Tales of 1,008 small molecules: phenomic profiling through live-cell imaging in a panel of reporter cell lines

Phenomic profiles are high-dimensional sets of readouts that can comprehensively capture the biological impact of chemical and genetic perturbations in cellular assay systems. Phenomic profiling of compound libraries can be used for compound target identification or mechanism of action (MoA) prediction and other applications in drug discovery. To devise an economical set of phenomic profiling assays, we assembled a library of 1,008 approved drugs and well-characterized tool compounds manually annotated to 218 unique MoAs, and we profiled each compound at four concentrations in live-cell, high-content imaging screens against a panel of 15 reporter cell lines, which expressed a diverse set of fluorescent organelle and pathway markers in three distinct cell lineages. For 41 of 83 testable MoAs, phenomic profiles accurately ranked the reference compounds (AUC-ROC ≥ 0.9). MoAs could be better resolved by screening compounds at multiple concentrations than by including replicates at a single concentration. Screening additional cell lineages and fluorescent markers increased the number of distinguishable MoAs but this effect quickly plateaued. There remains a substantial number of MoAs that were hard to distinguish from others under the current study's conditions. We discuss ways to close this gap, which will inform the design of future phenomic profiling efforts.

A joint modeling approach for uncovering associations between gene expression, bioactivity and chemical structure in early drug discovery to guide lead selection and genomic biomarker development

The modern drug discovery process involves multiple sources of high-dimensional data. This imposes the challenge of data integration. A typical example is the integration of chemical structure (fingerprint features), phenotypic bioactivity (bioassay read-outs) data for targets of interest, and transcriptomic (gene expression) data in early drug discovery to better understand the chemical and biological mechanisms of candidate drugs, and to facilitate early detection of safety issues prior to later and expensive phases of drug development cycles. In this paper, we discuss a joint model for the transcriptomic and the phenotypic variables conditioned on the chemical structure. This modeling approach can be used to uncover, for a given set of compounds, the association between gene expression and biological activity taking into account the influence of the chemical structure of the compound on both variables. The model allows to detect genes that are associated with the bioactivity data facilitating the identification of potential genomic biomarkers for compounds efficacy. In addition, the effect of every structural feature on both genes and pIC50 and their associations can be simultaneously investigated. Two oncology projects are used to illustrate the applicability and usefulness of the joint model to integrate multi-source high-dimensional information to aid drug discovery.

Transcriptional Characterization of Compounds: Lessons Learned from the Public LINCS Data

The NIH-funded LINCS program has been initiated to generate a library of integrated, network-based, cellular signatures (LINCS). A novel high-throughput gene-expression profiling assay known as L1000 was the main technology used to generate more than a million transcriptional profiles. The profiles are based on the treatment of 14 cell lines with one of many perturbation agents of interest at a single concentration for 6 and 24 hours duration. In this study, we focus on the chemical compound treatments within the LINCS data set. The experimental variables available include number of replicates, cell lines, and time points. Our study reveals that compound characterization based on three cell lines at two time points results in more genes being affected than six cell lines at a single time point. Based on the available LINCS data, we conclude that the most optimal experimental design to characterize a large set of compounds is to test them in duplicate in three different cell lines. Our conclusions are constrained by the fact that the compounds were profiled at a single, relative high concentration, and the longer time point is likely to result in phenotypic rather than mechanistic effects being recorded.

Weighted similarity-based clustering of chemical structures and bioactivity data in early drug discovery

The modern process of discovering candidate molecules in early drug discovery phase includes a wide range of approaches to extract vital information from the intersection of biology and chemistry. A typical strategy in compound selection involves compound clustering based on chemical similarity to obtain representative chemically diverse compounds (not incorporating potency information). In this paper, we propose an integrative clustering approach that makes use of both biological (compound efficacy) and chemical (structural features) data sources for the purpose of discovering a subset of compounds with aligned structural and biological properties. The datasets are integrated at the similarity level by assigning complementary weights to produce a weighted similarity matrix, serving as a generic input in any clustering algorithm. This new analysis work flow is semi-supervised method since, after the determination of clusters, a secondary analysis is performed wherein it finds differentially expressed genes associated to the derived integrated cluster(s) to further explain the compound-induced biological effects inside the cell. In this paper, datasets from two drug development oncology projects are used to illustrate the usefulness of the weighted similarity-based clustering approach to integrate multi-source high-dimensional information to aid drug discovery. Compounds that are structurally and biologically similar to the reference compounds are discovered using this proposed integrative approach.

