Uncovering vector, parasite, blood meal and microbiome patterns from mixed-DNA specimens of the Chagas disease vector Triatoma dimidiata

Abstract

Chagas disease, considered a neglected disease by the World Health Organization, is caused by the protozoan parasite Trypanosoma cruzi, and transmitted by >140 triatomine species across the Americas. In Central America, the main vector is Triatoma dimidiata, an opportunistic blood meal feeder inhabiting both domestic and sylvatic ecotopes. Given the diversity of interacting biological agents involved in the epidemiology of Chagas disease, having simultaneous information on the dynamics of the parasite, vector, the gut microbiome of the vector, and the blood meal source would facilitate identifying key biotic factors associated with the risk of T. cruzi transmission. In this study, we developed a RADseq-based analysis pipeline to study mixed-species DNA extracted from T. dimidiata abdomens. To evaluate the efficacy of the method across spatial scales, we used a nested spatial sampling design that spanned from individual villages within Guatemala to major biogeographic regions of Central America. Information from each biotic source was distinguished with bioinformatics tools and used to evaluate the prevalence of T. cruzi infection and predominant Discrete Typing Units (DTUs) in the region, the population genetic structure of T. dimidiata, gut microbial diversity, and the blood meal history. An average of 3.25 million reads per specimen were obtained, with approximately 1% assigned to the parasite, 20% to the vector, 11% to bacteria, and 4% to putative blood meals. Using a total of 6,405 T. cruzi SNPs, we detected nine infected vectors harboring two distinct DTUs: TcI and a second unidentified strain, possibly TcIV. Vector specimens were sufficiently variable for population genomic analyses, with a total of 25,710 T. dimidiata SNPs across all samples that were sufficient to detect geographic genetic structure at both local and regional scales. We observed a diverse microbiotic community, with significantly higher bacterial species richness in infected T. dimidiata abdomens than those that were not infected. Unifrac analysis suggests a common assemblage of bacteria associated with infection, which co-occurs with the typical gut microbial community derived from the local environment. We identified vertebrate blood meals from five T. dimidiata abdomens, including chicken, dog, duck and human; however, additional detection methods would be necessary to confidently identify blood meal sources from most specimens. Overall, our study shows this method is effective for simultaneously generating genetic data on vectors and their associated parasites, along with ecological information on feeding patterns and microbial interactions that may be followed up with complementary approaches such as PCR-based parasite detection, 18S eukaryotic and 16S bacterial barcoding.

Fig 1. Geographic locations of the sequenced T. dimidiata specimens.

Specimens from Madriz, Nicaragua, Quiché, Guatemala, Petén, Guatemala and Toledo, Belize were sampled to capture variation across countries. To assess within-country regional diversity, specimens from Guatemala and El Salvador were sampled more intensively to include regional and village-scale variation. Locations are color-coded by the Within-Country Regions.

Fig 2. Bioinformatics pipeline separating RADseq data obtained from the legs and abdomens of Triatoma dimidiata specimens.

Raw data from 32 T. dimidiata were trimmed and filtered using FastX tools, then mapped to the six available T. cruzi genomes using Bowtie. The unmapped reads from the host were assembled denovo using Stacks, converted to an index, and used as a catalog to map all to T. dimidiata; both sets of mapped reads were aligned in STACKS to obtain markers for the parasite and host. The NCBI nt database was queried (May, 2016) with the remaining unmapped reads to quantify matches obtained from chordates (blood meal hosts), bacteria and other taxa. Input and output parallelograms are color-coded to indicate the vector (yellow), parasite (pink) and all other taxa (orange).

Fig 3. Percentage of reads mapped to different DNA sources across all specimens.

(A) The overall percentage of reads mapped to Trypanosoma cruzi, Triatoma dimidiata, other taxa (BLAST results), and unmapped reads; and (B) the breakdown of taxa retrieved from a BLAST search using the nt database from NCBI.

Fig 4. Number of SNPs retrieved in relation to mapped reads and depth of coverage for T. dimidiata and T. cruzi.

Log-transformed number of single nucleotide polymorphisms (SNPs) in relation to the number of (A) T. dimidiata and (B) T. cruzi mapped reads, and the average depth of coverage for (C) T. dimidiata and (D) T. cruzi. In panels B and D, gray circles indicate putative TcI and black triangles indicate putative TcIV specimens.

Fig 5. Trypanosoma cruzi infection measured by the count of mapped reads detected from the 20 genotyped abdomens.

Star indicates positive T. cruzi infection detected by microscopy. CHCE -01 through QUI-01 have zero mapped reads.

Fig 6. Phylogenetic inference by maximum likelihood of T. cruzi from nine infected abdomens of T. dimidiata.

Specimens originating from Petén, Guatemala (Purple) Jutiapa, Guatemala (light-blue), Belize (red), Santa Ana, El Salvador (blue) and Nicaragua (orange), and six in-silico genotypes from the reference genomes of two TcI, one TcII, and two TcVI DTUs and the out-group T. c. marinkellei. The tree topology was tested with 10,000 bootstrap replications, using a total of 34,707 bi-allelic SNPs.

Fig 7. Population genetic structure of Triatoma dimidiata across Central America inferred with a discriminant analysis of principle components (DAPC) based on SNP markers.

DAPC shows the maximized differences among four genetic clusters of the vector. Clusters were determined using the k-mean clustering algorithm and choosing the lowest Bayesian Information Criterion (BIC). Ellipses show 95% confidence intervals. The first two eigenvalues explain 69.2% of the variation found in 21,461 SNPs.

Fig 8. Box-plot comparison of the asymptotic species richness of identified in SNPs from T. dimidiata legs, non-infected abdomens and T. cruzi-infected abdomens.

Letters indicate statistically significant groupings based on post-hoc Tukey’s tests (p <0.01).

Fig 9. NMDS plot of bacterial community structure based on weighted Unifrac distances.

Specimens are color-coded by within-country regions; stars indicate T. cruzi-positive abdomens. Colored polygons indicate statistically significant clusters from a post-hoc permutation test.

Fig 10. Outlier test of top chordate hits.

Specimens are sorted by top-hit percentage; legs were included in the analysis as baseline controls. Species identity of the top hit is indicated for the five abdomens above the upper Tukey range (+1.5*IQR).