CRISPR screens and quantitative proteomics reveal remodeling of the aryl hydrocarbon receptor–driven proteome through PARP7 activity
Bibliographic record
Abstract
to attach a single ADP-ribose moiety onto proteins related to immunity, transcription, and cell growth and motility. Despite the importance of PARP7 in these processes, PARP7 signaling networks remain underresearched. Here, we used genome-wide CRISPR screens and multiplex quantitative proteomics in distinct lung cancer cell lines treated with a PARP7 inhibitor to better understand PARP7 molecular functions. We find that manipulating the aryl hydrocarbon receptor (AHR) transcriptional activity mediates PARP7 inhibitor sensitivity and triggers robust changes to the AHR-controlled proteome (AHR-ome). One of the striking features of such AHR-ome remodeling was the downregulation of filamins A and B concurrent with the induction of the corresponding E3 ubiquitin ligase ASB2. We also show that suppressor of cytokine signaling 3 (SOCS3) crosstalks to AHR. Inhibition of PARP7 in SOCS3 knockout cells leads to reduced viability compared to wild-type cells treated with a PARP7 inhibitor. Our results reveal signaling interplay between PARP7, AHR, and SOCS3 and establish an invaluable resource to study the role of PARP7 in the regulation of AHR signaling and innate immunity through its ADP-ribosyl transferase activity.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".