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Anthropogenic activities have a major impact on our planet and rapidly drive biodiversity loss in ecosystems at a global scale. Particularly over the last century, rising CO2 emissions significantly raised global temperatures and increased the intensity and frequency of droughts and heatwaves. Additionally, agricultural land use and fossil fuel combustion contribute to the continuous release of nitrogen (N) and phosphorus (P) into ecosystems worldwide through extensive fertilization and deposition from the atmosphere. It is important to understand how these rapid changes affect the evolution of plant populations and their adaptive potential. Adaptation by natural selection (i.e., adaptive evolution) within a few generations is an essential process as a response to rapid environmental changes. Rapid evolution of plant populations can be detected by using the so-called resurrection approach. Here, diaspores (i.e., seeds) from a population are collected before (ancestors) and after (descendants) a potential selection pressure (e.g., consecutive years of drought or changes in nutrient supply). Comparing phenotypes of ancestors and descendants in a common environment such as an outside garden, greenhouse, or climate chamber, may then reveal evolutionary changes. Ideally, plants are first grown in a common environment for an intermediate refresher generation to reduce parental and storage effects.
The aim of this thesis was to investigate the occurrence of adaptive evolution in natural plant populations in response to rapidly changing environments over the past three decades. I conducted three experiments using the resurrection approach to generate comprehensive data on the adaptive processes that acted on three plant populations from three different species over the last three decades. Furthermore, I filled knowledge gaps in plant evolutionary ecology and conceptually developed the resurrection approach further.
In Chapter I, I performed a novel approach by testing for adaptive evolution in natural plant populations using the resurrection approach in combination with in-situ transplantations. I cultivated seedlings from ancestors (23 – 26 years old) and contemporary descendants of three perennial species (Melica ciliata, Leontodon hispidus and Clinopodium vulgare) from calcareous grasslands in the greenhouse and In Chapter III, I assessed the reproducibility of phenotypic differences between genotypes among three different growth facilities (climate chamber, greenhouse, and outdoor garden). I also evaluated differences in phenotypic expression between plants grown after one vs. two intermediate generations (i.e., refresher generations). I performed this experiment within the framework of the resurrection approach and compared ancestors and descendants of the same population of Leontodon hispidus.
I observed very strong differences among plants growing in the different growth facilities. I found a significant interaction between the growth facility and the temporal origin (ancestors vs. descendants): descendants had significantly larger rosettes than ancestors only in the greenhouse and they flowered significantly later than ancestors exclusively in the climate chamber. I did not find significant differences between intermediate generations within the growth facilities. Overall, Chapter III shows that the use of a particular experimental system can dictate the presence and magnitude of phenotypic differences. This implies that absence of evidence is not evidence of absence when it comes to investigating genetically based trait differentiation among plant origins (in space or time). Experimental systems should be carefully designed to provide meaningful conditions, ideally mimicking the environmental conditions of the population’s origins. Finally, growing a second intermediate generation did not impact the genetic differences of ancestors and descendants within the environments, supporting the idea that only one intermediate generation may be sufficient to reduce detectable parental and storage effects.
The resurrection approach allows a better understanding of rapid plant adaptation, but some limitations deserve to be highlighted. I only studied one population per species, and Chapters II and III only focus on one population of L. hispidus, which is also hampering generalizations, as adaptive potential can vary greatly among populations of the same species. I only compared the ancestral genotypes to one descendant sample with a long time span in between (26 – 28 years), which makes it hard to pinpoint the selection agents that caused the genetic differentiation among the sampling years. Hence, closely monitoring biotic and abiotic factors of the studied populations between the ancestral and descendant sampling in future studies, would make identifying the responsible selection pressures more precise. I also recommend sampling multiple populations over consecutive years to improve the robustness of results and make generalizations more approachable.Furthermore, combining the resurrection approach with other methods such as in-situ transplantations will be valuable to offset the limitation that adaptations cannot be proven under artificial conditions (e.g., in the greenhouse).
Proteins are biological macromolecules playing essential roles in all living organisms.
Proteins often bind with each other forming complexes to fulfill their function. Such protein complexes assemble along an ordered pathway. An assembled protein complex can often be divided into structural and functional modules. Knowing the order of assembly and the modules of a protein complex is important to understand biological processes and treat diseases related to misassembly.
