Circular Dichroism Sensing: Strategies and Applications

The analysis of the absolute configuration, enantiomeric composition, and concentration of chiral compounds are frequently encountered tasks across the chemical and health sciences. Chiroptical sensing methods can streamline this work and allow high-throughput screening with remarkable reduction of operational time and cost. During the last few years, significant methodological advances with innovative chirality sensing systems, the use of computer-generated calibration curves, machine learning assistance, and chemometric data processing, to name a few, have emerged and are now matched with commercially available multi-well plate CD readers. These developments have reframed the chirality sensing space and provide new opportunities that are of interest to a large group of chemists. This review will discuss chirality sensing strategies and applications with representative small-molecule CD sensors. Emphasis will be given to important milestones and recent advances that accelerate chiral compound analysis by outperforming traditional methods, conquer new directions, and pioneering efforts that lie at the forefront of chiroptical high-throughput screening developments. The goal is to provide the reader with a thorough understanding of the current state and a perspective of future directions of this rapidly emerging field.

CRISPRi: a way to integrate iPSC-derived neuronal models

The genetic landscape of neurodegenerative diseases encompasses genes affecting multiple cellular pathways which exert effects in an array of neuronal and glial cell-types. Deconvolution of the roles of genes implicated in disease and the effects of disease-associated variants remains a vital step in the understanding of neurodegeneration and the development of therapeutics. Disease modelling using patient induced pluripotent stem cells (iPSCs) has enabled the generation of key cell-types associated with disease whilst maintaining the genomic variants that predispose to neurodegeneration. The use of CRISPR interference (CRISPRi), alongside other CRISPR-perturbations, allows the modelling of the effects of these disease-associated variants or identifying genes which modify disease phenotypes. This review summarises the current applications of CRISPRi in iPSC-derived neuronal models, such as fluorescence-activated cell sorting (FACS)-based screens, and discusses the future opportunities for disease modelling, identification of disease risk modifiers and target/drug discovery in neurodegeneration.

Optimizing Nucleic Acid Delivery Systems through Barcode Technology

Conventional biological experiments often focus on in vitro assays because of the inherent limitations when handling multiple variables in vivo, including labor-intensive and time-consuming procedures. Often only a subset of samples demonstrating significant efficacy in the in vitro assays can be evaluated in vivo. Nonetheless, because of the low correlation between the in vitro and in vivo tests, evaluation of the variables under examination in vivo and not solely in vitro is critical. An emerging approach to achieve high-throughput in vivo tests involves using a barcode system consisting of various nucleotide combinations. Unique barcodes for each variant enable the simultaneous testing of multiple entities, eliminating the need for separate individual tests. Subsequently, to identify crucial parameters, samples were collected and analyzed using barcode sequencing. This review explores the development of barcode design and its applications, including the evaluation of nucleic acid delivery systems and the optimization of gene expression in vivo.

Fluorescence-Based Screens for Engineering Enzymes Linked to Halogenated Tryptophan

Directed evolution is often limited by the throughput of accurate screening methods. Here we demonstrate the feasibility of utilizing a singular transcription factor (TF)-system that can be refactored in two ways (both as an activator and repressor). Specifically, we showcase the use of previously evolved 5-halo- or 6-halo-tryptophan-specific TF biosensors suitable for the detection of a halogenated tryptophan molecule in vivo. We subsequently validate the biosensor's utility for two halogenase-specific halo-tryptophan accumulation screens. First, we isolated 5-tryptophan-halogenase, XsHal, from a mixed pool of halogenases with 100% efficiency. Thereafter, we generated a targeted library of the catalytic residue of 6-tryptophan halogenase, Th-Hal, and isolated functioning halogenases with 100% efficiency. Lastly, we refactor the TF circuit to respond to the depletion of halogenated tryptophan and prototype a high-throughput biosensor-directed evolution scheme to screen for downstream enzyme variants capable of promiscuously converting halogenated tryptophan. Altogether, this work takes a significant step toward the rapid and higher throughput screening of halogenases and halo-tryptophan converting enzymes to further reinforce efforts to enable high-level bioproduction of halogenated chemicals.

Advancing pathogen surveillance by nanopore sequencing and genotype characterization of Acheta domesticus densovirus in mass-reared house crickets

Rapid and reliable detection of pathogens is crucial to complement the growing industry of mass-reared insects, in order to safeguard the insect colonies from outbreak of diseases, which may cause significant economic loss. Current diagnostic methods are mainly based on conventional PCR and microscopic examination, requiring prior knowledge of disease symptoms and are limited to identifying known pathogens. Here, we present a rapid nanopore-based metagenomics approach for detecting entomopathogens from the European house cricket (Acheta domesticus). In this study, the Acheta domesticus densovirus (AdDV) was detected from diseased individuals using solely Nanopore sequencing. Virus reads and genome assemblies were obtained within twenty-four hours after sequencing. Subsequently, due to the length of the Nanopore reads, it was possible to reconstruct significantly large parts or even the entire AdDV genome to conduct studies for genotype identification. Variant analysis indicated the presence of three AdDV genotypes within the same house cricket population, with association to the vital status of the diseased crickets. This contrast provided compelling evidence for the existence of non-lethal AdDV genotypes. These findings demonstrated nanopore-based metagenomics sequencing as a powerful addition to the diagnostic tool kit for routine pathogen surveillance and diagnosis in the insect rearing industry.

Zebrafish as Versatile Model for Assessing Animal Venoms and Toxins: Current Applications and Future Prospects

Animal venoms and toxins hold promise as sources of novel drug candidates, therapeutic agents, and biomolecules. To fully harness their potential, it is crucial to develop reliable testing methods that provide a comprehensive understanding of their effects and mechanisms of action. However, traditional rodent assays encounter difficulties in mimicking venom-induced effects in human due to the impractical venom dosage levels. The search for reliable testing methods has led to the emergence of zebrafish (Danio rerio) as a versatile model organism for evaluating animal venoms and toxins. Zebrafish possess genetic similarities to humans, rapid development, transparency, and amenability to high-throughput assays, making it ideal for assessing the effects of animal venoms and toxins. This review highlights unique attributes of zebrafish and explores their applications in studying venom- and toxin-induced effects from various species, including snakes, jellyfish, cuttlefish, anemones, spiders, and cone snails. Through zebrafish-based research, intricate physiological responses, developmental alterations, and potential therapeutic interventions induced by venoms are revealed. Novel techniques such as CRISPR/Cas9 gene editing, optogenetics, and high-throughput screening hold great promise for advancing venom research. As zebrafish-based insights converge with findings from other models, the comprehensive understanding of venom-induced effects continues to expand, guiding the development of targeted interventions and promoting both scientific knowledge and practical applications.

