Establishment of a Pathogenicity Index for Mice to Pasteurella multocida Strains Isolated from Poultry and Swine

• Main Authors: BD Emery, TQ Furian, GZ Chitolina, RM Pilatti, KA Borges, CTP Salle, HLS Moraes
• Format: Article
• Language: English
• Published: Fundação APINCO de Ciência e Tecnologia Avícolas
• Series: Brazilian Journal of Poultry Science
• Subjects: Mice; Pasteurella multocida; pathogenicity index
• Online Access: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1516-635X2018000200237&lng=en&tlng=en

ABSTRACT Fowl cholera is a contagious disease that results from infection by the bacterium Pasteurella multocida. This microorganism is extensively distributed among animal species, but little is known regarding it’s pathogenesis and specificity to various hosts. Many studies using pathogenicity evaluation methods are subjective and difficult to quantify because they are often only involved in the observation of the lethal capacity of the agent in experimental inoculation. Due to a lack of more consistent data, this study aimed to establish a classification model of P. multocida pathogenicity in mice using strains isolated from poultry and swine. A total of 94 strains of P. multocida isolated from clinical cases of FC and from lungs of swine were tested. A volume of 0.1 mL of bacterial suspension was obtained from the concentration of 106 CFU/mL and inoculated by an intraperitoneal route in five mice. The animals were observed every six hours over seven days. In addition to the mortality observed, the time of death and gross lesions were also analyzed. The Pathogenicity Indexes obtained showed significant differences (p<0.05) according to the origin of the strains. Likewise, the number of gross lesions and isolation percentages were also varied (p<0.05) among strains isolated from poultry and swine. From the observed ratios, the isolates were grouped into three pathogenicity classes: high, medium and low. This study proposed a consistent measurement and classification of P. multocida pathogenicity. The obtained results will be used to generate other adjusted models, as well as to form the basis for disease diagnosis.