S100A9 promotes inflammatory response in diabetic nonalcoholic fatty liver disease

Neutrophil-secreted S100A8/A9 participates in fatty liver injury and fibrosis by promoting myofibroblast migration

Fatty liver, which is induced by abnormal lipid metabolism, is one of the most common causes of chronic liver disease globally and causes liver fibrosis. During this process, bone marrow-derived mesenchymal stromal cells (BMSCs) and hepatic stellate cells (HSCs) migrate toward the injured liver and participate in fibrogenesis by transdifferentiating into myofibroblasts. S100A8/A9 is a powerful inducer of cell migration and is involved in liver injury. But there are few reports about the effects of S100A8/A9 on BMSC/HSC migration. In the current study, we found that S100A8/A9 expression was increased during fatty liver injury/fibrogenesis. Moreover, S100A8/A9 expression had a positive correlation with fibrosis marker gene expressions in the injured liver. S100A8/A9 was mainly produced by neutrophils in the fibrotic liver. In vitro, neutrophil-secreted S100A8/A9 promoted BMSC/HSC migration via remodeling of microfilaments. Using specific siRNA and inhibitor, we proved that S100A8/A9-induced BMSC/HSC migration is dependent on TLR4/Rho GTPases signaling. Moreover, S100A8/A9 knock-down alleviated liver injury and fibrogenesis in vivo, while injection of S100A9 neutralizing antibody performed similar roles. We proved that S100A8/A9 was involved in liver injury and fibrogenesis via inducing BMSC/HSC migration. Our research reveals a new mechanism underlying BMSC/HSC migration in liver fibrosis and suggests S100A8/A9 as a potential therapeutic target of liver fibrosis. KEY MESSAGES: S100A8/A9 is secreted by neutrophils and increased in fatty liver injury. Neutrophil-secreted S100A8/A9 is a mediator of BMSC/HSC migration in vitro. S100A8/A9-induced BMSC/HSC migration is dependent on TLR4/Rho GTPases signaling. S100A8/A9 blockade alleviates liver injury and fibrogenesis in vivo.

Six-gene prognostic signature for non-alcoholic fatty liver disease susceptibility using machine learning

BACKGROUND

nonalcoholic fatty liver disease (NAFLD) is a common liver disease affecting the global population and its impact on human health will continue to increase. Genetic susceptibility is an important factor influencing its onset and progression, and there is a lack of reliable methods to predict the susceptibility of normal populations to NAFLD using appropriate genes.

METHODS

RNA sequencing data relating to nonalcoholic fatty liver disease was analyzed using the "limma" package within the R software. Differentially expressed genes were obtained through preliminary intersection screening. Core genes were analyzed and obtained by establishing and comparing 4 machine learning models, then a prediction model for NAFLD was constructed. The effectiveness of the model was then evaluated, and its applicability and reliability verified. Finally, we conducted further gene correlation analysis, analysis of biological function and analysis of immune infiltration.

RESULTS

By comparing 4 machine learning algorithms, we identified SVM as the optimal model, with the first 6 genes (CD247, S100A9, CSF3R, DIP2C, OXCT 2 and PRAMEF16) as predictive genes. The nomogram was found to have good reliability and effectiveness. Six genes' receiver operating characteristic curves (ROC) suggest an essential role in NAFLD pathogenesis, and they exhibit a high predictive value. Further analysis of immunology demonstrated that these 6 genes were closely connected to various immune cells and pathways.

CONCLUSION

This study has successfully constructed an advanced and reliable prediction model based on 6 diagnostic gene markers to predict the susceptibility of normal populations to NAFLD, while also providing insights for potential targeted therapies.

Single-cell atlas reveals the immunosuppressive microenvironment and Treg cells landscapes in recurrent Glioblastoma

Patients diagnosed with glioblastoma (GBM) have the most aggressive tumor progression and lethal recurrence. Research on the immune microenvironment landscape of tumor and cerebrospinal fluid (CSF) is limited. At the single-cell level, we aim to reveal the recurrent immune microenvironment of GBM and the potential CSF biomarkers and compare tumor locations. We collected four clinical samples from two patients: malignant samples from one recurrent GBM patient and non-malignant samples from a patient with brain tumor. We performed single-cell RNA sequencing (scRNA-seq) to reveal the immune landscape of recurrent GBM and CSF. T cells were enriched in the malignant tumors, while Treg cells were predominately found in malignant CSF, which indicated an inhibitory microenvironment in recurrent GBM. Moreover, macrophages and neutrophils were significantly enriched in malignant CSF. This indicates that they an important role in GBM progression. S100A9, extensively expressed in malignant CSF, is a promising biomarker for GBM diagnosis and recurrence. Our study reveals GBM's recurrent immune microenvironment after chemoradiotherapy and compares malignant and non-malignant CSF samples. We provide novel targets and confirm the promise of liquid CSF biopsy for patients with GBM.

