Project description:Meningitis is a life-threatening condition characterized by the inflammation of the leptomeningeal membranes surrounding the brain and spinal cord. The term meningitis is an umbrella term and includes several different etiologies. The majorities of meningitis cases are caused by viruses (viral meningitis; VM) and are often associated with low mortality rates and low risk of developing neurological. In contrast, meningitis caused by some viral infections, such as tick-borne encephalitis (TBE), can be life-threatening when left untreated with increased risk of developing neurological sequelae. Acute bacterial meningitis (ABM), however, is one of the leading causes of death due to infectious diseases worldwide and is associated with rapid disease progression, high mortality rates and increased risk of long-term neurological sequelae in survivors. As meningitis is caused by numerous different pathogens, the host-response is typically highly variable and it is currently unknown if different pathogens can introduce specific proteome changes in the cerebrospinal fluid (CSF). In this study we applied DIA-MS to provide novel insights for in-depth understanding of central nervous system functioning and host response during meningitis in a cohort of patients with differential diagnosis of meningitis, to account for variability contributed by different disease-causing pathogens. The results reveal drastic changes in the CSF proteome during meningitis, where in particular a massive increase of neutrophil derived proteins in the CSF correlated with ABM, suggesting that activated neutrophils play a particular role in ABM. Additionally both ABM and VM result in marked reduction of brain-specific proteins in the CSF, which could be indicative of pathophysiological mechanisms leading to brain damage. Furthermore, generation of lasso regression model enables separation of ABM with high sensitivity and specificity, demonstrating that several proteins are required to confidently discriminate between ABM, VM and BM.

Project description:Rationale: Lower respiratory tract infections continue to exact unacceptable worldwide mortality, often because the infecting pathogen cannot be identified. The respiratory epithelia provide protection from pneumonias through organism-specific generation of antimicrobial products, offering potential insight into the identity of infecting pathogens. Objective: This study assesses the capacity of the host gene expression response to infection to predict lower respiratory pathogens without reliance on culture data. Methods: Mice were inhalationally challenged with S. pneumoniae, P. aeruginosa, A. fumigatus or PBS prior to whole genome gene expression microarray analysis of their pulmonary parenchyma. Characteristic gene expression patterns for each condition were identified, allowing the derivation of prediction rules for each pathogen. After confirming the predictive capacity of gene expression data in blinded challenges, a computerized algorithm was devised to predict the infectious conditions of subsequent subjects. Measurements and Main Results: We observed robust, pathogen-specific gene expression patterns as early as 2 h after infection. We were able to develop a predictive model that used a limited number of specifically regulated transcripts to discriminate perfectly among infecting pathogens in the training data. Validation trials using an algorithmic decision tree revealed 94.4% diagnostic accuracy when discerning the presence of bacterial infection. The model subsequently differentiated between bacterial pathogens with 71.4% accuracy and between non-bacterial conditions with 70.0% accuracy, both far exceeding the expected diagnostic yield of standard culture-based bronchoscopy with bronchoalveolar lavage. Conclusions: These data substantiate the specificity of the pulmonary innate immune response and support the feasibility of a gene expression-based clinical tool for pneumonia diagnosis. Keywords: pathogen- and time-dependent host lung gene expression changes in pneumonia Mouse lungs were removed 6 h after challenge with P. aeruginosa, S. pneumoniae, A. fumigatus or sham (PBS), RNA was collected from leukoreduced lung homogenates, then cRNA was amplified and hybridized to Illumina Sentrix mouse-6 gene expression arrays v1.1. Gene expression data was analyzed to determine if there were distinct patterns of host trasnscription that corresponded with a specific infection.

Project description:Pneumonia is a serious problem worldwide. We recently demonstrated that innate defense mechanisms of the lung are highly inducible against pneumococcal pneumonia. To determine the breadth of protection conferred by stimulation of lung mucosal innate immunity, and to identify cells and signaling pathways activated by this treatment, mice were treated with an aerosolized bacterial lysate, then challenged with lethal doses of bacterial and fungal pathogens. Mice were highly protected against a broad array of Gram-positive, Gram-negative, and Class A bioterror bacterial pathogens, and Aspergillus fumigatus. Protection was associated with rapid pathogen killing within the lungs, and this effect was recapitulated in vitro using a respiratory epithelial cell line. Gene expression analysis of lung tissue showed marked activation of NF-kappaB, Type I and II interferon, and antifungal Card9-Bcl10-Malt1 pathways. Cytokines were the most strongly induced genes, but the inflammatory cytokines TNF and IL-6 were not required for protection. Lung-expressed antimicrobial peptides were also highly upregulated. Taken together, stimulated innate resistance (StIR) appears to occur through the activation of multiple host defense signaling pathways in lung epithelial cells, inducing rapid pathogen killing, and conferring broad protection against virulent bacterial and fungal pathogens. Augmentation of innate antimicrobial defenses of the lungs might have therapeutic value for protection of patients with neutropenia or impaired adaptive immunity against opportunistic pneumonia, and for defense of immunocompetent subjects against a bioterror threat or epidemic respiratory infection. Keywords: differential gene expression; time course; innate immunity; pneumonia; immunocompromised host; lung epithelium Gene expression patterns in mouse lung homogenates were analyzed 2h after exposure to aerosolized PBS (Sham treatment), 2h after exposure to aerosolized NTHi lysate or 4h after exposure to aerosolized NTHi lysate. Each group consisted of six mice.

Project description:Rationale: Lower respiratory tract infections continue to exact unacceptable worldwide mortality, often because the infecting pathogen cannot be identified. The respiratory epithelia provide protection from pneumonias through organism-specific generation of antimicrobial products, offering potential insight into the identity of infecting pathogens. Objective: This study assesses the capacity of the host gene expression response to infection to predict lower respiratory pathogens without reliance on culture data. Methods: Mice were inhalationally challenged with S. pneumoniae, P. aeruginosa, A. fumigatus or PBS prior to whole genome gene expression microarray analysis of their pulmonary parenchyma. Characteristic gene expression patterns for each condition were identified, allowing the derivation of prediction rules for each pathogen. After confirming the predictive capacity of gene expression data in blinded challenges, a computerized algorithm was devised to predict the infectious conditions of subsequent subjects. Measurements and Main Results: We observed robust, pathogen-specific gene expression patterns as early as 2 h after infection. We were able to develop a predictive model that used a limited number of specifically regulated transcripts to discriminate perfectly among infecting pathogens in the training data. Validation trials using an algorithmic decision tree revealed 94.4% diagnostic accuracy when discerning the presence of bacterial infection. The model subsequently differentiated between bacterial pathogens with 71.4% accuracy and between non-bacterial conditions with 70.0% accuracy, both far exceeding the expected diagnostic yield of standard culture-based bronchoscopy with bronchoalveolar lavage. Conclusions: These data substantiate the specificity of the pulmonary innate immune response and support the feasibility of a gene expression-based clinical tool for pneumonia diagnosis. Keywords: pathogen- and time-dependent host lung gene expression changes in pneumonia

Dataset Information

Design of a Diagnostic Pan-Pathogen Oligonucleotide Microarray including a sub-Array for Detection of Meningitis-Causing Bacterial Pathogens

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