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Ubiquitination is an important post-translational modification that maintains cellular homeostasis by regulating various biological processes. Deubiquitinases (DUBs) are enzymes that reverse the ubiquitination process by catalyzing the removal of ubiquitin from a substrate. Abnormal expression or function of DUBs is often associated with the onset and progression of various diseases, including cancer. Ubiquitin specific proteases (USPs), which constitute the largest family of DUBs in humans, have become the center of interest as potential targets in cancer therapy as many of them display increased activity or are overexpressed in a range of malignant tumors or the tumor microenvironment.
Two related members of the USP family, USP28 and USP25, share high sequence identities but play diverse biological roles. USP28 regulates cell proliferation, oncogenesis, DNA damage repair and apoptosis, whereas USP25 is involved in the anti-viral response, innate immunity and ER-associated degradation in addition to carcinogenesis. USP28 and USP25 also exhibit different oligomeric states – while USP28 is a constitutively active dimer, USP25 assumes an auto-inhibited tetrameric structure. The catalytic domains of both USP28 and USP25 comprise the canonical, globular USP-domain but contain an additional, extended insertion site called USP25/28 catalytic domain inserted domain (UCID) that mediates oligomerization of the proteins. Disruption of the USP25 tetramer leads to the formation of an activated dimeric protein. However, it is still not clear what triggers its activation.
Due to their role in maintaining and stabilizing numerous oncoproteins, USP28 and USP25 have emerged as interesting candidates for anti-cancer therapy. Recent advances in small-molecular inhibitor development have led to the discovery of relatively potent inhibitors of USP28 and USP25. This thesis focuses on the structural elucidation of USP28 and the biochemical characterization of USP28/USP25, both in complex with representatives of three out of the eight compound classes reported as USP28/USP25-specific inhibitors. The crystal structures of USP28 in complex with the AZ compounds, Vismodegib and FT206 reveal that all three inhibitor classes bind into the same allosteric pocket distant from the catalytic center, located between the palm and the thumb subdomains (the S1-site). Intriguingly, this binding pocket is identical to the UCID-tip binding interface in the USP25 tetramer, rendering the protein in a locked, inactive conformation. Formation of the binding pocket in USP28 requires a shift in the helix α5, which induces conformational changes and local distortion of the binding channel that typically accommodates the C-terminal tail of Ubiquitin, thus preventing catalysis and abrogating USP28 activity. The key residues of the USP28-inhibitor binding pocket are highly conserved in USP25. Mutagenesis studies of these residues accompanied by biochemical and biophysical assays confirm the proposed mechanism of inhibition and similar binding to USP25.
This work provides valuable insights into the inhibition mechanism of the small molecule compounds specifically for the DUBs USP28 and USP25. The USP28-inhibitor complex structures offer a framework to develop more specific and potent inhibitors.
The production of commodities such as cocoa, rubber, oil palm and cashew, is the main driver of deforestation in West Africa (WA). The practiced production systems correspond to a land managment approach referred to as agroforestry systems (AFS), which consist of managing trees and crops on the same unit of land.Because of the ubiquity of trees, AFS reported as viable solution for climate mitigation; the carbon sequestrated by the trees could be estimated with remote sensing (RS) data and methods and reported as emission reduction efforts. However, the diversity in AFS in relation to their composition, structure and spatial distribution makes it challenging for an accurate monitoring of carbon stocks using RS. Therefore, the aim of this research is to propose a RS-based approach for the estimation of carbon sequestration in AFS across the climatic regions of WA. The main objectives were to (i) provide an accurate classification map of AFS by modelling the spatial distribution of the classification error; (ii) estimate the carbon stock of AFS in the main climatic regions of WA using RS data; (iii) evaluate the dynamic of carbon stocks within AFS across WA. Three regions of interest (ROI) were defined in Cote d'Ivoire and Burkina Faso, one in each climatic region of WA namely the Guineo-Congolian, Guinean and Sudanian, and three field campaigns were carried out for data collection. The collected data consisted of reference points for image classification, biometric tree measurements (diameter, height, species) for biomass estimation. A total of 261 samples were collected in 12 AFS across WA. For the RS data, yearly composite images from Sentinel-1 and -2 (S1 and S2), ALOS-PALSAR and GEDI data were used. A supervised classification using random forest (RF) was implemented and the classification error was assessed using the Shannon entropy generated from the class probabilities. For carbon estimation, different RS data, machine learning algorithms and carbon reference sources were compared for the prediction of the aboveground biomass in AFS. The assessment of the carbon dynamic was carried between 2017 and 2021. An average carbon map was genrated and use as reference for the comparison of annual carbon estimations, using the standard deviation as threshold. As far as the results are concerned, the classification accuracy was higher than 0.9 in all the ROIs, and AFS were mainly represented by rubber (38.9%), cocoa (36.4%), palm (10.8%) in the ROI-1, mango (15.2%) and cashew (13.4%) in ROI-2, shea tree (55.7%) and African locust bean (28.1%) in ROI-3. However, evidence of misclassification was found in cocoa, mango, and shea butter. The assessment of the classification error suggested that the error level was higher in the ROI-3 and ROI-1. The error generated from the entropy was able to reduced the level of misclassification by 63% with 11% of loss of information. Moreover, the approach was able to accuretely detect encroachement in protected areas. On carbon estimation, the highest prediction accuracy (R²>0.8) was obtained for a RF model using the combination of S1 and S2 and AGB derived from field measurements. Predictions from GEDI could only be used as reference in the ROI-1 but resulted in a prediction error was higher in cashew, mango, rubber and cocoa plantations, and the carbon stock level was higher in African locust bean (43.9 t/ha), shea butter (15 t/ha), cashew (13.8 t/ha), mango (12.8 t/ha), cocoa (7.51 t/ha) and rubber (7.33 t/ha). The analysis showed that carbon stock is determined mainly by the diameter (R²=0.45) and height (R²=0.13) of trees. It was found that crop plantations had the lowest biodiversity level, and no significant relationship was found between the considered biodiversity indices and carbon stock levels. The assessment of the spatial distribution of carbon sources and sinks showed that cashew plantations are carbon emitters due to firewood collection, while cocoa plantations showed the highest potential for carbon sequestration. The study revealed that Sentinel data could be used to support a RS-based approach for modelling carbon sequestration in AFS. Entropy could be used to map crop plantations and to monitor encroachment in protected areas. Moreover, field measurements with appropriate allometric models could ensure an accurate estimation of carbon stocks in AFS. Even though AFS in the Sudanian region had the highest carbon stocks level, there is a high potential to increase the carbon level in cocoa plantations by integrating and/or maintaining forest trees.
