Nanoscale chemical mapping of exometabolites at fungal–mineral interfaces
Bibliographic record
Abstract
Mineral-associated organic matter is an integral part of soil carbon pool. Biological processes contribute to the formation of such organo-mineral complexes when soil microbes, and in particular soil fungi, deposit a suite of extracellular metabolic compounds and their necromass on the mineral surfaces. While studied in bulk, micro- to nanoscale fungal-mineral interactions remain elusive. Of particular interest are the mutual effects at the interface between the fungal exometabolites and proximal mineral particles. In this work, we have grown saprotrophic and symbiotic fungi in contact with two soil minerals with contrasting properties: quartz and goethite, on top of X-ray transparent silicon nitride membrane windows and analyzed fungal hyphae by synchrotron-based scanning transmission X-ray microscopy in combination with near edge X-ray fine structure spectroscopy at C(K) and Fe(L) absorption edges. In the resultant chemical maps, we were able to visualize and differentiate organic compounds constituting the fungal cells, their extracellular metabolites, and the exometabolites adsorbing on the minerals. We found that the composition of the exometabolites differed between the fungal functional guilds, particularly, in their sugar to protein ratio and potassium concentration. In samples with quartz and goethite, we observed adsorption of the exometabolic compounds on the mineral surfaces with variations in their chemical composition around the particles. Although we did not observe clear alteration in the exometabolite chemistry upon mineral encounters, we show that fungal-mineral interaction result in reduction of Fe(III) in goethite. This process has been demonstrated for bulk systems, but, to our knowledge, this is the first observation on a single hypha scale offering insight into its underlying biological mechanisms. This demonstrates the link between processes initiated at the single-cell level to macroscale phenomena. Thus, spatially resolved chemical characterization of the microbial-mineral interfaces is crucial for an increased understanding of overall carbon cycling in soil.
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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 | 0.000 | 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".