Alterations in the host transcriptome in vitro and in vivo following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection

Abstract Background. Exploring alterations in the host transcriptome following SARS-CoV-2 infection is not only highly warranted to help us understand molecular mechanisms of the disease, but also provide new prospective for screening effective antiviral drugs, finding new therapeutic targets, and evaluating the risk of systemic inflammatory response syndrome (SIRS) early.Methods. We downloaded three gene expression matrix files from the Gene Expression Omnibus (GEO) database, and extracted the gene expression data of the SARS-CoV-2 infection and non-infection in human samples and different cell line samples, and then performed gene set enrichment analysis (GSEA), respectively. Thereafter, we integrated the results of GSEA and obtained co-enriched gene sets and co-core genes in three various microarray data. Finally, we also constructed a protein-protein interaction (PPI) network and molecular modules for co-core genes and performed Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis for the genes from modules to clarify their possible biological processes and underlying signaling pathway. Results. A total of 11 co-enriched gene sets were identiﬁed from the three various microarray data. Among them, 10 gene sets were activated, and involved in immune response and inflammatory reaction. 1 gene set was suppressed, and participated in cell cycle. The analysis of molecular modules showed that 2 modules might play a vital role in the pathogenic process of SARS-CoV-2 infection. The KEGG enrichment analysis showed that genes from module one enriched in signaling pathways related to inflammation, but genes from module two enriched in signaling of cell cycle and DNA replication. Particularly, necroptosis signaling, a newly identified type of programmed cell death that differed from apoptosis, was also determined in our findings. Additionally, for patients with SARS-CoV-2 infection, genes from module one showed a relatively high-level expression while genes from module two showed low-level. Conclusions. We identified two molecular modules were used to assess severity and predict the prognosis of the patients with SARS-CoV-2 infection. In addition, these results provide a unique opportunity to explore more molecular pathways as new potential targets on therapy in COVID 19.

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Altered Genes and Biological Functions in Response to Severe Burns

BioMed Research International ◽

10.1155/2021/8836243 ◽

2021 ◽

Vol 2021 ◽

pp. 1-19

Author(s):

Xinheng Liu ◽

Yongxian Rong ◽

Donglin Huang ◽

Zhijie Liang ◽

Xiaolin Yi ◽

...

Keyword(s):

Gene Expression ◽

Roc Curve ◽

Molecular Mechanisms ◽

Metabolic Response ◽

Heat Exposure ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Ppi Network ◽

Gene Set ◽

Severe Burns

Severe burns are acute wounds caused by local heat exposure, resulting in life-threatening systemic effects and poor survival. However, the specific molecular mechanisms remain unclear. First, we downloaded gene expression data related to severe burns from the GEO database (GSE19743, GSE37069, and GSE77791). Then, a gene expression analysis was performed to identify differentially expressed genes (DEGs) and construct protein-protein interaction (PPI) network. The molecular mechanism was identified by enrichment analysis and Gene Set Enrichment Analysis. In addition, STEM software was used to screen for genes persistently expressed during response to severe burns, and receiver operating characteristic (ROC) curve was used to identify key DEGs. A total of 2631 upregulated and 3451 downregulated DEGs were identified. PPI network analysis clustered these DEGs into 13 modules. Importantly, module genes mostly related with immune responses and metabolism. In addition, we identified genes persistently altered during the response to severe burns corresponding to survival and death status. Among the genes with high area under the ROC curve in the PPI network gene, CCL5 and LCK were identified as key DEGs, which may affect the prognosis of burn patients. Gene set variation analysis showed that the immune response was inhibited and several types of immune cells were decreased, while the metabolic response was enhanced. The results showed that persistent gene expression changes occur in response to severe burns, which may underlie chronic alterations in physiological pathways. Identifying the key altered genes may reveal potential therapeutic targets for mitigating the effects of severe burns.

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Cross-Platform Microarray Data Integration Combining Meta-Analysis and Gene Set Enrichment Analysis

Bioinformatics ◽

10.4018/978-1-4666-3604-0.ch031 ◽

2013 ◽

pp. 570-585

Author(s):

Jian Yu ◽

Jun Wu ◽

Miaoxin Li ◽

Yajun Yi ◽

Yu Shyr ◽

...

