Research

This interdisciplinary project, led by collaborators from University of Innsbruck, explores the regulatory role of the GATA transcription factor Are1 in Trichoderma atroviride, focusing on its involvement in specialized metabolite (SM) biosynthesis and iron metabolism. Colleagues from Uni Innsbruck will employ advanced genetic tools, including an inducible Are1 overexpression system to map Are1’s regulatory network and identify its genomic targets. High-performance thin-layer chromatography (HPTLC) will be utilized to screen for abundant SM candidates under Are1-inducing conditions. Complementing this work, our group will contribute through advanced metabolomics, siderophore analysis, and the identification and purification of specialized metabolites. Untargeted liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS/MS) will be employed for comprehensive metabolomic profiling, while siderophore production will be analyzed under iron-deficient and iron-sufficient conditions. Structural elucidation of key metabolites will be performed using nuclear magnetic resonance (NMR), after purification by preparative HPLC. This collaborative effort aims to uncover novel bioactive compounds, elucidate Are1’s role in fungal metabolism, and provide a framework for targeted natural product discovery in filamentous fungi.

The Christian Doppler Laboratory for mycotoxin-induced metabolome changes & improved biomarkers in livestock farming (CDL-MyMetaboMarker) aims to investigate mycotoxin-induced changes in the metabolome of livestock and to discover improved biomarkers for animal production systems. Mycotoxins such as deoxynivalenol (DON) and ochratoxin A (OTA) are widespread contaminants in animal feed that cause significant health and productivity problems in livestock. While biomarkers of exposure for DON and OTA are already established, they are limited to assessing recent exposure and do not provide insight into long-term biological disturbances or health effects. Within the CDL-MyMetaboMarker, biomarkers of effect for DON and OTA will be identified in livestock by exposing piglets to controlled concentrations of mycotoxins in feed over several weeks. Subsequently, biological samples such as blood, plasma, saliva, urine, and feces will be analyzed by liquid chromatography-mass spectrometry based metabolomics to detect dose-, time-, and toxin-specific alterations in the profiles of endogenous metabolites. The resulting data will be evaluated by classical statistical methods as well as machine-learning approaches to identify potential biomarker candidates. These candidates will then be validated in further animal studies regarding robustness, reproducibility, and specificity. In the long term, the identified biomarkers will be used to investigate combined toxicity effects and to assess the efficacy of mycotoxin detoxifying agents. Overall, the research aims to improve the early detection of mycotoxin exposure, enable a better assessment of long-term health effects, and support the development of safe feed additives to sustainably enhance livestock health and productivity.

The Christian Doppler Laboratory for mycotoxin-induced metabolome changes & improved biomarkers in livestock farming (CDL-MyMetaboMarker) aims to investigate mycotoxin-induced changes in the metabolome of livestock and to discover improved biomarkers for animal production systems. Mycotoxins such as deoxynivalenol (DON) and ochratoxin A (OTA) are widespread contaminants in animal feed that cause significant health and productivity problems in livestock. While biomarkers of exposure for DON and OTA are already established, they are limited to assessing recent exposure and do not provide insight into long-term biological disturbances or health effects. Within the CDL-MyMetaboMarker, biomarkers of effect for DON and OTA will be identified in livestock by exposing piglets to controlled concentrations of mycotoxins in feed over several weeks. Subsequently, biological samples such as blood, plasma, saliva, urine, and feces will be analyzed by liquid chromatography-mass spectrometry based metabolomics to detect dose-, time-, and toxin-specific alterations in the profiles of endogenous metabolites. The resulting data will be evaluated by classical statistical methods as well as machine-learning approaches to identify potential biomarker candidates. These candidates will then be validated in further animal studies regarding robustness, reproducibility, and specificity. In the long term, the identified biomarkers will be used to investigate combined toxicity effects and to assess the efficacy of mycotoxin detoxifying agents. Overall, the research aims to improve the early detection of mycotoxin exposure, enable a better assessment of long-term health effects, and support the development of safe feed additives to sustainably enhance livestock health and productivity.