Multi-omics decipher the molecular mechanisms driving high-lipid production in an artificially-evolved <i>Chlamydomonas</i> mutant
Bibliographic record
Abstract
ABSTRACT Enhancing lipid accumulation in microalgae is critical for commercial viability but often compromises growth. We previously identified an artificially evolved Chlamydomonas reinhardtii mutant (H5) that retains wild-type growth (CC-503) while producing significantly more lipids. Here, we present multi-omic analyses that elucidate the molecular basis of this phenotype. Whole-genome sequencing revealed over 3,000 mutations in H5, including 45 in protein-coding genes (e.g., phosphofructokinase, acyl-carrier protein, glycerol kinase). Six corresponding CLiP insertion mutants also showed elevated lipid content. Transcriptomics revealed upregulation of key genes for glycolysis, nutrient uptake, and proliferation (e.g., pyruvate carboxylase, carbonic anhydrase) under nutrient-replete conditions. Metabolomics identified a striking increase in malonate, a metabolite that supports fatty acid synthesis and cell proliferation. Epigenomic profiling showed hypomethylation in triacylglycerol (TAG) biosynthesis genes and hypermethylation in energy balance regulators. Together, these data suggest that accelerated glycolysis and streamlined metabolism drive lipid accumulation in H5 without compromising growth. Our findings provide a blueprint for engineering high-lipid microalgal strains for industrial applications. HIGHLIGHTS High-lipid Chlamydomonas mutant (H5) exhibits cancer-like metabolism: pseudo-hypoxia and nutrient deprivation response Multi-omics reveals 45 high-impact mutations synergistically enhance lipid production in H5 Six CLiP mutants of H5-disrupted genes showed significantly increased lipid content Malonate levels increased 10-fold in H5, indicating altered mitochondrial function H5 upregulates glycolytic genes while maintaining wild-type growth rates Transcriptomes from H5 and CC-503 converge after nitrogen deprivation despite replete-state differences H5 shows altered lipid composition with increased TAG diversity, decreased DAGs Epigenomic profiling reveals 14,720 differentially methylated transcribed regions in H5
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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 | 0.000 | 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".