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. 2021 Oct 14;87(21):e01036-21. doi: 10.1128/AEM.01036-21

Recent Evolution and Genomic Profile of Salmonella enterica Serovar Heidelberg Isolates from Poultry Flocks in Brazil

Diéssy Kipper a, Renato H Orsi b, Laura M Carroll b, Andrea K Mascitti a, André F Streck c, André S K Fonseca d, Nilo Ikuta d, Eduardo C Tondo e, Martin Wiedmann b, Vagner R Lunge a,d,✉
Editor: Christopher A Elkinsf
PMCID: PMC8516049  PMID: 34406824

ABSTRACT

Salmonella enterica serovar Heidelberg is isolated from poultry-producing regions around the world. In Brazil, S. Heidelberg has been frequently detected in poultry flocks, slaughterhouses, and chicken meat. The goal of the present study was to assess the population structure, recent temporal evolution, and some important genetic characteristics of S. Heidelberg isolated from Brazilian poultry farms. Phylogenetic analysis of 68 S. Heidelberg genomes sequenced here and additional whole-genome data from NCBI demonstrated that all isolates from the Brazilian poultry production chain clustered into a monophyletic group, here called S. Heidelberg Brazilian poultry lineage (SH-BPL). Bayesian analysis defined the time of the most recent common ancestor (tMRCA) as 2004, and the overall population size (Ne) was constant until 2008, when an ∼10-fold Ne increase was observed until circa 2013. SH-BPL presented at least two plasmids with replicons ColpVC (n = 68; 100%), IncX1 (n = 66; 97%), IncA/C2 (n = 65; 95.5%), ColRNAI (n = 43; 63.2%), IncI1 (n = 32; 47%), ColMG828, Col156, IncHI2A, IncHI2, IncQ1, IncX4, IncY, and TrfA (each with n < 4; <4% each). Antibiotic resistance genes were found, with high frequencies of fosA7 (n = 68; 100%), mdf(A) (n = 68; 100%), tet(34) (n = 68; 100%), sul2 (n = 64; 94.1%), and blaCMY-2 (n = 56; 82.3%), along with an overall multidrug resistance (MDR) profile. Ten Salmonella pathogenicity islands (SPI1 to SPI5, SPI9, and SPI11 to SPI14) and 139 virulence genes were also detected. The SH-BPL profile was like those of other previous S. Heidelberg isolates from poultry around the world in the 1990s. In conclusion, the present study demonstrates the recent introduction (2004) and high level of dissemination of an MDR S. Heidelberg lineage in Brazilian poultry operations.

IMPORTANCE S. Heidelberg is the most frequent serovar in several broiler farms from the main Brazilian poultry-producing regions. Therefore, avian-source foods (mainly chicken carcasses) commercialized in the country and exported to other continents are contaminated with this foodborne pathogen, generating several national and international economic losses. In addition, isolates of this serovar are usually resistant to antibiotics and can cause human invasive and septicemic infection, representing a public health concern. This study demonstrates the use of whole-genome sequencing (WGS) to obtain epidemiological information for one S. Heidelberg lineage highly spread among Brazilian poultry farms. This information will help to define biosecurity measures to control this important Salmonella serovar in Brazilian and worldwide poultry operations.

KEYWORDS: Heidelberg, poultry farms, Salmonella

INTRODUCTION

Poultry farms have high-density flocks reared in large housing operations with 6,000 to 40,000 birds per unit (1). This situation is very favorable to a continuous exchange of bacterial and viral communities, including pathogenic ones. Salmonella is one of the main pathogenic bacteria detected in poultry flocks due to a remarkable genetic plasticity that responds to a wide variety of environmental threats and harmful conditions. Therefore, Salmonella spread is common in such animal production environments, and some serovars have been frequently detected in poultry farms and foods, especially chicken, turkey, and eggs (2).

Salmonella enterica serovar Heidelberg is a serovar detected in poultry flocks in many countries around the world since the 1990s (2–7). This high prevalence has been directly linked to poultry foods contamination and transmission to humans (8–11). In addition, this serovar is also capable of causing human invasive salmonellosis and septicemic infection (12). Besides this pathogenic profile, isolates of S. Heidelberg have also demonstrated resistance to different antibiotics, including third-generation cephalosporins (13, 14). Multidrug-resistant (MDR) strains have been reported in poultry farms and chicken carcasses from different countries over the last 3 decades (15–19).

Whole-genome sequencing (WGS) can be used as a molecular epidemiological tool and also allows the investigation of the general genetic characteristics of Salmonella with high resolution (20). The genomes of thousands of Salmonella isolates have been sequenced to improve the understanding of this bacteria evolutionary biology to differentiate sporadic from outbreak-related isolates and to study the antibiotic resistance genetic profiles and virulence markers of the main strains (21–26). Previous reports have already studied the differences among highly clonal S. Heidelberg isolates from humans, retail meats, and animals (27), as well as compared genomes of outbreak- versus nonoutbreak-causing isolates from diverse hosts and geographical regions (28) and characterized closely related MDR S. Heidelberg isolates from poultry meat (29).

In Brazil, S. Heidelberg has been the most frequently isolated serovar from broiler flocks and poultry products for final human consumption (chicken carcasses, legs, wings) in the last 2 decades (18, 30–33). Therefore, isolates of this serovar were frequently detected in poultry products commercialized in Brazil and exported to other countries and continents, mainly Europe, in recent years (29). The present study aimed to assess the population structure, the recent evolution, and genetic characteristics of S. Heidelberg isolates from commercial poultry flocks in Brazil using WGS.

RESULTS

WGS data and sequence types.

