Microbiome Assisted Triticum Resilience In X-dimensions (The MATRIX)

MATRIX will address six scientific Work Packages (WP1-WP6). The experimental Tasks within each WP will contribute to answer the Research Questions

WP1: Field logistics, environmental and phenotype monitoring

This WP is responsible for the design of field experiments at UCPH and NCSU in order to investigate how the flag leaf microbiome of wheat is influenced by cropping factors, microclimate and wheat genotypes.

WP Lead: Svend Christensen (UCPH); Co-lead: Gina Brown-Guedira (NCSU)

• Task 1.1 Establishment of field study infrastructure and field microbiome gradients.
• Task 1.2 Optimize logistics for targeted sampling.
• Task 1.3 Optimize logistics for basal environmental measurements.
• Task 1.4 Design field intervention experiments.

WP2: Microbiomics & hub taxa

This WP is responsible for sequencing the wheat flag leaf phyllosphere metagenome and metatranscriptome from samples obtained from the different fields in the US and DK (see WP1). The sequencing data will together with the environmental metadata create the backbone of the initial data input into the MATRIX predictive model.

WP Lead: Jos Raaijmakers (NIOO-KNAW); Co-lead: Lars Hestbjerg Hansen (UCPH)

• Task 2.1 Optimize protocols and pipelines.
• Task 2.2 Sequence the wheat phyllosphere metagenome and metatranscriptome.
• Task 2.3 Isolate, cultivate and identify phyllosphere microbes.
• Task 2.4 Isolate bacteriophages with potential to regulate hub taxa and pathogens.
• Task 2.5 Investigate microbe-microbe interactions and screening isolates for chemical signals.
  
  

WP3: Chemistry

This WP is responsible for providing the chemical profiling/characterization of the wheat flag leaf phyllosphere microbiome and in particular of hub microbial taxa that affect crop resilience. The targeted and untargeted analysis (metabolomics) performed in this WP covers finding carbohydrates, amino acids, organic acids as well as volatile organic compounds (VOCs) associated with the wheat phyllosphere microbiome.

WP Lead: Jan H. Christensen (UCPH); Co-lead: Jos Raaijmakers (NIOO-KNAW)

• Task 3.1 Build a chemical database for targeted screening and suspect screening analysis of phyllosphere metabolites.
• Task 3.2 Establish a pipeline for collection, handling and long-term storage of VOCs, semi-VOCs and non-VOCs in the phyllosphere.
• Task 3.3 Targeted and untargeted metabolomics of organic compounds and elements in field and greenhouse samples.
• Task 3.4 Metabolite profiling of isolated strains from flag leaves.
• Task 3.5 Tentative and full identification of key metabolites.

WP4: Effect of plant-microbiome interactions on resilience

This WP is responsible for empirically testing, under controlled settings in greenhouse experiments, how the wheat phyllosphere microbiome and its members affect wheat growth, physiology, and yield under stress. Putative mechanisms underlying microbiome effects, as well as interactions within microbial consortia, will be identified by examining changes in gene expression and secondary metabolites. The WP serves as validation of the results obtained from field microbiomes (WP1-3), informatics analysis (WP5) and modelling (WP6).

WP Lead: Christine Hawkes (NCSU); Co-lead: Ross Sozzani (NCSU) 

• Task 4.1 Optimize methods and select putative drivers of foliar microbiome effects on plant hosts.
• Task 4.2 Identify minimal microbiomes needed to achieve resilient phenotypes.
• Task 4.3 Identify putative mechanisms for how addition of hub taxa affects wheat phenotype resilience.
• Task 4.4 Examine how consortia of hub taxa affect wheat resilience.
• Task 4.5 Targeted microbiota manipulations by bacteriophage applications.
• Task 4.6 Verify genetic mechanisms underlying microbiome effects on wheat resilience.
• Task 4.7 Translate to real-world agriculture.
  
  

WP5: Informatics and data integration

This WP is responsible for establishing a common computational platform for handling major computational tasks related to storing, sharing and analysing large-scale data generated from the other WPs, such as metabolomics, environmental and microbiome data.

WP Lead: Simon Rasmussen (UCPH); Co-lead: Lars Hestbjerg Hansen (UCPH)

• Task 5.1 Establish informatics infrastructure and accessible databases for chemical, environmental and microbial data, which can be used by all WPs.
• Task 5.2 Preparation of microbiomics, metabolomics and environmental data for unsupervised learning and input to the deep learning model.
• Task 5.3 Identify data manifolds for reconstruction of microbial genomes and metabolomic signatures.
• Task 5.4 Network analysis and deep learning approaches for identification of microbial consortia, genes, and metabolites important for plant resilience.
• Task 5.5 Determining the relationship between leaf, root, and seed microbiomes.
• Task 5.6 Continuous update, validation and dissemination of data and results to the plant-microbiome experimental interactions (WP4) and the deep learning model (WP6).

WP6: Building a deep-learning model

This WP is responsible for the integration of all data and results produced in WP1-WP5, which will be based on deep neural networks. These networks will, by using representation learning, serve to integrate the environmental, microbial, metabolomics and network data in a single deep learning model that can be used to predict yield and crop resilience of the wheat system.

WP Lead: Mads Nielsen (UCPH); Co-lead: Simon Rasmussen (UCPH)  

• Task 6.1 Determine the prerequisites for building an integrative deep learning model and identify existing mechanistic models of plant growth.
• Task 6.2 Transform microbiome, chemical, environmental and imaging data to appropriate formats for feeding into the deep learning models.
• Task 6.3 Identify resilience trajectories through representation learning of the diverse omics datasets.
• Task 6.4 Create supervised deep learning models for prediction of phenotype membership.
• Task 6.5 Develop time resolved deep learning models for prediction of resilience trajectories and for identifying possible interventions in a given field.
• Task 6.6 Validating, refine and optimize the models after field trial data acquisition and test model transferability.