Plasma sCD146 is a potential biomarker for acute exacerbation of chronic obstructive pulmonary disease.

This study examined the levels of soluble CD146 (sCD146) in plasma samples from patients with chronic obstructive pulmonary disease (COPD) and assessed the relationship between sCD146 and the severity of COPD. A total of 97 COPD patients were recruited from 20 medical centers in Jiangsu, China, including 13 stable subjects and 84 exacerbated subjects. The plasma sCD146 level in exacerbated subjects (28.77 ± 10.80 ng/mL) was significantly lower than that in stable subjects (38.84 ± 15.00 ng/mL). In the high sCD146 group, the proportion of subjects with modified Medical Research Council (mMRC) scores of 0-1 was higher, the proportion of subjects with the Global Initiative for Chronic Obstructive Lung Disease (GOLD) stage 4 was lower, and the proportion of subjects with ≥1 hospitalizations in the past year was lower. The plasma sCD146 level was negatively correlated with the COPD Assessment Test (CAT) score (r = -0.2664, p = 0.0087). Logistic regression analysis showed that sCD146 was an independent risk factor for acute exacerbation of COPD (AECOPD). Receiver operating characteristic (ROC) analysis suggested that sCD146 combined with sex, age, pulmonary function, and acute exacerbations in the past year had clinical value for the accurate identification of AECOPD, with an area under the ROC curve (AUC) of 0.908 (95% CI: 0.810-1.000, p < 0.001). In addition, there was a significant negative correlation between plasma sCD146 and S100A9 (r = -0.3939, p < 0.001).

IFN-γ decreases PD-1 in T lymphocytes from convalescent COVID-19 patients via the AKT/GSK3ß signaling pathway.

Post-COVID-19 syndrome may be associated with the abnormal immune status. Compared with the unexposed age-matched elder group, PD-1 in the CD8+ T cells from recovered COVID-19 patients was significantly lower. IFN-γ in the plasma of COVID-19 convalescent patients was increased, which inhibited PD-1 expression in CD8+ T cells from COVID-19 convalescent patients. scRNA-seq bioinformatics analysis revealed that AKT/GSK3ß may regulate the INF-γ/PD-1 axis in CD8+ T cells from COVID-19 convalescent patients. In parallel, an IFN-γ neutralizing antibody reduced AKT and increased GSK3ß in PBMCs. An AKT agonist (SC79) significantly decreased p-GSK3ß. Moreover, AKT decreased PD-1 on CD8+ T cells, and GSK3ß increased PD-1 on CD8+ T cells according to flow cytometry analysis. Collectively, we demonstrated that recovered COVID-19 patients may develop long COVID. Increased IFN-γ in the plasma of recovered Wuhan COVID-19 patients contributed to PD-1 downregulation on CD8+ T cells by regulating the AKT/GSK3ß signaling pathway.

LincR-PPP2R5C Promotes Th2 Cell Differentiation Through PPP2R5C/PP2A by Forming an RNA-DNA Triplex in Allergic Asthma.

