Molecular network profiling of U373MG human glioblastoma cells following induction of apoptosis by novel marine-derived anti-cancer 1,2,3,4-tetrahydroisoquinoline alkaloids

Background

Glioblastoma is the most aggressive form of brain tumors showing resistance to treatment with various chemotherapeutic agents. The most effective way to eradicate glioblastoma requires the concurrent inhibition of multiple signaling pathways and target molecules involved in the progression of glioblastoma. Recently, we obtained a series of 1,2,3,4-tetrahydroisoquinoline alkaloids with potent anti-cancer activities, including ecteinascidin-770 (ET-770; the compound 1a) and renieramycin M (RM; the compound 2a) from Thai marine invertebrates, together with a 2’-N-4”-pyridinecarbonyl derivative of ET-770 (the compound 3). We attempted to characterize the molecular pathways responsible for cytotoxic effects of these compounds on a human glioblastoma cell line U373MG.

Methods

We studied the genome-wide gene expression profile on microarrays and molecular networks by using pathway analysis tools of bioinformatics.

Results

All of these compounds induced apoptosis of U373MG cells at nanomolar concentrations. The compound 3 reduced the expression of 417 genes and elevated the levels of 84 genes, while ET-770 downregulated 426 genes and upregulated 45 genes. RM decreased the expression of 274 genes and increased the expression of 9 genes. The set of 196 downregulated genes and 6 upregulated genes showed an overlap among all the compounds, suggesting an existence of the common pathways involved in induction of apoptosis. We identified the ErbB (EGFR) signaling pathway as one of the common pathways enriched in the set of downregulated genes, composed of PTK2, AKT3, and GSK3B serving as key molecules that regulate cell movement and the nervous system development. Furthermore, a GSK3B-specific inhibitor induced apoptosis of U373MG cells, supporting an anti-apoptotic role of GSK3B.

Conclusion

Molecular network analysis is a useful approach not only to characterize the glioma-relevant pathways but also to identify the network-based effective drug targets.     

链霉菌Streptomyces antibioticus NRRL 8167中Naphthgeranine A生物合成基因簇的分析鉴定

【背景】微生物来源的天然产物是小分子药物或药物先导物的重要来源。对链霉菌Streptomyces antibioticus NRRL 8167的基因组分析显示,其包含多个次级代谢产物的生物合成基因簇,具有产生多种新化合物的潜力。【目的】对链霉菌S. antibioticus NRRL 8167中次级代谢产物进行研究,以期发现结构新颖或生物活性独特的化合物,并对相应产物的生物合成基因簇和生物合成途径进行解析。【方法】利用HPLC图谱结合特征性紫外吸收和LC-MS方法,排除S. antibioticus NRRL 8167产生的已知化合物,确定具有特殊紫外吸收的化合物作为挖掘对象,然后利用正、反相硅胶柱色谱、高效液相色谱等技术对次级代谢产物进行分离纯化,分离化合物。利用质谱及核磁共振光谱技术对化合物结构进行解析和鉴定;提取链霉菌S. antibioticus NRRL 8167基因组DNA,利用PacBio测序平台进行基因组测序;利用生物信息学对基因组进行注释,并对合成该化合物的基因簇进行定位分析,推导其生物合成途径。【结果】确定这个化合物是NaphthgeranineA,属于聚酮类化合物。全基因组序列分析发现S.antibioticusNRRL8167基因组含有28个次级代谢产物生物合成基因簇,其中基因簇20可能负责Naphthgeranine A的生物合成,并对其生物合成途径进行了推导。【结论】基于紫外吸收光谱和质谱特征,从S. antibioticus NRRL 8167菌株的发酵提取物中分离鉴定了一个聚酮类化合物Naphthgeranine A。该菌株的全基因组测序为其生物合成基因簇的鉴定提供了前提,对Naphthgeranine A生物合成基因簇和生物合成途径的推测为进一步研究这个化合物的生物合成机制奠定了基础。     

