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The optimal probability and distance of dispersal largely depend on the risk to end up in unsuitable habitat. This risk is highest close to the habitat’s edge and consequently, optimal dispersal probability and distance should decline towards the habitat’s border. This selection should lead to the emergence of spatial gradients in dispersal strategies. However, gene flow caused by dispersal itself is counteracting local adaptation. Using an individual based model we investigate the evolution of local adaptations of dispersal probability and distance within a single, circular, habitat patch. We compare evolved dispersal probabilities and distances for six different dispersal kernels (two negative exponential kernels, two skewed kernels, nearest neighbour dispersal and global dispersal) in patches of different size. For all kernels a positive correlation between patch size and dispersal probability emerges. However, a minimum patch size is necessary to allow for local adaptation of dispersal strategies within patches. Beyond this minimum patch area the difference in mean dispersal distance between center and edge increases linearly with patch radius, but the intensity of local adaptation depends on the dispersal kernel. Except for global and nearest neighbour dispersal, the evolved spatial pattern are qualitatively similar for both, mean dispersal probability and distance. We conclude, that inspite of the gene-flow originating from dispersal local adaptation of dispersal strategies is possible if a habitat is of sufficient size. This presumably holds for any realistic type of dispersal kernel.
Background: According to the classical model of Macevicz and Oster, annual eusocial insects should show a clear dichotomous "bang-bang" strategy of resource allocation; colony fitness is maximised when a period of pure colony growth (exclusive production of workers) is followed by a single reproductive period characterised by the exclusive production of sexuals. However, in several species graded investment strategies with a simultaneous production of workers and sexuals have been observed. Such deviations from the "bang-bang" strategy are usually interpreted as an adaptive (bet-hedging) response to environmental fluctuations such as variation in season length or food availability. To generate predictions about the optimal investment pattern of insect colonies in fluctuating environments, we slightly modified Macevicz and Oster's classical model of annual colony dynamics and used a dynamic programming approach nested into a recurrence procedure for the solution of the stochastic optimal control problem. Results: 1) The optimal switching time between pure colony growth and the exclusive production of sexuals decreases with increasing environmental variance. 2) Yet, for reasonable levels of environmental fluctuations no deviation from the typical bang-bang strategy is predicted. 3) Model calculations for the halictid bee Lasioglossum malachurum reveal that bet-hedging is not likely to be the reason for the graded allocation into sexuals versus workers observed in this species. 4) When environmental variance reaches a critical level our model predicts an abrupt change from dichotomous behaviour to graded allocation strategies, but the transition between colony growth and production of sexuals is not necessarily monotonic. Both, the critical level of environmental variance as well as the characteristic pattern of resource allocation strongly depend on the type of function used to describe environmental fluctuations. Conclusion: Up to now bet-hedging as an evolutionary response to variation in season length has been the main argument to explain field observations of graded resource allocation in annual eusocial insect species. However, our model shows that the effect of moderate fluctuations of environmental conditions does not select for deviation from the classical bang-bang strategy and that the evolution of graded allocation strategies can be triggered only by extreme fluctuations. Detailed quantitative observations on resource allocation in eusocial insects are needed to analyse the relevance of alternative explanations, e.g. logistic colony growth or reproductive conflict between queen and workers, for the evolution of graded allocation strategies.
We present the results of individual-based simulation experiments on the evolution of dispersal rates of organisms living in metapopulations. We find conflicting results regarding the relationship between local extinction rate and evolutionarily stable (ES) dispersal rate depending on which principal mechanism causes extinction: if extinction is caused by environmental catastrophes eradicating local populations, we observe a positive correlation between extinction and ES dispersal rate; if extinction is a consequence of stochastic local dynamics and environmental fluctuations, the correlation becomes ambiguous; and in cases where extinction is caused by dispersal mortality, a negative correlation between local extinction rate and ES dispersal rate emerges. We conclude that extinction rate, which both affects and is affected by dispersal rates, is not an ideal predictor for optimal dispersal rates.
