High-throughput Screening Technologies Research Articles

Development and evaluation of a novel method for rapid screening of Pichia pastoris strains capable of efficiently expressing recombinant proteins

Pichia pastoris is one of the most widely used recombinant protein expression systems. In this study, a novel method for rapid screening of P. pastoris strains capable of efficiently expressing recombinant proteins was developed. Firstly, the ability to express recombinant proteins of the modified strain GS115-E in which a functional Sec63-EGFP (Enhanced green fluorescent protein) fusion protein replaced the endogenous endoplasmic reticulum transmembrane protein Sec63 was tested. Next, the plasmids carrying different copy numbers of phytase (phy) gene or xylanase (xyn) gene were transformed into GS115-E to obtain recombinant strains with different expression levels of phytase or xylanase, and the expression levels of EGFP and recombinant proteins in different strains were tested. Finally, a flow cytometer sorter was used to separate a mixture of cells with different phytase expression levels into sub-populations according to green fluorescence intensity. A good linear correlation was found between the fluorescence intensities of EGFP and the expression levels of the recombinant proteins in the recombinant strains (0.8<|R|<1). By using the flow cytometer, high-yielding P. pastoris cells were efficiently screened from a mixture of cells. The expression level of phytase of the selected high-fluorescence strains was 4.09 times higher than that of the low-fluorescence strains after 120 h of methanol induction. By detecting the EGFP fluorescence intensity instead of detecting the expression level and activity of the recombinant proteins in the recombinant strains, the method developed by the present study possesses the greatly improved performance of convenience and versatility in screening high-yielding P. pastoris strains. Combining the method with high-throughput screening instruments and technologies, such as flow cytometer and droplet microfluidics, the speed and throughput of this method will be further increased. This method will provide a simple and rapid approach for screening and obtaining P. pastoris with high abilities to express recombinant proteins.

Identification of Piperidine-3-carboxamide Derivatives Inducing Senescence-like Phenotype with Antimelanoma Activities.

This study evaluated the potential use of senescence-inducing small molecules in the treatment of melanoma. We screened commercially available small-molecule libraries with high-throughput screening and high-content screening image-based technology. Our findings showed an initial hit with the embedded N-arylpiperidine-3-carboxamide scaffold-induced senescence-like phenotypic changes in human melanoma A375 cells without serious cytotoxicity against normal cells. A focused library containing diversely modified analogues were constructed and examined to evaluate the structure-activity relationship of N-arylpiperidine-3-carboxamide derivatives starting from hit 1. This work identified a novel compound with remarkable antiproliferative activity in vitro and demonstrated the key structural moieties within.

DeepDSC: A Deep Learning Method to Predict Drug Sensitivity of Cancer Cell Lines.

High-throughput screening technologies have provided a large amount of drug sensitivity data for a panel of cancer cell lines and hundreds of compounds. Computational approaches to analyzing these data can benefit anticancer therapeutics by identifying molecular genomic determinants of drug sensitivity and developing new anticancer drugs. In this study, we have developed a deep learning architecture to improve the performance of drug sensitivity prediction based on these data. We integrated both genomic features of cell lines and chemical information of compounds to predict the half maximal inhibitory concentrations [Formula: see text] on the Cancer Cell Line Encyclopedia (CCLE) and the Genomics of Drug Sensitivity in Cancer (GDSC) datasets using a deep neural network, which we called DeepDSC. Specifically, we first applied a stacked deep autoencoder to extract genomic features of cell lines from gene expression data, and then combined the compounds' chemical features to these genomic features to produce final response data. We conducted 10-fold cross-validation to demonstrate the performance of our deep model in terms of root-mean-square error (RMSE) and coefficient of determination [Formula: see text]. We show that our model outperforms the previous approaches with RMSE of 0.23 and [Formula: see text] of 0.78 on CCLE dataset, and RMSE of 0.52 and [Formula: see text] of 0.78 on GDSC dataset, respectively. Moreover, to demonstrate the prediction ability of our models on novel cell lines or novel compounds, we left cell lines originating from the same tissue and each compound out as the test sets, respectively, and the rest as training sets. The performance was comparable to other methods.

