Identification of Bacillus anthracis, Brucella spp., and Coxiella burnetii DNA signatures from bushmeat.

Meat from wildlife species (bushmeat) represents a major source of dietary protein in low- and middle-income countries where humans and wildlife live in close proximity. Despite the occurrence of zoonotic pathogens in wildlife, their prevalence in bushmeat remains unknown. To assess the risk of exposure to major pathogens in bushmeat, a total of 3784 samples, both fresh and processed, were collected from three major regions in Tanzania during both rainy and dry seasons, and were screened by real-time PCR for the presence of DNA signatures of Bacillus anthracis (B. anthracis), Brucella spp. (Brucella) and Coxiella burnetii (Coxiella). The analysis identified DNA signatures of B. anthracis (0.48%), Brucella (0.9%), and Coxiella (0.66%) in a total of 77 samples. Highest prevalence rates of B. anthracis, Brucella, and Coxiella were observed in wildebeest (56%), dik-dik (50%), and impala (24%), respectively. Fresh samples, those collected during the rainy season, and samples from Selous or Serengeti had a greater relative risk of being positive. Microbiome characterization identified Firmicutes and Proteobacteria as the most abundant phyla. The results highlight and define potential risks of exposure to endemic wildlife diseases from bushmeat and the need for future investigations to address the public health and emerging infectious disease risks associated with bushmeat harvesting, trade, and consumption.

Microbial Diversity in Bushmeat Samples Recovered from the Serengeti Ecosystem in Tanzania.

Bushmeat, the meat and organs derived from wildlife species, is a common source of animal protein in the diets of those living in sub-Saharan Africa and is frequently associated with zoonotic spillover of dangerous pathogens. Given the frequent consumption of bushmeat in this region and the lack of knowledge about the microbial communities associated with this meat, the microbiome of 56 fresh and processed bushmeat samples ascertained from three districts in the Western Serengeti ecosystem in Tanzania was characterized using 16S rRNA metagenomic sequencing. The results show that the most abundant phyla present in bushmeat samples include Firmicutes (67.8%), Proteobacteria (18.4%), Cyanobacteria (8.9%), and Bacteroidetes (3.1%). Regardless of wildlife species, sample condition, season, or region, the microbiome is diverse across all samples, with no significant difference in alpha or beta diversity. The findings also suggest the presence of DNA signatures of potentially dangerous zoonotic pathogens, including those from the genus Bacillus, Brucella, Coxiella, and others, in bushmeat. Together, this investigation provides a better understanding of the microbiome associated with this major food source in samples collected from the Western Serengeti in Tanzania and highlights a need for future investigations on the potential health risks associated with the harvesting, trade, and consumption of bushmeat in Sub-Saharan Africa.

iMAP: an integrated bioinformatics and visualization pipeline for microbiome data analysis.

BACKGROUND: One of the major challenges facing investigators in the microbiome field is turning large numbers of reads generated by next-generation sequencing (NGS) platforms into biological knowledge. Effective analytical workflows that guarantee reproducibility, repeatability, and result provenance are essential requirements of modern microbiome research. For nearly a decade, several state-of-the-art bioinformatics tools have been developed for understanding microbial communities living in a given sample. However, most of these tools are built with many functions that require an in-depth understanding of their implementation and the choice of additional tools for visualizing the final output. Furthermore, microbiome analysis can be time-consuming and may even require more advanced programming skills which some investigators may be lacking. RESULTS: We have developed a wrapper named iMAP (Integrated Microbiome Analysis Pipeline) to provide the microbiome research community with a user-friendly and portable tool that integrates bioinformatics analysis and data visualization. The iMAP tool wraps functionalities for metadata profiling, quality control of reads, sequence processing and classification, and diversity analysis of operational taxonomic units. This pipeline is also capable of generating web-based progress reports for enhancing an approach referred to as review-as-you-go (RAYG). For the most part, the profiling of microbial community is done using functionalities implemented in Mothur or QIIME2 platform. Also, it uses different R packages for graphics and R-markdown for generating progress reports. We have used a case study to demonstrate the application of the iMAP pipeline. CONCLUSIONS: The iMAP pipeline integrates several functionalities for better identification of microbial communities present in a given sample. The pipeline performs in-depth quality control that guarantees high-quality results and accurate conclusions. The vibrant visuals produced by the pipeline facilitate a better understanding of the complex and multidimensional microbiome data. The integrated RAYG approach enables the generation of web-based reports, which provides the investigators with the intermediate output that can be reviewed progressively. The intensively analyzed case study set a model for microbiome data analysis.

Transcriptomics reveal potential vaccine antigens and a drastic increase of upregulated genes during Theileria parva development from arthropod to bovine infective stages.

Theileria parva is a protozoan parasite transmitted by the brown ear tick Rhipicephalus appendiculatus that causes East Coast fever (ECF) in cattle, resulting in substantial economic losses in the regions of southern, eastern and central Africa. The schizont form of the parasite transforms the bovine host lymphocytes into actively proliferating cancer-like cells. However, how T. parva causes bovine host cells to proliferate and maintain a cancerous phenotype following infection is still poorly understood. On the other hand, current efforts to develop improved vaccines have identified only a few candidate antigens. In the present paper, we report the first comparative transcriptomic analysis throughout the course of T. parva infection. We observed that the development of sporoblast into sporozoite and then the establishment in the host cells as schizont is accompanied by a drastic increase of upregulated genes in the schizont stage of the parasite. In contrast, the ten highest gene expression values occurred in the arthropod vector stages. A comparative analysis showed that 2845 genes were upregulated in both sporozoite and schizont stages compared to the sporoblast. In addition, 647 were upregulated only in the sporozoite whereas 310 were only upregulated in the schizont. We detected low p67 expression in the schizont stage, an unexpected finding considering that p67 has been reported as a sporozoite stage-specific gene. In contrast, we found that transcription of p67 was 20 times higher in the sporoblast than in the sporozoite. Using the expression profiles of recently identified candidate vaccine antigens as a benchmark for selection for novel potential vaccine candidates, we identified three genes with expression similar to p67 and several other genes similar to Tp1-Tp10 schizont vaccine antigens. We propose that the antigenicity or chemotherapeutic potential of this panel of new candidate antigens be further investigated. Structural comparisons of the transcripts generated here with the existing gene models for the respective loci revealed indels. Our findings can be used to improve the structural annotation of the T. parva genome, and the identification of alternatively spliced transcripts.