Keyword(s):

Microarray Data ◽

Molecular Mechanisms ◽

Meta Analysis ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Gene Set Enrichment ◽

Gene Set ◽

Gene Expression Studies ◽

Microarray Datasets ◽

Pathway Level

Integrative analysis of microarray data has been proven as a more reliable approach to deciphering molecular mechanisms underlying biological studies. Traditional integration such as meta-analysis is usually gene-centered. Recently, gene set enrichment analysis (GSEA) has been widely applied to bring gene-level interpretation to pathway-level. GSEA is an algorithm focusing on whether an a priori defined set of genes shows statistically significant differences between two biological states. However, GSEA does not support integrating multiple microarray datasets generated from different studies. To overcome this, the improved version of GSEA, ASSESS, is more applicable, after necessary modifications. By making proper combined use of meta-analysis, GSEA, and modified ASSESS, this chapter reports two workflow pipelines to extract consistent expression pattern change at pathway-level, from multiple microarray datasets generated by the same or different microarray production platforms, respectively. Such strategies amplify the advantage and overcome the disadvantage than if using each method individually, and may achieve a more comprehensive interpretation towards a biological theme based on an increased sample size. With further network analysis, it may also allow an overview of cross-talking pathways based on statistical integration of multiple gene expression studies. A web server where one of the pipelines is implemented is available at: http://lifecenter.sgst.cn/mgsea//home.htm.

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XGSEA: CROSS-species Gene Set Enrichment Analysis via domain adaptation

10.1101/2020.07.21.213645 ◽

2020 ◽

Author(s):

Menglan Cai ◽

Canh Hao Nguyen ◽

Hiroshi Mamitsuka ◽

Limin Li

Keyword(s):

Gene Expression ◽

Domain Adaptation ◽

Gene Knockout ◽

Enrichment Analysis ◽

Real Data ◽

Gene Set Enrichment Analysis ◽

Data Sets ◽

Gene Set Enrichment ◽

Gene Set ◽

Gene Sets

AbstractGene set enrichment analysis (GSEA) has been widely used to identify gene sets with statistically significant difference between cases and controls against a large gene set. GSEA needs both phenotype labels and expression of genes. However, gene expression are assessed more often for model organisms than minor species. More importantly, gene expression could not be measured under specific conditions for human, due to high healthy risk of direct experiments, such as non-approved treatment or gene knockout, and then often substituted by mouse. Thus predicting enrichment significance (on a phenotype) of a given gene set of a species (target, say human), by using gene expression measured under the same phenotype of the other species (source, say mouse) is a vital and challenging problem, which we call CROSS-species Gene Set Enrichment Problem (XGSEP). For XGSEP, we propose XGSEA (Cross-species Gene Set Enrichment Analysis), with three steps of: 1) running GSEA for a source species to obtain enrichment scores and p-values of source gene sets; 2) representing the relation between source and target gene sets by domain adaptation; and 3) using regression to predict p-values of target gene sets, based on the representation in 2). We extensively validated XGSEA by using four real data sets under various settings, proving that XGSEA significantly outperformed three baseline methods. A case study of identifying important human pathways for T cell dysfunction and reprogramming from mouse ATAC-Seq data further confirmed the reliability of XGSEA. Source code is available through https://github.com/LiminLi-xjtu/XGSEAAuthor summaryGene set enrichment analysis (GSEA) is a powerful tool in the gene sets differential analysis given a ranked gene list. GSEA requires complete data, gene expression with phenotype labels. However, gene expression could not be measured under specific conditions for human, due to high risk of direct experiments, such as non-approved treatment or gene knockout, and then often substituted by mouse. Thus no availability of gene expression leads to more challenging problem, CROSS-species Gene Set Enrichment Problem (XGSEP), in which enrichment significance (on a phenotype) of a given gene set of a species (target, say human) is predicted by using gene expression measured under the same phenotype of the other species (source, say mouse). In this work, we propose XGSEA (Cross-species Gene Set Enrichment Analysis) for XGSEP, with three steps of: 1) GSEA; 2) domain adaptation; and 3) regression. The results of four real data sets and a case study indicate that XGSEA significantly outperformed three baseline methods and confirmed the reliability of XGSEA.

