scholarly journals Transcriptomic Identification of Floral Transition and Development-Associated Genes in Styrax japonicus

Forests ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 10
Author(s):  
Wei Li ◽  
Zhengzhao Xu ◽  
Cuiping Zhang ◽  
Xinqiang Jiang ◽  
Kuiling Wang
Keyword(s):  
Gene Expression ◽  
Protein Kinase ◽  
Flower Development ◽  
Floral Development ◽  
Differentially Expressed ◽  
Cdna Libraries ◽  
Expression Data ◽  
Kegg Database ◽  
Pathway Enrichment ◽  
Gene Expression Dynamics

Styrax japonicus (S. japonicus) is an important flowering tree species in temperate regions, and it is regarded as a nectariferous plant. However, there have been few studies to date analyzing floral development in this species. In order to understand gene expression dynamics during S. japonicus flower development, we; therefore, prepared cDNA libraries from three distinct stages of S. japonicus. Illumina sequencing generated 31,471 differentially expressed unigenes during flower development. We additionally conducted pathway enrichment analyses using the GO and KEGG database in order to assess the functions of genes differentially expressed during different stages of the floral development process, revealing these genes to be associated with pathways including phytohormone signaling, Transcription factor, protein kinase, and circadian rhythms. In total, 4828 TF genes, 8402 protein kinase genes, and 78 DEGs related to hormone pathways were identified in flower development stages. Six genes were selected for confirmation of expression levels using quantitative real-time PCR. The gene expression data presented herein represent the most comprehensive dataset available regarding the flowering of S. japonicus, thus offering a reference for future studies of the flowering of this and other Styracaceae species.

Gene Set Correlation Analysis and Visualization Using Gene Expression Data

2020 ◽  
Vol 15 ◽  
Author(s):  
Chen-An Tsai ◽  
James J. Chen
Keyword(s):  
Gene Expression ◽  
Correlation Analysis ◽  
Gene Expression Data ◽  
Differentially Expressed Gene ◽  
Differentially Expressed ◽  
Superior Performance ◽  
Expression Data ◽  
Gene Set ◽  
Gene Sets ◽  
Set Correlation

Background: Gene set enrichment analyses (GSEA) provide a useful and powerful approach to identify differentially expressed gene sets with prior biological knowledge. Several GSEA algorithms have been proposed to perform enrichment analyses on groups of genes. However, many of these algorithms have focused on identification of differentially expressed gene sets in a given phenotype. Objective: In this paper, we propose a gene set analytic framework, Gene Set Correlation Analysis (GSCoA), that simultaneously measures within and between gene sets variation to identify sets of genes enriched for differential expression and highly co-related pathways. Methods: We apply co-inertia analysis to the comparisons of cross-gene sets in gene expression data to measure the costructure of expression profiles in pairs of gene sets. Co-inertia analysis (CIA) is one multivariate method to identify trends or co-relationships in multiple datasets, which contain the same samples. The objective of CIA is to seek ordinations (dimension reduction diagrams) of two gene sets such that the square covariance between the projections of the gene sets on successive axes is maximized. Simulation studies illustrate that CIA offers superior performance in identifying corelationships between gene sets in all simulation settings when compared to correlation-based gene set methods. Result and Conclusion: We also combine between-gene set CIA and GSEA to discover the relationships between gene sets significantly associated with phenotypes. In addition, we provide a graphical technique for visualizing and simultaneously exploring the associations of between and within gene sets and their interaction and network. We then demonstrate integration of within and between gene sets variation using CIA and GSEA, applied to the p53 gene expression data using the c2 curated gene sets. Ultimately, the GSCoA approach provides an attractive tool for identification and visualization of novel associations between pairs of gene sets by integrating co-relationships between gene sets into gene set analysis.

scholarly journals Transcriptome analysis ofSchistosoma mansonilarval development using serial analysis of gene expression (SAGE)

