BackgroundCommunity-acquired pneumonia (CAP) remains a major cause of death worldwide. Mechanisms underlying the detrimental outcome despite adequate antibiotic therapy and comorbidity management are still not fully understood.MethodsTo model timely versus delayed antibiotic therapy in patients, mice with pneumococcal pneumonia received ampicillin twice a day starting early (24 h) or late (48 h) after infection. Clinical readouts and local and systemic inflammatory mediators after early and late antibiotic intervention were examined.ResultsEarly antibiotic intervention rescued mice, limited clinical symptoms and restored fitness, whereas delayed therapy resulted in high mortality rates. Recruitment of innate immune cells remained unaffected by antibiotic therapy. However, both early and late antibiotic intervention dampened local levels of inflammatory mediators in the alveolar spaces. Early treatment protected from barrier breakdown, and reduced levels of vascular endothelial growth factor (VEGF) and perivascular and alveolar edema formation. In contrast, at 48 h post infection, increased pulmonary leakage was apparent and not reversed by late antibiotic treatment. Concurrently, levels of VEGF remained high and no beneficial effect on edema formation was evident despite therapy. Moreover, early but not late treatment protected mice from a vast systemic inflammatory response.ConclusionsOur data show that only early antibiotic therapy, administered prior to breakdown of the alveolar–capillary barrier and systemic inflammation, led to restored fitness and rescued mice from fatal streptococcal pneumonia. The findings highlight the importance of identifying CAP patients prior to lung barrier failure and systemic inflammation and of handling CAP as a medical emergency.Electronic supplementary materialThe online version of this article (10.1186/s13054-018-2224-5) contains supplementary material, which is available to authorized users.

Descriptive histopathology of mouse models of pneumonia is essential in assessing the outcome of infections, molecular manipulations, or therapies in the context of whole lungs. Quantitative comparisons between experimental groups, however, have been limited to laborious stereology or ill-defined scoring systems that depend on the subjectivity of a more or less experienced observer. Here, we introduce self-learning digital image analyses that allow us to transform optical information from whole mouse lung sections into statistically testable data. A pattern-recognition-based software and a nuclear count algorithm were adopted to quantify user-defined pathologies from whole slide scans of lungs infected with Streptococcus pneumoniae or influenza A virus compared with PBS-challenged lungs. The readout parameters "relative area affected" and "nuclear counts per area" are proposed as relevant criteria for the quantification of lesions from hematoxylin and eosin-stained sections, also allowing for the generation of a heat map of, for example, immune cell infiltrates with anatomical assignments across entire lung sections. Moreover, when combined with immunohistochemical labeling of marker proteins, both approaches are useful for the identification and counting of, for example, immune cell populations, as validated here by direct comparisons with flow cytometry data. The solutions can easily and flexibly be adjusted to specificities of different models or pathogens. Automated digital analyses of whole mouse lung sections may set a new standard for the user-defined, high-throughput comparative quantification of histological and immunohistochemical images. Still, our algorithms established here are only a start, and need to be tested in additional studies and other applications in the future.

Pneumonia is one of the leading causes of death worldwide. The course of the disease is often highly dynamic with unforeseen critical deterioration within hours in a relevant proportion of patients. Besides antibiotic treatment, novel adjunctive therapies are under development. Their additive value needs to be explored in preclinical and clinical studies and corresponding therapy schedules require optimization prior to introduction into clinical practice. Biomathematical modeling of the underlying disease and therapy processes might be a useful aid to support these processes. We here propose a biomathematical model of murine immune response during infection with Streptococcus pneumoniae aiming at predicting the outcome of different treatment schedules. The model consists of a number of non-linear ordinary differential equations describing the dynamics and interactions of the pulmonal pneumococcal population and relevant cells of the innate immune response, namely alveolar- and inflammatory macrophages and neutrophils. The cytokines IL-6 and IL-10 and the chemokines CCL2, CXCL1 and CXCL5 are considered as major mediators of the immune response. We also model the invasion of peripheral blood monocytes, their differentiation into macrophages and bacterial penetration through the epithelial barrier causing blood stream infections. We impose therapy effects on this system by modelling antibiotic therapy and treatment with the novel C5a-inactivator NOX-D19. All equations are derived by translating known biological mechanisms into equations and assuming appropriate response kinetics. Unknown model parameters were determined by fitting the predictions of the model to time series data derived from mice experiments with close-meshed time series of state parameters. Parameter fittings resulted in a good agreement of model and data for the experimental scenarios. The model can be used to predict the performance of alternative schedules of combined antibiotic and NOX-D19 treatment. We conclude that we established a comprehensive biomathematical model of pneumococcal lung infection, immune response and barrier function in mice allowing simulations of new treatment schedules. We aim to validate the model on the basis of further experimental data. We also plan the inclusion of further novel therapy principles and the translation of the model to the human situation in the near future.