The rise of antibiotic resistance in Campylobacter

Qin, Xiao; Wang, Xin; Shen, Zhangqi

doi:10.1097/mog.0000000000000901

Cited by 21 publications

(13 citation statements)

References 61 publications

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“…These include eight high-priority pathogens linked to the most AMR-related deaths [6], namely Escherichia coli , Staphylococcus aureus , Klebsiella pneumoniae , Streptococcus pneumoniae , Acinetobacter baumannii , Pseudomonas aeruginosa , Mycobacterium tuberculosis , and Enterococcus faecium , as well as Salmonella enterica , which is among the 11 most concerning pathogens. We further incorporated the genomes of two additional species of clinical and biological relevance, namely Campylobacter jejuni , the most common bacterial cause of gastroenteritis with the rise of AMR [79,80], and Neisseria gonorrhoeae , which poses a significant clinical challenge with the emergence of pan-resistant isolates [81]. For each species, we compiled genomes along with their phenotypic annotations, indicating susceptibility or resistance to a given antimicrobial.…”

Section: Resultsmentioning

confidence: 99%

Assessing computational predictions of antimicrobial resistance phenotypes from microbial genomes

Hu,

Meyer,

Deng

et al. 2024

Preprint

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The advent of rapid whole genome sequencing has created new opportunities for computational prediction of antimicrobial resistance (AMR) phenotypes directly from genomic data. Both rule-based and machine learning (ML) approaches have been explored for this task, but systematic benchmarking to evaluate the performance of these methods is still needed. Here, we evaluated four state-of-the-art ML methods (Kover, PhenotypeSeeker, Seq2Geno2Pheno, Aytan-Aktug), a ML baseline and the rule-based ResFinder, by training and testing each of them across 78 species-antibiotic datasets, using a rigorous benchmarking workflow that integrates three evaluation approaches, each paired with three distinct sample splitting methods. Our analysis revealed considerable performance variation across techniques and datasets. While ML methods generally excelled for closely related strains, ResFinder better handled divergent genomes. Overall, Kover most frequently ranked top among ML approaches, followed by PhenotypeSeeker and Seq2Geno2Pheno. AMR phenotypes for antibiotic classes like macrolides, and sulfonamides, were predicted with the highest accuracies. Prediction quality varied substantially across species-antibiotic combinations particularly for beta-lactams; across species, resistance phenotyping of the beta-lactams compound, aztreonam, amox-clav, cefoxitin, ceftazidime, and piperacillin/tazobactam, together with tetracyclines demonstrated more variable performances than the other benchmarked antibiotics. By organism,C. jejuniandE. faeciumphenotypes were more robustly predicted thanEscherichia coli,Staphylococcus aureus,Salmonella enterica,Neisseria gonorrhoeae,Klebsiella pneumoniae,Pseudomonas aeruginosa,Acinetobacter baumannii,Streptococcus pneumoniae, and Mycobacterium tuberculosis. In addition, our study provides software recommendations for each species-antibiotic combination. It furthermore highlights optimization needs for robust clinical application, particularly for strains that diverge substantially from those used for training.

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Section: Resultsmentioning

confidence: 99%

Assessing computational predictions of antimicrobial resistance phenotypes from microbial genomes

Hu,

Meyer,

Deng

et al. 2024

Preprint

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“…These include eight high-priority pathogens linked to the most AMR-related deaths [ 6 ], namely, Escherichia coli , Staphylococcus aureus , Klebsiella pneumoniae , Streptococcus pneumoniae , Acinetobacter baumannii , Pseudomonas aeruginosa , M. tuberculosis and Enterococcus faecium , as well as Salmonella enterica , which is among the 11 most concerning pathogens. We further incorporated the genomes of two additional species of clinical and biological relevance, namely, Campylobacter jejuni , the most common bacterial cause of gastroenteritis with the rise of AMR [ 79 , 80 ], and Neisseria gonorrhoeae , which poses a significant clinical challenge with the emergence of pan-resistant isolates [ 81 ] . For each species, we compiled genomes along with their phenotypic annotations, indicating susceptibility or resistance to a given antimicrobial.…”

Section: Resultsmentioning

confidence: 99%

Assessing computational predictions of antimicrobial resistance phenotypes from microbial genomes

