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      Timing the origin of human malarias: the lemur puzzle

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          Abstract

          Background

          Timing the origin of human malarias has been a focus of great interest. Previous studies on the mitochondrial genome concluded that Plasmodium in primates, including those parasitic to humans, radiated relatively recently during a process where host switches were common. Those investigations, however, assumed constant rate of evolution and tightly bound (fixed) calibration points based on host fossils or host distribution. We investigate the effect of such assumptions using different molecular dating methods. We include parasites from Lemuroidea since their distribution provides an external validation to time estimates allowing us to disregard scenarios that cannot explain their introduction in Madagascar.

          Results

          We reject the assumption that the Plasmodium mitochondrial genome, as a unit or each gene separately, evolves at a constant rate. Our analyses show that Lemuroidea parasites are a monophyletic group that shares a common ancestor with all Catarrhini malarias except those related to P. falciparum. However, we found no evidence that this group of parasites branched with their hosts early in the evolution of primates. We applied relaxed clock methods and different calibrations points to explore the origin of primate malarias including those found in African apes. We showed that previous studies likely underestimated the origin of malarial parasites in primates.

          Conclusions

          The use of fossils from the host as absolute calibration and the assumption of a strict clock likely underestimate time when performing molecular dating analyses on malarial parasites. Indeed, by exploring different calibration points, we found that the time for the radiation of primate parasites may have taken place in the Eocene, a time consistent with the radiation of African anthropoids. The radiation of the four human parasite lineages was part of such events. The time frame estimated in this investigation, together with our phylogenetic analyses, made plausible a scenario where gorillas and humans acquired malaria from a Pan lineage.

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          Most cited references33

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          TimeTree: a public knowledge-base of divergence times among organisms.

          Biologists and other scientists routinely need to know times of divergence between species and to construct phylogenies calibrated to time (timetrees). Published studies reporting time estimates from molecular data have been increasing rapidly, but the data have been largely inaccessible to the greater community of scientists because of their complexity. TimeTree brings these data together in a consistent format and uses a hierarchical structure, corresponding to the tree of life, to maximize their utility. Results are presented and summarized, allowing users to quickly determine the range and robustness of time estimates and the degree of consensus from the published literature. TimeTree is available at http://www.timetree.net
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            Paleontological evidence to date the tree of life.

            The role of fossils in dating the tree of life has been misunderstood. Fossils can provide good "minimum" age estimates for branches in the tree, but "maximum" constraints on those ages are poorer. Current debates about which are the "best" fossil dates for calibration move to consideration of the most appropriate constraints on the ages of tree nodes. Because fossil-based dates are constraints, and because molecular evolution is not perfectly clock-like, analysts should use more rather than fewer dates, but there has to be a balance between many genes and few dates versus many dates and few genes. We provide "hard" minimum and "soft" maximum age constraints for 30 divergences among key genome model organisms; these should contribute to better understanding of the dating of the animal tree of life.
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              Divergence time and evolutionary rate estimation with multilocus data.

              Bayesian methods for estimating evolutionary divergence times are extended to multigene data sets, and a technique is described for detecting correlated changes in evolutionary rates among genes. Simulations are employed to explore the effect of multigene data on divergence time estimation, and the methodology is illustrated with a previously published data set representing diverse plant taxa. The fact that evolutionary rates and times are confounded when sequence data are compared is emphasized and the importance of fossil information for disentangling rates and times is stressed.
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                Author and article information

                Journal
                BMC Evol Biol
                BMC Evolutionary Biology
                BioMed Central
                1471-2148
                2011
                12 October 2011
                : 11
                : 299
                Affiliations
                [1 ]Center for Evolutionary Medicine and Informatics, The Biodesign Institute, Arizona State University, Tempe, Arizona, USA
                [2 ]St. Louis Zoo, St. Louis, Missouri, USA
                [3 ]Department of Genetics, Stanford University School of Medicine, Stanford, California, USA
                [4 ]Duke Lemur Center, Duke University, Durham, North Carolina, USA
                [5 ]Parasitologie comparée et modèles expérimentaux, Muséum National d'Histoire Naturelle, Paris, France
                [6 ]Département de Biologie Animale, Faculté des Sciences, Université D'Antananarivo, Antananarivo 101, Madagascar
                [7 ]Université Pierre & Marie Curie, Faculté de Médecine Pitié-Salpêtrière, Paris, France
                [8 ]National Primate Research Center, University of Washington, Seattle, WA, USA
                [9 ]School of Life Sciences, Arizona State University, Tempe, Arizona, USA
                Article
                1471-2148-11-299
                10.1186/1471-2148-11-299
                3228831
                21992100
                4a9e05d3-78e7-4ae9-8ba6-49b842c859bb
                Copyright ©2011 Pacheco et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 7 July 2011
                : 12 October 2011
                Categories
                Research Article

                Evolutionary Biology
                Evolutionary Biology

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