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Nanoparticle-mediated mRNA delivery has emerged as a promising therapeutic modality, but its growth is still limited by the discovery and optimization of effective and well-tolerated delivery strategies. Lipid nanoparticles containing charged or ionizable lipids are an emerging standard for in vivo mRNA delivery, so creating facile, tunable strategies to synthesize these key lipid-like molecules is essential to advance the field. Here, we generate a library of N-substituted glycine oligomers, peptoids, and undertake a multistage down-selection process to identify lead candidate peptoids as the ionizable component in our Nutshell nanoparticle platform. First, we identify a promising peptoid structural motif by clustering a library of >200 molecules based on predicted physical properties and evaluate members of each cluster for reporter gene expression in vivo. Then, the lead peptoid motif is optimized using design of experiments methodology to explore variations on the charged and lipophilic portions of the peptoid, facilitating the discovery of trends between structural elements and nanoparticle properties. We further demonstrate that peptoid-based Nutshells leads to expression of therapeutically relevant levels of an anti-respiratory syncytial virus antibody in mice with minimal tolerability concerns or induced immune responses compared to benchmark ionizable lipid, DLin-MC3-DMA. Through this work, we present peptoid-based nanoparticles as a tunable delivery platform that can be optimized toward a range of therapeutic programs.

Ribosomally synthesized and post-translationally modified peptides (RiPPs) represent a significant potential for novel therapeutic applications because of their bioactive properties, stability, and specificity. RiPPs are synthesized on ribosomes, followed by intricate post-translational modifications (PTMs), crucial for their diverse structures and functions. PTMs, such as cyclization, methylation, and proteolysis, play crucial roles in enhancing RiPP stability and bioactivity. Advances in synthetic biology and bioinformatics have significantly advanced the field, introducing new methods for RiPP production and engineering. These methods encompass strategies for heterologous expression, genetic refactoring, and exploiting the substrate tolerance of tailoring enzymes to create novel RiPP analogs with improved or entirely new functions. Furthermore, the introduction and implementation of cutting-edge screening methods, including mRNA display, surface display, and two-hybrid systems, have expedited the identification of RiPPs with significant pharmaceutical potential. This comprehensive review not only discusses the current advancements in RiPP research but also the promising opportunities that leveraging these bioactive peptides for therapeutic applications presents, illustrating the synergy between traditional biochemistry and contemporary synthetic biology and genetic engineering approaches.

Whole-organism-based screen holds promise for discovering biologically active compounds. However, high-content imaging is challenging due to the difficulty of positioning live animals and individual variability of neuron counts. Here, we present a protocol to identify neuroprotective compounds for dopaminergic neurons in zebrafish using an image-based small-molecule screen. We describe steps for raising larvae, agarose embedding, and treatment to induce neurodegeneration. We then detail procedures for live confocal imaging, image processing, and data analysis. For complete details on the use and execution of this protocol, please refer to Kim et al. (2021).1.

Strategies in genetic and pharmacological modulation of innate immunity to enhance oncolytic virotherapy (OV) efficacy are being explored. We have recently characterized the ability for vanadium-based compounds, a class of pan-phosphatase (PP) inhibitors, to potentiate OVs. We next sought to identify PPs that could be targeted to enhance OVs, akin to vanadium. By conducting a high-throughput screen of a library of silencing RNA (siRNA) targeting human PPs, we uncovered several PPs that robustly enhanced infectivity and oncolysis of the oncolytic vesicular stomatitis virus (VSV∆51). Knockdown of our top validated hit, lysosomal acid phosphatase 2 (ACP2), increased VSV∆51 viral titers by over 20-fold. In silico analysis by RNA sequencing revealed ACP2 to regulate antiviral type I interferon (IFN-1) signaling pathways, similar to vanadium. To further exploit this mechanism for therapeutic gain, we encoded a short-hairpin RNA (shRNA) against ACP2 into oncolytic vesicular stomatitis virus (VSV∆51) under a miR-30 promoter. This bioengineered OV demonstrated expression of the miR-30 promoter, knockdown of ACP2, repression and ultimately, showed markedly enhanced viral VSV∆51 particle production compared to its non-targeting control counterpart. Altogether, this study identifies IFN-1 regulating PP targets, namely ACP2, that may prove instrumental in increasing the therapeutic efficacy of OVs. [ABSTRACT FROM AUTHOR]

Background: Target identification and hit identification can be transformed through the application of biomedical knowledge analysis, AI-driven virtual screening and robotic cloud lab systems. However there are few prospective studies that evaluate the efficacy of such integrated approaches. Results: We synergistically integrate our in-house-developed target evaluation (SpectraView) and deep-learning-driven virtual screening (HydraScreen) tools with an automated robotic cloud lab designed explicitly for ultra-high-throughput screening, enabling us to validate these platforms experimentally. By employing our target evaluation tool to select IRAK1 as the focal point of our investigation, we prospectively validate our structure-based deep learning model. We can identify 23.8% of all IRAK1 hits within the top 1% of ranked compounds. The model outperforms traditional virtual screening techniques and offers advanced features such as ligand pose confidence scoring. Simultaneously, we identify three potent (nanomolar) scaffolds from our compound library, 2 of which represent novel candidates for IRAK1 and hold promise for future development. Conclusion: This study provides compelling evidence for SpectraView and HydraScreen to provide a significant acceleration in the processes of target identification and hit discovery. By leveraging Ro5's HydraScreen and Strateos' automated labs in hit identification for IRAK1, we show how AI-driven virtual screening with HydraScreen could offer high hit discovery rates and reduce experimental costs. Scientific contribution: We present an innovative platform that leverages Knowledge graph-based biomedical data analytics and AI-driven virtual screening integrated with robotic cloud labs. Through an unbiased, prospective evaluation we show the reliability and robustness of HydraScreen in virtual and high-throughput screening for hit identification in IRAK1. Our platforms and innovative tools can expedite the early stages of drug discovery. [ABSTRACT FROM AUTHOR]

Adoptive T cell therapy is a pivotal strategy in cancer immunotherapy, demonstrating potent clinical efficacy. However, its limited durability often results in primary resistance. High-throughput screening technologies, which include both genetic and non-genetic approaches, facilitate the optimization of adoptive T cell therapies by enabling the selection of biologically significant targets or substances from extensive libraries. In this review, we examine advancements in high-throughput screening technologies and their applications in adoptive T cell therapies. We highlight the use of genetic screening for T cells, tumor cells, and other promising combination strategies, and elucidate the role of non-genetic screening in identifying small molecules and targeted delivery systems relevant to adoptive T cell therapies, providing guidance for future research and clinical applications. [ABSTRACT FROM AUTHOR]

Calcium oscillations in primary neuronal cultures and iPSCs have been employed to investigate arrhythmogenicity and epileptogenicity in drug development. Previous studies have demonstrated that Ca2+ influx via NMDA and nicotinic acetylcholine receptors (nAChRs) modulates Ca2+ oscillations. Nevertheless, there has been no comprehensive investigation into the impact of ischemia or nAChR-positive allosteric modulators (PAM) drugs on Ca2+ oscillations at a level that would facilitate high-throughput screening. We investigated the effects of ischemia and nAChR subtypes or nAChR PAM agonists on Ca2+ oscillations in high-density 2D and 3D-sphere primary neuronal cultures using 384-well plates with FDSS-7000. Ischemia for 1 and 2 h resulted in an increase in the frequency of Ca2+ oscillations and a decrease in their amplitude in a time-dependent manner. The NMDA and AMPA receptor inhibition significantly suppressed Ca2+ oscillation. Inhibition of NR2A or NR2B had the opposite effect on Ca oscillations. The potentiation of ischemia-induced Ca2+ oscillations was significantly inhibited by the NMDA receptor antagonist, MK-801, and the frequency of these oscillations was suppressed by the NR2B inhibitor, Ro-256981. In the 3D-neurosphere, the application of an α7nAChR agonist increased the frequency of Ca2+ oscillations, whereas the activation of α4β2 had no effect. The combination of nicotine and PNU-120596 (type II PAM) affected the frequency and amplitude of Ca2+ oscillations in a manner distinct from that of type I PAM. These systems may be useful not only for detecting epileptogenicity but also in the search for neuroprotective agents against cerebral ischemia. [ABSTRACT FROM AUTHOR]

