High-throughput Experiments Research Articles

Stringent and complex sequence constraints of an IGHV1-69 broadly neutralizing antibody to influenza HA stem

IGHV1-69 is frequently utilized by broadly neutralizing influenza antibodies to the hemagglutinin (HA) stem. These IGHV1-69 HA stem antibodies have diverse complementarity-determining region (CDR) H3 sequences. Besides, their light chains have minimal to no contact with the epitope. Consequently, sequence determinants that confer IGHV1-69 antibodies with HA stem specificity remain largely elusive. Using high-throughput experiments, this study reveals the importance of light-chain sequence for the IGHV1-69 HA stem antibody CR9114, which is the broadest influenza antibody known to date. Moreover, we demonstrate that the CDR H3 sequences from many other IGHV1-69 antibodies, including those to the HA stem, are incompatible with CR9114. Along with mutagenesis and structural analysis, our results indicate that light-chain and CDR H3 sequences coordinately determine the HA stem specificity of IGHV1-69 antibodies. Overall, this work provides molecular insights into broadly neutralizing antibody responses to influenza virus, which have important implications for universal influenza vaccine development.

High-throughput ultrasound neuromodulation in awake and freely behaving rats

Transcranial ultrasound neuromodulation is a promising potential therapeutic tool for the noninvasive treatment of neuropsychiatric disorders. However, the expansive parameter space and difficulties in controlling for peripheral auditory effects make it challenging to identify ultrasound sequences and brain targets that may provide therapeutic efficacy. Careful preclinical investigations in clinically relevant behavioral models are critically needed to identify suitable brain targets and acoustic parameters. However, there is a lack of ultrasound devices allowing for multi-target experimental investigations in awake and unrestrained rodents. We developed a miniaturized 64-element ultrasound array that enables neurointerventional investigations with within-trial active control targets in freely behaving rats. We first characterized the acoustic field with measurements in free water and with transcranial propagation. We then confirmed in vivo that the array can target multiple brain regions via electronic steering, and verified that wearing the device does not cause significant impairments to animal motility. Finally, we demonstrated the performance of our system in a high-throughput neuromodulation experiment, where we found that ultrasound stimulation of the rat central medial thalamus, but not an active control target, promotes arousal and increases locomotor activity.

O-29 - Development of Approaches for High-Throughput Experimentation in Radiochemistry

O-29 - Development of Approaches for High-Throughput Experimentation in Radiochemistry

Accelerated Engineering of Optimized Functional Composite Hydrogels via High-Throughput Experimentation.

The Materials Genome Initiative (MGI) seeks to accelerate the discovery and engineering of advanced materials via high-throughput experimentation (HTE), which is a challenging task, given the common trade-off between design for optimal processability vs performance. Here, we report a HTE method based on automated formulation, synthesis, and multiproperty characterization of bulk soft materials in well plate formats that enables accelerated engineering of functional composite hydrogels with optimized properties for processability and performance. The method facilitates rapid high-throughput screening of hydrogel composition-property relations for multiple properties in well plate formats. The feasibility and utility of the method were demonstrated by application to several functional composite hydrogel systems, including alginate/poly(N-isopropylacrylamide) (PNIPAM) and poly(ethylene glycol) dimethacrylate (PEGDMA)/poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOT:PSS) hydrogels. The HTE method was leveraged to identify formulations of conductive PEGDMA/PEDOT:PSS composite hydrogels for optimized performance and processability in three-dimensional (3D) printing. This work provides an advance in experimental methods based on automated dispensing, mixing, and sensing for the accelerated engineering of soft functional materials.

High-throughput synthesis provides data for predicting molecular properties and reaction success.

