Genomics accurately predicts antimicrobial resistance in Staphylococcus pseudintermedius collected as part of Vet-LIRN resistance monitoring

Gregory H. Tyson; Olgica Ceric; Jake Guag; Sarah Nemser; Stacey Borenstein; Durda Slavic; Sarah Lippert; Rebecca McDowell; Aparna Krishnamurthy; Shannon Korosec; Cheryl Friday; Neil Pople; Matthew E. Saab; Julie Hélène Fairbrother; Isabelle Janelle; Deanna McMillan; Yugendar R. Bommineni; David Simon; Shipra Mohan; Susan Sanchez; Ashley Phillips; Paula Bartlett; Hemant Naikare; Cynthia Watson; Orhan Sahin; Chloe Stinman; Leyi Wang; Carol Maddox; Vanessa DeShambo; Kenitra Hendrix; Debra Lubelski; Amy Burklund; Brian Lubbers; Debbie Reed; Tracie Jenkins; Erdal Erol; Mukeshbhai Patel; Stephan Locke; Jordan Fortner; Laura Peak; Udeni Balasuriya; Rinosh Mani; Niesa Kettler; Karen Olsen; Shuping Zhang; Zhenyu Shen; Martha Pulido Landinez; Jay Kay Thornton; Anil Thachil; Melissa Byrd; Megan Jacob; Darlene Krogh; Brett Webb; Lynn Schaan; Amar Patil; Sarmila Dasgupta; Shannon Mann; Laura B. Goodman; Rebecca June Franklin-Guild; Renee R. Anderson; Patrick K. Mitchell; Brittany D. Cronk; Missy Aprea; Jing Cui; Dominika Jurkovic; Melanie Prarat; Yan Zhang; Katherine Shiplett; Dubra Diaz Campos; Joany Van Balen Rubio; Akhilesh Ramanchandran; Scott Talent; Deepanker Tewari; Nagaraja Thirumalapura; Donna Kelly; Denise Barnhart; Lacey Hall; Shelley Rankin; Jaclyn Dietrich; Stephen Cole; Joy Scaria; Linto Antony; Sara D. Lawhon; Jing Wu; Christine McCoy; Kelly Dietz; Rebecca Wolking; Trevor Alexander; Claire Burbick; Renate Reimschuessel

doi:10.1016/j.vetmic.2021.109006

Genomics accurately predicts antimicrobial resistance in Staphylococcus pseudintermedius collected as part of Vet-LIRN resistance monitoring

Gregory H. Tyson, Olgica Ceric, Jake Guag, Sarah Nemser, Stacey Borenstein, Durda Slavic, Sarah Lippert, Rebecca McDowell, Aparna Krishnamurthy, Shannon Korosec, Cheryl Friday, Neil Pople, Matthew E. Saab, Julie Hélène Fairbrother, Isabelle Janelle, Deanna McMillan, Yugendar R. Bommineni, David Simon, Shipra Mohan, Susan SanchezAshley Phillips, Paula Bartlett, Hemant Naikare, Cynthia Watson, Orhan Sahin, Chloe Stinman, Leyi Wang, Carol Maddox, Vanessa DeShambo, Kenitra Hendrix, Debra Lubelski, Amy Burklund, Brian Lubbers, Debbie Reed, Tracie Jenkins, Erdal Erol, Mukeshbhai Patel, Stephan Locke, Jordan Fortner, Laura Peak, Udeni Balasuriya, Rinosh Mani, Niesa Kettler, Karen Olsen, Shuping Zhang, Zhenyu Shen, Martha Pulido Landinez, Jay Kay Thornton, Anil Thachil, Melissa Byrd, Megan Jacob, Darlene Krogh, Brett Webb, Lynn Schaan, Amar Patil, Sarmila Dasgupta, Shannon Mann, Laura B. Goodman, Rebecca June Franklin-Guild, Renee R. Anderson, Patrick K. Mitchell, Brittany D. Cronk, Missy Aprea, Jing Cui, Dominika Jurkovic, Melanie Prarat, Yan Zhang, Katherine Shiplett, Dubra Diaz Campos, Joany Van Balen Rubio, Akhilesh Ramanchandran, Scott Talent, Deepanker Tewari, Nagaraja Thirumalapura, Donna Kelly, Denise Barnhart, Lacey Hall, Shelley Rankin, Jaclyn Dietrich, Stephen Cole, Joy Scaria, Linto Antony, Sara D. Lawhon, Jing Wu, Christine McCoy, Kelly Dietz, Rebecca Wolking, Trevor Alexander, Claire Burbick, Renate Reimschuessel

