environmental sampling strategies for B. pseudomallei that are focused on understanding the virulence potential of clinical and environmental isolates. The characterization of genomic diversity and virulence of sympatric isolates will aid in the understanding of the evolution, ecology, and disease potential of this important human pathogen.

Methods

Ethics statement

Mouse challenge experiments were approved by the Animal Care and Use Committee of Colorado State University (CSU), protocol number 17-7497A. The experiments were performed under Select Agent and ABSL-3 containment practices at CSU and in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. Every effort was made to minimize animal suffering and pain.

B. pseudomallei isolation

A single site was chosen in the Ubon Rachathani Province (a.k.a. Ubon) of Thailand for sample collection (Fig 1A). The site, a rice field, was sampled in the dry season in a region where B. pseudomallei is highly endemic. The map of Thailand was created using ArcGIS software by Esri. ArcGIS and ArcMap are the intellectual property of Esri and are used herein under license, all rights reserved. The site was divided into a grid of 50 holes, each separated by 2m (Fig 1B). On 2 April 2007, soil was collected from all 50 holes at a depth of both 10cm and 30cm with a total of 100g of soil collected per sample; we report the results from just one of these 100 samples here as it yielded the largest number of B. pseudomallei isolates. All soil samples were mixed with 100ml of sterile distilled water in zipped bags and the bags were left to stand at room temperature overnight. A direct plating technique was used to isolate B. pseudomallei by inoculating 2 different volumes of the soil solution, 10 and 100μl, onto an Ashdown agar plate. The inoculum was spread evenly on the agar plate with an L-shape spreader while the plate was being spun on a plate rotator. The plates were incubated at 42°C for 7 days and were checked daily for growth. A latex agglutination test [29] was used as a presumptive test for B. pseudomallei identification from ~300 isolates collected from INT2 (Fig 1C). Each suspected B. pseudomallei colony was picked by a sterile loop and inoculated into each well of a 96-well plate containing 100μl of LB agar. The plate was sealed with adhesive aluminum foil and stored at room temperature before being shipped to Northern Arizona University under a CDC/APHIS permit. Isolates were picked, added to 70% glycerol, and stored at -80°C.
Fig 1

The sampling strategy performed in this study.

A) The location in Thailand (circle) where samples were obtained; the city of Ubon Ratchathani within Ubon Ratchathani province is marked by the star. The map of Thailand was created using ArcGIS software by Esri. ArcGIS and ArcMap are the intellectual property of Esri and are used herein under license, all rights reserved, B) the sampling strategy at the intensive sampling site; the location where the INT2 sample was collected is marked and boxed, C) a plate showing the B. pseudomallei colonies grown out of the single enriched INT2 soil sample; a total of 288 isolates were picked from this plate, and 169 of these isolates were sequenced.

The sampling strategy performed in this study.

A) The location in Thailand (circle) where samples were obtained; the city of Ubon Ratchathani within Ubon Ratchathani province is marked by the star. The map of Thailand was created using ArcGIS software by Esri. ArcGIS and ArcMap are the intellectual property of Esri and are used herein under license, all rights reserved, B) the sampling strategy at the intensive sampling site; the location where the INT2 sample was collected is marked and boxed, C) a plate showing the B. pseudomallei colonies grown out of the single enriched INT2 soil sample; a total of 288 isolates were picked from this plate, and 169 of these isolates were sequenced.

DNA extraction and sequencing

Isolates were grown on LB agar at 37°C for 24–48 hours and DNA was extracted from each isolate using the Qiagen DNeasy Blood and Tissue Extraction kit (Qiagen, Hilden, Germany) following the Gram-positive protocol with the addition of 1mg/ml of lysozyme and doubling the volumes. DNA was prepared for multiplexed, paired-end sequencing with a 500 base pair insert using Standard PCR Library Amplification (KAPA Biosystems, Woburn, MA). Genomes were sequenced on either a 2x100 bp paired-end HiSeq run or a 2x250 bp MiSeq run.

Genomic assembly and in silico genotyping

Illumina sequence data were assembled with the SPAdes assembler v.3.10.0 [30]. The per contig coverage was determined by mapping reads against contigs with Minimap2 v2.17 [31] and calculating coverage with Samtools v1.9 [32]. Each genome assembly was manually edited to remove contigs that had an anomalously low coverage compared to the rest of the assembly or aligned against known contaminants based on a BLASTN alignment [33] against the GenBank [34] nt database. Assembly statistics and accession numbers are shown in S1 Table. The multi-locus sequence type (MLST) for each isolate was calculated from each genome sequence with stringMLST [35] (S1 Table). Representative genome assemblies were submitted to GenBank (S1 Table).

Comparative pan-genomics

All genome assemblies were annotated with Prokka v1.14.6 [36]. The pan- and core-genomes were calculated for each ST with Panaroo v1.2.3 [37]. The resulting pan-genome representative sequences were mapped against genome assemblies with the large-scale BLAST score ratio (LS-BSR) tool v1.2.2 [38] in conjunction with BLAT v36x2 [39]. Variably-conserved genes, based on blast score ratio (BSR) values [40], were mapped against phylogenies from each ST with LS-BSR/BLAT and visualized with the interactive tree of life (iTOL) [41].

Single nucleotide polymorphism (SNP) identification and phylogenetics

To understand the placement of INT2 genomes within the global B. pseudomallei phylogeny, INT2 genome assemblies were combined with a global set of B. pseudomallei genomes (S2 Table); genomes were downloaded using the ncbi-genome-download tool (https://github.com/kblin/ncbi-genome-download) on December 13, 2019. SNPs were called with nucmer v3.1 [42] in conjunction with NASP v1.0.2 [43] for genome assemblies using B. pseudomallei K96243 (accession number GCA_000011545.1) [44] as the reference. A phylogeny was inferred on the concatenated SNP alignment with FastTree2 v2.1.10 [45]. For all other phylogenies, raw reads were aligned against B. pseudomallei K96243 with Minimap2 and SNPs were called using the HaplotypeCaller method in GATK v4.1.2 [46,47]. These functions were wrapped with NASP and positions were filtered if the coverage was <10X or the minimum allele frequency ratio was <0.90. All remaining polymorphic positions were concatenated into a single multi-FASTA file. Phylogenetic trees were inferred on concatenated SNP alignments with a maximum-likelihood algorithm implemented in IQ-TREE v1.6.1 [48] using the integrate model search method [49]. The Retention Index of SNP alignments, which provides information on the extent of homoplasy, was calculated with Phangorn v2.4 [50]. The functional annotation of SNPs was determined with SnpEff v4.3t [51]. Homoplastic SNPs were identified from the FASTA file with HomoplasyFinder [52].

Population structure

The NASP SNP matrix including the 169 B. pseudomallei genomes sequenced in this study was used as input into fastSTRUCTURE v1.0 [53] in order to investigate population structure. Using a variational Bayesian framework, the most likely number of populations as well as the probability that each strain belonged to each population was calculated. Only bi-allelic sites were included in this analysis and no phenotype data were provided for this sample set. The admixture model was applied assuming SNPs were unlinked. Individual ancestry and correlated allele frequencies were simulated between the range of K = 2 to K = 10 populations. Individual marginal likelihood values from each K simulation were compared to infer the most likely number of populations (K with the lowest likelihood) using the chooseK.py script included with fastSTRUCTURE [53].

