Development of an enhanced methodology for large-scale detection and quantification of antimicrobial resistant bacteria in livestock

Antimicrobial resistance (AMR) is a global health challenge for both humans and animals. A potential source of antimicrobial resistant bacteria is in livestock due to the widespread and unrestrained use of antimicrobials. This is further exacerbated by the presence of bacteria resistant to critically important antimicrobials (CIAs) that are classified as the last-line of treatment of infectious diseases in humans. AMR surveillance in livestock has become a key cornerstone of AMR control strategies by informing the presence and frequency of resistance including CIA-resistant bacteria. Established approach of AMR surveillance in livestock typically have a national-level focus that only acquire a maximum of 300 isolates nationwide for antimicrobial susceptibility testing (AST), with each isolate representing one sample from one farm. While this approach is sufficient for evaluating AMR at national-level, it is inadequate for AMR surveillance at herd-level as one isolate is not sufficient to represent AMR of each farm, leading to errors when implementing antimicrobial stewardship and AMR control measures at the herd-level. This project aimed to address this issue by developing an enhanced AMR surveillance method that combines a multiple samples per herd approach with automated laboratory robotics and selective agars incorporated with antimicrobials to provide accurate large-scale data on the presence, frequency and carriage levels of resistant bacteria within individual farms. The first step in developing the enhanced method was validating suitable selective agars for enumeration of resistant E. coli colonies. Of the three E. coli selective agars compared, MacConkey agar was found to be consistently inferior in E. coli growth performance than the two modern commercially available E. coli selective agars, Brilliance™ E. coli and CHROMagar™ ECC. This inferiority in E. coli growth performance was consistently seen regardless of whether pure cultures or homogenised faecal samples were used for inoculation onto E. coli selective agar with or without incorporation of antimicrobials. Brilliance™ ESBL and CHROMagar™ ESBL which are two modern commercially available selective agar targeting extended-spectrum cephalosporin (ESC)-resistant E. coli were also compared to determine which is better suited for quantifying ESC-resistant E. coli. The latter was found to be more suitable compared to the former due to being able to support a wider diversity of ESC-resistant E. coli strains. The chosen selective agars were subsequently applied to the enhanced method to describe the CIA-resistance scenario of Australian pigs in order demonstrate its capability to provide a more accurate and detailed AMR data at the herd, state and national-level. A major finding was the detection of CIA-resistant E. coli in Australian pigs. Fluoroquinolone (FQ)-resistant E. coli was present among majority of Australian pig farms nationwide, while the presence of ESC-resistant E. coli was detected among eight Australian pig farms nationwide, with the former having a higher frequency compared to the latter. However, compared to the commensal E. coli population, carriage levels of both resistant E. coli were lower, indicating that CIA-resistant E. coli has not yet spread throughout the commensal E. coli population. When subjected to AST, CIA-resistant E. coli harbouring phenotypic resistance towards FQ and ESC was detected but due to the nature of FQ-resistance mechanisms, it has limited clinical relevance. Whole genome sequencing (WGS) was also performed on CIA-resistant E. coli which revealed that ST744 and ST4981 are the current dominant FQ-resistant E. coli and ESC-resistant E. coli sequence types (STs) respectively present among Australian pigs nationwide. Further analysis suggests that both STs were likely introduced into Australian pigs via external sources. Nonetheless, the multiple samples per herd approach and quantitative focus of the enhanced method demonstrated that it is capable of delivering a more accurate and detailed AMR data at the herd-level compared to established AMR surveillance systems. The adaptability of the enhanced method towards a different livestock species was demonstrated through the performance of AMR surveillance on ten Australian meat chicken farms. While ESC-resistant E. coli was not detected, ciprofloxacin-resistant E. coli was detected on all farms, with carriage levels that were lower than commensal E. coli. This indicates that FQ-resistant E. coli is present among all ten farms but has not yet spread throughout its commensal E. coli population. When subjected to AST, only 57.1% of FQ-resistant E. coli isolates were multi-class resistant, and that the most common phenotypic resistance profile was one with resistance towards two antimicrobial classes. Though WGS will be conducted to ascertain the genomic characteristics of FQ-resistant E. coli isolates in these ten farms, the findings demonstrated that the enhanced method is also capable of delivering the same accurate and detailed AMR data at the flock-level for meat chickens. In conclusion, the findings demonstrated that the enhanced method is capable of delivering a more accurate and detailed AMR data than established AMR surveillance systems for livestock at all levels of governance, and with different livestock species. This ultimately leads to improved judgements when implementing AMR control strategies as part of biosecurity protocols to prevent further emergence and spread of CIA-resistant E. coli. Additionally, it provides further prospects for expanding the application of the enhanced method within the food and public health sectors, with further opportunities for enhancement via the inclusion of data pertaining to antimicrobial use and resistance transmission pathways

Exploratory analysis of methods for automated classification of laboratory test orders into syndromic groups in veterinary medicine