Raw sequencing reads obtained in this study (n = 68) and downloaded from NCBI (n = 248) were assembled with a median of 59 contigs (ranging from 21 to 425 contigs), a median N50 of 191,826 bp (ranging from 28,348 to 741,244 bp), and median average coverage of 608× (ranging from 53 to 997×). The median length of the 316 assembled S. Heidelberg genomes (made of contigs > 1,000 bp) was 4.93 Mbp (ranging from 4.67 to 5.44 Mbp). In silico multilocus sequence typing (MLST) showed that all isolates matched to ST15, except SRR8658220_UK_2016, SRR7187911_UK_2016, and SRR1968439_UK_2014, which matched to ST2071, and SRR1060665_US_2016, which matched to ST3377.

Poultry-associated S. Heidelberg strains of Brazilian origin are confined to a single lineage.

A maximum-parsimony phylogeny constructed using core single nucleotide polymorphisms (SNPs) (n = 8,872) revealed that all Brazilian S. Heidelberg genomes from poultry flocks sequenced here (n = 68) clustered together with other isolates from poultry or swine operations in Brazil (n = 26) as well as S. Heidelberg isolates from the United Kingdom (n = 42), United States (n = 25), Canada (n = 4), Chile (n = 10), Germany (n = 2), Colombia (n = 1), and South Korea (n = 1) (Fig. 1). All these isolates belonged to 14 different S. Heidelberg SNP clusters according to the NCBI pathogen detection tool in the search for WGSs of the Salmonella enterica species. There were also 14 other isolates that have not clustered with any other S. Heidelberg (Table S1 in the supplemental material).

FIG 1.

FIG 1

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Maximum-parsimony phylogeny constructed using core SNPs identified among 317 S. Heidelberg genomes using kSNP3. The area shaded represents all Brazilian S. Heidelberg isolates from poultry flocks sequenced here (n = 68), as well as publicly available genomes from Brazil (n = 26), United Kingdom (n = 42), United States (n = 25), Canada (n = 4), Chile (n = 3), Germany (n = 2), Colombia (n = 1), and South Korea (n = 1). Tip label colors denote the geographic origin of the isolates sequenced here (purple for SC, green for RS, red for SP, and blue for PR). The phylogeny was constructed using the maximum-parsimony method implemented in the kSNP3 and is midpoint rooted.

Of these sequences, all Brazilian WGS data (68 sequenced here and the other 26 from the NCBI), 3 genomes recently isolated from foods in Europe, and 13 North and South American previously referenced genomes were thus further investigated using the high-quality SNP calling (hqSNP) approach implemented in the Center for Food Safety and Applied Nutrition (CFSAN) SNP pipeline for a more detailed temporal evolutionary analysis. The 110 sampled WGSs included in the analysis differed from each other by 0 to 127 hqSNPs (median, 56). A maximum-likelihood (ML) phylogeny constructed using these hqSNPs clearly demonstrated that 96 S. Heidelberg genomes, representing 68 isolates from Brazilian poultry flocks (sequenced in this study), 25 isolates from Brazilian animal operations and foods from different states (16 from Santa Catarina [SC], 5 from São Paulo [SP], 4 with no origin information), and 3 isolates from unspecified food products clustered together (Fig. 2). This 96-isolate lineage is referred to here as the SH-BPL due to the high frequency of poultry-associated Brazilian isolates in this cluster (n = 91; 94.8%). The remaining 13 North and South American S. Heidelberg genomes and the single Brazilian isolate of swine origin clustered in 1 separate clade in the phylogenetic tree (Fig. 2).

FIG 2.

FIG 2

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Maximum-likelihood phylogeny constructed using high-quality SNPs (hqSNPs) identified among 110 S. Heidelberg genomes using the CFSAN SNP Pipeline. Almost all S. Heidelberg isolates sequenced in this study (68 out of 69) were grouped into one of three clades (I, II, and III; SH-BPL), denoted using Roman numerals. Tip label colors denote the geographic origin of isolates (purple for SC, green for RS, red for SP, and blue for PR). The phylogeny was constructed using IQ-TREE and is midpoint rooted, with branch lengths representing the number of substitutions per site. The bootstraps are presented in the ancestral nodes of each clade and in the ancestral node of the SH-BPL lineage.

The SH-BPL could be further divided into three clades. Clade I contained 43 isolates from different Brazilian states, including 12 from SP, 17 from SC, eight from Parana (PR), 4 from Rio Grande do Sul (RS), and 2 with unknown origin. Clade II contained 46 isolates, including 27 from SC, 9 from PR, 6 from RS, 2 from SP, and 2 with unknown origin. Clade III contained only four isolates, including two from SC and two with unknown origin (Fig. 2). One isolate from Germany clustered within clade II, while each of two isolates from the United Kingdom clustered within clades I and II (Fig. 2). Isolates differed by (i) 0 to 66 pairwise hqSNPs (median, 30) within clade I, (ii) 0 to 85 pairwise hpSNPs (median, 49) within clade II, and (iii) 0 to 24 pairwise hqSNPs (median, 12) within clade III. No relationship among these clades was observed.

The S. Heidelberg Brazilian poultry lineage emerged in Brazil circa 2004.

The 110 genome sequences were also used in a tip-dated Bayesian phylogenetic analysis to assess the time to the most recent common ancestor (tMRCA) of these isolates. The coefficient value of R2 of 0.6 indicated a correlation between isolation date and sequence divergence, which indicates that our data set is suitable for temporal analysis. The evolutionary rate for the 110-isolate clade was predicted to be 5.74 × 10−7 substitutions/site/year [95% highest posterior density (HPD) = (4.65 × 10−7) to (6.86 × 10−7)].