Progress on genome-scale metabolic models integrated with multi-omics data

The genome-scale metabolic network model (GEM) is a mathematical framework based on gene-protein-reaction associations combined with stoichiometric balance and is capable of facilitating the computation and prediction of multiscale phenotypes by optimizing the objective function of interest. It has been increasingly used as an important tool for understanding cellular metabolism and characterizing cell phenotypes. In cells, metabolism is tightly controlled by intricate regulatory mechanisms at the different system levels and is strictly regulated to ensure the dynamic adaptation of biochemical reaction fluxes for maintaining cell homeostasis to ultimately achieve optimal metabolic fitness. Advances in high-throughput screening and analysis technologies have generated massive amounts of genome sequences, along with transcriptomic, proteomic and metabolomic data, providing quantitative regulatory information to gain insights into cellular metabolism; however, integrating the available omics data into constraint-based metabolic models and quantitatively profiling genotype-phenotype relationships remains an outstanding challenge for computational biology. Here, we describe the recent developments in introducing macromolecular expression into GEMs and generating metabolic expression (ME) models, which increase the complexity and predictive capability of computational frameworks. Various algorithms employ different approaches to combine additional layers of omics data to limit the cone of allowable flux distributions in the metabolic model. In this review, we categorize all methods by five different grouping criteria and evaluate their practical perspectives. The first category of methods utilizes a threshold to distinguish active and inactive states of the corresponding reactions based on the gene expression measurement data. The second uses omics data to build cell- and tissue-specific models of human metabolism by removing unexpressed reactions from the global human metabolic network. The third category of methods involves modifying reaction bounds on the basis of mRNA and protein abundance, in which the width of the “flux cone” is adjusted via the maximum possible flux in the upper bound of the FBA optimization problem dependent on gene and protein expression levels. The imposition of constraints further defines the associated solution space of the model to improve the prediction accuracy. The fourth model incorporates transcriptional regulation networks (TRNs), which describe the phenomenological interactions between different biomolecules in response to genetic and environmental perturbation, into GEMs and avoids the obstacles of information formulation to achieve comprehensive knowledge regarding the metabolic and regulatory events occurring inside the cell. The last category integrates time-series transcriptomics data with flux-based bilevel optimization to comprehend the interplay between metabolism and regulation in time-dependent processes. We compare the advantages and limitations of different categories and explore the application areas of integrated models in analyzing metabolic characteristics, interpreting phenotypic states and the consequences of environmental and genetic perturbations while discovering potential drug targets and screening anti-metabolic drugs for cancer treatment. Finally, we also highlight the future perspectives and challenges for GEM-based reconstruction with omics data integration.

Interactions between Borrelia burgdorferi and its hosts across the enzootic cycle.

The bacterial pathogen Borrelia burgdorferi is the causative agent of Lyme disease and is transmitted to humans through an Ixodes tick vector. B.burgdorferi is able to survive in both mammalian and tick hosts through careful modulation of its gene expression. This allows B.burgdorferi to adapt to the environmental and nutritional changes that occur when it is transmitted between the two hosts. Distinct interactions between the spirochete and its host occur at every step of the enzootic cycle and dictate the ability of the spirochete to survive until the next stage of the cycle. Studying the interface between B.burgdorferi, the Ixodes tick vector and the natural mammalian reservoirs has been made significantly more feasible through the complete genome sequences of the organisms and the advent of high throughput screening technologies. Ultimately, a thorough investigation of the interplay between the two domains (and two phyla within one domain) is necessary in order to completely understand how the pathogen is transmitted.

