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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.
Spontaneous brain activity builds the foundation for human cognitive processing during external demands. Neuroimaging studies based on functional magnetic resonance imaging (fMRI) identified specific characteristics of spontaneous (intrinsic) brain dynamics to be associated with individual differences in general cognitive ability, i.e., intelligence. However, fMRI research is inherently limited by low temporal resolution, thus, preventing conclusions about neural fluctuations within the range of milliseconds. Here, we used resting-state electroencephalographical (EEG) recordings from 144 healthy adults to test whether individual differences in intelligence (Raven’s Advanced Progressive Matrices scores) can be predicted from the complexity of temporally highly resolved intrinsic brain signals. We compared different operationalizations of brain signal complexity (multiscale entropy, Shannon entropy, Fuzzy entropy, and specific characteristics of microstates) regarding their relation to intelligence. The results indicate that associations between brain signal complexity measures and intelligence are of small effect sizes (r ∼ 0.20) and vary across different spatial and temporal scales. Specifically, higher intelligence scores were associated with lower complexity in local aspects of neural processing, and less activity in task-negative brain regions belonging to the default-mode network. Finally, we combined multiple measures of brain signal complexity to show that individual intelligence scores can be significantly predicted with a multimodal model within the sample (10-fold cross-validation) as well as in an independent sample (external replication, N = 57). In sum, our results highlight the temporal and spatial dependency of associations between intelligence and intrinsic brain dynamics, proposing multimodal approaches as promising means for future neuroscientific research on complex human traits.
The phase space for the standard model of the basic four forces for n quanta includes all possible ensemble combinations of their quantum states m, a total of n**m states. Neighbor states reach according to transition possibilities (S-matrix) with emergent time from entropic ensemble gradients.
We replace the “big bang” by a condensation event (interacting qubits become decoherent) and inflation by a crystallization event – the crystal unit cell guarantees same symmetries everywhere. Interacting qubits solidify and form a rapidly growing domain where the n**m states become separated ensemble states, rising long-range forces stop ultimately further growth. After that very early events, standard cosmology with the hot fireball model takes over. Our theory agrees well with lack of inflation traces in cosmic background measurements, large-scale structure of voids and filaments, supercluster formation, galaxy formation, dominance of matter and life-friendliness.
We prove qubit interactions to be 1,2,4 or 8 dimensional (agrees with E8 symmetry of our universe). Repulsive forces at ultrashort distances result from quantization, long-range forces limit crystal growth. Crystals come and go in the qubit ocean. This selects for the ability to lay seeds for new crystals, for self-organization and life-friendliness.
We give energy estimates for free qubits vs bound qubits, misplacements in the qubit crystal and entropy increase during qubit decoherence / crystal formation. Scalar fields for color interaction and gravity derive from the permeating qubit-interaction field. Hence, vacuum energy gets low only inside the qubit crystal. Condensed mathematics may advantageously model free / bound qubits in phase space.
Mammalian embryonic development is subject to complex biological relationships that need to be understood. However, before the whole structure of development can be put together, the individual building blocks must first be understood in more detail. One of these building blocks is the second cell fate decision and describes the differentiation of cells of the inner cell mass of the embryo into epiblast and primitive endoderm cells. These cells then spatially segregate and form the subsequent bases for the embryo and yolk sac, respectively. In organoids of the inner cell mass, these two types of progenitor cells are also observed to form, and to some extent to spatially separate. This work has been devoted to these phenomena over the past three years. Plenty of studies already provide some insights into the basic mechanics of this cell differentiation, such that the first signs of epiblast and primitive endoderm differentiation, are the expression levels of transcription factors NANOG and GATA6. Here, cells with low expression of GATA6 and high expression of NANOG adopt the epiblast fate. If the expressions are reversed, a primitive endoderm cell is formed. Regarding the spatial segregation of the two cell types, it is not yet clear what mechanism leads to this. A common hypothesis suggests the differential adhesion of cell as the cause for the spatial rearrangement of cells. In this thesis however, the possibility of a global cell-cell communication is investigated. The approach chosen to study these phenomena follows the motto "mathematics is biology's next microscope". Mathematical modeling is used to transform the central gene regulatory network at the heart of this work into a system of equations that allows us to describe the temporal evolution of NANOG and GATA6 under the influence of an external signal. Special attention is paid to the derivation of new models using methods of statistical mechanics, as well as the comparison with existing models. After a detailed stability analysis the advantages of the derived model become clear by the fact that an exact relationship of the model parameters and the formation of heterogeneous mixtures of two cell types was found. Thus, the model can be easily controlled and the proportions of the resulting cell types can be estimated in advance. This mathematical model is also combined with a mechanism for global cell-cell communication, as well as a model for the growth of an organoid. It is shown that the global cell-cell communication is able to unify the formation of checkerboard patterns as well as engulfing patterns based on differently propagating signals. In addition, the influence of cell division and thus organoid growth on pattern formation is studied in detail. It is shown that this is able to contribute to the formation of clusters and, as a consequence, to breathe some randomness into otherwise perfectly sorted patterns.
