High-throughput Screening Research Articles

Bringing Artificial Intelligence (AI) into Environmental Toxicology Studies: A Perspective of AI-Enabled Zebrafish High-Throughput Screening.

The booming development of artificial intelligence (AI) has brought excitement to many research fields that could benefit from its big data analysis capability for causative relationship establishment and knowledge generation. In toxicology studies using zebrafish, the microscopic images and videos that illustrate the developmental stages, phenotypic morphologies, and animal behaviors possess great potential to facilitate rapid hazard assessment and dissection of the toxicity mechanism of environmental pollutants. However, the traditional manual observation approach is both labor-intensive and time-consuming. In this Perspective, we aim to summarize the current AI-enabled image and video analysis tools to realize the full potential of AI. For image analysis, AI-based tools allow fast and objective determination of morphological features and extraction of quantitative information from images of various sorts. The advantages of providing accurate and reproducible results while avoiding human intervention play a critical role in speeding up the screening process. For video analysis, AI-based tools enable the tracking of dynamic changes in both microscopic cellular events and macroscopic animal behaviors. The subtle changes revealed by video analysis could serve as sensitive indicators of adverse outcomes. With AI-based toxicity analysis in its infancy, exciting developments and applications are expected to appear in the years to come.

Disulfiram mediated anti-tumour effect in pituitary neuroendocrine tumours by inducing cuproptosis

ContextMedical treatment plays a critical role in pituitary neuroendocrine tumour (PitNET) treatment. Dopamine agonists and somatostatin receptor agonists are the only known drugs for effectively treating PitNET. Thus, the identification of potential therapeutic targets and drugs is urgently needed. ObjectiveTo discover potential drugs that can suppress PitNET growth and to further investigate the underlying mechanism involved. MethodsHigh-throughput drug screening of primary cultures of 17 patient-derived PitNETs was performed to identify potential therapeutic compounds. Cell viability assays, Western blot analysis and flow cytometry were used to investigate pituitary neuroendocrine tumour cell lines and patient-derived PitNET cultures in vitro. In vivo drug efficacy was examined in a mouse xenograft model. ResultsSeventeen primary PitNET samples were collected for high-throughput drug screening, and a class of copper ionophores that can effectively inhibit cell growth, such as zinc pyrithione, elesclomol, and disulfiram (DSF), was identified. Subsequent experiments initially validated the dose-dependent cell growth-suppressing effect of these copper ionophores on AtT20, GH3, and MMQ cells and several primary PitNET cell lines. Moreover, we confirmed that the cytotoxic effect of DSF depends on the presence of copper. Additionally, we determined that cell death occurs via cuproptosis, with events such as Fe-S cluster protein loss, dihydrolipoyl transacetylase oligomerization and heat shock protein 70 upregulation. Finally, we verified the cytotoxic effects of DSF in vivo. ConclusionThe present study revealed copper ionophores as a potential class of drugs for PitNET treatment. DSF induced PitNET cell death via cuproptosis and might be a promising option for PitNET therapy.

Development of a novel high-throughput culture system for hypoxic 3D hydrogel cell culture.

Animal models lack physiologic relevance to the human system which results in low clinical translation of results derived from animal testing. Besides spheroids or organoids, hydrogel-based 3D in vitro models are used to mimic the in vivo situation increasing the relevance while reducing animal testing. However, to establish hydrogel-based 3D models in applications such as drug development or personalized medicine, high-throughput culture systems are required. Furthermore, the integration of oxygen-reduced (hypoxic) conditions has become increasingly important to establish more physiologic culture models. Therefore, we developed a platform technology for the high-throughput generation of miniaturized hydrogels for 3D cell culture. The Oli-Up system is based on the shape of a well-plate and allows for the parallel culture of 48 hydrogel samples, each with a volume of 15µl. As a proof-of-concept, we established a 3D culture of gelatin-methacryloyl (GelMA)-encapsulated mesenchymal stem/stromal cells (MSCs). We used a hypoxia reporter cell line to establish a defined oxygen-reduced environment to precisely trigger cellular responses characteristicof hypoxia in MSCs. In detail, the expression of hypoxia response element (HRE) increased dependent on the oxygen concentration and cell density. Furthermore, MSCs displayed an altered glucose metabolism and increased VEGF secretion upon oxygen-reduction. In conclusion, the Oli-Up system is a platform technology for the high-throughput culture of hydrogel-based 3D models in a defined oxygen environment. As it is amenable for automation, it holds the potential for high-throughput screening applications such as drug development and testing in more physiologic 3D in vitro tissue models.

