High-throughput Screening Experiments Research Articles

DRABAL: novel method to mine large high-throughput screening assays using Bayesian active learning.

Background Mining high-throughput screening (HTS) assays is key for enhancing decisions in the area of drug repositioning and drug discovery. However, many challenges are encountered in the process of developing suitable and accurate methods for extracting useful information from these assays. Virtual screening and a wide variety of databases, methods and solutions proposed to-date, did not completely overcome these challenges. This study is based on a multi-label classification (MLC) technique for modeling correlations between several HTS assays, meaning that a single prediction represents a subset of assigned correlated labels instead of one label. Thus, the devised method provides an increased probability for more accurate predictions of compounds that were not tested in particular assays.ResultsHere we present DRABAL, a novel MLC solution that incorporates structure learning of a Bayesian network as a step to model dependency between the HTS assays. In this study, DRABAL was used to process more than 1.4 million interactions of over 400,000 compounds and analyze the existing relationships between five large HTS assays from the PubChem BioAssay Database. Compared to different MLC methods, DRABAL significantly improves the F1Score by about 22%, on average. We further illustrated usefulness and utility of DRABAL through screening FDA approved drugs and reported ones that have a high probability to interact with several targets, thus enabling drug-multi-target repositioning. Specifically DRABAL suggests the Thiabendazole drug as a common activator of the NCP1 and Rab-9A proteins, both of which are designed to identify treatment modalities for the Niemann–Pick type C disease.ConclusionWe developed a novel MLC solution based on a Bayesian active learning framework to overcome the challenge of lacking fully labeled training data and exploit actual dependencies between the HTS assays. The solution is motivated by the need to model dependencies between existing experimental confirmatory HTS assays and improve prediction performance. We have pursued extensive experiments over several HTS assays and have shown the advantages of DRABAL. The datasets and programs can be downloaded from https://figshare.com/articles/DRABAL/3309562.Graphical abstract. Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-016-0177-8) contains supplementary material, which is available to authorized users.

In silico target prediction for elucidating the mode of action of herbicides including prospective validation

The rapid emergence of pesticide resistance has given rise to a demand for herbicides with new mode of action (MoA). In the agrochemical sector, with the availability of experimental high throughput screening (HTS) data, it is now possible to utilize in silico target prediction methods in the early discovery phase to suggest the MoA of a compound via data mining of bioactivity data. While having been established in the pharmaceutical context, in the agrochemical area this approach poses rather different challenges, as we have found in this work, partially due to different chemistry, but even more so due to different (usually smaller) amounts of data, and different ways of conducting HTS. With the aim to apply computational methods for facilitating herbicide target identification, 48,000 bioactivity data against 16 herbicide targets were processed to train Laplacian modified Naïve Bayesian (NB) classification models. The herbicide target prediction model (“HerbiMod”) is an ensemble of 16 binary classification models which are evaluated by internal, external and prospective validation sets. In addition to the experimental inactives, 10,000 random agrochemical inactives were included in the training process, which showed to improve the overall balanced accuracy of our models up to 40%. For all the models, performance in terms of balanced accuracy of≥80% was achieved in five-fold cross validation. Ranking target predictions was addressed by means of z-scores which improved predictivity over using raw scores alone. An external testset of 247 compounds from ChEMBL and a prospective testset of 394 compounds from BASF SE tested against five well studied herbicide targets (ACC, ALS, HPPD, PDS and PROTOX) were used for further validation. Only 4% of the compounds in the external testset lied in the applicability domain and extrapolation (and correct prediction) was hence impossible, which on one hand was surprising, and on the other hand illustrated the utilization of using applicability domains in the first place. However, performance better than 60% in balanced accuracy was achieved on the prospective testset, where all the compounds fell within the applicability domain, and which hence underlines the possibility of using target prediction also in the area of agrochemicals.

Metabolic Response to XD14 Treatment in Human Breast Cancer Cell Line MCF-7.