The human genome has been sequenced since 2001. Most proteins have been characterized now and with everyday more bioinformatical predictions are experimentally verified. A project is underway to sequence thousand humans. But still, little is known about the evolution of the human proteome itself. Domains and their combinations are analysed in detail but not all of the human domain architectures at once. Like no one before, we have large datasets of high quality human protein-protein-protein interactions and complexes available which allow us to characterize the human proteome with unmatched accuracy. Advanced clustering algorithms and computing power enable us to gain new information about protein interactions without touching a pipette. In this work, the human proteome is analysed at three different levels. First, the origin of the different types of proteins was analysed based on their domain architectures. The second part focuses on the protein-protein interactions. Finally, in the third part, proteins are clustered based on their interactions and non-interactions. Most proteins are built of domains and their function is the sum of their domain functions. Proteins that share the same domain architecture, the linear order of domains are homologues and should have originated from one common ancestral protein. This ancestor was calculated for roughly 750 000 proteins from 1313 species. The relations between the species are based on the NCBI Taxonomy and additional molecular data. The resulting data set of 5817 domains and 32868 domain architectures was used to estimate the origin of these proteins based on their architectures. It could be observed, that new domain architectures are only in a small fraction composed of domains arisen at the same taxon. It was also found that domain architectures increase in length and complexity in the course of evolution and that different organisms like worm, and human share nearly the same amount of proteins but differ in their number of distinct domain architectures. The second part of this thesis focuses on protein-protein interactions. This chapter addresses the question how new evolved proteins form connections within the existing network. The network built of protein-protein interactions was shown to be scale free. Scale free networks, like the internet, consist of few hubs with many connections and many nodes with few connections. They are thought to arise by two mechanisms. First, newly emerged proteins interact with proteins of the network. Second, according to the theory of preferential attachment, new proteins have a higher chance to interact with already interaction rich proteins. The Human Protein Reference Database provides an on in-vivo interaction data based network for human. With the data obtained from chapter one, proteins were marked with their taxon of origin based on their domain architectures. The interaction ratio of proteins of the same taxa compared to all interactions was calculated and higher values than the random model showed for nearly every taxa. On the other hand, there was no enrichment of proteins originated at the taxon of cellular organisms for the node degree found. The node degree is the number of links for this node. According to the theorie of preferential attachment the oldest nodes should have the most interactions and newly arisen proteins should be preferably attached to them not together. Both could not be shown in this analysis, preferential attachment could therefore not be the only explanation for the forming of the human protein interaction network. Finally in part three, proteins and all their interactions in the network are analysed. Protein networks can be divided into smaller highly interacting parts carrying out specific functions. This can be done with high statistical significance but still, it does not reflect the biological significance. Proteins were clustered based on their interactions and non-interactions with other proteins. A version with eleven clusters showed high gene ontology based ratings and clusters related to specific cell parts. One cluster consists of proteins having very few interactions together but many to proteins of two other clusters. This first cluster is significantly enriched with transport proteins and the two others are enriched with extracellular and cytoplasm/membrane located proteins. The algorithm seems therefore well suited to reflect the biological importance behind functional modules. Although we are still far from understanding the origin of species, this work has significantly contributed to a better understanding of evolution at the protein level and has, in particular, shown the relation of protein domains and protein architectures and their preferences for binding partners within interaction networks.
In a nice assay published in Nature in 1993 the physicist Richard God III started from a human observer and made a number of witty conclusions about our future prospects giving estimates for the existence of the Berlin Wall, the human race and all the rest of the universe. In the same spirit, we derive implications for "the meaning of life, the universe and all the rest" from few principles. Adams´ absurd answer "42" tells the lesson "garbage in / garbage out" - or suggests that the question is non calculable. We show that experience of "meaning" and to decide fundamental questions which can not be decided by formal systems imply central properties of life: Ever higher levels of internal representation of the world and an escalating tendency to become more complex. An observer, "collecting observations" and three measures for complexity are examined. A theory on living systems is derived focussing on their internal representation of information. Living systems are more complex than Kolmogorov complexity ("life is NOT simple") and overcome decision limits (Gödel theorem) for formal systems as illustrated for cell cycle. Only a world with very fine tuned environments allows life. Such a world is itself rather complex and hence excessive large in its space of different states – a living observer has thus a high probability to reside in a complex and fine tuned universe.
