Back

Phagocytosis repurposed: infection strategies in a globalmarine diatom-parasite interaction

Mathur, V.; Irwin, N.; Galindo, L. J.; Alacid, E.; Cunliffe, M.; Kim, S.; Technau, U.; Richards, T.

2025-07-30 evolutionary biology
10.1101/2025.07.29.667418 bioRxiv
Show abstract

Microbial host-parasite interactions shape global biogeochemical cycles, yet their cellular and evolutionary mechanisms remain poorly understood. Here, we developed a novel model pathosystem to explore the interaction between the globally distributed eukaryotic microparasite, Pirsonia, and its host, the bloom-forming diatom, Coscinodiscus. Culture conditions recapitulated Pirsonias rapid life cycle in vitro and live-cell imaging enabled quantitative assessments of infection dynamics and host mortality. To investigate the genetic basis of this interaction, we assembled the Pirsonia genome and identified unique genetic innovations relative to their oomycete relatives. Time-resolved dual RNA-Seq enabled tracking of parasite gene expression through the infection cycle and revealed an upregulation of an expanded multi-gene family of integrin-like proteins in Pirsonia zoospores, analogous to the variable surface proteins in other parasitic lineages. Genes upregulated during infection were associated with cytoskeletal dynamics and drug inhibition assays confirmed that actin is required for host infection, consistent with the presence of parasitic pseudopodia on infected hosts. The dependence on both actin-based phagocytic mechanisms and parasitic-like surface proteins highlights Pirsonias intermediate state between predator and parasite, providing new insights into the evolution of parasite strategies and the complex cellular interactions controlling parasitic shunts in marine trophic networks.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.