Excessive host inflammatory responses negatively impact disease outcomes in respiratory infection. Host-pathogen interactions during the infective phase of influenza are well studied, but little is known about the host's response during the repair stage. Here, we show that influenza infection stimulated the expression of angiopoietin-like 4 (ANGPTL4) via a direct IL6-STAT3-mediated mechanism. ANGPTL4 enhanced pulmonary tissue leakiness and exacerbated inflammation-induced lung damage. Treatment of infected mice with neutralizing anti-ANGPTL4 antibodies significantly accelerated lung recovery and improved lung tissue integrity. ANGPTL4-deficient mice also showed reduced lung damage and recovered faster from influenza infection when compared to their wild-type counterparts. Retrospective examination of human lung biopsy specimens from infection-induced pneumonia with tissue damage showed elevated expression of ANGPTL4 when compared to normal lung samples. These observations underscore the important role that ANGPTL4 plays in lung infection and damage and may facilitate future therapeutic strategies for the treatment of influenza pneumonia.
Secondary bacterial lung infection by Streptococcus pneumoniae (S. pneumoniae) poses a serious health concern, especially in developing countries. We posit that the emergence of multiantibiotic-resistant strains will jeopardize current treatments in these regions. Deaths arising from secondary infections are more often associated with acute lung injury, a common consequence of hypercytokinemia, than with the infection per se. Given that secondary bacterial pneumonia often has a poor prognosis, newer approaches to improve treatment outcomes are urgently needed to reduce the high levels of morbidity and mortality. Using a sequential dual-infection mouse model of secondary bacterial lung infection, we show that host-directed therapy via immunoneutralization of the angiopoietin-like 4 c-isoform (cANGPTL4) reduced pulmonary edema and damage in infected mice. RNA sequencing analysis revealed that anti-cANGPTL4 treatment improved immune and coagulation functions and reduced internal bleeding and edema. Importantly, anti-cANGPTL4 antibody, when used concurrently with either conventional antibiotics or antipneumolysin antibody, prolonged the median survival of mice compared to monotherapy. Anti-cANGPTL4 treatment enhanced immune cell phagocytosis of bacteria while restricting excessive inflammation. This modification of immune responses improved the disease outcomes of secondary pneumococcal pneumonia. Taken together, our study emphasizes that host-directed therapeutic strategies are viable adjuncts to standard antimicrobial treatments. IMPORTANCE Despite extensive global efforts, secondary bacterial pneumonia still represents a major cause of death in developing countries and is an important cause of long-term functional disability arising from lung tissue damage. Newer approaches to improving treatment outcomes are needed to reduce the significant morbidity and mortality caused by infectious diseases. Our study, using an experimental mouse model of secondary S. pneumoniae infection, shows that a multimodal treatment that concurrently targets host and pathogen factors improved lung tissue integrity and extended the median survival time of infected mice. The immunoneutralization of host protein cANGPTL4 reduced the severity of pulmonary edema and damage. We show that host-directed therapeutic strategies as well as neutralizing antibodies against pathogen virulence factors are viable adjuncts to standard antimicrobial treatments such as antibiotics. In view of their different modes of action compared to antibiotics, concurrent immunotherapies using antibodies are potentially efficacious against secondary pneumococcal pneumonia caused by antibiotic-resistant pathogens.
Identifying Clostridioides difficile infection (CDI) patients at risk of mortality or recurrence facilitates prevention, timely treatment, and improves clinical outcomes. The aim herein is to establish an open‐access web‐based prediction system, which estimates CDI patients’ mortality and recurrence outcomes and explains machine learning prediction with patients’ characteristics. Prognostic models are developed using four various types of machine learning algorithms and the statistical logistics regression model utilizing over 15 000 CDI patients from 41 hospitals in Hong Kong. The boosting‐based machine learning algorithm gradient boosting machine (GBM) (Mortality AUC: 0.7878; Recurrence AUC: 0.7076) outperforms statistical models (Mortality AUC: 0.7573; Recurrence AUC: 0.6927) and other machine learning algorithms. As the difficulty to interpret complex machine learning results limits their use in the medical area, Shapley additive explanations (SHAP) are adapted to identify which features are crucial to the machine learning models and associate them with clinical findings. SHAP analysis shows that older age, reduced albumin levels, higher creatinine levels, and higher white blood cell count are the most highly associated mortality features, which is consistent with existing clinical findings. The open‐access prediction system for clinicians to assess and interpret the risk factors of CDI patients is now available at https://www.cdiml.care/.
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