Transcriptional profiling of the dose response: a more powerful approach for characterizing drug activities.

The dose response curve is the gold standard for measuring the effect of a drug treatment, but is rarely used in genomic scale transcriptional profiling due to perceived obstacles of cost and analysis. One barrier to examining transcriptional dose responses is that existing methods for microarray data analysis can identify patterns, but provide no quantitative pharmacological information. We developed analytical methods that identify transcripts responsive to dose, calculate classical pharmacological parameters such as the EC50, and enable an in-depth analysis of coordinated dose-dependent treatment effects. The approach was applied to a transcriptional profiling study that evaluated four kinase inhibitors (imatinib, nilotinib, dasatinib and PD0325901) across a six-logarithm dose range, using 12 arrays per compound. The transcript responses proved a powerful means to characterize and compare the compounds: the distribution of EC50 values for the transcriptome was linked to specific targets, dose-dependent effects on cellular processes were identified using automated pathway analysis, and a connection was seen between EC50s in standard cellular assays and transcriptional EC50s. Our approach greatly enriches the information that can be obtained from standard transcriptional profiling technology. Moreover, these methods are automated, robust to non-optimized assays, and could be applied to other sources of quantitative data.

Normalization strategies for metabonomic analysis of urine samples.

Unlike plasma and most biological fluids which have solute concentrations that are tightly controlled, urine volume can vary widely based upon water consumption and other physiological factors. As a result, the concentrations of endogenous metabolites in urine vary widely and normalizing for these effects is necessary. Normalization approaches that utilized urine volume, osmolality, creatinine concentration, and components that are common to all samples ("total useful MS signal") were compared in order to determine which strategies could be successfully used to differentiate between dose groups based upon the complete endogenous metabolite profile. Variability observed in LC/MS results obtained from targeted and non-targeted metabonomic analyses was highly dependent on the strategy used for normalization. We therefore recommend the use of two different normalization techniques in order to facilitate detection of statistically significant changes in the endogenous metabolite profile when working with urine samples.

Modulation of ascorbic acid metabolism by cytochrome P450 induction revealed by metabonomics and transcriptional profiling.

In the present study, NMR-based urinary metabonomic profiles resulting from dosing with widely recognized microsomal enzyme inducers were evaluated in male rats. Wistar or Sprague-Dawley rats were dosed daily by oral gavage with phenobarbital (PB; 100 mg/kg), diallyl sulfide (DAS; 500 mg/kg), the investigational compound DMP-904 (150 mg/kg), or beta-naphthoflavone (BNF; 100 mg/kg) for 4 days, and urine was collected daily for analysis. Compounds known to increase cytochrome P450 2B enzymes, including PB, DAS and DMP-904, increased the urinary excretion of gulonic and ascorbic acid in a time-dependent manner, reaching a maximum following 3-4 days of dosing. In contrast, BNF, an agent that induces primarily Cyp1A enzymes, did not increase gulonic or ascorbic acid excretion, despite inducing Cyp1A1 more than 200-fold. Given the metabonomic results, hepatic transcriptional changes in the regulation of ascorbic acid biosynthesis were determined by RT-PCR. All Cyp2B inducers increased hepatic mRNA levels of aldo-keto reductase 1A1, an enzyme that catalyzes the formation of gulonic acid from glucuronate with concurrent decreased expression of both regucalcin (Rgn), the enzyme responsible for conversion of gulonic acid to gulono-1, 4-lactone and gulonolactone oxidase (Gulo), the rate-limiting enzyme in ascorbate biosynthesis. These effects would be expected to increase levels of gulonic acid. In addition, Cyp2B inducers also increased hepatic expression of enzymes regulating ascorbic acid reutilization including glutaredoxin reductase (Glrx2) and thioredoxin reductase (Txnrd1). In contrast, BNF did not effect hepatic expression of any enzyme regulating gulonic or ascorbic acid biosynthesis. Thus, some microsomal enzyme inducers alter transcriptional regulation of ascorbic acid biosynthesis, and these changes are detected by noninvasive metabonomic profiling. However, not all microsomal enzyme inducers appear to alter ascorbic acid metabolism. Finally, the work illustrates how metabonomic results can direct additional studies to determine the biochemical mechanisms underlying changes in urinary metabolite excretion.