XD14 is a 4-acyl pyrrole derivative, which was discovered by a high-throughput virtual screening experiment. XD14 inhibits bromodomain and extra-terminal domain (BET) proteins (BRD2, BRD3, BRD4 and BRDT) and consequently suppresses cell proliferation. In this study, metabolic profiling reveals the molecular effects in the human breast cancer cell line MCF-7 (Michigan Cancer Foundation-7) treated by XD14. A three-day time series experiment with two concentrations of XD14 was performed. Gas chromatography-mass spectrometry (GC-MS) was applied for untargeted profiling of treated and non-treated MCF-7 cells. The gained data sets were evaluated by several statistical methods: analysis of variance (ANOVA), clustering analysis, principle component analysis (PCA), and partial least squares discriminant analysis (PLS-DA). Cell proliferation was strongly inhibited by treatment with 50 µM XD14. Samples could be discriminated by time and XD14 concentration using PLS-DA. From the 117 identified metabolites, 67 were significantly altered after XD14 treatment. These metabolites include amino acids, fatty acids, Krebs cycle and glycolysis intermediates, as well as compounds of purine and pyrimidine metabolism. This massive intervention in energy metabolism and the lack of available nucleotides could explain the decreased proliferation rate of the cancer cells.

Better screen for low-dose drug pollution

Researchers are increasingly concerned about the impact on microorganisms and wildlife from the long-term exposure to low-dose mixtures of antibiotics, lipid regulators, psychiatric drugs, and other medicines that end up in aquatic environments. A new study suggests that existing approaches to studying these environmental risks—which typically measure the risk of individual chemicals separately and then add them together—may not provide a realistic assessment of the harm being done, particularly for low-dose mixtures containing antibiotics. Seeking a better alternative, a team led by Francisca Fernández-Piñas of the Autonomous University of Madrid, Rafael Muñoz-Carpena of the University of Florida, and their collaborators has developed a combined computational and experimental high-throughput screening method that involves studying the effects of chemical mixtures on cyanobacteria, a group of microorganisms found abundantly in water systems (Sci. Adv. 2016, DOI: 10.1126/sciadv.1601272). The team created 180 mixtures of 16 pharmaceutical and personal care product chemicals at nanogram to

Comprehensive analysis of high-throughput screens with HiTSeekR

High-throughput screening (HTS) is an indispensable tool for drug (target) discovery that currently lacks user-friendly software tools for the robust identification of putative hits from HTS experiments and for the interpretation of these findings in the context of systems biology. We developed HiTSeekR as a one-stop solution for chemical compound screens, siRNA knock-down and CRISPR/Cas9 knock-out screens, as well as microRNA inhibitor and -mimics screens. We chose three use cases that demonstrate the potential of HiTSeekR to fully exploit HTS screening data in quite heterogeneous contexts to generate novel hypotheses for follow-up experiments: (i) a genome-wide RNAi screen to uncover modulators of TNFα, (ii) a combined siRNA and miRNA mimics screen on vorinostat resistance and (iii) a small compound screen on KRAS synthetic lethality. HiTSeekR is publicly available at http://hitseekr.compbio.sdu.dk. It is the first approach to close the gap between raw data processing, network enrichment and wet lab target generation for various HTS screen types.

Estimating Potency in High-Throughput Screening Experiments by Maximizing the Rate of Change in Weighted Shannon Entropy.

High-throughput in vitro screening experiments can be used to generate concentration-response data for large chemical libraries. It is often desirable to estimate the concentration needed to achieve a particular effect, or potency, for each chemical tested in an assay. Potency estimates can be used to directly compare chemical profiles and prioritize compounds for confirmation studies, or employed as input data for prediction modeling and association mapping. The concentration for half-maximal activity derived from the Hill equation model (i.e., AC50) is the most common potency measure applied in pharmacological research and toxicity testing. However, the AC50 parameter is subject to large uncertainty for many concentration-response relationships. In this study we introduce a new measure of potency based on a weighted Shannon entropy measure termed the weighted entropy score (WES). Our potency estimator (Point of Departure, PODWES) is defined as the concentration producing the maximum rate of change in weighted entropy along a concentration-response profile. This approach provides a new tool for potency estimation that does not depend on the assumption of monotonicity or any other pre-specified concentration-response relationship. PODWES estimates potency with greater precision and less bias compared to the conventional AC50 assessed across a range of simulated conditions.