Chapter 1 - Evolution of local adaptations in dispersal strategies The optimal probability and distance of dispersal largely depend on the risk to end up in unsuitable habitat. This risk is highest close to the habitat’s edge and consequently, optimal dispersal probability and distance should decline towards the habitat’s border. This selection should lead to the emergence of spatial gradients in dispersal strategies. However, gene flow caused by dispersal itself is counteracting local adaptation. Using an individual based model I investigate the evolution of local adaptations of dispersal probability and distance within a single, circular, habitat patch. I compare evolved dispersal probabilities and distances for six different dispersal kernels (two negative exponential kernels, two skewed kernels, nearest neighbour dispersal and global dispersal) in patches of different size. For all kernels a positive correlation between patch size and dispersal probability emerges. However, a minimum patch size is necessary to allow for local adaptation of dispersal strategies within patches. Beyond this minimum patch area the difference in mean dispersal distance between center and edge increases linearly with patch radius, but the intensity of local adaptation depends on the dispersal kernel. Except for global and nearest neighbour dispersal, the evolved spatial pattern are qualitatively similar for both, mean dispersal probability and distance. I conclude, that inspite of the gene-flow originating from dispersal local adaptation of dispersal strategies is possible if a habitat is of sufficient size. This presumably holds for any realistic type of dispersal kernel. Chapter 2 - How dispersal propensity and distance depend on the capability to assess population density We analyze the simultaneous evolution of emigration probability and dispersal distance for species with different abilities to assess habitat quality (population density) and which suffer from distance dependent dispersal costs. Using an individual-based model I simulate dispersal as a multistep (patch to patch) process in a world consisting of habitat patches surrounded by lethal matrix. Our simulations show that natal dispersal is strongly driven by kin-competition but that consecutive dispersal steps are mostly determined by the chance to immigrate into patches with lower population density. Consequently, individuals following an informed strategy where emigration probability depends on local population density disperse over larger distances than individuals performing density-independent emigration; this especially holds when variation in environmental conditions is spatially correlated. However, already moderate distance-dependent dispersal costs prevent the evolution of long-distance dispersal irrespectively of the chosen dispersal strategy. Chapter 3 - Evolution of sex-biased dispersal: the role of sex-specific dispersal costs, demographic stochasticity, and inbreeding Inbreeding avoidance and asymmetric competition over resources have both been identified as factors favouring the evolution of sex- biased dispersal. It has also been recognized that sex-specific costs of dispersal would promote selection for sexspecific dispersal, but there is little quantitative information on this aspect. In this paper I explore (i) the quantitative relationship between cost-asymmetry and a bias in dispersal, (ii) the influence of demographic stochasticity on this effect, and (iii) how inbreeding and cost-asymmetry interact in their effect on sex-specific dispersal. I adjust an existing analytical model to account for sex-specific costs of dispersal. Based on numerical calculations I predict a severe bias in dispersal already for small differences in dispersal costs. I corroborate these predictions in individualbased simulations, but show that demographic stochasticity generally leads to more balanced dispersal. In combination with inbreeding, cost asymmetries will usually determine which of the two sexes becomes the more dispersive. Chapter 4 - Evolution of sex-biased dispersal: the role of sex-specific dispersal costs, demographic stochasticity, and inbreeding Inbreeding depression, asymmetries in costs or benefits, and the mating system have been identified as potential factors underlying the evolution of sex-biased dispersal. We use individual-based simulations to explore how the mating system and demographic stochasticity influence the evolution of sex-specific dispersal in a metapopulation with females competing over breeding sites, and males over mating opportunities. Comparison of simulation results for random mating with those for a harem system (locally, a single male sires all offspring) reveal that even extreme variance in local male reproductive success (extreme male competition) does not induce a male bias in dispersal. The latter evolves if between-patch variance in reproductive success is larger for males than females. This can emerge due to demographic stochasticity if habitat patches are small. More generally, members of a group of individuals experiencing higher spatio-temporal variance in fitness expectations may evolve to disperse with greater probability than others.
Die Meiose ist eine besondere Art der Zellteilung, die während der Keimzellreifung stattfindet. Sie umfasst zwei aufeinander folgende Zellteilungen mit nur einer DNA-Repli-kationsrunde, wodurch aus einer diploiden Ausgangszelle vier haploide Gameten entstehen. In der ersten meiotischen Teilung werden die homologen Chromosomen miteinander rekombiniert und voneinander getrennt, in der Meiose II findet die Trennung der Schwesterchromatiden statt. Für den korrekten Ablauf dieser Prozesse musste sich eine spezielle molekulare Architektur des meiotischen Chromosoms entwickeln welche die Synapse der homologen Chromosomen durch den Synaptonemalkomplex (SC) beinhaltet. SCs sind evolutionär hochkonservierte, meiosespezifische Proteinkomplexe, die eine zentrale Bedeutung für Synapse, Rekombination und Segregation der homologen Chromosomen haben. Ein SC besteht aus zwei lateralen Elementen (LEs), die den Achsen der homologen Chromosomen aufgelagert sind, einer zentralen Region (CR) und einem zentralen Element (CE). Eine Hauptstrukturkomponente der LEs in Vertebraten ist das Synaptonemalkomplexprotein, SYCP3. Um die molekulare Architektur des SC besser zu verstehen und die Bedeutung von SYCP3 für die Zusammenlagerung der LE aufzudecken, wurden die Polymerisationseigenschaften von SYCP3, exprimiert in somatischen Zellen, erforscht. In diesem experimentellen Ansatz polymerisierte SYCP3 autonom zu stabilen, höher geordneten, filamentösen Strukturen. Die „Coiled-Coil“-Domäne und die flankierenden, evolutionär konservierten Motive sind dabei notwenig, und nach Deletion des weniger konservierten N-terminalen Bereichs auch ausreichend für die Bildung der höher geordneten Strukturen. Der N-Terminus hingegen spielt eine Rolle in der Stabilität der Polymärstrukturen, welche durch Phosphorylierung zweier Serinreste im N-terminalen Bereich beeinflusst werden könnte. Obwohl die Struktur des SC in der Evolution hochkonserviert ist, sind die Protein-komponenten auf Aminosäuresequenzebene sehr unterschiedlich und weisen wenn überhaupt eine strukturelle Homologie in ihrer Domänenorganisation auf. Um den SC-Aufbau und dessen Funktion besser verstehen zu können, wurden die orthologen SC-Proteine zwischen taxonomisch entfernten Spezies Ratte und Medaka verglichen. Es konnte gezeigt werden, dass trotz der Unterschiede in den Aminosäuresequenzen die sich in den letzen 450 Millionen Jahren zwischen Fisch- und Säugern-SYCP3 akkumuliert haben, die Eigenschaften der Proteine vergleichbar sind, und das sie unter experimentellen Bedingungen miteinander interagieren und zu höher geordneten Strukturen kopolymerisieren können.
