scholarly journals Transcriptome analysis of differential gene expression inDichomitus squalensduring interspecific mycelial interactions and the potential link with laccase induction

2018 ◽  
Author(s):  
Zixuan Zhong ◽  
Nannan Li ◽  
Binghui He ◽  
Yasuo Igarashi ◽  
Feng Luo
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Expression Patterns ◽  
White Rot Fungi ◽  
White Rot ◽  
Laccase Production ◽  
Pcr Analysis ◽  
Ros Generation ◽  
Mycelial Interactions ◽  
Differential Gene

ABSTRACTInterspecific mycelial interactions between white rot fungi are always accompanied by increased production of laccase. In this study, the potential of white rot fungiDichomitus squalensfor enhancing laccase production during interaction with two other white rot fungiTrametes versicolororPleurotus ostreatuswas identified. To probe the mechanism of laccase induction and the role of laccase played during the combative interaction, we analyzed the laccase induction response to stressful conditions during fungal interaction related to the differential gene expression profile. We further confirmed the expression patterns of 16 selected genes by qRT-PCR analysis. We noted that many differential expression genes (DEGs) encoding proteins were involved in xenobiotics detoxification and ROS generation or reduction, including aldo/keto reductase, glutathione S-transferases, cytochrome P450 enzymes, alcohol oxidases and dehydrogenase, manganese peroxidase and laccase. Furthermore, many DEG-encoding proteins were involved in antagonistic mechanisms of nutrient acquisition and antifungal properties, including glycoside hydrolase, glucanase, chitinase and terpenoid synthases. DEGs analysis effectively revealed that laccase induction was likely caused by protective responses to oxidative stress and nutrient competition during fungal interspecific interaction.

Differential Gene Expression Patterns and Interaction Networks in BCR/ABL Positive and Negative Adult Acute Lymphoblastic Leukemias.

Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 1836-1836
Author(s):  
Dejan Juric ◽  
Norman J. Lacayo ◽  
Meghan C. Ramsey ◽  
Janis Racevskis ◽  
Peter H. Wiernik ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Expression Profiles ◽  
Lymphoblastic Leukemia ◽  
Expression Patterns ◽  
Cellular Growth ◽  
Interaction Networks ◽  
Pcr Analysis ◽  
Interaction Patterns ◽  
Differential Gene

Abstract BCR/ABL is associated with an unfavorable prognosis in adults with acute lymphoblastic leukemia (ALL). We used DNA microarrays to identify gene expression profiles and molecular interaction networks related to BCR/ABL status and clinical outcome in a set of 54 adult ALL specimens from the MRC UKALL XII/ECOG E2993 intergroup study (21 p185BCR/ABL- and 16 p210BCR/ABL-positive and 17 BCR/ABL negative). In order to avoid biases associated with commonly used sample amplification procedures, we have implemented an indirect two-step labeling protocol based on signal amplification by use of dendrimer technology. Using a two-class, non-parametric t-test and a false discovery rate cutoff of 5%, we identified 271 genes (including GAB1, CIITA, XBP1, CD83, SERPINB9, PTP4A3, NOV, LOX, CTNND1, BAALC and RAB21) as differentially expressed in BCR/ABL positive ALL compared with BCR/ABL negative ALL. They separate these two classes of adult ALL with an overall accuracy of 93% and are enriched for three highly relevant biological functions: Cellular Growth and Proliferation (57 genes, p = 0.004–0.044), Cell Death (49 genes, p = 0.0007–0.049), and Hematological System Development and Function (40 genes, p = 0.00004–0.049). Network analysis demonstrated complex interaction patterns of these genes, and identified FYN and IL15 as the hubs of the top-scoring network. We confirmed these findings by both qRT-PCR analysis of the initial set of samples and by cross-platform validation in an independent cohort of 128 adult ALL specimens. In addition, within the BCR/ABL positive subgroup, we identified 14 clones found to be over-expressed (TSPAN16, ADAMTSL4) or under-expressed (PILRB, STS-1, SPRY1) in p185BCR-ABL- relative to p210BCR-ABL-ALL. In a nearest-centroid classification, these clones correctly predict the BCR/ABL subtype with a cross-validated prediction accuracy of 95%. No differential gene expression was detected among Rho family GTPases and their known interaction partners. Finally, we constructed a gene expression- and interaction-based outcome predictor consisting of 27 genes (including GRB2, GAB1, GLI1, IRS1, RUNX2 and SPP1), which strongly correlated with overall survival in BCR/ABL positive adult ALL (p=0.0001), independently of other clinical parameters such as age (p=0.25) and white blood cell count at presentation (p = 0.003). These findings may be useful for developing novel therapeutic targets and prognostic markers in adult ALL.

