scholarly journals Identification of Inflammatory Genes, Pathways, and Immune Cells in Necrotizing Enterocolitis of Preterm Infant by Bioinformatics Approaches

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Lili Zhang ◽  
Lizhen Sun ◽  
Mingli Wu ◽  
Jie Huang
Keyword(s):  
Gene Expression ◽  
B Cells ◽  
Rna Sequencing ◽  
Necrotizing Enterocolitis ◽  
Immune Cells ◽  
Gastrointestinal Disease ◽  
Gene Set Enrichment Analysis ◽  
High Morbidity ◽  
Expression Levels ◽  
Gene Expression Levels

Background. Necrotizing enterocolitis (NEC) is one of the most serious gastrointestinal disease-causing high morbidity and mortality in premature infants. However, the underlying mechanism of the pathogenesis of NEC is still not fully understood. Methods. RNA sequencing of intestinal specimens from 9 NEC and 5 controls was employed to quantify the gene expression levels. RNA sequencing was employed to quantify the gene expression levels. DESeq2 tool was used to identify the differentially expressed genes. The biological function, pathways, transcription factors, and immune cells dysregulated in NEC were characterized by gene set enrichment analysis. Results. In the present study, we analyzed RNA sequencing data of NECs and controls and revealed that immune-related pathways were highly activated, while some cellular responses to external stimuli-related pathways were inactivated in NEC. Moreover, B cells, macrophages M1, and plasma cells were identified as the major cell types involved in NEC. Furthermore, we also found that inflammation-related transcription factor genes, such as STAT1, STAT2, and IRF2, were significantly activated in NEC, further suggesting that these TFs might play critical roles in NEC pathogenesis. In addition, NEC samples exhibited heterogeneity to some extent. Interestingly, two subgroups in the NEC samples were identified by hierarchical clustering analysis. Notably, B cells, T cells, Th1, and Tregs involved in adaptive immune were predicted to highly infiltrate into subgroup I, while subgroup II was significantly infiltrated by neutrophils. The heterogeneity of immune cells in NEC indicated that both innate and adaptive immunes might induce NEC-related inflammatory response. Conclusions. In summary, we systematically analyzed inflammation-related genes, signaling pathways, and immune cells to characterize the NEC pathogenesis and samples, which greatly improved our understanding of the roles of inflammatory responses in NEC.

scholarly journals RNA sequencing reveals two major classes of gene expression levels in metazoan cells

2011 ◽  
Vol 7 (1) ◽  
pp. 497 ◽  
Author(s):  
Daniel Hebenstreit ◽  
Miaoqing Fang ◽  
Muxin Gu ◽  
Varodom Charoensawan ◽  
Alexander van Oudenaarden ◽  
...  
Keyword(s):  
Gene Expression ◽  
Rna Sequencing ◽  
Expression Levels ◽  
Gene Expression Levels

scholarly journals Calling genotypes from public RNA-sequencing data enables identification of genetic variants that affect gene-expression levels

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Patrick Deelen ◽  
Daria V Zhernakova ◽  
Mark de Haan ◽  
Marijke van der Sijde ◽  
Marc Jan Bonder ◽  
...  
Keyword(s):  
Gene Expression ◽  
Rna Sequencing ◽  
Genetic Variants ◽  
Sequencing Data ◽  
Expression Levels ◽  
Affect Gene Expression ◽  
Gene Expression Levels

scholarly journals An Efficient and Flexible Method for Deconvoluting Bulk RNA-Seq Data with Single-Cell RNA-Seq Data

Cells ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1161 ◽  
Author(s):  
Xifang Sun ◽  
Shiquan Sun ◽  
Sheng Yang
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type ◽  
Disease Etiology ◽  
Expression Levels ◽  
Cell Type Specific ◽  
Gene Expression Levels

