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In eukaryotes, the enormously long DNA molecules need to be packaged together with histone proteins into nucleosomes and further into compact chromatin structures to fit it into the nucleus. This nuclear organisation interferes with all phases of transcription that require the polymerase to bind to DNA. During transcription – the process in which the hereditary information stored in DNA is transferred to many transportable RNA molecules - nucleosomes form a physical obstacle for polymerase progression. Thus, transcription is usually accompanied by processes mediating nucleosome destabilisation, including post-translational histone modifications (PTMs) or exchange of canonical histones by their variant forms. To the best of our knowledge, acetylation of histones has the highest capability to induce chromatin opening. The lysine modification can destabilise histone-DNA interactions within a nucleosome and can serve as a binding site for various chromatin remodelers that can modify the nucleosome composition. For example, H4 acetylation can impede chromatin folding and can stimulate the exchange of canonical H2A histone by its variant form H2A.Z at transcription start sites (TSSs) in many eukaryotes, including humans. As histone H4, H2A.Z can be post-translationally acetylated and as acetylated H4, acetylated H2A.Z is enriched at TSSs suggested to be critical for transcription. However, thus far, it has been difficult to study the cause and consequence of H2A.Z acetylation.
Even though, genome-wide chromatin profiling studies such as ChIP-seq have already revealed the genomic localisation of many histone PTMs and variant proteins, they can only be used to study individual chromatin marks and not to identify all factors important for establishing a distinct chromatin structure. This would require a comprehensive understanding of all marks associated to a specific genomic locus. However, thus far, such analyses of locus-specific chromatin have only been successful for repetitive regions, such as telomeres.
In my doctoral thesis, I used the unicellular parasite Trypanosoma brucei as a model system for chromatin biology and took advantage of its chromatin landscape with TSSs comprising already 7% of the total T. brucei genome (humans: 0.00000156%). Atypical for a eukaryote, the protein-coding genes are arranged in long polycistronic transcription units (PTUs). Each PTU is controlled by its own ~10 kb-wide TSS, that lies upstream of the PTU. As observed in other eukaryotes, TSSs are enriched with nucleosomes containing acetylated histones and the histone variant H2A.Z. This is why I used T. brucei to particularly investigate the TSS-specific chromatin structures and to identify factors involved in H2A.Z deposition and transcription regulation in eukaryotes. To this end, I established an approach for locus-specific chromatin isolation that would allow me to identify the TSSs- and non-TSS-specific chromatin marks. Later, combining the approach with a method for quantifying lysine-specific histone acetylation levels, I found H2A.Z and H4 acetylation enriched in TSSs-nucleosomes and mediated by the histone acetyltransferases HAT1 and HAT2. Depletion of HAT2 reduced the levels of TSS-specific H4 acetylation, affected targeted H2A.Z deposition and shifted the sites of transcription initiation. Whereas HAT1 depletion had only a minor effect on H2A.Z deposition, it had a strong effect on H2A.Z acetylation and transcription levels. My findings demonstrate a clear link between histone acetylation, H2A.Z deposition and transcription initiation in the early diverged unicellular parasite T. brucei, which was thus far not possible to determine in other eukaryotes. Overall, my study highlights the usefulness of T. brucei as a model system for studying chromatin biology. My findings allow the conclusion that H2A.Z regardless of its modification state defines sites of transcription initiation, whereas H2A.Z acetylation is essential co-factor for transcription initiation. Altogether, my data suggest that TSS-specific chromatin establishment is one of the earliest developed mechanisms to control transcription initiation in eukaryotes.
Transcription describes the process of converting the information contained in DNA into RNA. Although, tremendous progress has been made in recent decades to uncover this complex mechanism, it is still not fully understood. Given the advances and reduction in cost of high-throughput sequencing experiments, more and more data have been generated to help elucidating this complex process. Importantly, these sequencing experiments produce massive amounts of data that are incomprehensible in their raw form for humans. Further, sequencing techniques are not always 100% accurate and are subject to a certain degree of variability and, in special cases, they might introduce technical artifacts. Thus, computational and statistical methods are indispensable to uncover the information buried in these datasets.
In this thesis, I worked with multiple high throughput datasets from herpes simplex virus 1 (HSV-1) and human cytomegalovirus (HCMV) infections. During the last decade, it has became clear that a gene might not have a single, but multiple sites at which transcription initiates. These multiple transcription start sites (TiSS) demonstrated to have regulatory effects on the gene itself depending on which TiSS is used. Specialized experimental approaches were developed to help identify TiSS (TiSS-profiling). In order to facilitate the identification of all potential TiSS that are used for cell type- and condition-specific transcription, I developed the tool iTiSS. By using a new general enrichment-based approach to predict TiSS, iTiSS proved to be applicable in integrated studies and made it less prone to false positives compared to other TiSS-calling tools. Another improvement in recent years was made in metabolic labeling experiments such as SLAM-seq. Here, they removed the time consuming and laborious step of physically separating new from old RNA in the samples. This was achieved by inducing specific nucleotide conversions in newly synthesized RNA that are later visible in the data. Consequently, the separation of new and old RNA is now done computationally and, hence, tools are needed that accurately quantify these fold-changes. My second tool that I developed, called GRAND-SLAM proved to be capable to accomplish this task and outperform competing programs. As both of my tools, iTiSS and GRAND-SLAM are not specifically tailored to my own goals, but could also facilitate the research of other groups in this field, I made them publicly available on GitHub.
I applied my tools to datasets generated in our lab as well as to publicly available data sets from HSV-1 and HCMV, respectively. For HSV-1, I was able to predict and validate TiSS with nucleotide precision using iTiSS. This has lead to the most comprehensive annotation for HSV-1 to date, which now serves as the fundamental basis of any future transcriptomic research on HSV-1. By combining both my tools, I was further able to uncover parts of the highly complex gene kinetics in HCMV and to resolve the limitations caused by the densely packed genome of HCMV.
With the ever-increasing advances in sequencing techniques and their decrease in cost, the amounts of data produced will continue to rise massively in the future. Additionally, more and more specialized omics approaches are appearing, calling for new tools to leverage their full information potential. Consequently, it has become apparent that specialized computational tools such as iTiSS and GRAND-SLAM are needed and will become an essential and indispensable part of the analysis.
