Understanding the origin of evolutionary innovation using water strider propelling fan as a model

---

Porteur du projet

Mirko Francesconi

Personnes

Laurent Modolo, Mirko Francesconi, Arnaud Badiane

Problématique biologique

During evolution organismal traits become gradually more adapted thanks to natural selection. But how do completely new traits arise in the first place? Understanding the evolutionary and developmental origin of innovations is currently a major goal in biology.

Arnaud Badiane, a postdoc shared between The Khila, and Francesconi teams (hired in the context of the ANR project Geisha which also includes Laurent Modolo) is studying the developmental and evolutionary origin of the propelling fan of the water strider Rhagovelia (Figure1A) as a model system of evolutionary innovation. The fan is composed of ~20 plume-like structures in the mid-leg that can be deployed or retracted as the animal rows on the water (Santos et al., 2017; Figure 1B) and it is a striking new trait that allows the Rhagovelia water strider to move fast on the water surface and colonize fast flowing streams, a previously unexploited ecological niche. The fan therefore likely contributed to the burst of speciation in Rhagovelia lineage which with over 400 species (all with fans) contributes to almost half of the 900 species of the Veliidae family that includes 61 genera.

The aim of the project is to investigate the origin of the fan by comparing single cell gene expression of leg development in Rhagovelia (mid-leg with fan and hind leg without fan) and in its sister genus Tetraripis which have two pairs of bushy fans, one in the mid- (Figure 2C) and one in the hind-legs (Figure 2D) as well as in a fan-less sister species.

To this end Arnaud is collecting single cell transcriptomic data using 10X genomics technology and he will analyze this data to find fan specific cell types, states, and developmental trajectories and use machine learning algorithms to build putative gene regulatory networks responsible for fan development that will have to be experimentally validated.

Questions

For this project Arnaud would benefit from the expertise of Laurent Modolo in setting up pipelines for processing and analyzing single cell transcriptomic data as well in machine learning algorithms for network reconstruction.

Données

scRNA-Seq

Date

La date du début du projet: 30/01/2023
La date d’obtention 30/01/2023
La date d’obtention de l’intégralité des données NA
La date souhaitée de fin du projet : 24/12/2023

Attentes

Mentoring of Arnaud for the analysis of the scRNA-Seq data generated by the project.