All 110 S. Heidelberg isolates shared a most recent common ancestor (MRCA) that existed circa 1986 (95% HPD, 1951 to 2018). Resembling what was observed in the ML phylogeny, the SH-BPL formed a monophyletic cluster that encompassed 3 clades, while the 13 North American S. Heidelberg genomes and the Brazilian isolate of swine origin formed a separate clade in the phylogenetic tree (Fig. 3). The monophyletic SH-BPL had an MRCA that existed circa 2004 (95% HPD, 1986 to 2015), indicating that the SH-BPL likely emerged in Brazilian poultry flocks at the beginning of this century. In the tip-dated phylogenetic tree, a subclade previously classified within SH-BPL clade II was placed within SH-BPL clade I (Fig. 3), although with a low branch support (posterior probability, 0.36). Thus, a tMRCA for clade II could not be obtained without including all isolates from clade I as well. Hence, a tMRCA was obtained for the combined clades I and II, which show an ancestral node with a high branch support (posterior probability, 1.0), with a common ancestor that existed circa 2008. Additionally, SH-BPL clades I and III were predicted to have emerged from common ancestors that existed circa 2008 (95% HPD, 1996 to 2015), and 2006 (95% HPD, 1990 to 2014), respectively. The effective population size of the SH-BPL lineage remained constant until 2008, when an increase and subsequent decrease (circa 2013) in population size were observed (Fig. 4).

FIG 3.

FIG 3

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Maximum clade credibility tree constructed using high-quality SNPs (hqSNPs) identified among 110 S. Heidelberg genomes using the CFSAN SNP Pipeline, rooted using BEAST. Three clades defined as I, II, and III (SH-BPL) are annotated with roman numerals to the right of the phylogeny. Tip label colors denote the geographic origin of isolates (purple for SC, green for RS, red for SP, and blue for PR). Time by year is plotted along the x axis. Branch labels denote posterior probabilities of branch support, and node bars correspond to 95% highest posterior density (HPD) intervals for node heights.

FIG 4.

FIG 4

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Bayesian skyline plot constructed via BEAST and Tracer, using high-quality SNPs (hqSNPs) detected among SH-BPL genomes. The x axis represents time by year, while the y axis denotes the effective population size. The dark-blue line represents the median, while light-blue shading represents the 95% highest posterior density (HPD) interval.

Genes that confer resistance to tetracyclines, sulfonamides, and cephalosporins are prevalent among the S. Heidelberg Brazilian poultry lineage.

SB-BPL genomes each possessed one to nine plasmid replicons, with the following replicons detected most frequently: IncX1 (n = 107; 97.2%), ColpVC (n = 105; 95.4%), IncA/C2 (n = 94; 85.4%), ColRNAI (n = 67; 60.9%), and IncI1 (n = 51; 46.3%). Plasmids replicons Col(MG828), Col156, IncFIA, IncFIB, IncFII, IncHI2A, IncHI2, IncN, IncQ1, IncR, IncX4_1, IncX4_2, IncY, TrfA, and p0111_1 were each detected among the 110 genomes, but in fewer than 5 isolates each (i.e., with frequencies less than 4%). A separate analysis of the 96 SH-BPL genomes demonstrated a very similar profile of replicons, with ColpVC (n = 95; 98.9%), IncX1 (n = 93; 96.8%), IncA/C2 (n = 92; 95.8%), ColRNAI (n = 63; 65.6%), and IncI1 (n = 46; 47.9%) detected most frequently. Plasmid replicons Col(MG828), Col156, IncFIA(HI1), IncFIB(K), IncHI2A, IncHI2, IncN, IncQ1, IncR, IncX4, IncY, and TrfA were each detected but in fewer than four isolates each and with frequencies lower than 4% (Fig. 5).

FIG 5.

FIG 5

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Presence and absence of 26 antimicrobial resistance genes and 20 plasmids replicons among 110 S. Heidelberg genomes. Black-and-white squares in the heatmap denote the presence and absence of a plasmid replicon, respectively. Black and white circles in the heat map denote the presence and absence of an antimicrobial resistance gene, respectively. The names of the antimicrobial resistance genes and plasmid replicons are along the bottom of the heatmap. Labels are at the tips of maximum-likelihood phylogeny constructed using high-quality SNPs (hqSNPs) and midpoint rooted (Fig. 2). iTOL was used to build this heat map.

Twenty-six different antimicrobial resistance genes were detected among the 110 genomes, with each harboring 3 to 13 genes and/or integrons. The fosA7 and mdf(A) genes conferring resistance to fosfomycins (fosA7), benzalkonium chloride [mdf(A)], and rhodamine [mdf(A)] were detected in all 110 genomes (100%). tet(A) and sul2, which confer resistance to tetracyclines and sulfonamides, respectively, were highly frequent (n = 96 [87.2%] and 92 [83.4%], respectively), while blaCMY-2, which confers resistance to cephamycins and cephalosporins, was detected in almost three-quarters of the isolates (n = 82; 74.5%). The remaining 20 genes, which confer resistance to aminoglycosides, cephalosporins, penems, monobactams, penams, diaminopyrimidines, phenicols, lincosamides, fluoroquinolones, and sulfonamides, were detected in fewer than 5% of the isolates (Fig. 5).

Among the 96 SH-BPL genomes, 22 different antimicrobial resistance genes were detected, with isolates harboring three to 13 genes and/or integrons. Apart from genes detected in 100% of the isolates [i.e., aac(6′)-Iaa, fosA7, and mdf(A)], antimicrobial resistance genes that were prevalent among the 110 isolates were present at even higher frequencies among the 96 SH-BPL genomes, including tet(A) in 93 isolates (96.8%), sul2 in 91 isolates (94.7%), and blaCMY-2 in 79 isolates (82.2%). The remaining 16 genes, which confer resistance to aminoglycosides, cephalosporins, penems, monobactams, penams, diaminopyrimidines, phenicols, lincosamides, fluoroquinolones, and sulfonamides, were detected in fewer than 5% of the SH-BPL isolates (Fig. 5). Genes sul2 and tet(A) were often codetected (i.e., present on the same contig) alongside the IncA/C2 plasmid replicon (66.6% and 65.6%, respectively). Other resistance genes were codetected alongside Incl (blaCMY-2, 7.2%), ColRNAI (qnrB19, 4.1%), and IncA/C2 (blaCMY-2, 3.1%) plasmid replicons.