PURPOSE: The roles and mechanisms of long noncoding RNAs (lncRNAs) in T helper 2 (Th2) differentiation from allergic asthma are poorly understood. We aimed to explore a novel lncRNA, LincR-protein phosphatase 2 regulatory subunit B' gamma (PPP2R5C), in Th2 differentiation in a mouse model of asthma. METHODS: LincR-PPP2R5C from RNA-seq data of CD4+ T cells of asthma-like mice were validated and confirmed by quantitative reverse transcription polymerase chain reaction, northern blotting, nuclear and cytoplasmic separation, and fluorescence in situ hybridization (FISH). Lentiviruses encoding LincR-PPP2R5C or shRNA were used to overexpress or silence LincR-PPP2R5C in CD4+ T cells. The interactions between LincR-PPP2R5C and PPP2R5C were explored with western blotting, chromatin isolation by RNA purification assay, and fluorescence resonance energy transfer. An ovalbumin-induced acute asthma model in knockout (KO) mice (LincR-PPP2R5C KO, CD4 conditional LincR-PPP2R5C KO) was established to explore the roles of LincR-PPP2R5C in Th2 differentiation. RESULTS: LncR-PPP2R5C was significantly higher in CD4+ T cells from asthmatic mice ex vivo and Th2 cells in vitro. The lentivirus encoding LincR-PPP2R5C suppressed Th1 differentiation; in contrast, the short hairpin RNA (shRNA) lentivirus decreased LincR-PPP2R5C and Th2 differentiation. Mechanistically, LincR-PPP2R5C deficiency suppressed the phosphatase activity of the protein phosphatase 2A (PP2A) holocomplex, resulting in a decline in Th2 differentiation. The formation of an RNA-DNA triplex between LincR-PPP2R5C and the PPP2R5C promoter enhanced PPP2R5C expression and activated PP2A. LincR-PPP2R5C KO and CD4 conditional KO decreased Th2 differentiation, airway hyperresponsiveness and inflammatory responses. CONCLUSIONS: LincR-PPP2R5C regulated PPP2R5C expression and PP2A activity by forming an RNA-DNA triplex with the PPP2R5C promoter, leading to Th2 polarization in a mouse model of acute asthma. Our data presented the first definitive evidence of lncRNAs in the regulation of Th2 cells in asthma.

CD146 deficiency aggravates chronic obstructive pulmonary disease via the increased production of S100A9 and MMP-9 in macrophages.

Chronic obstructive pulmonary disease (COPD) is a leading cause of global death. As a molecule beyond adhesion, CD146 is involved in COPD pathogenesis. However, the mechanisms of CD146 in COPD remain largely elusive. We hypothesized that CD146 regulates the production of matrix metalloproteinase-9 (MMP-9) in macrophages and thereby contributes to COPD. Here, we constructed a murine model of COPD using lipopolysaccharide (LPS) and porcine pancreatic elastase (PPE). In COPD-like mice, LPS and PPE decreased the pulmonary expression of CD146. MMP-9 expression and bioactivity were increased in CD146 knockout COPD-like mice. In vitro, LPS decreased CD146 expression in macrophages. With or without LPS challenge, CD146-defective macrophages produced more MMP-9. Transcriptome analysis based on next-generation sequencing (NGS) revealed that S100A9 regulated MMP-9 production in CD146-defective macrophages. Targeting S100A9 with paquinimod decreased lung inflammation and alleviated alveolar destruction in COPD-like mice. Collectively, our study suggests that CD146 negatively regulates MMP-9 production in macrophages via the S100A9 pathway in COPD.

CD146 deficiency promotes inflammatory type 2 responses in pulmonary cryptococcosis.

Cryptococcus neoformans (C. neoformans) is an important opportunistic fungal pathogen for pulmonary cryptococcosis. Previously, we demonstrated that CD146 mediated the adhesion of C. neoformans to the airway epithelium. CD146 is more than an adhesion molecule. In the present study, we aimed to explore the roles of CD146 in the inflammatory response in pulmonary cryptococcosis. CD146 was decreased in lung tissues from patients with pulmonary cryptococcosis. Similarly, C. neoformans reduced pulmonary CD146 expression in mice following intratracheal inoculation. To explore the pathological roles of CD146 reduction in pulmonary cryptococcosis, CD146 knockout (KO) mice were inoculated with C. neoformans via intratracheal instillation. CD146 deficiency aggravated C. neoformans infection, as evidenced by a shortened survival time and increased fungal burdens in the lung. Inflammatory type 2 cytokines (IL-4, IL-5, and TNF-α) and alternatively activated macrophages were increased in the pulmonary tissues of CD146 KO-infected mice. CD146 is expressed in immune cells (macrophages, etc.) and nonimmune cells, i.e., epithelial cells and endothelial cells. Bone marrow chimeric mice were established and infected with C. neoformans. CD146 deficiency in immune cells but not in nonimmune cells increased fungal burdens in the lung. Mechanistically, upon C. neoformans challenge, CD146 KO macrophages produced more neutrophil chemokine KC and inflammatory cytokine TNF-α. Meanwhile, CD146 KO macrophages decreased the fungicidity and production of reactive oxygen species. Collectively, C. neoformans infection decreased CD146 in pulmonary tissues, leading to inflammatory type 2 responses, while CD146 deficiency worsened pulmonary cryptococcosis.

