GPR34-mediated sensing of lysophosphatidylserine released by apoptotic neutrophils activates type 3 innate lymphoid cells to mediate tissue repair.

Neutrophils migrate rapidly to damaged tissue and play critical roles in host defense and tissue homeostasis. Here we investigated the mechanisms whereby neutrophils participate in tissue repair. In an intestinal epithelia injury model, neutrophil depletion exacerbated colitis and associated with reduced interleukin (IL)-22 and limited activation of type 3 innate lymphoid cells (ILC3s). Co-culture with neutrophils activated ILC3s in a manner dependent on neutrophil apoptosis. Metabolomic analyses revealed that lysophosphatidylserine (LysoPS) from apoptotic neutrophils directly stimulated ILC3 activation. ILC3-specific deletion of Gpr34, encoding the LysoPS receptor GPR34, or inhibition of downstream PI3K-AKT or ERK suppressed IL-22 production in response to apoptotic neutrophils. Gpr34-/- mice exhibited compromised ILC3 activation and tissue repair during colon injury, and neutrophil depletion abrogated these defects. GPR34 deficiency in ILC3s limited IL-22 production and tissue repair in vivo in settings of colon and skin injury. Thus, GPR34 is an ILC3-expressed damage-sensing receptor that triggers tissue repair upon recognition of dying neutrophils.

Dopamine controls systemic inflammation through inhibition of NLRP3 inflammasome.

Inflammasomes are involved in diverse inflammatory diseases, so the activation of inflammasomes needs to be tightly controlled to prevent excessive inflammation. However, the endogenous regulatory mechanisms of inflammasome activation are still unclear. Here, we report that the neurotransmitter dopamine (DA) inhibits NLRP3 inflammasome activation via dopamine D1 receptor (DRD1). DRD1 signaling negatively regulates NLRP3 inflammasome via a second messenger cyclic adenosine monophosphate (cAMP), which binds to NLRP3 and promotes its ubiquitination and degradation via the E3 ubiquitin ligase MARCH7. Importantly, in vivo data show that DA and DRD1 signaling prevent NLRP3 inflammasome-dependent inflammation, including neurotoxin-induced neuroinflammation, LPS-induced systemic inflammation, and monosodium urate crystal (MSU)-induced peritoneal inflammation. Taken together, our results reveal an endogenous mechanism of inflammasome regulation and suggest DRD1 as a potential target for the treatment of NLRP3 inflammasome-driven diseases.

RNA viruses promote activation of the NLRP3 inflammasome through a RIP1-RIP3-DRP1 signaling pathway.

The NLRP3 inflammasome functions as a crucial component of the innate immune system in recognizing viral infection, but the mechanism by which viruses activate this inflammasome remains unclear. Here we found that inhibition of the serine-threonine kinases RIP1 (RIPK1) or RIP3 (RIPK3) suppressed RNA virus-induced activation of the NLRP3 inflammasome. Infection with an RNA virus initiated assembly of the RIP1-RIP3 complex, which promoted activation of the GTPase DRP1 and its translocation to mitochondria to drive mitochondrial damage and activation of the NLRP3 inflammasome. Notably, the RIP1-RIP3 complex drove the NLRP3 inflammasome independently of MLKL, an essential downstream effector of RIP1-RIP3-dependent necrosis. Together our results reveal a specific role for the RIP1-RIP3-DRP1 pathway in RNA virus-induced activation of the NLRP3 inflammasome and establish a direct link between inflammation and cell-death signaling pathways.

A penalized linear mixed model with generalized method of moments for prediction analysis on high-dimensional multi-omics data.

With the advances in high-throughput biotechnologies, high-dimensional multi-layer omics data become increasingly available. They can provide both confirmatory and complementary information to disease risk and thus have offered unprecedented opportunities for risk prediction studies. However, the high-dimensionality and complex inter/intra-relationships among multi-omics data have brought tremendous analytical challenges. Here we present a computationally efficient penalized linear mixed model with generalized method of moments estimator (MpLMMGMM) for the prediction analysis on multi-omics data. Our method extends the widely used linear mixed model proposed for genomic risk predictions to model multi-omics data, where kernel functions are used to capture various types of predictive effects from different layers of omics data and penalty terms are introduced to reduce the impact of noise. Compared with existing penalized linear mixed models, the proposed method adopts the generalized method of moments estimator and it is much more computationally efficient. Through extensive simulation studies and the analysis of positron emission tomography imaging outcomes, we have demonstrated that MpLMMGMM can simultaneously consider a large number of variables and efficiently select those that are predictive from the corresponding omics layers. It can capture both linear and nonlinear predictive effects and achieves better prediction performance than competing methods.

