Systematic metabolome profiling and multi-omics analysis of the nitrogen-limited non-model oleaginous algae for biorefining
Bibliographic record
Abstract
Oleaginous microalga Chlorella is a promising microbial cell factory for producing lipids, transesterifiable to biodiesel, and phytochemical, high-value molecules (HVMs). To better understand the stress-induced oleaginous mechanism of Chlorella sp. FC2 IITG that closely matches with Chlorella sorokiniana based on 18s rRNA gene sequence, we performed 'Algomics' by systematically integrating metabolomics and proteomics data in response to time-resolved nitrogen-limitation: 40 h (mild), 88 h (moderate), and 120 h (severe). Ten HVMs belonging to four biological classes: triacylglycerol (TAG), polyunsaturated fatty acids (PUFA), phytosterols, and terpenoids were annotated using untargeted metabolomics and MS/MS fragmentation pattern match to the spectral library. In particular, the study evidenced 4× and 6× increased accumulation of two different PUFA: 9(S)HpOTrE and dihomo-gamma-linolenic acid, respectively, in nitrogen-limitation conditions. Co-extraction of TAG and PUFA could lower the biodiesel production cost for feasible commercialization. The investigation found a maximum accumulation of TAG 59:10 at 40 h, while that for TAG 54:4 was recorded at 88 h, which suggests different TAG species could be induced by regulating nitrogen-limitation severity. Elevated ꞵ-oxidation, glycolysis, and tricarboxylic acid (TCA) cycle identified in proteomics analysis could provide the substrates, phosphoenolpyruvate, pyruvate, and acetyl CoA, for different phytochemical accumulation in response to nitrogen limitation. The multiomics data unraveled a metabolic tug-of-war ongoing between biomass and storage lipid (TAG) accumulation during nitrogen-limitation, which involved multiple processes including hindered CO2 fixation, the supply of energy, reductants, and carbon reallocation from proteins and membrane lipids. These findings provide distinct oleaginous mechanisms in non-model microalgae, Chlorella sp. FC2 IITG, and engineerable targets for microalgal trait improvements.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".