Defensive behaviors in response to threats are key factors in maintaining mental and physical health, but their phenomenology remains poorly understood. Prior work reported an inhibition of oculomotor activity in response to avoidable threat in humans that reminded of freezing behaviors in rodents. This notion of a homology between defensive responding in rodents and humans was seconded by concomitant heart rate decrease and skin conductance increase. However, several aspects of this presumed defense state remained ambiguous. For example, it was unclear whether the observed oculomotor inhibition would 1) robustly occur during preparation for threat-avoidance irrespective of task demands, 2) reflect a threat-specific defensive state, 3) be related to an inhibition of somatomotor activity as both motion metrics have been discussed as indicators for freezing behaviors in humans, and 4) manifest in unconstrained settings.
We thus embarked on a series of experiments to unravel the robustness, threat-specificity, and validity of previously observed (oculo)motor and autonomic dynamics upon avoidable threat in humans. We provided robust evidence for reduced gaze dispersion, significantly predicting the speed of subsequent motor reactions across a wide range of stimulus contexts. Along this gaze pattern, we found reductions in body movement and showed that the temporal profiles between gaze and body activity were positively related within individuals, suggesting that both metrics reflect the same construct. A simultaneous activation of the parasympathetic (i.e., heart rate deceleration) and sympathetic (i.e., increased skin conductance and pupil dilation) nervous system was present in both defensive and appetitive contexts, suggesting that these autonomic dynamics are not only sensitive to threat but reflecting a more general action-preparatory mechanism. We further gathered evidence for two previously proposed defensive states involving a decrease of (oculo)motor activity in a naturalistic, unconstrained virtual reality environment. Specifically, we observed a state consisting of a cessation of ongoing behaviors and orienting upon relatively distal, ambiguous threat (Attentive Immobility) while an entire immobilization and presumed allocation of attention to the threat stimulus became apparent upon approaching potential threat (Immobility under Attack).
Taken together, we provided evidence for specific oculomotor and autonomic dynamics upon increasing levels of threat that may inspire future translational work in rodents and humans on shared mechanisms of threat processing, ultimately supporting the development of novel therapeutic approaches.
The relation between LV function and cardiac MRI tissue characteristics in separate myocardial segments and their change over time has yet to be explored in myocarditis. Thus, our research aimed to investigate possible associations between global and regional myocardial T1 and T2 times and peak strain in patients with suspected myocarditis.
From 2012 to 2015, 129 patients with clinically suspected myocarditis of the prospective, observational MyoRacer-Trial underwent systematic biventricular EMB at baseline and cardiac MRI at baseline and after three months as a follow-up. We divided the LV myocardium into 17 segments and estimated the segmental myocardial strain using FT. We registered T1 and T2 maps to the cine sequences and transferred the segmentations used for FT to ensure conformity of the myocardial segments. Multi-level multivariable linear mixed effects regression was applied to investigate the relation of segmental myocardial strain to relaxation times and their respective change from baseline to follow-up.
We found a significant improvement in myocardial peak strain from baseline to follow-up (p < 0.001; all p-values given for likelihood ratio tests) and significant associations between higher T1 and T2 times and lower segmental myocardial peak strain (p ranging from < 0.001 to 0.049). E.g., regression coefficient (Reg. coef.) for segmental radial peak strain in short axis view (SRPS_SAX) and T1 time: -1.9, 95% CI (-2.6;-1.2) %/100 ms, p < 0.001. A decrease in T1 and T2 times from baseline to follow-up was also significantly related to a recovery of segmental peak strains (p ranging from < 0.001 to 0.050). E.g., Reg. coef. for SRPS_SAX per ΔT1: -1.8, 95% CI (-2.5;-1.0) %/100 ms, p < 0.001. Moreover, the higher the baseline T1 time, the more substantial the functional recovery from baseline to follow-up (p ranging from 0.004 to 0.042, e.g., for SRPS_SAX: Reg. coef. 1.3, 95% CI (0.4;2.1) %/100 ms, p 0.004). We did not find an effect modification by the presence of myocarditis in the EMB (p > 0.1).
Our cross-sectional and longitudinal analyses provide evidence of dose-dependent correlations between T1 and T2 relaxation times and myocardial peak strain in patients with clinical presentation of myocarditis, regardless of the EMB result. Thus, assessing strain values and mapping relaxation times helps estimate the functional prognosis in patients with clinically suspected myocarditis.
Colorectal cancer (CRC) is the second most common tumour disease in Germany, with the sequential accumulation of certain mutations playing a decisive role in the transition from adenoma to carcinoma. In particular, deregulation of the Wnt signalling pathway and the associated deregulated expression of the MYC oncoprotein play a crucial role. Targeting MYC thus represents an important therapeutic approach in the treatment of tumours. Since direct inhibition of MYC is challenging, various approaches have been pursued to date to target MYC indirectly. The MYC 5' UTR contains an internal ribosomal entry site (IRES), which has a particular role in the initiation of MYC translation, especially in multiple myeloma. As basis for this work, it was hypothesised on the basis of previous data that translation of MYC potentially occurs via its IRES in CRC as well. Based on this, two IRES inhibitors were tested for their potential to regulate MYC expression in CRC cells. In addition, alternative, 5’ UTR-dependent translation of MYC and interacting factors were investigated. EIF3D was identified as a MYC 5' UTR binding protein which has the potential to regulate MYC expression in CRC. The results of this work suggest that there is a link between eIF3D and MYC expression/translation, rendering eIF3D a potential therapeutic target for MYC-driven CRCs.
Colon carcinomas (CRC) are statistically among the most fatal cancer types and hence one of the top reasons for premature mortality in the developed world. CRC cells are characterized by high proliferation rates caused by deregulation of gene transcription of proto-oncogenes and general chromosomal instability. On macroscopic level, CRC cells show a strongly altered nutrient and energy metabolism.
This work presents research to understand general links between the metabolism and transcription alteration. Mainly focussing on glutamine dependency, shown in colon carcinoma cells and expression pathways of the pro-proliferation protein c-MYC.
Previous studies showed that a depletion of glutamine in the cultivation medium of colon carcinoma cell lines caused a proliferation arrest and a strong decrease of overall c-MYC levels. Re-addition of glutamine quickly replenished c-MYC levels through an unknown mechanism. Several proteins altering this regulation mechanism were identified and proposed as possible starting point for further in detail studies to unveil the precise biochemical pathway controlling c-MYC translation repression and reactivation in a rapid manner.
