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Frontiers in Microbiology ·

Volume 17 - 2026 | https://doi.org/10.3389/fmicb.2026.1873321

Signaling in the gas phase: comparative volatile profiling of biocontrol Trichoderma spp. and aflatoxin-producing Aspergillus spp. in isolation and co-culture using HS-SPME GC-MS

  • Zilfa Irakoze1
  • Nataliya M. Voloshchuk1
  • Joshua D. Lambert1
  • Helene Hopfer1
  • Josephine Wee1,2*corresponding author
  1. 1Department of Food Science, The Pennsylvania State University, University Park, PA, United States
  2. 2One Health Microbiome Center, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, United States
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Abstract

Food contamination by aflatoxin-producing Aspergillus spp. remains a major food safety and public health concern. Trichoderma spp. and their metabolites are promising biocontrol agents, yet the volatile organic compound (VOC)-mediated interactions underlying growth inhibition of Aspergillus spp. are not fully understood. We applied untargeted metabolomics using Headspace Solid Phase Microextraction Gas Chromatography–Mass Spectrometry (HS-SPME-GC-MS) and multivariate analyses to profile VOCs from three Trichoderma strains ( T. asperellum BTU, T. virens OSK13, T. harzianum OSK34) in monoculture and in co-culture with Aspergillus flavus NRRL 3357 and A. parasiticus B62. Principal component analysis (PCA), ANOVA, PLS-DA, and Spearman correlation were used to characterize VOC profiles and identify discriminant compounds. Co-cultures produced 30 VOCs across 9–12 chemical classes, dominated by sesquiterpenes, alcohols, and ketones. Among the Trichoderma strains, BTU (against A. flavus 3357) and OSK34 (against A. parasiticus B62) showed the greatest VOC abundance and diversity. PCA confirmed Trichoderma -driven changes in VOC profiles during co-cultures, with OSK13 and OSK34 sharing greater VOC profile similarity compared to BTU. In addition, Trichoderma spp. produced both species-specific and shared VOCs, with some also shared with Aspergillus spp. ANOVA further identified significant upregulation and downregulation of VOCs during co-culture. Finally, PLS-DA, ANOVA, and Spearman correlation identified four key discriminant VOCs: 6-pentyl-2H-pyran-2-one, 2-pentylfuran, 1-decanol, and 1-ethenyl-4-methoxybenzene. These VOCs were most abundant in BTU- Aspergillus co-cultures with observed antifungal activity. These findings provide insight into potential mechanisms of Aspergillus growth inhibition by Trichoderma spp. through comparison of monocultures to co-cultures. In addition, the four identified key discriminant VOCs make promising VOC targets for selecting Trichoderma strains for biocontrol of toxigenic Aspergillus , particularly A. flavus . Together, this study establishes a metabolomic framework for VOC-mediated biocontrol and a basis for optimizing Trichoderma selection for biocontrol purposes. Future research should validate the bioactivity roles of these key VOCs and their effect on aflatoxin production, test their antifungal mechanisms, examine synergistic effects, and assess environmental influences to strengthen metabolomics-informed biocontrol strategies for reducing aflatoxin contamination in food systems.

Keywords

1 Introduction

The increasing global population is projected to reach nearly 10 billion by 2050, necessitating a substantial increase in food production to meet demand (United Nations, 2023). Unfortunately, mycotoxins continue to impose a burden on food safety and nutrition security. Mycotoxins contaminate 60%–80% of agricultural crops, with about one quarter of these contaminations exceeding regulatory limits, leading to significant food loss and waste (Eskola et al., 2020). One of the most potent and concerning groups of mycotoxins is aflatoxins, a class of secondary metabolites primarily produced by Aspergillus flavus and Aspergillus parasiticus. There are four major aflatoxin types, B1, B2, G1, and G2, with aflatoxin B1 (AFB1) being the most toxic. Aflatoxins contaminate staple food crops such as maize, rice, and peanuts (Eskola et al., 2020). Human consumption of aflatoxin-contaminated food has been strongly linked to hepatocellular carcinoma, with elevated risks observed in populations co-infected with the Hepatitis B virus or Human Immunodeficiency Virus (Kew, 2003; Cardwell and Henry, 2004). Additional health consequences include impaired child growth, developmental delays, and immunosuppression (Gong et al., 2016).

Mycotoxin contamination is further compounded by environmental changes. Climate change is projected to intensify mycotoxin occurrence by creating conditions favorable for aflatoxin-producing fungi. Rising temperatures and shifting rainfall patterns are expected to expand the range and prevalence of A. flavus and A. parasiticus, ultimately increasing contamination risks and food safety concerns worldwide (Leggieri et al., 2021; Yu et al., 2022; Kos et al., 2023). These projections highlight the need for a resilient and environmentally safe solution to reduce mycotoxin contamination. In response, there has been growing interest in alternative biocontrol strategies that reduce reliance on synthetic chemical fungicides for managing plant pathogens. Synthetic fungicides face growing public resistance due to increased consumer concerns over health risks from residue transfer into food, rising microbial resistance, and environmental pollution (Tsitsigiannis et al., 2012; Ali et al., 2021; Weaver et al., 2022). Currently, the only available commercial biocontrol strains used for aflatoxin management are atoxigenic A. flavus formulations, such as AF36, Afla-Guard®, and various versions of Aflasafe®, which reduce toxigenic Aspergillus strains through competitive exclusion in the same ecological niche (Probst et al., 2011; Atehnkeng et al., 2014; Agbetiameh et al., 2019). However, reliance on a single biocontrol species is concerning, especially with climate change altering fungal ecology and increasing the spread of toxigenic strains. In addition, atoxigenic A. flavus strains are limited by their strict competitive mode of action, as well as potential recombination with toxigenic isolates, underscoring the need for complementary biocontrol strategies (Lewis et al., 2019).

Among the most promising biocontrol candidates are Trichoderma spp., which represent nearly 60% of registered biocontrol agents for plant health improvement (Guzmán-Guzmán et al., 2023). Trichoderma is a rapidly growing, widely distributed fungus that utilizes diverse nutrient sources, suppresses a broad range of plant pathogens, including Aspergillus spp., and enhances host plant growth, nutrient uptake, and resistance to abiotic stresses both in vitro and in the field (Glare et al., 2012; Mishra et al., 2016; Hu et al., 2020; Ferreira and Musumeci, 2021). Although Trichoderma spp. have not yet been commercialized for aflatoxin management, multiple studies have demonstrated effective inhibition of both the growth of toxigenic Aspergillus strains and aflatoxin biosynthesis in the laboratory and in the field (Mostafa et al., 2013; Sivparsad and Laing, 2016; Kifle et al., 2017; Ren et al., 2020; Peles et al., 2021). The biocontrol potential of Trichoderma spp. is linked to the multifaceted mechanisms, including competitive exclusion, mycoparasitism, and production of antagonistic secondary metabolites (antibiosis), known as volatile organic compounds (VOCs) and non-VOCs.

