Transcriptome analysis highlights common pathways between Alzheimer’s disease, dementia with Lewy bodies and Parkinson’s disease
Bibliographic record
Abstract
Background Alzheimer’s disease (AD) and Parkinson’s disease (PD) are the two most common neurodegenerative disorders. Dementia with Lewy bodies (DLB) has common clinical and pathological features with both AD and PD. Additionally, some genes were discovered as top hits in genome-wide association studies (GWASs) for multiple traits (i.e. MAPT, SNCA, TMEM175). This pleiotropy and shared biology suggest common biological pathways. In the current study, we aim to apply genomic and transcriptomic approaches to study possible shared biological pathways across three disorders. Method Summary statistics from the most recent AD, PD and DLB GWASs were included in the analysis. To study genetic correlation, linkage disequilibrium score regression (LDSC) was applied. We performed a transcriptome-wide association study (TWAS) using the FUSION software. For the current study, we have selected expression data from 16 tissues including brain, colon and whole blood. Further, we used the RHOGE package to perform expression correlation between each pair of disorders in the selected tissues. We then performed a cross-tissue analysis of gene expression using the UTMOST package. We selected genes significantly associated with all diseases after false discovery rate (FDR) correction and analyzed common pathways across the three traits. Result We found a genetic correlation between AD and PD (rg=0.21; p=0.013), PD and DLB (rg=0.63; p=0.0002). Applying gene expression correlation across tissues, we found a significant correlation between PD and AD in the amygdala and spinal cord. We also identified correlations between PD and DLB, driven by other tissues including the cerebellum, hippocampus, substantia nigra and transverse colon. The strongest correlation between AD and DLB was in the cerebellum and caudate. We found a number of genes overlapping across three traits in the cross-tissue analysis (Figure 1). Genes overlapping between PD and DLB are mainly encoding proteins involved in lysosomal metabolism. Shared genes between AD and DLB are involved in lipid metabolism and related to cognitive dysfunction. For PD and AD, genes encoding proteins from the SNARE complex (STX1B, STX4, DOC2A) were identified. Conclusion We highlighted common genes and pathways between AD, PD and DLB, which could serve as targets for drug development.
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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 | 0.000 |
| 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) | 0.001 | 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".