Comparative Genomics of Drug-resistant Salmonella enterica Isolated from Dairy Cattle and HumansL. Carroll1, M. Wiedmann1, H. den Bakker2, J. Siler1, M. Davis3, W. Sischo3, T. Besser3, L. Warnick1, R. Pereira1&4

1Department of Food Science & Population Med. and Diag. Sci. , Cornell University, Ithaca, NY 2Department of Animal and Food Sciences, Texas Tech University, Lubbock, TX

3Paul G. Allen School for Global Animal Health, Washington State University, Pullman, WA4Department of Population Health and Reproduction, University of California, Davis, CA

Introduction•Salmonella spp. are estimated to cause 1.2 million illnesses each

year, approximately 450 of which result in death (6).

•Multidrug resistant isolates pose a unique threat, making the

illnesses they cause challenging to treat.

Results

Abstract

Salmonella enterica is a pathogen of concern for both humans and

cattle. It can be spread from cattle to human populations through direct

contact with animals shedding Salmonella, as well as through the food

chain. Infections caused by multidrug-resistant isolates can be

challenging to treat, making multidrug-resistant Salmonella a relevant

human health hazard. The objective of this study was to use whole

genome sequencing to study the evolutionary relationship of

antimicrobial-resistant S. Typhimurium, Newport, and Dublin isolated

from dairy cattle and humans in Washington state and New York state

from 2008 to 2012. A total of 91 drug-resistant Salmonella isolates

were selected for this study, 45 of which were from Washington state

(20 from dairy cattle and 25 from humans) and 46 from New York state

(22 from dairy cattle and 24 from humans). Isolates were selected

based on location, source, and serotype stratified by year. All isolates

were tested for phenotypic antimicrobial resistance to 12 drugs using

Kirby-Bauer disk diffusion. Genomes of all isolates were sequenced at

Cornell University using the Illumina HiSeq platform and

assembled de novo using SPAdes. In silico MLST and serotyping were

performed using SRST2 and SeqSero, respectively. SRST2 and ARG-

ANNOT were used to detect antimicrobial resistance genes in each

isolate. kSNP was used to assess overall phylogeny for the complete

set of 91 isolates. Cortex_var was used to detect SNPs within each

serotype, and maximum likelihood trees based on the variant sites were

generated using MEGA. Genotypic resistance predicted phenotypic

resistance with an overall sensitivity of 85.3% and specificity of

87.6%. Phylogenetic analyses by serotype showed evidence for

clustering geospatially and by antimicrobial resistance phenotype. By

studying the phylogenetic relationships of these isolates, we gain

further insight into the spread of drug-resistant Salmonella between

dairy cattle and humans in New York and Washington state.

Conclusions

Sample

References1. Bankevich, A., et al. SPAdes: A New Genome Assembly Algorithm and Its Applications to Single-Cell Sequencing.

Journal of Computational Biology 2012 May; 19(5): 455–477.

2. Gardner, S., and B. Hall. When Whole-Genome Alignments Just Won't Work: kSNP v2 Software for Alignment-Free SNP

Discovery and Phylogenetics of Hundreds of Microbial Genomes. PLOS One. 8(12): e81760. doi:

10.1371/journal.pone.0081760.

3. Gupta, S., et al. ARG-ANNOT, a new bioinformatic tool to discover antibiotic resistance genes in bacterial genomes.

Antimicrobial Agents and Chemotherapy. 2014;58(1):212-20.

4. Inouye, M., et al. SRST2: Rapid genomic surveillance for public health and hospital microbiology labs. Genome

Medicine 2014, 6:90 doi:10.1186/s13073-014-0090-6.

5. Iqbal, Z., eta l. De novo assembly and genotyping of variants using colored de Bruijn graphs. Nature Genetics.

2012(44):226-232.

6. Scallan, E., et al. Foodborne illness acquired in the United States--major pathogens. Emerging Infectious Diseases.

2011;17(1):7-15.

7. Tamura, K., et al. MEGA6: Molecular Evolutionary Genetics Analysis Version 6.0. Molecular Biology and Evolution.

2013. 30(12):2725–2729.

8. Zhang, S., et al. Salmonella serotype determination utilizing high-throughput genome sequencing data. Journal of Clinical

Microbiology. 2015 May;53(5):1685-92.

Figure 3. Maximum likelihood trees generated using a

reference-based SNP calling pipeline.Analysis Workflow

Figure 2. Genotypic and phenotypic resistance to various

antimicrobials within each serotype-source group

kSNP v2 was used to call SNPs in the core genomes of all 91 Salmonella isolates in the data set. The resulting tree.core file was converted to a cladogram

and annotated in FigTree. Within each distinct serotype cluster, the name of each strain used as a reference sequence for subsequent within-serotype

reference-based SNP calling is denoted in black.

