Sniffing fungi – phenotyping of volatile chemical diversity in Trichoderma species. Issue 1 (23rd April 2020)

• Record Type: Journal Article
• Authors: Guo, Yuan; Jud, Werner; Ghirardo, Andrea; Antritter, Felix; Benz, J. Philipp; Schnitzler, Jörg‐Peter; Rosenkranz, Maaria
• Is Part Of: New phytologist. Volume 227:Issue 1(2020)
• Page Start: 244
• Page End: 259
• Publication Date: 2020-04-23
• Subjects: automated cuvettes -- chemical diversity -- data mining -- fungi -- GC‐MS -- Proton transfer reaction‐time of flight‐MS -- Trichoderma -- volatile organic compound (VOC) emission; Botany -- Periodicals; 580
• DOI: 10.1111/nph.16530
• Languages: English
• ISSNs: 0028-646X

Abstract

Summary: Volatile organic compounds (VOCs) play vital roles in the interaction of fungi with plants and other organisms. A systematic study of the global fungal VOC profiles is still lacking, though it is a prerequisite for elucidating the mechanisms of VOC‐mediated interactions. Here we present a versatile system enabling a high‐throughput screening of fungal VOCs under controlled temperature. In a proof‐of‐principle experiment, we characterized the volatile metabolic fingerprints of four Trichoderma spp. over a 48 h growth period. The developed platform allows automated and fast detection of VOCs from up to 14 simultaneously growing fungal cultures in real time. The comprehensive analysis of fungal odors is achieved by employing proton transfer reaction‐time of flight‐MS and GC‐MS. The data‐mining strategy based on multivariate data analysis and machine learning allows the volatile metabolic fingerprints to be uncovered. Our data revealed dynamic, development‐dependent and extremely species‐specific VOC profiles from the biocontrol genus Trichoderma . The two mass spectrometric approaches were highly complementary to each other, together revealing a novel, dynamic view to the fungal VOC release. This analytical system could be used for VOC‐based chemotyping of diverse small organisms, or more generally, for any in vivo and in vitro real‐time headspace analysis.