Virulence mapping and pan-genome.

To understand the pathogenicity genetic repertoire of S. Heidelberg isolates, virulence factors were detected among the 110 genomes. Ten Salmonella pathogenicity islands (SPI1, SPI2, SPI3, SPI4, SPI5, SPI9, SPI11, SPI12, SPI13, and SPI14) were identified in all isolates. A total of 139 Salmonella virulence genes were also found, with 95 genes present in all isolates, among them some important operons such as cheWY, csgABCDEFG, entABCES, fepABCDG, fimCDFHI, fliAGMNP, lpfABCDE, spABDE, ssaCDEGHIJKLMNOPQRSTUV, and sseABCDEFGJKL. No major differences between the 96 SH-BPL genomes and the full 110-genome data sets were observed in the virulence genetic factors. However, 86.5% SH-BPL genomes (83/96) carried the virulence genes irp1, irp2, and ybtAEPQSTUX, while only 7.1% genomes (1/14) of the other cluster carried these genes (Fig. S1).

Of the 6,738 genes identified among the 110 S. Heidelberg genomes, 4,107 (60.9%) were core genes (i.e., present among all 110 genomes), while the remaining 2,631 genes (39.1%) comprised the pan-genome (Fig. S2) (P > 0.05). No clade-associated genes were identified for any clade (i.e., I, II, and III); additionally, no genes were found to be associated with the SH-BPL.

DISCUSSION

Salmonella is one of the main bacterial pathogens present on commercial poultry farms, causing direct and indirect losses in poultry production, in addition to its importance in public health. In the early 1900s, Salmonella enterica serovar Gallinarum, a serovar associated with poultry-specific diseases (fowl typhoid and pullorum disease), was widespread in flocks around the world. Biosecurity efforts eradicated S. Gallinarum from many poultry-producing countries in the mid-1960s (34). However, other Salmonella serovars emerged and quickly predominated in commercial poultry farms. S. Heidelberg is one such serovar and is frequently detected in many poultry-producing countries around the world, including Germany (3), Denmark (4), Italy (5), France (6), and the United States (2, 7). S. Heidelberg in poultry is more studied in the United States due to several Salmonella surveillance reports from the U.S. Department of Agriculture’s Food Safety and Inspection Services (USDA-FSIS), the National Antimicrobial Resistance Monitoring System (NARMS), and some independent studies (7, 9). These data demonstrated that S. Heidelberg supplanted Salmonella enterica serovar Enteritidis as the predominant “poultry” serovar from 1997 to 2006 (35).

In Brazil, S. Heidelberg was first identified in poultry and derived products in 1962 (36). This serovar was rarely detected for decades, but its frequency increased rapidly in the last 20 years. S. Heidelberg now is one of the most frequently isolated serovars from commercial broiler flocks in Brazil (18, 30–33). Consequently, it is frequently detected in commercialized raw chicken meat in Brazil and in other countries (29).

S. Heidelberg from Brazilian poultry operations forms a monophyletic lineage supporting a probable clonal origin.

In the present study, all 68 S. Heidelberg isolates isolated from Brazilian poultry flocks in four different states in the south and southeast regions of Brazil over a period of 5 years (2014 to 2018) showed a high level of genomic similarity, indicating a probable clonal origin. This is supported by three major pieces of evidence, including (i) all 68 genomes sequenced here, as well as the 25 publicly available Brazilian S. Heidelberg genomes, belonged to sequence type 15 (ST15), which is the most disseminated S. Heidelberg ST worldwide (29, 37, 38); (ii) most Brazilian genomes sequenced clustered the S. Heidelberg SNP cluster PDS000037185 according to CBI Pathogen Detection Isolates Browser (75% from this study and 78.7% from all S. Heidelberg WGSs in Brazil); and (iii) all Brazilian S. Heidelberg isolates clustered together as a monophyletic group with high bootstrap support in the ML tree (SH-BPL).

Furthermore, all Brazilian isolates in this monophyletic clade reported here were obtained from breeders and broilers flocks in four very important poultry producers states in the country (39) (Fig. S3 in the supplemental material). This high frequency of genetically related S. Heidelberg strains associated with different levels of the poultry chain across these producing states in a period of 5 years reinforces that poultry farms played an important role in the dissemination of S. Heidelberg into the slaughterhouses and the contamination of the chicken meat in Brazil. Previous reports have already described similar clonal population structure in other S. Heidelberg lineages from animal production chains in the United States (27, 28).

Importantly, there were several S. Heidelberg WGSs from other countries (such as Chile, Germany, South Korea, and the United Kingdom) in the monophyletic clade SH-BPL, most of them obtained from chicken meat or food in the last years. There is a strong possibility that these foods could be Brazilian chicken products exported to European, Asian, and other South American countries as previously demonstrated (29). However, the hypothesis could not be excluded that this S. Heidelberg lineage has also been disseminated in poultry operations outside Brazil. As more historical S. Heidelberg genomes are published and available in public databases, more complete analyses can be performed to assess more definitively the role of Brazilian poultry chain production in the dissemination of this serovar worldwide.

Temporal evolutionary analysis supports a single recent origin of poultry-associated S. Heidelberg in Brazil.

