How molecular profiling could revolutionize drug discovery

The Prodrug DHED Delivers 17β-Estradiol into the Retina for Protection of Retinal Ganglion Cells and Preservation of Visual Function in an Animal Model of Glaucoma

We report a three-pronged phenotypic evaluation of the bioprecursor prodrug 10β,17β-dihydroxyestra-1,4-dien-3-one (DHED) that selectively produces 17β-estradiol (E2) in the retina after topical administration and halts glaucomatous neurodegeneration in a male rat model of the disease. Ocular hypertension (OHT) was induced by hyperosmotic saline injection into an episcleral vein of the eye. Animals received daily DHED eye drops for 12 weeks. Deterioration of visual acuity and contrast sensitivity by OHT in these animals were markedly prevented by the DHED-derived E2 with concomitant preservation of retinal ganglion cells and their axons. In addition, we utilized targeted retina proteomics and a previously established panel of proteins as preclinical biomarkers in the context of OHT-induced neurodegeneration as a characteristic process of the disease. The prodrug treatment provided retina-targeted remediation against the glaucomatous dysregulations of these surrogate endpoints without increasing circulating E2 levels. Collectively, the demonstrated significant neuroprotective effect by the DHED-derived E2 in the selected animal model of glaucoma supports the translational potential of our presented ocular neuroprotective approach owing to its inherent therapeutic safety and efficacy.

Connectivity Mapping: Methods and Applications

Connectivity mapping resources consist of signatures representing changes in cellular state following systematic small-molecule, disease, gene, or other form of perturbations. Such resources enable the characterization of signatures from novel perturbations based on similarity; provide a global view of the space of many themed perturbations; and allow the ability to predict cellular, tissue, and organismal phenotypes for perturbagens. A signature search engine enables hypothesis generation by finding connections between query signatures and the database of signatures. This framework has been used to identify connections between small molecules and their targets, to discover cell-specific responses to perturbations and ways to reverse disease expression states with small molecules, and to predict small-molecule mimickers for existing drugs. This review provides a historical perspective and the current state of connectivity mapping resources with a focus on both methodology and community implementations.

Chloramphenicol inhibits eukaryotic Ser/Thr phosphatase and infection-specific cell differentiation in the rice blast fungus

Chloramphenicol (Cm) is a broad-spectrum classic antibiotic active against prokaryotic organisms. However, Cm has severe side effects in eukaryotes of which the cause remains unknown. The plant pathogenic fungus Magnaporthe oryzae, which causes rice blast, forms an appressorium to infect the host cell via single-cell differentiation. Chloramphenicol specifically inhibits appressorium formation, which indicates that Cm has a novel molecular target (or targets) in the rice blast fungus. Application of the T7 phage display method inferred that MoDullard, a Ser/Thr-protein phosphatase, may be a target of Cm. In animals Dullard functions in cell differentiation and protein synthesis, but in fungi its role is poorly understood. In vivo and in vitro analyses showed that MoDullard is required for appressorium formation, and that Cm can bind to and inhibit MoDullard function. Given that human phosphatase CTDSP1 complemented the MoDullard function during appressorium formation by M. oryzae, CTDSP1 may be a novel molecular target of Cm in eukaryotes.

Phenotype-Based High-Content Screening Using Fluorescent Chemical Bioprobes: Lipid Droplets and Glucose Uptake Quantification in Live Cells

Phenotypic screening in live cells has emerged as a promising strategy for drug discovery in pharmaceutical communities. For relevant phenotype-based screening setups, it is critical to develop adequate reporters in order to selectively visualize subcellular compartments or phenotypic changes that represent disease-related characteristics during compound screening. In this chapter, we introduce two phenotype-based high-content/high-throughput assays using fluorescent bioprobes that have been designed and refined to selectively stain cellular lipid droplets (LDs) and to show cellular glucose uptake. In conjunction with target identification process for the hit compounds from phenotypic screening, these fluorescent chemical probe-based screening techniques are expected to drive a great advancement for the discovery of novel first-in-class therapeutics.

Optimising parallel R correlation matrix calculations on gene expression data using MapReduce

Background

High-throughput molecular profiling data has been used to improve clinical decision making by stratifying subjects based on their molecular profiles. Unsupervised clustering algorithms can be used for stratification purposes. However, the current speed of the clustering algorithms cannot meet the requirement of large-scale molecular data due to poor performance of the correlation matrix calculation. With high-throughput sequencing technologies promising to produce even larger datasets per subject, we expect the performance of the state-of-the-art statistical algorithms to be further impacted unless efforts towards optimisation are carried out. MapReduce is a widely used high performance parallel framework that can solve the problem.