The holy grail of structural biology is to study a protein in situ, and this goal has been fast approaching since the resolution revolution and the achievement of atomic resolution. A cell's interior is not a dilute environment, and proteins have evolved to fold and function as needed in that environment; as such, an investigation of a cellular component should ideally include the full complexity of the cellular environment. Imaging whole cells in three dimensions using electron cryotomography is the best method to accomplish this goal, but it comes with a limitation on sample thickness and produces noisy data unamenable to direct analysis. This thesis establishes a novel workflow to systematically analyse whole-cell electron cryotomography data in three dimensions and to find and identify instances of protein complexes in the data to set up a determination of their structure and identity for success. Mycoplasma pneumoniae is a very small parasitic bacterium with fewer than 700 protein-coding genes, is thin enough and small enough to be imaged in large quantities by electron cryotomography, and can grow directly on the grids used for imaging, making it ideal for exploratory studies in structural proteomics. As part of the workflow, a methodology for training deep-learning-based particle-picking models is established.
As a proof of principle, a dataset of whole-cell Mycoplasma pneumoniae tomograms is used with this workflow to characterize a novel membrane-associated complex observed in the data. Ultimately, 25431 such particles are picked from 353 tomograms and refined to a density map with a resolution of 11 Å. Making good use of orthogonal datasets to filter search space and verify results, structures were predicted for candidate proteins and checked for suitable fit in the density map. In the end, with this approach, nine proteins were found to be part of the complex, which appears to be associated with chaperone activity and interact with translocon machinery.
Visual proteomics refers to the ultimate potential of in situ electron cryotomography: the comprehensive interpretation of tomograms. The workflow presented here is demonstrated to help in reaching that potential.
Adrenocortical carcinoma (ACC) is a rare, but highly aggressive endocrine malignancy. Tumor-related hypercortisolism is present in 60 % of patients and associated with worse outcome. While cancer immunotherapies have revolutionized the treatment of many cancer entities, the results of initial studies of different immune checkpoint inhibitors in ACC were heterogeneous. Up to now, five small clinical trials with a total of 121 patients have been published and demonstrated an objective response in only 17 patients. However, one of the studies, by Raj et al., reported a clinically meaningful disease control rate of 52 % and a median overall survival of almost 25 months suggesting that a subgroup of ACC patients may benefit from immunotherapeutic approaches. Following the hypothesis that some ACCs are characterized by a glucocorticoid-induced T lymphocytes depletion, several studies were performed as part of the presented thesis. First, the immune cell infiltration in a large cohort of 146 ACC specimens was investigated. It was demonstrated for the first time, and against the common assumption, that ACCs were infiltrated not only by FoxP3+ regulatory T cells (49.3 %), but also that a vast majority of tumor samples was infiltrated by CD4+ TH cells (74 %) and CD8+ cytotoxic T cells (84.3 %), albeit the immune cell number varied heterogeneously and was rather low (median: 7.7 CD3+ T cells / high power field, range: 0.1-376). Moreover, the presence of CD3+-, CD4+- and CD8+ ACC-infiltrating lymphocytes was associated with an improved recurrence-free (HR: 0.31 95 % CI 0.11-0.82) and overall survival (HR: 0.47 96 % CI 0.25-0.87). Particularly, patients with tumor-infiltrating CD4+ TH cells without glucocorticoid excess had a significantly longer overall survival compared to patients with T cell-depleted ACC and hypercortisolism (121 vs. 27 months, p = 0.004). Hence, the impact of glucocorticoids might to some extent be responsible for the modest immunogenicity in ACC as hypercortisolism was reversely correlated with the number of CD4+ TH cells. Accordingly, CD3+ T cells co-cultured with steroidogenic NCI-H295R ACC cells demonstrated in vitro an enhanced anti-tumoral cytotoxicity by secreting 747.96 ±225.53 pg/ml IFN-γ in a therapeutically hormone-depleted microenvironment (by incubation with metyrapone), versus only 276.02 ±117.46 pg/ml IFN-γ in a standard environment with glucocorticoid excess.
Other potential biomarkers to predict response to immunotherapies are the immunomodulatory checkpoint molecules, programmed cell death 1 (PD-1) and its ligand PD-L1, since both are targets of antibodies used therapeutically in different cancer entities. In a subcohort of 129 ACCs, expressions of both molecules were heterogeneous (PD-1 17.4 %, range 1-15; PD-L1 24.4 %, range 1 - 90) and rather low. Interestingly, PD-1 expression significantly influenced ACC patients´ overall (HR: 0.21 95 % CI 0.53-0.84) and progression- free survival (HR: 0.30 95 % CI 0.13-0.72) independently of established factors, like ENSAT tumor stage, resection status, Ki67 proliferation index and glucocorticoid excess, while PD-L1 had no impact.
In conclusion, this study provides several potential explanations for the heterogeneous results of the immune checkpoint therapy in advanced ACC. In addition, the establishment of PD-1 as prognostic marker can be easily applied in routine clinical care, because it is nowadays anyway part of a detailed histo-pathological work-up. Furthermore, these results provide the rationale and will pave the way towards a combination therapy using immune checkpoint inhibitors as well as glucocorticoid blockers. This will increase the likelihood of re-activating the immunological anti-tumor potential in ACC. However, this will have to be demonstrated by additional preclinical in vivo experiments and finally in clinical trials with patients.
Additive manufacturing processes such as 3D printing are booming in the industry due to their high degree of freedom in terms of geometric shapes and available materials. Focusing on patient-specific medicine, 3D printing has also proven useful in the Life Sciences, where it exploits the shape fidelity for individualized tissues in the field of bioprinting. In parallel, the current systems of bioreactor technology have adapted to the new manufacturing technology as well and 3D-printed bioreactors are increasingly being developed. For the first time, this work combines the manufacturing of the tissue and a tailored bioreactor, significantly streamlining the overall process and optimally merging the two processes. This way the production of the tissues can be individualized by customizing the reactor to the tissue and the patient-specific wound geometry. For this reason, a common basis and guideline for the cross-device and cross-material use of 3D printers was created initially. Their applicability was demonstrated by the iterative development of a perfusable bioreactor system, made from polydimethylsiloxane (PDMS) and a lignin-based filament, into which a biological tissue of flexible shape can be bioprinted. Cost-effective bioink-replacements and in silico computational fluid dynamics simulations were used for material sustainability and shape development. Also, nutrient distribution and shear stress could be predicted in this way pre-experimentally.
