The hidden power of secondary metabolites in plant-fungi interactions and sustainable phytoremediation
Bibliographic record
Abstract
The global environment is dominated by various small exotic substances, known as secondary metabolites, produced by plants and microorganisms. Plants and fungi are particularly plentiful sources of these molecules, whose physiological functions, in many cases, remain a mystery. Fungal secondary metabolites (SM) are a diverse group of substances that exhibit a wide range of chemical properties and generally fall into one of four main family groups: Terpenoids, polyketides, non-ribosomal peptides, or a combination of the latter two. They are incredibly varied in their functions and are often related to the increased fitness of the respective fungus in its environment, often competing with other microbes or interacting with plant species. Several of these metabolites have essential roles in the biological control of plant diseases by various beneficial microorganisms used for crop protection and biofertilization worldwide. Besides direct toxic effects against phytopathogens, natural metabolites can promote root and shoot development and/or disease resistance by activating host systemic defenses. The ability of these microorganisms to synthesize and store biologically active metabolites that are a potent source of novel natural compounds beneficial for agriculture is becoming a top priority for SM fungi research. In this review, we will discuss fungal-plant secondary metabolites with antifungal properties and the role of signaling molecules in induced and acquired systemic resistance activities. Additionally, fungal secondary metabolites mimic plant promotion molecules such as auxins, gibberellins, and abscisic acid, which modulate plant growth under biotic stress. Moreover, we will present a new trend regarding phytoremediation applications using fungal secondary metabolites to achieve sustainable food production and microbial diversity in an eco-friendly environment.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".