Glucocorticoid receptor ligands modulate Cardiovirus encephalomyocarditis virus internal ribosome entry site activity

The use of small molecules to modulate cellular processes is a powerful approach to investigate gene function as a complement to genetic approaches. The discovery and characterization of compounds that modulate translation initiation, the rate-limiting step of protein synthesis, is important both to provide tool compounds to explore this fundamental biological process and to further evaluate protein synthesis as a therapeutic target. While most messenger ribonucleic acids (mRNAs) recruit ribosomes via their 5' cap, some viral and cellular mRNAs initiate protein synthesis via an alternative "cap-independent" mechanism utilizing internal ribosome entry sites (IRES) elements, which are complex mRNA secondary structures, localized within the 5' nontranslated region of the mRNA upstream of the AUG start codon. This report describes the design of a functional, high throughput screen of small molecules miniaturized into a 1,536-well format and performed using the luciferase reporter gene under control of the viral Cardiovirus encephalomyocarditis virus (EMCV) IRES element to identify nontoxic compounds modulating translation initiated from the EMCV IRES. One activating compound, validated in a dose response manner, has previously been shown to bind the glucocorticoid receptor (GR). Subsequent testing of additional GR modulators further supported this as the possible mechanism of action. Detailed characterization of this compound activity supported the notion that this was due to an effect at the level of translation.

Integrated compound profiling screens identify the mitochondrial electron transport chain as the molecular target of the natural products manassantin, sesquicillin, and arctigenin

Phenotypic compound screens can be used to identify novel targets in signaling pathways and disease processes, but the usefulness of these screens depends on the ability to quickly determine the target and mechanism of action of the molecules identified as hits. One fast route to discovering the mechanism of action of a compound is to profile its properties and to match this profile with those of compounds of known mechanism of action. In this work, the Novartis collection of over 12,000 pure natural products was screened for effects on early zebrafish development. The largest phenotypic class of hits, which caused developmental arrest without necrosis, contained known electron transport chain inhibitors and many compounds of unknown mechanism of action. High-throughput transcriptional profiling revealed that these compounds are mechanistically related to one another. Metabolic and biochemical assays confirmed that all of the molecules that induced developmental arrest without necrosis inhibited the electron transport chain. These experiments demonstrate that the electron transport chain is the target of the natural products manassantin, sesquicillin, and arctigenin. The overlap between the zebrafish and transcriptional profiling screens was not perfect, indicating that multiple profiling screens are necessary to fully characterize molecules of unknown function. Together, zebrafish screening and transcriptional profiling represent sensitive and scalable approaches for identifying bioactive compounds and elucidating their mechanism of action.

The multidimensional perturbation value: a single metric to measure similarity and activity of treatments in high-throughput multidimensional screens

Screens using high-throughput, information-rich technologies such as microarrays, high-content screening (HCS), and next-generation sequencing (NGS) have become increasingly widespread. Compared with single-readout assays, these methods produce a more comprehensive picture of the effects of screened treatments. However, interpreting such multidimensional readouts is challenging. Univariate statistics such as t-tests and Z-factors cannot easily be applied to multidimensional profiles, leaving no obvious way to answer common screening questions such as "Is treatment X active in this assay?" and "Is treatment X different from (or equivalent to) treatment Y?" We have developed a simple, straightforward metric, the multidimensional perturbation value (mp-value), which can be used to answer these questions. Here, we demonstrate application of the mp-value to three data sets: a multiplexed gene expression screen of compounds and genomic reagents, a microarray-based gene expression screen of compounds, and an HCS compound screen. In all data sets, active treatments were successfully identified using the mp-value, and simulations and follow-up analyses supported the mp-value's statistical and biological validity. We believe the mp-value represents a promising way to simplify the analysis of multidimensional data while taking full advantage of its richness.

Determination of minimal transcriptional signatures of compounds for target prediction

The identification of molecular target and mechanism of action of compounds is a key hurdle in drug discovery. Multiplexed techniques for bead-based expression profiling allow the measurement of transcriptional signatures of compound-treated cells in high-throughput mode. Such profiles can be used to gain insight into compounds' mode of action and the protein targets they are modulating. Through the proxy of target prediction from such gene signatures we explored important aspects of the use of transcriptional profiles to capture biological variability of perturbed cellular assays. We found that signatures derived from expression data and signatures derived from biological interaction networks performed equally well, and we showed that gene signatures can be optimised using a genetic algorithm. Gene signatures of approximately 128 genes seemed to be most generic, capturing a maximum of the perturbation inflicted on cells through compound treatment. Moreover, we found evidence for oxidative phosphorylation to be one of the most general ways to capture compound perturbation.