Research output: Contribution to journal › Article › peer-review

27 Scopus citations

Abstract

Whole-genome sequencing (WGS) has changed our understanding of bacterial pathogens, aiding outbreak investigations and advancing our knowledge of their genetic features. However, there has been limited use of genomics to understand antimicrobial resistance of veterinary pathogens, which would help identify emerging resistance mechanisms and track their spread. The objectives of this study were to evaluate the correlation between resistance genotypes and phenotypes for Staphylococcus pseudintermedius, a major pathogen of companion animals, by comparing broth microdilution antimicrobial susceptibility testing and WGS. From 2017–2019, we conducted antimicrobial susceptibility testing and WGS on S. pseudintermedius isolates collected from dogs in the United States as a part of the Veterinary Laboratory Investigation and Response Network (Vet-LIRN) antimicrobial resistance monitoring program. Across thirteen antimicrobials in nine classes, resistance genotypes correlated with clinical resistance phenotypes 98.4 % of the time among a collection of 592 isolates. Our findings represent isolates from diverse lineages based on phylogenetic analyses, and these strong correlations are comparable to those from studies of several human pathogens such as Staphylococcus aureus and Salmonella enterica. We uncovered some important findings, including that 32.3 % of isolates had the mecA gene, which correlated with oxacillin resistance 97.0 % of the time. We also identified a novel rpoB mutation likely encoding rifampin resistance. These results show the value in using WGS to assess antimicrobial resistance in veterinary pathogens and to reveal putative new mechanisms of resistance.

Original language	English
Article number	109006
Journal	Veterinary Microbiology
Volume	254
DOIs	https://doi.org/10.1016/j.vetmic.2021.109006
State	Published - Mar 2021

Bibliographical note

Publisher Copyright:
© 2021

Funding

We would like to acknowledge Jill Johnson, Brooke Adams, Teresa Register, Danielle Kenne, the Bacteria K State Group, Hannah Rawza, Tamara Gull, Jesse Bowman, Jana Morgan, Megan Fauls, Jasmin Huang, Leah Scarborough, Gianna Goldman, Megan Cleary, Noah Allen, Christian Holcomb, Donna Krouse, the members of the Clinical Microbiology Laboratory of the Texas A&M Veterinary Medical Teaching Hospital, Daniel Bradway, Tim Baszler, Rachel Olson, and Marla Francis. This work was supported by FDA Vet-LIRN grants 1U18FD006453 , 5U18FD006180 , 1U18FD006460-01 , 5U18FD006670-02 , 1U18FD006562 , 1U18FD006862 , 5U18FD006172 , 5U18FD006157 , 5U18FD006173 , 5U18FD006667-02 , AWD00007382 , 5U18FD006245 , U18FD006175A , 1U18FD006593-01 , U18-FD-006713 , 1U18FD004849-01 , 1U18FD006164 , 1U18FD006671 , 1U18FD006673-01 , 5U18FD006156 , 5U18FD006160-02 , 5U18FD006170-04 , 5U18FD006179 , 5U18FD006377 , 5U18FD006378 , 5U18FD006379 , 1U18FD006567 , U18FD006171 , U18FD005164 , U18FD006151 , U18FD006181 , U18FD006558 , 1U18FD006165-01 , 1U18FD0051544 , 1U18FD0051544 , 3U18FD005144 , 1U18FD006716 , 5U18FD006379 , 3U18FD005144 , 1U18FD006716 , 1U18FD006448 , 1U18FD006567 , 5U18FD006379 , 5U18FD006155-04 , and 5U18FD006712 . We would like to acknowledge Jill Johnson, Brooke Adams, Teresa Register, Danielle Kenne, the Bacteria K State Group, Hannah Rawza, Tamara Gull, Jesse Bowman, Jana Morgan, Megan Fauls, Jasmin Huang, Leah Scarborough, Gianna Goldman, Megan Cleary, Noah Allen, Christian Holcomb, Donna Krouse, the members of the Clinical Microbiology Laboratory of the Texas A&M Veterinary Medical Teaching Hospital, Daniel Bradway, Tim Baszler, Rachel Olson, and Marla Francis. This work was supported by FDA Vet-LIRN grants 1U18FD006453, 5U18FD006180, 1U18FD006460-01, 5U18FD006670-02, 1U18FD006562, 1U18FD006862, 5U18FD006172, 5U18FD006157, 5U18FD006173, 5U18FD006667-02, AWD00007382, 5U18FD006245, U18FD006175A, 1U18FD006593-01, U18-FD-006713, 1U18FD004849-01, 1U18FD006164, 1U18FD006671, 1U18FD006673-01, 5U18FD006156, 5U18FD006160-02, 5U18FD006170-04, 5U18FD006179, 5U18FD006377, 5U18FD006378, 5U18FD006379, 1U18FD006567, U18FD006171, U18FD005164, U18FD006151, U18FD006181, U18FD006558, 1U18FD006165-01, 1U18FD0051544, 1U18FD0051544, 3U18FD005144, 1U18FD006716, 5U18FD006379, 3U18FD005144, 1U18FD006716, 1U18FD006448, 1U18FD006567, 5U18FD006379, 5U18FD006155-04, and 5U18FD006712.