Results

In this paper, we evaluate the current parallel modes for correlation calculation methods and introduce an efficient data distribution and parallel calculation algorithm based on MapReduce to optimise the correlation calculation. We studied the performance of our algorithm using two gene expression benchmarks. In the micro-benchmark, our implementation using MapReduce, based on the R package RHIPE, demonstrates a 3.26-5.83 fold increase compared to the default Snowfall and 1.56-1.64 fold increase compared to the basic RHIPE in the Euclidean, Pearson and Spearman correlations. Though vanilla R and the optimised Snowfall outperforms our optimised RHIPE in the micro-benchmark, they do not scale well with the macro-benchmark. In the macro-benchmark the optimised RHIPE performs 2.03-16.56 times faster than vanilla R. Benefiting from the 3.30-5.13 times faster data preparation, the optimised RHIPE performs 1.22-1.71 times faster than the optimised Snowfall. Both the optimised RHIPE and the optimised Snowfall successfully performs the Kendall correlation with TCGA dataset within 7 hours. Both of them conduct more than 30 times faster than the estimated vanilla R.

Conclusions

The performance evaluation found that the new MapReduce algorithm and its implementation in RHIPE outperforms vanilla R and the conventional parallel algorithms implemented in R Snowfall. We propose that MapReduce framework holds great promise for large molecular data analysis, in particular for high-dimensional genomic data such as that demonstrated in the performance evaluation described in this paper. We aim to use this new algorithm as a basis for optimising high-throughput molecular data correlation calculation for Big Data.

Profiling the kinome for drug discovery

The human kinome is made up of 518 distinctive serine/threonine and tyrosine kinases, which are key components of virtually every mammalian signal transduction pathway. Consequently, kinases provide a compelling target family for the development of small molecule inhibitors, which could be used as tools to delineate the mechanism of action for biological processes and potentially be used as therapeutics to treat human diseases such as cancer. A myriad of recent technological advances have accelerated our understanding of kinome function, its relationship to tumorigenic development, and have contributed to the progression of small molecule kinase inhibitors into the clinic. Essential to the continued growth of the field are informatics tools that can assist in interpreting disparate and voluminous data sets and correctly guide decision making processes. These advances are expected to have a dramatic impact on kinase drug development and clinical diagnoses and treatment in the near future.:

Proteomic approach to investigate pathogenicity and metabolism of methicillin-resistant Staphylococcus aureus

Over the last two decades, numerous genomes of pathogenic bacteria have been fully sequenced and annotated, while others are continuously being sequenced. To date, the sequences of more than 8,500 whole bacterial genomes are publicly available for research purposes. These efforts in high-throughput sequencing simultaneously to progresses in methods allowing to study whole transcriptome and proteome of bacteria provide the basis of comprehensive understanding of metabolism, adaptability to environment, regulation, resistance pathways, or pathogenicity mechanisms of bacterial pathogens. Staphylococcus aureus is a Gram-positive human pathogen causing a wide variety of infections ranging from benign skin infection to life-threatening diseases. Furthermore, the spreading of multidrug-resistant isolates requiring the use of last barrier drugs has resulted in a particular attention of the medical and scientific community to this pathogen. We describe here proteomic methods to prepare, identify, and analyze protein fractions, which allow studying Staphylococcus aureus on the organism level. Besides evaluation of the whole bacterial transcriptome, this approach might contribute to the development of rapid diagnostic tests and to the identification of new drug targets to improve public health.

Ayurvedic drug discovery

Ayurveda is a major traditional system of Indian medicine that is still being successfully used in many countries. Recapitulation and adaptation of the older science to modern drug discovery processes can bring renewed interest to the pharmaceutical world and offer unique therapeutic solutions for a wide range of human disorders. Eventhough time-tested evidences vouch immense therapeutic benefits for ayurvedic herbs and formulations, several important issues are required to be resolved for successful implementation of ayurvedic principles to present drug discovery methodologies. Additionally, clinical examination in the extent of efficacy, safety and drug interactions of newly developed ayurvedic drugs and formulations are required to be carefully evaluated. Ayurvedic experts suggest a reverse-pharmacology approach focusing on the potential targets for which ayurvedic herbs and herbal products could bring tremendous leads to ayurvedic drug discovery. Although several novel leads and drug molecules have already been discovered from ayurvedic medicinal herbs, further scientific explorations in this arena along with customization of present technologies to ayurvedic drug manufacturing principles would greatly facilitate a standardized ayurvedic drug discovery.