Pathway Reporter Assays Reveal Small Molecule Mechanisms of Action

Cell-based, phenotypic screening of small molecules often identifies compounds with provocative biological properties. However, determining the cellular target(s) and/or mechanism of action (MoA) of lead compounds remains an extremely challenging and time-consuming exercise. To provide insights into a compound's cellular action and greatly reduce the time required for MoA determination, we have developed a screening platform consisting of an extensive series of reporter gene assays (RGAs). A collection of > 11,000 compounds of known MoA (e.g., World Drug Index entries) were screened against the entire panel. The output provided evidence that an RGA signature could be ascribed to numerous, biologically diverse MoAs. The reference database generated suggested novel biological activity for particular compounds. For example, the profiling data led to the prediction that the cellular target of the natural product terprenin was dihydroorotate dehydrogenase (DHODH), which was confirmed experimentally. The screening methodology developed for this endeavor renders it amenable to the future examination of compounds with unknown MoA, in an automated, inexpensive, and time-efficient manner.

On the complete determination of biological systems

The nascent field of systems biology ambitiously proposes to integrate information from large-scale biology projects to create computational models that are, in some sense, complete. However, the details of what would constitute a complete systems-level model of an organism are far from clear. To provide a framework for this difficult question it is useful to define a model as a set of rules that maps a set of inputs (e.g. descriptions of the cell's environment) to a set of outputs (e.g. the concentrations of all its RNAs and proteins). We show how the properties of a model affect the required experimental sampling and estimate the number of experiments needed to "complete" a particular model. Based on these estimates, we suggest that the complete determination of a biological system is a concrete, achievable goal.

A microarray-based antibiotic screen identifies a regulatory role for supercoiling in the osmotic stress response of Escherichia coli

Changes in DNA supercoiling are induced by a wide range of environmental stresses in Escherichia coli, but the physiological significance of these responses remains unclear. We now demonstrate that an increase in negative supercoiling is necessary for transcriptional activation of a large subset of osmotic stress-response genes. Using a microarray-based approach, we have characterized supercoiling-dependent gene transcription by expression profiling under conditions of high salt, in conjunction with the microbial antibiotics novobiocin, pefloxacin, and chloramphenicol. Algorithmic clustering and statistical measures for gauging cellular function show that this subset is enriched for genes critical in osmoprotectant transport/synthesis and rpoS-driven stationary phase adaptation. Transcription factor binding site analysis also supports regulation by the global stress sigma factor rpoS. In addition, these studies implicate 60 uncharacterized genes in the osmotic stress regulon, and offer evidence for a broader role for supercoiling in the control of stress-induced transcription.

Global RNA half-life analysis in Escherichia coli reveals positional patterns of transcript degradation

Subgenic-resolution oligonucleotide microarrays were used to study global RNA degradation in wild-type Escherichia coli MG1655. RNA chemical half-lives were measured for 1036 open reading frames (ORFs) and for 329 known and predicted operons. The half-life of total mRNA was 6.8 min under the conditions tested. We also observed significant relationships between gene functional assignments and transcript stability. Unexpectedly, transcription of a single operon (tdcABCDEFG) was relatively rifampicin-insensitive and showed significant increases 2.5 min after rifampicin addition. This supports a novel mechanism of transcription for the tdc operon, whose promoter lacks any recognizable sigma binding sites. Probe by probe analysis of all known and predicted operons showed that the 5' ends of operons degrade, on average, more quickly than the rest of the transcript, with stability increasing in a 3' direction, supporting and further generalizing the current model of a net 5' to 3' directionality of degradation. Hierarchical clustering analysis of operon degradation patterns revealed that this pattern predominates but is not exclusive. We found a weak but highly significant correlation between the degradation of adjacent operon regions, suggesting that stability is determined by a combination of local and operon-wide stability determinants. The 16 ORF dcw gene cluster, which has a complex promoter structure and a partially characterized degradation pattern, was studied at high resolution, allowing a detailed and integrated description of its abundance and degradation. We discuss the application of subgenic resolution DNA microarray analysis to study global mechanisms of RNA transcription and processing.

RNA expression analysis using a 30 base pair resolution Escherichia coli genome array

We have developed a high-resolution "genome array" for the study of gene expression and regulation in Escherichia coli. This array contains on average one 25-mer oligonucleotide probe per 30 base pairs over the entire genome, with one every 6 bases for the intergenic regions and every 60 bases for the 4,290 open reading frames (ORFs). Twofold concentration differences can be detected at levels as low as 0.2 messenger RNA (mRNA) copies per cell, and differences can be seen over a dynamic range of three orders of magnitude. In rich medium we detected transcripts for 97% and 87% of the ORFs in stationary and log phases, respectively. We found that 1, 529 transcripts were differentially expressed under these conditions. As expected, genes involved in translation were expressed at higher levels in log phase, whereas many genes known to be involved in the starvation response were expressed at higher levels in stationary phase. Many previously unrecognized growth phase-regulated genes were identified, such as a putative receptor (b0836) and a 30S ribosomal protein subunit (S22), both of which are highly upregulated in stationary phase. Transcription of between 3,000 and 4,000 predicted ORFs was observed from the antisense strand, indicating that most of the genome is transcribed at a detectable level. Examples are also presented for high-resolution array analysis of transcript start and stop sites and RNA secondary structure.