High-throughput Screening Approach Research Articles

Identification of Src as a Therapeutic Target in Oesophageal Adenocarcinoma through Functional Genomic and High-Throughput Drug Screening Approaches.

Simple SummaryOesophageal adenocarcinoma (OAC) is a leading cause of cancer mortality in the United Kingdom with a 5-year survival rate of approximately 15%. Major contributors to poor outcome are late diagnosis and chemotherapy resistance, and while targeted therapies have benefitted certain cancer settings, they have had limited success in OAC. An understanding of the mechanisms mediating chemotherapy resistance could identify novel targets with the potential to improve standard treatments. This paper aimed to identify mediators of both OAC cancer cell pro-survival signalling and chemotherapy resistance, as well as determine potential small molecular compounds to counteract this. Gene set enrichment analysis of transcriptional data generated a gene-list with significant differential expression between responder and non-responder OAC patients. Gene functionality assessment using siRNA screening showed that targeting SRC had an anti-tumour effect in OAC cells with the potential to enhance chemotherapy treatment. In parallel to this, a compound screen showed the Src inihibitor dasatinib sensitised OAC cells to chemotherapy. Together, these findings suggest targeting SRC as a novel therapeutic strategy in OAC.Drug resistance limits the effectiveness of oesophageal adenocarcinoma (OAC) chemotherapies, leading to a poor prognosis for this disease. Elucidation of the underlying resistance mechanisms is key to enabling the identification of more effective treatments. This study, therefore, aims to identify novel therapeutic and/or chemotherapy sensitising drug targets in OAC. Transcriptional data from a cohort of 273 pre-treatment OAC biopsies, from patients who received neoadjuvant chemotherapy followed by surgical resection, were analysed using gene set enrichment analysis (GSEA) to determine differential gene expression between responding and non-responding OAC tumours. From this, 80 genes were selected for high-throughput siRNA screening in OAC cell lines with or without standard chemotherapy treatment. In parallel, cell viability assays were performed using a panel of FDA-approved drugs and combination index (CI) values were calculated to evaluate drug synergy with standard chemotherapy. Mechanisms of synergy were investigated using western blot, propidium iodide flow cytometry, and proliferation assays. Taken together, the screens identified that targeting Src, using either siRNA or the small molecule inhibitor dasatinib, enhanced the efficacy of chemotherapy in OAC cells. Further in vitro functional analysis confirmed Src inhibition to be synergistic with standard OAC chemotherapies, 5-fluorouracil (5-FU), and cisplatin (CDDP). In conclusion, a compound screen together with a functional genomic approach identified Src as a potential chemosensitising target in OAC, which could be assessed in a clinical study for poor prognosis OAC patients.

Identification of anti-schistosomal, anthelmintic and anti-parasitic compounds curated and text-mined from the scientific literature.

More than a billion people are infected with parasitic worms, including nematodes, such as hookworms, and flatworms, such as blood flukes. Few drugs are available to treat worm infections, but high-throughput screening approaches hold promise to identify novel drug candidates. One problem for researchers who find an interesting 'hit' from a high-throughput screen is to identify whether that compound, or a similar compound has previously been published as having anthelmintic or anti-parasitic activity. Here, we present (i) data sets of 2,828 anthelmintic compounds, and 1,269 specific anti-schistosomal compounds, manually curated from scientific papers and books, and (ii) a data set of 24,335 potential anthelmintic and anti-parasitic compounds identified by text-mining PubMed abstracts. We provide their structures in simplified molecular-input line-entry system (SMILES) format so that researchers can easily compare 'hits' from their screens to these anthelmintic compounds and anti-parasitic compounds and find previous literature on them to support/halt their progression in drug discovery pipelines.

The Main Protease of SARS-CoV-2 as a Target for Phytochemicals against Coronavirus.