High-Throughput Screening for Boride Superconductors.

A high-throughput screening using density functional calculations is performed to search for stable boride superconductors from the existing materials database. The workflow employs the fast frozen-phonon method as the descriptor to evaluate the superconducting properties quickly. Twenty-three stable candidates were identified during the screening. The superconductivity was obtained earlier experimentally or computationally for almost all found binary compounds. Previous studies on ternary borides are very limited. Our extensive search among ternary systems confirmed superconductivity in known systems and found several new compounds. Among these discovered superconducting ternary borides, TaMo2B2 shows the highest superconducting temperature of ∼12 K. Most predicted compounds were synthesized previously; therefore, our predictions can be examined experimentally. Our work also demonstrates that the boride systems can have diverse structural motifs that lead to superconductivity.

Integration of Computational Pipeline to Streamline Efficacious Drug Nomination and Biomarker Discovery in Glioblastoma.

Glioblastoma multiforme (GBM) is the deadliest, most heterogeneous, and most common brain cancer in adults. Not only is there an urgent need to identify efficacious therapeutics, but there is also a great need to pair these therapeutics with biomarkers that can help tailor treatment to the right patient populations. We built patient drug response models by integrating patient tumor transcriptome data with high-throughput cell line drug screening data as well as Bayesian networks to infer relationships between patient gene expression and drug response. Through these discovery pipelines, we identified agents of interest for GBM to be effective across five independent patient cohorts and in a mouse avatar model: among them are a number of MEK inhibitors (MEKis). We also predicted phosphoglycerate dehydrogenase enzyme (PHGDH) gene expression levels to be causally associated with MEKi efficacy, where knockdown of this gene increased tumor sensitivity to MEKi and overexpression led to MEKi resistance. Overall, our work demonstrated the power of integrating computational approaches. In doing so, we quickly nominated several drugs with varying known mechanisms of action that can efficaciously target GBM. By simultaneously identifying biomarkers with these drugs, we also provide tools to select the right patient populations for subsequent evaluation.

CRISPR Screen Identifies the RNA-Binding Protein Eef1a1 as a Key Regulator of Myogenesis.

Skeletal muscle myogenesis hinges on gene regulation, meticulously orchestrated by molecular mechanisms. While the roles of transcription factors and non-coding RNAs in myogenesis are widely known, the contribution of RNA-binding proteins (RBPs) has remained unclear until now. Therefore, to investigate the functions of post-transcriptional regulators in myogenesis and uncover new functional RBPs regulating myogenesis, we employed CRISPR high-throughput RBP-KO (RBP-wide knockout) library screening. Through this approach, we successfully identified Eef1a1 as a novel regulatory factor in myogenesis. Using CRISPR knockout (CRISPRko) and CRISPR interference (CRISPRi) technologies, we successfully established cellular models for both CRISPRko and CRISPRi. Our findings demonstrated that Eef1a1 plays a crucial role in promoting proliferation in C2C12 myoblasts. Through siRNA inhibition and overexpression methods, we further elucidated the involvement of Eef1a1 in promoting proliferation and suppressing differentiation processes. RIP (RNA immunoprecipitation), miRNA pull-down, and Dual-luciferase reporter assays confirmed that miR-133a-3p targets Eef1a1. Co-transfection experiments indicated that miR-133a-3p can rescue the effect of Eef1a1 on C2C12 myoblasts. In summary, our study utilized CRISPR library high-throughput screening to unveil a novel RBP, Eef1a1, involved in regulating myogenesis. Eef1a1 promotes the proliferation of myoblasts while inhibiting the differentiation process. Additionally, it acts as an antagonist to miR-133a-3p, thus modulating the process of myogenesis.