Beast timing analysis

BEAST analysis included all 169 genomes sequenced in this study. Putative recombinant SNPs were identified and removed with Gubbins v2.4.1 [54] prior to running BEAST. A SNP matrix based on B. pseudomallei chromosome I (BX571965.1) that included 5,252 total SNPs with 3,059 parsimony-informative SNPs was analyzed within a Bayesian framework separate from chromosome II (BX571966.1) data based on previous research [55]. The chromosome II dataset included 3,760 total SNPs with 2,484 informative SNPs used in the Bayesian analysis. Additionally, each ST was run through BEAST separately as well using a previous reported evolutionary rate for chromosome I and chromosome II (9.22E-7 and 6.71E-7 substitutions per site per year, respectively) [55]. To estimate the most recent common ancestor (TMRCA) for the INT2 isolates, a Bayesian strict clock was applied in BEAST version 1.8.4 [56]. MEGA7 [57] was used to infer the best nucleotide substitution model using the Bayesian information criterion (BIC) (GTR for chromosome I data and HKY for the chromosome II dataset). A correction for invariant sites was implemented in all BEAST analyses by specifying a Constant Patterns model. Additionally, a “path and stepping stone” sampling marginal-likelihood estimator was used to determine best-fitting clock (strict and relaxed) and demographic model combination (Constant, Bayesian Skygrid, Bayesian Skyline, Extended Bayesian Skyline Plot, GMRF) from ten clock and model combinations [58]. However, no appreciable difference of the marginal likelihood calculation was observed. We implemented the strict skyline clock and skyline demographic model combination to avoid overparameterization. Four independent chains of 1 billion iterations were run for the strict clock and skyline demographic model combination. The program Tracer v1.6.0 [59] was used to visually confirm convergence.

In vivo animal challenge

Female BALB/c mice, 6–8 weeks old, were purchased from Charles River laboratories and housed at Colorado State University. To assess virulence of a subset of 30 of the soil isolates collected from INT2, each isolate was grown at 37°C in BHI medium to an OD600 of 1, supplemented with glycerol to 10% v/v, and frozen in multiple aliquots at -80°C. A vial of these stocks was thawed and serial dilutions plated on BHI agar plates to determine titer 2 days prior to the animal challenge. On the day of the animal challenge, a new frozen aliquot was thawed and diluted with PBS to obtain approximately 500 CFU per 50μL of the bacterial inoculum. The development of the subcutaneous (SC) model in BALB/c mice found an LD50 of 1000 CFU [60]; this guided the use of 0.5x LD50 in this study to identify differential virulence. Each mouse (5 mice per strain) was subcutaneously inoculated in the medial right leg with 50μL of the bacterial inoculum; subcutaneous infection in mouse models represents percutaneous human infections, which are common in endemic areas [61]. The same volume was also back-titrated to confirm the given dose. In addition to INT2 isolates, mice were also challenged with control strains, NCTC13178 and NCTC13179 [60], at the same dose; strains were purchased from the National Collection of Type Culture (NCTC) in the UK. Mice were weighed and evaluated for clinical symptoms associated with B. pseudomallei for 21 days post infection. This monitoring was performed daily for the first 7 days and subsequently every 3–4 days to 21 days. The clinical scores indicated in the study were: 0 = normal, 1 = questionable illness, 2 = mild but definitive illness, 3 = moderate illness, 4 = severe illness, moribund-euthanized, 5 = found dead. The moribund and surviving mice at 21 days post infection were euthanized by a standard CO2 overdosing method. To compare the virulence of selected isolates, combined survival curves for each ST were graphed using the program GraphPad Prism version 7.00 for mac (GraphPad Software, La Jolla California, USA).

Comparative genomics of animal-challenged isolates

Genomes from virulent (n = 8) (killed all mice) and attenuated (n = 20) (killed no mice) strains, as determined by the mouse challenge, were processed with LS-BSR using BLAT to align coding region sequences (CDS) predicted by Prokka and clustered with CD-HIT v4.8.1 [62]; intermediate virulence strains (n = 2) were not included in this analysis. CDSs were identified that were more conserved in either the virulent or attenuated phenotypes with the compare_BSR.py script in the LS-BSR repository based on BSR values; the differential conservation of genes was verified thorough short read mapping approaches where the breadth of coverage was calculated with Samtools. For comparison, the pan-genome was also determined with Panaroo using Prokka-annotated genomes.

Genome-wide association analysis (GWAS) with pyseer

To identify genomic regions associated with differential virulence, a Kmer-based GWAS analysis was conducted across animal-challenged genomes. Briefly, assembled genomes were fragmented into 54 base pair Kmers using bbmap (https://sourceforge.net/projects/bbmap/) and Kmers with a frequency <4X coverage in any one genome were converted to “0”; only Kmers that were present in at least one genome were processed further. Kmers were then analyzed with pyseer [63] using default settings with phenotypic data represented by “1” for virulent and “0” for attenuated isolates. For the investigation of genes that contain associated Kmers, regions were extracted from BLASTN alignments, aligned with MUSCLE v3.8.31 [64], and visualized with Jalview [65].

Machine learning approaches for feature identification

In order to identify genes associated with virulence, a machine learning algorithm (Boruta) [66] was applied to a binary LS-BSR matrix; a BSR value ≥0.8 for a CDS was coded as present (“1”), whereas a score <0.8 for a CDS was coded as absent (“0”). Of the 7 parameters the Boruta_py module (https://github.com/scikit-learn-contrib/boruta_py) requires, all were kept at default except the estimator object, which is a supervised learning model in which feature importance can be calculated. The percentage strength of Boruta’s shadow features compared to the real features was also calculated, which was set to 99 instead of the default 100 to identify more potential associations. A random forest classifier from the sci-kit learn [67] module was tuned over 250 iterations of stratified 5-fold cross-validation with gridsearchCV and chosen for the estimator object. Finally, the Boruta algorithm was run for 125 iterations over the random seed parameter of the Boruta_py module on the tuned random forest model. Regions of interest were identified by only investigating features that were selected in 100% of the Boruta iterations.

Screen of previously-characterized virulence genes

The peptide sequences of 551 genes previously associated with virulence in B. pseudomallei [68] were screened against the 30 animal-challenged genomes with TBLASTN [69] in conjunction with LS-BSR.

Results

The diversity of B. pseudomallei within a single soil sample (intensive site 2, INT2) from the Ubon Ratchathani Province of Thailand was investigated by whole genome sequencing and comparative genomics. An analysis of 169 genomes demonstrated that seven different sequence types (STs) were observed, although three of the STs were single-locus variants of STs observed in INT2 genomes (S1 Table). The differential virulence of a subset of 30 of these isolates was investigated in a murine melioidosis model.

Global phylogenetics

To understand the global diversity of sequenced genomes, the 169 INT2 genomes were compared to 1,576 globally-diverse B. pseudomallei genomes available in GenBank (S2 Table). The core genome phylogeny demonstrated that genomes from this study shared a recent common ancestor (Fig 2), suggesting a common geographic origin. The retention index (RI) for the concatenated SNP alignment and phylogeny was calculated at 0.8378, indicating homoplasy throughout the B. pseudomallei core genome, which was expected based on previous studies [70].
Fig 2

An approximate maximum-likelihood phylogenetic tree inferred with FastTree2 [45] from a concatenation of 90,518 core genome SNPs called from 169 genomes sequenced in this study (red text) as well as 1,576 reference B. pseudomallei genomes from GenBank (S2 Table).

Genomes sequenced in this study are shown in red. The tree was rooted with MSHR668 based on its basal position in other studies [71].

An approximate maximum-likelihood phylogenetic tree inferred with FastTree2 [45] from a concatenation of 90,518 core genome SNPs called from 169 genomes sequenced in this study (red text) as well as 1,576 reference B. pseudomallei genomes from GenBank (S2 Table).

Genomes sequenced in this study are shown in red. The tree was rooted with MSHR668 based on its basal position in other studies [71].

Population genomics of INT2 genomes

To better understand their genetic backgrounds, we determined the population structure of the 169 B. pseudomallei genomes from INT2 using Bayesian methods implemented in fastSTRUCTURE. By comparing individual marginal likelihood values to each individual K value (signifying number of populations), the most reliable population distribution was determined. The marginal likelihood values identified K = 4 as the best scenario with four distinct populations largely corresponding to ST58, ST93, ST1005, and ST60 (Fig 3); these populations are highly correlated with phylogenetic groupings (Figs 2 and 3). The ST58 population is composed of a mostly unique clade (solid yellow) with isolates Bp1763 and Bp1752 representing recombination with the ST93 clade. The ST60 population is composed of admixtures between genomes from ST93, ST1005, and ST58 populations. The ST1005 population is represented by a unique genetic clade (solid red) with only one genome (Bp1818; ST1007) displaying an admixture. Furthermore, while ST93 has the largest number of genomes (n = 82), only three isolates displayed an admixture (Bp1740, Bp1805, Bp8880).
Fig 3

The population structure of INT2 genomes, as revealed by fastSTRUCTURE [53].

