Search

Transposable elements (TEs) are genomic repeated sequences that display complex evolutionary patterns. They are usually inherited vertically, but can occasionally be transmitted between sexually independent species, through so-called horizontal transposon transfers (HTTs). Recurrent HTTs are supposed to be essential in life cycle of TEs, which are otherwise destined for eventual decay. HTTs also impact the host genome evolution. However, the extent of HTTs in eukaryotes is largely unknown, due to the lack of efficient, statistically supportedmethods that can be applied to multiple species sequence data sets. Here, we developed a new automated method available as a R package "vhica" that discriminates whether a given TE family was vertically or horizontally transferred, and potentially infers donor and receptor species. Themethod is well suited for TE sequences extracted from complete genomes, and applicable tomultiple TEs and species at the same time. We first validated our method using Drosophila TE families with well-known evolutionary histories, displaying both HTTs and vertical transmission. We then tested 26 different lineages of mariner elements recently characterized in 20 Drosophila genomes, and found HTTs in 24 of them. Furthermore, several independent HTT events could often be detected within the same mariner lineage. The VHICA (Vertical and Horizontal Inheritance Consistence Analysis) method thus appears as a valuable tool to analyze the evolutionary history of TEs across a large range of species. [ABSTRACT FROM AUTHOR]

Drosophila yakuba is widespread in Africa. Here we compare the cuticular hydrocarbon (CHC) profiles and mating behavior of mainland (Kounden, Cameroon) and island (Mayotte, Sao-Tome, Bioko) populations. The strains each had different CHC profiles: Bioko and Kounden were the most similar, while Mayotte and Sao-Tome contained significant amounts of 7-heptacosene. The CHC profile of the Sao-Tome population differed the most, with half the 7-tricosene of the other populations and more 7-heptacosene and 7-nonacosene. We also studied the characteristics of the mating behavior of the four strains: copulation duration was similar but latency times were higher in Mayotte and Sao-Tome populations. We found partial reproductive isolation between populations, especially in male-choice experiments with Sao-Tome females. [ABSTRACT FROM AUTHOR]

Even though laboratory evolution experiments have demonstrated genetic variation for learning ability, we know little about the underlying genetic architecture and genetic relationships with other ecologically relevant traits. With a full diallel cross among twelve inbred lines of Drosophila melanogaster originating from a natural population (0.75 < F < 0.93), we investigated the genetic architecture of olfactory learning ability and compared it to that for another behavioral trait (unconditional preference for odors), as well as three traits quantifying the ability to deal with environmental challenges: egg-to-adult survival and developmental rate on a low-quality food, and resistance to a bacterial pathogen. Substantial additive genetic variation was detected for each trait, highlighting their potential to evolve. Genetic effects contributed more than nongenetic parental effects to variation in traits measured at the adult stage: learning, odorant perception, and resistance to infection. In contrast, the two traits quantifying larval tolerance to low-quality food were more strongly affected by parental effects. We found no evidence for genetic correlations between traits, suggesting that these traits could evolve at least to some degree independently of one another. Finally, inbreeding adversely affected all traits. [ABSTRACT FROM AUTHOR]

The impact of transposable elements (TEs) on genome structure, plasticity, and evolution is still not well understood. The recent availability of complete genome sequences makes it possible to get new insights on the evolutionary dynamics of TEs from the phylogenetic analysis of their multiple copies in a wide range of species. However, this source of information is not always fully exploited. Here, we show how the history of transposition activity may be qualitatively and quantitatively reconstructed by considering the distribution of transposition events in the phylogenetic tree, along with the tree topology. Using statistical models developed to infer speciation and extinction rates in species phylogenies, we demonstrate that it is possible to estimate the past transposition rate of a TE family, as well as how this rate varies with time. This methodological framework may not only facilitate the interpretation of genomic data, but also serve as a basis to develop new theoretical and statistical models. [ABSTRACT FROM AUTHOR]

Background: The genetic architecture of a quantitative trait influences the phenotypic response to natural or artificial selection. One of the main objectives of genetic mapping studies is to identify the genetic factors underlying complex traits and understand how they contribute to phenotypic expression. Presently, we are good at identifying and locating individual loci with large effects, but there is a void in describing more complex genetic architectures. Although large networks of connected genes have been reported, there is an almost complete lack of information on how polymorphisms in these networks contribute to phenotypic variation and change. To date, most of our understanding comes from theoretical, model-based studies, and it remains difficult to assess how realistic their conclusions are as they lack empirical support. Results: A previous study provided evidence that nearly half of the difference in eight-week body weight between two divergently selected lines of chickens was a result of four loci organized in a 'radial' network (one central locus interacting with three 'radial' loci that, in turn, only interacted with the central locus). Here, we study the relationship between phenotypic change and genetic polymorphism in this empirically detected network. We use a model-free approach to study, through individual-based simulations, the dynamic properties of this polymorphic and epistatic genetic architecture. The study provides new insights to how epistasis can modify the selection response, buffer and reveal effects of major loci leading to a progressive release of genetic variation. We also illustrate the difficulty of predicting genetic architecture from observed selection response, and discuss mechanisms that might lead to misleading conclusions on underlying genetic architectures from quantitative trait locus (QTL) experiments in selected populations. Conclusion: Considering both molecular (QTL) and phenotypic (selection response) data, as suggested in this work, provides additional insights into the genetic mechanisms involved in the response to selection. Such dissection of genetic architectures and in-depth studies of their ability to contribute to short- or long-term selection response represents an important step towards a better understanding of the genetic bases of complex traits and, consequently, of the evolutionary properties of populations. [ABSTRACT FROM AUTHOR]

