scholarly journals APETALA2 and APETALA3 Genes Expression Profiling on Floral Development of Teak (Tectona grandis Linn f.)

2016 ◽  
Vol 11 (4) ◽  
pp. 61-68 ◽  
Author(s):  
Srinanan M. Widiyanto ◽  
Eri Mustari ◽  
Diky Setya Diningrat ◽  
Rina Ratnasih
Keyword(s):  
Expression Profiling ◽  
Floral Development ◽  
Tectona Grandis ◽  
Genes Expression ◽  
Genes Expression Profiling

scholarly journals Pattern recognition receptor genes expression profiling in indigenous chickens of India and White Leghorn

2017 ◽  
Vol 96 (9) ◽  
pp. 3052-3057 ◽  
Author(s):  
S. Haunshi ◽  
Arun Kumar Burramsetty ◽  
T.R. Kannaki ◽  
K. S. Raja Ravindra ◽  
R.N. Chatterjee
Keyword(s):  
Pattern Recognition ◽  
Expression Profiling ◽  
Pattern Recognition Receptor ◽  
White Leghorn ◽  
Indigenous Chickens ◽  
Genes Expression ◽  
Genes Expression Profiling ◽  
Recognition Receptor

scholarly journals Candidate genes expression profiling during wilting in chickpea caused by Fusarium oxysporum f. sp. ciceris race 5

PLoS ONE ◽  
2019 ◽  
Vol 14 (10) ◽  
pp. e0224212 ◽  
Author(s):  
Cristina Caballo ◽  
Patricia Castro ◽  
Juan Gil ◽  
Teresa Millan ◽  
Josefa Rubio ◽  
...  
Keyword(s):  
Fusarium Oxysporum ◽  
Candidate Genes ◽  
Expression Profiling ◽  
Genes Expression ◽  
Genes Expression Profiling

Difference in apoptosis-associated genes expression profiling and immunohistology analysis between Kashin-Beck disease and primary osteoarthritis

2014 ◽  
Vol 59 (9) ◽  
pp. 833-839 ◽  
Author(s):  
Shixun Wu ◽  
Chen Duan ◽  
Feng Zhang ◽  
Robert Pierce McKenzie ◽  
Jingjing Zheng ◽  
...  
Keyword(s):  
Expression Profiling ◽  
Primary Osteoarthritis ◽  
Genes Expression ◽  
Genes Expression Profiling ◽  
Beck Disease

Genes expression profiling of peripheral blood cells of patients with hepatocellular carcinoma

2012 ◽  
Vol 36 (9) ◽  
pp. 803-809 ◽  
Author(s):  
Peng‑jun Zhang ◽  
Run Wei ◽  
Xin‑yu Wen ◽  
Liang Ping ◽  
Cheng‑bin Wang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Peripheral Blood ◽  
Expression Profiling ◽  
Blood Cells ◽  
Peripheral Blood Cells ◽  
Genes Expression ◽  
Genes Expression Profiling

Neurogenesis-related genes expression profiling of mouse fibroblastic stem cells induced by Wnt signaling

2009 ◽  
Vol 31 (2) ◽  
pp. 200-203 ◽  
Author(s):  
Dan Zhu ◽  
Quan Kang ◽  
Pei-Yu Huang ◽  
Tong-Chuan He ◽  
Peng Xie
Keyword(s):  
Stem Cells ◽  
Wnt Signaling ◽  
Expression Profiling ◽  
Genes Expression ◽  
Genes Expression Profiling

scholarly journals Identification of Terminal Flowering1 (TFL1) Genes Associated with the Teak (Tectona grandis) Floral Development Regulation Using RNA-seq

2015 ◽  
Vol 10 (1) ◽  
pp. 1-13 ◽  
Author(s):  
D.S. Diningrat ◽  
S.M. Widiyanto ◽  
A. Pancoro ◽  
Iriawati . ◽  
D. Shim ◽  
...  
Keyword(s):  
Floral Development ◽  
Tectona Grandis ◽  
Rna Seq ◽  
Development Regulation