Volatile organic compounds (VOCs) are carbon-containing low molecular weight compounds with high vapor pressure. These characteristics enable their high volatility, which, combined with lower toxicity, makes them increasingly interesting for postharvest biocontrol (Boukaew et al., 2024). Our group and others have demonstrated that VOCs produced by Trichoderma spp. can effectively control fungal growth and toxin production by toxigenic Aspergillus spp. Voloshchuk et al. (2024) used a sandwiched plate assay to demonstrate that VOCs from T. harzianum (OSK-34) and T. asperellum (BTU) inhibited the growth of A. flavus by approximately 21% and A. parasiticus by 34%–44%. Additionally, T. asperelloides VOCs suppressed mycelial growth of both A. parasiticus and A. flavus by around 75% and 71%, respectively (Boukaew et al., 2024). However, fungal growth inhibition and aflatoxin suppression do not always co-occur. Trichoderma VOCs can inhibit Aspergillus growth without reducing toxin production and vice versa. This highlights the need to dissect the mechanisms underlying VOC-mediated interactions (Ren et al., 2022; Voloshchuk et al., 2024). Despite extensive interest in the use of Trichoderma as a biocontrol agent, there remains limited knowledge of the specific mechanisms underlying its ability to suppress Aspergillus growth and aflatoxin production. This knowledge gap is partly because relatively few studies have been conducted, and these have (1) characterized the VOC profiles of a limited number of Trichoderma spp., and (2) often focused on Trichoderma grown in monoculture or in co-culture with plant pathogens other than toxigenic Aspergillus (Ruangwong et al., 2021; Stoppacher et al., 2010). When evaluated in monoculture, Trichoderma strains have been shown to release VOCs belonging to diverse chemical classes such as alcohols, ketones, sesquiterpenes, and lactones, many of which are known to possess antifungal properties (Boukaew et al., 2024; Stoppacher et al., 2010). Nonetheless, no study to date has comprehensively examined how VOC production by Trichoderma is modulated during co-culture with toxigenic Aspergillus spp.

This study employs untargeted metabolomics, integrating Headspace Solid Phase Microextraction Gas Chromatography–Mass Spectrometry (HS SPME GC-MS) and multivariate statistical analyses, to examine how the most potent Trichoderma strains alter their VOC profiles during co-culture with toxigenic Aspergillus spp., linking these changes to growth inhibition of Aspergillus spp. We hypothesized that (1) co-cultures of the most inhibitory Trichoderma strains and toxigenic Aspergillus spp. would exhibit the greatest diversity and abundance of VOCs, contributing to stronger growth inhibition; (2) the volatile profiles of these co-cultures would be predominantly driven by Trichoderma; and (3) the transition from monoculture to co-culture would result in both conserved and species-specific shifts in VOC compositions. Collectively, the findings of this study offer new insights into the metabolomic pathways and regulatory VOCs involved in the antifungal activity of Trichoderma, highlighting the role of signaling in the gas phase as a key mechanism in microbial interactions. Understanding these interactions provides a foundation for optimizing Trichoderma-based biocontrol strategies and developing complementary approaches to mitigate toxigenic Aspergillus growth in food crops.

2 Materials and methods

2.1 Fungal strains and culture conditions

Two Trichoderma strains used in this study were a gift from Dr. Seogchan Kang (Penn State): T. virens (OSK13), and T. harzianum (OSK34), and one from BTU Biotech company (Ukraine), Trichoderma asperellum (BTU). Aspergillus parasiticus B62 (ApB62), was a gift from Dr. John Linz (Michigan State University) and Aspergillus flavus NRRL 3357 (A.f. 3357) was from the Agricultural Research Service (ARS) Culture Collection (Peoria, IL, USA). A.f. 3357 is a reference strain known for high aflatoxin production, while ApB62 is a mutant derived from the high-producing parent strain SU-1 (ATCC 56775). ApB62 was genetically modified through a single amino acid substitution in nor-1 resulting in a mutant carrying a single amino acid substitution of a non-functional Nor-1. This mutant accumulates norsolorinic acid, an orange-colored precursor of aflatoxin biosynthesis, enabling fast visual detection of aflatoxin inhibition when screening multiple Trichoderma strains (Wee et al., 2016). All fungal strains were previously identified and validated using ITS and Tef1 primers (Voloshchuk et al., 2024). Fungal strains were cultured on potato dextrose agar (PDA; Difco™, Becton, Dickinson and Company, Franklin Lakes, NJ, USA) at 30 °C for 5 days. Spores were then harvested using 0.05% Tween-20 in sterile distilled water, filtered in a cell strainer, quantified using a hemocytometer, and stored as stocks in 25% glycerol at −80 °C.

2.2 Preparation of headspace vials for dual-culture VOC profiling

The experimental setup for automated solid phase microextraction (SPME) sampling of VOCs was adapted from Yalage Don et al. (2020) with modifications. Briefly, 2 mL of PDA was dispensed onto one side of a 20 mL headspace glass vial (Restek Corporation, Bellefonte, PA, USA) and left to solidify horizontally, creating a solid agar surface restricted to one side of the vial. After solidification, an additional 2 mL of PDA was poured into the opposite side of the vial, ensuring no contact between the two agar sections. Vials were left to solidify for 1 h at 25 °C prior to fungal inoculation. For fungal inoculation, each Aspergillus strain (ApB62 or A.f. 3357) was center inoculated on one side of the agar using 1 μL of a spore suspension containing 105 spores/mL. This volume and concentration were selected based on a preliminary study conducted to monitor Trichoderma growth and prevent overgrowth, maintaining no contact with Aspergillus spp. over 3 days of co-culture. On the opposite side of the agar, 1 μL of spore suspension (105 spores/mL) from one of the selected biocontrol Trichoderma spp. (OSK34, OSK13, or BTU) was point-inoculated. These strains were chosen based on previously reported high VOC-driven antifungal activity against both Aspergillus spp. in a sandwich plate assay (Voloshchuk et al., 2024). For each Trichoderma-Aspergillus combination, corresponding monocultures and non-inoculated PDA control were included to control for background volatiles, as summarized in Table 1. All experimental and control vials were prepared in triplicate. Following inoculation, vials were sealed with screw caps fitted with PTFE/silicone septa (Restek Corporation) and incubated horizontally at 30 °C for 72 h to allow fungal interaction and VOC accumulation.

TABLE 1. Summary of Trichoderma-Aspergillus combinations and controls evaluated in this study.

