Guanylyl Cyclase A/cGMP Signaling Slows Hidden, Age- and Acoustic Trauma-Induced Hearing Loss
Bibliographic record
Abstract
In the inner ear, cyclic guanosine monophosphate (cGMP) signaling has been described to facilitate otoprotection, which was previously observed through elevated cGMP levels achieved by phosphodiesterase 5 inhibition. However, to date, the upstream hormone receptor pathways eliciting cGMP production are unclear. Here, we show that mice with a genetic deletion of the gene encoding the cGMP generator guanylyl cyclase (GC)-A, the receptor for atrial and B type natriuretic peptides, display a greater vulnerability of hair cells to hidden hearing loss and noise- and age-dependent hearing loss. This vulnerability was associated with GC-A expression in spiral ganglia and outer hair cells (OHCs) but not in inner hair cells (IHCs). GC-A knockout mice exhibited elevated hearing thresholds, most pronounced for the detection of high-frequency tones. Deficits in OHC input-output function in high-frequency regions were already present in young GC-A-deficient mice, with no signs of accelerated progression of age-related hearing loss or higher vulnerability to acoustic trauma. OHCs in these frequency regions from young GC-A knockout mice exhibited diminished levels of KCNQ4 expression, which is the dominant K+ channel in OHCs, and decreased activation of poly (ADP-ribose) polymerase-1, an enzyme involved in DNA repair. Further, GC-A knockout mice had IHC synapse impairments and reduced amplitudes of auditory brainstem responses that progressed with age and with acoustic trauma, in contrast to OHCs, compared to GC-A wild-type littermates. We conclude that GC A/cGMP-dependent signaling pathways have otoprotective functions and GC-A deletion differentially contributes to hair cell damage in a healthy, aged, or injured system. Thus, augmentation of natriuretic peptide/GC-A signaling likely has potential to overcome hidden and noise-induced hearing loss as well as presbycusis.
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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".