scholarly journals RankerGUI: A Computational Framework to Compare Differential Gene Expression Profiles Using Rank Based Statistics

2019 ◽  
Vol 20 (23) ◽  
pp. 6098 ◽  
Author(s):  
Amarinder Singh Thind ◽  
Kumar Parijat Tripathi ◽  
Mario Rosario Guarracino
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Differential Gene Expression ◽  
Web Application ◽  
Cellular Response ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Experimental Conditions ◽  
Differential Gene

The comparison of high throughput gene expression datasets obtained from different experimental conditions is a challenging task. It provides an opportunity to explore the cellular response to various biological events such as disease, environmental conditions, and drugs. There is a need for tools that allow the integration and analysis of such data. We developed the “RankerGUI pipeline”, a user-friendly web application for the biological community. It allows users to use various rank based statistical approaches for the comparison of full differential gene expression profiles between the same or different biological states obtained from different sources. The pipeline modules are an integration of various open-source packages, a few of which are modified for extended functionality. The main modules include rank rank hypergeometric overlap, enriched rank rank hypergeometric overlap and distance calculations. Additionally, preprocessing steps such as merging differential expression profiles of multiple independent studies can be added before running the main modules. Output plots show the strength, pattern, and trends among complete differential expression profiles. In this paper, we describe the various modules and functionalities of the developed pipeline. We also present a case study that demonstrates how the pipeline can be used for the comparison of differential expression profiles obtained from multiple platforms’ data of the Gene Expression Omnibus. Using these comparisons, we investigate gene expression patterns in kidney and lung cancers.

scholarly journals Carbohydrate-induced Differential Gene Expression Patterns in the Hyperthermophilic BacteriumThermotoga maritima

2002 ◽  
Vol 278 (9) ◽  
pp. 7540-7552 ◽  
Author(s):  
Swapnil R. Chhabra ◽  
Keith R. Shockley ◽  
Shannon B. Conners ◽  
Kevin L. Scott ◽  
Russell D. Wolfinger ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Differential Gene

Differential gene expression patterns and colocalization of ATP-gated P2X6/P2X4 ion channels during rat small intestine ontogeny

2016 ◽  
Vol 21 (2) ◽  
pp. 81-88 ◽  
Author(s):  
Karla Padilla ◽  
David Gonzalez-Mendoza ◽  
Laura C. Berumen ◽  
Jesica E. Escobar ◽  
Ricardo Miledi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Small Intestine ◽  
Ion Channels ◽  
Differential Gene Expression ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Rat Small Intestine ◽  
Differential Gene

scholarly journals Differential Gene Expression Patterns of EBV Infected EBNA-3A Positive and Negative Human B Lymphocytes

2009 ◽  
Vol 5 (7) ◽  
pp. e1000506 ◽  
Author(s):  
Marie L. Hertle ◽  
Claudia Popp ◽  
Sabine Petermann ◽  
Sabine Maier ◽  
Elisabeth Kremmer ◽  
...  
Keyword(s):  
Gene Expression ◽  
B Lymphocytes ◽  
Differential Gene Expression ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Differential Gene

scholarly journals Differential Gene Expression in Sugarcane in Response to Challenge by Fungal PathogenUstilago scitamineaRevealed by cDNA-AFLP

2011 ◽  
Vol 2011 ◽  
pp. 1-10 ◽  
Author(s):  
Que You-Xiong ◽  
Lin Jian-Wei ◽  
Song Xian-Xian ◽  
Xu Li-Ping ◽  
Chen Ru-Kai
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Silver Staining ◽  
Molecular Basis ◽  
Pcr Analysis ◽  
Aflp Analysis ◽  
Rt Pcr ◽  
Resistant Genotype ◽  
Ustilago Scitaminea ◽  
Differential Gene

Differential gene expression in sugarcane during sugarcane-Ustilago scitamineainteraction was conducted in a smut-resistant genotype. Using cDNA-AFLP along with silver staining, a total of 136 transcript-derived fragments (TDFs) were found to be differentially expressed in response to challenge byU. scitaminea. Forty TDFs, 34 newly induced plus six with obvious upregulated expression after infection, were sequenced and validated by RT-PCR analysis. These results demonstrated that the expression of 37 out of these TDFs in RT-PCR analysis was consistent with that in cDNA-AFLP analysis. Based on BlastX in NCBI, 28 TDFs were assumed to function in sugarcane underU. scitamineastress. Analysis of expression profile of three TDFs revealed that they responded differently after infection withU. scitaminea, and the transcription was significantly enhanced. The response of two TDFs, SUC06 and SUC09, occurred before that of SUC10. This study enriches our knowledge of the molecular basis for sugarcane response toU. scitamineainfection.

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