Pathogen (Aspergillus)Biocontrol (Trichoderma)Culture typePercent growth inhibition (%)
A.f. 3357–Monoculture–
ApB62–Monoculture–
–BTU (T. asperellum)Monoculture–
–OSK13 (T. virens)Monoculture–
–OSK34 (T. harzianum)Monoculture–
A.f. 3357BTU (T. asperellum)Co-culture31.8b
A.f. 3357OSK13 (T. virens)Co-culture21.2a
A.f. 3357OSK34 (T. harzianum)Co-culture22.7ab
ApB62BTU (T. asperellum)Co-culture41.2a
ApB62OSK13 (T. virens)Co-culture35.3a
ApB62OSK34 (T. harzianum)Co-culture44.1a
––PDA Control–

Percent growth inhibition values reported in this table were derived from (Voloshchuk et al., 2024). Within each Aspergillus strain, co-culture growth inhibitions with different lowercase superscript letters are significantly different (p < 0.05), based on one-way ANOVA followed by Tukey’s HSD test.

2.3 HS-SPME gas chromatography and mass spectrometry analysis

Headspace VOCs produced in both fungal co-culture and monoculture vials were analyzed using headspace solid-phase microextraction (HS-SPME) coupled with gas chromatography–mass spectrometry (GC–MS). Just before HS-SPME analysis, an internal standard (IS), which comprised 1 μL of methanolic D8-naphthalene (Sigma-Aldrich, St. Louis, MO, USA) solution, was introduced directly into the headspace of each vial by piercing the septum with a glass syringe (Agilent Technologies, Santa Clara, CA, USA) without opening the vial. The IS concentration for A.f. 3357 (245 ng/mL) and ApB62 (98 ng/mL) was determined through preliminary optimization to ensure that the peak intensity of the IS was proportional to the dominant VOCs in each treatment.

To enhance VOC analysis from fungal cultures, key parameters of the SPME sampling process were systematically optimized and detailed here. First, two SPME fibers were evaluated: polydimethylsiloxane/divinylbenzene (PDMS/DVB) and polydimethylsiloxane/carboxen/divinylbenzene (PDMS/CAR/DVB), both 20 mm in length (Supelco®, Bellefonte, PA, USA). Each fiber (pre-conditioned at 250 °C for 30 min before use) was mounted on a Gerstel MPS robotic autosampler (Gerstel, Mülheim an der Ruhr, Germany) and tested under identical conditions to compare the diversity and relative abundance of VOCs detected. While both fibers yielded comparable overall VOC profiles, PDMS/DVB was selected for subsequent analyses due to its superior resolution of compounds eluting around 13 min (Supplementary Figure 1).

Second, headspace sample incubation and extraction times were optimized using a full factorial design to maximize VOC diversity and abundance while minimizing relative standard deviation across replicates. Holding incubation and extraction temperature constant at 35 °C, incubation and fiber exposure times were varied between 1 and 30 min. The combination of 15 min incubation and 30 min of extraction yielded the highest metabolite diversity and abundance with minimal variability and was therefore selected for all subsequent analyses.

All GC–MS analyses were performed on an Agilent 7890B GC system (Agilent Technologies, Santa Clara, CA, USA) coupled with an Agilent 5977B MSD (Agilent Technologies). The two Aspergillus-Trichoderma interaction analyses were run independently, and in each system, the samples were run in a randomized treatment order. Empty vials were used to control for contamination from the vial, septa, and instrument. SPME fibers were thermally cleaned for 10 min at 250 °C to control for carryover. Samples were analyzed in randomized treatment order, and replicates of the same treatment were spread out to account for potential order effects. Thermal desorption of VOCs was conducted into a SPME inlet liner (Restek Corporation) with the inlet operated in splitless mode for 1.2 min, and held at a temperature of 250°C. Ultra-pure Helium (99.999%, Linde Gas & Equipment Inc., State College, PA, USA) was used as the carrier gas at a constant flow of 1.2 mL/min. VOCs were separated on a StabilWax-MS capillary column (30 m × 250 μm ID × 0.25 μm df; Restek Corporation). The oven program started at 35 °C (2 min hold), ramped at 10 °C/min to 250 °C, with a final hold at 250 °C for 3 min. The MSD transfer line was held at 250 °C, the ion source at 230 °C, and the quadrupole at 150 °C. Data were acquired in electron ionization (EI) mode at 70 eV in full scanning mode (m/z range of 32–350, 8 scans/s). Retention indices (RI) of identified compounds were calculated using an alkane standard mix (C5-C40; Supelco®, Bellefonte, PA, USA) that was analyzed with the same parameters.

2.4 Data processing, peak identification, metabolomic profiling

Chromatogram files were converted to .cdf format using Agilent MSD Chemstation (version F.01.03.2357) for peak detection, alignment, and deconvolution using PARADISE (Johnsen et al., 2017). Aligned peaks were annotated based on mass spectral matching using the NIST-14 library (National Institute of Standards and Technology [NIST], 2014). We excluded VOCs that appeared in PDA-only controls at levels above 10% of the maximum found in treatment samples to remove media-derived compounds. Remaining VOCs were further validated by comparing their RI with entries in the NIST Chemistry WebBook. For VOCs with commercial standards in our possession, RI and mass spectra were confirmed under the same GC-MS conditions (Supplementary Table 1). Pure analytical standards were obtained from Thermo Fisher Scientific (Waltham, MA, USA), Tokyo Chemical Industries (TCI America, Portland, OR, USA), and Sigma-Aldrich (St. Louis, MO, USA) (Supplementary Table 1). Only compounds that were not from media with a validated RI, spectral match above 70%, or confirmed with a standard were retained for further analysis (Full list of VOCs before the 10% threshold, Spectral match and RI filtering uploaded on ScholarSphere (Irakoze et al., 2026). All identified VOC peak areas were normalized to the IS by dividing each VOC’s peak area by that of the IS and multiplying by the known IS concentration. The semi-quantitative IS-normalized concentrations (pg/vial) were used for all subsequent analyses.

