scholarly journals Hi-TOM: a platform for high-throughput tracking of mutations induced by CRISPR/Cas systems

2017 ◽  
Author(s):  
Qing Liu ◽  
Chun Wang ◽  
Xiaozhen Jiao ◽  
Huawei Zhang ◽  
Lili Song ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Large Scale ◽  
High Reliability ◽  
Mutation Screening ◽  
Screening Strategy ◽  
Library Construction ◽  
Genetic Modifications ◽  
Parameter Configuration ◽  
Next Generation Sequencing Ngs

AbstractThe CRISPR/Cas system has been extensively applied to make precise genetic modifications in various organisms. Despite its importance and widespread use, large-scale mutation screening remains time-consuming, labour-intensive and costly. Here, we describe a cheap, practicable and high-throughput screening strategy that allows parallel screening of 96 × N (N denotes the number of targets) genome-modified sites. The strategy simplified and streamlined the process of next-generation sequencing (NGS) library construction by fixing the bridge sequences and barcoding primers. We also developed Hi-TOM (available at http://www.hi-tom.net/hi-tom/), an online tool to track the mutations with precise percentage. Analysis of the samples from rice, hexaploid wheat and human cells reveals that the Hi-TOM tool has high reliability and sensitivity in tracking various mutations, especially complex chimeric mutations that frequently induced by genome editing. Hi-TOM does not require specially design of barcode primers, cumbersome parameter configuration or additional data analysis. Thus, the streamlined NGS library construction and comprehensive result output make Hi-TOM particularly suitable for high-throughput identification of all types of mutations induced by CRISPR/Cas systems.

scholarly journals Efficient online-group-screening designs for agent identification

2017 ◽  
Author(s):  
Tianxiang Gao ◽  
Omri Finkel ◽  
Jeff Dangl ◽  
Vladimir Jojic
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Large Scale ◽  
Screening Method ◽  
Screening Strategy ◽  
Online Version ◽  
Worst Case ◽  
Screening Designs ◽  
Complex Agent ◽  
Online Group

AbstractIdentifying significant causal agents among a large number of candidates is challenging. When experimental resources are limited, exhaustively screening a large number of agents for the desired effect could incur a large cost and take a substantial amount of time. However, in many large scale experiments, such as high-throughput screening (HTS), the ratio of causal to non-causal agents is usually very low.In this paper, we introduce a group-screening strategy to efficiently screen causal agents by grouping them into treatments. Our analysis shows that when a large number of candidates factors are screened and true agent percentage is very low (less than 1%), even in the worst case we could save up to 80% of the experiment runs. In the case where experiments span many rounds, we provide an online version of the group-screening that can determine the best strategy automatically based on the existing results. We applied this method to a real HTS experiment with 50,000 candidates that would require 9 rounds to finish in an exhaustive case. Our analysis showed that by applying the online-group-screening method, in the worst case, we can use 3 rounds and 19.7% (9828/50000) total tests to identify all the agents.Finally, we show that with minor modifications, this framework extends to more complex agent discovery problems.

Accelerated Discovery of High-Refractive-Index Polyimides via First-Principles Molecular Modeling, Virtual High-Throughput Screening, and Data Mining

2019 ◽  
Author(s):  
Mohammad Atif Faiz Afzal ◽  
Mojtaba Haghighatlari ◽  
Sai Prasad Ganesh ◽  
Chong Cheng ◽  
Johannes Hachmann
Keyword(s):  
Data Mining ◽  
Refractive Index ◽  
High Throughput ◽  
First Principles ◽  
High Throughput Screening ◽  
Large Scale ◽  
Computational Study ◽  
High Refractive Index ◽  
Structural Features ◽  
Learning Program

<div>We present a high-throughput computational study to identify novel polyimides (PIs) with exceptional refractive index (RI) values for use as optic or optoelectronic materials. Our study utilizes an RI prediction protocol based on a combination of first-principles and data modeling developed in previous work, which we employ on a large-scale PI candidate library generated with the ChemLG code. We deploy the virtual screening software ChemHTPS to automate the assessment of this extensive pool of PI structures in order to determine the performance potential of each candidate. This rapid and efficient approach yields a number of highly promising leads compounds. Using the data mining and machine learning program package ChemML, we analyze the top candidates with respect to prevalent structural features and feature combinations that distinguish them from less promising ones. In particular, we explore the utility of various strategies that introduce highly polarizable moieties into the PI backbone to increase its RI yield. The derived insights provide a foundation for rational and targeted design that goes beyond traditional trial-and-error searches.</div>