Development of a fluorescence-based excipient screening for improved stability and shelf-life of recombinant chitin deacetylase

Chitin deacetylase (CDA) modifies chitin into chitosan by removing acetyl groups, but its inherent instability poses a challenge for successful crystallisation. Despite limited successes in crystallizing CDAs, prior attempts with recombinant chitin deacetylase (BaCDA) failed due to poor stability. To address this, we propose an enzyme buffer formulation as a cost-effective strategy to enhance stability, prolong shelf life, and increase the likelihood of crystallisation. Utilizing the high-throughput screening technique FTSA, we developed a screening method correlating BaCDA stability with its activity. The optimised formulation comprises 50 mM Tris-HCl buffer pH 7, 1 M NaCl, 20 % glycerol, and 1 mM Mg2+ as excipients. This formulation significantly improves BaCDA's thermostability (140.47 % increase) and enzyme activity (2.9-fold enhancement). BaCDA remains stable in the formulated buffer at −20 °C and −80 °C for 30 days and at 4 °C for 15 days. The current study has designed a high-throughput screening method approach to assess the stability of CDA enzyme formulations. The results of this study could contribute to the exploration of formulation elements that enhance the structural stability of CDA, thereby facilitating investigations into the enzyme's structure-function relationships.

MMSyn: A New Multimodal Deep Learning Framework for Enhanced Prediction of Synergistic Drug Combinations.

Combination therapy is a promising strategy for the successful treatment of cancer. The large number of possible combinations, however, mean that it is laborious and expensive to screen for synergistic drug combinations in vitro. Nevertheless, because of the availability of high-throughput screening data and advances in computational techniques, deep learning (DL) can be a useful tool for the prediction of synergistic drug combinations. In this study, we proposed a multimodal DL framework, MMSyn, for the prediction of synergistic drug combinations. First, features embedded in the drug molecules were extracted: structure, fingerprint, and string encoding. Then, gene expression data, DNA copy number, and pathway activity were used to describe cancer cell lines. Finally, these processed features were integrated using an attention mechanism and an interaction module and then input into a multilayer perceptron to predict drug synergy. Experimental results showed that our method outperformed five state-of-the-art DL methods and three traditional machine learning models for drug combination prediction. We verified that MMSyn achieved superior performance in stratified cross-validation settings using both the drug combination and cell line data. Moreover, we performed a set of ablation experiments to illustrate the effectiveness of each component and the efficacy of our model. In addition, our visual representation and case studies further confirmed the effectiveness of our model. All results showed that MMSyn can be used as a powerful tool for the prediction of synergistic drug combinations.

A substitutional quantum defect in WS2 discovered by high-throughput computational screening and fabricated by site-selective STM manipulation

Point defects in two-dimensional materials are of key interest for quantum information science. However, the parameter space of possible defects is immense, making the identification of high-performance quantum defects very challenging. Here, we perform high-throughput (HT) first-principles computational screening to search for promising quantum defects within WS2, which present localized levels in the band gap that can lead to bright optical transitions in the visible or telecom regime. Our computed database spans more than 700 charged defects formed through substitution on the tungsten or sulfur site. We found that sulfur substitutions enable the most promising quantum defects. We computationally identify the neutral cobalt substitution to sulfur (CoS0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${}_{{{{{{{{\\rm{S}}}}}}}}}^{0}$$\\end{document}) and fabricate it with scanning tunneling microscopy (STM). The CoS0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${}_{{{{{{{{\\rm{S}}}}}}}}}^{0}$$\\end{document} electronic structure measured by STM agrees with first principles and showcases an attractive quantum defect. Our work shows how HT computational screening and nanoscale synthesis routes can be combined to design promising quantum defects.