A penalized linear mixed model with generalized method of moments estimators for complex phenotype prediction.

MOTIVATION: Linear mixed models (LMMs) have long been the method of choice for risk prediction analysis on high-dimensional data. However, it remains computationally challenging to simultaneously model a large amount of variants that can be noise or have predictive effects of complex forms. RESULTS: In this work, we have developed a penalized LMM with generalized method of moments (pLMMGMM) estimators for prediction analysis. pLMMGMM is built within the LMM framework, where random effects are used to model the joint predictive effects from all variants within a region. Different from existing methods that focus on linear relationships and use empirical criteria for variable screening, pLMMGMM can efficiently detect regions that harbor genetic variants with both linear and non-linear predictive effects. In addition, unlike existing LMMs that can only handle a very limited number of random effects, pLMMGMM is much less computationally demanding. It can jointly consider a large number of regions and accurately detect those that are predictive. Through theoretical investigations, we have shown that our method has the selection consistency and asymptotic normality. Through extensive simulations and the analysis of PET-imaging outcomes, we have demonstrated that pLMMGMM outperformed existing models and it can accurately detect regions that harbor risk factors with various forms of predictive effects. AVAILABILITY AND IMPLEMENTATION: The R-package is available at https://github.com/XiaQiong/GMMLasso. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A U-statistics for integrative analysis of multilayer omics data.

MOTIVATION: The emerging multilayer omics data provide unprecedented opportunities for detecting biomarkers that are associated with complex diseases at various molecular levels. However, the high-dimensionality of multiomics data and the complex disease etiologies have brought tremendous analytical challenges. RESULTS: We developed a U-statistics-based non-parametric framework for the association analysis of multilayer omics data, where consensus and permutation-based weighting schemes are developed to account for various types of disease models. Our proposed method is flexible for analyzing different types of outcomes as it makes no assumptions about their distributions. Moreover, it explicitly accounts for various types of underlying disease models through weighting schemes and thus provides robust performance against them. Through extensive simulations and the application to dataset obtained from the Alzheimer's Disease Neuroimaging Initiatives, we demonstrated that our method outperformed the commonly used kernel regression-based methods. AVAILABILITY AND IMPLEMENTATION: The R-package is available at https://github.com/YaluWen/Uomic. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Plant Lectins Activate the NLRP3 Inflammasome To Promote Inflammatory Disorders.

Plant-derived dietary lectins have been reported to be involved in the pathogenesis of several inflammatory diseases, including inflammatory bowel disease, diabetes, rheumatoid arthritis, and celiac disease, but little is known about the molecular mechanisms underlying lectin-induced inflammation. In this study, we showed that plant lectins can induce caspase-1 activation and IL-1ß secretion via the NLRP3 inflammasome. Lectins were internalized and subsequently escaped from the lysosome and then translocated to the endoplasmic reticulum. Endoplasmic reticulum-loaded plant lectins then triggered Ca2+ release and mitochondrial damage, and inhibition of Ca2+ release and mitochondrial reactive oxygen species by chemical inhibitors significantly suppressed NLRP3 inflammasome activation. In vivo, plant lectin-induced inflammation and tissue damage also depended on the NLRP3 inflammasome. Our findings indicate that plant lectins can act as an exogenous "danger signal" that can activate the NLRP3 inflammasome and suggest that dietary lectins might promote inflammatory diseases via the NLRP3 inflammasome.

GPR34 senses demyelination to promote neuroinflammation and pathologies.

Sterile neuroinflammation is a major driver of multiple neurological diseases. Myelin debris can act as an inflammatory stimulus to promote inflammation and pathologies, but the mechanism is poorly understood. Here, we showed that lysophosphatidylserine (LysoPS)-GPR34 axis played a critical role in microglia-mediated myelin debris sensing and the subsequent neuroinflammation. Myelin debris-induced microglia activation and proinflammatory cytokine expression relied on its lipid component LysoPS. Both myelin debris and LysoPS promoted microglia activation and the production of proinflammatory cytokines via GPR34 and its downstream PI3K-AKT and ERK signaling. In vivo, reducing the content of LysoPS in myelin or inhibition of GPR34 with genetic or pharmacological approaches reduced neuroinflammation and pathologies in the mouse models of multiple sclerosis and stroke. Thus, our results identify GPR34 as a key receptor to sense demyelination and CNS damage and promote neuroinflammation, and suggest it as a potential therapeutic target for demyelination-associated diseases.

