Adipocyte-derived PGE2 is required for intermittent fasting-induced Treg proliferation and improvement of insulin sensitivity.

The intermittent fasting (IF) diet has profound benefits for diabetes prevention. However, the precise mechanisms underlying IF's beneficial effects remain poorly defined. Here, we show that the expression levels of cyclooxygenase-2 (COX-2), an enzyme that produces prostaglandins, are suppressed in white adipose tissue (WAT) of obese humans. In addition, the expression of COX-2 in WAT is markedly upregulated by IF in obese mice. Adipocyte-specific depletion of COX-2 led to reduced fractions of CD4+Foxp3+ Tregs and a substantial decrease in the frequency of CD206+ macrophages, an increase in the abundance of γδT cells in WAT under normal chow diet conditions, and attenuation of IF-induced antiinflammatory and insulin-sensitizing effects, despite a similar antiobesity effect in obese mice. Mechanistically, adipocyte-derived prostaglandin E2 (PGE2) promoted Treg proliferation through the CaMKII pathway in vitro and rescued Treg populations in adipose tissue in COX-2-deficient mice. Ultimately, inactivation of Tregs by neutralizing anti-CD25 diminished IF-elicited antiinflammatory and insulin-sensitizing effects, and PGE2 restored the beneficial effects of IF in COX-2-KO mice. Collectively, our study reveals that adipocyte COX-2 is a key regulator of Treg proliferation and that adipocyte-derived PGE2 is essential for IF-elicited type 2 immune response and metabolic benefits.

Serum Fractalkine Levels Were Positively Associated with Disease Severity of Liver Cirrhosis.

OBJECTIVE: The chemokine receptor CX3CR1 and its specific ligand fractalkine (CX3CL1, FKN) has been implicated in modulating inflammatory and fibroproliferative diseases. The current study was performed to investigate the correlation of serum fractalkine levels with disease severity of liver fibrosis/cirrhosis (LC). METHODS: 162 LC patients and 140 healthy controls well enrolled in our study. Serum fractalkine levels were detected using commercial ELISA kit. Liver biopsy specimens were obtained using 16 G disposable needle in LC patients. The Child-Pugh grade was recorded to assess liver function. ROC curve analysis was performed to assess the potential diagnostic power of serum fractalkine with regard to the disease severity of Child-Pugh grade system. Pathological assessment of cirrhotic severity was performed by Laennec staging system. The L3 skeletal muscle index (L3SMI) was applicated to assess the nutrition status. RESULTS: Serum fractalkine levels were significantly higher in LC patients compared with healthy controls. The case group included 50 Child-Pugh A patients, 59 Child-Pugh B patients, and 53 Child-Pugh C patients. Cirrhosis patients with Child-Pugh C had drastically higher serum fractalkine levels compared with those with Child-Pugh B and A. Child-Pugh B patients showed significantly higher serum PACAP concentrations compared with those with Child-Pugh A. ROC curve analysis demonstrated that serum fractalkine may act as a potential indicator for disease progression of LC determined by Child-Pugh classification. Besides, serum fractalkine levels were positively related to ALT and AST concentrations and negatively related to L3SMI. CONCLUSION: Serum fractalkine levels were positively associated with disease severity of LC.

CircCCT3 Acts as a Sponge of miR-613 to Promote Tumor Growth of Pancreatic Cancer Through Regulating VEGFA/VEGFR2 Signaling.

BACKGROUND: Circular RNAs (CircRNAs) have been recently implicated in the progression of pancreatic cancer (PC). AIMS: To investigate the involvement of CircCCT3 in PC and studying its interactions and functioning during the progression of PC in vitro and in vivo, using methods of molecular biology and bioinformatics. STUDY DESIGN: Experimental study. METHODS: The expressions of CircCCT3 and miR-613 in pancreatic carcinoma tissues and cell lines were evaluated by quantitative real-time polymerase chain reaction (PCR). The relationship between clinical pathologic features as well as the survival rate and CircCCT3 expression was analyzed with chi-square test and the Kaplan-Meier method. CCK-8, wound healing, transwell assays, and the fluorescein isothiocyanate- AnnexinV/propidium iodide (FITC-AnnexinV/PI) assay were used to assess cell proliferation, migration, invasion, and apoptosis after CircCCT3 overexpression or downregulation. The Dual- Luciferase reporter assay, RNA immunoprecipitation (RIP), RNA pull-down and fluorescence in situ hybridization (FISH) assays were performed to validate the potential interaction of CircCCT3, miR-613, and vascular endothelial growth factor (VEGFA). The nude mouse xenograft tumor assay was used to detect CircCCT3 effects on pancreatic tumorigenesis in vivo. Western blotting analysis was performed to examine the VEGFA and the vascular endothelial growth factor receptor 2 (VEGFR2) protein expressions following. RESULTS: CircCCT3 expression was significantly increased in PC tissues (3.41 ± 0.57 vs. 1.00 ± 0.10, P < .01) and cell lines (Patu8988 2.57 ± 0.20; SW1990 2.88 ± 0.10; BxPC-3 2.45 ± 0.20; Panc02 2.99 ± 0.10 vs. H6c7 1.00 ± 0.10; all P < .001). CircCCT3 expression was negatively correlated with miR-613 expression. PC patients with high CircCCT3 expression exhibited significantly poorer overall survival rate than those patients with low CircCCT3 expression (P = .013). Moreover, it was found that CircCCT3 promoted cell proliferation, migration, and invasion, and inhibited cell apoptosis in PC cells. The CircCCT3 acted as a sponge for the miR-613 to facilitate VEGFA and VEGFR2 expression. si-CirCCT3 also inhibited tumor growth of PC in nude mice. si-CircCCT3 reduced VEGFA and VEGFR2 expression, whereas overexpression of CircCCT3 increased VEGFA and VEGFR2 expression. CONCLUSION: Increased CircCCT3 suggests a poor prognosis for PC patients and promotes the migration and invasion through targeting VEGFA/VEGFR2 signaling. CircCCT3 may serve as a potential and promising therapeutic target for PC treatment.

