The ALEX algorithm - estimating average lifetime antimicrobial exposure of danish slaughter pigs in a fast, automated and robust way.

Calculating and estimating antimicrobial exposure at specific batch level is key to understanding consumption patterns of antimicrobials in the Danish pig sector. Understanding consumption and trading patterns can assist in developing treatment plans at national levels and might lead to reducing antimicrobial resistance levels. The ALEX algorithm is a fast, automated and robust algorithm developed to estimate the average lifetime antimicrobial exposure of Danish slaughter pigs. The algorithm estimates antimicrobial exposure in the different life stages of the pig (piglet, weaner and finisher) together with the type of production network (the number of farms within a network and ownership of these). We present the algorithm and give two examples of usage. Furthermore, we compare the ALEX algorithm with an acknowledged exposure estimation algorithm, and we present a sensitivity analysis.

Mixed effect modeling of tetracycline resistance levels in Danish slaughter pigs.

Mathematical and statistical modeling can be a very useful tool in understanding and fighting antimicrobial resistance (AMR). Here we present investigations of mixed effect models of varying complexity in order to identify and address possible management factors affecting the tetracycline AMR levels in Danish pig farms. Besides antimicrobial exposure during pigs life cycle, the type of production seems to also have an influence. The results concludes that not only fully integrated farms (CHR integrated) but also farms in a production network with a single ownership (CVR integrated) might have a preventive effect on levels of tetracycline AMR compared to more complex trading patterns.

Robustness in quantifying the abundance of antimicrobial resistance genes in pooled faeces samples from batches of slaughter pigs using metagenomics analysis.

OBJECTIVES: With the continued spread of antimicrobial resistance (AMR) in animals, it is important to assess its occurrence throughout a microbiome quantitatively in order to evaluate significantly affecting factors, e.g. antimicrobial usage. Metagenomics methods make it possible to measure the abundance of AMR genes in complex samples such as pooled faeces samples from batches of slaughter pigs. This study was performed to determine the random error in pooled samples from batches of pigs at slaughter and the measurement error from the metagenomics processes. METHODS: In four farms, two pooled samples were obtained from a batch of slaughter pigs by two individual samplers, and each pooled sample was thereafter processed twice. Hierarchically clustered heatmaps were applied to evaluate dissimilarities between samples. The coefficient of variation was used to calculate the percentage difference between samples from the same farm. RESULTS: Results of the analysis revealed that it was not possible to quantitatively separate the variation arising from sampling and metagenomics processes. They both contributed to the overall measurement error in batches of slaughter pigs. CONCLUSION: Sampling of single pigs in 30 randomly selected pig pens within the farms provides a composition representative for frequently occurring AMR genes present within the farms, while rare genes were not dispersed in a similar manner. Aggregating the resistance abundance at gene family or antimicrobial class level will reduce the apparent variation originating from errors in sampling and metagenomics processing.

Computional algorithm for lifetime exposure to antimicrobials in pigs using register data-The LEA algorithm.

Accurate and detailed data on antimicrobial exposure in pig production are essential when studying the association between antimicrobial exposure and antimicrobial resistance. Due to difficulties in obtaining primary data on antimicrobial exposure in a large number of farms, there is a need for a robust and valid method to estimate the exposure using register data. An approach that estimates the antimicrobial exposure in every rearing period during the lifetime of a pig using register data was developed into a computational algorithm. In this approach data from national registers on antimicrobial purchases, movements of pigs and farm demographics registered at farm level are used. The algorithm traces batches of pigs retrospectively from slaughter to the farm(s) that housed the pigs during their finisher, weaner, and piglet period. Subsequently, the algorithm estimates the antimicrobial exposure as the number of Animal Defined Daily Doses for treatment of one kg pig in each of the rearing periods. Thus, the antimicrobial purchase data at farm level are translated into antimicrobial exposure estimates at batch level. A batch of pigs is defined here as pigs sent to slaughter at the same day from the same farm. In this study we present, validate, and optimise a computational algorithm that calculate the lifetime exposure of antimicrobials for slaughter pigs. The algorithm was evaluated by comparing the computed estimates to data on antimicrobial usage from farm records in 15 farm units. We found a good positive correlation between the two estimates. The algorithm was run for Danish slaughter pigs sent to slaughter in January to March 2015 from farms with more than 200 finishers to estimate the proportion of farms that it was applicable for. In the final process, the algorithm was successfully run for batches of pigs originating from 3026 farms with finisher units (77% of the initial population). This number can be increased if more accurate register data can be obtained. The algorithm provides a systematic and repeatable approach to estimating the antimicrobial exposure throughout the rearing period, independent of rearing site for finisher batches, as a lifetime exposure measurement.

A sampling and metagenomic sequencing-based methodology for monitoring antimicrobial resistance in swine herds.

OBJECTIVES: Reliable methods for monitoring antimicrobial resistance (AMR) in livestock and other reservoirs are essential to understand the trends, transmission and importance of agricultural resistance. Quantification of AMR is mostly done using culture-based techniques, but metagenomic read mapping shows promise for quantitative resistance monitoring. METHODS: We evaluated the ability of: (i) MIC determination for Escherichia coli; (ii) cfu counting of E. coli; (iii) cfu counting of aerobic bacteria; and (iv) metagenomic shotgun sequencing to predict expected tetracycline resistance based on known antimicrobial consumption in 10 Danish integrated slaughter pig herds. In addition, we evaluated whether fresh or manure floor samples constitute suitable proxies for intestinal sampling, using cfu counting, qPCR and metagenomic shotgun sequencing. RESULTS: Metagenomic read-mapping outperformed cultivation-based techniques in terms of predicting expected tetracycline resistance based on antimicrobial consumption. Our metagenomic approach had sufficient resolution to detect antimicrobial-induced changes to individual resistance gene abundances. Pen floor manure samples were found to represent rectal samples well when analysed using metagenomics, as they contain the same DNA with the exception of a few contaminating taxa that proliferate in the extraintestinal environment. CONCLUSIONS: We present a workflow, from sampling to interpretation, showing how resistance monitoring can be carried out in swine herds using a metagenomic approach. We propose metagenomic sequencing should be part of routine livestock resistance monitoring programmes and potentially of integrated One Health monitoring in all reservoirs.