Monolignol export by diffusion down a polymerization-induced concentration gradient
Bibliographic record
Abstract
Lignin, the second most abundant biopolymer, is a promising renewable energy source and chemical feedstock. A key element of lignin biosynthesis is unknown: how do lignin precursors (monolignols) get from inside the cell out to the cell wall where they are polymerized? Modeling indicates that monolignols can passively diffuse through lipid bilayers, but this has not been tested experimentally. We demonstrate significant monolignol diffusion occurs when laccases, which consume monolignols, are present on one side of the membrane. We hypothesize that lignin polymerization could deplete monomers in the wall, creating a concentration gradient driving monolignol diffusion. We developed a two-photon microscopy approach to visualize lignifying Arabidopsis thaliana root cells. Laccase mutants with reduced ability to form lignin polymer in the wall accumulated monolignols inside cells. In contrast, active transport inhibitors did not decrease lignin in the wall and scant intracellular phenolics were observed. Synthetic liposomes were engineered to encapsulate laccases, and monolignols crossed these pure lipid bilayers to form polymer within. A sink-driven diffusion mechanism explains why it has been difficult to identify genes encoding monolignol transporters and why the export of varied phenylpropanoids occurs without specificity. It also highlights an important role for cell wall oxidative enzymes in monolignol export.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".