Unraveling the Activity and Mechanism of TM@g-C4N3 Electrocatalysts in the Oxygen Reduction Reaction

In this work, a high-throughput screening strategy and density functional theory (DFT) are jointly employed to identify high-performance TM@g-C4N3 (TM = 3d, 4d, 5d transition metals) single-atom catalysts (SACs) for the oxygen reduction reaction (ORR). Comprehensive studies demonstrated that Cu@, Zn@, and Ag@g-C4N3 show high ORR catalytic activities under both acidic and alkaline conditions with favorable overpotentials (ηORR) of 0.70, 0.89, and 0.89 V, respectively; among them, Cu@g-C4N3 is the best candidate. The ORR follows a four-electron mechanism with the final product H2O/OH-. Cu@, Zn@, and Ag@g-C4N3 catalysts also exhibit good thermal (500 K) and electrochemical (0.93-3.14 V) stabilities. Cu@, Zn@, and Ag@g-C4N3 demonstrate superior activities with low ηORR due to its moderate adsorption strength of *OH. The ηORR and the Gibbs free energy changes of *OH (ΔG4(acidic)/ΔG4(alkaline)) resemble a volcano-type relationship under acidic/alkaline conditions, respectively. Additionally, the O-O bond length in *OOH emerged as an effective structural descriptor for rapidly identifying the promising electrocatalysts. This research provides valuable insights into the origin of the ORR activity on TM@g-C4N3 and offers useful guidance for the efficient exploration of high-performance catalyst candidates.

Exploiting the predictive power of educated spheroids to detect immune-mediated idiosyncratic drug-induced liver injury: the case of troglitazone

Idiosyncratic drug-induced liver injury (iDILI) is a major concern in drug development because its occurrence is unpredictable. Presently, iDILI prediction is a challenge, and cell toxicity is observed only at concentrations that are much higher than the therapeutic doses in preclinical models. Applying a proprietary cell educating technology, we developed a person-dependent spheroid system that contains autologous educated immune cells that can detect iDILI risk at therapeutic concentrations. Integrating this system into a high-throughput screening platform will help pharmaceutical companies accurately detect the iDILI risk of new molecules de-risking drug development.

Screening of factors inducing alveolar type 1 epithelial cells using human pluripotent stem cells

Alveolar type 2 (AT2) epithelial cells are tissue stem cells capable of differentiating into alveolar type 1 (AT1) cells for injury repair and maintenance of lung homeostasis. However, the factors involved in human AT2-to-AT1 cell differentiation are not fully understood. Here, we established SFTPCGFP and AGERmCherry-HiBiT dual-reporter induced pluripotent stem cells (iPSCs), which detected AT2-to-AT1 cell differentiation with high sensitivity and identified factors inducing AT1 cell differentiation from AT2 and their progenitor cells. We also established an "on-gel" alveolar epithelial spheroid culture suitable for medium-throughput screening. Among the 274 chemical compounds, several single compounds, including LATS-IN-1, converted AT1 cells from AT2 and their progenitor cells. Moreover, YAP/TAZ signaling activation and AKT signaling suppression synergistically recapitulated the induction of transcriptomic, morphological, and functionally mature AT1 cells. Our findings provide novel insights into human lung development and lung regenerative medicine.

Graph Convolutional Network-Enhanced Model for Screening Persistent, Mobile, and Toxic and Very Persistent and Very Mobile Substances

The global management for persistent, mobile, and toxic (PMT) and very persistent and very mobile (vPvM) substances has been further strengthened with the rapid increase of emerging contaminants. The development of a ready-to-use and publicly available tool for the high-throughput screening of PMT/vPvM substances is thus urgently needed. However, the current model building with the coupling of conventional algorithms, small-scale data set, and simplistic features hinders the development of a robust model for screening PMT/vPvM with wide application domains. Here, we construct a graph convolutional network (GCN)-enhanced model with feature fusion of a molecular graph and molecular descriptors to effectively utilize the significant correlation between critical descriptors and PMT/vPvM substances. The model is built with 213,084 substances following the latest PMT classification criteria. The application domains of the GCN-enhanced model assessed by kernel density estimation demonstrate the high suitability for high-throughput screening PMT/vPvM substances with both a high accuracy rate (86.6%) and a low false-negative rate (6.8%). An online server named PMT/vPvM profiler is further developed with a user-friendly web interface (http://www.pmt.zj.cn/). Our study facilitates a more efficient evaluation of PMT/vPvM substances with a globally accessible screening platform.

Mutational signature-based identification of DNA repair deficient gastroesophageal adenocarcinomas for therapeutic targeting

Homologous recombination (HR) and nucleotide excision repair (NER) are the two most frequently disabled DNA repair pathways in cancer. HR-deficient breast, ovarian, pancreatic and prostate cancers respond well to platinum chemotherapy and PARP inhibitors. However, the frequency of HR deficiency in gastric and esophageal adenocarcinoma (GEA) still lacks diagnostic and functional validation. Using whole exome and genome sequencing data, we found that a significant subset of GEA, but very few colorectal adenocarcinomas, show evidence of HR deficiency by mutational signature analysis (HRD score). High HRD gastric cancer cell lines demonstrated functional HR deficiency by RAD51 foci assay and increased sensitivity to platinum chemotherapy and PARP inhibitors. Of clinical relevance, analysis of three different GEA patient cohorts demonstrated that platinum treated HR deficient cancers had better outcomes. A gastric cancer cell line with strong sensitivity to cisplatin showed HR proficiency but exhibited NER deficiency by two photoproduct repair assays. Single-cell RNA-sequencing revealed that, in addition to inducing apoptosis, cisplatin treatment triggered ferroptosis in a NER-deficient gastric cancer, validated by intracellular GSH assay. Overall, our study provides preclinical evidence that a subset of GEAs harbor genomic features of HR and NER deficiency and may therefore benefit from platinum chemotherapy and PARP inhibitors.

Rebaudioside B Attenuates Lung Ischemia-reperfusion Injury-associated Apoptosis and Inflammation

OBJECTIVE

At present, no proven effective treatment is available for Lung Ischemiareperfusion Injury (LIRI). Natural compounds offer promising prospects for developing new drugs to address various diseases. This study sought to explore the potential of Rebaudioside B (Reb B) as a treatment compound for LIRI, both in vivo and in vitro.

METHODS

This study involved utilizing the human pulmonary alveolar cell line A549, consisting of epithelial type II cells, subjected to Oxygen-glucose Deprivation/recovery (OGD/R) for highthroughput in vitro cell viability screening. The aim was to identify the most promising candidate compounds. Additionally, an in vivo rat model of lung ischemia-reperfusion was employed to evaluate the potential protective effects of Reb B.

RESULTS

Through high-throughput screening, Reb B emerged as the most promising natural compound among those tested. In the A549 OGD/R models, Reb B exhibited a capacity to enhance cell viability by mitigating apoptosis. In the in vivo LIRI model, pre-treatment with Reb B notably decreased apoptotic cells, perivascular edema, and neutrophil infiltration within lung tissues. Furthermore, Reb B demonstrated its ability to attenuate lung inflammation associated with LIRI primarily by elevating IL-10 levels while reducing levels of IL-6, IL-8, and TNF-α.

CONCLUSION

The comprehensive outcomes strongly suggest Reb B's potential as a protective agent against LIRI. This effect is attributed to its inhibition of the mitochondrial apoptotic pathway and its ability to mitigate the inflammatory response.