Translation of disease associated gene signatures across tissues

It has recently been shown that disease associated gene signatures can be identified by profiling tissue other than the disease related tissue. In this paper, we investigate gene signatures for Irritable Bowel Syndrome (IBS) using gene expression profiling of both disease related tissue (colon) and surrogate tissue (rectum). Gene specific joint ANOVA models were used to investigate differentially expressed genes between the IBS patients and the healthy controls taken into account both intra and inter tissue dependencies among expression levels of the same gene. Classification algorithms in combination with feature selection methods were used to investigate the predictive power of gene expression levels from the surrogate and the target tissues. We conclude based on the analyses that expression profiles of the colon and the rectum tissue could result in better predictive accuracy if the disease associated genes are known.

Understanding drugs in breast cancer through drug sensitivity screening

With substantial numbers of breast tumors showing or acquiring treatment resistance, it is of utmost importance to develop new agents for the treatment of the disease, to know their effectiveness against breast cancer and to understand their relationships with other drugs to best assign the right drug to the right patient. To achieve this goal drug screenings on breast cancer cell lines are a promising approach. In this study a large-scale drug screening of 37 compounds was performed on a panel of 42 breast cancer cell lines representing the main breast cancer subtypes. Clustering, correlation and pathway analyses were used for data analysis. We found that compounds with a related mechanism of action had correlated IC50 values and thus grouped together when the cell lines were hierarchically clustered based on IC50 values. In total we found six clusters of drugs of which five consisted of drugs with related mode of action and one cluster with two drugs not previously connected. In total, 25 correlated and four anti-correlated drug sensitivities were revealed of which only one drug, Sirolimus, showed significantly lower IC50 values in the luminal/ERBB2 breast cancer subtype. We found expected interactions but also discovered new relationships between drugs which might have implications for cancer treatment regimens.

Connecting gene expression data from connectivity map and in silico target predictions for small molecule mechanism-of-action analysis

Integrating gene expression profiles with certain proteins can improve our understanding of the fundamental mechanisms in protein-ligand binding. This paper spotlights the integration of gene expression data and target prediction scores, providing insight into mechanism of action (MoA). Compounds are clustered based upon the similarity of their predicted protein targets and each cluster is linked to gene sets using Linear Models for Microarray Data. MLP analysis is used to generate gene sets based upon their biological processes and a qualitative search is performed on the homogeneous target-based compound clusters to identify pathways. Genes and proteins were linked through pathways for 6 of the 8 MCF7 and 6 of the 11 PC3 clusters. Three compound clusters are studied; (i) the target-driven cluster involving HSP90 inhibitors, geldanamycin and tanespimycin induces differential expression for HSP90-related genes and overlap with pathway response to unfolded protein. Gene expression results are in agreement with target prediction and pathway annotations add information to enable understanding of MoA. (ii) The antipsychotic cluster shows differential expression for genes LDLR and INSIG-1 and is predicted to target CYP2D6. Pathway steroid metabolic process links the protein and respective genes, hypothesizing the MoA for antipsychotics. A sub-cluster (verepamil and dexverepamil), although sharing similar protein targets with the antipsychotic drug cluster, has a lower intensity of expression profile on related genes, indicating that this method distinguishes close sub-clusters and suggests differences in their MoA. Lastly, (iii) the thiazolidinediones drug cluster predicted peroxisome proliferator activated receptor (PPAR) PPAR-alpha, PPAR-gamma, acyl CoA desaturase and significant differential expression of genes ANGPTL4, FABP4 and PRKCD. The targets and genes are linked via PPAR signalling pathway and induction of apoptosis, generating a hypothesis for the MoA of thiazolidinediones. Our analysis show one or more underlying MoA for compounds and were well-substantiated with literature.