Bayesian phylogenetic analysis demonstrated the temporal evolution of S. Heidelberg, mainly in the American continent. First, the evolutionary rate for the 110-isolate clade was predicted to be 5.74 × 10−7 substitutions/site/year [(95% HPD = (4.65 × 10−7) to (6.86 × 10−7)], a similar value observed to other Salmonella serovars (such as Minnesota and Enteritidis) with a fast dissemination in poultry flocks from Brazil (40, 41) as well as in other production animals worldwide (42, 43). Interestingly, some North American genomes in this analysis were from S. Heidelberg isolates obtained in the 1980s and 1990s, reinforcing the probable common origin for all S. Heidelberg disseminated in poultry in North and South America. Aside from the oldest isolate (SRR112727_US_1986), which was isolated from a dog in Texas in 1986 (44), the other two isolates were obtained from poultry in Oklahoma in 1993 and 1995 (SRR5278779_US_1993; SRR5409891_US_1995). This result reinforces previous data demonstrating that S. Heidelberg has been circulating in North American poultry farms for more than 30 years (2, 9, 11). In addition, S. Heidelberg is more frequently reported in North America than in other regions of the world (45).

In Brazil, the highly disseminated lineage identified here (SH-BPL) was probably introduced at the beginning of this century (2004), when it underwent a clonal expansion and diversification. This first ancestor may have been introduced horizontally, for example, in the feed or vertically from the hatchery via day-old chicks (4). The introduction by the poultry production chain pyramid, such as breeder flocks and hatcheries, is a probable explanation for rapid spreading in the first years. Afterward, both horizontal and vertical transmission of S. Heidelberg could have contributed to broiler contamination at preharvest and further downstream in the production chain as previously suggested for S. Typhimurium and S. Enteritidis (46). Carryover from one batch to another within the same barn has already been described in Salmonella enterica serovars Java and Heidelberg, even after cleaning and disinfection (47, 48).

Historical demographic reconstruction showed that the 110-isolate lineage underwent a dramatic increase between 2008 and 2013. In this range of time, there was a large expansion of poultry production in Brazil (49). In 2008, there were 10.94 million tons of poultry produced in Brazil, while this number increased to 12.31 million tons in 2013 (39). A previous study also reported that intensification of poultry production increased outbreaks of fowl typhoid (caused by Salmonella enterica serovar Gallinarum) in Brazil (50).

SH-BPL is characterized by a multidrug resistance profile associated with the presence of plasmids.

The replicons detected in the set of SH-BPL genomes included IncI, IncX1, and IncA/C2, all of which have been previously described in other S. Heidelberg isolates from other sources and origins (27, 29, 51, 52). The plasmids associated with these replicons have previously been shown to play an important role in the dissemination of resistance genes, generally associated with persistence of these strains in environments where antibiotics are used (53, 54). The results presented here also suggest that most antibiotic resistance genes [tet(A), sul2, and blaCMY-2] were harbored on IncA/C2 plasmids. These genes have been detected in this same replicon in other S. Heidelberg isolates from Brazil according to previous studies (33, 55). Detection of antibiotic resistance genes also demonstrated the high prevalence of genes conferring resistance to multiple different classes of antibiotics among the SH-BPL lineage [i.e., tet(A), sul2, blaCMY-2, and fosA7]. The fosA7 and blaCMY-2 genes have already been reported with high frequency in Heidelberg serovar isolates in a study of Salmonella genomes in Brazil of the last 4 decades, including the fosA7 gene, which was limited to a few serovars (56). In addition, the aac(6′)-Iaa gene was detected in all isolates. This gene codes for an aminoglycoside acetyltransferase, and it should inactivate aminoglycoside antibiotics by acetylating their substrates at the 6′ position (57). However, a more recent study has demonstrated that isolates carrying this gene as the only aminoglycoside resistance determinant usually do not show phenotypic resistance to an aminoglycoside antimicrobial (58).

The widespread occurrence and recent dissemination of the SH-BPL lineage in chicken flocks in Brazil are probably related to the selection of bacteria with resistance to multiple antibiotics due to the use of antibiotics in the production chain (59, 60). Antibiotic administration in combination with egg vaccination has been carried out in some hatcheries to decrease the presence of pathogenic bacteria in the development of the embryo, which may benefit the most resistant bacterial communities. It has already been demonstrated that these practices may generate resistance to third-generation cephalosporins, especially with the use of ceftiofur in combination with Marek's vaccine (spray or subcutaneous injection) in day-old poultry hatchers (59, 61). In addition, increased occurrence of beta-lactam resistance genes (e.g., blaCMY-2) has already been reported in S. Heidelberg isolated in Brazilian poultry farms, as well as chicken carcasses and other poultry food produced in Brazil (29, 60, 62, 63). Presence of beta-lactam resistance genes is a public health concern, as these genes inactivate third-generation cephalosporins used in the treatment of infections caused by Salmonella in humans and animals (64).

Virulence gene content was also analyzed across S. Heidelberg genomes. Most of the SPIs previously reported in S. Heidelberg were present in all isolates studied here. However, some virulence genes were absent in specific genomes from the different phylogenetic clades, including SH-BPL. Gene losses due to DNA insertions and deletions (as, for example, in fimbrial and antibiotic resistance genes) had already been reported previously in S. Heidelberg (28). The modification of the whole genetic profile of the circulating S. Heidelberg strains, mainly those related to antibiotic resistance, is a possible reason for the increased prevalence of this serovar, as well as its persistence in some specific environments, such as intensive poultry-producing farms (65).

Conclusion.

This study identified an MDR S. Heidelberg lineage that is circulating in Brazilian commercial poultry flocks (SH-BPL), providing important information about the recent introduction (2004) of this lineage and the increase in bacterial population size soon after (2006 to 2010). SH-BPL seems to be persisting in successive poultry flocks despite different management practices designed to eliminate this serovar on farms, as supported by previous studies indicating that typical litter management procedures were not capable of interrupting the cycle of residual contamination by S. Heidelberg in southern Brazil (18). This region is also an emerging antimicrobial resistance hot spot in animals among low- and middle-income countries (66). Future continued genomic monitoring of the spread and evolution of this lineage in Brazilian poultry operations will be necessary, including the development of control strategies. In addition, phenotypic evaluation of the isolates used in this study could refine genome-level results observed here, providing insight into the fitness of SH-BPL lineage.