Data-Driven Discovery of Gas-Selective Organic Linkers in Metal-Organic Frameworks for the Separation of Ethylene and Ethane.

Metal-organic frameworks (MOFs) are potential candidates for gas-selective adsorbents for the separation of an ethylene/ethane mixture. To accelerate material discovery, high-throughput computational screening is a viable solution. However, classical force fields, which were widely employed in recent studies of MOF adsorbents, have been criticized for their failure to cover complicated interactions such as those involving π electrons. Herein, we demonstrate that machine learning force fields (MLFFs) trained on quantum-chemical reference data can overcome this difficulty. We have constructed a MLFF to accurately predict the adsorption energies of ethylene and ethane on the organic linkers of MOFs and discovered that the π electrons from both the ethylene molecule and the aromatic rings in the linkers could substantially influence the selectivity for gas adsorption. Four kinds of MOF linkers are identified as having promise for the separation of ethylene and ethane, and our results could also offer a new perspective on the design of MOF building blocks for diverse applications.

Novel Scorpion Toxin ω-Buthitoxin-Hf1a Selectively Inhibits Calcium Influx via CaV3.3 and CaV3.2 and Alleviates Allodynia in a Mouse Model of Acute Postsurgical Pain.

Venom peptides have evolved to target a wide range of membrane proteins through diverse mechanisms of action and structures, providing promising therapeutic leads for diseases, including pain, epilepsy, and cancer, as well as unique probes of ion channel structure-function. In this work, a high-throughput FLIPR window current screening assay on T-type CaV3.2 guided the isolation of a novel peptide named ω-Buthitoxin-Hf1a from scorpion Hottentotta franzwerneri crude venom. At only 10 amino acid residues with one disulfide bond, it is not only the smallest venom peptide known to target T-type CaVs but also the smallest structured scorpion venom peptide yet discovered. Synthetic Hf1a peptides were prepared with C-terminal amidation (Hf1a-NH2) or a free C-terminus (Hf1a-OH). Electrophysiological characterization revealed Hf1a-NH2 to be a concentration-dependent partial inhibitor of CaV3.2 (IC50 = 1.18 μM) and CaV3.3 (IC50 = 0.49 μM) depolarized currents but was ineffective at CaV3.1. Hf1a-OH did not show activity against any of the three T-type subtypes. Additionally, neither form showed activity against N-type CaV2.2 or L-type calcium channels. The three-dimensional structure of Hf1a-NH2 was determined using NMR spectroscopy and used in docking studies to predict its binding site at CaV3.2 and CaV3.3. As both CaV3.2 and CaV3.3 have been implicated in peripheral pain signaling, the analgesic potential of Hf1a-NH2 was explored in vivo in a mouse model of incision-induced acute post-surgical pain. Consistent with this role, Hf1a-NH2 produced antiallodynia in both mechanical and thermal pain.

Phase stability and mechanical property trends for MAB phases by high-throughput ab initio calculations

MAB phases (MABs) are atomically-thin laminates of ceramic/metallic-like layers, having made a breakthrough in the development of 2D materials. Though offering a vast chemical and phase space, relatively few MABs have been synthesised. To guide experiments, we perform high-throughput ab initio screening of MABs that combine group 4–7 transition metals (M); Al, Si, Ga, Ge, or In (A); and boron (B) focusing on their phase stability trends and mechanical properties. Considering the 1:1:1, 2:1:1, 2:1:2, 3:1:2, 3:1:3, and 3:1:4 M:A:B ratios and 10 phase prototypes, synthesisability of a single-phase compound for each elemental combination is estimated through formation energy spectra of competing dynamically stable MABs. Based on the volumetric proximity of energetically-close phases, we identify systems in which volume-changing deformations may facilitate transformation toughening. Subsequently, chemistry- and phase-structure-related trends in the elastic stiffness and ductility are predicted using elastic-constants-based descriptors. The analysis of directional Cauchy pressures and Young's moduli allows comparing mechanical response parallel and normal to M–B/A layers. The suggested promising MABs include Nb3AlB4, Cr2SiB2, Mn2SiB2 or the already synthesised MoAlB.

