Skip to content

Advertisement

Gut Pathogens

What do you think about BMC? Take part in

Open Access

Whole genome sequencing-based detection of antimicrobial resistance and virulence in non-typhoidal Salmonella enterica isolated from wildlife

Gut Pathogens20179:66

https://doi.org/10.1186/s13099-017-0213-x

Received: 27 June 2017

Accepted: 6 November 2017

Published: 21 November 2017

Abstract

The aim of this study was to generate a reference set of Salmonella enterica genomes isolated from wildlife from the United States and to determine the antimicrobial resistance and virulence gene profile of the isolates from the genome sequence data. We sequenced the whole genomes of 103 Salmonella isolates sampled between 1988 and 2003 from wildlife and exotic pet cases that were submitted to the Oklahoma Animal Disease Diagnostic Laboratory, Stillwater, Oklahoma. Among 103 isolates, 50.48% were from wild birds, 0.9% was from fish, 24.27% each were from reptiles and mammals. 50.48% isolates showed resistance to at least one antibiotic. Resistance against the aminoglycoside streptomycin was most common while 9 isolates were found to be multi-drug resistant having resistance against more than three antibiotics. Determination of virulence gene profile revealed that the genes belonging to csg operons, the fim genes that encode for type 1 fimbriae and the genes belonging to type III secretion system were predominant among the isolates. The universal presence of fimbrial genes and the genes encoded by pathogenicity islands 1–2 among the isolates we report here indicates that these isolates could potentially cause disease in humans. Therefore, the genomes we report here could be a valuable reference point for future traceback investigations when wildlife is considered to be the potential source of human Salmonellosis.

Keywords

WildlifeSalmonellosisWhole genome sequencingAntimicrobial resistanceSalmonella virulenceFoodborne pathogen

Background

Salmonella enterica is the leading cause of foodborne illness in the United States accounting for approximately 1.2 million infections, 23,000 hospitalizations and 450 deaths annually. Over the past few decades, Salmonella has acquired new virulence determinants that influence host-tropism which helps these organisms to adapt to a wide range of hosts [1]. Multiple serovars of S. enterica originating from mammalian, reptilian and avian hosts have been reported to cause infections in humans [1]. Wildlife and exotic pets harboring Salmonella are potential sources for human infections [1]. Transmission of Salmonella from wildlife and exotic animals to humans occurs through multiple pathways. Increasing evidence suggests that there could be a bidirectional transmission of Salmonella between domesticated and wild animals. Farm animals acquiring Salmonella from wildlife could increase the risk of human infection. Salmonella infections in humans have also been reported through direct contact with exotic pets and wildlife, especially those in captivity. Consumption of contaminated game bird meat is also a potential source for foodborne salmonellosis. Furthermore, wildlife such as rodents and birds, harboring in the proximity of food production units can act as carriers and contaminate food products leading to indirect infections.

The threat posed by salmonellosis is further compounded by the presence of resistance genes that confer resistance to multiple antimicrobial drugs. According to the National Antimicrobial Resistance Monitoring System (NARMS) integrated report, 20% of human Salmonella isolates exhibit antimicrobial resistance (AMR). Antimicrobial-resistant Salmonella infections result in increased disease severity and longer hospitalizations in addition to economic losses [2]. Research indicates that Salmonella isolates from various wildlife species also possess AMR determinants and the prevalence rate of AMR genes in these isolates could be as high as 100% [3, 4]. Thus, Salmonella in wildlife poses a significant risk to human health underlining the need for an integrative ‘One Health’ approach for the surveillance of pathogens among humans, domestic animals, and wildlife population.

Whole genome sequencing (WGS) of foodborne pathogens could be adopted as an effective and rapid surveillance tool. Compared to conventional antimicrobial tests, WGS offers a more comprehensive information on the genotypic characteristics of pathogens including identification of AMR and virulence determinants, and serotypes. Recent studies have utilized WGS to reliably predict the antimicrobial characteristics in various pathogens including Salmonella [58]. In this study, WGS was utilized to predict AMR and virulence determinants in Salmonella isolated from exotic pets and wildlife.

Methods

Quality assurance

All strains were identified as Salmonella enterica following the American Association of Veterinary Laboratory Diagnosticians certified laboratory. For genome sequencing, each isolate was streaked on Salmonella selective medium and a single colony was picked and used for further steps as outlined below.

