Novel subtractive transcription-based amplification of mRNA (STAR) method and its application in search of rare and differentially expressed genes in AD brains
Bibliographic record
Abstract
BACKGROUND: Alzheimer's disease (AD) is a complex disorder that involves multiple biological processes. Many genes implicated in these processes may be present in low abundance in the human brain. DNA microarray analysis identifies changed genes that are expressed at high or moderate levels. Complementary to this approach, we described here a novel technology designed specifically to isolate rare and novel genes previously undetectable by other methods. We have used this method to identify differentially expressed genes in brains affected by AD. Our method, termed Subtractive Transcription-based Amplification of mRNA (STAR), is a combination of subtractive RNA/DNA hybridization and RNA amplification, which allows the removal of non-differentially expressed transcripts and the linear amplification of the differentially expressed genes. RESULTS: Using the STAR technology we have identified over 800 differentially expressed sequences in AD brains, both up- and down- regulated, compared to age-matched controls. Over 55% of the sequences represent genes of unknown function and roughly half of them were novel and rare discoveries in the human brain. The expression changes of nearly 80 unique genes were further confirmed by qRT-PCR and the association of additional genes with AD and/or neurodegeneration was established using an in-house literature mining tool (LitMiner). CONCLUSION: The STAR process significantly amplifies unique and rare sequences relative to abundant housekeeping genes and, as a consequence, identifies genes not previously linked to AD. This method also offers new opportunities to study the subtle changes in gene expression that potentially contribute to the development and/or progression of AD.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".