Functional analysis of lactate dehydrogenase during hypoxic stress in Arabidopsis
Bibliographic record
Abstract
During waterlogging conditions plants switch from aerobic respiration to anaerobic fermentation to cope with the lack of available oxygen. Plants have two main fermentation pathways: ethanol and lactic acid fermentation. In this paper we carry out a functional analysis of the Arabidopsis lactate dehydrogenase gene, LDH1. Our results indicate that LDH1, like some other anaerobic genes, is expressed in a root-specific manner and is affected by a variety of abiotic stresses (hypoxia, drought, cold) and mechanical wounding. Functional analysis of LDH1 was carried out using transgenic Arabidopsis overexpressing the gene (35S promoter) and a T-DNA knockout line. Overexpression of LDH1 resulted in improved survival of low oxygen stress conditions in roots but not in shoots. Increased lactic acid fermentation also resulted in significantly higher activities of pyruvate decarboxylase (PDC). Knockout mutants of LDH1 showed reduced survival under low oxygen conditions and PDC activity levels were not changed compared with the wild type. Our data suggest that there is an interdependency between the lactic and ethanol fermentation pathways and that lactic acid fermentation may play a role in stimulating ethanol fermentation and improving plant survival. We show also that Arabidopsis plants are able to exude lactate efficiently into the medium, preventing it accumulating to toxic levels in the cells.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".