Autoreactivity to Glucose Regulated Protein 78 Links Emphysema and Osteoporosis in Smokers

Rationale: Emphysema and osteoporosis are epidemiologically associated diseases of cigarette smokers. The causal mechanism(s) linking these illnesses is unknown. We hypothesized autoimmune responses may be involved in both disorders. Objectives: To discover an antigen-specific autoimmune response associated with both emphysema and osteoporosis among smokers. Methods: Replicate nonbiased discovery assays indicated that autoimmunity to glucose regulated protein 78 (GRP78), an endoplasmic reticulum chaperone and cell surface signaling receptor, is present in many smokers. Subject assessments included spirometry, chest CT scans, dual x-ray absorptiometry, and immunoblots for anti-GRP78 IgG. Anti-GRP78 autoantibodies were isolated from patient plasma by affinity chromatography, leukocyte functions assessed by flow cytometry, and soluble metabolites and mediators measured by immunoassays. Measurements and Main Results Circulating anti-GRP78 IgG autoantibodies were detected in plasma specimens from 86 (32%) of the 265 smoking subjects. Anti-GRP78 autoantibodies were singularly prevalent among subjects with radiographic emphysema (OR 3.1, 95%CI 1.7–5.7, p = 0.003). Anti-GRP78 autoantibodies were also associated with osteoporosis (OR 4.7, 95%CI 1.7–13.3, p = 0.002), and increased circulating bone metabolites (p = 0.006). Among emphysematous subjects, GRP78 protein was an autoantigen of CD4 T-cells, stimulating lymphocyte proliferation (p = 0.0002) and IFN-gamma production (p = 0.03). Patient-derived anti-GRP78 autoantibodies had avidities for osteoclasts and macrophages, and increased macrophage NFkB phosphorylation (p = 0.005) and productions of IL-8, CCL-2, and MMP9 (p = 0.005, 0.007, 0.03, respectively). Conclusions: Humoral and cellular GRP78 autoimmune responses in smokers have numerous biologically-relevant pro-inflammatory and other deleterious actions, and are associated with emphysema and osteoporosis. These findings may have relevance for the pathogenesis of smoking-associated diseases, and development of biomarker immunoassays and/or novel treatments for these disorders

ORM 1 as a biomarker of increased vascular invasion and decreased sorafenib sensitivity in hepatocellular carcinoma

This study aimed to clarify the role of Orosomucoid 1 (ORM1) in the development and therapy resistance in hepatocellular carcinoma (HCC). The mRNA expression level of ORM1 was analyzed via integrative analysis of Gene Express Omnibus (GEO) and The Cancer Genome Atlas (TCGA) datasets. The protein expression level of ORM1 in our cohort was determined using immunohistochemistry. Correlation analysis was used to investigate the relationship between ORM1 expression and clinical parameters. The Cell Counting Kit-8 assay was used to clarify the role of ORM1 in HCC malignant behaviors, including cell growth and sorafenib sensitivity, in vitro. The results indicated that ORM1 was significantly downregulated in the hepatic cancer cells compared to that in the non-cancerous cells. However, it was upregulated in microvascular invasion samples, especially in the cancer embolus compared to that in the surrounding tumor cells. Though Kaplan-Meier analysis did not show an association of ORM1 expression with the overall survival rates of HCC patients, univariate analysis indicated that ORM1 expression was highly correlated with tumor grade and stage. An in vitro assay also revealed that downregulation of ORM1 led to the suppression of tumor growth and enhancement of sorafenib sensitivity without epithelial-to-mesenchymal transition (EMT) alteration, which was consistent with our bioinformatic analysis. Hence, ORM1 played a key role in HCC tumorigenesis and may serve as a potential target for the development of therapeutics against HCC in the future