2.5 Statistical analyses

To compare VOC abundance and diversity across treatments, the identified VOCs were classified into chemical classes using PubChem and the literature. Summed concentrations of VOCs in each class were visualized as stacked bar plots in RStudio version 2024.09.0+375 (Posit team, 2024). The dataset containing VOCs and their IS-normalized concentrations for each pathogen-Trichoderma was independently analyzed in MetaboAnalyst 6.0 (Pang et al., 2024). After integrity checks, data were log10-transformed and mean-centered to improve comparability across treatments. Principal Component Analysis (PCA) was performed to assess clustering of Trichoderma and Aspergillus in monocultures and in co-cultures. To evaluate the effects of Trichoderma-Aspergillus interactions on each VOC, one-way ANOVA with false discovery rate (FDR) correction, followed by Tukey’s HSD post hoc testing, was conducted in RStudio version 2024.09.0+375 (Posit team, 2024). For all significantly different VOCs (p < 0.05), hierarchical clustered heatmaps of VOCs (rows) based on Euclidean distance and complete linkage were generated to visualize VOC abundance patterns across samples (columns). Partial Least Squares-Discriminant Analysis (PLS-DA) identified VOCs discriminating all Trichoderma co-cultures, with model validation by leave-one-out cross-validation retaining two components (Q2 = 0.93, R2 = 0.97 and Q2 = 0.93, R2 = 0.99 for A.f. 3357 and ApB62, respectively). VOCs with VIP ≥ 1.0 were considered putative key discriminants. To increase confidence in putative bioactive VOC detection, correlation analysis of VOC abundance and percent growth inhibition of the different Trichoderma strains in co-cultures was conducted. The IS-normalized VOC concentrations were averaged across replicates (n = 3) for each Trichoderma spp. co-culture and Aspergillus monoculture before correlation analysis. Spearman’s rank correlations were calculated between mean VOC levels and previously reported mean percent growth inhibition (Voloshchuk et al., 2024) to find potential bioactive VOCs associated with percent growth inhibition (rs ≥ 0.5, p < 0.05). Due to the small sample-to-variable ratio of the data, the PLS-DA model and Spearman correlation were used for ranking rather than predictive classification of VOCs. All statistical analyses were conducted independently for each Aspergillus spp., and the consensus VOCs were highlighted as putative antifungal VOCs based on high ranking by the multiple statistical analyses.

3 Results and discussion

3.1 The greatest VOC diversity and abundance were observed in the most inhibitory Trichoderma strains in co-cultures with Aspergillus spp.

Co-culturing of Trichoderma strains with A.f. 3357 and ApB62 resulted in distinct patterns of VOC abundance and distribution of chemical classes. Across all co-cultures, comprehensive profiling identified 30 VOCs per pathogen interaction (Tables 2, 3), grouped into 12 functional compound classes for A.f. 3357 and 9 for ApB62. In both cases, sesquiterpenes dominated (8 and 7 compounds, respectively), followed by alcohols (6 each) and ketones (4 each). Other classes included carboxylic acids, esters, aromatic hydrocarbons, furan derivatives, hydroxyketones, lactones, monoterpenes, monoterpenoid ketones, and sesquiterpenoid alcohol. These findings align with previous studies showing that Trichoderma VOCs are chemically diverse and rich in sesquiterpenes, alcohols, and ketones, a diversity that could explain their antibiosis activity against a range of pathogens (Stoppacher et al., 2010; Shahiri et al., 2016).

TABLE 2. VOCs released by the interaction of the three Trichoderma species with A.f. 3357.

VOC numberTentative annotationClassCAS numberRTRI (observed)Kováts RIConfirmation/ identification method
AflaVOC1EthanolAlcohol64-17-52.83–MS
AflaVOC22-methyl-1-propanolAlcohol78-83-14.5211031098MS+RI+Standard
AflaVOC3β-PhellandreneMonoterpene555-10-25.9112081200RI
AflaVOC42-methyl-1-butanolAlcohol137-32-65.9612111211RI + Standard
AflaVOC53-methyl-1-butanolAlcohol123-51-35.9812131211MS+RI+Standard
AflaVOC62-pentylfuranFuran derivative3777-69-36.2512331235MS+RI+Standard
AflaVOC73-OctanoneKetone106-68-36.6212601262MS+RI+Standard
AflaVOC8StyreneAromatic hydrocarbon100-42-56.6912661262RI +Standard
AflaVOC92-OctanoneKetone111-13-77.0512921297MS+RI+Standard
AflaVOC10AcetoinHydroxyketone513-86-07.1512991290MS+RI+Standard
AflaVOC113-Heptanone, 5-ethyl-4-methyl-Ketone27607-63-27.211304MS
AflaVOC122-Pentenoic acid, 4,4- dimethyl-, methyl esterCarboxylic acid ester20607-85-67.501326MS
AflaVOC132-n-HeptylfuranFuran derivative3777-71-78.961437MS
AflaVOC14Acetic acidCarboxylic acid64-19-79.2914631463MS+RI+Standard
AflaVOC15Propanoic acid, 2-methyl-Carboxylic acid79-31-210.7315781571MS+RI
AflaVOC16trans-α-BergamoteneSesquiterpene13474-59-410.9115921590RI
AflaVOC17α-GuaieneSesquiterpene13474-59-511.0216011596RI
AflaVOC18Cyclohexane, 1-ethenyl-1-methyl-2,4-bis(1-methylethenyl)-, [1S-(1α,2β,4β)]-Sesquiterpene515-13-911.0316021600RI
AflaVOC192-UndecanoneKetone112-12-911.0916081606MS+RI
AflaVOC20CaryophylleneSesquiterpene87-44-511.1216101612RI +Standard
AflaVOC211-DecanolAlcohol112-30-112.3617161714RI
AflaVOC22Germacrene DSesquiterpene23986-74-512.4717261726RI
AflaVOC23Naphthalene, 1,2,3,4,4a,5,6,8a-octahydro-7-methyl-4-methylene-1-(1-methylethyl)-, (1α,4aβ,8aα)-Sesquiterpene39029-41-912.5817361738RI
AflaVOC24β-BisaboleneSesquiterpene495-61-412.5917361730RI
AflaVOC252-Butanone, 4-(2,6,6-trimethyl-2-cyclohexen-1-yl)-Monoterpenoid ketone31499-72-613.671834MS
AflaVOC26Naphthalene, 1,2,3,4-tetrahydro-1,6-dimethyl-4-(1-methylethyl)-, (1S-cis)-Sesquiterpene483-77-213.8418491839RI
AflaVOC27Phenylethyl alcoholAlcohol60-12-814.6719291929MS+RI+Standard
AflaVOC286-pentyl-2H-pyran-2-oneLactone27593-23-317.46–Standard
AflaVOC29Ethyl hexadecanoateCarboxylic acid ester628-97-717.88–MS +Standard
AflaVOC30Spiro[4.5]dec-8-en-7-ol, 4,8-dimethyl-1-(1-methylethyl)-Sesquiterpenoid alcohol61050-89-318.38–MS

MS (match probability > 70%), RI (retention index) using literature compiled in NIST Chemistry WebBook.

TABLE 3. VOCs released by the interaction of the three Trichoderma species with ApB62.