The generation of attractive scaffolds for drug discovery efforts requires the expeditious synthesis of diverse analogues from readily available building blocks. This endeavor necessitates a trade-off between diversity and ease of access and is further complicated by uncertainty about the synthesizability and pharmacokinetic properties of the resulting compounds. Here, we document a platform that leverages photocatalytic N-heterocycle synthesis, high-throughput experimentation, automated purification, and physicochemical assays on 1152 discrete reactions. Together, the data generated allow rational predictions of the synthesizability of stereochemically diverse C-substituted N-saturated heterocycles with deep learning and reveal unexpected trends on the relationship between structure and properties. This study exemplifies how organic chemists can exploit state-of-the-art technologies to markedly increase throughput and confidence in the preparation of drug-like molecules.

Infer global, predict local: Quantity-relevance trade-off in protein fitness predictions from sequence data.

Predicting the effects of mutations on protein function is an important issue in evolutionary biology and biomedical applications. Computational approaches, ranging from graphical models to deep-learning architectures, can capture the statistical properties of sequence data and predict the outcome of high-throughput mutagenesis experiments probing the fitness landscape around some wild-type protein. However, how the complexity of the models and the characteristics of the data combine to determine the predictive performance remains unclear. Here, based on a theoretical analysis of the prediction error, we propose descriptors of the sequence data, characterizing their quantity and relevance relative to the model. Our theoretical framework identifies a trade-off between these two quantities, and determines the optimal subset of data for the prediction task, showing that simple models can outperform complex ones when inferred from adequately-selected sequences. We also show how repeated subsampling of the sequence data is informative about how much epistasis in the fitness landscape is not captured by the computational model. Our approach is illustrated on several protein families, as well as on in silico solvable protein models.

High-Throughput Experimentation and Machine Learning-Assisted Optimization of Iridium-Catalyzed Cross-Dimerization of Sulfoxonium Ylides.

A novel and convenient approach that combines high-throughput experimentation (HTE) with machine learning (ML) technologies to achieve the first selective cross-dimerization of sulfoxonium ylides via iridium catalysis is presented. A variety of valuable amide-, ketone-, ester-, and N-heterocycle-substituted unsymmetrical E-alkenes are synthesized in good yields with high stereoselectivities. This mild method avoids the use of diazo compounds and is characterized by simple operation, high step-economy, and excellent chemoselectivity and functional group compatibility. The combined experimental and computational studies identify an amide-sulfoxonium ylide as a carbene precursor. Furthermore, a comprehensive exploration of the reaction space is also performed (600 reactions) and a machine learning model for reaction yield prediction has been constructed.

Information sharing in high-dimensional gene expression data for improved parameter estimation in concentration-response modelling.

In toxicological concentration-response studies, a frequent goal is the determination of an 'alert concentration', i.e. the lowest concentration where a notable change in the response in comparison to the control is observed. In high-throughput gene expression experiments, e.g. based on microarray or RNA-seq technology, concentration-response profiles can be measured for thousands of genes simultaneously. One approach for determining the alert concentration is given by fitting a parametric model to the data which allows interpolation between the tested concentrations. It is well known that the quality of a model fit improves with the number of measured data points. However, adding new replicates for existing concentrations or even several replicates for new concentrations is time-consuming and expensive. Here, we propose an empirical Bayes approach to information sharing across genes, where in essence a weighted mean of the individual estimate for one specific parameter of a fitted model and the mean of all estimates of the entire set of genes is calculated as a result. Results of a controlled plasmode simulation study show that for many genes a notable improvement in terms of the mean squared error (MSE) between estimate and true underlying value of the parameter can be observed. However, for some genes, the MSE increases, and this cannot be prevented by using a more sophisticated prior distribution in the Bayesian approach.

A Normalization Protocol Reduces Edge Effect in High-Throughput Analyses of Hydroxyurea Hypersensitivity in Fission Yeast.

