High-throughput Screening Research Articles

D-allose, a typical rare sugar: properties, applications, and biosynthetic advances and challenges.

D-allose, a C-3 epimer of D-glucose and an aldose-ketose isomer of D-allulose, exhibits 80% of sucrose's sweetness while being remarkably low in calories and nontoxic, making it an appealing sucrose substitute. Its diverse physiological functions, particularly potent anticancer and antitumor effects, render it a promising candidate for clinical treatment, garnering sustained attention. However, its limited availability in natural sources and the challenges associated with chemical synthesis necessitate exploring biosynthetic strategies to enhance production. This overview encapsulates recent advancements in D-allose's physicochemical properties, physiological functions, applications, and biosynthesis. It also briefly discusses the crucial role of understanding aldoketose isomerase structure and optimizing its performance in D-allose synthesis. Furthermore, it delves into the challenges and future perspectives in D-allose bioproduction. Early efforts focused on identifying and characterizing enzymes responsible for D-allose production, followed by detailed crystal structure analysis to improve performance through molecular modification. Strategies such as enzyme immobilization and implementing multi-enzyme cascade reactions, utilizing more cost-effective feedstocks, were explored. Despite progress, challenges remain, including the lack of efficient high-throughput screening methods for enzyme modification, the need for food-grade expression systems, the establishment of ordered substrate channels in multi-enzyme cascade reactions, and the development of downstream separation and purification processes.

Engineered CRISPR-Cas12a for higher-order combinatorial chromatin perturbations.

Multiplexed genetic perturbations are critical for testing functional interactions among coding or non-coding genetic elements. Compared to double-stranded DNA cutting, repressive chromatin formation using CRISPR interference (CRISPRi) avoids genotoxicity and is more effective for perturbing non-coding regulatory elements in pooled assays. However, current CRISPRi pooled screening approaches are limited to targeting one to three genomic sites per cell. We engineer an Acidaminococcus Cas12a (AsCas12a) variant, multiplexed transcriptional interference AsCas12a (multiAsCas12a), that incorporates R1226A, a mutation that stabilizes the ribonucleoprotein-DNA complex via DNA nicking. The multiAsCas12a-KRAB fusion improves CRISPRi activity over DNase-dead AsCas12a-KRAB fusions, often rescuing the activities of lentivirally delivered CRISPR RNAs (crRNA) that are inactive when used with the latter. multiAsCas12a-KRAB supports CRISPRi using 6-plex crRNA arrays in high-throughput pooled screens. Using multiAsCas12a-KRAB, we discover enhancer elements and dissect the combinatorial function of cis-regulatory elements in human cells. These results instantiate a group testing framework for efficiently surveying numerous combinations of chromatin perturbations for biological discovery and engineering.

P35 Adaptation of a phenotypic screening assay to incorporate 384-well plates: opening the door to high-throughput screening

P35 Adaptation of a phenotypic screening assay to incorporate 384-well plates: opening the door to high-throughput screening

Cracking the code of the cardiovascular enigma: hPSC-derived endothelial cells unveil the secrets of endothelial dysfunction.

Endothelial dysfunction is a central contributor to the development of most cardiovascular diseases and is characterised by the reduced synthesis or bioavailability of the vasodilator nitric oxide together with other abnormalities such as inflammation, senescence, and oxidative stress. The use of patient-specific and genome-edited human pluripotent stem cell-derived endothelial cells (hPSC-ECs) has shed novel insights into the role of endothelial dysfunction in cardiovascular diseases with strong genetic components such as genetic cardiomyopathies and pulmonary arterial hypertension. However, their utility in studying complex multifactorial diseases such as atherosclerosis, metabolic syndrome and heart failure poses notable challenges. In this review, we provide an overview of the different methods used to generate and characterise hPSC-ECs before comprehensively assessing their effectiveness in cardiovascular disease modelling and high-throughput drug screening. Furthermore, we explore current obstacles that will need to be overcome to unleash the full potential of hPSC-ECs in facilitating patient-specific precision medicine. Addressing these challenges holds great promise in advancing our understanding of intricate cardiovascular diseases and in tailoring personalised therapeutic strategies.

E3 ubiquitin ligase UBR5 promotes gemcitabine resistance in pancreatic cancer by inducing O-GlcNAcylation-mediated EMT via destabilization of OGA.