Funders	Funder number
Clinical Microbiology Laboratory of the Texas A&M Veterinary Medical Teaching Hospital
FDA Vet-LIRN	1U18FD006165-01, U18FD005164, 5U18FD006160-02, 1U18FD006593-01, 5U18FD006180, U18FD006151, 1U18FD006673-01, 1U18FD004849-01, U18FD006558, 1U18FD006453, 1U18FD0051544, 1U18FD006671, U18-FD-006713, 1U18FD006716, 5U18FD006379, U18FD006171, 5U18FD006712, AWD00007382, 5U18FD006156, 5U18FD006670-02, 5U18FD006378, 5U18FD006157, 5U18FD006179, 5U18FD006377, 5U18FD006173, 5U18FD006172, 1U18FD006460-01, 5U18FD006667-02, 1U18FD006862, U18FD006175A, 1U18FD006562, 1U18FD006164, U18FD006181, 3U18FD005144, 5U18FD006155-04, 1U18FD006448, 5U18FD006170-04, 1U18FD006567, 5U18FD006245
U.S. Food and Drug Administration	U18FD006245

Keywords

Antimicrobial resistance
Genomics
Staphylococcus pseudintermedius

ASJC Scopus subject areas

Microbiology
General Veterinary

Access to Document

10.1016/j.vetmic.2021.109006

Cite this

Tyson, G. H., Ceric, O., Guag, J., Nemser, S., Borenstein, S., Slavic, D., Lippert, S., McDowell, R., Krishnamurthy, A., Korosec, S., Friday, C., Pople, N., Saab, M. E., Fairbrother, J. H., Janelle, I., McMillan, D., Bommineni, Y. R., Simon, D., Mohan, S., ... Reimschuessel, R. (2021). Genomics accurately predicts antimicrobial resistance in Staphylococcus pseudintermedius collected as part of Vet-LIRN resistance monitoring. Veterinary Microbiology, 254, Article 109006. https://doi.org/10.1016/j.vetmic.2021.109006

@article{e051c26a955c4ebe8c787da9148410ba,

title = "Genomics accurately predicts antimicrobial resistance in Staphylococcus pseudintermedius collected as part of Vet-LIRN resistance monitoring",