Mitofusin-mediated contacts between mitochondria and peroxisomes regulate mitochondrial fusion.

Mitofusins are large GTPases that trigger fusion of mitochondrial outer membranes. Similarly to the human mitofusin Mfn2, which also tethers mitochondria to the endoplasmic reticulum (ER), the yeast mitofusin Fzo1 stimulates contacts between Peroxisomes and Mitochondria when overexpressed. Yet, the physiological significance and function of these "PerMit" contacts remain unknown. Here, we demonstrate that Fzo1 naturally localizes to peroxisomes and promotes PerMit contacts in physiological conditions. These contacts are regulated through co-modulation of Fzo1 levels by the ubiquitin-proteasome system (UPS) and by the desaturation status of fatty acids (FAs). Contacts decrease under low FA desaturation but reach a maximum during high FA desaturation. High-throughput genetic screening combined with high-resolution cellular imaging reveal that Fzo1-mediated PerMit contacts favor the transit of peroxisomal citrate into mitochondria. In turn, citrate enters the TCA cycle to stimulate the mitochondrial membrane potential and maintain efficient mitochondrial fusion upon high FA desaturation. These findings thus unravel a mechanism by which inter-organelle contacts safeguard mitochondrial fusion.

Fission Yeast as a Platform for Antibacterial Drug Screens Targeting Bacterial Cytoskeleton Proteins.

Bacterial cytoskeletal proteins such as FtsZ and MreB perform essential functions such as cell division and cell shape maintenance. Further, FtsZ and MreB have emerged as important targets for novel antimicrobial discovery. Several assays have been developed to identify compounds targeting nucleotide binding and polymerization of these cytoskeletal proteins, primarily focused on FtsZ. Moreover, many of the assays are either laborious or cost-intensive, and ascertaining whether these proteins are the cellular target of the drug often requires multiple methods. Finally, the toxicity of the drugs to eukaryotic cells also poses a problem. Here, we describe a single-step cell-based assay to discover novel molecules targeting bacterial cytoskeleton and minimize hits that might be potentially toxic to eukaryotic cells. Fission yeast is amenable to high-throughput screens based on microscopy, and a visual screen can easily identify any molecule that alters the polymerization of FtsZ or MreB. Our assay utilizes the standard 96-well plate and relies on the ability of the bacterial cytoskeletal proteins to polymerize in a eukaryotic cell such as the fission yeast. While the protocols described here are for fission yeast and utilize FtsZ from Staphylococcus aureus and MreB from Escherichia coli, they are easily adaptable to other bacterial cytoskeletal proteins that readily assemble into polymers in any eukaryotic expression hosts. The method described here should help facilitate further discovery of novel antimicrobials targeting bacterial cytoskeletal proteins.

Quantifying Antibody-Dependent Cellular Cytotoxicity in a Tumor Spheroid Model: Application for Drug Discovery.

