Trichoderma atroviride from Predator to Prey: Role of the Mitogen-Activated Protein Kinase Tmk3 in Fungal Chemical Defense against Fungivory by Drosophila melanogaster Larvae (2024)

Alfons J. M. Stams, Editor

Alfons J. M. Stams, Wageningen University;

The response to injury represents an important strategy for animals and plants to survive mechanical damage and predation. Plants respond to injury by activating a defense response that includes the production of an important variety of compounds that help them withstand predator attack and recover from mechanical injury (MI). Similarly, the filamentous fungus Trichoderma atroviride responds to MI by strongly modifying its transcriptional profile and producing asexual reproduction structures (conidia). Here, we analyzed whether the response to MI in T. atroviride is related to a possible predator defense mechanism from a metabolic perspective. We found that the production of specific groups of secondary metabolites increases in response to MI but is reduced after fungivory by Drosophila melanogaster larvae. We further show that fungivory results in repression of the expression of genes putatively involved in the regulation of secondary metabolite production in T. atroviride. Activation of secondary metabolite production appears to depend on the mitogen-activated protein kinase (MAPK) Tmk3. Interestingly, D. melanogaster larvae preferred to feed on a tmk3 gene replacement mutant rather than on the wild-type strain. Consumption of the mutant strain, however, resulted in increased larval mortality.

IMPORTANCE Fungi, like other organisms, have natural predators, including fungivorous nematodes and arthropods that use them as an important food source. Thus, they require mechanisms to detect and respond to injury. Trichoderma atroviride responds to mycelial injury by rapidly regenerating its hyphae and developing asexual reproduction structures. Whether this injury response is associated with attack by fungivorous insects is unknown. Therefore, determining the possible conservation of a defense mechanism to predation in T. atroviride and plants and elucidating the mechanisms involved in the establishment of this response is of major interest. Here, we describe the chemical response of T. atroviride to mechanical injury and fungivory and the role of a MAPK pathway in the regulation of this response.

Drosophila larvae feed on Trichoderma.

To determine if D. melanogaster larvae, as generalist feeders, could feed on T. atroviride, we set up a simple interaction experiment, placing larvae on colonies of this fungus and allowing them to feed on mycelia for 30 min and 24 h (Fig. 1A). As shown by the results in Fig. 1B, 48 h after fungivory, all along the paths followed by larvae, formation of mature green conidia was observed. We then captured the behavior of the larvae on mycelium on video. As shown in Movie S1 in the supplemental material, D. melanogaster larvae pull and chew the mycelium, causing mechanical injury. To provide evidence that larvae indeed feed on Trichoderma, we weighed the mycelium of the fungus before and 4 h after placing the larvae on the mycelial mat. As shown by the results in Fig. 1C, larvae consumed 50% of the mycelium. Furthermore, we observed emergence of the fungus from surface-disinfected larval bodies (Fig. S1). Thus, in this interaction, the predator (Trichoderma) becomes prey.

Open in a separate window

FIG 1

Fungivory by D. melanogaster larvae induces conidiation in T. atroviride mycelium. (A) Photograph of D. melanogaster larva feeding on T. atroviride mycelium. (B) The photographs show responses of T. atroviride to mechanical damage (Injury) and at 30 min and 24 h after fungivory, as well as an untreated control. The Control and Injury images show the response 48 h after treatment. (C) Impact of fungivory on mycelial weight. The graph shows the mycelial weights of colonies where Drosophila larvae grazed for 30 min. Error bar shows standard error value, and different letters indicate significant differences (P < 0.05).

TMK3 mutants are more susceptible than the WT strain to fungivory by Drosophila larvae.

Given that Tmk3 mutants are affected in their capacity to produce conidia in response to mechanical injury, we examined the effect of the mutation on the corresponding gene in the response to fungivory by D. melanogaster. We therefore set up Drosophila-Trichoderma interaction experiments using three independent Δtmk3 mutant strains (designated Δtmk3-2, Δtmk3-13, and Δtmk3-17) and compared their responses to that of the wild type. As in the case of mechanical injury, the Δtmk3 mutants did not produce conidia in response to attack by Drosophila (Fig. 2A). However, the Δtmk3 mutants were more susceptible to predation than the WT, since the larvae consumed most of their mycelium (Fig. 2A). We thus determined mycelium weight before and 4 h after fungivory for both the WT and the Δtmk3 mutant strains. While the larvae consumed almost 25% (Fig. 1C) of the mycelium of the WT strain, they consumed 37 to 50% of the mycelium of the Δtmk3 mutant strains (Fig. 2B). Based on these results, we hypothesized that the Δtmk3 mutants may be affected in the production of metabolites involved in protection against or repulsion of the larvae. Given that the three mutants tested appear to respond in the same way, we decided to use only one of them (Δtmk3-2) in further experiments, and we refer to it simply as Δtmk3.

Tmk3 regulates production of secondary metabolites in T. atroviride.

To identify compounds that could be involved in protection against or repulsion of the larvae, we carried out metabolic fingerprinting on extracts from the Δtmk3 and WT strains upon mechanical injury and fungivory and from an undamaged control. We identified the 30 diffusible compounds which best explained the differences observed between the fungal strains and conditions. When we analyzed the metabolic changes in the Δtmk3 and WT strains in response to injury and fungivory, we observed clear differences between the patterns of diffusible compounds produced (Fig. 3A). In the control, the WT strain produced several ions but at low levels, except for a group of 5 ions that were strongly produced under this condition (Fig. 3A, purple bar at left). However, after injury, the levels of a group of 17 ions increased strongly and the levels of the compounds strongly produced under the control conditions decreased (Fig. 3A, pink bar at left). In contrast, after fungivory, the abundance of a specific group of eight ions increased even more than after injury (Fig. 3A, green bar at left). Overall, the Δtmk3 mutant’s production of these compounds was much lower than that of the wild type, regardless of the condition (Fig. 3A, orange bars at left). Interestingly, the production of this set of metabolites was even lower in response to fungivory; this was particularly evident in the case of the 323, 280, and 315 m/z ions, which increased slightly after injury (Fig. 3A, blue bars at left). A dendrogram constructed based on the patterns of metabolites produced by the two strains under the different conditions clearly shows that, although there are differential responses to injury and fungivory in both strains, the main difference in the production of metabolites is due to mutation of tmk3 (Fig. 3B).

