Table · dataset · 2026
Figure 1-6; Sup Figure 1-4 and Sup Table 1
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<p dir="ltr"><b>Figure S1:</b> Overview of static mycelial pellicle production, characterization, and mycelium-assisted anaerobic fermentation.
Description
The workflow illustrates the preparation of fungal mycelial pellicles by inoculating mycelial agar plugs into potato dextrose broth (PDB), followed by static cultivation for 2 weeks to obtain mature pellicles. <b>(A)</b> Mycelial pellicle characterization workflow, including separation of the pellicle from the culture broth, washing with deionized water, wet-sample analysis, freeze-drying, morphological examination by scanning electron microscopy (SEM), and determination of water content. <b>(B)</b> Mycelium-assisted anaerobic fermentation workflow, in which 100 mL of PDB was removed and replaced with 100 mL of MRS medium, followed by inoculation with 5% (v/v) anaerobic bacterial culture, N₂ purging, and incubation at 37 °C. Samples were collected at 0, 16, 41, and 65 h for analysis of optical density, glucose consumption, short-chain fatty acid production by HPLC, broader metabolite profiling, and pellicle morphology by SEM after fermentation.</p><p dir="ltr"><b>Figure S2:</b> Growth kinetics, glucose utilization, and short-chain fatty acid production during fungal–bacterial co-culture fermentation. <b>(A, B)</b> Optical density (OD₆₀₀) profiles, <b>(C, D)</b> residual glucose concentrations, and <b>(E, F)</b> short-chain fatty acid (SCFA) concentrations measured at different fermentation time points.
Panels <b>A, C, and E</b> represent <i>Clostridium kluyveri</i> co-cultured with <i>Trichoderma harzianum</i>, whereas panels <b>B, D, and F</b> represent <i>Clostridium tyrobutyricum</i> co-cultured with <i>T. harzianum</i>. Corresponding bacterial monocultures and <i>T. harzianum</i> monocultures were included for comparison. SCFAs analyzed included acetic acid (AA), propionic acid (PA), and butyric acid (BA).
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Data are presented as mean ± SD..</p><p dir="ltr"><b>Figure S3. </b>Molecular docking and network-based modeling of co-culture-derived metabolites against selected metabolic, inflammatory, and host-defense targets. (A) Molecular docking workflow and representative metabolites (GMP, guanosine, inosine, apigenin, and butyrate). (B–D) Representative docking poses illustrating interactions of GMP with IMPDH2, apigenin with PLD2, and butyrate with NF-κB1.
(E) Binding-energy profile of the selected metabolites across molecular targets associated with purine metabolism, phospholipid remodeling, inflammation, and host-defense pathways. (F) Functional classification of the investigated protein targets. (G) Representative two-dimensional protein-ligand interaction maps showing predicted hydrogen-bonding and other non-covalent interactions.
(H) Integrated interpretation highlighting predicted modulation of purine metabolism, phospholipid remodeling, inflammatory signaling, and antimicrobial/host-defense mechanisms. Docking interactions represent<i> in-silico</i> predictions and require experimental validation.</p><p dir="ltr"><b>Figure S4.</b> Antimicrobial activity of fungal–bacterial fermentation samples, short-chain fatty acids, and reference antibiotics.
Antimicrobial activity was evaluated by measuring the zone of inhibition (cm) against <i>Pseudomonas aeruginosa</i>, <i>Candida albicans</i>, <i>Salmonella Typhi</i>, <i>Klebsiella pneumoniae</i>, <i>Escherichia coli</i>, <i>Bacillus cereus</i>, and <i>Staphylococcus aureus</i>. The tested samples included the medium blank, fungal mycelium, <i>Clostridium tyrobutyricum</i> and <i>Clostridium kluyveri</i> monocultures, fungal–bacterial co-cultures collected at different fermentation times, standard short-chain fatty acid (SCFA) combinations, individual SCFAs, and reference antibiotics.
Samples 1–11 generally exhibited little or no antimicrobial activity, whereas several SCFA/reference treatments showed substantially greater inhibition against the tested microorganisms. Kanamycin and ciprofloxacin were included as positive antibiotic controls. Data were presented as mean ± SD.</p><p dir="ltr"><b>Table S1. </b>Bacterial molecular targets selected for in-silico screening of antimicrobial compounds.</p>
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