Typical structures of the Protein Data Bank (PDB) contain two to three subunits and a few thousand atoms. Recent developments have led to large protein complexes being resolved. The increasing number and size of the protein complexes demand for computational assistance for the visualization and analysis. One such large protein complex is respiratory complex I accounting for 45 subunits in Homo sapiens.
Complex I is a well understood protein complex that served as case study to validate our methods.
Our aim was to analyze time-resolved Molecular Dynamics (MD) simulation data, identify modules of a protein complex and generate hypotheses for the assembly pathway of a protein complex. For that purpose, we abstracted the topology of protein complexes to Complex Graphs of the Protein Topology Graph Library (PTGL). The subunits are represented as vertices, and spatial contacts as edges. The edges are weighted with the number of contacts based on a distance threshold. This allowed us to apply graph-theoretic methods to visualize and analyze protein complexes.
We extended the implementations of two methods to achieve a computation of Complex Graphs in feasible runtimes. The first method skipped checks for contacts using the information which residues are sequential neighbors. We extended the method to protein complexes and structures containing ligands. The second method introduced spheres encompassing all atoms of a subunit and skipped the check for contacts if the corresponding spheres do not overlap. Both methods combined allowed skipping up to 93 % of the checks for contacts for sample complexes of 40 subunits compared to up to 10 % of the previous implementation. We showed that the runtime of the combined method scaled linearly with the number of atoms compared to a non-linear scaling of the previous implementation We implemented a third method fixing the assignment of an orientation to secondary structure elements. We placed a three-dimensional vector in each secondary structure element and computed the angle between secondary structure elements to assign an orientation. This method sped up the runtime especially for large structures, such as the capsid of human immunodeficiency virus, for which the runtime decreased from 43 to less than 9 hours.
The feasible runtimes allowed us to investigate two data sets of MD trajectories of respiratory complex I of Thermus thermophilus that we received. The data sets differ only by whether ubiquinone is bound to the complex. We implemented a pipeline, PTGLdynamics, to compute the contacts and Complex Graphs for all time steps of the trajectories. We investigated different methods to track changes of contacts during the simulation and created a heat map put onto the three-dimensional structure visualizing the changes. We also created line plots to visualize the changes of contacts over the course of the simulation. Both visualizations helped spotting outstandingly flexible or rigid regions of the structure or time points of the simulation in which major dynamics occur.
We introduced normalizations of the edge weights of Complex Graphs for identi-fying modules and predicting the assembly pathway. The idea is to normalize the number of contacts for the number of residues of a subunit. We defined five different normalizations.
To identify structural and functional modules, we applied the Leiden graph clustering algorithm to the Complex Graphs of respiratory complex I and the respiratory supercomplex. We examined the results for the different normalizations of the weights of the Complex Graphs. The absolute edge weight produced the best result identifying three of four modules that have been defined in the literature for respiratory complex I.
We applied agglomerative hierarchical clustering to the edges of a Complex Graph to create hypotheses of the assembly pathway. The rationale was that subunits with an extensive interface in the final structure assemble early. We tested our method against two existing methods on a data set of 21 proteins with reported assembly pathways. Our prediction outperformed the other methods and ran in feasible runtimes of a few minutes at most.
We also tested our method on respiratory complex I, the respiratory supercomplex and the respiratory megacomplex. We compared the results for the different normalizations with an assembly pathway of respiratory complex I described in the literature. We transformed the assembly pathways to dendrograms and compared the predictions to the reference using the Robinson-Foulds distance and clustering information distance. We analyzed the landscape of the clustering information distance by generating random dendrograms and showed that our result is far better than expected at random. We showed in a detailed analysis that the assembly prediction using one normalization was able to capture key features of the assembly pathway that has been proposed in the literature.
In conclusion, we presented different applications of graph theory to automatically analyze the topology of protein complexes. Our programs run in feasible runtimes even for large complexes. We showed that graph-theoretic modeling of the protein structure can be used to analyze MD simulation data, identify modules of protein complexes and predict assembly pathways.