High throughput surface plasmon resonance imaging method for clinical detection of presence and strength of binding of IgM, IgG and IgA antibodies against SARS-CoV-2 during CoViD-19 infection

Surface Plasmon Resonance imaging (SPRi) was used to determine the presence and strength of binding of IgG, IgM and IgA against the Receptor Binding Domain (RBD) of SARS-CoV-2 in sera of 102 CoViD-19 and non-CoViD-19 patients. The SPRi assay simultaneously measures the antibody isotype levels and the strength of binding to the RBD of ultimate 384 patient samples in one run. It turns out that during the course of the disease, the IgG levels and strength of binding increased while generally the IgM and IgA levels go down. Recovered patients all show high strength of binding of the IgG type to the RBD protein. The anti-RBD immunoglobulins SPRi assay provides additional insights in the immune status of patients recovering from CoViD-19. This new high throughput method can be applied for the assessment of the quality of the immune reaction of healthy individuals to SARS-CoV-2 and its mutants in vaccination programs.•Surface Plasmon Resonance imaging is an unprecedented technology for high throughput screening of antibody profiling of CoViD19 patients.•Fingerprinting of isotypes IgM, IgG and IgA can be performed for 384 patients in one run.•An affinity maturation effect was shown for patients recovering from CoViD19.

Faster, better, and cheaper: harnessing microfluidics and mass spectrometry for biotechnology.

High-throughput screening technologies are widely used for elucidating biological activities. These typically require trade-offs in assay specificity and sensitivity to achieve higher throughput. Microfluidic approaches enable rapid manipulation of small volumes and have found a wide range of applications in biotechnology providing improved control of reaction conditions, faster assays, and reduced reagent consumption. The integration of mass spectrometry with microfluidics has the potential to create high-throughput, sensitivity, and specificity assays. This review introduces the widely-used mass spectrometry ionization techniques that have been successfully integrated with microfluidics approaches such as continuous-flow system, microchip electrophoresis, droplet microfluidics, digital microfluidics, centrifugal microfluidics, and paper microfluidics. In addition, we discuss recent applications of microfluidics integrated with mass spectrometry in single-cell analysis, compound screening, and the study of microorganisms. Lastly, we provide future outlooks towards online coupling, improving the sensitivity and integration of multi-omics into a single platform.

The maturation of DNA encoded libraries: opportunities for new users.

DNA-encoded combinatorial libraries (DECLs) represent an exciting new technology for high-throughput screening, significantly increasing its capacity and cost-effectiveness. Historically, DECLs have been the domain of specialized academic groups and industry;however, there has recently been a shift towardmore drug discovery academic centers and institutes adopting this technology. Key to this development has been the simplification, characterization and standardization of various DECL subprotocols, such as library design, affinity screening and data analysis of hits. This review examines the feasibility of implementing DECL screening technology as a first-time user, particularly in academia, exploring the some important considerations for this, and outlines some applications of the technology that academia could contribute to the field.

Synthetic Lethal Activity of Benzophenanthridine Alkaloids From Zanthoxylum coco Against BRCA1-Deficient Cancer Cells.

Several plants from South America show strong antitumoral properties based on anti-proliferative and/or pro-apoptotic activities. In this work we aimed to identify selective cytotoxic compounds that target BRCA1-deficient cancer cells by Synthetic Lethality (SL) induction. Using a high-throughput screening technology developed in our laboratory, we analyzed a collection of extracts from 46 native plant species from Argentina using a wide dose-response scheme. A highly selective SL-induction capacity was found in an alkaloidal extract from Zanthoxylum coco (Fam. Rutaceae). Bio-guided fractionation coupled to HPLC led to the identification of active benzophenanthridine alkaloids. The most potent SL activity was found with the compound oxynitidine, which showed a remarkably low relative abundance in the active fractions. Further validation experiments were performed using the commercially available and closely related analog nitidine, which showed SL-induction activity against various BRCA1-deficient cell lines with different genetic backgrounds, even in the nanomolar range. Exploration of the underlying mechanism of action using BRCA1-KO cells revealed AKT and topoisomerases as the potential targets responsible of nitidine-triggered SL-induction. Taken together, our findings expose an unforeseen therapeutic activity of alkaloids from Zanthoxylum-spp. that position them as novel lead molecules for drug discovery.

