Whole-genome sequencing and gene sharing network analysis powered by machine learning identifies antibiotic resistance sharing between animals, humans and environment in livestock farming

Impressive pan-genomic diversity of E. coli from a wild animal community near urban development reflects human impacts

Human and domesticated animal waste infiltrates global freshwater, terrestrial, and marine environments, widely disseminating fecal microbes, antibiotics, and other chemical pollutants. Emerging evidence suggests that guts of wild animals are being invaded by our microbes, including Escherichia coli, which face anthropogenic selective pressures to gain antimicrobial resistance (AMR) and increase virulence. However, wild animal sources remain starkly under-represented among genomic sequence repositories. We sequenced whole genomes of 145 E. coli isolates from 55 wild and 13 domestic animal fecal samples, averaging 2 (ranging 1-7) isolates per sample, on a preserve imbedded in a human-dominated landscape in California Bay Area, USA, to assess AMR, virulence, and pan-genomic diversity. With single nucleotide polymorphism analyses we predict potential transmission routes. We illustrate the usefulness of E. coli to aid our understanding of and ability to surveil the emergence of zoonotic pathogens created by the mixing of human and wild bacteria in the environment.

Convergence of resistance and evolutionary responses in Escherichia coli and Salmonella enterica co-inhabiting chicken farms in China

Sharing of genetic elements among different pathogens and commensals inhabiting same hosts and environments has significant implications for antimicrobial resistance (AMR), especially in settings with high antimicrobial exposure. We analysed 661 Escherichia coli and Salmonella enterica isolates collected within and across hosts and environments, in 10 Chinese chicken farms over 2.5 years using data-mining methods. Most isolates within same hosts possessed the same clinically relevant AMR-carrying mobile genetic elements (plasmids: 70.6%, transposons: 78%), which also showed recent common evolution. Supervised machine learning classifiers revealed known and novel AMR-associated mutations and genes underlying resistance to 28 antimicrobials, primarily associated with resistance in E. coli and susceptibility in S. enterica. Many were essential and affected same metabolic processes in both species, albeit with varying degrees of phylogenetic penetration. Multi-modal strategies are crucial to investigate the interplay of mobilome, resistance and metabolism in cohabiting bacteria, especially in ecological settings where community-driven resistance selection occurs.

Advancing food safety risk assessment in China: development of new approach methodologies (NAMs)

Novel techniques and methodologies are being developed to advance food safety risk assessment into the next-generation. Considering the shortcomings of traditional animal testing, new approach methodologies (NAMs) will be the main tools for the next-generation risk assessment (NGRA), using non-animal methodologies such as in vitro and in silico approaches. The United States Environmental Protection Agency and the European Food Safety Authority have established work plans to encourage the development and application of NAMs in NGRA. Currently, NAMs are more commonly used in research than in regulatory risk assessment. China is also developing NAMs for NGRA but without a comprehensive review of the current work. This review summarizes major NAM-related research articles from China and highlights the China National Center for Food Safety Risk Assessment (CFSA) as the primary institution leading the implementation of NAMs in NGRA in China. The projects of CFSA on NAMs such as the Food Toxicology Program and the strategies for implementing NAMs in NGRA are outlined. Key issues and recommendations, such as discipline development and team building, are also presented to promote NAMs development in China and worldwide.