Background: Microalgae have emerged as sustainable alternatives to fossil fuels and high-value petrochemicals. Despite the commercial potential of microalgae, their low biomass productivity is a significant limiting factor for large-scale production. In the photoautotrophic cultivation of microalgae, achievable cell density levels depend on the light transmittance of the production system, which can significantly decrease the photosynthetic rate and biomass production. In contrast, the mixotrophic cultivation of microalgae using heterotrophic carbon sources enables high-density cultivation, which significantly enhances biomass productivity. The identification of optimal production conditions is crucial for improving biomass productivity; however, it is typically time- and resource-consuming. To overcome this problem, high-throughput screening (HTS) system presents a practical approach to maximize biomass and lipid production and enhance the industrial applicability of microalgae. Results: In this study, we proposed a two-step HTS assay that allows effective screening of heterotrophic conditions compatible with new microalgal isolates. To confirm the effectiveness of the HTS assay, three microalgal isolates with distinctive morphological and genetic traits were selected. Suitable cultivation conditions, including various heterotrophic carbon sources, substrate concentrations, and temperatures, were investigated using a two-step HTS assay. The optimized conditions were validated at the flask scale, which confirmed a significant enhancement in the biomass and lipid productivity of each isolate. Moreover, the two-step HTS assay notably enhanced economic and temporal efficiency compared to conventional flask-based optimization. Conclusions: These results suggest that our two-step HTS assay is an efficient strategy for investigating and optimizing microalgal culture conditions to maximize biomass and lipid productivity. This approach has the potential to enhance the industrial applicability of microalgae and facilitate the seamless transition from laboratory to field applications. [ABSTRACT FROM AUTHOR]

Background: The efficacy of mRNA-based vaccines and therapies relies on lipid nanoparticles (LNPs) as carriers to deliver mRNA into cells. The chemical structure of ionizable lipids (ILs) within LNPs is crucial in determining their delivery efficiency. Results: In this study, we synthesized 623 alkyne-bearing ionizable lipids using the A3 coupling reaction and assessed their effectiveness in mRNA delivery. ILs with specific structural features—18-carbon alkyl chains, a cis-double bond, and ethanolamine head groups—demonstrated superior mRNA delivery capabilities. Variations in saturation, double bond placement, and chain length correlated with decreased efficacy. Alkynes positioned adjacent to nitrogen atoms in ILs reduced the acid dissociation constant (pKa) of LNPs, thereby hindering mRNA delivery efficiency. Conversion of alkynes to alkanes significantly enhanced mRNA delivery of ILs both in vitro and in vivo. Moreover, combining optimized ILs with cKK-E12 yields synergistic LNPs that showed markedly augmented mRNA expression levels in vivo. Conclusions: Overall, our study provides insights into the structure–function relationships of ILs, providing a foundation for the rational design of ILs to enhance the efficacy of LNPs in mRNA delivery. [ABSTRACT FROM AUTHOR]

Pregnane X receptor (PXR) is a xenobiotic-sensing nuclear receptor with a well-established role in regulating drug metabolism and clearance. Recent studies have shown that PXR is involved in cell proliferation, apoptosis, immune response, and energy homeostasis. It is important to identify compounds that may modulate PXR activity to prevent drug-drug interactions, distinguish chemicals which could potentially generate toxicity, and identify compounds for further development towards therapeutic usage. In this study, we have screened the National Center for Advancing Translational Sciences (NCATS) Pharmacologically Active Chemical Toolbox (NPACT) library, which consists of 5,099 unique pharmacologically active synthetic and naturally derived small molecules to identify PXR antagonists. Ninety-four compounds were identified as potential PXR antagonists through a primary screen and 66 were confirmed in a confirmation study. Of these compounds, twenty potential PXR antagonists, including gamma-secretase modulator 2 (GSM2) and fusidic acid, were selected for further study based on their efficacy, potency, and novelty. Their PXR inhibition abilities were assessed by examining their effects on cytrochrome P450 (CYP) 3A4 mRNA expression using metabolically competent HepaRG cells. Additionally, a pharmacological inhibition assay using various concentrations of rifampicin as a stimulator was performed in HepG2- CYP3A4 -hPXR cells to confirm the activity of the 20 selected compounds against PXR. Finally, HepaRG cells were used to confirm PXR antagonism by verification of a concentration-dependent decrease of CYP3A4 when co-treated with the known PXR agonist, rifampicin. Additionally, the potent actives were further investigated using molecular docking to find the potential interactions of the novel ligands with the active sites of hPXR. To our knowledge from the current study, GSM2 and fusidic acid have been identified as novel PXR antagonists, which provides useful information for further investigation regarding possible drug-drug interactions, as well as the detection of potential therapeutic effects or other toxic consequences. [ABSTRACT FROM AUTHOR]

Drosophila melanogaster usage has provided substantial insights into the pathogenesis of several nucleotide repeat expansion diseases (NREDs), a group of genetic diseases characterized by the abnormal expansion of DNA repeats. Leveraging the genetic simplicity and manipulability of Drosophila, researchers have successfully modeled close to 15 NREDs such as Huntington's disease (HD), several spinocerebellar ataxias (SCA), and myotonic dystrophies type 1 and 2 (DM1/DM2). These models have been instrumental in characterizing the principal associated molecular mechanisms: protein aggregation, RNA toxicity, and protein function loss, thus recapitulating key features of human disease. Used in chemical and genetic screenings, they also enable us to identify promising small molecules and genetic modifiers that mitigate the toxic effects of expanded repeats. This review summarizes the close to 150 studies performed in this area during the last seven years. The relevant highlights are the achievement of the first fly-based models for some NREDs, the incorporation of new technologies such as CRISPR for developing or evaluating transgenic flies containing repeat expanded motifs, and the evaluation of less understood toxic mechanisms in NREDs such as RAN translation. Overall, Drosophila melanogaster remains a powerful platform for research in NREDs. [ABSTRACT FROM AUTHOR]