Proline is increased in allergic asthma and promotes airway remodeling.

Proline and its synthesis enzyme pyrroline-5-carboxylate reductase 1 (PYCR1) are implicated in epithelial-mesenchymal transition (EMT), yet how proline and PYCR1 function in allergic asthmatic airway remodeling via EMT has not yet been addressed to our knowledge. In the present study, increased levels of plasma proline and PYCR1 were observed in patients with asthma. Similarly, proline and PYCR1 in lung tissues were high in a murine allergic asthma model induced by house dust mites (HDMs). Pycr1 knockout decreased proline in lung tissues, with reduced airway remodeling and EMT. Mechanistically, loss of Pycr1 restrained HDM-induced EMT by modulating mitochondrial fission, metabolic reprogramming, and the AKT/mTORC1 and WNT3a/ß-catenin signaling pathways in airway epithelial cells. Therapeutic inhibition of PYCR1 in wild-type mice disrupted HDM-induced airway inflammation and remodeling. Deprivation of exogenous proline relieved HDM-induced airway remodeling to some extent. Collectively, this study illuminates that proline and PYCR1 involved with airway remodeling in allergic asthma could be viable targets for asthma treatment.

Spatial-temporal data-augmentation-based functional brain network analysis for brain disorders identification.

Introduction: Due to the lack of devices and the difficulty of gathering patients, the small sample size is one of the most challenging problems in functional brain network (FBN) analysis. Previous studies have attempted to solve this problem of sample limitation through data augmentation methods, such as sample transformation and noise addition. However, these methods ignore the unique spatial-temporal information of functional magnetic resonance imaging (fMRI) data, which is essential for FBN analysis. Methods: To address this issue, we propose a spatial-temporal data-augmentation-based classification (STDAC) scheme that can fuse the spatial-temporal information, increase the samples, while improving the classification performance. Firstly, we propose a spatial augmentation module utilizing the spatial prior knowledge, which was ignored by previous augmentation methods. Secondly, we design a temporal augmentation module by random discontinuous sampling period, which can generate more samples than former approaches. Finally, a tensor fusion method is used to combine the features from the above two modules, which can make efficient use of spatial-temporal information of fMRI simultaneously. Besides, we apply our scheme to different types of classifiers to verify the generalization performance. To evaluate the effectiveness of our proposed scheme, we conduct extensive experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and REST-meta-MDD Project (MDD) dataset. Results: Experimental results show that the proposed scheme achieves superior classification accuracy (ADNI: 82.942%, MDD: 63.406%) and feature interpretation on the benchmark datasets. Discussion: The proposed STDAC scheme, utilizing both spatial and temporal information, can generate more diverse samples than former augmentation methods for brain disorder classification and analysis.

The reconfiguration pattern of individual brain metabolic connectome for Parkinson's disease identification.

18F-Fluorodeoxyglucose positron emission tomography (18F-FDG PET) is widely employed to reveal metabolic abnormalities linked to Parkinson's disease (PD) at a systemic level. However, the individual metabolic connectome details with PD based on 18F-FDG PET remain largely unknown. To alleviate this issue, we derived a novel brain network estimation method for individual metabolic connectome, that is, Jensen-Shannon Divergence Similarity Estimation (JSSE). Further, intergroup difference between the individual's metabolic brain network and its global/local graph metrics was analyzed to investigate the metabolic connectome's alterations. To further improve the PD diagnosis performance, multiple kernel support vector machine (MKSVM) is conducted for identifying PD from normal control (NC), which combines both topological metrics and connection. Resultantly, PD individuals showed higher nodal topological properties (including assortativity, modularity score, and characteristic path length) than NC individuals, whereas global efficiency and synchronization were lower. Moreover, 45 most significant connections were affected. Further, consensus connections in occipital, parietal, and frontal regions were decrease in PD while increase in subcortical, temporal, and prefrontal regions. The abnormal metabolic network measurements depicted an ideal classification in identifying PD of NC with an accuracy up to 91.84%. The JSSE method identified the individual-level metabolic connectome of 18F-FDG PET, providing more dimensional and systematic mechanism insights for PD.

