Transcriptional profiling of genes that are regulated by the endoplasmic reticulum-bound transcription factor AIbZIP/CREB3L4 in prostate cells
Bibliographic record
Abstract
The androgen-regulated protein androgen-induced bZIP (AIbZIP) is a bZIP transcription factor that localizes to the membrane of the endoplasmic reticulum (ER). The physiological role of AIbZIP is unknown, but other ER-bound transcription factors such as ATF6 and SREBPs play a crucial role in the regulation of protein processing and lipid synthesis, respectively. In response to alterations in the intracellular milieu, ATF6 and SREBPs are processed to their transcriptionally active forms by regulated intramembrane proteolysis. In humans, AIbZIP mRNA is expressed in several organs including the pancreas, liver, and gonads, but it is especially abundant in prostate epithelial cells. We therefore used LNCaP human prostate cancer cells as a model to identify stimuli that lead to AIbZIP activation and define the transcriptional targets of AIbZIP. In LNCaP cells, AIbZIP was processed to its transcriptionally active form by drugs that deplete ER calcium stores (i.e., A23187 and caffeine), but it was unaffected by an inhibitor of protein glycosylation (tunicamycin). To identify AIbZIP-regulated genes, we generated LNCaP cell lines that conditionally express the processed form of AIbZIP and used Affymetrix microarrays to screen for AIbZIP-regulated transcripts. Selected genes (n = 48) were validated by Northern blot hybridization. The results reveal that the downstream targets of AIbZIP include genes that are implicated in protein processing (e.g., BAG3, DNAJC12, KDELR3). Strikingly, a large number of AIbZIP-regulated transcripts encode proteins that are involved in transcriptional regulation, small molecule transport, signal transduction, and metabolism. These results suggest that AIbZIP plays a novel role in cell homeostasis.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".