Gene flow and gene flux shape evolutionary patterns of variation in Drosophila subobscura

Gene flow (defined as allele exchange between populations) and gene flux (defined as allele exchange during meiosis in heterokaryotypic females) are important factors decreasing genetic differentiation between populations and inversions. Many chromosomal inversions are under strong selection and their role in recombination reduction enhances the maintenance of their genetic distinctness. Here we analyze levels and patterns of nucleotide diversity, selection and demographic history, using 37 individuals of Drosophila subobscura from Mount Parnes (Greece) and Barcelona (Spain). Our sampling focused on two frequent O-chromosome arrangements that differ by two overlapping inversions (OST and O3+4), which are differentially adapted to the environment as observed by their opposing latitudinal clines in inversion frequencies. The six analyzed genes (Pif1A, Abi, Sqd, Yrt, Atpa and Fmr1) were selected for their location across the O-chromosome and their implication in thermal adaptation. Despite the extensive gene flux detected outside the inverted region, significant genetic differentiation between both arrangements was found inside it. However, high levels of gene flow were detected for all six genes when comparing the same arrangement among populations. These results suggest that the adaptive value of inversions is maintained, regardless of the lack of genetic differentiation within arrangements from different populations, and thus favors the Local Adaptation hypothesis over the Coadapted Genome hypothesis as the basis of the selection acting on inversions in these populations.

Evolutionary Games in Networked Populations: Models and Experiments

Cooperation and coordination are desirable behaviors that are fundamental for the harmonious development of society. People need to rely on cooperation with other individuals in many aspects of everyday life, such as teamwork and economic exchange in anonymous markets. However, cooperation may easily fall prey to exploitation by selfish individuals who only care about short- term gain. For cooperation to evolve, specific conditions and mechanisms are required, such as kinship, direct and indirect reciprocity through repeated interactions, or external interventions such as punishment. In this dissertation we investigate the effect of the network structure of the population on the evolution of cooperation and coordination. We consider several kinds of static and dynamical network topologies, such as Baraba´si-Albert, social network models and spatial networks. We perform numerical simulations and laboratory experiments using the Prisoner's Dilemma and co- ordination games in order to contrast human behavior with theoretical results. We show by numerical simulations that even a moderate amount of random noise on the Baraba´si-Albert scale-free network links causes a significant loss of cooperation, to the point that cooperation almost vanishes altogether in the Prisoner's Dilemma when the noise rate is high enough. Moreover, when we consider fixed social-like networks we find that current models of social networks may allow cooperation to emerge and to be robust at least as much as in scale-free networks. In the framework of spatial networks, we investigate whether cooperation can evolve and be stable when agents move randomly or performing Le´vy flights in a continuous space. We also consider discrete space adopting purposeful mobility and binary birth-death process to dis- cover emergent cooperative patterns. The fundamental result is that cooperation may be enhanced when this migration is opportunistic or even when agents follow very simple heuristics. In the experimental laboratory, we investigate the issue of social coordination between indi- viduals located on networks of contacts. In contrast to simulations, we find that human players dynamics do not converge to the efficient outcome more often in a social-like network than in a random network. In another experiment, we study the behavior of people who play a pure co- ordination game in a spatial environment in which they can move around and when changing convention is costly. We find that each convention forms homogeneous clusters and is adopted by approximately half of the individuals. When we provide them with global information, i.e., the number of subjects currently adopting one of the conventions, global consensus is reached in most, but not all, cases. Our results allow us to extract the heuristics used by the participants and to build a numerical simulation model that agrees very well with the experiments. Our findings have important implications for policymakers intending to promote specific, desired behaviors in a mobile population. Furthermore, we carry out an experiment with human subjects playing the Prisoner's Dilemma game in a diluted grid where people are able to move around. In contrast to previous results on purposeful rewiring in relational networks, we find no noticeable effect of mobility in space on the level of cooperation. Clusters of cooperators form momentarily but in a few rounds they dissolve as cooperators at the boundaries stop tolerating being cheated upon. Our results highlight the difficulties that mobile agents have to establish a cooperative environment in a spatial setting without a device such as reputation or the possibility of retaliation. i.e. punishment. Finally, we test experimentally the evolution of cooperation in social networks taking into ac- count a setting where we allow people to make or break links at their will. In this work we give particular attention to whether information on an individual's actions is freely available to poten- tial partners or not. Studying the role of information is relevant as information on other people's actions is often not available for free: a recruiting firm may need to call a job candidate's refer- ences, a bank may need to find out about the credit history of a new client, etc. We find that people cooperate almost fully when information on their actions is freely available to their potential part- ners. Cooperation is less likely, however, if people have to pay about half of what they gain from cooperating with a cooperator. Cooperation declines even further if people have to pay a cost that is almost equivalent to the gain from cooperating with a cooperator. Thus, costly information on potential neighbors' actions can undermine the incentive to cooperate in dynamical networks.