Salmonella bacterial isolates

A total of 103 Salmonella isolates were revived from archival cultures obtained from exotic pet or wildlife clinical specimens submitted to the Oklahoma Animal Disease Diagnostic Laboratory, Stillwater, Oklahoma during 1988–2003. The metadata for the samples used in this study is provided in Table 1 and the details of genome sequencing and assembly parameters are given in Additional file 1: Table S1. Isolates were streaked on Luria–Bertani agar slants and were transported to the Animal Disease Research and Diagnostic Laboratory, South Dakota State University, Brookings, South Dakota for WGS. Samples were streaked on Luria–Bertani agar plates upon arrival to the laboratory. A single bacterial colony from the agar plate was then inoculated to Luria–Bertani broth and cultured at 37 °C.
Table 1

List of Salmonella enterica strains isolated and sequenced from wild life and the corresponding metadata

Strain ID

Serovar

Year

Animal

NCBI SRA BioSample ID

NCBI SRA ID

ADRDL-001

Poona

1993

Alligator omentum

SAMN06330630

SRR5278825

ADRDL-002

Typhimurium

1993

Auodad feces

SAMN06330629

SRR5278822

ADRDL-003

Gaminara

1994

Ratite intestine

SAMN06330628

SRR5278823

ADRDL-004

Lille

1993

Gamebird embryo

SAMN06330627

SRR5278827

ADRDL-005

Typhimurium

1993

Ratite feces

SAMN06333495

SRR5278819

ADRDL-006

Typhimurium

1993

Ratite feces

SAMN06333494

SRR5278824

ADRDL-007

Thompson

1993

Ratite cecum

SAMN06333493

SRR5278802

ADRDL-008

Livington

1993

Ratite cecum

SAMN06333492

SRR5278806

ADRDL-009

Typhimurium

1993

Ratite feces

SAMN06333491

SRR5278801

ADRDL-010

Montevideo

1993

Ratite feces

SAMN06333489

SRR5278805

ADRDL-011

6,7-nonmotile

1993

Ratite intestine

SAMN06333488

SRR5278804

ADRDL-012

Arechavaleta

1994

Ratite intestine

SAMN06333486

SRR5278803

ADRDL-013

4,5,12:i-monophasic

1994

Ratite liver

SAMN06333485

SRR5380966

ADRDL-014

Berta

1994

Ratite intestine

SAMN06333484

SRR5278808

ADRDL-015

Ituri

1994

Ratite cecum

SAMN06333483

SRR5278773

ADRDL-016

Ituri

1994

Ratite intestine

SAMN06333482

SRR5278772

ADRDL-017

Heidelberg

1993

Wild turkey liver

SAMN06333481

SRR5278779

ADRDL-018

Heidelberg

1993

Wild turkey liver

SAMN06333480

SRR5278777

ADRDL-019

Godesberg

1993

Wild turkey cecum

SAMN06333479

SRR5278778

ADRDL-020

4,5,12:i-monophasic

1993

Eclectus colon

SAMN06333477

SRR5278771

ADRDL-021

Anatum

1993

Giraffe feces

SAMN06333476

SRR5278774

ADRDL-022

Anatum

1993

Giraffe feces

SAMN06333475

SRR5278780

ADRDL-023

Pomona

1993

Python abdominal swab

SAMN06333473

SRR5278767

ADRDL-024

Muenchen

1993

Ratite intestine

SAMN06333472

SRR5278776

ADRDL-025

Typhimurium

1994

Rodent intestine

SAMN06333471

SRR5278770

ADRDL-026

Hadar

1995

Wild chicken intestine

SAMN06333470

SRR5278768

ADRDL-027

Hadar

1994

Ratite intestine

SAMN06333469

SRR5278769

ADRDL-028

Typhimurium

1988

Primate intestine

SAMN06333465

SRR5278873

ADRDL-029

Albany

1988

Saiga intestine

SAMN06333464

SRR5278882

ADRDL-030

Arizona

1988

Snake

SAMN06333462

SRR5330438

ADRDL-031

Arizona

1989

Boa intestinal swab

SAMN06333460

SRR5330446

ADRDL-032

16:z10-e,n,xz15

1989

Cervine feces

SAMN06333459

SRR5330441

ADRDL-033

Enteritidis

1989

Hedgehog spleen

SAMN06333458

SRR5330440

ADRDL-034

Typhimurium(O5−)*

1992

Pigeon airsac swab

SAMN06333457

SRR5330448

ADRDL-035

Typhimurium

1989

Screech owl liver

SAMN06333455

SRR5330445

ADRDL-036

Braenderup

1989

Snow leopard intestine

SAMN06333454

SRR5330444

ADRDL-037

Saintpaul

1989

Snow leopard lung

SAMN06333453

SRR5330406

ADRDL-038

Montevideo

1992

Cervid intestine

SAMN06333451

SRR5329403

ADRDL-039

Enteriditis

1993

Emu feces

SAMN06333450

SRR5329404

ADRDL-040

Enteriditis

