scholarly journals Rewired Pathways and Disrupted Pathway Crosstalk in Schizophrenia Transcriptomes by Multiple Differential Coexpression Methods

Genes ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 665
Author(s):  
Hui Yu ◽  
Yan Guo ◽  
Jingchun Chen ◽  
Xiangning Chen ◽  
Peilin Jia ◽  
...  
Keyword(s):  
Gene Expression ◽  
Focal Adhesion ◽  
Biological Pathways ◽  
Postmortem Brain ◽  
Rna Seq ◽  
Hub Genes ◽  
Differential Coexpression ◽  
Pathway Crosstalk ◽  
Microarray Datasets ◽  
Transcriptomic Studies

Transcriptomic studies of mental disorders using the human brain tissues have been limited, and gene expression signatures in schizophrenia (SCZ) remain elusive. In this study, we applied three differential co-expression methods to analyze five transcriptomic datasets (three RNA-Seq and two microarray datasets) derived from SCZ and matched normal postmortem brain samples. We aimed to uncover biological pathways where internal correlation structure was rewired or inter-coordination was disrupted in SCZ. In total, we identified 60 rewired pathways, many of which were related to neurotransmitter, synapse, immune, and cell adhesion. We found the hub genes, which were on the center of rewired pathways, were highly mutually consistent among the five datasets. The combinatory list of 92 hub genes was generally multi-functional, suggesting their complex and dynamic roles in SCZ pathophysiology. In our constructed pathway crosstalk network, we found “Clostridium neurotoxicity” and “signaling events mediated by focal adhesion kinase” had the highest interactions. We further identified disconnected gene links underlying the disrupted pathway crosstalk. Among them, four gene pairs (PAK1:SYT1, PAK1:RFC5, DCTN1:STX1A, and GRIA1:MAP2K4) were normally correlated in universal contexts. In summary, we systematically identified rewired pathways, disrupted pathway crosstalk circuits, and critical genes and gene links in schizophrenia transcriptomes.

scholarly journals Comparative Transcriptome Analysis of Different Dendrobium Species Reveals Active Ingredients-Related Genes and Pathways

2020 ◽  
Vol 21 (3) ◽  
pp. 861 ◽  
Author(s):  
Yingdan Yuan ◽  
Bo Zhang ◽  
Xinggang Tang ◽  
Jinchi Zhang ◽  
Jie Lin
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Rna Seq ◽  
Active Ingredients ◽  
Hub Genes ◽  
Transcriptome Database ◽  
Gene Modules ◽  
Functional Investigation ◽  
First Time

Dendrobium is widely used in traditional Chinese medicine, which contains many kinds of active ingredients. In recent years, many Dendrobium transcriptomes have been sequenced. Hence, weighted gene co-expression network analysis (WGCNA) was used with the gene expression profiles of active ingredients to identify the modules and genes that may associate with particular species and tissues. Three kinds of Dendrobium species and three tissues were sampled for RNA-seq to generate a high-quality, full-length transcriptome database. Based on significant changes in gene expression, we constructed co-expression networks and revealed 19 gene modules. Among them, four modules with properties correlating to active ingredients regulation and biosynthesis, and several hub genes were selected for further functional investigation. This is the first time the WGCNA method has been used to analyze Dendrobium transcriptome data. Further excavation of the gene module information will help us to further study the role and significance of key genes, key signaling pathways, and regulatory mechanisms between genes on the occurrence and development of medicinal components of Dendrobium.

scholarly journals Diagnostic biomarker candidates for pulpitis revealed by bioinformatics analysis of merged microarray gene expression datasets

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Ming Chen ◽  
Junkai Zeng ◽  
Yeqing Yang ◽  
Buling Wu
Keyword(s):  
Gene Expression ◽  
Dental Pulp ◽  
Bioinformatics Analysis ◽  
Kegg Pathway ◽  
Enrichment Analysis ◽  
Biological Pathways ◽  
Signalling Pathway ◽  
Diagnostic Biomarker ◽  
Hub Genes ◽  
Geo Database

