The molecular epidemiology of parasite infections: tools and applications.
ABSTRACT Molecular epidemiology, broadly defined, is the application of molecular genetic techniques to the dynamics of disease in a population. In this review, we briefly describe molecular and analytical tools available for molecular epidemiological studies and then provide an overview of how they can be applied to better understand parasitic disease. A range of new molecular tools have been developed in recent years, allowing for the direct examination of parasites from clinical or environmental samples, and providing access to relatively cheap, rapid, high throughput molecular assays. At the same time, new analytical approaches, in particular those derived from coalescent theory, have been developed to provide more robust estimates of evolutionary processes and demographic parameters from multilocus, genotypic data. To date, the primary application of molecular epidemiology has been to provide specific and sensitive identification of parasites and to resolve taxonomic issues, particularly at the species level and below. Population genetic studies have also been used to determine the extent of genetic diversity among populations of parasites and the degree to which this diversity is associated with different host cycles or epidemiologically important phenotypes. Many of these studies have also shed new light on transmission cycles of parasites, particularly the extent to which zoonotic transmission occurs, and on the prevalence and importance of mixed infections with different parasite species or intraspecific variants (polyparasitism). A major challenge, and one which is now being addressed by an increasing number of studies, is to find and utilize genetic markers for complex traits of epidemiological significance, such as drug resistance, zoonotic potential and virulence.
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ABSTRACT: BACKGROUND: Over the last 6 decades, rodent Plasmodium species have become key model systems for understanding the basic biology of malaria parasites. Cell and molecular parasitology have made much progress in identifying genes underpinning interactions between malaria parasites, hosts, and vectors. However, little attention has been paid to the evolutionary genetics of parasites, which provides context for identifying potential therapeutic targets and for understanding the selective forces shaping parasites in natural populations. Additionally, understanding the relationships between species, subspecies, and strains, is necessary to maximize the utility of rodent malaria parasites as medically important infectious disease models, and for investigating the evolution of host-parasite interactions. RESULTS: Here, we collected multi-locus sequence data from 58 rodent malaria genotypes distributed throughout 13 subspecies belonging to P. berghei, P. chabaudi, P. vinckei, and P. yoelii. We employ multi-locus methods to infer the subspecies phylogeny, and use population-genetic approaches to elucidate the selective patterns shaping the evolution of these organisms. Our results reveal a time-line for the evolution of rodent Plasmodium and suggest that all the subspecies are independently evolving lineages (i.e. species). We show that estimates of species-level polymorphism are inflated if subspecies are not explicitly recognized, and detect purifying selection at most loci. CONCLUSIONS: Our work resolves previous inconsistencies in the phylogeny of rodent malaria parasites, provides estimates of important parameters that relate to the parasite's natural history and provides a much-needed evolutionary context for understanding diverse biological aspects from the cross-reactivity of immune responses to parasite mating patterns.BMC Evolutionary Biology 11/2012; 12(1):219. · 3.29 Impact Factor
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ABSTRACT: We propose that clonal evolution in micropathogens be defined as restrained recombination on an evolutionary scale, with genetic exchange scarce enough to not break the prevalent pattern of clonal population structure, a definition already widely used for all kinds of pathogens, although not clearly formulated by many scientists and rejected by others. The two main manifestations of clonal evolution are strong linkage disequilibrium (LD) and widespread genetic clustering ("near-clading"). We hypothesize that this pattern is not mainly due to natural selection, but originates chiefly from in-built genetic properties of pathogens, which could be ancestral and could function as alternative allelic systems to recombination genes ("clonality/sexuality machinery") to escape recombinational load. The clonal framework of species of pathogens should be ascertained before any analysis of biomedical phenotypes (phylogenetic character mapping). In our opinion, this model provides a conceptual framework for the population genetics of any micropathogen.Proceedings of the National Academy of Sciences 09/2012; · 9.74 Impact Factor
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ABSTRACT: Reliable and rapid genotyping of large number of Echinococcus granulosus sensu lato isolates is crucial for understanding the epidemiology and transmission of cystic echinococcosis. We have developed a method for distinguishing and discriminating common genotypes of E. granulosus s.l. (G1, G3, and G6) in Iran. This method is based on polymerase chain reaction coupled with high resolution melting curve (HRM), ramping from 70 to 86 °C with fluorescence data acquisition set at 0.1 °C increments and continuous fluorescence monitoring. Consistency of this technique was assessed by inter- and intra-assays. Assessment of intra- and inter-assay variability showed low and acceptable coefficient of variations ranging from 0.09 to 0.17 %. Two hundred and eighty E. granulosus s.l. isolates from sheep, cattle, and camel were used to evaluate the applicability and accuracy of the method. The isolates were categorized as G1 (93, 94, and 25 %), G3 (7, 4, and 4 %), and G6 (0, 2, and 71 %) for sheep, cattle, and camel, respectively. HRM results were completely compatible with those obtained from sequencing and rostellar hook measurement. This method proved to be a valuable screening tool for large-scale molecular epidemiological studies.Parasitology Research 07/2013; · 2.85 Impact Factor