In late December 2019, the first cases of COVID-19 emerged as an outbreak in Wuhan, China that later spread vastly around the world, evolving into a pandemic and one of the worst global health crises in modern history. The causative agent was identified as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although several vaccines were authorized for emergency use, constantly emerging new viral mutants and limited treatment options for COVID-19 drastically highlighted the need for developing an efficient treatment for this disease. One of the most important viral components to target for this purpose is the main protease of the coronavirus (Mpro). This enzyme is an excellent target for a potential drug, as it is essential for viral replication and has no closely related homologues in humans, making its inhibitors unlikely to be toxic. Our review describes a variety of approaches that could be applied in search of potential inhibitors among plant-derived compounds, including virtual in silico screening (a data-driven approach), which could be structure-based or fragment-guided, the classical approach of high-throughput screening, and antiviral activity cell-based assays. We will focus on several classes of compounds reported to be potential inhibitors of Mpro, including phenols and polyphenols, alkaloids, and terpenoids.

One-step high-throughput detection of low-abundance biomarker BDNF using a biolayer interferometry-based 3D aptasensor

Although biosensors for signal monitoring have been extensively developed, their application in one-step high-throughput detection of low-abundance disease biomarkers remains challenging. This study presents a 3D aptasensor based on a biolayer interferometry (BLI) technique, followed by the sensitive and rapid detection of the specific biomarker brain-derived neurotrophic factor (BDNF) for early screening of glaucoma, an irreversible disease that causes blindness. The developed 3D aptasensor enabled one-step batch conversion of the low-abundance biomarker BDNF binding into optical interference signal, which was mainly attributed to the following factors: (1) A dimeric aptamer with extremely high targeting affinity was constructed as a biorecognition molecule, (2) highly sensitive 3D matrix sensors were integrated as signal transduction elements, and (3) the BLI Octet system with automated, high-throughput, and real-time online monitoring capabilities was used for reporting. The 3D aptasensor exhibited a broad detection window from 0.41 to 250 ng/mL BDNF, with a limit of detection of 0.2 ng/mL. Furthermore, detection of BDNF in glaucoma patient serum using the aptasensor showed good agreement with ELISA findings as well as the clinical diagnosis of the patient, demonstrating the feasibility of the system as a screening tool for glaucoma. This one-step high-throughput screening approach provides a valuable solution for the early diagnosis of glaucoma and may reduce the risk of blindness in visually impaired people.

Identification of NAPRT Inhibitors with Anti-Cancer Properties by In Silico Drug Discovery.

Depriving cancer cells of sufficient NAD levels, mainly through interfering with their NAD-producing capacity, has been conceived as a promising anti-cancer strategy. Numerous inhibitors of the NAD-producing enzyme, nicotinamide phosphoribosyltransferase (NAMPT), have been developed over the past two decades. However, their limited anti-cancer activity in clinical trials raised the possibility that cancer cells may also exploit alternative NAD-producing enzymes. Recent studies show the relevance of nicotinic acid phosphoribosyltransferase (NAPRT), the rate-limiting enzyme of the Preiss–Handler NAD-production pathway for a large group of human cancers. We demonstrated that the NAPRT inhibitor 2-hydroxynicotinic acid (2-HNA) cooperates with the NAMPT inhibitor FK866 in killing NAPRT-proficient cancer cells that were otherwise insensitive to FK866 alone. Despite this emerging relevance of NAPRT as a potential target in cancer therapy, very few NAPRT inhibitors exist. Starting from a high-throughput virtual screening approach, we were able to identify and annotate two additional chemical scaffolds that function as NAPRT inhibitors. These compounds show comparable anti-cancer activity to 2-HNA and improved predicted aqueous solubility, in addition to demonstrating favorable drug-like profiles.

High-throughput screening of surface roughness during additive manufacturing

Smooth surfaces in the printed parts are essential for long fatigue life and high dimensional accuracy. They are currently achieved by post-process machining and grinding of external surfaces that add extra costs. Mitigating the surface roughness of internal channels remains a challenge. Here we use a high-throughput screening approach that analyzes the value of a dimensionless index for many experiments and provides a pathway for reducing the surface roughness without the need for post-processing. The index is derived by dimensional analysis of causative variables that affect the roughness of the surface such as heat input, powder diameter, layer thickness, pool aspect ratio, Marangoni force, contact angle, and enthalpy of melting of alloys. Using the results of high-throughput screening, we develop easy-to-use process maps that are consistent with the experimental observations. Among the causative variables, heat input and the contact angle of the molten material with the substrate have the highest and lowest influence on the smoothness of printed surfaces. An aluminum alloy, AlSi10Mg is found to be the best choice for printing smooth surfaces among the four alloys considered here. These findings can improve the surface quality of additively manufactured parts that now significantly hinder their wider industrial adaptation.

