• Process metagenomics and metatranscriptomics dataset with existing workflow from JGI (Joint Genome Institute), and local GLiCID Cluster facilities (including Magneto, MiBiOmics pipelines…).

• Identify the most prevalent and highly expressed microbial functions associated with Arabidopsis thaliana plant genotype, and assess their correlation with exudate-induced parasitic plant germination.

• Determine how specific exuded metabolites influence gene expression and microbial community function across different soil conditions.

• Explore how changes in microbial gene expression are mirrored in the chemical landscape of the rhizosphere.

Vascular plants release a variety of metabolites into the rhizosphere, influencing the surrounding organisms, including other plants and microbes. Among these metabolites, Strigolactones and Glucosinolates (and their derivatives, such as Isothiocyanates) play significant roles. These plant secondary metabolites can induce the germination of parasitic plants like branched broomrape (Phelipanche ramosa) and alter molecular signaling with microorganisms. In return, the rhizosphere microbiome can influence the success of parasitic plants.
To investigate these interactions, we grew A. thaliana (a model plant from the Brassicaceae family) mutant lines with knockouts in either the Strigolactone or Glucosinolate pathways in raw and modified soils. We monitored the metabolome and exudates activity of these plants at three weeks post-sowing. Currently, in the framework of a sequencing project with the Joint Genome Institute (JGI), we are acquiring data through meta-genomics (metaG) and meta-transcriptomics (metaT) sequencing and metabolomics by targeted and untargeted approaches.

Related literature:
Auger, B. Mol. Plant-Microbe Interact. 25, 993–1004 (2012). Open access*.
Martinez, L. Plant and Soil 483 (1), 667-691 (2023). Open access*.
Wang et al, 2025; Journal of Agri. Food and Chem [up-to-date discovered microbial myrosinase]

The work will be carried out under the supervision and support of a collaborative team, including participants of the Plant2Mi project and collaborators from the Joint Genome Institute (JGI), from LS2N (MAGNETO pipeline maintainers) as well as researchers from IGEPP contributing expertise to RNA-seq analyses.
It will recquire analysis of metaG and metaT dataset such as :

֎ Quality Control:
• Perform quality control on over 40 Illumina and PacBio sequencing datasets.

֎ Assembly & pipeline optimization:
• Assemble metagenomic data using MEGAHIT, implemented in MAGNETO.
• Optimize existing pipelines for long-read PacBio assembly.

֎ Dashboard & visualization development:
• Develop an automated dashboard in R for visualizing microbial gene functions, gene families, and KEGG pathway summaries (with a focus on GH3 gene families, likely involved in microbial myrosinase activity).

֎ Transcriptomics & workflow automation:
• Construct a semi-automated MAGNETO-based MetaT analysis workflow, enabling transcript mapping to reference metagenomes with minimal code modifications.

֎ Comparative genomics & functional profiling:
• Conduct comparative analyses of contigs, functional profiles, and MAGs between in-house and JGI-processed data.

֎ Incorporate related metabolomic data:
• Available full scans of corresponding metabolomic samples will be processed by the PhD student and can be integrated into the analysis if time allows.

• Ongoing M.Sc. in Bioinformatics/ Plant biology with a training in Bioinformatics.

• Background in, microbial ecology, or chemical ecology is advantageous.

• Very familiar with Bash, Python, Git and R.

• Strong interest in academic research.

Please send your complete CV, and cover letter to lucie.poulin@univ-nantes.fr and ahmed.choukri@univ-nantes.fr