QSAR Accelerated Discovery of Potent Ice Recrystallization Inhibitors.

Ice recrystallization is the main contributor to cell damage and death during the cryopreservation of cells and tissues. Over the past five years, many small carbohydrate-based molecules were identified as ice recrystallization inhibitors and several were shown to reduce cryoinjury during the cryopreservation of red blood cells (RBCs) and hematopoietic stems cells (HSCs). Unfortunately, clear structure-activity relationships have not been identified impeding the rational design of future compounds possessing ice recrystallization inhibition (IRI) activity. A set of 124 previously synthesized compounds with known IRI activities were used to calibrate 3D-QSAR classification models using GRid INdependent Descriptors (GRIND) derived from DFT level quantum mechanical calculations. Partial least squares (PLS) model was calibrated with 70% of the data set which successfully identified 80% of the IRI active compounds with a precision of 0.8. This model exhibited good performance in screening the remaining 30% of the data set with 70% of active additives successfully recovered with a precision of ~0.7 and specificity of 0.8. The model was further applied to screen a new library of aryl-alditol molecules which were then experimentally synthesized and tested with a success rate of 82%. Presented is the first computer-aided high-throughput experimental screening for novel IRI active compounds.

Rapid Discrimination of Polymorphic Crystal Forms by Nonlinear Optical Stokes Ellipsometric Microscopy.

The use of nonlinear optical Stokes ellipsometric (NOSE) microscopy for rapid discrimination of two polymorphic forms of the small molecule d-mannitol is presented. Fast (8 MHz) polarization modulated beam-scanning microscopy and a recently developed iterative, nonlinear least-squares fitting algorithm were combined to allow discrimination of orthorhombic and monoclinic crystal structures of d-mannitol with data acquisition times of <7 s per field of view with a signal-to-noise ratio (SNR) of ∼300. Discrimination between polymorphic forms within the 99.99% confidence interval was achieved by standard statistical tests of the recovered probability density functions for the measured observables following two class linear discriminant analysis. These measurements target bottlenecks in small-volume, high-throughput solid form screening experiments for polymorph discovery in the development of emerging active pharmaceutical ingredients.

Abstract P2-05-18: Mesofluidic platform for high throughput screening of inhibitors of metastasis

Abstract A fundamental limitation in the development of new therapies to prevent metastatic cancer is a lack of in vitro systems that can accurately recapitulate the steps of cancer cell metastasis. Currently, most assays for examining the steps of metastasis fail to incorporate the biophysical forces experienced by tumor cells due to blood flow, or are low throughput and thereby not amenable to drug screens or high throughput experimentation. We have developed a novel high throughput mesofluidic platform for assaying cell adhesion under flow in a 96-well format. This device functions like a cone and plate viscometer in each well by inducing shear stress on cells cultured in a standard 96-well plate. We validated the fluid flow and alignment of the device and studied the adhesion of cultured leukocytic monocytes (THP-1 cells) and multiple cancer cell lines (MDA-MB-231 and MCF-7 breast cancer cell lines) to purified extracellular matrix molecules (ECM), endothelial cells and immobilized platelets. Assays were carried out under flow (0.5 dynes/cm2 shear stress) and static conditions. Our studies show that adhesion assays performed under flow yield markedly different results from static adhesion assays. Treatment of breast cancer cells with a small library of integrin inhibitors demonstrated that these compounds had minimal effect on cancer cell adhesion to endothelial cells or immobilized platelets under static conditions, whereas under shear conditions many of these compounds significantly reduced adhesion of cancer cells. As well, this experiment elucidated integrins important for breast cancer adhesion to endothelial cells and platelets. A static adhesion assay of breast cancer cells to various types of ECM showed greater adhesion of the less aggressive MCF-7 cell line in comparison to the more aggressive MDA-MB-231 cell line. In contrast, flow incorporating assays showed increased adhesion of the more aggressive MDA-MB-231 breast cancer cell line. Specifically, the shear assay saw a significant increase in adhesion for multiple ECM as well as an increase in the strength of adhesion to laminin. Finally, we performed a high throughput screening experiment using a kinase inhibitor library of 80 compounds and found that the shear based assay yielded notably different results from a similar screen under static conditions for breast cancer cell adhesion to endothelial cells, immune cell adhesion to endothelial cells and breast cancer cell adhesion to platelets. This shear experiment yielded seven "hits", many of which match targets of drugs in clinical trials. In conclusion, our studies show that adhesion assays performed under flow yield markedly different results from static adhesion assays, and are better at identifying both aggressive cancer cells lines and known pathways for circulating cancer and immune cell adhesion. Thus, this high-throughput screening platform may enable the development of novel compounds to inhibit cancer metastasis and facilitate the study of the systems level behavior of cancer-endothelium adhesion. Citation Format: Spencer A, Spruell C, Nandi S, Le V, Crexiell M, Dunn AK, Baker AB. Mesofluidic platform for high throughput screening of inhibitors of metastasis. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P2-05-18.

