69 resultados para Genetic Algorithms, Adaptation, Internet Computing

em Université de Lausanne, Switzerland


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Heterozygosity-fitness correlations (HFCs) have been used to understand the complex interactions between inbreeding, genetic diversity and evolution. Although frequently reported for decades, evidence for HFCs was often based on underpowered studies or inappropriate methods, and hence their underlying mechanisms are still under debate. Here, we used 6100 genome-wide single nucleotide polymorphisms (SNPs) to test for general and local effect HFCs in maritime pine (Pinus pinaster Ait.), an iconic Mediterranean forest tree. Survival was used as a fitness proxy, and HFCs were assessed at a four-site common garden under contrasting environmental conditions (total of 16 288 trees). We found no significant correlations between genome-wide heterozygosity and fitness at any location, despite variation in inbreeding explaining a substantial proportion of the total variance for survival. However, four SNPs (including two non-synonymous mutations) were involved in significant associations with survival, in particular in the common gardens with higher environmental stress, as shown by a novel heterozygosity-fitness association test at the species-wide level. Fitness effects of SNPs involved in significant HFCs were stable across maritime pine gene pools naturally growing in distinct environments. These results led us to dismiss the general effect hypothesis and suggested a significant role of heterozygosity in specific candidate genes for increasing fitness in maritime pine. Our study highlights the importance of considering the species evolutionary and demographic history and different spatial scales and testing environments when assessing and interpreting HFCs.

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Local adaptation provides an opportunity to study the genetic basis of adaptation and investigate the allelic architecture of adaptive genes. We study delay of germination 1 (DOG1), a gene controlling natural variation in seed dormancy in Arabidopsis thaliana and investigate evolution of dormancy in 41 populations distributed in four regions separated by natural barriers. Using F(ST) and Q(ST) comparisons, we compare variation at DOG1 with neutral markers and quantitative variation in seed dormancy. Patterns of genetic differentiation among populations suggest that the gene DOG1 contributes to local adaptation. Although Q(ST) for seed dormancy is not different from F(ST) for neutral markers, a correlation with variation in summer precipitation supports that seed dormancy is adaptive. We characterize dormancy variation in several F(2) -populations and show that a series of functionally distinct alleles segregate at the DOG1 locus. Theoretical models have shown that the number and effect of alleles segregatin at quantitative trait loci (QTL) have important consequences for adaptation. Our results provide support to models postulating a large number of alleles at quantitative trait loci involved in adaptation.

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The M-Coffee server is a web server that makes it possible to compute multiple sequence alignments (MSAs) by running several MSA methods and combining their output into one single model. This allows the user to simultaneously run all his methods of choice without having to arbitrarily choose one of them. The MSA is delivered along with a local estimation of its consistency with the individual MSAs it was derived from. The computation of the consensus multiple alignment is carried out using a special mode of the T-Coffee package [Notredame, Higgins and Heringa (T-Coffee: a novel method for fast and accurate multiple sequence alignment. J. Mol. Biol. 2000; 302: 205-217); Wallace, O'Sullivan, Higgins and Notredame (M-Coffee: combining multiple sequence alignment methods with T-Coffee. Nucleic Acids Res. 2006; 34: 1692-1699)] Given a set of sequences (DNA or proteins) in FASTA format, M-Coffee delivers a multiple alignment in the most common formats. M-Coffee is a freeware open source package distributed under a GPL license and it is available either as a standalone package or as a web service from www.tcoffee.org.

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Uncovering the genetic basis of phenotypic variation and the population history under which it established is key to understand the trajectories along which local adaptation evolves. Here, we investigated the genetic basis and evolutionary history of a clinal plumage color polymorphism in European barn owls (Tyto alba). Our results suggest that barn owls colonized the Western Palearctic in a ring-like manner around the Mediterranean and meet in secondary contact in Greece. Rufous coloration appears to be linked to a recently evolved nonsynonymous-derived variant of the melanocortin 1 receptor (MC1R) gene, which according to quantitative genetic analyses evolved under local adaptation during or following the colonization of Central Europe. Admixture patterns and linkage disequilibrium between the neutral genetic background and color found exclusively within the secondary contact zone suggest limited introgression at secondary contact. These results from a system reminiscent of ring species provide a striking example of how local adaptation can evolve from derived genetic variation.