Combining imaging and pathway profiling: an alternative approach to cancer drug discovery

Conventional drug discovery strategies are typically 'target centric' based on the selection of lead compounds with optimised 'on-target' potency and selectivity profiles. However, high-attrition rates are often the result of compensatory or redundant cancer mechanisms and the fact that tumours do not find it difficult to escape inhibition of a single pathway. In this article, we highlight two emerging and complimentary technologies; namely phenotypic imaging and post-translational pathway profiling, which when combined with relevant disease models can provide pharmacodiagnostic and drug combination strategies that predict and counteract inherent and adaptive drug resistance. The implementation of such approaches at early stages of the drug discovery process enables more informed decisions on candidate drug selection and how to maximise and predict efficacy before clinical development.

Advancing cancer drug discovery towards more agile development of targeted combination therapies

Current drug-discovery strategies are typically 'target-centric' and are based upon high-throughput screening of large chemical libraries against nominated targets and a selection of lead compounds with optimized 'on-target' potency and selectivity profiles. However, high attrition of targeted agents in clinical development suggest that combinations of targeted agents will be most effective in treating solid tumors if the biological networks that permit cancer cells to subvert monotherapies are identified and retargeted. Conventional drug-discovery and development strategies are suboptimal for the rational design and development of novel drug combinations. In this article, we highlight a series of emerging technologies supporting a less reductionist, more agile, drug-discovery and development approach for the rational design, validation, prioritization and clinical development of novel drug combinations.

Proteomic approaches in understanding action mechanisms of metal-based anticancer drugs

Medicinal inorganic chemistry has been stimulating largely by the success of the anticancer drug, cisplatin. Various metal complexes are currently used as therapeutic agents (e.g., Pt, Au, and Ru) in the treatment of malignant diseases, including several types of cancers. Understanding the mechanism of action of these metal-based drugs is for the design of more effective drugs. Proteomic approaches combined with other biochemical methods can provide comprehensive understanding of responses that are involved in metal-based anticancer drugs-induced cell death, including insights into cytotoxic effects of metal-based anticancer drugs, correlation of protein alterations to drug targets, and prediction of drug resistance and toxicity. This information, when coupled with clinical data, can provide rational basses for the future design and modification of present used metal-based anticancer drugs.

On the intrinsic inevitability of cancer: from foetal to fatal attraction

The cracks in the paradigm of oncogenic mutations and somatic evolution as driving force of tumorigenesis, lucidly exposed by the dynamic heterogeneity of "cancer stem cells" or the diffuse results of cancer genome sequencing projects, indicate the need for a more encompassing theory of cancer that reaches beyond the current proximate explanations based on individual genetic pathways. One such integrative concept, derived from first principles of the dynamics of gene regulatory networks, is that cancerous cell states are attractor states, just like normal cell types are. Here we extend the concept of cancer attractors to illuminate a more profound property of cancer initiation: its inherent inevitability in the light of metazoan evolution. Using Waddington's Epigenetic Landscape as a conceptual aid, for which we present a mathematical and evolutionary foundation, we propose that cancer is intrinsically linked to ontogenesis and phylogenesis. This explanatory rather than enumerating review uses a formal argumentation structure that is atypical in modern experimental biology but may hopefully offer a new coherent perspective to reconcile many conflicts between new findings and the old thinking in the categories of linear oncogenic pathways.

Towards a holistic, yet gene-centered analysis of gene expression profiles: a case study of human lung cancers

Genome-wide gene expression profile studies encompass increasingly large number of samples, posing a challenge to their presentation and interpretation without losing the notion that each transcriptome constitutes a complex biological entity. Much like pathologists who visually analyze information-rich histological sections as a whole, we propose here an integrative approach. We use a self-organizing maps -based software, the gene expression dynamics inspector (GEDI) to analyze gene expression profiles of various lung tumors. GEDI allows the comparison of tumor profiles based on direct visual detection of transcriptome patterns. Such intuitive “gestalt” perception promotes the discovery of interesting relationships in the absence of an existing hypothesis. We uncovered qualitative relationships between squamous cell tumors, small-cell tumors, and carcinoid tumor that would have escaped existing algorithmic classifications. These results suggest that GEDI may be a valuable explorative tool that combines global and gene-centered analyses of molecular profiles from large-scale microarray experiments.

Kinome profiling of clinical cancer specimens

Over the past years novel technologies have emerged to enable the determination of the transcriptome and proteome of clinical samples. These data sets will prove to be of significant value to our elucidation of the mechanisms that govern pathophysiology and may provide biological markers for future guidance in personalized medicine. However, an equally important goal is to define those proteins that participate in signaling pathways during the disease manifestation itself or those pathways that are made active during successful clinical treatment of the disease: the main challenge now is the generation of large-scale data sets that will allow us to define kinome profiles with predictive properties on the outcome-of-disease and to obtain insight into tissue-specific analysis of kinase activity. This review describes the current techniques available to generate kinome profiles of clinical tissue samples and discusses the future strategies necessary to achieve new insights into disease mechanisms and treatment targets.