VOC numberTentative annotationClassCAS numberRTRI (observed)Kováts RIConfirmation/ identification method
AparVOC1EthanolAlcohol64-17-52.83–MS
AparVOC2Propanoic acid, 2- methyl-, ethyl esterCarboxylic acid ester97-62-13.11–MS + Standard
AparVOC3Butanoic acid, 2- methyl-, ethyl esterCarboxylic acid ester7452-79-13.991057–MS + Standard
AparVOC42-Methyl-1-propanolAlcohol78-83-14.5211051098MS+ RI+ Standard
AparVOC5β-PhellandreneMonoterpene555-10-25.7111951194RI
AparVOC62-methyl-1-butanolAlcohol137-32-65.9612131211MS+ RI+ Standard
AparVOC73-methyl-1-butanolAlcohol123-51-35.9712141211MS+ RI+ Standard
AparVOC82-pentylfuranFuran derivative3777-69-36.2512341235MS+ RI+ Standard
AparVOC9Ethyl hexanoateCarboxylic acid ester123-66-06.2912371240MS+ RI+ Standard
AparVOC10StyreneAromatic hydrocarbon100-42-56.6112611262RI + Standard
AparVOC113-OctanoneKetone106-68-36.6212621262MS+ RI+ Standard
AparVOC122-OctanoneKetone111-13-77.0612931297MS+ RI+ Standard
AparVOC133-Heptanone, 5-ethyl-4-methyl-Ketone27607-63-27.111297–MS
AparVOC14AcetoinHydroxyketone513-86-07.141299–MS + Standard
AparVOC152-Pentenoic acid, 4,4- dimethyl-, methyl esterCarboxylic acid ester20607-85-67.511327–MS
AparVOC163-Isopropyl-6,8a-dimethyl-1,2,4,5,8,8a-hexahydroazuleneSesquiterpene16661-00-09.7114951495RI
AparVOC17trans-α-bergamoteneSesquiterpene13474-59-410.8415871590RI
AparVOC18α-GuaieneSesquiterpene13474-59-510.9315941596RI
AparVOC192-UndecanoneKetone112-12-911.0916081606MS +RI
AparVOC20Butanoic acid, 2-methyl-Carboxylic acid116-53-011.9516811682MS+ RI+ Standard
AparVOC211-Ethenyl-4-methoxybenzeneAromatic hydrocarbon637-69-412.121696MS
AparVOC221-DecanolAlcohol112-30-112.3717171714RI
AparVOC23Germacrene DSesquiterpene23986-74-512.4617261726RI
AparVOC241,3-Cyclohexadiene, 5-(1,5-dimethyl-4-hexenyl)-2- methyl-, [S-(R,S)]-Sesquiterpene495-60-312.4717261726RI
AparVOC25Naphthalene, 1,2,3,4,4a,5,6,8a-octahydro-7-methyl-4-methylene-1-(1-methylethyl)-, (1α,4aβ,8aα)-Sesquiterpene39029-41-912.5917371738RI
AparVOC26Cyclohexene, 3-(1,5-dimethyl-4-hexenyl)-6- methylene-, [S-(R,S)]-Sesquiterpene20307-83-913.0417771775MS +RI
AparVOC27Benzene, 1-(1,5-dimethyl-4-hexenyl)-4-methyl-Aromatic hydrocarbon644-30-413.04817781777RI
AparVOC28Phenylethyl alcoholAlcohol60-12-814.6819311929MS+ RI+ Standard
AparVOC296-pentyl-2H-pyran-2-oneLactone27593-23-317.42––MS+ Standard
AparVOC30Spiro[4.5]dec-8-en-7-ol, 4,8-dimethyl-1-(1-methylethyl)-Sesquiterpenoid alcohol61050-89-318.34––MS

*MS (match probability > 70%), RI (retention index) using literature compiled in NIST Chemistry WebBook.

Strain-specific differences demonstrate that co-cultures involving Trichoderma strains BTU against A.f. 3357 and OSK34 against ApB62 exhibited greater VOC abundance and diversity. Against A.f. 3357, the BTU–Af3357 co-culture produced the highest total VOC abundance (pg/vial) and class diversity (Figure 1A); interestingly, BTU also had the highest percent growth inhibition (Table 1). Similarly, in ApB62 interactions, OSK34–ApB62, the strain with the highest growth inhibition (not statistically significant), displayed the highest VOC abundance and diversity (Figure 1B). These results suggest that enhanced VOC production may contribute to stronger antagonistic activity, a pattern that has also been observed in other studies, where Trichoderma isolates with higher VOC abundance/diversity showed greater inhibition of fungal pathogens such as Rhizoctonia solani (van Zijll de Jong et al., 2023). The elevated VOC abundance and diversity such as that observed between BTU and A.f. 3357, likely stem from an enhancement of secondary metabolism in response to pathogen detection through signaling in the gas phase (Li et al., 2018; Thomas et al., 2025). For example, the exposure of T. virens to Fusarium oxysporum VOCs led to a significant induction of the terpene synthase encoding gene tps, which could signify a potential upregulation of VOC production, such as terpenes, in exposure to pathogen VOCs (Li et al., 2018). VOC profile comparison between Trichoderma mono- and co-cultures is one approach to reveal pathogen-induced VOC changes, and give insights into the metabolomic mechanisms driving Trichoderma biocontrol against toxigenic Aspergillus spp.

Class-level distribution of VOCs produced by *Aspergillus* in monoculture and its co-cultures with *Trichoderma* spp. VOC composition of panel **(A)** *A. flavus* NRRL 3357 (A.f. 3357) and **(B)** *A. parasiticus B62* (ApB62) and their corresponding co-cultures with the three evaluated *Trichoderma* spp. Stacked bars represent the sum of IS-normalized concentrations for all VOC classes detected in each treatment. Different colors represent distinct VOC classes.
Figure 1. Class-level distribution of VOCs produced by Aspergillus in monoculture and its co-cultures with Trichoderma spp. VOC composition of panel (A) A. flavus NRRL 3357 (A.f. 3357) and (B) A. parasiticus B62 (ApB62) and their corresponding co-cultures with the three evaluated Trichoderma spp. Stacked bars represent the sum of IS-normalized concentrations for all VOC classes detected in each treatment. Different colors represent distinct VOC classes.

3.2 Distinct VOC profiles across Trichoderma spp. drive VOC changes in co-cultures

To evaluate similarities and differences in volatile profiles between Trichoderma monocultures and co-cultures, a PCA was conducted. The first two principal components explained 74.9% of the variance for A.f. 3357 interactions (Figure 2A) and 57.5% for ApB62 interactions (Figure 2B). The score plots (Figure 2) show clear separation between Trichoderma strains and Aspergillus spp., with co-culture profiles clustered more closely with monoculture Trichoderma spp. than Aspergillus spp., suggesting that Trichoderma primarily drives VOC composition in co-cultures.