Background: Recent focus on earlier detection of pathogen introduction in human and animal populations has led to the development of surveillance systems based on automated monitoring of health data. Real- or near real-time monitoring of pre-diagnostic data requires automated classification of records into syndromes-syndromic surveillance-using algorithms that incorporate medical knowledge in a reliable and efficient way, while remaining comprehensible to end users. Methods: This paper describes the application of two of machine learning (Naïve Bayes and Decision Trees) and rule-based methods to extract syndromic information from laboratory test requests submitted to a veterinary diagnostic laboratory. Results: High performance (F1-macro = 0.9995) was achieved through the use of a rule-based syndrome classifier, based on rule induction followed by manual modification during the construction phase, which also resulted in clear interpretability of the resulting classification process. An unmodified rule induction algorithm achieved an F1-micro score of 0.979 though this fell to 0.677 when performance for individual classes was averaged in an unweighted manner (F1-macro), due to the fact that the algorithm failed to learn 3 of the 16 classes from the training set. Decision Trees showed equal interpretability to the rule-based approaches, but achieved an F1-micro score of 0.923 (falling to 0.311 when classes are given equal weight). A Naïve Bayes classifier learned all classes and achieved high performance (F1-micro = 0.994 and F1-macro =. 955), however the classification process is not transparent to the domain experts. Conclusion: The use of a manually customised rule set allowed for the development of a system for classification of laboratory tests into syndromic groups with very high performance, and high interpretability by the domain experts. Further research is required to develop internal validation rules in order to establish automated methods to update model rules without user input

Understanding Animal Health in Southeast Asia: Advances in the Collection, Management and Use of Animal Health Information

Livestock Production/Industries,

Biotechnology in Cattle Reproduction.

End of Project ReportTeagasc acknowledges support from the European Union 3rd and 4th Framework Programmes (CT-92-0163 and CT-95-0190)Over the next decade the Irish agri-food industry will have to compete in a rapidly changing world environment arising from increased competitiveness, decreased world market prices and increased consumer demands for higher quality, healthier and safer food. To become competitive in this environment the scale and efficiency of production at both farm and factory level will have to increase significantly and this must be achieved with due regard for the protection of the environment and the welfare of animals. New technologies will be needed to achieve this. Biotechnology will be central to the development of these new technologies. This project has been concerned with the identification and evaluation of biotechnology developments that have the potential to increase reproductive efficiency in cattle. This includes a range of technologies relating to the in vitro production, manipulation, cryopreservation and transfer of cattle embryos. The potential of other emerging technologies such as embryo and sperm sexing, cloning and biopharming or the production of commercially desirable proteins in cows milk are also addressed in this report.European Unoi

Geographic information system (GIS) and epidemiological associations among foodborne pathogens at the farm

Geographic Information System (GIS), a computer mapping and analysis technology, has emerged as an innovative epidemiological tool in a variety of disciplines. However, the application of GIS to food safety research has received little attention. This study utilized GIS and automated riboprinting technology to examine relationships that existed between animals and their environments, monitoring transmission of pathogens on the farm environment and to nearby surface water environments. A comprehensive epidemiological survey was conducted at The University of Tennessee, Knoxville Experiment Station research dairy farm. More than 40,000 animal and environmental samples were analyzed for Salmonella, Campylobacter jejuni and Escherichia coli 0157:H?. A survey of the Tennessee River, adjacent The University of Tennessee research dairy farm, was also conducted to determine the incidence of these pathogens in the river. Automated riboprinting was used to compare bacterial isolates from various species, locations, and sample types. Salmonella (32%) was the most frequent pathogen isolated on the farm, followed by C. jejuni (21 %) and E. coli 0157:H? (2%). Feed, bedding, water, insects and bird droppings were identified as significant vectors of transmission of pathogens to animals and farm environments. Results of this study indicate that controlling access to animal feed and water sources by insects and wild birds could reduce transmission of pathogens to dairy animals and farm environments. Neither C. jejuni nor E. coli 0157:H? were recovered from the Tennessee River. However, Salmonella was isolated from sampling sites upstream and downstream from the dairy farm. Salmonella was recovered at increased frequency in the Tennessee River at the dairy farm and sites upstream from the farm. Salmonella ser. Senftenberg, Typhimurium, Havana and Newport were the most frequently isolated Serotypes at the dairy farm and from the river. Salmonella ser. Havana, isolated from farm and river water samples, was the only detected serotype showing similar riboprint patterns. Based on pathogens isolated at the farm and not in the river, the variable pattern of Salmonella isolation in the river, and detection of few similar Salmonella serotypes, it was concluded that the dairy farm did not contribute significantly to contamination of the river

Brucella 'HOOF-Prints': strain typing by multi-locus analysis of variable number tandem repeats (VNTRs)

