Control of the proinflammatory state in cystic fibrosis lung epithelial cells by genes from the TNF-alphaR/NFkappaB pathway

BACKGROUND: Cystic fibrosis (CF) is the most common, lethal autosomal recessive disease affecting children in the United States and Europe. Extensive work is being performed to develop both gene and drug therapies. The principal mutation causing CF is in the CFTR gene ([Delta F508]CFTR). This mutation causes the mutant protein to traffic poorly to the plasma membrane, and degrades CFTR chloride channel activity. CPX, a candidate drug for CF, binds to mutant CFTR and corrects the trafficking deficit. CPX also activates mutant CFTR chloride channel activity. CF airways are phenotypically inundated by inflammatory signals, primarily contributed by sustained secretion of the proinflammatory cytokine interleukin 8 (IL-8) from mutant CFTR airway epithelial cells. IL-8 production is controlled by genes from the TNF-alphaR/NFkappaB pathway, and it is possible that the CF phenotype is due to dysfunction of genes from this pathway. In addition, because drug therapy with CPX and gene therapy with CFTR have the same common endpoint of raising the levels of CFTR, we have hypothesized that either approach should have a common genomic endpoint. MATERIALS AND METHODS: To test this hypothesis, we studied IL-8 secretion and global gene expression in IB-3 CF lung epithelial cells. The cells were treated by either gene therapy with wild-type CFTR, or by pharmacotherapy with the CFTR-surrogate drug CPX. CF cells, treated with either CFTR or CPX, were also exposed to Pseudomonas aeruginosa, a common chronic pathogen in CF patients. cDNA microarrays were used to assess global gene expression under the different conditions. A novel bioinformatic algorithm (GENESAVER) was developed to identify genes whose expression paralleled secretion of IL-8. RESULTS: We report here that IB3 CF cells secrete massive levels of IL-8. However, both gene therapy with CFTR and drug therapy with CPX substantially suppress IL-8 secretion. Nonetheless, both gene and drug therapy allow the CF cells to respond with physiologic secretion of IL-8 when the cells are exposed to P. aeruginosa. Thus, neither CFTR nor CPX acts as a nonspecific suppressor of IL-8 secretion from CF cells. Consistently, pharmacogenomic analysis indicates that CF cells treated with CPX greatly resemble CF cells treated with CFTR by gene therapy. Additionally, the same result obtains in the presence of P. aeruginosa. Classical hierarchical cluster analysis, based on similarity of global gene expression, also supports this conclusion. The GENESAVER algorithm, using the IL-8 secretion level as a physiologic variable, identifies a subset of genes from the TNF-alphaR/NFkappaB pathway that is expressed in phase with IL-8 secretion from CF epithelial cells. Certain other genes, previously known to be positively associated with CF, also fall into this category. Identified genes known to code for known inhibitors are expressed inversely, out of phase with IL-8 secretion. CONCLUSIONS: Wild-type CFTR and CPX both suppress proinflammatory IL-8 secretion from CF epithelial cells. The mechanism, as defined by pharmacogenomic analysis, involves identified genes from the TNF-alphaR/NFkappaB pathway. The close relationship between IL-8 secretion and genes from the TNF-alphaR/NFkappaB pathway suggests that molecular or pharmaceutical targeting of these novel genes may have strategic use in the development of new therapies for CF. From the perspective of global gene expression, both gene and drug therapy have similar genomic consequences. This is the first example showing equivalence of gene and drug therapy in CF, and suggests that a gene therapy-defined endpoint may prove to be a powerful paradigm for CF drug discovery. Finally, because the GENESAVER algorithm is capable of isolating disease-relevant genes in a hypothesis-driven manner without recourse to any a priori knowledge about the system, this new algorithm may also prove useful in applications to other genetic diseases.     

