Immunosuppression for Parkinson's disease - a new therapeutic strategy?
Caroline Williams-Gray is a Principal Research Associate in the Department of Clinical Neurosciences, University of Cambridge, and an honorary consultant neurologist specializing in Parkinson’s disease and movement disorders. She leads a translational research group investigating the clinical and biological heterogeneity of PD, with the ultimate goal of developing more targeted therapies for different Parkinson’s subtypes. Her recent work has focused on the theory that the immune system plays a significant role in mediating the heterogeneity of PD and its progression. Her lab is investigating this using blood and CSF -based immune markers, PET neuroimaging and neuropathology in stratified PD cohorts; and she is leading the first randomized controlled trial repurposing a peripheral immunosuppressive drug (azathioprine) to slow the progression of PD.
Protective microglial signaling in Alzheimer's Disease
Recent studies have begun to reveal critical roles for the brain’s professional phagocytes, microglia, and their receptors in the control of neurotoxic amyloid beta (Aβ) and myelin debris accumulation in neurodegenerative disease. However, the critical intracellular molecules that orchestrate neuroprotective functions of microglia remain poorly understood. In our studies, we find that targeted deletion of SYK in microglia leads to exacerbated Aβ deposition, aggravated neuropathology, and cognitive defects in the 5xFAD mouse model of Alzheimer’s disease (AD). Disruption of SYK signaling in this AD model was further shown to impede the development of disease-associated microglia (DAM), alter AKT/GSK3β-signaling, and restrict Aβ phagocytosis by microglia. Conversely, receptor-mediated activation of SYK limits Aβ load. We also found that SYK critically regulates microglial phagocytosis and DAM acquisition in demyelinating disease. Collectively, these results broaden our understanding of the key innate immune signaling molecules that instruct beneficial microglial functions in response to neurotoxic material." https://doi.org/10.1016/j.cell.2022.09.030
Multimorbidity in the ageing human brain: lessons from neuropathological assessment
Age-associated dementias are neuropathologically characterized by the identification of hallmark intracellular and extracellular deposition of proteins, i.e., hyperphosphorylated-tau, amyloid-β, and α-synuclein, or cerebrovascular lesions. The neuropathological assessment and staging of these pathologies allows for a diagnosis of a distinct disease, e.g., amyloid-β plaques and hyperphosphorylated tau pathology in Alzheimer's disease. Neuropathological assessment in large scale cohorts, such as the UK’s Brains for Dementia Research (BDR) programme, has made it increasingly clear that the ageing brain is characterized by the presence of multiple age-associated pathologies rather than just the ‘pure’ hallmark lesion as commonly perceived. These additional pathologies can range from low/intermediate levels, that are assumed to have little if any clinical significance, to a full-blown mixed disease where there is the presence of two distinct diseases. In our recent paper (McAleese et al. 2021 Concomitant neurodegenerative pathologies contribute to the transition from mild cognitive impairment to dementia, https://alz-journals.onlinelibrary.wiley.com/doi/full/10.1002/alz.12291, Alzheimer's & Dementia), using the BDR cohort, we investigated the frequency of multimorbidity and specifically investigated the impact of additional low-level pathology on cognition. In this study, of 670 donated post-mortem brains, we found that almost 70% of cases exhibited multimorbidity and only 22% were considered a pure diagnosis. Importantly, no case of Lewy Body dementia or vascular dementia was considered pure. A key finding is that the presence of low levels of additional pathology increased the likelihood of having mild dementia vs mild cognitive impairment by almost 20-fold, indicating low levels of additional pathology do impact the clinical progression of a distinct disease. Given the high prevalence and the potential clinical impact, cerebral multimorbidity should be at the forefront of consideration in dementia research.
From genetics to neurobiology through transcriptomic data analysis
Over the past years, genetic studies have uncovered hundreds of genetic variants to be associated with complex brain disorders. While this really represents a big step forward in understanding the genetic etiology of brain disorders, the functional interpretation of these variants remains challenging. We aim to help with the functional characterization of variants through transcriptomic data analysis. For instance, we rely on brain transcriptome atlases, such as Allen Brain Atlases, to infer functional relations between genes. One example of this is the identification of signaling mechanisms of steroid receptors. Further, by integrating brain transcriptome atlases with neuropathology and neuroimaging data, we identify key genes and pathways associated with brain disorders (e.g. Parkinson's disease). With technological advances, we can now profile gene expression in single-cells at large scale. These developments have presented significant computational developments. Our lab focuses on developing scalable methods to identify cells in single-cell data through interactive visualization, scalable clustering, classification, and interpretable trajectory modelling. We also work on methods to integrate single-cell data across studies and technologies.