Anthropogenic interventions have altered all ecosystems around the world. One of those ecosystems are forests, the main resource for timber. They have been strongly transformed in their structure with large consequences on forest biodiversity. Especially the decrease in dead-wood volume due to the timber extraction and alternation of natural forest structures with even-aged stands of less diverse tree species composition has put especially saproxylic, i.e., dead-wood dependent species, under threat, which comprise about 20% of all forest species. Beetles, fungi and bacteria are three functional important groups for decomposition processes but we still lack much information about their sampling and the drivers of their diversity, thus it is difficult to comprehensively protect their diversity. Saproxylic fungi are a highly diverse species group and the main drivers of dead-wood decomposition; hence they play a major role in the global carbon cycle. Due to their cryptic lifestyle, many species are still unknown, but the recent advances in environmental DNA barcoding methods (metabarcoding) shed light on the formerly underestimated diversity. Yet, this method's accuracy and suitability in detecting specific species have not been assessed so far, limiting its current usefulness for species conservation. On the other hand, these methods are a convenient tool to study highly diverse areas with high numbers of unknown species, enabling the study of global diversity and its drivers, which are unknown for saproxylic fungi, but important to assess to predict the future impacts of global change. Since nature conservation concepts are usually not applied on a global scale, the drivers of diversity must also be assessed on smaller scales. Besides understanding the drivers of diversity, to identify focus scales to create comprehensive, evidence-based conservation concepts must utilize multi-taxonomic studies since saproxylic species are differently sensitive towards environmental variables and closely interact with each other. Filling these knowledge gaps is utterly needed to protect the high saproxylic diversity and ensure the functional continuity of decomposition processes, especially regarding the global change.
To address the usefulness of metabarcoding for fungal species conservation, I compared the traditional method of fruit body sampling with metabarcoding and their efficiency in detecting threatened fungal species in the first chapter of this thesis. Both methods have advantages and disadvantages. Their ability to detect threatened saproxylic fungal species and their dependencies on detecting specific fungal groups have not been compared, albeit they are important to inform species conservation like Red Lists properly. I found metabarcoding to generally detect more threatened fungal species than fruit body sampling with a higher frequency than fruit body sampling. Moreover, fruit body sampling detected a unique set of species, while fruit body sampling missed large parts of fungal diversity due to species-specific fruiting characteristics. Metabarcoding with high sampling intensity is thus a viable method to assess threatened saproxylic fungal diversity and inform nature conservation like Red Lists about distribution and abundances. Nevertheless, a complementary approach with fruit body sampling is indispensable for assessing all threatened fungal species.
In order to analyse the global diversity of saproxylic fungi and its drivers, I examined whether fungal species richness increases from the poles towards the equator and thus follows the latitudinal diversity gradient already found in many other species groups. I further investigated whether such an increase is caused by increasing ecological specialisation, i.e., niche partitioning, or local tree diversity, i.e., niche space. Gamma diversity per biome increased from the boreal, over the temperate to the tropics and thus confirmed the latitudinal diversity for saproxylic fungi. Contrastingly, alpha diversity at the log level did not significantly increase towards the tropics, suggesting a grain size dependency of the observed pattern and an equal niche space within dead-wood across latitudes. Ecological specialisation on the plot level was globally on a high level but did not increase significantly towards the equator. Additionally, I found local tree species richness to drive plot-based fungal diversity. Further analysis of gamma diversity against the total number of sampled tree species strengthened the assumption that tree species diversity and not increased ecological specialisation was the main driver of the latitudinal diversity gradient, as there was no significant difference between the gamma diversity of the temperate and tropical biome. Nonetheless, as the gamma diversity of the boreal biome was still significantly smaller, my results do not allow a complete neglection of the ecological specialisation hypothesis. The overall results indicate a strong dependency of saproxylic fungi diversity with host tree species diversity and that the global loss of tree species threatens saproxylic fungi with an unpredictable impact on carbon and nutrient cycling.
