Differential gene expressions of steroid- and catecholamine-synthesizing enzymes in adrenal gland of rats with different diseases but the same syndromes

2010 ◽  
Vol 8 (4) ◽  
pp. 352-357 ◽  
Author(s):  
ZQ Pan
Keyword(s):  
Adrenal Gland ◽  
Gene Expressions ◽  
Catecholamine Synthesizing Enzymes ◽  
Differential Gene

scholarly journals 3D genome alterations associated with dysregulated HOXA13 expression in high-risk T-lineage acute lymphoblastic leukemia

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Lu Yang ◽  
Fengling Chen ◽  
Haichuan Zhu ◽  
Yang Chen ◽  
Bingjie Dong ◽  
...  
Keyword(s):  
Acute Lymphoblastic Leukemia ◽  
Lymphoblastic Leukemia ◽  
T Cell Differentiation ◽  
Newly Diagnosed ◽  
Coding Region ◽  
Gene Expressions ◽  
3D Genome ◽  
Topologically Associated Domains ◽  
Differential Gene ◽  
Poor Outcomes

Abstract3D genome alternations can dysregulate gene expression by rewiring enhancer-promoter interactions and lead to diseases. We report integrated analyses of 3D genome alterations and differential gene expressions in 18 newly diagnosed T-lineage acute lymphoblastic leukemia (T-ALL) patients and 4 healthy controls. 3D genome organizations at the levels of compartment, topologically associated domains and loop could hierarchically classify different subtypes of T-ALL according to T cell differentiation trajectory, similar to gene expressions-based classification. Thirty-four previously unrecognized translocations and 44 translocation-mediated neo-loops are mapped by Hi-C analysis. We find that neo-loops formed in the non-coding region of the genome could potentially regulate ectopic expressions of TLX3, TAL2 and HOXA transcription factors via enhancer hijacking. Importantly, both translocation-mediated neo-loops and NUP98-related fusions are associated with HOXA13 ectopic expressions. Patients with HOXA11-A13 expressions, but not other genes in the HOXA cluster, have immature immunophenotype and poor outcomes. Here, we highlight the potentially important roles of 3D genome alterations in the etiology and prognosis of T-ALL.

scholarly journals Differential gene expressions in arbuscular mycorrhizal-colonized tomato grown under heavy metal stress

2005 ◽  
Vol 162 (6) ◽  
pp. 634-649 ◽  
Author(s):  
Fouad Ouziad ◽  
Ulrich Hildebrandt ◽  
Elmon Schmelzer ◽  
Hermann Bothe
Keyword(s):  
Heavy Metal ◽  
Arbuscular Mycorrhizal ◽  
Heavy Metal Stress ◽  
Metal Stress ◽  
Gene Expressions ◽  
Differential Gene

scholarly journals Gender Differences of B Cell Signature in Healthy Subjects Underlie Disparities in Incidence and Course of SLE Related to Estrogen

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
Hongye Fan ◽  
Guanjun Dong ◽  
Guangfeng Zhao ◽  
Fei Liu ◽  
Genghong Yao ◽  
...  
Keyword(s):  
Gender Differences ◽  
B Cells ◽  
Global Gene Expression ◽  
Gene Expressions ◽  
Significant Difference ◽  
Healthy Female ◽  
Nonsignificant Difference ◽  
Differential Gene ◽  
The Difference ◽  
Healthy Females

The aim of the present study was to investigate mechanism of the gender differences of B cells. The results showed that 358 differential gene expressions (DEGs) were displayed between healthy females and males. Compared with male, 226 and 132 genes were found to be up- and downregulated in the female. 116 genes displayed possible correlation with estrogen. Moreover, the upregulated DEGs (Cav1, CD200R1, TNFRSF17, and CXCR3) and downregulated DEGs (EIF1AY and DDX3Y) in healthy female may be involved in gender predominance of some immune diseases. Furthermore, signaling pathway analysis for estrogen-relevant DEGs showed that only 26 genes were downregulated in SLE female versus SLE male, of which expressions of 8 genes had significant difference between SLE females and SLE males but are having nonsignificant difference between healthy females and healthy males. Except for the 5 Y-chromosome-related genes or varients, only 3 DEGs (LTF, CAMP, and DEFA4) were selected and qRT-PCR confirmed that the expressions of LTF and CAMP decreased significantly in B cells from female SLE patients. These data indicated that the gender differences were existent in global gene expression of B cells and the difference may be related to estrogen.

scholarly journals A Novel Approach for Discovering Condition-Specific Correlations of Gene Expressions within Biological Pathways by Using Cloud Computing Technology

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Tzu-Hao Chang ◽  
Shih-Lin Wu ◽  
Wei-Jen Wang ◽  
Jorng-Tzong Horng ◽  
Cheng-Wei Chang
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cloud Computing ◽  
Cancer Recurrence ◽  
Computation Time ◽  
Gene Expression Omnibus ◽  
Biological Pathways ◽  
Data Sets ◽  
Gene Expressions ◽  
Differential Gene

Microarrays are widely used to assess gene expressions. Most microarray studies focus primarily on identifying differential gene expressions between conditions (e.g., cancer versus normal cells), for discovering the major factors that cause diseases. Because previous studies have not identified the correlations of differential gene expression between conditions, crucial but abnormal regulations that cause diseases might have been disregarded. This paper proposes an approach for discovering the condition-specific correlations of gene expressions within biological pathways. Because analyzing gene expression correlations is time consuming, an Apache Hadoop cloud computing platform was implemented. Three microarray data sets of breast cancer were collected from the Gene Expression Omnibus, and pathway information from the Kyoto Encyclopedia of Genes and Genomes was applied for discovering meaningful biological correlations. The results showed that adopting the Hadoop platform considerably decreased the computation time. Several correlations of differential gene expressions were discovered between the relapse and nonrelapse breast cancer samples, and most of them were involved in cancer regulation and cancer-related pathways. The results showed that breast cancer recurrence might be highly associated with the abnormal regulations of these gene pairs, rather than with their individual expression levels. The proposed method was computationally efficient and reliable, and stable results were obtained when different data sets were used. The proposed method is effective in identifying meaningful biological regulation patterns between conditions.

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