Cloning and expression analysis of differentially expressed genes in Chinese fir stems treated by different concentrations of exogenous IAA

2012 ◽  
Vol 34 (4) ◽  
pp. 472-484
Author(s):  
Li-Wei YANG ◽  
Ji-Sen SHI
Keyword(s):  
Differentially Expressed Genes ◽  
Expression Analysis ◽  
Chinese Fir ◽  
Differentially Expressed ◽  
Cloning And Expression ◽  
Exogenous Iaa

Isolation of differentially expressed genes in wheat caryopses with contrasting starch granule size

2013 ◽  
Vol 8 (3) ◽  
pp. 297-305
Author(s):  
Rita Armonienė ◽  
Kristina Jonavičienė ◽  
Vytautas Ruzgas ◽  
Gintaras Brazauskas
Keyword(s):  
Gene Expression ◽  
Winter Wheat ◽  
Differentially Expressed Genes ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Starch Granule ◽  
Wheat Breeding ◽  
Granule Size ◽  
Differentially Expressed ◽  
Breeding Lines

AbstractIn order to identify genes responsible for starch granule initiation during early development of wheat caryopsis, nine winter wheat breeding lines were studied. Two breeding lines, which are the most diverse in A-type granule size (26.85 µm versus 23.65 µm) were chosen for further differential gene expression analysis in developing caryopses at 10 and 15 days post-anthesis (DPA). cDNA-amplified fragment length polymorphism (cDNA-AFLP) analysis resulted in 384 transcript-derived fragments, out of which 18 were identified as being differentially expressed. Six differentially expressed genes, together with the six well-known starch biosynthesis genes, were chosen for semi-quantitative gene expression analysis in developing wheat caryopses at 10 and 15 DPA. This study provides genomic information on 18 genes differentially expressed at early stages of wheat caryopses development and reports on the identification of genes putatively involved in the production of large A-type granules. These genes are targets for further validation on their role in starch granule synthesis control and provide the basis for the development of DNA marker tools in winter wheat breeding for enhanced starch quality.

Gene-Expression Profiling of Graft-Versus-Host Disease by Oligonucleotide Microarrays.

Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 4645-4645
Author(s):  
Jan Verner ◽  
Jitka Kabathova ◽  
Boris Tichy ◽  
Zbynek Zdrahal ◽  
Alexandra Tomancova ◽  
...  
Keyword(s):  
Gene Expression ◽  
Czech Republic ◽  
Differentially Expressed Genes ◽  
Expression Analysis ◽  
Graft Versus Host Disease ◽  
Gene Expression Analysis ◽  
Differentially Expressed ◽  
Oligonucleotide Microarrays ◽  
Post Transplantation ◽  
Graft Versus Host

Abstract Abstract 4645 Background Graft-versus-host disease (GvHD) is the life-threatening complication of allogeneic hematopoietic stem cells transplantation (allo-HSCT). GvHD is mediated by an immune reaction of donor T lymphocytes against recipient's tissues/cells. Acute GvHD (aGvHD) appearing within the first 100 days post transplantation is the most frequent cause of recipient's death and characterization of biomarkers for early prediction of aGvHD or resistance to corticoid treatment could be of great clinical relevance. Biomarker panels for aGvHD are currently extensively studied by proteomic and gene expression based approaches but so far very few markers were described and validated (Kaiser et al., 2004; Baron et al., 2007; Weissinger et al., 2007; Paczesny et al., 2009). Aim In this study, we performed microarray gene expression analysis (whole genome Human OneArray, Phalanx) of 43 leukemia patients who received allo-HSCT. Mononuclear cells isolated from peripheral blood samples (Ficoll-Paque) were collected at i) 14 days before transplantation, ii) 20 and iii) 30 days after transplantation and iv) at the time of aGvHD manifestation. We also performed gene-expression analysis for corticoid-resistant vs. corticoid-sensitive aGvHD cases. Results The SAM supervised analysis of samples collected at day +20 post transplantation revealed set of differentially expressed genes between groups of patients that developed aGvHD vs. aGvHD-free recipients. Among others, genes CASP1 (encoding caspase 1, protein implicated in apoptosis), HLA-DRA (member of MHC class II family) and LILRA3 (leukocyte immunoglobulin-like receptor, subfamily A member 3) showed the highest difference in expression. Gene expression with regard to corticoid response was analyzed at the time of first aGvHD manifestation. The SAM supervised analysis of gene expression between patients with corticoid-sensitive aGvHD (n=10) or aGvHD resistant to corticoid treatment (n=4) revealed a set of significantly differentially expressed genes including NR4A2 (nuclear receptor subfamily 4; member of the steroid-thyroid hormone-retinoid receptor superfamily), DEDD2 (death effector domain containing 2), TREM1 (triggering receptor expressed on myeloid cells 1), TPK1 (thiamin pyrophosphokinase 1) and HBEGF (heparin-binding EGF-like growth factor). Conclusion Oligonucleotide microarrays proved to be a useful tool for expression studies of hematological malignancies and our work shows that they may help to identify markers for early diagnosis/treatment of aGvHD. The limited patients' cohort and their heterogeneity complicate such studies. Our future effort will be focused on experimental group extension, cohort uniformity and verification of the obtained data. This work is supported by the grant NS9683-4/2008 provided by the IGA MH of the Czech Republic, and MSM0021622430 provided by MEYS of Czech Republic Disclosures: No relevant conflicts of interest to declare.

