Several different models of Trypanosoma cruzi evolution have been proposed. These models suggest that scarce events of genetic exchange occurred during the evolutionary history of this parasite. In addition, the debate has focused on the existence of one or two hybridisation events during the evolution of T. cruzi lineages. Here, we reviewed the literature and analysed available sequence data to clarify the phylogenetic relationships among these different lineages. We observed that TcI, TcIII and TcIV form a monophyletic group and that TcIII and TcIV are not, as previously suggested, TcI-TcII hybrids. Particularly, TcI and TcIII are sister groups that diverged around the same time that a widely distributed TcIV split into two clades (TcIV_S and TcIV_N). In addition, we collected evidence that TcIII received TcIV_S kDNA by introgression on several occasions. Different demographic hypotheses (surfing and asymmetrical introgression) may explain the origin and expansion of the TcIII group. Considering these hypotheses, genetic exchange should have been relatively frequent between TcIII and TcIV_S in the geographic area in which their distributions overlapped. In addition, our results support the hypothesis that two independent hybridisation events gave rise to TcV and TcVI. Consequently, TcIV_S kDNA was first transferred to TcIII and later to TcV and TcVI in TcII/TcIII hybridisation events.

People living in areas with active vector-borne transmission of Chagas disease have multiple contacts with its causative agent, Trypanosoma cruzi. Reinfections by T. cruzi are possible at least in animal models leading to lower or even hardly detectable parasitaemia. In humans, although reinfections are thought to have major public health implications by increasing the risk of chronic manifestations of the disease, there is little quantitative knowledge about their frequency and the timing of parasite re-inoculation in the course of the disease. Here, we implemented stochastic agent-based models i) to estimate the rate of re-inoculation in humans and ii) to assess how frequent are reinfections during the acute and chronic stages of the disease according to alternative hypotheses on the adaptive immune response following a primary infection. By using a hybrid genetic algorithm, the models were fitted to epidemiological data of Argentinean rural villages where mixed infections by different genotypes of T. cruzi reach 56% in humans. To explain this percentage, the best model predicted 0.032 (0.008–0.042) annual reinfections per individual with 98.4% of them occurring in the chronic phase. In addition, the parasite escapes to the adaptive immune response mounted after the primary infection in at least 20% of the events of re-inoculation. With these low annual rates, the risks of reinfection during the typically long chronic stage of the disease stand around 14% (4%-18%) and 60% (21%-70%) after 5 and 30 years, with most individuals being re-infected 1–3 times overall. These low rates are better explained by the weak efficiency of the stercorarian mode of transmission than a highly efficient adaptive immune response. Those estimates are of particular interest for vaccine development and for our understanding of the higher risk of chronic disease manifestations suffered by infected people living in endemic areas. Chagas disease is caused by a unicellular parasite mainly transmitted by blood-feeding bugs. The disease starts with an acute phase when the parasite multiplies in tissues. The immune system is then activated and almost clears the parasites before the host enters the chronic phase of the disease. As hosts remain exposed to the vectors, secondary contacts may result in re-inoculation with the parasite. The re-inoculated parasites then have to face an adaptive immune response that may prevent reinfection. There is little quantitative knowledge about the frequency of such re-inoculation and the efficiency of the immune system to prevent them. Since reinfections can lead to people carrying two or more strains of the parasite, we can potentially infer on the frequency of reinfection from the detection of such mixed infections. We fitted agent-based models to field data to estimate the frequency of reinfection in humans and its timing after primary infection. Our models predicted that most reinfections occur during the chronic phase as the adaptive immune response fails to prevent them in 20% of re-inoculations. Such a rate of failure is particularly important as reinfections are thought to increase the risk of heart damage in the chronic phase of the disease.

The model of predominant clonal evolution (PCE) proposed for micropathogens does not state that genetic exchange is totally absent, but rather, that it is too rare to break the prevalent PCE pattern. However, the actual impact of this “residual” genetic exchange should be evaluated. Multilocus Sequence Typing (MLST) is an excellent tool to explore the problem. Here, we compared online available MLST datasets for seven eukaryotic microbial pathogens: Trypanosoma cruzi, the Fusarium solani complex, Aspergillus fumigatus, Blastocystis subtype 3, the Leishmania donovani complex, Candida albicans and Candida glabrata. We first analyzed phylogenetic relationships among genotypes within each dataset. Then, we examined different measures of branch support and incongruence among loci as signs of genetic structure and levels of past recombination. The analyses allow us to identify three types of genetic structure. The first was characterized by trees with well-supported branches and low levels of incongruence suggesting well-structured populations and PCE. This was the case for the T. cruzi and F. solani datasets. The second genetic structure, represented by Blastocystis spp., A. fumigatus and the L. donovani complex datasets, showed trees with weakly-supported branches but low levels of incongruence among loci, whereby genetic structuration was not clearly defined by MLST. Finally, trees showing weakly-supported branches and high levels of incongruence among loci were observed for Candida species, suggesting that genetic exchange has a higher evolutionary impact in these mainly clonal yeast species. Furthermore, simulations showed that MLST may fail to show right clustering in population datasets even in the absence of genetic exchange. In conclusion, these results make it possible to infer variable impacts of genetic exchange in populations of predominantly clonal micro-pathogens. Moreover, our results reveal different problems of MLST to determine the genetic structure in these organisms that should be considered.