Accurate Classification Of GCB/ABC and MYC/BCL2 Diffuse Large B-Cell Lymphoma With a 14 Genes Expression Signature and a Simple and Robust RT-MLPA Assay

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 84-84 ◽  
Author(s):  
Philippe Ruminy ◽  
Sylvain Mareschal ◽  
Cristina Bagacean ◽  
Vinciane Rainville ◽  
Karen Leroy ◽  
...  
Keyword(s):  
Gene Expression ◽  
B Cell ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Cell Lymphoma ◽  
B Cell Lymphoma ◽  
Large B Cell Lymphoma ◽  
Routine Diagnosis ◽  
Genes Expression

Abstract Diffuse large B-cell lymphoma (DLBCL) is the most common non-hodgkin lymphoma. It is subdivided into two molecular subtypes called germinal center B cell-like (GCB) and Activated B cell-like (ABC), deriving from different stages of B-cell differentiation. Recent studies have shown that ABC DLBCLs, which are associated with inferior survival, could benefit from new therapies targeting the NFkB pathway. Unfortunately, gene expression profiling methods to distinguish these lymphomas are not available in routine diagnosis and alternative immunohistochemical algorithms are often considered poorly reproducible. To circumvent these limitations we have developed a simple assay which allows a rapid and efficient classification of these tumours. By comparing gene expression data from the literature and from our own studies, we established a 10 genes expression signature which discriminates ABC from GCB cases (ABC: IRF4, FOXP1, IGHM, TNFRSF13B, CCND2; GCB: LMO2, MYBL1, BCL6, NEK6, TNFRSF9). These genes were incorporated into a Reverse Transcriptase Multiplex Ligation-dependant Probe Amplification assay (RT-MLPA), together with cMYC and BCL2, whose co-expression was recently shown to be prognostic in these diseases, and the CCND1 and MS4A1 (encoding CD20) genes used as controls (Figure 1A-B). We next applied this assay on a training series of 50 cases previously identified as belonging to the ABC or GCB subtypes by gene expression profiling using the Wright's algorithm to construct a Bayesian predictor. Within this first series, this predictor classified 44 cases within the expected subtypes, 5 were considered unclassified and only one ABC case was misclassified. The efficiency of this predictor was next verified on an independent validation series of 50 cases, where 46 cases were classified within the expected subtype, 3 were considered unclassified and again only one ABC case was misclassified. To test the prognostic strength of this assay we next included 85 additional cases, to reach a total of 185 DLBCLs (Figure 1C). 141 of these tumours, all treated between 2001 and 2011 at the centre H. Becquerel, Rouen, France, by a combination of Rituximab and chemotherapy, were retained for this analysis. Within this series, the RT-MLPA predictor identified 49 GCB and 64 ABC DLBCLs, and 28 cases remained unclassified. As expected, ABC cases were significantly associated with a worst event free survival (EFS)(P=0.015) and showed a trend toward a lower overall survival (OS)(P=0.060). Univariate analysis confirmed that the expression of several individual genes within this signature is significantly associated with poor prognosis (LMO2 low: OS P=0.005; BCL6 low: OS P=0.012; IRF4 high: OS P=0.008; TNFRSF13B high: OS P=0.004). We could also verify that the double MYC+/BCL2+ signature is significantly associated with a worst outcome (OS P=0.012) and identified a group of patients with particularly poor prognosis within the ABC subtype (OS P=0.004). Finally, we could demonstrate the robustness of our assay by classifying correctly 12 DLBCLs (6 ABC and 6 GCB) starting from RNA extracted from paraffin embedded (FFPE) biopsies. In conclusion, our RT-MLPA assay is a rapid (it can be achieved in less than one day, and we repeatedly tested up to 40 patients in parallel), cheap (the cost of one test is less than 5 dollars), and efficient gene expression profiling method to discriminate ABC from GCB DLBCLs and to obtain important information on others prognosis markers. It does not require specific equipments (only one conventional PCR module and one capillary genetic analyser), can be achieved from FFPE samples, and could easily be transferred in many routine diagnosis laboratories. Disclosures: No relevant conflicts of interest to declare.

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