1993

Emu feces

SAMN06333449

SRR5380965

ADRDL-041

Worthington

1992

Quail intestine

SAMN06333448

SRR5380958

ADRDL-042

 II 43:z4,z23:- or IIIa 43:z4,z23:- or Farmingdale or IV 43:z4,z23:-*

1992

Reptile eggsac

SAMN06333447

SRR5329405

ADRDL-043

Panama

1992

Rhea intestine

SAMN06333694

SRR5409894

ADRDL-044

Ituri

1994

Ratite cecum

SAMN06333692

SRR5409893

ADRDL-045

Newport

1995

Ratite feces

SAMN06333691

SRR5409493

ADRDL-046

Newport

1995

Dolphin lung

SAMN06333689

SRR5409890

ADRDL-047

Typhimurium

1997

Psittacine lung

SAMN06333684

SRR5409485

ADRDL-048

Typhimurium

1997

Psittacine intestine

SAMN06333683

SRR5409315

ADRDL-049

Muenchen

1996

Ratite intestine

SAMN06333682

SRR5409313

ADRDL-050

Schwazengrund

1997

Ratite intestine

SAMN06333681

SRR5409312

ADRDL-051

Archavaleta

1997

Antelope intestine

 SAMN06333692

 SRR5409893

ADRDL-052

Infantis

1997

Fish water

 SAMN06645614

 SRR5398012

ADRDL-053

Bredeney

1998

Llama intestine

 SAMN06333861

 SRR5409360

ADRDL-054

Plymouth

1997

Reptile liver

 SAMN06330627

 SRR5278827

ADRDL-055

Montevideo

1997

Reptile intestine

 SAMN06645663

 SRR5398013

ADRDL-056

Branderup

1995

Wild chicken intestine

 SAMN06645590

 SRR5387496

ADRDL-057

Enteriditis

1996

Wild chicken intestine

SAMN06645569

SRR5387492

ADRDL-058

Typhimurium

1996

Wild chicken feces

SAMN06645567

SRR5387491

ADRDL-059

Bredeney

1995

Gamebird intestine

SAMN06645592

SRR5387497

ADRDL-060

Livingston

1996

Gamebird intestine

SAMN06645590

SRR5387496

ADRDL-061

Enteriditis

1995

Psittacine intestine

SAMN06645588

SRR5387490

ADRDL-062

Montevideo

1996

Psittacine liver

SAMN06645587

SRR5387493

ADRDL-063

7,14:K-monophasic

1995

Ratite intestine

SAMN06645585

SRR5387523

ADRDL-064

Anatum

1995

Ratite feces

SAMN06645654

SRR5387521

ADRDL-065

Enteriditis

1995

Ratite

SAMN06645582

SRR5387527

ADRDL-066

Thompson

1995

Ratite cloacal swab

SAMN06645594

SRR5387519

ADRDL-067

Thompson

1995

Ratite cloacal swab

SAMN06645593

SRR5387517

ADRDL-068

4,5,12: i

1995

Ratite pericardial fluid

SAMN06645652

SRR5387518

ADRDL-069

Livingston

1996

Llama intestine

SAMN06645650

SRR5387514

ADRDL-070

Uganda

1999

Cervine intestine

SAMN06645664

SRR5398014

ADRDL-071

Lille

2000

Cervine intestine

SAMN06645663

SRR5398013

ADRDL-072

Parera

1998

Iguana cloacal swab

SAMN06645662

SRR5398016

ADRDL-073

Anatum

1998

Ratite feces

SAMN06645661

SRR5398025

ADRDL-074

Anatum

1998

Ratite feces

SAMN06645615

SRR5398018

ADRDL-075

Kiambu

1998

Ratite cloacal swab

SAMN06645614

SRR5398012

ADRDL-076

Marina

2000

Reptile feces

SAMN06645660

SRR5398017

ADRDL-077

Bredeney

2003

Alpaca liver

SAMN06645613

SRR5398015

ADRDL-078

Sandiego

2003

Alpaca feces

SAMN06645612

SRR5398009

ADRDL-079

Sandiego

2003

Alpaca feces

SAMN06645611

SRR5398010

ADRDL-080

Bredeney

2003

Antelope feces

SAMN06645610

SRR5398011

ADRDL-081

Virginia or Muenchen*

2002

Ratite

SAMN06645609

SRR5398008

ADRDL-082

Newport*

2002

Ratite

SAMN06645659

SRR5398007

ADRDL-083

Enteritidis*

2002

Ratite

SAMN06645658

SRR5398001

ADRDL-084

Oranienburg

2003

Iguana cloacal swab

SAMN06645657

SRR5398006

ADRDL-085

Give

2003

Iguana cloacal swab

SAMN06658957

SRR5409330

ADRDL-086

Chameleon

2003

Iguana cloacal swab

SAMN06333875

SRR5387539

ADRDL-087

Typhimurium

2002

Llama feces

SAMN06333874

SRR5387538

ADRDL-088

Anatum

2003

Llama feces

SAMN06333873

SRR5387533

ADRDL-089

Typhimurium

2003

Llama feces

SAMN06333872

SRR5387534

ADRDL-090

Agona

2003

Marsupial intestine

SAMN06333871

SRR5387532

ADRDL-091

Miami

2001

Reptile fecal swab

SAMN06658960

SRR5409328

ADRDL-092

Arizona

2001

Reptile liver

SAMN06658959

SRR5409327

ADRDL-093

 