On a transcriptional level the formation of RNA:DNA hybrids, so called R-loops, was observed under glutamine depleted conditions. The introduction and overexpression of RNaseH1, a R-loop degrading enzyme, in combination with an ectopically expressed c-MYC variant, independent of cellular regulation mechanisms by deleting the regulatory 3’-UTR of the c-MYC gene, lead to a high rate of apoptotic cells in culture. Expression of a functionally inactive variant of RNaseH1 abolished this effect. This indicates a regulatory function of R-loops formed during glutamine starvation in the presence of c-MYC protein in a cell. Degradation of R-loops and high c-MYC levels in this stress condition had no imminent effect on the cell cycle progression is CRC cells but disturbed the nucleotide metabolism. Nucleotide triphosphates were strongly reduced in comparison to starving cells without R-loop degradation and proliferating cells.
This study proposes a model of a terminal cycle of transcription termination, unregulated initiation and elongation of transcription leading to a depletion of energy resources of cells. This could finally lead to high apoptosis of the cells. Sequencing experiments to determine a coinciding of termination sites and R-loop formation sides failed so far but show a starting point for further studies in this essential survival mechanism involving R-loop formation and c-MYC downregulation.
Over the years, hydrogels have been developed and used for a huge variety of different applications ranging from drug delivery devices to medical products. In this thesis, a poly(2-methyl-2-oxazoline) (POx) / poly(2-n-propyl-2-oxazine) (POzi) bioink was modified and analyzed for the use in biofabrication and targeted drug delivery. In addition, the protein fibrinogen (Fbg) was genetically modified for an increased stability towards plasmin degradation for its use as wound sealant.
In Chapter 1, a thermogelling, printable POx/POzi-based hydrogel was modified with furan and maleimide moieties in the hydrophilic polymer backbone facilitating post-printing maturation of the constructs via Diels-Alder chemistry. The modification enabled long-term stability of the hydrogel scaffolds in aqueous solutions which is necessary for applications in biofabrication or tissue engineering. Furthermore, we incorporated RGD-peptides into the hydrogel which led to cell adhesion and elongated morphology of fibroblast cells seeded on top of the scaffolds. Additional printing experiments demonstrate that the presented POx/POzi system is a promising platform for the use as a bioink in biofabrication.
Chapter 2 highlights the versatility of the POx/POzi hydrogels by adapting the system to a use in targeted drug delivery. We used a bioinspired approach for a bioorthogonal conjugation of insulin-like growth factor I (IGF-I) to the polymer using an omega-chain-end dibenzocyclooctyne (DBCO) modification and a matrix metalloprotease-sensitive peptide linker. This approach enabled a bioresponsive release of IGF-I from hydrogels as well as spatial control over the protein distribution in 3D printed constructs which makes the system a candidate for the use in personalized medicine.
Chapter 3 gives a general overview over the necessity of wound sealants and the current generations of fibrin sealants on the market including advantages and challenges. Furthermore, it highlights trends and potential new strategies to tackle current problems and broadens the toolbox for future generations of fibrin sealants.
Chapter 4 applies the concepts of recombinant protein expression and molecular engineering to a novel generation of fibrin sealants. In a proof-of-concept study, we developed a new recombinant fibrinogen (rFbg) expression protocol and a Fbg mutant that is less susceptible to plasmin degradation. Targeted lysine of plasmin cleavage sites in Fbg were exchanged with alanine or histidine in different parts of the molecule. The protein was recombinantly produced and restricted plasmin digest was analyzed using high resolution mass spectrometry. In addition to that, we developed a novel time resolved screening protocol for the detection of new potential plasmin cleavage sites for further amino acid exchanges in the fibrin sealant.
This thesis investigates the charged moments and the symmetry-resolved
entanglement entropy in the context of the AdS3/CFT2 duality. In the
first part, I focus on the holographic U(1) Chern-Simons-Einstein gravity,
a toy model of AdS3/CFT2 with U(1) Kac-Moody symmetry. I
start with the vacuum background with a single entangling interval. I
show that, apart from a partition function in the grand canonical ensemble,
the charged moments can also be interpreted as the two-point
function of vertex operators on the replica surface. For the holographic
description, I propose a duality between the bulk U(1) Wilson line and
the boundary vertex operators. I verify this duality by deriving the
effective action for the Chern-Simons fields and comparing the result
with the vertex correlator. In the twist field approach, I show that the
charged moments are given by the correlation function of the charged
twist operators and the additional background operators. To solve the
correlation functions involved, I prove the factorization of the U(1) extended
conformal block into a U(1) block and a Virasoro block. The
general expression for the U(1) block is derived by directly summing
over the current descendant states, and the result shows that it takes
an identical form as the vertex correlators. This leads to the conclusion
that the disjoint Wilson lines compute the neutral U(1) block. The final
result for the symmetry-resolved entanglement entropy shows that
it is always charge-independent in this model. In the second part, I
study charged moments in higher spin holography, where the boundary
theory is a CFT with W3 symmetry. I define the notion of the
higher spin charged moments by introducing a spin-3 modular charge
operator. Restricting to the vacuum background with a single entangling
interval, I employ the grand canonical ensemble interpretation
and calculate the charged moments via the known higher spin black
hole solution. On the CFT side, I perform a perturbative expansion for
the higher spin charged moments in terms of the connected correlation
functions of the spin-3 modular charge operators. Using the recursion
relation for the correlation functions of the W3 currents, I evaluate the
charged moments up to the quartic order of the chemical potential. The
final expression matches with the holographic result. My results both
for U(1) Chern-Simons Einstein gravity and W3 higher spin gravity
constitute novel checks of the AdS3/CFT2 correspondence.
Interactions between host and pathogen determine the development, progression and outcomes
of disease. Medicine benefits from better descriptions of these interactions through increased
precision of prevention, diagnosis and treatment of diseases. Single-cell genomics is a
disruptive technology revolutionizing science by increasing the resolution with which we study
diseases. Cell type specific changes in abundance or gene expression are now routinely investigated
in diseases. Meanwhile, detecting cellular phenotypes across diseases can connect
scientific fields and fuel discovery. Insights acquired through systematic analysis of high resolution
data will soon be translated into clinical practice and improve decision making. Therefore,
the continued use of single-cell technologies and their application towards clinical samples will
improve molecular interpretation, patient stratification, and the prediction of outcomes.