Delayed bactericidal response of Mycobacterium tuberculosis to bedaquiline involves remodelling of bacterial metabolism

Bedaquiline (BDQ), an ATP synthase inhibitor, is the first drug to be approved for treatment of multidrug-resistant tuberculosis in decades. Though BDQ has shown excellent efficacy in clinical trials, its early bactericidal activity during the first week of chemotherapy is minimal. Here, using microfluidic devices and time-lapse microscopy of Mycobacterium tuberculosis, we confirm the absence of significant bacteriolytic activity during the first 3-4 days of exposure to BDQ. BDQ-induced inhibition of ATP synthesis leads to bacteriostasis within hours after drug addition. Transcriptional and proteomic analyses reveal that M. tuberculosis responds to BDQ by induction of the dormancy regulon and activation of ATP-generating pathways, thereby maintaining bacterial viability during initial drug exposure. BDQ-induced bacterial killing is significantly enhanced when the mycobacteria are grown on non-fermentable energy sources such as lipids (impeding ATP synthesis via glycolysis). Our results show that BDQ exposure triggers a metabolic remodelling in mycobacteria, thereby enabling transient bacterial survival.

FABIA: factor analysis for bicluster acquisition

MOTIVATION

Biclustering of transcriptomic data groups genes and samples simultaneously. It is emerging as a standard tool for extracting knowledge from gene expression measurements. We propose a novel generative approach for biclustering called 'FABIA: Factor Analysis for Bicluster Acquisition'. FABIA is based on a multiplicative model, which accounts for linear dependencies between gene expression and conditions, and also captures heavy-tailed distributions as observed in real-world transcriptomic data. The generative framework allows to utilize well-founded model selection methods and to apply Bayesian techniques.

RESULTS

On 100 simulated datasets with known true, artificially implanted biclusters, FABIA clearly outperformed all 11 competitors. On these datasets, FABIA was able to separate spurious biclusters from true biclusters by ranking biclusters according to their information content. FABIA was tested on three microarray datasets with known subclusters, where it was two times the best and once the second best method among the compared biclustering approaches.

AVAILABILITY

FABIA is available as an R package on Bioconductor (http://www.bioconductor.org). All datasets, results and software are available at http://www.bioinf.jku.at/software/fabia/fabia.html.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Response prediction to a multitargeted kinase inhibitor in cancer cell lines and xenograft tumors using high-content tyrosine peptide arrays with a kinetic readout

Multitargeted kinase inhibitors have shown clinical efficacy in a range of cancer types. However, two major problems associated with these drugs are the low fraction of patients for which these treatments provide initial clinical benefit and the occurrence of resistance during prolonged therapy. Several types of predictive biomarkers have been suggested, such as expression level and phosphorylation status of the major targeted kinase(s), mutational status of the kinases involved and of key components of the downstream signaling cascades, and gene expression signatures. In this work, we describe the development of a response prediction platform that does not require prior knowledge of the relevant kinases targeted by the inhibitor; instead, a phosphotyrosine peptide profile using peptide arrays with a kinetic readout is derived in lysates in the presence and absence of a kinase inhibitor. We show in a range of cell lines and in xenograft tumors that this approach allows for the stratification of responders and nonresponders to a multitargeted kinase inhibitor.

An investigation on performance of Significance Analysis of Microarray (SAM) for the comparisons of several treatments with one control in the presence of small-variance genes

One of multiple testing problems in drug finding experiments is the comparison of several treatments with one control. In this paper we discuss a particular situation of such an experiment, i.e., a microarray setting, where the many-to-one comparisons need to be addressed for thousands of genes simultaneously. For a gene-specific analysis, Dunnett's single step procedure is considered within gene tests, while the FDR controlling procedures such as Significance Analysis of Microarrays (SAM) and Benjamini and Hochberg (BH) False Discovery Rate (FDR) adjustment are applied to control the error rate across genes. The method is applied to a microarray experiment with four treatment groups (three microarrays in each group) and 16,998 genes. Simulation studies are conducted to investigate the performance of the SAM method and the BH-FDR procedure with regard to controlling the FDR, and to investigate the effect of small-variance genes on the FDR in the SAM procedure.