Deep learning using a residual deconvolutional network enables real-time high-density single-molecule localization microscopy.

High-density localization based on deep learning is a very effective method to accelerate single molecule localization microscopy (SMLM). Compared with traditional high-density localization methods, deep learning-based methods enable a faster data processing speed and a higher localization accuracy. However, the reported high-density localization methods based on deep learning are still not fast enough to enable real time data processing for large batches of raw images, which is probably due to the heavy computational burden and computation complexity in the U-shape architecture used in these models. Here we propose a high-density localization method called FID-STORM, which is based on an improved residual deconvolutional network for the real-time processing of raw images. In FID-STORM, we use a residual network to extract the features directly from low-resolution raw images rather than the U-shape network from interpolated images. We also use a model fusion from TensorRT to further accelerate the inference of the model. In addition, we process the sum of the localization images directly on GPU to obtain an additional speed gain. Using simulated and experimental data, we verified that the FID-STORM method achieves a processing speed of 7.31 ms/frame at 256 × 256 pixels @ Nvidia RTX 2080 Ti graphic card, which is shorter than the typical exposure time of 10∼30 ms, thus enabling real-time data processing in high-density SMLM. Moreover, compared with a popular interpolated image-based method called Deep-STORM, FID-STORM enables a speed gain of ∼26 times, without loss of reconstruction accuracy. We also provided an ImageJ plugin for our new method.

Deep-Learning-Based COVID-19 Diagnosis and Implementation in Embedded Edge-Computing Device.

The rapid spread of coronavirus disease 2019 (COVID-19) has posed enormous challenges to the global public health system. To deal with the COVID-19 pandemic crisis, the more accurate and convenient diagnosis of patients needs to be developed. This paper proposes a deep-learning-based COVID-19 detection method and evaluates its performance on embedded edge-computing devices. By adding an attention module and mixed loss into the original VGG19 model, the method can effectively reduce the parameters of the model and increase the classification accuracy. The improved model was first trained and tested on the PC X86 GPU platform using a large dataset (COVIDx CT-2A) and a medium dataset (integrated CT scan); the weight parameters of the model were reduced by around six times compared to the original model, but it still approximately achieved 98.80%and 97.84% accuracy, outperforming most existing methods. The trained model was subsequently transferred to embedded NVIDIA Jetson devices (TX2, Nano), where it achieved 97% accuracy at a 0.6-1 FPS inference speed using the NVIDIA TensorRT engine. The experimental results demonstrate that the proposed method is practicable and convenient; it can be used on a low-cost medical edge-computing terminal. The source code is available on GitHub for researchers.

Fine particulate matter (PM2.5) induces inhibitory memory alveolar macrophages through the AhR/IL-33 pathway.

Fine particulate matter (PM2.5) concentrations have decreased in the past decade. The adverse effects of acute PM2.5 exposure on respiratory diseases have been well recognized. To explore the long-term effects of PM2.5 exposure on chronic obstructive pulmonary disease (COPD), mice were exposed to PM2.5 for 7 days and rest for 21 days, followed by challenges with lipopolysaccharide (LPS) and porcine pancreatic elastase (PPE). Unexpectedly, PM2.5 exposure and rest alleviated the disease severity and airway inflammatory responses in COPD-like mice. Although acute PM2.5 exposure increased airway inflammation, rest for 21 days reversed the airway inflammatory responses, which was associated with the induction of inhibitory memory alveolar macrophages (AMs). Similarly, polycyclic aromatic hydrocarbons (PAHs) in PM2.5 exposure and rest decreased pulmonary inflammation, accompanied by inhibitory memory AMs. Once AMs were depleted, pulmonary inflammation was aggravated. PAHs in PM2.5 promoted the secretion of IL-33 from airway epithelial cells via the aryl hydrocarbon receptor (AhR)/ARNT pathway. High-throughput mRNA sequencing revealed that PM2.5 exposure and rest drastically changed the mRNA profiles in AMs, which was largely rescued in IL-33-/- mice. Collectively, our results indicate that PM2.5 may mitigate pulmonary inflammation, which is mediated by inhibitory trained AMs via IL-33 production from epithelial cells through the AhR/ARNT pathway. We provide the rationale that PM2.5 plays complicated roles in respiratory disease.

