Modelling staphylococcal pneumonia in a human 3D lung tissue model system delineates toxin-mediated pathology

Staphylococcus aureus necrotizing pneumonia is recognized as a toxin-mediated disease, yet the tissue-destructive events remain elusive, partly as a result of lack of mechanistic studies in human lung tissue. In this study, a three-dimensional (3D) tissue model composed of human lung epithelial cells and fibroblasts was used to delineate the role of specific staphylococcal exotoxins in tissue pathology associated with severe pneumonia. To this end, the models were exposed to the mixture of exotoxins produced by S. aureus strains isolated from patients with varying severity of lung infection, namely necrotizing pneumonia or lung empyema, or to purified toxins. The necrotizing pneumonia strains secreted high levels of alpha-toxin and Panton-Valentine leukocidin (PVL), and triggered high cytotoxicity, inflammation, necrosis and loss of E-cadherin from the lung epithelium. In contrast, the lung empyema strain produced moderate levels of PVL, but negligible amounts of alpha-toxin, and triggered limited tissue damage. alpha-toxin had a direct damaging effect on the epithelium, as verified using toxin-deficient mutants and pure alpha-toxin. Moreover, PVL contributed to pathology through the lysis of neutrophils. A combination of alpha-toxin and PVL resulted in the most severe epithelial injury. In addition, toxin-induced release of pro-inflammatory mediators from lung tissue models resulted in enhanced neutrophil migration. Using a collection of 31 strains from patients with staphylococcal pneumonia revealed that strains producing high levels of alpha-toxin and PVL were cytotoxic and associated with fatal outcome. Also, the strains that produced the highest toxin levels induced significantly greater epithelial disruption. Of importance, toxin-mediated lung epithelium destruction could be inhibited by polyspecific intravenous immunoglobulin containing antibodies against alpha-toxin and PVL. This study introduces a novel model system for study of staphylococcal pneumonia in a human setting. The results reveal that the combination and levels of alpha-toxin and PVL correlate with tissue pathology and clinical outcome associated with pneumonia.

High levels of catalytic antibodies correlate with favorable outcome in sepsis

Sepsis is the leading cause of death in intensive care units and results from a deleterious systemic host response to infection. Although initially perceived as potentially deleterious, catalytic antibodies have been proposed to participate in removal of metabolic wastes and protection against infection. Here we show that the presence in plasma of IgG endowed with serine protease-like hydrolytic activity strongly correlates with survival from sepsis. Variances of catalytic rates of IgG were greater in the case of patients with severe sepsis than healthy donors (P < 0.001), indicating that sepsis is associated with alterations in plasma levels of hydrolytic IgG. The catalytic rates of IgG from patients who survived were significantly greater than those of IgG from deceased patients (P < 0.05). The cumulative rate of survival was higher among patients exhibiting high rates of IgG-mediated hydrolysis as compared with patients with low hydrolytic rates (P < 0.05). An inverse correlation was also observed between the markers of severity of disseminated intravascular coagulation and rates of hydrolysis of patients' IgG. Furthermore, IgG from three surviving patients hydrolyzed factor VIII, one of which also hydrolyzed factor IX, suggesting that, in some patients, catalytic IgG may participate in the control of disseminated microvascular thrombosis. Our observations provide the first evidence that hydrolytic antibodies might play a role in recovery from a disease.

A systems perspective of host-pathogen interactions: predicting disease outcome in tuberculosis

The complex web of interactions between the host immune system and the pathogen determines the outcome of any infection. A computational model of this interaction network, which encodes complex interplay among host and bacterial components, forms a useful basis for improving the understanding of pathogenesis, in filling knowledge gaps and consequently to identify strategies to counter the disease. We have built an extensive model of the Mycobacterium tuberculosis host-pathogen interactome, consisting of 75 nodes corresponding to host and pathogen molecules, cells, cellular states or processes. Vaccination effects, clearance efficiencies due to drugs and growth rates have also been encoded in the model. The system is modelled as a Boolean network. Virtual deletion experiments, multiple parameter scans and analysis of the system's response to perturbations, indicate that disabling processes such as phagocytosis and phagolysosome fusion or cytokines such as TNF-alpha and IFN-gamma, greatly impaired bacterial clearance, while removing cytokines such as IL-10 alongside bacterial defence proteins such as SapM greatly favour clearance. Simulations indicate a high propensity of the pathogen to persist under different conditions.

