scholarly journals Comprehensive analysis of the gene expression profile of wheat at the crossroads of heat, drought and combined stress

2021 ◽  
pp. bs202104
Author(s):  
Alsamman M. Alsamman ◽  
Ratiba Bousba ◽  
Michael Baum ◽  
Aladdin Hamwieh ◽  
Nourhan Fouad
Keyword(s):  
Gene Expression ◽  
Heat Stress ◽  
Protein Interactions ◽  
Gene Annotation ◽  
Rubisco Activase ◽  
Gene Expression Omnibus ◽  
P Value ◽  
Lipid Transfer ◽  
Combined Stress ◽  
Protein Protein Interaction

Heat and drought are among the leading environmental stresses which have a major impact on plant development. In our research, identification and characterization of differentially expressed genes (DEGs) regulating the response of wheat to drought, heat and combined stress was carried out. We analyzed data from the Gene Expression Omnibus database (GEO) microarrays containing 24 samples of wheat, which were categorized by different treatments (control: ctrl, drought: D, heat: H, and mixed: HD). Significant DEGs were examined for gene annotation, gene ontology, co-expression, protein-protein interaction (PPI) and their heterogeneity and consistency through drought, heat and combined stress was also studied. Genes such as gyrB, C6orf132 homolog, PYR1 were highly associated with wheat response to drought with P-value (-log10) of 9.3, 7.3, 6.4, and logFC of -3.9, 2.0, 1.6, respectively. DEGs associated with drought tolerance were highly related to the protein domains of lipid-transfer (LTP). Wheat response to heat stress was strongly associated with genes such as RuBisCO activase B, small heat shock, LTP3, YLS3, At2g33490, PETH with p-values (-log10) ranging from 9.3 to 12.3. In addition, a relatively high number of protein interactions involved the SDH, PEPCK, and G6PD genes under heat stress.

scholarly journals Identification of hub genes and pathways in nonalcoholic steatohepatitis by integrated bioinformatics analysis

2021 ◽  
Author(s):  
Pegah Einaliyan ◽  
Ali Owfi ◽  
Mohammadamin Mahmanzar ◽  
Taha Aghajanzadeh ◽  
Morteza Hadizadeh ◽  
...  
Keyword(s):  
Gene Expression ◽  
Protein Interactions ◽  
Expression Profiles ◽  
Principal Component ◽  
Gene Expression Omnibus ◽  
P Value ◽  
Protein Protein Interactions ◽  
Hub Genes ◽  
Alcoholic Fatty Liver ◽  
Critical Pathways

AbstractBackgroundCurrently, non-alcoholic fatty liver disease (NAFLD) is one of the most common chronic liver diseases in the world. Forecasting the short-term, up to 2025, NASH due to fibrosis is one of the leading causes of liver transplantation. Cohort studies revealed that non-alcoholic steatohepatitis (NASH) has a higher risk of fibrosis progression among NAFLD patients. Identifying differentially expressed genes helps to determine NASH pathogenic pathways, make more accurate diagnoses, and prescribe appropriate treatment.Methods and ResultsIn this study, we found 11 NASH datasets by searching in the Gene Expression Omnibus (GEO) database. Subsequently, NASH datasets with low-quality control scores were excluded. Four datasets were analyzed with packages of R/Bioconductor. Then, all integrated genes were Imported into Cytoscape to illustrate the protein-protein interactions network. All hubs and nodes degree has been calculated to determine the hub genes with critical roles in networks.Possible correlations between expression profiles of mutual DEGs were identified employing Principal Component Analysis (PCA). Primary analyzed data were filtered based on gene expression (logFC > 1, logFC < −1) and adj-P-value (<0.05). Ultimately, among 379 DEGs, we selected the top 10 genes (MYC, JUN, EGR1, FOS, CCL2, IL1B, CXCL8, PTGS2, IL6, SERPINE1) as candidates among up and down regulated genes, and critical pathways such as IL-6, IL-17, TGF β, and TNFα were identified.ConclusionThe present study suggests an important DEGs, biological processes, and critical pathways involved in the pathogenesis of NASH disease. Further investigations are needed to clarify the exact mechanisms underlying the development and progression of NASH disease.