In the past years, I was fortunate to participate in interdisciplinary research groups bridging
biology, clinical research and data science. I was able to contribute to diverse projects through
computational analysis and biological interpretation of sequencing data. Together, we were
able to discover cellular phenotypes that influence disease progression and outcomes as well
as the response to treatment. Here, I will present four studies that I have conducted in my PhD.
First, we performed a case study of relapse from cell-based immunotherapy in Multiple Myeloma.
We identified genomic deletion of the epitope as mechanism of immune escape and implicate
heterozygosity or monosomy of the genomic locus at baseline as a potential risk factor. Second,
we investigated the pathomechanisms of severe COVID-19 at the earliest stage of the COVID-
19 pandemic in Germany in March 2020. We discovered that profibrotic macrophages and
lung fibrosis can be caused by SARS-CoV-2 infection. Third, we used a mouse model of chronic
infection with Staphylococcus aureus that causes Osteomyelitis similar to the human disease.
We were able to identify dysregulated immunometabolism associated with the generation of
myeloid-derived suppressor cells (MDSC). Fourth, we investigated Salmonella infection of the
human small intestine in an in vitro model and describe features of pathogen invasion and host
response.
Overall, I have been able to successfully employ single-cell sequencing to discover important
aspects of diseases ranging from development to treatment and outcome. I analyzed samples
from the clinics, human donors, mouse models and organoid models to investigate different
aspects of diseases and managed to integrate data across sample types, technologies and
diseases. Based on successful studies, we increased our efforts to combine data from multiple
sources to build comprehensive references for the integration of large collections of clinical
samples. Our findings exemplify how single-cell sequencing can improve clinical research and
highlights the potential of mechanistic discoveries to drive precision medicine.
African trypanosomes are unicellular parasites that cause nagana and sleeping sickness in livestock and man, respectively. The major pathogens for the animal disease include Trypanosoma vivax, T. congolense, and T. brucei brucei, whereas T. b. gambiense and T. b. rhodesiense are responsible for human infections. Given that the bloodstream form (BSF) of African trypanosomes is exclusively extracellular, its cell surface forms a critical boundary with the host environment. The cell surface of the BSF African trypanosomes is covered by a dense coat of immunogenic variant surface glycoproteins (VSGs). This surface protein acts as an impenetrable shield that protects the cells from host immune factors and is also involved in antibody clearance and antigenic variation, which collectively ensure that the parasite stays ahead of the host immune system. Gene expression in T. brucei is markedly different from other eukaryotes: most genes are transcribed as long polycistronic units, processed by trans-splicing a 39-nucleotide mini exon at the 5′ and polyadenylation at the 3′ ends of individual genes to generate the mature mRNA.
Therefore, gene expression in T. brucei is regulated post-transcriptionally, mainly by the action of RNA binding proteins (RBPs) and conserved elements in the 3′ untranslated regions (UTR) of transcripts. The expression of VSGs is highly regulated, and only a single VSG gene is expressed at a time from one of the ~15 subtelomeric domains termed bloodstream expression sites (BES). When cells are engineered to simultaneously express two VSGs, the total VSG mRNA do not exceed the wild type amounts. This suggests that a robust VSG mRNA balancing mechanism exists in T. brucei. The present study uses inducible and constitutive expression of ectopic VSG genes to show that the endogenous VSG mRNA is regulated only if the second VSG is properly targeted to the ER. Additionally, the endogenous VSG mRNA response is triggered when high amounts of the GFP reporter with a VSG 3′UTR is targeted to the ER. Further evidence that non-VSG ER import signals can efficiently target VSGs to the ER is presented. This study suggests that a robust trans-regulation of the VSG mRNA is elicited at the ER through a feedback loop to keep the VSG transcripts in check and avoid overshooting the secretory pathway capacity.
Further, it was shown that induction of expression of the T. vivax VSG ILDat1.2 in T. brucei causes a dual cell cycle arrest, with concomitant upregulation of the protein associated with differentiation (PAD1) expression. It could be shown that T. vivax VSG ILDat1.2 can only be sufficiently expressed in T. brucei after replacing its native GPI signal peptide with that of a T. brucei VSG. Taken together, these data indicate that inefficient VSG GPI anchoring and expression of low levels of the VSG protein can trigger differentiation from slender BSF to stumpy forms. However, a second T. vivax VSG, ILDat2.1, is not expressed in T. brucei even after similar modifications to its GPI signals. An X-ray crystallography approach was utilized to solve the N-terminal domain (NTD) structure of VSG ILDat1.2. This is first structure of a non-T. brucei VSG, and the first of a surface protein of T. vivax to be solved. VSG ILDat1.2 NTD maintains the three-helical bundle scaffold conserved in T. brucei surface proteins. However, it is likely that there are variations in the architecture of the membrane proximal region of the ILDat1.2 NTD and its CTD from T. brucei VSGs. The tractable T. brucei system is presented as a model that can be used to study surface proteins of related trypanosome species, thus creating avenues for further characterization of trypanosome surface coats.
Among the defense strategies developed in microbes over millions of years, the innate adaptive CRISPR-Cas immune systems have spread across most of bacteria and archaea. The flexibility, simplicity, and specificity of CRISPR-Cas systems have laid the foundation for CRISPR-based genetic tools. Yet, the efficient administration of CRISPR-based tools demands rational designs to maximize the on-target efficiency and off-target specificity. Specifically, the selection of guide RNAs (gRNAs), which play a crucial role in the target recognition of CRISPR-Cas systems, is non-trivial. Despite the fact that the emerging machine learning techniques provide a solution to aid in gRNA design with prediction algorithms, design rules for many CRISPR-Cas systems are ill-defined, hindering their broader applications.
CRISPR interference (CRISPRi), an alternative gene silencing technique using a catalytically dead Cas protein to interfere with transcription, is a leading technique in bacteria for functional interrogation, pathway manipulation, and genome-wide screens. Although the application is promising, it also is hindered by under-investigated design rules. Therefore, in this work, I develop a state-of-art predictive machine learning model for guide silencing efficiency in bacteria leveraging the advantages of feature engineering, data integration, interpretable AI, and automated machine learning. I first systematically investigate the influential factors that attribute to the extent of depletion in multiple CRISPRi genome-wide essentiality screens in Escherichia coli and demonstrate the surprising dominant contribution of gene-specific effects, such as gene expression level. These observations allowed me to segregate the confounding gene-specific effects using a mixed-effect random forest (MERF) model to provide a better estimate of guide efficiency, together with the improvement led by integrating multiple screens. The MERF model outperformed existing tools in an independent high-throughput saturating screen. I next interpret the predictive model to extract the design rules for robust gene silencing, such as the preference for cytosine and disfavoring for guanine and thymine within and around the protospacer adjacent motif (PAM) sequence. I further incorporated the MERF model in a web-based tool that is freely accessible at www.ciao.helmholtz-hiri.de.
