Trypanosoma cruzi exhibits remarkable genetic diversity, with six main evolutionary lineages or discrete typing units (DTUs), namely TcI to TcVI. Identifying these DTUs is essential for understanding the role of genetics in the biology, epidemiology, and clinical implications of Chagas disease. However, molecular methods proposed to identify T. cruzi lineages in clinical samples exhibit sensitivity limitations due to the low parasitemia in blood or tissue samples. The high diversity and copy number of the minicircle hypervariable regions (mHVRs) in kinetoplast DNA (kDNA) makes it a viable target for typing. This chapter presents a high-sensitivity and high-resolution typing protocol based on deep sequencing of the mHVRs using Illumina technology. This protocol includes two consecutive PCRs for amplification and indexing, followed by sequencing. The resulting reads are processed by a user-friendly pipeline on Google Colab. This pipeline compares the reads against reference mHVRs in our database in order to determine infecting DTUs in each sample.

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Deep Sequencing of mHVRs for Trypanosoma cruzi Genotyping

  • Anahí G. Díaz,
  • Fanny Rusman,
  • Nicolás Tomasini

摘要

Trypanosoma cruzi exhibits remarkable genetic diversity, with six main evolutionary lineages or discrete typing units (DTUs), namely TcI to TcVI. Identifying these DTUs is essential for understanding the role of genetics in the biology, epidemiology, and clinical implications of Chagas disease. However, molecular methods proposed to identify T. cruzi lineages in clinical samples exhibit sensitivity limitations due to the low parasitemia in blood or tissue samples. The high diversity and copy number of the minicircle hypervariable regions (mHVRs) in kinetoplast DNA (kDNA) makes it a viable target for typing. This chapter presents a high-sensitivity and high-resolution typing protocol based on deep sequencing of the mHVRs using Illumina technology. This protocol includes two consecutive PCRs for amplification and indexing, followed by sequencing. The resulting reads are processed by a user-friendly pipeline on Google Colab. This pipeline compares the reads against reference mHVRs in our database in order to determine infecting DTUs in each sample.