Pancreatic cancer (PC) is among the deadliest malignancies, with an extremely poor diagnosis and prognosis. Gemcitabine (GEM) remains the first-line drug for treating PC; however, only a small percentage of patients benefit from current immunotherapies or targeted therapies. Resistance to GEM is prevalent and affects long-term survival. We found that ubiquitin-protein ligase E3 module N-recognition 5 (UBR5) is a therapeutic target against GEM resistance. UBR5 was markedly upregulated in clinical GEM-resistant PC samples and GEM-resistant PC cells. UBR5 knockdown markedly increased GEM sensitivity in GEM-resistant PC cell lines. UBR5-mediated GEM resistance was accompanied by activation of epithelial-mesenchymal transition (EMT) and could be mitigated by inhibiting EMT. Further analysis revealed that UBR5 promoted GEM resistance in PC cells by enhancing O-GlcNAcylation-mediated EMT. In addition, UBR5 knockdown resulted in increased O-GlcNAase (OGA) levels, an essential negatively regulated enzyme in the O-GlcNAcylation process. We identified a negative association between OGA and UBR5 levels, which further supported the hypothesis that O-GlcNAcylation-mediated GEM resistance induced by UBR5 is OGA-dependent in PC cells. Mechanistic studies revealed that UBR5 acts as an E3 ubiquitin ligase of OGA and regulates O-GlcNAcylation by binding and modulating OGA, facilitating its degradation and ubiquitination. Additionally, high-throughput compound library screening using three-dimensional protein structure analysis and drug screening identified a Food and Drug Administration drug, Y-39983 dihydrochloride, as a potent GEM sensitiser and UBR5 inhibitor. The combination of Y-39983 dihydrochloride and GEM attenuated tumour growth in a mouse xenograft tumour model. Collectively, these data demonstrated that UBR5 plays a pivotal role in the sensitisation of PC to GEM and provides a potential therapeutic strategy to overcome GEM resistance.

A High-throughput Response to the SARS-CoV-2 Pandemic.

Four years after the beginning of the COVID-19 pandemic, it is important to reflect on the events that have occurred during that time and the knowledge that has been gained. The response to the pandemic was rapid and highly resourced; it was also built upon a foundation of decades of federally funded basic and applied research. Laboratories in government, pharmaceutical, academic, and non-profit institutions all played roles in advancing pre-2020 discoveries to produce clinical treatments. This perspective provides a summary of how the development of high-throughput screening methods in a biosafety level 3 (BSL-3) environment at Southern Research Institute (SR) contributed to pandemic response efforts. The challenges encountered are described, including those of a technical nature as well as those of working under the pressures of an unpredictable virus and pandemic.

Predicting Emission of Heteroleptic Iridium Complexes using Artificial Chemical Intelligence.

We report a deep learning-based approach to accurately predict the emission spectraof phosphorescent heteroleptic [Ir(C^N)2(NN)]+complexes, enabling the rapid discovery of novel Ir(III) chromophores for diverse applications including organic light-emitting diodes and solar fuel cells. The deep learning models utilize graph neural networks and other chemical features in architectures that reflect the inherent structure of the heteroleptic complexes, composed of C^Nand N^Nligands, and are thus geared towards efficient training over the dataset. By leveraging experimental emission data, our models reliably predict the full emission spectra of these complexes across various emission profiles, surpassing the accuracy of conventional DFT and correlated wavefunction methods, while simultaneously achieving robustness to the presence of imperfect (noisy, low-quality) training spectra. We showcase the potential applications for these and related models for \insilico\ prediction of complexes with tailored emission properties, as well as in "design of experiment'' contexts to reduce the synthetic burden of high-throughput screening. In the latter case, we demonstrate that the models allow to exploit a limited amount of experimental data to explore a wide range of chemical space, thus leveraging a modest synthetic effort.

WLB-87848, a Selective σ1 Receptor Agonist, with an Unusually Positioned NH Group as Positive Ionizable Moiety and Showing Neuroprotective Activity.