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Rubisco is responsible for the fixation of CO2 into organic compounds through photosynthesis and thus has a great agronomic importance. It is well established that this enzyme suffers from a slow catalysis, and its low specificity results into photorespiration, which is considered as an energy waste for the plant. However, natural variations exist, and some Rubisco lineages, such as in C4 plants, exhibit higher catalytic efficiencies coupled to lower specificities. These C4 kinetics could have evolved as an adaptation to the higher CO2 concentration present in C4 photosynthetic cells. In this study, using phylogenetic analyses on a large data set of C3 and C4 monocots, we showed that the rbcL gene, which encodes the large subunit of Rubisco, evolved under positive selection in independent C4 lineages. This confirms that selective pressures on Rubisco have been switched in C4 plants by the high CO2 environment prevailing in their photosynthetic cells. Eight rbcL codons evolving under positive selection in C4 clades were involved in parallel changes among the 23 independent monocot C4 lineages included in this study. These amino acids are potentially responsible for the C4 kinetics, and their identification opens new roads for human-directed Rubisco engineering. The introgression of C4-like high-efficiency Rubisco would strongly enhance C3 crop yields in the future CO2-enriched atmosphere.

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Therapeutic drug monitoring (TDM) aims to optimize treatments by individualizing dosage regimens based on the measurement of blood concentrations. Dosage individualization to maintain concentrations within a target range requires pharmacokinetic and clinical capabilities. Bayesian calculations currently represent the gold standard TDM approach but require computation assistance. In recent decades computer programs have been developed to assist clinicians in this assignment. The aim of this survey was to assess and compare computer tools designed to support TDM clinical activities. The literature and the Internet were searched to identify software. All programs were tested on personal computers. Each program was scored against a standardized grid covering pharmacokinetic relevance, user friendliness, computing aspects, interfacing and storage. A weighting factor was applied to each criterion of the grid to account for its relative importance. To assess the robustness of the software, six representative clinical vignettes were processed through each of them. Altogether, 12 software tools were identified, tested and ranked, representing a comprehensive review of the available software. Numbers of drugs handled by the software vary widely (from two to 180), and eight programs offer users the possibility of adding new drug models based on population pharmacokinetic analyses. Bayesian computation to predict dosage adaptation from blood concentration (a posteriori adjustment) is performed by ten tools, while nine are also able to propose a priori dosage regimens, based only on individual patient covariates such as age, sex and bodyweight. Among those applying Bayesian calculation, MM-USC*PACK© uses the non-parametric approach. The top two programs emerging from this benchmark were MwPharm© and TCIWorks. Most other programs evaluated had good potential while being less sophisticated or less user friendly. Programs vary in complexity and might not fit all healthcare settings. Each software tool must therefore be regarded with respect to the individual needs of hospitals or clinicians. Programs should be easy and fast for routine activities, including for non-experienced users. Computer-assisted TDM is gaining growing interest and should further improve, especially in terms of information system interfacing, user friendliness, data storage capability and report generation.

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Many new gene copies emerged by gene duplication in hominoids, but little is known with respect to their functional evolution. Glutamate dehydrogenase (GLUD) is an enzyme central to the glutamate and energy metabolism of the cell. In addition to the single, GLUD-encoding gene present in all mammals (GLUD1), humans and apes acquired a second GLUD gene (GLUD2) through retroduplication of GLUD1, which codes for an enzyme with unique, potentially brain-adapted properties. Here we show that whereas the GLUD1 parental protein localizes to mitochondria and the cytoplasm, GLUD2 is specifically targeted to mitochondria. Using evolutionary analysis and resurrected ancestral protein variants, we demonstrate that the enhanced mitochondrial targeting specificity of GLUD2 is due to a single positively selected glutamic acid-to-lysine substitution, which was fixed in the N-terminal mitochondrial targeting sequence (MTS) of GLUD2 soon after the duplication event in the hominoid ancestor approximately 18-25 million years ago. This MTS substitution arose in parallel with two crucial adaptive amino acid changes in the enzyme and likely contributed to the functional adaptation of GLUD2 to the glutamate metabolism of the hominoid brain and other tissues. We suggest that rapid, selectively driven subcellular adaptation, as exemplified by GLUD2, represents a common route underlying the emergence of new gene functions.

