A Surveillance Study on The Spleen Microbiome Of Wild Rodents And Shrews Caught In Marigat, Baringo County, Kenya

Abstract

There is an observed global increase in emerging infectious diseases, with 13% of over 1400 known human pathogens being classified as emerging or re-emerging. Majority of the emerging pathogens are zoonoses, majority of which have animals (domestic and wildlife) as reservoirs. Of the animals, small mammals represent 40% of mammalian species and because of their widespread distribution and accelerated human encroachment to their habitats; they provide great opportunities for disease transmission to humans. The nomadic pastoralists are most likely at greater risk of encountering zoonoses due to their animal husbandry lifestyle. In Kenya, the prevalence of this tick-borne bacteria has been seen to be on the rise. For instance, Coxiella burnetti ,Orientiachuto, Rickettsia spp, and Bartonella spp, have been detected in vectors, animals and humans in different regions in Kenya. This study investigated the bacterial microbiota of wild caught small mammals of Marigat in Baringo County, Kenya. Communities in this county are nomadic pastoralists. Fifty-four small mammals were trapped from different sites in Marigat. DNA was extracted from the spleen and used to amplify the hyper-variable V3-V4 region of bacterial 16S ribosomal RNA (rRNA). The spleen is a peripheral lymphoid organ in vertebrates that aids in filtering blood. It plays an important role in the modulation of immune responses and hematopoiesis. The spleen can be infected by bacteria during the blood filtration and can therefore be used as an indicator for microorganisms harbored by wild animals. This study assessed bacterial diversity in the spleen of wild caught small mammals. The V3-V4 region demonstrates considerable sequence diversity among different bacteria and can identify all bacteria species to the genus level, with an exception of the closely related enterobacteriaceae. The generated amplicon libraries were sequenced on the Illumina MiSeq. Sequence data were analyzed with Mothur v1.35, queried against the Silver database and visualized on R.For taxonomic classification of the small mammals, cytochrome B (Cytb) and cytochrome oxidase subunit 1 (COI) genes were amplified and thereafter sequenced using Sanger method on a Genetic Analyzer. CLC main workbench was used to assemble the data into contigs. The sequences were then queried against the reference sequence database. A phylogenetic tree was then inferred using the MEGA software v7. By molecular taxonomy, the small mammals were classified as 41 rodents and 13 shrews. 175,629 sequences were obtained and classed into 196 operational taxonomic units (OTUs), based on unique sequences that mapped to 18 bacteria phyla, with 4 phyla accounting for 97% of the total OTUs. 18 phyla and 196 bacteria genera were detected. Of these phyla, Proteobacteria was the most abundant contributing 64.7% of total contigs. Other phyla included Actinobacteria (18.0%), Firmicutes (6.1%), Chlamydiae (3.8%), Chloroflexi (2.6%) and Bacteroidetes (1.9%) among others. Of the pathogenic bacteria genera, Bartonella was the most abundant (41.5%), followed by Anaplasma (6.5%), Methylobacterium (3.6%), Delftia (3.2%), Coxiella 2.6%, Bradyrhizobium (1.6%) and Acinetobacter (1.3%). Other less abundant (<1%) pathogenic bacteria included Ehrlichia, Rickettsia, Leptospira, Borrelia, Brucella, Chlamydia and Streptococcus. Acomys carried higher bacteria diversity than other small mammals at Shannon diversity index of 3.0 compared to 2.3 for Rattus, 2.2 for Arvicathis and Crocidura and 1.8 for Mastomys. This study confirms the role of the spleen as a microbial repository and its suitability for studying microbial pathogens, and utility of 16S rRNA deep sequencing in characterizing the complex microbiota in the spleens of wild rodents and shrews. An inherent problem with the V3-V4 region of 16S rRNA is the inability to classify the bacteria reliably beyond the genera. Future studies should utilize the newer long read methods of 16S rRNA analysis that are able to delimit the species composition.