Investigating novelty-outcome relationships in engineering design

Design creativity involves developing novel and useful solutions to design problems The research in this article is an attempt to understand how novelty of a design resulting from a design process is related to the kind of outcomes. described here as constructs, involved in the design process A model of causality, the SAPPhIRE model, is used as the basis of the analysis The analysis is based on previous research that shows that designing involves development and exploration of the seven basic constructs of the SAPPhIRE model that constitute the causal connection between the various levels of abstraction at which a design can be described The constructs am state change, action, parts. phenomenon. input. organs. and effect The following two questions are asked. Is there a relationship between novelty and the constructs? If them is a relationship, what is the degree of this relationship? A hypothesis is developed to answer the questions an increase in the number and variety of ideas explored while designing should enhance the variety of concept space. leading to an increase in the novelty of the concept space Eight existing observational studies of designing sessions are used to empirically validate the hypothesis Each designing session involves an individual designer. experienced or novice. solving a design problem by producing concepts and following a think-aloud protocol. The results indicate dependence of novelty of concept space on variety of concept space and dependence of variety of concept space on variety of idea space. thereby validating the hypothesis The Jesuits also reveal a strong correlation between novelty and the constructs, correlation value decreases as the abstraction level of the constructs reduces. signifying the importance of using constructs at higher abstraction levels for enhancing novelty

A systems perspective of host-pathogen interactions: predicting disease outcome in tuberculosis

The complex web of interactions between the host immune system and the pathogen determines the outcome of any infection. A computational model of this interaction network, which encodes complex interplay among host and bacterial components, forms a useful basis for improving the understanding of pathogenesis, in filling knowledge gaps and consequently to identify strategies to counter the disease. We have built an extensive model of the Mycobacterium tuberculosis host-pathogen interactome, consisting of 75 nodes corresponding to host and pathogen molecules, cells, cellular states or processes. Vaccination effects, clearance efficiencies due to drugs and growth rates have also been encoded in the model. The system is modelled as a Boolean network. Virtual deletion experiments, multiple parameter scans and analysis of the system's response to perturbations, indicate that disabling processes such as phagocytosis and phagolysosome fusion or cytokines such as TNF-alpha and IFN-gamma, greatly impaired bacterial clearance, while removing cytokines such as IL-10 alongside bacterial defence proteins such as SapM greatly favour clearance. Simulations indicate a high propensity of the pathogen to persist under different conditions.

Computational framework to integrate the effect of antigen recognition on disease epidemiology outcome: multi-scale approach

Human Leukocyte Antigen (HLA) plays an important role, in presenting foreign pathogens to our immune system, there by eliciting early immune responses. HLA genes are highly polymorphic, giving rise to diverse antigen presentation capability. An important factor contributing to enormous variations in individual responses to diseases is differences in their HLA profiles. The heterogeneity in allele specific disease responses decides the overall disease epidemiological outcome. Here we propose an agent based computational framework, capable of incorporating allele specific information, to analyze disease epidemiology. This framework assumes a SIR model to estimate average disease transmission and recovery rate. Using epitope prediction tool, it performs sequence based epitope detection for a given the pathogenic genome and derives an allele specific disease susceptibility index depending on the epitope detection efficiency. The allele specific disease transmission rate, that follows, is then fed to the agent based epidemiology model, to analyze the disease outcome. The methodology presented here has a potential use in understanding how a disease spreads and effective measures to control the disease.