PhD position in phytoplankton metabolism
Phytoplankton carbon fixation in the surface ocean is a sink of atmospheric CO2, and is affected by seawater chemistry via its effect on phytoplankton composition (Falkowski et al. 2004). Studies have shown substantial differences in the types of algae dominating phytoplankton assemblages through time, with green algae being dominant in the Paleozoic and diatoms in present-day conditions. The causes of these changes may include interspecific differences in fitness as a function of seawater chemistry (Giordano et al., 2018), but many questions remain about the nature of phytoplankton metabolic responses to seawater chemistry at the cellular level. The project aims to evaluate how phytoplankton physiology is affected by alterations of seawater conditions using a combination of computational metabolic modelling and experimental work. A series of simple but elegant experiments mimicking different paleo-oceanic conditions will be carried out, with a specific focus on CO2 concentrations at different times in the last 500 million years, to evaluate their physiological and metabolic effects. The project will rely on complementary techniques and methods from metabolic modelling (Kim et al., 2017) along with transcriptional and metabolic profiling of cultures grown in different conditions. We will study representatives of different phytoplankton groups (cyanobacteria, diatoms, green algae, haptophytes, dinoflagellates) exposed to different seawater regimes. Once an understanding of metabolic responses of individual species has been gained, it becomes possible to study how different organisms act as part of a community and contribute to explaining the relative dominance of different phytoplankton groups in the ocean at different times.
- What are the genes and metabolites that show the largest changes between different sea water chemistry regimes? Are there cellular processes and molecular functions enriched in differentially expressed genes and metabolites common to all species or exclusive to a species?
- What are the differences in the physiology and metabolic responses of representatives of the main phytoplankton groups to different sea water chemistry?
- Can computational models be developed to mimic phytoplankton growth on different seawater chemistry?
- Do the predictions of the computational models match the observations from the omics profiling? What physiological constraints should be imposed to match the observations from omics data?
Changes in metabolism as a result of altered seawater chemistry alter the excretion and uptake of particular molecules, which in turn affect phytoplankton metabolism and composition.
Scope of work
This project will be based at University of Potsdam / Max Planck Institute of Molecular Plant Physiology with a minimum of 12 month stay at The University of Melbourne after the first year.
You will carry out an extensive literature review on algal nutrient stoichiometry and the influence of ocean chemistry on plankton physiology. Based on this knowledge, you will design a project that spans computational and experimental approaches. From the experimental perspective, you will design phytoplankton culturing experiments to measure growth of different species in media representing relevant present-day and paleo-ocean conditions, differing in critical nutrients like CO2, sulphate and trace elements. In addition to growth, you will prepare cultures for transcriptomics and metabolomics profiling to determine responses to these conditions at the cell physiological level. You will run experiments on individual species and on mixtures of species to evaluate competition by monitoring community dynamics, again at the cell count and physiological levels. The computational component of the project consists of metabolic modelling in two phases: (1) Metabolic networks of the cyanobacterium Synechocystis spp. (Knoop et al., 2013) and the diatom Phaeodactylum tricornutum (Smith et al., 2019) as well as several green algae are available and can be used to make predictions about changes in physiology due to altering inputs from the environment. (2) Metabolic networks will be created for the remaining species of interest to facilitate community modelling following well-established protocols.
You will have an interdisciplinary team of supervisors to work with: Heroen Verbruggen (Melbourne, algal biology), Zoran Nikoloski (Potsdam, metabolic modeling), Ute Roessner (Melbourne, metabolomics) and Harry McLelland (Melbourne, biogeochemistry).
Instructions to applicants:
The application deadline is 30 January. Please see this website for more information on how to apply.
- Falkowski et al. (2004) The evolution of modern eukaryotic phytoplankton. Science 305:354-360. https://dx.doi.org/10.1126/science.1095964
- Giordano et al. (2018) A tale of two eras: Phytoplankton composition influenced by oceanic paleochemistry. Geobiology 16:498-506. https://doi.org/10.1111/gbi.12290
- Kim et al. (2017) Current state and applications of microbial genome-scale metabolic models. Current Opinion in Systems Biology 2:10-18. https://doi.org/10.1016/j.coisb.2017.03.001
- Knoop et al. (2013) Flux Balance Analysis of Cyanobacterial Metabolism: The Metabolic Network of Synechocystis sp. PCC 6803. PLoS Comp. Biol. 9: e1003081. https://doi.org/10.1371/journal.pcbi.1003081
- Smith et al. (2019) Evolution and regulation of nitrogen flux through compartmentalized metabolic networks in a marine diatom. Nature Comm.10: 4552. https://doi.org/10.1038/s41467-019-12407-y