Prediction of drug efficacy for cancer treatment based on comparative analysis of chemosensitivity and gene expression data

The NCI60 database is the largest available collection of compounds with measured anti-cancer activity. The strengths and limitations for using the NCI60 database as a source of new anti-cancer agents are explored and discussed in relation to previous studies. We selected a sub-set of 2333 compounds with reliable experimental half maximum growth inhibitions (GI(50)) values for 30 cell lines from the NCI60 data set and evaluated their growth inhibitory effect (chemosensitivity) with respect to tissue of origin. This was done by identifying natural clusters in the chemosensitivity data set and in a data set of expression profiles of 1901 genes for the corresponding tumor cell lines. Five clusters were identified based on the gene expression data using self-organizing maps (SOM), comprising leukemia, melanoma, ovarian and prostate, basal breast, and luminal breast cancer cells, respectively. The strong difference in gene expression between basal and luminal breast cancer cells was reflected clearly in the chemosensitivity data. Although most compounds in the data set were of low potency, high efficacy compounds that showed specificity with respect to tissue of origin could be found. Furthermore, eight potential topoisomerase II inhibitors were identified using a structural similarity search. Finally, a set of genes with expression profiles that were significantly correlated with anti-cancer drug activity was identified. Our study demonstrates that the combined data sets, which provide comprehensive information on drug activity and gene expression profiles of tumor cell lines studied, are useful for identifying potential new active compounds.     

Identification of a four-gene metabolic signature predicting overall survival for hepatocellular carcinoma

While hundreds of consistently altered metabolic genes had been identified in hepatocellular carcinoma (HCC), the prognostic role of them remains to be further elucidated. Messenger RNA expression profiles and clinicopathological data were downloaded from The Cancer Genome Atlas—Liver Hepatocellular Carcinoma and GSE14520 data set from the Gene Expression Omnibus database. Univariate Cox regression analysis and lasso Cox regression model established a novel four-gene metabolic signature (including acetyl-CoA acetyltransferase 1, glutamic-oxaloacetic transaminase 2, phosphatidylserine synthase 2, and uridine-cytidine kinase 2) for HCC prognosis prediction. Patients in the high-risk group shown significantly poorer survival than patients in the low-risk group. The signature was significantly correlated with other negative prognostic factors such as higher α-fetoprotein. The signature was found to be an independent prognostic factor for HCC survival. Nomogram including the signature shown some clinical net benefit for overall survival prediction. Furthermore, gene set enrichment analyses revealed several significantly enriched pathways, which might help explain the underlying mechanisms. Our study identified a novel robust four-gene metabolic signature for HCC prognosis prediction. The signature might reflect the dysregulated metabolic microenvironment and provided potential biomarkers for metabolic therapy and treatment response prediction in HCC.     

Gene expression analysis of familial hypercholesterolemia

Familial hypercholesterolemia (FH) is a common cause of a variety of cardiovascular diseases. The aim of this study was to uncover the underlying mechanism of FH and provide a possible treatment project for FH. We tried to identify the differently expressed genes (DEGs) involved in FH by comparing the gene expression profiles between FH and normal cells. We performed GO and biological pathway analysis of differently expressed genes with DAVID. We searched for candidates for FH treatment by analyzing DEGs between normal cells and FH cells and compared the differences with the DEGs caused by the small interfering molecules in The Connectivity Map (cmap). Using a bioinformatics method, we identified the abnormal metabolic processes in the cells of FH patients, including cell adhesion, material transport, signal transduction and gene expression, and found that the small molecule trazodone could be a potential drug in restoring the dysregulated metabolic pathway. In conclusion, candidates for further evaluation as possible therapeutic agents for FH have been identified using bioinformatics analysis of differentially expressed genes. Phenotype targeting using genomic profiling is a rational approach to drug discovery, which provides a new guideline in treatment of FH and a potential new clinical drug for FH patients.     