As a proof of functionality and adaptability of the reactor, tissues made from a nanocellulose-based Cellink® Bioink, as well as an alginate-based ink mixed with Me-PMeOx100-b-PnPrOzi100-EIP (POx) (Alginate-POx bioink) were successfully cultured dynamically in the bioreactor together with C2C12 cell line. Tissue maturation was further demonstrated using hMSC which were successfully induced to adipocyte differentiation. For further standardization, a mobile electrical device for automated media exchange was developed, improving handling in the laboratory and thus reduces the probability of contamination.
Introduction.
Mobile health (mHealth) integrates mobile devices into healthcare, enabling remote monitoring, data collection, and personalized interventions. Machine Learning (ML), a subfield of Artificial Intelligence (AI), can use mHealth data to confirm or extend domain knowledge by finding associations within the data, i.e., with the goal of improving healthcare decisions. In this work, two data collection techniques were used for mHealth data fed into ML systems: Mobile Crowdsensing (MCS), which is a collaborative data gathering approach, and Ecological Momentary Assessments (EMA), which capture real-time individual experiences within the individual’s common environments using questionnaires and sensors. We collected EMA and MCS data on tinnitus and COVID-19. About 15 % of the world’s population suffers from tinnitus.
Materials & Methods.
This thesis investigates the challenges of ML systems when using MCS and EMA data. It asks: How can ML confirm or broad domain knowledge? Domain knowledge refers to expertise and understanding in a specific field, gained through experience and education. Are ML systems always superior to simple heuristics and if yes, how can one reach explainable AI (XAI) in the presence of mHealth data? An XAI method enables a human to understand why a model makes certain predictions. Finally, which guidelines can be beneficial for the use of ML within the mHealth domain? In tinnitus research, ML discerns gender, temperature, and season-related variations among patients. In the realm of COVID-19, we collaboratively designed a COVID-19 check app for public education, incorporating EMA data to offer informative feedback on COVID-19-related matters. This thesis uses seven EMA datasets with more than 250,000 assessments. Our analyses revealed a set of challenges: App user over-representation, time gaps, identity ambiguity, and operating system specific rounding errors, among others. Our systematic review of 450 medical studies assessed prior utilization of XAI methods.
Results.
ML models predict gender and tinnitus perception, validating gender-linked tinnitus disparities. Using season and temperature to predict tinnitus shows the association of these variables with tinnitus. Multiple assessments of one app user can constitute a group. Neglecting these groups in data sets leads to model overfitting. In select instances, heuristics outperform ML models, highlighting the need for domain expert consultation to unveil hidden groups or find simple heuristics.
Conclusion.
This thesis suggests guidelines for mHealth related data analyses and improves estimates for ML performance. Close communication with medical domain experts to identify latent user subsets and incremental benefits of ML is essential.
Glioblastoma (GBM) sind bösartige hirneigene Tumore, deren schlechte Prognose einer innovativen Therapie bedarf. Aus diesem Grund wurde ein neuer Therapieansatz entwickelt, der auf einer lokalen Ultraschall-vermittelten Zytostatika Applikation beruht. Hierfür wurden stabile Microbubbles (MB) bestehend aus Phospholipiden synthetisiert. Es konnte gezeigt werden, dass MB als auch fokussierter Ultraschall niedriger Intensität (LIFU) keinen negativen Einfluss auf GBM-Zellen hat. MB hingegen konnten mittels LIFU destruiert werden, wodurch das in den MB eingeschlossene Chemotherapeutikum freigesetzt werden kann. Es wurden verschiedene Platin(II)- und Palladium(II)-Komplexe auf GBM Zellen getestet. Zur Beladung der MB wurde Doxorubicin (Dox) verwendet. Es konnte eine Beladungseffizienz der MB mit Dox von 52 % erreicht werden, auch eine Aufreinigung dieser mittel Ionenaustausch-Chromatographie und Dialyse war erfolgreich. Die Austestung der mit Dox beladenen MB (MBDox) erfolgte auf GBM-Zellen in 2D- und 3D-Zelkulturmodellen. Dabei zeigte sich, dass die Behandlung mit MBDox und LIFU für 48 h eine zytotoxische Wirkung hatte, die sich signifikant von der Behandlung mit MBDox ohne LIFU unterschied. Zur Austestung der MBDox in 3D-Zellkulturmodellen wurden zwei Scaffold-Systeme eingesetzt. Es zeigte sich in den Versuchen, dass MBDox mit LIFU im Vergleich zu MBDox ohne LIFU Applikation einen zytotoxischen Effekt auf GBM-Zellen haben. Somit konnte die Wirksamkeit der Zytostatika Applikation mittels MB und LIFU in 2D- und 3D-Zellkulturmodellen erfolgreich etabliert werden. Als weiterer Schritt wurden zwei 3D in vitro Modelle erarbeitet. Dabei wurden zunächst organotypische hippocampale Slice Kulturen (organotypic hippocampal brain slice cultures, OHSC) aus der Maus hergestellt und anschließend mit fluoreszent-markierten Mikrotumoren aus GBM-Zelllinien, Primärzellen (PZ) und aus Patienten generierten GBM-Organoiden hergestellt. Diese GBM-Modelle wurden mit Tumor Treating Fields (TTFields) behandelt. Dabei war eine Abnahme der Tumorgröße von Mikrotumoren aus GBM-Zellen und PZ unter TTFields-Behandlung für 72 h messbar. Als weiteres in vitro Modell wurden humane Tumorschnitte aus intraoperativ entferntem GBM-Patientenmaterial hergestellt. Die Schnitte wiesen ein heterogenes Ansprechen nach 72 h TTFields-Applikation auf. Dies spiegelt die Heterogenität des GBM sehr gut wider und bestärkt die Eignung des Modelles zur Untersuchung von neuen Therapieansätzen zur Behandlung von GBM.
The platelet cytoskeleton ensures normal size and discoid shape under resting conditions and undergoes immediate reorganization in response to changes in the extracellular environment through integrin-based adhesion sites, resulting in actomyosin-mediated contractile forces. Mutations in the contractile protein non-muscle myosin heavy chain IIA display, among others, macrothrombocytopenia and a mild to moderate bleeding tendency in human patients. It is insufficiently understood which factors contribute to the hemostatic defect found in MYH9-related disease patients. Therefore, a better understanding of the underlying biophysical mechanisms in thrombus formation and stabilization is warranted.
