Browsing by Author "Hong, Honghai"

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    Aberrant Expression Profiles of lncRNAs and Their Associated Nearby Coding Genes in the Hippocampus of the SAMP8 Mouse Model with AD
    (Cell Press, 2020-06-05) Hong, Honghai; Mo, Yousheng; Li, Dongli; Xu, Zhiheng; Liao, Yanfang; Yin, Ping; Liu, Xinning; Xia, Yong; Fang, Jiansong; Wang, Qi; Fang, Shuhuan; Pathology and Laboratory Medicine, School of Medicine
    The senescence-accelerated mouse prone 8 (SAMP8) mouse model is a useful model for investigating the fundamental mechanisms involved in the age-related learning and memory deficits of Alzheimer's disease (AD), while the SAM/resistant 1 (SAMR1) mouse model shows normal features. Recent evidence has shown that long non-coding RNAs (lncRNAs) may play an important role in AD pathogenesis. However, a comprehensive and systematic understanding of the function of AD-related lncRNAs and their associated nearby coding genes in AD is still lacking. In this study, we collected the hippocampus, the main area of AD pathological processes, of SAMP8 and SAMR1 animals and performed microarray analysis to identify aberrantly expressed lncRNAs and their associated nearby coding genes, which may contribute to AD pathogenesis. We identified 3,112 differentially expressed lncRNAs and 3,191 differentially expressed mRNAs in SAMP8 mice compared to SAMR1 mice. More than 70% of the deregulated lncRNAs were intergenic and exon sense-overlapping lncRNAs. Gene Ontology (GO) and pathway analyses of the AD-related transcripts were also performed and are described in detail, which imply that metabolic process reprograming was likely related to AD. Furthermore, six lncRNAs and six mRNAs were selected for further validation of the microarray results using quantitative PCR, and the results were consistent with the findings from the microarray. Moreover, we analyzed 780 lincRNAs (also called long "intergenic" non-coding RNAs) and their associated nearby coding genes. Among these lincRNAs, AK158400 had the most genes nearby (n = 13), all of which belonged to the histone cluster 1 family, suggesting regulation of the nucleosome structure of the chromosomal fiber by affecting nearby genes during AD progression. In addition, we also identified 97 aberrant antisense lncRNAs and their associated coding genes. It is likely that these dysregulated lncRNAs and their associated nearby coding genes play a role in the development and/or progression of AD.
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    ATGL promotes the proliferation of hepatocellular carcinoma cells via the p‐AKT signaling pathway
    (Wiley, 2019-11) Liu, Meiling; Yu, Xuegao; Lin, Liying; Deng, Jiankai; Wang, Kanglong; Su, Baochang; Xia, Yong; Tang, Xiaohua; Hong, Honghai; Pathology and Laboratory Medicine, School of Medicine
    Abnormal metabolism, including abnormal lipid metabolism, is a hallmark of cancer cells. Some studies have demonstrated that the lipogenic pathway might promote the development of hepatocellular carcinoma (HCC). However, the role of adipose triglyceride lipase (ATGL) in hepatocellular carcinoma cells has not been elucidated. We evaluated the function of ATGL in hepatocellular carcinoma using methyl azazolyl blue and migration assay through overexpression of ATGL in HepG2 cells. Quantitative reverse‐transcription polymerase chain reaction and Western blot analyses were used to assess the mechanisms of ATGL in hepatocellular carcinoma. In the current study, we first constructed and transiently transfected ATGL into hepatocellular carcinoma cells. Secondly, we found that ATGL promoted the proliferation of hepatoma cell lines via upregulating the phosphorylation of AKT, but did not affect the metastatic ability of HCC cells. Moreover, the p‐AKT inhibitor significantly eliminated the effect of ATGL on the proliferation of hepatoma carcinoma cells. Taken together, our results indicated that ATGL promotes hepatocellular carcinoma cells proliferation through upregulation of the AKT signaling pathway.
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    Autophagy, Metabolism, and Alcohol-Related Liver Disease: Novel Modulators and Functions
    (MDPI, 2019-10-11) Yan, Shengmin; Khambu, Bilon; Hong, Honghai; Liu, Gang; Huda, Nazmul; Yin, Xiao-Ming; Pathology and Laboratory Medicine, School of Medicine
    Alcohol-related liver disease (ALD) is caused by over-consumption of alcohol. ALD can develop a spectrum of pathological changes in the liver, including steatosis, inflammation, cirrhosis, and complications. Autophagy is critical to maintain liver homeostasis, but dysfunction of autophagy has been observed in ALD. Generally, autophagy is considered to protect the liver from alcohol-induced injury and steatosis. In this review, we will summarize novel modulators of autophagy in hepatic metabolism and ALD, including autophagy-mediating non-coding RNAs (ncRNAs), and crosstalk of autophagy machinery and nuclear factors. We will also discuss novel functions of autophagy in hepatocytes and non-parenchymal hepatic cells during the pathogenesis of ALD and other liver diseases.