When comparing the MERF model with existing tools, the performance of the alternative gRNA design tool optimized for CRISPRi in eukaryotes when applied to bacteria was far from satisfying, questioning the robustness of prediction algorithms across organisms. In addition, the CRISPR-Cas systems exhibit diverse mechanisms albeit with some similarities. The captured predictive patterns from one dataset thereby are at risk of poor generalization when applied across organisms and CRISPR-Cas techniques. To fill the gap, the machine learning approach I present here for CRISPRi could serve as a blueprint for the effective development of prediction algorithms for specific organisms or CRISPR-Cas systems of interest. The explicit workflow includes three principle steps: 1) accommodating the feature set for the CRISPR-Cas system or technique; 2) optimizing a machine learning model using automated machine learning; 3) explaining the model using interpretable AI. To illustrate the applicability of the workflow and diversity of results when applied across different bacteria and CRISPR-Cas systems, I have applied this workflow to analyze three distinct CRISPR-Cas genome-wide screens. From the CRISPR base editor essentiality screen in E. coli, I have determined the PAM preference and sequence context in the editing window for efficient editing, such as A at the 2nd position of PAM, A/TT/TG downstream of PAM, and TC at the 4th to 5th position of gRNAs. From the CRISPR-Cas13a screen in E. coli, in addition to the strong correlation with the guide depletion, the target expression level is the strongest predictor in the model, supporting it as a main determinant of the activation of Cas13-induced immunity and better characterizing the CRISPR-Cas13 system. From the CRISPR-Cas12a screen in Klebsiella pneumoniae, I have extracted the design rules for robust antimicrobial activity across K. pneumoniae strains and provided a predictive algorithm for gRNA design, facilitating CRISPR-Cas12a as an alternative technique to tackle antibiotic resistance.
Overall, this thesis presents an accurate prediction algorithm for CRISPRi guide efficiency in bacteria, providing insights into the determinants of efficient silencing and guide designs. The systematic exploration has led to a robust machine learning approach for effective model development in other bacteria and CRISPR-Cas systems. Applying the approach in the analysis of independent CRISPR-Cas screens not only sheds light on the design rules but also the mechanisms of the CRISPR-Cas systems. Together, I demonstrate that applied machine learning paves the way to a deeper understanding and a broader application of CRISPR-Cas systems.
Cognition refers to the ability to of animals to acquire, process, store and use vital information from the environment. Cognitive processes are necessary to predict the future and reduce the uncertainty of the ever-changing environment. Classically, research on animal cognition focuses on decisive cognitive tests to determine the capacity of a species by the testing the ability of a few individuals. This approach views variability between these tested key individuals as unwanted noise and is thus often neglected. However, inter-individual variability provides important insights to behavioral plasticity, cognitive specialization and brain modularity. Honey bees Apis mellifera are a robust and traditional model for the study of learning, memory and cognition due to their impressive capabilities and rich behavioral repertoire. In this thesis I have applied a novel view on the learning abilities of honey bees by looking explicitly at individual differences in a variety of learning tasks. Are some individual bees consistently smarter than some of her sisters? If so, will a smart individual always perform good independent of the time, the context and the cognitive requirements or do bees show distinct isolated ‘cognitive modules’?
My thesis presents the first comprehensive investigation of consistent individual differences in the cognitive abilities of honey bees. To speak of an individual as behaving consistently, a crucial step is to test the individual multiple times to examine the repeatability of a behavior. I show that free-flying bees remain consistent in a visual discrimination task for three consecutive days. Successively, I explored individual consistency in cognitive proficiency across tasks involving different sensory modalities, contexts and cognitive requirements. I found that free-flying bees show a cognitive specialization between visual and olfactory learning but remained consistent across a simple discrimination task and a complex concept learning task. I wished to further explore individual consistency with respect to tasks of different cognitive complexity, a question that has never been tackled before in an insect. I thus performed a series of four experiments using either visual or olfactory stimuli and a different training context (free-flying and restrained) and tested bees in a discrimination task, reversal learning and negative patterning. Intriguingly, across all these experiments I evidenced the same results: The bees’ performances were consistent across the discrimination task and reversal learning and negative patterning respectively. No association was evidenced between reversal learning and negative patterning. After establishing the existence of consistent individual differences in the cognitive proficiency of honey bees I wished to determine factors which could underlie these differences. Since genetic components are known to underlie inter-individual variability in learning abilities, I studied the effects of genetics on consistency in cognitive proficiency by contrasting bees originating from either from a hive with a single patriline (low genetic diversity) or with multiple patrilines (high genetic diversity). These two groups of bees showed differences in the patterns of individually correlated performances, indicating a genetic component accounts for consistent cognitive individuality. Another major factor underlying variability in learning performances is the individual responsiveness to sucrose solution and to visual stimuli, as evidenced by many studies on restrained bees showing a positive correlation between responsiveness to task relevant stimuli and learning performances. I thus tested whether these relationships between sucrose/visual responsiveness and learning performances are applicable for free-flying bees. Free-flying bees were again subjected to reversal learning and negative patterning and subsequently tested in the laboratory for their responsiveness to sucrose and to light. There was no evidence of a positive relationship between sucrose/visual responsiveness and neither performances of free-flying bees in an elemental discrimination, reversal learning and negative patterning. These findings indicate that relationships established between responsiveness to task relevant stimuli and learning proficiency established in the laboratory with restrained bees might not hold true for a completely different behavioral context i.e. for free-flying bees in their natural environment.
These results show that the honey bee is an excellent insect model to study consistency in cognitive proficiency and to identify the underlying factors. I mainly discuss the results with respect to the question of brain modularity in insects and the adaptive significance of individuality in cognitive abilities for honey bee colonies. I also provide a proposition of research questions which tie in this theme of consistent cognitive proficiency and could provide fruitful areas for future research.
