Cross-talk within plant niches: endophytic and arbuscular mycorrhizal fungi for sustainable crop production
Bibliographic record
Abstract
World agriculture depends in part on the crop-associated microbiome for improved plant growth, health, and productivity. In particular, endophytic fungi (EF) with plant growth-promoting activities fulfill some of these roles and are central as bioinoculant agents. In the case of arbuscular mycorrhizal fungi (AMF), they form a symbiosis with their host plants, enhancing the uptake of water, phosphorus, nitrogen, and other micronutrients, while the plants provide them with photosynthates. This work reviews the differences in the colonization of internal plant niches between these beneficial fungi, as well as other distinctive ecological traits. It also explores mechanisms of seedborne vertical transmission in AMF and their classification. Genomic and transcriptomic advances in fungal endophytes are highlighted, shedding light on genes and expression profiles that define their lifestyle and plant associations. In addition, recent studies on their abilities to promote plant growth are analyzed, especially focusing on Trichoderma spp., Epichloë spp., Serendipita indica (formerly Piriformospora indica), and entomopathogens like Beauveria spp. and Metarhizium spp. Finally, the multiple interactions among EF, AMF, and other members of the plant microbiome-notably plant growth-promoting bacteria (PGPB)-are discussed, emphasizing how these organisms synergistically benefit the host. A deeper understanding of these fungi and their plant-beneficial effects should facilitate commercialization and help farmers achieve sustainable production, especially under challenges posed by global climate change.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".