Predicting how a genetic change affects a given character is a major challenge in biology, and being able to tackle this problem relies on our ability to develop realistic models of gene networks. However, such models are rarely tractable mathematically. In this paper, we propose a mathematical analysis of the sigmoid variant of the Wagner gene-network model. By considering the simplest case, that is, one unique self-regulating gene, we show that numerical simulations are not the only tool available to study such models: theoretical studies can be done too, by mathematical analysis of discrete dynamical systems. It is first shown that the particular sigmoid function can be theoretically investigated. Secondly, we provide an illustration of how to apply such investigations in the case of the dynamical system representing the one self-regulating gene. In this context, we focused on the composite function f a ( m. x ) where f a is the parametric sigmoid function and m is a scalar not in { 0 , 1 } and we have proven that the number of fixed-point can be deduced theoretically, according to the values of a and m. [ABSTRACT FROM AUTHOR]

Glycolysis and fatty acid (FA) synthesis directs the production of energy-carrying molecules and building blocks necessary to support cell growth, although the absolute requirement of these metabolic pathways must be deeply investigated. Here, we used Drosophila genetics and focus on the TOR (Target of Rapamycin) signaling network that controls cell growth and homeostasis. In mammals, mTOR (mechanistic-TOR) is present in two distinct complexes, mTORC1 and mTORC2; the former directly responds to amino acids and energy levels, whereas the latter sustains insulin-like-peptide (Ilp) response. The TORC1 and Ilp signaling branches can be independently modulated in most Drosophila tissues. We show that TORC1 and Ilp-dependent overgrowth can operate independently in fat cells and that ubiquitous over-activation of TORC1 or Ilp signaling affects basal metabolism, supporting the use of Drosophila as a powerful model to study the link between growth and metabolism. We show that cell-autonomous restriction of glycolysis or FA synthesis in fat cells retrains overgrowth dependent on Ilp signaling but not TORC1 signaling. Additionally, the mutation of FASN (Fatty acid synthase) results in a drop in TORC1 but not Ilp signaling, whereas, at the cell-autonomous level, this mutation affects none of these signals in fat cells. These findings thus reveal differential metabolic sensitivity of TORC1- and Ilp-dependent growth and suggest that cell-autonomous metabolic defects might elicit local compensatory pathways. Conversely, enzyme knockdown in the whole organism results in animal death. Importantly, our study weakens the use of single inhibitors to fight mTOR-related diseases and strengthens the use of drug combination and selective tissue-targeting. [ABSTRACT FROM AUTHOR]

Anticipating the genetic and phenotypic changes induced by natural or artificial selection requires reliable estimates of trait evolvabilities (genetic variances and covariances). However, whether or not multivariate quantitative genetics models are able to predict precisely the evolution of traits of interest, especially fitness-related, life history traits, remains an open empirical question. Here, we assessed to what extent the response to bivariate artificial selection on both body size and maturity in the medaka Oryzias latipes , a model fish species, fits the theoretical predictions. Three lines (Large, Small, and Control lines) were differentially selected for body length at 75 days of age, conditional on maturity. As maturity and body size were phenotypically correlated, this selection procedure generated a bi-dimensional selection pattern on two life history traits. After removal of nonheritable trends and noise with a random effect ("animal") model, the observed selection response did not match the expected bidirectional response. For body size, Large and Control lines responded along selection gradients (larger body size and stasis, respectively), but, surprisingly, the Small did not evolve a smaller body length and remained identical to the Control line throughout the experiment. The magnitude of the empirical response was smaller than the theoretical prediction in both selected directions. For maturity, the response was opposite to the expectation (the Large line evolved late maturity compared to the Control line, while the Small line evolved early maturity, while the opposite pattern was predicted due to the strong positive genetic correlation between both traits). The mismatch between predicted and observed response was substantial and could not be explained by usual sources of uncertainties (including sampling effects, genetic drift, and error in G matrix estimates)., Competing Interests: The authors declare no competing financial and/or nonfinancial interests., (© 2020 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.)

The decomposition of genetic variance into additive, dominance, and epistatic components is a common procedure in quantitative genetics. Yet, the interpretation of this variance partition is not trivial, especially concerning nonadditive components. In this chapter, we compile various uses of variance partitioning from published analyses, new simulations, and theoretical examples. We show ways in which advanced genetic modeling facilitates the analysis of data through variance partitioning, focusing on the natural and orthogonal interactions (NOIA) model. We also discuss how epistasis and epistatic variance may influence the outcome of selection, a topic that is still a matter of debate among quantitative and evolutionary geneticists.