The synthesis and pharmacological activity of a new series of thieno[2,3-d]pyrimidin-4(3H)-one derivatives as sigma-1 receptor (σ1R) ligands are reported. A hit from a high-throughput screening program was evolved into a highly potent and selective σ1R agonist (14qR) that contains a free NH group as positive ionizable moiety, not fulfilling the usual pharmacophoric features of the σ1R. The compound shows good physicochemical and ADMET characteristics, displays an agonist profile in the binding immunoglobulin protein/σ1R association assay, induces neuron viability in an in vitro model of β-amyloid peptide intoxication, and presents positive results against recognition memory impairment induced by hippocampal injection of Aβ peptide in rats after oral treatment, altogether making 14qR (WLB-87848) an interesting candidate for neuroprotection.

Electrophoretic Microfluidic Characterization of mRNA- and pDNA-Loaded Lipid Nanoparticles.

Lipid nanoparticles (LNPs) are clinically advanced nonviral gene delivery vehicles with a demonstrated ability to address viral, oncological, and genetic diseases. However, the further development of LNP therapies requires rapid analytical techniques to support their development and manufacturing. The method developed and described in this paper presents an approach to rapidly and accurately analyze LNPs for optimized therapeutic loading by utilizing an electrophoresis microfluidic platform to analyze the composition of LNPs with different clinical lipid compositions (Onpattro, Comirnaty, and Spikevax) and nucleic acid (plasmid DNA (pDNA) and messenger RNA (mRNA)) formulations. This method enables the high-throughput screening of LNPs using a 96- or 384-well plate with approximate times of 2-4 min per sample using a total volume of 11 μL. The lipid analysis requires concentrations approximately between 109 and 1010 particles/mL and has an average precision error of 10.4% and a prediction error of 19.1% when compared to using a NanoSight, while the nucleic acid analysis requires low concentrations of 1.17 ng/μL for pDNA and 0.17 ng/μL for mRNA and has an average precision error of 4.8% and a prediction error of 9.4% when compared to using a PicoGreen and RiboGreen assay. In addition, our method quantifies the relative concentration of nucleic acid per LNP. Utilizing this approach, we observed an average of 263 ± 62.2 mRNA per LNP and 126.3 ± 21.2 pDNA per LNP for the LNP formulations used in this study, where the accuracy of these estimations is dependent on reference standards. We foresee the utility of this technique in the high-throughput characterization of LNPs during manufacturing and formulation research and development.

Speeding up Glioblastoma Cancer Research: Highlighting the Zebrafish Xenograft Model

Glioblastoma multiforme (GBM) is a very aggressive and lethal primary brain cancer in adults. The multifaceted nature of GBM pathogenesis, rising from complex interactions between cells and the tumor microenvironment (TME), has posed great treatment challenges. Despite significant scientific efforts, the prognosis for GBM remains very poor, even after intensive treatment with surgery, radiation, and chemotherapy. Efficient GBM management still requires the invention of innovative treatment strategies. There is a strong necessity to complete cancer in vitro studies and in vivo studies to properly evaluate the mechanisms of tumor progression within the complex TME. In recent years, the animal models used to study GBM tumors have evolved, achieving highly invasive GBM models able to provide key information on the molecular mechanisms of GBM onset. At present, the most commonly used animal models in GBM research are represented by mammalian models, such as mouse and canine ones. However, the latter present several limitations, such as high cost and time-consuming management, making them inappropriate for large-scale anticancer drug evaluation. In recent years, the zebrafish (Danio rerio) model has emerged as a valuable tool for studying GBM. It has shown great promise in preclinical studies due to numerous advantages, such as its small size, its ability to generate a large cohort of genetically identical offspring, and its rapid development, permitting more time- and cost-effective management and high-throughput drug screening when compared to mammalian models. Moreover, due to its transparent nature in early developmental stages and genetic and anatomical similarities with humans, it allows for translatable brain cancer research and related genetic screening and drug discovery. For this reason, the aim of the present review is to highlight the potential of relevant transgenic and xenograft zebrafish models and to compare them to the traditionally used animal models in GBM research.

