In vitro susceptibility of swine pathogens to feed additives and active ingredients with potential as antibiotic replacements

AIMS

The rise in antibiotic resistance requires the reduction of antibiotic use in all sectors. In animal production, many commercial alternatives to antibiotics have been developed for incorporation into feeds, but a lack of evidence on their antibacterial activity limits confidence in their application. We aim to compare the antibacterial activity of feed additives and active ingredients to better understand their usefulness.

METHODS AND RESULTS

The antibacterial activity of 34 active ingredients and feed additives, including medium- and short-chain organic acids and essential oils, was tested against pure cultures of five bacterial swine pathogens. Antibacterial activity was observed using an agar plug diffusion method and quantified via broth microdilution. A diverse range of antibacterial activities were observed. The highest inhibitory activity against Staphylococcus aureus and Streptococcus suis was exhibited by the C12 monoglyceride (0.49 mg ml^-1 ). The monoglyceride of C12 was more effective than C12:0 against Strep. suis, but neither C12:0 nor its monoglyceride showed efficacy against the gram-negative micro-organisms tested. The most active against Escherichia coli were the C6:0 medium-chain organic acids and potassium diformate (1.95 mg ml^-1 ). For Salmonella Typhimurium, potassium diformate, sodium diformate, and a blend of C8:0/C10:0 (each 1.96 mg ml^-1 ), and for Actinobacillus pleuropneumoniae, eugenol (0.49 mg ml^-1 ) showed the most promising activity.

CONCLUSIONS

We identified broad-spectrum antibacterial activity, such as the C6:0 MCOA, and those with interesting narrow-spectrum activity, notably the killing of Strep. suis by C12 monoglyceride. We have identified additives that show the most promising bioactivity against specific pathogens.

SIGNIFICANCE AND IMPACT OF THE STUDY

We broadly compare a large collection of feed additives and active ingredients for their antibacterial activity against a diverse panel of bacterial swine pathogens. This provides a solid base of evidence which can drive the development of feed supplementation strategies with the aim of reducing dependency on antibiotic use in swine production.

Prediction of seasonal patterns of porcine reproductive and respiratory syndrome virus RNA detection in the U.S. swine industry

We developed a model to predict the cyclic pattern of porcine reproductive and respiratory syndrome virus (PRRSV) RNA detection by reverse-transcription real-time PCR (RT-rtPCR) from 4 major swine-centric veterinary diagnostic laboratories (VDLs) in the United States and to use historical data to forecast the upcoming year's weekly percentage of positive submissions and issue outbreak signals when the pattern of detection was not as expected. Standardized submission data and test results were used. Historical data (2015-2017) composed of the weekly percentage of PCR-positive submissions were used to fit a cyclic robust regression model. The findings were used to forecast the expected weekly percentage of PCR-positive submissions, with a 95% confidence interval (CI), for 2018. During 2018, the proportion of PRRSV-positive submissions crossed 95% CI boundaries at week 2, 14-25, and 48. The relatively higher detection on week 2 and 48 were mostly from submissions containing samples from wean-to-market pigs, and for week 14-25 originated mostly from samples from adult/sow farms. There was a recurring yearly pattern of detection, wherein an increased proportion of PRRSV RNA detection in submissions originating from wean-to-finish farms was followed by increased detection in samples from adult/sow farms. Results from the model described herein confirm the seasonal cyclic pattern of PRRSV detection using test results consolidated from 4 VDLs. Wave crests occurred consistently during winter, and wave troughs occurred consistently during the summer months. Our model was able to correctly identify statistically significant outbreak signals in PRRSV RNA detection at 3 instances during 2018.