The eIF2α kinase HRI triggers the autophagic clearance of cytosolic protein aggregates
Bibliographic record
Abstract
Large cytosolic protein aggregates are removed by two main cellular processes, autophagy and the ubiquitin-proteasome system, and defective clearance of these protein aggregates results in proteotoxicity and cell death. Recently, we found that the eIF2α kinase heme-regulated inhibitory (HRI) induced a cytosolic unfolded protein response to prevent aggregation of innate immune signalosomes, but whether HRI acts as a general sensor of proteotoxicity in the cytosol remains unclear. Here we show that HRI controls autophagy to clear cytosolic protein aggregates when the ubiquitin-proteasome system is inhibited. We further report that silencing the expression of HRI resulted in decreased levels of BAG3 and HSPB8, two proteins involved in chaperone-assisted selective autophagy, suggesting that HRI may control proteostasis in the cytosol at least in part through chaperone-assisted selective autophagy. Moreover, knocking down the expression of HRI resulted in cytotoxic accumulation of overexpressed α-synuclein, a protein known to aggregate in Parkinson's disease, dementia with Lewy bodies, and multiple system atrophy. In agreement with these data, protein aggregate accumulation and microglia activation were observed in the spinal cord white matter of 7-month-old Hri −/− mice as compared with Hri +/+ littermates. Moreover, aged Hri −/− mice showed accumulation of misfolded α-synuclein in the lateral collateral pathway, a region of the sacral spinal cord horn that receives visceral sensory afferents from the bladder and distal colon, a pathological feature common to α-synucleinopathies in humans. Together, these results suggest that HRI contributes to a general cytosolic unfolded protein response that could be leveraged to bolster the clearance of cytotoxic protein aggregates.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".