Identification of potential HIV restriction factors by combining evolutionary genomic signatures with functional analyses.

BACKGROUND: Known antiretroviral restriction factors are encoded by genes that are under positive selection pressure, induced during HIV-1 infection, up-regulated by interferons, and/or interact with viral proteins. To identify potential novel restriction factors, we performed genome-wide scans for human genes sharing molecular and evolutionary signatures of known restriction factors and tested the anti-HIV-1 activity of the most promising candidates. RESULTS: Our analyses identified 30 human genes that share characteristics of known restriction factors. Functional analyses of 27 of these candidates showed that over-expression of a strikingly high proportion of them significantly inhibited HIV-1 without causing cytotoxic effects. Five factors (APOL1, APOL6, CD164, TNFRSF10A, TNFRSF10D) suppressed infectious HIV-1 production in transfected 293T cells by >90% and six additional candidates (FCGR3A, CD3E, OAS1, GBP5, SPN, IFI16) achieved this when the virus was lacking intact accessory vpr, vpu and nef genes. Unexpectedly, over-expression of two factors (IL1A, SP110) significantly increased infectious HIV-1 production. Mechanistic studies suggest that the newly identified potential restriction factors act at different steps of the viral replication cycle, including proviral transcription and production of viral proteins. Finally, we confirmed that mRNA expression of most of these candidate restriction factors in primary CD4+ T cells is significantly increased by type I interferons. CONCLUSIONS: A limited number of human genes share multiple characteristics of genes encoding for known restriction factors. Most of them display anti-retroviral activity in transient transfection assays and are expressed in primary CD4+ T cells.

Evolutionary steps in the reproductive biology of Annonaceae

Flowers of Annonaceae are characterized by fleshy petals, many stamens with hard connective shields and numerous carpels with sessile stigmas often covered by sticky secretions. The petals of many representatives during anthesis form a closed pollination chamber. Protogynous dichogamy with strong scent emissions especially during the pistillate stage is a character of nearly all species. Scent emissions can be enhanced by thermogenesis. The prevailing reproductive system in the family seems to be self-compatibility. The basal genus Anaxagorea besides exhibiting several ancestral morphological characters has also many characters which reappear in other genera. Strong fruit-like scents consisting of fruit-esters and alcohols mainly attract small fruit-beetles (genus Colopterus, Nitidulidae) as pollinators, as well as several other beetles (Curculionidae, Chrysomelidae) and fruit-flies (Drosophilidae), which themselves gnaw on the thick petals or their larvae are petal or ovule predators. The flowers and the thick petals are thus a floral brood substrate for the visitors and the thick petals of Anaxagorea have to be interpreted as an antipredator structure. Another function of the closed thick petals is the production of heat by accumulated starch, which enhances scent emission and provides a warm shelter for the attracted beetles. Insight into floral characters and floral ecology of Anaxagorea, the sister group of the rest of the Annonaceae, is particularly important for understanding functional evolution and diversification of the family as a whole. As beetle pollination (cantharophily) is plesiomorphic in Anaxagorea and in Annonaceae, characters associated with beetle pollination appear imprinted in members of the whole family. Pollination by beetles (cantharophily) is the predominant mode of the majority of species worldwide. Examples are given of diurnal representatives (e.g., Guatteria, Duguetia, Annona) which function on the basis of fruit-imitating flowers attracting mainly fruit-inhabiting nitidulid beetles, as well as nocturnal species (e.g., large-flowered Annona and Duguetia species), which additionally to most of the diurnal species exhibit strong flower warming and provide very thick petal tissues for the voracious dynastid scarab beetles (Dynastinae, Scarabaeidae). Further examples will show that a few Annonaceae have adapted in their pollination also to thrips, flies, cockroaches and even bees. Although this non-beetle pollinated species have adapted in flower structure and scent compounds to their respective insects, they still retain some of the specialized cantharophilous characters of their ancestors.