Rational design of a near-infrared fluorescent probe for monitoring butyrylcholinesterase activity and its application in development of inhibitors

Butyrylcholinesterase (BChE) is widely expressed in multiple tissues and has a vital role in several key human disorders, such as Alzheimer's disease and tumorigenesis. However, the role of BChE in human disorders has not been investigated. Thus, to quantitatively detect and visualize dynamical variations in BChE activity is essential for exploring the biological roles of BChE in the progression of a number of human disorders. Herein, based on the substrate characteristics of BChE, we customized and synthesized three near-infrared (NIR) fluorescent probe substrates with cyanine-skeleton, and finally selected a NIR fluorescence probe substrate named CYBA. The CYBA demonstrated a significant increase in fluorescence when interacting with BChE, but mainly avoided AChE. Upon the addition of BChE, CYBA could be specifically hydrolyzed to TBO, resulting in a significant NIR fluorescence signal enhancement at 710 nm. Systematic evaluation revealed that CYBA exhibited exceptional chemical stability in complex biosamples and possessed remarkable selectivity and sensitivity towards BChE. Moreover, CYBA was successfully applied for real-time imaging of endogenous BChE activity in two types of nerve-related living cells. Additionally, CYBA demonstrated exceptional stability in the detection of complex biological samples in plasma recovery studies (97.51%-104.01%). Furthermore, CYBA was used to construct a high-throughput screening (HTS) method for BChE inhibitors using human plasma as the enzyme source. We evaluated inhibitory effects of a series of natural products and four flavonoids were identified as potent inhibitors of BChE. Collectively, CYBA can serve as a practical tool to track the changes of BChE activity in complicated biological environments due to its excellent capabilities.

AI is a viable alternative to high throughput screening: a 318-target study

High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery.

Insights from bioinformatics analysis reveal that lipopolysaccharide induces activation of chemokine-related signaling pathways in human nasal epithelial cells

Lipopolysaccharide (LPS) is known to elicit a robust immune response. This study aimed to investigate the impact of LPS on the transcriptome of human nasal epithelial cells (HNEpC). HNEpC were cultured and stimulated with LPS (1 μg/mL) or an equivalent amount of normal culture medium. Subsequently, total RNA was extracted, purified, and sequenced using next-generation RNA sequencing technology. Differentially expressed genes (DEGs) were identified and subjected to functional enrichment analysis. A protein-protein interaction (PPI) network of DEGs was constructed, followed by Ingenuity Pathway Analysis (IPA) to identify molecular pathways influenced by LPS exposure on HNEpC. Validation of key genes was performed using quantitative real-time PCR (qRT-PCR). A total of 97 DEGs, comprising 48 up-regulated genes and 49 down-regulated genes, were identified. Results from functional enrichment analysis, PPI, and IPA indicated that DEGs were predominantly enriched in chemokine-related signaling pathways. Subsequent qRT-PCR validation demonstrated significant upregulation of key genes in these pathways in LPS-treated HNEpC compared to control cells. In conclusion, LPS intervention profoundly altered the transcriptome of HNEpC, potentially exacerbating inflammatory responses through the activation of chemokine-related signaling pathways.

Pooled effector library screening in protoplasts rapidly identifies novel Avr genes

Crop breeding for durable disease resistance is challenging due to the rapid evolution of pathogen virulence. While progress in resistance (R) gene cloning and stacking has accelerated in recent years1-3, the identification of corresponding avirulence (Avr) genes in many pathogens is hampered by the lack of high-throughput screening options. To address this technology gap, we developed a platform for pooled library screening in plant protoplasts to allow rapid identification of interacting R-Avr pairs. We validated this platform by isolating known and novel Avr genes from wheat stem rust (Puccinia graminis f. sp. tritici) after screening a designed library of putative effectors against individual R genes. Rapid Avr gene identification provides molecular tools to understand and track pathogen virulence evolution via genotype surveillance, which in turn will lead to optimized R gene stacking and deployment strategies. This platform should be broadly applicable to many crop pathogens and could potentially be adapted for screening genes involved in other protoplast-selectable traits.

High-throughput measurement of the content and properties of nano-sized bioparticles with single-particle profiler

We introduce a method, single-particle profiler, that provides single-particle information on the content and biophysical properties of thousands of particles in the size range 5-200 nm. We use our single-particle profiler to measure the messenger RNA encapsulation efficiency of lipid nanoparticles, the viral binding efficiencies of different nanobodies, and the biophysical heterogeneity of liposomes, lipoproteins, exosomes and viruses.

High-Throughput Methods for the Discovery of Small Molecule Modulators of Pancreatic Beta-Cell Function and Regeneration

The progression of type II diabetes (T2D) is characterized by a complex and highly variable loss of beta-cell mass, resulting in impaired insulin secretion. Many T2D drug discovery efforts aimed at discovering molecules that can protect or restore beta-cell mass and function have been developed using limited beta-cell lines and primary rodent/human pancreatic islets. Various high-throughput screening methods have been used in the context of drug discovery, including luciferase-based reporter assays, glucose-stimulated insulin secretion, and high-content screening. In this context, a cornerstone of small molecule discovery has been the use of immortalized rodent beta-cell lines. Although insightful, this usage has led to a more comprehensive understanding of rodent beta-cell proliferation pathways rather than their human counterparts. Advantages gained in enhanced physiological relevance are offered by three-dimensional (3D) primary islets and pseudoislets in contrast to monolayer cultures, but these approaches have been limited to use in low-throughput experiments. Emerging methods, such as high-throughput 3D islet imaging coupled with machine learning, aim to increase the feasibility of integrating 3D microtissue structures into high-throughput screening. This review explores the current methods used in high-throughput screening for small molecule modulators of beta-cell mass and function, a potentially pivotal strategy for diabetes drug discovery.

Feasibility of functional precision medicine for guiding treatment of relapsed or refractory pediatric cancers

Children with rare, relapsed or refractory cancers often face limited treatment options, and few predictive biomarkers are available that can enable personalized treatment recommendations. The implementation of functional precision medicine (FPM), which combines genomic profiling with drug sensitivity testing (DST) of patient-derived tumor cells, has potential to identify treatment options when standard-of-care is exhausted. The goal of this prospective observational study was to generate FPM data for pediatric patients with relapsed or refractory cancer. The primary objective was to determine the feasibility of returning FPM-based treatment recommendations in real time to the FPM tumor board (FPMTB) within a clinically actionable timeframe (<4 weeks). The secondary objective was to assess clinical outcomes from patients enrolled in the study. Twenty-five patients with relapsed or refractory solid and hematological cancers were enrolled; 21 patients underwent DST and 20 also completed genomic profiling. Median turnaround times for DST and genomics were within 10 days and 27 days, respectively. Treatment recommendations were made for 19 patients (76%), of whom 14 received therapeutic interventions. Six patients received subsequent FPM-guided treatments. Among these patients, five (83%) experienced a greater than 1.3-fold improvement in progression-free survival associated with their FPM-guided therapy relative to their previous therapy, and demonstrated a significant increase in progression-free survival and objective response rate compared to those of eight non-guided patients. The findings from our proof-of-principle study illustrate the potential for FPM to positively impact clinical care for pediatric and adolescent patients with relapsed or refractory cancers and warrant further validation in large prospective studies. ClinicalTrials.gov registration: NCT03860376 .

