Study of crosstalk between G-protein coupled receptor-mediated signals and the Nuclear Factor-κB signal transduction cascade

Nuclear Factor-κB (NF-κB) is an ubiquitously expressed transcription factor that is activated in response to a broad spectrum of inflammatory stimuli, including the proinflammatory cytokine Tumour Necrosis Factor-α (TNF-α). Whereas NF-κB is pivotal for coordination of the immune/inflammatory response, its excessive activation is associated with the onset and propagation of multiple disease processes. NF-κB activity is mostly studied in cells subjected to proinflammatory stimuli, but in "real life" cells are simultaneously exposed to a plethora of signalling molecules that can modulate NF-κB activity. It has been known for many decades that sympathetic stress modulates immunity and inflammation, yet the molecular bases are not completely understood. Therefore, in this thesis, we focused on the activity of the β2- adrenergic receptor (β2-AR), one of the key mediators of the stress response, as a modulator of NF-κB function. In line with other reports describing the anti-inflammatory action of β2-AR agonists (β-agonists), we observed that cotreatment of human astrocytes with TNF-α and a β- agonist, inhibited the expression of several NF-κB-driven genes. However, we found that at the same time it potently enhanced the expression of other prototypical NF-κB target genes, including the proinflammatory cytokine Interleukin-6 (IL-6). We found that the IL-6 synergy, depended on the formation of an enhanceosome structure, and hypothesized that the IL-6 promoter acted as a "coincidence" detector, which requires input from multiple signalling cascades for maximal activation. Our previous research was limited to the study of β2-AR/NF-κB crosstalk in the central nervous system, using astrocytes as a cellular model system. In this thesis, we have extended our previous research to skeletal muscle cells. In addition, we have attempted to further unravel the molecular details of the very strong transcriptional synergy apparent at the IL-6 gene using a proteomics approach. Firstly, we have investigated signalling in response to TNF-α/β-agonist cotreatment in C2C12 cells, a murine skeletal muscle model, representing a physiologically relevant cell type to study β2-AR/NF-κB crosstalk. We observed many similarities in the outcome of β2-AR/NF-κB crosstalk in skeletal muscle cells as compared to astrocytes, although cell-type specific differences in the signalling cascades induced by β- agonists/TNF-α were also apparent. In particular, the very potent synergy at the IL-6 promoter was also detected in skeletal muscle cells. In addition, we found that the expression of several chemokines, influencing the migration potential of undifferentiated skeletal muscle cells, was upregulated upon TNF-α/β-agonist costimulation At the molecular level, we demonstrated that β-agonist-induced potentiation of NF-κB-dependent transcription of the IL-6 gene was associated with histone modifications, chromatin relaxation and formation of an enhanceosome structure. Secondly, using an unbiased proteomics approach, combining DNA-affinity purification and mass spectrometric analysis, we identified Transcription Enhancer Factor 1 (TEF-1) as a novel interactor of the IL-6 promoter. We found that TEF-1 recruitment to the IL-6 promoter was induced upon TNF-α/β-agonist costimulation and that it acted as a transcriptional repressor. Our results furthermore indicate that TEF-1 modulates the transcriptional activity of CREB, but not NF-κB, and that this is associated with altered accessibility of the IL-6 promoter to transcriptional regulators. Importantly, TEF-1 modulated NF-κB-dependent transcription in a gene selective manner. As the effects of β-agonists appear to be highly gene-selective, further elucidation of its molecular basis might lead to the identification of novel targets for the development of selective NF-κB inhibitors. In conclusion, these findings indicate that β2-AR/NF-κB crosstalk promotes potent transcriptional synergy for a subset of NF-κB target genes, including IL-6 and several chemokines. This synergy is apparent in multiple relevant cell types, suggesting it might have general significance. As IL-6 has been attributed with devastating properties in inflammatory disease, and as β-agonists are mainstream therapy for respiratory disease, our data warrant further investigation into the outcome of β2- AR/NF-κB crosstalk in vivo

Normalizing flows as an avenue to study overlapping gravitational wave signals

Due to its speed after training, machine learning is often envisaged as a solution to a manifold of the issues faced in gravitational-wave astronomy. Demonstrations have been given for various applications in gravitational-wave data analysis. In this work, we focus on a challenging problem faced by third-generation detectors: parameter inference for overlapping signals. Due to the high detection rate and increased duration of the signals, they will start to overlap, possibly making traditional parameter inference techniques difficult to use. Here, we show a proof-of-concept application of normalizing flows to perform parameter estimation on overlapped binary black hole systems.Comment: 7 pages, 6 figure

Generative Poisoning Using Random Discriminators

We introduce ShortcutGen, a new data poisoning attack that generates sample-dependent, error-minimizing perturbations by learning a generator. The key novelty of ShortcutGen is the use of a randomly-initialized discriminator, which provides spurious shortcuts needed for generating poisons. Different from recent, iterative methods, our ShortcutGen can generate perturbations with only one forward pass in a label-free manner, and compared to the only existing generative method, DeepConfuse, our ShortcutGen is faster and simpler to train while remaining competitive. We also demonstrate that integrating a simple augmentation strategy can further boost the robustness of ShortcutGen against early stopping, and combining augmentation and non-augmentation leads to new state-of-the-art results in terms of final validation accuracy, especially in the challenging, transfer scenario. Lastly, we speculate, through uncovering its working mechanism, that learning a more general representation space could allow ShortcutGen to work for unseen data.Comment: 6 pages, 2 figures, 4 tables, accepted as an oral presentation at RCV (ECCV 2022 Workshop

Hunting for Serine 276-Phosphorylated p65

The transcription factor nuclear factor kappaB (NF-κB) is one of the central mediators of inflammatory gene expression. Several posttranslational modifications of NF-κB, regulating its transactivation ability, have been described. Especially phosphorylation of the NF-κB subunit p65 has been investigated in depth and several commercial phosphospecific antibodies, targeting selected p65 residues, are available. One of the p65 residues, that is subject to phosphorylation by protein kinase A (PKA) as well as by mitogen-stimulated kinase-1 (MSK-1), is the serine at position 276. Here, we have performed a detailed analysis of the performance of the most commonly used commercial anti-P-p65 Ser276 antibodies. Our findings indicate that at least three widely used anti-P-p65 Ser276 antibodies do not detect p65 in vivo via Western Blot, but instead crossreact with PKA-regulated proteins. As PKA is one of the main kinases responsible for phosphorylation of p65 at Ser276, this observation warrants cautious interpretation of data generated using the tested antibodies

Fast sky localization of gravitational waves using deep learning seeded importance sampling

Fast, highly accurate, and reliable inference of the sky origin of gravitational waves would enable real-time multimessenger astronomy. Current Bayesian inference methodologies, although highly accurate and reliable, are slow. Deep learning models have shown themselves to be accurate and extremely fast for inference tasks on gravitational waves, but their output is inherently questionable due to the blackbox nature of neural networks. In this work, we merge Bayesian inference and deep learning by applying importance sampling on an approximate posterior generated by a multiheaded convolutional neural network. The neural network parametrizes Von Mises-Fisher and Gaussian distributions for the sky coordinates and two masses for given simulated gravitational wave injections in the LIGO and Virgo detectors. We generate skymaps for unseen gravitational-wave events that highly resemble predictions generated using Bayesian inference in a few minutes. Furthermore, we can detect poor predictions from the neural network, and quickly flag them