Open in a separate window

See Also

Toenail Fungus TreatmentsOver-the-Counter Antifungal Medications for Athlete's FootDon't scratch itchy skin: CityMD's tips for dealing with skin fungus.Antifungal medicines

FIG 3

The TMK3 pathway regulates the production of secondary metabolites in response to injury and fungivory in T. atroviride. (A) Heat map of diffusible compounds produced by the WT and the Δtmk3 mutant after injury (AI) and after fungivory (AF). Color bars at the left side of the heat map indicate the clusters of compounds differentially produced by the WT (purple, pink, and green bars) and the Δtmk3 mutant (orange and blue bars). Scale bar at the right side indicates ion intensity. (B) Dendrogram that supports the clusters formed in the heat map. Numbers in red indicate the approximately unbiased (au) values, and numbers in green indicate the bootstrap probability (bp). (C) Heat map of volatile compounds differentially produced in the WT and the Δtmk3 mutant after injury and fungivory. Color bars at the left side of the heat map indicate the clusters of compounds differentially produced by the WT (green, dark blue, and blue bars) and the Δtmk3 mutant (purple and yellow bars). Scale bar at the right side indicates ion intensity. (D) Dendrogram supporting clusters formed in the heat map. Numbers in red indicate the approximately unbiased values, and numbers in green indicate the bootstrap probability.

The production of volatile organic compounds (VOCs) was determined using a low-temperature plasma, which allows the detection of compounds of low molecular weight. VOCs produced in the WT show a medium to high intensity under control conditions. Three clusters of ions, however, increased specifically after injury in the WT strain (Fig. 3C, green bars to the left). Among these ions, we found signals indicating the presence of 6-penthyl-alpha-pyrone (6PP) at ∼167 and ∼184 m/z (oxygenated 6PP) (Fig. 3C), the oxygenated molecule being more abundant than in the control. Interestingly, predation by larvae resulted in decreased abundance of a large cluster of ions that were produced under the control conditions and after injury (Fig. 3C, dark-blue bar to the left). At the same time, however, fungivory greatly increased the production of a cluster of nine ions, among which we found the nonoxygenated 6PP (Fig. 3C, blue bar to the left). In the case of the Δtmk3 mutant, there was a set of volatile compounds that was strongly produced upon mechanical injury and detected with lower intensities in the control and at much lower intensities in the samples subjected to fungivory (Fig. 3C, purple bar to the left). It is important to notice that fungivory resulted in reduced production of most ions in the Δtmk3 mutant (Fig. 3C, yellow bar to the left). As in the case of diffusible compounds, the main difference in the patterns of VOC production under all conditions tested was due to a mutation of tmk3 (Fig. 3D). The difference between the patterns of VOCs produced in response to fungivory and injury was more marked than that observed for diffusible molecules. We therefore decided to identify and compare individually the levels of production of 6PP, C₈ compounds, and sesquiterpenes (including cyclonerodiol).

Fungivory, but not mechanical injury, resulted in increased production of 6PP and C₈ compounds in the WT (Fig. 4). In contrast with 6PP, C₈ compounds decreased and sesquiterpenes were produced at lower levels after fungivory in the WT strain (Fig. 4B; Fig. S2). Additionally, the Δtmk3 mutant was severely affected in the production of 6PP independently of the condition tested (Fig. 4A). While C₈ compound production in this mutant decreased upon injury, these compounds increased after fungivory (Fig. 4B). Interestingly, the Δtmk3 mutant produced more sesquiterpenes than the WT upon injury (Fig. S2).

Open in a separate window

FIG 4

Tmk3 is involved in the production of VOCs putatively related to fungal defense. (A) Comparison of induction of 6-penthyl-alpha-pyrone production by fungivory and injury and under control conditions in WT strain and Δtmk3 mutant. (B) Comparison of C₈ compound production in the WT and the Δtmk3 mutant after injury (AI) and after fungivory (AF). Shades of red show increases in abundance. Shades of blue represent reductions in production. Production is expressed as absolute intensity. Error bars show the standard error values, and different letters indicate significant differences (P < 0.05).

Injury and fungivory differentially induce the expression of genes putatively involved in secondary metabolism.

To investigate whether the differential response observed in secondary metabolite production could be related to underlying gene expression, we selected four orthologs of genes that in A. nidulans are involved in secondary metabolism biosynthesis. Using bidirectional BLAST, we identified T. atroviride orthologs of the A. nidulans genes napA (TanapA; JGI gene identification number 39837), metR (TametR; JGI gene identification number 314604), and laeA (Talae1; JGI gene identification number 319344).

Reverse transcription-quantitative PCR (RT-qPCR) showed that the T. atroviride ortholog of laeA (Talae1) was highly induced 30 min after mechanical injury, 575-fold, dropping to 4- and 2-fold by 1 and 24 h after injury (Fig. 5A). Interestingly, fungivory induced Talae1 expression, but at 40-times-lower levels than mechanical injury after 30 min (14-fold) (Fig. 5A), while at 1 and 24 h, its level of expression was similar to that observed upon mechanical injury (Fig. 5A). Surprisingly, the expression of Talae1 was repressed in the Δtmk3 mutant at all time points evaluated, regardless of the treatment (Fig. 5A), except for 1 h after injury, when it showed a slight induction. Similar behavior was observed for the T. atroviride ortholog of metR (TametR), which showed very strong induction (9,000-fold) upon mechanical injury in the WT strain (Fig. 5B), while after fungivory, the induction was much lower (94-fold) (Fig. 5B). In the Δtmk3 mutant, this gene showed very low induction early (30 min) after mechanical injury and was repressed by 1 h after the treatment, while it was slightly induced after fungivory (Fig. 5B). However, in the case of the T. atroviride ortholog of the transcription factor napA (TanapA), we observed strong induction (398-fold) in the WT 30 min after MI, which decreased dramatically (to 2-fold) 1 h after treatment and was only slightly induced during fungivory. In the Δtmk3 mutant, this gene was repressed early after mechanical injury, with a slight but significant induction (0.5-fold) at 24 h (Fig. 5B), whereas in response to fungivory, the expression of this gene increased very slightly (1.13- to 1.6-fold) at all time points tested (Fig. 5C).