Various types of cancer involve aberrant cell cycle regulation. Among the pathways responsible for tumor growth, the YAP oncogene, a key downstream effector of the Hippo pathway, is responsible for oncogenic processes including cell proliferation, and metastasis by controlling the expression of cell cycle genes. In turn, the MMB multiprotein complex (which is formed when B-MYB binds to the MuvB core) is a master regulator of mitotic gene expression, which has also been associated with cancer. Previously, our laboratory identified a novel crosstalk between the MMB-complex and YAP. By binding to enhancers of MMB target genes and promoting B-MYB binding to promoters, YAP and MMB co-regulate a set of mitotic and cytokinetic target genes which promote cell proliferation. This doctoral thesis addresses the mechanisms of YAP and MMB mediated transcription, and it characterizes the role of YAP regulated enhancers in transcription of cell cycle genes.
The results reported in this thesis indicate that expression of constitutively active, oncogenic YAP5SA leads to widespread changes in chromatin accessibility in untransformed human MCF10A cells. ATAC-seq identified that newly accessible and active regions include YAP-bound enhancers, while the MMB-bound promoters were found to be already accessible and remain open during YAP induction. By means of CRISPR-interference (CRISPRi) and chromatin immuniprecipitation (ChIP), we identified a role of YAP-bound enhancers in recruitment of CDK7 to MMB-regulated promoters and in RNA Pol II driven transcriptional initiation and elongation of G2/M genes. Moreover, by interfering with the YAP-B-MYB protein interaction, we can show that binding of YAP to B-MYB is also critical for the initiation of transcription at MMB-regulated genes. Unexpectedly, overexpression of YAP5SA also leads to less accessible chromatin regions or chromatin closing. Motif analysis revealed that the newly closed regions contain binding motifs for the p53 family of transcription factors. Interestingly, chromatin closing by YAP is linked to the reduced expression and loss of chromatin-binding of the p53 family member Np63. Furthermore, I demonstrate that downregulation of Np63 following expression of YAP is a key step in driving cellular migration.
Together, the findings of this thesis provide insights into the role of YAP in the chromatin changes that contribute to the oncogenic activities of YAP. The overexpression of YAP5SA not only leads to the opening of chromatin at YAP-bound enhancers which together with the MMB complex stimulate the expression of G2/M genes, but also promotes the closing of chromatin at ∆Np63 -bound regions in order to lead to cell migration.
Coxiella burnetii, a Gram negative obligate intracellular bacterium, is the causative
agent of Q fever. It has a world wide distribution and has been documented to
be capable of causing infections in several domestic animals, livestock species,
and human beings. Outbreaks of Q fever are still being observed in livestock
across animal farms in Europe, and primary transmission to humans still oc-
curs especially in animal handlers. Public health authorities in some countries
like Germany are required by law to report human acute cases denoting the
significance of the challenge posed by C. burnetii to public health.
In this thesis, I have developed a platform alongside methods to address the
challenges of genomic analyses of C. burnetii for typing purposes. Identification
of C. burnetii isolates is an important task in the laboratory as well as in the
clinics and genotyping is a reliable method to identify and characterize known
and novel isolates. Therefore, I designed and implemented several methods
to facilitate the genotyping analyses of C. burnetii genomes in silico via a web
platform. As genotyping is a data intensive process, I also included additional
features such as visualization methods and databases for interpretation and
storage of obtained results. I also developed a method to profile the resistome
of C. burnetii isolates using a machine learning approach. Data about antibiotic
resistance in C. burnetii are scarce majorly due to its lifestyle and the difficulty
of cultivation in laboratory media. Alternative methods that rely on homology
identification of resistance genes are also inefficient in C. burnetii, hence, I
opted for a novel approach that has been shown to be promising in other
bacteria species. The applied method relied on an artificial neural network as
well as amino acid composition of position specific scoring matrix profile for
feature extraction. The resulting model achieved an accuracy of ≈ 0.96 on test
data and the overall performance was significantly higher in comparison to
existing models. Finally, I analyzed two new C. burnetii isolates obtained from
an outbreak in Germany, I compared the genome to the RSA 493 reference
isolate and found extensive deletions across the genome landscape.