Abstract Background Pulpitis is an inflammatory disease, the grade of which is classified according to the level of inflammation. Traditional methods of evaluating the status of dental pulp tissue in clinical practice have limitations. The rapid and accurate diagnosis of pulpitis is essential for determining the appropriate treatment. By integrating different datasets from the Gene Expression Omnibus (GEO) database, we analysed a merged expression matrix of pulpitis, aiming to identify biological pathways and diagnostic biomarkers of pulpitis. Methods By integrating two datasets (GSE77459 and GSE92681) in the GEO database using the sva and limma packages of R, differentially expressed genes (DEGs) of pulpitis were identified. Then, the DEGs were analysed to identify biological pathways of dental pulp inflammation with Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and Gene Set Enrichment Analysis (GSEA). Protein–protein interaction (PPI) networks and modules were constructed to identify hub genes with the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and Cytoscape. Results A total of 470 DEGs comprising 394 upregulated and 76 downregulated genes were found in pulpitis tissue. GO analysis revealed that the DEGs were enriched in biological processes related to inflammation, and the enriched pathways in the KEGG pathway analysis were cytokine-cytokine receptor interaction, chemokine signalling pathway and NF-κB signalling pathway. The GSEA results provided further functional annotations, including complement system, IL6/JAK/STAT3 signalling pathway and inflammatory response pathways. According to the degrees of nodes in the PPI network, 10 hub genes were identified, and 8 diagnostic biomarker candidates were screened: PTPRC, CD86, CCL2, IL6, TLR8, MMP9, CXCL8 and ICAM1. Conclusions With bioinformatics analysis of merged datasets, biomarker candidates of pulpitis were screened and the findings may be as reference to develop a new method of pulpitis diagnosis.

scholarly journals Bioinformatic Identification of Hub Genes and Biological Pathways for Sepsis-Associated Acute Lung Injury

2021 ◽  
Author(s):  
Chao Zhang ◽  
Feng Xu ◽  
Fang Fang
Keyword(s):  
Gene Expression ◽  
Acute Lung Injury ◽  
Lung Injury ◽  
Function Analysis ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Biological Pathways ◽  
Gene Set Enrichment Analysis ◽  
Ppi Network ◽  
Hub Genes

Abstract Background: Sepsis-associated acute lung injury (ALI) is a potentially lethal complication associated with a poor prognosis and high mortality worldwide, especially in the outbreak of COVID-19. However, the fundamental mechanisms of this complication were still not fully elucidated. Thus, we conducted this study to identify hub genes and biological pathways of sepsis-associated ALI, mainly focus on two pathways of LPS and HMGB1. Methods: Gene expression profile GSE3037 were downloaded from Gene Expression Omnibus (GEO) database, including 8 patients with sepsis-induced acute lung injury, with 8 unstimulated blood neutrophils, 8 LPS- induced neutrophils and 8 HMGB1-induced neutrophils. Differentially expressed genes (DEGs) identifications, Gene Ontology (GO) function analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis, Gene Set Enrichment Analysis (GSEA) and protein-protein interaction (PPI) network constructions were performed to obtain hub genes and relevant biological pathways.Results: We identified 534 and 317 DEGs for LPS- and HMGB1-induced ALI, respectively. The biological pathways involved in LPS- and HMGB1-induced ALI were also identified accordingly. By PPI network analysis, we found that ten hub genes for LPS-induced ALI (CXCL8, TNF, IL6, IL1B, ICAM1, CXCL1, CXCL2, IL1A, IL1RN and CXCL3) and another ten hub genes for HMGB1-induced ALI (CCL20, CXCL2, CXCL1, CCL4, CXCL3, CXCL9, CCL21, CXCR6, KNG1 and SST). Furthermore, by combining analysis, the results revealed that genes of TNF, CCL20, IL1B, NFKBIA, CCL4, PTGS2, TNFAIP3, CXCL2, CXCL1 and CXCL3 were potential biomarkers for sepsis-associated ALI. Conclusions: Our study revealed that ten hub genes associated with sepsis-induced ALI were TNF, CCL20, IL1B, NFKBIA, CCL4, PTGS2, TNFAIP3, CXCL2, CXCL1 and CXCL3, which may serve as genetic biomarkers and be further verified in prospective experimental trials.

