Search bioRxivSearch

bioRxiv · 10.1101/418343

A single test approach for accurate and sensitive detection and taxonomic characterization of Trypanosomes by comprehensive analysis of ITS1 amplicons

Abstract

The World Health Organization has targeted stopping the transmission of Human African Trypanosomiasis by 2030. To achieve this, better tools are urgently required to identify and monitor Trypanosome infections in human, animals, and tsetse fly vectors. This study presents a single test approach for detection and identification of Trypanosomes and their comprehensive characterization at species and sub-group level. Our method uses newly designed ITS1 PCR primers (a widely used method for detection of African Trypanosomes, amplifying the ITS1 region of ribosomal RNA genes) coupled to Illumina sequencing of the amplicon. The protocol is based on the widely used Illuminas 16s bacterial metagenomic analysis procedure that makes use of multiplex PCR and dual indexing. We analyzed wild tsetse flies collected from Zambia and Zimbabwe. Our results show that the traditional method for Trypanosome species detection based on band size comparisons on a gel is unable to distinguish between T. vivax and T. godfreyi accurately. Additionally, this approach shows increased sensitivity of detection at species level. Through phylogenetic analysis, we identified Trypanosomes at species and sub-group level without the need for any additional tests. Our results show T. congolense Kilifi sub-group is more closely related to T. simiae than to other T. congolense sub-groups. This agrees with previous studies using satellite DNA and 18s RNA analysis. While current classification does not list any sub-groups for T. vivax and T. godfreyi, we observed distinct subgroups for these species. Interestingly, sequences matching T. congolense Tsavo (now classified as T. simiae Tsavo) clusters distinctly from the rest of the T. simiae Tsavo sequences suggesting that the Nannomonas group is more divergent than currently thought thus the need for a better classification criteria. This approach has the potential for refining classification of Trypanosomes and provide detailed molecular epidemiology information useful for surveillance and transmission control efforts.\n\nAuthor summaryDetection of Trypanosomes in the tsetse flies plays an important role in the control of African trypanosomiasis by providing information on circulating Trypanosome species in a given area. We have developed a method that combines multiplex PCR and next-generation sequencing for Trypanosome species detection. The method is based on the widely used bacterial metagenomic analysis protocol and uses a modular, two-step PCR process followed by sequencing of all amplicons in a single run, making sequencing of amplicons more efficient and cost-effective when dealing with large sample sizes. As part of this approach, we designed novel primers for amplifying the ITS1 region of the Trypanosome rRNA gene that is more sensitive than conventional primers. Identification of Trypanosome species is based on BLAST searches against the constantly updated NCBIs nt database, which facilitates the identification of Trypanosome subgroups. Our approach is more accurate than traditional gel-based analysis and shows how the latter is prone to misidentification. It is also sensitive and is able to discriminate between subgroups within Trypanosome species. Applied as an epidemiological tool, it has the potential to provide new, comprehensive and more accurate information on vector-pathogen-host interconnections which are key in the control and management of African trypanosomiasis.

Source connections

Explore related subjects

Keep this discovery

BibTeXRIS

Gaithuma, A. K., Yamagishi, J., Martinelli, A., Hayashida, K., Kawai, N., Marsela, M., Sugimoto, C.. 2018-09-14. A single test approach for accurate and sensitive detection and taxonomic characterization of Trypanosomes by comprehensive analysis of ITS1 amplicons. https://doi.org/10.1101/418343

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Translating surveillance data into incidence estimates

Monitoring a population for a disease requires the hosts to be sampled and tested for the pathogen. This results in sampling series from which to estimate the disease incidence, i.e. the proportion of hosts infected. Existing estimation methods assume that disease incidence is not changing between monitoring rounds, resulting in underestimation of the disease incidence. In this paper we develop an incidence estimation model accounting for epidemic growth with monitoring rounds sampling varying incidence. We also show how to accommodate the asymptomatic period characteristic to most diseases. For practical use, we produce an approximation of the model, which is subsequently shown accurate for relevant epidemic and sampling parameters. Both the approximation and the full model are applied to stochastic spatial simulations of epidemics. The results prove their consistency for a very wide range of situations.

epidemiology

The Swiss Primary Ciliary Dyskinesia registry: objectives, methods and first results

Primary Ciliary Dyskinesia (PCD) is a rare hereditary, multi-organ disease caused by defects in ciliary structure and function. It results in a wide range of clinical manifestations, most commonly in the upper and lower airways. Central data collection in national and international registries is essential to studying the epidemiology of rare diseases and filling in gaps in knowledge of diseases such as PCD. For this reason, the Swiss Primary Ciliary Dyskinesia Registry (CH-PCD) was founded in 2013 as a collaborative project between epidemiologists and adult and paediatric pulmonologists.\n\nThe registry records patients of any age, suffering from PCD, who are treated and resident in Switzerland. It collects information from patients identified through physicians, diagnostic facilities, and patient organisations. The registry dataset contains data on diagnostic evaluations, lung function, microbiology and imaging, symptoms, treatments, and hospitalizations.\n\nBy May 2018, CH-PCD has contacted 566 physicians of different specialties and identified 134 patients with PCD. At present this number represents an overall 1 in 63,000 prevalence of people diagnosed with PCD in Switzerland. Prevalence differs by age and region; it is highest in children and adults younger than 30 years, and in Espace Mittelland. The median age of patients in the registry is 25 years (range 5-73), and 49 patients have a definite PCD diagnosis based on recent international guidelines. Data from CH-PCD are contributed to international collaborative studies and the registry facilitates patient identification for nested studies.\n\nCH-PCD has proven to be a valuable research tool that already has highlighted weaknesses in PCD clinical practice in Switzerland. Development of centralised diagnostic and management centres and adherence to international guidelines are needed to improve diagnosis and management--particularly for adult PCD patients.

epidemiology

Perfect Counterfactuals for Epidemic Simulations

Simulation studies are often used to predict the expected impact of control measures in infectious disease outbreaks. Typically, two independent sets of simulations are conducted, one with the intervetnion, and one without, and epidemic sizes (or some related metric) are compared to estimate the effect of the intervention. Since it is possible that controlled epidemics are larger than uncontrolled ones if there is substantial stochastic variation between epidemics, uncertainty intervals from this approach can include a negative effect even for an effective intervention. To more precisely estimate the number of cases an intervention will prevent within a single epidemic, here we develop a single world approach to matching simulations of controlled epidemics to their exact uncontrolled counterfac-tual. Our method borrows concepts from percolation approaches prune out possible epidemic histories and create potential epidemic graph that can be realized to create perfectly matched controlled and uncontrolled epidemics. We present an implementation of this method for a common class of compartmental models, and its application in a simple SIR model. Results illustrate how, at the cost of some computation time, this method substantially narrows confidence intervals and avoids non-sensical inferences.

epidemiology