6,657 research outputs found

    Chagas Disease Diagnostic Applications: Present Knowledge and Future Steps

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    Chagas disease, caused by the protozoan Trypanosoma cruzi, is a lifelong and debilitating illness of major significance throughout Latin America and an emergent threat to global public health. Being a neglected disease, the vast majority of Chagasic patients have limited access to proper diagnosis and treatment, and there is only a marginal investment into R&D for drug and vaccine development. In this context, identification of novel biomarkers able to transcend the current limits of diagnostic methods surfaces as a main priority in Chagas disease applied research. The expectation is that these novel biomarkers will provide reliable, reproducible and accurate results irrespective of the genetic background, infecting parasite strain, stage of disease, and clinical-associated features of Chagasic populations. In addition, they should be able to address other still unmet diagnostic needs, including early detection of congenital T. cruzi transmission, rapid assessment of treatment efficiency or failure, indication/prediction of disease progression and direct parasite typification in clinical samples. The lack of access of poor and neglected populations to essential diagnostics also stresses the necessity of developing new methods operational in point-of-care settings. In summary, emergent diagnostic tests integrating these novel and tailored tools should provide a significant impact on the effectiveness of current intervention schemes and on the clinical management of Chagasic patients. In this chapter, we discuss the present knowledge and possible future steps in Chagas disease diagnostic applications, as well as the opportunity provided by recent advances in high-throughput methods for biomarker discovery.Fil: Balouz, Virginia. Consejo Nacional de Investigaciones CientĂ­ficas y TĂ©cnicas. Centro CientĂ­fico TecnolĂłgico Conicet - La Plata. Instituto de Investigaciones BiotecnolĂłgicas. Instituto de Investigaciones BiotecnolĂłgicas "Dr. RaĂșl AlfonsĂ­n" (sede ChascomĂșs). Universidad Nacional de San MartĂ­n. Instituto de Investigaciones BiotecnolĂłgicas. Instituto de Investigaciones BiotecnolĂłgicas "Dr. RaĂșl AlfonsĂ­n" (sede ChascomĂșs); ArgentinaFil: Fernandez Aguero, Maria Jose. Consejo Nacional de Investigaciones CientĂ­ficas y TĂ©cnicas. Centro CientĂ­fico TecnolĂłgico Conicet - La Plata. Instituto de Investigaciones BiotecnolĂłgicas. Instituto de Investigaciones BiotecnolĂłgicas "Dr. RaĂșl AlfonsĂ­n" (sede ChascomĂșs). Universidad Nacional de San MartĂ­n. Instituto de Investigaciones BiotecnolĂłgicas. Instituto de Investigaciones BiotecnolĂłgicas "Dr. RaĂșl AlfonsĂ­n" (sede ChascomĂșs); ArgentinaFil: Buscaglia, Carlos Andres. Consejo Nacional de Investigaciones CientĂ­ficas y TĂ©cnicas. Centro CientĂ­fico TecnolĂłgico Conicet - La Plata. Instituto de Investigaciones BiotecnolĂłgicas. Instituto de Investigaciones BiotecnolĂłgicas "Dr. RaĂșl AlfonsĂ­n" (sede ChascomĂșs). Universidad Nacional de San MartĂ­n. Instituto de Investigaciones BiotecnolĂłgicas. Instituto de Investigaciones BiotecnolĂłgicas "Dr. RaĂșl AlfonsĂ­n" (sede ChascomĂșs); Argentin

    Phylogeography of New Zealand’s coastal benthos

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    During the past 30 years, 42 molecular studies have been undertaken in New Zealand to examine the phylogeography of coastal benthic invertebrates and plants. Here, we identify generalities and/or patterns that have emerged from this research and consider the processes implicated in generating genetic structure within populations. Studies have used various molecular markers and examined taxonomic groups with a range of life histories and dispersal strategies. Genetic disjunctions have been identified at multiple locations, with the most frequently observed division occurring between northern and southern populations at the top of the South Island. Although upwelling has been implicated as a cause of this disjunction, oceanographic evidence is lacking and alternative hypotheses exist. A significant negative correlation between larval duration and genetic differentiation (r2 = 0.39, P < 0.001, n = 29) across all studies suggests that larval duration might be used as a proxy for dispersal potential. However, among taxa with short larval durations (<10 days) there was greater variability in genetic differentiation than among taxa with longer pelagic periods. This variability implies that when larval duration is short, other factors may determine dispersal and connectivity among populations. Although there has been little congruence between the phylogeographic data and recognised biogeographic regions, recent research has resolved population subdivision at finer spatial scales corresponding more closely with existing biogeographic classifications. The use of fast-evolving and ecologically significant molecular markers in hypothesis-driven research could further improve our ability to detect population subdivision and identify the processes structuring marine ecosystems

    Non-equilibrium theory of the allele frequency spectrum

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    A forward diffusion equation describing the evolution of the allele frequency spectrum is presented. The influx of mutations is accounted for by imposing a suitable boundary condition. For a Wright-Fisher diffusion with or without selection and varying population size, the boundary condition is lim⁥x↓0xf(x,t)=Ξρ(t)\lim_{x \downarrow 0} x f(x,t)=\theta \rho(t), where f(⋅,t)f(\cdot,t) is the frequency spectrum of derived alleles at independent loci at time tt and ρ(t)\rho(t) is the relative population size at time tt. When population size and selection intensity are independent of time, the forward equation is equivalent to the backwards diffusion usually used to derive the frequency spectrum, but the forward equation allows computation of the time dependence of the spectrum both before an equilibrium is attained and when population size and selection intensity vary with time. From the diffusion equation, we derive a set of ordinary differential equations for the moments of f(⋅,t)f(\cdot,t) and express the expected spectrum of a finite sample in terms of those moments. We illustrate the use of the forward equation by considering neutral and selected alleles in a highly simplified model of human history. For example, we show that approximately 30% of the expected heterozygosity of neutral loci is attributable to mutations that arose since the onset of population growth in roughly the last 150,000150,000 years.Comment: 24 pages, 7 figures, updated to accomodate referees' suggestions, to appear in Theoretical Population Biolog

    Microevolution of Helicobacter pylori during prolonged infection of single hosts and within families

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    Our understanding of basic evolutionary processes in bacteria is still very limited. For example, multiple recent dating estimates are based on a universal inter-species molecular clock rate, but that rate was calibrated using estimates of geological dates that are no longer accepted. We therefore estimated the short-term rates of mutation and recombination in Helicobacter pylori by sequencing an average of 39,300 bp in 78 gene fragments from 97 isolates. These isolates included 34 pairs of sequential samples, which were sampled at intervals of 0.25 to 10.2 years. They also included single isolates from 29 individuals (average age: 45 years) from 10 families. The accumulation of sequence diversity increased with time of separation in a clock-like manner in the sequential isolates. We used Approximate Bayesian Computation to estimate the rates of mutation, recombination, mean length of recombination tracts, and average diversity in those tracts. The estimates indicate that the short-term mutation rate is 1.4×10−6 (serial isolates) to 4.5×10−6 (family isolates) per nucleotide per year and that three times as many substitutions are introduced by recombination as by mutation. The long-term mutation rate over millennia is 5–17-fold lower, partly due to the removal of non-synonymous mutations due to purifying selection. Comparisons with the recent literature show that short-term mutation rates vary dramatically in different bacterial species and can span a range of several orders of magnitude
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