Critical roles of mucin 1 glycosylation by transactivated polypeptide N-acetylgalactosaminyltransferase 6 in mammary carcinogenesis

The structure of O-glycosylated proteins is altered in breast cancer cells, but the mechanisms of such an aberrant modification have been largely unknown. We here report critical roles of a novel druggable target, polypeptide N-acetylgalactosaminyltransferase 6 (GALNT6), which is upregulated in a great majority of breast cancers and encodes a glycosyltransferase responsible for initiating mucin-type O-glycosylation. Knockdown of GALNT6 by small interfering RNA significantly enhanced cell adhesion function and suppressed the growth of breast cancer cells. Western blot and immunostaining analyses indicated that wild-type GALNT6 protein could glycosylate and stabilize an oncoprotein mucin 1 (MUC1), which was upregulated with GALNT6 in breast cancer specimens. Furthermore, knockdown of GALNT6 or MUC1 led to similar morphologic changes of cancer cells accompanied by the increase of cell adhesion molecules beta-catenin and E-cadherin. Our findings implied that overexpression of GALNT6 might contribute to mammary carcinogenesis through aberrant glycosylation and stabilization of MUC1 and that screening of GALNT6 inhibitors would be valuable for the development of novel therapeutic modalities against breast cancer.

Prediction of human functional genetic networks from heterogeneous data using RVM-based ensemble learning

MOTIVATION

Three major problems confront the construction of a human genetic network from heterogeneous genomics data using kernel-based approaches: definition of a robust gold-standard negative set, large-scale learning and massive missing data values.

RESULTS

The proposed graph-based approach generates a robust GSN for the training process of genetic network construction. The RVM-based ensemble model that combines AdaBoost and reduced-feature yields improved performance on large-scale learning problems with massive missing values in comparison to Naïve Bayes.

CONTACT

dargenio@bmsr.usc.edu

SUPPLEMENTARY INFORMATION

Supplementary material is available at Bioinformatics online.

Pharmacogenetics of calcineurin inhibitors in renal transplantation

Cyclosporine A and tacrolimus (Tac) are inmunosuppresive drugs with a narrow therapeutic range. Underdosing is associated with organ rejection, whereas overdosing could result in toxicity. Therapeutic drug monitoring at different postdose times is necessary to maintain the blood concentrations within a target window. These calcineurin inhibitors are characterized by a broad interindividual pharmacokinetics variability, which makes the determination of the initial dose difficult. In a patient receiving a dose, the amount of the drug that is measured in the blood determines its bioavailability, which depends on the absorption, biotransformation, and elimination of the drug. These processes are primarily controlled by efflux pumps and enzymes of the cytochrome P (CYP) 450 family. DNA variants at the genes encoding these proteins contribute to the interindividual heterogeneity for calcineurin inhibitors metabolism. Cyclosporine A and Tac are metabolized by CYP3A4 and CYP3A5, and several single nucleotide polymorphisms in the two genes have been associated with differences in drug clearance. Carriers of the CYP3A5 wild-type allele have a higher CYP3A5 expression compared with individuals who are homozygous for a common DNA variant that affects gene splicing (CYP3A5*3). For renal transplant recipients receiving Tac, homozygotes for this nonexpression allele would exhibit significantly lower Tac clearance and may require a lower dose to remain within the blood target concentration compared with CYP3A5 expressors. To date, this CYP3A5 variant is the only reported genetic factor to predict the appropiate starting dosage of Tac, avoiding overdosing and improving the outcome of renal transplantation.

A multistep validation process of biomarkers for preclinical drug development

Biomarkers that can be measured in preclinical models in a high-throughput, reproducible manner offer the potential to increase the speed and efficacy of drug development. Development of therapeutic agents for many conditions is hampered by the limited number of validated preclinical biomarkers available to gauge pharmacoefficacy and disease progression, but the validation process for preclinical biomarkers has received limited attention. This report defines a five-step preclinical biomarker validation process and applies the process to a case study of diabetic retinopathy. By showing that a gene expression panel is highly reproducible, coincides with disease manifestation, accurately classifies individual animals and identifies animals treated with a known therapeutic agent, a biomarker panel can be considered validated. This particular biomarker panel consisting of 14 genes (C1inh, C1s, Carhsp1, Chi3l1, Gat3, Gbp2, Hspb1, Icam1, Jak3, Kcne2, Lama5, Lgals3, Nppa, Timp1) can be used in diabetic retinopathy pharmacotherapeutic research, and the biomarker development process outlined here is applicable to drug development efforts for other diseases.

