Cytosolic Gram-negative bacteria prevent apoptosis by inhibition of effector caspases through lipopolysaccharide.
Saskia D GüntherMelanie FritschJens M SeegerLars M SchiffmannScott J SnipasMaria CoutelleThomas A KuferPaul G HigginsVeit HornungMaria L BernardiniStefan HöningMartin KrönkeGuy S SalvesenHamid KashkarPublished in: Nature microbiology (2019)
The cytosolic appearance and propagation of bacteria cause overwhelming cellular stress responses that induce apoptosis under normal conditions. Therefore, successful bacterial colonization depends on the ability of intracellular pathogens to block apoptosis and to safeguard bacterial replicative niches. Here, we show that the cytosolic Gram-negative bacterium Shigella flexneri stalls apoptosis by inhibiting effector caspase activity. Our data identified lipopolysaccharide (LPS) as a bona fide effector caspase inhibitor that directly binds caspases by involving its O-antigen (O Ag) moiety. Bacterial strains that lacked the O Ag or failed to replicate within the cytosol were incapable of blocking apoptosis and exhibited reduced virulence in a murine model of bacterial infection. Our findings demonstrate how Shigella inhibits pro-apoptotic caspase activity, effectively delays coordinated host-cell demise and supports its intracellular propagation. Next to the recently discovered pro-inflammatory role of cytosolic LPS, our data reveal a distinct mode of LPS action that, through the disruption of the early coordinated non-lytic cell death response, ultimately supports the inflammatory breakdown of infected cells at later time points.
Keyphrases
- cell death
- cell cycle arrest
- endoplasmic reticulum stress
- induced apoptosis
- gram negative
- inflammatory response
- oxidative stress
- anti inflammatory
- multidrug resistant
- dendritic cells
- escherichia coli
- electronic health record
- pi k akt
- staphylococcus aureus
- quantum dots
- single cell
- stem cells
- immune response
- genome wide
- big data
- toll like receptor
- dna methylation
- antimicrobial resistance
- reactive oxygen species
- machine learning
- highly efficient
- data analysis