Pharmacogenomics of the cystic fibrosis transmembrane conductance regulator (CFTR) and the cystic fibrosis drug CPX using genome microarray analysis

BACKGROUND: Cystic fibrosis (CF) is the most common lethal recessive disease affecting children in the U.S. and Europe. For this reason, a number of ongoing attempts are being made to treat the disease either by gene therapy or pharmacotherapy. Several phase 1 gene therapy trials have been completed, and a phase 2 clinical trial with the xanthine drug CPX is in progress. The protein coded by the principal CFTR mutation, DeltaF508-CFTR, fails to traffic efficiently from the endoplasmic reticulum to the plasma membrane, and is the pathogenic basis for the missing cAMP-activated plasma membrane chloride channel. CPX acts by binding to the mutant DeltaF508-CFTR and correcting the trafficking deficit. CPX also activates mutant CFTR channels. The comparative genomics of wild-type and mutant CFTR has not previously been studied. However, we have hypothesized that the gene expression patterns of human cells expressing mutant or wild-type CFTR might differ, and that a drug such as CPX might convert the mutant gene expression pattern into one more characteristic of wild-type CFTR. To the extent that this is true, a pharmacogenomic profile for such corrective drugs might be deduced that could simplify the process of drug discovery for CF. MATERIALS AND METHODS: To test this hypothesis we used cDNA microarrays to study global gene expression in human cells permanently transfected with either wild-type or mutant CFTR. We also tested the effects of CPX on global gene expression when incubated with cells expressing either mutant or wild-type CFTR. RESULTS: Wild-type and mutant DeltaF508-CFTR induce distinct and differential changes in cDNA microarrays, significantly affecting up to 5% of the total genes in the array. CPX also induces substantial mutation-dependent and -independent changes in gene expression. Some of these changes involve movement of gene expression in mutant cells in a direction resembling expression in wild-type cells. CONCLUSIONS: These data clearly demonstrate that cDNA array analysis of cystic fibrosis cells can yield useful pharmacogenomic information with significant relevance to both gene and pharmacological therapy. We suggest that this approach may provide a paradigm for genome-based surrogate endpoint testing of CF therapeutics prior to human administration.     

Cystic fibrosis enters the proteomics scene: new answers to old questions

The discovery in 1989 of the gene encoding for the cystic fibrosis transmembrane conductance regulator (CFTR) and its mutation as the primary cause of cystic fibrosis (CF), generated an optimistic reaction with respect to the development of potential therapies. This extraordinary milestone, however, represented only the initial key step in a long path. Many of the mechanisms that govern the pathogenesis of CF, the most commonly inherited lethal pulmonary disorder in Caucasians, remain even today unknown. As a continuation to genomic research, proteomics now offers the unique advantage to examine global alterations in the protein expression patterns of CF cells and tissues. The systematic use of this approach will probably provide new insights into the cellular mechanisms involved in CF dysfunctions, and should ultimately result in the finding of new prognostic markers, and in the generation of new therapies. In this article we review the current status of proteomic research applied to the study of CF, including CFTR-related interactomics, and evaluate the potential of these technologies for future investigations.     

Detecting periodic patterns in unevenly spaced gene expression time series using Lomb-Scargle periodograms

MOTIVATION: Periodic patterns in time series resulting from biological experiments are of great interest. The commonly used Fast Fourier Transform (FFT) algorithm is applicable only when data are evenly spaced and when no values are missing, which is not always the case in high-throughput measurements. The choice of statistic to evaluate the significance of the periodic patterns for unevenly spaced gene expression time series has not been well substantiated. METHODS: The Lomb-Scargle periodogram approach is used to search time series of gene expression to quantify the periodic behavior of every gene represented on the DNA array. The Lomb-Scargle periodogram analysis provides a direct method to treat missing values and unevenly spaced time points. We propose the combination of a Lomb-Scargle test statistic for periodicity and a multiple hypothesis testing procedure with controlled false discovery rate to detect significant periodic gene expression patterns. RESULTS: We analyzed the Plasmodium falciparum gene expression dataset. In the Quality Control Dataset of 5080 expression patterns, we found 4112 periodic probes. In addition, we identified 243 probes with periodic expression in the Complete Dataset, which could not be examined in the original study by the FFT analysis due to an excessive number of missing values. While most periodic genes had a period of 48 h, some had a period close to 24 h. Our approach should be applicable for detection and quantification of periodic patterns in any unevenly spaced gene expression time-series data.     