To support saproxylic conservation, I conducted two analyses. First, I compared the beta diversity of the three main decomposer groups (beetles, fungal fruit bodies, mycelial fungi (metabarcoding), and bacteria (metabarcoding)) across different scales to assess the impact of different environmental variables on their overall diversity. I used an experimental design to disentangle two different spatial scales, influenced by differences in macroclimate, forest microclimate and spatial distance, and two host scales, driven by differences between tree lineages and tree species. I set these beta diversities in relation to the gamma diversity of the three main decomposer groups to identify whether a unified conservation concept could be applied to one scale to optimally protect the diversity of all three species groups. Second, I identified whether diversity and community composition of fungi and bacteria differed among climate and land use gradients. Further I explored whether specialisation and niche packing could explain the expected pattern. To do so I used an experimental design disentangling climate and land use across a large gradient in Germany. The results differed among the species groups, denying a unified conservation concept focusing on one scale. Saproxylic beetle and fruit body beta diversity was equally high on each scale, as they are more sensitive towards environmental factors like macro- and microclimate. On the other hand, mycelial fungi and bacteria beta diversity was highest on the host scale, especially the host tree scale, indicating a high host specificity of the two groups. The second study also identified tree species as the main driver of diversity and community composition of these two study groups. Specialisation of fungi was not influenced by land use or climate. Bacterial specialisation and diversity were under a strong influence of mean precipitation. Comprehensive conservation of multi-taxonomic diversity across regions thus requires the integration of several scales. Within different macroclimatic regions, forests of varying microclimates, i.e., forest management, must be implemented. In these forests, dead-wood of different tree lineages, i.e., angio- and gymnosperms and tree species, must be provided.
Taken together, I could demonstrate that metabarcoding is an efficient method to sample threatened fungal species and identify differing drivers of fungal diversity present as fruit bodies or mycelium. Its usefulness will further increase due to the ongoing improvement of sequencing databases and thus better inform conservation concepts. Using metabarcoding, I could demonstrate that high host specialisation of saproxylic fungi is not a European but a global phenomenon and identify tree species loss under global change as one major concern for saproxylic diversity. My dissertation further highlighted the importance of multi-taxonomic studies for evidence-based nature conservation, as different species groups require varying concepts. These results were especially important for saproxylic bacteria as the drivers of their diversity are still largely unknown. Howbeit, large research gaps still exist regarding the impacts of global change on species and processes. Moreover, the spatial coverage of studies is needed to confirm or neglect the generality of current research especially concerning the highly diverse tropical areas. An increased focus on the drivers of diversity in these areas is crucial to ensure a globally comprehensive saproxylic conservation and the various ecosystem functions they control.
Our mind has the function of representing the physical and social world we are in, so that we can efficiently interact with it. This results in a constant and dynamic interaction between mind and world that produces a balance when representations are at the same time accurate with respect to what the world is communicating to our organism, but also compatible with how our mind works.
A paradigmatic case of this interaction is offered by perception, which is the mental function that represents contingent aspects of the world built from what is captured by our senses. Indeed, the dominant philosophical view in cognitive science is that our perceptual states are representations of the world and not direct access to that world. These representational perceptual states therefor include the aspects of the world they represent and that initiate the perception by stimulating our sensory organs.
Perceptual representations are built using information from the sensory system, i.e., bottom-up information, but are also integrated with information previously acquired, i.e., top-down information, so that perception interacts with memory through language and other mental functions. Such organization is believed to reflect a general mechanism of our mind/brain, which is to acquire and use information to make efficient predictions about the future, continuously updating older information with present information.
This predictive processing works because the world is not random, but shows a regular structure from which reliable expectations can be built. One way that our minds make these predictions is by adapting to the structure of the world in an implicit, automatic and unconscious way, a process that has been called Implicit Statistical Learning (ISL). ISL is a learning process that does not require awareness and happens in an incidental and spontaneous way, with mere exposure to statistical regularities of the world. It is what happens when we learn a language during early childhood, and that allows us to be implicitly sensitive to the phonological structure of speech, or to associate speech patterns with objects and events to learn word meaning.
A specific case of ISL is the learning of spatial configuration in the visual world, which we apply to abstract arrays of items, but most importantly, also to more ecological settings such as the visual scenes we are immersed in during our everyday life. The knowledge we acquire about the structure of visual scenes has been called “Scene Grammar”, because it informs about presence and position of objects in a similar way to what linguistic grammar tells us about the presence and position of words. So, we implicitly acquire the semantics of scenes, learning which objects are consistent with a certain scene, as well as the syntax of scenes, learning where objects are positioned in a consistent way within a certain scene.