FOXA2 plays a critical role in hepatocellular carcinoma progression and lenvatinib-associated drug resistance.

Hepatic forkhead box protein A2 (FOXA2) is a crucial transcription factor for liver development and metabolic homeostasis. However, its role in hepatocellular carcinoma (HCC) progression and lenvatinib-related drug resistance remains unknown. In this study, the level of FOXA2 expression was found to be lower in HCC tissues than in paired adjacent tumor tissues. A low level of FOXA2 expression was associated with aggressive tumor characteristics (vascular invasion and poor differentiation). A low level of FOXA2 expression was found to be an independent risk factor for tumor recurrence (hazard ratio (HR): 1.899, P < 0.001) and long-term survival (HR: 2.011, P = 0.003) in HCC patients after hepatectomy. In xenograft animal models, FOXA2 overexpression significantly inhibited tumor growth. Moreover, FOXA2 overexpression was found to enhance the inhibitory effect of lenvatinib on HCC cells by upregulating the adenosine monophosphate-activated protein kinase-mechanistic target of rapamycin (AMPK-mTOR) pathway. Conversely, inhibition of adenosine monophosphate-activated protein kinase (AMPK) or stimulation of mechanistic target of rapamycin (mTOR) attenuated the sensitization of cells overexpressing FOXA2 to lenvatinib. Similarly, FOXA2 overexpression augmented the antitumor effect of lenvatinib in animal models with xenograft tumors. FOXA2 overexpression increased autophagy in HCC cells treated with lenvatinib. Lenvatinib treatment activated the platelet-derived growth factor receptor-extracellular regulated protein kinase (PDGFR-ERK) pathway in HCC. FOXA2 overexpression further downregulated the PDGFR-ERK pathway through the activation of the AMPK-mTOR axis. In conclusion, FOXA2 was identified as an independent risk factor for HCC after hepatectomy. FOXA2 was found to be closely associated with the biological progression of HCC. By modulating the AMPK-mTOR-autophagy signaling pathway, FOX2 significantly augmented antitumor effect of lenvatinib in HCC.

A Similarity Measure-Based Approach Using RS-fMRI Data for Autism Spectrum Disorder Diagnosis.

Autism spectrum disorder (ASD) is a lifelong neurological disease, which seriously reduces the patients' life quality. Generally, an early diagnosis is beneficial to improve ASD children's life quality. Current methods based on samples from multiple sites for ASD diagnosis perform poorly in generalization due to the heterogeneity of the data from multiple sites. To address this problem, this paper presents a similarity measure-based approach for ASD diagnosis. Specifically, the few-shot learning strategy is used to measure potential similarities in the RS-fMRI data distributions, and, furthermore, a similarity function for samples from multiple sites is trained to enhance the generalization. On the ABIDE database, the presented approach is compared to some representative methods, such as SVM and random forest, in terms of accuracy, precision, and F1 score. The experimental results show that the experimental indicators of the proposed method are better than those of the comparison methods to varying degrees. For example, the accuracy on the TRINITY site is more than 5% higher than that of the comparison method, which clearly proves that the presented approach achieves a better generalization performance than the compared methods.

A comparative study of IL-33 and its receptor ST2 in a C57BL/6 J mouse model of pulmonary Cryptococcus neoformans infection.

