Aggressive nitrogen assimilation during exponential growth in <i>Chlamydomonas reinhardtii</i>
Bibliographic record
Abstract
Abstract Bioavailable nitrogen (N) is a key limiting factor for global biomass production. In dynamic environments, organisms must employ effective nitrogen use strategies (NUS) to capitalize on episodic N availability. The recent discovery of intracellular guanine crystals in diverse algal species has prompted questions about their roles in NUS. To investigate NUS in Chlamydomonas reinhardtii , a model photosynthetic eukaryote, we compared N-to-biomass conversion rate of mixotrophic batch cultures fed with three common N sources, ammonium, nitrate, and urea. Saturating growth was achieved at 4 mM ammonium, 4 mM nitrate, and 2 mM urea, indicating comparable molar N utilization efficiency. Residual N measurements revealed that approximately 1.2 unit of optical density (O.D. at 680 nm) biomass was produced per mM N under sub-saturating conditions, while biomass accumulation per N decreased at above-saturating conditions. To estimate N storage capacity, we tracked N uptake kinetics in high-density culture following an N pulse, showing rapid assimilation of 3~6 mM N per O.D. biomass. N source-specific transcriptome revealed N source-specific regulation of assimilation pathways and transporter genes in support of effective NUS in C. reinhardtii . These findings support a model in which C. reinhardtii can rapidly acquire and store N during exponential growth, enabling sustained growth and metabolism during periods of N scarcity. Observed variation in N storage capacity across growth stages and N conditions predicts the regulatory mechanisms governing N partitioning and storage. This study highlights flexible NUS in microalgae, offering insights for improving N assimilation capacity and resilience in agricultural and aquacultural crops.
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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".