De-Risking Drug Discovery with ADDME — Avoiding Drug Development Mistakes Early

The advent of early Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) screening has increased the elimination rate of weak drug candidates early in the drug-discovery process, and decreased the proportion of compounds failing in clinical trials for ADMET reasons. This paper reviews the history of ADMET screening and why it has become so important in drug discovery and development. Assays that have been developed in response to specific needs, and improvements in technology that result in higher throughput and greater accuracy of prediction of human mechanisms of toxicity, are discussed. The paper concludes with the authors’ forecast of new models that will better predict human efficacy and toxicity.

Multi-parameter phenotypic profiling: using cellular effects to characterize small-molecule compounds

Multi-parameter phenotypic profiling of small molecules provides important insights into their mechanisms of action, as well as a systems level understanding of biological pathways and their responses to small molecule treatments. It therefore deserves more attention at an early step in the drug discovery pipeline. Here, we summarize the technologies that are currently in use for phenotypic profiling--including mRNA-, protein- and imaging-based multi-parameter profiling--in the drug discovery context. We think that an earlier integration of phenotypic profiling technologies, combined with effective experimental and in silico target identification approaches, can improve success rates of lead selection and optimization in the drug discovery process.

ADDME--Avoiding Drug Development Mistakes Early: central nervous system drug discovery perspective

The advent of early absorption, distribution, metabolism, excretion, and toxicity (ADMET) screening has increased the attrition rate of weak drug candidates early in the drug-discovery process, and decreased the proportion of compounds failing in clinical trials for ADMET reasons. This paper reviews the history of ADMET screening and its place in pharmaceutical development, and central nervous system drug discovery in particular. Assays that have been developed in response to specific needs and improvements in technology that result in higher throughput and greater accuracy of prediction of human mechanisms of absorption and toxicity are discussed. The paper concludes with the authors' forecast of new models that will better predict human efficacy and toxicity.

Regulatory potential for concerted modulation of Nrf2- and Nfkb1-mediated gene expression in inflammation and carcinogenesis

Many studies have implicated nuclear factor E2-related factor 2 (Nrf2) and nuclear factor-kappaB1 (Nfkb1) in inflammation and cancer. However, the regulatory potential for crosstalk between these two important transcription factors in inflammation and carcinogenesis has not been explored. To delineate conserved transcription factor-binding site signatures, we performed bioinformatic analyses on the promoter regions of human and murine Nrf2 and Nfkb1. We performed multiple sequence alignment of Nrf2 and Nfkb1 genes in five mammalian species - human, chimpanzee, dog, mouse and rat - to explore conserved biological features. We constructed a canonical regulatory network for concerted modulation of Nrf2 and Nfkb1 involving several members of the mitogen-activated protein kinase (MAPK) family and present a putative model for concerted modulation of Nrf2 and Nfkb1 in inflammation/carcinogenesis. Our results reflect potential for putative crosstalk between Nrf2 and Nfkb1 modulated through the MAPK cascade that may influence inflammation-associated etiopathogenesis of cancer. Taken together, the elucidation of potential relationships between Nrf2 and Nfkb1 may help to better understand transcriptional regulation, as well as transcription factor networks, associated with the etiopathogenesis of inflammation and cancer.

Use of genome-wide high-throughput technologies in biomarker development

In recent years, the usage of high-throughput technologies in the fields of genomics, transcriptomics, proteomics and metabolomics for biomarker discovery has expanded enormously. Biomarkers can be applied for many purposes, including diagnosis, prognosis, staging and selecting appropriate patient therapy. In addition, biomarkers can provide information on disease mechanism or progression. Biomarker development for clinical application encompasses phases for their discovery and characterization, assay development and, finally, implementation using automated platforms employed in clinical laboratories. However, translation from bench to bedside outside a research-oriented environment has proven to be more difficult. This is reflected by only few new biomarkers being integrated into clinical application in the last years. This article reviews currently used high-throughput technologies for the identification of biomarkers, as well as present approaches to increase the percentage of biomarkers that pass the barriers for clinical application.

Genomic-based high throughput screening identifies small molecules that differentially inhibit the antiviral and immunomodulatory effects of IFN-alpha

Multiple lines of evidence suggest that inhibition of Type I Interferons, including IFN-alpha, may provide a therapeutic benefit for autoimmune diseases. Using a chemical genomics approach integrated with cellular and in vivo assays, we screened a small compound library to identify modulators of IFN-alpha biological effects. A genomic fingerprint was developed from both ex vivo patient genomic information and in vitro gene modulation from IFN-alpha cell-based stimulation. A high throughput genomic-based screen then was applied to prioritize 268 small molecule inhibitors targeting 41 different intracellular signaling pathways. Active compounds were profiled further for their ability to inhibit the activation and differentiation of human monocytes using disease-related stimuli. Inhibitors targeting NF-kappaB or Janus Kinase/Signal Transducer and Activator of Transcription (JAK/STAT) signaling emerged as "dissociated inhibitors" because they did not modulate IFN-alpha anti-viral effects against HSV-1 but potently inhibited other immune-related functions. This work describes a novel strategy to identify small molecule inhibitors for the treatment of autoimmune disorders.