Parasitology ◽  
2009 ◽  
Vol 136 (5) ◽  
pp. 469-485 ◽  
Author(s):  
A. S. TAFT ◽  
J. J. VERMEIRE ◽  
J. BERNIER ◽  
S. R. BIRKELAND ◽  
M. J. CIPRIANO ◽  
...  
Keyword(s):  
Gene Expression ◽  
Sequence Data ◽  
Subsequent Development ◽  
Differentially Expressed ◽  
Cdna Libraries ◽  
Genome Wide ◽  
A Genome ◽  
Genome Wide Expression ◽  
Cell Conditioned Medium

SUMMARYInfection of the snail,Biomphalaria glabrata, by the free-swimming miracidial stage of the human blood fluke,Schistosoma mansoni, and its subsequent development to the parasitic sporocyst stage is critical to establishment of viable infections and continued human transmission. We performed a genome-wide expression analysis of theS. mansonimiracidia and developing sporocyst using Long Serial Analysis of Gene Expression (LongSAGE). Five cDNA libraries were constructed from miracidia andin vitrocultured 6- and 20-day-old sporocysts maintained in sporocyst medium (SM) or in SM conditioned by previous cultivation with cells of theB. glabrataembryonic (Bge) cell line. We generated 21 440 SAGE tags and mapped 13 381 to theS. mansonigene predictions (v4.0e) either by estimating theoretical 3′ UTR lengths or using existing 3′ EST sequence data. Overall, 432 transcripts were found to be differentially expressed amongst all 5 libraries. In total, 172 tags were differentially expressed between miracidia and 6-day conditioned sporocysts and 152 were differentially expressed between miracidia and 6-day unconditioned sporocysts. In addition, 53 and 45 tags, respectively, were differentially expressed in 6-day and 20-day cultured sporocysts, due to the effects of exposure to Bge cell-conditioned medium.

scholarly journals Comparative pathway enrichment analysis in gastrointestinal cell lines Caco-2, HT-29, HEPG2, and colon fibroblasts using a custom expression panel for tight-junction and cytoskeletal regulatory genes

2019 ◽  
Author(s):  
JM Robinson
Keyword(s):  
Gene Expression ◽  
Comparative Analysis ◽  
Tight Junction ◽  
Cell Lines ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Intestinal Epithelial ◽  
Expression Data ◽  
Pathway Enrichment ◽  
Ht 29

AbstractThis brief report details results from a comparative analysis of Nanostring expression data between cell lines HEPG2, Caco-2, HT-29, and colon fibroblasts. Raw and normalized data are available publicly in the NCBI GEO/Bioproject databases. Results identify cell-line specific variations in gene expression relevant to intestinal epithelial function.

scholarly journals Generation of patterns from gene expression data by assigning confidence to differentially expressed genes

2000 ◽  
Vol 16 (8) ◽  
pp. 685-698 ◽  
Author(s):  
E. Manduchi ◽  
G. R. Grant ◽  
S. E. McKenzie ◽  
G. C. Overton ◽  
S. Surrey ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Gene Expression Data ◽  
Differentially Expressed ◽  
Expression Data

scholarly journals Identification of Differentially Expressed Genes and miRNAs Associated with Esophageal Squamous Cell Carcinoma by Integrated Analysis of Microarray Data

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Lemeng Zhang ◽  
Jianhua Chen ◽  
Tianli Cheng ◽  
Hua Yang ◽  
Changqie Pan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Squamous Cell Carcinoma ◽  
Survival Analysis ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Differentially Expressed Genes ◽  
Microarray Data ◽  
Regulatory Network ◽  
Differentially Expressed ◽  
Pathway Enrichment