Monoclonal antibody-based immunotherapy targeting tumor antigens is now a mainstay of cancer treatment. One of the clinically relevant mechanisms of action of the antibodies is antibody-dependent cellular cytotoxicity (ADCC), where the antibody binds to the cancer cells and engages the cellular component of the immune system, e.g., natural killer (NK) cells, to kill the tumor cells. The effectiveness of these therapies could be improved by identifying adjuvant compounds that increase the sensitivity of the cancer cells or the potency of the immune cells. In addition, undiscovered drug interactions in cancer patients co-medicated for previous conditions or cancer-associated symptoms may determine the success of the antibody therapy; therefore, such unwanted drug interactions need to be eliminated. With these goals in mind, we created a cancer ADCC model and describe here a simple protocol to find ADCC-modulating drugs. Since 3D models such as cancer cell spheroids are superior to 2D cultures in predicting in vivo responses of tumors to anticancer therapies, spheroid co-cultures of EGFP-expressing HER2+ JIMT-1 breast cancer cells and the NK92.CD16 cell lines were set up and induced with Trastuzumab, a monoclonal antibody clinically approved against HER2-positive breast cancer. JIMT-1 spheroids were allowed to form in cell-repellent U-bottom 96-well plates. On day 3, NK cells and Trastuzumab were added. The spheroids were then stained with Annexin V-Alexa 647 to measure apoptotic cell death, which was quantitated in the peripheral zone of the spheroids with an automated microscope. The applicability of our assay to identify ADCC-modulating molecules is demonstrated by showing that Sunitinib, a receptor tyrosine kinase inhibitor approved by the FDA against metastatic cancer, almost completely abolishes ADCC. The generation of the spheroids and image acquisition and analysis pipelines are compatible with high-throughput screening for ADCC-modulating compounds in cancer cell spheroids.

A new high-throughput screening methodology for the discovery of cancer-testis antigen using multi-omics data

BackgroundCancer/testis antigens (CTAs), also known as tumor-specific antigens (TSAs) are specifically expressed in cancer cells and exhibit high immunogenicity, making them promising targets for immunotherapy and cancer vaccines. MethodsA new integrated high-throughput screening methodology for CTAs was proposed in this study through combining DNA methylation and RNA sequencing data. Briefly, the genes with increased transcript level and decreased DNA methylation were identified by multi-omics analysis. RNA sequencing studies in cell lines exposed to DNA methyltransferase (DNMT) inhibitors were performed to validate the inherent causal relationship between DNA hypomethylation and gene expression upregulation. ResultsWe proposed a new integrated high-throughput screening methodology for identification of CTAs using multi-omics analysis. In addition, we tested the feasibility of this method using gastric cancer (GC) as an example. In GC, we identified over 2000 primary candidate CTAs and ultimately identified 20 CTAs with significant tissue-specificity, including a testis-specific serine protease TESSP1/PRSS41. Integrated analysis confirmed that PRSS41 expression was reactivated in gastrointestinal cancers by promoter DNA hypomethylation at the CpG site (cg08104780). Additionally, DNA hypomethylation of PRSS41 predicted a poor prognosis in GC. ConclusionWe propose a new high-throughput screening method for the identification of CTAs in cancer and validate its effectiveness. Our work emphasizes that serine protease PRSS41 is a novel TSA that is reactivated in GC due to promoter DNA hypomethylation.

Engineering CRISPR/Cas9 therapeutics for cancer precision medicine.

The discovery of Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) and CRISPR-associated protein 9 (Cas9) technology has revolutionized field of cancer treatment. This review explores usage of CRISPR/Cas9 for editing and investigating genes involved in human carcinogenesis. It provides insights into the development of CRISPR as a genetic tool. Also, it explores recent developments and tools available in designing CRISPR/Cas9 systems for targeting oncogenic genes for cancer treatment. Further, we delve into an overview of cancer biology, highlighting key genetic alterations and signaling pathways whose deletion prevents malignancies. This fundamental knowledge enables a deeper understanding of how CRISPR/Cas9 can be tailored to address specific genetic aberrations and offer personalized therapeutic approaches. In this review, we showcase studies and preclinical trials that show the utility of CRISPR/Cas9 in disrupting oncogenic targets, modulating tumor microenvironment and increasing the efficiency of available anti treatments. It also provides insight into the use of CRISPR high throughput screens for cancer biomarker identifications and CRISPR based screening for drug discovery. In conclusion, this review offers an overview of exciting developments in engineering CRISPR/Cas9 therapeutics for cancer treatment and highlights the transformative potential of CRISPR for innovation and effective cancer treatments.