scholarly journals P22 Micro-RNA enriched pathway impact analysis applied to synovial RNA-seq in early rheumatoid arthritis identifies response prediction pathways

Rheumatology ◽  
2020 ◽  
Vol 59 (Supplement_2) ◽  
Author(s):  
Elisabetta Sciacca ◽  
Salvatore Alaimo ◽  
Alfredo Pulvirenti ◽  
Vito Latora ◽  
Frances Humby ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Gene Expression ◽  
Pathway Analysis ◽  
Impact Analysis ◽  
Regulatory Elements ◽  
Biological Pathways ◽  
Pairwise Comparisons ◽  
Rna Seq ◽  
Dmard Therapy ◽  
Expression Levels

Abstract Background We use a new pathway analysis tool MITHrIL (Mirna enriched paTHway Impact anaLysis) to analyse RNA-seq patterns in synovial biopsies from patients with rheumatoid arthritis from the Pathobiology of Early Arthritis Cohort (PEAC). MITHrIL augments pathways with missing regulatory elements, such as microRNAs, and their interactions with genes to enhance quantification of immunological pathway activation in bulk RNA-seq. MITHrIL pathways were compared with three major pathotypes (lympho-myeloid, diffuse myeloid and pauci-immune fibroid) in early treatment-naïve RA patients and responders vs non-responders to methotrexate-based DMARD regimens. Methods A differential gene expression analysis is performed on the PEAC observational cohort. The Log-Fold Changes retrieved from the pairwise comparisons between responders/non responders and across different pathotypes are used as input to the MITHrIL software. Using the KEGG biological pathways, MITHrIL proportionally spreads the known differential perturbation of a gene to the downstream nodes in the pathway. In so doing, a perturbation factor is assigned to each gene and, as a result, a list of differentially altered pathways is returned together with the corresponding statistical significance (p-values). Particular attention is given to immunological pathways. We show a list of differentially altered pathways for each comparison and provide specific insight to those more characterising pathways. Results The results show coherence with the previous findings providing greater granularity on how the gene level alterations can propagate through biological pathways. In particular, we found different gene expression levels in the pairwise comparisons across pathotypes highlighting different perturbations in the following pathways: chemokine signalling (adjusted P = 3.3E-13), Jak-STAT signalling (adjusted P = 6.5E-04), Leukocyte transendothelial migration (adjusted P = 3.7E-02), PI3K-Akt signalling (adjusted P = 4.6E-04), T cell receptor signalling (adjusted P = 3.7E-02), Antigen processing and presentation (adjusted P = 1.1E-08) and NF-kappa B signalling (adjusted P = 4.1E-02). We also found confirmation of changes in lymphoid and myeloid subtypes over time, while fibroid RA patients present no significant alteration in their expression levels after the methotrexate-based DMARD therapy. Finally, we found different levels of activation in MAPK and AMPK signalling pathways for patients that do not respond to the DMARD therapy in contrast to those who do. Conclusion A novel pathway analysis approach is used to show the most differentially active biological pathways between different RA pathotypes and responder-resistant patients to methotrexate-based DMARD therapy. The results identify responder-resistant gene expression pathway patterns in early RA which may help to stratify patients to biologic therapy at an earlier stage. Disclosures E. Sciacca None. S. Alaimo None. A. Pulvirenti None. V. Latora None. F. Humby None. A. Ferro None. M.J. Lewis None. C. Pitzalis None.

scholarly journals Integrative RNA-seq and microarray data analysis reveals GC content and gene length biases in the psoriasis transcriptome

