Physiological function as regulation of large transcriptional programs: the cellular response to genotoxic stress

The responses to ionizing radiation and other genotoxic environmental stresses are complex and are regulated by a number of overlapping molecular pathways. One such stress signaling pathway involves p53, which regulates the expression of over 100 genes already identified. It is also becoming increasingly apparent that the pattern of stress gene expression has some cell type specificity. It may be possible to exploit these differences in stress gene responsiveness as molecular markers through the use of a combined informatics and functional genomics approach. The techniques of microarray analysis potentially offer the opportunity to monitor changes in gene expression across the entire set of expressed genes in a cell or organism. As an initial step in the development of a functional genomics approach to stress gene analysis, we have recently demonstrated the utility of cDNA microarray hybridization to measure radiation-stress gene responses and identified a number of previously unknown radiation-regulated genes. The responses of some of these genes to DNA-damaging agents vary widely in cell lines from different tissues of origin and different genetic backgrounds. While this again highlights the importance of a cellular context to genotoxic stress responses, it also raises the prospect of expression-profiling of cell lines, tissues, and tumors. Such profiles may have a predictive value if they can define regions of ‘expression space’ that correlate with important endpoints, such as response to cancer therapy regimens, or identification of exposures to environmental toxins.     

"Omic" approaches for unraveling signaling networks

Signaling pathways are crucial for cell differentiation and response to cellular environments. Recently, a large number of approaches for the global analysis of genes and proteins have been described. These have provided important new insights into the components of different pathways and the molecular and cellular responses of these pathways. This review covers genomic and proteomic (collectively referred to as "omic") approaches for the global analysis of cell signaling, including gene expression profiling and analysis, protein-protein interaction methods, protein microarrays, mass spectroscopy and gene-disruption and engineering approaches.     

Changes in MicroRNA expression patterns in human fibroblasts after low-LET radiation

Exposure to radiation provokes cellular responses controlled in part by gene expression networks. MicroRNAs (miRNAs) are small non-coding RNAs which mostly regulate gene expression by degrading the messages or inhibiting translation. Here, we investigated changes in miRNA expression patterns after low (0.1 Gy) and high (2.0 Gy) doses of X-ray in human fibroblasts. At early (0.5 h) and late (6 and 24 h) time points, irradiation caused qualitative and quantitative differences in the down-regulation of miRNA levels, including miR-92b, 137, 660, and 656. A transient up-regulation of miRNAs was observed after 2 h post-irradiation following high doses of radiation, including miR-558 and 662. MicroRNA levels were inversely correlated with targets from mRNA and proteomic profiling after 2.0 Gy of radiation. MicroRNAs miR-579, 608, 548-3p, and 585 are noted for targeting genes involved in radioresponsive mechanisms, such as cell cycle checkpoint and apoptosis. We suggest here a model in which miRNAs may act as "hub" regulators of specific cellular responses, immediately down-regulated so as to stimulate DNA repair mechanisms, followed by up-regulation involved in suppressing apoptosis for cell survival. Taken together, miRNAs may mediate signaling pathways in sequential fashion in response to radiation, and may serve as biodosimetric markers of radiation exposure.     

Gene expression profiles of normal human fibroblasts after exposure to ionizing radiation: a comparative study of low and high doses

Several types of cellular responses to ionizing radiation, such as the adaptive response or the bystander effect, suggest that low-dose radiation may possess characteristics that distinguish it from its high-dose counterpart. Accumulated evidence also implies that the biological effects of low-dose and high-dose ionizing radiation are not linearly distributed. We have investigated, for the first time, global gene expression changes induced by ionizing radiation at doses as low as 2 cGy and have compared this to expression changes at 4 Gy. We applied cDNA microarray analyses to G1-arrested normal human skin fibroblasts subjected to X irradiation. Our data suggest that both qualitative and quantitative differences exist between gene expression profiles induced by 2 cGy and 4 Gy. The predominant functional groups responding to low-dose radiation are those involved in cell-cell signaling, signal transduction, development and DNA damage responses. At high dose, the responding genes are involved in apoptosis and cell proliferation. Interestingly, several genes, such as cytoskeleton components ANLN and KRT15 and cell-cell signaling genes GRAP2 and GPR51, were found to respond to low-dose radiation but not to high-dose radiation. Pathways that are specifically activated by low-dose radiation were also evident. These quantitative and qualitative differences in gene expression changes may help explain the non-linear correlation of biological effects of ionizing radiation from low dose to high dose.     

Computational methods for analysis of cellular functions and pathways collectively targeted by differentially expressed microRNA.

