Discriminating Micropathogen Lineages and Their Reticulate Evolution through Graph Theory-Based Network Analysis: The Case of Trypanosoma cruzi, the Agent of Chagas Disease

Micropathogens (viruses, bacteria, fungi, parasitic protozoa) share a common trait, which is partial clonality, with wide variance in the respective influence of clonality and sexual recombination on the dynamics and evolution of taxa. The discrimination of distinct lineages and the reconstruction of their phylogenetic history are key information to infer their biomedical properties. However, the phylogenetic picture is often clouded by occasional events of recombination across divergent lineages, limiting the relevance of classical phylogenetic analysis and dichotomic trees. We have applied a network analysis based on graph theory to illustrate the relationships among genotypes of Trypanosoma cruzi, the parasitic protozoan responsible for Chagas disease, to identify major lineages and to unravel their past history of divergence and possible recombination events. At the scale of T. cruzi subspecific diversity, graph theory-based networks applied to 22 isoenzyme loci (262 distinct Multi-Locus-Enzyme-Electrophoresis -MLEE) and 19 microsatellite loci (66 Multi-Locus-Genotypes -MLG) fully confirms the high clustering of genotypes into major lineages or "near-clades''. The release of the dichotomic constraint associated with phylogenetic reconstruction usually applied to Multilocus data allows identifying putative hybrids and their parental lineages. Reticulate topology suggests a slightly different history for some of the main "near-clades'', and a possibly more complex origin for the putative hybrids than hitherto proposed. Finally the sub-network of the near-clade T. cruzi I (28 MLG) shows a clustering subdivision into three differentiated lesser near-clades ("Russian doll pattern''), which confirms the hypothesis recently proposed by other investigators. The present study broadens and clarifies the hypotheses previously obtained from classical markers on the same sets of data, which demonstrates the added value of this approach. This underlines the potential of graph theory-based network analysis for describing the nature and relationships of major pathogens, thereby opening stimulating prospects to unravel the organization, dynamics and history of major micropathogen lineages

Multilocus polymerase chain reaction restriction fragment-length polymorphism genotyping of Trypanosoma cruzi (Chagas disease) : Taxonomic and clinical applications

Background. Trypanosoma cruzi, the agent of Chagas disease, is subdivided into 6 discrete typing units (DTUs); their identification is important to understand clinical pleomorphism and track sylvatic DTUs that might (re-) invade domestic foci of the disease and jeopardize the running control programs. Methods. The genetic polymorphism of 12 loci was analyzed by multilocus polymerase chain reaction restriction fragment-length polymorphism (PCR-RFLP) analysis (MLP analysis) in a sample representative of the diversity within T. cruzi. We paid particular attention to genes involved in host-parasite relationships, because these may be prone to polymorphism as an adaptive answer to the immune selective pressure. Results. The results of MLP analysis were shown to agree with the current multilocus enzyme electrophoresis and random amplified polymorphic DNA-based classification of T. cruzi in 6 DTUs, thereby providing a taxonomic validation of our method. Our data supported hypotheses of genetic recombination within T. cruzi. We demonstrated direct applicability of PCR-RFLP analysis to blood of mammal hosts and intestine content of vector insects. Domestic DTUs were encountered in wild animals, and, reciprocally, sylvatic DTUs were encountered in humans, raising questions about changes of transmission patterns. Conclusions. MLP analysis represents a new alternative to existing molecular methods for T. cruzi typing. It might offer an invaluable support to clinical and epidemiological studies and to control programs

Deep learning and wing interferential patterns identify Anopheles species and discriminate amongst Gambiae complex species

Abstract We present a new and innovative identification method based on deep learning of the wing interferential patterns carried by mosquitoes of the Anopheles genus to classify and assign 20 Anopheles species, including 13 malaria vectors. We provide additional evidence that this approach can identify Anopheles spp. with an accuracy of up to 100% for ten out of 20 species. Although, this accuracy was moderate (> 65%) or weak (50%) for three and seven species. The accuracy of the process to discriminate cryptic or sibling species is also assessed on three species belonging to the Gambiae complex. Strikingly, An. gambiae, An. arabiensis and An. coluzzii, morphologically indistinguishable species belonging to the Gambiae complex, were distinguished with 100%, 100%, and 88% accuracy respectively. Therefore, this tool would help entomological surveys of malaria vectors and vector control implementation. In the future, we anticipate our method can be applied to other arthropod vector-borne diseases

Observed <i>F</i>_is of the six <i>Trypanosoma cruzi</i> populations under study and three artificial clusters (all stocks from Sapini, all stocks from Quillacollo, and all TcI Bolivian stocks) and their 95% confidence intervals.

<p>Observed <i>F</i>_is of the six <i>Trypanosoma cruzi</i> populations under study and three artificial clusters (all stocks from Sapini, all stocks from Quillacollo, and all TcI Bolivian stocks) and their 95% confidence intervals.</p

Map of Bolivia: localization of the six populations of <i>Trypanosoma cruzi</i> under study isolated from sylvatic <i>Triatoma infestans</i> and distances between populations.

<p>Map of Bolivia: localization of the six populations of <i>Trypanosoma cruzi</i> under study isolated from sylvatic <i>Triatoma infestans</i> and distances between populations.</p