Plant Phenotypic Rhizobiome Abiotic Stress Responses to Variations in Root Exudates


Alfano*, Walia*, Clemente, Drijber, Guda, Herr, Schnable, Van Dijk


  • Dr. Alfano and others in field
  • Innovation Campus greenhouse chamber
  • Research in the field
  • Potted plants in NIC Greenhouse
  • Microbiome Sampling
Aim 4 will determine the impact of plant root exudate composition on both plant phenotypes and rhizobiomes under various growth conditions. Variation in root-associated microbiota has been shown to influence plant health and agronomic performance. Some of these effects may arise through microbe-mediated improvement of plant tolerance of stresses such as water and nitrogen (N) deficiencies. These intriguing observations suggest that plant-microbe interactions affect plant phenotypes. The community structure of the maize rhizobiome has a genetic component, as it shows significant heritability, but is also strongly influenced by soil type. It is likely that plant genotypes can regulate the rhizobiome through the composition of root exudates, however current methods cannot directly measure root exudates under field conditions in real time, a gap we seek to address. Results from this aim will elucidate how rhizobiomes change over time and in response to low soil nitrogen and water stress conditions. These results will also provide critical data to explore the idea that wild maize relatives express root traits to recruit superior rhizobiomes.


  1. Quantify changes in phenotypes of plants under low soil nitrogen and water stress
  2. Effects of variations in root exudates on root microbiota
  3. Identifying changes in phenotype and rhizobiomes of plants with variant exudates under field conditions

Associated Publications

Chenyong Miao, Jinliang Yang and James C. Schnable. 2018. Optimizing the identification of causal variants across varying genetic architectures in crops. bioRxiv

Herr J. R., Scully E. D., Geib S. M., Hoover K., Geiser D. M., Tien M., Carlson J. E.. 2016. Genome sequence of a Fusarium species (MYA-4552) isolated from the midgut of Anoplophora glabripennis, an invasive, wood-boring beetle. Genome Announcements 4: e00544-16
DOI: 10.1128/genomeA.00544-16

Hibbett D. S., Abarenkov K., Koljalg U., Opik M., Chai B., Cole J. R., Wang Q., Crous P. W., Robert V. A. R. G., Helgason T., Herr J. R., Kirk P., Lueschow S., O’Donnell K., Nilsson H., Oono R., Schoch C. L., Smyth C., Walker D., Porras-Alfaro A., Taylor J. W., Geiser D. M.. 2016. Sequence-based classification and identificationof Fungi. Mycologia 108: 1049-1068
DOI: 10.3852/16-130

Li Y, Heavican TB, Vellichirammal NN, Iqbal J, Guda C. . 2017. ChimeRScope: a novel alignment-free algorithm for fusion gene prediction using paired-end RNA-Seq data. Nucleic Acids Res.45:e120.

Liang Z., Schnable J.C.. 2018. Functional Divergence between Subgenomes and Gene Pairs after Whole Genome Duplications. Molecular Plant, Volume 11, Issue 3, 5 March 2018, Pages 388-397

Liang Z, Pandey P, Stoerger V, Xu Y, Qiu Y, Ge Y, Schnable JC.. 2017. Conventional and hyperspectral time-series imaging of maize lines widely used in field trials. GigaScience

Malachy T Campbell, Harkamal Walia, Gota Morota. 2018. Utilizing random regression models for genomic prediction of a longitudinal trait derived from high-throughput phenotyping. bioRxiv

Scully, E.D., T. Gries, N.A. Palmer, G. Sarath, D.L. Funnell-Harris, L. Baird, P. Twigg, J. Seravelli, T.E. Clemente, & S. Sattler. . 2018. Overexpression of SbMyb60 in Sorghum bicolor impacts both primary and secondary metabolism. New Phytol. 217: 82-104.

Zhang Y, Ngu DW, Carvalho D, Liang Z, Qiu Y, Roston RL, Schnable JC. 2017. Differentially regulated orthologs in sorghum and the subgenomes of maize. The Plant Cell