Digital Phenotyping: A Game Changer for the Broiler Industry

In response to escalating global demand for poultry, the industry grapples with an array of intricate challenges, from enhancing productivity to improving animal welfare and attenuating environmental impacts. This comprehensive review explores the transformative potential of digital phenotyping, an emergent technological innovation at the cusp of dramatically reshaping broiler production. The central aim of this study is to critically examine digital phenotyping as a pivotal solution to these multidimensional industry conundrums. Our investigation spotlights the profound implications of 'digital twins' in the burgeoning field of broiler genomics, where the production of exact digital counterparts of physical entities accelerates genomics research and its practical applications. Further, this review probes into the ongoing advancements in the research and development of a context-sensitive, multimodal digital phenotyping platform, custom-built to monitor broiler health. This paper critically evaluates this platform's potential in revolutionizing health monitoring, fortifying the resilience of broiler production, and fostering a harmonious balance between productivity and sustainability. Subsequently, the paper provides a rigorous assessment of the unique challenges that may surface during the integration of digital phenotyping within the industry. These span from technical and economic impediments to ethical deliberations, thus offering a comprehensive perspective. The paper concludes by highlighting the game-changing potential of digital phenotyping in the broiler industry and identifying potential future directions for the field, underlining the significance of continued research and development in unlocking digital phenotyping's full potential. In doing so, it charts a course towards a more robust, sustainable, and productive broiler industry. The insights garnered from this study hold substantial value for a broad spectrum of stakeholders in the broiler industry, setting the stage for an imminent technological evolution in poultry production.

Machine learning and metagenomics reveal shared antimicrobial resistance profiles across multiple chicken farms and abattoirs in China

China is the largest global consumer of antimicrobials and improving surveillance methods could help to reduce antimicrobial resistance (AMR) spread. Here we report the surveillance of ten large-scale chicken farms and four connected abattoirs in three Chinese provinces over 2.5 years. Using a data mining approach based on machine learning, we analysed 461 microbiomes from birds, carcasses and environments, identifying 145 potentially mobile antibiotic resistance genes (ARGs) shared between chickens and environments across all farms. A core set of 233 ARGs and 186 microbial species extracted from the chicken gut microbiome correlated with the AMR profiles of Escherichia coli colonizing the same gut, including Arcobacter, Acinetobacter and Sphingobacterium, clinically relevant for humans, and 38 clinically relevant ARGs. Temperature and humidity in the barns were also correlated with ARG presence. We reveal an intricate network of correlations between environments, microbial communities and AMR, suggesting multiple routes to improving AMR surveillance in livestock production.

The effect of antibiotic usage on resistance in humans and food-producing animals: a longitudinal, One Health analysis using European data

This paper estimates the effect of antibiotic usage in humans and food-producing animals on the prevalence of resistance in zoonotic bacteria in both humans and animals. Using comprehensive longitudinal data from annual surveillance reports on resistance and usage in Europe, we find that antibiotic usage in food-producing animals and antibiotic usage in humans are independently and causally related to the prevalence of resistance in both humans and animals. The study considers simultaneous and total usage of antibiotics in humans and food-producing animals to identify the marginal effects and joint effects of usage on resistance of both groups. By employing lagged-dependent variable and fixed-effects specifications, we provide a lower and an upper bound on the effects on resistance. The paper also contributes to the scant literature on how antibiotic use in humans is related to resistance in other animals.

Metagenomic next generation sequencing for studying antibiotic resistance genes in the environment

Bacterial antimicrobial resistance (AMR) is a persisting and growing threat to human health. Characterization of antibiotic resistance genes (ARGs) in the environment is important to understand and control ARG-associated microbial risks. Numerous challenges exist in monitoring ARGs in the environment, due to the extraordinary diversity of ARGs, low abundance of ARGs with respect to the complex environmental microbiomes, difficulties in linking ARGs with bacterial hosts by molecular methods, difficulties in achieving quantification and high throughput simultaneously, difficulties in assessing mobility potential of ARGs, and difficulties in determining the specific AMR determinant genes. Advances in the next generation sequencing (NGS) technologies and related computational and bioinformatic tools are facilitating rapid identification and characterization ARGs in genomes and metagenomes from environmental samples. This chapter discusses NGS-based strategies, including amplicon-based sequencing, whole genome sequencing, bacterial population-targeted metagenome sequencing, metagenomic NGS, quantitative metagenomic sequencing, and functional/phenotypic metagenomic sequencing. Current bioinformatic tools for analyzing sequencing data for studying environmental ARGs are also discussed.

Addressing antibiotic resistance: computational answers to a biological problem?