Edge effect denotes better growth of microbial organisms situated at the edge of the solid agar media. Although the precise reason underlying edge effect is unresolved, it is generally attributed to greater nutrient availability with less competing neighbors at the edge. Nonetheless, edge effect constitutes an unavoidable confounding factor that results in misinterpretation of cell fitness, especially in high-throughput screening experiments widely employed for genome-wide investigation using microbial gene knockout or mutant libraries. Here, we visualize edge effect in high-throughput high-density pinning arrays and report a normalization approach based on colony growth rate to quantify drug (hydroxyurea)-hypersensitivity in fission yeast strains. This normalization procedure improved the accuracy of fitness measurement by compensating cell growth rate discrepancy at different locations on the plate and reducing false-positive and -negative frequencies. Our work thus provides a simple and coding-free solution for a struggling problem in robotics-based high-throughput screening experiments.

Design and use of a new primer pair for the characterization of the cyanobacteria Synechococcus and Prochlorococcus communities targeting petB gene through metabarcoding approaches

During the last years, the application of next-generation sequencing (NGS) technologies to search for specific genetic markers has become a crucial method for the characterization of microbial communities. Illumina MiSeq, likely the most widespread NGS platform for metabarcoding experiments and taxonomic classification, allows processing shorter reads than the classical SANGER sequencing method and therefore requires specific primer pairs that produce shorter amplicons. Specifically, for the analysis of the commonly studied Prochlorococcus and Synechococcus communities, the petB marker gene has recently stood out as able to provide deep coverage to determine the microdiversity of the community. However, current petB primer set produce a 597 bp amplicon that is not suitable for MiSeq chemistry.Here, we designed and tested a petB primer pair that targets both Prochlorococcus and Synechococcus communities producing an appropriate amplicon to be used with state-of-the-art Illumina MiSeq. This new primer set allows the classification of both groups to a low taxonomic level and is therefore suitable for high throughput experiments using MiSeq technologies, therefore constituting a useful, novel tool to facilitate further studies on Prochlorococcus and Synechococcus communities.•This work describes the de novo design of a Prochlorococcus and Synechococcus-specific petB primer pair, allowing the characterization of both populations to a low taxonomic level.•This primer pair is suitable for widespread Illumina MiSeq sequencing technologies.•petB was confirmed as an adequate target for the characterization of both picocyanobacteria.

Single-Cell Measurements Using Acoustic Force Spectroscopy (AFS).

Single-molecule force spectroscopy is a powerful tool to investigate the forces and motions related to interactions of biological molecules. Acoustic force spectroscopy (AFS) is a developed measurement tool to study single molecules or cells making use of acoustic standing waves. AFS permits high experimental throughput because many individual molecules can be manipulated and tracked in parallel. Moreover, a wide range of forces can be applied as well as a force loading rate with range of six orders of magnitude. At the same time, AFS stands out because of its simplicity and the compactness of the experimental setup. Even though the AFS setup is simple, it can still be challenging to perform high-quality measurements. Here we describe, in detail, how to setup, perform, and analyze an AFS measurement to determine cell adhesion.

From network analysis to experimental validation: identification of regulators of non-muscle myosin II contractility using the folded-gastrulation signaling pathway

The morphogenetic process of apical constriction, which relies on non-muscle myosin II (NMII) generated constriction of apical domains of epithelial cells, is key to the development of complex cellular patterns. Apical constriction occurs in almost all multicellular organisms, but one of the most well-characterized systems is the Folded-gastrulation (Fog)-induced apical constriction that occurs in Drosophila. The binding of Fog to its cognizant receptors Mist/Smog results in a signaling cascade that leads to the activation of NMII-generated contractility. Despite our knowledge of key molecular players involved in Fog signaling, we sought to explore whether other proteins have an undiscovered role in its regulation. We developed a computational method to predict unidentified candidate NMII regulators using a network of pairwise protein–protein interactions called an interactome. We first constructed a Drosophila interactome of over 500,000 protein–protein interactions from several databases that curate high-throughput experiments. Next, we implemented several graph-based algorithms that predicted 14 proteins potentially involved in Fog signaling. To test these candidates, we used RNAi depletion in combination with a cellular contractility assay in Drosophila S2R + cells, which respond to Fog by contracting in a stereotypical manner. Of the candidates we screened using this assay, two proteins, the serine/threonine phosphatase Flapwing and the putative guanylate kinase CG11811 were demonstrated to inhibit cellular contractility when depleted, suggestive of their roles as novel regulators of the Fog pathway.