The use of diversity profiling to characterize chemical modulators of the histone deacetylases

Target specificity and off-target liabilities are routinely monitored during the early phases of drug discovery for most kinase projects. Typically these criteria are evaluated using a profiling panel comprised of a diverse collection of in vitro kinase assays and relates compound structure to potency and selectivity. The success of these efforts has led to the design of similar panels for phosphatase, protease, and epigenetic targets. Here the implementation of an epigenetic profiling panel, comprised of eleven histone deacetylases (HDACs) and one histone acetyltransferase (HAT), was used to evaluate chemical modulators of these enzymes. HDAC inhibitors (HDACi) such as sodium butyrate and trichostatin A demonstrate diverse biological effects which have led to broad speculation about their therapeutic potential in multiple disease states. Some HDACi have demonstrated tumor suppression in vivo and recently Zolinza was the first HDACi approved by the FDA for the treatment of cutaneous T-cell lymphoma. While HDACi have demonstrated therapeutic utility, many of the first generation compounds are pan-inhibitors. Thus, use of an HDAC profiling panel will be essential in achieving isoform specificity of the next generation of inhibitors. To this end, twenty-one compounds, twelve of which are known to have activities against the HDACs, were tested to evaluate the utility of the epigenetic panel. Additionally, these compounds were tested against a larger 72 member enzyme panel comprised of kinase, phosphatase and protease activities. This effort represents the first time these compounds have been profiled with such a broad range of biochemical activities.

Gene response profiles for Daphnia pulex exposed to the environmental stressor cadmium reveals novel crustacean metallothioneins

Background

Genomic research tools such as microarrays are proving to be important resources to study the complex regulation of genes that respond to environmental perturbations. A first generation cDNA microarray was developed for the environmental indicator species Daphnia pulex, to identify genes whose regulation is modulated following exposure to the metal stressor cadmium. Our experiments revealed interesting changes in gene transcription that suggest their biological roles and their potentially toxicological features in responding to this important environmental contaminant.

Results

Our microarray identified genes reported in the literature to be regulated in response to cadmium exposure, suggested functional attributes for genes that share no sequence similarity to proteins in the public databases, and pointed to genes that are likely members of expanded gene families in the Daphnia genome. Genes identified on the microarray also were associated with cadmium induced phenotypes and population-level outcomes that we experimentally determined. A subset of genes regulated in response to cadmium exposure was independently validated using quantitative-realtime (Q-RT)-PCR. These microarray studies led to the discovery of three genes coding for the metal detoxication protein metallothionein (MT). The gene structures and predicted translated sequences of D. pulex MTs clearly place them in this gene family. Yet, they share little homology with previously characterized MTs.

Conclusion

The genomic information obtained from this study represents an important first step in characterizing microarray patterns that may be diagnostic to specific environmental contaminants and give insights into their toxicological mechanisms, while also providing a practical tool for evolutionary, ecological, and toxicological functional gene discovery studies. Advances in Daphnia genomics will enable the further development of this species as a model organism for the environmental sciences.

Recent development in pharmacogenomics: from candidate genes to genome-wide association studies

Genetic diversity, most notably through single nucleotide polymorphisms and copy-number variation, together with specific environmental exposures, contributes to both disease susceptibility and drug response variability. It has proved difficult to isolate disease genes that confer susceptibility to complex disorders, and as a consequence, even fewer genetic variants that influence clinical drug responsiveness have been uncovered. As such, the candidate gene approach has largely failed to deliver and, although the family-based linkage approach has certain theoretical advantages in dealing with common/complex disorders, progress has been slower than was hoped. More recently, genome-wide association studies have gained increasing popularity, as they enable scientists to robustly associate specific variants with the predisposition for complex disease, such as age-related macular degeneration, Type 2 diabetes, inflammatory bowel disease, obesity, autism and leukemia. This relatively new methodology has stirred new hope for the mapping of genes that regulate drug response related to these conditions. Collectively, these studies support the notion that modern high-throughput single nucleotide polymorphism genotyping technologies, when applied to large and comprehensively phenotyped patient cohorts, will readily reveal the most clinically relevant disease-modifying and drug response genes. This review addresses both recent advances in the genotyping field and highlights from genome-wide association studies, which have conclusively uncovered variants that underlie disease susceptibility and/or variability in drug response in common disorders.