Principal Component Analysis (PCA) score plot of VOC profiles from *Trichoderma* and *Aspergillus* monocultures and their co-cultures with **(A)** *A. flavus* NRRL 3357 (A.f. 3357) and **(B)** *A. parasiticus B62* (ApB62). Each point represents an individual replicate with shaded ellipses indicating the 95% confidence region for each group (monocultures and co-cultures). Ellipses used to visualize the group separation and not statistical differences. OSK13: *T. virens*, OSK34: *T. harzianum*, and BTU: *T. asperellum*.
Figure 2. Principal Component Analysis (PCA) score plot of VOC profiles from Trichoderma and Aspergillus monocultures and their co-cultures with (A) A. flavus NRRL 3357 (A.f. 3357) and (B) A. parasiticus B62 (ApB62). Each point represents an individual replicate with shaded ellipses indicating the 95% confidence region for each group (monocultures and co-cultures). Ellipses used to visualize the group separation and not statistical differences. OSK13: T. virens, OSK34: T. harzianum, and BTU: T. asperellum.

Among Trichoderma strains, OSK13 consistently grouped closer to OSK34 than to BTU, suggesting phylogenetic relatedness and VOC similarity between OSK13 and OSK34. In contrast, BTU exhibited a more distinct VOC profile, suggesting this species might employ a different inhibitory strategy with unique or amplified VOC biosynthesis pathways. T. harzianum (OSK34) and T. virens (OSK13) belong to a closely related lineage in the Harzianum/Virens clade, distinct from T. asperellum (BTU), which belongs to section Trichoderma (Kubicek et al., 2019). Species within the same phylogenetic clade tend to share more similar biosynthetic gene clusters, which likely translates to overlapping VOC production capabilities; however, each species also possesses unique biosynthetic gene clusters that likely contribute to the observed distinct VOC profiles (Li et al., 2021).

3.3 Shared and species-specific VOCs profiles in Trichoderma-Aspergillus interactions

A hierarchical clustered heatmap was generated to visualize overall VOC patterns and shifts from monocultures to co-cultures. The heatmap of VOC profiles revealed distinct clustering patterns among the three Trichoderma strains and their interactions with Aspergillus spp. (Figures 3A, B). Here, we focused on VOCs in three groups: (I) VOCs significantly higher in the Trichoderma strain with the most overall Aspergillus spp. inhibition (BTU; Voloshchuk et al., 2024), (II) VOCs significantly higher in Trichoderma strains and conserved across multiple species, and (III) VOCs shared by both Aspergillus spp. and Trichoderma spp. During antagonistic interactions, Trichoderma strains produced both strain-specific and shared VOCs (Figures 3A, B). VOCs significantly higher in BTU monoculture and co-cultures (p < 0.05) likely account for its distinct profile observed in the PCA and may contribute to its associated higher percent growth inhibition, which was significant for the A.f. 3357 co-culture (p < 0.05). Against A.f. 3357, three compounds were significantly higher in BTU (p < 0.05), both in monoculture and in co-culture, compared to all other Trichoderma interactions (Figures 3A, I). These metabolites have previously been reported for antifungal properties, such as 6-pentyl-2H-pyran-2-one (6PP), 2-Octanone, and 2-pentylfuran (Ismaiel and Ali, 2017; Wu et al., 2023; Thomas et al., 2025). 6PP is a well-characterized antifungal metabolite known to inhibit A. flavus growth and suppress aflatoxin B1 biosynthesis. Specifically, Ismaiel and Ali (2017) demonstrated that 6PP, at a concentration of 70 μg/mL, inhibited the growth of various Aspergillus spp. by up to 21% and reduced aflatoxin B1 production by 54%. In contrast, compounds such as 2-Octanone and 2-pentylfuran, along with others in this cluster, remain less studied as individual or synergistic antifungal agents, particularly against Aspergillus spp.

Heatmaps of VOC profiles from three *Trichoderma* strains and their interactions with **(A)** A.f. 3357 and **(B)** ApB62. Boxplots of example VOCs that are (I) significantly higher in BTU; (II) *Trichoderma* specific and shared across multiple species; and (III) produced by both *Aspergillus* and *Trichoderma.* Only VOCs that were significantly different by one-way ANOVA after FDR correction (adjusted *p* < 0.05) were visualized. Boxplot values represent the average of three biological replicates per treatment. Hierarchical clustering of VOC was conducted using Euclidean distance and complete linkage with a fixed sample order. For each VOC, treatments with a different letter are significantly different based on ANOVA followed by Tukey’s HSD *post hoc* testing (*p* < 0.05).
Figure 3. Heatmaps of VOC profiles from three Trichoderma strains and their interactions with (A) A.f. 3357 and (B) ApB62. Boxplots of example VOCs that are (I) significantly higher in BTU; (II) Trichoderma specific and shared across multiple species; and (III) produced by both Aspergillus and Trichoderma. Only VOCs that were significantly different by one-way ANOVA after FDR correction (adjusted p < 0.05) were visualized. Boxplot values represent the average of three biological replicates per treatment. Hierarchical clustering of VOC was conducted using Euclidean distance and complete linkage with a fixed sample order. For each VOC, treatments with a different letter are significantly different based on ANOVA followed by Tukey’s HSD post hoc testing (p < 0.05).

Some VOCs were shared across multiple Trichoderma spp. and were produced in significantly higher levels than in Aspergillus, though their responses in co-culture varied by species. Most of these VOCs belonged to compound classes previously reported to have antifungal properties, particularly sesquiterpenoids and alcohol derivatives (Boukaew et al., 2024; Stoppacher et al., 2010; Figure 3A, II). One example, phenylethyl alcohol, is produced by multiple Trichoderma spp. and has demonstrated efficacy in inhibiting Aspergillus spp. growth, sporulation, and aflatoxin production (Hua et al., 2014; Angel et al., 2016; Chan et al., 2023). Phenylethyl alcohol suppresses phytopathogens by disrupting cell membrane integrity and altering cellular processes. For instance, this compound induces reactive oxygen species stress against Botrytis cinerea, and causes downregulation of aflatoxin biosynthetic genes such as aflR, pks (polyketide synthase), and nor-1 (norsolorinic acid reductase) in A. flavus (Hua et al., 2014; Zou et al., 2022). Phenylethyl alcohol concentration needed to completely inhibit AFB1 production is lower than the concentration needed to completely inhibit mycelial growth, meaning that they might have different mechanisms (Prakash et al., 2015). Teasing apart VOC mechanisms targeting toxin production versus growth could enable the development of biofungicides that reduce aflatoxin production without killing the fungus, which in turn could reduce the development of resistance. Other Trichoderma-specific VOCs, such as trans-α-bergamotene, β-bisabolene, and Spiro[4.5]dec-8-en-7-ol, 4,8-dimethyl-1-(1-methylethyl)- remain poorly characterized in Trichoderma spp. and have not been tested against Aspergillus spp., even though related bisabolene-types have shown antifungal activity against species like Candida albicans (Li et al., 2022; Bai et al., 2023; Yang et al., 2024). Future studies should validate these VOCs with a standard, test their antifungal activities and mechanisms against toxigenic Aspergillus spp., as they may represent a conserved VOC fingerprint underlying Trichoderma biocontrol activity across multiple species. This VOC fingerprint represents a starting point for studying conserved antifungal activities of VOCs produced by Trichoderma spp. against plant pathogens which could lead to the development of new biocontrol formulations.