This thesis demonstrates that an amino acid exchange at the positions 702, 1424 and 1841 in the heavy chain of the contractile protein non-muscle myosin IIA, caused by heterozygous point mutations in the gene, resulted in macrothrombocytopenia and increased bleeding in mice, reflecting the clinical hallmark of the MYH9-related disease in human patients. Basic characterization of biological functions of Myh9 mutant platelets revealed overall normal surface glycoprotein expression and agonist-induced activation when compared to wildtype platelets. However, myosin light chain phosphorylation after thrombin-activation was reduced in mutant platelets, resulting in less contractile forces and a defect in clot retraction. Altered biophysical characteristics with lower adhesion and interaction forces of Myh9 mutant platelets led to reduced thrombus formation and stability. Platelets from patients with the respective mutations recapitulated the findings obtained with murine platelets, such as impaired thrombus formation and stiffness.
Besides biological and biophysical characterization of mutant platelets from mice and men, treatment options were investigated to prevent increased bleeding caused by reduced platelet forces. The antifibrinolytic agent tranexamic acid was applied to stabilize less compact thrombi, which are presumably more vulnerable to fibrinolysis. The hemostatic function in Myh9 mutant mice was improved by interfering with the fibrinolytic system. These results show the beneficial effect of fibrin stabilization to reduce bleeding in MYH9-related disease.
Studies on the role of cytoskeletal-regulatory and -crosslinking proteins in platelet function
(2023)
Cytoskeletal reorganization in platelets is highly regulated and important for proper platelet function during activation and aggregation at sites of vascular injury. In this thesis, the role of three different cytoskeletal-regulatory and -crosslinking proteins was studied in platelet physiology using megakaryocyte- and platelet-specific knockout mice. The generation of branched actin filaments is regulated by nucleation promoting factors (NPF) and the Arp2/3 complex.
(1.) The WAVE complex is a NPF, which upregulates the Arp2/3 complex activity at the plasma membrane. As shown in this thesis, the loss of the WAVE complex subunit Cyfip1 in mice did not alter platelet production and had only a minor impact on platelet activation. However, Cyfip1 played an essential role for branching of actin filaments and consequently for lamellipodia formation in vitro. The importance of lamellipodia for thrombus formation and stability has been controversially discussed. Cyfip1-deficient platelets were able to form a stable thrombus ex vivo and in vivo and a hemostatic plug comparable to controls. Moreover, Cyfip1-deficient mice maintained vascular integrity at the site of inflammation. These data show that platelet lamellipodia formation is not required for hemostatic function and pathophysiological thrombus formation.
(2.) The WASH complex is another NPF, which mediates actin filament polymerization on endosomal vesicles via the Arp2/3 complex. Loss of the WASH complex subunit Strumpellin led to a decreased protein abundance of the WASH protein and to a 20% reduction in integrin αIIbβ3 surface expression on platelets and megakaryocytes, whereas the expression of other surface receptors as well as the platelet count, size, ex vivo thrombus formation and bleeding time remained unaltered. These data point to a distinct role of Strumpellin in maintaining integrin αIIbβ3 expression and provide new insights into regulatory mechanisms of platelet integrins.
(3.) MACF1 has been described as a cytoskeletal crosslinker of microtubules and F-actin. However, MACF1-deficient mice displayed no alterations in platelet production, activation, thrombus formation and hemostatic function. Further, no compensatory up- or downregulation of other proteins could be found that contain an F-actin- and a microtubule-binding domain. These data indicate that MACF1 is dispensable for platelet biogenesis, activation and thrombus formation. Nevertheless, functional redundancy among different proteins mediating the cytoskeletal crosstalk may exist.
Neurobiology is widely supported by bioinformatics. Due to the big amount of data generated from the biological side a computational approach is required. This thesis presents four different cases of bioinformatic tools applied to the service of Neurobiology.
The first two tools presented belong to the field of image processing. In the first case, we make use of an algorithm based on the wavelet transformation to assess calcium activity events in cultured neurons. We designed an open source tool to assist neurobiology researchers in the analysis of calcium imaging videos. Such analysis is usually done manually which is time consuming and highly subjective. Our tool speeds up the work and offers the possibility of an unbiased detection of the calcium events. Even more important is that our algorithm not only detects the neuron spiking activity but also local spontaneous activity which is normally discarded because it is considered irrelevant. We showed that this activity is determinant in the calcium dynamics in neurons and it is involved in important functions like signal modulation and memory and learning.
The second project is a segmentation task. In our case we are interested in segmenting the neuron nuclei in electron microscopy images of c.elegans. Marking these structures is necessary in order to reconstruct the connectome of the organism. C.elegans is a great study case due to the simplicity of its nervous system (only 502 neurons). This worm, despite its simplicity has taught us a lot about neuronal mechanisms. There is still a lot of information we can extract from the c.elegans, therein lies the importance of reconstructing its connectome. There is a current version of the c.elegans connectome but it was done by hand and on a single subject which leaves a big room for errors. By automatizing the segmentation of the electron microscopy images we guarantee an unbiased approach and we will be able to verify the connectome on several subjects.
For the third project we moved from image processing applications to biological modeling. Because of the high complexity of even small biological systems it is necessary to analyze them with the help of computational tools. The term in silico was coined to refer to such computational models of biological systems. We designed an in silico model of the TNF (Tumor necrosis factor) ligand and its two principal receptors. This biological system is of high relevance because it is involved in the inflammation process. Inflammation is of most importance as protection mechanism but it can also lead to complicated diseases (e.g. cancer). Chronic inflammation processes can be particularly dangerous in the brain. In order to better understand the dynamics that govern the TNF system we created a model using the BioNetGen language. This is a rule based language that allows one to simulate systems where multiple agents are governed by a single rule. Using our model we characterized the TNF system and hypothesized about the relation of the ligand with each of the two receptors. Our hypotheses can be later used to define drug targets in the system or possible treatments for chronic inflammation or lack of the inflammatory response.