The maximum likelihood tree was inferred with IQ-TREE [48] on 22,080 concatenated SNPs using the TVM+F+ASC+R2 substitution model [49]. Four clear populations are present, corresponding to ST58, ST60, ST1005, and ST93, with limited admixing between STs. Each color represents a population and each individual genome is displayed as a horizontal line subdivided into color blocks whose lengths represent the admixture proportions from K = 4 populations.

The population structure of INT2 genomes, as revealed by fastSTRUCTURE [53].

The maximum likelihood tree was inferred with IQ-TREE [48] on 22,080 concatenated SNPs using the TVM+F+ASC+R2 substitution model [49]. Four clear populations are present, corresponding to ST58, ST60, ST1005, and ST93, with limited admixing between STs. Each color represents a population and each individual genome is displayed as a horizontal line subdivided into color blocks whose lengths represent the admixture proportions from K = 4 populations. A concatenated SNP alignment from 169 INT2 genomes identified 7,960 core genome SNPs, out of 22,080 total SNPs, with a consistency index ≤0.5. The locus tags in K96243 with the highest number of homoplastic SNPs in INT2 genomes include: BPSS1007 (polyketide synthase), BPSS0409 (hypothetical protein), BPSS0306 (polyketide synthase), and BPSL1712 (non-ribosomal antibiotic-related protein synthase); the functions of these and many other regions containing homoplastic SNPS are largely unknown.

Comparative genomics of representative STs

Sub-trees were generated for the four major STs (ST60, ST58, ST1005, ST93) (S1–S4 Figs, respectively). Although gene content variability within each ST was largely due to convergent gene loss, unique genes were also identified in a subset of strains; unique and lost genes are represented as a heatmap correlated to the phylogeny (S1–S4 Figs). A pan-genomics analysis determined that the core and pan-genome for each ST were largely similar in size (Table 1), suggesting minimal, yet observable gene differences within each ST. When compared to the global set of B. pseudomallei genomes, unique gene variants were identified within each of the four STs (Table 1); although homologs were observed for these unique regions in other B. pseudomallei genomes, each ST-specific sequence contained unique insertions/deletions that were not observed in these other genomes and, thus, represent genes that encode distinct proteins.
Table 1

Coding region stats for each major INT2 sequence type.

PopulationPan-genome size (#CDSs)Core-genome size (#CDSs)#Unique variants
#genomesUnique locus tags
9382618548851AQ771_RS06540
5816583456041C1W81_11530
100534589656742C1W93_05955,C1W93_24425
6037596355512C1W75_00010,C1W75_27835

Bayesian timing analysis

The 169 INT2 B. pseudomallei genomes representing the four populations identified by fastStructure were included in the Bayesian convergence analysis. The SNP matrix included reference positions present in >90% of reads and had a minimum coverage of 10X that spanned 90.1% of reference chromosome I in K96243 (NC_006350.1) and 75.1% of reference chromosome II (NC_006351.1). Based on previous research [55], the estimated mutation rates for chromosome I and chromosome II differ slightly, 9.22E-7 vs 6.71E-7. As such, the time to most recent common ancestor (TMRCA) was calculated in the BEAST analysis separately for each chromosomal dataset using these previously reported mutation rates. For the 169 INT2 genomes, the mean TMRCA for chromosome I was estimated at 1258.5 years ago (95% highest posterior density (HPD), 1229.7, 1286.5), and the mean TMRCA for chromosome II was estimated at 791.0 years ago (95% HPD, 757.8, 823.1; Fig 4). The TMRCA for the ST1005 clade was estimated at 80.7 years ago (95% HPD, 72.6, 88.9) for chromosome I and 48.7 years ago (95% HPD, 40.1, 57.3) for chromosome II. The TMRCA for ST58 clade was estimated at 243.8 years ago (95% HPD, 229.6, 258.1) for chromosome I and 63.6 years ago (95% HPD, 53.2, 73.8) for chromosome II. The ST93 lineage TMRCA was estimated to be 95.4 years ago (95% HPD, 87.1, 104.0) for chromosome I and 74.5 years ago (95% HPD, 63.4, 85.0) for chromosome II. The ST60 clade TMRCA was estimated to be 250.3 (95% HPD, 238.9, 262.0) for chromosome I and 67.4 years ago (95% HPD, 58.4, 76.1) for chromosome II.
Fig 4

Estimate of times to most recent common ancestor for the 169 INT2 B. pseudomallei genomes using BEAST [56].

Node dating ranges for both chromosomes (chromosome I in red, chromosome II in black) are shown on the Bayesian phylogeny. STs are indicated for each population.

Estimate of times to most recent common ancestor for the 169 INT2 B. pseudomallei genomes using BEAST [56].

Node dating ranges for both chromosomes (chromosome I in red, chromosome II in black) are shown on the Bayesian phylogeny. STs are indicated for each population.

Differential virulence

The virulence of 30 INT2 B. pseudomallei isolates assigned to five different sequence types was examined in a murine infection model (5 mice per strain); these included isolates from the four main STs (ST58, ST60, ST93, ST1005), as well as isolate Bp8873 from ST176 (S1 Table); ST176 is a single locus variant of ST60. In addition, two controls strains with known variable virulence in this model (NCTC13178, NTCT13179) were processed at the same dosage. The probability of survival was calculated from the pooled challenged mice per tested ST (Fig 5, raw data in S3 Table). A core genome phylogeny of 169 INT2 isolate genomes demonstrates the phylogenetic distribution of the 30 isolates utilized for virulence studies within this larger set (S5 Fig). Isolates were considered virulent if they killed all tested mice, attenuated if they killed no mice, and intermediate if they killed between 1 and 5 mice. None of the tested ST93 isolates (n = 10) killed any mice based on the subcutaneous inoculation pathway. In contrast, all tested ST58 isolates (n = 6) were highly virulent, killing all challenged mice. Interestingly, six ST1005 strains killed no mice, whereas 1 ST1005 strain (Bp8884) killed all mice challenged (Fig 5 and S3 Table). Four ST60 isolates did not kill any challenged mice, whereas one isolate (Bp8881) killed one mouse. The single ST176 isolate (Bp8873) was highly virulent, killing all challenged mice before the end of the 21-day experiment. The virulent control strain (NTCT13178) killed all mice at 500 CFU by day 5 and the less virulent control strain killed no mice over the 21-day experiment.
Fig 5

A kill curve based on subcutaneous injection of 500 CFUs in 5 mice for each isolate.

The mortality curves were plotted in Prism. For each ST, all mice were pooled, and mortality curves plotted. Raw survival data per isolate is shown in S3 Table.

A kill curve based on subcutaneous injection of 500 CFUs in 5 mice for each isolate.

The mortality curves were plotted in Prism. For each ST, all mice were pooled, and mortality curves plotted. Raw survival data per isolate is shown in S3 Table.

Genome wide association studies

To identify genotypic differences that may explain the virulent phenotype, the program pyseer was run on a set of all 54-mers from Illumina reads from each of the 30 INT2 isolates used for virulence studies. Pyseer identified 31 virulence-associated 54-mers, all of which mapped to a single annotated gene, mgtA (C1W34_19470). However, all of these Kmers had “bad chisq” values signifying possible spurious associations; a “bad chisq” value is reported by pyseer and no additional information is available. These 31 associated Kmers were conserved in 7 of 8 virulent genomes and 8 of 20 attenuated genomes. However, a manual inspection of an alignment of this gene demonstrated that SNPs were highly correlated with population structure, with the exception of a single SNP found in all ST58 and ST60 genomes (including ST176), most likely due to homologous recombination. In addition to pyseer, we also used a machine learning (ML) Boruta model to identify CDSs potentially associated with virulence. ML has been previously used to accurately predict phenotype from genotype [72,73]. Although ML methods used in this study have not been previously used for bacterial GWAS, the Boruta method has been established by statisticians and human GWAS researchers as an efficient and specific feature selection algorithm [66,74]. By iterating over the random seed of Boruta, we were able to identify 45 associated CDSs that were consistently selected as associated with virulence, although only 11 of them (S6 Fig) were more conserved in virulent genomes. Some of these regions demonstrate a population structure effect, whereas others represent a polyphyletic distribution, most likely due to recombination.