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Application of biclustering of gene expression data and gene set enrichment analysis methods to identify potentially disease causing nanomaterials

Beilstein Journal of Nanotechnology ◽

10.3762/bjnano.6.252 ◽

2015 ◽

Vol 6 ◽

pp. 2438-2448 ◽

Cited By ~ 14

Author(s):

Andrew Williams ◽

Sabina Halappanavar

Keyword(s):

Gene Expression ◽

Pulmonary Fibrosis ◽

Expression Profiles ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Data Driven ◽

Gene Set Enrichment ◽

Gene Set ◽

Analysis Methods ◽

Gene Sets

Background: The presence of diverse types of nanomaterials (NMs) in commerce is growing at an exponential pace. As a result, human exposure to these materials in the environment is inevitable, necessitating the need for rapid and reliable toxicity testing methods to accurately assess the potential hazards associated with NMs. In this study, we applied biclustering and gene set enrichment analysis methods to derive essential features of altered lung transcriptome following exposure to NMs that are associated with lung-specific diseases. Several datasets from public microarray repositories describing pulmonary diseases in mouse models following exposure to a variety of substances were examined and functionally related biclusters of genes showing similar expression profiles were identified. The identified biclusters were then used to conduct a gene set enrichment analysis on pulmonary gene expression profiles derived from mice exposed to nano-titanium dioxide (nano-TiO2), carbon black (CB) or carbon nanotubes (CNTs) to determine the disease significance of these data-driven gene sets. Results: Biclusters representing inflammation (chemokine activity), DNA binding, cell cycle, apoptosis, reactive oxygen species (ROS) and fibrosis processes were identified. All of the NM studies were significant with respect to the bicluster related to chemokine activity (DAVID; FDR p-value = 0.032). The bicluster related to pulmonary fibrosis was enriched in studies where toxicity induced by CNT and CB studies was investigated, suggesting the potential for these materials to induce lung fibrosis. The pro-fibrogenic potential of CNTs is well established. Although CB has not been shown to induce fibrosis, it induces stronger inflammatory, oxidative stress and DNA damage responses than nano-TiO2 particles. Conclusion: The results of the analysis correctly identified all NMs to be inflammogenic and only CB and CNTs as potentially fibrogenic. In addition to identifying several previously defined, functionally relevant gene sets, the present study also identified two novel genes sets: a gene set associated with pulmonary fibrosis and a gene set associated with ROS, underlining the advantage of using a data-driven approach to identify novel, functionally related gene sets. The results can be used in future gene set enrichment analysis studies involving NMs or as features for clustering and classifying NMs of diverse properties.

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High BLM Expression Predicts Poor Clinical Outcome and Contributes to Malignant Progression in Human Cholangiocarcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.633899 ◽

2021 ◽

Vol 11 ◽

Author(s):

Xiaolong Du ◽

Chen Zhang ◽

Chuanzheng Yin ◽

Wenjie Wang ◽

Xueke Yan ◽

...

Keyword(s):

Cell Cycle ◽

Molecular Mechanisms ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Roc Curve Analysis ◽

Migration Ability ◽

Data Set ◽

Hub Gene

Molecular mechanisms underlying the tumorigenesis of a highly malignant cancer, cholangiocarcinoma (CCA), are still obscure. In our study, the CCA expression profile data were acquired from The Cancer Genome Atlas (TCGA) database, and differentially expressed genes (DEGs) in the TCGA-Cholangiocarcinoma (TCGA-CHOL) data set were utilized to construct a co-expression network via weighted gene co-expression network analysis (WGCNA). The blue gene module associated with the histopathologic grade of CCA was screened. Then, five candidate hub genes were screened by combining the co-expression network with protein–protein interaction (PPI) network. After progression and survival analyses, bloom syndrome helicase (BLM) was ultimately identified as a real hub gene. Moreover, the receiver operating characteristic (ROC) curve analysis suggested that BLM had a favorable diagnostic and predictive recurrence value for CCA. The gene set enrichment analysis (GSEA) results for a single hub gene revealed the importance of cell cycle-related pathways in the CCA progression and prognosis. Furthermore, we detected the BLM expression in vitro, and the results demonstrated that the expression level of BLM was much higher in the CCA tissues and cells relative to adjacent non-tumor samples and normal bile duct epithelial cells. Additionally, after further silencing the BLM expression by small interfering RNA (siRNA), the proliferation and migration ability of CCA cells were all inhibited, and the cell cycle was arrested. Altogether, a real hub gene (BLM) and cell cycle-related pathways were identified in the present study, and the gene BLM may be involved in the CCA progression and could act as a reliable biomarker for potential diagnosis and prognostic evaluation.