Prediction of Parkinson's Disease Using Machine Learning Methods.

The detection of Parkinson's disease (PD) in its early stages is of great importance for its treatment and management, but consensus is lacking on what information is necessary and what models should be used to best predict PD risk. In our study, we first grouped PD-associated factors based on their cost and accessibility, and then gradually incorporated them into risk predictions, which were built using eight commonly used machine learning models to allow for comprehensive assessment. Finally, the Shapley Additive Explanations (SHAP) method was used to investigate the contributions of each factor. We found that models built with demographic variables, hospital admission examinations, clinical assessment, and polygenic risk score achieved the best prediction performance, and the inclusion of invasive biomarkers could not further enhance its accuracy. Among the eight machine learning models considered, penalized logistic regression and XGBoost were the most accurate algorithms for assessing PD risk, with penalized logistic regression achieving an area under the curve of 0.94 and a Brier score of 0.08. Olfactory function and polygenic risk scores were the most important predictors for PD risk. Our research has offered a practical framework for PD risk assessment, where necessary information and efficient machine learning tools were highlighted.

Integrated proteomic and transcriptomic landscape of macrophages in mouse tissues.

Macrophages are involved in tissue homeostasis and are critical for innate immune responses, yet distinct macrophage populations in different tissues exhibit diverse gene expression patterns and biological processes. While tissue-specific macrophage epigenomic and transcriptomic profiles have been reported, proteomes of different macrophage populations remain poorly characterized. Here we use mass spectrometry and bulk RNA sequencing to assess the proteomic and transcriptomic patterns, respectively, of 10 primary macrophage populations from seven mouse tissues, bone marrow-derived macrophages and the cell line RAW264.7. The results show distinct proteomic landscape and protein copy numbers between tissue-resident and recruited macrophages. Construction of a hierarchical regulatory network finds cell-type-specific transcription factors of macrophages serving as hubs for denoting tissue and functional identity of individual macrophage subsets. Finally, Il18 is validated to be essential in distinguishing molecular signatures and cellular function features between tissue-resident and recruited macrophages in the lung and liver. In summary, these deposited datasets and our open proteome server ( http://macrophage.mouseprotein.cn ) integrating all information will provide a valuable resource for future functional and mechanistic studies of mouse macrophages.

Synthetic vitamin K analogs inhibit inflammation by targeting the NLRP3 inflammasome.

Vitamin K refers to a group of structurally similar vitamins that are essential for proper blood coagulation, as well as bone and cardiovascular health. Previous studies have indicated that vitamin K may also have anti-inflammatory properties, although the underlying mechanisms of its anti-inflammatory effects remain unclear. The NLRP3 inflammasome is a multiprotein complex, and its activation leads to IL-1ß and IL-18 secretion and contributes to the pathogenesis of various human inflammatory diseases. Here, we show that synthetic vitamins K3 and K4 are selective, potent inhibitors of the NLRP3 inflammasome and specifically block the interaction between NLRP3 and ASC, thereby inhibiting NLRP3 inflammasome assembly. Moreover, we show that treatment with vitamin K3 or K4 attenuates the severity of inflammation in a mouse model of peritonitis. Our results demonstrate that vitamins K3 and K4 exert their anti-inflammatory effects by inhibiting NLRP3 inflammasome activation and indicate that vitamin K supplementation may be a treatment option for NLRP3-associated inflammatory diseases.

Tranilast directly targets NLRP3 to treat inflammasome-driven diseases.

