Mots-Clés

bioinformatics omics integration obesity neurosciences

Description

Internship M2: Integrative Multi-Omics Analysis of Sex-Specific Microglial Mechanisms in Response to Obesity

Obesity affects 2.1 billion people globally and is the 5th leading cause of death. While it is a widespread epidemic, men are more prone to obesity-related metabolic issues like diabetes, heart attacks, and strokes, whereas women are generally more protected. However, there is limited knowledge on the sex-specific brain response to an obesogenic diet, crucial for developing personalized treatments.

In murine models, we (Nadjar lab) and others have shown that male brains, particularly the hypothalamus, exhibit strong microglial inflammation, which drives weight gain. This response is minimal in females, suggesting that sex differences in microglial activity may influence obesity complications. Our study aims to explore the molecular mechanisms behind these differences using advanced omics techniques. We used a combination of transcriptomics (bulk RNAseq), metabolomics, lipidomics and proteomics analyses, on microglial cells sorted from the hypothalamus of mice to an obesogenic diet.

The primary objective of this internship is to integrate multi-omics data to investigate the mechanisms driving sex-dependent microglial responses to a high-calorie diet. This will involve utilizing advanced bioinformatics methodologies to integrate these datasets to uncover comprehensive biological insights.
During the internship, the student must develop (in Python or R) a methodology to join the mentioned omics data types onto a meaningful, combined representation, using the knowledge from metabolic pathways databases. The intern will begin investigating metabolic networks by applying pathway scoring methods (such as GSVA [Hänzelmann et al., 2013]) to deepen the understanding of complex mechanisms underlying microglial response differences. Finally, advanced bioinformatics techniques for multi-omics integration will be employed, using statistical methods such as linear modeling and regression with tools like PathIntegrate [Wieder et al., 2024] and DIABLO [Singh et al., 2019], to identify key biomarkers relevant to our study. This work should help to identify functionally related groups of markers that a) confer a resilient phenotype to females, and b) are associated with inflammatory changes and a pathological outcome for males. 

Eventually, the intern will explore standard analytical workflows for individual omics data processing (standard preprocessing and differential expression analysis for transcriptomics data (using e.g. DESeq2 [Love et al., 2014]), as well as specialized metabolomics analyses, leveraging the Bordeaux bioinformatics lab's (Nikolski lab) expertise with the DIMet tool [Galvis et al., 2024]), but this individual omics analysis is not the main focus of the internship.

We are looking for an intern who is interested to work in an interdisciplinary context anchored in the collaboration between the 2 teams (Nadjar lab and Nikolski lab). The candidate must be proficient in Bash, Python and R . Code versioning (Git) knowledge is a plus. Biostatistics knowledge would be a plus. Moreover, basic knowledge of relational databases, as well as general molecular biology and metabolism is required.

Contacts

Agnès Nadjar agnes.nadjar@u-bordeaux.fr
Macha Nikolski macha.nikolski@u-bordeaux.fr
Gauthier Delrot gauthier.delrot@u-bordeaux.fr
Johanna Galvis deisy-johanna.galvis-rodriguez@u-bordeaux.fr

References

Love MI, Huber W, Anders S (2014). “Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.” Genome Biology, 15, 550. doi:10.1186/s13059-014-0550-8.

Galvis J, Guyon J, Dartigues B, Hecht H, Grüning B, Specque F, Soueidan H, Karkar S, Daubon T and Nikolski M. (2024). DIMet: an open-source tool for differential analysis of targeted isotope-labeled metabolomics data.Bioinformatics, 40(5), btae282. https://doi.org/10.1093/bioinformatics/btae282

Hänzelmann S, Castelo R,  Guinney, J. (2013). GSVA: gene set variation analysis for microarray and RNA-seq data. BMC bioinformatics, 14, 1-15.

Wieder C, Cooke J, Frainay C, Poupin N, Bowler R, Jourdan F, et al (2024). PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data integration. PLOS Computational Biology, 20(3), e1011814.

Singh A, Shannon CP, Gautier B, et al. DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays. Bioinformatics. 2019;35(17):3055-3062. doi:10.1093/bioinformatics/bty1054