scholarly journals Non-coding loci without epigenomic signals can be essential for maintaining global chromatin organization and cell viability

2020 ◽  
Author(s):  
Bo Ding ◽  
Ying Liu ◽  
Zhiheng Liu ◽  
Lina Zheng ◽  
Ping Xu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Protein Binding ◽  
Cell Viability ◽  
Single Cell ◽  
Functional Element ◽  
Chromatin Organization ◽  
Coding Regions ◽  
Structural Importance ◽  
Powerful Approach ◽  
Disease Specific

ABSTRACTThe majority of the non-coding regions in the human genome do not harbor any annotated element and are even not marked with any epigenomic signal or protein binding. An understudied aspect of these regions is their possible roles in stabilizing the 3D chromatin organization. To illuminate their “structural importance”, we chose to start with the non-coding regions forming many 3D contacts (referred to as hubs) and identified dozens of hubs essential for cell viability. Hi-C and single cell transcriptomic analyses showed that their deletion could significantly alter chromatin organization and impact gene expression located distal in the genome. This study revealed the 3D structural importance of non-coding loci that are not associated with any functional element, providing a new mechanistic understanding of the disease-associated genetic variations (GVs). Furthermore, our analyses also suggested a powerful approach to develop “one-drug-multiple-targets” therapeutics targeting the disease-specific non-coding regions.

Effect of 5'-fluoro-2'-deoxycytidine and sodium butyrate on the genes of the intrinsic apoptotic pathway, p21, p53, cell viability, and apoptosis in human hepatocellular carcinoma cell lines

2021 ◽  
Author(s):  
Masumeh Sanaei ◽  
Fraidoon Kavoosi ◽  
Mohammad Amin Moezzi
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Cell Viability ◽  
Cell Lines ◽  
Sodium Butyrate ◽  
Cell Apoptosis ◽  
Chromatin Organization ◽  
Expression Level ◽  
Intrinsic Apoptotic Pathway ◽  
Apoptotic Pathway

Backgrounds: Epigenetic regulation such as DNA methylation plays a major role in chromatin organization and gene transcription. Additionally, histone modification is an epigenetic regulator of chromatin structure and influences chromatin organization and gene expression. The relationship between DNA methyltransferase (DNMTs) expression and promoter methylation of the tumor suppressor genes (TSGs) has been reported in various cancers. Previously, the effect of 5-aza-2'-deoxycytidine (5-AZA-CdR), trichostatin A (TSA), and valproic acid (VPA) was shown on various cancers. This study aimed to investigate the effect of 5'-fluoro-2'-deoxycytidine (FdCyd) and sodium butyrate on the genes of the intrinsic apoptotic pathway, p21, p53, cell viability, and apoptosis in human hepatocellular carcinoma SNU449, SNU475, and SNU368 cell lines. Materials and Methods: In this lab trial study, the SNU449, SNU475, and SNU368 cells were cultured and treated with 5'-fluoro-2'-deoxycytidine and sodium butyrate. To determine cell viability, cell apoptosis, and the relative gene expression level, MTT assay, flow cytometry assay, and qRT-PCR were done respectively. Results: 5'-fluoro-2'-deoxycytidine and sodium butyrate changed the expression level of the BAX, BAK, APAF1, Bcl-2, Bcl-xL, p21, and p53 gene (P<0.0001) by which induced cell apoptosis and inhibit cell growth in all three cell lines, SNU449, SNU475, and SNU368.  Conclusion: Both compounds played their roles through the intrinsic apoptotic pathway to induce cell apoptosis.

scholarly journals Identifying cell types from single-cell data based on similarities and dissimilarities between cells

2021 ◽  
Vol 22 (S3) ◽  
Author(s):  
Yuanyuan Li ◽  
Ping Luo ◽  
Yi Lu ◽  
Fang-Xiang Wu
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Spectral Clustering ◽  
Incidence Matrix ◽  
Expression Patterns ◽  
Cell Types ◽  
Clustering Method ◽  
Different Types ◽  
Cell Data ◽  
Spectral Clustering Method

Abstract Background With the development of the technology of single-cell sequence, revealing homogeneity and heterogeneity between cells has become a new area of computational systems biology research. However, the clustering of cell types becomes more complex with the mutual penetration between different types of cells and the instability of gene expression. One way of overcoming this problem is to group similar, related single cells together by the means of various clustering analysis methods. Although some methods such as spectral clustering can do well in the identification of cell types, they only consider the similarities between cells and ignore the influence of dissimilarities on clustering results. This methodology may limit the performance of most of the conventional clustering algorithms for the identification of clusters, it needs to develop special methods for high-dimensional sparse categorical data. Results Inspired by the phenomenon that same type cells have similar gene expression patterns, but different types of cells evoke dissimilar gene expression patterns, we improve the existing spectral clustering method for clustering single-cell data that is based on both similarities and dissimilarities between cells. The method first measures the similarity/dissimilarity among cells, then constructs the incidence matrix by fusing similarity matrix with dissimilarity matrix, and, finally, uses the eigenvalues of the incidence matrix to perform dimensionality reduction and employs the K-means algorithm in the low dimensional space to achieve clustering. The proposed improved spectral clustering method is compared with the conventional spectral clustering method in recognizing cell types on several real single-cell RNA-seq datasets. Conclusions In summary, we show that adding intercellular dissimilarity can effectively improve accuracy and achieve robustness and that improved spectral clustering method outperforms the traditional spectral clustering method in grouping cells.

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