OR35-03 Uncovering Causative Mutations in Polycystic Ovary Syndrome

Abstract Disclosure: J. Srivastava: None. I. Ovcharenko: None. PCOS is a complex disorder manifesting as reproductive and metabolic abnormalities in women. The undefined etiology stems from our incomplete understanding of genetic aberrations contributing to the diseased phenotype. This has resulted in speculations surrounding the cause-consequence relationship between phenotypic features of PCOS and limited our knowledge of comprehensive genetic signatures of PCOS. Pleiotropic effects are salient features of genetic regulation in mammalian genomes. The contribution of a set of regulatory elements (REs) to upregulation of a single gene, or the genetic control of multigene loci by a single RE, give rise to heterogenous phenotypes. Enhancers in particular dictate the spatio-temporal scale of gene expression by controlling transcription factor (TF) binding events. The complex traits of PCOS are suggestive of pleiotropy originating from genetic and epigenetic changes that arise from nucleotide variations in REs of the genome. Genome Wide Association Studies (GWAS) have identified ∼100 risk variants associated with PCOS and implicated the involvement of several genes such as FSHB/R, THADA, DENND1A, etc. However, mechanisms by which they contribute to pathophysiology are unknown. Additionally, twin studies have implicated the heritability of PCOS to be ∼80% and yet, the heritability from GWAS variants accounts for less than 10%. Experimental data may account for the ‘missing heritability problem’ specifically through rare variants that are missed in GWAS. To address this issue, we developed a Deep Learning model of gene regulation in PCOS identifying hundreds of active REs orchestrating the activity of PCOS genes. We also established a foundation for quantifying the impact of single-nucleotide noncoding mutations on TF binding and RE activity. By combining thousands of experimental assays associated with a large number of biological events such as tissue-specific epigenetic maps of the human genome, multi-tissue profiling of TF binding and gene expression using data from high throughput experiments we devised a strategy to accurately identify functionally map causative mutations in the GWAS loci of PCOS genes. MPRA experimental data validates a large fraction of our predicted causative variants thus suggesting that our studies have a potential of a leap forward accurate dissection of genomic and epigenetic mechanisms of PCOS. Consolidated results of this analysis and experimental validation of our predictions will lead to detailing the regulatory mechanisms of PCOS. Presentation Date: Sunday, June 18, 2023

Identification of formononetin as the active compound of CR-SR in hepatocellular carcinoma treatment: An integrated approach combining network pharmacology and weighted gene co-expression networks.

Hepatocellular carcinoma (HCC) is a life-threatening disease for which there is no cure. Traditional Chinese medicine is a treasure trove of Medicinals that has been used for thousands of years. In China, the traditional herb pair, Curcumae Rhizoma and Sparganii Rhizoma (CR-SR) represent a classic herbal combination used for the treatment of HCC. However, the drug targets and pharmacological mechanism of action of CR-SR in the treatment of HCC are unclear. To address this, we screened the active components and drug targets of CR-SR from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database and a high-throughput experiment- and reference-guided database of traditional Chinese medicines (HERB database). Combined with the weighted co-expression network analysis of dataset GSE76427, we constructed an active component-target-disease regulatory network. It was found that CR-SR's active components for HCC treatment included trans-gondoic acid, beta-sitosterol, stigmasterol, hederagenin, and formononetin. These compounds specifically targeted the genes Estrogen Receptor 1 (ESR1), Cyclin A2 (CCNA2), Checkpoint Kinase 1 (CHEK1), and Nuclear Receptor Coactivator 2 (NCOA2). ESR1, CCNA2, and CHEK1 genes showed significant differences in survival prognosis, expression levels, and statistical significance during the pathological stage. Moreover, their high affinity for formononetin was determined through molecular docking analysis. Cell assays and high-throughput sequencing were performed to reveal that the inhibitory effect of formononetin on HepG2 cell proliferation was related to hepatocyte metabolism and cell cycle regulation-related pathways. This study provides insights into potential HCC treatments.