Predicting physiologically relevant SH3 domain mediated protein-protein interactions in yeast.

Motivation: Many intracellular signaling processes are mediated by interactions involving peptide recognition modules such as SH3 domains. These domains bind to small, linear protein sequence motifs which can be identified using high-throughput experimental screens such as phage display. Binding motif patterns can then be used to computationally predict protein interactions mediated by these domains. While many protein–protein interaction prediction methods exist, most do not work with peptide recognition module mediated interactions or do not consider many of the known constraints governing physiologically relevant interactions between two proteins.Results: A novel method for predicting physiologically relevant SH3 domain-peptide mediated protein–protein interactions in S. cerevisae using phage display data is presented. Like some previous similar methods, this method uses position weight matrix models of protein linear motif preference for individual SH3 domains to scan the proteome for potential hits and then filters these hits using a range of evidence sources related to sequence-based and cellular constraints on protein interactions. The novelty of this approach is the large number of evidence sources used and the method of combination of sequence based and protein pair based evidence sources. By combining different peptide and protein features using multiple Bayesian models we are able to predict high confidence interactions with an overall accuracy of 0.97.Availability and implementation: Domain-Motif Mediated Interaction Prediction (DoMo-Pred) command line tool and all relevant datasets are available under GNU LGPL license for download from http://www.baderlab.org/Software/DoMo-Pred. The DoMo-Pred command line tool is implemented using Python 2.7 and C ++.Contact: gary.bader@utoronto.caSupplementary information: Supplementary data are available at Bioinformatics online.

Global Biological Network Alignment by Using Efficient Memetic Algorithm

High-throughput experimental screening techniques have resulted in a large number of biological network data such as protein-protein interactions (PPI) data. The analysis of these data can enhance our understanding of cellular processes. PPI network alignment is one of the comparative analysis methods for analyzing biological networks. Research on PPI networks can identify conserved subgraphs and help us to understand evolutionary trajectories across species. Some evolutionary algorithms have been proposed for coping with PPI network alignment, but most of them are limited by the lower search efficiency due to the lack of the priori knowledge. In this paper, we propose a memetic algorithm, denoted as MeAlgn, to solve the biological network alignment by optimizing an objective function which introduces topological structure and sequence similarities. MeAlign combines genetic algorithm with a local search refinement. The genetic algorithm is to find interesting alignment solution regions, and the local search is to find optimal solutions around the regions. The proposed algorithm first develops a coarse similarity score matrix for initialization and then it uses a specific neighborhood heuristic local search strategy to find an optimal alignment. In MeAlign, the information of topological structure and sequence similarities is used to guide our mapping. Experimental results demonstrate that our algorithm can achieve a better mapping than some state-of-the-art algorithms and it makes a better balance between the network topology and nodes sequence similarities.

Mining Chemical Activity Status from High-Throughput Screening Assays.

