Genomic epidemiology of third-generation cephalosporin-resistant Escherichia coli from Argentinian pig and dairy farms reveals animal-specific patterns of co-resistance and resistance mechanisms.

Control measures are being introduced globally to reduce the prevalence of antibiotic resistance (ABR) in bacteria on farms. However, little is known about the current prevalence and molecular ecology of ABR in bacterial species with the potential to be key opportunistic human pathogens, such as Escherichia coli, on South American farms. Working with 30 dairy cattle farms and 40 pig farms across two provinces in central-eastern Argentina, we report a comprehensive genomic analysis of third-generation cephalosporin-resistant (3GC-R) E. coli, which were recovered from 34.8% (cattle) and 47.8% (pigs) of samples from fecally contaminated sites. Phylogenetic analysis revealed substantial diversity suggestive of long-term horizontal and vertical transmission of 3GC-R mechanisms. CTX-M-15 and CTX-M-2 were more often produced by isolates from dairy farms, while CTX-M-8 and CMY-2 and co-carriage of amoxicillin/clavulanate resistance and florfenicol resistance were more common in isolates from pig farms. This suggests different selective pressures for antibiotic use in these two animal types. We identified the ß-lactamase gene blaROB, which has previously only been reported in the family Pasteurellaceae, in 3GC-R E. coli. blaROB was found alongside a novel florfenicol resistance gene, ydhC, also mobilized from a pig pathogen as part of a new composite transposon. As the first comprehensive genomic survey of 3GC-R E. coli in Argentina, these data set a baseline from which to measure the effects of interventions aimed at reducing on-farm ABR and provide an opportunity to investigate the zoonotic transmission of resistant bacteria in this region. IMPORTANCE: Little is known about the ecology of critically important antibiotic resistance among bacteria with the potential to be opportunistic human pathogens (e.g., Escherichia coli) on South American farms. By studying 70 pig and dairy cattle farms in central-eastern Argentina, we identified that third-generation cephalosporin resistance (3GC-R) in E. coli was mediated by mechanisms seen more often in certain species and that 3GC-R pig E. coli were more likely to be co-resistant to florfenicol and amoxicillin/clavulanate. This suggests that on-farm antibiotic usage is key to selecting the types of E. coli present on these farms. 3GC-R E. coli and 3GC-R plasmids were diverse, suggestive of long-term circulation in this region. We identified the de novo mobilization of the resistance gene blaROB from pig pathogens into E. coli on a novel mobile genetic element, which shows the importance of surveying poorly studied regions for antibiotic resistance that might impact human health.

Do you see what I see? Testing horses' ability to recognise real-life objects from 2D computer projections.

The use of 2-dimensional representations (e.g. photographs or digital images) of real-life physical objects has been an important tool in studies of animal cognition. Horses are reported to recognise objects and individuals (conspecifics and humans) from printed photographs, but it is unclear whether image recognition is also true for digital images, e.g. computer projections. We expected that horses trained to discriminate between two real-life objects would show the same learnt response to digital images of these objects indicating that the images were perceived as objects, or representations of such. Riding-school horses (N = 27) learnt to touch one of two objects (target object counterbalanced between horses) to instantly receive a food reward. After discrimination learning (three consecutive sessions of 8/10 correct trials), horses were immediately tested with on-screen images of the objects over 10 image trials interspersed with five real object trials. At first image presentation, all but two horses spontaneously responded to the images with the learnt behaviour by contacting one of the two images, but the number of horses touching the correct image was not different from chance (14/27 horses, p > 0.05). Only one horse touched the correct image above chance level across 10 image trials (9/10 correct responses, p = 0.021). Our findings thus question whether horses recognise real-life objects from digital images. We discuss how methodological factors and individual differences (i.e. age, welfare state) might have influenced animals' response to the images, and the importance of validating the suitability of stimuli of this kind for cognitive studies in horses.