Modulation of human bronchial epithelial cells by pneumococcal choline binding protein A

Tomatidine activates autophagy to improve lung injury and inflammation in sepsis by inhibiting NF-κB and MAPK pathways

Sepsis-induced acute lung injury (ALI) is a life-threatening medical condition with high mortality and morbidity. Autophagy is involved in the pathophysiological process of sepsis-induced ALI, including inflammation, which indicates that regulating autophagy may be beneficial for this disease. Tomatidine, a natural compound abundant in unripe tomatoes, has been reported to have anti-inflammatory, anti-tumorigenic, and lipid-lowering effects. However, the biological functions and mechanisms of tomatidine in sepsis-induced ALI remain unknown. The principal objective of this study was to investigate the effect of tomatidine on sepsis-induced ALI. Cecal ligation and puncture (CLP) was used to induce septic lung injury in mice, and 10 mg/kg tomatidine was intraperitoneally injected into mice 2 h after the operation. The results of hematoxylin and eosin staining and assessment of lung edema and total protein levels in bronchoalveolar lavage fluid (BALF) demonstrated that tomatidine alleviated CLP-induced severe lung injuries such as hemorrhage, infiltration of inflammatory cells, and interstitial and alveolar edema in mice. Additionally, the levels of proinflammatory cytokines in BALF and lung tissues were measured by enzyme-linked immunosorbent assay (ELISA), and the results showed that tomatidine inhibited CLP-induced inflammatory damage to lungs. Moreover, the results of western blotting showed that tomatidine promoted autophagy during CLP-induced ALI. Mechanistically, immunofluorescence staining and western blotting were used to measure the protein levels of TLR4, phosphorylated NF-κB, phosphorylated IκBα, and phosphorylated MAPKs, showing that tomatidine inactivated NF-κB and MAPK signaling in lung tissues of CLP-induced ALI mice. In conclusion, tomatidine exerts protective effects against sepsis-induced severe damage to the lungs by inhibiting inflammation and activating autophagy in CLP-treated mice through inactivating the NF-κB and MAPK pathways, which may be an effective candidate for treating septic ALI.

OGDH is involved in sepsis induced acute lung injury through the MAPK pathway

Background

Acute lung injury (ALI) induced by sepsis is a common cause of death in clinical practice, and there remains a lack of clinical effective treatment. Cecal ligation and puncture (CLP) is a classic animal model of sepsis, which can induce ALI. Studies have shown that in the lung injury cell model, OGDH (oxoglutarate dehydrogenase) transcription is up-regulated, which is a potential therapeutic target for acute pneumonia. The purpose of this study was to confirm the effects of OGDH on lung injury and inflammation in animal and cell models, and to explore its mechanism.

Methods

By analyzing the GSE16650 gene set, the upregulated OGDH gene was detected in the lung injury cell model. In a sepsis animal model established by CLP and a lung injury cell model, RT-PCR, immunohistochemistry, WB, and other techniques were used to verify the upregulation of OGDH expression, which was then was down-regulated with shRNA to confirm its relationship with ALI. Further, ELISA, RT-PCR, and WB were used to detect the effect of OGDH on the expression of pro-inflammatory factors including IL-1β, IL-6, IL-18, and TNF-α. The downstream pathway of OGDH was predicted using KEGG and GSEA tools and verified by WB and immunofluorescence.

Results

The results showed OGDH was highly expressed in a lung injury cell model and the lung tissue of ALI mice induced by CLP, and downregulation of OGDH alleviated sepsis induced ALI. In animal models and cell models, the expression of OGDH was positively correlated with the expression of pro-inflammatory factors. OGDH may act through the MAPK pathway.

Conclusions

Under the pathological condition of sepsis, OGDH amplifies the inflammatory response through the MAPK pathway, releases pro-inflammatory factors, and induces ALI.

Jenner-predict server: prediction of protein vaccine candidates (PVCs) in bacteria based on host-pathogen interactions

Background

Subunit vaccines based on recombinant proteins have been effective in preventing infectious diseases and are expected to meet the demands of future vaccine development. Computational approach, especially reverse vaccinology (RV) method has enormous potential for identification of protein vaccine candidates (PVCs) from a proteome. The existing protective antigen prediction software and web servers have low prediction accuracy leading to limited applications for vaccine development. Besides machine learning techniques, those software and web servers have considered only protein’s adhesin-likeliness as criterion for identification of PVCs. Several non-adhesin functional classes of proteins involved in host-pathogen interactions and pathogenesis are known to provide protection against bacterial infections. Therefore, knowledge of bacterial pathogenesis has potential to identify PVCs.

Results

A web server, Jenner-Predict, has been developed for prediction of PVCs from proteomes of bacterial pathogens. The web server targets host-pathogen interactions and pathogenesis by considering known functional domains from protein classes such as adhesin, virulence, invasin, porin, flagellin, colonization, toxin, choline-binding, penicillin-binding, transferring-binding, fibronectin-binding and solute-binding. It predicts non-cytosolic proteins containing above domains as PVCs. It also provides vaccine potential of PVCs in terms of their possible immunogenicity by comparing with experimentally known IEDB epitopes, absence of autoimmunity and conservation in different strains. Predicted PVCs are prioritized so that only few prospective PVCs could be validated experimentally. The performance of web server was evaluated against known protective antigens from diverse classes of bacteria reported in Protegen database and datasets used for VaxiJen server development. The web server efficiently predicted known vaccine candidates reported from Streptococcus pneumoniae and Escherichia coli proteomes. The Jenner-Predict server outperformed NERVE, Vaxign and VaxiJen methods. It has sensitivity of 0.774 and 0.711 for Protegen and VaxiJen dataset, respectively while specificity of 0.940 has been obtained for the latter dataset.

Conclusions

Better prediction accuracy of Jenner-Predict web server signifies that domains involved in host-pathogen interactions and pathogenesis are better criteria for prediction of PVCs. The web server has successfully predicted maximum known PVCs belonging to different functional classes. Jenner-Predict server is freely accessible at http://117.211.115.67/vaccine/home.html

Pneumococcal ClpP modulates the maturation and activation of human dendritic cells: implications for pneumococcal infections

DCs are essential for host immune response to pathogens. Pneumococcal diseases still remain to be a major global-health issue, and HSP100/ClpP is a ubiquitously present virulence determinant for Streptococcus pneumoniae. Here, we show that ClpP expression facilitates the uptake and phagocytosis of pneumococci by human DCs, and it could increase apoptosis of DCs infected with pneumococci. Furthermore, pneumococcal ClpP is required for optimal production of inflammatory cytokines and chemokines and an efficient activation of adaptive immune response in DCs. Complementary, purified rClpP protein recognizes TLR4 and functionally activates human DCs by augmenting the expression of surface molecules and the production of inflammatory cytokines and chemokines dependent on MAPKs and NF-κB signaling pathways. Besides, ClpP-treated DCs induce T cell proliferation and contribute to Th1 immune response. This study describes a novel role of ClpP in the interaction of DCs with pneumococci that could provide new insight for the progression of pneumococcal diseases and has important implications for designing pneumococcal protein vaccines.