Optimizing the growth of vertically aligned carbon nanotubes by literature mining and high-throughput experiments

Vertically aligned carbon nanotube (VACNT) arrays with good mechanical properties and high thermal conductivity can be used as effective thermal interface materials in thermal management. In order to take advantage of the high thermal conductivity along the axis of nanotubes, the quality and height of the arrays need to be optimized. However, the immense synthesis parameter space for VACNT arrays and the interdependence of structural features make it challenging to improve both their height and quality. We have developed a literature mining approach combined with machine learning and high-throughput design to efficiently optimize the height and quality of the arrays. To reveal the underlying relationship between VACNT structures and their key growth parameters, we used random forest regression (RFR) and SHapley Additive exPlanation (SHAP) methods to model a set of published sample data (864 samples). High-throughput experiments were designed to change 4 key parameters: growth temperature, growth time, catalyst composition, and concentration of the carbon source. It was found that a screened Fe/Gd/Al2O3 catalyst was able to grow VACNT arrays with millimeter-scale height and improved quality. Our results demonstrate that this approach can effectively deal with multi-parameter processes such as nanotube growth and improve control over their structures.

High-Entropy Alloy Catalysts toward Multi-Functionality: Synthesis, Application, and Material Discovery

High-entropy alloy (HEA) catalysts have attracted tremendous research interest owing to their versatile performances in various applications. However, research on HEA catalysts remains in the early stages of exploration, and the fabrication process, element selection, and application remain difficult. Herein, we summarize the current literature on HEA catalysts from the viewpoint of facile synthesis routes, tunable morphologies, attractive applications, and material discoveries related to machine learning and high-throughput experiments. Finally, perspectives and concepts are presented to design the desired multifunctionality HEA catalysts.

Network module analysis and molecular docking-based study on the mechanism of astragali radix against non-small cell lung cancer

BackgroundMost lung cancer patients worldwide (stage IV non-small cell lung cancer, NSCLC) have a poor survival: 25%-30% patients die < 3 months. Yet, of those surviving > 3 months, 10%-15% patients survive (very) long. Astragali radix (AR) is an effective traditional Chinese medicine widely used for non-small cell lung cancer (NSCLC). However, the pharmacological mechanisms of AR on NSCLC remain to be elucidated.MethodsUltra Performance Liquid Chromatography system coupled with Q-Orbitrap HRMS (UPLC-Q-Orbitrap HRMS) was performed for the qualitative analysis of AR components. Then, network module analysis and molecular docking-based approach was conducted to explore underlying mechanisms of AR on NSCLC. The target genes of AR were obtained from four databases including TCMSP (Traditional Chinese Medicine Systems Pharmacology) database, ETCM (The Encyclopedia of TCM) database, HERB (A high-throughput experiment- and reference-guided database of TCM) database and BATMAN-TCM (a Bioinformatics Analysis Tool for Molecular mechanism of TCM) database. NSCLC related genes were screened by GEO (Gene Expression Omnibus) database. The STRING database was used for protein interaction network construction (PIN) of AR-NSCLC shared target genes. The critical PIN were further constructed based on the topological properties of network nodes. Afterwards the hub genes and network modules were analyzed, and enrichment analysis were employed by the R package clusterProfiler. The Autodock Vina was utilized for molecular docking, and the Gromacs was utilized for molecular dynamics simulations Furthermore, the survival analysis was performed based on TCGA (The Cancer Genome Atlas) database.ResultsSeventy-seven AR components absorbed in blood were obtained. The critical network was constructed with 1447 nodes and 28,890 edges. Based on topological analysis, 6 hub target genes and 7 functional modules were gained. were obtained including TP53, SRC, UBC, CTNNB1, EP300, and RELA. After module analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that AR may exert therapeutic effects on NSCLC by regulating JAK-STAT signaling pathway, PI3K-AKT signaling pathway, ErbB signaling pathway, as well as NFkB signaling pathway. After the intersection calculation of the hub targets and the proteins participated in the above pathways, TP53, SRC, EP300, and RELA were obtained. These proteins had good docking affinity with astragaloside IV. Furthermore, RELA was associated with poor prognosis of NSCLC patients.ConclusionsThis study could provide chemical component information references for further researches. The potential pharmacological mechanisms of AR on NSCLC were elucidated, promoting the clinical application of AR in treating NSCLC. RELA was selected as a promising candidate biomarker affecting the prognosis of NSCLC patients.