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Based on ecological and metabolic arguments, some authors predict that adaptation to novel, harsh environments should involve alleles showing negative (diminishing return) epistasis and/or that it should be mediated in part by evolution of maternal effects. Although the first prediction has been supported in microbes, there has been little experimental support for either prediction in multicellular eukaryotes. Here we use a line-cross design to study the genetic architecture of adaptation to chronic larval malnutrition in a population of Drosophila melanogaster that evolved on an extremely nutrient-poor larval food for 84 generations. We assayed three fitness-related traits (developmental rate, adult female weight and egg-to-adult viability) under the malnutrition conditions in 14 crosses between this selected population and a nonadapted control population originally derived from the same base population. All traits showed a pattern of negative epistasis between alleles improving performance under malnutrition. Furthermore, evolutionary changes in maternal traits accounted for half of the 68% increase in viability and for the whole of 8% reduction in adult female body weight in the selected population (relative to unselected controls). These results thus support both of the above predictions and point to the importance of nonadditive effects in adaptive microevolution.

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Both stress during development and response to directional selection were proposed to lead to reduced developmental stability of an organism, commonly measured as fluctuating asymmetry. Here, we investigated the direct physiological (plastic) effect of larval malnutrition and the effect of evolutionary adaptation to this form of stress on developmental stability, measured as fluctuating asymmetry of several wing measurements. The measurements were made on female Drosophila melanogaster from populations which, in the course of 84 generations of experimental evolution, adapted to malnutrition and from non-adapted controls, raised either under standard conditions or under nutritional stress. We detected no changes in the levels of fluctuating asymmetry as either a plastic or an evolutionary response. Thus, neither nutritional stress within lifetime nor directional selection it imposes seems to affect developmental stability in flies.

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The introduction of Next Generation Sequencing (NGS) facilitated the task of localizing DNA variation and identifying the genetic cause of yet unsolved Mendelian disorders. Using Whole Exome Capture method and NGS, we identified the causative genetic aberration responsible for a number of monogenic disorders previously undetermined. Due to the novelty of the NGS method we benchmarked different algorithms to assess their merits and defects. This allowed us to establish a pipeline that we successfully used to pinpoint genes responsible for a form of West's syndrome, a Complex Intellectual Disability syndrome associated with patellar dislocation and celiac disease, and correcting some erroneous molecular diagnosis of Alport's syndrome in a Saudi Arabian family.

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MOTIVATION: Regulatory gene networks contain generic modules such as feedback loops that are essential for the regulation of many biological functions. The study of the stochastic mechanisms of gene regulation is instrumental for the understanding of how cells maintain their expression at levels commensurate with their biological role, as well as to engineer gene expression switches of appropriate behavior. The lack of precise knowledge on the steady-state distribution of gene expression requires the use of Gillespie algorithms and Monte-Carlo approximations. METHODOLOGY: In this study, we provide new exact formulas and efficient numerical algorithms for computing/modeling the steady-state of a class of self-regulated genes, and we use it to model/compute the stochastic expression of a gene of interest in an engineered network introduced in mammalian cells. The behavior of the genetic network is then analyzed experimentally in living cells. RESULTS: Stochastic models often reveal counter-intuitive experimental behaviors, and we find that this genetic architecture displays a unimodal behavior in mammalian cells, which was unexpected given its known bimodal response in unicellular organisms. We provide a molecular rationale for this behavior, and we implement it in the mathematical picture to explain the experimental results obtained from this network.