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    Hepatic senescence, the good and the bad
    (Baishideng Publishing Group, 2019-09-14) Huda, Nazmul; Liu, Gang; Hong, Honghai; Yan, Shengmin; Khambu, Bilon; Yin, Xiao-Ming; Pathology and Laboratory Medicine, School of Medicine
    Gradual alterations of cell's physiology and functions due to age or exposure to various stresses lead to the conversion of normal cells to senescent cells. Once becoming senescent, the cell stops dividing permanently but remains metabolically active. Cellular senescence does not have a single marker but is characterized mainly by a combination of multiple markers, such as, morphological changes, expression of cell cycle inhibitors, senescence associated β-galactosidase activity, and changes in nuclear membrane. When cells in an organ become senescent, the entire organism can be affected. This may occur through the senescence-associated secretory phenotype (SASP). SASP may exert beneficial or harmful effects on the microenvironment of tissues. Research on senescence has become a very exciting field in cell biology since the link between age-related diseases, including cancer, and senescence has been established. The loss of regenerative and homeostatic capacity of the liver over the age is somehow connected to cellular senescence. The major contributors of senescence properties in the liver are hepatocytes and cholangiocytes. Senescent cells in the liver have been implicated in the etiology of chronic liver diseases including cirrhosis and hepatocellular carcinoma and in the interference of liver regeneration. This review summarizes recently reported findings in the understanding of the molecular mechanisms of senescence and its relationship with liver diseases.
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    The HMGB1-RAGE axis modulates the growth of autophagy-deficient hepatic tumors
    (Springer Nature, 2020-05) Khambu, Bilon; Hong, Honghai; Liu, Sheng; Liu, Gang; Chen, Xiaoyun; Dong, Zheng; Wan, Jun; Yin, Xiao-Ming; Pathology and Laboratory Medicine, School of Medicine
    Autophagy is an intracellular lysosomal degradative pathway important for tumor surveillance. Autophagy deficiency can lead to tumorigenesis. Autophagy is also known to be important for the aggressive growth of tumors, yet the mechanism that sustains the growth of autophagy-deficient tumors is not unclear. We previously reported that progression of hepatic tumors developed in autophagy-deficient livers required high mobility group box 1 (HMGB1), which was released from autophagy-deficient hepatocytes. In this study we examined the pathological features of the hepatic tumors and the mechanism of HMGB1-mediated tumorigenesis. We found that in liver-specific autophagy-deficient (Atg7ΔHep) mice the tumors cells were still deficient in autophagy and could also release HMGB1. Histological analysis using cell-specific markers suggested that fibroblast and ductular cells were present only outside the tumor whereas macrophages were present both inside and outside the tumor. Genetic deletion of Hmgb1 or one of its receptors, receptor for advanced glycated end product (Rage), retarded liver tumor development. HMGB1 and RAGE enhanced the proliferation capability of the autophagy-deficient hepatocytes and tumors. However, RAGE expression was only found on ductual cells and Kupffer's cells but not on hepatoctyes, suggesting that HMGB1 might promote hepatic tumor growth through a paracrine mode, which altered the tumor microenvironment. Finally, RNAseq analysis of the tumors indicated that HMGB1 induced a much broad changes in tumors. In particular, genes related to mitochondrial structures or functions were enriched among those differentially expressed in tumors in the presence or absence of HMGB1, revealing a potentially important role of mitochondria in sustaining the growth of autophagy-deficient liver tumors via HMGB1 stimulation.
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    Recent Advances of Deep Learning for Computational Histopathology: Principles and Applications
    (MDPI, 2022-02-25) Wu, Yawen; Cheng, Michael; Huang, Shuo; Pei, Zongxiang; Zuo, Yingli; Liu, Jianxin; Yang, Kai; Zhu, Qi; Zhang, Jie; Hong, Honghai; Zhang, Daoqiang; Huang, Kun; Cheng, Liang; Shao, Wei; Medicine, School of Medicine
    With the remarkable success of digital histopathology, we have witnessed a rapid expansion of the use of computational methods for the analysis of digital pathology and biopsy image patches. However, the unprecedented scale and heterogeneous patterns of histopathological images have presented critical computational bottlenecks requiring new computational histopathology tools. Recently, deep learning technology has been extremely successful in the field of computer vision, which has also boosted considerable interest in digital pathology applications. Deep learning and its extensions have opened several avenues to tackle many challenging histopathological image analysis problems including color normalization, image segmentation, and the diagnosis/prognosis of human cancers. In this paper, we provide a comprehensive up-to-date review of the deep learning methods for digital H&E-stained pathology image analysis. Specifically, we first describe recent literature that uses deep learning for color normalization, which is one essential research direction for H&E-stained histopathological image analysis. Followed by the discussion of color normalization, we review applications of the deep learning method for various H&E-stained image analysis tasks such as nuclei and tissue segmentation. We also summarize several key clinical studies that use deep learning for the diagnosis and prognosis of human cancers from H&E-stained histopathological images. Finally, online resources and open research problems on pathological image analysis are also provided in this review for the convenience of researchers who are interested in this exciting field.