Estimating cell type compositions for complex diseases is an important step to investigate the cellular heterogeneity for understanding disease etiology and potentially facilitate early disease diagnosis and prevention. Here, we developed a computationally statistical method, referring to Multi-Omics Matrix Factorization (MOMF), to estimate the cell-type compositions of bulk RNA sequencing (RNA-seq) data by leveraging cell type-specific gene expression levels from single-cell RNA sequencing (scRNA-seq) data. MOMF not only directly models the count nature of gene expression data, but also effectively accounts for the uncertainty of cell type-specific mean gene expression levels. We demonstrate the benefits of MOMF through three real data applications, i.e., Glioblastomas (GBM), colorectal cancer (CRC) and type II diabetes (T2D) studies. MOMF is able to accurately estimate disease-related cell type proportions, i.e., oligodendrocyte progenitor cells and macrophage cells, which are strongly associated with the survival of GBM and CRC, respectively.

scTSSR: gene expression recovery for single-cell RNA sequencing using two-side sparse self-representation

2020 ◽  
Vol 36 (10) ◽  
pp. 3131-3138
Author(s):  
Ke Jin ◽  
Le Ou-Yang ◽  
Xing-Ming Zhao ◽  
Hong Yan ◽  
Xiao-Fei Zhang
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Patterns ◽  
Differential Expression Analysis ◽  
Supplementary Information ◽  
Expression Levels ◽  
Single Cell Rna Sequencing ◽  
Downstream Analysis ◽  
Gene Expression Levels

Abstract Motivation Single-cell RNA sequencing (scRNA-seq) methods make it possible to reveal gene expression patterns at single-cell resolution. Due to technical defects, dropout events in scRNA-seq will add noise to the gene-cell expression matrix and hinder downstream analysis. Therefore, it is important for recovering the true gene expression levels before carrying out downstream analysis. Results In this article, we develop an imputation method, called scTSSR, to recover gene expression for scRNA-seq. Unlike most existing methods that impute dropout events by borrowing information across only genes or cells, scTSSR simultaneously leverages information from both similar genes and similar cells using a two-side sparse self-representation model. We demonstrate that scTSSR can effectively capture the Gini coefficients of genes and gene-to-gene correlations observed in single-molecule RNA fluorescence in situ hybridization (smRNA FISH). Down-sampling experiments indicate that scTSSR performs better than existing methods in recovering the true gene expression levels. We also show that scTSSR has a competitive performance in differential expression analysis, cell clustering and cell trajectory inference. Availability and implementation The R package is available at https://github.com/Zhangxf-ccnu/scTSSR. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Fc Receptor-Like Gene Expression in Renal Transplantation Patients

2020 ◽  
Vol 9 ◽  
Author(s):  
Narges Jamshidian Tehrani ◽  
Zahra Amirghofran ◽  
Ali Reza Shamsaeefar ◽  
Aida Karachi ◽  
Mohammad Hossein Karimi
Keyword(s):  
Gene Expression ◽  
B Cells ◽  
Mononuclear Cells ◽  
Control Group ◽  
Healthy Controls ◽  
Peripheral Blood Mononuclear ◽  
Expression Levels ◽  
Transplant Patients ◽  
Blood Mononuclear Cells ◽  
Gene Expression Levels

Background: It has been well-documented that the Fc receptor-like (FCRL) molecule contributes to the pathogenesis of certain autoimmune disorders. FCRL molecules belong to the immunoglobulin superfamily produced by B cells. Also, these molecules induce activating or inhibitory signals of B cells.  According to this information and also considering the critical role of immune reactions in organ transplantation, the following experiment was performed to analyze the gene expression level of FCRLs in peripheral blood mononuclear cells of kidney transplant recipients. Materials and Methods: Blood samples were obtained from 32 renal transplant patients on days 1, 3, and 7 post-transplantations. Patients were divided into two groups according to the presence or absence of rejection. Also, 24 age-matched healthy subjects were enrolled as control group. After total RNA extraction from peripheral blood mononuclear cells (PBMC) and cDNA synthesis, the gene expression levels of FCRL1, FCRL2, and FCRL4 in each group were measured by real-time polymerase chain reaction. Results: Our results showed that FCRL1 expression levels in kidney transplant patients were significantly less than healthy controls. The overall FCRL2 expression level was not significantly different between them. However, at days 1 and 7, following transplantation in the non-rejected group FCRL2 level was significantly higher than the control group. Comparing the FCRL4 gene expression levels of both groups with healthy controls showed a significant decrease in the third and seventh days post-transplantation. Conclusion: It can be concluded that mononuclear cells, mainly B cells, have an essential role to play in kidney transplantation. [GMJ.2020;9:e1730]