To identify candidate key genes and miRNAs associated with esophageal squamous cell carcinoma (ESCC) development and prognosis, the gene expression profiles and miRNA microarray data including GSE20347, GSE38129, GSE23400, and GSE55856 were downloaded from the Gene Expression Omnibus (GEO) database. Clinical and survival data were retrieved from The Cancer Genome Atlas (TCGA). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of differentially expressed genes (DEGs) was analyzed via DAVID, while the DEG-associated protein-protein interaction network (PPI) was constructed using the STRING database. Additionally, the miRNA target gene regulatory network and miRNA coregulatory network were constructed, using the Cytoscape software. Survival analysis and prognostic model construction were performed via the survival (version 2.42-6) and rbsurv R packages, respectively. The results showed a total of 2575, 2111, and 1205 DEGs, and 226 differentially expressed miRNAs (DEMs) were identified. Pathway enrichment analyses revealed that DEGs were mainly enriched in 36 pathways, such as the proteasome, p53, and beta-alanine metabolism pathways. Furthermore, 448 nodes and 1144 interactions were identified in the PPI network, with MYC having the highest random walk score. In addition, 7 DEMs in the microarray data, including miR-196a, miR-21, miR-205, miR-194, miR-103, miR-223, and miR-375, were found in the regulatory network. Moreover, several reported disease-related miRNAs, including miR-198a, miR-103, miR-223, miR-21, miR-194, and miR-375, were found to have common target genes with other DEMs. Survival analysis revealed that 85 DEMs were related to prognosis, among which hsa-miR-1248, hsa-miR-1291, hsa-miR-421, and hsa-miR-7-5p were used for a prognostic survival model. Taken together, this study revealed the important roles of DEGs and DEMs in ESCC development, as well as DEMs in the prognosis of ESCC. This will provide potential therapeutic targets and prognostic predictors for ESCC.

scholarly journals Can We Assume the Gene Expression Profile as a Proxy for Signaling Network Activity?

Biomolecules ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 850 ◽  
Author(s):  
Mehran Piran ◽  
Reza Karbalaei ◽  
Mehrdad Piran ◽  
Jehad Aldahdooh ◽  
Mehdi Mirzaie ◽  
...  
Keyword(s):  
Gene Expression ◽  
Signaling Pathways ◽  
Differentially Expressed Genes ◽  
Expression Profile ◽  
Signaling Network ◽  
Differentially Expressed ◽  
Network Activity ◽  
Gene Products ◽  
Expression Data ◽  
Gene Pairs

Studying relationships among gene products by expression profile analysis is a common approach in systems biology. Many studies have generalized the outcomes to the different levels of central dogma information flow and assumed a correlation of transcript and protein expression levels. However, the relation between the various types of interaction (i.e., activation and inhibition) of gene products to their expression profiles has not been widely studied. In fact, looking for any perturbation according to differentially expressed genes is the common approach, while analyzing the effects of altered expression on the activity of signaling pathways is often ignored. In this study, we examine whether significant changes in gene expression necessarily lead to dysregulated signaling pathways. Using four commonly used and comprehensive databases, we extracted all relevant gene expression data and all relationships among directly linked gene pairs. We aimed to evaluate the ratio of coherency or sign consistency between the expression level as well as the causal relationships among the gene pairs. Through a comparison with random unconnected gene pairs, we illustrate that the signaling network is incoherent, and inconsistent with the recorded expression profile. Finally, we demonstrate that, to infer perturbed signaling pathways, we need to consider the type of relationships in addition to gene-product expression data, especially at the transcript level. We assert that identifying enriched biological processes via differentially expressed genes is limited when attempting to infer dysregulated pathways.

Association Between Gene Expression Profiles and Commonly Mutated Genes In The Hematopoietic Stem Cells Of Patients With Myelodysplastic Syndromes

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 2779-2779 ◽  
Author(s):  
Andrea Pellagatti ◽  
Moritz Gerstung ◽  
Elli Papaemmanuil ◽  
Luca Malcovati ◽  
Aristoteles Giagounidis ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Gene Expression Data ◽  
Expression Profiles ◽  
Gene Mutations ◽  
Gene Expression Profiles ◽  
Differentially Expressed ◽  
Expression Data ◽  
Common Gene ◽  
Mutational Status

