Title: Interactions between the human and dengue virus genomes

Supervisor: Vincent Pedergnana, CRCN, vincent.pedergnana@cnrs.fr
Co-supervisor: Stéphane Guindon, CRHC, stephane.guindon@lirmm.fr

Host Units: MIVEGEC, UMR5290 & LIRMM, UMR 5506
Institution: University of Montpellier

Subject description:
Our main objective is to identify new genes in the host and virus genomes that explain the pathogenicity of the dengue virus. We will also aim at identifying new targets to potentially develop a new therapy and a vaccine against dengue. Indeed, the targets identified over the last 15 years have not been able to control the epidemic. Drug development needs the support of genomics to identify promising new targets, as these have been shown to be more likely to lead to effective therapeutics. However, to date, only two human targets have been identified for dengue.

We propose to apply the new genetic analysis techniques that Dr. Pedergnana and colleagues have recently developed to identify more targets and more important interaction points between the viral and human genomes at the same time. This approach represents a significant improvement over current solutions by integrating the genomes of both interacting organisms in the same analysis.
In addition, we will explore, through phylogenetic reconstruction, inter- and intra-host viral diversity during acute infection, which will allow us to better understand the revealed genomic interactions.

PhD student role:
Dr. Pedergnana and Dr. Guindon will co-supervise the PhD student. Their laboratories are both located in Montpellier, which will be an asset for this double supervision, allowing frequent interactions with both supervisors for the PhD student as well as providing a stimulating and complementary environment and scientific communities. Thus, the doctoral student will share his/her time between the two sites, depending on the specific objectives of his/her thesis.

The PhD student will have a strong background in mathematics, population genomics and statistical genetics, and be able to code and work on a cluster. He/she will be primarily responsible for the genetic analysis and the development of statistical methods under the supervision of Drs Pedergnana and Guindon. He/she will first reconstruct viral genomes from the viral sequences obtained by full genome sequencing. He/she will then construct consensus genomes and call the viral variants. Based on these data, she/he will reconstruct viral phylogenies and estimate intra- and inter-host viral diversity. Dr. Guindon will supervise this first part. She/he will then cut the human genetic data and deduce the missing genotypes and HLA alleles. Finally, he/she will be able to integrate the human clinical and genomic data and perform a genome-to-genome analysis under the supervision of Dr. Pedergnana.
The PhD student will be fully involved in the development of statistical methods. He/she will also participate in national or international conferences to present the results of this project. Finally, he/she will be the first author of any manuscript resulting from this work. He/she will be able to attend any courses that may help in the development of his/her career.

Expected Profile:
This position is ideal for someone with a combination of interest in theory, real world data, and public health. Curiosity and the ability to learn are more important than specific training. Essential requirements include.

• Master's degree in statistical genetics, genetic epidemiology, evolutionary genetics or statistics and strong analytical and quantitative skills.
• Proficiency in at least one programming language (e.g., R, Python).
• Excellent written and oral communication skills in English.

Publications directly associated with this thesis:

Sigera PC, Amarasekara R, Rodrigo C, Rajapakse S, Weeratunga P, De Silva NL, et al. Risk prediction for severe disease and better diagnostic accuracy in early dengue infection; the Colombo dengue study. BMC infectious diseases. 2019;19(1):680.
Ansari, […], Pedergnana V; Interferon lambda 4 impacts the genetic diversity of hepatitis C virus; eLife 2019
Ansari MA*, Pedergnana V* et al.; Genome-to-genome analysis reveals the impact of the human innate and adaptive immune systems on the hepatitis C virus ; Nature Genetics, 2017
Accounting for spatial sampling patterns in Bayesian phylogeography. S Guindon, N De Maio. Proceedings of the National Academy of Sciences 118 (52). 2021.

Accounting for calibration uncertainty: Bayesian molecular dating as a “doubly intractable” problem. S Guindon. Systematic Biology. 67 (4). 2018.

New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. S Guindon et al.. Systematic Biology 59 (3). 2010.