abstract = "Whole-genome sequencing (WGS) has changed our understanding of bacterial pathogens, aiding outbreak investigations and advancing our knowledge of their genetic features. However, there has been limited use of genomics to understand antimicrobial resistance of veterinary pathogens, which would help identify emerging resistance mechanisms and track their spread. The objectives of this study were to evaluate the correlation between resistance genotypes and phenotypes for Staphylococcus pseudintermedius, a major pathogen of companion animals, by comparing broth microdilution antimicrobial susceptibility testing and WGS. From 2017–2019, we conducted antimicrobial susceptibility testing and WGS on S. pseudintermedius isolates collected from dogs in the United States as a part of the Veterinary Laboratory Investigation and Response Network (Vet-LIRN) antimicrobial resistance monitoring program. Across thirteen antimicrobials in nine classes, resistance genotypes correlated with clinical resistance phenotypes 98.4 \% of the time among a collection of 592 isolates. Our findings represent isolates from diverse lineages based on phylogenetic analyses, and these strong correlations are comparable to those from studies of several human pathogens such as Staphylococcus aureus and Salmonella enterica. We uncovered some important findings, including that 32.3 \% of isolates had the mecA gene, which correlated with oxacillin resistance 97.0 \% of the time. We also identified a novel rpoB mutation likely encoding rifampin resistance. These results show the value in using WGS to assess antimicrobial resistance in veterinary pathogens and to reveal putative new mechanisms of resistance.",

keywords = "Antimicrobial resistance, Genomics, Staphylococcus pseudintermedius",

author = "Tyson, \{Gregory H.\} and Olgica Ceric and Jake Guag and Sarah Nemser and Stacey Borenstein and Durda Slavic and Sarah Lippert and Rebecca McDowell and Aparna Krishnamurthy and Shannon Korosec and Cheryl Friday and Neil Pople and Saab, \{Matthew E.\} and Fairbrother, \{Julie H{\'e}l{\`e}ne\} and Isabelle Janelle and Deanna McMillan and Bommineni, \{Yugendar R.\} and David Simon and Shipra Mohan and Susan Sanchez and Ashley Phillips and Paula Bartlett and Hemant Naikare and Cynthia Watson and Orhan Sahin and Chloe Stinman and Leyi Wang and Carol Maddox and Vanessa DeShambo and Kenitra Hendrix and Debra Lubelski and Amy Burklund and Brian Lubbers and Debbie Reed and Tracie Jenkins and Erdal Erol and Mukeshbhai Patel and Stephan Locke and Jordan Fortner and Laura Peak and Udeni Balasuriya and Rinosh Mani and Niesa Kettler and Karen Olsen and Shuping Zhang and Zhenyu Shen and Landinez, \{Martha Pulido\} and Thornton, \{Jay Kay\} and Anil Thachil and Melissa Byrd and Megan Jacob and Darlene Krogh and Brett Webb and Lynn Schaan and Amar Patil and Sarmila Dasgupta and Shannon Mann and Goodman, \{Laura B.\} and Franklin-Guild, \{Rebecca June\} and Anderson, \{Renee R.\} and Mitchell, \{Patrick K.\} and Cronk, \{Brittany D.\} and Missy Aprea and Jing Cui and Dominika Jurkovic and Melanie Prarat and Yan Zhang and Katherine Shiplett and Campos, \{Dubra Diaz\} and Rubio, \{Joany Van Balen\} and Akhilesh Ramanchandran and Scott Talent and Deepanker Tewari and Nagaraja Thirumalapura and Donna Kelly and Denise Barnhart and Lacey Hall and Shelley Rankin and Jaclyn Dietrich and Stephen Cole and Joy Scaria and Linto Antony and Lawhon, \{Sara D.\} and Jing Wu and Christine McCoy and Kelly Dietz and Rebecca Wolking and Trevor Alexander and Claire Burbick and Renate Reimschuessel",