Abstract A particular profile of gene expression can reflect an underlying molecular abnormality in malignancy. Distinct gene expression profiles and deregulated gene pathways can be driven by specific gene mutations and may shed light on the biology of the disease and lead to the identification of new therapeutic targets. We selected 143 cases from our large-scale gene expression profiling (GEP) dataset on bone marrow CD34+ cells from patients with myelodysplastic syndromes (MDS), for which matching genotyping data were obtained using next-generation sequencing of a comprehensive list of 111 genes involved in myeloid malignancies (including the spliceosomal genes SF3B1, SRSF2, U2AF1 and ZRSR2, as well as TET2, ASXL1and many other). The GEP data were then correlated with the mutational status to identify significantly differentially expressed genes associated with each of the most common gene mutations found in MDS. The expression levels of the mutated genes analyzed were generally lower in patients carrying a mutation than in patients wild-type for that gene (e.g. SF3B1, ASXL1 and TP53), with the exception of RUNX1 for which patients carrying a mutation showed higher expression levels than patients without mutation. Principal components analysis showed that the main directions of gene expression changes (principal components) tend to coincide with some of the common gene mutations, including SF3B1, SRSF2 and TP53. SF3B1 and STAG2 were the mutated genes showing the highest number of associated significantly differentially expressed genes, including ABCB7 as differentially expressed in association with SF3B1 mutation and SULT2A1 in association with STAG2 mutation. We found distinct differentially expressed genes associated with the four most common splicing gene mutations (SF3B1, SRSF2, U2AF1 and ZRSR2) in MDS, suggesting that different phenotypes associated with these mutations may be driven by different effects on gene expression and that the target gene may be different. We have also evaluated the prognostic impact of the GEP data in comparison with that of the genotype data and importantly we have found a larger contribution of gene expression data in predicting progression free survival compared to mutation-based multivariate survival models. In summary, this analysis correlating gene expression data with genotype data has revealed that the mutational status shapes the gene expression landscape. We have identified deregulated genes associated with the most common gene mutations in MDS and found that the prognostic power of gene expression data is greater than the prognostic power provided by mutation data. AP and MG contributed equally to this work. JB and PJC are co-senior authors. Disclosures: No relevant conflicts of interest to declare.

Gene expression panel (TheraPrint) analyzed as predictors of response to neoadjuvant chemotherapy (NCT) in patients (pts) with stage II-III and inflammatory breast cancer (BC).

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. e21013-e21013
Author(s):  
Femke De Snoo ◽  
Justine Peeters ◽  
Kim Robinson ◽  
Lisette Stork-Sloots ◽  
Iris Simon ◽  
...  
Keyword(s):  
Gene Expression ◽  
Survival Data ◽  
Stage Ii ◽  
Differentially Expressed ◽  
P Value ◽  
Expression Data ◽  
Global Test ◽  
Patient Cohort ◽  
Predictors Of Response ◽  
Canonical Pathways

e21013 Background: TheraPrint is a microarray-based gene expression panel of 125 genes identified as potential targets for prognosis and therapeutic response. These genes may hold the key to a greater level of personalized prognosis and therapy for BC pts. The aim of the current study was to assess the clinical relevance of the TheraPrint genes for either predictive and/or prognostic value in 2 patient cohorts treated with NCT. Methods: The 1st patient cohort are 68 Stage II-III BC pts treated with NCT. Expression data from Agilent full genome arrays, containing the MammaPrint, BluePrint and TheraPrint diagnostic profiles/probes (Somlo et al, 2009). Median FU 2.3 years. The 2nd patient cohort are 230 Stage I-III BC pts treated with NCT. Expression data from Affymetrix probe sets was publically available (Iwamoto et al, 2011). Median FU 5.2 years. To identify genes that are differentially expressed between responders (pCR/RCBI) and non-responders, a supervised analysis was performed. The analysis was performed across all pts and also within groups of HER2+ and HER2-. Univariate t-tests were performed, with results filtered by permutation p-value (p<0.05) and fold change of >1.5. Global test was also reported. In addition, survival data analysis was performed across all pts. Results: Overlapping genes between the 2 datasets that were significantly differentially expressed between responders and non-responders include: BCL2 (down-regulated) and CDH3, GRB7, KRT6B, KRT17 (up-regulated). When analysing the HER2- subgroup, 3 genes turned out to be differentially expressed between responders and non-responders in the 2 datasets: FLT1, PIK3R1 (down-regulated) and KRT6B (up-regulated). For the HER2+ subgroup, only one gene overlapped for the 2 datasets: IL2RA (up-regulated). The top canonical pathways for the significant genes have been analyzed, and in addition correlation of the TheraPrint gene expression with survival for these pt groups. Conclusions: This study has identified several genes from a panel of 125 TheraPrint genes with statistically significant correlation between expression and response to NCT.