2001

Reptile cloacal swab

SAMN06658958

SRR5409325

ADRDL-094

Marina

2002

Reptile cloacal swab

SAMN06658962

SRR5409322

ADRDL-095

Marina

2002

Reptile abscess swab

SAMN06658961

SRR5409324

ADRDL-096

Arizona

2002

Reptile lung

SAMN06333869

SRR5387526

ADRDL-097

Parera

2002

Reptile cloacal swab

SAMN06333866

SRR5397979

ADRDL-098

Chameleon

2002

Reptile cloacal swab

SAMN06333865

SRR5397978

ADRDL-099

Senftenberg

2002

Reptile cloacal swab

SAMN06333864

SRR5397977

ADRDL-100

Arizona

2002

Reptile cloacal swab

SAMN06333863

SRR5409363

ADRDL-101

Arizona

2002

Reptile cloacal swab

SAMN06333862

SRR5409361

ADRDL-102

Kisarwe

2003

Reptile cloacal swab

SAMN06333861

SRR5409360

ADRDL-103

Newport

2003

Turtle intestine

SAMN06333859

SRR5409359

* Predicted serovar using Seqsero

Genomic DNA isolation and WGS

Genomic DNA was isolated from 1.0 mL overnight cultures using the Qiagen DNeasy kits (Qiagen, Valencia, CA, USA) according to manufacturer’s protocol. The quality of isolated DNA was analyzed using NanoDrop™ One (Thermo Scientific™, DE) and was quantified using Qubit® 3.0 (Thermo Fisher Scientific Inc., MA) fluorometer and stored at − 20 °C until use. Whole-genome sequencing was performed on Illumina Miseq platform using V2 chemistry with 2 × 250 paired-end chemistry Briefly, the concentrations of genomic DNA samples were adjusted to 0.3 ng/µL concentration and were processed using Nextera XT DNA Sample Prep Kit (Illumina Inc. San Diego, CA). The libraries were normalized using bead-based procedure and pooled together at equal volume. The pooled library was denatured and sequenced using Miseq reagent version 2 (Illumina, Inc., CA).

Genome assembly and identification of resistance and virulence genes

The raw data files were de-multiplexed and converted to FASTQ files using Casava v.1.8.2. (Illumina, Inc, San Diego, CA). The FASTQ files were trimmed and assembled de novo using CLC Genomics workbench 9.4 (Qiagen Bioinformatics, CA). The antibiotic resistance genes in the assembled Salmonella genomes were identified by BLAST search against a local copy of the antibiotic resistance gene sequence data from ResFinder [9] and CARD [10]. The parameters used for BLAST search were ≥ 95% gene identity and 50% sequence length of the resistance gene. The virulence genes in the genomes were predicted using a similar approach. Salmonella virulence gene sequences were extracted from Virulence Factor Database [11] and Salmonella genome assemblies were searched against these sequences using BLAST with ≥ 90% gene identity and 50% sequence length cut off.

Serotyping and antimicrobial susceptibility test

Serotypes of the strains were determined at the National Veterinary Service Laboratory, Ames, IA. Antimicrobial susceptibility of all Salmonella isolates was determined using the Sensititre NARMS Gram Negative Plate (CMV3AGNF, Thermofisher). The antibiotics used were gentamicin, streptomycin, amoxicillin–clavulanic acid, ampicillin, cefoxitin, ceftiofur, ceftriaxone, azithromycin, chloramphenicol, nalidixic acid, ciprofloxacin, sulfisoxazole, trimethoprim–sulfamethoxazole, and tetracycline. The AMR was determined according to Clinical and Laboratory Standards Institute guidelines except for azithromycin and sulfisoxazole where the data obtained was indeterminate and were not included in further analysis.