Identification of the Key Genes of Autism Spectrum Disorder Through Protein-Protein Interaction Network

2019 ◽  
Vol 8 ◽  
Author(s):  
Mona Zamanian Azodi ◽  
Mostafa Rezaei-Tavirani ◽  
Majid Rezaei-Tavirani
Keyword(s):  
Gene Expression ◽  
Autism Spectrum Disorder ◽  
Protein Interactions ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
The Given

Background: Currently, the prevalence of autism spectrum disorder (ASD) is increasing, which widely spurs the interest in the molecular investigation. Thereby, a better understanding of the given disorder mechanisms is likely to be achieved. Bioinformatics suiting protein-protein interactions analysis via the application of high-throughput studies, such as protein array, is one of these achievements.Materials and Methods: The gene expression data from Gene Expression Omnibus (GEO) database were downloaded, and the expression profile of patients with developmental delay and autistic features were analyzed via Cytoscape and its relevant plug-ins.Results: Our findings indicated that EGFR, ACTB, RHOA, CALM1, MAPK1, and JUN genes as the hub-bottlenecks and their related terms could be important in ASD risk. In other words, any expression modification in these genes could trigger dysfunctions in the corresponding biological processes.Conclusion: We suggest that differentially expressed genes could be used as suitable targets for ASD after being validated.[GMJ.2019;8:e1367]

scholarly journals Investigation of the Mechanism of Complement System in Diabetic Nephropathy via Bioinformatics Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Bojun Xu ◽  
Lei Wang ◽  
Huakui Zhan ◽  
Liangbin Zhao ◽  
Yuehan Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Diabetic Nephropathy ◽  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Bioinformatics Analysis ◽  
Topological Analysis ◽  
Gene Expression Omnibus ◽  
Complement Cascade ◽  
Hub Genes ◽  
Novel Biomarkers

Objectives. Diabetic nephropathy (DN) is a major cause of end-stage renal disease (ESRD) throughout the world, and the identification of novel biomarkers via bioinformatics analysis could provide research foundation for future experimental verification and large-group cohort in DN models and patients. Methods. GSE30528, GSE47183, and GSE104948 were downloaded from Gene Expression Omnibus (GEO) database to find differentially expressed genes (DEGs). The difference of gene expression between normal renal tissues and DN renal tissues was firstly screened by GEO2R. Then, the protein-protein interactions (PPIs) of DEGs were performed by STRING database, the result was integrated and visualized via applying Cytoscape software, and the hub genes in this PPI network were selected by MCODE and topological analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out to determine the molecular mechanisms of DEGs involved in the progression of DN. Finally, the Nephroseq v5 online platform was used to explore the correlation between hub genes and clinical features of DN. Results. There were 64 DEGs, and 32 hub genes were identified, enriched pathways of hub genes involved in several functions and expression pathways, such as complement binding, extracellular matrix structural constituent, complement cascade related pathways, and ECM proteoglycans. The correlation analysis and subgroup analysis of 7 complement cascade-related hub genes and the clinical characteristics of DN showed that C1QA, C1QB, C3, CFB, ITGB2, VSIG4, and CLU may participate in the development of DN. Conclusions. We confirmed that the complement cascade-related hub genes may be the novel biomarkers for DN early diagnosis and targeted treatment.

scholarly journals Bioinformatics Analysis of the Molecular Mechanism and Potential Treatment Target of Ankylosing Spondylitis

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Fanyan Meng ◽  
Ningna Du ◽  
Daoming Xu ◽  
Li Kuai ◽  
Lanying Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ankylosing Spondylitis ◽  
Molecular Mechanism ◽  
Soft Tissues ◽  
Bioinformatics Analysis ◽  
Enrichment Analysis ◽  
R Package ◽  
Gene Expression Omnibus ◽  
Treatment Targets ◽  
Protein Protein Interaction

Ankylosing spondylitis (AS) is an autoimmune disease that mainly affects the spinal joints, sacroiliac joints, and adjacent soft tissues. We conducted bioinformatics analysis to explore the molecular mechanism related to AS pathogenesis and uncover novel potential molecular targets for the treatment of AS. The profiles of GSE25101, containing gene expression data extracted from the blood of 16 AS patients and 16 matched controls, were acquired from the Gene Expression Omnibus (GEO) database. The background correction and standardization were carried out utilizing the transcript per million (TPM) method. After analysis of AS patients and the normal groups, we identified 199 differentially expressed genes (DEGs) with upregulation and 121 DEGs with downregulation by the limma R package. The results of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) biological process enrichment analysis revealed that the DEGs with upregulation were mainly associated with spliceosome, ribosome, RNA-catabolic process, electron transport chain, etc. And the DEGs with downregulation primarily participated in T cell-associated pathways and processes. After analysis of the protein-protein interaction (PPI) network, our data revealed that the hub genes, comprising MRPL13, MRPL22, LSM3, COX7A2, COX7C, EP300, PTPRC, and CD4, could be the treatment targets in AS. Our data furnish new hints to uncover the features of AS and explore more promising treatment targets towards AS.