Pan-genomics of animal-challenged genomes

A pan-genomics analysis of the 30 INT2 isolates utilized in the virulence study demonstrated a core-genome size of 5,369 CDSs and a pan-genome size of 6,338 CDSs. To identify potential differences in the pan-genome between virulent and attenuated strains, an LS-BSR analysis was performed, which failed to identify any genomic regions that fully differentiated between these two phenotypes. However, at a BSR threshold of 0.75, there were two CDSs (C1W87_17265; hypothetical protein, AQ770_28965; non-ribosomal peptide synthetase) that were highly conserved in the genomes of 7 of 8 virulent isolates and less conserved in the genomes of all 20 attenuated isolates (S6 Fig); these same two regions were also identified by the machine learning method. Of the seven virulent isolates possessing these regions, six were assigned to ST58 and one to ST176 (Bp8873). ST176 is a single locus variant of ST60 and none of the ST60 isolates exhibited the virulent phenotype. The polyphyletic distribution of these 2 genes (S6 Fig) suggests that these regions were a product of recombination and could be the subject of additional investigation. For ST1005, six of the isolates were attenuated, one demonstrated intermediate virulence, and one of the isolates was virulent. A comparative analysis failed to identify CDSs that fully differentiated between phenotypes within this ST. A Kmer analysis identified 31 Kmers that were associated with a single locus (C1W93_20275) that was enriched in the genome from the virulent isolate; this was the same locus that was identified by pyseer on the complete set of animal-challenged genomes. A screen of previously characterized virulence factors across the genomes of the 30 INT2 isolates included in the virulence study identified that Hcp1, which has previously been linked with virulence in B. pseudomallei [23], was associated with the virulent phenotype; this region was also identified with the ML method. All 8 of the virulent strains had the same version of Hcp1 found in K96243 (BPSS1498). However, 14 of 20 attenuated isolates, plus one intermediate virulence isolate (Bp8881), contain a variant of Hcp1 (C1W89_RS19320) with significant amino acid differences to BPSS1498 (S7 Fig). This demonstrates that this Hcp1 homolog has a different peptide composition and may have a different protein activity associated with attenuated virulence. A screen of the nucleotide sequences of Hcp1 against all ST93 genomes in the GenBank assembly database demonstrated that both alleles are present in INT2 ST93 genomes (S8 Fig), demonstrating recombination of this region; INT2 genome Bp1782, in particular, has an annotated Hcp1 that is highly divergent at both the nucleotide and amino acid level (identical to another annotated Hpc1 gene (BBU_3899)), suggesting that it may have altered function. Six attenuated strains from other STs (Bp8894, Bp8892, Bp8871, Bp8890, Bp8886, Bp1927) had the K96243 version of Hcp1, suggesting that a different mechanism of attenuation exists in those strains. Interestingly, Panaroo grouped both Hcp1 variants (BPSS1498, C1W89_RS19320) together into a single gene and the binary presence/absence results demonstrate that all animal-challenged genomes contain the K96243 Hcp1 allele; using Panaroo alone on this dataset would not have identified the ST93 Hcp1 differences. For the remaining 6 attenuated ST1005 isolates with the K96243 Hcp1 allele (Bp8892, Bp8886, Bp8890, Bp8894, Bp8871, Bp1927), a comparison was performed with all virulent INT2 isolate genomes (n = 8) using a set of previously characterized virulence genes in B. pseudomallei. The results demonstrate that a cluster of genes (BPSS0417-BPSS0428) associated with virulence was conserved in 7 of 8 virulent isolates from the other STs (BSR value ≥0.8) and was missing from all ST1005 isolates (6 attenuated and 1 virulent isolates); these genes are all part of a polysaccharide biosynthetic operon that has been demonstrated to react strongly with patient sera [75].

Discussion

In this study, we explored the genomic diversity and differential virulence of B. pseudomallei within a single soil sample collected from a rice paddy in the Ubon Ratchathani Province of Thailand. MLST analysis identified 7 unique sequence types (STs) within a single soil sample, highlighting genotypic diversity and prompting the investigation into whole genome comparisons and in vivo animal challenge. Comparative genomics identified variably conserved coding region sequence (CDS) differences between STs as well as within STs. Genomes from a single ST were not monomorphic, demonstrating within-ST diversity from a single soil sample. The within-ST diversity in these isolates was only apparent by sequencing multiple bacterial colonies from the same isolation plate. If only a single colony was picked from this soil sample during propagation, the comprehensive sample diversity described here would have been missed. If a plate sweep had been sequenced, a ST mixture would have been recovered and would have complicated all downstream population analyses. As demonstrated by this study, an analysis of individual colonies was the best option to identify and understand the sample diversity and should be considered for comprehensive environmental sampling of B. pseudomallei. An investigation into population structure identified 4 distinct populations (Fig 3), with demonstrated mixing between a small number of genomes. Horizontal gene transfer and recombination have been demonstrated to help drive the diversification in B. pseudomallei [19], and the observation of recombination in genomes from a single sample is not surprising given their proximity and related genomic background. The CDSs that demonstrated the highest levels of homoplasy were associated with non-ribosomal protein synthetases; the function of many of these regions is not known but suggests potential mechanisms of environmental adaptation. Other recombination events were observed in virulence-associated genes (S6 Fig), including in Hcp1, a gene associated with secretion of type six effectors and disease in mammals. This result suggests that recombination could drive virulence in environmental B. pseudomallei strains. The subcutaneous (SC) BALB/c model was used to identify variable virulence as it simulates percutaneous infection [7], has published LD50 values [60], and has been used in other virulence studies [76,77]. We did not perform histology in this study and it is unclear if surviving mice were colonized by B. pseudomallei. The results do demonstrate that different strains kill mice at a similar dosage, which may correlate to variable disease presentation in human infections. Variable virulence was observed among isolates from INT2 and was largely segregated by ST. For example, ST93 was found to be highly attenuated in the mouse model based on the dosage and route of exposure. A screen of public genomes in GenBank identified 8 ST93 genomes in addition to those sequenced in this study; 6 of these additional ST93 genomes were isolated from the environment whereas the other 2 were reported to be isolated from humans, although details on isolation source were missing. In general, the results of this study demonstrate that ST alone is insufficient to predict virulence in B. pseudomallei. A recent study identified genomic differences between clinical and environmental isolates [16], suggesting that some environmental isolates may not contain genomic elements required for virulence in humans. The animal study prompted analysis into genes associated with the virulent phenotype. A screen of genes previously associated with virulence identified that all attenuated ST93 genomes have an alternative allele for Hcp1, an important virulence factor in B. pseudomallei. Of external ST93 genome assemblies, 3 have the K96243 version of Hcp1, while 5 have the alternate allele; one of the external genomes with the alternate Hcp1 allele was associated with humans (SAMN04208633), although details of the isolation source are not available in the public record. Previous results demonstrated that even single amino acid differences can result in different phenotypes against a similar genomic background [78]. Although most ST93 genomes sequenced in this study (80/82) have the alternative Hcp1 allele, two genomes have an identical allele to Hcp1 in B. pseudomallei K96243 (S8 Fig); the presence of both alleles within a single soil sample is most likely explained by homologous recombination. Testing these naturally occurring, related strains in a murine melioidosis model will demonstrate if this one allele difference is responsible for the differential virulence observed in this sequence type; if virulence is observed in the wild strain, allele replacement will confirm that Hcp1 confers the virulent phenotype. For other genomes associated with virulence, machine learning methods identified additional regions that were not strictly associated with population structure, suggesting that the apparent recombination may be associated with the observed virulence phenotype in the murine model, but additional experimentation is required to validate these results and benchmark the Boruta ML method. The lack of a single region that explains the virulence phenotype suggests that either multiple independent mechanisms are associated with virulence or that complex interactions between mechanisms result in the virulent phenotype. Our findings indicate that environmental exposure to B. pseudomallei, especially inoculation via contact with contaminated soil and water, but possibly also via inhalation and ingestion, likely means exposure to a mixed community of B. pseudomallei strains. Attenuated strains are likely to be quickly cleared by the host immune system, although the virulent strains may cause disease in sensitive patients. A recent longitudinal study of melioidosis in a single patient demonstrated the selection for specific genotypes with significantly reduced virulence in chronic infections [78], suggesting that either the immune system can select for strains from the environment that cause attenuated, but chronic infection, or that strains that evade the immune system persist in the human host. In addition to being a significant public health threat, B. pseudomallei is also a biothreat agent and our results have implications for studies of the pathogen in this context. The diversity of INT2 isolate genomes, measured by total pairwise SNPs as well as pan-genome size, is greater than the known, global diversity of other biothreat pathogens, including Bacillus anthracis [79], Yersinia pestis [80], and Francisella tularensis [81]. This diversity complicates sample matching [82], suggesting that matching disease to a specific strain using shared mutations may need to be supplemented by overlap in the accessory genome. A recent study identified unique genomic islands in isolates from Puerto Rico [10], suggesting that whole genome comparisons are needed for robust sample matching. This study presents a focused survey into the evolution, diversity, and virulence of B. pseudomallei isolates from a single soil sample. Based on the projected distribution of B. pseudomallei throughout the world [83], there likely exists an underexplored diversity of strains that have variable risk to humans. This study provides a framework for studying this diversity and identifies genomic targets that may focus additional studies to better understand the virulence of B. pseudomallei.