Diarylquinolines are bactericidal for dormant mycobacteria as a result of disturbed ATP homeostasis

An estimated one-third of the world population is latently infected with Mycobacterium tuberculosis. These nonreplicating, dormant bacilli are tolerant to conventional anti-tuberculosis drugs, such as isoniazid. We recently identified diarylquinoline R207910 (also called TMC207) as an inhibitor of ATP synthase with a remarkable activity against replicating mycobacteria. In the present study, we show that R207910 kills dormant bacilli as effectively as aerobically grown bacilli with the same target specificity. Despite a transcriptional down-regulation of the ATP synthase operon and significantly lower cellular ATP levels, we show that dormant mycobacteria do possess residual ATP synthase enzymatic activity. This activity is blocked by nanomolar concentrations of R207910, thereby further reducing ATP levels and causing a pronounced bactericidal effect. We conclude that this residual ATP synthase activity is indispensable for the survival of dormant mycobacteria, making it a promising drug target to tackle dormant infections. The unique dual bactericidal activity of diarylquinolines on dormant as well as replicating bacterial subpopulations distinguishes them entirely from the current anti-tuberculosis drugs and underlines the potential of R207910 to shorten tuberculosis treatment.

Alterations in mucosal immunity identified in the colon of patients with irritable bowel syndrome

BACKGROUND & AIMS

Irritable bowel syndrome (IBS) has been associated with mucosal dysfunction, mild inflammation, and altered colonic bacteria. We used microarray expression profiling of sigmoid colon mucosa to assess whether there are stably expressed sets of genes that suggest there are objective molecular biomarkers associated with IBS.

METHODS

Gene expression profiling was performed using Human Genome U133 Plus 2.0 (Affymetrix) GeneChips with RNA from sigmoid colon mucosal biopsy specimens from 36 IBS patients and 25 healthy control subjects. Real-time quantitative polymerase chain reaction was used to confirm the data in 12 genes of interest. Statistical methods for microarray data were applied to search for differentially expressed genes, and to assess the stability of molecular signatures in IBS patients.

RESULTS

Mucosal gene expression profiles were consistent across different sites within the sigmoid colon and were stable on repeat biopsy over approximately 3 months. Differentially expressed genes suggest functional alterations of several components of the host mucosal immune response to microbial pathogens. The most strikingly increased expression involved a yet uncharacterized gene, DKFZP564O0823. Identified specific genes suggest the hypothesis that molecular signatures may enable distinction of a subset of IBS patients from healthy controls. By using 75% of the biopsy specimens as a validation set to develop a gene profile, the test set (25%) was predicted correctly with approximately 70% accuracy.

CONCLUSIONS

Mucosal gene expression analysis shows there are relatively stable alterations in colonic mucosal immunity in IBS. These molecular alterations provide the basis to test the hypothesis that objective biomarkers may be identified in IBS and enhance understanding of the disease.

I/NI-calls for the exclusion of non-informative genes: a highly effective filtering tool for microarray data

MOTIVATION

DNA microarray technology typically generates many measurements of which only a relatively small subset is informative for the interpretation of the experiment. To avoid false positive results, it is therefore critical to select the informative genes from the large noisy data before the actual analysis. Most currently available filtering techniques are supervised and therefore suffer from a potential risk of overfitting. The unsupervised filtering techniques, on the other hand, are either not very efficient or too stringent as they may mix up signal with noise. We propose to use the multiple probes measuring the same target mRNA as repeated measures to quantify the signal-to-noise ratio of that specific probe set. A Bayesian factor analysis with specifically chosen prior settings, which models this probe level information, is providing an objective feature filtering technique, named informative/non-informative calls (I/NI calls).