Background: Microalgae are potential sustainable resources for the production of value-added chemicals that can be used as biofuels, pharmaceuticals, and nutritional supplements. Arachidonic acid (ARA), a omega-6 fatty acid, plays a crucial role in infant development and immune response, and can be used in cosmetics and pharmaceuticals. Demand for industrial-scale ARA production is continuously increasing because of its broad applicability. To address this demand, there has been a significant shift towards microorganism-based ARA production. To accelerate large-scale ARA production, it is crucial to select suitable strains and establish optimal culture conditions. Results: Here, we isolated a novel microalga Lobosphaera incisa CFRC-1, a valuable strain that holds promise as a feedstock for ARA production. Optimal cultivation conditions were investigated using a high-throughput screening method to enhance ARA production in this novel strain. Out of 71 candidates, four organic carbon substrates were identified that could be utilized by L. incisa CFRC-1. Through flask-scale verification, fructose was confirmed as the optimal organic carbon substrate for promoting microalgal growth, total lipid accumulation, and ARA production. Subsequently, we investigated appropriate substrate concentration and cultivation temperature, confirming that the optimal conditions were 30 g L− 1 of fructose and 27 ℃ of temperature. Under these optimized conditions, biomass and ARA production reached 13.05 ± 0.40 g L− 1 and 97.98 ± 7.33 mg L− 1, respectively, representing 9.6-fold and 5.3-fold increases compared to the conditions before optimization conditions. These results achieved the highest biomass and ARA production in flask-scale cultivation, indicating that our approach effectively improved both production titer and productivity. Conclusions: This study presents a novel microalgae and optimized conditions for enhancing biomass and ARA production, suggesting that this approach is a practical way to accelerate the production of valuable microalgae-based chemicals. These findings provide a basis for large-scale production of ARA-utilizing microalgae for industrial applications. [ABSTRACT FROM AUTHOR]

The educational landscape of toxicology is increasingly integrating computational methodologies due to ethical concerns about animal testing and advancements in biotechnological and data analysis tools. This paper examines the evolution and significance of the Toxicology in the 21st century (Tox21) initiative and its impact on computational toxicology education. It contrasts computational toxicology with traditional methods, highlighting the limitations of conventional approaches and the new perspectives offered by computational techniques. The study emphasizes the importance of incorporating computational toxicology into curricula, including case studies that demonstrate how this integration enhances students' problem-solving abilities, real-time data analysis skills, and innovation capabilities. Furthermore, it outlines effective teaching content and methods, including software tools, online resources, and academic literature. The paper also addresses the challenges and limitations faced in this educational shift and explores prospects for advancing computational toxicology education. By documenting these developments, the study aims to clarify the current advancements in toxicology education and the preparedness of students to address global chemical safety challenges with innovative solutions. [ABSTRACT FROM AUTHOR]

The recently developed high-throughput system for cell spheroid generation (SpheroWell) is a promising technology for cost- and time-efficient in vitro analysis of, for example, chondrogenic differentiation. It is a compartmental growth surface where spheroids develop from a cell monolayer by self-assembling and aggregation. In order to automatize the analysis of spheroids, we aimed to develop imaging software and improve the localization of cell compartments and fully formed spheroids. Our workflow provides automated detection and localization of spheroids in different formation stages within Petri dishes based on images created with a low-budget camera imaging setup. This automated detection enables a fast and inexpensive analysis workflow by processing a stack of images within a short period of time, which is essential for the extraction of early readout parameters. Our workflow combines image processing algorithms and deep learning-based image localization/segmentation methods like Mask R-CNN and Unet++. These methods are refined by an evolution strategy for automated grid detection, which is able to improve the overall segmentation and classification quality. Besides the already pre-trained neural networks and predefined image processing parameters, our evolution-based post-processing provides the required adaptability for our workflow to deliver a consistent and reproducible quality. This is especially important due to the use of a low-budget imaging setup with various light conditions. The to-be-detected objects of the three different stages show improved results using our evolutionary post-processing for monolayer and starting aggregation with Dice coefficients of 0.7301 and 0.8562, respectively, compared with the raw scores of 0.2879 and 0.8187. The Dice coefficient of the fully formed spheroids in both cases is 0.8829. With our algorithm, we provide automated analyses of cell spheroid by self-assembling in SpheroWell dishes, even if the images are created using a low-budget camera setup. [ABSTRACT FROM AUTHOR]

Copyright of Food Research & Development is the property of Food Research & Development Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Building extensive drug candidate libraries as early in the development pipeline as possible, with high-throughput in vitro absorption, distribution, metabolism, and excretion (ADME) profiling, is crucial for the selection of lead compounds to guide subsequent research and production phases. Traditionally, the analysis of metabolic stability assays heavily relies on high-throughput LC-MS/MS (liquid chromatography tandem mass spectrometry) techniques to meet with the lead profiling demands. Laser-assisted rapid evaporative ionization mass spectrometry (LA-REIMS) is a quick and efficient technique for characterizing complex biological samples without laborious sample preparation. Objective: In this study, using an automated LA-REIMS well plate reader, achieving an 8 s per sample measurement time, the oxidative metabolic stability of active drug agents was assessed using biomimetic metalloporphyrin-based oxidative model reactions. Results: The results obtained using the novel LA-REIMS-based protocol were compared to and corroborated by those obtained using conventional HPLC-UV-MS (high performance liquid chromatography with ultra-violet detection coupled with mass spectrometry) measurements. Conclusions: LA-REIMS emerges as a promising technique, demonstrating potential suitability for semi-quantitative high-throughput metabolic stability in an optimized solvent environment. [ABSTRACT FROM AUTHOR]

[Display omitted] • A human iPSCs-based high-throughput anti-seizure compounds screening platform was built. • The human iPSCs-derived neurons can effectively and efficiently predict positive compounds. • This integrated anti-seizure drug discovering approach combines the advantages of human genetic background, HTS, and animal brain network. • Piperine, magnolol, α-asarone, and osthole can reduce epileptiform activity and seizures. • Natural compounds are promising candidates used for anti-seizure drug discovery. One-third of people with epilepsy continue to experience seizures despite treatment with existing anti-seizure medications (ASMs). The failure of modern ASMs to substantially improve epilepsy prognosis has been partly attributed to overreliance on acute rodent models in preclinical drug development as they do not adequately recapitulate the mechanisms of human epilepsy, are labor-intensive and unsuitable for high-throughput screening (HTS). There is an urgent need to find human-relevant HTS models in preclinical drug development to identify novel anti-seizure compounds. This paper developed high-throughput preclinical screening models to identify new ASMs. 14 natural compounds (α-asarone, curcumin, vinpocetine, magnolol, ligustrazine, osthole, tanshinone IIA, piperine, gastrodin, quercetin, berberine, chrysin, schizandrin A and resveratrol) were assessed for their ability to suppress epileptiform activity as measured by multi-electrode arrays (MEA) in neural cultures derived from human induced pluripotent stem cells (iPSCs). In parallel, they were tested for anti-seizure effects in zebrafish and mouse models, which have been widely used in development of modern ASMs. The effects of the compounds in these models were compared. Two approved ASMs were used as positive controls. Epileptiform activity could be induced in iPSCs-derived neurons following treatment with 4-aminopyridine (4-AP) and inhibited by standard ASMs, carbamazepine, and phenytoin. Eight of the 14 natural compounds significantly inhibited the epileptiform activity in iPSCs-derived neurons. Among them, piperine, magnolol, α-asarone, and osthole showed significant anti-seizure effects both in zebrafish and mice. Comparative analysis showed that compounds ineffective in the iPSCs-derived neural model also showed no anti-seizure effects in the zebrafish or mouse models. Our findings support the use of iPSCs-derived human neurons for first-line high-throughput screening to identify compounds with anti-seizure properties and exclude ineffective compounds. Effective compounds may then be selected for animal evaluation before clinical testing. This integrated approach may improve the efficiency of developing novel ASMs. [ABSTRACT FROM AUTHOR]

