SP2: Investigating the physiological, biochemical, and molecular responses of maize to concurrent biotic and abiotic stresses
How does maize cope when drought, heat, nitrogen deficiency, and attacks by pathogens or insects occur simultaneously? SP2 unlocks the physiological, biochemical, and molecular mechanisms underlying maize responses to combined stresses, from root water uptake and stomatal regulation to ABA signalling and oxidative stress, using cutting-edge imaging, machine learning, and functional genomics to support breeding for maize under a changing climate.
Project description
Main research questions: How do maize plants integrate responses to concurrent abiotic (drought, heat, nitrogen deficiency) and biotic (Setosphaeria turcica, stem borer) stresses? What are the key hydraulic, biochemical, and molecular bottlenecks limiting performance under multistress conditions? Can we identify functional variation in ABA signalling components that confers enhanced multistress tolerance? And finally, can hyperspectral imaging combined with machine learning reveal interpretable spectral signatures that reflect underlying physiological and biochemical stress responses?
Methods/approaches (keywords): Soil-plant hydraulics; automated root pressure chamber; X-ray micro-CT; neutron radiography with D₂O labelling; hyperspectral imaging (400-1000); vegetation indices (NDVI, PRI, WBI, DWSI); thermal imaging; chlorophyll fluorescence; machine learning (SVM, PLSR, ANN); phytohormone profiling (UHPLC-HESI-HRMS); ROS and antioxidant enzyme assays (SOD, POD, CAT, APX, GPX); protoplast-based functional assays of ABA signalling variants; controlled environment and field experiments in Germany & Kenya.
Expected outcomes and relevance: SP2 will deliver a mechanistic understanding of how soil-plant hydraulic limitations, root-soil interactions, stomatal behaviour, oxidative stress balance, and ABA signalling are coordinated under multistress scenarios. By integrating high-resolution phenotyping, including hyperspectral imaging linked to physiological and biochemical reference data, with molecular functional analysis across six commercial hybrids and two contrasting environments, we will identify key traits and regulatory nodes underlying multiple stresses. Machine learning models will extract meaningful patterns from multidimensional spectral data, enabling stress-specific signature identification and supporting trait-based interpretation. These insights will directly inform the MultiStress modelling platform (SP6), provide physiological context for genomic and transcriptomic data (SP1, SP3), and guide the identification of targets for breeding more resilient maize varieties under climate change.
Research Team SP2
Prof. Ahmed, PI
Root-Soil TUM
Dr. Ejaz, PI
Agronomy
Dr. Yang, CoPI
Root-soil TUM
Prof. Otieno, CoPa
JOOUST
Dr. Bulli, CoPa
JOOUST
Dr. Nyongesa, CoPa
JOOUST
PhD
Root-Soil, TUM
Angura Louis
Agronomy
Christoph Heidersberger, TA
Root-Soil TUM
Michael Schmidt, TA
Root-Soil, TUM
Gabrielle Kolle, TA
Agronomy
Christiane Münter, TA
Agronomy
Schnellnavigation → MultiStress Forschungsverbund
Entdecken Sie das zentrale Projekt, das Koordinationsprojekt und 6 Teilprojekte
ZP – Zentrales Projekt
Experimente, Daten-Hub und Synthese von Erkenntnissen
SP1
Auswirkungen von Stress-Genotyp-Interaktionen auf die ober- und unterirdische Kohlenstoffallokation, die Nährstoffnutzungseffizienz und Prozesse in der Wurzeltiefe
SP2
Untersuchung der physiologischen, biochemischen und molekularen Reaktionen von Mais auf gleichzeitige biotische und abiotische Stressfaktoren
SP3
Molekulare Anpassung an kontrastierende Stressregime
SP4
Kombinierte Auswirkungen von Maiszünsler und abiotischen Stressfaktoren auf kommerzielle Maishybriden
SP5
Kombinierte Effekte von Setosphaeria turcica und abiotischen Stressfaktoren auf
Maissorten
SP6
Integration der Genetik in Pflanzenwachstumsmodelle zum Verständnis der Genotyp-Reaktion auf kombinierte (abiotische + biotische) Stressfaktoren und Synthese von Modellierungen
COP – Koordinationsprojekt
Strategie, Verbreitung und Kapazitätsaufbau