Advances in succinic acid production: the enhancement of CO2 fixation for the carbon sequestration benefits

Succinic acid (SA), one of the 12 top platform chemicals produced from biomass, is a precursor of various high value-added derivatives. Specially, 1 mol CO2 is assimilated in 1 mol SA biosynthetic route under anaerobic conditions, which helps to achieve carbon reduction goals. In this review, methods for enhanced CO2 fixation in SA production and utilization of waste biomass for SA production are reviewed. Bioelectrochemical and bioreactor coupling systems constructed with off-gas reutilization to capture CO2 more efficiently were highlighted. In addition, the techno-economic analysis and carbon sequestration benefits for the synthesis of bio-based SA from CO2 and waste biomass are analyzed. Finally, a droplet microfluidics-based high-throughput screening technique applied to the future bioproduction of SA is proposed as a promising approach.

Functional screening in human HSPCs identifies optimized protein-based enhancers of Homology Directed Repair

Homology Directed Repair (HDR) enables precise genome editing, but the implementation of HDR-based therapies is hindered by limited efficiency in comparison to methods that exploit alternative DNA repair routes, such as Non-Homologous End Joining (NHEJ). In this study, we develop a functional, pooled screening platform to identify protein-based reagents that improve HDR in human hematopoietic stem and progenitor cells (HSPCs). We leverage this screening platform to explore sequence diversity at the binding interface of the NHEJ inhibitor i53 and its target, 53BP1, identifying optimized variants that enable new intermolecular bonds and robustly increase HDR. We show that these variants specifically reduce insertion-deletion outcomes without increasing off-target editing, synergize with a DNAPK inhibitor molecule, and can be applied at manufacturing scale to increase the fraction of cells bearing repaired alleles. This screening platform can enable the discovery of future gene editing reagents that improve HDR outcomes.

High-throughput Compositional Screening of PdxTi1-xHy and PdxNb1-xHy Hydrides for CO2 Reduction

Electrochemical experiments and theoretical calculations have shown that Pd-based metal hydrides can perform well for the CO2 reduction reaction (CO2RR). Our previous work on doped-PdH showed that doping Ti and Nb into PdH can improve the CO2RR activity, suggesting that the Pd alloy hydrides with better performance are likely to be found in the PdxTi1-xHy and PdxNb1-xHy phase space. However, the vast compositional and structural space with different alloy hydride compositions and surface adsorbates, makes it intractable to screen out the stable and active PdxM1-xHy catalysts using density functional theory calculations. Herein, an active learning cluster expansion (ALCE) surrogate model equipped with Monte Carlo simulated annealing (MCSA), a CO* binding energy filter and a kinetic model are used to identify promising PdxTi1-xHy and PdxNb1-xHy catalysts with high stability and superior activity. Using our approach, we identify 24 stable and active candidates of PdxTi1-xHy and 5 active candidates of PdxNb1-xHy. Among these candidates, the Pd0.23Ti0.77H, Pd0.19Ti0.81H0.94, and Pd0.17Nb0.83H0.25 are predicted to display current densities of approximately 5.1, 5.1 and 4.6 μA cm-2 at -0.5 V overpotential, respectively, which are significantly higher than that of PdH at 3.7 μA cm-2.

Validating ΔΔG Selectivity Descriptor for Electrosynthesis of H2O2 from Oxygen Reduction Reaction

Understanding selectivity trends is a crucial hurdle in the developing innovative catalysts for generating hydrogen peroxide through the two-electron oxygen reduction reaction (2e-ORR). The identification of selectivity patterns has been made more accessible through the introduction of a newly developed selectivity descriptor derived from thermodynamics, denoted as ΔΔG introduced in Chem Catal. 2023, 3(3), 100568. To validate the suitability of this parameter as a descriptor for 2e-ORR selectivity, we utilize an extensive library of 155 binary alloys. We validate that ΔΔG reliably depicts the selectivity trends in binary alloys reported for their high activity in the 2e-ORR. This analysis also enables the identification of nine selective 2e-ORR catalysts underscoring the efficacy of ΔΔG as 2e-ORR selectivity descriptor. This work highlights the significance of concurrently considering both selectivity and activity trends. This holistic approach is crucial for obtaining a comprehensive understanding in the identification of high-performance catalyst materials for optimal efficiency in various applications.

GAS41 modulates ferroptosis by anchoring NRF2 on chromatin

YEATS domain-containing protein GAS41 is a histone reader and oncogene. Here, through genome-wide CRISPR-Cas9 screenings, we identify GAS41 as a repressor of ferroptosis. GAS41 interacts with NRF2 and is critical for NRF2 to activate its targets such as SLC7A11 for modulating ferroptosis. By recognizing the H3K27-acetylation (H3K27-ac) marker, GAS41 is recruited to the SLC7A11 promoter, independent of NRF2 binding. By bridging the interaction between NRF2 and the H3K27-ac marker, GAS41 acts as an anchor for NRF2 on chromatin in a promoter-specific manner for transcriptional activation. Moreover, the GAS41-mediated effect on ferroptosis contributes to its oncogenic role in vivo. These data demonstrate that GAS41 is a target for modulating tumor growth through ferroptosis. Our study reveals a mechanism for GAS41-mediated regulation in transcription by anchoring NRF2 on chromatin, and provides a model in which the DNA binding activity on chromatin by transcriptional factors (NRF2) can be directly regulated by histone markers (H3K27-ac).

A universal system for boosting gene expression in eukaryotic cell-lines

We demonstrate a transcriptional regulatory design algorithm that can boost expression in yeast and mammalian cell lines. The system consists of a simplified transcriptional architecture composed of a minimal core promoter and a synthetic upstream regulatory region (sURS) composed of up to three motifs selected from a list of 41 motifs conserved in the eukaryotic lineage. The sURS system was first characterized using an oligo-library containing 189,990 variants. We validate the resultant expression model using a set of 43 unseen sURS designs. The validation sURS experiments indicate that a generic set of grammar rules for boosting and attenuation may exist in yeast cells. Finally, we demonstrate that this generic set of grammar rules functions similarly in mammalian CHO-K1 and HeLa cells. Consequently, our work provides a design algorithm for boosting the expression of promoters used for expressing industrially relevant proteins in yeast and mammalian cell lines.

Development of phenotyping algorithms for hypertensive disorders of pregnancy (HDP) and their application in more than 22,000 pregnant women

Recently, many phenotyping algorithms for high-throughput cohort identification have been developed. Prospective genome cohort studies are critical resources for precision medicine, but there are many hurdles in the precise cohort identification. Consequently, it is important to develop phenotyping algorithms for cohort data collection. Hypertensive disorders of pregnancy (HDP) is a leading cause of maternal morbidity and mortality. In this study, we developed, applied, and validated rule-based phenotyping algorithms of HDP. Two phenotyping algorithms, algorithms 1 and 2, were developed according to American and Japanese guidelines, and applied into 22,452 pregnant women in the Birth and Three-Generation Cohort Study of the Tohoku Medical Megabank project. To precise cohort identification, we analyzed both structured data (e.g., laboratory and physiological tests) and unstructured clinical notes. The identified subtypes of HDP were validated against reference standards. Algorithms 1 and 2 identified 7.93% and 8.08% of the subjects as having HDP, respectively, along with their HDP subtypes. Our algorithms were high performing with high positive predictive values (0.96 and 0.90 for algorithms 1 and 2, respectively). Overcoming the hurdle of precise cohort identification from large-scale cohort data collection, we achieved both developed and implemented phenotyping algorithms, and precisely identified HDP patients and their subtypes from large-scale cohort data collection.