The heatmap also highlights VOCs in the co-cultures that are produced by both Aspergillus and Trichoderma spp., with no significant differences between Aspergillus monocultures and the co-cultures (Figure 3A, III). These VOCs primarily include carboxylic acid/fatty acid esters such as ethyl hexadecanoate and short-chain alcohols such as 3-methyl-1-butanol. The production of these VOCs and others within the same chemical classes by multiple Trichoderma and Aspergillus spp. has been widely reported (Xu et al., 2014; Elsherbiny et al., 2020; Josselin et al., 2021; Elshahawy and Marrez, 2024). Interestingly, these compounds are consistently reported as some of the most abundant VOCs and key contributors to Trichoderma spp. antifungal activity when Trichoderma VOCs are profiled in monoculture (Elsherbiny et al., 2020; Elshahawy and Marrez, 2024). However, their inhibitory effects against toxigenic Aspergillus spp. appear complex. For example, it was reported that both 3-methyl-1-butanol and 2-methyl-1-butanol inhibited A. flavus growth by approximately 20% but increased aflatoxin B1 production by 1.5–2-fold (Zeringue and McCormick, 1990). This dual effect likely reflects the ecological versatility of these VOCs. Since these VOCs are produced by both the Aspergillus and the Trichoderma strain, their role in the co-culture as a defense, signaling, or antagonistic VOC depends on the timing and abundance produced by each fungus. This observation emphasizes the importance of comparing co-cultures to monocultures to better understand metabolite dynamics and their influence on interactions.

The clustered heatmap patterns and VOCs were mainly maintained across interactions with A.f. 3357 and ApB62 interactions, with a few differing VOCs (Figure 3B, I–III). This trend emphasizes the existence of both conserved and pathogen-specific VOC changes during the interactions of Trichoderma spp. and toxigenic Aspergillus spp. Differences in metabolite profiles among Aspergillus spp. may influence their VOC-mediated communication with Trichoderma, shaping the observed pathogen-specific interactions. The observed pathogen-specific variations could partly result from genetic mutations in strains such as ApB62, which are known to alter a variety of secondary metabolites (Roze et al., 2010). For both Aspergillus interactions, changes in VOC concentration from monoculture to co-culture also differed depending on the Trichoderma spp., reflecting adaptive modulation of antifungal metabolites and potentially explaining the difference in bioactivity across interactions.

3.4 Putative VOCs driving metabolomic shifts and informing selection of promising biocontrol strains in Trichoderma–Aspergillus interactions

We applied PLS-DA to identify putative VOCs discriminating between Trichoderma co-cultures. Alcohols, ketones, lactones, and terpenoids emerged as major discriminants, separating the different Trichoderma-Aspergillus co-cultures (Figures 4A, B). These VOC classes are well-documented for their production by Trichoderma with broad antifungal and anti-aflatoxigenic mechanisms of action, including disruption of membrane integrity, inhibition of spore germination, suppression of mycelial growth, and reduction of toxin biosynthesis (Almeida et al., 2023; Boukaew et al., 2024; Stoppacher et al., 2010). PLS-DA showed that most VOCs with high VIP scores were more abundant in BTU co-cultures compared to other Trichoderma strains. Specifically, four of six VOCs in the A.f. 3357 interaction and six of nine in the ApB62 interaction showed higher abundance in BTU, as indicated by deep red (Figures 4A, B). It is important to note that compounds with lower abundances can also play a role in bioactivity.

Variable importance in projection (VIP) score plots from PLS-DA showing VOCs with VIP score > 1 driving separation in the three *Trichoderma* interactions with **(A)** A.f. 3357 and **(B)** ApB62.
Figure 4. Variable importance in projection (VIP) score plots from PLS-DA showing VOCs with VIP score > 1 driving separation in the three Trichoderma interactions with (A) A.f. 3357 and (B) ApB62.

To identify putative antifungal VOCs, we supplemented PLS-DA results with statistical significance (ANOVA) and functional association (correlation with growth inhibition; Supplementary Figures 2A, B). This multi-criteria approach revealed four consensus VOCs that met all three criteria: (1) ranked high by PLS-DA (VIP > 1.0), (2) significantly different in co-cultures compared to their respective monocultures (ANOVA, p < 0.05), and (3) associated with antifungal activity (rs ≥ 0.5, p < 0.05). Notably, this approach identified four consensus VOCs: 2-pentylfuran, 1-decanol, 6-pentyl-2H-pyran-2-one, and 1-ethenyl-4-methoxybenzene, which were all predominantly enriched in BTU interactions with A.f. 3357 and ApB62 compared to all the other evaluated Trichoderma spp. (summarized in Supplementary Table 2). Amongst the four, 2-pentylfuran and 6-pentyl-2H-pyran-2-one have been confirmed with a standard, while 1-decanol and 1-ethenyl-4-methoxybenzene are putatively annotated with RI and MS, respectively.

Interestingly, these consensus VOCs exhibited distinct dynamics during co-culture. 2-pentylfuran was significantly upregulated in BTU co-culture with A.f. 3357 (p < 0.05), suggesting a potential upregulation of biosynthetic genes encoding this secondary metabolite during antagonism (Thomas et al., 2025). In contrast, the remaining three putative antifungal VOCs (1-decanol, 6-pentyl-2H-pyran-2-one, and 1-ethenyl-4-methoxybenzene) were significantly decreased during co-culture with ApB62 (p < 0.05), potentially reflecting biotransformation or active use in antibiosis during antagonistic interactions. These findings are consistent with previous reports showing that Trichoderma spp. alter their secondary metabolites in response to environmental cues and competing fungi (Nieto-Jacobo et al., 2017; Li et al., 2018; Gualtieri et al., 2022). Despite these dynamic changes, BTU maintained significantly higher abundances of all four compounds compared to other Trichoderma strains in both monoculture and co-culture conditions. This pattern suggests that initial high levels of VOC production, rather than dynamic upregulation alone, may also be a defining trait of superior biocontrol strains, ensuring sufficient bioactive concentrations are produced and maintained during fungal antagonism. While these putative antifungal VOCs have been reported in Trichoderma monocultures previously, this study is the first to identify them as discriminating compounds that distinguish strains associated with stronger inhibition of aflatoxin-producing Aspergillus strains, and to demonstrate how their abundance changes from monoculture to co-culture conditions within the context of a controlled experimental condition.