A maximum-likelihood phylogeny of ST93 genomes inferred from a concatenation of 15,249 SNPs with IQ-TREE [48] using the TVM+F+ASC+R3 substitution model [49].

The phylogeny was annotated with coding regions that show variable distribution, based on an analysis with LS-BSR [38]. The phylogeny and heatmap were visualized with the interactive tree of life [41] and rooted with B. pseudomallei K96243 [44] as it represents an outgroup genome from Thailand. (TIF) Click here for additional data file.

A maximum-likelihood phylogeny of ST58 genomes inferred from a concatenation of 27,500 SNPs with IQ-TREE [48] using the GTR+F+ASC+R2 substitution model [49].

The phylogeny was annotated with coding regions that show variable distribution, based on an analysis with LS-BSR [38]. The phylogeny and heatmap were visualized with the interactive tree of life [41] and rooted with B. pseudomallei K96243 [44] as it represents an outgroup genome from Thailand. (TIF) Click here for additional data file.

A maximum-likelihood phylogeny of ST60 genomes inferred from a concatenation of 23,735 SNPs with IQ-TREE [48] using the TVM+F+ASC+R4 substitution model [49].

The phylogeny was annotated with coding regions that show variable distribution, based on an analysis with LS-BSR [38]. The phylogeny and heatmap were visualized with the interactive tree of life [41] and rooted with B. pseudomallei K96243 [44] as it represents an outgroup genome from Thailand. (TIF) Click here for additional data file.

A maximum-likelihood phylogeny of ST1005 genomes inferred from a concatenation of 20,923 SNPs with IQ-TREE [48].

The phylogeny was annotated with coding regions that show variable distribution, based on an analysis with LS-BSR [38] using the TVMe+ASC+R3 substitution model [49]. The phylogeny and heatmap were visualized with the interactive tree of life [41] and rooted with B. pseudomallei K96243 [44] as it represents an outgroup genome from Thailand. (TIF) Click here for additional data file.

A maximum-likelihood phylogeny of all sequenced INT2 genomes (n = 169) inferred from a concatenation of 22,080 SNPs with IQ-TREE [48] using the TVM+F+ASC+R2 substitution model [49].

Animal-passed genomes are colored by attenuated, intermediate, or virulent phenotypes. The phylogeny is rooted by B. pseudomallei K96243 [44] as it represents an outgroup genome from Thailand. (TIF) Click here for additional data file.

A maximum-likelihood phylogeny of genomes from animal-challenged INT2 isolates inferred from a concatenation of 32,821 SNPs with IQ-TREE [48] using the TVM+F+ASC substitution model [49].

Genomes are colored by the virulence outcome in an animal challenge model. Each genome was screened with LS-BSR [38] using genes identified through machine learning methods. The phylogeny and heatmap were visualized with the interactive tree of life [41] and rooted with B. pseudomallei K96243 [44] as it represents an outgroup genome from Thailand. (TIF) Click here for additional data file.

An alignment of protein sequences from Hcp1 (BPSS1498) and a novel allele variant seen in ST93 genomes (C1W89_RS19320).

Blue boxes surround amino acid differences in the alignment, which was visualized with Jalview [65]. (TIF) Click here for additional data file.

A maximum-likelihood phylogeny of ST93 genomes sequenced in this study as well as GenBank external genomes inferred from a concatenation of 37,746 SNPs with IQ-TREE [48] using the TVM+F+ASC substitution model [49].

The phylogeny was rooted with B. pseudomallei K96243 [44]. Each genome was screened with LS-BSR [38] using two Hcp1 variants. The phylogeny and heatmap were visualized with the interactive tree of life [41] and rooted with B. pseudomallei K96243 [44] as it represents an outgroup genome from Thailand. (TIF) Click here for additional data file.

Accession information for genomes sequenced in this study.

(XLSX) Click here for additional data file.

Accession information for a global set of B. pseudomallei genomes.

(XLSX) Click here for additional data file.

Number of mice remaining after each day of the mouse challenge experiment.