Engineering Bacillus subtilis Cells as Factories: Enzyme Secretion and Value-added Chemical Production

Bacillus subtilis has been studied for more than half a century, ever since the dawn of molecular biology, as a representative Gram-positive bacterium and cell factory. Two characteristic capacities of B. subtilis, namely its natural competence for DNA uptake and high-level enzyme secretion, have been investigated and exploited intensively during these long years. As a consequence, this bacterium has evolved into an excellent platform for synthetic biological research and development. In this review, we outline basic concepts for B. subtilis cell factory engineering, and we describe several examples of its applications in the production of proteins and high-value metabolites. In particular, we highlight engineering approaches that can make the already very efficient Bacillus protein secretion pathways even more efficient for the production of enzymes and pharmaceutical proteins. We further showcase examples of metabolic engineering in B. subtilis based on synthetic biology principles to produce various high-value or health-promoting substances, especially inositol stereoisomers. We conclude that the versatile traits of B. subtilis, combined with multi-omics approaches and rapidly developing technologies for genome engineering and high-throughput screening enable us to modify and optimize this bacterium’s metabolic circuits to deliver compounds that are needed for a green and sustainable society as well as a healthy population.

Addressing Data Gaps in Toxicity of Food Additive Chemicals using High-throughput Screening Technology

Background: More than 8,000 chemical additives are permitted in food in the United States, however, most lack adequate health and safety information. High-throughput screening tools, such as Toxicity Forecaster (ToxCast), provide opportunities to quickly assess and prioritize thousands of food additives for further investigation. Given the significant lack of in vivo data for most chemicals, we aimed to characterize the toxicity data available for chemical food additives using ToxCast.Methods: We developed an up-to-date inventory of chemical food additives utilizing information from the U.S. Food and Drug Administration, the Environmental Protection Agency, and private manufacturers. We quantified the number of chemicals with ToxCast data available, and conducted a preliminary analysis of toxicity data for selected phthalates and phthalate alternatives (di(2-ethylhexyl) phthalate (DEHP), diisononyl cyclohexane-1,2-dicarboxylate (DINCH), N,N-Diethylhydroxylamine (DEHA), and bis (2-ethylhexyl) terephthalate (DEHT)).Results: Our updated inventory indicates that there are more than 9,000 chemicals allowed in contact with food, including direct and indirect additives, food packaging chemicals, pesticide residues, and substances generally recognized as safe. Preliminary analyses suggest that approximately 23% of these have ToxCast data available. Comparing bioassay data in ToxCast for selected phthalates and phthalate alternatives, we found DEHP to be the most toxic chemical, with 40 active endpoints related to cell morphology, DNA binding, and nuclear receptors. DEHT had seven bioactive endpoints, followed by DINCH and DEHA each with 3 active endpoints. DEHA was the only plasticizer with no bioactivity for nuclear receptor assays, one important biological pathway for toxicity in humans.Conclusions: ToxCast may be a useful tool for characterizing the toxicity of chemical food additives. Future analyses will focus on more diverse biological endpoints and compare ToxCast results to available in vivo data. These analyses will help advance understanding on the appropriateness of ToxCast for regulatory decision-making.

Enlightening Allostery: Designing Switchable Proteins by Photoreceptor Fusion.