Quantitative trait associated microarray gene expression data analysis

Selection on phenotypes may cause genetic change. To understand the relationship between phenotype and gene expression from an evolutionary viewpoint, it is important to study the concordance between gene expression and profiles of phenotypes. In this study, we use a novel method of clustering to identify genes whose expression profiles are related to a quantitative phenotype. Cluster analysis of gene expression data aims at classifying genes into several different groups based on the similarity of their expression profiles across multiple conditions. The hope is that genes that are classified into the same clusters may share underlying regulatory elements or may be a part of the same metabolic pathways. Current methods for examining the association between phenotype and gene expression are limited to linear association measured by the correlation between individual gene expression values and phenotype. Genes may be associated with the phenotype in a nonlinear fashion. In addition, groups of genes that share a particular pattern in their relationship to phenotype may be of evolutionary interest. In this study, we develop a method to group genes based on orthogonal polynomials under a multivariate Gaussian mixture model. The effect of each expressed gene on the phenotype is partitioned into a cluster mean and a random deviation from the mean. Genes can also be clustered based on a time series. Parameters are estimated using the expectation-maximization algorithm and implemented in SAS. The method is verified with simulated data and demonstrated with experimental data from 2 studies, one clusters with respect to severity of disease in Alzheimer's patients and another clusters data for a rat fracture healing study over time. We find significant evidence of nonlinear associations in both studies and successfully describe these patterns with our method. We give detailed instructions and provide a working program that allows others to directly implement this method in their own analyses.     

Genomic signatures to guide the use of chemotherapeutics

Using in vitro drug sensitivity data coupled with Affymetrix microarray data, we developed gene expression signatures that predict sensitivity to individual chemotherapeutic drugs. Each signature was validated with response data from an independent set of cell line studies. We further show that many of these signatures can accurately predict clinical response in individuals treated with these drugs. Notably, signatures developed to predict response to individual agents, when combined, could also predict response to multidrug regimens. Finally, we integrated the chemotherapy response signatures with signatures of oncogenic pathway deregulation to identify new therapeutic strategies that make use of all available drugs. The development of gene expression profiles that can predict response to commonly used cytotoxic agents provides opportunities to better use these drugs, including using them in combination with existing targeted therapies.     

Mechanisms of Metabonomic for a Gateway Drug: Nicotine Priming Enhances Behavioral Response to Cocaine with Modification in Energy Metabolism and Neurotransmitter Level

Nicotine, one of the most commonly used drugs, has become a major concern because tobacco serves as a gateway drug and is linked to illicit drug abuse, such as cocaine and marijuana. However, previous studies mainly focused on certain genes or neurotransmitters which have already been known to participate in drug addiction, lacking endogenous metabolic profiling in a global view. To further explore the mechanism by which nicotine modifies the response to cocaine, we developed two conditioned place preference (CPP) models in mice. In threshold dose model, mice were pretreated with nicotine, followed by cocaine treatment at the dose of 2 mg/kg, a threshold dose of cocaine to induce CPP in mice. In high-dose model, mice were only treated with 20 mg/kg cocaine, which induced a significant CPP. ¹H nuclear magnetic resonance based on metabonomics was used to investigate metabolic profiles of the nucleus accumbens (NAc) and striatum. We found that nicotine pretreatment dramatically increased CPP induced by 2 mg/kg cocaine, which was similar to 20 mg/kg cocaine-induced CPP. Interestingly, metabolic profiles showed considerable overlap between these two models. These overlapped metabolites mainly included neurotransmitters as well as the molecules participating in energy homeostasis and cellular metabolism. Our results show that the reinforcing effect of nicotine on behavioral response to cocaine may attribute to the modification of some specific metabolites in NAc and striatum, thus creating a favorable metabolic environment for enhancing conditioned rewarding effect of cocaine. Our findings provide an insight into the effect of cigarette smoking on cocaine dependence and the underlying mechanism.     

EST analysis of gene expression in testis of the ascidian Ciona intestinalis

To explore the gene expression underlying spermatogenesis, a large-scale analysis has been done on the cDNAs from testis of the ascidian, Ciona intestinalis. A set of 5,461 expressed sequence tags was analyzed and grouped into 2,806 independent clusters. Approximately 30% of the clusters showed significant sequence matches to the proteins reported in DDBJ/GenBank/EMBL database including a set of proteins closely related to the gene regulation during spermatogenesis, functional and morphological changes of spermatogenic cells during spermiogenesis, and physiological functions of sperm, as well as those with housekeeping functions commonly expressed in other cells. Some clones show similarities to the proteins present in vertebrate lymphocytes, suggesting a primitive immune system in ascidians. We have also found some genes that are known to participate in hormonal regulation of spermatogenesis in vertebrates. The large majority of the genes expressed in Ciona testis show no significant matches to known proteins and the further analysis of these genes may shed new light on the molecular mechanism of spermatogenesis and sperm functions.     