HER2参与的基因表达调控

HER2与许多恶性肿瘤的发生、发展密切相关。HER2可通过信号转导途径间接调控许多肿瘤相关基因的表达,亦可作为转录因子直接调控某些基因的表达,而一些基因表达产物又进而增强HER2或其他基因的表达,这就构成了以HER2为中心的基因表达调控网络,这些基因表达产物和HER2可能共同成为肿瘤诊断和预后的标志物。阐明这个网络中各个分子间的相互作用关系,将为HER2过表达肿瘤的治疗提供新的药物设计靶标。     

Molecular Genetics Evidence for the in Vivo Roles of the Two Major NADPH-dependent Disulfide Reductases in the Malaria Parasite

Malaria-associated pathology is caused by the continuous expansion of Plasmodium parasites inside host erythrocytes. To maintain a reducing intracellular milieu in an oxygen-rich environment, malaria parasites have evolved a complex antioxidative network based on two central electron donors, glutathione and thioredoxin. Here, we dissected the in vivo roles of both redox pathways by gene targeting of the respective NADPH-dependent disulfide reductases. We show that Plasmodium berghei glutathione reductase and thioredoxin reductase are dispensable for proliferation of the pathogenic blood stages. Intriguingly, glutathione reductase is vital for extracellular parasite development inside the insect vector, whereas thioredoxin reductase is dispensable during the entire parasite life cycle. Our findings suggest that glutathione reductase is the central player of the parasite redox network, whereas thioredoxin reductase fulfils a specialized and dispensable role for P. berghei. These results also indicate redundant roles of the Plasmodium redox pathways during the pathogenic blood phase and query their suitability as promising drug targets for antimalarial intervention strategies.     

The amitochondriate eukaryote Trichomonas vaginalis contains a divergent thioredoxin-linked peroxiredoxin antioxidant system

Trichomonas is an amitochondriate parasitic protozoon specialized for an anaerobic lifestyle. Nevertheless, it is exposed to oxygen and is able to cope with the resultant oxidative stress. In the absence of glutathione, cysteine has been thought to be the major antioxidant. We now report that the parasite contains thioredoxin reductase, which functions together with thioredoxin and thioredoxin peroxidase to detoxify potentially damaging oxidants. Thioredoxin reductase and thioredoxin also reduce cystine and so may play a role in maintaining the cellular cysteine levels. The importance of the thioredoxin system as one of the major antioxidant defense mechanisms in Trichomonas was confirmed by showing that the parasite responds to environmental changes resulting in increased oxidative stress by up-regulating thioredoxin and thioredoxin peroxidases levels. Sequence data indicate that the thioredoxin reductase of Trichomonas differs fundamentally in structure from that of its human host and thus may represent a useful drug target. The protein is generally similar to thioredoxin reductases present in other lower eukaryotes, all of which probably originated through horizontal gene transfer from a prokaryote. The phylogenetic signal in thioredoxin peroxidase is weak, but evidence from trees suggests that this gene has been subject to repeated horizontal gene transfers from different prokaryotes to different eukaryotes. The data are thus consistent with the complexity hypothesis that predicts that the evolution of simple pathways such as the thioredoxin cascade are likely to be affected by horizontal gene transfer between species.     

Identifying disease related sub-pathways for analysis of genome-wide association studies

Most methods for genome-wide association studies (GWAS) focus on discovering a single genetic variant, but the pathogenesis of complex diseases is thought to arise from the joint effect of multiple genetic variants. Information about pathway structure, such as the interactions and distances between gene products within pathways, can help us learn more about the functions and joint effect of genes associated with disease risk. We developed a novel sub-pathway based approach to study the joint effect of multiple genetic variants that are modestly associated with disease. The approach prioritized sub-pathways based on the significance values of single nucleotide polymorphisms (SNPs) and the interactions and distances between gene products within pathways. We applied the method to seven complex diseases. The result showed that our method can efficiently identify statistically significant sub-pathways associated with the pathogenesis of complex diseases. The approach identified sub-pathways that may inform the interpretation of GWAS data.     

Discovering molecular pathways from protein interaction and gene expression data

In this paper, we describe an approach for identifying 'pathways' from gene expression and protein interaction data. Our approach is based on the assumption that many pathways exhibit two properties: their genes exhibit a similar gene expression profile, and the protein products of the genes often interact. Our approach is based on a unified probabilistic model, which is learned from the data using the EM algorithm. We present results on two Saccharomyces cerevisiae gene expression data sets, combined with a binary protein interaction data set. Our results show that our approach is much more successful than other approaches at discovering both coherent functional groups and entire protein complexes.     