The final project deals with the protein folding problem. In our organism proteins are folded all the time, because only in their folded conformation are proteins capable of doing their job (with some very few exceptions). This folding process presents a great challenge for science because it has been shown to be an NP problem. NP means non deterministic Polynomial time problem. This basically means that this kind of problems cannot be efficiently solved. Nevertheless, somehow the body is capable of folding a protein in just milliseconds. This phenomenon puzzles not only biologists but also mathematicians. In mathematics NP problems have been studied for a long time and it is known that given the solution to one NP problem we could solve many of them (i.e. NP-complete problems). If we manage to understand how nature solves the protein folding problem then we might be able to apply this solution to many other problems. Our research intends to contribute to this discussion. Unfortunately, not to explain how nature solves the protein folding problem, but to explain that it does not solve the problem at all. This seems contradictory since I just mentioned that the body folds proteins all the time, but our hypothesis is that the organisms have learned to solve a simplified version of the NP problem. Nature does not solve the protein folding problem in its full complexity. It simply solves a small instance of the problem. An instance which is as simple as a convex optimization problem. We formulate the protein folding problem as an optimization problem to illustrate our claim and present some toy examples to illustrate the formulation. If our hypothesis is true, it means that protein folding is a simple problem. So we just need to understand and model the conditions of the vicinity inside the cell at the moment the folding process occurs. Once we understand this starting conformation and its influence in the folding process we will be able to design treatments for amyloid diseases such as Alzheimer's and Parkinson's.
In summary this thesis project contributes to the neurobiology research field from four different fronts. Two are practical contributions with immediate benefits, such as the calcium imaging video analysis tool and the TNF in silico model. The neuron nuclei segmentation is a contribution for the near future. A step towards the full annotation of the c.elegans connectome and later for the reconstruction of the connectome of other species. And finally, the protein folding project is a first impulse to change the way we conceive the protein folding process in nature. We try to point future research in a novel direction, where the amino code is not the most relevant characteristic of the process but the conditions within the cell.
A fundamental question in current biology concerns the translational mechanisms leading from genetic variability to phenotypes. Technologies have evolved to the extent that they can efficiently and economically determine an individual’s genomic composition, while at the same time big data on clinical profiles and diagnostics have substantially accumulated. Genome-wide association studies linking genomic loci to certain traits, however, remain limited in their capacity to explain the cellular mechanisms that underlie the given association. For most associations, gene expression has been blamed; yet given that transcript and protein abundance oftentimes do not correlate, that finding does not necessarily decrypt the underlying mechanism. Thus, the integration of further information is crucial to establish a model that could prove more accurate in predicting genotypic effects on the human organism.
In this work we describe the so-called proteotype as a feature of the cell that could provide a substantial link between genotype and phenotype. Rather than looking at the proteome as a set of independent molecules, we demonstrate a consistent modular architecture of the proteome that is driven by molecular cooperativity. Functional modules, especially protein complexes, can be further interrogated for differences between individuals and tackled as imprints of genetic and environmental variability. We also show that subtle stoichiometric changes of protein modules could have broader effects on the cellular system, such as the transport of specific molecular cargos.
The presented work also delineates to what extent temporal events and processes influence the stoichiometry of protein complexes and functional modules. The re-wiring of the glycolytic pathway for example is illustrated as a potential cause for an increased Warburg effect during the ageing of the human bone marrow. On top of analyzing protein abundances we also interrogate proteome dynamics in terms of stability and solubility transitions during the short temporal progression of the cell cycle. One of our main observations in the thesis encompass the delineation of protein complexes into respective sub-complexes according to distinct stability patterns during the cell cycle. This has never been demonstrated before, and is functionally relevant for our understanding of the dis- and assembly of large protein modules.
The insights presented in this work imply that the proteome is more than the sum of its parts, and primarily driven by variability in entire protein ensembles and their cooperative nature. Analyzing protein complexes and functional modules as molecular reflections of genetic and environmental variations could indeed prove to be a stepping stone in closing the gap between genotype and phenotype and customizing clinical treatments in the future.
Kritische Knochendefekte stellen heutzutage ein ungelöstes Problem in der klinischen Praxis dar, da die verfügbaren prothetischen Optionen oft die mechanische Anpassung an das Gewebe nicht gewährleisten oder zu wichtigen immunologischen und Implantat-bedingten Komplikationen führen.
In diesem Kontext ermöglichen Tissue Engineering-Ansätze neue Strategien, um in vitro Zell-Material Interaktionen zu untersuchen und so die Implantatmaterialien zu optimieren.
In dieser Arbeit habe ich Zell-Material Interaktionen eines neuen Kollagen-basierten Scaffolds untersucht, das langfristig als Trägerstruktur für eine zellbasierte Therapie für kritische Knochendefekte entwickelt werden soll. Im Rahmen der Dissertation konnte ich belegen, dass die Kollagen-basierten makroporöse Mikrocarrier für die Zellvermehrung humaner mesenchymaler Stammzellen (MSC) und deren osteogene Differenzierung unter GMP Bedingungen verwendet werden können. Außerdem habe ich die die Kokultur von hämatopoietischen Stammzellen des Knochenmarks und multiplen Myelomzellen funktionell charakterisiert. Ich konnte erstmals Kulturbedingungen etablieren, die die Langzeitkultur ohne die Verwendung von Zytokinen ermöglicht. Mittels dieser Kokultur konnte ich ein Knochenmarknischen-Modell etablieren und die Untersuchung der Expression von zentralen Signalkaskaden der Homöostase dieser Nische untersuchen. Ich konnte die Expression von zwei verschiedenen Isoformen von Osteopontin nachweisen, die in Tiermodellen nicht gefunden werden. Diese Isoformen des Osteopontins habe ich kloniert und die rekombinanten Isoformen exprimiert und ihre Rollen in der Homöostase der Knochenmarknische untersucht.
Critical size bone defects represent nowadays an unresolved problem in the clinical practice, where the available prosthetic options often lack adequate mechanical matching to the host tissue or lead to important immunological and implant-related complications.
In this context, Tissue Engineering approaches promise more effective strategies to study cell-material interactions in vitro and consequently optimize implant materials.
In this work, I investigated the cell-scaffold interactions of a new collagen-based scaffold for a putative cell-based therapy for critical size defects to be developed. In the context of this thesis, I could demonstrate that the collagen-based macroporous microcarriers could be employed for the expansion and osteogenic differentiation of human mesenchymal stromal cells (MSCs) under GMP-compliant conditions. Moreover, I functionally characterized the co-culture of bone marrow hematopoietic stem cells and multiple myeloma cells. I was for the first time able to establish culture conditions allowing their long-term culture in absence of externally supplemented cytokines. Using this co-culture, I was able to establish a bone marrow niche model to investigate the expression of key signaling pathways involved in the niche´s homeostasis. I was able to demonstrate the expression of two different isoforms of Osteopontin, that could not previously be detected in animal models. Finally, I cloned these Osteopontin isoforms, expressed recombinant versions of the isoforms, and investigated their roles in the homeostasis of the bone marrow niche.
