title: Discriminating micropathogen lineages and their reticulate evolution through graph theory-based network analysis: the case of Trypanosoma cruzi, the agent of Chagas disease.
abstract: 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.
authors.affiliation: IFREMER (Institut Français de Recherche pour l'Exploitation de la Mer) - Département Ecosystèmes Marins Exploités, Sète, France.
authors.forename: Sophie
authors.initials: S
authors.lastname: Arnaud-Haond
authors.affiliation: IFREMER (Institut Français de Recherche pour l'Exploitation de la Mer) - Département Ecosystèmes Marins Exploités, Sète, France.
authors.forename: Yann
authors.initials: Y
authors.lastname: Moalic
authors.affiliation: Interactions hôte-vecteur-parasite dans les maladies dues aux Trypanosomatidés, INTERTRYP (IRD-CIRAD), Montpellier, France.
authors.forename: Christian
authors.initials: C
authors.lastname: Barnabé
authors.affiliation: Department of Ecology and Evolutionary Biology, University of California Irvine, Irvine, California, United States of America.
authors.forename: Francisco José
authors.initials: FJ
authors.lastname: Ayala
authors.affiliation: Maladies Infectieuses et Vecteurs Ecologie, Génétique, Evolution et Contrôle, MIVEGEC (IRD 224-CNRS 5290-UM1-UM2), Montpellier, France.
authors.forename: Michel
authors.initials: M
authors.lastname: Tibayrenc
mesh_terms: D005075:Biological Evolution; D014355:Chagas Disease / Q000469:parasitology* / Q000635:transmission; D016000:Cluster Analysis; D005838:Genotype; D006801:Humans; D018895:Microsatellite Repeats; D008954:Models, Biological; D014349:Trypanosoma cruzi / Q000145:classification / Q000235:genetics*