More recent developments have proposed that scene grammar knowledge might be organized based on a hierarchical system: objects are arranged in the scene, which offers the more general context, but within a scene we can identify different spatial and functional clusters of objects, called “phrases”, that offer a second level of context; within every phrase, then, objects have different status, with usually one object (“anchor object”) offering strong prediction of where and which are the other objects within the phrase (“local objects”). However, these further aspects of the organization of objects In scenes remain poorly understood.
Another problem relates to the way we measure the structure of scenes to compare the organization of the visual world with the organization in the mind. Typically, to decide if an object appears or not in a certain scene, and whether or not it appears in a certain position within a scene, researchers based their decision on intuition and common-sense, maybe validating those decisions with independent raters. But it has been shown that often these decisions can be limited and more complex information about objects’ arrangement in scenes can be lost.
A potential solution to this problem might be using large set of real-world images, that have annotations and segmentations of objects, to measures statistics about how objects are arranged in the environment. This idea exploits the nowadays larger availability of this kind of datasets due to increasing developments of computer vision algorithms, and also parallels with the established usage of large text corpora in language research.
The goals of the current investigation were to extract object statistics from this image datasets and test if they reliably predict behavioural responses during object processing, as well as to use these statistics to investigate more complex aspects of scene grammar, such as its hierarchical organization, to see if this organization is reflected in the organization of objects in our mind.
The role of USP22 in nucleic acid sensing pathways and interferon-induced necroptotic cell death
(2023)
Every day, living organisms are challenged by internal and external factors that threaten to bring imbalance to their tightly regulated systems and disrupt homeostasis, leading to degeneration, and ultimately death. More than ever, we face the challenge of combating diseases such as COVID-19 caused by infection with the SARS-CoV-2 coronavirus. It is therefore crucial to identify host factors that control antiviral defense mechanisms. In addition, in the fight against cancer, it is becoming increasingly important to identify markers that could be used for targeted therapy to influence cellular processes and determine cell fate.
As a deubiquitylating enzyme, ubiquitin specific peptidase 22 (USP22) mediates the removal of the small molecule ubiquitin, which is post-translationally added to target proteins, thereby regulating several important processes such as protein degradation, activation or localization. Through its deubiquitylating function, USP22 controls several biological processes such as cell cycle regulation, proliferation and cancer immunoresistance by modulating key proteins involved in these pathways. Lately, USP22 was reported to positively regulate TNFα-mediated necroptosis, an inflammatory type of programmed cell death, in various human tumor cell lines by affecting RIPK3 phosphorylation. In addition, USP22 as a part of the Spt-Ada-Gcn5 acetyltransferase (SAGA) transcription complex is known to regulate gene expression by removing ubiquitin from histones H2A and H2B. However, little is known about the role of USP22 in global gene expression.
In this study, we performed a genome-wide screen in the human colon carcinoma cell line HT-29 and identified USP22 as a key negative regulator of basal interferon (IFN) expression. We further demonstrated that the absence of USP22 results in increased STING activity and ubiquitylation, both basally and in response to stimulation with the STING agonist 2'3'-cGAMP, thereby affecting IFNλ1 expression and basal expression of antiviral ISGs. In addition, we were able to establish USP22 as a critical host factor in controlling SARS-CoV-2 infection by regulating infection, replication, and the generation of infectious virus particles, which we attribute in part to its role in regulating STING signaling.
In the second part of the study, we connected the findings of USP22-dependent regulation of IFN signaling and TNFα-induced necroptosis and investigated the role of USP22 during necroptosis induced by the synergistic action of IFN and the Smac mimetic BV6 in caspase-deficient settings. We identified USP22 as a negative regulator of IFN-induced necroptosis, which does not depend on STING expression, but relies on a yet unknown mechanism.
In summary, we identify USP22 as an important regulator of IFN signaling with important implications for the defense against viral infections and regulation of the necroptotic pathway that could be exploited for devising targeted therapeutic strategies against viral infections and related diseases like COVID-19, and advancing precision medicine in cancer treatment.