L-type voltage-gated calcium channels (LTCC) are heteromultimeric membrane proteins that allow Ca2+ entry into the cell upon plasma membrane depolarization. The β subunit of voltage-dependent calcium channels (Cavβ) binds to the α-interaction domain in the pore-forming α1 subunit and regulates the trafficking and biophysical properties of these channels. Of the four Cavβ isoforms, Cavβ2 is predominantly expressed in cardiomyocytes. This subunit associates with diverse proteins besides LTCC, but the molecular composition of the Cavβ2 nanoenvironments in cardiomyocytes is yet unresolved. Here, we used a protein-labeling technique in living cells based on an engineered ascorbate peroxidase 2 (APEX2). In this strategy, Cavβ2b was fused to APEX2 and expressed in adult rat cardiomyocytes using an adenovirus system. Nearby proteins covalently labeled with biotin-phenol were purified using streptavidin-coated beads and identified by mass spectrometry (MS). Analysis of the in situ APEX2-based biotin labeling by MS revealed 61 proteins located in the nanoenvironments of Cavβ2b, with a high specificity and consistency in all the replicates. These proteins are involved in diverse cellular functions such as cellular trafficking, sarcomere organization and excitation-contraction coupling. Among these proteins, we demonstrated an interaction between the ryanodine receptor 2 (RyR2) and Cavβ2b, probably coupling LTCC and the RyR2 into a supramolecular complex at the dyads. This interaction is mediated by the Src homology 3 (SH3) domain of Cavβ2b and is necessary for an effective pacing frequency‐dependent increase in Ca2+-induced Ca2+ release in cardiomyocytes.
The field of genetics faces a lot of challenges and opportunities in both research and diagnostics due to the rise of next generation sequencing (NGS), a technology that allows to sequence DNA increasingly fast and cheap.
NGS is not only used to analyze DNA, but also RNA, which is a very similar molecule also present in the cell, in both cases producing large amounts of data.
The big amount of data raises both infrastructure and usability problems, as powerful computing infrastructures are required and there are many manual steps in the data analysis which are complicated to execute.
Both of those problems limit the use of NGS in the clinic and research, by producing a bottleneck both computationally and in terms of manpower, as for many analyses geneticists lack the required computing skills.
Over the course of this thesis we investigated how computer science can help to improve this situation to reduce the complexity of this type of analysis.
We looked at how to make the analysis more accessible to increase the number of people that can perform OMICS data analysis (OMICS groups various genomics data-sources).
To approach this problem, we developed a graphical NGS data analysis pipeline aimed at a diagnostics environment while still being useful in research in close collaboration with the Human Genetics Department at the University of Würzburg.
The pipeline has been used in various research papers on covering subjects, including works with direct author participation in genomics, transcriptomics as well as epigenomics.
To further validate the graphical pipeline, a user survey was carried out which confirmed that it lowers the complexity of OMICS data analysis.
We also studied how the data analysis can be improved in terms of computing infrastructure by improving the performance of certain analysis steps.
We did this both in terms of speed improvements on a single computer (with notably variant calling being faster by up to 18 times), as well as with distributed computing to better use an existing infrastructure.
The improvements were integrated into the previously described graphical pipeline, which itself also was focused on low resource usage.
As a major contribution and to help with future development of parallel and distributed applications, for the usage in genetics or otherwise, we also looked at how to make it easier to develop such applications.
Based on the parallel object programming model (POP), we created a Java language extension called POP-Java, which allows for easy and transparent distribution of objects.
Through this development, we brought the POP model to the cloud, Hadoop clusters and present a new collaborative distributed computing model called FriendComputing.
The advances made in the different domains of this thesis have been published in various works specified in this document.
Understanding the causal relationship between genotype and phenotype is a major objective in biology. The main interest is in understanding trait architecture and identifying loci contributing to the respective traits. Genome-wide association mapping (GWAS) is one tool to elucidate these relationships and has been successfully used in many different species. However, most studies concentrate on marginal marker effects and ignore epistatic and gene-environment interactions. These interactions are problematic to account for, but are likely to make major contributions to many phenotypes that are not regulated by independent genetic effects, but by more sophisticated gene-regulatory networks. Further complication arises from the fact that these networks vary in different natural accessions. However, understanding the differences of gene regulatory networks and gene-gene interactions is crucial to conceive trait architecture and predict phenotypes.
The basic subject of this study – using data from the Arabidopsis 1001 Genomes Project – is the analysis of pre-mature stop codons. These have been incurred in nearly one-third of the ~ 30k genes. A gene-gene interaction network of the co-occurrence of stop codons has been built and the over and under representation of different pairs has been statistically analyzed. To further classify the significant over and under- represented gene-gene interactions in terms of molecular function of the encoded proteins, gene ontology terms (GO-SLIM) have been applied. Furthermore, co- expression analysis specifies gene clusters that co-occur over different genetic and phenotypic backgrounds. To link these patterns to evolutionary constrains, spatial location of the respective alleles have been analyzed as well. The latter shows clear patterns for certain gene pairs that indicate differential selection.
Since the advent of high-throughput sequencing technologies in the mid-2010s, RNA se-
quencing (RNA-seq) has been established as the method of choice for studying gene
expression. In comparison to microarray-based methods, which have mainly been used to
study gene expression before the rise of RNA-seq, RNA-seq is able to profile the entire
transcriptome of an organism without the need to predefine genes of interest. Today,
a wide variety of RNA-seq methods and protocols exist, including dual RNA sequenc-
ing (dual RNA-seq) and multi RNA sequencing (multi RNA-seq). Dual RNA-seq and
multi RNA-seq simultaneously investigate the transcriptomes of two or more species, re-
spectively. Therefore, the total RNA of all interacting species is sequenced together and
only separated in silico. Compared to conventional RNA-seq, which can only investi-
gate one species at a time, dual RNA-seq and multi RNA-seq analyses can connect the
transcriptome changes of the species being investigated and thus give a clearer picture of
the interspecies interactions. Dual RNA-seq and multi RNA-seq have been applied to a
variety of host-pathogen, mutualistic and commensal interaction systems.
