PREVALENCE AND CHARACTERISTICS OF LISTERIA SPECIES FROM SELECTED AFRICAN COUNTRIES

Abstract

Listeriosis, caused by Listeria spp., presents varying clinical manifestations among individuals, from moderate fecal infections such as diarrhea to severe infections such as septicemia, meningitis and abortion or newborn listeriosis in perinatal patients. In Africa, listeriosis is attributed to poor sanitation and cross-contamination in food processing environments, particularly ready to eat (RTE) foods including dairy products, leafy vegetables, fish and meat. Despite the global increase in reported cases and research on listeriosis, data from Africa remains scarce and this could lead to possible underestimation of the importance of listeriosis on the continent. This paper therefore presents a comprehensive overview of currently available reports on Listeria spp. in Africa with emphasis on molecular characteristics, antimicrobial susceptibility, and prevalence in food, animal and environmental samples. The majority of studies on Listeria spp. in Africa have so far focused on the prevalence and antibiotic susceptibility of L. monocytogenes isolated from RTE foods and raw meat but rarely from humans, animals, and the environment. The overall calculated average prevalence values from the available reports are 23.7 and 22.2% for Listeria spp. and L. monocytogenes, respectively. Listeria spp. isolated from different parts of Africa are generally sensitive to ciprofloxacin, but resistant to penicillin. The majority of these studies employed conventional culture and biochemical tests to characterize Listeria spp. However, the use of modern molecular techniques such as PCR and whole-genome sequencing is on the rise. Most of the studies employing molecular tools were carried out in South Africa and Nigeria, with the predominant strain reported in South Africa being ST6. In order to provide a better understanding of the importance of listeria in Africa, there is the need for extensive and coordinated studies using modern molecular-based techniques to characterize the various Listeria species, and to assess the disease epidemiology using the one health concept.