Open in a separate window

FIG 5

Tmk3 regulates expression of genes putatively involved in regulating transcription of genes related to secondary metabolite production in response to injury and fungivory. The graph shows the levels of messengers detected by RT-qPCR of the genes Talae1 (A), TametR (B), and TanapA (C) at the indicated times after injury (AI) and after fungivory (AF) in the WT (gray) and the Δtmk3 mutant (black). The y axes are in log₂. Error bars show the standard error values, and different letters indicate significant differences (P < 0.05).

Metabolite production induced by injury in the Δtmk3 mutant make it more attractive than the WT strain to D. melanogaster larvae.

Given that the different patterns of metabolite production detected between the WT and the Δtmk3 strains could affect their interaction with the Drosophila larvae, we conducted food choice assays. First, we tested the effects of mechanical injury of the WT strain on the preference of the larvae. As shown by the results in Fig. 6A, larvae preferred the damaged fungus, maybe due to the stimulation of VOC production resulting from injury (Movie S2). When examining the preference of the larvae for the WT or the Δtmk3 mutant without damage, we observed no difference (Fig. 6B). However, when performing the food choice assays using the mechanically injured WT and Δtmk3 strains, we observed that larvae had a much stronger preference for the damaged mutant than for the WT (Fig. 6C; Movie S3).

Open in a separate window

FIG 6

Injury induces production of compounds in T. atroviride that influence larval food choice. (A) Food choice assay between the undamaged (light gray bar) and damaged (dark gray bar) WT strain. (B) Food choice assay between the undamaged WT strain (light gray bar) and Δtmk3 mutant (dark gray bar). (C) Food choice assay between the damaged WT and Δtmk3 strains. The bars show the number of larvae that chose each of the colonies, as indicated. “Free larvae” refers to larvae that that did not choose either of the strains. Error bars show standard error values, and different letters indicate significant differences (P < 0.05).

Feeding on Trichoderma strongly affects development and survival of D. melanogaster larvae.

To determine if the interaction with Trichoderma had any detrimental effect on the larvae, we determined larval mortality and development after feeding on the fungus. For this purpose, we allowed third-instar larvae to feed for 24 h on mycelia of the two Trichoderma strains and then followed their development for up to 14 days. As shown by the results in Fig. 7A, consumption of either fungal strain (WT or Δtmk3) resulted in delayed development of the flies, which took ≈13 days to reach adulthood, compared to the 3 to 4 days required by the controls. While a considerable number of larvae died after interaction with the fungus, mortality was much higher when feeding on the Δtmk3 mutant (75%) than upon consumption of the WT (37.5%) (Fig. 7B).

Open in a separate window

FIG 7

Fungal consumption slows the development of D. melanogaster larvae, while the Δtmk3 mutant kills a major number of larvae compared with the WT strain. (A) Larval development during 14 days after fungal consumption. Graph shows that both fungal strains affected development of larvae into adult flies. (B) Graph shows the numbers of dead larvae according to consumption of WT or Δtmk3 strain mycelium. Black bars show the numbers of live larvae and gray bars the numbers of dead larvae after mycelium consumption. Error bars show standard error values, and different letters indicate significant differences (P < 0.05).

The immune system of D. melanogaster is not activated upon fungivory of Trichoderma.

D. melanogaster flies activate their immune system to produce antimicrobial peptides in response to attack by potentially pathogenic microorganisms. Due to the fact that Trichoderma consumption affects larval development and causes larval death, we considered the possibility of Trichoderma behaving as a pathogen of D. melanogaster. Accordingly, we proposed that antimicrobial peptide production, involved in Drosophila’s defense against fungi, could be affected by this interaction. To explain the high larval mortality and why one fungal genotype is more lethal, we determined the expression levels of D. melanogaster immune response genes. We asked whether differences in D. melanogaster larval mortality could be explained by differences in the recognition or production of antifungal compounds. Thus, based on the literature, we selected the genes involved in diptericin (dptB; FlyBase number CG10794) (67) and drosomycin (drs; FlyBase number CG10810) production (67) and toll1 (FlyBase number CG5490) to determine their expression patterns in the Trichoderma-Drosophila interaction by RT-qPCR.

Figure 8 shows the expression patterns of genes related to antimicrobial response in D. melanogaster. Expression of the Toll1 receptor-encoding gene is not activated after fungivory in any of the T. atroviride strains. In fact, the Trichoderma WT strain represses toll1 expression (Fig. 8A). Interestingly, the effect of the interaction on toll1 expression in the Δtmk3 mutant strain is almost negligible (Fig. 8A). Whereas expression of the genes dptB and drs was activated in both Trichoderma strains during fungivory (Fig. 8B and ​andC),C), predation on the Δtmk3 strain activated them to a slightly lesser extent (Fig. 8B and ​andCC).

Open in a separate window

FIG 8

Analysis of the expression of D. melanogaster genes related to immune response to fungal attack. The graphs show the levels of messengers detected by RT-qPCR of the genes toll1 (A), drs1 (B), and dptB (C) at the indicated times after consumption of mycelium of T. atroviride WT (light gray) and the Δtmk3 mutant (dark gray). Error bars show the standard error values, and different letters indicate significant differences (P < 0.05).

Biological material and growth conditions. (i) Fungal strains.

Trichoderma atroviride strain IMI 2060040 was used as the wild type (WT). The T. atroviride mutants Δtmk3-2, Δtmk3-13, and Δtmk3-17 were previously described (60). All strains were propagated on potato dextrose agar (PDA; Difco) at 39 g/liter in constant light at 27°C for 5 days.

(ii) Insects and larvae.