High-throughput screening (HTS) experiments provide a valuable resource that reports biological activity of numerous chemical compounds relative to their molecular targets. Building computational models that accurately predict such activity status (active vs. inactive) in specific assays is a challenging task given the large volume of data and frequently small proportion of active compounds relative to the inactive ones. We developed a method, DRAMOTE, to predict activity status of chemical compounds in HTP activity assays. For a class of HTP assays, our method achieves considerably better results than the current state-of-the-art-solutions. We achieved this by modification of a minority oversampling technique. To demonstrate that DRAMOTE is performing better than the other methods, we performed a comprehensive comparison analysis with several other methods and evaluated them on data from 11 PubChem assays through 1,350 experiments that involved approximately 500,000 interactions between chemicals and their target proteins. As an example of potential use, we applied DRAMOTE to develop robust models for predicting FDA approved drugs that have high probability to interact with the thyroid stimulating hormone receptor (TSHR) in humans. Our findings are further partially and indirectly supported by 3D docking results and literature information. The results based on approximately 500,000 interactions suggest that DRAMOTE has performed the best and that it can be used for developing robust virtual screening models. The datasets and implementation of all solutions are available as a MATLAB toolbox online at www.cbrc.kaust.edu.sa/dramote and can be found on Figshare.

High-throughput single cell multidrug resistance analysis with multifunctional gradients-customizing microfluidic device

Multidrug resistance analysis represents a great challenge in cancer chemotherapy, drug development and pathological study. In this paper, multifunctional gradients-customizing microfluidic devices were developed for high-throughput single-cell multidrug resistance (MDR) analysis. The gradient profile was determined by the lengths of the distribution microchannels regardless of flow rates and pressure, which provided good stability and remarkably reduced redundancy of microfluidic architecture. The drug of gradient concentrations consecutively stimulate upon the cells in the downstreaming cell cultivation chamber. Time-dependent drug efflux kinetics of HepG2 cells were firstly investigated on our device using both the different-single-cell and the same-single-cell strategies. Furthermore, hepatic polarized HepG2 cells, which collected the secreted cholephilic substances in the apical vacuoles, were used as model to investigate the inhibition of MDR-associated protein with secretion inhibitor cyclosporine A of varied concentrations on single organelle level. Finally, a high-throughput drug screening experiment was conducted to examine both the chemo-sensitizing effect and the cytotoxity of the potential chemo-sensitizing agents. Conclusively, the results confirmed that our method was a highly efficient way to analyze multidrug resistance (MDR) at single-cell or even single-organelle level with advantages of high-throughput, flexibility, stability and low sample consumption.

Solvent-free [Ru]-catalyzed isomerization of allyl glycidyl ether: The scope, effectiveness and recycling of catalysts, and exothermal effect

The effectiveness of selected simple ruthenium catalyst precursors, mainly [RuClH(CO)(PPh3)3] and {[RuCl2(p-cymene)]2}, in the solvent-free isomerization of a model allyl glycidyl ether (AGE) into a 1-propenyl derivative has been examined. The high throughput screening experiment method was adopted for the investigation of the catalytic activity of ruthenium complexes under various reaction conditions. The presence and amount of allyl hydroperoxides were recognized to be crucial for this reaction. For the first time it was found that the neat {[RuCl2(p-cymene)]2} displayed a high catalytic activity in the isomerization of the AGE containing allyl hydroperoxides. The real effectiveness and the recycling of the selected ruthenium catalyst, neat or coupled with some additives such as bases and/or the hydride ligand donor, have been evaluated under various conditions. The method can also be readily scaled up without any practical problems. New, technologically significant facts, important for the assessment of the process reliability have been discovered.

Impact of normalization methods on high-throughput screening data with high hit rates and drug testing with dose-response data.

Motivation: Most data analysis tools for high-throughput screening (HTS) seek to uncover interesting hits for further analysis. They typically assume a low hit rate per plate. Hit rates can be dramatically higher in secondary screening, RNAi screening and in drug sensitivity testing using biologically active drugs. In particular, drug sensitivity testing on primary cells is often based on dose–response experiments, which pose a more stringent requirement for data quality and for intra- and inter-plate variation. Here, we compared common plate normalization and noise-reduction methods, including the B-score and the Loess a local polynomial fit method under high hit-rate scenarios of drug sensitivity testing. We generated simulated 384-well plate HTS datasets, each with 71 plates having a range of 20 (5%) to 160 (42%) hits per plate, with controls placed either at the edge of the plates or in a scattered configuration.Results: We identified 20% (77/384) as the critical hit-rate after which the normalizations started to perform poorly. Results from real drug testing experiments supported this estimation. In particular, the B-score resulted in incorrect normalization of high hit-rate plates, leading to poor data quality, which could be attributed to its dependency on the median polish algorithm. We conclude that a combination of a scattered layout of controls per plate and normalization using a polynomial least squares fit method, such as Loess helps to reduce column, row and edge effects in HTS experiments with high hit-rates and is optimal for generating accurate dose–response curves.Contact: john.mpindi@helsinki.fiAvailability and implementation, Supplementary information: R code and Supplementary data are available at Bioinformatics online.