(XLSX) Click here for additional data file. 20 Oct 2021 Dear Dr. Sahl, Thank you very much for submitting your manuscript "Multiple phylogenetically-diverse, differentially-virulent Burkholderia pseudomallei isolated from a single soil sample collected in Thailand" 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. All reviewers think the study is potentially interesting and can contribute to the field. However, all have point out problems with some interpretation of the data, that the authors have to address. Particularly, reviewer 2 highlights major flaws in the animal model chosen for virulence. I would agree that the authors have to provide evidence why this is a valid mouse model that approximates virulence either through well defined virulent vs non-virulent isolates performed in this model, to have a "calibration" standard. Otherwise, the authors should consider using more standard and well established models of virulence either through ip, iv, intranasal or intratracheal routes. We cannot make any decision about publication until we have seen the revised manuscript and your response to the reviewers' comments. Your revised manuscript is also likely to be sent to reviewers for further evaluation. When you are ready to resubmit, please upload the following: [1] A letter containing a detailed list of your responses to the review comments and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out. [2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file). Important additional instructions are given below your reviewer comments. Please prepare and submit your revised manuscript within 60 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email. Please note that revised manuscripts received after the 60-day due date may require evaluation and peer review similar to newly submitted manuscripts. Thank you again for your submission. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments. Sincerely, Yunn-Hwen Gan Associate Editor PLOS Neglected Tropical Diseases Elsio Wunder Jr Deputy Editor PLOS Neglected Tropical Diseases *********************** All reviewers think the study is potentially interesting and can contribute to the field. However, all have point out problems with some interpretation of the data, that the authors have to address. Particularly, reviewer 2 highlights major flaws in the animal model chosen for virulence. I would agree that the authors have to provide evidence why this is a valid mouse model that approximates virulence either through well defined virulent vs non-virulent isolates performed in this model, to have a "calibration" standard. Otherwise, the authors should consider using more standard and well established models of virulence either through ip, iv, intranasal or intratracheal routes. 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: Methods are sound and comprehensive Reviewer #2: (No Response) Reviewer #3: The study clearly describes its objective of investigating the genomic diversity and differential virulence of B.pseudomallei isolates obtained from a single soil sample. The approaches taken to investigate the stated goals were appropriate. To investigate genomic diversity, the authors subjected B. pseudomallei isolates from the soil sample to genome sequencing, in silico genotyping and phylogenetic analysis. To investigate the virulence of the strains, a murine infection model via subcutaneous injection of B. pseudomallei was adopted. However, only 30 out of the 169 B. pseudomallei isolates sequenced were investigated for their virulence potential in the in vivo animal challenge. There is no explanation on why these 30 strains were chosen. There are no obvious identifiable ethical concerns. The authors had declared the use of IACUC- approved protocols, appropriate risk group 3 biosafety practices and compliance with the Guide for the Care and Use of Laboratory Animals of NIH. The genomic data of all sequenced strains are available on GenBank. -------------------- 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: Results are clear and thorough Reviewer #2: (No Response) Reviewer #3: The analysis performed matched the objectives of the study. However, clarity of the results and terms used can be improved. The authors should have included explanations of why certain strains were chosen for rooting of the phylogenetic trees. For example, it should have been explained that B. pseudomallei strain MSHR0668 is the most ancestral B. pseudomallei strain identified in a previous phylogenetic study. It is not apparent what is the rationale for using K96243 for rooting the trees in Figures S1 to S4. The term “unique genomic regions” (Line 345-346) is confusing since homologs of these genomic regions are found in other B. pseudomallei strains and not only in the STs examined. “Unique insertions/deletions” (Line 348) suggest that each ST has distinct variants but not a “unique genomic region”. The consolidated survival curve (Fig 5) provides only a general overview of the virulence potentials of each ST. While the authors did provide supplementary data on the number of mice surviving throughout their in vivo mouse challenge experiment (Table S3), the data presented is raw. The strains in the table should be organized according to ST for easier viewing. The survival curves for all the strains should also be plotted and presented for readers to quickly identify which strains are more virulent/attenuated or behaved exceptionally in comparison to the other strains within the same ST. The terms “virulent”, “attenuated” and “intermediate”; which were used to describe strains that killed all mice, no mice and some mice respectively; were only defined in the Materials and Methods section. The authors should also clearly define these terms within the main text (Under the section on “Differential virulence” which examines virulence of the different strains in the in vivo mouse model) for clarity in the subsequent sections. The authors mentioned “14 out of the 20 attenuated isolates… contain a variant of Hcp1” (in Line 433) and “Five attenuated strains from other STs had the K96243 version of Hcp1” (in Line 442). These descriptions are vague, and it is unclear which isolates have the C1W89_RS19320 Hcp1 variant or K96243 version of BPSS1498. A table should be included to clarify this. A more thorough explanation of the datasets and the concluding statements would also enhance readability. For instance, in lines 340 to 343, the authors stated that gene variability in each ST “was largely due to convergent gene loss” without explaining how the data shows that. Is the loss/gain of genes relative to a more ancestral strain in each ST (i.e. in ST93, Bp1740) from the INT2 soil sample? This should be explicitly stated. -------------------- 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: Conclusions are mostly well supported. Detail provided in comments to the authors Reviewer #2: (No Response) Reviewer #3: Key discussion of the data, implications of the study, and conclusions of the results were generally appropriate, with some points requiring clarification. In the abstract, the authors wrote “Five different sequence types were identified” (Lines 58-59). All the data described and shown indicates that there were 7 sequence types observed in the one soil sample. “The core genome phylogeny demonstrated that genomes from this study shared a recent common ancestor (Figure 2), suggesting a common geographic origin.” (Lines 301-302). Does this really suggest a common geographic origin? In the phylogenetic tree (Fig 2), there were GenBank strains that branched out from the INT2 common ancestor. Are those strains also isolated from the same region in Thailand? In Lines 343-345, the authors mentioned that “A pan-genomics analysis determined that the core and pan-genome for each ST were largely similar in size (Table 1), suggesting minimal, yet observable gene differences within each ST.” This statement needs to be revised. Size does not necessarily correlate to minimal gene differences. The integration of new genes coupled with the loss of others could also result in minimal changes in size. The authors found that virulence was largely segregated by the STs (Fig 5 and line 485-486). This suggests that ST may be able to provide a general indication of virulence, although it may not be the most accurate, since certain strains within the ST may display exceptions. The author’s statement of ST being “a poor predictor of virulence” (Line 493) will therefore need to be further justified. Discussions on ST93 (in lines 486-492) are not able to support the author’s view of ST being a poor virulence predictor. Further discussion on the other STs - ST58 (which was virulent), ST1005 and ST60 (largely attenuated with several exceptions) – should be made to justify the author’s stand. -------------------- 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: (No Response) Reviewer #3: The recommended modifications have been described in all the other sections. -------------------- 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: The authors present an interesting study on the genomic diversity and virulence potential of the population of B. pseudomallei in a single soil sample from Thailand. I enjoyed reading the manuscript and have only minor comments to the authors. Minor points Line 36. Suggest “all identified STs” rather than “all STs” for clarity. Line 36. You can use the abbreviation STs here. Line 42-44: Suggest re-wording for clarity. Is virulence being stratified by ST suggestive of convergent evolution or is one virulent isolate and six attenuated isolates belonging to the same ST suggestive of convergent evolution? Differing virulence within the same ST would be the opposite of convergent evolution. Line 83. The study by McRobb et al., 2014 (https://doi.org/10.1128/AEM.00128-14) is important to mention here to capture the Australian context. This study used MLST to look at the population of ~170 Australian B. pseudomallei from the environment across the Top End region. Line 357. The mutation rate for chromosome two has been listed here as 9.71e-7 but listed previously as 6.71e-7. Please check for consistency. Line 398. What exactly is a “bad chisq” value? Are you saying that they haven’t reached statistical significance? More explanation is needed here. What cut-offs were used to establish a bad or good chisq test? Was the data corrected for multiple testing? Line 415-416. The locus names are not particularly useful here. Is there a homolog in K96243 that could be used to define these two loci? Line 471-2. As a comment. Yes, the ST mixture would have complicated all downstream analyses, but it may have captured additional diversity not observed with the colony picking methods. We need improved methodology to sample complete diversity and be able to analyse this complex milieux of data. Line 483. A reference around hcp function would be good here to back-up the statement about disease in mammals. Line 485-6. Suggest using the word “sequence type” rather than “genotype”. Line 490-492. “This suggests that either this ST exhibits low virulence in humans and is therefore not showing up in clinical surveillance or has not been observed in clinical samples due to insufficient sampling.” This statement is unsupported by the data. B. pseudomallei is not considered a commensal organism; therefore, according to the previous line, ST93 has been isolated from two human cases of melioidosis. The data presented suggests that ST93 may not display virulence in the murine model but can still infect humans and cause melioidosis. There are multiple cases of a single ST being isolated from both environmental and clinical sources so finding slightly more ST93 in the environment vs clinical studies does not support the argument of this being an attenuated ST in humans. Line 492. “In general, the results of this study demonstrate that ST alone is a poor predictor of virulence in B. pseudomallei.” This is in perfect agreement with a much larger study that looked at the distribution of STs in a large clinical cohort and in the environment. They saw that there was no difference in the clinical vs environmental ST distribution across the entire Top End of Australia (McRobb et al., 2014 - https://doi.org/10.1128/AEM.00128-14). Line 501. B. pseudomallei is not considered a commensal. Granted that this case may have been a mild case of melioidosis but an isolate from a human is from a case of melioidosis. Line 505-507. This experiment may help determine the role of Hcp1, however any additional variation in the genomes would still have to be taken into account. A better approach would be to use isogenic strains with a genetically modified hcp1 allele to ensure that no other differences are present. As stated, the experiment has been oversimplified. Line 510-513. Worth restating that this is a murine model of virulence that has been investigated. Line 515. Suggest removing “and water”. The study did not investigate population diversity in water and makes no claim as to the diversity present. Line 520-1 As a comment. “immune system can select for strains from the environment that cause attenuated, but chronic infection” or the immune system can eliminate some strains but only those that can evade the immune system survive and cause chronic infection. In addition to P314, the same virulence attenuation has been observed in multiple cases of B. pseudomallei infection in cystic fibrosis patients (10.1128/mBio.00356-17). Line 526-528. The addition of indels has also helped differentiate strains in cases where SNPs fail to provide enough resolution. This approach has been well established for B. pseudomallei too (e.g. 10.1099/mgen.0.000067) Reviewer #2: This manuscript describes a range of studies conducted on B. pseudomallei isolates obtained from a grid of soil samples collected in 2007 from a rice field located in a region of Thailand endemic for the disease melioidosis. The data presented are interesting in that they are suggestive of variability of B. pseudomallei sequences within a small geographic area. However, the samples are obtained from soil samples collected 14 years ago, the animal model experiments lack rigour and some of the bioinformatic analysis lacks clarity in its approach and is questionable in its interpretation. These weaknesses in the study unfortunately undermine the conclusions of the authors who suggest there are significant levels of differential virulence that could be ascribed to genomic differences. Specific areas of concern/improvement are listed below. 