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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.
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.
Asymptomatische Bakteriurie (ABU) stellt eine bakterielle Infektion der Harnblase über einen langen Zeitraum dar, die häufig von Escherichia coli hervorgerufen wird, ohne dass typische Symptome einer Harnwegsinfektion auftreten. Um die Charakteristika von ABU E. coli Isolaten genauer zu untersuchen, wurden die Geno- und Phänotypen von 11 ABU-Isolaten verglichen. Außerdem wurden in mehreren aufeinanderfolgenden in vivo-Reisolaten des Modell-ABU Stammes 83972 die Veränderungen im Transkriptom, Proteom und Genom während einer langfristigen Persistenz in der menschlichen Blase charakterisiert. Schließlich wurde der Effekt des menschlichen Wirtes auf die bakterielle Adaptation durch einen Vergleich von in vitro- mit in vivo-kultivierten Stämmen abgeschätzt. ABU-Isolate stellt eine heterogene Gruppe von Organismen dar. Diese können den vier phylogenetischen Hauptgruppen von E. coli sowie unterschiedlichen klonalen Gruppen zugeordnet werden. Dementsprechend unterscheiden sie sich erheblich bezüglich der Zusammensetzung des Genomes, der Genomgröße und auch der Ausstattung mit UPEC-typischen Virulenz-assoziierten Genen. Multi-Lokus-Sequenz-Typisierung legt nahe, dass bestimmte ABU Stämme sich durch Genomreduktion aus UPEC Stämmen entwickelt haben, die eine Harnwegsinfektion mit charakteristischen Symptomen auslösen konnten. Folglich erlaubt die hohe Genomplastizität von E. coli keine generalisierte Betrachtung einzelner Isolate eines Klons. Genomreduktion über Punktmutationen, Genom-Reorganisation und Deletionen resultierte in der Inaktivierung einiger Gene, die für einige UPEC Virulenz-Faktoren kodieren. Dies stützt die Vorstellung, dass eine verminderte bakterielle Aktivierung der Entzündung der Wirtsschleimhaut den Lebensstil von ABU (bei diesen E. coli-)Isolaten fördert. Genregulation und genetische Diversität sind Strategien, die es Bakterien ermöglichen unter sich fortlaufend ändernden Bedingungen zu leben bzw. zu überleben. Um die anpassungsbedingten Veränderungen bei einem langfristigen Wachstum in der Blase zu untersuchen, wurden aufeinanderfolgende Reisolate, denen eine langfristige in vivo-Kolonisierung im menschlichen Wirt beziehungsweise eine in vitro-Kultivierung vorausgegangen ist, im Hinblick auf Veränderungen Genexpression und Genomorganisation analysiert. In diesem Zusammenhang konnte gezeigt werden, dass E. coli in der Lage ist, seine metabolischen Netzwerke verschiedenen Wachstumsbedingungen anzupassen und individuelle bakterielle Kolonisierungsstrategien entwickeln kann. Transkriptom- und Proteom-Analysen zeigten verschiedene metabolische Strategien zur Nährstoffbeschaffung und Energieproduktion bei untersuchten in vivo-Reisolaten vom Stamm 83972, die es ihnen ermöglichen, den Wirt zu kolonisieren. Das Zurückgreifen auf D-Serin, Deoxy- und Ribonucleoside sowie die bidirektionale Umwandlung zwischen Pentose und Glucuronat waren hoch-regulierte Stoffwechselwege, die die in vivo-Reisolate mit zusätzlicher Energie für ein effizientes Wachstum in der Blase versorgen. Zudem wurden in dieser Studie die Netzwerke für eine Reaktion auf Abwehrmechanismen des Wirtes erforscht: Erstmals wurde hier die Rolle der Klasse-III-Alkoholdehydrogenase AdhC, bekannt durch ihre Bedeutung bei der Entgiftung von Stickstoffmonoxid, bei der Wirtsantwort während einer asymptomatischen Bakteriurie gezeigt. Aufeinanderfolgende in vivo- und in vitro-Reisolate vom Stamm 83972 wurden ebenfalls bezüglich ihrer Genomstruktur analysiert. Einige Veränderungen in der Genomstruktur der aufeinanderfolgenden Reisolate, die von einer humanen Kolonisierungsstudie stammen, implizieren die Bedeutung einer Interaktion der Bakterien mit dem Wirt bei der Mikroevolution der Bakterien. Dagegen war die Genomstruktur von Reisolaten eines langfristigen in vitro-Kultivierungsexperiments, bei dem sich der Stamm 83972 ohne Wirtskontakt vermehrt hat, nicht von Veränderungen betroffen. Das legt nahe, dass die Immunantwort eine Genomplastizität fördert und somit eine treibende Kraft für den ABU Lebensstil und die Evolution im Harnwegstrakt ist.