This work has provided a new digital infrastructure to analyze and character-
ize C. burnetii genomes that was not in existence before and it has also made a
significant contribution to the existing information about antibiotic resistance
genes in C. burnetii.
The original habitat of native European honey bees (\(Apis\) \(mellifera\)) is forest, but currently there is a lack of data about the occurrence of wild honey bee populations in Europe. Prior to being kept by humans in hives, honey bees nested as wild species in hollow trees in temperate forests. However, in the 20th century, intensification of silviculture and agriculture with accompanying losses of nesting sites and depletion of food resources caused population declines in Europe. When the varroa mite (Varroa destructor), an invasive ectoparasite from Asia, was introduced in the late 1970s, wild honey bees were thought to be eradicated in Europe. Nevertheless, sporadic, mostly anecdotal, reports from ornithologists or forest ecologists indicated that honey bee colonies still occupy European forest areas. In my thesis I hypothesize that near-natural deciduous forests may provide sufficient large networks of nesting sites representing refugia for wild-living honey bees. Using two special search techniques, i.e. the tracking of flight routes of honey bee foragers (the “beelining” method) and the inspection of known cavity trees, I collected for the first time data on the occurrence and density of wild-living honey bees in forest areas in Germany (CHAPTER 3). I found wild-living honey bee colonies in the Hainich national park at low densities in two succeeding years. In another forest region, I checked known habitat trees containing black woodpecker cavities for occupation by wild-living honey bee colonies. It turned out that honey bees regularly use these cavities and occur in similar densities in both studied forest regions, independent of the applied detection method. Extrapolating these densities to all German forest areas, I estimate several thousand wild-living colonies in Germany that potentially interact in different ways with the forest environment. I conclude that honey bees regularly colonize forest areas in Germany and that networks of mapped woodpecker cavities offer unique possibilities to study the ecology of wild-living honey bees over several years.
While their population status is ambiguous and the density of colonies low, the fact that honey bees can still be found in forests poses questions about food supply in forest environments. Consequently, I investigated the suitability of woodlands as a honey bee foraging habitat (CHAPTER 4). As their native habitat, forests are assumed to provide important pollen and nectar sources for honey bee colonies. However, resource supply might be spatially and temporally restricted and landscape-scale studies in European forest regions are lacking. Therefore, I set up twelve honey bee colonies in observation hives at locations with varying degree of forest cover. Capitalizing on the unique communication behaviour, the waggle dance, I examined the foraging distances and habitat preferences of honey bees over almost an entire foraging season. Moreover, by connecting this decoded dance information with colony weight recordings, I could draw conclusions about the contribution of the different habitat types to honey yield. Foraging distances generally increased with the amount of forest in the surrounding landscape. Yet, forest cover did not have an effect on colony weight. Compared to expectations based on the proportions of different habitats in the surroundings, colonies foraged more frequently in cropland and grasslands than in deciduous and coniferous forests, especially in late summer when pollen foraging in the forest is most difficult. In contrast, colonies used forests for nectar/honeydew foraging in early summer during times of colony weight gain emphasizing forests as a temporarily significant source of carbohydrates. Importantly, my study shows that the ecological and economic value of managed forest as habitat for honey bees and other wild pollinators can be significantly increased by the continuous provision of floral resources, especially for pollen foraging.
