Host-pathogen interactions in the Plasmodium-infected mouse liver at spatial and single-cell resolution.
Franziska HildebrandtMiren Urrutia IturritzaChristian ZwickerBavo VannesteNoémi K M van HulElisa SemleJaclyn QuinTales PasciniSami SaarenpääMengxiao HeEmma Rachel AnderssonCharlotte L ScottJoel Vega-RodríguezJoakim LundebergJohan AnkarklevPublished in: Nature communications (2024)
Upon infecting its vertebrate host, the malaria parasite initially invades the liver where it undergoes massive replication, whilst remaining clinically silent. The coordination of host responses across the complex liver tissue during malaria infection remains unexplored. Here, we perform spatial transcriptomics in combination with single-nuclei RNA sequencing over multiple time points to delineate host-pathogen interactions across Plasmodium berghei-infected liver tissues. Our data reveals significant changes in spatial gene expression in the malaria-infected tissues. These include changes related to lipid metabolism in the proximity to sites of Plasmodium infection, distinct inflammation programs between lobular zones, and regions with enrichment of different inflammatory cells, which we term 'inflammatory hotspots'. We also observe significant upregulation of genes involved in inflammation in the control liver tissues of mice injected with mosquito salivary gland components. However, this response is considerably delayed compared to that observed in P. berghei-infected mice. Our study establishes a benchmark for investigating transcriptome changes during host-parasite interactions in tissues, it provides informative insights regarding in vivo study design linked to infection and offers a useful tool for the discovery and validation of de novo intervention strategies aimed at malaria liver stage infection.
Keyphrases
- plasmodium falciparum
- gene expression
- single cell
- oxidative stress
- rna seq
- randomized controlled trial
- high throughput
- public health
- small molecule
- dna methylation
- induced apoptosis
- type diabetes
- candida albicans
- metabolic syndrome
- genome wide
- big data
- deep learning
- electronic health record
- cell cycle arrest
- artificial intelligence