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Modulating the innate immune response and bacterial fitness by combinatorial engineering of endotoxin

Gram-negative bacteria decorate their outermost surface structure, lipopolysaccharide, with elaborate chemical moieties, which effectively disguises them from immune surveillance and protects them from the onslaught of host defenses. Many of these changes occur on the lipid A component of lipopolysaccharide, which is crucial for host recognition of Gram-negative infection. Despite its highly inflammatory nature, LPS is a molecule with remarkable therapeutic potential. Lipid A is a glycolipid that serves as the hydrophobic anchor of LPS and constitutes a potent ligand of the TLR4/MD2 receptor of the innate immune system. A less toxic mixture of mono-phosphorylated lipid A species (MPL) recently became the first new FDA-approved adjuvant in over 70 years. Whereas wild-type E. coli LPS provokes strong inflammatory MyD88-mediated TLR4 signaling, MPL preferentially induces less inflammatory TRIF-mediated responses. Here, we developed a system for combinatorial structural diversification of E. coli lipid A yielding a spectrum of bioactive variants that display distinct TLR4 agonist activities and cytokine induction. Mice immunized with engineered lipid A/antigen emulsions exhibited robust IgG titers indicating the efficacy of these molecules as adjuvants. Other types of modification to the lipid A domain, such as altering the length of the fatty acyl chains that anchor LPS to the cell membrane, were found to affect bacterial fitness but not drastically influence detection by the TLR4/MD2 receptor. Overall, this combinatorial approach demonstrates how engineering lipid A can be exploited to generate a spectrum of immunostimulatory molecules for vaccine and therapeutics development as well as for a deeper understanding of bacterial membrane biogenesis. / text

Identiferoai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/31290
Date10 September 2015
CreatorsNeedham, Brittany Dawn
ContributorsTrent, Michael Stephen
Source SetsUniversity of Texas
LanguageEnglish
Detected LanguageEnglish
TypeThesis
Formatapplication/pdf

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