Abstract LB-008: Mesofluidic platform for high throughput screening for inhibitors of metastasis

Abstract Background: A fundamental limitation in the development of new therapies to prevent metastatic cancer is a lack of in vitro systems that can accurately recapitulate the steps of cancer cell metastasis. Currently, most assays for examining the steps of metastasis fail to incorporate the biophysical forces experienced by tumor cells due to blood flow, or are low throughput and therefore not amenable to drug screening and high throughput experimentation. Methods: We have developed a novel high throughput mesofluidic platform for assaying cell adhesion under flow in a 96-well format. This device functions like a cone and plate viscometer in each well by inducing shear stress on cells cultured in a standard 96-well plate. We validated the fluid flow and alignment of the device and studied the adhesion of cultured leukocytic monocytes (THP-1 cells) and multiple cancer cell lines (HCT116, MDA-MB-231 and MCF-7 cells) to purified extracellular matrix molecules (ECM), endothelial cells and immobilized platelets. All assays were carried out under flow (0.5 dynes/cm2 of shear stress) and static conditions. Results: Our studies show that adhesion assays performed under flow yield markedly different results from static adhesion assays, and are better at identifying both aggressive cancer cells lines and known pathways for circulating cancer and immune cell adhesion. Treatment of breast cancer cells with a small library of integrin inhibitors demonstrated that these compounds had minimal effect on cancer cell adhesion to endothelial cells under static conditions, whereas under shear conditions many of these compounds reduced adhesion of cancer cells. In addition, a static adhesion assay of breast cancer cells to various types of ECM showed higher adhesion of the less aggressive MCF-7 cell line in comparison to the more aggressive MDA-MB-231 cell line. In contrast, flow incorporating assays showed increased adhesion of the MDA-MB-231 in comparison to the MCF-7 cell line. Finally, we performed a high throughput screening experiment using a kinase inhibitor library with 80 compounds and found that the shear based assay yielded notably different results from a similar screen under static conditions for cancer cell adhesion to endothelial cells, immune cell adhesion to endothelial cells and cancer cell adhesion to platelets. Conclusions: Our studies show that adhesion assays performed under flow yield markedly different results from static adhesion assays, and are better at identifying both aggressive cancer cells lines and known pathways for circulating cancer and immune cell adhesion. Thus, this high-throughput screening platform may enable the development of novel compounds to inhibit cancer metastasis and facilitate the study of the systems level behavior of cancer-endothelium adhesion. Citation Format: Adrianne Shearer, Chris Spruell, Victoria Le, Mar Creixell, Seema Nandi, Aaron B. Baker. Mesofluidic platform for high throughput screening for inhibitors of metastasis. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr LB-008. doi:10.1158/1538-7445.AM2015-LB-008

Rapid parallel measurements of macroautophagy and mitophagy in mammalian cells using a single fluorescent biosensor.