2014 ◽  
Vol 46 (15) ◽  
pp. 533-546 ◽  
Author(s):  
William R. Swindell ◽  
Xianying Xing ◽  
John J. Voorhees ◽  
James T. Elder ◽  
Andrew Johnston ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Profiling ◽  
Gc Content ◽  
Microarray Data Analysis ◽  
Gene Length ◽  
Rna Seq ◽  
Data Types ◽  
Factors Affecting ◽  
Microarray Studies ◽  
Microarray Datasets

Gene expression profiling of psoriasis has driven research advances and may soon provide the basis for clinical applications. For expression profiling studies, RNA-seq is now a competitive technology, but RNA-seq results may differ from those obtained by microarray. We therefore compared findings obtained by RNA-seq with those from eight microarray studies of psoriasis. RNA-seq and microarray datasets identified similar numbers of differentially expressed genes (DEGs), with certain genes uniquely identified by each technology. Correspondence between platforms and the balance of increased to decreased DEGs was influenced by mRNA abundance, GC content, and gene length. Weakly expressed genes, genes with low GC content, and long genes were all biased toward decreased expression in psoriasis lesions. The strength of these trends differed among array datasets, most likely due to variations in RNA quality. Gene length bias was by far the strongest trend and was evident in all datasets regardless of the expression profiling technology. The effect was due to differences between lesional and uninvolved skin with respect to the genome-wide correlation between gene length and gene expression, which was consistently more negative in psoriasis lesions. These findings demonstrate the complementary nature of RNA-seq and microarray technology and show that integrative analysis of both data types can provide a richer view of the transcriptome than strict reliance on a single method alone. Our results also highlight factors affecting correspondence between technologies, and we have established that gene length is a major determinant of differential expression in psoriasis lesions.

scholarly journals Subgenome Bias and Temporal Postponement of Gene Expression Contributes to the Distinctions of Fiber Quality in Gossypium Species

2021 ◽  
Vol 12 ◽  
Author(s):  
Huan Mei ◽  
Bowen Qi ◽  
Zegang Han ◽  
Ting Zhao ◽  
Menglan Guo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Fiber Quality ◽  
Expression Patterns ◽  
Rna Seq ◽  
Hub Genes ◽  
Hub Gene ◽  
Expression Of Genes ◽  
Multiple Dimensions ◽  
Cotton Species ◽  
Expression Bias

As two cultivated widely allotetraploid cotton species, although Gossypium hirsutum and Gossypium barbadense evolved from the same ancestor, they differ in fiber quality; the molecular mechanism of that difference should be deeply studied. Here, we performed RNA-seq of fiber samples from four G. hirsutum and three G. barbadense cultivars to compare their gene expression patterns on multiple dimensions. We found that 15.90–37.96% of differentially expressed genes showed biased expression toward the A or D subgenome. In particular, interspecific biased expression was exhibited by a total of 330 and 486 gene pairs at 10 days post-anthesis (DPA) and 20 DPA, respectively. Moreover, 6791 genes demonstrated temporal differences in expression, including 346 genes predominantly expressed at 10 DPA in G. hirsutum (TM-1) but postponed to 20 DPA in G. barbadense (Hai7124), and 367 genes predominantly expressed at 20 DPA in TM-1 but postponed to 25 DPA in Hai7124. These postponed genes mainly participated in carbohydrate metabolism, lipid metabolism, plant hormone signal transduction, and starch and sucrose metabolism. In addition, most of the co-expression network and hub genes involved in fiber development showed asymmetric expression between TM-1 and Hai7124, like three hub genes detected at 10 DPA in TM-1 but not until 25 DPA in Hai7124. Our study provides new insights into interspecific expression bias and postponed expression of genes associated with fiber quality, which are mainly tied to asymmetric hub gene network. This work will facilitate further research aimed at understanding the mechanisms underlying cotton fiber improvement.

scholarly journals Integrated Analysis of Microarray and RNA-Seq Data for the Identification of Hub Genes and Networks Involved in the Pancreatic Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Maryum Nisar ◽  
Rehan Zafar Paracha ◽  
Iqra Arshad ◽  
Sidra Adil ◽  
Sabaoon Zeb ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Early Stage ◽  
Therapeutic Targets ◽  
Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Receptor Interaction ◽  
Integrated Analysis ◽  
Rna Seq ◽  
Hub Genes