G protein-coupled receptors (GPCRs) are pivotal in mediating diverse physiological and pathological processes, rendering them promising targets for drug discovery. GPCRs account for about 40% of FDA-approved drugs, representing the most successful drug targets. However, only approximately 15% of the 800 human GPCRs are targeted by market drugs, leaving numerous opportunities for drug discovery among the remaining receptors. Cell expression systems play crucial roles in the GPCR drug discovery field, including novel target identification, structural and functional characterization, potential ligand screening, signal pathway elucidation, and drug safety evaluation. Here, we discuss the principles, applications, and limitations of widely used cell expression systems in GPCR-targeted drug discovery, GPCR function investigation, signal pathway characterization, and pharmacological property studies. We also propose three strategies for constructing genome-wide pan-GPCR cell libraries, which will provide a powerful platform for GPCR ligand screening, and facilitate the study of GPCR mechanisms and drug safety evaluation, ultimately accelerating the process of GPCR-targeted drug discovery. The genome-wide pan-GPCR cell line libraries, which utilize overexpression, PRESTO-Tango, and CRISPRa/i technologies, provide a powerful platform for GPCR-targeted drug discovery, which will facilitate the study of GPCR function, drug safety evaluation, and accelerate GPCR drug discovery. [Display omitted] [ABSTRACT FROM AUTHOR]

Droplet microfluidic-based technology is a powerful tool for biotechnology, and it is also expected that it will be applied to culturing and screening methods. Using this technology, a new high-throughput screening method for lactic acid bacteria was developed. In this study, the conventional culture of lactic acid bacteria that form clear zones on an agar medium was reproduced in water-in-oil droplets, and only the droplets in which lactic acid bacteria grew were collected one by one. Using this method, the specific recovery of Lactiplantibacillus plantarum from a mixture of L. plantarum and Escherichia coli and the acquirement of lactic acid bacteria from an environmental sample were successful. This method could be applied to various conventional screening methods using the clear zone as a microbial growth indicator. This has expanded the possibilities of applying droplet microfluidic-based technology to microbial cultivations. [ABSTRACT FROM AUTHOR]

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; and (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. [ABSTRACT FROM AUTHOR]

During the deamination and amination processes of meso -diaminopimelate dehydrogenase (meso -DAPDH) from Symbiobacterium thermophilum (StDAPDH), residue R71 was observed to display distinct functions. H154 has been proposed as a basic residue that facilitates water molecules to attack the D-chiral carbon of meso -DAP during deamination. Inspired by the phenomenon of R71, the effects of H154 during deamination and amination were investigated in this study with the goal of enhancing the amination activities of StDAPDH. Single site saturation mutagenesis indicated that almost all of the H154 mutants completely lost their deamination activity towards meso -DAP. However, some H154 variants showed enhanced k cat / K m values towards pyruvic acid and other bulky 2-keto acids, such as 2-oxovaleric acid, 4-methyl-2-oxopentanoic acid, 2-ketobutyric acid, and 3-methyl-2-oxobutanoic acid. When combined with the previously reported W121L/H227I mutant, triple mutants with significantly improved k cat / K m values (2.4-, 2.5-, 2.5-, and 4.0-fold) towards these 2-keto acids were obtained. Despite previous attempts, mutations at the H154 site did not yield the desired results. Moreover, this study not only recognizes the distinctive impact of H154 on both the deamination and amination reactions, but also provides guidance for further high-throughput screening in protein engineering and understanding the catalytic mechanism of StDAPDH. • H154 was first experimentally identified to be the fatal residue for deamination. • H154 mutation boosted the amination towards pyruvic acid and bulky 2-keto acids. • Coupling H154 variants with W121L/H227I resulted in enhanced amination activities. • The first report on the contrary effects of H154 during deamination and amination. • A reminder to update the high-throughput screening for DAPDH amination activity. [ABSTRACT FROM AUTHOR]

Copyright of Science & Technology of Food Industry is the property of Science & Technology of Food Industry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Addressing the critical need for swift and precise nutritional profiling in healthcare and in food industry, this study pioneers the integration of vision‐language models (VLMs) with chemical analysis techniques. A cutting‐edge VLM is unveiled, utilizing the expansive UMDFood‐90k database, to significantly improve the speed and accuracy of nutrient estimation processes. Demonstrating a macro‐AUCROC of 0.921 for lipid quantification, the model exhibits less than 10% variance compared to traditional chemical analyses for over 82% of the analyzed food items. This innovative approach not only accelerates nutritional screening by 36.9% when tested amongst students but also sets a new benchmark in the precision of nutritional data compilation. This research marks a substantial leap forward in food science, employing a blend of advanced computational models and chemical validation to offer a rapid, high‐throughput solution for nutritional analysis. [ABSTRACT FROM AUTHOR]

Genetically encoded circuits have been successfully utilized to assess and characterize target variants with desirable traits from large mutant libraries. Adenosylcobalamin is an essential coenzyme that is required in many intracellular physiological reactions and is widely used in the pharmaceutical and food industries. Highthroughput screening techniques capable of detecting adenosylcobalamin productivity and selecting superior adenosylcobalamin biosynthesis strains are critical for the creation of an effective microbial cell factory for the production of adenosylcobalamin at an industrial level. In this study, we developed an RNA-protein hybrid biosensor whose input part was an endogenous RNA riboswitch to specifically respond to adenosylcobalamin, the inverter part was an orthogonal transcriptional repressor to obtain signal inversion, and the output part was a fluorescent protein to be easily detected. The hybrid biosensor could specifically and positively correlate adenosylcobalamin concentrations to green fluorescent protein expression levels in vivo. This study also improved the operating concentration and dynamic range of the hybrid biosensor by systematic optimization. An individual cell harboring the hybrid biosensor presented over 20-fold higher fluorescence intensity than the negative control. Then, using such a biosensor combined with fluorescence-activated cell sorting, we established a highthroughput screening platform for screening adenosylcobalamin overproducers. This study demonstrates that this platform has significant potential to quickly isolate high-productive strains to meet industrial demand and that the framework is acceptable for various metabolites. [ABSTRACT FROM AUTHOR]

Understanding the correlation between the fundamental descriptors and catalytic performance is meaningful to guide the design of high‐performance electrochemical catalysts. However, exploring key factors that affect catalytic performance in the vast catalyst space remains challenging for people. Herein, to accurately identify the factors that affect the performance of N2 reduction, we apply interpretable machine learning (ML) to analyze high‐throughput screening results, which is also suited to other surface reactions in catalysis. To expound on the paradigm, 33 promising catalysts are screened from 168 carbon‐supported candidates, specifically single‐atom catalysts (SACs) supported by a BC3 monolayer (TM@VB/C‐Nn = 0–3‐BC3) via high‐throughput screening. Subsequently, the hybrid sampling method and XGBoost model are selected to classify eligible and non‐eligible catalysts. Through feature interpretation using Shapley Additive Explanations (SHAP) analysis, two crucial features, that is, the number of valence electrons (Nv) and nitrogen substitution (Nn), are screened out. Combining SHAP analysis and electronic structure calculations, the synergistic effect between an active center with low valence electron numbers and reasonable C‐N coordination (a medium fraction of nitrogen substitution) can exhibit high catalytic performance. Finally, six superior catalysts with a limiting potential lower than −0.4 V are predicted. Our workflow offers a rational approach to obtaining key information on catalytic performance from high‐throughput screening results to design efficient catalysts that can be applied to other materials and reactions. [ABSTRACT FROM AUTHOR]