HoughFeature,a novel method for assessing drug effects in three-color cDNA microarray experiments

Background  

Three-color microarray experiments can be performed to assess drug effects on the genomic scale. The methodology may be useful in shortening the cycle, reducing the cost, and improving the efficiency in drug discovery and development compared with the commonly used dual-color technology. A visualization tool, the hexaMplot, is able to show the interrelations of gene expressions in normal-disease-drug samples in three-color microarray data. However, it is not enough to assess the complicated drug therapeutic effects based on the plot alone. It is important to explore more effective tools so that a deeper insight into gene expression patterns can be gained with three-color microarrays.     

Is cystic fibrosis genetic medicine's canary?

In 1989 the gene that causes cystic fibrosis (CF) was identified in a search accompanied by intense anticipation that the gene, once discovered, would lead rapidly to gene therapy. Many hoped that the disease would effectively disappear. Those affected were going to inhale vectors packed with functioning genes, which would go immediately to work in the lungs. It was a bewitching image, repeatedly invoked in both scientific and popular texts. Gene therapy clinical trials were carried out with a range of strategies and occasionally success seemed close, but by 1996 the idea that gene therapy for CF would quickly provide a cure was being abandoned by the communities engaged with treatment and research. While conventional wisdom holds that the death of Jesse Gelsinger in an unrelated gene therapy trial in 1999 produced new skepticism about gene therapy, the CF story suggests a different trajectory, and some different lessons. This article considers the rise and fall of gene therapy for CF and suggests that CF may provide a particularly compelling case study of a failed genomic technology, perhaps even of a medical "canary." The story of CF might be a kind of warning to us that genetic medicine may create as many problems as it solves, and that moving forward constructively with these techniques and practices requires many kinds of right information, not just about biology, but also about values, priorities, market forces, uncertainty, and consumer choice.     

Retroviral infection of hES cells produces random-like integration patterns

Retroviral integration provides us with a powerful tool to realize prolonged gene expressions that are often critical to gene therapy. However, the perturbation of gene regulations in host cells by viral genome integration can lead to detrimental effects, yielding cancer. The oncogenic potential of retroviruses is linked to the preference of retroviruses to integrate into genomic regions that are enriched in gene regulatory elements. To better navigate the double-edged sword of retroviral integration we need to understand how retroviruses select their favored genomic loci during infections. In this study I showed that in addition to host proteins that tether retroviral pre-integration complexes to specific genomic regions, the epigenetic architecture of host genome might strongly affect retroviral integration patterns. Specifically, retroviruses showed their characteristic integration preference in differentiated somatic cells. In contrast, retroviral infections of hES cells, which are known to display decondensed chromatin, produced random-like integration patterns lacking of strong preference for regulatory-element-rich genomic regions. Better identification of the cellular and viral factors that determine retroviral integration patterns will facilitate the design of retroviral vectors for safer use in gene therapy.     

CDA: Combinatorial Drug Discovery Using Transcriptional Response Modules

Background

Anticancer therapies that target single signal transduction pathways often fail to prevent proliferation of cancer cells because of overlapping functions and cross-talk between different signaling pathways. Recent research has identified that balanced multi-component therapies might be more efficacious than highly specific single component therapies in certain cases. Ideally, synergistic combinations can provide 1) increased efficacy of the therapeutic effect 2) reduced toxicity as a result of decreased dosage providing equivalent or increased efficacy 3) the avoidance or delayed onset of drug resistance. Therefore, the interest in combinatorial drug discovery based on systems-oriented approaches has been increasing steadily in recent years.

Methodology

Here we describe the development of Combinatorial Drug Assembler (CDA), a genomics and bioinformatics system, whereby using gene expression profiling, multiple signaling pathways are targeted for combinatorial drug discovery. CDA performs expression pattern matching of signaling pathway components to compare genes expressed in an input cell line (or patient sample data), with expression patterns in cell lines treated with different small molecules. Then it detects best pattern matching combinatorial drug pairs across the input gene set-related signaling pathways to detect where gene expression patterns overlap and those predicted drug pairs could likely be applied as combination therapy. We carried out in vitro validations on non-small cell lung cancer cells and triple-negative breast cancer (TNBC) cells. We found two combinatorial drug pairs that showed synergistic effect on lung cancer cells. Furthermore, we also observed that halofantrine and vinblastine were synergistic on TNBC cells.

