ABSTRACT
Parkinson's disease (PD) is a progressive neurodegenerative disorder that is neuropathologically characterized by degeneration of dopaminergic neurons of the substantia nigra (SN) and formation of Lewy bodies and Lewy neurites composed of aggregated α-synuclein. Proteolysis of α-synuclein by matrix metalloproteinases was shown to facilitate its aggregation and to affect cell viability. One of the proteolysed fragments, Gln79-α-synuclein, possesses a glutamine residue at its N-terminus. We argue that glutaminyl cyclase (QC) may catalyze the pyroglutamate (pGlu)79-α-synuclein formation and, thereby, contribute to enhanced aggregation and compromised degradation of α-synuclein in human synucleinopathies. Here, the kinetic characteristics of Gln79-α-synuclein conversion into the pGlu-form by QC are shown using enzymatic assays and mass spectrometry. Thioflavin T assays and electron microscopy demonstrated a decreased potential of pGlu79-α-synuclein to form fibrils. However, size exclusion chromatography and cell viability assays revealed an increased propensity of pGlu79-α-synuclein to form oligomeric aggregates with high neurotoxicity. In brains of wild-type mice, QC and α-synuclein were co-expressed by dopaminergic SN neurons. Using a specific antibody against the pGlu-modified neo-epitope of α-synuclein, pGlu79-α-synuclein aggregates were detected in association with QC in brains of two transgenic mouse lines with human α-synuclein overexpression. In human brain samples of PD and dementia with Lewy body subjects, pGlu79-α-synuclein was shown to be present in SN neurons, in a number of Lewy bodies and in dystrophic neurites. Importantly, there was a spatial co-occurrence of pGlu79-α-synuclein with the enzyme QC in the human SN complex and a defined association of QC with neuropathological structures. We conclude that QC catalyzes the formation of oligomer-prone pGlu79-α-synuclein in human synucleinopathies, which may-in analogy to pGlu-Aß peptides in Alzheimer's disease-act as a seed for pathogenic protein aggregation.
Subject(s)
Aminoacyltransferases/metabolism , Synucleinopathies/genetics , alpha-Synuclein/metabolism , Animals , Brain/pathology , Cell Survival , Chromatography, Gel , Dopaminergic Neurons/metabolism , Glutamine/metabolism , Humans , Kinetics , Lewy Body Disease/genetics , Lewy Body Disease/metabolism , Mice , Mice, Transgenic , Parkinson Disease/genetics , Parkinson Disease/metabolism , Protein Processing, Post-Translational , Sambucus nigra/cytology , Sambucus nigra/metabolismABSTRACT
Synucleinopathies are a group of neurodegenerative disorders, classically characterized by the accumulation of aggregated alpha synuclein (aSyn) in the central nervous system. Parkinson's disease (PD) and multiple system atrophy (MSA) are the two prominent members of this family. Current treatment options mainly focus on the motor symptoms of these diseases. However, non-motor symptoms, including gastrointestinal (GI) symptoms, have recently gained particular attention, as they are frequently associated with synucleinopathies and often arise before motor symptoms. The gut-origin hypothesis has been proposed based on evidence of an ascending spreading pattern of aggregated aSyn from the gut to the brain, as well as the comorbidity of inflammatory bowel disease and synucleinopathies. Recent advances have shed light on the mechanisms underlying the progression of synucleinopathies along the gut-brain axis. Given the rapidly expanding pace of research in the field, this review presents a summary of the latest findings on the gut-to-brain spreading of pathology and potential pathology-reinforcing mediators in synucleinopathies. Here, we focus on 1) gut-to-brain communication pathways, including neuronal pathways and blood circulation, and 2) potential molecular signalling mediators, including bacterial amyloid proteins, microbiota dysbiosis-induced alterations in gut metabolites, as well as host-derived effectors, including gut-derived peptides and hormones. We highlight the clinical relevance and implications of these molecular mediators and their possible mechanisms in synucleinopathies. Moreover, we discuss their potential as diagnostic markers in distinguishing the subtypes of synucleinopathies and other neurodegenerative diseases, as well as for developing novel individualized therapeutic options for synucleinopathies.
Subject(s)
Multiple System Atrophy , Parkinson Disease , Synucleinopathies , Humans , Synucleinopathies/metabolism , Synucleinopathies/pathology , alpha-Synuclein/metabolism , Parkinson Disease/metabolism , Multiple System Atrophy/metabolism , Multiple System Atrophy/pathology , Brain/metabolism , Neurons/metabolismABSTRACT
Purpose: Automatic outlining of different tissue types in digitized histological specimen provides a basis for follow-up analyses and can potentially guide subsequent medical decisions. The immense size of whole-slide-images (WSIs), however, poses a challenge in terms of computation time. In this regard, the analysis of nonoverlapping patches outperforms pixelwise segmentation approaches but still leaves room for optimization. Furthermore, the division into patches, regardless of the biological structures they contain, is a drawback due to the loss of local dependencies. Approach: We propose to subdivide the WSI into coherent regions prior to classification by grouping visually similar adjacent pixels into superpixels. Afterward, only a random subset of patches per superpixel is classified and patch labels are combined into a superpixel label. We propose a metric for identifying superpixels with an uncertain classification and evaluate two medical applications, namely tumor area and invasive margin estimation and tumor composition analysis. Results: The algorithm has been developed on 159 hand-annotated WSIs of colon resections and its performance is compared with an analysis without prior segmentation. The algorithm shows an average speed-up of 41% and an increase in accuracy from 93.8% to 95.7%. By assigning a rejection label to uncertain superpixels, we further increase the accuracy by 0.4%. While tumor area estimation shows high concordance to the annotated area, the analysis of tumor composition highlights limitations of our approach. Conclusion: By combining superpixel segmentation and patch classification, we designed a fast and accurate framework for whole-slide cartography that is AI-model agnostic and provides the basis for various medical endpoints.