Optogenetics harnesses natural photoreceptors to non-invasively control selected processes in cells with previously unmet spatiotemporal precision. Linking the activity of a protein of choice to the conformational state of a photosensor domain through allosteric coupling represents a powerful method for engineering light-responsive proteins. It enables the design of compact and highly potent single-component optogenetic systems with fast on- and off-switching kinetics. However, designing protein-photoreceptor chimeras, in which structural changes of the photoreceptor are effectively propagated to the fused effector protein, is a challenging engineering problem and often relies on trial and error. Here, recent advances in the design and application of optogenetic allosteric switches are reviewed. First, an overview of existing optogenetic tools based on inducible allostery is provided and their utility for cell biology applications is highlighted. Focusing on light-oxygen-voltage domains, a widely applied class of small blue light sensors, the available strategies for engineering light-dependent allostery are presented and their individual advantages and limitations are highlighted. Finally, high-throughput screening technologies based on comprehensive insertion libraries, which could accelerate the creation of stimulus-responsive receptor-protein chimeras for use in optogenetics and beyond, are discussed.

Patient-Derived Induced Pluripotent Stem Cell-Based Models in Parkinson's Disease for Drug Identification.

Parkinson’s disease (PD) is a common progressive neurodegenerative disorder characterized by loss of striatal-projecting dopaminergic neurons of the ventral forebrain, resulting in motor and cognitive deficits. Despite extensive efforts in understanding PD pathogenesis, no disease-modifying drugs exist. Recent advances in cell reprogramming technologies have facilitated the generation of patient-derived models for sporadic or familial PD and the identification of early, potentially triggering, pathological phenotypes while they provide amenable systems for drug discovery. Emerging developments highlight the enhanced potential of using more sophisticated cellular systems, including neuronal and glial co-cultures as well as three-dimensional systems that better simulate the human pathophysiology. In combination with high-throughput high-content screening technologies, these approaches open new perspectives for the identification of disease-modifying compounds. In this review, we discuss current advances and the challenges ahead in the use of patient-derived induced pluripotent stem cells for drug discovery in PD. We address new concepts implicating non-neuronal cells in disease pathogenesis and highlight the necessity for functional assays, such as calcium imaging and multi-electrode array recordings, to predict drug efficacy. Finally, we argue that artificial intelligence technologies will be pivotal for analysis of the large and complex data sets obtained, becoming game-changers in the process of drug discovery.

Gradient electrodeposition enables high-throughput fabrication and screening of alloy anodes for high-energy lithium-ion batteries

Alloy anodes provide high capacity for high-energy lithium-ion batteries. Multi-element alloys require high-throughput fabrication technologies to facilitate the screening of alloy composition, morphologies, and structures. Here, we report a gradient electrodeposition method to prepare Sn–Co–Sb alloy anodes with varied Sn:Co:Sb ratios and demonstrate the effectiveness of gradient electrodeposition on alloy anode development. Using this technology, we can vary each metal element to form a gradient distribution in one direction by tilting the sample alternatively during each electrodeposition. Such gradient electrodeposition realizes the complex composition of Sn–Co–Sb alloys in one large sample, enabling high-throughput fabrication simultaneously. After annealing, the obtained Sn–Co–Sb alloy forms varied phases such as Sn, SnSb, and CoSn2. It is noted that the elemental ratio has a significant influence on the microstructures and electrochemical performances of the deposited Sn–Co–Sb alloy. The Sn–Co–Sb alloy with a ratio of 71.3:12.8:15.9 delivers a high reversible capacity of 671.8 mAh g−1 and simultaneously shows excellent cyclability, which can be attributed to the optimal morphology structure and Sn:Co:Sb ratio. The optimized alloy can maintain a high capacity without sacrificing cyclability, which was usually limited by strain accumulation caused by high capacity. This work reports a general gradient electrodeposition technology for high-throughput screening of alloy anodes, which can also be applied to other alloy applications.

Single-Chain Lanthanide Luminescence Biosensors for Cell-Based Imaging and Screening of Protein-Protein Interactions.