RESULTS

Based on 30 real-life data sets (including various human, rat, mice and Arabidopsis studies) and a spiked-in data set, it is shown that I/NI calls is highly effective, with exclusion rates ranging from 70% to 99%. Consequently, it offers a critical solution to the curse of high-dimensionality in the analysis of microarray data.

AVAILABILITY

This filtering approach is publicly available as a function implemented in the R package FARMS (www.bioinf.jku.at/software/farms/farms.html).

The high-level similarity of some disparate gene expression measures

Probe-level data from Affymetrix GeneChips can be summarized in many ways to produce probe-set level gene expression measures (GEMs). Disturbingly, the different approaches not only generate quite different measures but they could also yield very different analysis results. Here, we explore the question of how much the analysis results really do differ, first at the gene level, then at the biological process level. We demonstrate that, even though the gene level results may not necessarily match each other particularly well, as long as there is reasonably strong differentiation between the groups in the data, the various GEMs do in fact produce results that are similar to one another at the biological process level. Not only that the results are biologically relevant. As the extent of differentiation drops, the degree of concurrence weakens, although the biological relevance of findings at the biological process level may yet remain.

Analysis of Affymetrix GeneChip data using amplified RNA

When small biological samples are collected by microdissection or other methods, amplification techniques are required to provide sufficient target for hybridization to expression arrays. One such technique is to perform two successive rounds of T7-based in vitro transcription. However the use of random primers, required to regenerate cDNA from the first round of transcription, results in shortened copies of cDNA from which the 5' end is missing. In this paper we describe an experiment designed to compare the quality of data obtained from labeling small RNA samples using the Affymetrix Two-Cycle Eukaryotic. Target Labeling procedure to that of data obtained using the One-Cycle Eukaryotic Target Labeling protocol. We utilized different preprocessing algorithms to compare the data generated using both labeling methods and present a new algorithm that improves upon existing ones in this setting.

A diarylquinoline drug active on the ATP synthase of Mycobacterium tuberculosis

The incidence of tuberculosis has been increasing substantially on a worldwide basis over the past decade, but no tuberculosis-specific drugs have been discovered in 40 years. We identified a diarylquinoline, R207910, that potently inhibits both drug-sensitive and drug-resistant Mycobacterium tuberculosis in vitro (minimum inhibitory concentration 0.06 mug/ml). In mice, R207910 exceeded the bactericidal activities of isoniazid and rifampin by at least 1 log unit. Substitution of drugs included in the World Health Organization's first-line tuberculosis treatment regimen (rifampin, isoniazid, and pyrazinamide) with R207910 accelerated bactericidal activity, leading to complete culture conversion after 2 months of treatment in some combinations. A single dose of R207910 inhibited mycobacterial growth for 1 week. Plasma levels associated with efficacy in mice were well tolerated in healthy human volunteers. Mutants selected in vitro suggest that the drug targets the proton pump of adenosine triphosphate (ATP) synthase.

Transcription profiles of the bacterium Mycoplasma pneumoniae grown at different temperatures

Applying microarray technology, we have investigated the transcriptome of the small bacterium Mycoplasma pneumoniae grown at three different temperature conditions: 32, 37 and 32 degrees C followed by a heat shock for 15 min at 43 degrees C, before isolating the RNA. From 688 proposed open-reading frames, 676 were investigated and 564 were found to be expressed (P < 0.001; 606 with P < 0.01) and at least 33 (P < 0.001; 77 at P < 0.01) regulated. By quantitative real-time PCR of selected mRNA species, the expression data could be linked to absolute molecule numbers. We found M.pneumoniae to be regulated at the transcriptional level. Forty-seven genes were found to be significantly up-regulated after heat shock (P < 0.01). Among those were the conserved heat shock genes like dnaK, lonA and clpB, but also several genes coding for ribosomal proteins and 10 genes of unassigned functions. In addition, 30 genes were found to be down-regulated under the applied heat shock conditions. Further more, we have compared different methods of cDNA synthesis (random hexamer versus gene-specific primers, different RNA concentrations) and various normalization strategies of the raw microarray data.