Intracellular bacterial pathogens pose significant public health challenges due to their ability to evade immune defenses and conventional antibiotics. Drug repurposing has recently been explored as a strategy to discover new therapeutic uses for established drugs to combat these infections. Utilizing high-throughput screening, bioinformatics, and systems biology, several existing drugs have been identified with potential efficacy against intracellular bacteria. For instance, neuroleptic agents like thioridazine and antipsychotic drugs such as chlorpromazine have shown effectiveness against Staphylococcus aureus and Listeria monocytogenes. Furthermore, anticancer drugs including tamoxifen and imatinib have been repurposed to induce autophagy and inhibit bacterial growth within host cells. Statins and anti-inflammatory drugs have also demonstrated the ability to enhance host immune responses against Mycobacterium tuberculosis. The review highlights the complex mechanisms these pathogens use to resist conventional treatments, showcases successful examples of drug repurposing, and discusses the methodologies used to identify and validate these drugs. Overall, drug repurposing offers a promising approach for developing new treatments for bacterial infections, addressing the urgent need for effective antimicrobial therapies. [ABSTRACT FROM AUTHOR]

Abstract Adoptive T cell therapy is a pivotal strategy in cancer immunotherapy, demonstrating potent clinical efficacy. However, its limited durability often results in primary resistance. High-throughput screening technologies, which include both genetic and non-genetic approaches, facilitate the optimization of adoptive T cell therapies by enabling the selection of biologically significant targets or substances from extensive libraries. In this review, we examine advancements in high-throughput screening technologies and their applications in adoptive T cell therapies. We highlight the use of genetic screening for T cells, tumor cells, and other promising combination strategies, and elucidate the role of non-genetic screening in identifying small molecules and targeted delivery systems relevant to adoptive T cell therapies, providing guidance for future research and clinical applications.

Abstract Strategies in genetic and pharmacological modulation of innate immunity to enhance oncolytic virotherapy (OV) efficacy are being explored. We have recently characterized the ability for vanadium-based compounds, a class of pan-phosphatase (PP) inhibitors, to potentiate OVs. We next sought to identify PPs that could be targeted to enhance OVs, akin to vanadium. By conducting a high-throughput screen of a library of silencing RNA (siRNA) targeting human PPs, we uncovered several PPs that robustly enhanced infectivity and oncolysis of the oncolytic vesicular stomatitis virus (VSV∆51). Knockdown of our top validated hit, lysosomal acid phosphatase 2 (ACP2), increased VSV∆51 viral titers by over 20-fold. In silico analysis by RNA sequencing revealed ACP2 to regulate antiviral type I interferon (IFN-1) signaling pathways, similar to vanadium. To further exploit this mechanism for therapeutic gain, we encoded a short-hairpin RNA (shRNA) against ACP2 into oncolytic vesicular stomatitis virus (VSV∆51) under a miR-30 promoter. This bioengineered OV demonstrated expression of the miR-30 promoter, knockdown of ACP2, repression and ultimately, showed markedly enhanced viral VSV∆51 particle production compared to its non-targeting control counterpart. Altogether, this study identifies IFN-1 regulating PP targets, namely ACP2, that may prove instrumental in increasing the therapeutic efficacy of OVs.

Abstract Calcium oscillations in primary neuronal cultures and iPSCs have been employed to investigate arrhythmogenicity and epileptogenicity in drug development. Previous studies have demonstrated that Ca2+ influx via NMDA and nicotinic acetylcholine receptors (nAChRs) modulates Ca2+ oscillations. Nevertheless, there has been no comprehensive investigation into the impact of ischemia or nAChR-positive allosteric modulators (PAM) drugs on Ca2+ oscillations at a level that would facilitate high-throughput screening. We investigated the effects of ischemia and nAChR subtypes or nAChR PAM agonists on Ca2+ oscillations in high-density 2D and 3D-sphere primary neuronal cultures using 384-well plates with FDSS-7000. Ischemia for 1 and 2 h resulted in an increase in the frequency of Ca2+ oscillations and a decrease in their amplitude in a time-dependent manner. The NMDA and AMPA receptor inhibition significantly suppressed Ca2+ oscillation. Inhibition of NR2A or NR2B had the opposite effect on Ca oscillations. The potentiation of ischemia-induced Ca2+ oscillations was significantly inhibited by the NMDA receptor antagonist, MK-801, and the frequency of these oscillations was suppressed by the NR2B inhibitor, Ro-256981. In the 3D-neurosphere, the application of an α7nAChR agonist increased the frequency of Ca2+ oscillations, whereas the activation of α4β2 had no effect. The combination of nicotine and PNU-120596 (type II PAM) affected the frequency and amplitude of Ca2+ oscillations in a manner distinct from that of type I PAM. These systems may be useful not only for detecting epileptogenicity but also in the search for neuroprotective agents against cerebral ischemia.

Abstract Background The efficacy of mRNA-based vaccines and therapies relies on lipid nanoparticles (LNPs) as carriers to deliver mRNA into cells. The chemical structure of ionizable lipids (ILs) within LNPs is crucial in determining their delivery efficiency. Results In this study, we synthesized 623 alkyne-bearing ionizable lipids using the A3 coupling reaction and assessed their effectiveness in mRNA delivery. ILs with specific structural features—18-carbon alkyl chains, a cis-double bond, and ethanolamine head groups—demonstrated superior mRNA delivery capabilities. Variations in saturation, double bond placement, and chain length correlated with decreased efficacy. Alkynes positioned adjacent to nitrogen atoms in ILs reduced the acid dissociation constant (pKa) of LNPs, thereby hindering mRNA delivery efficiency. Conversion of alkynes to alkanes significantly enhanced mRNA delivery of ILs both in vitro and in vivo. Moreover, combining optimized ILs with cKK-E12 yields synergistic LNPs that showed markedly augmented mRNA expression levels in vivo. Conclusions Overall, our study provides insights into the structure–function relationships of ILs, providing a foundation for the rational design of ILs to enhance the efficacy of LNPs in mRNA delivery. Graphical Abstract

Abstract Background Microalgae have emerged as sustainable alternatives to fossil fuels and high-value petrochemicals. Despite the commercial potential of microalgae, their low biomass productivity is a significant limiting factor for large-scale production. In the photoautotrophic cultivation of microalgae, achievable cell density levels depend on the light transmittance of the production system, which can significantly decrease the photosynthetic rate and biomass production. In contrast, the mixotrophic cultivation of microalgae using heterotrophic carbon sources enables high-density cultivation, which significantly enhances biomass productivity. The identification of optimal production conditions is crucial for improving biomass productivity; however, it is typically time- and resource-consuming. To overcome this problem, high-throughput screening (HTS) system presents a practical approach to maximize biomass and lipid production and enhance the industrial applicability of microalgae. Results In this study, we proposed a two-step HTS assay that allows effective screening of heterotrophic conditions compatible with new microalgal isolates. To confirm the effectiveness of the HTS assay, three microalgal isolates with distinctive morphological and genetic traits were selected. Suitable cultivation conditions, including various heterotrophic carbon sources, substrate concentrations, and temperatures, were investigated using a two-step HTS assay. The optimized conditions were validated at the flask scale, which confirmed a significant enhancement in the biomass and lipid productivity of each isolate. Moreover, the two-step HTS assay notably enhanced economic and temporal efficiency compared to conventional flask-based optimization. Conclusions These results suggest that our two-step HTS assay is an efficient strategy for investigating and optimizing microalgal culture conditions to maximize biomass and lipid productivity. This approach has the potential to enhance the industrial applicability of microalgae and facilitate the seamless transition from laboratory to field applications.