SummaryLanthanide-based, Förster resonance energy transfer (LRET) biosensors enabled sensitive, time-gated luminescence (TGL) imaging or multiwell plate analysis of protein-protein interactions (PPIs) in living cells. We prepared stable cell lines that expressed polypeptides composed of an alpha helical linker flanked by a Tb(III) complex-binding domain, GFP, and two interacting domains at each terminus. The PPIs examined included those between FKBP12 and the rapamycin-binding domain of m-Tor (FRB) and between p53 (1–92) and HDM2 (1–128). TGL microscopy revealed dramatic differences (>500%) in donor- or acceptor-denominated, Tb(III)-to-GFP LRET ratios between open (unbound) and closed (bound) states of the biosensors. We observed much larger signal changes (>2,500%) and Z′-factors of 0.5 or more when we grew cells in 96- or 384-well plates and analyzed PPI changes using a TGL plate reader. The modular design and exceptional dynamic range of lanthanide-based LRET biosensors will facilitate versatile imaging and cell-based screening of PPIs.

Abstract 6171: High throughput screening identified new radiosensitizers which inhibit DNA damage repair pathway

Abstract Radiotherapy (RT) plays a crucial role in the treatment of cervical cancer. However, the efficacy of existing radiosensitizers in clinical applications is limited. The aim of this study was to established an efficient method of identifying new radiosensitizers and to investigated the specific mechanisms when used in combination with RT. An effective platform for radiosensitizers discovery was created based on high-throughput screening (HTS) technology. We established an automated and powerful HTS approach to identify potent radiosensitizers from the LOPAC1280 chemical library. We used the Geldanamycin for validation of our HTS method, which had been known as a radiosensitizer for cervical cancer cells. Z'-score of our HTS platform was 0.6. From 1280 compounds, we identified four hit compounds. Among them, we found Dihydroouabain (DHO) and Ro90-7501 which have not been previously known as radiosensitizers. Clonogenic survival assay showed that DHO and Ro90-7501 significantly enhanced radiosensitivity in cervical cancer cells. Western blot analysis showed that DHO inhibited the phosphorylation of Chk1, and Ro90-7501 inhibited the phosphorylation of ATM. These results indicated that DHO and Ro90-7501 inhibited DNA damage response pathway. In conclusion, we established the HTS method for radiosensitizer screening, and found DHO and Ro90-7501 as new radiosensitizers targeting DNA damage repair pathway. Citation Format: Keisuke Tamari, Zhihao Li, Hideki Matsutani, Yuji Seo, Yutaka Takahashi, Kazumasa Minami, Fumiaki Isohashi, Kazuhiko Ogawa. High throughput screening identified new radiosensitizers which inhibit DNA damage repair pathway [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 6171.

Progress in the application of exposomics in risk assessment of environmental chemicals

With the increasing number of environmental chemicals, the risk assessment system based on animal toxicity testing is difficult to satisfy the current demands and needs to be reformed urgently. With the wide application of high-throughput screening technology, the advantage of exposomics in risk assessment is gradually emerging. Exposomics aims to study the health effects of mixed environmental exposure at different stages of life (especially in the early stage of life) through the dynamic monitoring of external and internal exposure levels. Exposomics is introduced into risk assessment to systematically study gene-environment interaction so as to provide a reliable basis for the construction of exposure-biomarker-disease network. This review summarized the importance and application prospect of exposomics in risk assessment of environmental chemicals in terms of the research progress, application in risk assessment and existing problems, opportunities and challenges.

Transcriptomic and Proteostasis Networks of CFTR and the Development of Small Molecule Modulators for the Treatment of Cystic Fibrosis Lung Disease.

