Diaporisoindole B Reduces Lipid Accumulation in THP-1 Macrophage Cells via MAPKs and PPARγ-LXRα Pathways and Promotes the Reverse Cholesterol Transport by Upregulating SR-B1 and LDLR in HepG2 Cells.

Diaporisoindole B (DPB), an isoprenylisoindole alkaloid isolated from the mangrove endophytic fungus Diaporthe sp. SYSU-HQ3, has been proved to have a good anti-inflammatory activity in macrophage cells. In this study, we found that DPB was able to reduce lipid accumulation in THP-1 macrophage-derived foam cells. DPB could inhibit the lipid influx-related gene CD36 and increase the expression of lipid efflux-related genes ATP binding cassette transporter A1 (ABCA1), ATP binding cassette transporter G1 (ABCG1), and scavenger receptor B1 (SR-B1). Moreover, DPB elevated low-density lipoprotein receptor (LDLR) protein expression in HepG2 cells, which can increase the transport of LDL. Meanwhile, DPB could downregulate the expression levels of proteins related to cholesterol and fatty acid synthesis. Further study showed that DPB could activate peroxisome proliferator-activated receptor gamma (PPARγ) and inhibit mitogen-activated protein kinase (MAPK) phosphorylation. Taken together, our findings demonstrated that DPB could reduce lipid accumulation in THP-1 macrophage cells by reducing the intake of lipids and promoting the efflux of lipids and also could promote the reverse cholesterol transport (RCT) mechanism by upregulating SR-B1 and LDLR in HepG2 cells.

Fucose as a potential therapeutic molecule against the immune-mediated inflammation in IgA nepharopathy: An unrevealed link.

Background: IgA nephropathy (IgAN) is an autoimmune disease that affects people of any age and is an important cause of end-stage renal disease. However, the pathogenesis and pathophysiology of IgAN is not clear. This article aimed to explore the immune-mediated inflammation and genetic mechanisms in IgAN. Methods: The transcriptome sequencing data of IgAN glomeruli in the Gene Expression Omnibus database were downloaded. Single-sample gene set enrichment analysis was used to estimate the immune microenvironment of the merged microarray data and GSE141295. IgAN samples were divided into two clusters by cluster analysis. "limma" and "DEseq2" package in R were used to identify differentially expressed genes (DEGs). The weighted gene co-expression network analysis (WGCNA) was used to identify the co-expression modules related to inflammation in IgAN. R software package "clusterProfiler" was used for enrichment analysis, whereas Short Time-Series Expression Miner (STEM) analysis was used to identify the trend of gene expression. Machine-learn (ML) was performed using the shiny app. Finally, Drug Signatures Database (DSigDB) was used to identify potential molecules for treating IgAN. Results: The infiltration of macrophages in IgAN glomeruli was increased, whereas CD4+ T cells, especially inducedregulatory T cells (iTregs) were decreased. A total of 1,104 common DEGs were identified from the merged data and GSE141295. Brown module was identified to have the highest inflammatory correlation with IgAN using WGCNA, and 15 hub genes were screened from this module. Among these 15 hub genes, 14 increased with the severity of IgAN inflammation based on STEM analysis. Neural network (nnet) is considered as the best model to predict the severity of IgAN. Fucose identified from DSigDB has a potential biological activity to treat IgAN. Conclusion: The increase of macrophages and the decrease of iTregs in glomeruli represent the immune-mediated inflammation of IgAN, and fucose may be a potential therapeutic molecule against IgAN because it affects genes involved in the severe inflammation of IgAN.

Differentiation of T Helper 17 Cells May Mediate the Abnormal Humoral Immunity in IgA Nephropathy and Inflammatory Bowel Disease Based on Shared Genetic Effects.

