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Chronic hepatitis B is a significant public health problem and complex pathologic process, and unraveling the underlying mechanisms and pathophysiology is of great significance. Data independent acquisition mass spectrometry (DIA-MS) is a label-free quantitative proteomics method that has been successfully applied to the study of a wide range of diseases. The aim of this study was to apply DIA-MS for proteomic analysis of patients with chronic hepatitis B. We performed comprehensive proteomics analysis of protein expression in serum samples from HBV patients and healthy controls by using DIA-MS. Gene Ontology (GO) terms, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and protein network analysis were performed on differentially expressed proteins and were further combined with literature analysis. We successfully identified a total of 3786 serum proteins with a high quantitative performance from serum samples in this study. We identified 310 differentially expressed proteins (DEPs) (fold change > 1.5 and P value < 0.05 as the criteria for a significant difference) between HBV and healthy samples. A total of 242 upregulated proteins and 68 downregulated proteins were among the DEPs. Some protein expression levels were significantly elevated or decreased in patients with chronic hepatitis B, indicating a relation to chronic liver disease, which should be further investigated. [ABSTRACT FROM AUTHOR]

Background: Hypertension and its associated dysfunction of the blood-brain barrier (BBB) are considered to contribute to cerebral small vessel disease (cSVD). Angiotensin II (Ang II), as an important vasoactive peptide of the renin-angiotensin system (RAS), is not only a pivotal molecular signal in hypertension, but also causes BBB leakage, cSVD and its related cognitive impair. Hyperoside (Hyp), a flavone glycoside, has antioxidant, antiphlogistic and anti-apoptosis effects. In this study, we investigate the protection of Hyp on apoptosis of bEnd.3 cells and BBB disruption in vitro induced by Ang II. Methods: We used bEnd.3 cells to imitate a BBB monolayer model and explored the protection of Hyp on Ang II-induced BBB leakage. The apoptotic activity was assessed by TUNEL staining and flow cytometry. The expression of apoptosis pathway related proteins, tight junction proteins and transcytosis related proteins were detected by western blot assay. The BBB model permeability was detected through measuring the flux of sodium fluorescein (Na-F). Results: We found that Hyp can not only effectively inhibit the apoptosis of bEnd.3 induced by Ang II, but also protect the structural soundness and functional integrity of BBB model by affecting the expression levels of junctional adhesion molecule A (JAM-A), Claudin-5, zonula occludens-1 (ZO-1), Caveolin-1 (Cav-1) and major facilitator superfamily domain-containing protein 2a (Mfsd2a). Conclusion: Hyp might be a potent compound for preventing Ang II-induced BBB disruption. [ABSTRACT FROM AUTHOR]

Background: Identifying cancer biomarkers from transcriptomics data is of importance to cancer research. However, transcriptomics data are often complex and heterogeneous, which complicates the identification of cancer biomarkers in practice. Currently, the heterogeneity still remains a challenge for detecting subtle but consistent changes of gene expression in cancer cells. Results: In this paper, we propose to adaptively capture the heterogeneity of expression across samples in a gene regulation space instead of in a gene expression space. Specifically, we transform gene expression profiles into gene regulation profiles and mathematically formulate gene regulation probabilities (GRPs)-based statistics for characterizing differential expression of genes between tumor and normal tissues. Finally, an unbiased estimator (aGRP) of GRPs is devised that can interrogate and adaptively capture the heterogeneity of gene expression. We also derived an asymptotical significance analysis procedure for the new statistic. Since no parameter needs to be preset, aGRP is easy and friendly to use for researchers without computer programming background. We evaluated the proposed method on both simulated data and real-world data and compared with previous methods. Experimental results demonstrated the superior performance of the proposed method in exploring the heterogeneity of expression for capturing subtle but consistent alterations of gene expression in cancer. Conclusions: Expression heterogeneity largely influences the performance of cancer biomarker identification from transcriptomics data. Models are needed that efficiently deal with the expression heterogeneity. The proposed method can be a standalone tool due to its capacity of adaptively capturing the sample heterogeneity and the simplicity in use. Software availability: The source code of aGRP can be downloaded from https://github.com/hqwang126/aGRP. [ABSTRACT FROM AUTHOR]