Novel Tetrazolium-Based Colorimetric Assay for Helicase nsp13 in SARS-CoV-2

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a human pathogenic virus that encodes for a helicase (SC2Hel) that is essential for viral replication. SC2Hel has the ability to unravel dsRNA or dsDNA in an NTP-dependent manner from the 5′ to 3′ directionality. The standard helicase assay from studies involving SARS-CoV and SARS-CoV-2 have relied on the concept of fluorescence resonance energy transfer. Adding to the collection of helicase assays, herein, we have developed a novel tetrazolium-based colorimetric assay system for the detection of ADP that is produced via SC2Hel activity. This SC2Hel assay combines three enzyme-coupled steps involving the ADP-dependent Thermococcus litoralis glucokinase (TlGlcK), Leuconostoc mesenteroides glucose-6-phosphate dehydrogenase (LmG6PDH), and Clostridium kluyveri diaphorase (CkDIA). Iodonitrotetrazolium chloride (INT), a colorimetric tetrazolium reagent, was used in the final step of the assay that converted into INT-formazan during reduction. INT-formazan in the assay’s buffered solution at pH 7.6 exhibited an intense colorimetric response at a wavelength maximum of 505 nm. The assay exhibited excellent performance characteristics as it revealed a Z’ factor of 0.87 and it has the potential to be further adopted into high-throughput screening studies for therapeutic drug discovery research.

HDAC6 inhibition releases HR23B to activate proteasomes, expand the tumor immunopeptidome and amplify T-cell antimyeloma activity.

Proteasomes degrade intracellular proteins to generate antigenic peptides that are recognized by the adaptive immune system and promote anticancer immunity. However, tumors subvert the antigen presentation machinery to escape immunosurveillance. We hypothesized that proteasome activation could concomitantly increase antigen abundance and diversity in multiple myeloma (MM) cells. High-throughput screens revealed that histone deacetylase 6 (HDAC6) inhibitors activated proteasomes to unmask neoantigens and amplify the tumor-specific antigenic landscape. Treatment of patient CD138+ cells with HDAC6 inhibitors significantly promoted the anti-myeloma activity of autologous CD8+ T-cells. Pharmacologic blockade and genetic ablation of the HDAC6 ubiquitin-binding domain released HR23B, which shuttles ubiquitinylated cargo to proteasomes, while silencing HDAC6 or HR23B in MM cells abolished the effect of HDAC6 inhibitors on proteasomes, antigen presentation and T-cell cytotoxicity. Taken together, our results demonstrate the paradigm-shifting translational impact of proteasome activators to expand the myeloma immunopeptidome and have revealed novel, actionable antigenic targets for T-cell-directed immunotherapy.

Identification and characterization of a nonbiological small-molecular mimic of a Zika virus conformational neutralizing epitope

Antigenic similarities between Zika virus (ZIKV) and other flaviviruses pose challenges to the development of virus-specific diagnostic tools and effective vaccines. Starting with a DNA-encoded one-bead-one-compound combinatorial library of 508,032 synthetic, non-natural oligomers, we selected and characterized small molecules that mimic ZIKV epitopes. High-throughput fluorescence-activated cell sorter-based bead screening was used to select molecules that bound IgG from ZIKV-immune but not from dengue-immune sera. Deep sequencing of the DNA from the "Zika-only" beads identified 40 candidate molecular structures. A lead candidate small molecule "CZV1-1" was selected that correctly identifies serum specimens from Zika-experienced patients with good sensitivity and specificity (85.3% and 98.4%, respectively). Binding competition studies of purified anti-CZV1-1 IgG against known ZIKV-specific monoclonal antibodies (mAbs) showed that CZV1-1 mimics a nonlinear, neutralizing conformational epitope in the domain III of the ZIKV envelope. Purified anti-CZV1-1 IgG neutralized infection of ZIKV in cell cultures with potencies comparable to highly specific ZIKV-neutralizing mAbs. This study demonstrates an innovative approach for identification of synthetic non-natural molecular mimics of conformational virus epitopes. Such molecular mimics may have value in the development of accurate diagnostic assays for Zika, as well as for other viruses.

LigandDiff: de Novo Ligand Design for 3D Transition Metal Complexes with Diffusion Models.