Docking-Based Virtual Screening Enables Prioritizing Protein Kinase Inhibitors With In Vitro Phenotypic Activity Against Schistosoma mansoni.

Schistosomiasis is a parasitic neglected disease with praziquantel (PZQ) utilized as the main drug for treatment, despite its low effectiveness against early stages of the worm. To aid in the search for new drugs to tackle schistosomiasis, computer-aided drug design has been proved a helpful tool to enhance the search and initial identification of schistosomicidal compounds, allowing fast and cost-efficient progress in drug discovery. The combination of high-throughput in silico data followed by in vitro phenotypic screening assays allows the assessment of a vast library of compounds with the potential to inhibit a single or even several biological targets in a more time- and cost-saving manner. Here, we describe the molecular docking for in silico screening of predicted homology models of five protein kinases (JNK, p38, ERK1, ERK2, and FES) of Schistosoma mansoni against approximately 85,000 molecules from the Managed Chemical Compounds Collection (MCCC) of the University of Nottingham (UK). We selected 169 molecules predicted to bind to SmERK1, SmERK2, SmFES, SmJNK, and/or Smp38 for in vitro screening assays using schistosomula and adult worms. In total, 89 (52.6%) molecules were considered active in at least one of the assays. This approach shows a much higher efficiency when compared to using only traditional high-throughput in vitro screening assays, where initial positive hits are retrieved from testing thousands of molecules. Additionally, when we focused on compound promiscuity over selectivity, we were able to efficiently detect active compounds that are predicted to target all kinases at the same time. This approach reinforces the concept of polypharmacology aiming for “one drug-multiple targets”. Moreover, at least 17 active compounds presented satisfactory drug-like properties score when compared to PZQ, which allows for optimization before further in vivo screening assays. In conclusion, our data support the use of computer-aided drug design methodologies in conjunction with high-throughput screening approach.

High-throughput Screening Computation for Discovery of Porous Zeolites for Hydrogen Storage

Hydrogen is considered an attractive energy resource because it is eco-friendly in contrast with fossil fuels. Hydrogen storage remains as essential technology for increasing the use of the hydrogen in applications such as hydrogen vehicles and fuel cells. Hydrogen storage requires retaining a high density of hydrogen molecules at ambient temperature in a suitable tank. Zeolites are one of the promising hydrogen storage materials, but experimentally investigating them for hydrogen storage is difficult since the number of the zeolites in the largescale material database has been increasing. In the present study I developed an efficient method of exploring potential zeolites in the database that had high volumetric hydrogen storage capacity. To do this I employed a high-throughput screening approach to automatically construct a zeolite database for hydrogen storage in the Inorganic Crystal Structural Database (ICSD). Also, I performed grand canonical Monte Carlo (GCMC) simulations to estimate hydrogen adsorption isotherms at operating ambient temperatures, to determine the volumetric hydrogen storage capacity of the zeolites. Finally, I found 10 top ranked materials in the zeolite database for H2 storage, and I calculated Pearson’s correlation coefficient to revealed the linear correlations between the hydrogen storage capacities and 3 structural characteristics (i.e., surface area, largest cavity diameter, pore limiting diameter). Furthermore, I investigated atom species in the 10 materials to show the relation between the hydrogen storage capacities and chemical elements. In future works, I expect the method can be easily applied to accelerate the discovery and design of porous materials for storing CO2 or toxic gases.

What Makes GPCRs from Different Families Bind to the Same Ligand?

