Hypercapnia induces cleavage and nuclear localization of RelB protein, giving insight into CO2 sensing and signaling

Carbon dioxide (CO(2)) is increasingly being appreciated as an intracellular signaling molecule that affects inflammatory and immune responses. Elevated arterial CO(2) (hypercapnia) is encountered in a range of clinical conditions, including chronic obstructive pulmonary disease, and as a consequence of therapeutic ventilation in acute respiratory distress syndrome. In patients suffering from this syndrome, therapeutic hypoventilation strategy designed to reduce mechanical damage to the lungs is accompanied by systemic hypercapnia and associated acidosis, which are associated with improved patient outcome. However, the molecular mechanisms underlying the beneficial effects of hypercapnia and the relative contribution of elevated CO(2) or associated acidosis to this response remain poorly understood. Recently, a role for the non-canonical NF-?B pathway has been postulated to be important in signaling the cellular transcriptional response to CO(2). In this study, we demonstrate that in cells exposed to elevated CO(2), the NF-?B family member RelB was cleaved to a lower molecular weight form and translocated to the nucleus in both mouse embryonic fibroblasts and human pulmonary epithelial cells (A549). Furthermore, elevated nuclear RelB was observed in vivo and correlated with hypercapnia-induced protection against LPS-induced lung injury. Hypercapnia-induced RelB processing was sensitive to proteasomal inhibition by MG-132 but was independent of the activity of glycogen synthase kinase 3ß or MALT-1, both of which have been previously shown to mediate RelB processing. Taken together, these data demonstrate that RelB is a CO(2)-sensitive NF-?B family member that may contribute to the beneficial effects of hypercapnia in inflammatory diseases of the lung.

PDNAsite:identification of DNA-binding site from protein sequence by incorporating spatial and sequence context

Protein-DNA interactions are involved in many fundamental biological processes essential for cellular function. Most of the existing computational approaches employed only the sequence context of the target residue for its prediction. In the present study, for each target residue, we applied both the spatial context and the sequence context to construct the feature space. Subsequently, Latent Semantic Analysis (LSA) was applied to remove the redundancies in the feature space. Finally, a predictor (PDNAsite) was developed through the integration of the support vector machines (SVM) classifier and ensemble learning. Results on the PDNA-62 and the PDNA-224 datasets demonstrate that features extracted from spatial context provide more information than those from sequence context and the combination of them gives more performance gain. An analysis of the number of binding sites in the spatial context of the target site indicates that the interactions between binding sites next to each other are important for protein-DNA recognition and their binding ability. The comparison between our proposed PDNAsite method and the existing methods indicate that PDNAsite outperforms most of the existing methods and is a useful tool for DNA-binding site identification. A web-server of our predictor (http://hlt.hitsz.edu.cn:8080/PDNAsite/) is made available for free public accessible to the biological research community.