Pom1 gradient buffering through intermolecular auto-phosphorylation.

Concentration gradients provide spatial information for tissue patterning and cell organization, and their robustness under natural fluctuations is an evolutionary advantage. In rod-shaped Schizosaccharomyces pombe cells, the DYRK-family kinase Pom1 gradients control cell division timing and placement. Upon dephosphorylation by a Tea4-phosphatase complex, Pom1 associates with the plasma membrane at cell poles, where it diffuses and detaches upon auto-phosphorylation. Here, we demonstrate that Pom1 auto-phosphorylates intermolecularly, both in vitro and in vivo, which confers robustness to the gradient. Quantitative imaging reveals this robustness through two system's properties: The Pom1 gradient amplitude is inversely correlated with its decay length and is buffered against fluctuations in Tea4 levels. A theoretical model of Pom1 gradient formation through intermolecular auto-phosphorylation predicts both properties qualitatively and quantitatively. This provides a telling example where gradient robustness through super-linear decay, a principle hypothesized a decade ago, is achieved through autocatalysis. Concentration-dependent autocatalysis may be a widely used simple feedback to buffer biological activities.

Evolutionary patchwork of an insecticidal toxin shared between plant-associated pseudomonads and the insect pathogens Photorhabdus and Xenorhabdus.

BACKGROUND: Root-colonizing fluorescent pseudomonads are known for their excellent abilities to protect plants against soil-borne fungal pathogens. Some of these bacteria produce an insecticidal toxin (Fit) suggesting that they may exploit insect hosts as a secondary niche. However, the ecological relevance of insect toxicity and the mechanisms driving the evolution of toxin production remain puzzling. RESULTS: Screening a large collection of plant-associated pseudomonads for insecticidal activity and presence of the Fit toxin revealed that Fit is highly indicative of insecticidal activity and predicts that Pseudomonas protegens and P. chlororaphis are exclusive Fit producers. A comparative evolutionary analysis of Fit toxin-producing Pseudomonas including the insect-pathogenic bacteria Photorhabdus and Xenorhadus, which produce the Fit related Mcf toxin, showed that fit genes are part of a dynamic genomic region with substantial presence/absence polymorphism and local variation in GC base composition. The patchy distribution and phylogenetic incongruence of fit genes indicate that the Fit cluster evolved via horizontal transfer, followed by functional integration of vertically transmitted genes, generating a unique Pseudomonas-specific insect toxin cluster. CONCLUSIONS: Our findings suggest that multiple independent evolutionary events led to formation of at least three versions of the Mcf/Fit toxin highlighting the dynamic nature of insect toxin evolution.

Inference of Evolutionary Forces Acting on Human Biological Pathways.

