A transcription factor contributes to pathogenesis and virulence in streptococcus pneumoniae

To date, the role of transcription factors (TFs) in the progression of disease for many pathogens is yet to be studied in detail. This is probably due to transient, and generally low expression levels of TFs, which are the central components controlling the expression of many genes during the course of infection. However, a small change in the expression or specificity of a TF can radically alter gene expression. In this study, we combined a number of quality-based selection strategies including structural prediction of modulated genes, gene ontology and network analysis, to predict the regulatory mechanisms underlying pathogenesis of Streptococcus pneumoniae (the pneumococcus). We have identified two TFs (SP_0676 and SP_0927 [SmrC]) that might control tissue-specific gene expression during pneumococcal translocation from the nasopharynx to lungs, to blood and then to brain of mice. Targeted mutagenesis and mouse models of infection confirmed the role of SP_0927 in pathogenesis and virulence, and suggests that SP_0676 might be essential to pneumococcal viability. These findings provide fundamental new insights into virulence gene expression and regulation during pathogenesis.Layla K. Mahdi, Esmaeil Ebrahimie, David L. Adelson, James C. Paton, Abiodun D. Ogunniy

Nasopharyngeal Colonization and Invasive Disease Are Enhanced by the Cell Wall Hydrolases LytB and LytC of Streptococcus pneumoniae

Background: Streptococcus pneumoniae is a common colonizer of the human nasopharynx and one of the major pathogens causing invasive disease worldwide. Dissection of the molecular pathways responsible for colonization, invasion, and evasion of the immune system will provide new targets for antimicrobial or vaccine therapies for this common pathogen. Methodology/Principal Findings: We have constructed mutants lacking the pneumococcal cell wall hydrolases (CWHs) LytB and LytC to investigate the role of these proteins in different phases of the pneumococcal pathogenesis. Our results show that LytB and LytC are involved in the attachment of S. pneumoniae to human nasopharyngeal cells both in vitro and in vivo. The interaction of both proteins with phagocytic cells demonstrated that LytB and LytC act in concert avoiding pneumococcal phagocytosis mediated by neutrophils and alveolar macrophages. Furthermore, C3b deposition was increased on the lytC mutant confirming that LytC is involved in complement evasion. As a result, the lytC mutant showed a reduced ability to successfully cause pneumococcal pneumonia and sepsis. Bacterial mutants lacking both LytB and LytC showed a dramatically impaired attachment to nasopharyngeal cells as well as a marked degree of attenuation in a mouse model of colonization. In addition, C3b deposition and phagocytosis was more efficient for the double lytB lytC mutant and its virulence was greatly impaired in both systemic and pulmonary models of infection. Conclusions/Significance: This study confirms that the CWHs LytB and LytC of S. pneumoniae are essential virulence factor

Metabolomic Analysis in Severe Childhood Pneumonia in The Gambia, West Africa: Findings from a Pilot Study

Pneumonia remains the leading cause of death in young children globally and improved diagnostics are needed to better identify cases and reduce case fatality. Metabolomics, a rapidly evolving field aimed at characterizing metabolites in biofluids, has the potential to improve diagnostics in a range of diseases. The objective of this pilot study is to apply metabolomic analysis to childhood pneumonia to explore its potential to improve pneumonia diagnosis in a high-burden setting. and Random Forests (RF). ‘Unsupervised’ (blinded) data were analyzed by Principal Component Analysis (PCA), while ‘supervised’ (unblinded) analysis was by Partial Least Squares-Discriminant Analysis (PLS-DA) and Orthogonal Projection to Latent Structures (OPLS). Potential markers were extracted from S-plots constructed following analysis with OPLS, and markers were chosen based on their contribution to the variation and correlation within the data set. The dataset was additionally analyzed with the machine-learning algorithm RF in order to address issues of model overfitting and markers were selected based on their variable importance ranking. Unsupervised PCA analysis revealed good separation of pneumonia and control groups, with even clearer separation of the groups with PLS-DA and OPLS analysis. Statistically significant differences (p<0.05) between groups were seen with the following metabolites: uric acid, hypoxanthine and glutamic acid were higher in plasma from cases, while L-tryptophan and adenosine-5′-diphosphate (ADP) were lower; uric acid and L-histidine were lower in urine from cases. The key limitation of this study is its small size.Metabolomic analysis clearly distinguished severe pneumonia patients from community controls. The metabolites identified are important for the host response to infection through antioxidant, inflammatory and antimicrobial pathways, and energy metabolism. Larger studies are needed to determine whether these findings are pneumonia-specific and to distinguish organism-specific responses. Metabolomics has considerable potential to improve diagnostics for childhood pneumonia