Nuclear magnetic resonance metabonomics: methods for drug discovery and development.

Nuclear magnetic resonance (NMR)-based metabonomics is gaining popularity in drug discovery and development and in academia in a variety of settings, ranging from toxicology, preclinical, and clinical approaches to nutrition research, studies on microorganisms, and research on plants. This chapter focuses on the basic steps in a metabonomics study and emphasizes experience and lessons learned in our lab where we focused on metabonomic analyses of plant extracts, cell lines, and a variety of animal tissues and biofluids. We emphasize that a comprehensive and suitable study design is pivotal for a correct biological interpretation of the results, as well as highly controlled experimental conditions. Sample preparation and NMR protocols are detailed for a wide range of sample types. We discuss alternative data processing strategies and considerations for a general data analysis approach, paying particular attention to the statistical interpretation and validation of the results while also highlighting approaches to avoid possible pitfalls resulting from systematic and random errors. A tutorial written for the R statistical package and other small utilities are available from the authors upon request.

Metabonomics classifies pathways affected by bioactive compounds. Artificial neural network classification of NMR spectra of plant extracts.

The biochemical mode-of-action (MOA) for herbicides and other bioactive compounds can be rapidly and simultaneously classified by automated pattern recognition of the metabonome that is embodied in the 1H NMR spectrum of a crude plant extract. The ca. 300 herbicides that are used in agriculture today affect less than 30 different biochemical pathways. In this report, 19 of the most interesting MOAs were automatically classified. Corn (Zea mays) plants were treated with various herbicides such as imazethapyr, glyphosate, sethoxydim, and diuron, which represent various biochemical modes-of-action such as inhibition of specific enzymes (acetohydroxy acid synthase [AHAS], protoporphyrin IX oxidase [PROTOX], 5-enolpyruvylshikimate-3-phosphate synthase [EPSPS], acetyl CoA carboxylase [ACC-ase], etc.), or protein complexes (photosystems I and II), or major biological process such as oxidative phosphorylation, auxin transport, microtubule growth, and mitosis. Crude isolates from the treated plants were subjected to 1H NMR spectroscopy, and the spectra were classified by artificial neural network analysis to discriminate the herbicide modes-of-action. We demonstrate the use and refinement of the method, and present cross-validated assignments for the metabolite NMR profiles of over 400 plant isolates. The MOA screen also recognizes when a new mode-of-action is present, which is considered extremely important for the herbicide discovery process, and can be used to study deviations in the metabolism of compounds from a chemical synthesis program. The combination of NMR metabolite profiling and neural network classification is expected to be similarly relevant to other metabonomic profiling applications, such as in drug discovery.

Cannabinoid receptor antagonist-induced striated muscle toxicity and ethylmalonic-adipic aciduria in beagle dogs.

