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Did the neurons evolve twice?

Primary Supervisor: Dr Roberto Feuda

Secondary supervisor: Ezio Rosato and Alexander Gorban

PhD project title: Did the neurons evolve twice?

University of Registration: University of Leicester

Project outline:

Background

Neurons are fundamental for everyday processes in animals, such as seeing, reading and responding to sensory stimuli and required the sophisticated integration of different functional modules to work1. However, it remains entirely unclear whether neurons evolved once or multiple times 2,3. It has been recently proposed, based on phylogenetic and molecular evidence, that comb jellies might have evolved their neurons independently (see 3,4)

The recent development of single-cell RNA-sequencing, combined with the observation that related cell-types (e.g. photoreceptors cells) are characterized by the shared expression of orthologous genes5, will enable to test whether the neurons evolved once or multiple time. That is, if ctenophores evolved their neurons independently, they should express a different combination of regulatory genes. Alternatively, a common development toolkit will indicate a common origin of the neurons.

In this respect, current single-cell-RNA-seq data for ctenophores reached a contradictory conclusion, suggesting that while cells expressing typical neuronal markers can be identified they have a dramatically different molecular composition6. This finding indicates that to clarify whether neurons evolved independently in ctenophores, additional data (i.e. single-cell data for different ctenophores species), better cell-type classification methods and finally a phylogenetic analysis of each genes expressed in the neurons is required.

Objectives

The objective of this proposal is to understand how many times the neurons evolved in animals by integrating cutting-edge molecular biology, machine learning and phylogenetic methods.

Methodology

This PhD will use state-of-the-art techniques in molecular biology (i.e. single-cell RNA sequencing) and computational methods (e.g. machine learning and phylogenetic methods) to test whether neurons evolved once or multiple times. Specifically, the PhD student will:

  1. Generate single-cell RNA-seq data for Pleurobrachia pileus.
  2. Use machine learning to classify cell-types and validate them using in-situ hybridization methods.
  3. Reconstruct the evolutionary origin of neuronal modules in animals using phylogenetic methods.

This project will equip you with a unique combination of expertise in experimental and computational biology and data analysis that can be applied to a large, diverse set of problems.

You will be part of the Feuda’s research group at the University of Leicester. The group currently includes 2 master students, 3 PhD students and 2 researchers with different background and expertise, from molecular biology to paleontology and computer science. The lab is part of the neurogenetic research group https://neurogeneticsleicester.com/the-lab/.

This position offers ample opportunity for training and collaboration in the U.K. and Europe.

References:

  1. Arendt, D. The Evolutionary Assembly of Neuronal Machinery. Curr. Biol. 30, R603–R616 (2020).
  2. Moroz, L. L. On the independent origins of complex brains and neurons. Brain Behav Evol 74, 177–90 (2009).
  3. Moroz, L. L. et al. The ctenophore genome and the evolutionary origins of neural systems. Nature (2014) doi:10.1038/nature13400.
  4. Feuda, R. et al. Improved Modeling of Compositional Heterogeneity Supports Sponges as Sister to All Other Animals. Curr. Biol. 27, 3864-3870.e4 (2017).
  5. Arendt, D. et al. The origin and evolution of cell types. Nat. Rev. Genet. 17, 744–757 (2016).
  6. Sebé-Pedrós, A. et al. Early metazoan cell type diversity and the evolution of multicellular gene regulation. Nat. Ecol. Evol. 2, 1176–1188 (2018).

BBSRC Strategic Research Priority: Understanding the Rules of Life: Neuroscience and behaviour & Systems Biology

    Techniques that will be undertaken during the project:

    • Single cell RNA sequencing
    • Phylogenomics
    • In-situ hybridization
    • Machine Learning

    Contact: Dr Roberto Feuda, University of Leicester