Citations

Mutations in the PINK1 and PRKN genes are the most frequent genetic cause of early-onset Parkinson disease. The pathogenic p.R275W substitution in PRKN is the most frequent substitution observed in patients, and thus far has been characterized mostly through overexpression models that suggest a possible gain of toxic misfunction. However, its effects under endogenous conditions are largely unknown. We used patient fibroblasts, isogenic neurons, and post-mortem human brain samples from carriers with and without PRKN p.R275W to assess functional impact. Immunoblot analysis and immunofluorescence were used to study mitophagy activation, and mitophagy execution was analyzed by flow cytometry of the reporter mitoKeima. The functional analysis was accompanied by structural investigation of PRKN p.R275W. We observed lower PRKN protein in fibroblasts with compound heterozygous p.R275W mutations. Isogenic neurons showed an allele-dose dependent decrease in PRKN protein. Lower PRKN protein levels were accompanied by diminished phosphorylated ubiquitin and decreased MFN2 modification. Mitochondrial degradation was also allele-dose dependently impaired. Consistently, PRKN protein levels were drastically reduced in human brain samples from p.R275W carriers. Finally, structural simulations showed significant changes in the closed form of PRKN p.R275W. Our data suggest that under endogenous conditions the p.R275W mutation results in a loss-of-function by destabilizing PRKN.

Aggregated α-synuclein (α-SYN) proteins, encoded by the SNCA gene, are hallmarks of Lewy body disease (LBD), affecting multiple brain regions. However, the specific mechanisms underlying α-SYN pathology in cortical neurons, crucial for LBD-associated dementia, remain unclear. Here, we recapitulated α-SYN pathologies in human induced pluripotent stem cells (iPSCs)–derived cortical organoids generated from patients with LBD with SNCA gene triplication. Single-cell RNA sequencing, combined with functional and molecular validation, identified synaptic and mitochondrial dysfunction in excitatory neurons exhibiting high expression of the SNCA gene, aligning with observations in the cortex of autopsy-confirmed LBD human brains. Furthermore, we screened 1280 Food and Drug Administration–approved drugs and identified four candidates (entacapone, tolcapone, phenazopyridine hydrochloride, and zalcitabine) that inhibited α-SYN seeding activity in real-time quaking-induced conversion assays with human brains, reduced α-SYN aggregation, and alleviated mitochondrial dysfunction in SNCA triplication organoids and excitatory neurons. Our findings establish human cortical LBD models and suggest potential therapeutic drugs targeting α-SYN aggregation for LBD.

Abstract INTRODUCTION Multi‐omics studies in Alzheimer's disease (AD) revealed many potential disease pathways and therapeutic targets. Despite their promise of precision medicine, these studies lacked Black Americans (BA) and Latin Americans (LA), who are disproportionately affected by AD. METHODS To bridge this gap, Accelerating Medicines Partnership in Alzheimer's Disease (AMP‐AD) expanded brain multi‐omics profiling to multi‐ethnic donors. RESULTS We generated multi‐omics data and curated and harmonized phenotypic data from BA ( n = 306), LA ( n = 326), or BA and LA ( n = 4) brain donors plus non‐Hispanic White ( n = 252) and other ( n = 20) ethnic groups, to establish a foundational dataset enriched for BA and LA participants. This study describes the data available to the research community, including transcriptome from three brain regions, whole genome sequence, and proteome measures. DISCUSSION The inclusion of traditionally underrepresented groups in multi‐omics studies is essential to discovering the full spectrum of precision medicine targets that will be pertinent to all populations affected with AD. Highlights Accelerating Medicines Partnership in Alzheimer's Disease Diversity Initiative led brain tissue profiling in multi‐ethnic populations. Brain multi‐omics data is generated from Black American, Latin American, and non‐Hispanic White donors. RNA, whole genome sequencing and tandem mass tag proteomicsis completed and shared. Multiple brain regions including caudate, temporal and dorsolateral prefrontal cortex were profiled.

Abstract Metabolic disorders such as insulin resistance and hypertension are potential risk factors for aging and neurodegenerative diseases. These conditions are reversed in Chromogranin A knockout (CgA-KO) mice. This study investigates the role of CgA in Alzheimer’s disease (AD) and corticobasal degeneration (CBD). CgA ablation in tauopathy mice (hTau) (CgA-KO/hTau) exhibited reduced tau aggregation, spreading, extended lifespan, and improved cognitive function. Transcriptomic and metabolite analysis of mouse cortices revealed altered alpha1-adrenergic receptors (Adra1) and high epinephrine (EPI) levels in hTau mice compared to WT mice, mirroring observations in AD and CBD patients. CgA-KO/hTau mice exhibited a reversal of EPI levels in the cortex and the expression of Adra1, nearly returning them to WT levels. Treatment of hippocampal slices with EPI or Adra1 agonist intensified, while an Adra1 antagonist inhibited tau hyperphosphorylation and aggregation. These findings highlight the interplay between the EPI-Adra signaling system and CgA in tauopathy.