G protein-coupled receptors (GPCRs) are the largest class of cell-surface receptor proteins with important functions in signal transduction and often serve as therapeutic drug targets. With the rapidly growing public data on three dimensional (3D) structures of GPCRs and GPCR-ligand interactions, computational prediction of GPCR ligand binding becomes a convincing option to high throughput screening and other experimental approaches during the beginning phases of ligand discovery. In this work, we set out to computationally uncover and understand the binding of a single ligand to GPCRs from several different families. Three-dimensional structural comparisons of the GPCRs that bind to the same ligand revealed local 3D structural similarities and often these regions overlap with locations of binding pockets. These pockets were found to be similar (based on backbone geometry and side-chain orientation using APoc), and they correlate positively with electrostatic properties of the pockets. Moreover, the more similar the pockets, the more likely a ligand binding to the pockets will interact with similar residues, have similar conformations, and produce similar binding affinities across the pockets. These findings can be exploited to improve protein function inference, drug repurposing and drug toxicity prediction, and accelerate the development of new drugs.

Gene Expression Analysis of the Stress Response to Lithium, Nickel, and Zinc in Paracentrotus lividus Embryos.

Many anthropogenic pollutants such as metals are discharged into the marine environment through modern sources. Among these, lithium (Li), nickel (Ni), and zinc (Zn) can interfere with biological processes in many organisms when their concentration rises. These metals are toxic to sea urchin embryos, affecting their development. Indeed, animal/vegetal and dorso/ventral embryonic axes are differently perturbed: Li is a vegetalizing agent, Ni can disrupt dorso-ventral axis, Zn can be animalizing. To address the molecular response adopted by embryos to cope with these metals or involved in the gene networks regulating embryogenesis, and to detect new biomarkers for evaluating hazards in polluted environments in a well-known in vivo model, we applied a high-throughput screening approach to sea urchin embryos. After fertilization, Paracentrotus lividus embryos were exposed to Li, Ni, and Zn for 24/48 h. At both endpoints, RNAs were analyzed by NanoString nCounter technology. By in silico analyses, we selected a panel of 127 transcripts encoding for regulatory and structural proteins, ranked in categories: Apoptosis, Defense, Immune, Nervous, Development, and Biomineralization. The data analysis highlighted the dysregulation of many genes in a metal-dependent manner. A functional annotation analysis was performed by the KEEG Orthology database. This study provides a platform for research on metals biomarkers in sea urchins.

A high-throughput screening approach for bacterial quorum sensing inhibitors (QSIs) against Aeromonas hydrophila infection

Bacterial quorum sensing (QS) is a well-known behavior for bacterial communication and plays an important role in various bacterial biological functions. Several approaches have been developed to interrupt the QS mechanism in bacterial pathogens. This study developed a novel, high-throughput screening method combined with phage peptide display libraries and a QS biosensor system (PDQBS) to identify quorum-sensing inhibitors (QSIs). We screened 11 hit peptide candidates for QSI activity, which inhibited the C4-AHL mediated quorum-sensing system. Although the synthetic peptides showed moderate inhibitory effects and six candidate peptides ligated with a cell-penetrating peptide (CPP) dramatically inhibited QS signals. The protective efficacies of candidate DNA expression vector ligated with CPP-QSIs were tested against Aeromonas hydrophila infection in the zebrafish model. The obtained result revealed that at least two peptides (CPP21 and CPP23) exhibited an increased protective ability against A. hydrophila infection with RPSs of >50%. Furthermore, the three candidates CPP-QSIs (CPP21, CPP23, and CPP24), also showed high protective abilities with RPSs of >50% against A. hydrophila infection in the mice model. The qPCR analysis further confirmed that these selected CPP-QSIs might stimulate host immunity and inhibit the virulence of A. hydrophila. Finally, the results of dot blot and ELISA assays indicated that these CPP-QSI peptides could bind to the QS receptor protein, AhyR/CviR. Overall, this study provides a promising approach to screening novel QSI candidates for controlling QS-mediated bacterial infections.