scholarly journals Data mining of immune-related prognostic genes in metastatic melanoma microenvironment

2020 ◽  
Vol 40 (11) ◽  
Author(s):  
Wei Han ◽  
Biao Huang ◽  
Xiao-Yu Zhao ◽  
Guo-Liang Shen
Keyword(s):  
Gene Expression ◽  
Tumor Microenvironment ◽  
Metastatic Melanoma ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Protein Protein Interaction ◽  
Subtype Identification ◽  
Cancer Genome Atlas ◽  
Immune Related Genes

Abstract Skin cutaneous melanoma (SKCM) is one of the most deadly malignancies. Although immunotherapies showed the potential to improve the prognosis for metastatic melanoma patients, only a small group of patients can benefit from it. Therefore, it is urgent to investigate the tumor microenvironment in melanoma as well as to identify efficient biomarkers in the diagnosis and treatments of SKCM patients. A comprehensive analysis was performed based on metastatic melanoma samples from the Cancer Genome Atlas (TCGA) database and ESTIMATE algorithm, including gene expression, immune and stromal scores, prognostic immune-related genes, infiltrating immune cells analysis and immune subtype identification. Then, the differentially expressed genes (DEGs) were obtained based on the immune and stromal scores, and a list of prognostic immune-related genes was identified. Functional analysis and the protein–protein interaction network revealed that these genes enriched in multiple immune-related biological processes. Furthermore, prognostic genes were verified in the Gene Expression Omnibus (GEO) databases and used to predict immune infiltrating cells component. Our study revealed seven immune subtypes with different risk values and identified T cells as the most abundant cells in the immune microenvironment and closely associated with prognostic outcomes. In conclusion, the present study thoroughly analyzed the tumor microenvironment and identified prognostic immune-related biomarkers for metastatic melanoma.

Uncovering potential lncRNAs and nearby mRNAs in systemic lupus erythematosus from the Gene Expression Omnibus dataset

Epigenomics ◽  
2019 ◽  
Vol 11 (16) ◽  
pp. 1795-1809 ◽  
Author(s):  
Haiyu Cao ◽  
Dong Li ◽  
Huixiu Lu ◽  
Jing Sun ◽  
Haibin Li
Keyword(s):  
Gene Expression ◽  
Systemic Lupus Erythematosus ◽  
Lupus Erythematosus ◽  
Noncoding Rnas ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Systemic Lupus ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

Aim: The aim of this study was to find potential differentially expressed long noncoding RNAs (lncRNAs) and mRNAs in systemic lupus erythematosus. Materials & methods: Differentially expressed lncRNAs and mRNAs were obtained in the Gene Expression Omnibus dataset. Functional annotation of differentially expressed mRNAs was performed, followed by protein–protein interaction network analysis. Then, the interaction network of lncRNA-nearby targeted mRNA was built. Results: Several interaction pairs of lncRNA-nearby targeted mRNA including NRIR-RSAD2, RP11-153M7.5-TLR2, RP4-758J18.2-CCNL2, RP11-69E11.4-PABPC4 and RP11-496I9.1-IRF7/ HRAS/ PHRF1 were identified. Measles and MAPK were significantly enriched signaling pathways of differentially expressed mRNAs. Conclusion: Our study identified several differentially expressed lncRNAs and mRNAs. And their interactions may play a crucial role in the process of systemic lupus erythematosus.