The density of these wild-living honey bee colonies and their survival is driven by several factors that vary locally, making it crucial to compare results in different regions. Therefore, I investigated a wild-living honey bee population in Galicia in north-western Spain, where colonies were observed to reside in hollow electric poles (CHAPTER 5). The observed colony density only in these poles was almost twice as high as in German forest areas, suggesting generally more suitable resource conditions for the bees in Galicia. Based on morphometric analyses of their wing venation patterns, I assigned the colonies to the native evolutionary lineage (M-lineage) where the particularly threatened subspecies \(Apis\) \(mellifera\) \(iberiensis\) also belongs to. Averaged over two consecutive years, almost half of the colonies survived winter (23 out of 52). Interestingly, semi-natural areas both increased abundance and subsequent colony survival. Colonies surrounded by more semi-natural habitat (and therefore less intensive cropland) had an elevated overwintering probability, indicating that colonies need a certain amount of semi-natural habitat in the landscape to survive. Due to their ease of access these power poles in Galicia are, ideally suited to assess the population demography of wild-living Galician honey bee colonies through a long-term monitoring.
In a nutshell, my thesis indicates that honey bees in Europe always existed in the wild. I performed the first survey of wild-living bee density yet done in Germany and Spain. My thesis identifies the landscape as a major factor that compromises winter survival and reports the first data on overwintering rates of wild-living honey bees in Europe. Besides, I established methods to efficiently detect wild-living honey bees in different habitat. While colonies can be found all over Europe, their survival and viability depend on unpolluted, flower rich habitats. The protection of near-natural habitat and of nesting sites is of paramount importance for the conservation of wild-living honey bees in Europe.
The monarch butterfly (Danaus plexippus) performs one of the most astonishing behaviors in the animal kingdom: every fall millions of these butterflies leave their breeding grounds in North Amerika and migrate more than 4.000 km southwards until they reach their overwintering habitat in Central Mexico. To maintain their migratory direction over this enormous distance, the butterflies use a time-compensated sun compass. Beside this, skylight polarization, the Earth’s magnetic field and specific mountain ranges seem to guide the butterflies as well the south. In contrast to this fascinating orientation ability, the behavior of the butterflies in their non-migratory state received less attention. Although they do not travel long distances, they still need to orient themselves to find food, mating partners or get away from competitors. The aim of the present doctoral thesis was to investigate use of visual cues for orientation in migrating as well as non-migrating monarch butterflies. For this, field experiments investigating the migration of the butterflies in Texas (USA) were combined with experiments testing the orientation performance of non-migratory butterflies in Germany.
In the first project, I recorded the heading directions of tethered butterflies during their annual fall migration. In an outdoor flight simulator, the butterflies maintained a southwards direction as long as they had a view of the sun’s position. Relocating the position of the sun by 180° using a mirror, revealed that the sun is the animals’ main orientation reference. Furthermore, I demonstrated that when the sun is blocked and a green light stimulus (simulated sun) is introduced, the animals interpreted this stimulus as the ‘real’ sun. However, this cue was not sufficient to set the migratory direction when simulated as the only visual cue in indoor experiments. When I presented the butterflies a linear polarization pattern additionally to the simulated sun, the animals headed in the correct southerly direction showing that multiple skylight cues are required to guide the butterflies during their migration.
In the second project, I, furthermore, demonstrated that non-migrating butterflies are able to maintain a constant direction with respect to a simulated sun. Interestingly, they ignored the spectral component of the stimulus and relied on the intensity instead. When a panoramic skyline was presented as the only orientation reference, the butterflies maintained their direction only for short time windows probably trying to stabilize their flight based on optic-flow information. Next, I investigated whether the butterflies combine celestial with local cues by simulating a sun stimulus together with a panoramic skyline. Under this conditions, the animals’ directedness was increased demonstrating that they combine multiple visual cues for spatial orientation.
Following up on the observation that a sun stimulus resulted in a different behavior than the panoramic skyline, I investigated in my third project which orientation strategies the butterflies use by presenting different simulated cues to them. While a bright stripe on a dark background elicited a strong attraction of the butterflies steering in the direction of the stimulus, the inverted version of the stimulus was used for flight stabilization. In contrast to this, the butterflies maintained arbitrary directions with a high directedness with respect to a simulated sun. In an ambiguous scenery with two identical stimuli (two bright stripes, two dark stripes, or two sun stimuli) set 180° apart, a constant flight course was only achieved when two sun stimuli were displayed suggesting an involvement of the animals’ internal compass. In contrast, the butterflies used two dark stripes for flight stabilization and were alternatingly attracted by two bright stripes. This shows that monarch butterflies use stimulus-dependent orientation strategies and gives the first evidence for different neuronal pathways controlling the output behavior.