High-throughput single-cell antibody secretion quantification and enrichment using droplet microfluidics-based FRET assay

SummaryHigh-throughput screening and enrichment of antibody-producing cells have many important applications. Herein, we present a droplet microfluidic approach for high-throughput screening and sorting of antibody-secreting cells using a Förster resonance electron transfer (FRET)-based assay. The FRET signal is mediated by the specific binding of the secreted antibody to two fluorescently labeled probes supplied within a droplet. Functional hybridoma cells expressing either membrane-bound or secreted monoclonal antibodies (mAbs), or both, were efficiently differentiated in less than 30 min. The antibody secretion rate by individual hybridoma cells was recorded in the range of 14,000 Abs/min, while the density of membrane-bound fraction was approximately 100 Abs/μm2. Combining the FRET assay with droplet-based single-cell sorting, an 800-fold enrichment of antigen-specific cells was achieved after one round of sorting. The presented system overcomes several key limitations observed in conventional FACS-based screening methods and should be applicable to assaying various other secreted proteins.

High-throughput virtual screening of small-molecule inhibitors targeting immune cell checkpoints to discover new immunotherapeutics for human diseases.

Immunotherapy is widely used to treat various cancers, and the drugs used are called immune checkpoint (ICP) inhibitors. Overexpression of immune cell checkpoints is reported for other human diseases such as acute infections (malaria), chronic viral infection (HIV, hepatitis B virus, TB infections), allergy, asthma, neurodegeneration, and autoimmune diseases. Some mAbs (monoclonal antibodies) are available against ICPs, but they have side effects. Small molecule seems to be safer in comparison with mAbs. Three independent small-molecule inhibitor libraries consisting of 9466 compounds were screened against seven immune cell checkpoints by applying high-throughput virtual screening approach. A total of 13 ICP inhibitors were finalized based on docking, MM-GBSA scores, and ADME properties. Six compounds were selected for MD simulation, and then, rutin hydrate (targeting all seven immune cell checkpoints), amikacin hydrate (targeting six), and 6-hydroxyluteolin (targeting three) were found to be the best immune cell checkpoint inhibitors. These three potential inhibitors have shown the potential to activate human immune cells and thus may control the spread of human lifestyle or infectious diseases. Proposed inhibitors warrant the in vitro and in vivo validation to develop it as an immunotherapeutic.

Simplified choice of suitable excipients within biologics formulation design using protein-protein interaction- and water activity-maps

Still today, high-concentration protein formulations are often developed based on high-throughput experimental screening approaches. Although likely delivering working formulations, these approaches do not lead to a deep/mechanistic understanding of the protein phase behavior in solution. Within this work, we thus optimized and enhanced a recent approach for an initial low effort selection of potential excipients and excipient mixtures to be used in high-concentration protein formulations. This approach considers both: molecular interactions and thermodynamic determinants to access the phase behavior of the proteins in solution, as well as pharmaceutical engineering boundaries (such as osmotic pressure and osmolality) to deliver on optimal formulation conditions. Water activity coefficient γW-calculations (used to describe the protein environment in solution), unfolding temperature (conformational stability) and protein-protein interactions (colloidal stability) are used as determinants. Amino acids (20 proteinogenic amino acids), selected amino acid mixtures, as well as mixtures of amino acids and trehalose (l-arginine-trehalose; l-histidine-trehalose) are considered as model excipients. The approach is extends by studying the long-term stability of the predicted formulation conditions for a γ-globulin from human blood and denosumab. The results reveal, that by combining protein-specific experiments as well as model-based studies for the selection of excipient mixtures in high concentration protein formulations, the effort as well as the resource requirements can be reduced significantly.

Measuring Protein Aggregation and Stability Using High-Throughput Biophysical Approaches.

Structure-function relationships of biological macromolecules, in particular proteins, provide crucial insights for fundamental biochemistry, medical research and early drug discovery. However, production of recombinant proteins, either for structure determination, functional studies, or to be used as biopharmaceutical products, is often hampered by their instability and propensity to aggregate in solution in vitro. Protein samples of poor quality are often associated with reduced reproducibility as well as high research and production expenses. Several biophysical methods are available for measuring protein aggregation and stability. Yet, discovering and developing means to improve protein behaviour and structure-function integrity remains a demanding task. Here, we discuss workflows that are made possible by adapting established biophysical methods to high-throughput screening approaches. Rapid identification and optimisation of conditions that promote protein stability and reduce aggregation will support researchers and industry to maximise sample quality, stability and reproducibility, thereby reducing research and development time and costs.