Because natural selection is likely to act on multiple genes underlying a given phenotypic trait, we study here the potential effect of ongoing and past selection on the genetic diversity of human biological pathways. We first show that genes included in gene sets are generally under stronger selective constraints than other genes and that their evolutionary response is correlated. We then introduce a new procedure to detect selection at the pathway level based on a decomposition of the classical McDonald-Kreitman test extended to multiple genes. This new test, called 2DNS, detects outlier gene sets and takes into account past demographic effects and evolutionary constraints specific to gene sets. Selective forces acting on gene sets can be easily identified by a mere visual inspection of the position of the gene sets relative to their two-dimensional null distribution. We thus find several outlier gene sets that show signals of positive, balancing, or purifying selection but also others showing an ancient relaxation of selective constraints. The principle of the 2DNS test can also be applied to other genomic contrasts. For instance, the comparison of patterns of polymorphisms private to African and non-African populations reveals that most pathways show a higher proportion of nonsynonymous mutations in non-Africans than in Africans, potentially due to different demographic histories and selective pressures.

Computational toolbox towards evolutionary domain mapping of membrane proteins

Membrane proteins account for about 20% to 30% of all proteins encoded in a typical genome. They play central roles in multiple cellular processes mediating the interaction of the cell with its surrounding. Over 60% of all drug targets contain a membrane domain. The experimental difficulties of obtaining a crystal structural severely limits our ability or understanding of membrane protein function. Computational evolutionary studies of proteins are crucial for the prediction of 3D structures. In this project, we construct a tool able to quantify the evolutionary positive selective pressure on each residue of membrane proteins through maximum likelihood phylogeny reconstruction. The conservation plot combined with a structural homology model is also a potent tool to predict those residues that have essentials roles in the structure and function of a membrane protein and can be very useful in the design of validation experiments.

Evolutionary dynamics of collective action in spatially structured populations.

Many models proposed to study the evolution of collective action rely on a formalism that represents social interactions as n-player games between individuals adopting discrete actions such as cooperate and defect. Despite the importance of spatial structure in biological collective action, the analysis of n-player games games in spatially structured populations has so far proved elusive. We address this problem by considering mixed strategies and by integrating discrete-action n-player games into the direct fitness approach of social evolution theory. This allows to conveniently identify convergence stable strategies and to capture the effect of population structure by a single structure coefficient, namely, the pairwise (scaled) relatedness among interacting individuals. As an application, we use our mathematical framework to investigate collective action problems associated with the provision of three different kinds of collective goods, paradigmatic of a vast array of helping traits in nature: "public goods" (both providers and shirkers can use the good, e.g., alarm calls), "club goods" (only providers can use the good, e.g., participation in collective hunting), and "charity goods" (only shirkers can use the good, e.g., altruistic sacrifice). We show that relatedness promotes the evolution of collective action in different ways depending on the kind of collective good and its economies of scale. Our findings highlight the importance of explicitly accounting for relatedness, the kind of collective good, and the economies of scale in theoretical and empirical studies of the evolution of collective action.

Decreasing Proportion of Recent Infections among Newly Diagnosed HIV-1 Cases in Switzerland, 2008 to 2013 Based on Line-Immunoassay-Based Algorithms.

BACKGROUND: HIV surveillance requires monitoring of new HIV diagnoses and differentiation of incident and older infections. In 2008, Switzerland implemented a system for monitoring incident HIV infections based on the results of a line immunoassay (Inno-Lia) mandatorily conducted for HIV confirmation and type differentiation (HIV-1, HIV-2) of all newly diagnosed patients. Based on this system, we assessed the proportion of incident HIV infection among newly diagnosed cases in Switzerland during 2008-2013. METHODS AND RESULTS: Inno-Lia antibody reaction patterns recorded in anonymous HIV notifications to the federal health authority were classified by 10 published algorithms into incident (up to 12 months) or older infections. Utilizing these data, annual incident infection estimates were obtained in two ways, (i) based on the diagnostic performance of the algorithms and utilizing the relationship 'incident = true incident + false incident', (ii) based on the window-periods of the algorithms and utilizing the relationship 'Prevalence = Incidence x Duration'. From 2008-2013, 3'851 HIV notifications were received. Adult HIV-1 infections amounted to 3'809 cases, and 3'636 of them (95.5%) contained Inno-Lia data. Incident infection totals calculated were similar for the performance- and window-based methods, amounting on average to 1'755 (95% confidence interval, 1588-1923) and 1'790 cases (95% CI, 1679-1900), respectively. More than half of these were among men who had sex with men. Both methods showed a continuous decline of annual incident infections 2008-2013, totaling -59.5% and -50.2%, respectively. The decline of incident infections continued even in 2012, when a 15% increase in HIV notifications had been observed. This increase was entirely due to older infections. Overall declines 2008-2013 were of similar extent among the major transmission groups. CONCLUSIONS: Inno-Lia based incident HIV-1 infection surveillance proved useful and reliable. It represents a free, additional public health benefit of the use of this relatively costly test for HIV confirmation and type differentiation.