Abstract The educational landscape of toxicology is increasingly integrating computational methodologies due to ethical concerns about animal testing and advancements in biotechnological and data analysis tools. This paper examines the evolution and significance of the Toxicology in the 21st century (Tox21) initiative and its impact on computational toxicology education. It contrasts computational toxicology with traditional methods, highlighting the limitations of conventional approaches and the new perspectives offered by computational techniques. The study emphasizes the importance of incorporating computational toxicology into curricula, including case studies that demonstrate how this integration enhances students’ problem-solving abilities, real-time data analysis skills, and innovation capabilities. Furthermore, it outlines effective teaching content and methods, including software tools, online resources, and academic literature. The paper also addresses the challenges and limitations faced in this educational shift and explores prospects for advancing computational toxicology education. By documenting these developments, the study aims to clarify the current advancements in toxicology education and the preparedness of students to address global chemical safety challenges with innovative solutions.

Abstract Background Microalgae are potential sustainable resources for the production of value-added chemicals that can be used as biofuels, pharmaceuticals, and nutritional supplements. Arachidonic acid (ARA), a omega-6 fatty acid, plays a crucial role in infant development and immune response, and can be used in cosmetics and pharmaceuticals. Demand for industrial-scale ARA production is continuously increasing because of its broad applicability. To address this demand, there has been a significant shift towards microorganism-based ARA production. To accelerate large-scale ARA production, it is crucial to select suitable strains and establish optimal culture conditions. Results Here, we isolated a novel microalga Lobosphaera incisa CFRC-1, a valuable strain that holds promise as a feedstock for ARA production. Optimal cultivation conditions were investigated using a high-throughput screening method to enhance ARA production in this novel strain. Out of 71 candidates, four organic carbon substrates were identified that could be utilized by L. incisa CFRC-1. Through flask-scale verification, fructose was confirmed as the optimal organic carbon substrate for promoting microalgal growth, total lipid accumulation, and ARA production. Subsequently, we investigated appropriate substrate concentration and cultivation temperature, confirming that the optimal conditions were 30 g L− 1 of fructose and 27 ℃ of temperature. Under these optimized conditions, biomass and ARA production reached 13.05 ± 0.40 g L− 1 and 97.98 ± 7.33 mg L− 1, respectively, representing 9.6-fold and 5.3-fold increases compared to the conditions before optimization conditions. These results achieved the highest biomass and ARA production in flask-scale cultivation, indicating that our approach effectively improved both production titer and productivity. Conclusions This study presents a novel microalgae and optimized conditions for enhancing biomass and ARA production, suggesting that this approach is a practical way to accelerate the production of valuable microalgae-based chemicals. These findings provide a basis for large-scale production of ARA-utilizing microalgae for industrial applications.

G protein-coupled receptors (GPCRs) are pivotal in mediating diverse physiological and pathological processes, rendering them promising targets for drug discovery. GPCRs account for about 40% of FDA-approved drugs, representing the most successful drug targets. However, only approximately 15% of the 800 human GPCRs are targeted by market drugs, leaving numerous opportunities for drug discovery among the remaining receptors. Cell expression systems play crucial roles in the GPCR drug discovery field, including novel target identification, structural and functional characterization, potential ligand screening, signal pathway elucidation, and drug safety evaluation. Here, we discuss the principles, applications, and limitations of widely used cell expression systems in GPCR-targeted drug discovery, GPCR function investigation, signal pathway characterization, and pharmacological property studies. We also propose three strategies for constructing genome-wide pan-GPCR cell libraries, which will provide a powerful platform for GPCR ligand screening, and facilitate the study of GPCR mechanisms and drug safety evaluation, ultimately accelerating the process of GPCR-targeted drug discovery.

Abstract Cortisol is established as a reliable biomarker for stress prompting intensified research in developing wearable sensors to detect it via eccrine sweat. Since cortisol is present in sweat in trace quantities, typically 8–140 ng/mL, developing such biosensors necessitates the design of bioreceptors with appropriate sensitivity and selectivity. In this work, we present a systematic biomimetic methodology and a semi-automated high-throughput screening tool which enables rapid selection of bioreceptors as compared to ab initio design of peptides via computational peptidology. Candidate proteins from databases are selected via molecular docking and ranked according to their binding affinities by conducting automated AutoDock Vina scoring simulations. These candidate proteins are then validated via full atomistic steered molecular dynamics computations including umbrella sampling to estimate the potential of mean force using GROMACS version 2022.6. These explicit molecular dynamic calculations are carried out in an eccrine sweat environment taking into consideration the protein dynamics and solvent effects. Subsequently, we present a candidate baseline peptide bioreceptor selected as a contiguous sequence of amino acids from the selected protein binding pocket favourably interacting with the target ligand (i.e., cortisol) from the active binding site of the proteins and maintaining its tertiary structure. A unique cysteine residue introduced at the N-terminus allows orientation-specific surface immobilization of the peptide onto the gold electrodes and to ensure exposure of the binding site. Comparative binding affinity simulations of this peptide with the target ligand along with commonly interfering species e.g., progesterone, testosterone and glucose are also presented to demonstrate the validity of this proposed peptide as a candidate baseline bioreceptor for future cortisol biosensor development.

The flavor profile is an important factor that affects consumer preference and purchase intention and plays a vital role in food production as well as in food science research. Traditional methods for determining food flavor are based on sensory evaluation, instrumental analysis, or a combination of the two. With the rapid development of computer technology, machine learning technology with high predictive ability and accuracy has been widely used for food flavor analysis and prediction. This method overcomes the limitations of traditional methods in food flavor evaluation, which are time-consuming and cannot be used to process large quantities of data. This review discusses the latest progress in food flavor analysis technology and machine learning technology in food flavor research and presents commonly used food flavor analysis techniques and machine learning algorithms. Based on an overview of common machine learning models, this review systematically summarizes the application of machine learning in the high-throughput screening of food flavor substances, flavor perception, and flavor quality control. The challenges and future research trends in the application of machine learning to food flavor analysis and prediction are summarized and discussed. The purpose of this review is to provide theoretical references for machine learning technology to predict flavor formation mechanisms, synthesize desirable flavor compounds, and control food quality.

Differential scanning fluorimetry (DSF) is a widely used technique for determining the apparent melting temperature (Tma) of a purified protein. Here, we present a protocol for performing and optimizing DSF experiments. We describe steps for designing and performing the experiment, analyzing data, and optimization. We provide benchmarks for typical Tmas and ΔTmas, standard assay conditions, and upper and lower limits of commonly altered experimental variables. We also detail common pitfalls of DSF and ways to avoid, identify, and overcome them.

Proteomic studies have identified moesin (MSN), a protein containing a four-point-one, ezrin, radixin, moesin (FERM) domain, and the receptor CD44 as hub proteins found within a coexpression module strongly linked to Alzheimers disease (AD) traits and microglia. These proteins are more abundant in Alzheimers patient brains, and their levels are positively correlated with cognitive decline, amyloid plaque deposition, and neurofibrillary tangle burden. The MSN FERM domain interacts with the phospholipid phosphatidylinositol 4,5-bisphosphate (PIP2) and the cytoplasmic tail of CD44. Inhibiting the MSN-CD44 interaction may help limit AD-associated neuronal damage. Here, we investigated the feasibility of developing inhibitors that target this protein-protein interaction. We have employed structural, mutational, and phage-display studies to examine how CD44 binds to the FERM domain of MSN. Interestingly, we have identified an allosteric site located close to the PIP2 binding pocket that influences CD44 binding. These findings suggest a mechanism in which PIP2 binding to the FERM domain stimulates CD44 binding through an allosteric effect, leading to the formation of a neighboring pocket capable of accommodating a receptor tail. Furthermore, high-throughput screening of a chemical library identified two compounds that disrupt the MSN-CD44 interaction. One compound series was further optimized for biochemical activity, specificity, and solubility. Our results suggest that the FERM domain holds potential as a drug development target. Small molecule preliminary leads generated from this study could serve as a foundation for additional medicinal chemistry efforts with the goal of controlling microglial activity in AD by modifying the MSN-CD44 interaction.