Achieving confidence in mechanism for drug discovery and development

Decisions in drug development are made on the basis of determinations of cause and effect from experimental observations that span drug development phases. Despite advances in our powers of observation, the ability to determine compound mechanisms from large-scale multi-omic technologies continues to be a major bottleneck. This can only be overcome by utilizing computational learning methods that identify from compound data the circuits and connections between drug-affected molecular constituents and physiological observables. The marriage of multi-omics technologies with network inference approaches will provide missing insights needed to improve drug development success rates.

CSR1, the sole target of imidazolinone herbicide in Arabidopsis thaliana

The imidazolinone-tolerant mutant of Arabidopsis thaliana, csr1-2(D), carries a mutation equivalent to that found in commercially available Clearfield crops. Despite their widespread usage, the mechanism by which Clearfield crops gain imidazolinone herbicide tolerance has not yet been fully characterized. Transcription profiling of imazapyr (an imidazolinone herbicide)-treated wild-type and csr1-2(D) mutant plants using Affymetrix ATH1 GeneChip microarrays was performed to elucidate further the biochemical and genetic mechanisms of imidazolinone resistance. In wild-type shoots, the genes which responded earliest to imazapyr treatment were detoxification-related genes which have also been shown to be induced by other abiotic stresses. Early-response genes included steroid sulfotransferase (ST) and 1-aminocyclopropane-1-carboxylic acid oxidase (ACO), as well as members of the glycosyltransferase, glutathione transferase (GST), cytochrome P450, ATP-binding cassette (ABC) transporter, multidrug and toxin extrusion (MATE) and alternative oxidase (AOX) protein families. Later stages of the imazapyr response involved regulation of genes participating in biosynthesis of amino acids, secondary metabolites and tRNA. In contrast to the dynamic changes in the transcriptome profile observed in imazapyr-treated wild-type plants, the transcriptome of csr1-2(D) did not exhibit significant changes following imazapyr treatment, compared with mock-treated csr1-2(D). Further, no substantial difference was observed between wild-type and csr1-2(D) transcriptomes in the absence of imazapyr treatment. These results indicate that CSR1 is the sole target of imidazolinone and that the csr1-2(D) mutation has little or no detrimental effect on whole-plant fitness.

Universal strategies in research and drug discovery based on protein-fragment complementation assays

Changes in the interactions among proteins that participate in a biochemical pathway can reflect the immediate regulatory responses to intrinsic or extrinsic perturbations of the pathway. Thus, methods that allow for the direct detection of the dynamics of protein-protein interactions can be used to probe the effects of any perturbation on any pathway of interest. Here we describe experimental strategies - based on protein-fragment complementation assays (PCAs) - that can achieve this. PCA-based strategies can be used with or instead of traditional target-based drug discovery strategies to identify novel pathway-component proteins of therapeutic interest, to increase the quantity and quality of information about the actions of potential drugs, and to gain insight into the intricate networks that make up the molecular machinery of living cells.

Dynamic range and mass accuracy of wide-scan direct infusion nanoelectrospray fourier transform ion cyclotron resonance mass spectrometry-based metabolomics increased by the spectral stitching method

Direct infusion nanoelectrospray Fourier transform ion cyclotron resonance mass spectrometry (DI nESI FT-ICR MS) offers high mass accuracy and resolution for analyzing complex metabolite mixtures. High dynamic range across a wide mass range, however, can only be achieved at the expense of mass accuracy, since the large numbers of ions entering the ICR detector induce adverse space-charge effects. Here we report an optimized strategy for wide-scan DI nESI FT-ICR MS that increases dynamic range but maintains high mass accuracy. It comprises the collection of multiple adjacent selected ion monitoring (SIM) windows that are stitched together using novel algorithms. The final SIM-stitching method, derived from several optimization experiments, comprises 21 adjoining SIM windows each of width m/z 30 (from m/z 70 to 500; adjacent windows overlap by m/z 10) with an automated gain control (AGC) target of 1 x 10(5) charges. SIM-stitching and wide-scan range (WSR; Thermo Electron) were compared using a defined standard to assess mass accuracy and a liver extract to assess peak count and dynamic range. SIM-stitching decreased the maximum mass error by 1.3- and 4.3-fold, and increased the peak count by 5.3- and 1.8-fold, versus WSR (AGC targets of 1 x 10(5) and 5 x 10(5), respectively). SIM-stitching achieved an rms mass error of 0.18 ppm and detected over 3000 peaks in liver extract. This novel approach increases metabolome coverage, has very high mass accuracy, and at 5.5 min/sample is conducive for high-throughput metabolomics.