False normal Lung Clearance Index in infants with cystic fibrosis due to software algorithms.

BACKGROUND: Lung clearance index (LCI), a marker of ventilation inhomogeneity, is elevated early in children with cystic fibrosis (CF). However, in infants with CF, LCI values are found to be normal, although structural lung abnormalities are often detectable. We hypothesized that this discrepancy is due to inadequate algorithms of the available software package. AIM: Our aim was to challenge the validity of these software algorithms. METHODS: We compared multiple breath washout (MBW) results of current software algorithms (automatic modus) to refined algorithms (manual modus) in 17 asymptomatic infants with CF, and 24 matched healthy term-born infants. The main difference between these two analysis methods lies in the calculation of the molar mass differences that the system uses to define the completion of the measurement. RESULTS: In infants with CF the refined manual modus revealed clearly elevated LCI above 9 in 8 out of 35 measurements (23%), all showing LCI values below 8.3 using the automatic modus (paired t-test comparing the means, P < 0.001). Healthy infants showed normal LCI values using both analysis methods (n = 47, paired t-test, P = 0.79). The most relevant reason for false normal LCI values in infants with CF using the automatic modus was the incorrect recognition of the end-of-test too early during the washout. CONCLUSION: We recommend the use of the manual modus for the analysis of MBW outcomes in infants in order to obtain more accurate results. This will allow appropriate use of infant lung function results for clinical and scientific purposes. Pediatr Pulmonol. 2015; 50:970-977. © 2015 Wiley Periodicals, Inc.

Comment on 'The Molecular Evolutionary Patterns of the Insulin/FOXO Signaling Pathway'

Letter to the Editor on Wang M, Wang Q, Wang Z, Zhang X, Pan Y. The molecular evolutionary patterns of the insulin/FOXO signaling pathway

Moths behaving like butterflies. Evolutionary loss of long range attractant pheromones in Castniid Moths: A Paysandisia archon model

Background: In the course of evolution butterflies and moths developed two different reproductive behaviors. Whereas butterflies rely on visual stimuli for mate location, moths use the"female calling plus male seduction" system, in which females release long-range sex pheromones to attract conspecific males. There are few exceptions from this pattern but in all cases known female moths possess sex pheromone glands which apparently have been lost in female butterflies. In the day-flying moth family Castniidae ("butterfly-moths"), which includes some important crop pests, no pheromones have been found so far. Methodology/Principal Findings: Using a multidisciplinary approach we described the steps involved in the courtship of P. archon, showing that visual cues are the only ones used for mate location; showed that the morphology and fine structure of the antennae of this moth are strikingly similar to those of butterflies, with male sensilla apparently not suited to detect female-released long range pheromones; showed that its females lack pheromone-producing glands, and identified three compounds as putative male sex pheromone (MSP) components of P. archon, released from the proximal halves of male forewings and hindwings. Conclusions/Significance: This study provides evidence for the first time in Lepidoptera that females of a moth do not produce any pheromone to attract males, and that mate location is achieved only visually by patrolling males, which may release a pheromone at short distance, putatively a mixture of Z,E-farnesal, E,E-farnesal, and (E,Z)-2,13-octadecadienol. The outlined behavior, long thought to be unique to butterflies, is likely to be widespread in Castniidae implying a novel, unparalleled butterfly-like reproductive behavior in moths. This will also have practical implications in applied entomology since it signifies that the monitoring/control of castniid pests should not be based on the use of female-produced pheromones, as it is usually done in many moths.