MATERIALS AND METHODS

Bacterial isolates.

Sixty-eight S. Heidelberg isolates from broiler producing flocks were obtained in different commercial poultry farms from the Brazilian states of Rio Grande do Sul (RS; n = 10), Santa Catarina (SC; n = 30), Paraná (PR; n = 17), and São Paulo (SP; n = 9) from 2014 to 2018 (2 isolates had no origin information) (Table 1).

TABLE 1.

Metadata of S. Heidelberg isolates sequenced in this study

Identification Accession no. Yr Statea Sourceb SNP cluster
ULBRA_SA344 SRR8520104 2014 Parana Slaughterhouse PDS000037185.91
ULBRA_SA345 SRR8520145 2014 Parana Culture PDS000037185.91
ULBRA_SA348 SRR8520112 2014 Parana Flock environment PDS000037185.91
ULBRA_SA349 SRR8520141 2014 Parana Dead poultry PDS000037185.91
ULBRA_SA350 SRR8520111 2014 Parana Flock environment PDS000037185.91
ULBRA_SA170 SRR8177014 2015 R.G. do Sul Drag swab PDS000037185.91
ULBRA_SA174 SRR8177024 2015 R.G. do Sul Drag swab PDS000037185.91
ULBRA_SA175 JAAQPM000000000 2015 R.G. do Sul Drag swab No
ULBRA_SA180 SRR7284814 2015 Missing Drag swab PDS000037185.91
ULBRA_SA181 SRR8177073 2015 Missing Field strain PDS000029703.311
ULBRA-SF327 SRR8508326 2015 S. Catarina Raw chicken No
ULBRA-SF328 SRR8508325 2015 S. Catarina Raw chicken PDS000037185.91
ULBRA_SA185 SRR8177066 2016 S. Catarina Drag swab PDS000029160.8
ULBRA_SA186 SRR8177072 2016 S. Catarina Drag swab PDS000029160.8
ULBRA_SA197 SRR8177020 2016 S. Catarina Drag swab PDS000038552.1
ULBRA_SA198 SRR8177051 2016 S. Catarina Drag swab PDS000038552.1
ULBRA_SA207 SRR7282629 2016 S. Catarina Drag swab PDS000037185.91
ULBRA_SA242 SRR8177058 2016 S. Catarina Drag swab PDS000037185.91
ULBRA_SA225 SRR8177033 2017 R.G. do Sul Drag swab PDS000037185.91
ULBRA_SA226 SRR7284821 2017 R.G. do Sul Drag swab PDS000037185.91
ULBRA_SA227 SRR8177046 2017 Parana Feces (poultry) PDS000037185.91
ULBRA_SA228 SRR8177026 2017 Parana Feces (poultry) PDS000037185.91
ULBRA_SA230 SRR8177028 2017 S. Catarina Drag swab PDS000037185.91
ULBRA_SA231 SRR8177015 2017 S. Catarina Drag swab PDS000037185.91
ULBRA_SA235 SRR8177047 2017 Parana Feces (poultry) PDS000037185.91
ULBRA_SA237 SRR8177027 2017 Parana Feces (poultry) PDS000037185.91
ULBRA_SA239 SRR8177019 2017 S. Catarina Drag swab PDS000029703.311
ULBRA_SA240 SRR7284713 2017 S. Catarina Drag swab PDS000029703.311
ULBRA_SA241 SRR8177045 2017 R.G. do Sul Drag swab PDS000037185.91
ULBRA_SA243 SRR8177061 2017 Parana Feces (poultry) PDS000037185.91
ULBRA_SA244 SRR8177050 2017 R.G. do Sul Meconium (breeder) PDS000037185.91
ULBRA_SA245 JAAQPM000000000 2017 R.G. do Sul Shoe cover PDS000037185.91
ULBRA_SA248 SRR8177064 2017 S. Catarina Drag swab No
ULBRA_SA250 SRR7284811 2017 Parana Feces (poultry) PDS000037185.91
ULBRA_SA251 SRR8177017 2017 R.G. do Sul Shoe cover PDS000029160.8
ULBRA_SA252 SRR8177013 2017 R.G. do Sul Shoe cover PDS000029160.8
ULBRA_SA364 SRR8520092 2017 S. Catarina Flock environment PDS000037185.91
ULBRA_SA365 SRR8519245 2017 S. Catarina Flock environment PDS000037185.91
ULBRA-SF334 SRR8508332 2017 S. Catarina Raw chicken PDS000037185.91
ULBRA-SF343 SRR8508331 2017 S. Catarina Raw chicken PDS000029160.8
ULBRA_SA354 SRR8520144 2018 S. Catarina Slaughterhouse PDS000029160.8
ULBRA_SA355 SRR8520207 2018 S. Catarina Slaughterhouse PDS000037185.91
ULBRA_SA356 SRR8520143 2018 S. Catarina Slaughterhouse PDS000037185.91
ULBRA_SA357 SRR8520113 2018 S. Catarina Slaughterhouse PDS000037185.91
ULBRA_SA358 SRR8520140 2018 S. Catarina Slaughterhouse PDS000037185.91
ULBRA_SA359 SRR8520208 2018 S. Catarina Slaughterhouse PDS000037185.91
ULBRA_SA360 SRR8520093 2018 S. Catarina Slaughterhouse PDS000037185.91
ULBRA_SA361 SRR8520086 2018 S. Catarina Slaughterhouse PDS000029160.8
ULBRA_SA362 SRR8520085 2018 S. Catarina Flock environment PDS000037185.91
ULBRA_SA363 SRR8520087 2018 S. Catarina Flock environment PDS000037185.91
ULBRA_SA366 SRR8519235 2018 S. Catarina Flock environment PDS000037185.91
ULBRA_SA367 SRR8519240 2018 S. Catarina Flock environment PDS000037185.91
ULBRA_SA371 SRR8519241 2018 S. Catarina Flock environment PDS000037185.91
ULBRA_SA372 SRR8519233 2018 Parana Flock environment No
ULBRA-SA373 SRR8508320 2018 Parana Flock environment PDS000037185.91
ULBRA-SA378 SRR8508319 2018 Parana Flock environment PDS000037185.91
ULBRA-SA379 SRR8508318 2018 Sao Paulo Slaughterhouse PDS000037185.91
ULBRA-SA380 SRR8508317 2018 Sao Paulo Slaughterhouse PDS000037185.91
ULBRA-SA381 SRR8508316 2018 Sao Paulo Slaughterhouse PDS000037185.91
ULBRA-SA382 SRR8508315 2018 Sao Paulo Slaughterhouse PDS000037185.91
ULBRA-SA383 SRR8508314 2018 Sao Paulo Slaughterhouse PDS000037185.91
ULBRA-SA385 SRR8508313 2018 Sao Paulo Flock environment PDS000037185.91
ULBRA-SA386 SRR8508328 2018 Sao Paulo Flock environment PDS000037185.91
ULBRA-SA387 SRR8508327 2018 Sao Paulo Flock environment PDS000037185.91
ULBRA-SA388 SRR8508330 2018 Sao Paulo Flock environment PDS000037185.91
ULBRA-SA389 SRR8508329 2018 Parana Slaughterhouse No
ULBRA-SA390 SRR8508324 2018 Parana Slaughterhouse PDS000037185.91
ULBRA-SA391 SRR8508323 2018 Parana Slaughterhouse PDS000037185.91
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a

