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Marie Morel

(INCEPTION PhD 2018 – 2022)

 

What was the aim of your PhD project?
 

During my thesis, I approached the question of the viruses' adaptation to antiviral treatments from two different angles. Firstly, I used large sequence datasets to identify cases of molecular convergence, i.e., identical mutations' repeated and independent emergence. Secondly, I studied genomic variations within viral populations subjected to antiviral treatment. In the first case, I developed a method for detecting evolutionary convergence in protein sequences, considering that convergent mutations indicated the selection pressures exerted on viral sequences. In the second case, I examined the evolution of intra-host viral diversity in the presence of antiviral treatment.

 

What motivated you to work on this topic?
 

These two parts of my thesis project required different skills. The first involved bioinformatics development and much programming work, while the second consisted of data analysis using existing tools. I enjoyed working on these different aspects of bioinformatics, which enabled me to acquire new skills during my thesis. This is one of the reasons why I chose this subject. I also loved learning more about viruses, particularly the human immunodeficiency virus (HIV) and the hepatitis C virus (HCV), which were the focus of my studies.

 

The main results
 

The HCV project was a partnership with Médecins Sans Frontières. We were asked to sequence and assemble the genomes of HCV genotype 6, which is widespread in Cambodia but still poorly characterised. Some patients infected with HCV and treated with antiviral drugs still had traces of the virus in their bodies after several weeks of treatment, which could indicate resistance to the antiviral therapy. The analyses entrusted to us by MSF, in collaboration with the Institut Pasteur du Cambodge, were designed to determine whether the resistance was present before treatment or had been acquired. We demonstrated that, in most cases, resistance was acquired in response to treatment, which means that prior sequencing of the virus before treatment was unnecessary. In addition, we identified several mutations potentially linked to resistance that had not yet been characterised, and these are now being tested to determine their potential role in resistance to antiviral treatments. In addition to these results, we have assembled around thirty complete genomes of HCV genotype 6, which will be available to the scientific community.

What is the impact of your result in socio-economic terms/real life?
 

The short-term impact of my method development project on molecular convergence may be less obvious to define in everyday life. However, this method, called ConDor, is now available as a website and a downloadable, easy-to-install pipeline. Evolutionary convergence is an essential topic in evolutionary biology, and this method can be applied to a wide range of organisms such as viruses, bacteria, mammals, plants, etc. We hope it will be helpful to the scientific community and can be used in other research projects. The article corresponding to this project is available as a preprint: https://www.biorxiv.org/content/10.1101/2021.06.30.450558v2

 

What was your next career step?
 

I am currently a postdoctoral fellow at the Laboratoire de Biométrie et Biologie Évolutive in Lyon. I'm studying the origins of flower development in Angiosperms by trying to reconstruct the evolutionary history of certain transcription factors involved in flower development. My future project is to become a senior evolutionary biology/bioinformatics lecturer.

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Vincent Mallet

(INCEPTION PhD 2019 – 2022)

 

What was the aim of your PhD project?
 

My PhD was aiming to develop learning algorithms for structural biological data. Such data is produced in large amounts experimentally and, more recently, computationally, it provides a mechanistic understanding of life. Algorithms for this data can be developed with applications in fundamental biology and drug design, such as detecting binding sites in proteins. Classical learning algorithms fail to capture the structural aspect of the data; hence dedicated learning tools must be developed and used.


The main results and their impact
 

I developed new algorithms to capture DNA sequence symmetry during my PhD research. I used a graph formalism on RNA to help drug design and find regular motifs, and I used a volumetric approach to investigate the druggability of protein-protein interactions. All of these results are building blocks to find new drugs.


What motivated you to work on this topic?
 

I chose this area of research because I like the final application of curing diseases as well as the one of deciphering the puzzles of life. Moreover, from a methodological point of view, this data holds challenges, and analyzing it requires the usual mathematical background of machine learning and notions from geometry.

 

What was your next career step?
 

I kept researching this field by taking on a PostDoc with Maks Ovsjanikov and then plan to keep on making an academic career. Further information about my research can be found here (PhD manuscript).

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