Bioactive metabolites in Hypsizygus marmoreus spent substrate enhanced Pleurotus ostreatus growth
Bibliographic record
Abstract
The rapid accumulation of spent mushroom substrate (SMS) presents a critical environmental challenge globally, but efficient and cost-effective recycling approaches remain limited. Here, to advance valorization of SMS, a proof-of-concept study was conducted in which Hypsizygus marmoreus SMS (HmSMS) was recycled in the cultivation of Pleurotus ostreatus. Impacts of the HmSMS-derived bioactive metabolites were further examined systematically. Results showed that integration of 10 %-30 % HmSMS shortened the harvest time by 2-6 days, significantly improved fruiting body differentiation and productivity of P. ostreatus. The HmSMS-derived water-soluble and volatile metabolites significantly enhanced P. ostreatus growth, albeit through different metabolic pathways: the former act by reprogramming of the amino acid-related metabolisms, whereas the latter act by upregulating purine metabolism, nucleotide metabolism, and the biosynthesis of phenylalanine, tyrosine and tryptophan. Chemical profiles of SMS were comprehensively investigated using a combined multi-omics approach. The Water-soluble metabolites within HmSMS comprised primarily lipids and lipid-like molecules (26.8 %; LC-MS) and organic acids and derivatives (31.9 %; GC-MS). Volatile metabolites in HmSMS were primarily lipids and lipid-like molecules (33.6 %; GC-MS). Five of the examined water-soluble metabolites (fulvic acid, dl-arabinose, erythritol, l-valine and l-isoleucine) significantly promoted P. ostreatus productivity. These findings highlight the untapped potential of SMS-derived bioactive metabolites and imply their potential high-value valorization in forestry and agricultural practices.
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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 | 0.000 | 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".