The founder of the isofemale strain Drosophila melanogaster SD-5 was collected in San Diego, CA, USA. Adult flies were preserved on standard banana medium with a photoperiod of 12 h dark/12 h light at 25°C. For egg collection, 300 flies were placed into a collector with a petri dish containing flour-agar medium (10 g/liter agar, 25 g/liter sucrose, 105.5 g/liter wheat flour, and 18 g/liter Bacto yeast extract). Eggs were incubated in flour-agar medium for 5 days to obtain third-instar larvae. Larvae were collected using dissection forceps and washed three times with sterile 1× phosphate-buffered saline (PBS) (8 g/liter NaCl, 0.2 g/liter KCl, 1.44 g/liter Na₂HPO₄, 0.24 g/liter KH₂PO₄, pH adjusted to 7.4 with HCl).

Interactions between D. melanogaster and T. atroviride.

For interaction experiments, spores collected from a fresh culture of T. atroviride (5 days of incubation) were counted in a Newbauer chamber, and 10,000 were dispersed on a petri dish containing PDA and incubated for 36 h at 27°C in darkness. Forty larvae were then placed on the mycelial fungal mat of the fungus and allowed to feed for 30 min, 1 h, or 24 h. Larvae were placed and removed with dissection forceps under red light to avoid photoinduction of conidiation. Fungi were incubated for an additional 48-h period at 26°C in darkness. As controls, we used fungal colonies injured with a sterile scalpel and untreated colonies of the same age.

Trichoderma survival in the larvae.

To confirm consumption of Trichoderma by larvae, we collected the larval bodies after they had fed on the mycelia of the WT and the tmk3 mutants. Larvae were washed two times with ethanol (70%), once with sodium hypochlorite (20%), and three times with distilled sterile water to eliminate any living fungal structures from outside the body. Larvae were then frozen at −20°C for 2 h and placed in Trichoderma selective culture medium (rose Bengal medium containing 0.2 g/liter MgSO₄·7H₂O, 0.9 g/liter K₂HPO₄, 0.15 g/liter KCl, 1 g/liter NH₄NO₃, 3 g/liter glucose, 0.075 g/liter rose Bengal, and 20 g/liter agar, which was sterilized by heat for 20 min at 120°C before 0.25 g/liter chloramphenicol and 0.2 g/liter quintozene were added). Plates were incubated in darkness for 3 days at 27°C. After 3 days, larval bodies from which fungal colonies emerged were counted. We used triplicates of 40 larvae in three independent experiments for statistical analysis.

Mycelial weight loss analysis.

To determine the effect of the larvae on the mycelia, interaction experiments were performed in petri dishes containing PDA medium covered with sterile cellophane sheets (8 cm in diameter, heat sterilized). Four hundred milligrams of mycelium of each strain was used to feed larvae. The mycelium was placed in the middle of the plate, and then 40 larvae were placed on the mycelium. Four hours later, the mycelium was weighed in an analytical balance. For statistical analyses, we used three replicates in three independent experiments.

Metabolic fingerprinting by direct mass spectrometry.

Metabolic fingerprinting of T. atroviride after injury and fungivory was conducted using two techniques: direct liquid injection mass spectrometry (DLI-MS) and low-temperature plasma (LTP) ionization mass spectrometry (LTP-MS).

For the DLI-MS analysis, we obtained extracts based on a previously reported methodology (61), with some modifications based on the protocol reported by Frisvad (62), as follows: 100 mg of mycelium was hom*ogenized and extracted with high-performance liquid chromatography (HPLC)-grade ethyl acetate, methanol, and dichloromethane with proportions of 3:2:1, respectively, and 1% formic acid (Sigma-Aldrich, Mexico). The sample was then disrupted using a sonicator (Branson 1510) at a frequency of 40 kHz for 50 min at room temperature, after which the solvent was evaporated with nitrogen gas. Residual compounds were resuspended in 400 μl of HPLC-grade methanol (Fisher Scientific, Mexico) and filtered through a 0.2-μm nylon membrane (Thermo Scientific, Mexico). Mass spectra were acquired in positive ESI mode, using an LCQ Fleet ion trap mass spectrometer (Thermo Scientific, USA).

For the measurements, the capillary voltage was set to 35 V, the capillary temperature to 280°C, the tube lens to 80 V, and the spray voltage to 4.5 kV. Spectra were acquired in positive profile mode, in an m/z range of 50 to 500.

For the direct analysis with LTP-MS, we used a device designed in the laboratory (63, 64). The complete plate of T. atroviride was placed under the plasma beam; extraction of metabolites was not required. The measurements were performed in triplicates. The LTP was mounted perpendicular to the MS inlet. The technical parameters were the same as reported previously (63, 64). Spectra were acquired in positive and profile mode, in an m/z range of 50 to 500. The DLI-MS and LTP-MS analyses were carried out in three independent experiments.

Metabolic data processing and statistical analyses.

The raw mass spectrometry data were transformed to the HUPO standard data format, mzML, using msconvert version 3.0.91 (65). The spectra then were processed using a workflow designed in R (https://www.r-project.org), employing the package MALDIquant (65). All scans of a sample were summarized and smoothed using a Savitzky-Golay filter. Peaks were detected with a signal-to-noise ratio of 2 and aligned to allow the comparison of peaks across different samples.

To identify the most important ion signals, we generated a random forest tree model with 500 tree decisions. The algorithm ranks the most important ions based on the Gini index. We used the 30 most important signals, i.e., the ions that best explain the differences between the wild-type and mutant samples. For data mining and model building, we used the R package Rattle (27) and the mean values from triplicate experiments. We applied a hierarchical clustering analysis (HCA) to the filtered data to create metabolic heat maps and evaluated differences in the metabolic profiles (66).

RT-qPCR.