Pancreatic cancer (PaCa) is the seventh most fatal malignancy, with more than 90% mortality rate within the first year of diagnosis. Its treatment can be improved the identification of specific therapeutic targets and their relevant pathways. Therefore, the objective of this study is to identify cancer specific biomarkers, therapeutic targets, and their associated pathways involved in the PaCa progression. RNA-seq and microarray datasets were obtained from public repositories such as the European Bioinformatics Institute (EBI) and Gene Expression Omnibus (GEO) databases. Differential gene expression (DE) analysis of data was performed to identify significant differentially expressed genes (DEGs) in PaCa cells in comparison to the normal cells. Gene co-expression network analysis was performed to identify the modules co-expressed genes, which are strongly associated with PaCa and as well as the identification of hub genes in the modules. The key underlaying pathways were obtained from the enrichment analysis of hub genes and studied in the context of PaCa progression. The significant pathways, hub genes, and their expression profile were validated against The Cancer Genome Atlas (TCGA) data, and key biomarkers and therapeutic targets with hub genes were determined. Important hub genes identified included ITGA1, ITGA2, ITGB1, ITGB3, MET, LAMB1, VEGFA, PTK2, and TGFβ1. Enrichment analysis characterizes the involvement of hub genes in multiple pathways. Important ones that are determined are ECM–receptor interaction and focal adhesion pathways. The interaction of overexpressed surface proteins of these pathways with extracellular molecules initiates multiple signaling cascades including stress fiber and lamellipodia formation, PI3K-Akt, MAPK, JAK/STAT, and Wnt signaling pathways. Identified biomarkers may have a strong influence on the PaCa early stage development and progression. Further, analysis of these pathways and hub genes can help in the identification of putative therapeutic targets and development of effective therapies for PaCa.

scholarly journals Diagnostic biomarker candidates for pulpitis revealed by bioinformatics analysis of merged microarray gene expression datasets

2020 ◽  
Author(s):  
Ming Chen ◽  
Junkai Zeng ◽  
Yeqing Yang ◽  
Buling Wu
Keyword(s):  
Gene Expression ◽  
Dental Pulp ◽  
Kegg Pathway ◽  
Enrichment Analysis ◽  
Biological Pathways ◽  
Signalling Pathway ◽  
Diagnostic Biomarker ◽  
Hub Genes ◽  
Go Analysis ◽  
Geo Database

Abstract Background: Pulpitis is an inflammatory disease, the grade of which is classified according to the level of inflammation. Traditional methods of evaluating the status of dental pulp tissue in clinical practice have limitations. The rapid and accurate diagnosis of pulpitis is essential for determining the appropriate treatment. By integrating different datasets from the Gene Expression Omnibus (GEO) database, we analysed a merged expression matrix of pulpitis, aiming to identify biological pathways and diagnostic biomarkers of pulpitis. Methods: By integrating two datasets (GSE77459 and GSE92681) in the GEO database using the sva and limma packages of R, differentially expressed genes (DEGs) of pulpitis were identified. Then, the DEGs were analysed to identify biological pathways of dental pulp inflammation with Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and Gene Set Enrichment Analysis (GSEA). Protein–protein interaction (PPI) networks and modules were constructed to identify hub genes with the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and Cytoscape.Results: A total of 470 DEGs comprising 394 upregulated and 76 downregulated genes were found in pulpitis tissue. GO analysis revealed that the DEGs were enriched in biological processes related to inflammation, and the enriched pathways in the KEGG pathway analysis were cytokine-cytokine receptor interaction, chemokine signalling pathway and NF-κB signalling pathway. The GSEA results provided further functional annotations, including complement system, IL6/JAK/STAT3 signalling pathway and inflammatory response pathways. According to the degrees of nodes in the PPI network, 10 hub genes were identified, and 8 diagnostic biomarker candidates were screened: PTPRC, CD86, CCL2, IL6, TLR8, MMP9, CXCL8 and ICAM1.

