A delivered DNase toxin creates population heterogeneity through transient intoxication of siblings.
Eriksson, H.; Schlegel, S.; Kjellin, J.; Koskiniemi, S.
Show abstract
Population heterogeneity is important for multicellular behavior and division of labor. Bacterial toxin delivery has been implicated in generating population heterogeneity, but the molecular mechanisms behind this are not well understood. Here we investigate how CdiA toxins generate heterogeneity in isogenic populations. Using a DNase toxin as proxy, we find that E. coli populations able to deliver the toxin show a heterogeneous expression of the SOS-response gene sulA. Heterogeneity results from excessive delivery of toxin into some cells, which become intoxicated due to insufficient immunity. Intoxication is transiently reversible, and intoxicated cells can be rescued by de novo synthesis of cognate immunity protein. Expression of sulA is regulated by both DNA damage and redox status. Interestingly, kin-delivery changes redox status, whereas intoxicated non-kin cells induce the SOS DNA damage response. The former results in changed expression of metabolic genes whereas the latter induces prophage excision, which may promote horizontal gene transfer. In conclusion, we identify a molecular mechanism by which heterogeneity is generated through toxin delivery among kin, and the consequences of said heterogeneity. Significance statementBacteria communicate through secretion of chemical signaling molecules to perform multicellular behavior. Recent advances suggest that contact-mediated toxin delivery allow bacteria to participate also in direct cell-cell communication. How such toxin-mediated communication would work mechanistically is however unclear. Here we elucidate a molecular mechanism of a toxin-mediated communication, where kin-cells transiently intoxicate each other, resulting in physiological changes. These changes depend on the toxic activity, i.e. other toxins with different activities are likely to give rise to other responses. Thus, the arsenal of toxins that a bacterium harbors could affect their ability to communicate. Understanding the molecular mechanism of how toxins could mediate polyphenism is important for our understanding of what this signaling is used for.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Loss of Bacterial Cell Pole Stabilization in Caulobacter crescentus Sensitizes to Outer Membrane Stress and Peptidoglycan-Directed Antibiotics 95%
- A Second Role for the Second Messenger Cyclic-di-GMP in E. coli: Arresting Cell Growth by Altering Metabolic Flow 95%
- Modulation of bacterial cell size and growth rate via activation of a cell envelope stress response 94%
Similar papers in this journal
- Prevalence and mechanisms of high-level carbapenem antibiotic tolerance in clinical isolates of Klebsiella pneumoniae 95%
- Klebsiella pneumoniae type VI secretion system-mediated microbial competition is PhoPQ controlled and reactive oxygen species dependent 94%
- SPI-1 virulence gene expression modulates motility of Salmonella Typhimurium in a proton motive force- and adhesins-dependent manner 94%
Similar papers in this journal
- The quorum sensing transcription factor AphA directly regulates natural competence in Vibrio cholerae 94%
- Activation of ChvG-ChvI regulon by cell wall stress confers resistance to β-lactam antibiotics and initiates surface spreading in Agrobacterium tumefaciens 94%
- Cell splitting in Staphylococcus aureus is controlled by an adaptor protein facilitating degradation of a peptidoglycan hydrolase 94%
Similar papers in this journal
- Panacea: a hyperpromiscuous antitoxin protein domain for the neutralisation of diverse toxin domains 94%
- Fluoride triggers lysis in Streptococcus mutans by inhibition of Clp protease complex leading to an unabated competence cascade 93%
- Pulsatile basal gene expression as a fitness determinant in bacteria 93%
"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.