For analysis of the expression of Trichoderma genes, mycelia were collected from Trichoderma-Drosophila interaction (see “Interactions between D. melanogaster and T. atroviride,” above) or mechanical injury experiments, collected, and immediately frozen in liquid nitrogen. For analysis of the expression of Drosophila genes, 40 larvae were collected and used for RNA extraction. Total RNA was extracted using TRIzol, following the manufacturer’s instructions. For RT-qPCR, primers were designed to produce amplicons of around 150 bp (Table 1) (28, 67, 68) for all selected genes and for a gene encoding DNA polymerase family B as an internal control housekeeping gene. cDNA was synthesized using Superscript reverse transcriptase II (Invitrogen). The reaction mixture for quantitative PCR was as follows: 10 μl of SYBR green master mix (Applied Biosystems), 3 ml of cDNA template (3 ng/ml), and 1 μl (10 μM) of each of the primers. The PCR program was as follows: one cycle at 95°C for 5 min and 40 cycles at 95°C for 30 s, 65°C for 30 s, and 72°C for 40 s. Melting curves for each product, starting from 60°C and increasing to 95°C at 0.2°C/s, produced a single melting point. All experiments were repeated at least three times. All calculations and analyses were performed using ABI 7500 software, version 2.0.1 (Applied Biosystems), and the cycle threshold (2^−ΔΔCT) method (69).

Food choice assays.

The food choice assays were performed in 150- by 150-mm petri dishes containing PDA. One thousand conidia of the WT strain of T. atroviride or the Δtmk3 mutant were inoculated at 1 cm from the end of the plate and incubated for 36 h at 26°C in darkness. One third-instar larva was placed in the middle of the plate and video recorded for 15 min. Twenty replicates were used per food choice assay, in three independent experiments. For statistical analysis, we counted the number of replicates in which larvae were at the side of the indicated fungal strain at the end of the 15-min period.

Larval survival.

To determine the effect of Trichoderma consumption on the larvae, analyses of interactions between D. melanogaster larvae and T. atroviride were performed as described above in “Interactions between D. melanogaster and T. atroviride.” After 30 min of interaction of the larvae with the indicated fungal strains, the larvae were placed on fresh PDA plates and incubated until they completed their life cycle. Larval development and death were monitored every 24 h for 14 days. Once all living larvae reached adulthood, the numbers of dead and living flies were computed. As a control, we used larvae that did not feed on Trichoderma. The experiment was performed using three technical replicates of 40 larvae in three independent assays.

Statistical analysis.

Single-factor analysis of variance (ANOVA) was used, followed by a Fisher’s exact test. Statistically significant differences in mean values at a P value of <0.05 are indicated with different letters in the figures.

Supplemental file 1:

Supplemental file 2:

Supplemental file 3:

Supplemental file 4:

We thank Pedro Martínez-Hernández, Mariana R. Loustalot-Laclette, and Nestor Nazario-Yepiz for technical assistance.

K.A.-H. and A.M.-P. are indebted to Conacyt for doctoral fellowships. This work was supported by a CONACYT grant (Investigación en Fronteras de la Ciencia; grant number FON.INST./117/2016) to A.H.-E. Current research of the group of R.W. is supported by grants Fronteras 2015-2/814 and CONACyT-DFG bilateral 2016/277850.

[This article was published on 9 January 2019 with a standard copyright line (“© 2019 American Society for Microbiology. All Rights Reserved.”). The authors elected to pay for open access for the article after publication, necessitating replacement of the original copyright line with the one above, and this change was made on 29 January 2019.]

Supplemental material for this article may be found at https://doi.org/10.1128/AEM.01825-18.

1. Lima SL.1998. Stress and decision making under the risk of predation: recent developments from behavioral, reproductive, and ecological perspectives. Adv Study Behav27:215–290. doi: 10.1016/S0065-3454(08)60366-6. [CrossRef] [Google Scholar]

2. Pandey G, Sharma N, Sahu PP, Prasad M. 2016. Chromatin-based epigenetic regulation of plant abiotic stress response. Curr Genomics17:490–498. doi: 10.2174/1389202917666160520103914. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

3. Hernández-Oñate MA, Herrera-Estrella A. 2015. Damage response involves mechanisms conserved across plants, animals and fungi. Curr Genet61:359–372. doi: 10.1007/s00294-014-0467-5. [PubMed] [CrossRef] [Google Scholar]

4. Asano Y, Hagiwara D, Yahino T, Mizuno T. 2007. Characterization of the bZip-type transcription factor NapA with reference to oxidative stress response in Aspergillus nidulans. Biosci Biotechnol Biochem71:1800–1803. doi: 10.1271/bbb.70133. [PubMed] [CrossRef] [Google Scholar]

5. Dighton J. (ed). 2016. Fungi in ecosystem processes, 2nd edCRC Press, Boca Raton, FL. [Google Scholar]

6. Trienens M, Keller N, Rohlfs M. 2010. Fruit, flies and filamentous fungi—experimental analysis of animal–microbe competition using Drosophila melanogaster and Aspergillus mold as a model system. Oikos119:1765–1775. doi: 10.1111/j.1600-0706.2010.18088.x. [CrossRef] [Google Scholar]

7. Caballero Ortiz S, Trienens M, Rohlfs M. 2013. Induced fungal resistance to insect grazing: reciprocal fitness consequences and fungal gene expression in the Drosophila-Aspergillus model system. PLoS One8:e74951. doi: 10.1371/journal.pone.0074951. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

8. Trienens M, Rohlfs M. 2012. Insect–fungus interference competition—the potential role of global secondary metabolite regulation, pathway-specific mycotoxin expression and formation of oxylipins. Fungal Ecol5:191–199. doi: 10.1016/j.funeco.2011.07.009. [CrossRef] [Google Scholar]

9. Plaza DF, Schmieder SS, Lipzen A, Lindquist E, Künzler M. 2015. Identification of a novel nematotoxic protein by challenging the model mushroom Coprinopsis cinerea with a fungivorous nematode. G3 (Bethesda)6:87–98. doi: 10.1534/g3.115.023069. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

10. Bleuler‐Mínez S, Butschi A, Garni M, Wälti MA, Wohlschlager T, Potthoff E, Sabotiĉ J, Pohleven J, Lüthy P, Hengartner MO, Aebi M, Künzler M. 2011. A lectin‐mediated resistance of higher fungi against predators and parasites. Mol Ecol20:3056–3070. doi: 10.1111/j.1365-294X.2011.05093.x. [PubMed] [CrossRef] [Google Scholar]

