Bibliographic record
Abstract
Plants and fungi interact in complex ways that can benefit or harm a plant. To better understand how plants defend against pathogens and enhance interactions with beneficial fungi, I used several different approaches to identify genes and pathways involved in these processes. First, I developed a bioinformatics pipeline to search for antimicrobial peptides (AMPs) in public DNA sequence databases. I found 16,870 novel candidate AMPs from 1,003 species, and demonstrated that transcripts of heveins and cyclolinopeptides (CLPs), especially, increased abundance in response to a pathogenic fungus (Fusarium oxysporum f. sp. lini) but not a mutualistic fungus (Rhizoglomus irregulare). Second, I developed an in vitro system to evaluate the effects of the interaction of two mutualistic fungi (Rhizoglomus irregulare and Clonostachys rosea) on flax roots infected by F. oxysporum. I found that both R. irregulare and C. rosea have bio-protective effects against F. oxysporum. R. irregulare also increased flax biomass production, while mitigating the negative effects caused by F. oxysporum on shoot length and biomass. Third, I used RNA-Seq to compare the pre-colonization responses of flax roots to inoculation with R. irregulare or F. oxysporum, separately and in combination. I found distinct classes of genes that responded uniquely to the pathogen and the mutualist. Finally, I tested the activity of flax CLPs, polyamines (PAs), and carbendazim against three pathogenic fungi: F. oxysporum, S. linicola, and Alternaria sp. I found that CLPs and PAs have antifungal activity in vitro against the fungi studied at a concentration range that might be biologically relevant. We suggest that CLPs and PAs should be tested for the mitigation of Alternaria sp. in vivo, since these compounds had better effects on the growth inhibition of this fungus in comparison to a commercial antifungal agent. Together, these data will improve the understanding of mechanisms underlying pathogenicity and mutualism, and will lead to the development of better strategies to control fungal diseases.
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 | 0.001 |
| 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".