Epstein-Barr virus(EBV)is an important human dsDNA virus,which has been shown to be associated with several malignancies including about 10%of gastric carcinomas.How EBV enters an epithelial cell has been an interesti...Epstein-Barr virus(EBV)is an important human dsDNA virus,which has been shown to be associated with several malignancies including about 10%of gastric carcinomas.How EBV enters an epithelial cell has been an interesting project for investigation."Cell-in-cell"infection was recently reported an efficient way for the entry of EBV into nasopharynx epithelial cells.The present approach was to explore the feasibility of this mode for EBV infection in gastric epithelial cells and the dynamic change of host inflammatory reaction.The EBV-positive lymphoblastic cells of Akata containing a GFP tag in the viral genome were co-cultured with the gastric epithelial cells(GES-1).The infection situation was observed under fluorescence and electron microscopies.Real-time quantitative PCR and Western-blotting assay were employed to detect the expression of a few specific cytokines and inflammatory factors.The results demonstrated that EBV could get into gastric epithelial cells by"cell-in-cell"infection but not fully successful due to the host fighting.IL-1β,IL-6 and IL-8 played prominent roles in the cellular response to the infection.The activation of NF-κB and HSP70 was also required for the host antiviral response.The results imply that the gastric epithelial cells could powerfully resist the virus invader via cell-in-cell at the early stage through inflammatory and innate immune responses.展开更多
Cell-in-cell is a unique phenomenon mostly documented in human cancer tissues.A recent study demonstrated that cell-in-cell might promote lymphopenia by internalizing and killing immune cells in COVID-19,which implica...Cell-in-cell is a unique phenomenon mostly documented in human cancer tissues.A recent study demonstrated that cell-in-cell might promote lymphopenia by internalizing and killing immune cells in COVID-19,which implicates cell-in-cell as an emerging player in a broader spectrum of pathological processes,such as immune dysregulation.展开更多
Whereas biochemical markers are available for most types of cell death, current studies on non-autonomous cell death by entosis rely strictly on the identification of cell-in-cell structures (CICs), a unique morpholog...Whereas biochemical markers are available for most types of cell death, current studies on non-autonomous cell death by entosis rely strictly on the identification of cell-in-cell structures (CICs), a unique morphological readout that can only be quantified manually at present. Moreover, the manual CIC quantification is generally over-simplified as CIC counts, which represents a major hurdle against profound mechanistic investigations. In this study, we take advantage of artificial intelligence technology to develop an automatic identification method for CICs (AIM-CICs), which performs comprehensive CIC analysis in an automated and efficient way. The AIM-CICs, developed on the algorithm of convolutional neural network, can not only differentiate between CICs and non-CICs (the area under the receiver operating characteristic curve (AUC) > 0.99), but also accurately categorize CICs into five subclasses based on CIC stages and cell number involved (AUC > 0.97 for all subclasses). The application of AIM-CICs would systemically fuel research on CIC-mediated cell death, such as high-throughput screening.展开更多
基金supported by the National Key Research & Development ProgramNational Natural Science Foundations of China (2017YFC1200204, 31670171, 81728011)Innovation Foundations for Postgraduates of Central South University (2018zzts817)
文摘Epstein-Barr virus(EBV)is an important human dsDNA virus,which has been shown to be associated with several malignancies including about 10%of gastric carcinomas.How EBV enters an epithelial cell has been an interesting project for investigation."Cell-in-cell"infection was recently reported an efficient way for the entry of EBV into nasopharynx epithelial cells.The present approach was to explore the feasibility of this mode for EBV infection in gastric epithelial cells and the dynamic change of host inflammatory reaction.The EBV-positive lymphoblastic cells of Akata containing a GFP tag in the viral genome were co-cultured with the gastric epithelial cells(GES-1).The infection situation was observed under fluorescence and electron microscopies.Real-time quantitative PCR and Western-blotting assay were employed to detect the expression of a few specific cytokines and inflammatory factors.The results demonstrated that EBV could get into gastric epithelial cells by"cell-in-cell"infection but not fully successful due to the host fighting.IL-1β,IL-6 and IL-8 played prominent roles in the cellular response to the infection.The activation of NF-κB and HSP70 was also required for the host antiviral response.The results imply that the gastric epithelial cells could powerfully resist the virus invader via cell-in-cell at the early stage through inflammatory and innate immune responses.
基金supported by the National Natural Science Foundation of China(31970685).
文摘Cell-in-cell is a unique phenomenon mostly documented in human cancer tissues.A recent study demonstrated that cell-in-cell might promote lymphopenia by internalizing and killing immune cells in COVID-19,which implicates cell-in-cell as an emerging player in a broader spectrum of pathological processes,such as immune dysregulation.
基金This workwas supported by Beijing Municipal Natural Science Foundation(KZ202110025029 to H.H.)the National Key R&D Program of China(2022YFC3600100 to Q.S.and H.H.)+2 种基金the National Natural Science Foundation of China(32100608 to C.W.,82002918 and 31970685 to Q.S.)Beijing Municipal Administration of Hospitals Incubating Program(PX2021033 to H.H.)Beijing Postdoctoral Research Foundation(2021-ZZ-027 to M.T.).
文摘Whereas biochemical markers are available for most types of cell death, current studies on non-autonomous cell death by entosis rely strictly on the identification of cell-in-cell structures (CICs), a unique morphological readout that can only be quantified manually at present. Moreover, the manual CIC quantification is generally over-simplified as CIC counts, which represents a major hurdle against profound mechanistic investigations. In this study, we take advantage of artificial intelligence technology to develop an automatic identification method for CICs (AIM-CICs), which performs comprehensive CIC analysis in an automated and efficient way. The AIM-CICs, developed on the algorithm of convolutional neural network, can not only differentiate between CICs and non-CICs (the area under the receiver operating characteristic curve (AUC) > 0.99), but also accurately categorize CICs into five subclasses based on CIC stages and cell number involved (AUC > 0.97 for all subclasses). The application of AIM-CICs would systemically fuel research on CIC-mediated cell death, such as high-throughput screening.