What can ecosystems learn? Expanding evolutionary ecology with learning theory.

BACKGROUND: The structure and organisation of ecological interactions within an ecosystem is modified by the evolution and coevolution of the individual species it contains. Understanding how historical conditions have shaped this architecture is vital for understanding system responses to change at scales from the microbial upwards. However, in the absence of a group selection process, the collective behaviours and ecosystem functions exhibited by the whole community cannot be organised or adapted in a Darwinian sense. A long-standing open question thus persists: Are there alternative organising principles that enable us to understand and predict how the coevolution of the component species creates and maintains complex collective behaviours exhibited by the ecosystem as a whole? RESULTS: Here we answer this question by incorporating principles from connectionist learning, a previously unrelated discipline already using well-developed theories on how emergent behaviours arise in simple networks. Specifically, we show conditions where natural selection on ecological interactions is functionally equivalent to a simple type of connectionist learning, 'unsupervised learning', well-known in neural-network models of cognitive systems to produce many non-trivial collective behaviours. Accordingly, we find that a community can self-organise in a well-defined and non-trivial sense without selection at the community level; its organisation can be conditioned by past experience in the same sense as connectionist learning models habituate to stimuli. This conditioning drives the community to form a distributed ecological memory of multiple past states, causing the community to: a) converge to these states from any random initial composition; b) accurately restore historical compositions from small fragments; c) recover a state composition following disturbance; and d) to correctly classify ambiguous initial compositions according to their similarity to learned compositions. We examine how the formation of alternative stable states alters the community's response to changing environmental forcing, and we identify conditions under which the ecosystem exhibits hysteresis with potential for catastrophic regime shifts. CONCLUSIONS: This work highlights the potential of connectionist theory to expand our understanding of evo-eco dynamics and collective ecological behaviours. Within this framework we find that, despite not being a Darwinian unit, ecological communities can behave like connectionist learning systems, creating internal conditions that habituate to past environmental conditions and actively recalling those conditions. REVIEWERS: This article was reviewed by Prof. Ricard V Solé, Universitat Pompeu Fabra, Barcelona and Prof. Rob Knight, University of Colorado, Boulder.

Co-evolutionary dynamics between public good producers and cheats in the bacterium Pseudomonas aeruginosa.

The production of beneficial public goods is common in the microbial world, and so is cheating - the exploitation of public goods by nonproducing mutants. Here, we examine co-evolutionary dynamics between cooperators and cheats and ask whether cooperators can evolve strategies to reduce the burden of exploitation, and whether cheats in turn can improve their exploitation abilities. We evolved cooperators of the bacterium Pseudomonas aeruginosa, producing the shareable iron-scavenging siderophore pyoverdine, together with cheats, defective in pyoverdine production but proficient in uptake. We found that cooperators managed to co-exist with cheats in 56% of all replicates over approximately 150 generations of experimental evolution. Growth and competition assays revealed that co-existence was fostered by a combination of general adaptions to the media and specific adaptions to the co-evolving opponent. Phenotypic screening and whole-genome resequencing of evolved clones confirmed this pattern, and suggest that cooperators became less exploitable by cheats because they significantly reduced their pyoverdine investment. Cheats, meanwhile, improved exploitation efficiency through mutations blocking the costly pyoverdine-signalling pathway. Moreover, cooperators and cheats evolved reduced motility, a pattern that likely represents adaptation to laboratory conditions, but at the same time also affects social interactions by reducing strain mixing and pyoverdine sharing. Overall, we observed parallel evolution, where co-existence of cooperators and cheats was enabled by a combination of adaptations to the abiotic and social environment and their interactions.