Alzheimer’s disease (AD) is an age-related neurodegenerative disorder. Transgenic and pharmacological AD models are extensively studied to understand AD mechanisms and drug discovery. However, they are time-consuming and relatively costly, which hinders the discovery of potential anti-AD therapeutics. Here, we established a new model of AD in larval zebrafish by co-treatment with aluminum chloride (AlCl3) and D-galactose (D-gal) for 72 h. In particular, exposure to 150 μM AlCl3 + 40 mg/mL D-gal, 200 μM AlCl3 + 30 mg/mL D-gal, or 200 μM AlCl3 + 40 mg/mL D-gal successfully induced AD-like symptoms and aging features. Co-treatment with AlCl3 and D-gal caused significant learning and memory deficits, as well as impaired response ability and locomotor capacity in the plus-maze and light/dark test. Moreover, increased acetylcholinesterase and β-galactosidase activities, β-amyloid 1–42 deposition, reduced telomerase activity, elevated interleukin 1 beta mRNA expression, and enhanced reactive oxygen species production were also observed. In conclusion, our zebrafish model is simple, rapid, effective and affordable, incorporating key features of AD and aging, thus may become a unique and powerful tool for high-throughput screening of anti-AD compounds in vivo.

Heterogeneous catalysis remains at the core of various bulk chemical manufacturing and energy conversion processes, and its revolution necessitates the hunt for new materials with ideal catalytic activities and economic feasibility. Computational high-throughput screening presents a viable solution to this challenge, as machine learning (ML) has demonstrated its great potential in accelerating such processes by providing satisfactory estimations of surface reactivity with relatively low-cost information. This review focuses on recent progress in applying ML in adsorption energy prediction, which predominantly quantifies the catalytic potential of a solid catalyst. ML models that leverage inputs from different categories and exhibit various levels of complexity are classified and discussed. At the end of the review, an outlook on the current challenges and future opportunities of ML-assisted catalyst screening is supplied. We believe that this review summarizes major achievements in accelerating catalyst discovery through ML and can inspire researchers to further devise novel strategies to accelerate materials design and, ultimately, reshape the chemical industry and energy landscape.

Abstract Intrahepatic cholangiocarcinoma (iCCA) presents a challenging diagnosis due to its nonspecific early clinical manifestations, often resulting in late-stage detection and high mortality. Diagnosing iCCA is further complicated by its limited accuracy, often necessitating multiple invasive procedures for precise identification. Despite carbohydrate antigen 19–9 (CA19-9) having been investigated and employed for iCCA diagnosis, it demonstrates modest diagnostic performance. Consequently, the identification of novel biomarkers with improved sensitivity and specificity remains an imperative yet formidable task. Autoantibodies, as early indicators of the immune response against cancer, offer a promising avenue for enhancing diagnostic accuracy. Our study aimed to identify non-invasive blood-based autoantibody biomarkers capable of distinguishing iCCA patients from healthy individuals (CTRs). We profiled autoantibodies in 26 serum samples (16 iCCAs and 10 CTRs) using protein microarrays containing 1622 functional proteins. Leveraging machine learning techniques, we identified a signature composed of three autoantibody biomarkers (NDE1, PYCR1, and VIM) in conjunction with CA19-9 for iCCA detection. This combined signature demonstrated superior diagnostic performance with an AUC of 96.9%, outperforming CA19-9 alone (AUC: 83.8%). These results suggest the potential of autoantibody biomarkers to develop a complementary non-invasive diagnostic utility for routine iCCA screening.

Abstract Background Base editing is a powerful tool for artificial evolution to create allelic diversity and improve agronomic traits. However, the great evolutionary potential for every sgRNA target has been overlooked. And there is currently no high-throughput method for generating and characterizing as many changes in a single target as possible based on large mutant pools to permit rapid gene directed evolution in plants. Results In this study, we establish an efficient germline-specific evolution system to screen beneficial alleles in Arabidopsis which could be applied for crop improvement. This system is based on a strong egg cell-specific cytosine base editor and the large seed production of Arabidopsis, which enables each T1 plant with unedited wild type alleles to produce thousands of independent T2 mutant lines. It has the ability of creating a wide range of mutant lines, including those containing atypical base substitutions, and as well providing a space- and labor-saving way to store and screen the resulting mutant libraries. Using this system, we efficiently generate herbicide-resistant EPSPS, ALS, and HPPD variants that could be used in crop breeding. Conclusions Here, we demonstrate the significant potential of base editing-mediated artificial evolution for each sgRNA target and devised an efficient system for conducting deep evolution to harness this potential.

Pulmonary fibrosis poses a significant health threat with very limited therapeutic options available. In this study, we reported the enhanced expression of mesenchymal homobox 1 (MEOX1) in pulmonary fibrosis patients, especially in their fibroblasts and endothelial cells, and confirmed MEOX1 as a central orchestrator in the activation of profibrotic genes. By high-throughput screening, we identified Ailanthone (AIL) from a natural compound library as the first small molecule capable of directly targeting and suppressing MEOX1. AIL demonstrated the ability to inhibit both the activation of fibroblasts and endothelial-to-mesenchymal transition of endothelial cells when challenged by transforming growth factor-β1 (TGF-β1). In an animal model of bleomycin-induced pulmonary fibrosis, AIL effectively mitigated the fibrotic process and restored respiratory functions. Mechanistically, AIL acted as a suppressor of MEOX1 by disrupting the interaction between the transcription factor JUN and the promoter of MEOX1, thereby inhibiting MEOX1 expression and activity. In summary, our findings pinpointed MEOX1 as a cell-specific and clinically translatable target in fibrosis. Moreover, we demonstrated the potent anti-fibrotic effect of AIL in pulmonary fibrosis, specifically through the suppression of JUN-dependent MEOX1 activation.

The rapid rise of antibiotic resistance and slow discovery of new antibiotics have threatened global health. While novel phage lysins have emerged as potential antibacterial agents, experimental screening methods for novel lysins pose significant challenges due to the enormous workload. Here, the first unified software package, namely DeepLysin, is developed to employ artificial intelligence for mining the vast genome reservoirs ("dark matter") for novel antibacterial phage lysins. Putative lysins are computationally screened from uncharacterized Staphylococcus aureus phages and 17 novel lysins are randomly selected for experimental validation. Seven candidates exhibit excellent in vitro antibacterial activity, with LLysSA9 exceeding that of the best‐in‐class alternative. The efficacy of LLysSA9 is further demonstrated in mouse bloodstream and wound infection models. Therefore, this study demonstrates the potential of integrating computational and experimental approaches to expedite the discovery of new antibacterial proteins for combating increasing antimicrobial resistance. [ABSTRACT FROM AUTHOR]

