High-throughput profiling of key enzyme activities of carbon, nitrogen, and antioxidant metabolism is emerging as a valuable approach to integrate cell physiological phenotyping into a holistic functional phenomics approach. However, the analyses of the large datasets generated by this method represent a bottleneck, often keeping researchers from exploiting the full potential of their studies. We address these limitations through the exemplary application of a set of data evaluation and visualization tools within a case study. This includes the introduction of multivariate statistical analyses that can easily be implemented in similar studies, allowing researchers to extract more valuable information to identify enzymatic biosignatures. Through a literature meta-analysis, we demonstrate how enzyme activity profiling has already provided functional information on the mechanisms regulating plant development and response mechanisms to abiotic stress and pathogen attack. The high robustness of the distinct enzymatic biosignatures observed during developmental processes and under stress conditions underpins the enormous potential of enzyme activity profiling for future applications in both basic and applied research. Enzyme activity profiling will complement molecular -omics approaches to contribute to the mechanistic understanding required to narrow the genotype-to-phenotype knowledge gap and to identify predictive biomarkers for plant breeding to develop climate-resilient crops.

Department of Adaptive Biotechnologies Global Change Research Institute Czech Academy of Sciences Brno Czech Republic

Department of Plant and Environmental Sciences Section of Crop Science University of Copenhagen Copenhagen Denmark

Department of Plant and Environmental Sciences Section of Microbial Ecology and Biotechnology University of Copenhagen Copenhagen Denmark

Institute for Computational Medicine University of Veterinary Medicine Vienna Vienna Austria

Institute of Biology University of Graz NAWI Graz Schubertstraße 51 8010 Graz Austria

International AI Future Lab Technical University of Munich Munich Germany

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