miR-155 in the progression of lung fibrosis in systemic sclerosis

Background\ud MicroRNA (miRNA) control key elements of mRNA stability and likely contribute to the dysregulated lung gene expression observed in systemic sclerosis associated interstitial lung disease (SSc-ILD). We analyzed the miRNA gene expression of tissue and cells from patients with SSc-ILD. A chronic lung fibrotic murine model was used.\ud \ud Methods\ud RNA was isolated from lung tissue of 12 patients with SSc-ILD and 5 controls. High-resolution computed tomography (HRCT) was performed at baseline and 2–3 years after treatment. Lung fibroblasts and peripheral blood mononuclear cells (PBMC) were isolated from healthy controls and patients with SSc-ILD. miRNA and mRNA were analyzed by microarray, quantitative polymerase chain reaction, and/or Nanostring; pathway analysis was performed by DNA Intelligent Analysis (DIANA)-miRPath v2.0 software. Wild-type and miR-155 deficient (miR-155ko) mice were exposed to bleomycin.\ud \ud Results\ud Lung miRNA microarray data distinguished patients with SSc-ILD from healthy controls with 185 miRNA differentially expressed (q < 0.25). DIANA-miRPath revealed 57 Kyoto Encyclopedia of Genes and Genomes pathways related to the most dysregulated miRNA. miR-155 and miR-143 were strongly correlated with progression of the HRCT score. Lung fibroblasts only mildly expressed miR-155/miR-21 after several stimuli. miR-155 PBMC expression strongly correlated with lung function tests in SSc-ILD. miR-155ko mice developed milder lung fibrosis, survived longer, and weaker lung induction of several genes after bleomycin exposure compared to wild-type mice.\ud \ud Conclusions\ud miRNA are dysregulated in the lungs and PBMC of patients with SSc-ILD. Based on mRNA-miRNA interaction analysis and pathway tools, miRNA may play a role in the progression of the disease. Our findings suggest that targeting miR-155 might provide a novel therapeutic strategy for SSc-ILD

Effect of Sulindac Sulfide on Metallohydrolases in the Human Colon Cancer Cell Line HT-29

Matrix metalloproteinase 7 (MMP7), a metallohydrolase involved in the development of several cancers, is downregulated in the ApcMin/+ colon cancer mouse model following sulindac treatment. To determine whether this effect is relevant to the human condition, HT-29 human colon cancer cells were treated with sulindac and its metabolites, and compared to results obtained from in vivo mouse studies. The expression of MMP7 was monitored. The results demonstrated that sulindac sulfide effectively downregulated both MMP7 expression and activity. Furthermore, activity-based proteomics demonstrated that sulindac sulfide dramatically decreased the activity of leukotriene A4 hydrolase in HT-29 cells as reflected by a decrease in the level of its product, leukotriene B4. This study demonstrates that the effect of sulindac treatment in a mouse model of colon cancer may be relevant to the human counterpart and highlights the effect of sulindac treatment on metallohydrolases

Treatment With Treprostinil and Metformin Normalizes Hyperglycemia and Improves Cardiac Function in Pulmonary Hypertension Associated With Heart Failure With Preserved Ejection Fraction

Objective: Pulmonary hypertension (PH) due to left heart disease (group 2), especially in the setting of heart failure with preserved ejection fraction (HFpEF), is the most common cause of PH worldwide; however, at present, there is no proven effective therapy available for its treatment. PH-HFpEF is associated with insulin resistance and features of metabolic syndrome. The stable prostacyclin analog, treprostinil, is an effective and widely used Food and Drug Administration-approved drug for the treatment of pulmonary arterial hypertension. While the effect of treprostinil on metabolic syndrome is unknown, a recent study suggests that the prostacyclin analog beraprost can improve glucose intolerance and insulin sensitivity. We sought to evaluate the effectiveness of treprostinil in the treatment of metabolic syndrome-associated PH-HFpEF. Approach and Results: Treprostinil treatment was given to mice with mild metabolic syndrome-associated PH-HFpEF induced by high-fat diet and to SU5416/obese ZSF1 rats, a model created by the treatment of rats with a more profound metabolic syndrome due to double leptin receptor defect (obese ZSF1) with a vascular endothelial growth factor receptor blocker SU5416. In high-fat diet-exposed mice, chronic treatment with treprostinil reduced hyperglycemia and pulmonary hypertension. In SU5416/Obese ZSF1 rats, treprostinil improved hyperglycemia with similar efficacy to that of metformin (a first-line drug for type 2 diabetes mellitus); the glucose-lowering effect of treprostinil was further potentiated by the combined treatment with metformin. Early treatment with treprostinil in SU5416/Obese ZSF1 rats lowered pulmonary pressures, and a late treatment with treprostinil together with metformin improved pulmonary artery acceleration time to ejection time ratio and tricuspid annular plane systolic excursion with AMPK (AMP-activated protein kinase) activation in skeletal muscle and the right ventricle. Conclusions: Our data suggest a potential use of treprostinil as an early treatment for mild metabolic syndrome-associated PH-HFpEF and that combined treatment with treprostinil and metformin may improve hyperglycemia and cardiac function in a more severe disease