Study on Potential Differentially Expressed Genes in Idiopathic Pulmonary Fibrosis by Bioinformatics and Next-Generation Sequencing Data Analysis

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive lung disease with reduced quality of life and earlier mortality, but its pathogenesis and key genes are still unclear. In this investigation, bioinformatics was used to deeply analyze the pathogenesis of IPF and related key genes, so as to investigate the potential molecular pathogenesis of IPF and provide guidance for clinical treatment. Next-generation sequencing dataset GSE213001 was obtained from Gene Expression Omnibus (GEO), and the differentially expressed genes (DEGs) were identified between IPF and normal control group. The DEGs between IPF and normal control group were screened with the DESeq2 package of R language. The Gene Ontology (GO) and REACTOME pathway enrichment analyses of the DEGs were performed. Using the g:Profiler, the function and pathway enrichment analyses of DEGs were performed. Then, a protein-protein interaction (PPI) network was constructed via the Integrated Interactions Database (IID) database. Cytoscape with Network Analyzer was used to identify the hub genes. miRNet and NetworkAnalyst databaseswereused to construct the targeted microRNAs (miRNAs), transcription factors (TFs), and small drug molecules. Finally, receiver operating characteristic (ROC) curve analysis was used to validate the hub genes. A total of 958 DEGs were screened out in this study, including 479 up regulated genes and 479 down regulated genes. Most of the DEGs were significantly enriched in response to stimulus, GPCR ligand binding, microtubule-based process, and defective GALNT3 causes HFTC. In combination with the results of the PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network, hub genes including LRRK2, BMI1, EBP, MNDA, KBTBD7, KRT15, OTX1, TEKT4, SPAG8, and EFHC2 were selected. Cyclothiazide and rotigotinethe are predicted small drug molecules for IPF treatment. Our findings will contribute to identification of potential biomarkers and novel strategies for the treatment of IPF, and provide a novel strategy for clinical therapy.

IS FECAL CALPROTECTIN DETERMINATION USEFUL FOR PATIENTS WITH METABOLIC ASSOCIATED FATTY LIVER DISEASE?

OBJECTIVE

Aim: To investigate the possible relationship between fecal calprotectin (FC) level and ultrasound indicators of steatosis and fibrosis wich defined by attenuation coefficient (AC) and liver stiffness (LS) from two-dimensional (2D) shear-wave elastography (SWE) in patients with metabolically associated fatty liver disease (MAFLD).

PATIENTS AND METHODS

Materials and Methods: The study included 110 persons with MAFLD; mean age 51.3±4.8 years, 65 (59.1%) men. There were used laboratory, sonography and statistical methods.

RESULTS

Results: Stage S1 of steatosis was diagnosed in 42 (38.2%), S2 - in 56 (50.9%), S3 - only in 12 (10.9%) MAFLD patients. The carbohydrate metabolism disorders were found in 62 (56.4%); 38 (34.5%) patients among them suffered from type 2 diabetes. The lipid metabolism disorders were diagnosed in the vast majority of patients included in this study. The minimal excess of fecal calprotectin (FC) was detected in 72 MAFLD patients (65.5%), the moderate increase of FC was found in 12 persons, the FC more than 10-fold excess of the norm was observed in only 8 MAFLD patients. FC levels were significantly elevated in MAFLD patients with a S2-S3 compared to those with a S1 (75.8 [42.9-112.1] vs. 46.3 [28.2-65.4], p<0.01).

CONCLUSION

Conclusions: Fecal calprotectin levels are significantly elevated in patients with MAFLD. Future studies are warranted to establish the definitive role and clinical utility of FC as a potential biomarker of probably liver steatosis as well as other diseases associated with methabolic syndrome and its complications.

Plasma Calprotectin Levels Associate with Suspected Metabolic-Associated Fatty Liver Disease and All-Cause Mortality in the General Population

Metabolic-associated fatty liver disease (MAFLD) is characterized by hepatic steatosis, metabolic dysregulation, and neutrophilic inflammation. In this study, we hypothesized that systemic levels of plasma calprotectin, as a biomarker of neutrophilic inflammation, may be associated with suspected MAFLD. Plasma calprotectin levels were measured in subjects (n = 5446) participating in the Prevention of Renal and Vascular ENd-stage Disease (PREVEND) cohort study. Suspected MAFLD was defined by the fatty liver index (FLI ≥ 60) and hepatic steatosis index (HSI ≥ 36) as proxies. Plasma calprotectin levels were significantly higher in subjects with FLI ≥ 60 (0.57 [IQR: 0.42−0.79] mg/L, n = 1592) (p < 0.001) compared to subjects with FLI < 60 (0.46 [0.34−0.65] mg/L, n = 3854). Multivariable logistic regression analyses revealed that plasma calprotectin levels were significantly associated with suspected MAFLD (FLI ≥ 60), even after adjustment for potential confounding factors, including current smoking, alcohol consumption, hypertension, diabetes, cardiovascular diseases, insulin resistance (HOMA-IR), hs-CRP, eGFR, and total cholesterol levels (OR 1.19 [95% CI: 1.06−1.33], p = 0.003). Interaction analyses revealed significant effect modifications for the association between plasma calprotectin and suspected MAFLD by BMI (p < 0.001) and hypertension (p = 0.003), with the strongest associations in subjects with normal BMI and without hypertension. Prospectively, plasma calprotectin levels were significantly associated with all-cause mortality after adjustment for potential confounding factors, particularly in subjects without suspected MAFLD (FLI < 60) (hazard ratio (HR) per doubling: 1.34 (1.05−1.72), p < 0.05). In conclusion, higher plasma calprotectin levels are associated with suspected MAFLD and with the risk of all-cause mortality, the latter especially in subjects without suspected MAFLD.