Transition metal complexes are a class of compounds with varied and versatile properties, making them of great technological importance. Their applications cover a wide range of fields, either as metallodrugs in medicine or as materials, catalysts, batteries, solar cells, etc. The demand for the novel design of transition metal complexes with new properties remains of great interest. However, the traditional high-throughput screening approach is inherently expensive and laborious since it depends on human expertise. Here, we present LigandDiff, a generative model for the de novo design of novel transition metal complexes. Unlike the existing methods that simply extract and combine ligands with the metal to get new complexes, LigandDiff aims at designing configurationally novel ligands from scratch, which opens new pathways for the discovery of organometallic complexes. Moreover, it overcomes the limitations of current methods, where the diversity of new complexes highly relies on the diversity of available ligands, while LigandDiff can design numerous novel ligands without human intervention. Our results indicate that LigandDiff designs unique and novel ligands under different contexts, and these generated ligands are synthetically accessible. Moreover, LigandDiff shows good transferability by generating successful ligands for any transition metal complex.

Framework for a High-Throughput Screening Method to Assess Polymer/Plasticizer Miscibility: The Case of Hydrocarbons in Polyolefins

Framework for a High-Throughput Screening Method to Assess Polymer/Plasticizer Miscibility: The Case of Hydrocarbons in Polyolefins

Improving Anticancer Drug Selection and Prioritization via Neural Learning to Rank.

Personalized cancer treatment requires a thorough understanding of complex interactions between drugs and cancer cell lines in varying genetic and molecular contexts. To address this, high-throughput screening has been used to generate large-scale drug response data, facilitating data-driven computational models. Such models can capture complex drug-cell line interactions across various contexts in a fully data-driven manner. However, accurately prioritizing the most effective drugs for each cell line still remains a significant challenge. To address this, we developed multiple neural ranking approaches that leverage large-scale drug response data across multiple cell lines from diverse cancer types. Unlike existing approaches that primarily utilize regression and classification techniques for drug response prediction, we formulated the objective of drug selection and prioritization as a drug ranking problem. In this work, we proposed multiple pairwise and listwise neural ranking methods that learn latent representations of drugs and cell lines and then use those representations to score drugs in each cell line via a learnable scoring function. Specifically, we developed neural pairwise and listwise ranking methods, Pair-PushC and List-One on top of the existing methods, pLETORg and ListNet, respectively. Additionally, we proposed a novel listwise ranking method, List-All, that focuses on all the effective drugs instead of the top effective drug, unlike List-One. We also provide an exhaustive empirical evaluation with state-of-the-art regression and ranking baselines on large-scale data sets across multiple experimental settings. Our results demonstrate that our proposed ranking methods mostly outperform the best baselines with significant improvements of as much as 25.6% in terms of selecting truly effective drugs within the top 20 predicted drugs (i.e., hit@20) across 50% test cell lines. Furthermore, our analyses suggest that the learned latent spaces from our proposed methods demonstrate informative clustering structures and capture relevant underlying biological features. Moreover, our comprehensive evaluation provides a thorough and objective comparison of the performance of different methods (including our proposed ones).

Combining computational screening and machine learning to explore MOFs and COFs for methane purification

Metal-organic frameworks (MOFs) and covalent organic frameworks (COFs) have great potential to be used as porous adsorbents and membranes to achieve high-performance methane purification. Although the continuous increase in the number and diversity of MOFs and COFs is a great opportunity for the discovery of novel adsorbents and membranes with superior performances, evaluating such a vast number of materials in the quickest and most effective manner requires the development of computational approaches. High-throughput computational screening based on molecular simulations has been extensively used to identify the most promising MOFs and COFs for methane purification. However, the enormous and ever-growing material space necessitates more efficient approaches in terms of time and effort. Combining data science with molecular simulations has recently accelerated the discovery of optimal MOF and COF materials for methane purification and revealed the hidden structure–performance relationships. In this perspective, we highlighted the recent developments in combining high-throughput molecular simulations and machine learning to accurately identify the most promising MOF and COF adsorbents and membranes among thousands of candidates for separating methane from other gases including acetylene, carbon dioxide, helium, hydrogen, and nitrogen. After providing a brief overview of the topic, we reviewed the pioneering contributions in the field and discussed the current opportunities and challenges that we need to direct our efforts for the design and discovery of adsorbent and membrane materials.

Visualization of oxidized guanine nucleotides accumulation in living cells with split MutT.