OrgaSegment: deep-learning based organoid segmentation to quantify CFTR dependent fluid secretion

Epithelial ion and fluid transport studies in patient-derived organoids (PDOs) are increasingly being used for preclinical studies, drug development and precision medicine applications. Epithelial fluid transport properties in PDOs can be measured through visual changes in organoid (lumen) size. Such organoid phenotypes have been highly instrumental for the studying of diseases, including cystic fibrosis (CF), which is characterized by genetic mutations of the CF transmembrane conductance regulator (CFTR) ion channel. Here we present OrgaSegment, a MASK-RCNN based deep-learning segmentation model allowing for the segmentation of individual intestinal PDO structures from bright-field images. OrgaSegment recognizes spherical structures in addition to the oddly-shaped organoids that are a hallmark of CF organoids and can be used in organoid swelling assays, including the new drug-induced swelling assay that we show here. OrgaSegment enabled easy quantification of organoid swelling and could discriminate between organoids with different CFTR mutations, as well as measure responses to CFTR modulating drugs. The easy-to-apply label-free segmentation tool can help to study CFTR-based fluid secretion and possibly other epithelial ion transport mechanisms in organoids.

A Fully-Automated Senescence Test (FAST) for the high-throughput quantification of senescence-associated markers

Cellular senescence is a major driver of aging and age-related diseases. Quantification of senescent cells remains challenging due to the lack of senescence-specific markers and generalist, unbiased methodology. Here, we describe the Fully-Automated Senescence Test (FAST), an image-based method for the high-throughput, single-cell assessment of senescence in cultured cells. FAST quantifies three of the most widely adopted senescence-associated markers for each cell imaged: senescence-associated β-galactosidase activity (SA-β-Gal) using X-Gal, proliferation arrest via lack of 5-ethynyl-2'-deoxyuridine (EdU) incorporation, and enlarged morphology via increased nuclear area. The presented workflow entails microplate image acquisition, image processing, data analysis, and graphing. Standardization was achieved by i) quantifying colorimetric SA-β-Gal via optical density; ii) implementing staining background controls; iii) automating image acquisition, image processing, and data analysis. In addition to the automated threshold-based scoring, a multivariate machine learning approach is provided. We show that FAST accurately quantifies senescence burden and is agnostic to cell type and microscope setup. Moreover, it effectively mitigates false-positive senescence marker staining, a common issue arising from culturing conditions. Using FAST, we compared X-Gal with fluorescent C12FDG live-cell SA-β-Gal staining on the single-cell level. We observed only a modest correlation between the two, indicating that those stains are not trivially interchangeable. Finally, we provide proof of concept that our method is suitable for screening compounds that modify senescence burden. This method will be broadly useful to the aging field by enabling rapid, unbiased, and user-friendly quantification of senescence burden in culture, as well as facilitating large-scale experiments that were previously impractical.

Unearthing Superior Inorganic UV Second-Order Nonlinear Optical Materials: A Mineral-Inspired Method Integrating First-Principles High-Throughput Screening and Crystal Engineering

Natural minerals, with their adaptable framework structures exemplified by perovskite and lyonsite, have sparked substantial interest as potential templates for the design of advanced functional solid-state materials. Nonetheless, the quest for new materials with desired properties remains a substantial challenge, primarily due to the scarcity of effective and practical synthetic approaches. In this study, we have harnessed a synergistic approach that seamlessly integrates first-principles high-throughput screening and crystal engineering to reinvigorate the often-overlooked fresnoite mineral, Ba2 TiOSi2 O7 . This innovative strategy has culminated in the successful synthesis of two superior inorganic UV nonlinear optical materials, namely Rb2 TeOP2 O7 and Rb2 SbFP2 O7 . Notably, Rb2 SbFP2 O7 demonstrates a comprehensive enhancement in nonlinear optical performance, featuring a shortened UV absorption edge (260 nm) and a more robust second-harmonic generation response (5.1×KDP). Particularly striking is its significantly increased birefringence (0.15@546 nm), which is approximately 30 times higher than the prototype Ba2 TiOSi2 O7 (0.005@546 nm). Our research has not only revitalized the potential of the fresnoite mineral for the development of new high-performance UV nonlinear optical materials but has also provided a clearly defined roadmap for the efficient exploration of novel structure-driven functional materials with targeted properties.

A high-throughput target-based screening approach for the identification and assessment of Mycobacterium tuberculosis mycothione reductase inhibitors

The World Health Organization's goal to combat tuberculosis (TB) is hindered by the emergence of anti-microbial resistance, therefore necessitating the exploration of new drug targets. Multidrug regimens are indispensable in TB therapy as they provide synergetic bactericidal effects, shorten treatment duration, and reduce the risk of resistance development. The research within our European RespiriTB consortium explores Mycobacterium tuberculosis energy metabolism to identify new drug candidates that synergize with bedaquiline, with the aim of discovering more efficient combination drug regimens. In this study, we describe the development and validation of a luminescence-coupled, target-based assay for the identification of novel compounds inhibiting Mycobacterium tuberculosis mycothione reductase (MtrMtb), an enzyme with a role in the protection against oxidative stress. Recombinant MtrMtb was employed for the development of a highly sensitive, robust high-throughput screening (HTS) assay by coupling enzyme activity to a bioluminescent readout. Its application in a semi-automated setting resulted in the screening of a diverse library of ~130,000 compounds, from which 19 hits were retained after an assessment of their potency, selectivity, and specificity. The selected hits formed two clusters and four fragment molecules, which were further evaluated in whole-cell and intracellular infection assays. The established HTS discovery pipeline offers an opportunity to deliver novel MtrMtb inhibitors and lays the foundation for future efforts in developing robust biochemical assays for the identification and triaging of inhibitors from high-throughput library screens.

IMPORTANCE

The growing anti-microbial resistance poses a global public health threat, impeding progress toward eradicating tuberculosis. Despite decades of active research, there is still a dire need for the discovery of drugs with novel modes of action and exploration of combination drug regimens. Within the European RespiriTB consortium, we explore Mycobacterium tuberculosis energy metabolism to identify new drug candidates that synergize with bedaquiline, with the aim of discovering more efficient combination drug regimens. In this study, we present the development of a high-throughput screening pipeline that led to the identification of M. tuberculosis mycothione reductase inhibitors.

Protein3D: Enabling analysis and extraction of metal-containing sites from the Protein Data Bank with molSimplify

Metalloenzymes catalyze a wide range of chemical transformations, with the active site residues playing a key role in modulating chemical reactivity and selectivity. Unlike smaller synthetic catalysts, a metalloenzyme active site is embedded in a larger protein, which makes interrogation of electronic properties and geometric features with quantum mechanical calculations challenging. Here we implement the ability to fetch crystallographic structures from the Protein Data Bank and analyze the metal binding sites in the program molSimplify. We show the usefulness of the newly created protein3D class to extract the local environment around non-heme iron enzymes containing a two histidine motif and prepare 372 structures for quantum mechanical calculations. Our implementation of protein3D serves to expand the range of systems molSimplify can be used to analyze and will enable high-throughput study of metal-containing active sites in proteins.

