Biology subjects

Tobon-Castano, A.

Publications and source records attributed to Tobon-Castano, A..

2 recordsLinked to original sources

bioRxiv2023

Diversity of the bacterial and viral communities in the tropical horse tick, Dermacentor nitens in Colombia

Ticks are obligatory hematophagous ectoparasites that transmit pathogens among various vertebrates, including humans. The composition of the microbial and viral communities in addition to the pathogenic microorganisms is highly diverse in ticks, but the factors driving the diversity are not well understood. The tropical horse tick, Dermacentor nitens, is distributed throughout the Americas and it is recognized as a natural vector of Babesia caballi and Theileria equi, the causal agents of equine piroplasmosis. We characterized the bacterial and viral communities associated with partially-fed D. nitens females collected by a passive survey on horses from field sites representing three distinct geographical areas in Colombia (Bolivar, Antioquia, and Cordoba). RNA-seq and sequencing of the V3 and V4 hypervariable regions of the 16S rRNA gene were performed using the Illumina-Miseq platform. A total of 356 operational taxonomic units (OTUs) were identified, in which the presumed endosymbiotic Francisellaceae/Francisella spp. was predominantly found. Nine contigs corresponding to six different viruses were identified in three viral families: Chuviridae, Rhabdoviridae, and Flaviviridae. Differences in the relative abundance of the microbial composition among the geographical regions were found to be independent of the presence of Francisella-Like Endosymbiont (FLE). The most prevalent bacteria found on each region were Corynebacterium in Bolivar, Staphylococcus in Antioquia, and Pseudomonas in Cordoba. Rickettsia-like endosymbionts, mainly recognized as the etiological agent of rickettsioses in Colombia were detected in the Cordoba samples. Metatranscriptomics revealed 13 contigs containing FLE genes, suggesting a trend of regional differences. These findings suggest regional distinctions among the ticks and their bacterial compositions.

bioinformatics↗

bioRxiv2019

A molecular barcode and online tool to identify and map imported infection with Plasmodium vivax

Imported cases present a considerable challenge to the elimination of malaria. Traditionally, patient travel history has been used to identify imported cases, but the long-latency liver stages confound this approach in Plasmodium vivax. Molecular tools to identify and map imported cases offer a more robust approach, that can be combined with drug resistance and other surveillance markers in high-throughput, population-based genotyping frameworks. Using a machine learning approach incorporating hierarchical FST (HFST) and decision tree (DT) analysis applied to 831 P. vivax genomes from 20 countries, we identified a 28-Single Nucleotide Polymorphism (SNP) barcode with high capacity to predict the country of origin. The Matthews correlation coefficient (MCC), which provides a measure of the quality of the classifications, ranging from -1 (total disagreement) to 1 (perfect prediction), exceeded 0.9 in 15 countries in cross-validation evaluations. When combined with an existing 37-SNP P. vivax barcode, the 65-SNP panel exhibits MCC scores exceeding 0.9 in 17 countries with up to 30% missing data. As a secondary objective, several genes were identified with moderate MCC scores (median MCC range from 0.54-0.68), amenable as markers for rapid testing using low-throughput genotyping approaches. A likelihood-based classifier framework was established, that supports analysis of missing data and polyclonal infections. To facilitate investigator-lead analyses, the likelihood framework is provided as a web-based, open-access platform (vivaxGEN-geo) to support the analysis and interpretation of data produced either at the 28-SNP core or full 65-SNP barcode. These tools can be used by malaria control programs to identify the main reservoirs of infection so that resources can be focused to where they are needed most.

molecular biology↗