Bioinformatics analysis of aberrantly methylated- differentially expressed genes in gastric cancer

2021 ◽  
Author(s):  
Song Siyuan ◽  
Shu Peng
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Cytochrome P450 ◽  
Differentially Expressed Genes ◽  
Focal Adhesion ◽  
Biological Process ◽  
Akt Signaling ◽  
Receptor Interaction ◽  
Akt Signaling Pathway ◽  
Hub Genes

Abstract Background: This study was carried out to identify the aberrantly methylated-differentially expressed genes in gastric cancer (GC). Methods: We downloaded data of gene expression microarrays GSE118916 and gene methylation microarrays GSE25869 from the Gene Expression Omnibus (GEO) database. The DEGs and DMGs were analyzed by the limma software package and Venn diagram. The PPI network was mapped and the enrichment analysis was conducted by the DAVID database. GEPIA online tool, Oncomine database, HPA, and cBioPortal tool were used to verify hub genes. Result: We obtained 110 Hypo-HGs, 9 high-regulation hypomethylation oncogenes, 23 Hyper-LGs, and 2 low-regulation hypermethylation tumor suppressor genes. Hypo-HGs biological process mainly involves cell adhesion and extracellular matrix organization, Hyper-LGs biological process mainly involves response to nicotine and xenobiotic metabolic process. KEGG analysis showed that Hypo-HGs significantly enriched in Focal adhesion, PI3K-Akt signaling pathway, and ECM-receptor interaction. Hyper-LGs significantly enriched in Drug metabolism-cytochrome P450, Chemical carcinogenesis, and Metabolism of xenobiotics by cytochrome P450. The database identified the hub genes were COL1A1, THBS1, COL5A2, COL12A1, and CXCR. Conclusion: COL1A1, THBS1, COL5A2, COL12A1, and CXCR4 can be used as a target for precise diagnosis and treatment of GC. Focal adhesion, PI3K-Akt signaling pathway, and ECM-receptor interaction are important mechanisms of GC.

scholarly journals A Panel of rSNPs Demonstrating Allelic Asymmetry in Both ChIP-seq and RNA-seq Data and the Search for Their Phenotypic Outcomes through Analysis of DEGs

2021 ◽  
Vol 22 (14) ◽  
pp. 7240
Author(s):  
Elena E. Korbolina ◽  
Leonid O. Bryzgalov ◽  
Diana Z. Ustrokhanova ◽  
Sergey N. Postovalov ◽  
Dmitry V. Poverin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Specific Binding ◽  
Transcription Factor Binding ◽  
Postmortem Brain ◽  
Rna Seq ◽  
Factor Binding ◽  
Protein Protein Interaction ◽  
Human Postmortem Brain ◽  
H3k4me3 Mark

Currently, the detection of the allele asymmetry of gene expression from RNA-seq data or the transcription factor binding from ChIP-seq data is one of the approaches used to identify the functional genetic variants that can affect gene expression (regulatory SNPs or rSNPs). In this study, we searched for rSNPs using the data for human pulmonary arterial endothelial cells (PAECs) available from the Sequence Read Archive (SRA). Allele-asymmetric binding and expression events are analyzed in paired ChIP-seq data for H3K4me3 mark and RNA-seq data obtained for 19 individuals. Two statistical approaches, weighted z-scores and predicted probabilities, were used to improve the efficiency of finding rSNPs. In total, we identified 14,266 rSNPs associated with both allele-specific binding and expression. Among them, 645 rSNPs were associated with GWAS phenotypes; 4746 rSNPs were reported as eQTLs by GTEx, and 11,536 rSNPs were located in 374 candidate transcription factor binding motifs. Additionally, we searched for the rSNPs associated with gene expression using an SRA RNA-seq dataset for 281 clinically annotated human postmortem brain samples and detected eQTLs for 2505 rSNPs. Based on these results, we conducted Gene Ontology (GO), Disease Ontology (DO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses and constructed the protein–protein interaction networks to represent the top-ranked biological processes with a possible contribution to the phenotypic outcome.

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