The fusion of methods from several disciplines is a crucial component of scientific development. Artificial Neural Networks, based on the principle of biological neuronal networks, demonstrate how nature provides the best templates for technological advancement. These innovations can then be employed to solve the remaining mysteries of biology, including, in particular, processes that take place on microscopic scales and can only be studied with sophisticated techniques. For instance, direct Stochastic Optical Reconstruction Microscopy combines tools from chemistry, physics, and computer science to visualize biological processes at the molecular level. One of the key components is the computer-aided reconstruction of super-resolved images. Improving the corresponding algorithms increases the quality of the generated data, providing further insights into our biology. It is important, however, to ensure that the heavily processed images are still a reflection of reality and do not originate in random artefacts.
Expansion microscopy is expanding the sample by embedding it in a swellable hydrogel. The method can be combined with other super-resolution techniques to gain additional resolution. We tested this approach on microtubules, a well-known filamentous reference structure, to evaluate the performance of different protocols and labelling techniques.
We developed LineProfiler an objective tool for data collection. Instead of collecting perpendicular profiles in small areas, the software gathers line profiles from filamentous structures of the entire image. This improves data quantity, quality and prevents a biased choice of the evaluated regions. On the basis of the collected data, we deployed theoretical models of the expected intensity distribution across the filaments. This led to the conclusion that post-expansion labelling significantly reduces the labelling error and thus, improves the data quality. The software was further used to determine the expansion factor and arrangement of synaptonemal complex data.
Automated Simple Elastix uses state-of-the-art image alignment to compare pre- and post-expansion images. It corrects linear distortions occurring under isotropic expansion, calculates a structural expansion factor and highlights structural mismatches in a distortion map. We used the software to evaluate expanded fungi and NK cells. We found that the expansion factor differs for the two structures and is lower than the overall expansion of the hydrogel.
Assessing the fluorescence lifetime of emitters used for direct Stochastic Optical Reconstruction Microscopy can reveal additional information about the molecular environment or distinguish dyes emitting with a similar wavelength. The corresponding measurements require a confocal scanning of the sample in combination with the fluorescent switching of the underlying emitters. This leads to non-linear, interrupted Point Spread Functions. The software ReCSAI targets this problem by combining the classical algorithm of compressed sensing with modern methods of artificial intelligence. We evaluated several different approaches to combine these components and found, that unrolling compressed sensing into the network architecture yields the best performance in terms of reconstruction speed and accuracy.
In addition to a deep insight into the functioning and learning of artificial intelligence in combination with classical algorithms, we were able to reconstruct the described non-linearities with significantly improved resolution, in comparison to other state-of-the-art architectures.
Small bacterial regulatory RNAs (sRNAs) have been implicated in the regulation of numerous metabolic pathways. In most of these studies, sRNA-dependent regulation of mRNAs or proteins of enzymes in metabolic pathways has been predicted to affect the metabolism of these bacteria. However, only in a very few cases has the role in metabolism been demonstrated. Here, we performed a combined transcriptome and metabolome analysis to define the regulon of the sibling sRNAs NgncR_162 and NgncR_163 (NgncR_162/163) and their impact on the metabolism of Neisseria gonorrhoeae. These sRNAs have been reported to control genes of the citric acid and methylcitric acid cycles by posttranscriptional negative regulation. By transcriptome analysis, we now expand the NgncR_162/163 regulon by several new members and provide evidence that the sibling sRNAs act as both negative and positive regulators of target gene expression. Newly identified NgncR_162/163 targets are mostly involved in transport processes, especially in the uptake of glycine, phenylalanine, and branched-chain amino acids. NgncR_162/163 also play key roles in the control of serine-glycine metabolism and, hence, probably affect biosyntheses of nucleotides, vitamins, and other amino acids via the supply of one-carbon (C\(_1\)) units. Indeed, these roles were confirmed by metabolomics and metabolic flux analysis, which revealed a bipartite metabolic network with glucose degradation for the supply of anabolic pathways and the usage of amino acids via the citric acid cycle for energy metabolism. Thus, by combined deep RNA sequencing (RNA-seq) and metabolomics, we significantly extended the regulon of NgncR_162/163 and demonstrated the role of NgncR_162/163 in the regulation of central metabolic pathways of the gonococcus.