The increasing prevalence of infections caused by antibiotic-resistant bacteria is a global healthcare crisis. Understanding the spread of resistance is predicated on the surveillance of antibiotic resistance genes within an environment. Bioinformatics and artificial intelligence (AI) methods applied to metagenomic sequencing data offer the capacity to detect known and infer yet-unknown resistance mechanisms, and predict future outbreaks of antibiotic-resistant infections. Machine learning methods, in particular, could revive the waning antibiotic discovery pipeline by helping to predict the molecular structure and function of antibiotic resistance compounds, and optimising their interactions with target proteins. Consequently, AI has the capacity to play a central role in guiding antibiotic stewardship and future clinical decision-making around antibiotic resistance.

Staphylococcus aureus and MRSA in Livestock: Antimicrobial Resistance and Genetic Lineages

Animal production is associated with the frequent use of antimicrobial agents for growth promotion and for the prevention, treatment, and control of animal diseases, thus maintaining animal health and productivity. Staphylococcus aureus, in particular methicillin-resistant S. aureus (MRSA), can cause a variety of infections from superficial skin and soft tissue infections to life-threatening septicaemia. S. aureus represents a serious public health problem in hospital and community settings, as well as an economic and animal welfare problem. Livestock-associated MRSA (LA-MRSA) was first described associated with the sequence (ST) 398 that was grouped within the clonal complex (CC) 398. Initially, LA-MRSA strains were restricted to CC398, but over the years it has become clear that its diversity is much greater and that it is constantly changing, a trend increasingly associated with multidrug resistance. Therefore, in this review, we aimed to describe the main clonal lineages associated with different production animals, such as swine, cattle, rabbits, and poultry, as well as verify the multidrug resistance associated with each animal species and clonal lineage. Overall, S. aureus ST398 still remains the most common clone among livestock and was reported in rabbits, goats, cattle, pigs, and birds, often together with spa-type t011. Nevertheless, a wide diversity of clonal lineages was reported worldwide in livestock.

Dissecting microbial communities and resistomes for interconnected humans, soil, and livestock

A debate is currently ongoing as to whether intensive livestock farms may constitute reservoirs of clinically relevant antimicrobial resistance (AMR), thus posing a threat to surrounding communities. Here, combining shotgun metagenome sequencing, machine learning (ML), and culture-based methods, we focused on a poultry farm and connected slaughterhouse in China, investigating the gut microbiome of livestock, workers and their households, and microbial communities in carcasses and soil. For both the microbiome and resistomes in this study, differences are observed across environments and hosts. However, at a finer scale, several similar clinically relevant antimicrobial resistance genes (ARGs) and similar associated mobile genetic elements were found in both human and broiler chicken samples. Next, we focused on Escherichia coli, an important indicator for the surveillance of AMR on the farm. Strains of E. coli were found intermixed between humans and chickens. We observed that several ARGs present in the chicken faecal resistome showed correlation to resistance/susceptibility profiles of E. coli isolates cultured from the same samples. Finally, by using environmental sensing these ARGs were found to be correlated to variations in environmental temperature and humidity. Our results show the importance of adopting a multi-domain and multi-scale approach when studying microbial communities and AMR in complex, interconnected environments.

Current Insights Regarding the Role of Farm Animals in the Spread of Antimicrobial Resistance from a One Health Perspective

Antimicrobial resistance (AMR) represents a global threat to both human and animal health and has received increasing attention over the years from different stakeholders. Certain AMR bacteria circulate between humans, animals, and the environment, while AMR genes can be found in all ecosystems. The aim of the present review was to provide an overview of antimicrobial use in food-producing animals and to document the current status of the role of farm animals in the spread of AMR to humans. The available body of scientific evidence supported the notion that restricted use of antimicrobials in farm animals was effective in reducing AMR in livestock and, in some cases, in humans. However, most recent studies have reported that livestock have little contribution to the acquisition of AMR bacteria and/or AMR genes by humans. Overall, strategies applied on farms that target the reduction of all antimicrobials are recommended, as these are apparently associated with notable reduction in AMR (avoiding co-resistance between antimicrobials). The interconnection between human and animal health as well as the environment requires the acceleration of the implementation of the ‘One Health’ approach to effectively fight AMR while preserving the effectiveness of antimicrobials.