Related Experiment Video
Updated: Jun 3, 2025
Biosensor for Detection of Antibiotic Resistant Staphylococcus Bacteria
Published on: May 8, 2013
Phenotypic antibiotic resistance prediction using antibiotic resistance genes and machine learning models in
Carmen L Wickware1, Audrey C Ellis1, Mohit Verma2
1Purdue University, Department of Animal Sciences, West Lafayette, IN 47907 USA.
Abstract:
Mannheimia haemolytica is one of the most common causative agents of bovine respiratory disease (BRD); however, antibiotic resistance in this species is increasing, making treatment more difficult. Integrative-conjugative elements (ICE), a subset of mobile genetic elements (MGE), encoding up to 100 genes have been reported in Mannheimia haemolytica genomes to confer multidrug resistance, including resistance to antibiotics commonly used in the treatment of BRD. However, the presence of antibiotic resistance genes (ARGs) does not always agree with phenotypic resistance. Prior investigations have reported an overall phenotype-genotype concordance less than 75 % in BRD pathogens. The objective of the current study was to compare genotype-phenotype concordance either by annotating known resistance genes in genomes or predicting antibiotic resistance determinants de novo with machine learning (ML). The genotype-phenotype concordance rates of ARGs were generally > 90 %, while that of ML models were > 80 %. For all seven antibiotics (danofloxacin, enrofloxacin, florfenicol, tetracycline, tildipirosin, tilmicosin, and tulathromycin), the genotype-phenotype concordance rates were more accurate with ARGs. The annotations of ML models for all antibiotics included various types of sequences, including coding sequences such as DNA topoisomerase IV (danofloxacin) and non-coding sequences near tetracycline genes (multiple antibiotics), MGE (tetracycline and tildipirosin), or virulence genes (danofloxacin and enrofloxacin). When tested on an external set of isolates for validation, the best predictor of antibiotic resistance performed similarly to the training/testing datasets for each antibiotic. Incorporating single nucleotide polymorphisms and ARGs unknown during previous studies resulted in higher concordance rates (>90 %) for fluoroquinolones and tilmicosin, respectively. By finding increased concordance rates for known ARGs, this study was able to show that ARGs should continue to be utilized to predict phenotypic resistance.
Insights
Antibiotic resistance in Mannheimia haemolytica is a growing concern for bovine respiratory disease (BRD). This study found that known antibiotic resistance genes (ARGs) are more accurate than machine learning for predicting resistance, supporting their continued use in BRD treatment strategies.
Area of Science:
- Veterinary Microbiology
- Antimicrobial Resistance
- Genomics
Background:
- Mannheimia haemolytica is a primary cause of bovine respiratory disease (BRD).
- Increasing antibiotic resistance in M. haemolytica complicates BRD treatment.
- Integrative-conjugative elements (ICE) contribute to multidrug resistance in M. haemolytica.
Purpose of the Study:
- To compare the accuracy of genotype-phenotype concordance for predicting antibiotic resistance in M. haemolytica.
- To evaluate the effectiveness of annotated antibiotic resistance genes (ARGs) versus machine learning (ML) models.
- To assess the utility of ARGs for predicting phenotypic resistance in BRD pathogens.
Main Methods:
- Genomic annotation of known antibiotic resistance genes (ARGs).
- Development and testing of machine learning (ML) models for de novo prediction of antibiotic resistance determinants.
- Comparison of genotype-phenotype concordance rates for ARGs and ML models across seven antibiotics.
- Validation of predictive models on an external set of isolates.
Main Results:
- Genotype-phenotype concordance rates for ARGs were generally >90%, outperforming ML models (>80%).
- ARGs provided more accurate predictions than ML models for all seven tested antibiotics.
- ML models identified diverse genetic elements, including coding, non-coding, MGE, and virulence genes, associated with resistance.
- Models incorporating novel ARGs and single nucleotide polymorphisms improved concordance rates for specific antibiotics.
Conclusions:
- Known antibiotic resistance genes (ARGs) are reliable predictors of phenotypic resistance in Mannheimia haemolytica.
- The study supports the continued use of ARGs for predicting and managing antibiotic resistance in BRD.
- Further research into novel ARGs and genetic elements can enhance resistance prediction accuracy.
More Related Videos
08:58Isolation and Identification of Waterborne Antibiotic-Resistant Bacteria and Molecular Characterization of their Antibiotic Resistance Genes
Published on: March 3, 2023
12:32Quantification of Plasmid-Mediated Antibiotic Resistance in an Experimental Evolution Approach
Published on: December 14, 2019
Related Concept Videos
00:57Antibiotic Selection
01:33Bacterial Transformation
Griffith made an unexpected discovery when he killed the pathogenic strain and mixed its remains with the live, non-pathogenic strain. Not only did the mixture kill host mice, but it also contained living pathogenic bacteria that...