Faculty Opinions recommendation of Abundance, diversity and functional gene expression of denitrifier communities in adjacent riparian and agricultural zones.

2012 ◽  
Author(s):  
Gilles Pinay
Keyword(s):  
Gene Expression ◽  
Functional Gene ◽  
Denitrifier Communities

scholarly journals Abundance, diversity and functional gene expression of denitrifier communities in adjacent riparian and agricultural zones

2011 ◽  
Vol 77 (1) ◽  
pp. 69-82 ◽  
Author(s):  
Catherine E. Dandie ◽  
Sophie Wertz ◽  
Caissie L. Leclair ◽  
Claudia Goyer ◽  
David L. Burton ◽  
...  
Keyword(s):  
Gene Expression ◽  
Functional Gene ◽  
Denitrifier Communities

Utilizing Cancer - Functional Gene Set - Compound Networks to Identify Putative Drugs for Breast Cancer

2018 ◽  
Vol 21 (2) ◽  
pp. 74-83
Author(s):  
Tzu-Hung Hsiao ◽  
Yu-Chiao Chiu ◽  
Yu-Heng Chen ◽  
Yu-Ching Hsu ◽  
Hung-I Harry Chen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cancer Therapy ◽  
Cancer Treatment ◽  
Cancer Survival ◽  
Expression Profiles ◽  
Functional Gene ◽  
Gene Set Enrichment Analysis ◽  
Gene Set ◽  
Gene Sets

Aim and Objective: The number of anticancer drugs available currently is limited, and some of them have low treatment response rates. Moreover, developing a new drug for cancer therapy is labor intensive and sometimes cost prohibitive. Therefore, “repositioning” of known cancer treatment compounds can speed up the development time and potentially increase the response rate of cancer therapy. This study proposes a systems biology method for identifying new compound candidates for cancer treatment in two separate procedures. Materials and Methods: First, a “gene set–compound” network was constructed by conducting gene set enrichment analysis on the expression profile of responses to a compound. Second, survival analyses were applied to gene expression profiles derived from four breast cancer patient cohorts to identify gene sets that are associated with cancer survival. A “cancer–functional gene set– compound” network was constructed, and candidate anticancer compounds were identified. Through the use of breast cancer as an example, 162 breast cancer survival-associated gene sets and 172 putative compounds were obtained. Results: We demonstrated how to utilize the clinical relevance of previous studies through gene sets and then connect it to candidate compounds by using gene expression data from the Connectivity Map. Specifically, we chose a gene set derived from a stem cell study to demonstrate its association with breast cancer prognosis and discussed six new compounds that can increase the expression of the gene set after the treatment. Conclusion: Our method can effectively identify compounds with a potential to be “repositioned” for cancer treatment according to their active mechanisms and their association with patients’ survival time.

scholarly journals Functional Gene-Expression Analysis Shows Involvement of Schizophrenia-Relevant Pathways in Patients with 22q11 Deletion Syndrome

PLoS ONE ◽  
2012 ◽  
Vol 7 (3) ◽  
pp. e33473 ◽  
Author(s):  
Nico J. M. van Beveren ◽  
Lianne C. Krab ◽  
Sigrid Swagemakers ◽  
Gabriella Buitendijk ◽  
Erik Boot ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Functional Gene ◽  
Deletion Syndrome ◽  
22Q11 Deletion ◽  
22Q11 Deletion Syndrome

Nuclear transformation and functional gene expression in the oleaginous microalga Monoraphidium neglectum

2017 ◽  
Vol 249 ◽  
pp. 10-15 ◽  
Author(s):  
Daniel Jaeger ◽  
Wolfgang Hübner ◽  
Thomas Huser ◽  
Jan H. Mussgnug ◽  
Olaf Kruse
Keyword(s):  
Gene Expression ◽  
Functional Gene ◽  
Nuclear Transformation

6 GENE EXPRESSION ANALYSIS OF IN VIVO- AND IN VITRO-MATURED PORCINE METAPHASE II OOCYTES

2014 ◽  
Vol 26 (1) ◽  
pp. 117 ◽  
Author(s):  
L. Cox ◽  
G. Saunders ◽  
J. Stevens ◽  
S. C. Isom
Keyword(s):  
Gene Expression ◽  
Rna Degradation ◽  
Functional Gene ◽  
Gene Set Enrichment Analysis ◽  
Developmental Competence ◽  
Mrna Levels ◽  
Multiple Testing Correction ◽  
Metaphase Ii Oocytes

In vitro-matured (IVM) oocytes lack the same developmental competence as oocytes that are matured in vivo (IVV), yet no compelling explanation for this discrepancy has been provided at the molecular level. The aim of this study was to quantify and compare mRNA levels in IVM and IVV oocytes for genes from a wide variety of functional gene categories, including RNA degradation, pluripotency, epigenome modification, oocyte-specific, and apoptosis. Quantitative real-time PCR (qPCR) was used to evaluate the relative gene expression levels of 70 genes in each of 33 individual IVM oocytes from 4 different collection days and 29 individual IVV oocytes from 4 different donor animals. The qPCR data were analysed using ANOVA and significance was assigned at P < 0.05. After a multiple testing correction was applied, relative transcript abundances for 32 of the 70 genes tested were found to be significantly different (q < 0.05) between the IVM and IVV oocytes. Of these significantly different genes, 23 were higher in the IVM oocytes and only 9 were higher in the IVV oocytes. The 32 significantly differentially expressed genes were then evaluated in relation to their corresponding functional gene categories. Of particular interest, transcripts for 7/14 RNA degradation-related genes (CNOT3, DCP1A, DDX6, LSM1, PABPN1, PABPN1L, PARN) and 3/9 oocyte specific genes (BMP15, YBX2, H1FOO) were significantly more abundant in the IVM oocytes. In contrast, transcripts for 4/8 epigenetic related transcripts (ASH2l, DNMT1, EHMT2, EZH2), 2/2 apoptosis related genes (BCL2, XIAP), and 1/4 pluripotency factors (LIN28) were significantly more abundant in the IVV oocytes. Gene set enrichment analysis confirmed that, within the context of this experimental design, RNA degradation and chromatin remodelling pathways are significantly perturbed in IVM oocytes. We conclude that in vitro maturation has profound effects on transcript populations of metaphase-II oocytes, with most transcripts being higher in IVM oocytes. We expect that this data will lead to a better understanding of how we can improve the quality of oocytes that are matured in vitro as well as provide information to help to identify markers that could be indicative of oocyte quality.

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