Methods, applications and concepts of metabolite profiling: primary metabolism

In the 1990s the concept of a comprehensive analysis of the metabolic complement in biological systems, termed metabolomics or alternately metabonomics, was established as the last of four cornerstones for phenotypic studies in the post-genomic era. With genomic, transcriptomic, and proteomic technologies in place and metabolomic phenotyping under rapid development all necessary tools appear to be available today for a fully functional assessment of biological phenomena at all major system levels of life. This chapter attempts to describe and discuss crucial steps of establishing and maintaining a gas chromatography/electron impact ionization/ mass spectrometry (GC-EI-MS)-based metabolite profiling platform. GC-EI-MS can be perceived as the first and exemplary profiling technology aimed at simultaneous and non-biased analysis of primary metabolites from biological samples. The potential and constraints of this profiling technology are among the best understood. Most problems are solved as well as pitfalls identified. Thus GC-EI-MS serves as an ideal example for students and scientists who intend to enter the field of metabolomics. This chapter will be biased towards GC-EI-MS analyses but aims at discussing general topics, such as experimental design, metabolite identification, quantification and data mining.

Chemical genetic transcriptional fingerprinting for selectivity profiling of kinase inhibitors

The importance of protein kinases as a major class of drug targets across multiple diseases has generated a critical need for technologies that enable the identification of potent and selective kinase inhibitors. Bruton's tyrosine kinase (Btk) is a compelling drug target in multiple therapeutic areas, including systemic lupus erythematosus, asthma, rheumatoid arthritis, and B cell malignancies. We have combined potent, selective kinase inhibition through chemical genetics with gene expression profiling to identify a "fingerprint" of transcriptional changes associated with selective Btk kinase inhibition. The Btk transcriptional fingerprint shows remarkable relevance for Btk's biological roles and was used for functional selectivity profiling of two kinase inhibitor compounds. The fingerprint was able to rank the compounds by relative selectivity for Btk, and revealed broader off-target effects than observed in a broad panel of biochemical kinase cross screens. In addition to being useful for functional selectivity profiling, the fingerprint genes are themselves potential preclinical and clinical biomarkers for developing Btk-directed therapies.

Can pharmacology possibly have a role for bioinformatics?

In today's information-driven culture, there is virtually no walk of life that is not impacted on by computing. As a bridging discipline in the health sciences with activities that span both basic science and clinical interests, modern pharmacology is no exception. As the plethora of data and databases spawned by the 'omics' generation expand in number and complexity, bioinformatics is necessary to manage, integrate and exploit this cohort of data so that the appropriate links to molecular pathology and therapeutic response can be made. Bioinformatics is now an integral part of drug discovery and development. This article reviews the use of bioinformatics in this process, from target identification and validation, to pharmacogenomics, toxicogenomics and systems biology.

Proteomic approach to investigate MRSA

Over the past decade numerous genomes of pathogenic bacteria were fully sequenced and annotated, while others are continuously being sequenced and published. To date, the sequences of >440 bacterial genomes are publicly available for research purposes. These efforts in high-throughput sequencing parallel major improvements in methods permitting the study of whole transcriptome and proteome of bacteria. This provides a basis for a comprehensive understanding of the bacterial metabolism, adaptability to the environment, regulation, resistance pathways, and pathogenicity mechanisms of pathogens. Staphylococcus aureus is a Gram-positive human pathogen causing a wide variety of infections ranging from benign skin infections to life-threatening diseases. Furthermore, the spreading of multiresistance strains requiring the use of last-barrier drugs has resulted in the medical and scientific community focusing particularly on this pathogen. We describe here proteomic methods to prepare, identify, and analyze protein fractions, allowing the study of S. aureus on the organism level. Coupled with methods analyzing the whole bacterial transcriptome, this approach might contribute to the development of rapid diagnostic tests and to the identification of new drug targets.

Enantiomeric separation and determination of absolute stereochemistry of asymmetric molecules in drug discovery—Building chiral technology toolboxes

The application of Chiral Technology, or the (extensive) use of techniques or tools for the determination of absolute stereochemistry and the enantiomeric or chiral separation of racemic small molecule potential lead compounds, has been critical to successfully discovering and developing chiral drugs in the pharmaceutical industry. This has been due to the rapid increase over the past 10-15 years in potential drug candidates containing one or more asymmetric centers. Based on the experiences of one pharmaceutical company, a summary of the establishment of a Chiral Technology toolbox, including the implementation of known tools as well as the design, development, and implementation of new Chiral Technology tools, is provided.