Results and discussion

Distribution of Salmonella isolates among wildlife and exotic pets

A total of 103 Salmonella isolates sampled between 1988 and 2003 from wildlife and exotic pets were included in the present study for determining the antimicrobial susceptibility using whole genome sequencing. Among 103 isolates, 52 isolates (50.48%) were from wild birds, 1 isolate (0.9%) was from fish, 25 isolates each (24.27%) were from reptiles and mammals (Table 1). The serovars of 96 isolates in this study were determined at the National Veterinary Service Laboratory, Ames, IA, and the remaining 6 serovars were predicted using Seqsero [12]. The serovar of one isolate (ADRDL-093) was not identified under Kauffmann-White classification. A total of 45 serovars were identified among the 103 isolates, of which Typhimurium (12.62%) was the most frequent serovar. Other serovars that had higher prevalence were Enteritidis (6.8%), Anatum (5.8%), Arizona (5.8%), Bredeney (3.9%) and Montevideo (3.9%). The presence of multiple serotypes in wildlife has also been reported from previous epidemiological studies. Nine Salmonella samples isolated from marine mammals and birds in California yielded 7 serovars [4]. Similar to our findings, Salmonella Typhimurium was reportedly the predominant serovar present in wildlife [1315] in various parts of the world.

Phenotypic resistance to antimicrobials

Antimicrobial susceptibility test of 103 Salmonella bacterial isolates was performed using Sensititre NARMS gram-negative plate. The results were classified into 3 categories: resistant, intermediate, or susceptible. Fifty-two out of the 103 isolates (50.48%) showed resistance to at least one antibiotic (Fig. 1a). Resistance against the aminoglycoside streptomycin was most commonly observed. Forty-eight of the 103 isolates (46.6%) exhibited this phenotype. However, only three isolates (2.9%) showed resistance to gentamicin which also belonged to the aminoglycoside class of antibiotics. The isolates with resistance against gentamicin were also resistant to streptomycin. In the beta-lactam group, ampicillin resistance was the most common phenotype and was seen in 11 of the isolates (10.67%). Among these 11 isolates, few also shared resistance against other beta-lactams such as amoxicillin–clavulanic acid (4), cefoxitin (3), and ceftiofur (3). All the isolates were susceptible to ceftriaxone except one with intermediate resistance. The isolates that were susceptible to ampicillin were also susceptible to all other beta-lactams. Chloramphenicol resistance was observed for seven isolates (6.7%), trimethoprim–sulfamethoxazole resistance in 4 (3.88%) and tetracycline resistance in 19 (18.44%) of the isolates. All the isolates were susceptible to ciprofloxacin and all except one isolate was susceptible to nalidixic acid. Nine isolates were found to be multi-drug resistant having resistance against more than three antibiotics.
Fig. 1

Phenotypic and genotypic anti-microbial resistance of 103 wildlife salmonella isolates. a Heatmap of phenotypic resistance against 12 antibiotics was measured using Sensititre NARMS gram-negative plate. The CLSI breakoff points for resistance against various antibiotics was used for determining the antimicrobial susceptibility of 103 salmonella strains. Legend description: 0 = susceptible, 1 = intermediate, and 2 = resistant. b Heatmap of genotypic resistance against antimicrobials detected using CLC workbench 9.0 by BLAST against ResFinder 2.1 and CARD database. Legend description: 0 = absent, 1 = present. The golS, mdsABC complex, and mdtK genes associated with multidrug resistance was present in all except 16 isolates. Similarly, multidrug efflux pump regulator gene sdiA was absent in 14 isolates. Complete data used to generate b is given in Additional file 2: Table S2

Genotypic resistance to antimicrobials

The presence of genes that could contribute to AMR was detected by BLAST searching the assembled Salmonella genomes against a local copy of Resfinder and CARD sequence data (Fig. 1b). Additional details on the query length and percentage of gene identity for the BLAST results are provided in Additional file 2: Table S2. Bacterial isolates showing “intermediate” resistance on antimicrobial susceptibility test was grouped with “susceptible” isolates for the calculation of sensitivity and specificity of AMR genotype. Twenty-two genes that provided resistance to aminoglycosides were detected and the genes were present in 100 isolates. The sensitivity was 100% and specificity was 5.45% for resistance against aminoglycosides. The low specificity was probably due to the lack of resistance genes being expressed in vitro. Genes responsible for resistance to beta-lactam antibiotics were detected in 11 isolates which were also resistant by antimicrobial susceptibility test. The plasmid-mediated cephalosporinase gene blaLAT-1, plasmid-borne class C beta-lactamase gene blaBIL-1, and blaCMY (Class C) genes were found together and were detected in three isolates. Genes belonging to blaTEM (class A) were found in eight isolates. Collectively, there were 280 beta-lactamase genes present in those 11 isolates. The sensitivity and specificity was 100% for beta-lactams. Phenicol resistance encoded by cat, catA1, and floR genes was present in 8 isolates. The sensitivity was 100% and specificity was 98.96% for phenicol resistance. dfrA1, dfrA10, dfrA12, sul1, sul2, and sul3 genes conferring resistance to trimethoprim–sulfamethoxazole drugs were present in 12 isolates. The sensitivity was 100% and specificity was 91.92% for trimethoprim–sulfamethoxazole. The sul1, sul2, and sul3 genes could also contribute to resistance against sulfisoxazole. However, a definite conclusion of genotype–phenotype correlation is lacking due to the absence of antimicrobial susceptibility test data that matches the CLSI recommended breakpoint for resistance against sulfisoxazole. Tetracycline resistance encoded by tet(A), tet(B), tet(C), and tet(D) genes for tetracycline efflux pumps were detected in 18 samples all of which were also resistant by antimicrobial susceptibility test. The sensitivity was 94.74% and specificity was 100% for tetracycline resistance. Two isolates carried the mph(A) gene which confers resistance to macrolides. However, the only macrolide that was tested was azithromycin and the genotype–phenotype relation could not be established due to lack of data from antimicrobial susceptibility test that matches with the breakpoint recommended by CLSI (> 32 mg/L).

