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author:("Shi, minghui")
1.  Producing aglycons of ginsenosides in bakers' yeast 
Scientific Reports  2014;4:3698.
Ginsenosides are the primary bioactive components of ginseng, which is a popular medicinal plant that exhibits diverse pharmacological activities. Protopanaxadiol, protopanaxatriol and oleanolic acid are three basic aglycons of ginsenosides. Producing aglycons of ginsenosides in Saccharomyces cerevisiae was realized in this work and provides an alternative route compared to traditional extraction methods. Synthetic pathways of these three aglycons were constructed in S. cerevisiae by introducing β-amyrin synthase, oleanolic acid synthase, dammarenediol-II synthase, protopanaxadiol synthase, protopanaxatriol synthase and NADPH-cytochrome P450 reductase from different plants. In addition, a truncated 3-hydroxy-3-methylglutaryl-CoA reductase, squalene synthase and 2,3-oxidosqualene synthase genes were overexpressed to increase the precursor supply for improving aglycon production. Strain GY-1 was obtained, which produced 17.2 mg/L protopanaxadiol, 15.9 mg/L protopanaxatriol and 21.4 mg/L oleanolic acid. The yeast strains engineered in this work can serve as the basis for creating an alternative way for producing ginsenosides in place of extractions from plant sources.
doi:10.1038/srep03698
PMCID: PMC3892717  PMID: 24424342
2.  Semiparametric prognosis models in genomic studies 
Briefings in Bioinformatics  2010;11(4):385-393.
Development of high-throughput technologies makes it possible to survey the whole genome. Genomic studies have been extensively conducted, searching for markers with predictive power for prognosis of complex diseases such as cancer, diabetes and obesity. Most existing statistical analyses are focused on developing marker selection techniques, while little attention is paid to the underlying prognosis models. In this article, we review three commonly used prognosis models, namely the Cox, additive risk and accelerated failure time models. We conduct simulation and show that gene identification can be unsatisfactory under model misspecification. We analyze three cancer prognosis studies under the three models, and show that the gene identification results, prediction performance of all identified genes combined, and reproducibility of each identified gene are model-dependent. We suggest that in practical data analysis, more attention should be paid to the model assumption, and multiple models may need to be considered.
doi:10.1093/bib/bbp070
PMCID: PMC2905523  PMID: 20123942
genomic studies; semiparametric prognosis models; model comparison
3.  Incorporating gene co-expression network in identification of cancer prognosis markers 
BMC Bioinformatics  2010;11:271.
Background
Extensive biomedical studies have shown that clinical and environmental risk factors may not have sufficient predictive power for cancer prognosis. The development of high-throughput profiling technologies makes it possible to survey the whole genome and search for genomic markers with predictive power. Many existing studies assume the interchangeability of gene effects and ignore the coordination among them.
Results
We adopt the weighted co-expression network to describe the interplay among genes. Although there are several different ways of defining gene networks, the weighted co-expression network may be preferred because of its computational simplicity, satisfactory empirical performance, and because it does not demand additional biological experiments. For cancer prognosis studies with gene expression measurements, we propose a new marker selection method that can properly incorporate the network connectivity of genes. We analyze six prognosis studies on breast cancer and lymphoma. We find that the proposed approach can identify genes that are significantly different from those using alternatives. We search published literature and find that genes identified using the proposed approach are biologically meaningful. In addition, they have better prediction performance and reproducibility than genes identified using alternatives.
Conclusions
The network contains important information on the functionality of genes. Incorporating the network structure can improve cancer marker identification.
doi:10.1186/1471-2105-11-271
PMCID: PMC2881088  PMID: 20487548

Results 1-3 (3)