R.G. do Sul, Rio Grande do Sul; S. Catarina, Santa Catarina.

b

All isolates were isolated from poultry or poultry-associated environments.

Single bacterial colonies were removed from xylose lysine desoxycholate (XLD) agar plates and placed in brain heart infusion (BHI) broth, followed by overnight incubation at 35°C. All 68 S. Heidelberg isolates were further analyzed with species- and serovar-specific PCR assays. Briefly, DNA was extracted with a commercial kit according to the supplier´s instructions (NewGene, Simbios Biotecnologia, Cachoeirinha, RS, Brazil). Two real-time PCRs, one specific to Salmonella and another to S. Heidelberg, were carried out as previously described (67). Amplification conditions included an initial denaturation cycle of 3 min at 95°C followed by 40 cycles of 15 s at 95°C and 60 s at 60°C performed on a StepOne Plus instrument (Applied Biosystems, Carlsbad CA, USA).

Whole-genome sequencing.

The PureLink Genomic DNA minikit was used to extract genomic DNA following the manufacturer’s instructions (Thermo Fisher Scientific, Waltham, MA, USA), and DNA was visualized on 2% agarose gel stained with ethidium bromide to assess its integrity. Sequencing libraries were prepared using the Nextera XT kit (Illumina, Inc., San Diego, CA, USA). DNA concentration was adjusted to 0.5 ng/μl, and sequencing was performed on an Illumina NextSeq platform with 150-bp paired-end reads (Wadsworth Center, New York State Department of Health, Albany, NY, USA).

Assembly of genomic data and in silico multilocus sequence typing and serotyping.

Raw sequencing data for additional 248 S. Heidelberg genomes from 69 single nucleotide polymorphism (SNP) clusters (https://www.ncbi.nlm.nih.gov/pathogens/isolates) and some independent samples (not forming SNP clusters) were downloaded from the NCBI Sequence Read Archive database. These 248 genomes were selected from 3,570 bacterial WGSs belonging to more than 130 SNP clusters (that included at least one genome registered as S. Heidelberg). All of them were carefully reviewed to detect clusters of other Salmonella serovars, including one or more S. Heidelberg WGSs (due to incorrect identification of the samples). All WGSs from clusters of other serovars were removed from the data set, resulting in 69 specific SNPs clusters of S. Heidelberg and 27 independent samples. At least one WGS from each of the 69 SNP clusters specific to S. Heidelberg was selected for the novel phylogenetic analysis. Other criteria were also used to select and to download genomes, including quality of the sequences (i.e., genome length, contig number) and metadata availability (i.e., collection date, isolation source, and country). The selected genomes were from Brazil (n = 26) and other countries worldwide (n = 222). This sampling included all published genomes from South America (from Argentina, Chile, and Colombia) and representative genomes from all other continents (from Australia, Canada, China, Ethiopia, Germany, Ireland, Israel, Kenya, Netherlands, South Korea, Taiwan, Thailand, United Kingdom, and the United States) published until November 2020. Also, they were obtained from different sources (poultry, feed, cattle, dog, equine, egg, food, human, swine, and water) (Table S1 in the supplemental material).

Trimmomatic version 0.33 (68) was used to trim raw sequence reads and remove low-quality bases. The quality of trimmed reads was assessed using FastQC version 0.11.2 (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) prior to de novo assembly using SPAdes version 3.6.0 (69). The quality of draft genomes was evaluated using QUAST version 4.0, and average coverage was estimated using BBMap version 38.26 (https://sourceforge.net/projects/bbmap/) and Samtools version 1.8. Assemblies were annotated using Prokka version 1.12 (70). SISTR version 0.3.1 (71) was used to perform in silico serotyping of each isolate. Genomes were also assigned to a sequence type using a seven-gene multilocus sequence typing scheme (MLST) (https://github.com/tseemann/mlst).