It has been reported that IL-33 receptor ST2 deficiency mitigates Cryptococcus neoformans (C. neoformans) pulmonary infection in BALB/c mice. IL-33 may modulate immune responses in ST2-dependent and ST2-independent manners. The host genetic background (i.e., BALB/c, C57BL/6 J) influences immune responses against C. neoformans. In the present study, we aimed to explore the roles of IL-33 and ST2 in pulmonary C. neoformans-infected mice on a C57BL/6 J genetic background. C. neoformans infection increased IL-33 expression in lung tissues. IL-33 deficiency but not ST2 deficiency significantly extended the survival time of C. neoformans-infected mice. In contrast, either IL-33 or ST2 deficiency reduced fungal burdens in lung, spleen and brain tissues from the mice following C. neoformans intratracheal inoculation. Similarly, inflammatory responses in the lung tissues were more pronounced in both the IL-33-/- and ST2-/- infected mice. However, mucus production was decreased in IL-33-/- infected mice alone, and the level of IL-5 in bronchoalveolar lavage fluid (BALF) was substantially decreased in the IL-33-/- infected mice but not ST2-/- infected mice. Moreover, IL-33 deficiency but not ST2 deficiency increased iNOS-positive macrophages. At the early stage of infection, the reduced pulmonary fungal burden in the IL-33-/- and ST2-/- mice was accompanied by increased neutrophil infiltration. Collectively, IL-33 regulated pulmonary C. neoformans infection in an ST2-dependent and ST2-independent manner in C57BL/6 J mice.

Exosomes in malignant pleural effusion from lung cancer patients impaired the cytotoxicity of double-negative T cells.

CD3+CD4-CD8- double-negative T (DNT) cells are new weapons in cancer immunotherapy. Here, we explored DNT cells in malignant pleural effusions (MPEs) from lung cancer patients. DNT cells, especially TCRαß+CD56- DNT cells, were increased in MPE from lung cancer patients. DNT cells highly expressed PD-1, TRAIL, NKG2D and DNAM-1. In contrast, FasL was barely detected in DNT cells. Compared with non-MPE cells, MPE-derived DNT cells expressed much higher levels of PD-1 and TRAIL. DNT cells from healthy peripheral blood donors potentially killed lung cancers, which was decreased by MPE supernatant. Exosomes from MPE supernatant expressed PD-1 and CEACAM1 and impaired the cytotoxicity of DNT cells. Blocking PD-1 and TIM3 rescued the cytotoxicity of DNT cells treated with MPE-derived exosomes. Overall, we demonstrated that the frequency of DNT cells in MPE from lung cancer patients was increased and that MPE-derived exosomes impaired the cytotoxicity of DNT cells via the PD-1/PD-L1 and CEACAM1/TIM3 pathways.

ResNet-based image inpainting method for enhancing the imaging speed of single molecule localization microscopy.

Single molecule localization microscopy (SMLM) is a mainstream method in the field of super-resolution fluorescence microscopy that can achieve a spatial resolution of 20∼30 nm through a simple optical system. SMLM usually requires thousands of raw images to reconstruct a super-resolution image, and thus suffers from a slow imaging speed. Recently, several methods based on image inpainting have been developed to enhance the imaging speed of SMLM. However, these image inpainting methods may also produce erroneous local features (or called image artifacts), for example, incorrectly joined or split filaments. In this study, we use the ResNet generator, a network with strong local feature extraction capability, to replace the popularly-used U-Net generator to minimize the image artifact problem in current image inpainting methods, and develop an image inpainting method called DI-STORM. We validate our method using both simulated and experimental data, and demonstrate that DI-STORM has the best acceleration capability and produces the least artifacts in the repaired images, as compared with VDSR (the simplest CNN-based image inpainting method in SMLM) and ANNA-PALM (the best GAN-based image inpainting method in SMLM). We believe that DI-STORM could facilitate the application of deep learning-based image inpainting methods for SMLM.

Selecting Multiple Node Statistics Jointly from Functional Connectivity Networks for Brain Disorders Identification.