We applied dual RNA-seq to a host-pathogen system of human mast cells and Staphylo-
coccus aureus (S. aureus). S. aureus, a commensal gram-positive bacterium, can become
an opportunistic pathogen and infect skin lesions of atopic dermatitis (AD) patients.
Among the first immune cells S. aureus encounters are mast cells, which have previously
been shown to be able to kill the bacteria by discharging antimicrobial products and re-
leasing extracellular traps made of protein and deoxyribonucleic acid (DNA). However,
S. aureus is known to evade the host’s immune response by internalizing within mast
cells. Our dual RNA-seq analysis of different infection settings revealed that mast cells
and S. aureus need physical contact to influence each other’s gene expression. We could
show that S. aureus cells internalizing within mast cells undergo profound transcriptome
changes to adjust their metabolism to survive in the intracellular niche. On the host side,
we found out that infected mast cells elicit a type-I interferon (IFN-I) response in an
autocrine manner and in a paracrine manner to non-infected bystander-cells. Our study
provides the first evidence that mast cells are capable to produce IFN-I upon infection
with a bacterial pathogen.
Das zentrale Paradigma der Systembiologie zielt auf ein möglichst umfassendes Ver-ständnis der komplexen Zusammenhänge biologischer Systeme. Die in dieser Arbeit angewandten Methoden folgen diesem Grundsatz.
Am Beispiel von drei auf Basis von Datenbanken und aktueller Literatur rekonstruier-ten Netzwerkmodellen konnte in der hier vorliegenden Arbeit die Gültigkeit analyti-scher und prädiktiver Algorithmen nachgewiesen werden, die in Form der Analy-sesoftware Jimena angewandt wurden. Die daraus resultierenden Ergebnisse sowohl für die Berechnung von stabilen Systemzuständen, der dynamischen Simulation, als auch der Identifikation zentraler Kontrollknoten konnten experimentell validiert wer-den. Die Ergebnisse wurden in einem iterativen Prozess verwendet werden um das entsprechende Netzwerkmodell zu optimieren.
Beim Vergleich des Verhaltens des semiquantitativ ausgewerteten regulatorischen Netzwerks zur Kontrolle der Differenzierung humaner mesenchymaler Stammzellen in Chondrozyten (Knorpelbildung), Osteoblasten (Knochenbildung) und Adipozyten (Fett-zellbildung) konnten 12 wichtige Faktoren (darunter: RUNX2, OSX/SP7, SOX9, TP53) mit Hilfe der Berechnung der Bedeutung (Kontrollzentralität der Netzwerkknoten identifi-ziert werden). Der Abgleich des simulierten Verhaltens dieses Netzwerkes ergab eine Übereinstimmung mit experimentellen Daten von 47,2%, bei einem widersprüchlichen Verhalten von ca. 25%, dass unter anderem durch die temporäre Natur experimentel-ler Messungen im Vergleich zu den terminalen Bedingungen des Berechnung der stabilen Systemzustände erklärt werden kann.
Bei der Analyse des Netzwerkmodells der menschlichen Immunantwort auf eine Infek-tion durch A. fumigatus konnten vier Hauptregulatoren identifiziert werden (A. fumi-gatus, Blutplättchen, hier Platelets genannt, und TNF), die im Zusammenspiel mit wei-teren Faktoren mit hohen Zentralitätswerten (CCL5, IL1, IL6, Dectin-1, TLR2 und TLR4) fähig sind das gesamte Netzwerkverhalten zu beeinflussen. Es konnte gezeigt werden, dass sich das Aktivitätsverhalten von IL6 in Reaktion auf A. fumigatus und die regulato-rische Wirkung von Blutplättchen mit den entsprechenden experimentellen Resultaten deckt.
Die Simulation, sowie die Berechnung der stabilen Systemzustände der Immunantwort von A. thaliana auf eine Infektion durch Pseudomonas syringae konnte zeigen, dass die in silico Ergebnisse mit den experimentellen Ergebnissen übereinstimmen. Zusätzlich konnten mit Hilfe der Analyse der Zentralitätswerte des Netzwerkmodells fünf Master-regulatoren identifiziert werden: TGA Transkriptionsfaktor, Jasmonsäure, Ent-Kaurenoate-Oxidase, Ent-kaurene-Synthase und Aspartat-Semialdehyd-Dehydrogenase.
Während die ersteren beiden bereits lange als wichtige Regulatoren für die Gib-berellin-Synthese bekannt sind, ist die immunregulatorische Funktion von Aspartat-Semialdehyd-Dehydrogenase bisher weitgehend unbekannt.
Sponges (phylum Porifera) are evolutionary ancient, sessile filter-feeders that harbor a largely diverse microbial community within their internal mesohyl matrix. Throughout this thesis project, I aimed at exploring the adaptations of these symbionts to life within their sponge host by sequencing and analyzing the genomes of a variety of bacteria from the microbiome of the Mediterranean sponge Aplysina aerophoba. Employed methods were fluorescence-activated cell sorting with subsequent multiple displacement amplification and single-cell / ‘mini-metagenome’ sequencing, and metagenomic sequencing followed by differential coverage binning. These two main approaches both aimed at obtaining genome sequences of bacterial symbionts of A. aerophoba, that were then compared to each other and to references from other environments, to gain information on adaptations to the host sponge environment and on possible interactions with the host and within the microbial community.
Cyanobacteria are frequent members of the sponge microbial community. My ‘mini-metagenome’ sequencing project delivered three draft genomes of “Candidatus Synechococcus spongiarum,” the cyanobacterial symbiont of A. aerophoba and many more sponges inhabiting the photic zone. The most complete of these genomes was compared to other clades of this symbiont and to closely related free-living cyanobacterial references in a collaborative project published in Burgsdorf I*, Slaby BM* et al. (2015; *shared first authorship). Although the four clades of “Ca. Synechococcus spongiarum” from the four sponge species A. aerophoba, Ircinia variabilis, Theonella swinhoei, and Carteriospongia foliascens were approximately 99% identical on the level of 16S rRNA gene sequences, they greatly differed on the genomic level. Not only the genome sizes were different from clade to clade, but also the gene content and a number of features including proteins containing the eukaryotic-type domains leucine-rich repeats or tetratricopeptide repeats. On the other hand, the four clades shared a number of features such as ankyrin repeat domain-containing proteins that seemed to be conserved also among other microbial phyla in different sponge hosts and from different geographic locations. A possible novel mechanism for host phagocytosis evasion and phage resistance by means of an altered O antigen of the lipopolysaccharide was identified.