Identification of SNPs and construction of maximum likelihood phylogenies.

SNPs were identified among the 316 S. Heidelberg assemblies (Table 1 and Table S1) using kSNP3 version 3.1 (72) and the optimal k-mer size determined by Kchooser (k = 19). The maximum-parsimony tree produced by kSNP3 clustered the sequences based on the core SNPs identified.

The CFSAN SNP pipeline (73) was used to identify high-quality SNPs (hqSNPs) within a monophyletic clade containing 110 WGSs, including all Brazilian S. Heidelberg genomes (n = 94), as well as genomes from Europe (n = 3) and North America (n = 13). The resulting SNP matrix of preserved sites was used to build a phylogeny using the maximum-likelihood (ML) method implemented in W-IQ-TREE (the IQ-TREE web server; accessed 27 February 2020) (74), the optimal nucleotide substitution model selected using ModelFinder (75) and 1,000 replicates of the ultrafast bootstrap approximation (76).

Tip-dated evolutionary analysis.

The linear regression approach implemented in TempEst version 1.5 (77) was used to evaluate the temporal signal and clock-likeness of the ML phylogeny constructed using hqSNPs detected among the 110 genomes belonging to the monophyletic clade. The resulting R2 value produced by TempEst was 0.6 when the best-fitting root was used.

A tip-dated phylogeny was constructed using BEAUti version 1.8.2 and BEAST version 1.8.2 (78). The HKY substitution model was used, and each of the following combinations of clock and population models was tested: (i) strict clock and coalescent constant-size population model, (ii) strict clock and coalescent Bayesian skyline population model, (iii) lognormal relaxed clock and coalescent constant-size population model, and (iv) lognormal relaxed clock and coalescent Bayesian skyline population model. For all models, the initial substitution rate was set to 2.79 × 10−7 substitutions/site/year as previously demonstrated for Salmonella enterica serovar Typhimurium (79), and a lognormal prior was placed on the respective clock rate parameter (clockRate and ucldMean for strict and lognormal relaxed clocks, respectively; in real space, mean [M] = 1.0, standard deviation [S] = 1.25). After running all possible parameters, the one that presented the best effective sample size (ESS) values was chosen for the analyses.

The Markov chain Monte Carlo (MCMC) algorithm was run for 1 × 108 generations, and parameters were logged every 10,000 generations. The best model (i.e., the lognormal relaxed clock and coalescent Bayesian skyline model) was identified using marginal-likelihood estimates obtained via path sampling using 10 steps of at least 1 × 108 generations and by assessing ESS values and mixing of parameters in Tracer version 1.6.0 (80). Five independent MCMC runs using the best model were performed, using chain lengths of 1 × 108 generations, sampling every 10,000 generations. The resulting log files were viewed in Tracer to ensure that ESS values were sufficiently high (i.e., >200 for all parameters) and that all parameters had mixed adequately with 10% burn-in. LogCombiner version 1.8.3 was used to combine the log and tree files of five independent runs, and TreeAnnotator version 1.8.2 (81) was used to construct a maximum clade credibility (MCC) tree, using 10% burn-in and common ancestor node heights. FigTree version 1.4.2 (http://tree.bio.ed.ac.uk/software/figtree/) was used to annotate the resulting phylogeny, using bars to denote 95% highest posterior density (HPD) intervals for node heights and branch labels to denote posterior probabilities.

Identification of plasmid replicons, antimicrobial resistance genes, and virulence factors.

ABRicate version 0.8 (https://github.com/tseemann/abricate) was used to detect antimicrobial resistance genes, virulence factors, and plasmid replicons in each assembled genome, using ResFinder (82), Virulence Factor Database (VFDB) (83), and PlasmidFinder (84) databases, respectively (accessed 11 June 2018). Salmonella pathogenicity islands (SPIs) were detected in each genome (https://bitbucket.org/genomicepidemiology/spifinder_db/src/master/) using nucleotide BLAST (BLASTn) (85). For all searches, minimum nucleotide identity and coverage thresholds of 75% and 50% were used, respectively.

Identification of clade-associated genes within the S. Heidelberg Brazilian poultry lineage.

Roary version 3.12.0 (86) was used to identify orthologous genes present in the S. Heidelberg core and pan-genome, using a minimum protein BLAST (BLASTp) identity value of 90% (-i 90). Scoary version 1.6.14 (87) was used to identify genes associated with each of three clades (i.e., I, II, and III) within the S. Heidelberg Brazilian poultry lineage, using a P value cutoff of 0.05 (-p 0.05) and a Bonferroni correction to account for multiple testing.

ACKNOWLEDGMENTS

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brasil (CAPES)-Finance Code 001. This study was also financed by Simbios Biotecnologia. V.R.L. was financially supported by the National Council for Scientific and Technological Development from Brazil (Conselho Nacional de Desenvolvimento Científico e Tecnológico [CNPq]; process number 311010/2017-2).

We thank Porto Belo Laboratory, Simbios Biotecnologia, and UDESC Microbiology Laboratory for providing the samples. We thank the FDA GenomeTrakr network for support through the collaborative research agreement U18 FD006229 and the Wadsworth Center Advanced Genomic Technologies Cluster for sequencing 69 of the isolates. We also thank the technical personnel (especially Fernanda Kieling Moreira Lehmann) and students of the Laboratory of Molecular Diagnostic for the technical support.

Footnotes

Supplemental material is available online only.

Supplemental file 1
Table S1, Fig. S1 to S3. Download AEM.01036-21-s0001.pdf, PDF file, 0.5 MB (523KB, pdf)

Contributor Information

Vagner R. Lunge, Email: lunge@ulbra.br.

Christopher A. Elkins, Centers for Disease Control and Prevention

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