Under supervision of Yiyun Li (yiyun.li@wur.nl) and Dr. Aalt-Jan van Dijk

Aim: Identifying candidate regulators of Root System Architecture during salt stress

As a major abiotic stress, high soil salinity severely affects plant growth and crop productivity globally. Plants are unable to move from their location, and therefore require various effective mechanisms to cope with salinity. In presence of salt, the root architecture is reshaped, which is often characterized by changes in lateral root (LR) development [1]. Though several interactions of genes involved in LR development have been identified under control condition [2], genes and pathways involved in root branching in response to salt remain unclear.

In this project, you will use bioinformatics approaches to analyze salt-related root transcriptome data to identify candidate genes contributes to the salt-induced root branching. You will implement comparative analysis among multiple in-house and publicly available salt-induced root transcriptomic datasets to highlight the similarities and differences in the tissue-specific mechanism of root branching in salt. With the identified candidate genes, you will study the regulatory interactions using gene regulatory network (GRN) inference [3] to map the salt-induced transcriptional regulatory networks that are involved in root branching.

Further reading:
[1] Van Zelm, E., Zhang, Y., & Testerink, C. (2020). Salt Tolerance Mechanisms of Plants. Annual Review of Plant Biology, 71, 403–433. https://doi.org/10.1146/annurev-arplant-050718-100005

[2] Lavenus, J., Goh, T., Guyomarc’H, S., Hill, K., Lucas, M., Voß, U., Kenobi, K., Wilson, M. H., Farcot, E., Hagen, G., Guilfoyle, T. J., Fukaki, H., Laplaze, L., & Bennett, M. J. (2015). Inference of the arabidopsis lateral root gene regulatory network suggests a bifurcation mechanism that defines primordia flanking and central zones. Plant Cell, 27(5), 1368–1388. https://doi.org/10.1105/tpc.114.132993

[3] Van den Broeck, L., Gordon, M., Inzé, D., Williams, C., & Sozzani, R. (2020). Gene Regulatory Network Inference: Connecting Plant Biology and Mathematical Modeling. Frontiers in Genetics, 11(May), 1–12. https://doi.org/10.3389/fgene.2020.00457

Techniques:

  • Data analysis in R or Python
  • RNA-seq analysis
  • Computational Regulatory Network inference

Requirements:

(Advanced) Bioinformatics (e.g. BIF-30806 Advanced Bioinformatics, SSB-30306 Molecular Systems Biology or relevant course) and basic knowledge of plant physiology