Ibipinabant (IBI), a potent cannabinoid-1 receptor (CB1R) antagonist, previously in development for the treatment of obesity, causes skeletal and cardiac myopathy in beagle dogs. This toxicity was characterized by increases in muscle-derived enzyme activity in serum and microscopic striated muscle degeneration and accumulation of lipid droplets in myofibers. Additional changes in serum chemistry included decreases in glucose and increases in non-esterified fatty acids and cholesterol, and metabolic acidosis, consistent with disturbances in lipid and carbohydrate metabolism. No evidence of CB1R expression was detected in dog striated muscle as assessed by polymerase chain reaction, immunohistochemistry, Western blot analysis, and competitive radioligand binding. Investigative studies utilized metabonomic technology and demonstrated changes in several intermediates and metabolites of fatty acid metabolism including plasma acylcarnitines and urinary ethylmalonate, methylsuccinate, adipate, suberate, hexanoylglycine, sarcosine, dimethylglycine, isovalerylglycine, and 2-hydroxyglutarate. These results indicated that the toxic effect of IBI on striated muscle in beagle dogs is consistent with an inhibition of the mitochondrial flavin-containing enzymes including dimethyl glycine, sarcosine, isovaleryl-CoA, 2-hydroxyglutarate, and multiple acyl-CoA (short, medium, long, and very long chain) dehydrogenases. All of these enzymes converge at the level of electron transfer flavoprotein (ETF) and ETF oxidoreductase. Urinary ethylmalonate was shown to be a biomarker of IBI-induced striated muscle toxicity in dogs and could provide the ability to monitor potential IBI-induced toxic myopathy in humans. We propose that IBI-induced toxic myopathy in beagle dogs is not caused by direct antagonism of CB1R and could represent a model of ethylmalonic-adipic aciduria in humans.

FDR-FET: an optimizing gene set enrichment analysis method.

Gene set enrichment analysis for analyzing large profiling and screening experiments can reveal unifying biological schemes based on previously accumulated knowledge represented as "gene sets". Most of the existing implementations use a fixed fold-change or P value cutoff to generate regulated gene lists. However, the threshold selection in most cases is arbitrary, and has a significant effect on the test outcome and interpretation of the experiment. We developed a new gene set enrichment analysis method, ie, FDR-FET, which dynamically optimizes the threshold choice and improves the sensitivity and selectivity of gene set enrichment analysis. The procedure translates experimental results into a series of regulated gene lists at multiple false discovery rate (FDR) cutoffs, and computes the P value of the overrepresentation of a gene set using a Fisher's exact test (FET) in each of these gene lists. The lowest P value is retained to represent the significance of the gene set. We also implemented improved methods to define a more relevant global reference set for the FET. We demonstrate the validity of the method using a published microarray study of three protease inhibitors of the human immunodeficiency virus and compare the results with those from other popular gene set enrichment analysis algorithms. Our results show that combining FDR with multiple cutoffs allows us to control the error while retaining genes that increase information content. We conclude that FDR-FET can selectively identify significant affected biological processes. Our method can be used for any user-generated gene list in the area of transcriptome, proteome, and other biological and scientific applications.

Trends in kinase selectivity: insights for target class-focused library screening.

A kinome-wide selectivity screen of >20000 compounds with a rich representation of many structural classes has been completed. Analysis of the selectivity patterns for each class shows that a broad spectrum of structural scaffolds can achieve specificity for many kinase families. Kinase selectivity and potency are inversely correlated, a trend that is also found in a large set of kinase functional data. Although selective and nonselective compounds are mostly similar in their physicochemical characteristics, we identify specific features that are present more frequently in compounds that bind to many kinases. Our results support a scaffold-oriented approach for building compound collections to screen kinase targets.

Metabolomic analysis using optimized NMR and statistical methods.

NMR-based metabolomics requires robust automated methodologies, and the accuracy of NMR-based metabolomics data is greatly influenced by the reproducibility of data acquisition and processing methods. Effective water resonance signal suppression and reproducible spectral phasing and baseline traces across series of related samples are crucial for statistical analysis. We assess robustness, repeatability, sensitivity, selectivity, and practicality of commonly used solvent peak suppression methods in the NMR analysis of biofluids with respect to the automated processing of the NMR spectra and the impact of pulse sequence and data processing methods on the sensitivity of pattern recognition and statistical analysis of the metabolite profiles. We introduce two modifications to the excitation sculpting pulse sequence whereby the excitation solvent suppression pulse cascade is preceded by low-power water resonance presaturation pulses during the relaxation delay. Our analysis indicates that combining water presaturation with excitation sculpting water suppression delivers the most reproducible and information-rich NMR spectra of biofluids.