1. The introduction suggests that the most probable route of infection occurs through bacterial contamination of wounds. The limited literature cited to support this statement lack specific evidence for this and the literature cited is not a primary reference. The statement also ignores the presentation of cutaneous melioidosis, which can also be a self-limiting infection. Whilst infection via wounds is considered a viable route of infection, the authors must cite more published data to indicate that this is the most probable route of infection compared to ingestion and inhalation, assuming that is true. 2. Another point of concern is the source of the material. It appears that the samples are recently isolated from soil obtained in 2007. It is not clear why aged samples were used and how these samples were stored over such a long period of time. Could sample age and the nature of the storage (along with other microbes that are present in these samples) have influenced the sequencing diversity reported via recombination or other mechanisms that result in genetic modifications? 3. The animal model used (sub-cutaneous inoculation with 5x10^2) is not well established for assessing virulence. There is no literature cited in the manuscript that indicates studies have been done to establish lethal doses of B. pseudomallei in this murine model as compared to other murine models that use i.p. or i.n. as a route of infection. Developing this s.c. model should include prototypical bacterial strains including the K96243 and the l attenuated strain B. thailandensis E264. MOIs could differ significantly between the strains studied, which in turn does not mean that any one strain is less lethal than another – particularly given the very low challenge dose. The complete lack of any pathology – even bacterial counts on vital organs is a considerable weakness if not a fatal flaw that is also standard practice in assessing virulence in melioidosis infection models. 4. The genomic assembly, genotyping, comparative pan-genomics, and SNP identification and polymorphism analysis are well-established methods are data presented appear to be appropriately analysed with acceptable statistics. However, feature identification using the ML was not particularly well-justified or convincing. Benchmark studies are needed here to demonstrate that such an approach is viable – perhaps using genomes from related type strains of B. pseudomallei, B. mallei and B. thailandensis – and/or the collection of B. pseudomallei strains already deposited in GenBank/NCBI. 5. The identification of Hcp1 as displaying significant amino acid differences that in turn may suggest it could have a different function is not supported by the data. There is no citation to indicate how they conducted their pairwise alignment nor statistics to indicate that these differences are truly significant. A standard pairwise comparison using the standard Needle-Wunsch algorithm gives identity and similarity percentages of approximately 82% and 89%, respectively - and a slightly modified alignment as well. These percentages are suggestive of conserved structure and function and are nowhere near “twilight” values (e.g. <40%) that are typically indicative of an different function and possibly significant structural differences. Similarly, the identification the loss of a gene cluster involved in polysaccharide synthesis may be significant but would need some minimal demonstration comparisons with patient sera and even API data that could support the presumption this might influence reduced virulence of the ST1005 isolate. The importance of this strain in human infections is speculative at this point and it is possible that more virulent strains would simple out-compete this strain in the more likely scenario of a poly-microbial inoculation. Hence, while the authors do rightly point out that there likely exists an under-explored diversity of strains that could pose risks to humans, a considerable amount of data presented in this manuscript are too preliminary or unconvincing to adequately support this statement. It is questionable whether these strains demonstrate significant levels of differential virulence/attenuation in this study given lack of rigour in the animal model studies. The authors might consider revising this manuscript to focus on the genomic analysis, comparing their sequences with existing sequences already available in the public domain. Given that a protocol now exists for isolation and transfer of materials it may also be worth considering obtaining fresh (as opposed to aged) soil samples as another means to support the sequence diversity findings reported in this manuscript. Reviewer #3: In this study, the authors did a systematic and comprehensive analysis of the genomic diversity and virulence potentials of B. pseudomallei strains in a single soil sample . They discovered that B. pseudomallei isolates display high genotypic diversity, where 4 distinct populations, admixing between STs and within-ST diversity was observed in the single soil sample. Amongst the 169 B. pseudomallei strains sequenced, the authors selected 30 isolates for further investigations on their virulence potentials in an in vivo murine model. Virulence of the strains were largely segregated according to the STs (i.e. all ST93 strains were attenuated, all ST58 strains highly virulent, ST60 and ST1005 were largely attenuated) with several exceptions. Subsequent GWAS and pan genomics analysis of the 30 isolates utilized in the virulence study revealed 11 CDS that were more conserved in virulent genomes, including Hcp1, a known virulence factor. Hcp1 associated with the virulent phenotype, where all virulent strains possess the Hcp1 version of BPSS1498 and 14 out of 20 attenuated strains has the C1W89_RS19320 Hcp1 variant. The high genomic diversity identified has implications on environmental sampling strategies that examines the risk to humans and virulence potential of B. pseudomallei strains. Further insights on which genes could be potentially contributing to the bacteria’s fitness in in vivo infections or virulent phenotypes have also been gained from the identification of conserved genes in more virulent genomes. However, there are some concerns in terms of the interpretation of results and clarity of methods. -------------------- PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: No Reviewer #2: No Reviewer #3: No Figure Files: While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org. Data Requirements: Please note that, as a condition of publication, PLOS' data policy requires that you make available all data used to draw the conclusions outlined in your manuscript. Data must be deposited in an appropriate repository, included within the body of the manuscript, or uploaded as supporting information. This includes all numerical values that were used to generate graphs, histograms etc.. For an example see here: http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5. Reproducibility: To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols 12 Nov 2021 Submitted filename: INT2_response_to_reviewers.docx Click here for additional data file. 17 Dec 2021 Dear Dr. Sahl, Thank you very much for submitting your manuscript "Multiple phylogenetically-diverse, differentially-virulent Burkholderia pseudomallei isolated from a single soil sample collected in Thailand" 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. Dear authors, The reviewer has given detailed constructive comments pertaining to revision 1 and there are two aspects that require further revision. Although it is classified as major revision, the 2 points raised pertain to the interpretation and the conclusion of the findings which require the authors to acknowledge limitations and further discussion with inclusion of relevant references. I agree with the reviewer's recommendations and would ask the authors to consider the following. 1. The study did not undertake a proper virulence comparison either with surrogate models or further characterization of the mouse infection organ loads, LD50 etc. Therefore, discussion of limitations of this approach is warranted and the virulence comparison of the strains interpreted in this light. 2. More description of ML algorithms and justification, with limitations explained. It is indeed highly speculative that Hcp sequence variants result in altered protein function and virulence as Hcp is one of the highest conserved protein across species in T6SS secretion and function. Unless the authors have preliminary data to show that T6SS secretion is altered, this statement is very problematic. More analyses as suggested by reviewer should be considered. We cannot make any decision about publication until we have seen the revised manuscript and your response to the reviewers' comments. Your revised manuscript is also likely to be sent to reviewers for further evaluation. When you are ready to resubmit, please upload the following: [1] A letter containing a detailed list of your responses to the review comments and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out. [2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file). Important additional instructions are given below your reviewer comments. Please prepare and submit your revised manuscript within 60 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email. Please note that revised manuscripts received after the 60-day due date may require evaluation and peer review similar to newly submitted manuscripts. Thank you again for your submission. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments. Sincerely, Yunn-Hwen Gan Associate Editor PLOS Neglected Tropical Diseases Elsio Wunder Jr Deputy Editor PLOS Neglected Tropical Diseases *********************** Dear authors, The reviewer has given detailed constructive comments pertaining to revision 1 and there are two aspects that require further revision. Although it is classified as major revision, the 2 points raised pertain to the interpretation and the conclusion of the findings which require the authors to acknowledge limitations and further discussion with inclusion of relevant references. I agree with the reviewer's recommendations and would ask the authors to consider the following. 1. The study did not undertake a proper virulence comparison either with surrogate models or further characterization of the mouse infection organ loads, LD50 etc. Therefore, discussion of limitations of this approach is warranted and the virulence comparison of the strains interpreted in this light. 2. More description of ML algorithms and justification, with limitations explained. It is indeed highly speculative that Hcp sequence variants result in altered protein function and virulence as Hcp is one of the highest conserved protein across species in T6SS secretion and function. Unless the authors have preliminary data to show that T6SS secretion is altered, this statement is very problematic. More analyses as suggested by reviewer should be considered. 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 #2: Please see attached review -------------------- 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 #2: Please see attached review Figures and tables are clear -------------------- 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 #2: Please see attached review -------------------- 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 #2: (No Response) -------------------- 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 #2: Please see attached review -------------------- PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #2: No Figure Files: While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org. Data Requirements: Please note that, as a condition of publication, PLOS' data policy requires that you make available all data used to draw the conclusions outlined in your manuscript. Data must be deposited in an appropriate repository, included within the body of the manuscript, or uploaded as supporting information. This includes all numerical values that were used to generate graphs, histograms etc.. For an example see here: http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5. Reproducibility: To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols Submitted filename: PLOSNTD_comments.pdf Click here for additional data file. 13 Jan 2022 Submitted filename: response_to_reviewers_V2.docx Click here for additional data file. 14 Jan 2022 Dear Dr. Sahl, We are pleased to inform you that your manuscript 'Multiple phylogenetically-diverse, differentially-virulent Burkholderia pseudomallei isolated from a single soil sample collected in Thailand' 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. Should you, your institution's press office or the journal office choose to press release your paper, you will automatically be opted out of early publication. We ask that you notify us now if you or your institution is planning to press release the article. All press must be co-ordinated with PLOS. Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Neglected Tropical Diseases. Best regards, Yunn-Hwen Gan Associate Editor PLOS Neglected Tropical Diseases Elsio Wunder Jr Deputy Editor PLOS Neglected Tropical Diseases *********************************************************** 7 Feb 2022 Dear Dr. Sahl, We are delighted to inform you that your manuscript, "Multiple phylogenetically-diverse, differentially-virulent Burkholderia pseudomallei isolated from a single soil sample collected in Thailand," has been formally accepted for publication in PLOS Neglected Tropical Diseases. We have now passed your article onto the PLOS Production Department who will complete the rest of the publication process. All authors will receive a confirmation email upon publication. The corresponding author will soon be receiving a typeset proof for review, to ensure errors have not been introduced during production. Please review the PDF proof of your manuscript carefully, as this is the last chance to correct any scientific or type-setting errors. Please note that major changes, or those which affect the scientific understanding of the work, will likely cause delays to the publication date of your manuscript. Note: Proofs for Front Matter articles (Editorial, Viewpoint, Symposium, Review, etc...) are generated on a different schedule and may not be made available as quickly. Soon after your final files are uploaded, the early version of your manuscript will be published online unless you opted out of this process. The date of the early version will be your article's publication date. The final article will be published to the same URL, and all versions of the paper will be accessible to readers. Thank you again for supporting open-access publishing; we are looking forward to publishing your work in PLOS Neglected Tropical Diseases. Best regards, Shaden Kamhawi co-Editor-in-Chief PLOS Neglected Tropical Diseases Paul Brindley co-Editor-in-Chief PLOS Neglected Tropical Diseases
  80 in total