Microfluidic Giant Polymer Vesicles Equipped with Biopores for High-Throughput Screening of Bacteria

Understanding the mechanisms of antibiotic resistance is critical for the development of new therapeutics. Traditional methods for testing bacteria are often limited in their efficiency and reusability. Single bacterial cells can be studied at high throughput using double emulsions, although the lack of control over the oil shell permeability and limited access to the droplet interior present serious drawbacks. Here, a straightforward strategy for studying bacteria-encapsulating double emulsion-templated giant unilamellar vesicles (GUVs) is introduced. This microfluidic approach serves to simultaneously load bacteria inside synthetic GUVs and to permeabilize their membrane with the pore-forming peptide melittin. This enables antibiotic delivery or the influx of fresh medium into the GUV lumen for highly parallel cultivation and antimicrobial efficacy testing. Polymer-based GUVs proved to be efficient culture and analysis microvessels, as microfluidics allow easy selection and encapsulation of bacteria and rapid modification of culture conditions for antibiotic development. Further, a method for in situ profiling of biofilms within GUVs for high-throughput screening is demonstrated. Conceivably, synthetic GUVs equipped with biopores can serve as a foundation for the high-throughput screening of bacterial colony interactions during biofilm formation and for investigating the effect of antibiotics on biofilms.

Activation of human STING by a molecular glue-like compound

Stimulator of interferon genes (STING) is a dimeric transmembrane adapter protein that plays a key role in the human innate immune response to infection and has been therapeutically exploited for its antitumor activity. The activation of STING requires its high-order oligomerization, which could be induced by binding of the endogenous ligand, cGAMP, to the cytosolic ligand-binding domain. Here we report the discovery through functional screens of a class of compounds, named NVS-STGs, that activate human STING. Our cryo-EM structures show that NVS-STG2 induces the high-order oligomerization of human STING by binding to a pocket between the transmembrane domains of the neighboring STING dimers, effectively acting as a molecular glue. Our functional assays showed that NVS-STG2 could elicit potent STING-mediated immune responses in cells and antitumor activities in animal models.

Simultaneous inhibition of PI3K and PAK in preclinical models of neurofibromatosis type 2-related schwannomatosis

Neurofibromatosis Type 2 (NF2)-related schwannomatosis is a genetic disorder that causes development of multiple types of nervous system tumors. The primary and diagnostic tumor type is bilateral vestibular schwannoma. There is no cure or drug therapy for NF2. Recommended treatments include surgical resection and radiation, both of which can leave patients with severe neurological deficits or increase the risk of future malignant tumors. Results of our previous pilot high-throughput drug screen identified phosphoinositide 3-kinase (PI3K) inhibitors as strong candidates based on loss of viability of mouse merlin-deficient Schwann cells (MD-SCs). Here we used novel human schwannoma model cells to conduct combination drug screens. We identified a class I PI3K inhibitor, pictilisib and p21 activated kinase (PAK) inhibitor, PF-3758309 as the top combination due to high synergy in cell viability assays. Both single and combination therapies significantly reduced growth of mouse MD-SCs in an orthotopic allograft mouse model. The inhibitor combination promoted cell cycle arrest and apoptosis in mouse merlin-deficient Schwann (MD-SCs) cells and cell cycle arrest in human MD-SCs. This study identifies the PI3K and PAK pathways as potential targets for combination drug treatment of NF2-related schwannomatosis.

Synergistic D-Amino Acids Based Antimicrobial Cocktails Formulated via High-Throughput Screening and Machine Learning

Antimicrobial resistance (AMR) from pathogenic bacterial biofilms has become a global health issue while developing novel antimicrobials is inefficient and costly. Combining existing multiple drugs with enhanced efficacy and/or reduced toxicity may be a promising approach to treat AMR. D-amino acids mixtures coupled with antibiotics can provide new therapies for drug-resistance infection with reduced toxicity by lower drug dosage requirements. However, iterative trial-and-error experiments are not tenable to prioritize credible drug formulations, owing to the extremely large number of possible combinations. Herein, a new avenue is provide to accelerate the exploration of desirable antimicrobial formulations via high-throughput screening and machine learning optimization. Such an intelligent method can navigate the large search space and rapidly identify the D-amino acid mixtures with the highest anti-biofilm efficiency and also the synergisms between D-amino acid mixtures and antibiotics. The optimized drug cocktails exhibit high antimicrobial efficacy while remaining non-toxic, which is demonstrated not only from in vitro assessments but also the first in vivo study using a lung infection mouse model.

Designing patient-oriented combination therapies for acute myeloid leukemia based on efficacy/toxicity integration and bipartite network modeling

Acute myeloid leukemia (AML), a heterogeneous and aggressive blood cancer, does not respond well to single-drug therapy. A combination of drugs is required to effectively treat this disease. Computational models are critical for combination therapy discovery due to the tens of thousands of two-drug combinations, even with approved drugs. While predicting synergistic drugs is the focus of current methods, few consider drug efficacy and potential toxicity, which are crucial for treatment success. To find effective new drug candidates, we constructed a bipartite network using patient-derived tumor samples and drugs. The network is based on drug-response screening and summarizes all treatment response heterogeneity as drug response weights. This bipartite network is then projected onto the drug part, resulting in the drug similarity network. Distinct drug clusters were identified using community detection methods, each targeting different biological processes and pathways as revealed by enrichment and pathway analysis of the drugs' protein targets. Four drugs with the highest efficacy and lowest toxicity from each cluster were selected and tested for drug sensitivity using cell viability assays on various samples. Results show that ruxolitinib-ulixertinib and sapanisertib-LY3009120 are the most effective combinations with the least toxicity and the best synergistic effect on blast cells. These findings lay the foundation for personalized and successful AML therapies, ultimately leading to the development of drug combinations that can be used alongside standard first-line AML treatment.

The integrated stress response-related expression of CHOP due to mitochondrial toxicity is a warning sign for DILI liability

BACKGROUND AND AIMS

Drug-induced liver injury (DILI) is one of the most frequent reasons for failure of drugs in clinical trials or market withdrawal. Early assessment of DILI risk remains a major challenge during drug development. Here, we present a mechanism-based weight-of-evidence approach able to identify certain candidate compounds with DILI liabilities due to mitochondrial toxicity.

METHODS

A total of 1587 FDA-approved drugs and 378 kinase inhibitors were screened for cellular stress response activation associated with DILI using an imaging-based HepG2 BAC-GFP reporter platform including the integrated stress response (CHOP), DNA damage response (P21) and oxidative stress response (SRXN1).

