Treatment with a selective histone deacetylase (HDAC) 1 and 2 inhibitor in aged mice rejuvenates multiple organ systems
Bibliographic record
Abstract
ABSTRACT The process of aging increases the risk of developing age-related diseases, which come at great societal healthcare costs and suffering to individuals. Meanwhile, targeting the basic mechanisms of aging can reduce the risk of developing age-related diseases during aging, essentially resulting in a ‘healthy aging’ process. Multiple aging pathways exist, which over past decades have systematically been confirmed through gene knockout or overexpression studies in mammals and the ability to increase healthy lifespan. In this work, we perform transcriptome-based drug screening to identify small molecules that mimic the transcriptional profiles of long-lived genetic interventions in mammals. We identify one small molecule whose transcriptional effects mimic diverse known genetic longevity interventions: compound 60 (Cmpd60), which is a selective inhibitor of histone deacetylase 1 (HDAC1) and 2 (HDAC2). In line with this, in a battery of molecular, phenotypic, and bioinformatic analyses, in multiple disease cell and animal models, we find that Cmpd60 treatment rejuvenates multiple organ systems. These included the kidney, brain, and heart. In renal aging, Cmpd60 reduced partial epithelial-mesenchymal transition (EMT) in vitro and decreased fibrosis in vivo . For the aging brain, Cmpd60 reduced dementia-related gene expression in vivo , effects that were recapitulated when treating the APPSWE-1349 Alzheimer mouse. In cardiac aging, Cmpd60 treatment activated favorable developmental gene expression in vivo and in line with this, improved ventricular cardiomyocyte contraction and relaxation in a cell model of cardiac hypertrophy. Our work establishes that a systemic, two-week treatment with an HDAC1/2 inhibitor serves as a multi-tissue, healthy aging intervention in mammals. This holds potential for translation towards therapeutics that promote healthy aging in humans.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".