Molecular basis for structural diversity in the core regions of the lipopolysaccharides of Escherichia coli and Salmonella enterica

Bacterial lipopolysaccharides (LPS) are unique and complex glycolipids that provide characteristic components of the outer membranes of Gram-negative bacteria. In LPS of the Enterobacteriaceae, the core oligosaccharide links a highly conserved lipid A to the antigenic O-polysaccharide. Structural diversity in the core oligosaccharide is limited by the constraints imposed by its essential role in outer membrane stability and provides a contrast to the hypervariable O-antigen. The genetics of core oligosaccharide biosynthesis in Salmonella and Escherichia coli K-12 have served as prototypes for studies on the LPS and lipo-oligosaccharides from a growing range of bacteria. However, despite the wealth of knowledge, there remains a number of unanswered questions, and direct experimental data are not yet available to define the precise mechanism of action of many gene products. Here we present a comparative analysis of the recently completed sequences of the major core oligosaccharide biosynthesis gene clusters from the five known core types in E. coli and the Ra core type of Salmonella enterica serovar Typhimurium and discuss advances in the understanding of the related biosynthetic pathways. Differences in these clusters reflect important structural variations in the outer core oligosaccharides and provide a basis for ascribing functions to the genes in these model clusters, whereas highly conserved regions within these clusters suggest a critical and unalterable function for the inner region of the core.     

Discovering Functional Modules across Diverse Maize Transcriptomes Using COB,the Co-Expression Browser

Tools that provide improved ability to relate genotype to phenotype have the potential to accelerate breeding for desired traits and to improve our understanding of the molecular variants that underlie phenotypes. The availability of large-scale gene expression profiles in maize provides an opportunity to advance our understanding of complex traits in this agronomically important species. We built co-expression networks based on genome-wide expression data from a variety of maize accessions as well as an atlas of different tissues and developmental stages. We demonstrate that these networks reveal clusters of genes that are enriched for known biological function and contain extensive structure which has yet to be characterized. Furthermore, we found that co-expression networks derived from developmental or tissue atlases as compared to expression variation across diverse accessions capture unique functions. To provide convenient access to these networks, we developed a public, web-based Co-expression Browser (COB), which enables interactive queries of the genome-wide networks. We illustrate the utility of this system through two specific use cases: one in which gene-centric queries are used to provide functional context for previously characterized metabolic pathways, and a second where lists of genes produced by mapping studies are further resolved and validated using co-expression networks.     

Identifying clusters of functionally related genes in genomes

MOTIVATION: An increasing body of literature shows that genomes of eukaryotes can contain clusters of functionally related genes. Most approaches to identify gene clusters utilize microarray data or metabolic pathway databases to find groups of genes on chromosomes that are linked by common attributes. A generalized method that can find gene clusters regardless of the mechanism of origin would provide researchers with an unbiased method for finding clusters and studying the evolutionary forces that give rise to them. RESULTS: We present an algorithm to identify gene clusters in eukaryotic genomes that utilizes functional categories defined in graph-based vocabularies such as the Gene Ontology (GO). Clusters identified in this manner need only have a common function and are not constrained by gene expression or other properties. We tested the algorithm by analyzing genomes of a representative set of species. We identified species-specific variation in percentage of clustered genes as well as in properties of gene clusters including size distribution and functional annotation. These properties may be diagnostic of the evolutionary forces that lead to the formation of gene clusters. AVAILABILITY: A software implementation of the algorithm and example output files are available at http://fcg.tamu.edu/C_Hunter/.     

Linking tumor cell cytotoxicity to mechanism of drug action: an integrated analysis of gene expression, small-molecule screening and structural databases