Overall, the sensitivity for detecting AMR using genotype was 100% except for tetracycline where 1 isolate was phenotypically resistant even in the absence of the (tet) gene. The specificity for aminoglycosides had the highest degree of incongruence between genotype and phenotype. Fifty-two isolates that were positive for aminoglycoside resistance genes were phenotypically susceptible. Although not to the degree found in this study, a mismatch in phenotype-genotype correlation was also reported previously in E. coli and Salmonella for aminoglycoside resistance, especially for streptomycin [5, 16]. There was 100% phenotype-genotype correlation for beta-lactam resistance. Phenicols and tetracycline also had > 98% specificity, while trimethoprim–sulfamethoxazole had lower specificity (91.2%) because of four isolates that were genotypically resistant but were phenotypically susceptible. These results are also similar to those obtained in previous studies [5, 16] where correlation approaching 100% was obtained for antimicrobials other than aminoglycosides.

In addition to the genes that confer AMR, we also analyzed the genes that could confer multi-drug resistance (Fig. 1b). The golS gene is a promoter for multidrug efflux pump, mdsABC [17] and was detected among 84.46% (n = 87) isolates. Similarly, mdsABC (multidrug transporter of Salmonella) complex which is made up of mdsA, mdsB, and mdsC units, was found in all isolates that had golS gene except one isolate which lacked mdsB and mdsC genes. The mdsABC complex is known to provide resistance against a variety of drugs and toxins and is involved in Salmonella virulence and pathogenicity [17, 18]. The mdtK gene, a multi-efflux pump which could provide resistance against norfloxacin, doxorubicin and acriflavin [18] and sdiA, a regulator for multi-drug resistance pump AcraB [19], were present in 84.46 and 86.41% of the isolates respectively. The presence of these genes could contribute to the virulence and pathogenicity of these Salmonella isolates and also indicates the potential for these isolates to resist various antibiotics and toxins.

Analysis of virulence determinants

The genes that are associated with virulence among 103 wildlife Salmonella isolates were analyzed (Fig. 2) using CLC workbench 9.4. The parameters used were the minimum identity of 90% and minimum length of 50%. Additional details on the query length and percentage of gene identity for the BLAST results are given in Additional file 3: Table S3. A total of 197 virulence genes were detected by BLAST search against a local copy of the Virulence Factor Database. The virulence-associated determinants collectively were grouped under 9 categories: fimbrial adherence determinants, macrophage inducible genes, determinants associated with magnesium uptake, nonfimbrial adherence determinants, genes associated with secretion system, serum resistance determinants, stress proteins, toxins, and two-component regulatory systems.
Fig. 2

Heatmap of virulence genes present in 103 wildlife salmonella isolates. Salmonella genome Assemblies were searched against a local copy of the Virulence Factor Database using BLAST. In the figure, each row represents a virulence gene and each column denotes a sample. Legend on the left side of the figure denotes the following categories of virulence genes: (I) Fimbrial adherence determinants, (II) Macrophage inducible genes, (III) Magnesium uptake, (IV) Non-fimbrial adherence determinants, (V) Secretion system, (VI) Serum resistance, (VII) Stress protein, (VIII) Toxin, and (IX) Two-component system. The virulence genes belonged 5 categories—PSLT, SeHA, SEN, SeSA, and STM. Seventeen isolates had fewer virulence genes compared to others and this correlated with the absence of genes associated with multidrug resistance. Legend description: 0 = absent, 1 = present. Data underlying this figure is given in Additional file 3: Table S3

Among fimbrial adherence determinants, the genes belonging to two csg operons csgBAC and csgDEFG were present universally in all isolates. These genes encode for curli fimbriae or thin aggregative fimbriae and mediate binding to various serum and tissues matrix proteins [20]. Another gene cluster that was ubiquitously present were the fim genes that encodes for type 1 fimbriae. This cluster is comprised of the fimAICDHF operon and three regulatory genes fimW, fimY, and fimZ and mediates adherence to eukaryotic cells [21]. However, the fimY gene was not detected in ten isolates at the BLAST search cut-off level we used.