Conclusions

CDA provides a new way for rational drug combination. Together with phExplorer, CDA also provides functional insights into combinatorial drugs. CDA is freely available at http://cda.i-pharm.org.     

cDNA microarray technology and its applications

The cDNA microarray is the most powerful tool for studying gene expression in many different organisms. It has been successfully applied to the simultaneous expression of many thousands of genes and to large-scale gene discovery, as well as polymorphism screening and mapping of genomic DNA clones. It is a high throughput, highly parallel RNA expression assay technique that permits quantitative analysis of RNAs transcribed from both known and unknown genes. This technique provides diagnostic fingerprints by comparing gene expression patterns in normal and pathological cells, and because it can simultaneously track expression levels of many genes, it provides a source of operational context for inference and predication about complex cell control systems. This review describes this recently developed cDNA microarray technology and its application to gene discovery and expression, and to diagnostics for certain diseases.     

Gene expression profiling and its practice in drug development

The availability of sequenced genomes of human and many experimental animals necessitated the development of new technologies and powerful computational tools that are capable of exploiting these genomic data and ask intriguing questions about complex nature of biological processes. This gave impetus for developing whole genome approaches that can produce functional information of genes in the form of expression profiles and unscramble the relationships between variation in gene expression and the resulting physiological outcome. These profiles represent genetic fingerprints or catalogue of genes that characterize the cell or tissue being studied and provide a basis from which to begin an investigation of the underlying biology. Among the most powerful and versatile tools are high-density DNA microarrays to analyze the expression patterns of large numbers of genes across different tissues or within the same tissue under a variety of experimental conditions or even between species. The wide spread use of microarray technologies is generating large sets of data that is stimulating the development of better analytical tools so that functions can be predicted for novel genes. In this review, the authors discuss how these profiles are being used at various stages of the drug discovery process and help in the identification of new drug targets, predict the function of novel genes, and understand individual variability in response to drugs.     

Genomic studies of gene expression: regulation of the Wilson disease gene

Bacterial artificial chromosomes (BACs) have many advantages over other large-insert cloning vectors and have been used for a variety of genetic applications, including the final contigs of the human genome. We describe the utilization of a BAC construct to study gene regulation in a tissue culture-based system, using a 170-kb clone containing the entire Wilson disease (WND) locus as a model. A second BAC construct that lacked a putative negatively regulating promoter sequence was created. A nonviral method of gene delivery was applied to transfect three human cell lines stably with each construct. Our results show correct WND gene expression from the recombinant locus and quantification revealed significantly increased expression from the clone lacking the negative regulator. Comparison with conventional methods confirms the reliability of the genomic approach for thorough examination of gene expression. This experimental system illustrates the potential of BAC clones in genomic gene expression studies, new gene therapy strategies, and validation of potential molecular targets for drug discovery.     

A polynomial time biclustering algorithm for finding approximate expression patterns in gene expression time series

Background  

The ability to monitor the change in expression patterns over time, and to observe the emergence of coherent temporal responses using gene expression time series, obtained from microarray experiments, is critical to advance our understanding of complex biological processes. In this context, biclustering algorithms have been recognized as an important tool for the discovery of local expression patterns, which are crucial to unravel potential regulatory mechanisms. Although most formulations of the biclustering problem are NP-hard, when working with time series expression data the interesting biclusters can be restricted to those with contiguous columns. This restriction leads to a tractable problem and enables the design of efficient biclustering algorithms able to identify all maximal contiguous column coherent biclusters.     

Integrated analysis of gene expression and copy number data on gene shaving using independent component analysis

DNA microarray gene expression and microarray-based comparative genomic hybridization (aCGH) have been widely used for biomedical discovery. Because of the large number of genes and the complex nature of biological networks, various analysis methods have been proposed. One such method is "gene shaving," a procedure which identifies subsets of the genes with coherent expression patterns and large variation across samples. Since combining genomic information from multiple sources can improve classification and prediction of diseases, in this paper we proposed a new method, "ICA gene shaving" (ICA, independent component analysis), for jointly analyzing gene expression and copy number data. First we used ICA to analyze joint measurements, gene expression and copy number, of a biological system and project the data onto statistically independent biological processes. Next, we used these results to identify patterns of variation in the data and then applied an iterative shaving method. We investigated the properties of our proposed method by analyzing both simulated and real data. We demonstrated that the robustness of our method to noise using simulated data. Using breast cancer data, we showed that our method is superior to the Generalized Singular Value Decomposition (GSVD) gene shaving method for identifying genes associated with breast cancer.     

