Isolation and Characterization of a Type II Peroxiredoxin Gene from Panax ginseng C. A. Meyer

Kim, Yujin; Lee, Junghye; Lee, Ok Ran; Shim, Ju-Sun; Jung, Seok-Kyu; Son, Na-Ri; Kim, Ju Han; Kim, Seyoung; Yang, Deok-Chun

doi:10.5142/jgr.2010.34.4.296

Cited by 11 publications

(3 citation statements)

References 29 publications

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“…Its expression patterns indicated that is induced by ultraviolet irradiation, low temperature and salt stress. The induction of Prx in response to abiotic stimuli may suggest that Prx may protect the host against environmental stresses [60] . It looks like gene 42 affects gene 41 (HSP70T-2; ATP binding) and gene 7 (PSBS, (Nonphotochemical quenching), 16 (lipase, putative) and 38 (APX4 (Ascorbate peroxidase 4); peroxidase) and it is itself affected by 39 (GDCH (Glycine decarboxylase complex H)).…”

Section: Discussionmentioning

confidence: 99%

Discovering Study-Specific Gene Regulatory Networks

et al. 2014

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Microarrays are commonly used in biology because of their ability to simultaneously measure thousands of genes under different conditions. Due to their structure, typically containing a high amount of variables but far fewer samples, scalable network analysis techniques are often employed. In particular, consensus approaches have been recently used that combine multiple microarray studies in order to find networks that are more robust. The purpose of this paper, however, is to combine multiple microarray studies to automatically identify subnetworks that are distinctive to specific experimental conditions rather than common to them all. To better understand key regulatory mechanisms and how they change under different conditions, we derive unique networks from multiple independent networks built using glasso which goes beyond standard correlations. This involves calculating cluster prediction accuracies to detect the most predictive genes for a specific set of conditions. We differentiate between accuracies calculated using cross-validation within a selected cluster of studies (the intra prediction accuracy) and those calculated on a set of independent studies belonging to different study clusters (inter prediction accuracy). Finally, we compare our method's results to related state-of-the art techniques. We explore how the proposed pipeline performs on both synthetic data and real data (wheat and Fusarium). Our results show that subnetworks can be identified reliably that are specific to subsets of studies and that these networks reflect key mechanisms that are fundamental to the experimental conditions in each of those subsets.

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Section: Discussionmentioning

confidence: 99%

Discovering Study-Specific Gene Regulatory Networks

et al. 2014

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“…The total RNA was stored at À70 1C until use. Semi-quantitative RT reactions were conducted as reported previously (Kim et al, 2010c). The mRNA was quantified by realtime RT-PCR with SYBR Premix Ex Taq according to the manufacturer's instructions (Takara, Japan) using a real-time thermal cycler (Bio-Rad, USA) as reported previously (Khorolragchaa et al, 2010).…”

Section: Etoh/hcl-induced Gastritismentioning

confidence: 99%

Src and Syk are targeted to an anti-inflammatory ethanol extract of Aralia continentalis

Jeong

Moh

Yang

et al. 2012

Journal of Ethnopharmacology

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“…Total RNA was stored at -70 °C until use. Analysis of mRNA was also performed using semiquantitative RT-PCR according to the manufacturer's instructions (Bioneer, Daejeon, Korea) as reported previously [21] . The results were expressed as the ratio of the optical density at 280 nm to the GAPDH mRNA concentration.…”

Section: Cell Culturementioning

confidence: 99%

8-(Tosylamino)quinoline inhibits macrophage-mediated inflammation by suppressing NF-κB signaling

et al. 2012

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Aim:The macrophage-mediated inflammatory response may contribute to the development of cancer, diabetes, atherosclerosis and septic shock. This study was to characterize several new compounds to suppress macrophage-mediated inflammation. Methods: Peritoneal macrophages from C57BL/6 male mice and RAW264.7 cells were examined. Anti-inflammatory activity was evaluated in the cells exposed to lipopolysaccharide (LPS). The mechanisms of the anti-inflammatory activity were investigated via measuring transcription factor activation in response to specific signals and via assaying the activities of the target kinases. Results: Of 7 candidate compounds tested, 8-(tosylamino)quinoline (8-TQ, compound 7) exhibited the strongest activities in suppressing the production of NO, TNF-α, and PGE 2 in LPS-activated RAW264.7 cells and peritoneal macrophages (the IC 50 values=1-5 μmol/L). This compound (1.25-20 μmol/L) dose-dependently suppressed the expression of the pro-inflammatory genes for iNOS, COX-2, TNF-α, and the cytokines IL-1β and IL-6 at the level of transcription in LPS-activated RAW264.7 cells. 8-TQ (20 μmol/L) significantly suppressed the activation of NF-κB and its upstream signaling elements, including inhibitor of κB (IκBα), IκBα kinase (IKK) and Akt in LPSactivated RAW264.7 cells. In in vivo experiments, oral administration of 20 and 40 mg/kg 8-TQ for 3 d significantly alleviated the signs of LPS-induced hepatitis and HCl/EtOH-induced gastritis, respectively, in ICR mice. Conclusion: 8-TQ (compound 7) exerts significant anti-inflammatory activity through the inhibition of the Akt/NF-κB pathway, thus may be developed as a novel anti-inflammatory drug.

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Isolation and Characterization of a Type II Peroxiredoxin Gene from Panax ginseng C. A. Meyer

Cited by 11 publications

References 29 publications

Discovering Study-Specific Gene Regulatory Networks

Discovering Study-Specific Gene Regulatory Networks

Src and Syk are targeted to an anti-inflammatory ethanol extract of Aralia continentalis

8-(Tosylamino)quinoline inhibits macrophage-mediated inflammation by suppressing NF-κB signaling

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