Analysis of molecular mechanisms responsible for the assembly and regulation of effector complexes in RNA silencing

Das Herzstück aller RNA silencing Phänomene, hervorgerufen durch kleine RNA-Stränge, ist ein Ribonukleoproteinkomplex (RNP), der in der Minimalvariante aus einem kurzen, einzelsträngigen RNA-Molekül und einem Argonaute Protein besteht. Im Falle von short interfering RNAs (siRNAs) und microRNAs (miRNAs) wird dieser funktionelle Effektorkomplex RNA Induced Silencing Complex (RNA-induzierter Silencing Komplex), kurz RISC, genannt. In dieser Arbeit habe ich analysiert, (a) wie dieser Komplex zusammengefügt wird und (b) wie derartige Assemblierungskaskaden, die letztendlich zu einem funktionellen RNP führen, reguliert werden können. Ich konnte zeigen, dass Argonaute mit einer siRNA beladen wird, die noch doppelsträngig ist. Zudem muss Argonaute den passenger-Strang schneiden, der am guide-Strang gebunden ist, um ihn effizient freizugeben und um RISC in die aktive und funktionelle Konfiguration umzuwandeln. Dieser irreversible Vorgang verstärkt die Asymmetrie einer siRNA, weshalb dies eine wichtige Rolle bei der zukünftigen Konzeption von wirksamen siRNAs spielen könnte, um unspezifische Nebeneffekte zu vermeiden. Kann der passenger-Strang nicht geschnitten werden, wird er durch einen Umgehungsmechanismus intakt freigegeben, allerdings mit einer in hohem Maße reduzierten Geschwindigkeit. Diese Resultate haben Relevanz für miRNA-Duplexe, die interne Wölbungen aufweisen, und für Argonaute Proteine, die keine endonukleolytische Schneideaktivität aufweisen, wo in beiden Fällen der passenger-Strang nicht geschnitten werden kann. Ich konnte auch zeigen, dass die miRNA-Prozessierungskaskade keine starres Standardprogramm ist, das ausgeführt wird, sobald ein miRNA-Gen transkribiert wird. Im Falle von miR-138 wird die Prozessierung des Vorläufertranskripts von einem Inhibitorprotein gesteuert. Dieser Faktor erkennt die Vorläufer-RNA-Haarnadelstruktur und blockiert die finale Prozessierung in allen Geweben mit der Ausnahme von spezifischen neuronalen Zellen und Zellen der fötalen Leber. Diese Resultate haben zu einem besseren Verständnis der miRNA-Genexpression geführt, wo differenzielles Prozessieren einer Vorläufer-miRNA die Funktion der miRNA von ihrem transkriptionellen Status am genomischen Lokus entkoppeln könnte. Wir glauben, dass eine derartige Regulation eine straffe, aber gleichzeitig dynamische Kontolle der miRNA-Genexpression erlaubt und dass dies eine etwas mehr verbreitetere Möglichkeit darstellen könnte, wie die Funktion von miRNAs reguliert wird.The core of all small RNA silencing phenomena resides in a ribonucleoprotein (RNP) complex that minimally consists of a short, single-stranded RNA molecule and an Argonaute protein. In the case of short interfering RNAs (siRNAs) and microRNAs (miRNAs), the functional effector complex is called RNA Induced Silencing Complex, or RISC for short. In this work, I have analyzed (a) how this complex is assembled and (b) how such assembly cascades that ultimately lead to a functional RNP can be regulated. I could show that Argonaute becomes loaded with an siRNA, when it is still double-stranded. Furthermore, Argonaute has to cleave the passenger strand, which is bound to the guide strand, to efficiently release it and convert RISC into its active, functional configuration. This irreversible cleavage step enforces siRNA asymmetry. Thus, it may play an important role for the future design of potent siRNAs to mimimize off-target effects. If the passenger strand cannot be cleaved, it becomes removed intact by a bypass pathway, albeit at a greatly reduced speed. This is of relevance for miRNA duplexes displaying internal bulges or for Argonaute proteins lacking endonucleolytic cleavage activity, where in both cases passenger strand cleavage cannot occur. I could also demonstrate that the miRNA production cascade is not a rigid program that is being executed by default whenever a miRNA gene is transcribed. In the case of miR-138, the processing of its precursor transcript is regulated by the action of an inhibitory protein. This factor recognizes the precursor RNA hairpin and blocks the final cleavage step in all tissues with the exception of specific neuronal cells and fetal liver cells. These findings have contributed to a better understanding of how miRNA expression can be regulated. Differential processing of a precursor miRNA may allow the uncoupling of miRNA function from the transcriptional status of its genomic locus. We envision that such regulation enables tight but also dynamic control of miRNA expression and could be a more general way of regulating miRNA function

