AU-rich elements (AREs), residing in the 3= untranslated region (UTR) of many labile mRNAs, are important cis-acting elements that modulate the stability of these mRNAs by collaborating with trans-acting factors such as tristetraprolin (TTP). AREs also regulate translation, but the underlying mechanism is not fully understood. Here we examined the function and mechanism of TTP in ARE-mRNA translation. Through a luciferase-based reporter system, we used knockdown, overexpression, and tethering assays in 293T cells to demonstrate that TTP represses ARE reporter mRNA translation. Polyribosome fractionation experiments showed that TTP shifts target mRNAs to lighter fractions. In murine RAW264.7 macrophages, knocking down TTP produces significantly more tumor necrosis factor alpha (TNF-␣) than the control, while the corresponding mRNA level has a marginal change. Furthermore, knockdown of TTP increases the rate of biosynthesis of TNF-␣, suggesting that TTP can exert effects at translational levels. Finally, we demonstrate that the general translational repressor RCK may cooperate with TTP to regulate ARE-mRNA translation. Collectively, our studies reveal a novel function of TTP in repressing ARE-mRNA translation and that RCK is a functional partner of TTP in promoting TTP-mediated translational repression.

Numerous test data accumulates in the process of gas reservoir exploration and development, so it is necessary to apply the data mining technology to this process. Influenced by the geologic factors such as structure, deposition and diagenesis, tight sandstone gas reservoir formation types are so diversified that traditional cross-plot analysis technique hardly identify the formation types. In this paper, the formation types of tight sandstone gas reservoir in Daniudi area are successfully identified using the decision tree algorithm of data mining based on hierarchical decomposition theory, facilitating the development of the gas reservoir.

Daniudi gas field is a tight sandstone gas field in the northeast of Ordos Basin. How to use the successful experience in developing area to predict favorable gas-rich area in other areas in this gas field is very important to the next exploration and development in this field. This paper proposes a multi-information integrated method to predict favorable gas-rich area. Firstly describe sedimentary microfacies by integrating seismic, logging and geological information; and then summarize and analyze the seismic reflection patterns of medium-high productivity wells; finally determine the favorable gas-rich area with the distribution of storage coefficient based on the previous analysis. The welltest of newly drilled wells shows that the coincidence rate of favorable gas-rich area predicted by this method could be up to 90%,and this method could be extended to use in the other tight sandstone gas reservoirs.