In 2020, cancer was the leading cause of death worldwide, accounting for nearly 10 million deaths. Lung cancer was the most common cancer, with 2.21 million cases per year in both sexes. This non-homogeneous disease is further subdivided into small cell lung cancer (SCLC, 15%) and non-small cell lung cancer (NSCLC, 85%). By 2023, the American Cancer Society estimates that NSCLC will account for 13% of all new cancer cases and 21% of all estimated cancer deaths. In recent years, the treatment of patients with NSCLC has improved with the development of new therapeutic interventions and the advent of targeted and personalised therapies. However, these advances have only marginally improved the five-year survival rate, which remains alarmingly low for patients with NSCLC. This observation highlights the importance of having more appropriate experimental and preclinical models to recapitulate, identify and test novel susceptibilities in NSCLC. In recent years, the Trp53fl/fl KRaslsl-G12D/wt mouse model developed by Tuveson, Jacks and Berns has been the main in vivo model used to study NSCLC. This model mimics ADC and SCC to a certain extent. However, it is limited in its ability to reflect the genetic complexity of NSCLC. In this work, we use CRISPR/Cas9 genome editing with targeted mutagenesis and gene deletions to recapitulate the conditional model. By comparing the Trp53fl/fl KRaslsl- G12D/wt with the CRISPR-mediated Trp53mut KRasG12D, we demonstrated that both showed no differences in histopathological features, morphology, and marker expression. Furthermore, next-generation sequencing revealed a very high similarity in their transcriptional profile. Adeno-associated virus-mediated tumour induction and the modular design of the viral vector allow us to introduce additional mutations in a timely manner. CRISPR-mediated mutation of commonly mutated tumour suppressors in NSCLC reliably recapitulated the phenotypes described in patients in the animal model. Lastly, the dual viral approach could induce the formation of lung tumours not only in constitutive Cas9 expressing animals, but also in wildtype animals. Thus, the implementation of CRISPR genome editing can rapidly advance the repertoire of in vivo models for NSCLC research. Furthermore, it can reduce the necessity of extensive breeding.
Physical regimes characterized by low Mach numbers and steep stratifications pose severe challenges to standard finite volume methods. We present three new methods specifically designed to navigate these challenges by being both low Mach compliant and well-balanced. These properties are crucial for numerical methods to efficiently and accurately compute solutions in the regimes considered.
First, we concentrate on the construction of an approximate Riemann solver within Godunov-type finite volume methods. A new relaxation system gives rise to a two-speed relaxation solver for the Euler equations with gravity. Derived from fundamental mathematical principles, this solver reduces the artificial dissipation in the subsonic regime and preserves hydrostatic equilibria. The solver is particularly stable as it satisfies a discrete entropy inequality, preserves positivity of density and internal energy, and suppresses checkerboard modes.
The second scheme is designed to solve the equations of ideal MHD and combines different approaches. In order to deal with low Mach numbers, it makes use of a low-dissipation version of the HLLD solver and a partially implicit time discretization to relax the CFL time step constraint. A Deviation Well-Balancing method is employed to preserve a priori known magnetohydrostatic equilibria and thereby reduces the magnitude of spatial discretization errors in strongly stratified setups.
The third scheme relies on an IMEX approach based on a splitting of the MHD equations. The slow scale part of the system is discretized by a time-explicit Godunov-type method, whereas the fast scale part is discretized implicitly by central finite differences. Numerical dissipation terms and CFL time step restriction of the method depend solely on the slow waves of the explicit part, making the method particularly suited for subsonic regimes. Deviation Well-Balancing ensures the preservation of a priori known magnetohydrostatic equilibria.
The three schemes are applied to various numerical experiments for the compressible Euler and ideal MHD equations, demonstrating their ability to accurately simulate flows in regimes with low Mach numbers and strong stratification even on coarse grids.
The WHO-designated neglected-disease pathogen Chlamydia trachomatis (CT) is a gram-negative bacterium responsible for the most frequently diagnosed sexually transmitted infection worldwide. CT infections can lead to infertility, blindness and reactive arthritis, among others. CT acts as an infectious agent by its ability to evade the immune response of its host, which includes the impairment of the NF-κB mediated inflammatory response and the Mcl1 pro-apoptotic pathway through its deubiquitylating, deneddylating and transacetylating enzyme ChlaDUB1 (Cdu1). Expression of Cdu1 is also connected to host cell Golgi apparatus fragmentation, a key process in CT infections.
Cdu1 may this be an attractive drug target for the treatment of CT infections. However, a lead molecule for the development of novel potent inhibitors has been unknown so far. Sequence alignments and phylogenetic searches allocate Cdu1 in the CE clan of cysteine proteases. The adenovirus protease (adenain) also belongs to this clan and shares a high degree of structural similarity with Cdu1. Taking advantage of topological similarities between the active sites of Cdu1 and adenain, a target-hopping approach on a focused set of adenain inhibitors, developed at Novartis, has been pursued. The thereby identified cyano-pyrimidines represent the first active-site directed covalent reversible inhibitors for Cdu1. High-resolution crystal structures of Cdu1 in complex with the covalently bound cyano-pyrimidines as well as with its substrate ubiquitin have been elucidated. The structural data of this thesis, combined with enzymatic assays and covalent docking studies, provide valuable insights into Cdu1s activity, substrate recognition, active site pocket flexibility and potential hotspots for ligand interaction. Structure-informed drug design permitted the optimization of this cyano-pyrimidine based scaffold towards HJR108, the first molecule of its kind specifically designed to disrupt the function of Cdu1. The structures of potentially more potent and selective Cdu1 inhibitors are herein proposed.
This thesis provides important insights towards our understanding of the structural basis of ubiquitin recognition by Cdu1, and the basis to design highly specific Cdu1 covalent inhibitors.
This dissertation explores the development and assessment of inhibitory control – a crucial component of executive functions – in young children. Inhibitory control, defined as the ability to suppress inappropriate responses (Verbruggen & Logan, 2008), is essential for adaptable and goal-oriented behavior. The rapid and non-linear development of this cognitive function in early childhood presents unique challenges for accurate assessment. As children age, they often exhibit a ceiling effect in terms of response accuracy (Petersen et al., 2016), underscoring the need to consider response latency as well. Ideally, combining response latency with accuracy could yield a more precise measure of inhibitory control (e.g., Magnus et al., 2019), facilitating a detailed tracking of developmental changes in inhibitory control across a wider age spectrum. The three studies of this dissertation collectively aim to clarify the relationship between response accuracy, response latency, and inhibitory control across different stages of child development. Each study utilizes a computerized Pointing Stroop Task (Berger et al., 2000) to measure inhibitory control, examining the task's validity and the integration of dual metrics for a more comprehensive evaluation.