SVD-DIP: Overcoming the Overfitting Problem in DIP-based CT Reconstruction

The deep image prior (DIP) is a well-established unsupervised deep learning method for image reconstruction; yet it is far from being flawless. The DIP overfits to noise if not early stopped, or optimized via a regularized objective. We build on the regularized fine-tuning of a pretrained DIP, by adopting a novel strategy that restricts the learning to the adaptation of singular values. The proposed SVD-DIP uses ad hoc convolutional layers whose pretrained parameters are decomposed via the singular value decomposition. Optimizing the DIP then solely consists in the fine-tuning of the singular values, while keeping the left and right singular vectors fixed. We thoroughly validate the proposed method on real-measured $\mu$CT data of a lotus root as well as two medical datasets (LoDoPaB and Mayo). We report significantly improved stability of the DIP optimization, by overcoming the overfitting to noise

End-User Programming of Mobile Services: Empowering Domain Experts to Implement Mobile Data Collection Applications

The widespread use of smart mobile devices (e.g., in clinical trials or online surveys) offers promising perspectives with respect to the controlled collection of high-quality data. The design, implementation and deployment of such mobile data collection applications, however, is challenging in several respects. First, various mobile operating systems need to be supported, taking the short release cycles of vendors into account as well. Second, domain-specific requirements need to be flexibly aligned with mobile application development. Third, usability styleguides need to be obeyed. Altogether, this turns both programming and maintaining mobile applications into a costly, time-consuming, and error-prone endeavor. To remedy these drawbacks, a model-driven framework empowering domain experts to implement robust mobile data collection applications in an intuitive way was realized. The design of this end-user programming framework is based on experiences gathered in real-life mobile data collection projects. Facets of various stakeholders involved in such projects are discussed and an overall architecture as well as its components are presented. In particular, it is shown how the framework enables domain experts (i.e., end users) to flexibly implement mobile data collection applications on their own. Overall, the framework allows for the effective support of mobile services in a multitude of application domains

Learning-based approaches for reconstructions with inexact operators in nanoCT applications

Imaging problems such as the one in nanoCT require the solution of an inverse problem, where it is often taken for granted that the forward operator, i.e., the underlying physical model, is properly known. In the present work we address the problem where the forward model is inexact due to stochastic or deterministic deviations during the measurement process. We particularly investigate the performance of non-learned iterative reconstruction methods dealing with inexactness and learned reconstruction schemes, which are based on U-Nets and conditional invertible neural networks. The latter also provide the opportunity for uncertainty quantification. A synthetic large data set in line with a typical nanoCT setting is provided and extensive numerical experiments are conducted evaluating the proposed methods

An Educated Warm Start For Deep Image Prior-Based Micro CT Reconstruction

Deep image prior (DIP) was recently introduced as an effective unsupervised approach for image restoration tasks. DIP represents the image to be recovered as the output of a deep convolutional neural network, and learns the network's parameters such that the output matches the corrupted observation. Despite its impressive reconstructive properties, the approach is slow when compared to supervisedly learned, or traditional reconstruction techniques. To address the computational challenge, we bestow DIP with a two-stage learning paradigm: (i) perform a supervised pretraining of the network on a simulated dataset; (ii) fine-tune the network's parameters to adapt to the target reconstruction task. We provide a thorough empirical analysis to shed insights into the impacts of pretraining in the context of image reconstruction. We showcase that pretraining considerably speeds up and stabilizes the subsequent reconstruction task from real-measured 2D and 3D micro computed tomography data of biological specimens. The code and additional experimental materials are available at https://educateddip.github.io/docs.educated_deep_image_prior/

Image Reconstruction via Deep Image Prior Subspaces

Deep learning has been widely used for solving image reconstruction tasks but its deployability has been held back due to the shortage of high-quality training data. Unsupervised learning methods, such as the deep image prior (DIP), naturally fill this gap, but bring a host of new issues: the susceptibility to overfitting due to a lack of robust early stopping strategies and unstable convergence. We present a novel approach to tackle these issues by restricting DIP optimisation to a sparse linear subspace of its parameters, employing a synergy of dimensionality reduction techniques and second order optimisation methods. The low-dimensionality of the subspace reduces DIP's tendency to fit noise and allows the use of stable second order optimisation methods, e.g., natural gradient descent or L-BFGS. Experiments across both image restoration and tomographic tasks of different geometry and ill-posedness show that second order optimisation within a low-dimensional subspace is favourable in terms of optimisation stability to reconstruction fidelity trade-off