This thesis extends the classical theoretical work of Macevicz and Oster (1976, expanded by Oster and Wilson, 1978) on adaptive life history strategies in social insects. It focuses on the evolution of dynamic behavioural patterns (reproduction and activity) as a consequence of optimal allocation of energy and time resources. Mathematical modelling is based on detailed empirical observations in the model species Lasioglossum malachurum (Halictidae; Hymenoptera). The main topics are field observations, optimisation models for eusocial life histories, temporal variation in life history decisions, and annual colony cycles of eusocial insects.
Insights into the evolution of protein domains give rise to improvements of function prediction
(2005)
The growing number of uncharacterised sequences in public databases has turned the prediction of protein function into a challenging research field. Traditional annotation methods are often error-prone due to the small subset of proteins with experimentally verified function. Goal of this thesis was to analyse the function and evolution of protein domains in order to understand molecular processes in the cell. The focus was on signalling domains of little understood function, as well as on functional sites of protein domains in general. Glucosaminidases (GlcNAcases) represent key enzymes in signal transduction pathways. Together with glucosamine transferases, they serve as molecular switches, similar to kinases and phosphatases. Little was known about the molecular function and structure of the GlcNAcases. In this thesis, the GlcNAcases were identified as remote homologues of N-acetyltransferases. By comparing the homologous sequences, I was able to predict functional sites of the GlcNAcase family and to identify the GlcNAcases as the first family member of the acetyltransferase superfamily with a distinct catalytic mechanism, which is not involved in the transfer of acetyl groups. In a similar approach, the sensor domain of a plant hormone receptor was studied. I was able to predict putative ligand-binding sites by comparing evolutionary constraints in functionally diverged subfamilies. Most of the putative ligand-binding sites have been experimentally confirmed in the meantime. Due to the importance of enzymes involved in cellular signalling, it seems impossible to find substitutions of catalytic amino acids that turn them catalytically inactive. Nevertheless, by scanning catalytic positions of the protein tyrosine phosphatase families, I found many inactive domains among single domain and tandem domain phosphatases in metazoan proteomes. In addition, I found that inactive phosphatases are conserved throughout evolution, which led to the question about the function of these catalytically inactive phosphatase domains. An analysis of evolutionary site rates of amino acid substitutions revealed a cluster of conserved residues in the apparently redundant domain of tandem phosphatases. This putative regulatory center might be responsible for the experimentally verified dimerization of the active and inactive domain in order to control the catalytic activity of the active phosphatase domain. Moreover, I detected a subgroup of inactive phosphatases, which presumably functions in substrate recognition, based on different evolutionary site rates within the phosphatase family. The characterization of these new regulatory modules in the phosphatase family raised the question whether inactivation of enzymes is a more general evolutionary mechanism to enlarge signalling pathways and whether inactive domains are also found in other enzyme families. A large-scale analysis of substitutions at catalytic positions of enzymatic domains was performed in this work. I identified many domains with inactivating substitutions in various enzyme families. Signalling domains harbour a particular high occurrence of catalytically inactive domains indicating that these domains have evolved to modulate existing regulatory pathways. Furthermore, it was shown that inactivation of enzymes by single substitutions happened multiple times independently in evolution. The surprising variability of amino acids at catalytic positions was decisive for a subsequent analysis of the diversity of functional sites in general. Using functional residues extracted from structural complexes I could show that functional sites of protein domains do not only vary in their type of amino acid but also in their structural location within the domain. In the process of evolution, protein domains have arisen from duplication events and subsequently adapted to new binding partners and developed new functions, which is reflected in the high variability of functional sites. However, great differences exist between domain families. The analysis demonstrated that functional sites of nuclear domains are more conserved than functional sites of extracellular domains. Furthermore, the type of ligand influences the degree of conservation, for example ion binding sites are more conserved than peptide binding sites. The work presented in this thesis has led to the detection of functional sites in various protein domains involved in signalling pathways and it has resulted in insights into the molecular function of those domains. In addition, properties of functional sites of protein domains were revealed. This knowledge can be used in the future to improve the prediction of protein function and to identify functional sites of proteins.