To test previous hypotheses on adaptations of sponge-associated bacteria on a broader spectrum of the microbiome of A. aerophoba while also taking a step forward in methodology, I developed a bioinformatic pipeline to combine metagenomic Illumina short-read sequencing data with PacBio long-read data. At the beginning of this project, no pipelines to combine short-read and long-read data for metagenomics were published, and at time of writing, there are still no projects published with a comparable aim of un-targeted assembly, binning and analysis of a metagenome. I tried a variety of assembly programs and settings on a simulated test dataset reflecting the properties of the real metagenomic data. The developed assembly pipeline improved not only the overall assembly statistics, but also the quality of the binned genomes, which was evaluated by comparison to the originally published genome assemblies.
The microbiome of A. aerophoba was studied from various angles in the recent years, but only genomes of the candidate phylum Poribacteria and the cyanobacterial sequences from my above-described project have been published to date. By applying my newly developed assembly pipeline to a metagenomic dataset of A. aerophoba consisting of a PacBio long-read dataset and six Illumina short-read datasets optimized for subsequent differential coverage binning, I aimed at sequencing a larger number and greater diversity of symbionts. The results of this project are currently in review by The ISME Journal. The complementation of Illumina short-read with PacBio long-read sequencing data for binning of this highly complex metagenome greatly improved the overall assembly statistics and improved the quality of the binned genomes. Thirty-seven genomes from 13 bacterial phyla and candidate phyla were binned representing the most prominent members of the microbiome of A. aerophoba. A statistical comparison revealed an enrichment of genes involved in restriction modification and toxin-antitoxin systems in most symbiont genomes over selected reference genomes. Both are defense features against incoming foreign DNA, which may be important for sponge symbionts due to the sponge’s filtration and phagocytosis activity that exposes the symbionts to high levels of free DNA. Also host colonization and matrix utilization features were significantly enriched. Due to the diversity of the binned symbiont genomes, a within-symbionts genome comparison was possible, that revealed three guilds of symbionts characterized by i) nutritional specialization on the metabolization of carnitine, ii) specialization on sulfated polysaccharides, and iii) apparent nutritional generalism. Both carnitine and sulfated polysaccharides are abundant in the sponge extracellular matrix and therefore available to the sponge symbionts as substrates. In summary, the genomes of the diverse community of symbionts in A. aerophoba were united in their defense features, but specialized regarding their nutritional preferences.
Clostridioides difficile is a bacterial species well known for its ability to cause C. difficile
infection (also known as CDI). The investigation of the role of this species in the human
gut has been so far dominated by a disease-centred perspective, focused on studying
C. difficile in relation to its associated disease.
In this context, the first aim of this thesis was to combine publicly available
metagenomic data to analyse the microbial composition of stool samples from patients
diagnosed with CDI, with a particular focus on identifying a CDI-specific microbial
signature.
However, similarly to many other bacterial species inhabiting the human gut, C.
difficile association with disease is not valid in absolute terms, as C. difficile can be
found also among healthy subjects. Further aims of this thesis were to 1) identify
potential C. difficile reservoirs by screening a wide range of habitats, hosts, body sites
and age groups, and characterize the biotic context associated with C. difficile
presence, and 2) investigate C. difficile within-species diversity and its toxigenic
potential across different age groups.
The first part of the thesis starts with the description of the concepts and
definitions used to identify bacterial species and within-species diversity, and then
proceeds to provide an overview of the bacterial species at the centre of my
investigation, C. difficile. The first Chapter includes a detailed description of the
discovery, biology and physiology of this clinically relevant species, followed by an
overview of the diagnostic protocols used in the clinical setting to diagnose CDI.
The second part of the thesis describes the methodology used to investigate
the questions mentioned above, while the third part presents the results of such
investigative effort. I first show that C. difficile could be found in only a fraction of the
CDI samples and that simultaneous colonization of multiple enteropathogenic species
able to cause CDI-like clinical manifestations is more common than previously
thought, raising concerns about CDI overdiagnosis. I then show that the CDIassociated
gut microbiome is characterized by a specific microbial signature,
distinguishable from the community composition associated with non-CDI diarrhea.
Beyond the nosocomial and CDI context, I show that while rarely found in adults, C.
difficile is a common member of the infant gut microbiome, where its presence is
associated with multiple indicators typical of a desirable healthy microbiome
development.
In addition, I describe C. difficile extensive carriage among asymptomatic
subjects, of all age groups and a potentially novel clade of C. difficile identified
exclusively among infants.
Finally, I discuss the limitations, challenges and future perspectives of my
investigation.
In this work models for molecular networks consisting of ordinary differential equations are extended by terms that include the interaction of the corresponding molecular network with the environment that the molecular network is embedded in. These terms model the effects of the external stimuli on the molecular network. The usability of this extension is demonstrated with a model of a circadian clock that is extended with certain terms and reproduces data from several experiments at the same time.
Once the model including external stimuli is set up, a framework is developed in order to calculate external stimuli that have a predefined desired effect on the molecular network. For this purpose the task of finding appropriate external stimuli is formulated as a mathematical optimal control problem for which in order to solve it a lot of mathematical methods are available. Several methods are discussed and worked out in order to calculate a solution for the corresponding optimal control problem. The application of the framework to find pharmacological intervention points or effective drug combinations is pointed out and discussed. Furthermore the framework is related to existing network analysis tools and their combination for network analysis in order to find dedicated external stimuli is discussed.
The total framework is verified with biological examples by comparing the calculated results with data from literature. For this purpose platelet aggregation is investigated based on a corresponding gene regulatory network and associated receptors are detected. Furthermore a transition from one to another type of T-helper cell is analyzed in a tumor setting where missing agents are calculated to induce the corresponding switch in vitro. Next a gene regulatory network of a myocardiocyte is investigated where it is shown how the presented framework can be used to compare different treatment strategies with respect to their beneficial effects and side effects quantitatively. Moreover a constitutively activated signaling pathway, which thus causes maleficent effects, is modeled and intervention points with corresponding treatment strategies are determined that steer the gene regulatory network from a pathological expression pattern to physiological one again.