The first study focuses on establishing the validity of using both response accuracy and latency as indicators of inhibitory control. Utilizing the framework of explanatory item-response modeling (De Boeck & Wilson, 2004), the study revealed how the task characteristics congruency and item position influence both the difficulty level and timing aspects in young children’s responses in the computerized Pointing Stroop task. Further, this study found that integrating response accuracy with latency, even in a basic manner, provides additional insights. Building upon these findings, the second study investigates the nuances of integrating response accuracy and latency, examining whether this approach can account for age-related differences in inhibitory control. It also explores whether response latencies may contain different information depending on the age and proficiency of the children. The study leverages novel and established methodological perspectives to integrate response accuracy and latency into a single metric, showing the potential applicability of different approaches for assessing inhibitory control development. The third study extends the investigation to a longitudinal perspective, exploring the dynamic relationship between response accuracy, latency, and inhibitory control over time. It assesses whether children who achieve high accuracy at an earlier age show faster improvement in response latency, suggesting a non-linear maturation pathway of inhibitory control. The study also examines if the predictive value of early response latency for later fluid intelligence is dependent on the response accuracy level.
Together, these empirical studies contribute to a more robust understanding of the complex interaction between inhibitory control, response accuracy, and response latency, facilitating valid evaluations of cognitive capabilities in children. Moreover, the findings may have practical implications for designing educational strategies and clinical interventions that address the developmental trajectory of inhibitory control. The nuanced approach advocated in this dissertation suggests prioritizing accuracy in assessment and interventions during the early stages of children's cognitive development, gradually shifting the focus to response latency as children mature and secure their inhibitory control abilities.
The focus of this thesis is on analysing a linear stochastic partial differential equation (SPDE) with a bounded domain. The first part of the thesis commences with an examination of a one-dimensional SPDE. In this context, we construct estimators for the parameters of a parabolic SPDE based on discrete observations of a solution in time and space on a bounded domain. We establish central limit theorems for a high-frequency asymptotic regime, showing substantially smaller asymptotic variances compared to existing estimation methods. Moreover, asymptotic confidence intervals are directly feasible. Our approach builds upon realized volatilities and their asymptotic illustration as the response of a log-linear model with a spatial explanatory variable. This yields efficient estimators based on realized volatilities with optimal rates of convergence and minimal variances. We demonstrate our results by Monte Carlo simulations.
Extending this framework, we analyse a second-order SPDE model in multiple space dimensions in the second part of this thesis and develop estimators for the parameters of this model based on discrete observations in time and space on a bounded domain. While parameter estimation for one and two spatial dimensions was established in recent literature, this is the first work that generalizes the theory to a general, multi-dimensional framework. Our methodology enables the construction of an oracle estimator for volatility within the underlying model. For proving central limit theorems, we use a high-frequency observation scheme. To showcase our results, we conduct a Monte Carlo simulation, highlighting the advantages of our novel approach in a multi-dimensional context.
Two-dimensional lattices are in the focus of research in modern solid state physics due to their novel and exotic electronic properties with tremendous potential for seminal future applications. Of particular interest within this research field are quantum spin Hall insulators which are characterized by an insulating bulk with symmetry-protected metallic edge states. For electrons within these one-dimensional conducting channels, spin-momentum locking enables dissipationless transport - a property which promises nothing short of a revolution for electronic devices. So far, however, quantum spin Hall materials require enormous efforts to be realized such as cryogenic temperatures or ultra-high vacuum. A potential candidate to overcome these shortcomings are two-dimensional lattices of the topological semi-metal antimony due to their potential to host the quantum spin Hall effect while offering improved resilience against oxidation.
In this work, two-dimensional lattices of antimony on different substrates, namely Ag(111), InSb(111) and SiC(0001), are investigated regarding their atomic structure and electronic properties with complimentary surface sensitive techniques. In addition, a systematic oxidation study compares the stability of Sb-SiC(0001) with that of the two-dimensional topological insulators bismuthene-SiC(0001) and indenene-SiC(0001).
A comprehensive experimental analysis of the \((\sqrt{3}\times\sqrt{3})R30^\circ\) Sb-Ag(111) surface, including X-ray standing wave measurements, disproves the proclaimed formation of a buckled antimonene lattice in literature. The surface lattice can instead be identified as a metallic Ag\(_2\)Sb surface alloy.
Antimony on InSb(111) shows an unstrained Volmer-Weber island growth due to its large lattice mismatch to the substrate. The concomitant moir\'{e} situation at the interface imprints mainly in a periodic height corrugation of the antimony islands which as observed with scanning tunneling microscopy. On islands with various thicknesses, quasiparticle interference patterns allow to trace the topological surface state of antimony down to the few-layer limit.
On SiC(0001), two different two-dimensional antimony surface reconstructions are identified. Firstly, a metallic triangular $1\times1$ lattice which constitutes the antimony analogue to the topological insulator indenene. Secondly, an insulating asymmetric kagome lattice which represents the very first realized atomic surface kagome lattice.
A comparative, systematic oxidation study of elemental (sub-)monolayer materials on SiC(0001) reveals a high sensitivity of indenene and bismuthene to small dosages of oxygen. An improved resilience is found for Sb-SiC(0001) which, however, oxidizes nevertheless if exposed to oxygen. These surface lattices are therefore not suitable for future applications without additional protective measures.
In this thesis, a variety of Fokker--Planck (FP) optimal control problems are investigated. Main emphasis is put on a first-- and second--order analysis of different optimal control problems, characterizing optimal controls, establishing regularity results for optimal controls, and providing a numerical analysis for a Galerkin--based numerical scheme.
The Fokker--Planck equation is a partial differential equation (PDE) of linear parabolic type deeply connected to the theory of stochastic processes and stochastic differential equations. In essence, it describes the evolution over time of the probability distribution of the state of an object or system of objects under the influence of both deterministic and stochastic forces.
The FP equation is a cornerstone in understanding and modeling phenomena ranging from the diffusion and motion of molecules in a fluid to the fluctuations in financial markets.
Two different types of optimal control problems are analyzed in this thesis. On the one hand, Fokker--Planck ensemble optimal control problems are considered that have a wide range of applications in controlling a system of multiple non--interacting objects. In this framework, the goal is to collectively drive each object into a desired state.
On the other hand, tracking--type control problems are investigated, commonly used in parameter identification problems or stemming from the field of inverse problems.
In this framework, the aim is to determine certain parameters or functions of the FP equation, such that the resulting probability distribution function takes a desired form, possibly observed by measurements.