RESULTS

In total 389, 219 and 104 drugs were able to induce CHOP-GFP, P21-GFP and SRXN1-GFP expression at 50 μM respectively. Concentration response analysis identified 154 FDA-approved drugs as critical CHOP-GFP inducers. Based on predicted and observed (pre-)clinical DILI liabilities of these drugs, nine antimycotic drugs (e.g. butoconazole, miconazole, tioconazole) and 13 central nervous system (CNS) agents (e.g. duloxetine, fluoxetine) were selected for transcriptomic evaluation using whole-genome RNA-sequencing of primary human hepatocytes. Gene network analysis uncovered mitochondrial processes, NRF2 signalling and xenobiotic metabolism as most affected by the antimycotic drugs and CNS agents. Both the selected antimycotics and CNS agents caused impairment of mitochondrial oxygen consumption in both HepG2 and primary human hepatocytes.

CONCLUSIONS

Together, the results suggest that early pre-clinical screening for CHOP expression could indicate liability of mitochondrial toxicity in the context of DILI, and, therefore, could serve as an important warning signal to consider during decision-making in drug development.

A novel class of inhibitors that disrupts the stability of integrin heterodimers identified by CRISPR-tiling-instructed genetic screens

The plasma membrane is enriched for receptors and signaling proteins that are accessible from the extracellular space for pharmacological intervention. Here we conducted a series of CRISPR screens using human cell surface proteome and integrin family libraries in multiple cancer models. Our results identified ITGAV (integrin αV) and its heterodimer partner ITGB5 (integrin β5) as the essential integrin α/β pair for cancer cell expansion. High-density CRISPR gene tiling further pinpointed the integral pocket within the β-propeller domain of ITGAV for integrin αVβ5 dimerization. Combined with in silico compound docking, we developed a CRISPR-Tiling-Instructed Computer-Aided (CRISPR-TICA) pipeline for drug discovery and identified Cpd_AV2 as a lead inhibitor targeting the β-propeller central pocket of ITGAV. Cpd_AV2 treatment led to rapid uncoupling of integrin αVβ5 and cellular apoptosis, providing a unique class of therapeutic action that eliminates the integrin signaling via heterodimer dissociation. We also foresee the CRISPR-TICA approach to be an accessible method for future drug discovery studies.

Genetic determinants of micronucleus formation in vivo

Genomic instability arising from defective responses to DNA damage1 or mitotic chromosomal imbalances2 can lead to the sequestration of DNA in aberrant extranuclear structures called micronuclei (MN). Although MN are a hallmark of ageing and diseases associated with genomic instability, the catalogue of genetic players that regulate the generation of MN remains to be determined. Here we analyse 997 mouse mutant lines, revealing 145 genes whose loss significantly increases (n = 71) or decreases (n = 74) MN formation, including many genes whose orthologues are linked to human disease. We found that mice null for Dscc1, which showed the most significant increase in MN, also displayed a range of phenotypes characteristic of patients with cohesinopathy disorders. After validating the DSCC1-associated MN instability phenotype in human cells, we used genome-wide CRISPR-Cas9 screening to define synthetic lethal and synthetic rescue interactors. We found that the loss of SIRT1 can rescue phenotypes associated with DSCC1 loss in a manner paralleling restoration of protein acetylation of SMC3. Our study reveals factors involved in maintaining genomic stability and shows how this information can be used to identify mechanisms that are relevant to human disease biology1.

Improved fluorescence-based assay for rapid screening and evaluation of SARS-CoV-2 main protease inhibitors

The outbreak of coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a global threat to human health. In parallel with vaccines, efficacious antivirals are urgently needed. SARS-CoV-2 main protease (Mpro) is an attractive drug target for antiviral development owing to its key roles in virus replication and host immune evasion. Due to the limitations of currently available methods, the development of novel high-throughput screening assays is of the highest importance for the discovery of Mpro inhibitors. In this study, we first developed an improved fluorescence-based assay for rapid screening of Mpro inhibitors from an anti-infection compound library using a versatile dimerization-dependent red fluorescent protein (ddRFP) biosensor. Utilizing this assay, we identified MG-101 as a competitive Mpro inhibitor in vitro. Moreover, our results revealed that ensitrelvir is a potent Mpro inhibitor, but baicalein, chloroquine, ebselen, echinatin, and silibinin are not. Therefore, this robust ddRFP assay provides a faithful avenue for rapid screening and evaluation of Mpro inhibitors to fight against COVID-19.

Directed evolution of Baeyer-Villiger monooxygenase for highly secretory expressed in Pichia pastoris and efficient preparation of chiral pyrazole sulfoxide

The methylotrophic yeast Pichia pastoris (Komagataella phaffii) is a highly distinguished expression platform for the excellent synthesis of various heterologous proteins in recent years. With the advantages of high-density fermentation, P. pastoris can produce gram amounts of recombinant proteins. While not every protein of interest can be expressed to such high titers, such as Baeyer-Villiger monooxygenase (BVMO) (AcPSMO) which is responsible for pyrazole sulfide asymmetric oxidation. In this work, an excellent yeast expression system was established to facilitate efficient AcPSMO expression, which exhibited 9.5-fold enhanced secretion. Subsequently, an ultrahigh throughput screening method based on fluorescence-activated cell sorting by fusing super folder green fluorescent protein (sfGFP) in the C-terminal of AcPSMO was developed, and directed evolution was performed. The protein expression level of the superior mutant AcPSMOP1 (S58T/T252P/E336N/H456D) reached 84.6 mg/L at 100 mL shaking flask, which was 4.7 times higher than the levels obtained with the wild-type. Finally, the optimized chassis cells were used for high-density fermentation on a 5-L scale, and AcPSMOP1 protein yield of 3.4 g/L was achieved, representing approximately 85% of the total protein secreted. By directly employing the pH-adjusted supernatant as a biocatalyst, 20 g/L pyrmetazole sulfide was completely transformed into the corresponding (S)-sulfoxide, with a 78.8% isolated yield. This work confers dramatic benefits for efficient secretion of other BVMOs in P. pastoris.

Cellular reprogramming as a tool to model human aging in a dish

The design of human model systems is highly relevant to unveil the underlying mechanisms of aging and to provide insights on potential interventions to extend human health and life span. In this perspective, we explore the potential of 2D or 3D culture models comprising human induced pluripotent stem cells and transdifferentiated cells obtained from aged or age-related disorder-affected donors to enhance our understanding of human aging and to catalyze the discovery of anti-aging interventions.

Mirror-image ligand discovery enabled by single-shot fast-flow synthesis of D-proteins

Widespread adoption of mirror-image biological systems presents difficulties in accessing the requisite D-protein substrates. In particular, mirror-image phage display has the potential for high-throughput generation of biologically stable macrocyclic D-peptide binders with potentially unique recognition modes but is hindered by the individualized optimization required for D-protein chemical synthesis. We demonstrate a general mirror-image phage display pipeline that utilizes automated flow peptide synthesis to prepare D-proteins in a single run. With this approach, we prepare and characterize 12 D-proteins - almost one third of all reported D-proteins to date. With access to mirror-image protein targets, we describe the successful discovery of six macrocyclic D-peptide binders: three to the oncoprotein MDM2, and three to the E3 ubiquitin ligase CHIP. Reliable production of mirror-image proteins can unlock the full potential of D-peptide drug discovery and streamline the study of mirror-image biology more broadly.

