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miRNA regulatory circuits in ES cells differentiation: a chemical kinetics modeling approach.

Luo Z, Xu X, Gu P, Lonard D, Gunaratne PH, Cooney AJ, Azencott R - PLoS ONE (2011)

Bottom Line: For each pair (M,G) of potentially interacting miRMA gene M and mRNA gene G, we parameterize our associated kinetic equations by optimizing their fit with microarray data.When this fit is high enough, we validate the pair (M,G) as a highly probable repressive interaction.This approach leads to the computation of a highly selective and drastically reduced list of repressive pairs (M,G) involved in ES cells differentiation.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics, University of Houston, Houston, Texas, United States of America. boluomiduo1@gmail.com

ABSTRACT
MicroRNAs (miRNAs) play an important role in gene regulation for Embryonic Stem cells (ES cells), where they either down-regulate target mRNA genes by degradation or repress protein expression of these mRNA genes by inhibiting translation. Well known tables TargetScan and miRanda may predict quite long lists of potential miRNAs inhibitors for each mRNA gene, and one of our goals was to strongly narrow down the list of mRNA targets potentially repressed by a known large list of 400 miRNAs. Our paper focuses on algorithmic analysis of ES cells microarray data to reliably detect repressive interactions between miRNAs and mRNAs. We model, by chemical kinetics equations, the interaction architectures implementing the two basic silencing processes of miRNAs, namely "direct degradation" or "translation inhibition" of targeted mRNAs. For each pair (M,G) of potentially interacting miRMA gene M and mRNA gene G, we parameterize our associated kinetic equations by optimizing their fit with microarray data. When this fit is high enough, we validate the pair (M,G) as a highly probable repressive interaction. This approach leads to the computation of a highly selective and drastically reduced list of repressive pairs (M,G) involved in ES cells differentiation.

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Western blots for 4 proteins and actins.Oct4 and Nanog levels exhibit quite strong decrease for WT cells and very slow decrease for GCNF-KO cells. Sox2 levels vanish after 1.5 days for WT cells and slowly decrease for GCNF-KO cells. GCNF levels are initially low, peak at day 3 and fall back on day 6 for WT cells. For GCNF-KO cells, GCNF levels naturally vanish.
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pone-0023263-g001: Western blots for 4 proteins and actins.Oct4 and Nanog levels exhibit quite strong decrease for WT cells and very slow decrease for GCNF-KO cells. Sox2 levels vanish after 1.5 days for WT cells and slowly decrease for GCNF-KO cells. GCNF levels are initially low, peak at day 3 and fall back on day 6 for WT cells. For GCNF-KO cells, GCNF levels naturally vanish.

Mentions: By Western blots analysis, we have also recorded protein expression profiles For during ES cell differentiation (see Figure 1), for both WT and GCNF-KO, at time points (0, 1.5, 3, 6).


miRNA regulatory circuits in ES cells differentiation: a chemical kinetics modeling approach.

Luo Z, Xu X, Gu P, Lonard D, Gunaratne PH, Cooney AJ, Azencott R - PLoS ONE (2011)

Western blots for 4 proteins and actins.Oct4 and Nanog levels exhibit quite strong decrease for WT cells and very slow decrease for GCNF-KO cells. Sox2 levels vanish after 1.5 days for WT cells and slowly decrease for GCNF-KO cells. GCNF levels are initially low, peak at day 3 and fall back on day 6 for WT cells. For GCNF-KO cells, GCNF levels naturally vanish.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3198445&req=5

pone-0023263-g001: Western blots for 4 proteins and actins.Oct4 and Nanog levels exhibit quite strong decrease for WT cells and very slow decrease for GCNF-KO cells. Sox2 levels vanish after 1.5 days for WT cells and slowly decrease for GCNF-KO cells. GCNF levels are initially low, peak at day 3 and fall back on day 6 for WT cells. For GCNF-KO cells, GCNF levels naturally vanish.
Mentions: By Western blots analysis, we have also recorded protein expression profiles For during ES cell differentiation (see Figure 1), for both WT and GCNF-KO, at time points (0, 1.5, 3, 6).

Bottom Line: For each pair (M,G) of potentially interacting miRMA gene M and mRNA gene G, we parameterize our associated kinetic equations by optimizing their fit with microarray data.When this fit is high enough, we validate the pair (M,G) as a highly probable repressive interaction.This approach leads to the computation of a highly selective and drastically reduced list of repressive pairs (M,G) involved in ES cells differentiation.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics, University of Houston, Houston, Texas, United States of America. boluomiduo1@gmail.com

ABSTRACT
MicroRNAs (miRNAs) play an important role in gene regulation for Embryonic Stem cells (ES cells), where they either down-regulate target mRNA genes by degradation or repress protein expression of these mRNA genes by inhibiting translation. Well known tables TargetScan and miRanda may predict quite long lists of potential miRNAs inhibitors for each mRNA gene, and one of our goals was to strongly narrow down the list of mRNA targets potentially repressed by a known large list of 400 miRNAs. Our paper focuses on algorithmic analysis of ES cells microarray data to reliably detect repressive interactions between miRNAs and mRNAs. We model, by chemical kinetics equations, the interaction architectures implementing the two basic silencing processes of miRNAs, namely "direct degradation" or "translation inhibition" of targeted mRNAs. For each pair (M,G) of potentially interacting miRMA gene M and mRNA gene G, we parameterize our associated kinetic equations by optimizing their fit with microarray data. When this fit is high enough, we validate the pair (M,G) as a highly probable repressive interaction. This approach leads to the computation of a highly selective and drastically reduced list of repressive pairs (M,G) involved in ES cells differentiation.

Show MeSH