In both cases, we consider FP models where the control functions are part of the drift, arising only from the deterministic forces of the system. Therefore, the FP optimal control problem has a bilinear control structure.
Box constraints on the controls may be present, and the focus is on time--space dependent controls for ensemble--type problems and on only time--dependent controls for tracking--type optimal control problems.
In the first chapter of the thesis, a proof of the connection between the FP equation and stochastic differential equations is provided. Additionally, stochastic optimal control problems, aiming to minimize an expected cost value, are introduced, and the corresponding formulation within a deterministic FP control framework is established.
For the analysis of this PDE--constrained optimal control problem, the existence, and regularity of solutions to the FP problem are investigated. New $L^\infty$--estimates for solutions are established for low space dimensions under mild assumptions on the drift. Furthermore, based on the theory of Bessel potential spaces, new smoothness properties are derived for solutions to the FP problem in the case of only time--dependent controls. Due to these properties, the control--to--state map, which associates the control functions with the corresponding solution of the FP problem, is well--defined, Fréchet differentiable and compact for suitable Lebesgue spaces or Sobolev spaces.
The existence of optimal controls is proven under various assumptions on the space of admissible controls and objective functionals. First--order optimality conditions are derived using the adjoint system. The resulting characterization of optimal controls is exploited to achieve higher regularity of optimal controls, as well as their state and co--state functions.
Since the FP optimal control problem is non--convex due to its bilinear structure, a first--order analysis should be complemented by a second--order analysis.
Therefore, a second--order analysis for the ensemble--type control problem in the case of $H^1$--controls in time and space is performed, and sufficient second--order conditions are provided. Analogous results are obtained for the tracking--type problem for only time--dependent controls.
The developed theory on the control problem and the first-- and second--order optimality conditions is applied to perform a numerical analysis for a Galerkin discretization of the FP optimal control problem. The main focus is on tracking-type problems with only time--dependent controls. The idea of the presented Galerkin scheme is to first approximate the PDE--constrained optimization problem by a system of ODE--constrained optimization problems. Then, conditions on the problem are presented such that the convergence of optimal controls from one problem to the other can be guaranteed.
For this purpose, a class of bilinear ODE--constrained optimal control problems arising from the Galerkin discretization of the FP problem is analyzed. First-- and second--order optimality conditions are established, and a numerical analysis is performed. A discretization with linear finite elements for the state and co--state problem is investigated, while the control functions are approximated by piecewise constant or piecewise quadratic continuous polynomials. The latter choice is motivated by the bilinear structure of the optimal control problem, allowing to overcome the discrepancies between a discretize--then--optimize and optimize--then--discretize approach. Moreover, second--order accuracy results are shown using the space of continuous, piecewise quadratic polynomials as the discrete space of controls. Lastly, the theoretical results and the second--order convergence rates are numerically verified.
This work presents the first ILT observations of high redshift blazars and their study in terms of jet evolution, morphology, and interaction with the surrounding medium. Each of these represents a highly topical area of astronomywith a large number of open questions. To better understand Active Galactic Nuclei (AGN) and their fundamental inner workings, new techniques are needed to exploit the full potential of the next generation of radio interferometers. Some of these tools are presented here and applied to one of the latest generation of software radio telescopes. A major focus of the studies presented is on the unification model, where the observed blazars are discussed for their properties to be rotated counterparts of Fanaroff-Riley Class II (FR-II) radio galaxies, when classified as Flat Spectrum Radio Quasars (FSRQs). In addition, multiwavelength information has been included in the analysis. Both studies are feasibility studies that will serve as a basis for future similar studies. The characteristics discussed and their interpretation do not allow conclusions to be drawn for their respective populations. However, by applying them to a larger number of targets, population studies will be possible. The first chapters introduce the necessary topics, AGN, principles of radio observations and ILT, in the necessary depth to provide the reader with a solid knowledge base. They are particularly important for understanding the current limits and influences of uncertainties in the observation, calibration and imaging process. But they also shed light on realistic future improvements. A particular focus is on the development and evolution of the LOw-Frequency ARray (LOFAR)-Very Long Baseline Interferometry (VLBI) pipeline. With the tools at hand, the first study addresses the high redshift blazar S5 0836+710 $(z=2.218)$, which has been observed at various wavelengths and resolutions. It has a disrupted one-sided jet with an associated extended region further out. Despite the excellent wavelength coverage, only the additional ILT observations provided a complete picture of the source. With the data, the extended region could be classified as a hotspot moving at slightly relativistic speeds.. With the ILT data it was also possible to extract the flux of the core region of the AGN, and in projection to reveal the mixed counter-hotspot behind it. This also allowed constraints on jet parameters and environmental properties to be modelled, which were previously inconclusive. Technically, this study shows that the ILT can be used as an effective VLBI array for compact sources with small angular scales. However, the detection of faint components beyond redshifts of $z=2$ may require the capabilities of the Square Kilometre Array (SKA) to provide a significant number of detections to enable statistical conclusions. The second study uses a much improved calibration pipeline to analyse the high redshift blazar GB1508+5714 $(z=4.30)$. The ILT data revealed a previously unseen component in the eastern direction. A spectral index map was generated from the Karl G. Jansky Very Large Array (VLA) data, showing spectral index values of $-1.2_{-0.2}^{+0.4}$ for the western component, steeper than $-1.1$ for the eastern region, and $0.023 \pm 0.007$ for the core. Using the information provided by the ILT observation, as well as multi-wavelength information from other observations ranging from the long radio wavelengths to the $\gamma$ regime, four models were developed to interpret the observed flux with different emission origins. This also allowed to test a proposed interaction channel of the electrons provided by the jet, to cool off via inverse compton scattering with the Cosmic Microwave Background (CMB) photons, rather than by the usual synchrotron emission. This is referred to as cmb quenching in the literature, which could be shown in the study, to be necessary in any case. Finally, one of the four models was considered in which the hotspots in the detected components are unresolved and mixed by the lobe emission, with the X-ray emission coming from the lobes and partially mixed by the bright core region. The results of this preferred model are consistent with hotspots in a state of equipartition and lobes almost so. The study shows that high redshift blazars can be studied with the ILT, and expanding the sample of high redshift blazars resolved at multiple frequencies will allow a statistical study of the population. Finally, this work successfully demonstrates the powerful capabilities of the ILT to address questions that were previously inaccessible. The current state of the LOFAR-VLBI pipeline, when properly executed, allows work on the most challenging objects and will only improve in the future. In particular, this gives a glimpse of the possibilities that SKA will bring to astronomy.