Serine/threonine kinase activity associates with brain glucose metabolism changes in Alzheimer's Disease
Povala, G.; De Bastiani, M. A.; Bellaver, B.; Ferreira, P. C. L.; Ferrari-Souza, J. P.; Lussier, F. Z.; Souza, D. O.; Rosa-Neto, P.; Zatt, B.; Pascoal, T. A.; Zimmer, E. R.
Show abstract
BackgroundPositron emission tomography (PET) imaging has greatly improved the diagnosis and monitoring of Alzheimers disease (AD). The recently developed neuroinformatic field is expanding analytical and computational strategies to study multimodal neuroscience data. One approach is integrating PET imaging and omics to provide new insights into AD pathophysiology. MethodsHippocampal and blood transcriptomic data of cognitively unimpaired (CU) and cognitively impaired (CI) individuals were obtained from Gene Expression Omnibus (GEO) databases and the Alzheimers Disease Neuroimaging Initiative (ADNI). We used the differentially expressed genes (DEGs) from these datasets to implement a modular dimension reduction approach based on Gene Ontology (GO) and reverse engineering of transcriptional networks centered on transcription factors (TF). GO clusters and regulatory units of TF were selected to undergo integration with [18F]Fluorodeoxyglucose ([18F]FDG)-PET images using voxel-wise linear regression models adjusted for age, gender, years of education, and APOE {varepsilon}4 status. ResultsThe GO semantic similarity resulted in 16 GO clusters enriched with overlapping DEGs in blood and the brain. Voxel-wise analysis revealed a strong association between the cluster related to the regulation of protein serine/threonine kinase activity and the [18F]FDG-PET signal in the brain. The master regulator analysis showed 61 regulatory units of TF significantly enriched with DEGs. The voxel-wise analysis of these regulons showed that zinc-finger-related regulatory units had the closest association with brain glucose metabolism. ConclusionWe identified multiple biological processes and regulatory units of TF associated with [18F]FDG-PET metabolism in the brain of individuals across the aging and AD clinical spectrum. Furthermore, the prominent enrichment of protein serine/threonine kinase activity-related GO cluster and the zinc-finger-related regulatory units highlight the potential gene signatures associated with changes in glucose metabolism due to AD pathology.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- AlzGPS: A Genome-wide Positioning Systems Platform to Catalyze Multi-omics for Alzheimer's Therapeutic Discovery 95%
- Entorhinal cortex epigenome-wide association study highlights four novel loci showing differential methylation in Alzheimer's disease 95%
- TREM2 Risk Variants with Alzheimer’s Disease Differ in Rate of Cognitive Decline 94%
Similar papers in this journal
- Prediction of brain age using structural magnetic resonance imaging: A comparison of clinical validity of publicly available software packages 91%
- 1 H-NMR metabolomics-guided DNA methylation mortality predictors 91%
- 1 H-NMR metabolomics-based surrogates to impute common clinical risk factors and endpoints 90%
Similar papers in this journal
- Peripheral inflammation is associated with structural brain atrophy and cognitive decline linked to mild cognitive impairment and Alzheimer's disease 94%
- PTPRS is a novel marker for early tau pathology and synaptic integrity in Alzheimer's disease 94%
- Proteomic characterization of aging-driven changes in the mouse brain by co-expression network analysis 92%
Similar papers in this journal
- Single-synapse analyses of Alzheimers disease implicate pathologic tau, DJ1, CD47, and ApoE 96%
- Integrated Proteomics Reveals Brain-Based Cerebrospinal Fluid Biomarkers in Asymptomatic and Symptomatic Alzheimer’s Disease 93%
- Atrophy associated with tau pathology precedes overt cell death in a mouse model of progressive tauopathy 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.