Integrating Genomics and Proteomics Data to Predict Drug Effects Using Binary Linear Programming

The Library of Integrated Network-Based Cellular Signatures (LINCS) project aims to create a network-based understanding of biology by cataloging changes in gene expression and signal transduction that occur when cells are exposed to a variety of perturbations. It is helpful for understanding cell pathways and facilitating drug discovery. Here, we developed a novel approach to infer cell-specific pathways and identify a compound''s effects using gene expression and phosphoproteomics data under treatments with different compounds. Gene expression data were employed to infer potential targets of compounds and create a generic pathway map. Binary linear programming (BLP) was then developed to optimize the generic pathway topology based on the mid-stage signaling response of phosphorylation. To demonstrate effectiveness of this approach, we built a generic pathway map for the MCF7 breast cancer cell line and inferred the cell-specific pathways by BLP. The first group of 11 compounds was utilized to optimize the generic pathways, and then 4 compounds were used to identify effects based on the inferred cell-specific pathways. Cross-validation indicated that the cell-specific pathways reliably predicted a compound''s effects. Finally, we applied BLP to re-optimize the cell-specific pathways to predict the effects of 4 compounds (trichostatin A, MS-275, staurosporine, and digoxigenin) according to compound-induced topological alterations. Trichostatin A and MS-275 (both HDAC inhibitors) inhibited the downstream pathway of HDAC1 and caused cell growth arrest via activation of p53 and p21; the effects of digoxigenin were totally opposite. Staurosporine blocked the cell cycle via p53 and p21, but also promoted cell growth via activated HDAC1 and its downstream pathway. Our approach was also applied to the PC3 prostate cancer cell line, and the cross-validation analysis showed very good accuracy in predicting effects of 4 compounds. In summary, our computational model can be used to elucidate potential mechanisms of a compound''s efficacy.     

A high-throughput induction gene trap approach defines C4ST as a target of BMP signaling

Here we describe a novel gene trap protocol to screen for target genes that are regulated during inductive events in undifferentiated and differentiated mouse embryonic stem cells. This approach integrates several features that allows in vitro screening of large numbers of gene trap clones prior to generating lines of mutant mice. Moreover, targets of spatially and temporally restricted signaling pathways can be analyzed by screening undifferentiated ES cells versus ES cells differentiated into embryoid bodies. We employed this protocol to screen 1920 gene trap lines to identify targets and mediators of signaling through three growth factors of the TGFbeta superfamily--BMP2, activin and nodal. We identified two genes that are induced by BMP2 in a differentiation-dependent manner. One of the genes encodes for Chondroitin-4-sulfotransferase and displays a highly specific temporal and spatial expression pattern during mouse embryogenesis. These results demonstrate the feasibility of a high-throughput gene trap approach as a means to identify mediators and targets of multiple growth factor signaling pathways that function during different stages of development.     

Expression-level optimization of a multi-enzyme pathway in the absence of a high-throughput assay

Engineered metabolic pathways often suffer from flux imbalances that can overburden the cell and accumulate intermediate metabolites, resulting in reduced product titers. One way to alleviate such imbalances is to adjust the expression levels of the constituent enzymes using a combinatorial expression library. Typically, this approach requires high-throughput assays, which are unfortunately unavailable for the vast majority of desirable target compounds. To address this, we applied regression modeling to enable expression optimization using only a small number of measurements. We characterized a set of constitutive promoters in Saccharomyces cerevisiae that spanned a wide range of expression and maintained their relative strengths irrespective of the coding sequence. We used a standardized assembly strategy to construct a combinatorial library and express for the first time in yeast the five-enzyme violacein biosynthetic pathway. We trained a regression model on a random sample comprising 3% of the total library, and then used that model to predict genotypes that would preferentially produce each of the products in this highly branched pathway. This generalizable method should prove useful in engineering new pathways for the sustainable production of small molecules.