Cystic fibrosis (CF) is a lethal autosomal recessive disease caused by mutations in the CF transmembrane conductance regulator (CFTR) gene. The diversity of mutations and the multiple ways by which the protein is affected present challenges for therapeutic development. The observation that the Phe508del-CFTR mutant protein is temperature sensitive provided proof of principle that mutant CFTR could escape proteosomal degradation and retain partial function. Several specific protein interactors and quality control checkpoints encountered by CFTR during its proteostasis have been investigated for therapeutic purposes, but remain incompletely understood. Furthermore, pharmacological manipulation of many CFTR interactors has not been thoroughly investigated for the rescue of Phe508del-CFTR. However, high-throughput screening technologies helped identify several small molecule modulators that rescue CFTR from proteosomal degradation and restore partial function to the protein. Here, we discuss the current state of CFTR transcriptomic and biogenesis research and small molecule therapy development. We also review recent progress in CFTR proteostasis modulators and discuss how such treatments could complement current FDA-approved small molecules.

Screening Technologies for Inward Rectifier Potassium Channels: Discovery of New Blockers and Activators.

K+ channels play a critical role in maintaining the normal electrical activity of excitable cells by setting the cell resting membrane potential and by determining the shape and duration of the action potential. In nonexcitable cells, K+ channels establish electrochemical gradients necessary for maintaining salt and volume homeostasis of body fluids. Inward rectifier K+ (Kir) channels typically conduct larger inward currents than outward currents, resulting in an inwardly rectifying current versus voltage relationship. This property of inward rectification results from the voltage-dependent block of the channels by intracellular polyvalent cations and makes these channels uniquely designed for maintaining the resting potential near the K+ equilibrium potential (EK). The Kir family of channels consist of seven subfamilies of channels (Kir1.x through Kir7.x) that include the classic inward rectifier (Kir2.x) channel, the G-protein-gated inward rectifier K+ (GIRK) (Kir3.x), and the adenosine triphosphate (ATP)-sensitive (KATP) (Kir 6.x) channels as well as the renal Kir1.1 (ROMK), Kir4.1, and Kir7.1 channels. These channels not only function to regulate electrical/electrolyte transport activity, but also serve as effector molecules for G-protein-coupled receptors (GPCRs) and as molecular sensors for cell metabolism. Of significance, Kir channels represent promising pharmacological targets for treating a number of clinical conditions, including cardiac arrhythmias, anxiety, chronic pain, and hypertension. This review provides a brief background on the structure, function, and pharmacology of Kir channels and then focuses on describing and evaluating current high-throughput screening (HTS) technologies, such as membrane potential-sensitive fluorescent dye assays, ion flux measurements, and automated patch clamp systems used for Kir channel drug discovery.

Dihydroouabain, a novel radiosensitizer for cervical cancer identified by automated high-throughput screening

Background and purposeRadiotherapy plays a crucial role in the treatment of cervical cancer, but existing radiosensitizers have limited efficacy in clinical applications. The aims of this study were to establish and verify an efficient method for identifying new radiosensitizers, to use this to identify candidate radiosensitizers for cervical cancer, and to investigate the specific mechanisms of these when used in combination with radiotherapy. Materials and methodsAn automated platform for identifying radiosensitizers for cervical cancer was created based on high-throughput screening technology. The radiosensitizing effects of candidate compounds from the LOPAC1280 chemical library were evaluated in radiosensitive and radioresistant cervical cancer cells using a clonogenic survival assay, with cell cycle analyses, and western blot analyses performed for both cell lines. ResultsThe automated high-throughput screening approach identified four hit compounds. One of the most potent candidates was dihydroouabain (DHO), an inhibitor of Na+/K+-ATPase that has not previously been classified as a radiosensitizer. DHO significantly enhanced radiosensitivity in cervical cancer cells. It also abrogated radiation-induced S phase arrest in cervical cancer cells. Combination treatment significantly caused the inhibition of Chk1 and increased DNA double-strand breaks (DSB). ConclusionsDHO is a novel radiosensitizer for the treatment of cervical cancer. The automated high-throughput screening platform developed in this study proved to be powerful and effective, with the potential to be widely used in the future identification of radiosensitizers.