Analysis of genetic polymorphism is a powerful tool for epidemiological surveillance and research. Powerful inference from pathogen genetic variation, however, is often restrained by limited access to representative target DNA, especially in the study of obligate parasitic species for which ex vivo culture is resource-intensive or bias-prone. Modern sequence capture methods enable pathogen genetic variation to be analyzed directly from host/vector material but are often too complex and expensive for resource-poor settings where infectious diseases prevail. This study proposes a simple, cost-effective 'genome-wide locus sequence typing' (GLST) tool based on massive parallel amplification of information hotspots throughout the target pathogen genome. The multiplexed polymerase chain reaction amplifies hundreds of different, user-defined genetic targets in a single reaction tube, and subsequent agarose gel-based clean-up and barcoding completes library preparation at under 4 USD per sample. Our study generates a flexible GLST primer panel design workflow for Trypanosoma cruzi, the parasitic agent of Chagas disease. We successfully apply our 203-target GLST panel to direct, culture-free metagenomic extracts from triatomine vectors containing a minimum of 3.69 pg/μl T. cruzi DNA and further elaborate on method performance by sequencing GLST libraries from T. cruzi reference clones representing discrete typing units (DTUs) TcI, TcIII, TcIV, TcV and TcVI. The 780 SNP sites we identify in the sample set repeatably distinguish parasites infecting sympatric vectors and detect correlations between genetic and geographic distances at regional (< 150 km) as well as continental scales. The markers also clearly separate TcI, TcIII, TcIV and TcV + TcVI and appear to distinguish multiclonal infections within TcI. We discuss the advantages, limitations and prospects of our method across a spectrum of epidemiological research.
Culture-free genome-wide locus sequence typing (GLST) provides new perspectives on Trypanosoma cruzi dispersal and infection complexity.
阅读:10
作者:Schwabl Philipp, Maiguashca Sánchez Jalil, Costales Jaime A, Ocaña-Mayorga SofÃa, Segovia Maikell, Carrasco Hernán J, Hernández Carolina, RamÃrez Juan David, Lewis Michael D, Grijalva Mario J, Llewellyn Martin S
| 期刊: | PLoS Genetics | 影响因子: | 3.700 |
| 时间: | 2020 | 起止号: | 2020 Dec 16; 16(12):e1009170 |
| doi: | 10.1371/journal.pgen.1009170 | ||
特别声明
1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。
2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。
3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。
4、投稿及合作请联系:info@biocloudy.com。