Establishing an ANO1-Based Cell Model for High-Throughput Screening Targeting TRPV4 Regulators

Transient receptor potential vanilloid 4 (TRPV4) is a widely expressed cation channel that plays an important role in many physiological and pathological processes. However, most TRPV4 drugs carry a risk of side effects. Moreover, existing screening methods are not suitable for the high-throughput screening (HTS) of drugs. In this study, a cell model and HTS method for targeting TRPV4 channel drugs were established based on a calcium-activated chloride channel protein 1 Anoctamin 1 (ANO1) and a double mutant (YFP-H148Q/I152L) of the yellow fluorescent protein (YFP). Patch-clamp experiments and fluorescence quenching kinetic experiments were used to verify that the model could sensitively detect changes in intracellular Ca2+ concentration. The functionality of the TRPV4 cell model was examined through temperature variations and different concentrations of TRPV4 modulators, and the performance of the model in HTS was also evaluated. The model was able to sensitively detect changes in the intracellular Ca2+ concentration and also excelled at screening TRPV4 drugs, and the model was more suitable for HTS. We successfully constructed a drug cell screening model targeting the TRPV4 channel, which provides a tool to study the pathophysiological functions of TRPV4 in vitro.

High-Throughput Screening Techniques for the Selection of Thermostable Enzymes

The acquisition of a thermostable enzyme is an indispensable prerequisite for its successful implementation in industrial applications and the development of novel functionalities. Various protein engineering approaches, including rational design, semirational design, and directed evolution, have been employed to enhance thermostability. However, all of these approaches require sensitive and reliable high-throughput screening (HTS) technologies to efficiently and rapidly identify variants with improved properties. While numerous reviews focus on modification strategies for enhancing enzyme thermostability, there is a dearth of literature reviewing HTS methods specifically aimed at this objective. Herein, we present a comprehensive overview of various HTS methods utilized for modifying enzyme thermostability across different screening platforms. Additionally, we highlight significant recent examples that demonstrate the successful application of these methods. Furthermore, we address the technical challenges associated with HTS technologies used for screening thermostable enzyme variants and discuss valuable perspectives to promote further advancements in this field. This review serves as an authoritative reference source offering theoretical support for selecting appropriate screening strategies tailored to specific enzymes with the aim of improving their thermostability.

Discovering New Substrates of a UDP-Glycosyltransferase with a High-Throughput Method

UDP-glycosyltransferases (UGTs) form a large enzyme family that is found in a wide range of organisms. These enzymes are known for accepting a wide variety of substrates, and they derivatize xenobiotics and metabolites for detoxification. However, most UGT homologs have not been well characterized, and their potential for biomedical and environmental applications is underexplored. In this work, we have used a fluorescent assay for screening substrates of a plant UGT homolog by monitoring the formation of UDP. We optimized the assay such that it could be used for high-throughput screening of substrates of the Medicago truncatula UGT enzyme, UGT71G1, and our results show that 34 of the 159 screened compound samples are potential substrates. With an LC-MS/MS method, we confirmed that three of these candidates indeed were glycosylated by UGT71G1, which includes bisphenol A (BPA) and 7-Ethyl-10-hydroxycamptothecin (SN-38); derivatization of these toxic compounds can lead to new environmental and medical applications. This work suggests that UGT homologs may recognize a substrate profile that is much broader than previously anticipated. Additionally, it demonstrates that this screening method provides a new means to study UDP-glycosyltransferases, facilitating the use of these enzymes to tackle a wide range of problems.

Design and high-throughput implementation of MALDI-TOF/MS-based assays for Parkin E3 ligase activity

Parkinson's disease (PD) is a progressive neurological disorder that manifests clinically as alterations in movement as well as multiple non-motor symptoms including but not limited to cognitive and autonomic abnormalities. Loss-of-function mutations in the gene encoding the ubiquitin E3 ligase Parkin are causal for familial and juvenile PD. Among several therapeutic approaches being explored to treat or improve the prognosis of patients with PD, the use of small molecules able to reinstate or boost Parkin activity represents a potential pharmacological treatment strategy. A major barrier is the lack of high-throughput platforms for the robust and accurate quantification of Parkin activity in vitro. Here, we present two different and complementary Matrix-Assisted Laser Desorption/Ionization Time-Of-Flight Mass Spectrometry (MALDI-TOF/MS)-based approaches for the quantification of Parkin E3 ligase activity in vitro. Both approaches are scalable for high-throughput primary screening to facilitate the identification of Parkin modulators.

A 3D Bioprinted Human Neurovascular Unit Model of Glioblastoma Tumor Growth

A 3D bioprinted neurovascular unit (NVU) model is developed to study glioblastoma (GBM) tumor growth in a brain-like microenvironment. The NVU model includes human primary astrocytes, pericytes and brain microvascular endothelial cells, and patient-derived glioblastoma cells (JHH-520) are used for this study. Fluorescence reporters are used with confocal high content imaging to quantitate real-time microvascular network formation and tumor growth. Extensive validation of the NVU-GBM model includes immunostaining for brain relevant cellular markers and extracellular matrix components; single cell RNA sequencing (scRNAseq) to establish physiologically relevant transcriptomics changes; and secretion of NVU and GBM-relevant cytokines. The scRNAseq reveals changes in gene expression and cytokines secretion associated with wound healing/angiogenesis, including the appearance of an endothelial mesenchymal transition cell population. The NVU-GBM model is used to test 18 chemotherapeutics and anti-cancer drugs to assess the pharmacological relevance of the model and robustness for high throughput screening.

A deep learning dataset for sample preparation artefacts detection in multispectral high-content microscopy

High-content image-based screening is widely used in Drug Discovery and Systems Biology. However, sample preparation artefacts may significantly deteriorate the quality of image-based screening assays. While detection and circumvention of such artefacts could be addressed using modern-day machine learning and deep learning algorithms, this is widely impeded by the lack of suitable datasets. To address this, here we present a purpose-created open dataset of high-content microscopy sample preparation artefact. It consists of high-content microscopy of laboratory dust titrated on fixed cell culture specimens imaged with fluorescence filters covering the complete spectral range. To ensure this dataset is suitable for supervised machine learning tasks like image classification or segmentation we propose rule-based annotation strategies on categorical and pixel levels. We demonstrate the applicability of our dataset for deep learning by training a convolutional-neural-network-based classifier.

Protein design using structure-based residue preferences

Recent developments in protein design rely on large neural networks with up to 100s of millions of parameters, yet it is unclear which residue dependencies are critical for determining protein function. Here, we show that amino acid preferences at individual residues-without accounting for mutation interactions-explain much and sometimes virtually all of the combinatorial mutation effects across 8 datasets (R2 ~ 78-98%). Hence, few observations (~100 times the number of mutated residues) enable accurate prediction of held-out variant effects (Pearson r > 0.80). We hypothesized that the local structural contexts around a residue could be sufficient to predict mutation preferences, and develop an unsupervised approach termed CoVES (Combinatorial Variant Effects from Structure). Our results suggest that CoVES outperforms not just model-free methods but also similarly to complex models for creating functional and diverse protein variants. CoVES offers an effective alternative to complicated models for identifying functional protein mutations.