Detection and disease diagnosis trends (2017-2022) for Streptococcus suis, Glaesserella parasuis, Mycoplasma hyorhinis, Actinobacillus suis and Mycoplasma hyosynoviae at Iowa State University Veterinary Diagnostic Laboratory

Abstract

Background: Accurate measurement of disease associated with endemic bacterial agents in pig populations is challenging due to their commensal ecology, the lack of disease-specific antemortem diagnostic tests, and the polymicrobial nature of swine diagnostic cases. The main objective of this retrospective study was to estimate temporal patterns of agent detection and disease diagnosis for five endemic bacteria that can cause systemic disease in porcine tissue specimens submitted to the Iowa State University Veterinary Diagnostic Laboratory (ISU VDL) from 2017 to 2022. The study also explored the diagnostic value of specific tissue specimens for disease diagnosis, estimated the frequency of polymicrobial diagnosis, and evaluated the association between phase of pig production and disease diagnosis.

Results: S. suis and G. parasuis bronchopneumonia increased on average 6 and 4.3%, while S. suis endocarditis increased by 23% per year, respectively. M. hyorhinis and A. suis associated serositis increased yearly by 4.2 and 12.8%, respectively. A significant upward trend in M. hyorhinis arthritis cases was also observed. In contrast, M. hyosynoviae arthritis cases decreased by 33% average/year. Investigation into the diagnostic value of tissues showed that lungs were the most frequently submitted sample, However, the use of lung for systemic disease diagnosis requires caution due to the commensal nature of these agents in the respiratory system, compared to systemic sites that diagnosticians typically target. This study also explored associations between phase of production and specific diseases caused by each agent, showcasing the role of S. suis arthritis in suckling pigs, meningitis in early nursery and endocarditis in growing pigs, and the role of G. parasuis, A. suis, M. hyorhinis and M. hyosynoviae disease mainly in post-weaning phases. Finally, this study highlighted the high frequency of co-detection and -disease diagnosis with other infectious etiologies, such as PRRSV and IAV, demonstrating that to minimize the health impact of these endemic bacterial agents it is imperative to establish effective viral control programs.

Conclusions: Results from this retrospective study demonstrated significant increases in disease diagnosis for S. suis, G. parasuis, M. hyorhinis, and A. suis, and a significant decrease in detection and disease diagnosis of M. hyosynoviae. High frequencies of interactions between these endemic agents and with viral pathogens was also demonstrated. Consequently, improved control programs are needed to mitigate the adverse effect of these endemic bacterial agents on swine health and wellbeing. This includes improving diagnostic procedures, developing more effective vaccine products, fine-tuning antimicrobial approaches, and managing viral co-infections.

Keywords: Actinobacillus suis; Detection; Diagnosis; Disease; Endemic; Glaesserella parasuis; Monitoring; Mycoplasma hyorhinis; Mycoplasma hyosynoviae; Polymicrobial; Streptococcus suis; Swine.

Fig. 1

Trendlines (raw data and estimated rate) of overall detection of Streptococcus suis, Glaesserella parasuis, and Actinobacillus suis using bacteriologic culture. The annual estimated rate (solid line) and 95% confidence interval (dashed line) referred to the output of a Binomial regression model which modeled the total number of cases with detection of each agent (colored lines in raw data plot) divided by the total number of bacterial cultures for each year (black line in raw data plot). Black line is based on specimens of interest (see Fig. 2)

Fig. 2

Distribution of specimens with isolation of Streptococcus suis, Glaesserella parasuis, and Actinobacillus suis over a 6-year period. The percent are shown with specimens that were included > 10% of cases

Fig. 3

Trendlines (raw data and estimated rate) of overall detection of Glaesserella parasuis, Mycoplasma hyorhinis, and Mycoplasma hyosynoviae using PCR testing in specimens to diagnose disease. The red line represents the positive test results and black line the total testing. The annual estimated rate (solid line) and 95% confidence interval (dashed line) referred to the output of a Binomial regression model which modeled the total number of PCR-positive results from each agent (red line in raw data plot) divided by the total number of PCR tests from each agent for each year (black line in raw data plot)

Fig. 4

Distribution of specimens with Glaesserella parasuis and Mycoplasma hyorhinis DNA detection by PCR over a 6-year period. The percent are shown with specimens that were included > 10% of cases

Fig. 5

Distribution of lesions observed with S. suis, G. parasuis, M. hyorhinis, and A. suis diagnosis over a 6-year period. M. hyosynoviae was only observed with arthritis cases. The percent are shown with lesions that were included > 10% of cases

Fig. 6

Trendlines (raw data and estimated rate) of arthritis and bronchopneumonia observed with Streptococcus suis, Glaesserella parasuis, Mycoplasma hyorhinis, Actinobacillus suis, and Mycoplasma hyosynoviae disease diagnoses. The annual estimated rate (solid line) and 95% confidence interval (dashed line) referred to the output of a binomial regression model which modeled the total number of cases of each agent (colored lines in raw data plot) divided by the total number of cases of the specific lesion for each year (black line in raw data plot)

Fig. 7

Trendlines (raw data and estimated rate) of endocarditis and meningitis observed with Streptococcus suis and Glaesserella parasuis disease diagnosis. The annual estimated rate referred to the output of a binomial regression model in which modeled the total number of cases of each agent (colored lines in raw data plot) divided by the total number of cases of the specific lesion for each year (black line in raw data plot)

Fig. 8

Trendlines (raw data and estimated rate) of sepsis and serositis observed with Streptococcus suis, Glaesserella parasuis, Mycoplasma hyorhinis, Actinobacillus suis, and Mycoplasma hyosynoviae disease diagnoses. The annual estimated rate (solid line) and 95% confidence interval (dashed line) referred to the output of a binomial regression model which modeled the total number of cases of each agent (colored lines in raw data plot) divided by the total number of cases of the specific lesion for each year (black line in raw data plot)

Fig. 9

Distribution of lesions observed with Streptococcus suis, Glaesserella parasuis, Mycoplasma hyorhinis, Actinobacillus suis, and Mycoplasma hyosynoviae diseases by age of pigs. Suckling piglets (0 < x ≤ 3-week-old); early-nursery (3 < x ≤ 6-week-old); late-nursery (6 < x ≤ 10-week-old); growing (10 < x ≤ 16-week-old); and finishers and adults (16-week-old < x). The percent are shown with pig production phases that were included > 10% of cases