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms

Although targeted therapies are initially effective, resistance inevitably emerges. Several methods, such as genetic analysis of resistant clinical specimens, have been applied to uncover these resistance mechanisms to facilitate follow-up care. Although these approaches have led to clinically relevant discoveries, difficulties in attaining the relevant patient material or in deconvoluting the genomic data collected from these specimens have severely hampered the path towards a cure. To this end, we here describe a tool for expeditious discovery that may guide improvement in first-line therapies and alternative clinical management strategies. By coupling preclinical in vitro or in vivo drug selection with next-generation sequencing, it is possible to identify genomic structural variations and/or gene expression alterations that may serve as functional drivers of resistance. This approach facilitates the spontaneous emergence of alterations, enhancing the probability that these mechanisms may be observed in the patients. In this protocol we provide guidelines to maximize the potential for uncovering single nucleotide variants that drive resistance using adherent lines.     

Natural products discovery from micro-organisms in the post-genome era†

With the decision to award the Nobel Prize in Physiology or Medicine to Drs. S. ōmura, W.C. Campbell, and Y. Tu, the importance and usefulness of natural drug discovery and development have been revalidated. Since the end of the twentieth century, many genome analyses of organisms have been conducted, and accordingly, numerous microbial genomes have been decoded. In particular, genomic studies of actinomycetes, micro-organisms that readily produce natural products, led to the discovery of biosynthetic gene clusters responsible for producing natural products. New explorations for natural products through a comprehensive approach combining genomic information with conventional methods show great promise for the discovery of new natural products and even systematic generation of unnaturally occurring compounds.     

Use of gene networks for identifying and validating drug targets

We propose a new method for identifying and validating drug targets by using gene networks, which are estimated from cDNA microarray gene expression profile data. We created novel gene disruption and drug response microarray gene expression profile data libraries for the purpose of drug target elucidation. We use two types of microarray gene expression profile data for estimating gene networks and then identifying drug targets. The estimated gene networks play an essential role in understanding drug response data and this information is unattainable from clustering methods, which are the standard for gene expression analysis. In the construction of gene networks, we use the Bayesian network model. We use an actual example from analysis of the Saccharomyces cerevisiae gene expression profile data to express a concrete strategy for the application of gene network information to drug discovery.     

In silico gene function prediction using ontology-based pattern identification

MOTIVATION: With the emergence of genome-wide expression profiling data sets, the guilt by association (GBA) principle has been a cornerstone for deriving gene functional interpretations in silico. Given the limited success of traditional methods for producing clusters of genes with great amounts of functional similarity, new data-mining algorithms are required to fully exploit the potential of high-throughput genomic approaches. RESULTS: Ontology-based pattern identification (OPI) is a novel data-mining algorithm that systematically identifies expression patterns that best represent existing knowledge of gene function. Instead of relying on a universal threshold of expression similarity to define functionally related groups of genes, OPI finds the optimal analysis settings that yield gene expression patterns and gene lists that best predict gene function using the principle of GBA. We applied OPI to a publicly available gene expression data set on the life cycle of the malarial parasite Plasmodium falciparum and systematically annotated genes for 320 functional categories based on current Gene Ontology annotations. An ontology-based hierarchical tree of the 320 categories provided a systems-wide biological view of this important malarial parasite.     

Mining bacterial genomes for antimicrobial targets

The elucidation of whole-genome sequences is expected to have a revolutionary impact on the discovery of novel medicines. With the availability of complete genome sequences of more than 30 different species, the field of antimicrobial drug discovery has the opportunity to access a remarkable diversity of genomic information. In this review, I summarize how microbial genomics has changed strategies of drug discovery by applying bioinformatics, novel genetic approaches and genomics-based technologies, including analysis of gene expression using DNA microarrays.