Background: IgA nephropathy (IgAN) is the most frequent glomerulonephritis in inflammatory bowel disease (IBD). However, the inter-relational mechanisms between them are still unclear. This study aimed to explore the shared gene effects and potential immune mechanisms in IgAN and IBD. Methods: The microarray data of IgAN and IBD in the Gene Expression Omnibus (GEO) database were downloaded. The differential expression analysis was used to identify the shared differentially expressed genes (SDEGs). Besides, the shared transcription factors (TFs) and microRNAs (miRNAs) in IgAN and IBD were screened using humanTFDB, HMDD, ENCODE, JASPAR, and ChEA databases. Moreover, weighted gene co-expression network analysis (WGCNA) was used to identify the shared immune-related genes (SIRGs) related to IgAN and IBD, and R software package org.hs.eg.db (Version3.1.0) were used to identify common immune pathways in IgAN and IBD. Results: In this study, 64 SDEGs and 28 SIRGs were identified, and the area under the receiver operating characteristic curve (ROC) of 64 SDEGs was calculated and two genes (MVP, PDXK) with high area under the curve (AUC) in both IgAN and IBD were screened out as potential diagnostic biomarkers. We then screened 3 shared TFs (SRY, MEF2D and SREBF1) and 3 miRNAs (hsa-miR-146, hsa-miR-21 and hsa-miR-320), and further found that the immune pathways of 64SDEGs, 28SIRGs and 3miRNAs were mainly including B cell receptor signaling pathway, FcγR-mediated phagocytosis, IL-17 signaling pathway, toll-like receptor signaling pathway, TNF signaling pathway, TRP channels, T cell receptor signaling pathway, Th17 cell differentiation, and cytokine-cytokine receptor interaction. Conclusion: Our work revealed the differentiation of Th17 cells may mediate the abnormal humoral immunity in IgAN and IBD patients and identified novel gene candidates that could be used as biomarkers or potential therapeutic targets.

The Diagnostic and Predictive Significance of Immune-Related Genes and Immune Characteristics in the Occurrence and Progression of IgA Nephropathy.

Objective: To investigate the potential diagnostic and predictive significance of immune-related genes in IgA nephropathy (IgAN) and discover the abnormal glomerular inflammation in IgAN. Methods: GSE116626 was used as a training set to identify different immune-related genes (DIRGs) and establish machine learning models for the diagnosis of IgAN; then, a nomogram model was generated based on GSE116626, and GSE115857 was used as a test set to evaluate its clinical value. Short Time-Series Expression Miner (STEM) analysis was also performed to explore the changing trend of DIRGs with the progression of IgAN lesions. GSE141344 was used with DIRGs to establish the ceRNA network associated with IgAN progression. Finally, ssGSEA analysis was performed on the GSE141295 dataset to discover the abnormal inflammation in IgAN. Results: Machine learning (ML) performed excellently in diagnosing IgAN using six DIRGs. A nomogram model was constructed to predict IgAN based on the six DIRGs. Three trends related to IgAN lesions were identified using STEM analysis. A ceRNA network associated with IgAN progression which contained 8 miRNAs, 14 lncRNAs, and 3 mRNAs was established. A higher macrophage ratio and lower CD4+ T cell ratio in IgAN compared to controls were observed, and the correlation between macrophages and monocytes in the glomeruli of IgAN patients was inverse compared to controls. Conclusion: This study reveals the diagnostic and predictive significance of DIRGs in IgAN and finds that the imbalance between macrophages and CD4+ immune cells may be an important pathomechanism of IgAN. These results provide potential directions for the treatment and prevention of IgAN.

Anti-inflammatory activities of alkaloids from the mangrove endophytic fungus Phomopsis sp. SYSUQYP-23.

Five new maleimide derivatives, (+)- and (-)- farinomalein F (1), (+)- and (-)- farinomalein G (2), farinomalein H (3) and one new linearly fused prenylated indole alkaloid phomoamide (8), along with five known compounds 4-7 and 9 were isolated from the mangrove endophytic fungus Phomopsis sp. SYSUQYP-23. Their structures and absolute configurations were determined by HRESIMS, spectroscopic and electronic circular dichroism (ECD) calculations. Bioassay results showed that compounds 3-9 exhibited significant inhibitory activities against nitric oxide (NO) production in lipopolysaccharides (LPS) induced RAW 264.7 cells, with IC50 values ranging from 4.5 to 25 µM. Moreover, the molecular docking study implied the probable binding interaction of compounds 4 and 5 with nitric oxide synthase.