11. Staaden S, Milcu A, Rohlfs M, Scheu S. 2011. Olfactory cues associated with fungal grazing intensity and secondary metabolite pathway modulate Collembola foraging behavior. Soil Biol Biochem43:1411–1416. doi: 10.1016/j.soilbio.2010.10.002. [CrossRef] [Google Scholar]

12. Bok J, Keller N. 2004. LaeA, a regulator of secondary metabolism in Aspergillus spp. Eukaryot Cell3:527–535. doi: 10.1128/EC.3.2.527-535.2004. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

13. Janssens TKS, Staaden S, Scheu S, Mariën J, Ylstra B. 2010. Transcriptional responses of Folsomia candida upon exposure to Aspergillus nidulans secondary metabolites in single and mixed diets. Pedobiologia54:45–52. doi: 10.1016/j.pedobi.2010.09.002. [CrossRef] [Google Scholar]

14. Yin W-BB, Amaike S, Wohlbach DJ, Gasch AP, Chiang Y-MM, Wang CCC, Bok JW, Rohlfs M, Keller NP. 2012. An Aspergillus nidulans bZIP response pathway hardwired for defensive secondary metabolism operates through aflR. Mol Microbiol83:1024–1034. doi: 10.1111/j.1365-2958.2012.07986.x. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

15. Spiteller P.2008. Chemical defense strategies of higher fungi. Chem Eur J14:9100–9110. doi: 10.1002/chem.200800292. [PubMed] [CrossRef] [Google Scholar]

16. Spiteller P.2015. Chemical ecology of fungi. Nat Prod Rep32:971–993. doi: 10.1039/C4NP00166D. [PubMed] [CrossRef] [Google Scholar]

17. Harman GE, Howell CR, Viterbo A, Chet I, Lorito M. 2004. Trichoderma species—opportunistic, avirulent plant symbionts. Nat Rev Microbiol2:43–56. doi: 10.1038/nrmicro797. [PubMed] [CrossRef] [Google Scholar]

18. Druzhinina IS, Seidl-Seiboth V, Herrera-Estrella A, Horwitz BA, Kenerley CM, Monte E, Mukherjee PK, Zeilinger S, Grigoriev IV, Kubicek CP. 2011. Trichoderma: the genomics of opportunistic success. Nat Rev Microbiol9:749–759. doi: 10.1038/nrmicro2637. [PubMed] [CrossRef] [Google Scholar]

19. Schmoll M, Dattenböck C, Carreras-Villaseñor N, Mendoza-Mendoza A, Tisch D, Alemán MI, Baker SE, Brown C, Cervantes-Badillo MG, Cetz-Chel J, Cristobal-Mondragon GR, Delaye L, Esquivel-Naranjo EU, Frischmann A, de Jesus Gallardo-Negrete J, García-Esquivel M, Gomez-Rodriguez EY, Greenwood DR, Hernández-Oñate M, Kruszewska JS, Lawry R, Mora-Montes HM, Muñoz-Centeno T, Nieto-Jacobo MF, Nogueira Lopez G, Olmedo-Monfil V, Osorio-Concepcion M, Piłsyk S, Pomraning KR, Rodriguez-Iglesias A, Rosales-Saavedra MT, Sánchez-Arreguín JA, Seidl-Seiboth V, Stewart A, Uresti-Rivera EE, Wang C-LL, Wang T-FF, Zeilinger S, Casas-Flores S, Herrera-Estrella A. 2016. The genomes of three uneven siblings: footprints of the lifestyles of three Trichoderma species. Microbiol Mol Biol Rev80:205–327. doi: 10.1128/MMBR.00040-15. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

20. Hermosa R, Cardoza RE, Rubio MB, Gutiérrez SE. 2014. Secondary metabolism and antimicrobial metabolites of Trichoderma, p 125–137. InGupta V, Schmoll M, Herrera-Estrella A, Upadhyay R, Druzhinina I, Tuohy M (ed), Biotechnology and biology of Trichoderma. Elsevier, Amsterdam, The Netherlands. [Google Scholar]

21. Zeilinger S, Gruber S, Bansal R, Mukherjee PK. 2016. Secondary metabolism in Trichoderma—chemistry meets genomics. Fungal Biol Rev30:74–90. doi: 10.1016/j.fbr.2016.05.001. [CrossRef] [Google Scholar]

22. Um S, Fraimout A, Sapountzis P, Oh DC, Poulsen M. 2013. The fungus-growing termite Macrotermes natalensis harbors bacillaene-producing Bacillus sp. that inhibit potentially antagonistic fungi. Sci Rep3:3250. doi: 10.1038/srep03250. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

23. Rocha SL, Evans HC, Jorge VL, Cardoso LA, Pereira FS, Rocha FB, Barreto RW, Hart AG, Elliot SL. 2017. Recognition of endophytic Trichoderma species by leaf-cutting ants and their potential in a Trojan-horse management strategy. R Soc Open Sci4:160628. doi: 10.1098/rsos.160628. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

24. Sabatini M, Grazioso P, Altomare C, Innocenti G. 2002. Interactions between Onychiurus armatus and Trichoderma harzianum in take-all disease suppression in a simple experimental system. Eur J Soil Biol38:71–74. doi: 10.1016/S1164-5563(01)01129-3. [CrossRef] [Google Scholar]

25. Hernández-Oñate MA, Esquivel-Naranjo EU, Mendoza-Mendoza A, Stewart A, Herrera-Estrella AH. 2012. An injury-response mechanism conserved across kingdoms determines entry of the fungus Trichoderma atroviride into development. Proc Natl Acad Sci U S A109:14918–14923. doi: 10.1073/pnas.1209396109. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

26. Medina-Castellanos E, Esquivel-Naranjo EU, Heil M, Herrera-Estrella A. 2014. Extracellular ATP activates MAPK and ROS signaling during injury response in the fungus Trichoderma atroviride. Front Plant Sci5:659. doi: 10.3389/fpls.2014.00659. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

27. Williams G.2011. Data mining with rattle and R: the art of excavating data for knowledge discovery. Springer-Verlag, New York, NY. [Google Scholar]

28. Zhang Z-TT, Zhu S-YY. 2009. Drosomycin, an essential component of antifungal defense in Drosophila. Insect Mol Biol18:549–556. doi: 10.1111/j.1365-2583.2009.00907.x. [PubMed] [CrossRef] [Google Scholar]