Mitochondrial dysfunction is implicated in many human diseases and occurs in normal aging. Mitochondrial health is maintained through organelle biogenesis and repair or turnover of existing mitochondria. Mitochondrial turnover is principally mediated by mitophagy, the trafficking of damaged mitochondria to lysosomes via macroautophagy (autophagy). Mitophagy requires autophagy, but is itself a selective process that relies on specific autophagy-targeting mechanisms, and thus can be dissociated from autophagy under certain circumstances. Therefore, it is important to assess autophagy and mitophagy together and separately. We sought to develop a robust, high-throughput, quantitative method for monitoring both processes in parallel. Here we report a flow cytometry-based assay capable of rapid parallel measurements of mitophagy and autophagy in mammalian cells using a single fluorescent protein biosensor. We demonstrate the ability of the assay to quantify Parkin-dependent selective mitophagy in CCCP-treated HeLa cells. In addition, we show the utility of the assay for measuring mitophagy in other cell lines, as well as for Parkin-independent mitophagy stimulated by deferiprone. The assay makes rapid measurements (10,000 cells per 6 seconds) and can be combined with other fluorescent indicators to monitor distinct cell populations, enabling design of high-throughput screening experiments to identify novel regulators of mitophagy in mammalian cells.

Global tests for novelty

Outlier detection covers the wide range of methods aiming at identifying observations that are considered unusual. Novelty detection, on the other hand, seeks observations among newly generated test data that are exceptional compared with previously observed training data. In many applications, the general existence of novelty is of more interest than identifying the individual novel observations. For instance, in high-throughput cancer treatment screening experiments, it is meaningful to test whether any new treatment effects are seen compared with existing compounds. Here, we present hypothesis tests for such global level novelty. The problem is approached through a set of very general assumptions, making it innovative in relation to the current literature. We introduce test statistics capable of detecting novelty. They operate on local neighborhoods and their null distribution is obtained by the permutation principle. We show that they are valid and able to find different types of novelty, e.g. location and scale alternatives. The performance of the methods is assessed with simulations and with applications to real data sets.

Pituitary specific retinoid-X receptor ligand interactions with thyroid hormone receptor signaling revealed by high throughput reporter and endogenous gene responses

Disruption of thyroid hormone (TH) signaling can compromise vital processes both during development and in the adult. We previously reported on high-throughput screening experiments for man-made TH disruptors using a stably integrated line of rat pituitary cells, GH3.TRE-Luc, in which a thyroid hormone receptor (TR) response element drives luciferase (Luc) expression. In these experiments, several retinoid/rexinoid compounds activated the reporter. Here we show that all-trans and 13-cis retinoic acid appear to function through the heterodimer partners of TRs, retinoid-X receptors (RXRs), as RXR antagonists abrogated retinoid-induced activation. The retinoids also induced known endogenous TR target genes, showing good correlation with Luc activity. Synthetic RXR-specific agonists significantly activated all tested TR target genes, but interestingly, retinoid/rexinoid activation was more consistent between genes than the extent of T3-induced activation. In contrast, the retinoids neither activated the Luc reporter construct in transient transfection assays in the human hepatocarcinoma cell line HuH7, nor two of the same T3-induced genes examined in pituitary cells. These data demonstrate the suitability and sensitivity of GH3.TRE-Luc cells for screening chemical compound libraries for TH disruption and suggest that the extent of disruption can vary on a cell type and gene-specific bases, including an underappreciated contribution by RXRs.

Ebselen inhibits QSOX1 enzymatic activity and suppresses invasion of pancreatic and renal cancer cell lines.

Quiescin sulfhydryl oxidase 1 (QSOX1) is a highly conserved disulfide bond-generating enzyme that is overexpressed in diverse tumor types. Its enzymatic activity promotes the growth and invasion of tumor cells and alters extracellular matrix composition. In a nude mouse-human tumor xenograft model, tumors containing shRNA for QSOX1 grew significantly more slowly than controls, suggesting that QSOX1 supports a proliferative phenotype in vivo. High throughput screening experiments identified ebselen as an in vitro inhibitor of QSOX1 enzymatic activity. Ebselen treatment of pancreatic and renal cancer cell lines stalled tumor growth and inhibited invasion through Matrigel in vitro. Daily oral treatment with ebselen resulted in a 58% reduction in tumor growth in mice bearing human pancreatic tumor xenografts compared to controls. Mass spectrometric analysis of ebselen-treated QSOX1 mechanistically revealed that C165 and C237 of QSOX1 covalently bound to ebselen. This report details the anti-neoplastic properties of ebselen in pancreatic and renal cancer cell lines. The results here offer a "proof-of-principle" that enzymatic inhibition of QSOX1 may have clinical relevancy.