This report presents computational methods of analysis of cellular processes, functions, and pathways affected by differentially expressed microRNA, a statistical basis of the gene enrichment analysis method, a modification of enrichment analysis method accounting for combinatorial targeting of Gene Ontology categories by multiple miRNAs and examples of the global functional profiling of predicted targets of differentially expressed miRNAs in cancer. We have also summarized an application of Ingenuity Pathway Analysis tools for in depth analysis of microRNA target sets that may be useful for the biological interpretation of microRNA profiling data. To illustrate the utility of these methods, we report the main results of our recent computational analysis of five published datasets of aberrantly expressed microRNAs in five human cancers (pancreatic cancer, breast cancer, colon cancer, lung cancer, and lymphoma). Using a combinatorial target prediction algorithm and statistical enrichment analysis, we have determined Gene Ontology categories as well as biological functions, disease categories, toxicological categories, and signaling pathways that are: targeted by multiple microRNAs; statistically significantly enriched with target genes; and known to be affected in specific cancers. Our recent computational analysis of predicted targets of co-expressed miRNAs in five human cancers suggests that co-expressed miRNAs provide systemic compensatory response to the abnormal phenotypic changes in cancer cells by targeting a broad range of functional categories and signaling pathways reportedly affected in a particular cancer.     

Deciphering the regulatory mechanisms of abiotic stress tolerance in plants by genomic approaches

Environmental constraints that include abiotic stress factors such as salt, drought, cold and extreme temperatures severely limit crop productivity. Improvement of crop plants with traits that confer tolerance to these stresses was practiced using traditional and modern breeding methods. Molecular breeding and genetic engineering contributed substantially to our understanding of the complexity of stress response. Mechanisms that operate signal perception, transduction and downstream regulatory factors are now being examined and an understanding of cellular pathways involved in abiotic stress responses provide valuable information on such responses. This review presents genomic-assisted methods which have helped to reveal complex regulatory networks controlling abiotic stress tolerance mechanisms by high-throughput expression profiling and gene inactivation techniques. Further, an account of stress-inducible regulatory genes which have been transferred into crop plants to enhance stress tolerance is discussed as possible modes of integrating information gained from functional genomics into knowledge-based breeding programs. In addition, we envision an integrative genomic and breeding approach to reveal developmental programs that enhance yield stability and improve grain quality under unfavorable environmental conditions of abiotic stresses.     

Mammalian cell protein expression for biopharmaceutical production

Mammalian cell expression has become the dominant recombinant protein production system for clinical applications because of its capacity for post-translational modification and human protein-like molecular structure assembly. While expression and production have been fully developed and Chinese hamster ovary cells are used for the majority of products both on the market and in clinical development, significant progresses in developing and engineering new cell lines, introducing novel genetic mechanisms in expression, gene silencing, and gene targeting, have been reported in the last several years. With the latest analytical methods development, more attention is being devoted towards product quality including glycol profiling, which leads to better understanding the impact of culture condition during production. Additionally, transient gene expression technology platform plays more important role in biopharmaceutical early development stages. This review focused on the latest advancements in the field, especially in active areas such as expression systems, glycosylation impact factors, and transient gene expression.     

Exploring metabolic pathway disruption in the subchronic phencyclidine model of schizophrenia with the Generalized Singular Value Decomposition

Background

One of the major goals in gene and protein expression profiling of cancer is to identify biomarkers and build classification models for prediction of disease prognosis or treatment response. Many traditional statistical methods, based on microarray gene expression data alone and individual genes' discriminatory power, often fail to identify biologically meaningful biomarkers thus resulting in poor prediction performance across data sets. Nonetheless, the variables in multivariable classifiers should synergistically interact to produce more effective classifiers than individual biomarkers.

Results

We developed an integrated approach, namely network-constrained support vector machine (netSVM), for cancer biomarker identification with an improved prediction performance. The netSVM approach is specifically designed for network biomarker identification by integrating gene expression data and protein-protein interaction data. We first evaluated the effectiveness of netSVM using simulation studies, demonstrating its improved performance over state-of-the-art network-based methods and gene-based methods for network biomarker identification. We then applied the netSVM approach to two breast cancer data sets to identify prognostic signatures for prediction of breast cancer metastasis. The experimental results show that: (1) network biomarkers identified by netSVM are highly enriched in biological pathways associated with cancer progression; (2) prediction performance is much improved when tested across different data sets. Specifically, many genes related to apoptosis, cell cycle, and cell proliferation, which are hallmark signatures of breast cancer metastasis, were identified by the netSVM approach. More importantly, several novel hub genes, biologically important with many interactions in PPI network but often showing little change in expression as compared with their downstream genes, were also identified as network biomarkers; the genes were enriched in signaling pathways such as TGF-beta signaling pathway, MAPK signaling pathway, and JAK-STAT signaling pathway. These signaling pathways may provide new insight to the underlying mechanism of breast cancer metastasis.

Conclusions

We have developed a network-based approach for cancer biomarker identification, netSVM, resulting in an improved prediction performance with network biomarkers. We have applied the netSVM approach to breast cancer gene expression data to predict metastasis in patients. Network biomarkers identified by netSVM reveal potential signaling pathways associated with breast cancer metastasis, and help improve the prediction performance across independent data sets.