Subject of this thesis was the investigation of the actin-interacting and glucocorticoid-sensitive Protein DRR1 (or Fam107a) and its role in promoting stress resilience in the murine hippocampus.
We proposed the hypothesis that DRR1 through its actin-binding properties specifically modulates neuronal actin dynamics and promotes resilience through synaptic plasticity leading to subsequently improvement of cognitive performance and social behavior. The accompanied AMPA-receptor transport could create an efficient way regulating neural function and complex behavior during stress episodes.
By utilizing fluorescent immunohistochemistry, we showed basal expression of DRR1 primarily in the murine cerebellum and hippocampal CA3 and CA1 area. Co-staining with different cell marker proteins showed DRR1 expression in neurons, microglia and especially in astrocytic end-feet, which create contact to the brain vasculature.
To test whether DRR1 and AMPA receptor function correlate to modulate stress-associated consequences, primary hippocampal neuron cultures were transduced with adeno-associated virus (AAV) for overexpression or suppression of the protein. Western Blot analysis showed a positive correlation between the AMPA-receptor subunit GluR2 and DRR1 amounts. Further the application of the proximity ligation assay (PLA) in untreated neural cultures indicated interaction between DRR1 and the AMPA receptor subunit GluR2. To address whether DRR1 even affects AMPAR trafficking we performed the “newly inserted assay” after AAV-treatment of primary hippocampal neuron cultures. Suppression of DRR1 revealed less newly inserted GluR2 subunits as compared to controls. Inconclusive were the results upon DRR1 overexpression, however they point to no changes.
In the second part we correlated behavioral phenotypes originating from in vivo overexpression and suppression of DRR1 in the murine hippocampus with potential alterations in neuronal morphology. Therefore, in vitro analysis was performed utilizing AAV transduced primary hippocampal cultures overexpressing or suppressing DRR1. Synchronously the viral vector included a green fluorescent protein (GFP) being expressed throughout the complete neural cell. GFP staining was used to verify successful transfection and for reconstruction of dendritic arbors and dendritic stretches for spine classification. DRR1 suppression showed reduced total spine numbers especially evoked by reduced numbers of immature spine classes – namely long thin spines and filopodia. Whereas mature mushroom spines and stubby spines were unaffected. By overexpressing DRR1, tendencies inclined against higher total dendritic lengths, branch points and increased dendritic arbors in comparison to controls. In regard of spines, total numbers were unaffected. However, mature mushroom spines were significantly declined in numbers, but compensated by increased numbers of immature long thin spines and filopodia.
Chronic social defeat stress (CSDS) is widely used in mouse models to study the effects of stress and resilience. We exposed C57Bl/6J mice expressing GFP under the Thy1 promoter CSDS and categorized them into resilient (R+/-), susceptible (R-/-) and non-learning (R+/+) mice following a modified social interaction test (MSIT). We found alterations in CA1 spine compositions with resilient animals resembling the untreated phenotype. Stress susceptible and non-learning animals displayed reduced numbers in stubby spines with simultaneous increases in mature mushroom spines. In addition, we could detect a tendency towards more immature spines in susceptible animals and non-learners, mirroring our in vitro results.
Finally, we present a different investigative approach in this thesis. Sequenced acute stress was previously found to compromise cognition including spine loss.
We aimed to investigate the implication of acute stress on DRR1 levels and its occurrence in diverse cell types of the brain. We subjected one group of C57Bl/6J mice to acute stress and injected another group with the artificial glucocorticoid DEX. Six hours post stress, animals were perfused and brains were subsequently immunobiologically analyzed. We found DRR1 protein levels elevated in the hippocampus of stressed and DEX-treated animals compared to controls. Interestingly, DRR1 seemed was especially elevated in endothelial cells. This coincides with our investigations finding DRR1 present in astrocytic end-feet under basal conditions and might claim a participation of DRR1 in the blood-brain-barrier integrity.
Our results show DRR1 as actin-interacting and glucocorticoid-sensitive gene affecting structural plasticity of hippocampal spines. Moreover, DRR1 directly interacts with AMPA glutamate receptors and presumably is involved in AMPA trafficking to the postsynaptic membrane. In addition, this study could demonstrate that DRR1 is expressed by other cell types of the brain. Of special interest is DRR1’s occurrence in astrocytic end-feet and endothelial cells suggesting a role as integrator of cell-cell communication and to this end also acting as modifier of stress-induced consequences at the neurovascular unit.
