Pathogen Species Identification from Metagenomes in Ancient Remains: The Challenge of Identifying Human Pathogenic Species of Trypanosomatidae via Bioinformatic Tools
trypanosomatidae, kraken taxonomic assignment tool, bowtie2 fast short reads aligner, ANCIENT DNA, parasitome, co-infection
Accurate species identification from ancient DNA samples is a difficult task that would shed light on the evolutionary history of pathogenic microorganisms. The field of palaeomicrobiology has undoubtedly benefited from the advent of untargeted metagenomic approaches that use next-generation sequencing methodologies. Nevertheless, assigning ancient DNA at the species level is a challenging process. Recently, the gut microbiome analysis of three pre-Columbian Andean mummies (Santiago-Rodriguez et al., 2016) has called into question the identification of Leishmania in South America. The accurate assignment would be important because it will provide some key elements that are linked to the evolutionary scenario for visceral leishmaniasis agents in South America. Here, we recovered the metagenomic data filed in the metagenomics RAST server (MG-RAST) to identify the different members of the Trypanosomatidae family that have infected these ancient remains. For this purpose, we used the ultrafast metagenomic sequence classifier, based on an exact alignment of k-mers (Kraken) and Bowtie2, an ultrafast and memory-efficient tool for aligning sequencing reads to long reference sequences. The analyses, which have been conducted on the most exhaustive genomic database possible on Trypanosomatidae, show that species assignments could be biased by a lack of some genomic sequences of Trypanosomatidae species (strains). Nevertheless, our work raises the issue of possible co-infections by multiple members of the Trypanosomatidae family in these three pre-Columbian mummies. In the three mummies, we show the presence of DNA that is reminiscent of a probable co-infection with Leptomonas seymouri, a parasite of insect’s gut, and Lotmaria.
Denis Sereno, Franck Dorkeld, Mohammad Akhoundi, Pascale Perrin