Selective whole-genome amplification reveals population genetics of Leishmania braziliensis directly from patient skin biopsies.
Olivia A PillingJoão L Reis-CunhaCooper A GraceAlexander S F BerryMatthew W MitchellJane A YuClara R MalekshahiElise KrespanChristina K GoCláudia LombanaYun S SongCamila F AmorimAlexsandro S LagoLucas P CarvalhoEdgar M CarvalhoDustin BrissonPhillip ScottDaniel C JeffaresDaniel P BeitingPublished in: PLoS pathogens (2023)
In Brazil, Leishmania braziliensis is the main causative agent of the neglected tropical disease, cutaneous leishmaniasis (CL). CL presents on a spectrum of disease severity with a high rate of treatment failure. Yet the parasite factors that contribute to disease presentation and treatment outcome are not well understood, in part because successfully isolating and culturing parasites from patient lesions remains a major technical challenge. Here we describe the development of selective whole genome amplification (SWGA) for Leishmania and show that this method enables culture-independent analysis of parasite genomes obtained directly from primary patient skin samples, allowing us to circumvent artifacts associated with adaptation to culture. We show that SWGA can be applied to multiple Leishmania species residing in different host species, suggesting that this method is broadly useful in both experimental infection models and clinical studies. SWGA carried out directly on skin biopsies collected from patients in Corte de Pedra, Bahia, Brazil, showed extensive genomic diversity. Finally, as a proof-of-concept, we demonstrated that SWGA data can be integrated with published whole genome data from cultured parasite isolates to identify variants unique to specific geographic regions in Brazil where treatment failure rates are known to be high. SWGA provides a relatively simple method to generate Leishmania genomes directly from patient samples, unlocking the potential to link parasite genetics with host clinical phenotypes.
Keyphrases
- case report
- plasmodium falciparum
- soft tissue
- toxoplasma gondii
- end stage renal disease
- trypanosoma cruzi
- ejection fraction
- randomized controlled trial
- endothelial cells
- wound healing
- prognostic factors
- magnetic resonance imaging
- copy number
- systematic review
- magnetic resonance
- machine learning
- deep learning
- dna methylation
- peritoneal dialysis
- combination therapy
- image quality
- genetic diversity
- human health
- artificial intelligence