Exposure to Trypanosoma parasites induces changes in the microbiome of the Chagas disease vector Rhodnius prolixus

Abstract

Background: The causative agent of Chagas disease, Trypanosoma cruzi, and its nonpathogenic relative, Trypanosoma rangeli, are transmitted by haematophagous triatomines and undergo a crucial ontogenetic phase in the insect's intestine. In the process, the parasites interfere with the host immune system as well as the microbiome present in the digestive tract potentially establishing an environment advantageous for development. However, the coherent interactions between host, pathogen and microbiota have not yet been elucidated in detail. We applied a metagenome shotgun sequencing approach to study the alterations in the microbiota of Rhodnius prolixus, a major vector of Chagas disease, after exposure to T. cruzi and T. rangeli focusing also on the functional capacities present in the intestinal microbiome of the insect.

Results: The intestinal microbiota of R. prolixus was dominated by the bacterial orders Enterobacterales, Corynebacteriales, Lactobacillales, Clostridiales and Chlamydiales, whereas the latter conceivably originated from the blood used for pathogen exposure. The anterior and posterior midgut samples of the exposed insects showed a reduced overall number of organisms compared to the control group. However, we also found enriched bacterial groups after exposure to T. cruzi as well as T rangeli. While the relative abundance of Enterobacterales and Corynebacteriales decreased considerably, the Lactobacillales, mainly composed of the genus Enterococcus, developed as the most abundant taxonomic group. This applies in particular to vectors challenged with T. rangeli and at early timepoints after exposure to vectors challenged with T. cruzi. Furthermore, we were able to reconstruct four metagenome-assembled genomes from the intestinal samples and elucidate their unique metabolic functionalities within the triatomine microbiome, including the genome of a recently described insect symbiont, Candidatus Symbiopectobacterium, and the secondary metabolites producing bacteria Kocuria spp.

Conclusions: Our results facilitate a deeper understanding of the processes that take place in the intestinal tract of triatomine vectors during colonisation by trypanosomal parasites and highlight the influential aspects of pathogen-microbiota interactions. In particular, the mostly unexplored metabolic capacities of the insect vector's microbiome are clearer, underlining its role in the transmission of Chagas disease. Video Abstract.

Keywords: Host-parasite interaction; Intestinal bacterial community; Metagenomic shotgun sequencing; Secondary metabolites; Triatominae; Trypanosoma cruzi; Trypanosoma rangeli.

Fig. 1

Applied methodology for data analysis. A Preparation and collection of the metagenomic DNA samples of the midgut of R. prolixus. B Shotgun sequencing and filtering of the reads. C Quality check and statistical analysis of the microbial community. D In-depth analysis of reconstructed MAGs

Fig. 2

Reduction in the abundance of identified organisms in the anterior (AM) and posterior (PM) midgut of T. cruzi- and T. rangeli-exposed insects in comparison with nonexposed insects from T0 to T7 after exposure

Fig. 3

Reduction in the abundance of identified bacteria, fungi and viruses in the anterior (AM) and posterior (PM) midgut of T. cruzi- and T. rangeli-exposed insects in comparison with nonexposed insects from T0 to T7 after exposure

Fig. 4

Relative abundance of bacterial orders in T. cruzi-exposed (Tc), T. rangeli-exposed (Tr) and nonexposed (Ctrl) anterior and posterior midgut samples of R. prolixus at days 0, 1, 2, 3 and 7 after exposure. The figure was created with the package ggplot2 (v.3.3.3) in R

Fig. 5

Pangenomic analyses of the metagenome-assembled genomes and closely related bacterial species obtained from NCBI. A Rr_FE21. B Ef_FE21. C Ko_FE21. D Sp_FE21. The colour-coded layers represent the gene clusters of the indicated species. The number of contributing genomes, the number of genes in the respective gene cluster, the maximum number of paralogs and the presence of SCG clusters are also given for each gene cluster. The columns indicate the number of gene clusters for each genome, the single gene clusters, the number of genes per 1000 bp, the GC-content and the total length of the genomes. The centrally located tree shows each split of the gene clusters, while the tree on the right is based on the gene cluster frequencies in each genome. Accession numbers of reference genomes and bacterial strains used are provided in Additional file 1

Fig. 6

Phylogenomic tree of soft rot causing Enterobacteriaceae and Sp_FE21 based on homologous SCGs. Sp_FE21 presents as sister to the genera Brenneria and Pectobacterium. Escherichia coli was used as an outgroup in order to root the tree. Phylogeny was constructed using anvi-get-sequences-for-hmm-hits, MAFFT 7 and MEGA7 and tested by bootstrapping with 1000 replications. Confidence values are indicated

Fig. 7

Comparison of the completeness of functional pathways present in the MAGs Re_FE21, Ef_FE21, Ko_FE21 and Sp_FE21. KEGG module categories and subcategories are shown with remarkable pathways highlighted. The figure was created with the package pheatmap (v.1.0.12) in R