Browsing by Author "Pham, Hoang Nam"

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    Bioinformatic analysis identified novel candidate genes with the potentials for diagnostic blood testing of primary biliary cholangitis
    (Public Library of Science, 2023-10-16) Pham, Hoang Nam; Pham, Linh; Sato, Keisaku; Medicine, School of Medicine
    Primary biliary cholangitis (PBC) is an autoimmune disorder characterized by intrahepatic bile duct destruction and cholestatic liver injury. Diagnosis of PBC is generally based on the existence of anti-mitochondrial antibody (AMA) in blood samples; however, some PBC patients are negative for serum AMA tests, and invasive liver histological testing is required in rare PBC cases. The current study seeks novel candidate genes that are associated with PBC status and have potentials for blood diagnostic testing. Human transcriptomic profiling data of liver and blood samples were obtained from Gene Expression Omnibus (GEO). Three GEO data series (GSE79850, GSE159676, and GSE119600) were downloaded, and bioinformatic analyses were performed. Various differentially expressed genes were identified in three data series by comparing PBC patients and control individuals. Twelve candidate genes were identified, which were upregulated in both liver tissues and blood samples of PBC patients in all three data series. The enrichment analysis demonstrated that 8 out of 12 candidate genes were associated with biological functions, which were closely related to autoimmune diseases including PBC. Candidate genes, especially ITGAL showed good potentials to distinguish PBC with other diseases. These candidate genes could be useful for diagnostic blood testing of PBC, although further clinical studies are required to evaluate their potentials as diagnostic biomarkers.
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    Deconvolution analysis identified altered hepatic cell landscape in primary sclerosing cholangitis and primary biliary cholangitis
    (Frontiers Media, 2024-05-15) Pham, Hoang Nam; Pham, Linh; Sato, Keisaku; Medicine, School of Medicine
    Introduction: Primary sclerosing cholangitis (PSC) and primary biliary cholangitis (PBC) are characterized by ductular reaction, hepatic inflammation, and liver fibrosis. Hepatic cells are heterogeneous, and functional roles of different hepatic cell phenotypes are still not defined in the pathophysiology of cholangiopathies. Cell deconvolution analysis estimates cell fractions of different cell phenotypes in bulk transcriptome data, and CIBERSORTx is a powerful deconvolution method to estimate cell composition in microarray data. CIBERSORTx performs estimation based on the reference file, which is referred to as signature matrix, and allows users to create custom signature matrix to identify specific phenotypes. In the current study, we created two custom signature matrices using two single cell RNA sequencing data of hepatic cells and performed deconvolution for bulk microarray data of liver tissues including PSC and PBC patients. Methods: Custom signature matrix files were created using single-cell RNA sequencing data downloaded from GSE185477 and GSE115469. Custom signature matrices were validated for their deconvolution performance using validation data sets. Cell composition of each hepatic cell phenotype in the liver, which was identified in custom signature matrices, was calculated by CIBERSORTx and bulk RNA sequencing data of GSE159676. Deconvolution results were validated by analyzing marker expression for the cell phenotype in GSE159676 data. Results: CIBERSORTx and custom signature matrices showed comprehensive performance in estimation of population of various hepatic cell phenotypes. We identified increased population of large cholangiocytes in PSC and PBC livers, which is in agreement with previous studies referred to as ductular reaction, supporting the effectiveness and reliability of deconvolution analysis in this study. Interestingly, we identified decreased population of small cholangiocytes, periportal hepatocytes, and interzonal hepatocytes in PSC and PBC liver tissues compared to healthy livers. Discussion: Although further studies are required to elucidate the roles of these hepatic cell phenotypes in cholestatic liver injury, our approach provides important implications that cell functions may differ depending on phenotypes, even in the same cell type during liver injury. Deconvolution analysis using CIBERSORTx could provide a novel approach for studies of specific hepatic cell phenotypes in liver diseases.