Field-collected Glossina bacteriome and trypanosome infection

A number of reports have demonstrated the role of insect bacterial flora in their host's physiology and metabolism. For example, tsetse flies, the vector of human sleeping sickness (Human African Trypanosomiasis HAT) and nagana in animals (Animal African Trypanosomiasis AAT), carry bacteria that influence vector diet and immune processes. However, the mechanisms involved in these processes remain poorly documented. This underscores the need for increased research into the bacterial flora composition and structure of tsetse flies, especially with reference to certain species and the environment of certain foci. Accordingly, the aim of this study was to identify the diversity and relative abundance of bacterial genera in 190 Glossina palpalis palpalis flies collected in two trypanosomiasis foci located in southern Cameroon (Campo and Bipindi), which were either uninfected or naturally trypanosome-infected. Using the V3V4 and V4 variable regions of the small subunit of the 16S ribosomal RNA gene, we identified ten such bacterial genera. In addition, we observed that the relative abundance of the obligate endosymbiont Wigglesworthia was highly prominent (around 99%), regardless of the analyzed region. The remaining genera represented approximately 1% of the bacterial flora, and were composed of Salmonella, Spiroplasma, Sphingomonas, Methylobacterium, Acidibacter, Tsukamurella, Serratia, Kluyvera and an unidentified bacterium. The Sodalis genus was present but with a very low prevalence (below the bacterial enrollment threshold). Globally, no statistically significant difference was found between the bacterial compositions of flies from the two foci (Campo and Bipindi), and between infected and uninfected flies. However, Salmonella and Serratia were only described in uninfected flies and not in infected flies, suggesting a potential role for these two bacteria in fly refractoriness to trypanosome infection. Finally, from a technical point of view, our analysis of the V4 region was more efficient than the analysis of the V3V4 region, since it was capable of identifying nine out of the ten bacterial genera. Keywords: Glossina; bacterial flora; sleeping sickness; nagana; trypanosome; metabarcoding.