Assessment of Life Cycle Modeling Systems as Prediction Tools for a Possible Attenuation of Recombinant Ebola Viruses.

Ebola virus (EBOV) causes hemorrhagic fever in humans with high case fatality rates. In the past, a number of recombinant EBOVs expressing different reporters from additional transcription units or as fusion proteins have been rescued. These viruses are important tools for the study of EBOV, and their uses include high throughput screening approaches, the analysis of intercellular localization of viral proteins and of tissue distribution of viruses, and the study of pathogenesis in vivo. However, they all show, at least in vivo, attenuation compared to wild type virus, and the basis of this attenuation is only poorly understood. Unfortunately, rescue of these viruses is a lengthy and not always successful process, and working with them is restricted to biosafety level (BSL)-4 laboratories, so that the search for non-attenuated reporter-expressing EBOVs remains challenging. However, several life cycle modeling systems have been developed to mimic different aspects of the filovirus life cycle under BSL-1 or -2 conditions, but it remains unclear whether these systems can be used to predict the viability and possible attenuation of recombinant EBOVs. To address this question, we systematically fused N- or C-terminally either a flag-HA tag or a green fluorescent protein (GFP) to different EBOV proteins, and analyzed the impact of these additions with respect to protein function in life cycle modeling systems. Based on these results, selected recombinant EBOVs encoding these tags/proteins were then rescued and characterized for a possible attenuation in vitro, and results compared with data from the life cycle modeling systems. While the results for the small molecular tags showed mostly good concordance, GFP-expressing viruses were more attenuated than expected based on the results from the life cycle modeling system, demonstrating a limitation of these systems and emphasizing the importance of work with infectious virus. Nevertheless, life cycle modeling system remain useful tools to exclude non-viable tagging strategies.

Abstract 122: Rabeprazole Enhances Vascular Repair And Resolution Of Sepsis-induced Lung Injury Through HIF1/FoxM1 Signaling

Objective: There are currently no effective treatments for sepsis-induced inflammatory lung injury, including acute lung injury (ALI) and acute respiratory distress syndrome (ARDS). The repositioning of existing drugs is one possible strategy for the treatment of ALI/ARDS. We previously showed that vascular repair and resolution of ALI is dependent upon hypoxia-inducible factor 1 alpha (HIF1a) and forkhead box M1 (FoxM1) in endothelial cells. The aim of this study was to identify a candidate agonist of HIF1a/FoxM1 signaling to treat ALI/ARDS. Approach and Results: First, we adopted a high throughput screening approach of a library of 1200 FDA-approved drugs by using hypoxia response element (HRE)-driven luciferase reporter assays, cell toxicity assays, and molecular analyses of human lung endothelial cells, to identify candidate HIF1 agonists in vitro . One of the top candidate drugs, Rabeprazole (also known as Aciphex), gave rise to dose-dependent increases in HRE luciferase activity without concurrent increases in cell death. Next, we performed dose-response studies of Rabeprazole in a mouse model of sepsis-induced ALI. By treating wild type mice orally with Rabeprazole on 2 consecutive days after endotoxic sepsis challenge, we were able to identify a dose of Rabeprazole that is well tolerated and enhanced vascular repair and resolution of ALI. In timeline studies of murine inflammatory lung injury, we found that Rabeprazole treatment resulted in reductions in lung vascular leakage, edema, and inflammatory cytokine expression during the repair phrase following sepsis-induced ALI. Finally, we used conditional knockout mice to show that Rabeprazole increases vascular repair and resolution of ALI via HIF1a/FoxM1 signaling. Conclusions: Rabeprazole is a HIF1a agonist that improves vascular repair and resolution of sepsis-induced inflammatory lung injury. Rabeprazole represents a promising candidate for repurposing to treat ALI/ARDS.

Discovery of Natural Products With Antifungal Potential Through Combinatorial Synergy.