The dysregulation of NLRP3 inflammasome can cause uncontrolled inflammation and drive the development of a wide variety of human diseases, but the medications targeting NLRP3 inflammasome are not available in clinic. Here, we show that tranilast (TR), an old anti-allergic clinical drug, is a direct NLRP3 inhibitor. TR inhibits NLRP3 inflammasome activation in macrophages, but has no effects on AIM2 or NLRC4 inflammasome activation. Mechanismly, TR directly binds to the NACHT domain of NLRP3 and suppresses the assembly of NLRP3 inflammasome by blocking NLRP3 oligomerization. In vivo experiments show that TR has remarkable preventive or therapeutic effects on the mouse models of NLRP3 inflammasome-related human diseases, including gouty arthritis, cryopyrin-associated autoinflammatory syndromes, and type 2 diabetes. Furthermore, TR is active ex vivo for synovial fluid mononuclear cells from patients with gout. Thus, our study identifies the old drug TR as a direct NLRP3 inhibitor and provides a potentially practical pharmacological approach for treating NLRP3-driven diseases.

Perfluorodecanoic acid stimulates NLRP3 inflammasome assembly in gastric cells.

Perfluorodecanoic acid (PFDA), a perfluorinated carboxylic acid, presents in the environment and accumulates in human blood and organs, but its association with tumor promotion are not clear. Given that inflammation plays a significant role in the development of gastric malignancies, we evaluated the effects of PFDA on activation of the inflammasome and inflammation regulation in the gastric cell line AGS. When added to cell cultures, PFDA significantly stimulated IL-1ß and IL18 secretion and their mRNA levels compared with control cells. By RT-PCR and western-blot we found that up-regulation of NLRP3 were associated with promotion of IL-1ß and IL-18 production. Then expression variation of cIAP1/2, c-Rel and p52 were analyzed, the results demonstrated raised mRNA expression in all the tested genes concomitant with enhanced inflammasome activity after exposure to PFDA. Assays with cIAP2 siRNA and NFκB reporter provided additional evidence that these genes were involved in PFDA-induced inflammasome assembly. Furthermore, increased secretion of IL-1ß and IL-18 were detected in stomach of PFDA-treated mice, disorganized alignment of epithelial cells and inflammatory cell infiltration were also observed in the stomach tissues upon PFDA treatment. This study reports for the first time that PFDA regulates inflammasome assembly in human cells and mice tissues.

CLICs-dependent chloride efflux is an essential and proximal upstream event for NLRP3 inflammasome activation.

The NLRP3 inflammasome can sense different pathogens or danger signals, and has been reported to be involved in the development of many human diseases. Potassium efflux and mitochondrial damage are both reported to mediate NLRP3 inflammasome activation, but the underlying, orchestrating signaling events are still unclear. Here we show that chloride intracellular channels (CLIC) act downstream of the potassium efflux-mitochondrial reactive oxygen species (ROS) axis to promote NLRP3 inflammasome activation. NLRP3 agonists induce potassium efflux, which causes mitochondrial damage and ROS production. Mitochondrial ROS then induces the translocation of CLICs to the plasma membrane for the induction of chloride efflux to promote NEK7-NLRP3 interaction, inflammasome assembly, caspase-1 activation, and IL-1ß secretion. Thus, our results identify CLICs-dependent chloride efflux as an essential and proximal upstream event for NLRP3 activation.The NLRP3 inflammasome is key to the regulation of innate immunity against pathogens or stress, but the underlying signaling regulation is still unclear. Here the authors show that chloride intracellular channels (CLIC) interface between mitochondria stress and inflammasome activation to modulate inflammatory responses.

Recognition of gut microbiota by NOD2 is essential for the homeostasis of intestinal intraepithelial lymphocytes.

NOD2 functions as an intracellular sensor for microbial pathogen and plays an important role in epithelial defense. The loss-of-function mutation of NOD2 is strongly associated with human Crohn's disease (CD). However, the mechanisms of how NOD2 maintains the intestinal homeostasis and regulates the susceptibility of CD are still unclear. Here we found that the numbers of intestinal intraepithelial lymphocytes (IELs) were reduced significantly in Nod2(-/-) mice and the residual IELs displayed reduced proliferation and increased apoptosis. Further study showed that NOD2 signaling maintained IELs via recognition of gut microbiota and IL-15 production. Notably, recovery of IELs by adoptive transfer could reduce the susceptibility of Nod2(-/-) mice to the 2,4,6-trinitrobenzene sulfonic acid (TNBS)-induced colitis. Our results demonstrate that recognition of gut microbiota by NOD2 is important to maintain the homeostasis of IELs and provide a clue that may link NOD2 variation to the impaired innate immunity and higher susceptibility in CD.