An integrated, bioinformatic analysis of three databases comprising tumor-cell-based small molecule screening data, gene expression measurements, and PDB (Protein Data Bank) ligand-target structures has been developed for probing mechanism of drug action (MOA). Clustering analysis of GI50 profiles for the NCI's database of compounds screened across a panel of tumor cells (NCI60) was used to select a subset of unique cytotoxic responses for about 4000 small molecules. Drug-gene-PDB relationships for this test set were examined by correlative analysis of cytotoxic response and differential gene expression profiles within the NCI60 and structural comparisons with known ligand-target crystallographic complexes. A survey of molecular features within these compounds finds thirteen conserved Compound Classes, each class exhibiting chemical features important for interactions with a variety of biological targets. Protein targets for an additional twelve Compound Classes could be directly assigned using drug-protein interactions observed in the crystallographic database. Results from the analysis of constitutive gene expressions established a clear connection between chemo-resistance and overexpression of gene families associated with the extracellular matrix, cytoskeletal organization, and xenobiotic metabolism. Conversely, chemo-sensitivity implicated overexpression of gene families involved in homeostatic functions of nucleic acid repair, aryl hydrocarbon metabolism, heat shock response, proteasome degradation and apoptosis. Correlations between chemo-responsiveness and differential gene expressions identified chemotypes with nonselective (i.e., many) molecular targets from those likely to have selective (i.e., few) molecular targets. Applications of data mining strategies that jointly utilize tumor cell screening, genomic, and structural data are presented for hypotheses generation and identifying novel anticancer candidates.     

Metabolomics study on the toxicity of aconite root and its processed products using ultraperformance liquid-chromatography/electrospray-ionization synapt high-definition mass spectrometry coupled with pattern recognition approach and ingenuity pathways analysis

The mother and lateral root of Aconitum carmichaelii Debx, named "Chuanwu" (CW) and "Fuzi", respectively, has been used to relieve joint pain and treat rheumatic diseases for over 2000 years. However, it has a very narrow therapeutic range, and the toxicological risk of its usage remains very high. The traditional Chinese processing approach, Paozhi (detoxifying measure),can decompose poisonous Aconitum alkaloids into less or nontoxic derivatives and plays an important role in detoxification. The difference in metabolomic characters among the crude and processed preparations is still unclear, limited by the lack of sensitive and reliable biomarkers. Therefore, this paper was designed to investigate comprehensive metabolomic characters of the crude and its processed products by UPLC-Q-TOF-HDMS combined with pattern recognition methods and ingenuity pathway analysis (IPA). The significant difference in metabolic profiles and changes of metabolite biomarkers of interest between the crude and processed preparations were well observed. The underlying regulations of Paozhi-perturbed metabolic pathways are discussed according to the identified metabolites, and four metabolic pathways are identified using IPA. The present study demonstrates that metabolomic analysis could greatly facilitate and provide useful information to further comprehensively understand the pharmacological activity and potential toxicity of processed Aconite roots in the clinic.     

Natural biocombinatorics in the polyketide synthase genes of the actinobacterium Streptomyces avermitilis

Modular polyketide synthases (PKSs) of bacteria provide an enormous reservoir of natural chemical diversity. Studying natural biocombinatorics may aid in the development of concepts for experimental design of genes for the biosynthesis of new bioactive compounds. Here we address the question of how the modularity of biosynthetic enzymes and the prevalence of multiple gene clusters in Streptomyces drive the evolution of metabolic diversity. The phylogeny of ketosynthase (KS) domains of Streptomyces PKSs revealed that the majority of modules involved in the biosynthesis of a single compound evolved by duplication of a single ancestor module. Using Streptomyces avermitilis as a model organism, we have reconstructed the evolutionary relationships of different domain types. This analysis suggests that 65% of the modules were altered by recombinational replacements that occurred within and between biosynthetic gene clusters. The natural reprogramming of the biosynthetic pathways was unambiguously confined to domains that account for the structural diversity of the polyketide products and never observed for the KS domains. We provide examples for natural acyltransferase (AT), ketoreductase (KR), and dehydratase (DH)–KR domain replacements. Potential sites of homologous recombination could be identified in interdomain regions and within domains. Our results indicate that homologous recombination facilitated by the modularity of PKS architecture is the most important mechanism underlying polyketide diversity in bacteria.     

Glucagon counteracts interleukin-6-dependent gene expression by redundant action of Epac and PKA

Inflammation is the biological response to injurious stimuli. In the initial phase of the inflammatory process, interleukin-6 (IL-6) is the main inducer of acute phase protein expression in the liver. A prolonged acute phase response is characterised by a disturbed glucose homeostasis and elevated levels of IL-6, insulin, and counterregulatory hormones such as glucagon. Several studies deal with the impact of IL-6 on glucagon-dependent gene expression. In contrast, only very little is known about the influence of G-protein-coupled receptors on IL-6 signalling. Therefore, the aim of this study is to elucidate the regulation of IL-6-induced gene expression by glucagon. We could reveal a novel mechanism of negative regulation of IL-6-induced MAP kinase activation by glucagon in primary murine hepatocytes. IL-6-dependent induction of the ERK-dependent target gene Tfpi2, coding for a Kunitz-type serine protease inhibitor, was strongly down-regulated by glucagon treatment. Studying the underlying mechanism revealed a redundant action of the signalling molecules exchange protein activated by cyclic AMP (Epac) and protein kinase A. The metabolic hormone glucagon interferes in IL-6-induced gene expression. This observation is indicative for a regulatory role of G-protein-coupled receptors in the IL-6-dependent inflammatory response.     

