Activation of leukotriene B4 receptor 1 is a prerequisite for complement receptor 3-mediated antifungal responses of neutrophils.

Invasive fungal infections are life-threatening, and neutrophils are vital cells of the innate immune system that defend against them. The role of LTA4H-LTB4-BLT1 axis in regulation of neutrophil responses to fungal infection remains poorly understood. Here, we demonstrated that the LTA4H-LTB4-BLT1 axis protects the host against Candida albicans and Aspergillus fumigatus, but not Cryptococcus neoformans infection, by regulating the antifungal activity of neutrophils. Our results show that deleting Lta4h or Blt1 substantially impairs the fungal-specific phagocytic capacity of neutrophils. Moreover, defective activation of the spleen tyrosine kinase (Syk) and extracellular signal-related kinase (ERK1/2) pathways in neutrophils accompanies this impairment. Mechanistically, BLT1 regulates CR3-mediated, ß-1,3-glucan-induced neutrophil phagocytosis, while a physical interaction with CR3 with slight influence on its dynamics is observed. Our findings thus demonstrate that the LTA4H-LTB4-BLT1 axis is essential for the phagocytic function of neutrophils in host antifungal immune response against Candida albicans and Aspergillus fumigatus.

Intrinsic Abnormalities of Keratinocytes Initiate Skin Inflammation through the IL-23/T17 Axis in a MALT1-Dependent Manner.

Increasing evidence has supported the crucial role of CARD14 in the pathogenesis of psoriasis, whereas the precise cellular signaling involved in skin physiopathology remains poorly understood. In this article, we show that neither genetic ablation of Il17a nor elimination of T cells was sufficient to restrain the skin inflammation in a CARD14-E138A-mutation-induced psoriasis-like mouse model, whereas depletion of Il23, which extremely blocked the IL-23/T17 axis, was more effective. Targeting CBM complex by conditional deletion of MALT1 or BCL10 in keratinocytes abrogated both the cutaneous and systemic inflammation of heterozygous Card14 E138A/+ mice. Selective inactivation of keratinocyte-specific MALT1 proteolytic activity strongly ameliorated the Card14 E138A/+- and Card14 ΔQ136/+-induced skin disease, which was reproduced by using the imiquimod-induced mouse model. Together, our results suggest a sequence of events under CARD14-mutation-induced psoriasis condition that keratinocyte-intrinsic activation of CBM complex initiates the skin inflammation depending on the IL-23/T17 axis. Targeting keratinocytes by inactivation of MALT1 paracaspase activity might be a promising therapeutic target for early psoriasis treatment.

C-Type Lectin Receptor CD23 Is Required for Host Defense against Candida albicans and Aspergillus fumigatus Infection.

Infection by invasive fungi, such as Candida albicans, Aspergillus fumigatus, and Cryptococcus neoformans, is one of the leading death causes for the increasing population of immunocompromised and immunodeficient patients. Several C-type lectin receptors (CLRs), including Dectin-1, -2, and -3 and Mincle can recognize fungal surface components and initiate the host antifungal immune responses. Nevertheless, it remains to be determined whether other CLRs are involved in antifungal immunity. Our recent study suggests that CD23 (CLEC4J), a CLR and also a well-known B cell surface marker, may function to sense C. albicans components in antifungal immunity. However, it is not clear how CD23 functions as a fungal pattern recognition receptor and whether the antifungal role of CD23 is specific to C. albicans or not. In this study, we show that CD23 can recognize both α-mannan and ß-glucan from the cell wall of C. albicans or A. fumigatus but cannot recognize glucuronoxylomannan from Cryptococcus Through forming a complex with FcRγ, CD23 can induce NF-κB activation. Consistently, CD23-deficient mice were highly susceptible to C. albicans and A. fumigatus but not to C. neoformans infection. The expression of CD23 in activated macrophages is critical for the activation of NF-κB. CD23 deficiency results in impaired expression of NF-κB-dependent genes, especially iNOS, which induces NO production to suppress fungal infection. Together, our studies reveal the CD23-induced signaling pathways and their roles in antifungal immunity, specifically for C. albicans and A. fumigatus, which provides the molecular basis for designing potential therapeutic agents against fungal infection.

Bayesian Nonparametric Regression Modeling of Panel Data for Sequential Classification.

This paper proposes a Bayesian nonparametric regression model of panel data for sequential pattern classification. The proposed method provides a flexible and parsimonious model that allows both time-independent spatial variables and time-dependent exogenous variables to be predictors. Not only this method improves the accuracy of parameter estimation for limited data, but also it facilitates model interpretation by identifying statistically significant predictors with hypothesis testing. Moreover, as the data length approaches infinity, posterior consistency of the model is guaranteed for general data-generating processes under regular conditions. The resulting model of panel data can also be used for sequential classification. The proposed method has been tested by numerical simulation, then validated on an econometric public data set, and subsequently validated for detection of combustion instabilities with experimental data that have been generated in a laboratory environment.

Bayesian Nonparametric Modeling of Categorical Data for Information Fusion and Causal Inference.

This paper presents a nonparametric regression model of categorical time series in the setting of conditional tensor factorization and Bayes network. The underlying algorithms are developed to provide a flexible and parsimonious representation for fusion of correlated information from heterogeneous sources, which can be used to improve the performance of prediction tasks and infer the causal relationship between key variables. The proposed method is first illustrated by numerical simulation and then validated with two real-world datasets: (1) experimental data, collected from a swirl-stabilized lean-premixed laboratory-scale combustor, for detection of thermoacoustic instabilities and (2) publicly available economics data for causal inference-making.