Patho-epigenetics: histone deacetylases as targets of pathogens and therapeutics
Bibliographic record
Abstract
In recent years, the interplay of epigenetics and infection moved into the limelight. Epigenetic regulation describes modifications in gene expression without alterations of the DNA sequence. In eukaryotes, this mechanism is central for fundamental cellular processes such as cell development and differentiation, but it is also involved in more specific tasks such as the response to infection by a pathogen. One of the most common types of epigenetic changes is the modification of histones. Histones, the small protein building blocks that are wrapped with DNA are the fundamental packaging unit of chromatin. Histones can be modified by linking different moieties to them-one of the most abundant ones is acetylation. Histone acetylation is regulated by two main classes of enzymes, histone acetyl transferases (HAT) and their counterparts, histone deacetylases (HDAC). Given the high abundance and importance in regulating gene expression, histone acetylation is an excellent target for pathogens to manipulate the host cell to their advantage. Targeting HDACs gained particular interest in recent years, due to the increased use of HDAC inhibitors in clinical practice. Recently, the possibility to fight an infection with HDAC inhibitors was suggested as an alternative to overcome the ever-growing problem of antibiotic resistance. In this review, we focus on the regulation of HDACs and their involvement in immune cell function. We then highlight different mechanisms employed by pathogens to manipulate histone deacetylases and we discuss the possibility of HDAC inhibitors as therapeutics to fight infections.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".