29. Cloonan KR, Andreadis SS, Chen H, Jenkins NE, Baker TC. 2016. Attraction, oviposition and larval survival of the fungus gnat, Lycoriella ingenua, on fungal species isolated from adults, larvae, and mushroom compost. PLoS One11:e0167074. doi: 10.1371/journal.pone.0167074. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

30. Keller NP.2015. Translating biosynthetic gene clusters into fungal armor and weaponry. Nat Chem Biol11:671–677. doi: 10.1038/nchembio.1897. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

31. Demain AL, Fang A. 2000. The natural functions of secondary metabolites. Adv Biochem Eng Biotechnol69:1–39. [PubMed] [Google Scholar]

32. Macheleidt J, Mattern DJ, Fischer J, Netzker T, Weber J, Schroeckh V, Valiante V, Brakhage AA. 2016. Regulation and role of fungal secondary metabolites. Annu Rev Genet50:371–392. doi: 10.1146/annurev-genet-120215-035203. [PubMed] [CrossRef] [Google Scholar]

33. Calvo A, Wilson R, Bok J, Keller N. 2002. Relationship between secondary metabolism and fungal development. Microbiol Mol Biol Rev66:447–459. doi: 10.1128/MMBR.66.3.447-459.2002. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

34. Calvo AM, Cary JW. 2015. Association of fungal secondary metabolism and sclerotial biology. Front Microbiol6:62. doi: 10.3389/fmicb.2015.00062. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

35. Bayram O, Braus GH. 2012. Coordination of secondary metabolism and development in fungi: the velvet family of regulatory proteins. FEMS Microbiol Rev36:1–24. doi: 10.1111/j.1574-6976.2011.00285.x. [PubMed] [CrossRef] [Google Scholar]

36. Rohlfs M.2014. Fungal secondary metabolite dynamics in fungus-grazer interactions: novel insights and unanswered questions. Front Microbiol5:788. [PMC free article] [PubMed] [Google Scholar]

37. Rohlfs M, Churchill A. 2011. Fungal secondary metabolites as modulators of interactions with insects and other arthropods. Fungal Genet Biol48:23–34. doi: 10.1016/j.fgb.2010.08.008. [PubMed] [CrossRef] [Google Scholar]

38. Zhao Y, Ding J, Yuan W, Huang J, Huang W, Wang Y, Zheng W. 2017. Production of a fungal furocoumarin by a polyketide synthase gene cluster confers the chemo-resistance of Neurospora crassa to the predation by fungivorous arthropods. Environ Microbiol19:3920–3929. doi: 10.1111/1462-2920.13791. [PubMed] [CrossRef] [Google Scholar]

39. Mithöfer A, Wanner G, Boland W. 2005. Effects of feeding Spodoptera littoralis on lima bean leaves. II. Continuous mechanical wounding resembling insect feeding is sufficient to elicit herbivory-related volatile emission. Plant Physiol137:1160–1168. doi: 10.1104/pp.104.054460. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

40. Heil M.2009. Damaged-self recognition in plant herbivore defense. Trends Plant Sci14:356–363. doi: 10.1016/j.tplants.2009.04.002. [PubMed] [CrossRef] [Google Scholar]

41. Piesik D, Panka D, Delaney KJ, Skoczek A, Lamparski R, Weaver DK. 2011. Cereal crop volatile organic compound induction after mechanical injury, beetle herbivory (Oulema spp.), or fungal infection (Fusarium spp.). J Plant Physiol168:878–886. doi: 10.1016/j.jplph.2010.11.010. [PubMed] [CrossRef] [Google Scholar]

42. Kant MR, Sabelis MW, Haring MA, Schuurink RC. 2008. Intraspecific variation in a generalist herbivore accounts for induction and impact of host-plant defenses. Proc R Soc B Biol Sci275:443–452. doi: 10.1098/rspb.2007.1277. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

43. Eichenseer H, Mathews MC, Powell JS, Felton GW. 2010. Survey of a salivary effector in caterpillars: glucose oxidase variation and correlation with host range. J Chem Ecol36:885–897. doi: 10.1007/s10886-010-9830-2. [PubMed] [CrossRef] [Google Scholar]

44. Alba JM, Glas JJ, Schimmel BCJ, Kant MR. 2011. Avoidance and suppression of plant defenses by herbivores and pathogens. J Plant Interact6:221–227. doi: 10.1080/17429145.2010.551670. [CrossRef] [Google Scholar]

45. Trienens M, Kraaijeveld K, Wertheim B. 2017. Defensive repertoire of Drosophila larvae in response to toxic fungi. Mol Ecol26:5043–5057. doi: 10.1111/mec.14254. [PubMed] [CrossRef] [Google Scholar]

46. Caballero Ortiz S, Trienens M, Pfohl K, Karlovsky P, Holighaus G, Rohlfs M. 2018. Phenotypic responses to microbial volatiles render a mold fungus more susceptible to insect damage. Ecol Evol8:4328–4339. doi: 10.1002/ece3.3978. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

47. Nemčovič M, Jakubíková L, Víden I, Vladimír F. 2008. Induction of conidiation by endogenous volatile compounds in Trichoderma spp. FEMS Microbiol Lett284:231–236. [PubMed] [Google Scholar]

48. Reithner B, Schuhmacher R, Stoppacher N, Pucher M, Brunner K, Zeilinger S. 2007. Signaling via the Trichoderma atroviride mitogen-activated protein kinase Tmk1 differentially affects mycoparasitism and plant protection. Fungal Genet Biol44:1123–1133. doi: 10.1016/j.fgb.2007.04.001. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

49. Inamdar A, Zaman T, Morath S, Pu D, Bennett J. 2014. Drosophila melanogaster as a model to characterize fungal volatile organic compounds. Environ Toxicol29:829–836. doi: 10.1002/tox.21825. [PubMed] [CrossRef] [Google Scholar]