Large-scale High-throughput Screening Revealed 5'-(carbonylamino)-2,3'- bithiophene-4'-carboxylate as Novel Template for Antibacterial Agents

2020 ◽  
Vol 17 (5) ◽  
pp. 716-724
Author(s):  
Yan A. Ivanenkov ◽  
Renat S. Yamidanov ◽  
Ilya A. Osterman ◽  
Petr V. Sergiev ◽  
Vladimir A. Aladinskiy ◽  
...  
Keyword(s):  
Antibacterial Activity ◽  
High Throughput ◽  
High Throughput Screening ◽  
Large Scale ◽  
Antibacterial Agents ◽  
Sos Response ◽  
Luciferase Assay ◽  
Translational Machinery ◽  
E Coli ◽  
New Class

Background: The key issue in the development of novel antimicrobials is a rapid expansion of new bacterial strains resistant to current antibiotics. Indeed, World Health Organization has reported that bacteria commonly causing infections in hospitals and in the community, e.g. E. Coli, K. pneumoniae and S. aureus, have high resistance vs the last generations of cephalosporins, carbapenems and fluoroquinolones. During the past decades, only few successful efforts to develop and launch new antibacterial medications have been performed. This study aims to identify new class of antibacterial agents using novel high-throughput screening technique. Methods: We have designed library containing 125K compounds not similar in structure (Tanimoto coeff.< 0.7) to that published previously as antibiotics. The HTS platform based on double reporter system pDualrep2 was used to distinguish between molecules able to block translational machinery or induce SOS-response in a model E. coli system. MICs for most active chemicals in LB and M9 medium were determined using broth microdilution assay. Results: In an attempt to discover novel classes of antibacterials, we performed HTS of a large-scale small molecule library using our unique screening platform. This approach permitted us to quickly and robustly evaluate a lot of compounds as well as to determine the mechanism of action in the case of compounds being either translational machinery inhibitors or DNA-damaging agents/replication blockers. HTS has resulted in several new structural classes of molecules exhibiting an attractive antibacterial activity. Herein, we report as promising antibacterials. Two most active compounds from this series showed MIC value of 1.2 (5) and 1.8 μg/mL (6) and good selectivity index. Compound 6 caused RFP induction and low SOS response. In vitro luciferase assay has revealed that it is able to slightly inhibit protein biosynthesis. Compound 5 was tested on several archival strains and exhibited slight activity against gram-negative bacteria and outstanding activity against S. aureus. The key structural requirements for antibacterial potency were also explored. We found, that the unsubstituted carboxylic group is crucial for antibacterial activity as well as the presence of bulky hydrophobic substituents at phenyl fragment. Conclusion: The obtained results provide a solid background for further characterization of the 5'- (carbonylamino)-2,3'-bithiophene-4'-carboxylate derivatives discussed herein as new class of antibacterials and their optimization campaign.

scholarly journals Identification of a juvenile-hormone signaling inhibitor via high-throughput screening of a chemical library

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Takumi Kayukawa ◽  
Kenjiro Furuta ◽  
Keisuke Nagamine ◽  
Tetsuro Shinoda ◽  
Kiyoaki Yonesu ◽  
...  
Keyword(s):  
Juvenile Hormone ◽  
Cell Line ◽  
Signaling Pathway ◽  
High Throughput ◽  
High Throughput Screening ◽  
Large Scale ◽  
Ex Vivo ◽  
Agricultural Field ◽  
Chemical Library ◽  
Field Development

Abstract Insecticide resistance has recently become a serious problem in the agricultural field. Development of insecticides with new mechanisms of action is essential to overcome this limitation. Juvenile hormone (JH) is an insect-specific hormone that plays key roles in maintaining the larval stage of insects. Hence, JH signaling pathway is considered a suitable target in the development of novel insecticides; however, only a few JH signaling inhibitors (JHSIs) have been reported, and no practical JHSIs have been developed. Here, we established a high-throughput screening (HTS) system for exploration of novel JHSIs using a Bombyx mori cell line (BmN_JF&AR cells) and carried out a large-scale screening in this cell line using a chemical library. The four-step HTS yielded 69 compounds as candidate JHSIs. Topical application of JHSI48 to B. mori larvae caused precocious metamorphosis. In ex vivo culture of the epidermis, JHSI48 suppressed the expression of the Krüppel homolog 1 gene, which is directly activated by JH-liganded receptor. Moreover, JHSI48 caused a parallel rightward shift in the JH response curve, suggesting that JHSI48 possesses a competitive antagonist-like activity. Thus, large-scale HTS using chemical libraries may have applications in development of future insecticides targeting the JH signaling pathway.