Functional connectivity networks (FCN) analysis is instructive for the diagnosis of brain diseases, such as mild cognitive impairment (MCI) and major depressive disorder (MDD) at their early stages. As the critical step of FCN analysis, feature representation provides the basis for finding potential biomarkers of brain diseases. In previous studies, different node statistics (e.g. local efficiency and local clustering coefficients) are usually extracted from FCNs as features for the diagnosis/classification task, which can specifically locate disease-related regions on the node level, so as to help us understand the neurodevelopmental roots of brain disorders. However, each node statistic is proposed only considering a kind of specific network property, which has one-sidedness and limitations. As a result, it is incomplete to represent a node with only one statistic. To resolve this issue, we put forward a novel scheme to select multiple node statistics jointly from the estimated FCNs for automated classification, called multiple node statistics feature selection (MNSFS). Specifically, we first extract multiple statistics from the same nodes and assign each kind of statistic into a group. Then, sparse group least absolute shrinkage and selection operator (sgLASSO) is used to select groups (nodes) and statistics in the groups towards a better classification performance. Such a technique enables us to simultaneously locate the discriminative brain regions, as well as the specific statistics associated with these brain regions, making the classification results more interpretable. We conducted our scheme on two public databases for identifying subjects with MCI and MDD from normal controls. Experimental results show that the proposed scheme achieves superior classification accuracy and features interpreted on the benchmark datasets.

KIF2A decreases IL-33 production and attenuates allergic asthmatic inflammation.

BACKGROUND: The microtubule-dependent molecular motor protein Kinesin Family Member 2A (KIF2A) is down-regulated in asthmatic human airway epithelium. However, little is known about the roles of KIF2A as well as the possible underlying mechanisms in asthma. METHODS: House dust mite (HDM) extract was administered to establish a murine model of asthma. The expression of KIF2A, IL-33 and the autophagy pathways were detected. The plasmid pCMV-KIF2A was used to overexpress KIF2A in the airway epithelial cells in vitro and in vivo. IL-4, IL-5, IL-33 and other cytokines in bronchoalveolar lavage fluid (BALF) and lung tissues homogenates were measured. RESULTS: In response to the challenge of house dust mite (HDM) in vitro and in vivo, airway epithelial cells displayed decreased production of KIF2A. Meanwhile, autophagy and IL-33 were increased in HMD-treated epithelial cells. Mechanistically, KIF2A decreased autophagy via suppressing mTORC1 pathway in HDM-treated epithelial cells, which contributed to the reduced production of IL-33. Moreover, in vivo KIF2A transfection reduced IL-33 and autophagy in the lung, leading to the attenuation of allergic asthma. CONCLUSION: KIF2A suppressed mTORC1-mediated autophagy and decreased the production of epithelial-derived cytokine IL-33 in allergic airway inflammation. These data indicate that KIF2A may be a novel target in allergic asthma.

SARS-CoV-2 in the pancreas and the impaired islet function in COVID-19 patients.

Diabetes mellitus (DM) is one of the most common underlying diseases that may aggravates COVID-19. In the present study, we explored islet function, the presence of SARS-CoV-2 and pathological changes in the pancreas of patients with COVID-19. Oral glucose tolerance tests (OGTTs) and the C-peptide release test demonstrated a decrease in glucose-stimulated C-peptide secretory capacity and an increase in HbA1c levels in patients with COVID-19. The prediabetic conditions appeared to be more significant in the severe group than in the moderate group. SARS-CoV-2 receptors (ACE2, CD147, TMPRSS2 and neuropilin-1) were expressed in pancreatic tissue. In addition to SARS-CoV-2 virus spike protein and virus RNA, coronavirus-like particles were present in the autophagolysosomes of pancreatic acinar cells of a patient with COVID-19. Furthermore, the expression and distribution of various proteins in pancreatic islets of patients with COVID-19 were altered. These data suggest that SARS-CoV-2 in the pancreas may directly or indirectly impair islet function.