1.  The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

Authors:  Aaron McKenna; Matthew Hanna; Eric Banks; Andrey Sivachenko; Kristian Cibulskis; Andrew Kernytsky; Kiran Garimella; David Altshuler; Stacey Gabriel; Mark Daly; Mark A DePristo
Journal:  Genome Res       Date:  2010-07-19       Impact factor: 9.043

2.  Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences.

Authors:  Weizhong Li; Adam Godzik
Journal:  Bioinformatics       Date:  2006-05-26       Impact factor: 6.937

3.  Fine-scale genetic diversity among Burkholderia pseudomallei soil isolates in northeast Thailand.

Authors:  Jana M U'ren; Heidie Hornstra; Talima Pearson; James M Schupp; Benjamin Leadem; Shalamar Georgia; Rasana W Sermswan; Paul Keim
Journal:  Appl Environ Microbiol       Date:  2007-08-24       Impact factor: 4.792

4.  Minimap2: pairwise alignment for nucleotide sequences.

Authors:  Heng Li
Journal:  Bioinformatics       Date:  2018-09-15       Impact factor: 6.937

5.  Global and regional dissemination and evolution of Burkholderia pseudomallei.

Authors:  Claire Chewapreecha; Matthew T G Holden; Minna Vehkala; Niko Välimäki; Zhirong Yang; Simon R Harris; Alison E Mather; Apichai Tuanyok; Birgit De Smet; Simon Le Hello; Chantal Bizet; Mark Mayo; Vanaporn Wuthiekanun; Direk Limmathurotsakul; Rattanaphone Phetsouvanh; Brian G Spratt; Jukka Corander; Paul Keim; Gordon Dougan; David A B Dance; Bart J Currie; Julian Parkhill; Sharon J Peacock
Journal:  Nat Microbiol       Date:  2017-01-23       Impact factor: 17.745

6.  Evolutionary analysis of Burkholderia pseudomallei identifies putative novel virulence genes, including a microbial regulator of host cell autophagy.

Authors:  Arvind Pratap Singh; Shu-chin Lai; Tannistha Nandi; Hui Hoon Chua; Wen Fong Ooi; Catherine Ong; John D Boyce; Ben Adler; Rodney J Devenish; Patrick Tan
Journal:  J Bacteriol       Date:  2013-10-04       Impact factor: 3.490

7.  Route of infection in melioidosis.

Authors:  Jodie L Barnes; Natkunam Ketheesan
Journal:  Emerg Infect Dis       Date:  2005-04       Impact factor: 6.883

8.  Virulence of the Melioidosis Pathogen Burkholderia pseudomallei Requires the Oxidoreductase Membrane Protein DsbB.

Authors:  Róisín M McMahon; Philip M Ireland; Derek S Sarovich; Guillaume Petit; Christopher H Jenkins; Mitali Sarkar-Tyson; Bart J Currie; Jennifer L Martin
Journal:  Infect Immun       Date:  2018-04-23       Impact factor: 3.441

9.  Burkholderia pseudomallei in Soil, US Virgin Islands, 2019.

Authors:  Nathan E Stone; Carina M Hall; A Springer Browne; Jason W Sahl; Shelby M Hutton; Ella Santana-Propper; Kimberly R Celona; Irene Guendel; Cosme J Harrison; Jay E Gee; Mindy G Elrod; Joseph D Busch; Alex R Hoffmaster; Esther M Ellis; David M Wagner
Journal:  Emerg Infect Dis       Date:  2020-11       Impact factor: 6.883

10.  A Bacillus anthracis Genome Sequence from the Sverdlovsk 1979 Autopsy Specimens.

Authors:  Jason W Sahl; Talima Pearson; Richard Okinaka; James M Schupp; John D Gillece; Hannah Heaton; Dawn Birdsell; Crystal Hepp; Viacheslav Fofanov; Ramón Noseda; Antonio Fasanella; Alex Hoffmaster; David M Wagner; Paul Keim
Journal:  MBio       Date:  2016-09-27       Impact factor: 7.867
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