The genes belonging to type III secretion system (TTSS/T3SS) encoded by Salmonella pathogenicity island-1 (SPI-1) and -2 (SPI-2) were also predominantly present among the isolates. This included SPI-1 regulator genes hilACD, and SPI-1 encoded inv/spa, and prg/org operons that were detected in all the isolates. Similarly, SPI-2 regulatory gene ssrB, chaperone protein-encoding genes—sscA and sscB, and ssa genes that encode for T3SS2 apparatus were also present among 103 isolates. However, the sse genes which encode for the effectors were observed only in fewer isolates. Another set of genes that were present in all isolates were the genes that respond to magnesium level in the extracellular environment [22]. This included mgtc, which mediates magnesium uptake and phoPphoQ genes that are regulators of the two-component system.

The least abundant virulence determinants were the tcf, sta, and pef fimbrial operons and spv gene cluster. These genes belonging to the fimbrial adherence determinants category were detected in less than 25% of the isolates. Additionally, rck gene that provides protection against the complement-mediated immune response of the host was also found in low abundance. There were 16 isolates that possessed fewer than 50% of the total virulence genes in the database (Fig. 2). These isolates include ADRDL-002, -003, -019, -020, -021, -022, -023, -024, -032, -033, -042, -052, -060, -061, -075, and -076. Importantly, these isolates also had a lower abundance of genes that contributed to multi-drug resistance (Fig. 1). However, these isolates come under various serotypes and were isolated from different host species. Therefore, a common factor responsible for the observed low abundance of virulence genes is not evident. The universal presence of fimbrial genes and the genes encoded by pathogenicity islands 1–2 among the isolates we report here indicates that these isolates could potentially cause disease in humans. Therefore, the genomes we report here could be a valuable reference point for future traceback investigations in instances where wildlife may be considered as a potential source of human Salmonellosis.

Abbreviations

AMR: 

antimicrobial resistance

NARMS: 

The National Antimicrobial Resistance Monitoring System

WGS: 

whole genome sequencing

Declarations

Authors’ contributions

JS and AR conceived and designed the study. MT, GJF, LA and SG performed the experiments. RW originally developed the culture archive. MT analyzed the data. MT, JS and AR wrote the manuscript. All authors read and approved the final manuscript.

Acknowledgements

Authors thank the Section of Bacteriology, Animal Disease Research and Diagnostic Laboratory, South Dakota for helping with the antimicrobial susceptibility testing of the Salmonella isolates. We also thank Scott Talent and Leanne Tillman (Oklahoma Animal Disease Diagnostic Laboratory, Stillwater, OK) for reviving archival cultures necessary for this study.

Competing interests

The authors declare that they have no competing interests.

Availability of data and materials

Genome sequence data of 103 Salmonella enterica isolates have been submitted to NCBI Sequence Read Archive (NCBI SRA) for public access. NCBI SRA accession number for 103 isolates described in this manuscript is given in Table 1.

Consent for publication

All authors gave the consent for publication.

Ethics approval and consent to participate

Not applicable.

Funding

This work was supported in part by the USDA National Institute of Food and Agriculture, Hatch Projects SD00H532-14 and SD00R540-15, and the United States Food and Drug Administration GenomeTrakr project subcontract to awarded JS. The funding agencies had no role in study design, data collection, and interpretation, or the decision to submit the work for publication.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Veterinary and Biomedical Sciences, South Dakota State University
(2)
South Dakota Center for Biologics Research and Commercialization
(3)
Oklahoma Animal Disease Diagnostic Laboratory, Oklahoma State University