For each serotype, Cortex_var was used to call variants based on reference sequences S. Typhimurium str.

LT2 (RefSeq NC_003197.1), S. Newport str. SL254 (RefSeq NC_011080.1), and S. Dublin str.

CT_02021853 (RefSeq Assembly Accession GCF_000020925.1). Only calls classified as SNPs were used

to generate maximum likelihood trees using MEGA6.Genotypic resistance was determined using SRST2 and the ARG-ANNOT database. Isolates were classified as having a resistant

genotype if the antimicrobial resistance gene was detected by SRST2 with 100% sequence identity. Phenotypic resistance was

tested using Kirby-Bauer disk diffusion. Percentages were calculated using the ratio of resistant isolates to total isolates in each

serotype-source group (n = 20 for S. Typhimurium Bovine, n = 19 for S. Typhimurium Human, n = 14 for S. Newport Bovine, n =

18 for S. Newport Human, n = 8 for S. Dublin Bovine, and n = 12 for S. Dublin Human).

gDNA

extraction

and

Illumina

sequencing

91 Salmonella

isolates

Genome

assembly

(SPAdes)

Construction

of overall

phylogeny

(kSNP)

Variant calling

(Cortex_var)

In silico

serotyping

(SeqSero)

In silico MLST

(SRST2)

In silico ABR

gene detection

(SRST2 and

ARG-ANNOT)

Within-serotype

model selection

and phylogeny

(MEGA)

Kirby-Bauer

disk diffusion

for phenotypic

resistance

• Genotypic resistance predicted phenotypic resistance with an overall

sensitivity of 85.3% and specificity of 87.6% at a sequence identity threshold

of 100%.

• S. Typhimurium showed phenotypic resistance to a minimum of 1

antimicrobial and a maximum of 11. For S. Newport, the range was 6 to 10.

For S. Dublin, the range was 6 to 9.

• S. Typhimurium was the only serotype that had isolates with a nalidixic acid-

resistant phenotype, all 4 of which were isolated from humans in Washington

state.

• 12 out of 18 S. Newport isolates from New York state formed a clade with a

bootstrap value of 1.

• 28 out of 32 S. Newport isolates showed identical antimicrobial resistance

phenotypes. 3 of the 4 isolates with a non-identical resistance pattern had

additional resistance to SZD.

• S. Dublin isolates formed two large clades, one of which contained only

isolates from Washington state. 7 of 10 of these isolates had identical

antimicrobial resistance phenotypes.

Figure 1. Cladogram generated using all SNPs called within the core

genomes of all 91 Salmonella isolates.

S. Typhimurium

S. Newport

S. Dublin

S. Typhimurium

S. Newport

S. Dublin

AntimicrobialSensitivity

(%)

Specificity

(%)

AmClav 89.5 80.0

Amp 100.0 75.0

Cefo 98.5 91.7

Ceft 87.0 95.5

Ceftr 95.7 90.9

Chlo 98.6 94.7

Nal 0 95.5

Strep 78.4 66.7

Sulf 98.8 88.9

SZD 100 97.6

Tet 91.6 87.5

Overall 85.3 87.6

Table 1. Analysis of

genotype prediction of

phenotype resistance in

Salmonella isolated from

cattle and humans.

Washington state only

New York state only

(bootstrap value = 1)

SZD-resistant isolates

AcknowledgmentsThis material is based upon work supported by the National Science Foundation Graduate Research Fellowship Program under grant no. DGE-1144153.

Research reported in this publication was also supported by the Agriculture and Food Research Initiative Competitive Grant no. 2010-51110-21131 from the USDA

National Institute of Food and Agriculture. The content is solely the responsibility of the authors and does not necessarily represent the official views of the USDA.

The authors would like to thank Drs. Matt Stasiewicz, Jasna Kovac, and Michael Stanhope for their help with this project.

Phenotypic Antimicrobial resistance profiles

0%

20%

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60%

80%

100%

Phen

otype

Genotype

Phen

otype

Genotype

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otype

Genotype

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otype

Genotype

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otype

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otype

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otype

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otype

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otype

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AmClav Amp Cefo Ceft Ceftr Chlo Nal Strep Sulf SZD Tetra

Perc

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S. Dublin Bovine

S. Newport Bovine

S. Typhimurium Bovine

S. Dublin Human

S. Newport Human

S. Typhimurium Human