OSkirc: a web tool for identifying prognostic biomarkers in kidney renal clear cell carcinoma

2019 ◽  
Vol 15 (27) ◽  
pp. 3103-3110 ◽  
Author(s):  
Longxiang Xie ◽  
Qiang Wang ◽  
Yifang Dang ◽  
Linna Ge ◽  
Xiaoxiao Sun ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Carcinoma ◽  
Web Application ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
P Value ◽  
Survival Plot

Aim: To develop a free and quick analysis online tool that allows users to easily investigate the prognostic potencies of interesting genes in kidney renal clear cell carcinoma (KIRC). Patients & methods: A total of 629 KIRC cases with gene expression profiling data and clinical follow-up information are collected from public Gene Expression Omnibus and The Cancer Genome Atlas databases. Results: One web application called Online consensus Survival analysis for KIRC (OSkirc) that can be used for exploring the prognostic implications of interesting genes in KIRC was constructed. By OSkirc, users could simply input the gene symbol to receive the Kaplan–Meier survival plot with hazard ratio and log-rank p-value. Conclusion: OSkirc is extremely valuable for basic and translational researchers to screen and validate the prognostic potencies of genes for KIRC, publicly accessible at http://bioinfo.henu.edu.cn/KIRC/KIRCList.jsp

scholarly journals Identification of Differential Gene Expression and Related Signaling Pathways In SARS-COV-2 Infected Human Cells

2021 ◽  
Author(s):  
Tian-Ao Xie ◽  
Hou-He Li ◽  
Zu-En Lin ◽  
Xiao-Ye Lin ◽  
Xin Meng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Vaccine Development ◽  
Virus Disease ◽  
Expression Profiles ◽  
Biological Significance ◽  
Gene Expression Profiles ◽  
Gene Expression Omnibus ◽  
Human Cells ◽  
Hub Genes ◽  
Protein Protein Interaction

Abstract Background: The Corona Virus Disease 2019 (COVID-19) pandemic poses a serious public health threat to the survival and health of people all over the world. We analyzed related mRNA data and gene expression profiles of human cell lines infected with SARS-CoV-2 obtained from GEO (GSE148729), using bioinformatics tools. Differentially expressed genes (DEGs) of human cells infected with SARS-CoV-2 were identified.Method: The GSE148729 datasets were downloaded from the Gene Expression Omnibus (GEO) database. To explore the Biological significance of DEGs, Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment of the DEGs was performed. Protein-protein interaction (PPI) networks of the DEGs were constructed by using the STRING database. The hub genes were selected using the Cytoscape Software, and a t-test was performed to validate the hub genes.Result: A total of 1241 DEGs were screened, including 1049 up-regulated genes and 192 down-regulated genes. Besides, 10 hub genes were obtained from the PPI network, among which the expression level of CXCL2, Etv7, and HIST1H2BG was found to be statistically significant.Conclusion: In conclusion, bioinformatics analysis reveals genes and cellular pathways that are significantly altered in SARS-CoV-2 infected cells. This is conducive to further guide the clinical study of SARS-CoV-2 and provides new perspectives for vaccine development.

scholarly journals Identification of TFE3/TFEB and its Related Genes as Prognostic Biomarkers in Acute Myeloid Leukemia

2021 ◽  
Author(s):  
Qiaoli Li
Keyword(s):  
Gene Expression ◽  
Acute Myeloid Leukemia ◽  
Prognostic Factors ◽  
Cell Fate ◽  
Protein Interactions ◽  
Myeloid Leukemia ◽  
Sequence Data ◽  
Gene Expression Omnibus ◽  
Prognostic Biomarkers ◽  
Acute Myeloid

Abstract Background: Acute myeloid leukemia (AML) is one of the most common hematologic malignances with an ever-increasing incidence and high mortality. TFE3 and TFEB, two transcription factors that mediate cellular adaptation to stress by simultaneously promoting lysosomal biogenesis, autophagy induction, as well as expression of critical mitochondrial and metabolic regulators, which are substantial contributors to cell fate and cancer progress. However, the expression and prognostic values of TFE3/TFEB in AML have not been clarified.Objective: To explore the expression and role of TFE3/TFEB in AML and thus to find potential therapy. Methods: RNA sequence data from AML patients and healthy donors were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) analysis were performed by GEO2R. TFE3/TFEB related genes were obtained from UALCAN. Gene ontology (GO) and KEGG pathway were analyzed by WEB-based GEne SeT AnaLysis Toolkit (WebGestalt) and DAVID. Protein-protein interactions (PPIs) network construction and module analysis were performed by STRING and Cytoscape. The Kaplan-Meier survival curves were drawn in TCGA portal. Results: We found TFE3 and TFEB can be used prognostic factors for AML, and most of their positively related genes were worse prognostic factors too. ITGB2, FGR, ITGAM, ITGAX and SELPLG were identified as the most significant genes in survival-related genes contributed by TFE3 and TFEB.Conclusions: In this study, we performed a comprehensive analysis of gene expression and gene function to identify key prognostic biomarkers in AML.

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