Emergent scientists discuss Alzheimer's disease
This seminar is part of our “Emergent Scientists” series, an initiative that provides a platform for scientists at the critical PhD/postdoc transition period to share their work with a broad audience and network. Summary: These talks cover Alzheimer’s disease (AD) research in both mice and humans. Christiana will discuss in particular the translational aspects of applying mouse work to humans and the importance of timing in disease pathology and intervention (e.g. timing between AD biomarkers vs. symptom onset, timing of therapy, etc.). Siddharth will discuss a rare variant of Alzheimer’s disease called “Logopenic Progressive Aphasia”, which presents with temporo-parietal atrophy yet relative sparing of hippocampal circuitry. Siddharth will discuss how, despite the unusual anatomical basis underlying this AD variant, degeneration of the angular gyrus in the left inferior parietal lobule contributes to memory deficits similar to those of typical amnesic Alzheimer’s disease. Christiana’s abstract: Alzheimer’s disease (AD) is a debilitating neurodegenerative disorder that causes severe deterioration of memory, cognition, behavior, and the ability to perform daily activities. The disease is characterized by the accumulation of two proteins in fibrillar form; Amyloid-β forms fibrils that accumulate as extracellular plaques while tau fibrils form intracellular tangles. Here we aim to translate findings from a commonly used AD mouse model to AD patients. Here we initiate and chronically inhibit neuropathology in lateral entorhinal cortex (LEC) layer two neurons in an AD mouse model. This is achieved by over-expressing P301L tau virally and chronically activating hM4Di DREADDs intracranially using the ligand dechloroclozapine. Biomarkers in cerebrospinal fluid (CSF) is measured longitudinally in the model using microdialysis, and we use this same system to intracranially administer drugs aimed at halting AD-related neuropathology. The models are additionally tested in a novel contextual memory task. Preliminary findings indicate that viral injections of P301L tau into LEC layer two reveal direct projections between this region and the outer molecular layer of dentate gyrus and the rest of hippocampus. Additionally, phosphorylated tau co-localize with ‘starter cells’ and appear to spread from the injection site. Preliminary microdialysis results suggest that the concentrations of CSF amyloid-β and tau proteins mirror changes observed along the disease cascade in patients. The disease-modifying drugs appear to halt neuropathological development in this preclincial model. These findings will lead to a novel platform for translational AD research, linking the extensive research done in rodents to clinical applications. Siddharth’s abstract: A distributed brain network supports our ability to remember past events. The parietal cortex is a critical member of this network, yet, its exact contributions to episodic remembering remain unclear. Neurodegenerative syndromes affecting the posterior neocortex offer a unique opportunity to understand the importance and role of parietal regions to episodic memory. In this talk, I introduce and explore the rare neurodegenerative syndrome of Logopenic Progressive Aphasia (LPA), an aphasic variant of Alzheimer’s disease presenting with early, left-lateralized temporo-parietal atrophy, amidst relatively spared hippocampal integrity. I then discuss two key studies from my recent Ph.D. work showcasing pervasive episodic and autobiographical memory dysfunction in LPA, to a level comparable to typical, amnesic Alzheimer’s disease. Using multimodal neuroimaging, I demonstrate how degeneration of the angular gyrus in the left inferior parietal lobule, and its structural connections to the hippocampus, contribute to amnesic profiles in this syndrome. I finally evaluate these findings in the context of memory profiles in other posterior cortical neurodegenerative syndromes as well as recent theoretical models underscoring the importance of the parietal cortex in the integration and representation of episodic contextual information.
Machine reasoning in histopathologic image analysis
Deep learning is an emerging computational approach inspired by the human brain’s neural connectivity that has transformed machine-based image analysis. By using histopathology as a model of an expert-level pattern recognition exercise, we explore the ability for humans to teach machines to learn and mimic image-recognition and decision making. Moreover, these models also allow exploration into the ability for computers to independently learn salient histological patterns and complex ontological relationships that parallel biological and expert knowledge without the need for explicit direction or supervision. Deciphering the overlap between human and unsupervised machine reasoning may aid in eliminating biases and improving automation and accountability for artificial intelligence-assisted vision tasks and decision-making. Aleksandar Ivanov Title: