Back

Antagonistic effects of actin-specific toxins on Salmonella Typhimurium invasion into mammalian cells

Heisler, D. B.; Kudryashova, E.; Hitt, R.; Williams, B.; Dziejman, M.; Gunn, J. S.; KUDRYASHOV, D. S.

2024-07-02 microbiology
10.1101/2024.07.01.601609 bioRxiv
Show abstract

Competition between bacterial species is a major factor shaping microbial communities. In this work, we explored the hypothesis that competition between bacterial pathogens can be mediated through antagonistic effects of bacterial effector proteins on host systems, particularly the actin cytoskeleton. Using Salmonella Typhimurium invasion into cells as a model, we demonstrate that invasion is inhibited if the host actin cytoskeleton is disturbed by any of the four tested actin-specific toxins: Vibrio cholerae MARTX actin crosslinking and Rho GTPase inactivation domains (ACD and RID, respectively), TccC3 from Photorhabdus luminescens, and Salmonellas own SpvB. We noticed that ACD, being an effective inhibitor of tandem G-actin binding assembly factors, is likely to inhibit the activity of another Vibrio effector, VopF. In reconstituted actin polymerization assays confirmed by live-cell microscopy, we confirmed that ACD potently halted the actin nucleation and pointed-end elongation activities of VopF, revealing competition between these two V. cholerae effectors. Together, the results suggest bacterial effectors from different species that target the same host machinery or proteins may represent an effective but largely overlooked mechanism of indirect bacterial competition in host-associated microbial communities. Whether the proposed inhibition mechanism involves the actin cytoskeleton or other host cell compartments, such inhibition deserves investigation and may contribute to a documented scarcity of human enteric co-infections by different pathogenic bacteria.

Matching journals

The top 5 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.