Spermidine Feeding Decreases Age-Related Locomotor Activity Loss and Induces Changes in Lipid Composition

Spermidine is a natural polyamine involved in many important cellular functions, whose supplementation in food or water increases life span and stress resistance in several model organisms. In this work, we expand spermidine’s range of age-related beneficial effects by demonstrating that it is also able to improve locomotor performance in aged flies. Spermidine’s mechanism of action on aging has been primarily related to general protein hypoacetylation that subsequently induces autophagy. Here, we suggest that the molecular targets of spermidine also include lipid metabolism: Spermidine-fed flies contain more triglycerides and show altered fatty acid and phospholipid profiles. We further determine that most of these metabolic changes are regulated through autophagy. Collectively, our data suggests an additional and novel lipid-mediated mechanism of action for spermidine-induced autophagy.     

Mechanisms underlying the radioprotective properties of γ-tocotrienol: comparative gene expression profiling in tocol-treated endothelial cells

Among the eight naturally occurring vitamin E analogs, γ-tocotrienol (GT3) is a particularly potent radioprophylactic agent in vivo. Moreover, GT3 protects endothelial cells from radiation injury not only by virtue of its antioxidant properties but also by inhibition of 3-hydroxy-3-methyl-glutaryl-CoA (HMG-CoA) reductase and by improving the availability of the nitric oxide synthase cofactor tetrahydrobiopterin. Nevertheless, the precise mechanisms underlying the superior radioprotective properties of GT3 compared with other tocols are not known. This study, therefore, examined the differences in gene expression profiles between GT3 and its tocopherol counterpart, γ-tocopherol, as well as between GT3 and α-tocopherol in human endothelial cells. Cells were treated with vehicle or the appropriate tocol for 24 h, after which total RNA was isolated and genome-wide gene expression profiles were obtained using the Illumina platform. GT3 was far more potent in inducing gene-expression changes than α-tocopherol or γ-tocopherol. In particular, GT3 induced multiple changes in pathways known to be of importance in the cellular response to radiation exposure. Affected GO functional clusters included response to oxidative stress, response to DNA damage stimuli, cell cycle phase, regulation of cell death, regulation of cell proliferation, hematopoiesis, and blood vessel development. These results form the basis for further studies to determine the exact importance of differentially affected GO functional clusters in endothelial radioprotection by GT3.

Electronic supplementary material

The online version of this article (doi:10.1007/s12263-011-0228-8) contains supplementary material, which is available to authorized users.     

Genetic spell‐checking: gene editing using single‐stranded DNA oligonucleotides

Single‐stranded oligonucleotides (ssODNs) can be used to direct the exchange of a single nucleotide or the repair of a single base within the coding region of a gene in a process that is known, generically, as gene editing. These molecules are composed of either all DNA residues or a mixture of RNA and DNA bases and utilize inherent metabolic functions to execute the genetic alteration within the context of a chromosome. The mechanism of action of gene editing is now being elucidated as well as an understanding of its regulatory circuitry, work that has been particularly important in establishing a foundation for designing effective gene editing strategies in plants. Double‐strand DNA breakage and the activation of the DNA damage response pathway play key roles in determining the frequency with which gene editing activity takes place. Cellular regulators respond to such damage and their action impacts the success or failure of a particular nucleotide exchange reaction. A consequence of such activation is the natural slowing of replication fork progression, which naturally creates a more open chromatin configuration, thereby increasing access of the oligonucleotide to the DNA template. Herein, how critical reaction parameters influence the effectiveness of gene editing is discussed. Functional interrelationships between DNA damage, the activation of DNA response pathways and the stalling of replication forks are presented in detail as potential targets for increasing the frequency of gene editing by ssODNs in plants and plant cells.