Mots-Clés

Computational biology / OMICS / Machine learnings

Description

OVERVIEW Our laboratory examines the role of platelets and their mother cell, the megakaryocyte, in systemic lupus erythematosus (SLE). Megakaryocytes are giant cells, capable of replicating their DNA content 128 times without cellular division, which afford these cells an extraordinary RNA content.

PRELIMINARY OBSERVATIONS The accumulation of autoantibodies and autoantigens in blood, the formation of immune complexes (ICs) and their deposition in organs play a central role in this pathogenesis. Up-regulation of the type I interferon (IFN), leading to expression of IFN-regulated genes, also characterizes SLE. We demonstrated that dependently of the expression of FcγRIIA, a receptor for ICs, platelets displayed an altered transcriptome presenting IFN-regulated genes and dysregulated mitochondrial pathways in SLE. As platelets are anucleate, these data indicate that megakaryocytes, the cells that produce platelets and afford them their RNA content, are altered in SLE. We will integrate these intriguing findings by examining megakaryocytes in SLE with emphasis on megakaryocyte spatial transcriptomics and bioenergetics.

HYPOTHESIS: An SLE-prone environment reprograms megakaryocytes into a pro-inflammatory phenotype that plays a significant role in driving SLE pathology.

APPROACH In this project, the candidate will evaluate megakaryocytes in healthy conditions and SLE with special attention to mitochondrial bioenergetics. The candidate will evaluate whether certain subpopulations of megakaryocytes are preferentially affected in SLE using spatial omics. Studies will implicate tissues and cells isolated from mouse models of SLE that we have developed. Then, the candidate will perform various comparisons between spatial locations, including differential analysis and machine learning multivariate analysis. Other analyses will include pathway analysis to identify dysregulated mitochondrial pathways, spatial organization of gene expression within tissues, integration of transcriptomics data with mitochondrial bioenergetics data to examine the relationship between gene expression and cellular metabolism, and data visualization tools to aid in the interpretation and presentation of results.

OUTCOMES As megakaryocytes produce billions of platelets each day, we may reveal how to target the pathogenic megakaryocyte progeny, platelets, directly at their source to cure SLE.

Required skills

• Master degree in one of these domains: biology, computer science, bioinformatics, mathematics
• Knowledge in molecular biology
• Proficiency in programming (Python, R)
• Knowledge of statistics and machine learning techniques
• Experience in working with large datasets
• Familiarity with common bioinformatics tools and databases (BLAST, NCBI, Enrichment analysis, etc.)
• Ability to develop and implement bioinformatics pipelines and workflows
• Excellent communication and collaboration skills
• Attention to detail and ability to work independently
• Experience with high-performance computing (HPC) environments and cloud computing platforms.