Search

IntroductionIn Alzheimer’s disease clinical research, glial fibrillary acidic protein (GFAP) released into the cerebrospinal fluid and blood is widely measured and perceived as a biomarker of reactive astrogliosis. However, it was demonstrated that GFAP levels differ in individuals presenting with amyloid-β (Aβ) or tau pathology. The molecular underpinnings behind this specificity are unexplored. Here we investigated biomarker and transcriptomic associations of GFAP-positive astrocytes with Aβ and tau pathologies in humans and mouse models.MethodsWe studied 90 individuals with plasma GFAP, Aβ- and Tau-PET to investigate the association between biomarkers. Then, transcriptomic analysis in hippocampal GFAP-positive astrocytes isolated from mouse models presenting Aβ (PS2APP) or tau (P301S) pathologies was applied to explore differentially expressed genes (DEGs), Gene Ontology processes, and protein-protein interaction networks associated with each phenotype.ResultsIn humans, we found that plasma GFAP associates with Aβ but not tau pathology. Unveiling the unique nature of GFAP-positive astrocytic responses to Aβ or tau pathology, mouse transcriptomics showed scarce overlap of DEGs between the Aβ and tau mouse models, While Aβ GFAP-positive astrocytes were overrepresented with genes associated with proteostasis and exocytosis-related processes, tau hippocampal GFAP-positive astrocytes presented greater abnormalities in functions related to DNA/RNA processing and cytoskeleton dynamics.ConclusionOur results offer insights into Aβ- and tau-driven specific signatures in GFAP-positive astrocytes. Characterizing how different underlying pathologies distinctly influence astrocyte responses is critical for the biological interpretation of astrocyte-related biomarker and suggests the need to develop context-specific astrocyte targets to study AD.FundingThis study was supported by Instituto Serrapilheira, Alzheimer’s Association, CAPES, CNPq and FAPERGS.

Background: Changes in soluble amyloid-beta (Aβ) levels in cerebrospinal fluid (CSF) are detectable at early preclinical stages of Alzheimer's disease (AD). However, whether Aβ levels can predict downstream AD pathological features in cognitively unimpaired (CU) individuals remains unclear. With this in mind, we aimed at investigating whether a combination of soluble Aβ isoforms can predict tau pathology (T+) and neurodegeneration (N+) positivity. Methods: We used CSF measurements of three soluble Aβ peptides (Aβ1‑38, Aβ1‑40 and Aβ1‑42) in CU individuals (n = 318) as input features in machine learning (ML) models aiming at predicting T+ and N+. Input data was used for building 2046 tuned predictive ML models with a nested cross-validation technique. Additionally, proteomics data was employed to investigate the functional enrichment of biological processes altered in T+ and N+ individuals. Results: Our findings indicate that Aβ isoforms can predict T+ and N+ with an area under the curve (AUC) of 0.929 and 0.936, respectively. Additionally, proteomics analysis identified 17 differentially expressed proteins (DEPs) in individuals wrongly classified by our ML algorithm. More specifically, enrichment analysis of gene ontology biological processes revealed an upregulation in myelinization and glucose metabolism-related processes in CU individuals wrongly predicted as T+. A significant enrichment of DEPs in pathways including biosynthesis of amino acids, glycolysis/gluconeogenesis, carbon metabolism, cell adhesion molecules and prion disease was also observed. Conclusions: Our results demonstrate that, by applying a refined ML analysis, a combination of Ab isoforms can predict T+ and N+ with a high AUC. CSF proteomics analysis highlighted a promising group of proteins that can be further explored for improving T+ and N+ prediction.

Introduction The World Health Organization recognizes dementia as a public health priority and highlights research as an action to respond to the consequences, with early career dementia researchers (ECDRs) representing the key driving force. Due to the COVID-19 pandemic, however, biomedical and psychosocial dementia research was strained worldwide. The aim of this study was to understand the impact of the pandemic on ECDRs. Methods In autumn 2021, the Alzheimer’s Association International Society to Advance Alzheimer’s Research and Treatment (ISTAART) Professional Interest Area to Elevate Early Career Researchers (PEERs) and University College London conducted an online survey querying ECDRs’ experiences during the COVID-19 pandemic. The survey was shared through the ISTAART network, social media, podcasts, and emailing lists. Data were analyzed using descriptive and inferential statistics. Results Survey data from n = 321 ECDRs from 34 countries were analyzed (67.6% women; 78.8% working in academia). Overall, 77.8% of ECDRs surveyed indicated research delays, 53.9% made project adjustments, 37.9% required additional or extended funding, and 41.8% reported a negative impact on career progression. Moreover, 19.9% felt unsupported by their institutions and employers (33% felt well supported, 42.7% somewhat supported). ECDR’s conference attendance remained the same (26.5%) or increased (More: 28.6%; a lot more: 5.6%) since the start of the pandemic. Continental differences were visible, while the impact of the pandemic did not differ greatly based on ECDRs’ sociodemographic characteristics. Conclusions The COVID-19 pandemic had a substantial impact on ECDRs worldwide and institutions, employers, and funding bodies are urged to consider the implications and lessons-learned when working with, managing, and promoting ECDRs. Strategies related to the pandemic and general career support to improve ECDRs career progression are discussed, including social media training, digital networking, and benefits of hybrid events. Global resources specific for ECDRs are highlighted.

The human brain’s interregional communication is vital for its proper functioning. A promising direction for investigating how these regions communicate relies on the assumption that the brain is a complex network. In this context, images derived from positron emission tomography (PET) have been proposed as a potential source for understanding brain networks. However, such networks are often assembled via direct computation of inter-subject correlations, neglecting variabilities between subjects and jeopardizing the construction of group representative networks. Here, we used [18F]FDG-PET data from 1027 individuals at different syndromal stages (352 CU, 621 MCI and 234 AD) to develop a novel method for constructing stable (i.e. resilient to spurious data points) metabolic brain networks. Our multiple sampling (MS) scheme generates brain networks with higher stability when compared to the conventional approach. In addition, the proposed method is robust to imbalanced datasets and requires 50% fewer subjects to achieve stability than the conventional approach. Our method has the potential to considerably boost PET data reutilization and advance our understating of human brain network patterns in health and disease.