note = "Publisher Copyright: {\textcopyright} 2021",

year = "2021",

month = mar,

doi = "10.1016/j.vetmic.2021.109006",

language = "English",

volume = "254",

}

Tyson, GH, Ceric, O, Guag, J, Nemser, S, Borenstein, S, Slavic, D, Lippert, S, McDowell, R, Krishnamurthy, A, Korosec, S, Friday, C, Pople, N, Saab, ME, Fairbrother, JH, Janelle, I, McMillan, D, Bommineni, YR, Simon, D, Mohan, S, Sanchez, S, Phillips, A, Bartlett, P, Naikare, H, Watson, C, Sahin, O, Stinman, C, Wang, L, Maddox, C, DeShambo, V, Hendrix, K, Lubelski, D, Burklund, A, Lubbers, B, Reed, D, Jenkins, T, Erol, E, Patel, M, Locke, S, Fortner, J, Peak, L, Balasuriya, U, Mani, R, Kettler, N, Olsen, K, Zhang, S, Shen, Z, Landinez, MP, Thornton, JK, Thachil, A, Byrd, M, Jacob, M, Krogh, D, Webb, B, Schaan, L, Patil, A, Dasgupta, S, Mann, S, Goodman, LB, Franklin-Guild, RJ, Anderson, RR, Mitchell, PK, Cronk, BD, Aprea, M, Cui, J, Jurkovic, D, Prarat, M, Zhang, Y, Shiplett, K, Campos, DD, Rubio, JVB, Ramanchandran, A, Talent, S, Tewari, D, Thirumalapura, N, Kelly, D, Barnhart, D, Hall, L, Rankin, S, Dietrich, J, Cole, S, Scaria, J, Antony, L, Lawhon, SD, Wu, J, McCoy, C, Dietz, K, Wolking, R, Alexander, T, Burbick, C & Reimschuessel, R 2021, 'Genomics accurately predicts antimicrobial resistance in Staphylococcus pseudintermedius collected as part of Vet-LIRN resistance monitoring', Veterinary Microbiology, vol. 254, 109006. https://doi.org/10.1016/j.vetmic.2021.109006

TY - JOUR

T1 - Genomics accurately predicts antimicrobial resistance in Staphylococcus pseudintermedius collected as part of Vet-LIRN resistance monitoring

AU - Tyson, Gregory H.

AU - Ceric, Olgica

AU - Guag, Jake

AU - Nemser, Sarah

AU - Borenstein, Stacey

AU - Slavic, Durda

AU - Lippert, Sarah

AU - McDowell, Rebecca

AU - Krishnamurthy, Aparna

AU - Korosec, Shannon

AU - Friday, Cheryl

AU - Pople, Neil

AU - Saab, Matthew E.

AU - Fairbrother, Julie Hélène

AU - Janelle, Isabelle

AU - McMillan, Deanna

AU - Bommineni, Yugendar R.

AU - Simon, David

AU - Mohan, Shipra

AU - Sanchez, Susan

AU - Phillips, Ashley

AU - Bartlett, Paula

AU - Naikare, Hemant

AU - Watson, Cynthia

AU - Sahin, Orhan

AU - Stinman, Chloe

AU - Wang, Leyi

AU - Maddox, Carol

AU - DeShambo, Vanessa

AU - Hendrix, Kenitra

AU - Lubelski, Debra

AU - Burklund, Amy

AU - Lubbers, Brian

AU - Reed, Debbie

AU - Jenkins, Tracie

AU - Erol, Erdal

AU - Patel, Mukeshbhai

AU - Locke, Stephan

AU - Fortner, Jordan

AU - Peak, Laura

AU - Balasuriya, Udeni

AU - Mani, Rinosh

AU - Kettler, Niesa

AU - Olsen, Karen

AU - Zhang, Shuping

AU - Shen, Zhenyu

AU - Landinez, Martha Pulido

AU - Thornton, Jay Kay

AU - Thachil, Anil

AU - Byrd, Melissa

AU - Jacob, Megan

AU - Krogh, Darlene

AU - Webb, Brett

AU - Schaan, Lynn

AU - Patil, Amar

AU - Dasgupta, Sarmila

AU - Mann, Shannon

AU - Goodman, Laura B.

AU - Franklin-Guild, Rebecca June

AU - Anderson, Renee R.

AU - Mitchell, Patrick K.

AU - Cronk, Brittany D.

AU - Aprea, Missy

AU - Cui, Jing

AU - Jurkovic, Dominika

AU - Prarat, Melanie

AU - Zhang, Yan

AU - Shiplett, Katherine

AU - Campos, Dubra Diaz

AU - Rubio, Joany Van Balen

AU - Ramanchandran, Akhilesh

AU - Talent, Scott

AU - Tewari, Deepanker

AU - Thirumalapura, Nagaraja

AU - Kelly, Donna

AU - Barnhart, Denise

AU - Hall, Lacey

AU - Rankin, Shelley

AU - Dietrich, Jaclyn

AU - Cole, Stephen

AU - Scaria, Joy

AU - Antony, Linto

AU - Lawhon, Sara D.

