基于microRNA共调控网络的microRNA调控机制研究

已有研究通过计算和实验的手段,证明了不同的microRNA(miRNA)通过相互之间的合作,来共同调控它们所共有的靶基因。对miRNA之间这种合作行为的特性的研究,能够帮助我们更好的理解miRNA的调控机理。本文建立了一个网络来描述miRNA之间的合作关系,并通过对该网络的分析,得出了四点关于miRNA调控机制的性质。第一,基因靶标数目越多的miRNA倾向于与越多的miRNA伙伴进行合作。第二,进化上保守的miRNA所具有的共调控伙伴的数目显著多于非保守的miRNA。第三,以上的性质是跨物种的存在的(人与小鼠)。第四,miRNA与蛋白质在系统层面性质存在一定的相似。     

MiRNA在TLR信号通路中的负性调控作用

TLR信号是生物体重要的病原体模式识别信号,在免疫识别和炎症反应中具有重要作用,其信号异常会导致许多免疫和炎症相关疾病的发生,因此探讨和明确TLR信号通路的调控机制具有非常重要的意义。近年来研究发现,作为重要的基因表达调控的小分子RNA,微RNA(microRNA,miRNA)能与TLR信号通路中众多靶基因mRNA的3’UTR区结合,从而抑制翻译过程或降解mRNA来发挥负性调控作用。本文就miRNA对TLR信号通路中的一些受体、信号分子、调节因子和细胞因子的负性调控作用方面进行阐述。     

Galectin genes: regulation of expression

In this review we have summarized the more recent studies on the expression of mammalian galectins. One interesting observation that can be made is that in most of microarrays and/or differential display analysis performed in recent years one or more galectins have been picked up. From a critical evaluation of the pertinent studies the main conclusion that can be drawn is that, although it is not yet clear whether the 14 galectins identified so far have functions in common, a striking common feature of all galectins is the strong modulation of their expression during development, differentiation stages and under different physiological or pathological conditions. This suggests that the expression of different galectins is finely tuned and possibly coordinated. In spite of these observations it is rather unexpected that very few studies have been performed on the molecular mechanisms governing the activity of galectin genes.     

Combinatorial network of transcriptional regulation and microRNA regulation in human cancer

Background

In order to reduce time and efforts to develop microbial strains with better capability of producing desired bioproducts, genome-scale metabolic simulations have proven useful in identifying gene knockout and amplification targets. Constraints-based flux analysis has successfully been employed for such simulation, but is limited in its ability to properly describe the complex nature of biological systems. Gene knockout simulations are relatively straightforward to implement, simply by constraining the flux values of the target reaction to zero, but the identification of reliable gene amplification targets is rather difficult. Here, we report a new algorithm which incorporates physiological data into a model to improve the model??s prediction capabilities and to capitalize on the relationships between genes and metabolic fluxes.

Results

We developed an algorithm, flux variability scanning based on enforced objective flux (FVSEOF) with grouping reaction (GR) constraints, in an effort to identify gene amplification targets by considering reactions that co-carry flux values based on physiological omics data via ??GR constraints??. This method scans changes in the variabilities of metabolic fluxes in response to an artificially enforced objective flux of product formation. The gene amplification targets predicted using this method were validated by comparing the predicted effects with the previous experimental results obtained for the production of shikimic acid and putrescine in Escherichia coli. Moreover, new gene amplification targets for further enhancing putrescine production were validated through experiments involving the overexpression of each identified targeted gene under condition-controlled batch cultivation.

Conclusions

FVSEOF with GR constraints allows identification of gene amplification targets for metabolic engineering of microbial strains in order to enhance the production of desired bioproducts. The algorithm was validated through the experiments on the enhanced production of putrescine in E. coli, in addition to the comparison with the previously reported experimental data. The FVSEOF strategy with GR constraints will be generally useful for developing industrially important microbial strains having enhanced capabilities of producing chemicals of interest.