Microalgal glycerol-3-phosphate acyltransferase role in galactolipids and high-value storage lipid biosynthesis
Bibliographic record
Abstract
Glycerolipids are the most abundant lipids in microalgae, and glycerol-3-phosphate:acyl-CoA acyltransferase (GPAT) plays an important role in their biosynthesis. However, the biochemical and biological functions of algal GPAT remain poorly characterized. Here, we characterized the endoplasmic reticulum (ER)-associated GPAT of the model unicellular green alga Chlamydomonas reinhardtii (CrGPATer). Enzymatic assays indicated that CrGPATer is an sn-1 acyltransferase using a variety of acyl-CoAs as the acyl donor. Subcellular localization revealed that CrGPATer was associated with ER membranes and lipid droplets. We constructed overexpression (OE) and knockdown (KD) transgenic C. reinhardtii lines to investigate the in vivo function of CrGPATer. Lipidomic analysis indicated that CrGPATer OE enhanced the cellular content of galactolipids, especially monogalactosyldiacylglycerol, under nitrogen deficiency stress. Correspondingly, CrGPATer KD lines contained lower contents of galactolipids than the control. Feeding experiments with labeled phosphatidic acid revealed that the intermediate of the eukaryotic Kennedy pathway could be used for galactolipid biosynthesis in the chloroplasts. These results provided multiple lines of evidence that the eukaryotic Kennedy pathway mediated by CrGPATer may be involved in galactolipid biosynthesis in C. reinhardtii. OE of CrGPATer significantly increased the content of triacylglycerol and the yield of biomass. Moreover, the content and yield of 1, 3-olein-2-palmitin, a high-value lipid that can be used as an alternative for human milk fat in infant formula, were significantly enhanced in the OE transgenic lines. Taken together, this study provided insights into the biochemical and biological functions of CrGPATer and its potential as a genetic engineering target in functional lipid manufacturing.
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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".