Regulation of cyanobacterial CO<sub>2</sub>-concentrating mechanisms through transcriptional induction of high-affinity C<sub>i</sub>-transport systems
Bibliographic record
Abstract
Approximately 50% of global CO2-based productivity is now attributed to the activity of phytoplankton, including ocean-dwelling cyanobacteria. In response to inherent restrictions on the rate of CO2 supply in the aquatic environment, cyanobacteria have evolved a very efficient means of capturing inorganic carbon (Ci), as either CO2 or HCO3. for photosynthetic carbon fixation. This capturing mechanism, known as a CO2-concentrating mechanism (CCM), involves the operation of active CO2 and HCO3 transporters and results in the concentration of CO2 around RuBisCO, in a unique microcompartment called the carboxysome. The CCM exhibits two basic physiological states: a constitutive, low-affinity state; and a high-affinity state, which is induced in response to Ci limitation. Many of the genetic components of the CCM, including genes encoding Ci transporters, have been identified. It is apparent that the expression of genes encoding the inducible, high-affinity Ci transporters is particularly sensitive to Ci availability, and we are now interested in defining how cyanobacterial cells sense and respond to Ci limitation at the transcriptional level. Current theories include direct sensing of external Ci; sensing of internal Ci-pool fluctuations; and detection of changes in photorespiratory intermediates, carbon metabolites, or redox potential. At present, there is no consensual view. We have investigated the physiological and transcriptional responses of CCM mutants and wildtype strains to pharmacological treatments and various light, O2, and Ci regimes. Our data suggest that perception of Ci limitation by a cyanobacterial cell is either directly or indirectly related to the size of the internal Ci pool within the cell, in an oxygen-dependent manner.Key words: CO2-concentrating mechanisms, CO2 sensing, Ci transporters, Synechococcus PCC7942.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".