Hu,

Meyer,

Deng

et al. 2024

Briefings in Bioinformatics

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The advent of rapid whole-genome sequencing has created new opportunities for computational prediction of antimicrobial resistance (AMR) phenotypes from genomic data. Both rule-based and machine learning (ML) approaches have been explored for this task, but systematic benchmarking is still needed. Here, we evaluated four state-of-the-art ML methods (Kover, PhenotypeSeeker, Seq2Geno2Pheno and Aytan-Aktug), an ML baseline and the rule-based ResFinder by training and testing each of them across 78 species–antibiotic datasets, using a rigorous benchmarking workflow that integrates three evaluation approaches, each paired with three distinct sample splitting methods. Our analysis revealed considerable variation in the performance across techniques and datasets. Whereas ML methods generally excelled for closely related strains, ResFinder excelled for handling divergent genomes. Overall, Kover most frequently ranked top among the ML approaches, followed by PhenotypeSeeker and Seq2Geno2Pheno. AMR phenotypes for antibiotic classes such as macrolides and sulfonamides were predicted with the highest accuracies. The quality of predictions varied substantially across species–antibiotic combinations, particularly for beta-lactams; across species, resistance phenotyping of the beta-lactams compound, aztreonam, amoxicillin/clavulanic acid, cefoxitin, ceftazidime and piperacillin/tazobactam, alongside tetracyclines demonstrated more variable performance than the other benchmarked antibiotics. By organism, Campylobacter jejuni and Enterococcus faecium phenotypes were more robustly predicted than those of Escherichia coli, Staphylococcus aureus, Salmonella enterica, Neisseria gonorrhoeae, Klebsiella pneumoniae, Pseudomonas aeruginosa, Acinetobacter baumannii, Streptococcus pneumoniae and Mycobacterium tuberculosis. In addition, our study provides software recommendations for each species–antibiotic combination. It furthermore highlights the need for optimization for robust clinical applications, particularly for strains that diverge substantially from those used for training.

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“…Its resistance to FQ antimicrobials is of particular concern to public health because FQs (e.g., ciprofloxacin) are an important class of antimicrobials used for clinical therapy of campylobacteriosis and other enteric infections [35]. The prevalence of FQ-resistant Campylobacter is continuing to rise on a global scale and has reached alarming levels in some countries [17,21,[36][37][38]. This has led WHO to list FQ-resistant Campylobacter as a high-priority for research and development of new antibiotics [18].…”

Section: Fluoroquinolone (Fq) Resistance In Campylobacter: a One Heal...mentioning

confidence: 99%

“…Campylobacter is a major zoonotic and foodborne pathogen [16], and is increasingly resistant to antibiotics used for human and veterinary medicine [17]. Due to its importance in public health and its rising resistance to antibiotics, particularly fluoroquinolones (FQ), Campylobacter has been recognized as one of the serious antibiotic resistant threats of high priority by both WHO and the CDC [18].…”

Section: Introductionmentioning

confidence: 99%

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Zoonotic and antibiotic-resistant Campylobacter: a view through the One Health lens

2023

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As a pathogen of a major public health concern with animal health importance, Campylobacter constitutes a clear and present threat to One Health. This organism colonizes the intestinal tract and is widely distributed among various animal species, including livestock and poultry, companion animals, and wildlife. As a result of its broad distribution, Campylobacter is exposed to antibiotics used in both human and veterinary medicine, which creates antibiotic selection pressure that has driven the development and rising prevalence of antibiotic resistant Campylobacter. This is particularly evident with the resistance to fluoroquinolone (FQ), which has become a great concern for public health. However, the increased prevalence of antibiotic-resistant Campylobacter cannot be solely attributed to antibiotic usage, as interspecies transmission and subsequent clonal expansion also contribute to the dissemination of antibiotic-resistant Campylobacter. This is exemplified by the emergence and expansion of FQ-resistant Campylobacter clones in animal production systems where FQ antibiotics were never used, the transmission of extensively drug resistant Campylobacter from dogs to human patients, and the spread of antibiotic-resistant and hypervirulent Campylobacter from ruminants to humans. Another notable finding from recently published work is the emergence of antibiotic resistance genes of Gram-positive origin in Campylobacter, suggesting that genetic exchange between Campylobacter and Gram-positive bacteria occurs in the natural environment and is more frequent than previously realized. Once these “foreign” antibiotic resistance genes are presented in Campylobacter, they can further disseminate by clonal expansion or horizontal gene transfer among different Campylobacter species/strains. These findings indicate that the emergence and transmission of antibiotic-resistant Campylobacter in the ecosystem are complex and multidirectional, and are affected by multiple factors. Thus, a holistic and One Health approach is necessary to fully comprehend and mitigate antibiotic resistant Campylobacter.

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The rise of antibiotic resistance in Campylobacter

Cited by 21 publications

References 61 publications

Assessing computational predictions of antimicrobial resistance phenotypes from microbial genomes

Assessing computational predictions of antimicrobial resistance phenotypes from microbial genomes

Assessing computational predictions of antimicrobial resistance phenotypes from microbial genomes

Zoonotic and antibiotic-resistant Campylobacter: a view through the One Health lens

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