Improving the Synthesis Efficiency of Amino Acids by Analyzing the Key Sites of Intracellular Self-Assembly of Artificial Cellulosome

To explore the key sites affecting the intracellular assembly of key components of cellulosomes and obtain DocA mutants independent of Ca2+, Swiss-model, GROMACS, PyMOL, and other molecular dynamics simulation software were used for modeling and static and dynamic combination analysis. Site-specific mutation technology was used to mutate DocA, and Biacore was used to test the dependence of Ca2+ on the binding ability of protein DocA mutants and protein Coh, and to analyze the interaction and binding effect of mutant proteins in vitro. Forward intracellular mutant screening was performed based on semi-rational design and high throughput screening techniques. The orientation of mutations suitable for intracellular assembly was determined, and three directional mutant proteins, DocA-S1, DocA-S2, and DocA-S3, were obtained. Ca2+ independent DocA mutants were obtained gradually and their potential interaction mechanisms were analyzed. In the present study, intracellular self-assembly of key components of cellulosomes independent of Ca2+ was achieved, and DocA-S3 was applied to the assembly of key enzymes of L-lysine biosynthesis, in which DapA and DapB intracellular assembly increased L-lysine accumulation by 29.8% when compared with the control strains, providing a new strategy for improving the intracellular self-assembly of cellulosomes and amino acid fermentation efficiency.

Development of a high throughput oxidation profiling strategy for monoclonal antibody products

Oxidation is one of the most common degradation pathways of biopharmaceutics, potentially leading to altered product stability, pharmacokinetics, reduced biological activity and/or an increased immunogenicity. However, it is often insufficiently assessed in early development stages, leaving potential molecule liabilities undiscovered. Aim of the present work was the development of a high throughput oxidation profiling strategy, applicable throughout various stages of biopharmaceutical development. The study demonstrates that the combination of multiple stress assays, including peroxide-based, visible light, and metal-catalyzed oxidation (MCO), enables a comprehensive understanding of a mAb's oxidation susceptibility. The most effective parameters to evaluate oxidation in a high-throughput screening workflow are aggregation, tryptophan oxidation and changes in the hydrophobicity profile of the Fc and Fab subunit measured via Size Exclusion Chromatography, Intrinsic Tryptophan Fluorescence Emission spectroscopy and Reversed-Phase Chromatography subunit analysis, respectively. This oxidation profiling approach is valuable tool to systematically characterize the oxidation susceptibility under relevant conditions, time effective and with minimal sample consumption.

Improvement of Chalcone Synthase Activity and High-Efficiency Fermentative Production of (2S)-Naringenin via In Vivo Biosensor-Guided Directed Evolution.

Chalcone synthase (CHS) catalyzes the rate-limiting step of (2S)-naringenin (the essential flavonoid skeleton) biosynthesis. Improving the activity of the CHS by protein engineering enhances (2S)-naringenin production by microbial fermentation and can facilitate the production of valuable flavonoids. A (2S)-naringenin biosensor based on the TtgR operon was constructed in Escherichia coli and its detection range was expanded by promoter optimization to 0-300 mg/L, the widest range for (2S)-naringenin reported. The high-throughput screening scheme for CHS was established based on this biosensor. A mutant, SjCHS1S208N with a 2.34-fold increase in catalytic activity, was discovered by directed evolution and saturation mutagenesis. A pathway for de novo biosynthesis of (2S)-naringenin by SjCHS1S208N was constructed in Saccharomyces cerevisiae, combined with CHS precursor pathway optimization, increasing the (2S)-naringenin titer by 65.34% compared with the original strain. Fed-batch fermentation increased the titer of (2S)-naringenin to 2513 ± 105 mg/L, the highest reported so far. These findings will facilitate efficient flavonoid biosynthesis and further modification of the CHS in the future.