In vivo data of chronically stressed mice displayed no phenotypic differences in hippocampal pyramidal neurons of resilient animals as compared to unstressed mice. Morphological alterations of spine structures were particularly visible in stress susceptible and non-learning animals. Integrating our findings with existing behavioral data, we can conclude that DRR1 plays a role in stress resilience whereby it needs to be expressed in a tightly managed homeostatic equilibrium.
The EMT-transcription factor ZEB1 has been intensively studied in solid cancers, where it is expressed at the invasive front and in cancer-associated fibroblasts (CAFs). In tumour cells, ZEB1 has been involved in multiple steps of cancer progression including stemness, metastasis and therapy resistance, yet its role in the tumour-microenvironment is largely unknown. Here, the role of Zeb1 in CAFs was investigated using mouse models reflecting different tumour stages in immunocompetent fibroblast specific Zeb1 KO mice. Fibroblast-specific depletion of Zeb1 accelerated tumour growth in the inflammation driven AOM/DSS tumour initiation model, reduced tumour growth and invasion in the sporadic AOM/P53 model and reduced liver metastasis in a progressed orthotopic transplantation model. Immunohistochemical and single cell RNA-sequencing analysis showed that Zeb1 ablation resulted in attenuated expression of the myofibroblast marker aSMA and reduced ECM deposition, indicating a shift among fibroblast subpopulations. Modulation of CAFs was furthermore associated with increased inflammatory signaling in fibroblasts resulting in immune infiltration into primary tumours and exaggerated inflammatory signaling in T cells, B cells and macrophages. These changes in the tumour microenvironment were associated with increased efficacy of immune checkpoint inhibition therapy. In summary, Zeb1 expression in CAFs was identified as a potential target to block immunosuppression and metastatic dissemination in colon cancer.
The capacity of pathogenic bacteria to adhere to host cells and to avoid subsequent clearance by the host´s immune response is the initial and most decisive step leading to infections. Human pathogenic bacteria circulating in the bloodstream need to find ways to interact with endothelial cells (ECs) lining the blood vessels to infect and colonise the host. The extracellular matrix (ECM) of ECs might represent an attractive initial target for bacterial interaction, as many bacterial adhesins have reported affinities to ECM proteins, particularly fibronectin (Fn). Trimeric autotransporter adhesins (TAA) have been described as important pathogenicity factors of Gram-negative bacteria. The TAA from human pathogenic Bartonella henselae, Bartonella adhesin A (BadA), is one of the longest and best characterised adhesin and represents a prototypic TAA due to its domain architecture. B. henselae, the causative agent of cat scratch disease, endocarditis, and bacillary angiomatosis, adheres to ECs and ECM proteins via BadA interaction.
In this research, it was determined that the interaction between BadA and Fn is essential for B. henselae host cell adhesion. BadA interactions were identified within the heparin-binding domains of Fn, and the exact binding sites were revealed by mass spectrometry analysis of chemically crosslinked whole-cell bacteria and Fn. It turned out that specific BadA interactions with defined Fn regions represent the molecular basis for bacterial adhesion to ECs. These data were confirmed by using BadA-deficient bacteria and CRISPR-Cas FN1 knockout ECs. It was also identified that BadA binds to Fn from both cellular and plasma origin, suggesting that B. henselae binding to Fn might possibly take part in other infection processes apart from bacterial adherence, e.g. evasion from the host cell immune system.
Interactions between TAAs and Fn represent a key step for adherence of B. henselae to ECs. Still, Fn-mediated binding is of more significant importance for pathogenic bacteria than broadly recognised. Fn removal from the ECM environment of ECs, also reduced adherence of Staphylococcus aureus, Borrelia burgdorferi, and Acinetobacter baumannii to host cells Interactions between adhesins and Fn might therefore represent a crucial step for the adhesion of human-pathogenic Gram-negative and Gram-positive bacteria targeting the ECs as a niche of infection or as means for persistence.
