Crop genetic diversity uncovers metabolites, elements, and gene networks predicted to be associated with high plant biomass yields in maize

Author:

Hajheidari Mohsen1ORCID,Gerlach Nina1,Dorau Kristof2,Omidbakhshfard M Amin3,Pesch Lina1,Hofmann Jörg4,Hallab Asis5,Ponce-Soto Gabriel Y5,Kuhalskaya Anastasiya3ORCID,Medeiros David B3,Bourceret Amélia6,Usadel Björn57,Mayer Jochen8,Fernie Alisdair3ORCID,Mansfeldt Tim2,Sonnewald Uwe4ORCID,Bucher Marcel1ORCID,

Affiliation:

1. Institute for Plant Sciences, Cologne Biocenter, Cluster of Excellence on Plant Sciences, University of Cologne , D-50674 Cologne, Germany

2. Faculty of Mathematics and Natural Sciences, Department of Geosciences, Institute of Geography, University of Cologne , Albertus-Magnus-Platz, D-50923 Köln, Germany

3. Max Planck Institute of Molecular Plant Physiology, Department of Molecular Physiology , D-14476 Potsdam-Golm, Germany

4. Division of Biochemistry, Department of Biology, Friedrich-Alexander-University Erlangen-Nürnberg , D-91054 Erlangen, Germany

5. Bioinformatics (IBG-4), Forschungszentrum Jülich GmbH , D-52425 Jülich, Germany

6. Department of Plant Microbe Interactions, Max Planck Institute for Plant Breeding Research , D-50829 Cologne, Germany

7. HHU Düsseldorf, Institute of Biological Data Science, Cluster of Excellence on Plant Sciences , D-40225 Düsseldorf, Germany

8. Agroscope, Department of Agroecology and Environment , CH-8046 Zurich, Switzerland

Abstract

Abstract Rapid population growth and increasing demand for food, feed, and bioenergy in these times of unprecedented climate change require breeding for increased biomass production on the world's croplands. To accelerate breeding programs, knowledge of the relationship between biomass features and underlying gene networks is needed to guide future breeding efforts. To this end, large-scale multiomics datasets were created with genetically diverse maize lines, all grown in long-term organic and conventional cropping systems. Analysis of the datasets, integrated using regression modeling and network analysis revealed key metabolites, elements, gene transcripts, and gene networks, whose contents during vegetative growth substantially influence the build-up of plant biomass in the reproductive phase. We found that S and P content in the source leaf and P content in the root during the vegetative stage contributed the most to predicting plant performance at the reproductive stage. In agreement with the Gene Ontology enrichment analysis, the cis-motifs and identified transcription factors associated with upregulated genes under phosphate deficiency showed great diversity in the molecular response to phosphate deficiency in selected lines. Furthermore, our data demonstrate that genotype-dependent uptake, assimilation, and allocation of essential nutrient elements (especially C and N) during vegetative growth under phosphate starvation plays an important role in determining plant biomass by controlling root traits related to nutrient uptake. These integrative multiomics results revealed key factors underlying maize productivity and open new opportunities for efficient, rapid, and cost-effective plant breeding to increase biomass yield of the cereal crop maize under adverse environmental factors.

Funder

Bundesministerium für Bildung und Frauen

Publisher

Oxford University Press (OUP)

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