scholarly journals A Computational Framework to Identify Transcriptional and Network Differences between Hepatocellular Carcinoma and Normal Liver Tissue and Their Applications in Repositioning Drugs

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Aimin Hu ◽  
Zheng Wei ◽  
Zuxiang Zheng ◽  
Bichao Luo ◽  
Jieming Yi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Differentially Expressed Genes ◽  
Expression Analysis ◽  
Molecular Mechanisms ◽  
Drug Repositioning ◽  
Differential Expression Analysis ◽  
Differentially Expressed ◽  
Computational Framework ◽  
Normal Tissues

Hepatocellular carcinoma (HCC) is one of the most common and lethal malignancies worldwide. Although there have been extensive studies on the molecular mechanisms of its carcinogenesis, FDA-approved drugs for HCC are rare. Side effects, development time, and cost of these drugs are the major bottlenecks, which can be partially overcome by drug repositioning. In this study, we developed a computational framework to study the mechanisms of HCC carcinogenesis, in which drug perturbation-induced gene expression signatures were utilized for repositioning of potential drugs. Specifically, we first performed differential expression analysis and coexpression network module analysis on the HCC dataset from The Cancer Genome Atlas database. Differential gene expression analysis identified 1,337 differentially expressed genes between HCC and adjacent normal tissues, which were significantly enriched in functions related to various pathways, including α-adrenergic receptor activity pathway and epinephrine binding pathway. Weighted gene correlation network analysis (WGCNA) suggested that the number of coexpression modules was higher in HCC tissues than in normal tissues. Finally, by correlating differentially expressed genes with drug perturbation-related signatures, we prioritized a few potential drugs, including nutlin and eribulin, for the treatment of hepatocellular carcinoma. The drugs have been reported by a few experimental studies to be effective in killing cancer cells.

Expression analysis of subtractively enriched libraries (EASEL): a widely applicable approach to the identification of differentially expressed genes

2003 ◽  
Vol 57 (1) ◽  
pp. 17-33 ◽  
Author(s):  
M.Paola Lombardi ◽  
Maurice J.B. van den Hoff ◽  
Jan M. Ruijter ◽  
Marjanka Luijerink ◽  
Anita A. Buffing ◽  
...  
Keyword(s):  
Differentially Expressed Genes ◽  
Expression Analysis ◽  
Differentially Expressed

scholarly journals Co-expression analysis of differentially expressed genes in hepatitis C virus-induced hepatocellular carcinoma

2014 ◽  
Vol 11 (1) ◽  
pp. 21-28 ◽  
Author(s):  
QINGFENG SONG ◽  
CHANG ZHAO ◽  
SHENGQIU OU ◽  
ZHIBIN MENG ◽  
PING KANG ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Hepatitis C Virus ◽  
Hepatitis C ◽  
Differentially Expressed Genes ◽  
Expression Analysis ◽  
Differentially Expressed

scholarly journals Changes in gene expression at the CD160 locus in the whole blood of sepsis patients.

2020 ◽  
Author(s):  
Shahan Mamoor
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Expression Analysis ◽  
Whole Blood ◽  
Gene Expression Analysis ◽  
Differentially Expressed ◽  
Blood Gene Expression ◽  
Non Coding Rna ◽  
Long Non Coding Rna

Sepsis is a leading cause of mortality (1). We mined published datasets from the whole blood of patients with sepsis to identify differentially expressed genes in the septic state (2, 3). We found changes in CD160 expression as among the most significant quantitative differences in sepsis whole blood gene expression. Analysis of a separate dataset (4) demonstrated significant repression of a long non-coding RNA produced at the CD160 locus in the blood of patients with sepsis. In the datasets we analyzed, changes in coding and non-coding gene expression at the CD160 locus were among the most significant changes in gene expression in the blood of patients with sepsis.

scholarly journals Confronting false discoveries in single-cell differential expression

2021 ◽  
Author(s):  
Jordan W. Squair ◽  
Matthieu Gautier ◽  
Claudia Kathe ◽  
Mark A. Anderson ◽  
Nicholas D. James ◽  
...  
Keyword(s):  
Single Cell ◽  
Differential Expression ◽  
Differentially Expressed Genes ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Experimental Manipulation ◽  
Differentially Expressed ◽  
Cell Type Specific ◽  
False Discoveries ◽  
Cell Data

Differential expression analysis in single-cell transcriptomics enables the dissection of cell-type-specific responses to perturbations such as disease, trauma, or experimental manipulation. While many statistical methods are available to identify differentially expressed genes, the principles that distinguish these methods and their performance remain unclear. Here, we show that the relative performance of these methods is contingent on their ability to account for variation between biological replicates. Methods that ignore this inevitable variation are biased and prone to false discoveries. Indeed, the most widely used methods can discover hundreds of differentially expressed genes in the absence of biological differences. Our results suggest an urgent need for a paradigm shift in the methods used to perform differential expression analysis in single-cell data.

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