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Investigations into the Pathogenic Antibody-Antigen-Interference of Glycine Receptor Autoantibodies
(2021)
Anti-glycine receptor (GlyR) autoantibodies belong to the novel group of autoantibodies that target neuronal cell-surface antigens (NCS), which are accompanied with various neurologic and neuropsychiatric conditions. The inhibitory ionotropic GlyR is one of the major inhibitory neurotransmitter receptors and therefore involved in maintaining homeostasis of neuronal excitation levels at brain stem and spinal cord. Anti-GlyR autoantibodies are associated with progressive encephalomyelitis with rigidity and myoclonus or stiff person syndrome. These neuromotor disorders are characterized by exaggerated startle, muscle stiffness, and painful spasms, leading to immobility and fatal outcome in some cases. It was hypothesized that imbalance of motoneuronal inhibition by functional impairment of GlyR and receptor internalization are direct consequences of antibody-antigen interference. Here, serum samples of four patients were tested for anti-GlyR autoantibodies and were used for the analysis of the functional impact on the electrophysiological properties of recombinant GlyRs, transiently expressed in HEK293 cells. Furthermore, the recognition pattern of anti- GlyR autoantibodies to human, zebrafish and chimeric GlyRα1 located the epitope to the far N-terminal region. The pathogenicity of anti-GlyR autoantibodies and thereby the autoimmunologic etiology of the disease was confirmed by passive transfer of patient serum to zebrafish (Danio rerio) larvae, that yielded an abnormal escape response – a brain stem reflex that corresponds to the exaggerated startle of afflicted patients. The phenotype was accompanied by profound reduction of GlyR clusters in spinal cord cryosections of treated zebrafish larvae. Together, these novel insights into the pathogenicity of GlyR autoantibodies confirm the concept of a novel neurologic autoimmune disease and might contribute to the development of innovative therapeutic strategies.
The tropomysin receptor kinase B (TrkB), the receptor for the neurotrophin brain-derived neurotrophic factor (BDNF), plays an important role in neuronal survival, neuronal differentiation, and cellular plasticity. Conventionally, TrkB activation is induced by binding of BDNF at extracellular sites and subsequent dimerization of receptor monomers. Classical Trk signaling concepts have failed to explain ligand-independent signaling of intracellular TrkB or oncogenic NTRK-fusion proteins. The intracellular activation domain of TrkB consists of a tyrosine kinase core, with three tyrosine (Y) residues at positions 701, 705 and 706, that catalyzes the phosphorylation reaction between ATPγ and tyrosine. The release of cisautoinhibition of the kinase domain activates the kinase domain and tyrosine residues outside of the catalytic domain become phosphorylated. The aim of this study was to find out how ligand-independent activation of TrkB is brought about. With the help of phosphorylation mutants of TrkB, it has been found that a high, local abundance of the receptor is sufficient to activate TrkB in a ligand-independent manner. This self-activation of TrkB was blocked when either the ATP-binding site or Y705 in the core domain was mutated. The vast majority of this self-active TrkB was found at intracellular locations and was preferentially seen in roundish cells, lacking filopodia. Live cell imaging of actin dynamics showed that self-active TrkB changed the cellular morphology by reducing actin filopodia formation. Signaling cascade analysis confirmed that self-active TrkB is a powerful activator of focal adhesion kinase (FAK). This might be the reason why self-active TrkB is able to disrupt actin filopodia formation. The signaling axis from Y705 to FAK could be mimicked by expression of the soluble, cytosolic TrkB kinase domain. However, the signaling pathway was inactive, when the TrkB kinase domain was targeted to the plasmamembrane with the help of artificial myristoylation membrane anchors. A cancer-related intracellular NTRK2-fusion protein (SQSTM1-NTRK2) also underwent constitutive kinase activation. In glioblastoma-like U87MG cells, self-active TrkB kinase reduced cell migration. These constitutive signaling pathways could be fully blocked within minutes by clinically approved, anti-tumorigenic Trk inhibitors. Moreover, this study found evidences for constitutively active, intracellular TrkB in tissue of human grade IV glioblastoma. In conclusion, the data provide an explanation and biological function for selfactive, constitutive TrkB kinase domain signaling, in the absence of a ligand.
Significant advances in fluorescence imaging techniques enable life scientists today to gain insights into biological systems at an unprecedented scale. The interpretation of image features in such bioimage datasets and their subsequent quantitative analysis is referred to as bioimage analysis. A substantial proportion of bioimage analyses is still performed manually by a human expert - a tedious process that is long known to be subjective. Particularly in tasks that require the annotation of image features with a low signal-to-noise ratio, like in fluorescence images of tissue samples, the inter-rater agreement drops. However, like any other scientific analysis, also bioimage analysis has to meet the general quality criteria of quantitative research, which are objectivity, reliability, and validity. Thus, the automation of bioimage analysis with computer-aided approaches is highly desirable. Albeit conventional hard-coded algorithms are fully unbiased, a human user has to set its respective feature extraction parameters. Thus, also these approaches can be considered subjective.
Recently, deep learning (DL) has enabled impressive advances in computer vision research. The predominant difference between DL and conventional algorithms is the capability of DL models to learn the respective task on base of an annotated training dataset, instead of following user-defined rules for feature extraction. This thesis hypothesized that DL can be used to increase the objectivity, reliability, and validity of bioimage analyses, thus going beyond mere automation. However, in absence of ground truth annotations, DL models have to be trained on manual and thus subjective annotations, which could cause the model to incorporate such a bias. Moreover, model training is stochastic and even training on the same data could result in models with divergent outputs. Consequently, both the training on subjective annotations and the model-to-model variability could impair the quality of DL-based bioimage analyses. This thesis systematically assessed the impacts of these two limitations experimentally by analyzing fluorescence signals of a protein called cFOS in mouse brain sections. Since the abundance of cFOS correlates with mouse behavior, behavioral analyses could be used for cross-validation of the bioimage analysis results. Furthermore, this thesis showed that pooling the input of multiple human experts during model training and integration of multiple trained models in a model ensemble can mitigate the impact of these limitations. In summary, the present study establishes guidelines for how DL can be used to increase the general quality of bioimage analyses.