Overall, this work establishes a metabolomic framework for understanding VOC-mediated antagonism in Trichoderma and provides a volatile fingerprinting approach for selecting and optimizing strains with enhanced antifungal activity against aflatoxigenic Aspergillus spp. These findings provide initial insights for the development of metabolomics-informed biocontrol strategies for managing the growth of aflatoxin-producing fungi in food and agricultural systems. Some potential limitations of the study include that temporal fluctuations and compound degradation might have influenced the observed VOC concentrations, and the inability to definitely conclude the mechanism behind the final VOC changes in co-cultures. Hence, future studies integrating time-course VOC profiling with transcriptomic analysis could dissect the metabolite-induced mechanisms Trichoderma uses to inhibit the growth of toxigenic Aspergillus spp. Also, the use of deuterium-labeling to trace specific metabolites to their source organism in co-culture would help further parse out the contributions of Trichoderma vs. Aspergillus to VOC concentration changes in co-cultures.

In addition, while associations between VOC profiles and percent growth inhibition were observed, these 4 potential antifungal VOC findings remain exploratory due to the small sample-to-variable ratio of the data. Hence, independent and functional validation is still needed before any causal role is concluded. Future studies should include compound isolation, targeted bioassays, and transcriptomic analyses to confirm antifungal roles and elucidate specific molecular mechanisms. Finally, this study was conducted under laboratory conditions using nutrient-rich media, which may not reflect field performance. Evaluating how environmental factors and substrate variability affect VOC production will be essential to ensure reproducible biocontrol outcomes in real-world applications.

4 Conclusion

This study provides a comprehensive evaluation of volatile metabolomic shifts in Trichoderma spp. during interactions with A. flavus NRRL3357 and A. parasiticus B62, offering new insights into VOC-mediated biocontrol. Our findings reveal clear differences in VOC profiles among Trichoderma and Aspergillus monocultures and their co-cultures. Co-cultures displayed intermediate profiles closer to Trichoderma, supporting its dominant role in shaping the volatile composition during antagonism. Studying co-cultures offers deeper insights into potential mechanisms when compared to monocultures alone because co-cultures reveal specific VOC changes that are associated with the antagonistic interactions.

Metabolomic profiling underscored that antifungal efficacy is not driven by single compounds but by the collective action of multiple VOC classes, with both conserved and species-specific metabolites shaping bioactivity. Conserved compounds with previously noted antifungal activities, mainly terpenoids and alcohol, across Trichoderma spp. suggest a potential fingerprint of candidate VOCs driving the Trichoderma biocontrol strategy against toxigenic Aspergillus. T. asperellum (BTU) exhibited an enriched and distinct volatile profile, with elevated abundances of alcohols, ketones, lactones, and terpenoids compared to other strains. Several discriminant VOCs, including 2-pentylfuran, 1-decanol, 6-pentyl-2H-pyran-2-one, and 1-ethenyl-4-methoxybenzene, were consistently produced at higher levels by the Trichoderma strain that showed the strongest inhibition of toxigenic A. flavus. This association suggests that these compounds serve as candidate antifungal VOCs for identifying Trichoderma strains with enhanced biocontrol potential. Because aflatoxin production was not measured in the present study, the identified VOCs should be considered candidate biomarkers associated with Aspergillus growth inhibition rather than aflatoxin suppression.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb.2026.1873321/full#supplementary-material

Supplementary material Supplementary material at the source

Comparison of DVB/CAR/PDMS (gray, top two chromatograms) and PDMS/DVB (pink, bottom two chromatograms) fibers using raw chromatograms comparing the effect of fiber coating on volatile profiling of OSK13 using HS-SPME GC-MS. Two independent samples (1 and 2) were used to test each fiber. Peaks represent the relative abundance of compounds across retention time, and the arrows point to the retention time area of difference between the fibers. DVB, Divinylbenzene; CAR, Carboxen; PDMS, Polydimethylsiloxane. Supplementary material at the source

Spearman rank correlations between mean VOC abundance and percent growth inhibition of panel (A) A.f. 3357 and (B) ApB62. Semi-quantitative IS-normalized VOC concentrations were averaged across replicates of each Trichoderma spp. co-culture before analysis. Strong correlations were determined based on absolute correlation coefficients (rs > 0.5 with p < 0.05). Supplementary material at the source

Footnotes

Edited by: Rogelio Valadez Blanco, Technological University of the Mixteca, Mexico

Reviewed by: Gareth Thomas, Independent Researcher, London, United Kingdom

Jaime Alioscha Cuervo Parra, Autonomous University of the State of Hidalgo, Mexico

Statements

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This work is supported by the USDA National Institute of Food and Agriculture and Hatch Appropriations under Project #PEN04991 and Accession #7007024 (JW), Project #PEN04792 and accession #7002577 (HH), and Project #PEN05008 and Accession #7007470 (JL). ZI was partially supported by a Graduate Student Research Grant Program by The Northeast Sustainable Agriculture Research and Education (SARE).

Data availability

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://scholarsphere.psu.edu/resources/6cce6049-c4d5-4e40-9ff5-8ed9115d867a.

Author contributions

ZI: Software, Investigation, Funding acquisition, Writing – review & editing, Conceptualization, Formal analysis, Writing – original draft, Visualization, Data curation, Resources, Methodology, Validation. NV: Resources, Methodology, Investigation, Writing – review & editing, Supervision. JL: Funding acquisition, Resources, Formal analysis, Visualization, Supervision, Writing – review & editing, Methodology, Investigation. HH: Writing – review & editing, Resources, Validation, Funding acquisition, Project administration, Supervision, Data curation, Methodology, Visualization, Conceptualization. JW: Project administration, Methodology, Conceptualization, Visualization, Formal analysis, Supervision, Resources, Writing – review & editing, Funding acquisition, Investigation, Data curation.

Acknowledgements

We are grateful to BTU Biotech company (Ukraine), Seogchan Kang, and John Linz for providing fungal strains that were used in this study.

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Citation

Irakoze, Z., et al. (2026). Signaling in the gas phase: comparative volatile profiling of biocontrol Trichoderma spp. and aflatoxin-producing Aspergillus spp. in isolation and co-culture using HS-SPME GC-MS. Frontiers in Microbiology, Volume 17. https://doi.org/
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Copyright © 2026 Irakoze, Voloshchuk, Lambert, Hopfer and Wee.

License: https://creativecommons.org/licenses/by/4.0/

*Correspondence: Josephine Wee

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