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Guo et al. (2021) Volatile organic compound patterns predict fungal trophic mode and lifestyle Publication Abstract: Fungi produce a wide variety of volatile organic compounds (VOCs), which play central roles in the initiation and regulation of fungal interactions. Here we introduce a global overview of fungal VOC patterns and chemical diversity across phylogenetic clades and trophic modes. The analysis is based on measurements of comprehensive VOC profiles of forty-three fungal species. Our data show that the VOC patterns can describe the phyla and the trophic mode of fungi. We show different levels of phenotypic integration (PI) for different chemical classes of VOCs within distinct functional guilds. Further computational analyses reveal that distinct VOC patterns can predict trophic modes, (non)symbiotic lifestyle, substrate-use and host-type of fungi. Thus, depending on trophic mode, either individual VOCs or more complex VOC patterns (i.e. chemical communication displays) may be ecologically important. Present results stress the ecological importance of VOCs and serve as prerequisite for more comprehensive VOCs-involving ecological studies. Supplemental Tables S1, S2, S3, S4, S5 and S6 to the manuscript: Table S1. Summary of fungal species included in this study. n.a., not available; DSM, DSMZ Fungal Collection Number (Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures); FUNGuild: trophic mode suggested by FUNGuild-tool*. The numbers in front of some species names indicate the fungal species easily moving among trophic modes (two different analyses were done using the different groupings as shown in Fig. 4 and Supplementary Fig. S2). Table S2. Mass to charge ratios (m/z) of protonated ions, sum formula and tentative compound identification of fungal volatiles detected by Proton Transfer Reaction Time-of-Flight Mass Spectrometry (PTR-ToF-MS). Isotopes and known common fragments were omitted from the list. Same background colors in the “mass” column indicate m/z that are strongly correlated (R2 >0.9). Sensitivites (ncps/ppbv) of compounds available as standards (in green) were calculated using calibration curves, otherwise derived from chemically similar (molecular mass, dipole moment and reaction rate constants) calibrated compounds. Sensitivities of tentatively identified compounds with unknown fragmentation and isotopic patterns were roughly estimated (*) based on the mass-dependent detection efficiency of the mass spectrometer and the mean values of calibrated compounds. N/A: Not Available; Footnotes refer to: 1(Mancuso et al., 2015); 2(Asensio et al., 2007); 3(Infantino et al., 2017); 4(Seewald et al., 2010); 5(Misztal et al., 2018); 6(Ladygina et al., 2006); 7(Brilli et al., 2011); 8(Bunge et al., 2008); 9(Aprea et al., 2015); 10(Mayrhofer et al., 2006); 11(Bäck et al., 2010); 12(Lippolis et al., 2014); 13(Morath et al., 2012); 14(Maleknia et al., 2007); 15(Demarcke et al., 2009); 16(Kim et al., 2009); 17(Minerdi et al., 2009). The references (list can be found in the end of the table) refer to studies that investigated soil and fungal volatile emission using PTR-ToF-MS. Table S3. Identities of the fungal volatile organic compounds (fVOCs) detected using gas chromatography – mass spectrometer (GC-MS). RT: Retention time in min; RI: Kovats retention index; n.a.: not available. Table S4. Emission intensity of all detected fungal volatile organic compounds (fVOCs) from each fungus. (a) The compounds in bold were detected by PTR-ToF-MS as m/z ratio (ncps cm-2 s-1) and the rest were detected by GC-MS (pmol cm-2 h-1). Mean +/- SD are given after the raw data for each fungal species. (b) The compounds detected by PTR-ToF-MS (pmol cm-2 h-1). The compounds were converted from ncps to pmol knowing the sensitivity (ncps/ppbv; Supplementary Table S2) of the specific compounds. In absence of commercial standards for all the compounds, part of the sensitivities was estimated (as shown in Supplementary Table S2) leading to approximate concentration values for these compounds (marked with asteriks). Mean +/- SD are given after the raw data for each fungal species, N/A: not available. (c) The mean +/- SD of the emission intensities (ncps cm-2 s-1) as measured by PTR-ToF-MS sequentially each 70 minutes from each fungal culture over the 48h measurement period. Table S5. Exact phenotypic integration values of the chemical communication displays shown in Figure 5. The value of two-sided null model expectation is 23.760692 for all the samples. The significant PI values are shown in bold. NA: not available. Table S6. List of biological functions of the biomarkers. The names in brackets indicate the tentatively assigned compounds detected by PTR-ToF-MS. Fungal volatile organic compounds (fVOCs) in bold denote the ones which have known biological functions as shown in the following columns.

Air chemistry is affected by the emission of biogenic volatile organic compounds (BVOCs), which originate from almost all plants in varying qualities and quantities. They also vary widely among different crops, an aspect that has been largely neglected in emission inventories. In particular, bioenergy-related species can emit mixtures of highly reactive compounds that have received little attention so far. For such species, long-term field observations of BVOC exchange from relevant crops covering different phenological phases are scarcely available. Therefore, we measured and modeled the emission of three prominent European bioenergy crops (maize, ryegrass, and oil-seed rape) for full rotations in north-eastern Germany. Using a proton transfer reaction–mass spectrometer combined with automatically moving large canopy chambers, we were able to quantify the characteristic seasonal BVOC flux dynamics of each crop species. The measured BVOC fluxes were used to parameterize and evaluate the BVOC emission module (JJv) of the physiology-oriented LandscapeDNDC model, which was enhanced to cover de novo emissions as well as those from plant storage pools. Parameters are defined for each compound individually. The model is used for simulating total compound-specific reactivity over several years and also to evaluate the importance of these emissions for air chemistry. We can demonstrate substantial differences between the investigated crops with oil-seed rape having 37-fold higher total annual emissions than maize. However, due to a higher chemical reactivity of the emitted blend in maize, potential impacts on atmospheric OH-chemistry are only 6-fold higher.

Isoprene and other terpenoids are important biogenic volatile organic compounds in terms of atmospheric chemistry. Isoprene can aid plant performance under abiotic stresses, but the fundamental biological reasons for the high emissions are not completely understood. Here, we provide evidence of a previously unrecognized ecological function for isoprene and for the sesquiterpene, ß-caryophyllene. We show that isoprene and ß-caryophyllene act as core components of plant signalling networks, inducing resistance against microbial pathogens in neighbouring plants. We challenged Arabidopsis thaliana with Pseudomonas syringae, after exposure to pure volatile terpenoids or to volatile emissions of transformed poplar or Arabidopsis plants. The data suggest that isoprene induces a defence response in receiver plants that is similar to that elicited by monoterpenes and depended on salicylic acid (SA) signalling. In contrast, the sesquiterpene, ß-caryophyllene, induced resistance via jasmonic acid (JA)-signalling. The experiments in an open environment show that natural biological emissions are enough to induce resistance in neighbouring Arabidopsis. Our results show that both isoprene and ß-caryophyllene function as allelochemical components in complex plant signalling networks. Knowledge of this system may be used to boost plant immunity against microbial pathogens in various crop management schemes.