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Cross-Platform Microarray Data Integration Combining Meta-Analysis and Gene Set Enrichment Analysis

Interdisciplinary Research and Applications in Bioinformatics, Computational Biology, and Environmental Sciences - Advances in Bioinformatics and Biomedical Engineering ◽

10.4018/978-1-60960-064-8.ch002 ◽

2011 ◽

pp. 12-27

Author(s):

Jian Yu ◽

Jun Wu ◽

Miaoxin Li ◽

Yajun Yi ◽

Yu Shyr ◽

...

Keyword(s):

Microarray Data ◽

Molecular Mechanisms ◽

Meta Analysis ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Gene Set Enrichment ◽

Gene Set ◽

Gene Expression Studies ◽

Microarray Datasets ◽

Pathway Level

Integrative analysis of microarray data has been proven as a more reliable approach to deciphering molecular mechanisms underlying biological studies. Traditional integration such as meta-analysis is usually gene-centered. Recently, gene set enrichment analysis (GSEA) has been widely applied to bring gene-level interpretation to pathway-level. GSEA is an algorithm focusing on whether an a priori defined set of genes shows statistically significant differences between two biological states. However, GSEA does not support integrating multiple microarray datasets generated from different studies. To overcome this, the improved version of GSEA, ASSESS, is more applicable, after necessary modifications. By making proper combined use of meta-analysis, GSEA, and modified ASSESS, this chapter reports two workflow pipelines to extract consistent expression pattern change at pathway-level, from multiple microarray datasets generated by the same or different microarray production platforms, respectively. Such strategies amplify the advantage and overcome the disadvantage than if using each method individually, and may achieve a more comprehensive interpretation towards a biological theme based on an increased sample size. With further network analysis, it may also allow an overview of cross-talking pathways based on statistical integration of multiple gene expression studies. A web server where one of the pipelines is implemented is available at: http://lifecenter.sgst.cn/mgsea//home.htm.

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Comprehensive gene expression profiling of rat lung reveals distinct acute and chronic responses to cigarette smoke inhalation

AJP Lung Cellular and Molecular Physiology ◽

10.1152/ajplung.00105.2007 ◽

2007 ◽

Vol 293 (5) ◽

pp. L1183-L1193 ◽

Cited By ~ 65

Author(s):

Christopher S. Stevenson ◽

Cerys Docx ◽

Ruth Webster ◽

Cliff Battram ◽

Debra Hynx ◽

...

Keyword(s):

Gene Expression ◽

Stress Response ◽

Cigarette Smoke ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Smoke Inhalation ◽

Whole Body ◽

Gene Set Enrichment ◽

Gene Set ◽

Gene Sets

Chronic obstructive pulmonary disease (COPD) is a smoking-related disease that lacks effective therapies due partly to the poor understanding of disease pathogenesis. The aim of this study was to identify molecular pathways that could be responsible for the damaging consequences of smoking. To do this, we employed Gene Set Enrichment Analysis to analyze differences in global gene expression, which we then related to the pathological changes induced by cigarette smoke (CS). Sprague-Dawley rats were exposed to whole body CS for 1 day and for various periods up to 8 mo. Gene Set Enrichment Analysis of microarray data identified that metabolic processes were most significantly increased early in the response to CS. Gene sets involved in stress response and inflammation were also upregulated. CS exposure increased neutrophil chemokines, cytokines, and proteases (MMP-12) linked to the pathogenesis of COPD. After a transient acute response, the CS-exposed rats developed a distinct molecular signature after 2 wk, which was followed by the chronic phase of the response. During this phase, gene sets related to immunity and defense progressively increased and predominated at the later time points in smoke-exposed rats. Chronic CS inhalation recapitulated many of the phenotypic changes observed in COPD patients including oxidative damage to macrophages, a slowly resolving inflammation, epithelial damage, mucus hypersecretion, airway fibrosis, and emphysema. As such, it appears that metabolic pathways are central to dealing with the stress of CS exposure; however, over time, inflammation and stress response gene sets become the most significantly affected in the chronic response to CS.