Cancer cells produce vast quantities of reactive oxygen species, leading to the accumulation of toxic nucleotides as 8-oxo-7,8-dihydro-2'-deoxyguanosine 5'-triphosphate (8-oxo-dGTP). The human MTH1 protein catalyzes the hydrolysis of 8-oxo-dGTP, and cancer cells are dependent on MTH1 for their survival. MTH1 inhibitors are possible candidates for a class of anticancer drugs; however, a reliable screening system using live cells has not been developed. Here we report a visualization method for 8-oxo-dGTP and its related nucleotides in living cells. Escherichia coli MutT, a functional homologue of MTH1, is divided into the N-terminal (1-95) and C-terminal (96-129) parts (Mu95 and 96tT, respectively). Mu95 and 96tT were fused to Ash (assembly helper tag) and hAG (Azami Green), respectively, to visualize the nucleotides as fluorescent foci formed upon the Ash-hAG association. The foci were highly increased when human cells expressing Ash-Mu95 and hAG-96tT were treated with 8-oxo-7,8-dihydro-2'-deoxyguanosine (8-oxo-dG) and 8-oxo-dGTP. The foci formation by 8-oxo-dG(TP) was strikingly enhanced by the MTH1 knockdown. Moreover, known MTH1 inhibitors and oxidizing reagents also increased foci. This is the first system that visualizes damaged nucleotides in living cells, provides an excellent detection method for the oxidized nucleotides and oxidative stress, and enables high throughput screening for MTH1 inhibitors.

Engineering non-conventional yeast Rhodotorula toruloides for ergothioneine production

BackgroundErgothioneine (EGT) is a distinctive sulfur-containing histidine derivative, which has been recognized as a high-value antioxidant and cytoprotectant, and has a wide range of applications in food, medical, and cosmetic fields. Currently, microbial fermentation is a promising method to produce EGT as its advantages of green environmental protection, mild fermentation condition, and low production cost. However, due to the low-efficiency biosynthetic process in numerous cell factories, it is still a challenge to realize the industrial biopreparation of EGT. The non-conventional yeast Rhodotorula toruloides is considered as a potential candidate for EGT production, thanks to its safety for animals and natural ability to synthesize EGT. Nevertheless, its synthesis efficiency of EGT deserves further improvement.ResultsIn this study, out of five target wild-type R. toruloides strains, R. toruloides 2.1389 (RT1389) was found to accumulate the highest EGT production, which could reach 79.0 mg/L at the shake flask level on the 7th day. To achieve iterative genome editing in strain RT1389, CRISPR-assisted Cre recombination (CACR) method was established. Based on it, an EGT-overproducing strain RT1389-2 was constructed by integrating an additional copy of EGT biosynthetic core genes RtEGT1 and RtEGT2 into the genome, the EGT titer of which was 1.5-fold increase over RT1389. As the supply of S-adenosylmethionine was identified as a key factor determining EGT production in strain RT1389, subsequently, a series of gene modifications including S-adenosylmethionine rebalancing were integrated into the strain RT1389-2, and the resulting mutants were rapidly screened according to their EGT production titers with a high-throughput screening method based on ergothionase. As a result, an engineered strain named as RT1389-3 was selected with a production titer of 267.4 mg/L EGT after 168 h in a 50 mL modified fermentation medium.ConclusionsThis study characterized the EGT production capacity of these engineered strains, and demonstrated that CACR and high-throughput screening method allowed rapid engineering of R. toruloides mutants with improved EGT production. Furthermore, this study provided an engineered RT1389-3 strain with remarkable EGT production performance, which had potential industrial application prospects.

Rola i wykorzystanie badań wysokoprzepustowych w poszukiwaniu związków chemicznych o działaniu terapeutycznym

With the development of medicine and the aging population, the demand for more effective therapies is escalating. A tool that facilitates the discovery and introduction of new therapeutic drugs is High-Throughput Screening (HTS). These tests, consisting of a wide set of various assays, allow testing hundreds of thousands of compounds in a short period of time. The aim is to accurately identify active compounds that could become potential therapeutic candidates in the pharmaceutical industry. HTS is the first step in the quest for potential drugs, therefore it is one of the crucial tests that determine whether a particular drug candidate will be discovered. In this review, different stages of high-throughput screening studies will be described, as well as methods utilized in these studies. The necessary steps in the optimization of these tests, selection of equipment, automation and key quality control parameters for reliably performed screening will also be presented.