[ 18 F]-Flortaucipir positron emission tomography (PET) is considered a good biomarker of Alzheimer’s disease. However, it is unknown how flortaucipir is associated with the distribution of tau across brain regions and how these associations are influenced by amyloid-β. It is also unclear whether flortaucipir can detect tau in definite primary age–related tauopathy (PART). We identified 248 individuals at Mayo Clinic who had undergone [ 18 F]-flortaucipir PET during life, had died, and had undergone an autopsy, 239 cases of which also had amyloid-β PET. We assessed nonlinear relationships between flortaucipir uptake in nine medial temporal and cortical regions, Braak tau stage, and Thal amyloid-β phase using generalized additive models. We found that flortaucipir uptake was greater with increasing tau stage in all regions. Increased uptake at low tau stages in medial temporal regions was only observed in cases with a high amyloid-β phase. Flortaucipir uptake linearly increased with the amyloid-β phase in medial temporal and cortical regions. The highest flortaucipir uptake occurred with high Alzheimer’s disease neuropathologic change (ADNC) scores, followed by low-intermediate ADNC scores, then PART, with the entorhinal cortex providing the best differentiation between groups. Flortaucipir PET had limited ability to detect PART, and imaging-defined PART did not correspond with pathologically defined PART. In summary, spatial patterns of flortaucipir mirrored the histopathological tau distribution, were influenced by the amyloid-β phase, and were useful for distinguishing different ADNC scores and PART.

AbstractAimsRecent advances in artificial intelligence, particularly with large language models like GPT‐4Vision (GPT‐4V)—a derivative feature of ChatGPT—have expanded the potential for medical image interpretation. This study evaluates the accuracy of GPT‐4V in image classification tasks of histopathological images and compares its performance with a traditional convolutional neural network (CNN).MethodsWe utilised 1520 images, including haematoxylin and eosin staining and tau immunohistochemistry, from patients with various neurodegenerative diseases, such as Alzheimer's disease (AD), progressive supranuclear palsy (PSP) and corticobasal degeneration (CBD). We assessed GPT‐4V's performance using multi‐step prompts to determine how textual context influences image interpretation. We also employed few‐shot learning to enhance improvements in GPT‐4V's diagnostic performance in classifying three specific tau lesions—astrocytic plaques, neuritic plaques and tufted astrocytes—and compared the outcomes with the CNN model YOLOv8.ResultsGPT‐4V accurately recognised staining techniques and tissue origin but struggled with specific lesion identification. The interpretation of images was notably influenced by the provided textual context, which sometimes led to diagnostic inaccuracies. For instance, when presented with images of the motor cortex, the diagnosis shifted inappropriately from AD to CBD or PSP. However, few‐shot learning markedly improved GPT‐4V's diagnostic capabilities, enhancing accuracy from 40% in zero‐shot learning to 90% with 20‐shot learning, matching the performance of YOLOv8, which required 100‐shot learning to achieve the same accuracy.ConclusionsAlthough GPT‐4V faces challenges in independently interpreting histopathological images, few‐shot learning significantly improves its performance. This approach is especially promising for neuropathology, where acquiring extensive labelled datasets is often challenging.

AbstractAmyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder characterized by upper and lower motor neuron signs. There are, however, cases where upper motor neurons (UMNs) are predominantly affected, leading to clinical presentations of UMN‐dominant ALS or primary lateral sclerosis. Furthermore, cases exhibiting an UMN‐predominant pattern of motor neuron disease (MND) presenting with corticobasal syndrome (CBS) have been sparsely reported. This study aims to clarify the clinicopathological features of patients with UMN‐predominant MND. We reviewed 24 patients with UMN‐predominant MND with TDP‐43 pathology in the presence or absence of frontotemporal lobar degeneration. Additionally, we reviewed the medical records of patients with pathologically‐confirmed corticobasal degeneration (CBD) who received a final clinical diagnosis of CBS (n = 10) and patients with pathologically‐confirmed progressive supranuclear palsy (PSP) who received a final clinical diagnosis of PSP syndrome (n = 10). Of 24 UMN‐predominant MND patients, 20 had a clinical diagnosis of an atypical parkinsonian disorder, including CBS (n = 11) and PSP syndrome (n = 8). Only two patients had antemortem diagnoses of motor neuron disease. UMN‐predominant MND patients with CBS less frequently exhibited apraxia than those with CBD, and they were less likely to meet clinical criteria for possible or probable CBS. Similarly, UMN‐predominant MND patients with PSP syndrome less often met clinical criteria for probable PSP than PSP patients with PSP syndrome. Our findings suggest that UMN‐predominant MND can mimic atypical parkinsonism, and should be considered in the differential diagnosis of CBS and PSP syndrome, in particular when criteria are not met.

Abstract Lewy body dementia and Alzheimer's disease (AD) are leading causes of cognitive impairment, characterized by distinct but overlapping neuropathological hallmarks. Lewy body disease (LBD) is characterized by α-synuclein aggregates in the form of Lewy bodies as well as the deposition of extracellular amyloid plaques, with many cases also exhibiting neurofibrillary tangle (NFT) pathology. In contrast, AD is characterized by amyloid plaques and neurofibrillary tangles. Both conditions often co-occur with additional neuropathological changes, such as vascular disease and TDP-43 pathology. To elucidate shared and distinct molecular signatures underlying these mixed neuropathologies, we extensively analysed transcriptional changes in the anterior cingulate cortex, a brain region critically involved in cognitive processes. We performed bulk tissue RNA sequencing from the anterior cingulate cortex and determined differentially expressed genes (q-value <0.05) in control (n = 81), LBD (n = 436), AD (n = 53) and pathological amyloid cases consisting of amyloid pathology with minimal or no tau pathology (n = 39). We used gene set enrichment and weighted gene correlation network analysis to understand the pathways associated with each neuropathologically defined group. LBD cases had strong upregulation of inflammatory pathways and downregulation of metabolic pathways. The LBD cases were further subdivided into either high Thal amyloid, Braak NFT, or low pathological burden cohorts. Compared to the control cases, the LBD cohorts consistently showed upregulation for genes involved in protein folding and cytokine immune response, as well as downregulation of fatty acid metabolism. Surprisingly, concomitant tau pathology within the LBD cases resulted in no additional changes. Some core inflammatory pathways were shared between AD and LBD but with numerous disease-specific changes. Direct comparison of LBD cohorts versus AD cases revealed strong enrichment of synaptic signalling, behaviour and neuronal system pathways. Females had a stronger response overall in both LBD and AD, with several sex-specific changes. Overall, the results identify genes commonly and uniquely dysregulated in neuropathologically defined LBD and AD cases, shedding light on shared and distinct molecular pathways. Additionally, the study underscores the importance of considering sex-specific changes in understanding the complex transcriptional landscape of these neurodegenerative diseases.

Abstract Introduction: Recent advancements in artificial intelligence, particularly with large language models like GPT-4Vision (GPT-4V)—a derivative feature of ChatGPT—have expanded the potential for medical image interpretation. This study evaluates the accuracy of GPT-4V in image classification tasks of histopathological images and compares its performance with traditional convolutional neural network (CNN). Methods: We utilized 1515 images, including hematoxylin and eosin staining and tau immunohistochemistry, from patients with various neurodegenerative diseases, such as Alzheimer’s disease (AD), progressive supranuclear palsy (PSP), and corticobasal degeneration (CBD). We assessed GPT-4V’s performance using multi-step prompts to determine how textual context influences image interpretation. We also employed few-shot learning to enhance improvements in GPT-4V’s diagnostic accuracy in classifying three specific tau lesions—astrocytic plaques, neuritic plaques, and tufted astrocytes—and compared the outcomes with the CNN model YOLOv8. Results: GPT-4V accurately recognized staining techniques and tissue origin but struggled with specific lesion identification. The interpretation of images was notably influenced by the provided textual context, which sometimes led to diagnostic inaccuracies. For instance, when presented with images of the motor cortex, the diagnosis shifted inappropriately from AD to CBD or PSP. However, few-shot learning markedly improved GPT-4V’s diagnostic capabilities, enhancing accuracy from 40% in zero-shot learning to 90% with 20-shot learning, matching the performance of YOLOv8, which required 100-shot learning to achieve the same accuracy. Conclusions: Although GPT-4V faces challenges in independently interpreting histopathological images, few-shot learning significantly improves its accuracy. This approach is especially promising for neuropathology, where acquiring extensive labeled datasets is often challenging.