Identifying Mitochondrial Stress Response Factors using CRISPR/Cas9 GenomicScreening
Bibliographic record
Abstract
Mitochondria are energy-producing organelles with diverse roles in cellular homeostasis and fate. Due to their pre-endosymbiotic prokaryotic origin, mitochondria retain a small portion of their own genome, referred to as mtDNA. While nuclear DNA repair mechanisms have been fairly well characterized, mtDNA repair mechanisms remain elusive. A major impediment to the characterization of mtDNA repair mechanisms –relative to nuclear DNA repair mechanisms –is the difficulty in specifically addressing damage occurring in mitochondria due to its highly impermeable double membrane. Another challenge lies in the specificity of DNA-damaging probes, leaving questions regarding which cellular responses are due to mitochondrial versus nuclear DNA damage. Using a mitochondria-penetrating peptide, we are able to deliver DNA-damaging agents directly to mitochondria and bypass the nuclear compartment. Here, we describe the use of a mtDNA-damaging agent in tandem with CRISPR/Cas9 screening for the genome-wide discovery of factors essential for mtDNA damage response. Using mitochondria-targeted doxorubicin (mtDox), we generate mtDNA double-strand breaks (mtDSBs) specifically in this organelle. Using an untargeted Dox screen, we identify genes with significantly greater essentiality during mitochondrial versus nuclear DNA damage. We characterize the essentiality of our top hit - WRNIP1 - observed for the first time to respond to mtDNA damage. We further elucidate the mitochondrial role of WRNIP1 in innate immune signaling and nuclear genome maintenance, outlining a model that experimentally supports mitochondrial turnover rather than repair in response to mtDSBs.
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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) | 0.001 | 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".