Darwin’s theory of sexual selection explains the evolution of flamboyant male traits through female choice. It does not, however, address the question why males typically court and females choose. This asymmetry is now thought to be the result of the dichotomy in reproductive expenditures: Females invest primarily in parental care and males invest predominantly in mate attraction or competition. Based on this view, several hypotheses for the origin and maintenance of female preferences have been proposed. They include the classical sexual selection models, i.e. female choice for direct and indirect benefits as well as the more recent concepts of female choice for genetic compatibility and receiver bias models. The complementary choice scenario assumes that females choose mates with regard to genetic compatibility. The receiver bias concept views male traits and female preferences within the framework of communication theory and encompasses various more or less distinct models, two of which are sensory exploitation and sensory trap. Both models postulate that male signals evolved in response to pre-existing perceptual biases of females. The sensory trap hypothesis additionally emphasizes that pre-existing female preferences for certain cues evolved in non-sexual contexts, like e.g. foraging. Males that mimic these cues and elicit a favourable out-of-context response by females may increase their reproductive success. This thesis examines the evolution of the pheromone communication in the European Beewolf Philanthus triangulum. Beewolf females are specialized hunters of honeybees and provision their progeny with paralyzed prey. Male beewolves establish and scent mark territories with a pheromone from a head gland to court females. The concordant occurrence of the otherwise rare alcohol (Z)-11-eicosen-1-ol in the male pheromone and in the alarm pheromone of honeybees, the exclusive prey of the females, suggests a sensory trap process as an explanation for the evolution of the male pheromone in P. triangulum. According to this hypothesis, we tested three predictions: First, foraging honeybees should emit eicosenol. Via chemical analysis we could show that honeybee workers in fact smell of eicosenol during foraging. The occurrence of eicosenol on the cuticle and in the headspace of honeybees is a new finding. Second, beewolf females should use eicosenol as a cue for prey detection or identification. Using behavioural assays, we demonstrated that prey recognition in beewolf females is accomplished by olfactory cues and that eicosenol is an essential cue in this process. The sensory sensitivity of beewolf females to eicosenol must be extremely high, since they perceive the trace amounts present in the head space of honeybees. This sensitivity may be due to specialized olfactory receptors on the antennae of beewolf females. An inventory of the flagellar sensilla of both sexes showed that females carry one type of sensillum that is missing in males, the large sensillum basiconicum. This chemo-sensitive sensillum most likely plays a role in prey recognition. The third prediction is that beewolf males incorporate bee-like substances, including eicosenol, into their pheromone, and possibly catch females in a sensory trap. A reanalysis of the male pheromone revealed, among others, eicosenol and several alkanes and alkenes as pheromonal compounds. Our own analyses of the chemical profiles of honeybee workers and beewolf pheromone disclosed a surprisingly strong resemblance between the two. Eight of the eleven substances of the male pheromone are also present on the cuticle and in the headspace of honeybees. Notwithstanding this similarity, the male pheromone does not function as a sensory trap for females. Nevertheless, the extensive congruence between the odour bouquets of the females’ prey and the male pheromone strongly suggests that the male signal evolved to exploit a pre-existing female sensory bias towards bee odour, and, thus represents a case of sensory exploitation. In addition to the above described scenario concerning mostly the ‘design’ of the male pheromone, we addressed possible indirect benefits female beewolves may gain by basing their mating decisions on signal ‘content’. We show that the pheromone of male beewolves varies between families and may, thus, contain information about the degree of relatedness between the female and a potential mate. Females could use this information to choose genetically complementary males to avoid inbreeding and the production of infertile diploid sons. Collectively, our results provide strong evidence for a receiver bias process in the evolution of the male pheromone of P. triangulum. They further indicate that the pheromone composition may subsequently have been influenced by other natural or sexual selection pressures, like e.g. complementary female choice.