Naphthoquinone Derivatives with Anti-Inflammatory Activity from Mangrove-Derived Endophytic Fungus Talaromyces sp. SK-S009.

Twelve 1, 4-naphthoquinone derivatives, including two new (1 and 2) and 10 known (3-12), were obtained from endophytic fungus Talaromyces sp. SK-S009 isolated from the fruit of Kandelia obovata. All structures were identified through extensive analysis of the nuclear magnetic resonance (NMR), mass spectrometry (MS) and circular dichroism (CD), as well as by comparison with literature data. These compounds significantly inhibited the lipopolysaccharide (LPS)-induced nitric oxide (NO) production in the murine macrophage cell line (RAW 264.7 cells). The half maximal inhibitory concentration (IC50) values, except for compound 2, were lower than that of indomethacin (26.3 µM). Compound 9 inhibited the LPS-induced inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2) mRNA expressions in RAW 264.7 macrophages. Additionally, compound 9 reduced the mRNA levels of pro-inflammatory factors interleukin (IL)1ß, IL-6, and tumor necrosis factor (TNF)-α. The results of this study demonstrated that these 1, 4-naphthoquinone derivatives can inhibit LPS-induced inflammation.

A classification scheme for analyzing mobile apps used to prevent and manage disease in late life.

BACKGROUND: There are several mobile apps that offer tools for disease prevention and management among older adults, and promote health behaviors that could potentially reduce or delay the onset of disease. A classification scheme that categorizes apps could be useful to both older adult app users and app developers. OBJECTIVE: The objective of our study was to build and evaluate the effectiveness of a classification scheme that classifies mobile apps available for older adults in the "Health & Fitness" category of the iTunes App Store. METHODS: We constructed a classification scheme for mobile apps according to three dimensions: (1) the Precede-Proceed Model (PPM), which classifies mobile apps in terms of predisposing, enabling, and reinforcing factors for behavior change; (2) health care process, specifically prevention versus management of disease; and (3) health conditions, including physical health and mental health. Content analysis was conducted by the research team on health and fitness apps designed specifically for older adults, as well as those applicable to older adults, released during the months of June and August 2011 and August 2012. Face validity was assessed by a different group of individuals, who were not related to the study. A reliability analysis was conducted to confirm the accuracy of the coding scheme of the sample apps in this study. RESULTS: After applying sample inclusion and exclusion criteria, a total of 119 apps were included in the study sample, of which 26/119 (21.8%) were released in June 2011, 45/119 (37.8%) in August 2011, and 48/119 (40.3%) in August 2012. Face validity was determined by interviewing 11 people, who agreed that this scheme accurately reflected the nature of this application. The entire study sample was successfully coded, demonstrating satisfactory inter-rater reliability by two independent coders (95.8% initial concordance and 100% concordance after consensus was reached). The apps included in the study sample were more likely to be used for the management of disease than prevention of disease (109/119, 91.6% vs 15/119, 12.6%). More apps contributed to physical health rather than mental health (81/119, 68.1% vs 47/119, 39.5%). Enabling apps (114/119, 95.8%) were more common than reinforcing (20/119, 16.8%) or predisposing apps (10/119, 8.4%). CONCLUSIONS: The findings, including face validity and inter-rater reliability, support the integrity of the proposed classification scheme for categorizing mobile apps for older adults in the "Health and Fitness" category available in the iTunes App Store. Using the proposed classification system, older adult app users would be better positioned to identify apps appropriate for their needs, and app developers would be able to obtain the distributions of available mobile apps for health-related concerns of older adults more easily.