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Le présent cahier documente les différents programmes informatiques nécessaires à l'adaptation suisse des DRG.

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The European genus Ophrys (Orchidaceae) is famous for its insect-like floral morphology, an adaptation for a pseudocopulatory pollination strategy involving Hymenoptera males. A large number of endemic Ophrys species have recently been described, especially within the Mediterranean Basin, which is one of the major species diversity hotspots. Subtle morphological variation and specific pollinator dependence are the two main perceptible criteria for describing numerous endemic taxa. However, the degree to which endemics differ genetically remains a challenging question. Additionally, knowledge regarding the factors underlying the emergence of such endemic entities is limited. To achieve new insights regarding speciation processes in Ophrys, we have investigated species boundaries in the Fly Orchid group (Ophrys insectifera sensu lato) by examining morphological, ecological and genetic evidence. Classically, authors have recognized one widespread taxon (O. insectifera) and two endemics (O. aymoninii from France and O. subinsectifera from Spain). Our research has identified clear morphological and ecological factors segregating among these taxa; however, genetic differences were more ambiguous. Insights from cpDNA sequencing and amplified fragment length polymorphisms genotyping indicated a recent diversification in the three extant Fly Orchid species, which may have been further obscured by active migration and admixture across the European continent. Our genetic results still indicate weak but noticeable phylogeographic clustering that partially correlates with the described species. Particularly, we report several isolated haplotypes and genetic clusters in central and southeastern Europe. With regard to the morphological, ecological and genetic aspects, we discuss the endemism status within the Fly Orchid group from evolutionary, taxonomical and conservation perspectives.

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Major climatic and geological events but also population history (secondary contacts) have generated cycles of population isolation and connection of long and short periods. Recent empirical and theoretical studies suggest that fast evolutionary processes might be triggered by such events, as commonly illustrated in ecology by the adaptive radiation of cichlid fishes (isolation and reconnection of lakes and watersheds) and in epidemiology by the fast adaptation of the influenza virus (isolation and reconnection in hosts). We test whether cyclic population isolation and connection provide the raw material (standing genetic variation) for species evolution and diversification. Our analytical results demonstrate that population isolation and connection can provide, to populations, a high excess of genetic diversity compared with what is expected at equilibrium. This excess is either cyclic (high allele turnover) or cumulates with time depending on the duration of the isolation and the connection periods and the mutation rate. We show that diversification rates of animal clades are associated with specific periods of climatic cycles in the Quaternary. We finally discuss the importance of our results for macroevolutionary patterns and for the inference of population history from genomic data.

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Species range shifts in response to climate and land use change are commonly forecasted with species distribution models based on species occurrence or abundance data. Although appealing, these models ignore the genetic structure of species, and the fact that different populations might respond in different ways because of adaptation to their environment. Here, we introduced ancestry distribution models, that is, statistical models of the spatial distribution of ancestry proportions, for forecasting intra-specific changes based on genetic admixture instead of species occurrence data. Using multi-locus genotypes and extensive geographic coverage of distribution data across the European Alps, we applied this approach to 20 alpine plant species considering a global increase in temperature from 0.25 to 4 °C. We forecasted the magnitudes of displacement of contact zones between plant populations potentially adapted to warmer environments and other populations. While a global trend of movement in a north-east direction was predicted, the magnitude of displacement was species-specific. For a temperature increase of 2 °C, contact zones were predicted to move by 92 km on average (minimum of 5 km, maximum of 212 km) and by 188 km for an increase of 4 °C (minimum of 11 km, maximum of 393 km). Intra-specific turnover-measuring the extent of change in global population genetic structure-was generally found to be moderate for 2 °C of temperature warming. For 4 °C of warming, however, the models indicated substantial intra-specific turnover for ten species. These results illustrate that, in spite of unavoidable simplifications, ancestry distribution models open new perspectives to forecast population genetic changes within species and complement more traditional distribution-based approaches.