Imprint Desorption Electrospray Ionization Mass Spectrometry Imaging for Monitoring Secondary Metabolites Production during Antagonistic Interaction of Fungi
Bibliographic record
Abstract
Direct analysis of microbial cocultures grown on agar media by desorption electrospray ionization mass spectrometry (DESI-MS) is quite challenging. Due to the high gas pressure upon impact with the surface, the desorption mechanism does not allow direct imaging of soft or irregular surfaces. The divots in the agar, created by the high-pressure gas and spray, dramatically change the geometry of the system decreasing the intensity of the signal. In order to overcome this limitation, an imprinting step, in which the chemicals are initially transferred to flat hard surfaces, was coupled to DESI-MS and applied for the first time to fungal cocultures. Note that fungal cocultures are often disadvantageous in direct imaging mass spectrometry. Agar plates of fungi present a complex topography due to the simultaneous presence of dynamic mycelia and spores. One of the most devastating diseases of cocoa trees is caused by fungal phytopathogen Moniliophthora roreri. Strategies for pest management include the application of endophytic fungi, such as Trichoderma harzianum, that act as biocontrol agents by antagonizing M. roreri. However, the complex chemical communication underlying the basis for this phytopathogen-dependent biocontrol is still unknown. In this study, we investigated the metabolic exchange that takes place during the antagonistic interaction between M. roreri and T. harzianum. Using imprint-DESI-MS imaging we annotated the secondary metabolites released when T. harzianum and M. roreri were cultured in isolation and compared these to those produced after 3 weeks of coculture. We identified and localized four phytopathogen-dependent secondary metabolites, including T39 butenolide, harzianolide, and sorbicillinol. In order to verify the reliability of the imprint-DESI-MS imaging data and evaluate the capability of tape imprints to extract fungal metabolites while maintaining their localization, six representative plugs along the entire M. roreri/T. harzianum coculture plate were removed, weighed, extracted, and analyzed by liquid chromatography-high-resolution mass spectrometry (LC-HRMS). Our results not only provide a better understanding of M. roreri-dependent metabolic induction in T. harzianum, but may seed novel directions for the advancement of phytopathogen-dependent biocontrol, including the generation of optimized Trichoderma strains against M. roreri, new biopesticides, and biofertilizers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".