AU - Wu, Jing

AU - McCoy, Christine

AU - Dietz, Kelly

AU - Wolking, Rebecca

AU - Alexander, Trevor

AU - Burbick, Claire

AU - Reimschuessel, Renate

PY - 2021/3

Y1 - 2021/3

N2 - Whole-genome sequencing (WGS) has changed our understanding of bacterial pathogens, aiding outbreak investigations and advancing our knowledge of their genetic features. However, there has been limited use of genomics to understand antimicrobial resistance of veterinary pathogens, which would help identify emerging resistance mechanisms and track their spread. The objectives of this study were to evaluate the correlation between resistance genotypes and phenotypes for Staphylococcus pseudintermedius, a major pathogen of companion animals, by comparing broth microdilution antimicrobial susceptibility testing and WGS. From 2017–2019, we conducted antimicrobial susceptibility testing and WGS on S. pseudintermedius isolates collected from dogs in the United States as a part of the Veterinary Laboratory Investigation and Response Network (Vet-LIRN) antimicrobial resistance monitoring program. Across thirteen antimicrobials in nine classes, resistance genotypes correlated with clinical resistance phenotypes 98.4 % of the time among a collection of 592 isolates. Our findings represent isolates from diverse lineages based on phylogenetic analyses, and these strong correlations are comparable to those from studies of several human pathogens such as Staphylococcus aureus and Salmonella enterica. We uncovered some important findings, including that 32.3 % of isolates had the mecA gene, which correlated with oxacillin resistance 97.0 % of the time. We also identified a novel rpoB mutation likely encoding rifampin resistance. These results show the value in using WGS to assess antimicrobial resistance in veterinary pathogens and to reveal putative new mechanisms of resistance.

AB - Whole-genome sequencing (WGS) has changed our understanding of bacterial pathogens, aiding outbreak investigations and advancing our knowledge of their genetic features. However, there has been limited use of genomics to understand antimicrobial resistance of veterinary pathogens, which would help identify emerging resistance mechanisms and track their spread. The objectives of this study were to evaluate the correlation between resistance genotypes and phenotypes for Staphylococcus pseudintermedius, a major pathogen of companion animals, by comparing broth microdilution antimicrobial susceptibility testing and WGS. From 2017–2019, we conducted antimicrobial susceptibility testing and WGS on S. pseudintermedius isolates collected from dogs in the United States as a part of the Veterinary Laboratory Investigation and Response Network (Vet-LIRN) antimicrobial resistance monitoring program. Across thirteen antimicrobials in nine classes, resistance genotypes correlated with clinical resistance phenotypes 98.4 % of the time among a collection of 592 isolates. Our findings represent isolates from diverse lineages based on phylogenetic analyses, and these strong correlations are comparable to those from studies of several human pathogens such as Staphylococcus aureus and Salmonella enterica. We uncovered some important findings, including that 32.3 % of isolates had the mecA gene, which correlated with oxacillin resistance 97.0 % of the time. We also identified a novel rpoB mutation likely encoding rifampin resistance. These results show the value in using WGS to assess antimicrobial resistance in veterinary pathogens and to reveal putative new mechanisms of resistance.

KW - Antimicrobial resistance

KW - Genomics

KW - Staphylococcus pseudintermedius

UR - https://www.scopus.com/pages/publications/85100655419

UR - https://www.scopus.com/inward/citedby.url?scp=85100655419&partnerID=8YFLogxK

U2 - 10.1016/j.vetmic.2021.109006

DO - 10.1016/j.vetmic.2021.109006

M3 - Article

C2 - 33581494

AN - SCOPUS:85100655419

SN - 0378-1135

VL - 254

JO - Veterinary Microbiology

JF - Veterinary Microbiology

M1 - 109006

ER -

Genomics accurately predicts antimicrobial resistance in Staphylococcus pseudintermedius collected as part of Vet-LIRN resistance monitoring

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