References

  1. Hoelzer K, Switt AI, Wiedmann M. Animal contact as a source of human non-typhoidal salmonellosis. Vet Res. 2011;42:34.View ArticlePubMedPubMed CentralGoogle Scholar
  2. Krueger AL, et al. Clinical outcomes of nalidixic acid, ceftriaxone, and multidrug-resistant nontyphoidal salmonella infections compared with pansusceptible infections in FoodNet sites, 2006–2008. Foodborne Pathog Dis. 2014;11(5):335–41.View ArticlePubMedGoogle Scholar
  3. Botti V, et al. Salmonella spp. and antibiotic-resistant strains in wild mammals and birds in north-western Italy from 2002 to 2010. Vet Ital. 2013;49(2):195–202.PubMedGoogle Scholar
  4. Smith WA, Mazet JA, Hirsh DC. Salmonella in California wildlife species: prevalence in rehabilitation centers and characterization of isolates. J Zoo Wildl Med. 2002;33(3):228–35.View ArticlePubMedGoogle Scholar
  5. McDermott PF, et al. Whole-genome sequencing for detecting antimicrobial resistance in nontyphoidal Salmonella. Antimicrob Agents Chemother. 2016;60(9):5515–20.View ArticlePubMedPubMed CentralGoogle Scholar
  6. Bradley P, et al. Rapid antibiotic-resistance predictions from genome sequence data for Staphylococcus aureus and Mycobacterium tuberculosis. Nat Commun. 2015;6:10063.View ArticlePubMedPubMed CentralGoogle Scholar
  7. Metcalf BJ, et al. Using whole genome sequencing to identify resistance determinants and predict antimicrobial resistance phenotypes for year 2015 invasive pneumococcal disease isolates recovered in the United States. Clin Microbiol Infect. 2016;22(12):1002e1–8.View ArticleGoogle Scholar
  8. Metcalf BJ, et al. Short-read whole genome sequencing for determination of antimicrobial resistance mechanisms and capsular serotypes of current invasive Streptococcus agalactiae recovered in the USA. Clin Microbiol Infect. 2017;23(8):574.e7–574.e14Google Scholar
  9. Zankari E, et al. Identification of acquired antimicrobial resistance genes. J Antimicrob Chemother. 2012;67(11):2640–4.View ArticlePubMedPubMed CentralGoogle Scholar
  10. Jia B, et al. CARD 2017: expansion and model-centric curation of the comprehensive antibiotic resistance database. Nucleic Acids Res. 2017;45(D1):D566–73.View ArticlePubMedGoogle Scholar
  11. Chen L, et al. VFDB 2012 update: toward the genetic diversity and molecular evolution of bacterial virulence factors. Nucleic Acids Res. 2012;40(Database issue):D641–5.View ArticlePubMedGoogle Scholar
  12. Zhang S, et al. Salmonella serotype determination utilizing high-throughput genome sequencing data. J Clin Microbiol. 2015;53(5):1685–92.View ArticlePubMedPubMed CentralGoogle Scholar
  13. Refsum T, et al. Salmonellae in avian wildlife in Norway from 1969 to 2000. Appl Environ Microbiol. 2002;68(11):5595–9.View ArticlePubMedPubMed CentralGoogle Scholar
  14. Gopee NV, Adesiyun AA, Caesar K. Retrospective and longitudinal study of salmonellosis in captive wildlife in Trinidad. J Wildl Dis. 2000;36(2):284–93.View ArticlePubMedGoogle Scholar
  15. Chambers DL, Hulse AC. Salmonella serovars in the herpetofauna of Indiana County, Pennsylvania. Appl Environ Microbiol. 2006;72(5):3771–3.View ArticlePubMedPubMed CentralGoogle Scholar
  16. Tyson GH, et al. WGS accurately predicts antimicrobial resistance in Escherichia coli. J Antimicrob Chemother. 2015;70(10):2763–9.View ArticlePubMedGoogle Scholar
  17. Pontel LB, et al. GolS controls the response to gold by the hierarchical induction of Salmonella-specific genes that include a CBA efflux-coding operon. Mol Microbiol. 2007;66(3):814–25.View ArticlePubMedGoogle Scholar
  18. Nishino K, Latifi T, Groisman EA. Virulence and drug resistance roles of multidrug efflux systems of Salmonella enterica serovar Typhimurium. Mol Microbiol. 2006;59(1):126–41.View ArticlePubMedGoogle Scholar
  19. Rahmati S, et al. Control of the AcrAB multidrug efflux pump by quorum-sensing regulator SdiA. Mol Microbiol. 2002;43(3):677–85.View ArticlePubMedGoogle Scholar
  20. Romling U, et al. Curli fibers are highly conserved between Salmonella typhimurium and Escherichia coli with respect to operon structure and regulation. J Bacteriol. 1998;180(3):722–31.PubMedPubMed CentralGoogle Scholar
  21. Zeiner SA, Dwyer BE, Clegg S. FimA, FimF, and FimH are necessary for assembly of type 1 fimbriae on Salmonella enterica serovar Typhimurium. Infect Immun. 2012;80(9):3289–96.View ArticlePubMedPubMed CentralGoogle Scholar
  22. Groisman EA. The pleiotropic two-component regulatory system PhoP–PhoQ. J Bacteriol. 2001;183(6):1835–42.View ArticlePubMedPubMed CentralGoogle Scholar

Copyright

© The Author(s) 2017

Advertisement