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Background The spread of antibiotic-resistant bacteria (ARB) and antibiotic resistance genes (ARGs) among humans and food-producing animals has been widely reported. However, the transmission routes and associated risk factors remain incompletely understood. Methods Here, we used commensal Escherichia coli bacteria strains from faeces of pigs and local citizens [HEG: high exposure group (pig breeders, butchers or restaurant chefs) and LEG: low exposure group (other occupations)] to explore the dynamics of ARB and ARG transmission between animals and humans. Results Most ARGs (96%) present in pigs were shared with humans. Carriage rates of the shared ARGs suggest two transmission patterns among pigs, the HEG and LEG: one pattern was highest in pigs, gradually decreasing in the HEG and LEG (e.g. floR and cmlA1); the other pattern was increasing from pigs to the HEG but then decreasing in the LEG (e.g. mcr-1.1). Carriage rates of the HEG were higher than in the LEG in both patterns, implicating the HEG as a crucial medium in transmitting ARB and ARGs between food-producing animals and humans. Moreover, frequent inter/intragroup transmission via strains, plasmids and/or mobile elements was evident. Carriage of mcr-1.1 on human-gut-prevalent plasmids possibly promoted its enrichment in the HEG. Conclusions The HEG is a crucial factor in transmitting ARB and ARGs between food-producing animals and humans. Rational measures to contain the risks of occupational exposure are urgently needed to keep dissemination of antibiotic resistance in check and safeguard public health. [ABSTRACT FROM AUTHOR]

The near-identical plasmid sequences and epidemiological data suggest that the ST585 strains were the donors of the resistance plasmid to ST218 strains. In this study, we found that the hvKP clone ST218 developed carbapenem resistance in a single patient over time by acquiring the I bla i SB KPC sb plasmid. [Extracted from the article]

Streptococcus pneumoniae is the most common human respiratory pathogen, and β-lactam antibiotics have been employed to treat infections caused by S. pneumoniae for decades. β-lactam resistance is steadily increasing in pneumococci and is mainly associated with the alteration in penicillin-binding proteins (PBPs) that reduce binding affinity of antibiotics to PBPs. However, the high variability of PBPs in clinical isolates and their mosaic gene structure hamper the predication of resistance level according to the PBP gene sequences. In this study, we developed a systematic strategy for applying supervised machine learning to predict S. pneumoniae antimicrobial susceptibility to β-lactam antibiotics. We combined published PBP sequences with minimum inhibitory concentration (MIC) values as labelled data and the sequences from NCBI database without MIC values as unlabelled data to develop an approach, using only a fragment from pbp2x (750 bp) and a fragment from pbp2b (750 bp) to predicate the cefuroxime and amoxicillin resistance. We further validated the performance of the supervised learning model by constructing mutants containing the randomly selected pbps and testing more clinical strains isolated from Chinese hospital. In addition, we established the association between resistance phenotypes and serotypes and sequence type of S. pneumoniae using our approach, which facilitate the understanding of the worldwide epidemiology of S. pneumonia. [ABSTRACT FROM AUTHOR]