The growing prevalence of antifungal drug resistance coupled with the slow development of new, acceptable drugs and fungicides has raised interest in natural products (NPs) for their therapeutic potential and level of acceptability. However, a number of well-studied NPs are considered promiscuous molecules. In this study, the advantages of drug–drug synergy were exploited for the discovery of pairwise NP combinations with potentiated antifungal activity and, potentially, increased target specificity. A rational approach informed by previously known mechanisms of action of selected NPs did not yield novel antifungal synergies. In contrast, a high-throughput screening approach with yeast revealed 34 potential synergies from 800 combinations of a diverse NP library with four selected NPs of interest (eugenol, EUG; β-escin, ESC; curcumin, CUR; berberine hydrochloride, BER). Dedicated assays validated the most promising synergies, namely, EUG + BER, CUR + sclareol, and BER + pterostilbene (PTE) [fractional inhibitory concentrations (FIC) indices ≤ 0.5 in all cases], reduced to as low as 35 (BER) and 7.9 mg L–1 (PTE). These three combinations synergistically inhibited a range of fungi, including human or crop pathogens Candida albicans, Aspergillus fumigatus, Zymoseptoria tritici, and Botrytis cinerea, with synergy also against azole-resistant isolates and biofilms. Further investigation indicated roles for mitochondrial membrane depolarization and reactive oxygen species (ROS) formation in the synergistic mechanism of EUG + BER action. This study establishes proof-of-principle for utilizing high-throughput screening of pairwise NP interactions as a tool to find novel antifungal synergies. Such NP synergies, with the potential also for improved specificity, may help in the management of fungal pathogens.

MiR-324-5p and miR-30c-2-3p Alter Renal Mineralocorticoid Receptor Signaling under Hypertonicity.

The Mineralocorticoid Receptor (MR) mediates the sodium-retaining action of aldosterone in the distal nephron, but mechanisms regulating MR expression are still poorly understood. We previously showed that RNA Binding Proteins (RBPs) regulate MR expression at the post-transcriptional level in response to variations of extracellular tonicity. Herein, we highlight a novel regulatory mechanism involving the recruitment of microRNAs (miRNAs) under hypertonicity. RT-qPCR validated miRNAs candidates identified by high throughput screening approaches and transfection of a luciferase reporter construct together with miRNAs Mimics or Inhibitors demonstrated their functional interaction with target transcripts. Overexpression strategies using Mimics or lentivirus revealed the impact on MR expression and signaling in renal KC3AC1 cells. miR-324-5p and miR-30c-2-3p expression are increased under hypertonicity in KC3AC1 cells. These miRNAs directly affect Nr3c2 (MR) transcript stability, act with Tis11b to destabilize MR transcript but also repress Elavl1 (HuR) transcript, which enhances MR expression and signaling. Overexpression of miR-324-5p and miR-30c-2-3p alter MR expression and signaling in KC3AC1 cells with blunted responses in terms of aldosterone-regulated genes expression. We also confirm that their expression is increased by hypertonicity in vivo in the kidneys of mice treated with furosemide. These findings may have major implications for the pathogenesis of renal dysfunctions, sodium retention, and mineralocorticoid resistance.

CDK inhibitor Palbociclib targets STING to alleviate autoinflammation.

Aberrant activation of stimulator of interferon genes (STING) is tightly associated with multiple types of disease, including cancer, infection, and autoimmune diseases. However, the development of STING modulators for the therapy of STING-related diseases is still an unmet clinical need. We employed a high-throughput screening approach based on the interaction of small-molecule chemical compounds with recombinant STING protein to identify functional STING modulators. Intriguingly, the cyclin-dependent protein kinase (CDK) inhibitor Palbociclib was found to directly bind STING and inhibit its activation in both mouse and human cells. Mechanistically, Palbociclib targets Y167 of STING to block its dimerization, its binding with cyclic dinucleotides, and its trafficking. Importantly, Palbociclib alleviates autoimmune disease features induced by dextran sulphate sodium or genetic ablation of three prime repair exonuclease 1 (Trex1) in mice in a STING-dependent manner. Our work identifies Palbociclib as a novel pharmacological inhibitor of STING that abrogates its homodimerization and provides a basis for the fast repurposing of this Food and Drug Administration-approved drug for the therapy of autoinflammatory diseases.