Controllable Phycobilin Modification: An Alternative Photoacclimation Response in Cryptophyte Algae
Bibliographic record
Abstract
Cryptophyte algae are well-known for their ability to survive under low light conditions using their auxiliary light harvesting antennas, phycobiliproteins. Mainly acting to absorb light where chlorophyll cannot (500-650 nm), phycobiliproteins also play an instrumental role in helping cryptophyte algae respond to changes in light intensity through the process of photoacclimation. Until recently, photoacclimation in cryptophyte algae was only observed as a change in the cellular concentration of phycobiliproteins; however, an additional photoacclimation response was recently discovered that causes shifts in the phycobiliprotein absorbance peaks following growth under red, blue, or green light. Here, we reproduce this newly identified photoacclimation response in two species of cryptophyte algae and elucidate the origin of the response on the protein level. We compare isolated native and photoacclimated phycobiliproteins for these two species using spectroscopy and mass spectrometry, and we report the X-ray structures of each phycobiliprotein and the corresponding photoacclimated complex. We find that neither the protein sequences nor the protein structures are modified by photoacclimation. We conclude that cryptophyte algae change one chromophore in the phycobiliprotein β subunits in response to changes in the spectral quality of light. Ultrafast pump-probe spectroscopy shows that the energy transfer is weakly affected by photoacclimation.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".