Epitranscriptomic subtyping, visualization, and denoising by global motif visualization

Advances in sequencing technologies have empowered epitranscriptomic profiling at the single-base resolution. Putative RNA modification sites identified from a single high-throughput experiment may contain one type of modification deposited by different writers or different types of modifications, along with false positive results because of the challenge of distinguishing signals from noise. However, current tools are insufficient for subtyping, visualization, and denoising these signals. Here, we present iMVP, which is an interactive framework for epitranscriptomic analysis with a nonlinear dimension reduction technique and density-based partition. As exemplified by the analysis of mRNA m5C and ModTect variant data, we show that iMVP allows the identification of previously unknown RNA modification motifs and writers and the discovery of false positives that are undetectable by traditional methods. Using putative m6A/m6Am sites called from 8 profiling approaches, we illustrate that iMVP enables comprehensive comparison of different approaches and advances our understanding of the difference and pattern of true positives and artifacts in these methods. Finally, we demonstrate the ability of iMVP to analyze an extremely large human A-to-I editing dataset that was previously unmanageable. Our work provides a general framework for the visualization and interpretation of epitranscriptomic data.

Ruthenium-Catalyzed Aminocarbonylation with Isocyanates Through Weak Coordinating Groups.

Introducing amide functional groups under mild conditions has growing importance owing to the prevalence of such moiety in biologically active molecules. Herein, we disclose a mild protocol for the directed ruthenium-catalyzed C-H aminocarbonylation with isocyanates as the amidating agents developed through high-throughput experimentation (HTE). The redox-neutral and base-free reaction is guided by weakly Lewis basic functional groups, including anilides, lactams and carbamates to access anthranilamide derivatives. The synthetic utility of this transformation is reflected by large-scale synthesis and late-stage functionalization.

Development of high-throughput rapid heat-treatment and characterization process

Heat treatment plays an important part in materials development, but traditional heat treatment methods can only develop one target temperature/cycle at a time, which impairs the experimental efficiency in the development of new materials. We have therefore introduced the use of high-throughput experiments, changed the traditional sequential iterative method to parallel processing, and developed an efficient multi-temperature-field heat-treatment method. Microwave radiation is used as the heating source, and the method takes advantage of the selective heating ability of microwaves to generate 100 temperature zones that can heat treat 100 individual sheet samples simultaneously. The method incorporates various rapid characterization techniques, which enables a large amount of characterization data to be obtained in a short time. The obtained data can be uploaded to a high-throughput materials database to provide data to support subsequent studies. In this study, high-carbon, high-chromium steel was subjected to parallel tempering heat treatment at 109–786 °C. The relationship between the tempering temperature and the material microstructure and properties was derived from the changes in the microstructure and properties after parallel tempering, and used to verify the feasibility of this method.