50. Sawahata T, Shimano S, Suzuki M. 2008. Tricholoma matsutake 1-Ocen-3-ol and methyl cinnamate repel mycophagous Proisotoma minuta (Collembola: Insecta). Mycorrhiza18:111–114. doi: 10.1007/s00572-007-0158-x. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

51. Ipcho S, Sundelin T, Erbs G, Kistler HC, Newman M-AA, Olsson S. 2016. Fungal innate immunity induced by bacterial microbe-associated molecular patterns (MAMPs.). G3 (Bethesda)6:1585–1595. doi: 10.1534/g3.116.027987. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

52. Bleuler-Martinez S, Schmieder S, Aebi M, Künzler M. 2012. Biotin-binding proteins in the defense of mushrooms against predators and parasites. Appl Environ Microbiol78:8485–8487. doi: 10.1128/AEM.02286-12. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

54. Bennett JW, Hung R, Lee S, Padhi S. 2012. Chapter 18, Fungal and bacterial volatile organic compounds: an overview and their role as ecological signaling agents, p 373–393InHock B. (ed), The Mycota: a comprehensive treatise on fungi as experimental systems for basic and applied research, vol 9Fungal associations. Springer, Berlin, Germany. [Google Scholar]

55. Imler JL, Bulet P. 2005. Antimicrobial peptides in Drosophila: structures, activities and gene regulation. Chem Immunol Allergy86:1–21. doi: 10.1159/000086648. [PubMed] [CrossRef] [Google Scholar]

56. Tauszig S, Jouanguy E, Hoffmann JA, Imler JL. 2000. Toll-related receptors and the control of antimicrobial peptide expression in Drosophila. Proc Natl Acad Sci U S A97:10520–10525. doi: 10.1073/pnas.180130797. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

57. Lemaitre B, Reichhart JM, Hoffmann JA. 1997. Drosophila host defense: differential induction of antimicrobial peptide genes after infection by various classes of microorganisms. Proc Natl Acad Sci U S A94:14614–14619. doi: 10.1073/pnas.94.26.14614. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

58. Luo C, Shen B, Manley JL, Zheng L. 2001. Tehao functions in the Toll pathway in Drosophila melanogaster: possible roles in development and innate immunity. Insect Mol Biol10:457–464. doi: 10.1046/j.0962-1075.2001.00284.x. [PubMed] [CrossRef] [Google Scholar]

59. Halfon MS, Keshishian H. 1998. The Toll pathway is required in the epidermis for muscle development in the Drosophila embryo. Dev Biol199:164–174. doi: 10.1006/dbio.1998.8915. [PubMed] [CrossRef] [Google Scholar]

60. Esquivel-Naranjo EU, Garcia-Esquivel M, Medina-Castellanos E, Correa-Pérez VA, Parra-Arriaga JL, Landeros-Jaime F, Cervantes-Chávez JA, Herrera-Estrella A. 2016. A Trichoderma atroviride stress-activated MAPK pathway integrates stress and light signals. Mol Microbiol100:860–876. doi: 10.1111/mmi.13355. [PubMed] [CrossRef] [Google Scholar]

61. Montero-Vargas JM, González-González LH, Gálvez-Ponce E, Ramírez-Chávez E, Molina-Torres J, Chagolla A, Montagnon C, Winkler R. 2013. Metabolic phenotyping for the classification of coffee trees and the exploration of selection markers. Mol Biosyst9:693–699. doi: 10.1039/c3mb25509c. [PubMed] [CrossRef] [Google Scholar]

62. Frisvad JC.2012. Media and growth conditions for induction of secondary metabolite production. Methods Mol Biol944:47–58. doi: 10.1007/978-1-62703-122-6_3. [PubMed] [CrossRef] [Google Scholar]

63. Martínez-Jarquín S, Moreno-Pedraza A, Guillén-Alonso H, Winkler R. 2016. Template for 3D printing a low-temperature plasma probe. Anal Chem88:6976–6980. doi: 10.1021/acs.analchem.6b01019. [PubMed] [CrossRef] [Google Scholar]

64. Martínez‐Jarquín S, Winkler R. 2013. Design of a low‐temperature plasma (LTP) probe with adjustable output temperature and variable beam diameter for the direct detection of organic molecules. Rapid Commun Mass Spectrom27:629–634. doi: 10.1002/rcm.6494. [PubMed] [CrossRef] [Google Scholar]

65. Gibb S, Strimmer K. 2012. MALDIquant: a versatile R package for the analysis of mass spectrometry data. Bioinformatics28:2270–2271. doi: 10.1093/bioinformatics/bts447. [PubMed] [CrossRef] [Google Scholar]

66. Gamboa-Becerra R, Montero-Vargas JM, Martínez-Jarquín S, Gálvez-Ponce E, Moreno-Pedraza A, Winkler R. 2017. Rapid classification of coffee products by data mining models from direct electrospray and plasma-based mass spectrometry analyses. Food Anal Methods10:1359–1368. doi: 10.1007/s12161-016-0696-y. [CrossRef] [Google Scholar]

67. Jin LH, Shim J, Yoon JS, Kim B, Kim J, Kim-Ha J, Kim Y-JJ. 2008. Identification and functional analysis of antifungal immune response genes in Drosophila. PLoS Pathog4:e1000168. doi: 10.1371/journal.ppat.1000168. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

68. Nazario-Yepiz NO, Loustalot-Laclette MR, Carpinteyro-Ponce J, Abreu-Goodger C, Markow TA. 2017. Transcriptional responses of ecologically diverse Drosophila species to larval diets differing in relative sugar and protein ratios. PLoS One12:e0183007. doi: 10.1371/journal.pone.0183007. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

69. Livak KJ, Schmittgen TD. 2001. Analysis of relative gene expression data using real-time quantitative PCR and the 2-[Delta][Delta]CT method. Methods25:402–408. doi: 10.1006/meth.2001.1262. [PubMed] [CrossRef] [Google Scholar]

70. Medina-Castellanos E, Villalobos-Escobedo JM, Riquelme M, Read ND, Abreu-Goodger C, Herrera-Estrella A. Danger signals activate a putative innate immune system during regeneration in a filamentous fungus. PLoS Genet, in press. [PMC free article] [PubMed] [Google Scholar]