This research demonstrated that combining large-scale analysis approaches to describe protein-protein interactions with supportive functional readouts (binding assays) allows for the discrimination of crucial interactions involved in bacterial adhesion to the host. The herein-described experimental approaches and tools might guide future research for other pathogenic bacteria and represent an initial point for the future generation of anti-virulence strategies to inhibit bacterial binding to host cells.
The role of Apelin signaling and endocardial protrusions during cardiac development in zebrafish
(2023)
During cardiac development, cardiomyocytes (CMs) are delaminated from the compact muscle wall to increase the muscle mass of the heart. This process is also known as cardiac trabeculation. It has been shown that growth factors produced by endocardial cells (EdCs) are required for myocardial morphogenesis and growth. In particular, Neuregulin produced by EdCs promotes myocardial trabeculation. The deficiency of Neuregulin signaling leads to hypotrabeculation. Endocardial protrusions project from the endocardium to the myocardium are also essential for the trabeculae onset. Yet current studies only introduce the function of endocardial sprouts descriptively. This article first reports the mechanisms of endocardial sprouting during myocardial trabeculation. By living imaging, we first demonstrate that EdCs interact with CMs through membrane protrusions in zebrafish embryos. More interestingly, these protrusions stay in close contact with their target CMs in spite of the cardiac contraction. We utilize loss-of-function strategies to report the importance of myocardial apelin, which induces endocardial protrusion formation. Zebrafish lacking Apelin signaling exhibit defects in endocardial protrusion formation as well as excessive deposition of cardiac jelly and hypotrabeculation. Notably, we also present data that blocking protrusion formation in endocardial cells phenocopies the trabeculation defects in apelin mutants. Mechanistically, endocardial-derived Neuregulin requires Apelin signaling mediated endocardial protrusions, and Neuregulin dependent pERK expression is attenuated in the condition of reduced endocardial protrusion formation. Together, our data suggest that endocardial-myocardial communication through endocardial protrusions acts as an underlying principle allowing myocardial growth.
In this thesis, we use lattice QCD to study a part of the QCD phase diagram, specifically the QCD phase transition at mu=0, where the QCD matter changes from hadron gas to quark-gluon plasma (QGP) with increasing temperature.
This phase transition takes place as a crossover, but when theoretically changing the masses of the quarks, the order of the phase transition changes as well.
We focus on the region of heavy quark masses with Nf=2 flavours, where we investigate the critical quark mass at the second order phase transition in the form of a Z2 point between the first-order and the crossover region.
The first-order region is positioned at infinitely heavy quarks. As the quark masses decrease, the associated Z3 centre symmetry breaks explicitly, causing the first-order phase transition to weaken until it turns into the Z2 point and finally into a crossover.
We study this Z2 point using simulations at Nf=2 and lattices of the sizes Nt = {6, 8, 10, 12}, partially building on previous work, in which the simulations for Nt = {6, 8, 10} were started.
The simulations for Nt=12 are not finished yet though, but we were able to draw some preliminary conclusions. These simulations are run on GPUs and CPUs, using the codes Cl2QCD and open-QCD-FASTSUM, respectively. Afterwards, the data goes through a first analysis step in the form of the Python program PLASMA, preparing it for the two techniques we use to analyse the nature of the phase transition.
As a first, reliable analysis method, we perform a finite size scaling analysis of the data to find the location of the Z2 point. Since we are using lattice QCD, performing a continuum extrapolation is necessary to reach the continuum result.
In regard to this, the finite size scaling analysis method is hampered by the excessive amount of simulated data that is needed regarding statistics and the total number of simulations, which is why this thesis is only an intermediate step towards the continuum limit.
This also leads to the second analysis technique we explore in this thesis.
We start to design a Landau theory which describes the phase boundary for heavy masses at Nf=2 based on the simulated data.
We develop a Landau functional for every Nt we have simulation data for.
Albeit the results are not at the same precision as the ones from the finite size scaling analysis, we are able to reproduce the position of the Z2 point for every Nt.
Even though we are not able to take a continuum extrapolation right now, after more development takes place in future works, this approach might, in the long run, lead to a continuum result that won't need as many simulations as the finite size scaling analysis.