Chronic obstructive lung disease is characterized by persistent abnormalities in epithelial and immune cell function that are driven, at least in part, by infection. Analysis of parainfluenza virus infection in mice revealed an unexpected role for innate immune cells in IL-13-dependent chronic lung disease, but the upstream driver for the immune axis in this model and in humans with similar disease was undefined. We demonstrate here that lung levels of IL-33 are selectively increased in postviral mice with chronic obstructive lung disease and in humans with very severe chronic obstructive pulmonary disease (COPD). In the mouse model, IL-33/IL-33 receptor signaling was required for Il13 and mucin gene expression, and Il33 gene expression was localized to a virus-induced subset of airway serous cells and a constitutive subset of alveolar type 2 cells that are both linked conventionally to progenitor function. In humans with COPD, IL33 gene expression was also associated with IL13 and mucin gene expression, and IL33 induction was traceable to a subset of airway basal cells with increased capacities for pluripotency and ATP-regulated release of IL-33. Together, these findings provide a paradigm for the role of the innate immune system in chronic disease based on the influence of long-term epithelial progenitor cells programmed for excess IL-33 production.

Genetic variations in the myeloid immune receptor TREM2 are linked to several neurodegenerative diseases. To determine how TREM2 variants contribute to these diseases, we performed structural and functional studies of wild-type and variant proteins. Our 3.1 Å TREM2 crystal structure revealed that mutations found in Nasu-Hakola disease are buried whereas Alzheimer’s disease risk variants are found on the surface, suggesting that these mutations have distinct effects on TREM2 function. Biophysical and cellular methods indicate that Nasu-Hakola mutations impact protein stability and decrease folded TREM2 surface expression, whereas Alzheimer’s risk variants impact binding to a TREM2 ligand. Additionally, the Alzheimer’s risk variants appear to epitope map a functional surface on TREM2 that is unique within the larger TREM family. These findings provide a guide to structural and functional differences among genetic variants of TREM2, indicating that therapies targeting the TREM2 pathway should be tailored to these genetic and functional differences with patient-specific medicine approaches for neurodegenerative disorders.DOI: http://dx.doi.org/10.7554/eLife.20391.001

Wu et al. use a mouse model to show that active respiratory viral infection triggers TREM-2 expression on the macrophage cell surface and thereby prevents macrophage apoptosis during the acute illness. In addition, long after viral clearance, IL-13 and DAP12 promote TREM-2 cleavage to its soluble form that unexpectedly also enhances macrophage survival and promotes chronic inflammatory disease.

An abnormal immune response to environmental agents is generally thought to be responsible for causing chronic respiratory diseases, such as asthma and chronic obstructive pulmonary disease (COPD). Based on studies of experimental models and human subjects, there is increasing evidence that the response of the innate immune system is crucial for the development of this type of airway disease. Airway epithelial cells and innate immune cells represent key components of the pathogenesis of chronic airway disease and are emerging targets for new therapies. In this Review, we summarize the innate immune mechanisms by which airway epithelial cells and innate immune cells regulate the development of chronic respiratory diseases. We also explain how these pathways are being targeted in the clinic to treat patients with these diseases.

Some expressions and notations related to Equations 1 and 2 were presented incorrectly. The correct text and equations are below.The coefficients (β) in Cox's regression model are estimated by maximizing the partial likelihood function subject to a constraint on the L1-norm of the coefficients. The lasso estimator (β ) maximizes the objective function given below:Here l(β) is the log partial likelihood in the Cox model; for the exact form of this function, see ref. 41. The tuning parameter, λ in Equation 1, was chosen by 10-fold cross-validation. For the implementation, we used the R package "glmnet" (39).PROVAR was defined for each of the 222 TCGA samples as the sum of the estimated coefficients multiplied by protein expression levels, as shown below. Here i represents patients (i = 1,...,222), j represents proteins with nonzero coefficients (j = 1, ..., m), β j is the lasso coefficient of the jth protein marker, and X ij is the expression level of the jth protein for the ith patient. (Equation 2)The JCI regrets the error.