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Single nucleotide polymorphisms and copy-number variations in the Trypanosoma brucei repeat (TBR) sequence can be used to enhance amplification and genotyping of Trypanozoon strains.

Nick Van ReetPati Patient PyanaSara DehouNicolas BebronneStijn DeborggraevePhilippe Büscher
Published in: PloS one (2021)
The Trypanosoma brucei repeat (TBR) is a tandem repeat sequence present on the Trypanozoon minichromosomes. Here, we report that the TBR sequence is not as homogenous as previously believed. BLAST analysis of the available T. brucei genomes reveals various TBR sequences of 177 bp and 176 bp in length, which can be sorted into two TBR groups based on a few key single nucleotide polymorphisms. Conventional and quantitative PCR with primers matched to consensus sequences that target either TBR group show substantial copy-number variations in the TBR repertoire within a collection of 77 Trypanozoon strains. We developed the qTBR, a novel PCR consisting of three primers and two probes, to simultaneously amplify target sequences from each of the two TBR groups into one single qPCR reaction. This dual probe setup offers increased analytical sensitivity for the molecular detection of all Trypanozoon taxa, in particular for T.b. gambiense and T. evansi, when compared to existing TBR PCRs. By combining the qTBR with 18S rDNA amplification as an internal standard, the relative copy-number of each TBR target sequence can be calculated and plotted, allowing for further classification of strains into TBR genotypes associated with East, West or Central Africa. Thus, the qTBR takes advantage of the single-nucleotide polymorphisms and copy number variations in the TBR sequences to enhance amplification and genotyping of all Trypanozoon strains, making it a promising tool for prevalence studies of African trypanosomiasis in both humans and animals.
Keyphrases
  • copy number
  • mitochondrial dna
  • genome wide
  • escherichia coli
  • dna methylation
  • machine learning
  • nucleic acid
  • high resolution
  • living cells
  • deep learning
  • single cell
  • single molecule
  • case control