Omics-derived biological modules reflect metabolic brain changes in Alzheimer's disease.

INTRODUCTION: Brain glucose hypometabolism, indexed by the fluorodeoxyglucose positron emission tomography ([18F]FDG-PET) imaging, is a metabolic signature of Alzheimer's disease (AD). However, the underlying biological pathways involved in these metabolic changes remain elusive. METHODS: Here, we integrated [18F]FDG-PET images with blood and hippocampal transcriptomic data from cognitively unimpaired (CU, n = 445) and cognitively impaired (CI, n = 749) individuals using modular dimension reduction techniques and voxel-wise linear regression analysis. RESULTS: Our results showed that multiple transcriptomic modules are associated with brain [18F]FDG-PET metabolism, with the top hits being a protein serine/threonine kinase activity gene cluster (peak-t(223) = 4.86, P value < 0.001) and zinc-finger-related regulatory units (peak-t(223) = 3.90, P value < 0.001). DISCUSSION: By integrating transcriptomics with PET imaging data, we identified that serine/threonine kinase activity-associated genes and zinc-finger-related regulatory units are highly associated with brain metabolic changes in AD. HIGHLIGHTS: We conducted an integrated analysis of system-based transcriptomics and fluorodeoxyglucose positron emission tomography ([18F]FDG-PET) at the voxel level in Alzheimer's disease (AD). The biological process of serine/threonine kinase activity was the most associated with [18F]FDG-PET in the AD brain. Serine/threonine kinase activity alterations are associated with brain vulnerable regions in AD [18F]FDG-PET. Zinc-finger transcription factor targets were associated with AD brain [18F]FDG-PET metabolism.

Association of the fibronectin type III domain-containing protein 5 rs1746661 single nucleotide polymorphism with reduced brain glucose metabolism in elderly humans.

Fibronectin type III domain-containing protein 5 (FNDC5) and its derived hormone, irisin, have been associated with metabolic control in humans, with described FNDC5 single nucleotide polymorphisms being linked to obesity and metabolic syndrome. Decreased brain FNDC5/irisin has been reported in subjects with dementia due to Alzheimer's disease. Since impaired brain glucose metabolism develops in ageing and is prominent in Alzheimer's disease, here, we examined associations of a single nucleotide polymorphism in the FNDC5 gene (rs1746661) with brain glucose metabolism and amyloid-ß deposition in a cohort of 240 cognitively unimpaired and 485 cognitively impaired elderly individuals from the Alzheimer's Disease Neuroimaging Initiative. In cognitively unimpaired elderly individuals harbouring the FNDC5 rs1746661(T) allele, we observed a regional reduction in low glucose metabolism in memory-linked brain regions and increased brain amyloid-ß PET load. No differences in cognition or levels of cerebrospinal fluid amyloid-ß42, phosphorylated tau and total tau were observed between FNDC5 rs1746661(T) allele carriers and non-carriers. Our results indicate that a genetic variant of FNDC5 is associated with low brain glucose metabolism in elderly individuals and suggest that FNDC5 may participate in the regulation of brain metabolism in brain regions vulnerable to Alzheimer's disease pathophysiology. Understanding the associations between genetic variants in metabolism-linked genes and metabolic brain signatures may contribute to elucidating genetic modulators of brain metabolism in humans.

Hippocampal GFAP-positive astrocyte responses to amyloid and tau pathologies.

INTRODUCTION: In Alzheimer's disease clinical research, glial fibrillary acidic protein (GFAP) released/leaked 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-ß (Aß) or tau pathologies. The molecular underpinnings behind this specificity are little explored. Here we investigated biomarker and transcriptomic associations of hippocampal GFAP-positive astrocytes with Aß and tau pathologies in humans and mouse models. METHODS: We studied 90 individuals with plasma GFAP, Aß- and Tau-PET to investigate the association between biomarkers. Then, transcriptomic analysis in hippocampal GFAP-positive astrocytes isolated from mouse models presenting Aß (PS2APP) or tau (P301S) pathologies was conducted to explore differentially expressed genes (DEGs), Gene Ontology terms, and protein-protein interaction networks associated with each phenotype. RESULTS: In humans, we found that plasma GFAP associates with Aß but not tau pathology. Unveiling the unique nature of hippocampal GFAP-positive astrocytic responses to Aß or tau pathologies, mouse transcriptomics showed scarce overlap of DEGs between the Aß. and tau mouse models. While Aß GFAP-positive astrocytes were overrepresented with DEGs 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. CONCLUSION: Our results offer insights into Aß- and tau-driven specific signatures in hippocampal GFAP-positive astrocytes. Characterizing how different underlying pathologies distinctly influence astrocyte responses is critical for the biological interpretation of astrocyte biomarkers and suggests the need to develop context-specific astrocyte targets to study AD. FUNDING: This study was supported by Instituto Serrapilheira, Alzheimer's Association, CAPES, CNPq and FAPERGS.

Endocannabinoid System Biomarkers in Alzheimer's Disease.

Background: Alterations in the endocannabinoid system (ES) have been described in Alzheimer's disease (AD) pathophysiology. In the past years, multiple ES biomarkers have been developed, promising to advance our understanding of ES changes in AD. Discussion: ES biomarkers, including positron emission tomography with cannabinoid receptors tracers and biofluid-based endocannabinoids, are associated with AD disease progression and pathological features. Conclusion: Although not specific enough for AD diagnosis, ES biomarkers hold promise for prognosis, drug-target engagement, and a better understanding of the disease. Here, we summarize currently available ES biomarker findings and discuss their potential applications in the AD research field.

Soluble amyloid-beta isoforms predict downstream Alzheimer's disease pathology.

BACKGROUND: Changes in soluble amyloid-beta (Aß) levels in cerebrospinal fluid (CSF) are detectable at early preclinical stages of Alzheimer's disease (AD). However, whether Aß 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 Aß isoforms can predict tau pathology (T+) and neurodegeneration (N+) positivity. METHODS: We used CSF measurements of three soluble Aß peptides (Aß1-38, Aß1-40 and Aß1-42) 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 Aß 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 model. 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 Aß 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.

Low-energy motion estimation memory system with dynamic management.

The digital video coding process imposes severe pressure on memory traffic, leading to considerable power consumption related to frequent DRAM accesses. External off-chip memory demand needs to be minimized by clever architecture/algorithm co-design, thus saving energy and extending battery lifetime during video encoding. To exploit temporal redundancies among neighboring frames, the motion estimation (ME) algorithm searches for good matching between the current block and blocks within reference frames stored in external memory. To save energy during ME, this work performs memory accesses distribution analysis of the test zone search (TZS) ME algorithm and, based on this analysis, proposes both a multi-sector scratchpad memory design and dynamic management for the TZS memory access. Our dynamic memory management, called neighbor management, reduces both static consumption-by employing sector-level power gating-and dynamic consumption-by reducing the number of accesses for ME execution. Additionally, our dynamic management was integrated with two previously proposed solutions: a hardware reference frame compressor and the Level C data reuse scheme (using a scratchpad memory). This system achieves a memory energy consumption savings of 99.8 % and, when compared to the baseline solution composed of a reference frame compressor and data reuse scheme, the memory energy consumption was reduced by 44.1 % at a cost of just 0.35 % loss in coding efficiency, on average. When compared with related works, our system presents better memory bandwidth/energy savings and coding efficiency results.