Low levels of 3,3′-diindolylmethane activate estrogen receptor α and induce proliferation of breast cancer cells in the absence of estradiol
Bibliographic record
Abstract
BACKGROUND: 3,3'-diindolylmethane (DIM) is an acid-catalyzed dimer of idole-3-carbinol (I3C), a phytochemical found in cruciferous vegetables that include broccoli, Brussels sprouts and cabbage. DIM is an aryl hydrocarbon receptor (AhR) ligand and a potential anticancer agent, namely for the treatment of breast cancer. It is also advertised as a compound that regulates sex hormone homeostasis. METHODS: Here we make use of RNA expression assays coupled to Chromatin Immunoprecipitation (ChIP) in breast cancer cell lines to study the effect of DIM on estrogen signaling. We further make use of growth assays, as well as fluorescence-activated cell sorting (FACS) assays, to monitor cell growth. RESULTS: In this study, we report that 'physiologically obtainable' concentrations of DIM (10 μM) activate the estrogen receptor α (ERα) signaling pathway in the human breast cancer cell lines MCF7 and T47D, in a 17β-estradiol (E2)-independent manner. Accordingly, we observe induction of ERα target genes such as GREB1 and TFF1, and an increase in cellular proliferation after treatment with 10 μM DIM in the absence of E2. By using an ERα specific inhibitor (ICI 182 780), we confirm that the transcriptional and proliferative effects of DIM treatment are mediated by ERα. We further show that the protein kinase A signaling pathway participates in DIM-mediated activation of ERα. In contrast, higher concentrations of DIM (e.g. 50 μM) have an opposite and expected effect on cells, which is to inhibit proliferation. CONCLUSIONS: We document an unexpected effect of DIM on cell proliferation, which is to stimulate growth by inducing the ERα signaling pathway. Importantly, this proliferative effect of DIM happens with potentially physiological concentrations that can be provided by the diet or by taking caplet supplements.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".