Prodepth: Predict Residue Depth by Support Vector Regression Approach from Protein Sequences Only

Residue depth (RD) is a solvent exposure measure that complements the information provided by conventional accessible surface area (ASA) and describes to what extent a residue is buried in the protein structure space. Previous studies have established that RD is correlated with several protein properties, such as protein stability, residue conservation and amino acid types. Accurate prediction of RD has many potentially important applications in the field of structural bioinformatics, for example, facilitating the identification of functionally important residues, or residues in the folding nucleus, or enzyme active sites from sequence information. In this work, we introduce an efficient approach that uses support vector regression to quantify the relationship between RD and protein sequence. We systematically investigated eight different sequence encoding schemes including both local and global sequence characteristics and examined their respective prediction performances. For the objective evaluation of our approach, we used 5-fold cross-validation to assess the prediction accuracies and showed that the overall best performance could be achieved with a correlation coefficient (CC) of 0.71 between the observed and predicted RD values and a root mean square error (RMSE) of 1.74, after incorporating the relevant multiple sequence features. The results suggest that residue depth could be reliably predicted solely from protein primary sequences: local sequence environments are the major determinants, while global sequence features could influence the prediction performance marginally. We highlight two examples as a comparison in order to illustrate the applicability of this approach. We also discuss the potential implications of this new structural parameter in the field of protein structure prediction and homology modeling. This method might prove to be a powerful tool for sequence analysis

TANGLE: Two-Level Support Vector Regression Approach for Protein Backbone Torsion Angle Prediction from Primary Sequences

Protein backbone torsion angles (Phi) and (Psi) involve two rotation angles rotating around the Cα-N bond (Phi) and the Cα-C bond (Psi). Due to the planarity of the linked rigid peptide bonds, these two angles can essentially determine the backbone geometry of proteins. Accordingly, the accurate prediction of protein backbone torsion angle from sequence information can assist the prediction of protein structures. In this study, we develop a new approach called TANGLE (Torsion ANGLE predictor) to predict the protein backbone torsion angles from amino acid sequences. TANGLE uses a two-level support vector regression approach to perform real-value torsion angle prediction using a variety of features derived from amino acid sequences, including the evolutionary profiles in the form of position-specific scoring matrices, predicted secondary structure, solvent accessibility and natively disordered region as well as other global sequence features. When evaluated based on a large benchmark dataset of 1,526 non-homologous proteins, the mean absolute errors (MAEs) of the Phi and Psi angle prediction are 27.8° and 44.6°, respectively, which are 1% and 3% respectively lower than that using one of the state-of-the-art prediction tools ANGLOR. Moreover, the prediction of TANGLE is significantly better than a random predictor that was built on the amino acid-specific basis, with the p-value<1.46e-147 and 7.97e-150, respectively by the Wilcoxon signed rank test. As a complementary approach to the current torsion angle prediction algorithms, TANGLE should prove useful in predicting protein structural properties and assisting protein fold recognition by applying the predicted torsion angles as useful restraints. TANGLE is freely accessible at http://sunflower.kuicr.kyoto-u.ac.jp/~sjn/TANGLE/

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe