Microglial Expression of the Wnt Signaling Modulator DKK2 Differs between Human Alzheimer's Disease Brains and Mouse Neurodegeneration Models

Unlocking Hope: Therapeutic Advances and Approaches in Modulating the Wnt Pathway for Neurodegenerative Diseases

Neurodegenerative diseases (NDs) are conditions characterized by sensory, motor, and cognitive impairments due to alterations in the structure and function of neurons in the central nervous system (CNS). Despite their widespread occurrence, the exact causes of NDs remain largely elusive, and existing treatments fall short in efficacy. The Wnt signaling pathway is an emerging molecular pathway that has been linked to the development and progression of various NDs. Wnt signaling governs numerous cellular processes, such as survival, polarity, proliferation, differentiation, migration, and fate specification, via a complex network of proteins. In the adult CNS, Wnt signaling regulates synaptic transmission, plasticity, memory formation, neurogenesis, neuroprotection, and neuroinflammation, all essential for maintaining neuronal function and integrity. Dysregulation of both canonical and non-canonical Wnt signaling pathways contributes to neurodegeneration through various mechanisms, such as amyloid-β accumulation, tau protein hyperphosphorylation, dopaminergic neuron degeneration, and synaptic dysfunction, prompting investigations into Wnt modulation as a therapeutic target to restore neuronal function and prevent or delay neurodegenerative processes. Modulating Wnt signaling has the potential to restore neuronal function and impede or postpone neurodegenerative processes, offering a therapeutic approach for targeting NDs. In this article, the current knowledge about how Wnt signaling works in Alzheimer's disease and Parkinson's disease is discussed. Our study aims to explore the molecular mechanisms, recent discoveries, and challenges involved in developing Wnt-based therapies.

Tau Accumulation Induces Microglial State Alterations in Alzheimer's Disease Model Mice

Unique microglial states have been identified in Alzheimer's disease (AD) model mice and postmortem AD brains. Although it has been well documented that amyloid-β accumulation induces the alteration of microglial states, the relationship between tau pathology and microglial states remains incompletely understood because of a lack of suitable AD models. In the present study, we generated a novel AD model mouse by the intracerebral administration of tau purified from human brains with primary age-related tauopathy into App knock-in mice with humanized tau. Immunohistochemical analyses revealed that Dectin-1-positive disease-associated microglia were increased in the AD model mice after tau accumulation in the brain. We then performed single-nucleus RNA sequencing on the AD model mice to evaluate the differences in microglial states with and without tau propagation and accumulation. By taking advantage of spatial transcriptomics and existing single-cell RNA sequencing datasets, we showed for the first time that tau propagation and accumulation induce a disease-associated microglial phenotype at the expense of an age-related nonhomeostatic counterpart (namely, white matter-associated microglia) in an AD model mouse brain. Future work using spatial transcriptomics at single-cell resolution will pave the way for a more appropriate interpretation of microglial alterations in response to tau pathology in the AD brain.

Identification of Novel Biomarkers for Alzheimer's Disease and Related Dementias Using Unbiased Plasma Proteomics

Alzheimer's disease (AD) and related dementias (ADRD) is a complex disease with multiple pathophysiological drivers that determine clinical symptomology and disease progression. These diseases develop insidiously over time, through many pathways and disease mechanisms and continue to have a huge societal impact for affected individuals and their families. While emerging blood-based biomarkers, such as plasma p-tau181 and p-tau217, accurately detect Alzheimer neuropthology and are associated with faster cognitive decline, the full extension of plasma proteomic changes in ADRD remains unknown. Earlier detection and better classification of the different subtypes may provide opportunities for earlier, more targeted interventions, and perhaps a higher likelihood of successful therapeutic development. In this study, we aim to leverage unbiased mass spectrometry proteomics to identify novel, blood-based biomarkers associated with cognitive decline. 1,786 plasma samples from 1,005 patients were collected over 12 years from partcipants in the Massachusetts Alzheimer's Disease Research Center Longitudinal Cohort Study. Patient metadata includes demographics, final diagnoses, and clinical dementia rating (CDR) scores taken concurrently. The Proteograph™ Product Suite (Seer, Inc.) and liquid-chromatography mass-spectrometry (LC-MS) analysis were used to process the plasma samples in this cohort and generate unbiased proteomics data. Data-independent acquisition (DIA) mass spectrometry results yielded 36,259 peptides and 4,007 protein groups. Linear mixed effects models revealed 138 differentially abundant proteins between AD and healthy controls. Machine learning classification models for AD diagnosis identified potential candidate biomarkers including MBP, BGLAP, and APoD. Cox regression models were created to determine the association of proteins with disease progression and suggest CLNS1A, CRISPLD2, and GOLPH3 as targets of further investigation as potential biomarkers. The Proteograph workflow provided deep, unbiased coverage of the plasma proteome at a speed that enabled a cohort study of almost 1,800 samples, which is the largest, deep, unbiased proteomics study of ADRD conducted to date.

A characteristic cerebellar biosignature for bipolar disorder, identified with fully automatic machine learning

Background

Transcriptomic profile differences between patients with bipolar disorder and healthy controls can be identified using machine learning and can provide information about the potential role of the cerebellum in the pathogenesis of bipolar disorder.With this aim, user-friendly, fully automated machine learning algorithms can achieve extremely high classification scores and disease-related predictive biosignature identification, in short time frames and scaled down to small datasets.

Method

A fully automated machine learning platform, based on the most suitable algorithm selection and relevant set of hyper-parameter values, was applied on a preprocessed transcriptomics dataset, in order to produce a model for biosignature selection and to classify subjects into groups of patients and controls. The parent GEO datasets were originally produced from the cerebellar and parietal lobe tissue of deceased bipolar patients and healthy controls, using Affymetrix Human Gene 1.0 ST Array.

Results

Patients and controls were classified into two separate groups, with no close-to-the-boundary cases, and this classification was based on the cerebellar transcriptomic biosignature of 25 features (genes), with Area Under Curve 0.929 and Average Precision 0.955. The biosignature includes both genes connected before to bipolar disorder, depression, psychosis or epilepsy, as well as genes not linked before with any psychiatric disease. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed participation of 4 identified features in 6 pathways which have also been associated with bipolar disorder.

Conclusion

Automated machine learning (AutoML) managed to identify accurately 25 genes that can jointly - in a multivariate-fashion - separate bipolar patients from healthy controls with high predictive power. The discovered features lead to new biological insights. Machine Learning (ML) analysis considers the features in combination (in contrast to standard differential expression analysis), removing both irrelevant as well as redundant markers, and thus, focusing to biological interpretation.

Primary cilia-mediated regulation of microglial secretion in Alzheimer's disease

Alzheimer's disease (AD) is a brain disorder manifested by a gradual decline in cognitive function due to the accumulation of extracellular amyloid plaques, disruptions in neuronal substance transport, and the degeneration of neurons. In affected neurons, incomplete clearance of toxic proteins by neighboring microglia leads to irreversible brain inflammation, for which cellular signaling is poorly understood. Through single-cell transcriptomic analysis, we discovered distinct regional differences in the ability of microglia to clear damaged neurites. Specifically, microglia in the septal region of wild type mice exhibited a transcriptomic signature resembling disease-associated microglia (DAM). These lateral septum (LS)-enriched microglia were associated with dense axonal bundles originating from the hippocampus. Further transcriptomic and proteomic approaches revealed that primary cilia, small hair-like structures found on cells, played a role in the regulation of microglial secretory function. Notably, primary cilia were transiently observed in microglia, and their presence was significantly reduced in microglia from AD mice. We observed significant changes in the secretion and proteomic profiles of the secretome after inhibiting the primary cilia gene intraflagellar transport particle 88 (Ift88) in microglia. Intriguingly, inhibiting primary cilia in the septal microglia of AD mice resulted in the expansion of extracellular amyloid plaques and damage to adjacent neurites. These results indicate that DAM-like microglia are present in the LS, a critical target region for hippocampal nerve bundles, and that the primary ciliary signaling system regulates microglial secretion, affecting extracellular proteostasis. Age-related primary ciliopathy probably contributes to the selective sensitivity of microglia, thereby exacerbating AD. Targeting the primary ciliary signaling system could therefore be a viable strategy for modulating neuroimmune responses in AD treatments.

Generation and Characterization of the First Murine Model of Alzheimer's Disease with Mutated AβPP Inserted in a BALB/c Background (C.B6/J-APPswe)

BACKGROUND

Numerous mouse models of Alzheimer's disease (AD) are available, but all suffer from certain limitations, thus prompting further attempts. To date, no one model exists with amyloidopathy in a BALB/c strain.

OBJECTIVE

To generate and characterize the C.B6/J-APPswe mouse, a model of AD with a mutated human gene for the amyloid-β protein precursor (AβPP) inserted in a BALB/c background.

METHODS

We analyzed five groups at different ages (3, 6, 9, 12, and 16-18 months) of C.B6/J-APPswe and wild-type mice (50% males and 50% females) for the main hallmarks of AD by western blotting, amyloid-β (Aβ) ELISA, immunocytochemistry, electrophysiology, and behavioral tests.

RESULTS

The C.B6/J-APPswe mouse displays early AβPP and Aβ production, late amyloid plaques formation, high level of tau phosphorylation, synaptic deficits (reduced density and functional impairment due to a reduced post-synaptic responsiveness), neurodegeneration caused by apoptosis and necroptosis/necrosis, microgliosis, astrocytic abnormalities, and sex-related differences in explorative behavior, anxiety-like behavior, and spatial long-term and working memories. Social housing is feasible despite the intra-cage aggressiveness of male animals.

CONCLUSION

C.B6/J-APPswe mice develop most of the distinctive features of AD and is a suitable model for the study of brain atrophy mechanisms and of the differences between males and females in the onset of cognitive/non-cognitive deficits.