ZNF653 transcription factor activity is associated with FDG‐PET changes in AD brain‐vulnerable regions
Bibliographic record
Abstract
Abstract Background Positron emission tomography (PET) imaging greatly impacted Alzheimer’s disease (AD) research and diagnosis. which makes predicting PET brain imaging alterations using blood data is of high interest. Additionally, integrating PET and omics data can provide new insights into AD pathophysiology. Here, we implemented a module‐based framework combining blood transcriptomics with PET to search transcription factors (TFs) activities associated with brain metabolic changes in AD. We hypothesized that integrating omics and PET data will help advance our understanding of AD neurobiology and may reveal relevant new peripheral biomarkers. Methods [18F]Fluorodeoxyglucose ([18F]FDG)‐PET imaging and transcriptomics data were acquired from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Blood microarray gene expression from ADNI, GSE63063 and GSE97760 ( https://www.ncbi.nlm.nih.gov/geo/ ) datasets were submitted to differential expression (DE) analysis. Regulatory units (regulons) of TFs and their predicted target genes were reconstructed using the ARACNe method. Altered regulons in AD submitted gene set variation analysis to infer their TF activity prior to neuroimaging integration with [18F]FDG‐PET images using voxel‐wise eneralized linear regression (GLR) models adjusted for age, gender, years of education, and APOEε4 (RMINC package). Results Sixty‐one regulatory units were significantly enriched with altered genes in at least ⅔ of the datasets explored, and 12 were altered in all three (Figure A‐B). The voxel‐wise correlation between [18F]FDG‐PET and regulons resulted in t‐statistical maps, where uncorrected t‐value > 2.0 was used as the threshol. We observed that ZNF653 has a positive correlation with [18F]FDG‐PET in the precuneus (24.24% left, 39.51% right), medial frontal gyrus (17.26% left), medial frontal‐orbital gyrus (12.50% left) and precentral gyrus (9.08% left, 8.28% right). Interestingly, the ZNF653 regulatory unit is composed majoritarily by genes related to energetic metabolism and protein kinase activity (Figure 1C‐D). Conclusion We identified the activity of the ZNF653 regulatory unit associated with [18 F]FDG‐PET metabolism in the brain of AD individuals. Furthermore, ZNF653 activity could be modulating metabolic and protein kinase activity‐related genes, highlighting a potential role of this TF in AD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".