BACKGROUND: Currently, there are very few tools available for subtyping Brucella isolates for epidemiological trace-back. Subtyping is difficult because of the genetic homogeneity within the genus. Sequencing of the genomes from three Brucella species has facilitated the search for DNA sequence variability. Recently, hypervariability among short tandem repeat sequences has been exploited for strain-typing of several bacterial pathogens. RESULTS: An eight-base pair tandem repeat sequence was discovered in nine genomic loci of the B. abortus genome. Eight loci were hypervariable among the three Brucella species. A PCR-based method was developed to identify the number of repeat units (alleles) at each locus, generating strain-specific fingerprints. None of the loci exhibited species- or biovar-specific alleles. Sometimes, a species or biovar contained a specific allele at one or more loci, but the allele also occurred in other species or biovars. The technique successfully differentiated the type strains for all Brucella species and biovars, among unrelated B. abortus biovar 1 field isolates in cattle, and among B. abortus strains isolated from bison and elk. Isolates from the same herd or from short-term in vitro passage exhibited little or no variability in fingerprint pattern. Sometimes, isolates from an animal would have multiple alleles at a locus, possibly from mixed infections in enzootic areas, residual disease from incomplete depopulation of an infected herd or molecular evolution within the strain. Therefore, a mixed population or a pool of colonies from each animal and/or tissue was tested. CONCLUSION: This paper describes a new method for fingerprinting Brucella isolates based on multi-locus characterization of a variable number, eight-base pair, tandem repeat. We have named this technique "HOOF-Prints" for Hypervariable Octameric Oligonucleotide Finger-Prints. The technique is highly discriminatory among Brucella species, among previously characterized Brucella strains, and among unrelated field isolates that could not be differentiated by classical methods. The method is rapid and the results are reproducible. HOOF-Printing will be most useful as a follow-up test after identification by established methods since we did not find species-specific or biovar-specific alleles. Nonetheless, this technology provides a significant advancement in brucellosis epidemiology, and consequently, will help to eliminate this disease worldwide

Animal welfare management in a digital world

Simple SummaryThe digital revolution opens possibilities to use multiple sensors, a data infrastructure and data analytics to monitor animals or their environment 24/7. Precision Livestock Farming (PLF) offers significant opportunities for a holistic, evidence-based approach to the monitoring and surveillance of farmed animal welfare. To date, the emphasis of PLF has been on animal health and productivity. If PLF develops further along these lines, there is a risk that animal health and productivity define welfare. A combined multi-actor approach that brings together industry, scientists, food chain actors, policy-makers and NGOs is needed to develop and use the promise of PLF for the creative and effective improvement of farmed animal welfare, not only taking into account their physical welfare but also their mental one.Although there now exists a wide range of policies, instruments and regulations, in Europe and increasingly beyond, to improve and safeguard the welfare of farmed animals, there remain persistent and significant welfare issues in virtually all types of animal production systems ranging from high prevalence of lameness to limited possibilities to express natural behaviours. Protocols and indicators, such as those provided by Welfare Quality, mean that animal welfare can nowadays be regularly measured and surveyed at the farm level. However, the digital revolution in agriculture opens possibilities to quantify animal welfare using multiple sensors and data analytics. This allows daily monitoring of animal welfare at the group and individual animal level, for example, by measuring changes in behaviour patterns or physiological parameters. The present paper explores the potential for developing innovations in digital technologies to improve the management of animal welfare at the farm, during transport or at slaughter. We conclude that the innovations in Precision Livestock Farming (PLF) offer significant opportunities for a more holistic, evidence-based approach to the monitoring and surveillance of farmed animal welfare. To date, the emphasis in much PLF technologies has been on animal health and productivity. This paper argues that this emphasis should not come to define welfare. What is now needed is a coming together of industry, scientists, food chain actors, policy-makers and NGOs to develop and use the promise of PLF for the creative and effective improvement of farmed animal welfare

South Dakota Farm and Home Research

The better part of communication is listening: Ag Experiment Station Director Fred Cholick plans to communicate by doing a lot of listening. What he hears will help him develop a better understanding of the needs of farmers and ranchers, scientists, and other South Dakotans. [p] 1New director encourages integrated approach to ag research: All the resources necessary for the production of food and fiber-humans, soil, air, plants, animals-come under the umbrella of agriculture. All these resources will affect the course of SDSU ag research. [p] 2New approach unlocks secrets of soybean plant: Take scientists from a variety of disciplines-each tackling soybean problems from a different direction---put them together, establish strong ties and easy communication, and you deliver greater productivity and larger profits for producers. [p] 5Grape genes give clues to winter survival: SDSU plant physiologist Anne Fennell chose grapes for her winter hardiness research because they are easy to grow and reach fruiting stage in just 2 or 3 years. The study results will be applicable to all woody, fruit-bearing plants. [p] 10A day in the life of a research station: A research station is part farm and part outdoor laboratory. Most people see their area station only during its field day tours. But stations are buzzing with activity the rest of the year too. [p] 12107th Annual Report: The 107th Annual Report presents the people and projects that make up the South Dakota Agricultural Experiment Station. [p] 14https://openprairie.sdstate.edu/agexperimentsta_sd-fhr/1164/thumbnail.jp