Application of a Deep Matrix Factorization Model on Integrated Gene Expression Data

2020 ◽  
Vol 15 (4) ◽  
pp. 359-367
Author(s):  
Yong-Jing Hao ◽  
Mi-Xiao Hou ◽  
Ying-Lian Gao ◽  
Jin-Xing Liu ◽  
Xiang-Zhen Kong
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Gene Expression Data ◽  
Matrix Factorization ◽  
Coefficient Matrix ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Complex Data ◽  
Expression Data ◽  
Factorization Model

Background: Non-negative Matrix Factorization (NMF) has been extensively used in gene expression data. However, most NMF-based methods have single-layer structures, which may achieve poor performance for complex data. Deep learning, with its carefully designed hierarchical structure, has shown significant advantages in learning data features. Objective: In bioinformatics, on the one hand, to discover differentially expressed genes in gene expression data; on the other hand, to obtain higher sample clustering results. It can provide the reference value for the prevention and treatment of cancer. Method: In this paper, we apply a deep NMF method called Deep Semi-NMF on the integrated gene expression data. In each layer, the coefficient matrix is directly decomposed into the basic and coefficient matrix of the next layer. We apply this factorization model on The Cancer Genome Atlas (TCGA) genomic data. Results: The experimental results demonstrate the superiority of Deep Semi-NMF method in identifying differentially expressed genes and clustering samples. Conclusion: The Deep Semi-NMF model decomposes a matrix into multiple matrices and multiplies them to form a matrix. It can also improve the clustering performance of samples while digging out more accurate key genes for disease treatment.

scholarly journals Potential genes and pathways associated with heterotopic ossification derived from analyses of gene expression profiles

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Zhanyu Yang ◽  
Delong Liu ◽  
Rui Guan ◽  
Xin Li ◽  
Yiwei Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Protein Kinase ◽  
Heterotopic Ossification ◽  
Signaling Pathway ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Differentially Expressed ◽  
Heterotopic Bone ◽  
Hub Genes ◽  
Ppi Networks

Abstract Background Heterotopic ossification (HO) represents pathological lesions that refer to the development of heterotopic bone in extraskeletal tissues around joints. This study investigates the genetic characteristics of bone marrow mesenchymal stem cells (BMSCs) from HO tissues and explores the potential pathways involved in this ailment. Methods Gene expression profiles (GSE94683) were obtained from the Gene Expression Omnibus (GEO), including 9 normal specimens and 7 HO specimens, and differentially expressed genes (DEGs) were identified. Then, protein–protein interaction (PPI) networks and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed for further analysis. Results In total, 275 DEGs were differentially expressed, of which 153 were upregulated and 122 were downregulated. In the biological process (BP) category, the majority of DEGs, including EFNB3, UNC5C, TMEFF2, PTH2, KIT, FGF13, and WISP3, were intensively enriched in aspects of cell signal transmission, including axon guidance, negative regulation of cell migration, peptidyl-tyrosine phosphorylation, and cell-cell signaling. Moreover, KEGG analysis indicated that the majority of DEGs, including EFNB3, UNC5C, FGF13, MAPK10, DDIT3, KIT, COL4A4, and DKK2, were primarily involved in the mitogen-activated protein kinase (MAPK) signaling pathway, Ras signaling pathway, phosphatidylinositol-3-kinase/protein kinase B (PI3K/Akt) signaling pathway, and Wnt signaling pathway. Ten hub genes were identified, including CX3CL1, CXCL1, ADAMTS3, ADAMTS16, ADAMTSL2, ADAMTSL3, ADAMTSL5, PENK, GPR18, and CALB2. Conclusions This study presented novel insight into the pathogenesis of HO. Ten hub genes and most of the DEGs intensively involved in enrichment analyses may be new candidate targets for the prevention and treatment of HO in the future.

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