Discovery of Urinary Proteomic Signature for Differential Diagnosis of Acute Appendicitis.

Acute appendicitis is one of the most common acute abdomens, but the confident preoperative diagnosis is still a challenge. In order to profile noninvasive urinary biomarkers that could discriminate acute appendicitis from other acute abdomens, we carried out mass spectrometric experiments on urine samples from patients with different acute abdomens and evaluated diagnostic potential of urinary proteins with various machine-learning models. Firstly, outlier protein pools of acute appendicitis and controls were constructed using the discovery dataset (32 acute appendicitis and 41 control acute abdomens) against a reference set of 495 normal urine samples. Ten outlier proteins were then selected by feature selection algorithm and were applied in construction of machine-learning models using naïve Bayes, support vector machine, and random forest algorithms. The models were assessed in the discovery dataset by leave-one-out cross validation and were verified in the validation dataset (16 acute appendicitis and 45 control acute abdomens). Among the three models, random forest model achieved the best performance: the accuracy was 84.9% in the leave-one-out cross validation of discovery dataset and 83.6% (sensitivity: 81.2%, specificity: 84.4%) in the validation dataset. In conclusion, we developed a 10-protein diagnostic panel by the random forest model that was able to distinguish acute appendicitis from confusable acute abdomens with high specificity, which indicated the clinical application potential of noninvasive urinary markers in disease diagnosis.

Urine Proteome Profiling Predicts Lung Cancer from Control Cases and Other Tumors.

Development of noninvasive, reliable biomarkers for lung cancer diagnosis has many clinical benefits knowing that most of lung cancer patients are diagnosed at the late stage. For this purpose, we conducted proteomic analyses of 231 human urine samples in healthy individuals (n=33), benign pulmonary diseases (n=40), lung cancer (n=33), bladder cancer (n=17), cervical cancer (n=25), colorectal cancer (n=22), esophageal cancer (n=14), and gastric cancer (n=47) patients collected from multiple medical centers. By random forest modeling, we nominated a list of urine proteins that could separate lung cancers from other cases. With a feature selection algorithm, we selected a panel of five urinary biomarkers (FTL: Ferritin light chain; MAPK1IP1L: Mitogen-Activated Protein Kinase 1 Interacting Protein 1 Like; FGB: Fibrinogen Beta Chain; RAB33B: RAB33B, Member RAS Oncogene Family; RAB15: RAB15, Member RAS Oncogene Family) and established a combinatorial model that can correctly classify the majority of lung cancer cases both in the training set (n=46) and the test sets (n=14-47 per set) with an AUC ranging from 0.8747 to 0.9853. A combination of five urinary biomarkers not only discriminates lung cancer patients from control groups but also differentiates lung cancer from other common tumors. The biomarker panel and the predictive model, when validated by more samples in a multi-center setting, may be used as an auxiliary diagnostic tool along with imaging technology for lung cancer detection.

Proof-of-Concept Workflow for Establishing Reference Intervals of Human Urine Proteome for Monitoring Physiological and Pathological Changes.

Urine as a true non-invasive sampling source holds great potential for biomarker discovery. While approximately 2000 proteins can be detected by mass spectrometry in urine from healthy people, the amount of these proteins vary considerably. A systematic evaluation of a large number of samples is needed to determine the range of the variations. Current biomarker studies often measure limited number of urine samples in the discovery phase, which makes it difficult to determine whether proteins differentially expressed between control and disease groups represent actual difference, or are just physiological variations among the individuals, leads to failures in the validation phase with the increased sample numbers. Here, we report a streamlined workflow with capacity of measuring 8 urine proteomes per day at the coverage of >1500 proteins. With this workflow, we evaluated variations in 497 urine proteomes from 167 healthy donors, establishing reference intervals (RIs) that covered urine protein variations. We demonstrated that RIs could be used to monitor physiological changes by detecting transient outlier proteins. Furthermore, we provided a RIs-based algorithm for biomarker discovery and validation to screen for diseases such as cancer. This study provided a proof-of-principle workflow for the use of urine proteome for health monitoring and disease screening.