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Selection and characterization of anti-NF-kappaB p65 RNA aptamers.

ABSTRACT: NF-kappaB transcription factors include a group of five mammalian proteins that form hetero- or homodimers and regulate hundreds of target genes involved in acute inflammation, HIV-1 transcription activation, and resistance to cancer therapy. We previously used in vitro selection to develop a small RNA aptamer (anti-p50) that binds the DNA-binding domain of NF-kappaB p50(2) with low nanomolar affinity but does not bind NF-kappaB p65(2). Here, we report the in vitro selection of anti-NF-kappaB p65 RNA aptamers using parallel in vitro selections with either a fully randomized RNA library or a degenerate RNA library based on the primary sequence of the 31-nucleotide anti-p50 RNA aptamer. We report the characterization of these aptamers with respect to NF-kappaB target specificity, affinity, minimal sequence requirements, secondary structure, and competition with DNA kappaB sites. These results expand opportunities for artificial inhibition of NF-kappaB transcription factor dimers containing p65 subunits.

SUBMITTER: Wurster SE

PROVIDER: S-EPMC2390809 | biostudies-literature | 2008 Jun

REPOSITORIES: biostudies-literature

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Selection and characterization of anti-NF-kappaB p65 RNA aptamers.

Wurster Susan E SE Maher L James LJ

RNA (New York, N.Y.) 20080421 6

NF-kappaB transcription factors include a group of five mammalian proteins that form hetero- or homodimers and regulate hundreds of target genes involved in acute inflammation, HIV-1 transcription activation, and resistance to cancer therapy. We previously used in vitro selection to develop a small RNA aptamer (anti-p50) that binds the DNA-binding domain of NF-kappaB p50(2) with low nanomolar affinity but does not bind NF-kappaB p65(2). Here, we report the in vitro selection of anti-NF-kappaB p6 ...[more]

PMID: 18426920

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Project description:Picroliv, an iridoid glycoside derived from the plant Picrorhiza kurroa, is used traditionally to treat fever, asthma, hepatitis, and other inflammatory conditions. However, the exact mechanism of its therapeutic action is still unknown. Because nuclear factor-kappaB (NF-kappaB) activation plays a major role in inflammation and carcinogenesis, we postulated that picroliv must interfere with this pathway by inhibiting the activation of NF-kappaB-mediated signal cascade. Electrophoretic mobility shift assay showed that pretreatment with picroliv abrogated tumor necrosis factor (TNF)-induced activation of NF-kappaB. The glycoside also inhibited NF-kappaB activated by carcinogenic and inflammatory agents, such as cigarette smoke condensate, phorbol 12-myristate 13-acetate, okadaic acid, hydrogen peroxide, lipopolysaccharide, and epidermal growth factor. When examined for the mechanism of action, we found that picroliv inhibited activation of IkappaBalpha kinase, leading to inhibition of phosphorylation and degradation of IkappaBalpha. It also inhibited phosphorylation and nuclear translocation of p65. Further studies revealed that picroliv directly inhibits the binding of p65 to DNA, which was reversed by the treatment with reducing agents, suggesting a role for a cysteine residue in interaction with picroliv. Mutation of Cys(38) in p65 to serine abolished this effect of picroliv. NF-kappaB inhibition by picroliv leads to suppression of NF-kappaB-regulated proteins, including those linked with cell survival (inhibitor of apoptosis protein 1, Bcl-2, Bcl-xL, survivin, and TNF receptor-associated factor 2), proliferation (cyclin D1 and cyclooxygenase-2), angiogenesis (vascular endothelial growth factor), and invasion (intercellular adhesion molecule-1 and matrix metalloproteinase-9). Suppression of these proteins enhanced apoptosis induced by TNF. Overall, our results show that picroliv inhibits the NF-kappaB activation pathway, which may explain its anti-inflammatory and anticarcinogenic effects.

Dataset Information

Selection and characterization of anti-NF-kappaB p65 RNA aptamers.

Publications

Selection and characterization of anti-NF-kappaB p65 RNA aptamers.

Similar Datasets

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