A tool-kit for cDNA microarray and promoter analysis

A novel systems pharmacology platform to dissect action mechanisms of traditional Chinese medicines for bovine viral diarrhea disease

Due to the large direct and indirect productivity losses in the livestock industry caused by bovine viral diarrhea (BVD) and the lack of effective pharmacological therapies, developing an efficient treatment is extremely urgent. Traditional Chinese medicines (TCMs) that simultaneously address multiple targets have been proven to be effective therapies for BVD. However, the potential molecular action mechanisms of TCMs have not yet been systematically explored. In this work, take the example of a herbal remedy Huangqin Zhizi (HQZZ) for BVD treatment in China, a systems pharmacology approach combining with the pharmacokinetics and pharmacodynamics evaluation was developed to screen out the active ingredients, predict the targets and analyze the networks and pathways. Results show that 212 active compounds were identified. Utilizing these lead compounds as probes, we predicted 122 BVD related-targets. And in vitro experiments were conducted to evaluate the reliability of some vital active compounds and targets. Network and pathway analysis displayed that HQZZ was effective in the treatment of BVD by inhibiting inflammation, enhancing immune responses in hosts toward virus infection. In summary, the analysis of the complete profile of the pharmacological activities, as well as the elucidation of targets, networks and pathways can further elucidate the underlying anti-inflammatory, antiviral and immune regulation mechanisms of HQZZ against BVD.

Methods for the inference of biological pathways and networks

In this chapter, we discuss a number of approaches to network inference from large-scale functional genomics data. Our goal is to describe current methods that can be used to infer predictive networks. At present, one of the most effective methods to produce networks with predictive value is the Bayesian network approach. This approach was initially instantiated by Friedman et al. and further refined by Eric Schadt and his research group. The Bayesian network approach has the virtue of identifying predictive relationships between genes from a combination of expression and eQTL data. However, the approach does not provide a mechanistic bases for predictive relationships and is ultimately hampered by an inability to model feedback. A challenge for the future is to produce networks that are both predictive and provide mechanistic understanding. To do so, the methods described in several chapters of this book will need to be integrated. Other chapters of this book describe a number of methods to identify or predict network components such as physical interactions. At the end of this chapter, we speculate that some of the approaches from other chapters could be integrated and used to "annotate" the edges of the Bayesian networks. This would take the Bayesian networks one step closer to providing mechanistic "explanations" for the relationships between the network nodes.

Genome-wide identification of osmotic stress response gene in Arabidopsis thaliana

In this paper, we present a cis-regulatory element based computational approach to genome-wide identification of genes putatively responding to various osmotic stresses in Arabidopsis thaliana. The rationale of our method is that gene expression is largely controlled at the transcriptional level through the interactions between transcription factors and cis-regulatory elements. Using cis-regulatory motifs known to regulate osmotic stress response, we therefore built an artificial neural network model to identify other functionally relevant genes involved in the same process. We performed Gene Ontology enrichment analysis on the 500 top-scoring predictions and found that, except for un-annotated ORFs ( approximately 40%), 91.3% of the enriched GO classification was related to stress response and ABA response. Publicly available gene expression profiling data of Arabidopsis under various stresses were used for cross validation. We also conducted RT-PCR analysis to experimentally verify selected predictions. According to our results, transcript levels of 27 out of 41 top-ranked genes (65.8%) altered under various osmotic stress treatments. We believe that a similar approach could be extensively adopted elsewhere to infer gene function in various cellular processes from different species.

PlantQTL-GE: a database system for identifying candidate genes in rice and Arabidopsis by gene expression and QTL information

We have designed and implemented a web-based database system, called PlantQTL-GE, to facilitate quantitatine traits locus (QTL) based candidate gene identification and gene function analysis. We collected a large number of genes, gene expression information in microarray data and expressed sequence tags (ESTs) and genetic markers from multiple sources of Oryza sativa and Arabidopsis thaliana. The system integrates these diverse data sources and has a uniform web interface for easy access. It supports QTL queries specifying QTL marker intervals or genomic loci, and displays, on rice or Arabidopsis genome, known genes, microarray data, ESTs and candidate genes and similar putative genes in the other plant. Candidate genes in QTL intervals are further annotated based on matching ESTs, microarray gene expression data and cis-elements in regulatory sequences. The system is freely available at .

Temporal evolution of the Arabidopsis oxidative stress response

We have carried out a detailed analysis of the changes in gene expression levels in Arabidopsis thaliana ecotype Columbia (Col-0) plants during and for 6 h after exposure to ozone (O3) at 350 parts per billion (ppb) for 6 h. This O3 exposure is sufficient to induce a marked transcriptional response and an oxidative burst, but not to cause substantial tissue damage in Col-0 wild-type plants and is within the range encountered in some major metropolitan areas. We have developed analytical and visualization tools to automate the identification of expression profile groups with common gene ontology (GO) annotations based on the sub-cellular localization and function of the proteins encoded by the genes, as well as to automate promoter analysis for such gene groups. We describe application of these methods to identify stress-induced genes whose transcript abundance is likely to be controlled by common regulatory mechanisms and summarized our findings in a temporal model of the stress response.