Epigenetic Phosphorylation Control of<i>Mycobacterium tuberculosis</i>Infection and Persistence
Bibliographic record
Abstract
Protein phosphorylation is known to occur across all three kingdoms of life; however, the study of posttranslational modification in bacteria was neglected for a considerable amount of time. Early attempts to detect its presence were unsuccessful, generating the dogma that protein phosphorylation was a regulatory mechanism that emerged late in evolution to meet the needs of organisms composed of multiple and differentiated cells. The pioneering work of several groups in the 1970s identified protein kinase activity in both Escherichia coli and Salmonella typhimurium (1 – 3), which soon led to the discovery of the histidine/aspartate kinases of the two-component systems (4, 5). The first aspect of this system involves the stimulation of a histidine kinase by a particular environmental or intracellular signal resulting in autophosphorylation on a key histidine residue. The phospho-histidine can then be used as a substrate by the cognate response regulator for its own autophosphorylation on an aspartate residue. The majority of response regulators are DNA binding proteins that trigger expression from target promoters. Unlike the cross-reactivity observed with serine/threonine/tyrosine (Ser/Thr/Tyr) kinases in eukaryotic cell signaling cascades, two-component systems work in isolation, where a given pairing of histidine kinase and response regulator is highly selective for each other via protein-protein interaction.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".