Antimicrobial resistance (AMR) is a major global concern responsible for an estimated 700,000 deaths per year, and without efforts to tackle this issue, AMR infections are predicted to rise to 10 million per year by the year 2050¹. AMR occurs when bacteria evolve or acquire changes to their DNA that allow them to survive in the presence of antibiotics, reducing therapeutic options².  Historically AMR research has focused on clinical settings and high-income countries, but outside of these particularly in low-middle income countries (LMICs) resistant bacteria can circulate largely undetected and unmonitored³.

AMR can develop in response to selective pressure caused by the presence of antibiotics. The food production industry represents a major consumer of antibiotics, and hence may be high-risk environments for the development of AMR. Increased demand for animal protein has driven some countries, particularly low-middle income countries (LMICs) such as China, to shift towards intensive livestock farming, routinely using antimicrobials to keep animals healthy and improve productivity. Although food production settings are emphasized as high-risk transmission points for AMR³, only a few studies to date have considered the AMR present in livestock, the humans in direct contact with them (i.e. farm/slaughterhouse workers), and their environment^4-9. In addition, most of these investigations have been conducted in high-income countries^4,5,7,8 and there are few studies in LMICs, where risks are likely to be higher³. The greater human-animal contact and inadequate biosecurity, typical of LMICs, are likely to lead to increased risk of transmission among livestock, humans, and the environment, with different patterns of dissemination compared to high-income countries or hospital settings. Several studies have found both direct (in food production) ^4-10 and indirect (through food consumption) ^11-13 evidence of similar antibiotic-resistant bacteria and ARGs between humans and animals/meat. According to the World Health Organization, there are 600 million cases of foodborne diseases per year responsible for 420,000 deaths¹⁴.  It is therefore critical to set out studies and improved methods optimized to scenarios occurring in LMICs settings, to prospectively compare ARBs and ARGs from animals with those from humans, addressing both direct and indirect contact. This will help to gather more evidence to understand if, where, and how AMR can reach humans^1,15.

 In this study, we collected samples from a poultry meat farm and its connected slaughterhouse in China. We collected samples targeting workers and their households, chickens, carcasses, and soil. Samples were taken over two independent production cycles. DNA extracted from samples was subjected to metagenomic sequencing to obtain the genomic sequence of all bacteria in the microbiome of each sample. The sequences were annotated to obtain the gene content for each sample including the presence of antibiotic resistance genes (ARGs), then analysed using a machine learning (ML) based approach.

Our study produced three key findings. Firstly, we found similarities between mobile ARGs found in chicken and human samples. These antibiotic resistance genes were considered to be mobile as they were found close to mobile genetic elements, which facilitate the movement of genes within genomes¹⁶ and between bacteria¹⁷. Our study found that, despite the microbiome and resistome of these different hosts being different, 11 types of clinically important antibiotic resistance genes were present in both chicken and human samples as mobile ARGs, with conserved mobile ARG gene structures found between samples from different hosts. Whilst causes and directionality of the similarities between chicken and human samples could not be ascertained in our study the results indicated an interconnectedness between these two environments (chicken and human gut microbiomes).

Secondly, using a novel ML and statistical modelling approach, which combined metagenomics data with culture-based methods, we were able to uncover correlations between the ARGs present in the chicken gut resistome and the observed antibiotic resistance of isolates of E. coli taken from the same samples. Using these methods, we uncovered 52 genes that were correlated to resistance to at least one of the 28 antibiotics tested. Of these 52 genes, which are of interest for further study, 67% were not found in the E. coli strains we isolated so would likely have been missed by a conventional culture-based approach alone. This suggests the importance of the microbial reservoir to resistance phenotypes and the relevance of monitoring them in parallel.

Finally, to look more closely at the 52 genes of interest we uncovered with machine learning, we investigated whether changes in the barn temperature and humidity were correlated with the presence-absence of these genes in chicken gut samples. We found several genes were correlated with changes in either humidity or temperature, including genes conferring resistance to aminoglycoside, amphenicol, beta-lactam, MLSB, nucleoside, tetracycline, and trimethoprim classes as well as multidrug resistance genes. The full extent that the chicken gut ARGs may have been affected by these two co-dependent environmental factors, temperature, and humidity, could not be fully addressed by our study and so our results suggested the need for further studies with a larger sample size accounting for confounding factors.

Our study has provided a proof of principle, especially in consideration of how this knowledge and methods could be used to mitigate AMR within the landscape of increasing adoption of sensing/monitoring technologies, digitalisation in livestock farming^18-20, and the adoption of the latest technologies in ML and big data mining to implement precision poultry farming^21,22 to fight AMR and infections.

References

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21        Astill, J., Dara, R. A., Fraser, E. D. G. & Sharif, S. Detecting and predicting emerging disease in poultry with the implementation of new technologies and big data: A focus on avian influenza virus. Frontiers in Veterinary Science 5, doi:10.3389/fvets.2018.00263 (2018).

22        Ahmed, G. et al. An approach towards IoT-based predictive service for early detection of diseases in poultry chickens. Sustainability 13, 13396 (2021).