Theodor-Boveri-Institut für Biowissenschaften
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New antimycotic drugs are challenging to find, as potential target proteins may have close human orthologs. We here focus on identifying metabolic targets that are critical for fungal growth and have minimal similarity to targets among human proteins. We compare and combine here: (I) direct metabolic network modeling using elementary mode analysis and flux estimates approximations using expression data, (II) targeting metabolic genes by transcriptome analysis of condition-specific highly expressed enzymes, and (III) analysis of enzyme structure, enzyme interconnectedness (“hubs”), and identification of pathogen-specific enzymes using orthology relations. We have identified 64 targets including metabolic enzymes involved in vitamin synthesis, lipid, and amino acid biosynthesis including 18 targets validated from the literature, two validated and five currently examined in own genetic experiments, and 38 further promising novel target proteins which are non-orthologous to human proteins, involved in metabolism and are highly ranked drug targets from these pipelines.
Bees are subject to permanent threat from predators such as ants. Their nests with large quantities of brood, pollen and honey represent lucrative targets for attacks whereas foragers have to face rivalry at food sources. This thesis focused on the role of stingless bees as third party interactor on ant-aphid-associations as well as on the predatory potential represented by ants and defense mechanisms against this threat. Regular observations of an aphid infested Podocarpus for approaching stingless bees yielded no results. Another aim of this thesis was the observation of foraging habits of four native and one introduced ant species for assessment of their predatory potential to stingless bees. All species turned out to be dietary balanced generalists with one mostly carnivorous species and four species predominantly collecting nectar roughly according to optimal foraging theory. Two of the species monitored, Rhytidoponera metallica and Iridomyrmex rufoniger were considered potential nest robbers. As the name implies, stingless bees lack the powerful weapon of their distant relatives; hence they specialized on other defense strategies. Resin is an important, multipurpose resource for stingless bees that is used as material for nest construction, antibiotic and for defensive means. For the latter purpose highly viscous resin is either directly used to stick down aggressors or its terpenic compounds are included in the bees cuticular surface. In a feeding choice experiment, three ant species were confronted with the choice between two native bee species - Tetragonula carbonaria and Austroplebeia australis - with different cuticular profiles and resin collection habits. Two of the ant species, especially the introduced Tetramorium bicarinatum did not show any preferences. The carnivorous R. metallica predominantly took the less resinous A. australis as prey. The reluctance towards T. carbonaria disappeared when the resinous compounds on its cuticle had been washed off with hexane. To test whether the repulsive reactions were related to the stickiness of the resinous surface or to chemical substances, hexane extracts of bees’ cuticles, propolis and three natural tree resins were prepared. In the following assay responses of ants towards extract treated surfaces were observed. Except for one of the resin extracts, all tested substances had repellent effects to the ants. Efficacy varied with the type of extract and species. Especially to the introduced T. bicarinatum the cuticular extract had no effect. GCMS-analyses showed that some of the resinous compounds were also found in the cuticular profile of T. carbonaria which featured reasonable analogies to the resin of Corymbia torelliana that is highly attractive for stingless bees. The results showed that repellent effects were only partially related to the sticky quality of resin but were rather caused by chemical substances, presumably sesqui- and diterpenes. Despite its efficacy this defense strategy only provides short time repellent effects sufficient for escape and warning of nest mates to initiate further preventive measures.
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.