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Utilizing Cancer - Functional Gene Set - Compound Networks to Identify Putative Drugs for Breast Cancer

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1574888x13666180105125347 ◽

2018 ◽

Vol 21 (2) ◽

pp. 74-83

Author(s):

Tzu-Hung Hsiao ◽

Yu-Chiao Chiu ◽

Yu-Heng Chen ◽

Yu-Ching Hsu ◽

Hung-I Harry Chen ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Cancer Therapy ◽

Cancer Treatment ◽

Cancer Survival ◽

Expression Profiles ◽

Functional Gene ◽

Gene Set Enrichment Analysis ◽

Gene Set ◽

Gene Sets

Aim and Objective: The number of anticancer drugs available currently is limited, and some of them have low treatment response rates. Moreover, developing a new drug for cancer therapy is labor intensive and sometimes cost prohibitive. Therefore, “repositioning” of known cancer treatment compounds can speed up the development time and potentially increase the response rate of cancer therapy. This study proposes a systems biology method for identifying new compound candidates for cancer treatment in two separate procedures. Materials and Methods: First, a “gene set–compound” network was constructed by conducting gene set enrichment analysis on the expression profile of responses to a compound. Second, survival analyses were applied to gene expression profiles derived from four breast cancer patient cohorts to identify gene sets that are associated with cancer survival. A “cancer–functional gene set– compound” network was constructed, and candidate anticancer compounds were identified. Through the use of breast cancer as an example, 162 breast cancer survival-associated gene sets and 172 putative compounds were obtained. Results: We demonstrated how to utilize the clinical relevance of previous studies through gene sets and then connect it to candidate compounds by using gene expression data from the Connectivity Map. Specifically, we chose a gene set derived from a stem cell study to demonstrate its association with breast cancer prognosis and discussed six new compounds that can increase the expression of the gene set after the treatment. Conclusion: Our method can effectively identify compounds with a potential to be “repositioned” for cancer treatment according to their active mechanisms and their association with patients’ survival time.

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LMNB2 promotes the progression of colorectal cancer by silencing p21 expression

Cell Death and Disease ◽

10.1038/s41419-021-03602-1 ◽

2021 ◽

Vol 12 (4) ◽

Author(s):

Chen-Hua Dong ◽

Tao Jiang ◽

Hang Yin ◽

Hu Song ◽

Yi Zhang ◽

...

Keyword(s):

Colorectal Cancer ◽

Cell Cycle ◽

Cell Proliferation ◽

Cell Cycle Progression ◽

Molecular Mechanisms ◽

Gene Set Enrichment Analysis ◽

Molecular Therapy ◽

Cycle Progression ◽

Cancer Tissues

AbstractColorectal cancer is the second common cause of death worldwide. Lamin B2 (LMNB2) is involved in chromatin remodeling and the rupture and reorganization of nuclear membrane during mitosis, which is necessary for eukaryotic cell proliferation. However, the role of LMNB2 in colorectal cancer (CRC) is poorly understood. This study explored the biological functions of LMNB2 in the progression of colorectal cancer and explored the possible molecular mechanisms. We found that LMNB2 was significantly upregulated in primary colorectal cancer tissues and cell lines, compared with paired non-cancerous tissues and normal colorectal epithelium. The high expression of LMNB2 in colorectal cancer tissues is significantly related to the clinicopathological characteristics of the patients and the shorter overall and disease-free cumulative survival. Functional analysis, including CCK8 cell proliferation test, EdU proliferation test, colony formation analysis, nude mouse xenograft, cell cycle, and apoptosis analysis showed that LMNB2 significantly promotes cell proliferation by promoting cell cycle progression in vivo and in vitro. In addition, gene set enrichment analysis, luciferase report analysis, and CHIP analysis showed that LMNB2 promotes cell proliferation by regulating the p21 promoter, whereas LMNB2 has no effect on cell apoptosis. In summary, these findings not only indicate that LMNB2 promotes the proliferation of colorectal cancer by regulating p21-mediated cell cycle progression, but also suggest the potential value of LMNB2 as a clinical prognostic marker and molecular therapy target.

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