We developed a novel hepatitis B virus (HBV) infection-monitoring system using a luminescent, 11-amino acid reporter (HiBiT). We performed high-throughput antiviral screening using this system to identify anti-HBV compounds. After the infection of primary human hepatocytes with the recombinant virus HiBiT-HBV, which contains HiBiT at its preS1, 1262 compounds were tested in a first screening using extracellular HiBiT activity as an indicator of viral infection. Following a second screening, we focused on the compound skimmianine, which showed a potent antiviral effect. When skimmianine was added at the same time as HiBiT-HBV infection, skimmianine inhibited HiBiT activity with EC50 of 0.36 pM, CC50 of 1.67 μM and a selectivity index (CC50:EC50 ratio) of 5,100,000. When skimmianine was added 72 h after HiBiT-HBV infection, the EC50, CC50 and selectivity index were 0.19 μM, 1.87 μM and 8.79, respectively. Time-lapse fluorescence imaging analysis using another recombinant virus, ReAsH-TC155HBV, with the insertion of tetra-cysteine within viral capsid, revealed that skimmianine inhibited the accumulation of the capsid into hepatocytes. Furthermore, skimmianine did not inhibit either attachment or internalization. These results imply that skimmianine inhibits the retrograde trafficking of the virus after internalization. This study demonstrates the usefulness of the recombinant virus, HiBiT-HBV, for high-throughput screening to identify anti-HBV compounds. [ABSTRACT FROM AUTHOR]

Emergence of newer variants of SARS-CoV-2 underscores the need for effective antivirals to complement the vaccination program in managing COVID-19. The multi-functional papain-like protease (PLpro) of SARS-CoV-2 is an essential viral protein that not only regulates the viral replication but also modulates the host immune system, making it a promising therapeutic target. To this end, we developed an in vitro interferon stimulating gene 15 (ISG15)-based Förster resonance energy transfer (FRET) assay and screened the National Cancer Institute (NCI) Diversity Set VI compound library, which comprises 1584 small molecules. Subsequently, we assessed the PLpro enzymatic activity in the presence of screened molecules. We identified three potential PLpro inhibitors, namely, NSC338106, 651084, and 679525, with IC50 values in the range from 3.3 to 6.0 µM. These molecules demonstrated in vitro inhibition of the enzyme activity and exhibited antiviral activity against SARS-CoV-2, with EC50 values ranging from 0.4 to 4.6 µM. The molecular docking of all three small molecules to PLpro suggested their specificity towards the enzyme's active site. Overall, our study contributes promising prospects for further developing potential antivirals to combat SARS-CoV-2 infection. [ABSTRACT FROM AUTHOR]

Sake, which is produced mainly from japonica rice (Oryza sativa subsp. japonica), is one of the most important alcohol products in Japan. In this study, we aimed to investigate a hypothesis that the early root endophytic bacterial communities in Japanese sake rice cultivars would be distinct from those in table rice cultivars, comparing four sake rice cultivars and two table rice cultivars. Rice roots in the vegetative stage were collected 0, 3, and 6 weeks after transplanting, and 16S rRNA gene amplicon sequencing revealed significant differences in bacterial community composition diversity between the sake and table rice cultivars. The root endophytic bacterial communities at the transplanting differed significantly between the rice cultivars, indicating differences in each seed-derived endophytic community. After an overall dominance of Pantoea and Methylobacterium-Methylorubrum at the transplanting, the endophytic community was gradually replaced by soil-derived bacteria that varied by the rice cultivars. Notably, PERMANOVA results showed that the rice endophytic bacterial community composition differed significantly between the sake and table rice cultivars (p < 0.001). These results highlight the distinct root endophytic bacterial composition in the sake rice cultivars compared to those in the table rice cultivars, supporting our hypothesis. [ABSTRACT FROM AUTHOR]

Pulmonary fibrosis poses a significant health threat with very limited therapeutic options available. In this study, we reported the enhanced expression of mesenchymal homobox 1 (MEOX1) in pulmonary fibrosis patients, especially in their fibroblasts and endothelial cells, and confirmed MEOX1 as a central orchestrator in the activation of profibrotic genes. By high-throughput screening, we identified Ailanthone (AIL) from a natural compound library as the first small molecule capable of directly targeting and suppressing MEOX1. AIL demonstrated the ability to inhibit both the activation of fibroblasts and endothelial-to-mesenchymal transition of endothelial cells when challenged by transforming growth factor- β 1 (TGF- β 1). In an animal model of bleomycin-induced pulmonary fibrosis, AIL effectively mitigated the fibrotic process and restored respiratory functions. Mechanistically, AIL acted as a suppressor of MEOX1 by disrupting the interaction between the transcription factor JUN and the promoter of MEOX1, thereby inhibiting MEOX1 expression and activity. In summary, our findings pinpointed MEOX1 as a cell-specific and clinically translatable target in fibrosis. Moreover, we demonstrated the potent anti-fibrotic effect of AIL in pulmonary fibrosis, specifically through the suppression of JUN-dependent MEOX1 activation. MEOX1 is a central orchestrator during pulmonary fibrosis and promotes fibroblasts activation and EndMT. High-throughput screening identifies Ailanthone as the first direct suppressor of MEOX1 by disrupting the interaction between JUN and MEOX1 promoter. [Display omitted] [ABSTRACT FROM AUTHOR]

The cell painting (CP) assay has emerged as a potent imaging-based highthroughput phenotypic profiling (HTPP) tool that provides comprehensive input data for in silico prediction of compound activities and potential hazards in drug discovery and toxicology. CP enables the rapid, multiplexed investigation of various molecular mechanisms for thousands of compounds at the single-cell level. The resulting large volumes of image data provide great opportunities but also pose challenges to image and data analysis routines as well as property prediction models. This review addresses the integration of CP-based phenotypic data together with or in substitute of structural information from compounds into machine (ML) and deep learning (DL) models to predict compound activities for various human-relevant disease endpoints and to identify the underlying modesof- action (MoA) while avoiding unnecessary animal testing. The successful application of CP in combination with powerful ML/DL models promises further advances in understanding compound responses of cells guiding therapeutic development and risk assessment. Therefore, this review highlights the importance of unlocking the potential of CP assays when combined with molecular fingerprints for compound evaluation and discusses the current challenges that are associated with this approach. [ABSTRACT FROM AUTHOR]

Chemotherapy-induced peripheral neuropathy (CIPN) is a disabling side effect of cancer chemotherapy that can often limit treatment options for cancer patients or have life-long neurodegenerative consequences that reduce the patient's quality of life. CIPN is caused by the detrimental actions of various chemotherapeutic agents on peripheral axons. Currently, there are no approved preventative measures or treatment options for CIPN, highlighting the need for the discovery of novel therapeutics and improving our understanding of disease mechanisms. In this study, we utilized human-induced pluripotent stem cell (hiPSC)-derived motor neurons as a platform to mimic axonal damage after treatment with vincristine, a chemotherapeutic used for the treatment of breast cancers, osteosarcomas, and leukemia. We screened a total of 1902 small molecules for neuroprotective properties in rescuing vincristine-induced axon growth deficits. From our primary screen, we identified 38 hit compounds that were subjected to secondary dose response screens. Six compounds showed favorable pharmacological profiles – AZD7762, A-674563, Blebbistatin, Glesatinib, KW-2449, and Pelitinib, all novel neuroprotectants against vincristine toxicity to neurons. In addition, four of these six compounds also showed efficacy against vincristine-induced growth arrest in human iPSC-derived sensory neurons. In this study, we utilized high-throughput screening of a large library of compounds in a therapeutically relevant assay. We identified several novel compounds that are efficacious in protecting different neuronal subtypes from the toxicity induced by a common chemotherapeutic agent, vincristine which could have therapeutic potential in the clinic. [ABSTRACT FROM AUTHOR]