Microarray Analysis as a Tool to Elucidate the Mechanism of Action of St. John's Wort
Bibliographic record
Abstract
Hypericum perforatum L. , known as St. John's wort (SJW) is indicated as phytotherapeutic agent for the treatment of mild to moderate forms of depression. Data from literature support the hypothesis that chronic stress is a major risk factor for psychiatric illnesses. The aim of the present study was to evaluate the effect of SJW extract (STW3-VI; 250 and 500mg/kg; p.o.) and fluoxetine (10mg/kg, p.o.) on genes involved in the pathogenesis of depression using a chronic restraint stress (CRS) model in rats. Hypothalamic and hippocampal tissues were analyzed using the Affymetrix gene chip Rat Genome 230 2.0 Array, which comprises more than 30,000 rat transcripts. Limma analysis and PANTHER database were used to evaluate the microarray data. Treatment with SJW extract in both concentrations and fluoxetine reversed expression levels of several genes changed by CRS. Genes involved in pathways of inflammatory processes ( Mapk8 ) oxidative stress ( Gpx3, Gstm3, Sod3 ) or Alzheimer's disease ( Sncb, Apbb1ip ) were altered by both, fluoxetine and SJW. In all groups several pathways were identified which provide a link between the various hypotheses of depression. Gene expression results for both brain regions were verified using quantitative rPCR. In conclusion, Microarray analysis prooved to be a valuable tool to identify a large number of genes and resulting pathways that may serve as novel drug targets or predict drug responsiveness. Based on our results it was possible to identify new candidate pathways that help to understand molecular principles of depression and in addition help to find novel treatment strategies for neuropsychiatric disorders.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".