Project 5

Metabolic exchanges, regulation and equilibrium of an ancient insect symbiosis

Supervisors:
Dr. Mariana Galvão Ferrarini (main supervisor)
Department of Insect Symbiosis, Max Planck Institute for Chemical Ecology, Jena
Prof. Dr. Martin Kaltenpoth (co supervisor)
Department of Insect Symbiosis, Max Planck Institute for Chemical Ecology, Jena
Dr. Tobias Engl (co supervisor)
Department of Insect Symbiosis, Max Planck Institute for Chemical Ecology, Jena

Background:
The long-standing symbiosis between the grain beetle Oryzaephilus surinamensis and its bacterial endosymbiont, Shikimatogenerans silvanidophilus is a notable example of nutritional symbiosis, where the bacterium provides metabolites that are vital for the insect's fitness. Like other insect pests, the host's diet primarily consists of stored grains, which are rich in sugars but deficient in amino acids and vitamins. Previous work had already established that the endosymbiont bears a complete shikimate pathway, enabling it to produce tyrosine precursors for the insect, an important amino acid for cuticle biosynthesis, sclerotization, and melanization. Even though the endosymbionts are enclosed and protected within an organ called the bacteriome, our group has recently shown that the virulent bacterium Sodalis praecaptivus is able to replace the native endosymbiont.

Project description:
Through metabolic modeling, integration of omics data and targeted manipulation, this project aims at studying the molecular bases of metabolic exchanges and regulatory mechanisms between host and bacteria. The goal is to derive insights on how the beetle and its nutritional endosymbiont are metabolically intertwined, and how the virulent Sodalis symbiont disrupts this interaction, leading to the replacement of the native endosymbiont. The project will be part of an international collaborative team of scientists from the Department of Insect Symbiosis, focused on unravelling insect-microbe symbioses at the molecular level to the ecological and evolutionary implications.

Objectives/Methodology

1. Identify genome-wide elements involved in host-bacterial interactions through single-cell RNAseq, small RNAseq, and dual-/triple-RNAseq.
2. Investigate the metabolism of each species by constructing and refining genome-scale metabolic networks.
3. Integrate these models with regulatory elements by building genome-wide regulatory networks.
4. Combine these networks into a multi-organism model to pinpoint key interaction elements between the species.
5. Functionally characterize promising candidates.

Candidate profile:
Although this project aims to perform extensive bioinformatics analysis, formal training or a degree in bioinformatics are not required. We will provide training given that the candidate is willing to perform the computational analyses necessary for the development of the project. Ideally, a good candidate will have:

• critical thinking
• a keen interest in molecular biology, evolution, and computational biology of insect-microbe interactions
• strong background in any of the following fields: entomology, microbiology, evolutionary biology, molecular biology, and/or bioinformatics
• patience and precision for handling minute structures and performing state-of-the-art techniques
• attention to detail, particularly with command line tools
• a collaborative spirit, curiosity, creativity
• time management and organizational skills
• proficiency in written and spoken English and good communication skills

Reading:

1. Nielsen J. Systems Biology of Metabolism. Annu Rev Biochem., 20, 245-275 (2017).
2. Ankrah N. et al. Cooperative Metabolism in a Three-Partner Insect-Bacterial Symbiosis Revealed by Metabolic Modeling. J Bacteriol, 199:10.1128/jb.00872-16 (2017).
3. Engl, T. et al. Ancient symbiosis confers desiccation resistance to stored grain pest beetles. Mol. Ecol. 27, 2095–2108 (2018).
4. Kiefer J. et al. Inhibition of a nutritional endosymbiont by glyphosate abolishes mutualistic benefit on cuticle synthesis in Oryzaephilus surinamensis. Commun Biol., 11; 4(1):554 (2021).
5. Chen Y. et al. Single-cell omics analysis with genome-scale metabolic modeling. Curr Opin Biotechnol., 86:103078 (2024).