scholarly journals A Sparse and Low-Rank Regression Model for Identifying the Relationships Between DNA Methylation and Gene Expression Levels in Gastric Cancer and the Prediction of Prognosis

Genes ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 854
Author(s):  
Yishu Wang ◽  
Lingyun Xu ◽  
Dongmei Ai
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Dna Methylation ◽  
Regression Model ◽  
The Cancer Genome Atlas ◽  
Low Rank ◽  
Expression Levels ◽  
Rank Regression ◽  
Prediction Of Prognosis ◽  
Gene Expression Levels

DNA methylation is an important regulator of gene expression that can influence tumor heterogeneity and shows weak and varying expression levels among different genes. Gastric cancer (GC) is a highly heterogeneous cancer of the digestive system with a high mortality rate worldwide. The heterogeneous subtypes of GC lead to different prognoses. In this study, we explored the relationships between DNA methylation and gene expression levels by introducing a sparse low-rank regression model based on a GC dataset with 375 tumor samples and 32 normal samples from The Cancer Genome Atlas database. Differences in the DNA methylation levels and sites were found to be associated with differences in the expressed genes related to GC development. Overall, 29 methylation-driven genes were found to be related to the GC subtypes, and in the prognostic model, we explored five prognoses related to the methylation sites. Finally, based on a low-rank matrix, seven subgroups were identified with different methylation statuses. These specific classifications based on DNA methylation levels may help to account for heterogeneity and aid in personalized treatments.

scholarly journals High heterogeneity undermines generalization of differential expression results in RNA-Seq analysis

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Weitong Cui ◽  
Huaru Xue ◽  
Lei Wei ◽  
Jinghua Jin ◽  
Xuewen Tian ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Small Sample ◽  
Differentially Expressed ◽  
Cancer Type ◽  
Rna Seq ◽  
Sample Sizes ◽  
Large Sample ◽  
Expression Levels ◽  
Gene Expression Levels

Abstract Background RNA sequencing (RNA-Seq) has been widely applied in oncology for monitoring transcriptome changes. However, the emerging problem that high variation of gene expression levels caused by tumor heterogeneity may affect the reproducibility of differential expression (DE) results has rarely been studied. Here, we investigated the reproducibility of DE results for any given number of biological replicates between 3 and 24 and explored why a great many differentially expressed genes (DEGs) were not reproducible. Results Our findings demonstrate that poor reproducibility of DE results exists not only for small sample sizes, but also for relatively large sample sizes. Quite a few of the DEGs detected are specific to the samples in use, rather than genuinely differentially expressed under different conditions. Poor reproducibility of DE results is mainly caused by high variation of gene expression levels for the same gene in different samples. Even though biological variation may account for much of the high variation of gene expression levels, the effect of outlier count data also needs to be treated seriously, as outlier data severely interfere with DE analysis. Conclusions High heterogeneity exists not only in tumor tissue samples of each cancer type studied, but also in normal samples. High heterogeneity leads to poor reproducibility of DEGs, undermining generalization of differential expression results. Therefore, it is necessary to use large sample sizes (at least 10 if possible) in RNA-Seq experimental designs to reduce the impact of biological variability and DE results should be interpreted cautiously unless soundly validated.

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