Literature DB >> 33450871

Development of a Multiplex Real-Time PCR Assay for Predicting Macrolide and Tetracycline Resistance Associated with Bacterial Pathogens of Bovine Respiratory Disease.

Enakshy Dutta1, John Dustin Loy2, Caitlyn A Deal2, Emily L Wynn3, Michael L Clawson3, Jennifer Clarke1,4, Bing Wang4.   

Abstract

Antimicrobial resistance (AMR) in bovine respiratory disease (BRD) is an emerging concern that may threaten both animal and public health. Rapid and accurate detection of AMR is essential for prudent drug therapy selection during BRD outbreaks. This study aimed to develop a multiplex quantitative real-time polymerase chain reaction assay (qPCR) to provide culture-independent information regarding the phenotypic AMR status of BRD cases and an alternative to the gold-standard, culture-dependent test. Bovine clinical samples (297 lung and 111 nasal) collected in Nebraska were subjected to qPCR quantification of macrolide (MAC) and tetracycline (TET) resistance genes and gold-standard determinations of AMR of BRD pathogens. Receiver operating characteristic curve analysis was used to classify AMR based on the qPCR results. For lung tissues, the qPCR method showed good agreement with the gold-standard test for both MACs and TETs, with a sensitivity of 67-81% and a specificity higher than 80%. For nasal swabs, qPCR results passed validation criteria only for TET resistance detection, with a sensitivity of 88%, a specificity of 80% and moderate agreement. The culture-independent assay developed here provides the potential for more rapid AMR characterization of BRD cases directly from clinical samples at equivalent accuracy and higher time efficiency compared with the gold-standard, culture-based test.

Entities:  

Keywords:  bovine clinical samples; culture independent; prudent antibiotic use; quantitative PCR; rapid detection; receiver operating characteristic

Year:  2021        PMID: 33450871      PMCID: PMC7828349          DOI: 10.3390/pathogens10010064

Source DB:  PubMed          Journal:  Pathogens        ISSN: 2076-0817


  23 in total

1.  Interpretative reading: recognizing the unusual and inferring resistance mechanisms from resistance phenotypes.

Authors:  D M Livermore; T G Winstanley; K P Shannon
Journal:  J Antimicrob Chemother       Date:  2001-07       Impact factor: 5.790

2.  Sample size for positive and negative predictive value in diagnostic research using case-control designs.

Authors:  David M Steinberg; Jason Fine; Rick Chappell
Journal:  Biostatistics       Date:  2008-06-12       Impact factor: 5.899

3.  Development of a multiplex real-time PCR assay using two thermocycling platforms for detection of major bacterial pathogens associated with bovine respiratory disease complex from clinical samples.

Authors:  John D Loy; Laura Leger; Aspen M Workman; Michael L Clawson; Ece Bulut; Bing Wang
Journal:  J Vet Diagn Invest       Date:  2018-09-21       Impact factor: 1.279

Review 4.  Bacterial pathogens of the bovine respiratory disease complex.

Authors:  Dee Griffin; M M Chengappa; Jennifer Kuszak; D Scott McVey
Journal:  Vet Clin North Am Food Anim Pract       Date:  2010-07       Impact factor: 3.357

5.  Phenotypic antimicrobial susceptibility and occurrence of selected resistance genes in gram-positive mastitis pathogens isolated from Wisconsin dairy cows.

Authors:  P L Ruegg; L Oliveira; W Jin; O Okwumabua
Journal:  J Dairy Sci       Date:  2015-04-23       Impact factor: 4.034

6.  A ten-year (2000-2009) study of antimicrobial susceptibility of bacteria that cause bovine respiratory disease complex--Mannheimia haemolytica, Pasteurella multocida, and Histophilus somni--in the United States and Canada.

Authors:  Ellen Portis; Cynthia Lindeman; Lacie Johansen; Gillian Stoltman
Journal:  J Vet Diagn Invest       Date:  2012-09       Impact factor: 1.279

7.  Pathogens of bovine respiratory disease in North American feedlots conferring multidrug resistance via integrative conjugative elements.

Authors:  Cassidy L Klima; Rahat Zaheer; Shaun R Cook; Calvin W Booker; Steve Hendrick; Trevor W Alexander; Tim A McAllister
Journal:  J Clin Microbiol       Date:  2013-11-20       Impact factor: 5.948

8.  Prevalence and antimicrobial susceptibility of Mannheimia haemolytica, Pasteurella multocida, and Histophilus somni isolated from the lower respiratory tract of healthy feedlot cattle and those diagnosed with bovine respiratory disease.

Authors:  Edouard Timsit; Jennyka Hallewell; Calvin Booker; Nicolas Tison; Samat Amat; Trevor W Alexander
Journal:  Vet Microbiol       Date:  2017-07-27       Impact factor: 3.293

9.  Summarising and validating test accuracy results across multiple studies for use in clinical practice.

Authors:  Richard D Riley; Ikhlaaq Ahmed; Thomas P A Debray; Brian H Willis; J Pieter Noordzij; Julian P T Higgins; Jonathan J Deeks
Journal:  Stat Med       Date:  2015-03-20       Impact factor: 2.373

10.  Whole-Genome Sequencing and Concordance Between Antimicrobial Susceptibility Genotypes and Phenotypes of Bacterial Isolates Associated with Bovine Respiratory Disease.

Authors:  Joseph R Owen; Noelle Noyes; Amy E Young; Daniel J Prince; Patricia C Blanchard; Terry W Lehenbauer; Sharif S Aly; Jessica H Davis; Sean M O'Rourke; Zaid Abdo; Keith Belk; Michael R Miller; Paul Morley; Alison L Van Eenennaam
Journal:  G3 (Bethesda)       Date:  2017-09-07       Impact factor: 3.154

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  3 in total

1.  Evaluating the potential of third generation metagenomic sequencing for the detection of BRD pathogens and genetic determinants of antimicrobial resistance in chronically ill feedlot cattle.

Authors:  Claire N Freeman; Emily K Herman; Jennifer Abi Younes; Dana E Ramsay; Nathan Erikson; Paul Stothard; Matthew G Links; Simon J G Otto; Cheryl Waldner
Journal:  BMC Vet Res       Date:  2022-06-02       Impact factor: 2.792

Review 2.  Application of Nanopore Sequencing in the Detection of Foodborne Microorganisms.

Authors:  You Zhou; Meishen Ren; Pengfei Zhang; Dike Jiang; Xueping Yao; Yan Luo; Zexiao Yang; Yin Wang
Journal:  Nanomaterials (Basel)       Date:  2022-05-02       Impact factor: 5.719

3.  Genes and regulatory mechanisms associated with experimentally-induced bovine respiratory disease identified using supervised machine learning methodology.

Authors:  Matthew A Scott; Amelia R Woolums; Cyprianna E Swiderski; Andy D Perkins; Bindu Nanduri
Journal:  Sci Rep       Date:  2021-11-25       Impact factor: 4.379

  3 in total

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