High-Throughput Screening to Identify Small Molecules That Selectively Inhibit APOL1 Protein Level in Podocytes

2021 ◽  
pp. 247255522110262
Author(s):  
Jonathan Choy ◽  
Yanqing Kan ◽  
Steve Cifelli ◽  
Josephine Johnson ◽  
Michelle Chen ◽  
...  
Keyword(s):  
Small Molecule ◽  
High Throughput ◽  
High Throughput Screening ◽  
Screening Strategy ◽  
Time Resolved ◽  
Protein Levels ◽  
Increased Risk ◽  
Compound Screening ◽  
High Throughput Screens ◽  
Screening Library

High-throughput phenotypic screening is a key driver for the identification of novel chemical matter in drug discovery for challenging targets, especially for those with an unclear mechanism of pathology. For toxic or gain-of-function proteins, small-molecule suppressors are a targeting/therapeutic strategy that has been successfully applied. As with other high-throughput screens, the screening strategy and proper assays are critical for successfully identifying selective suppressors of the target of interest. We executed a small-molecule suppressor screen to identify compounds that specifically reduce apolipoprotein L1 (APOL1) protein levels, a genetically validated target associated with increased risk of chronic kidney disease. To enable this study, we developed homogeneous time-resolved fluorescence (HTRF) assays to measure intracellular APOL1 and apolipoprotein L2 (APOL2) protein levels and miniaturized them to 1536-well format. The APOL1 HTRF assay served as the primary assay, and the APOL2 and a commercially available p53 HTRF assay were applied as counterscreens. Cell viability was also measured with CellTiter-Glo to assess the cytotoxicity of compounds. From a 310,000-compound screening library, we identified 1490 confirmed primary hits with 12 different profiles. One hundred fifty-three hits selectively reduced APOL1 in 786-O, a renal cell adenocarcinoma cell line. Thirty-one of these selective suppressors also reduced APOL1 levels in conditionally immortalized human podocytes. The activity and specificity of seven resynthesized compounds were validated in both 786-O and podocytes.

scholarly journals Cytotoxic Profiling of Annotated and Diverse Chemical Libraries Using Quantitative High-Throughput Screening

2019 ◽  
Vol 25 (1) ◽  
pp. 9-20 ◽  
Author(s):  
Olivia W. Lee ◽  
Shelley Austin ◽  
Madison Gamma ◽  
Dorian M. Cheff ◽  
Tobie D. Lee ◽  
...  
Keyword(s):  
Cell Lines ◽  
Cancer Cell ◽  
High Throughput ◽  
High Throughput Screening ◽  
Drug Targets ◽  
Large Scale ◽  
Early Stage ◽  
Cancer Cell Line ◽  
Hek 293 ◽  
Cytotoxic Compounds

Cell-based phenotypic screening is a commonly used approach to discover biological pathways, novel drug targets, chemical probes, and high-quality hit-to-lead molecules. Many hits identified from high-throughput screening campaigns are ruled out through a series of follow-up potency, selectivity/specificity, and cytotoxicity assays. Prioritization of molecules with little or no cytotoxicity for downstream evaluation can influence the future direction of projects, so cytotoxicity profiling of screening libraries at an early stage is essential for increasing the likelihood of candidate success. In this study, we assessed the cell-based cytotoxicity of nearly 10,000 compounds in the National Institutes of Health, National Center for Advancing Translational Sciences annotated libraries and more than 100,000 compounds in a diversity library against four normal cell lines (HEK 293, NIH 3T3, CRL-7250, and HaCat) and one cancer cell line (KB 3-1, a HeLa subline). This large-scale library profiling was analyzed for overall screening outcomes, hit rates, pan-activity, and selectivity. For the annotated library, we also examined the primary targets and mechanistic pathways regularly associated with cell death. To our knowledge, this is the first study to use high-throughput screening to profile a large screening collection (>100,000 compounds) for cytotoxicity in both normal and cancer cell lines. The results generated here constitute a valuable resource for the scientific community and provide insight into the extent of cytotoxic compounds in screening libraries, allowing for the identification and avoidance of compounds with cytotoxicity during high-throughput screening campaigns.

scholarly journals A high-throughput screening strategy identifies cardiotonic steroids as alternative splicing modulators

2008 ◽  
Vol 105 (32) ◽  
pp. 11218-11223 ◽  
Author(s):  
P. Stoilov ◽  
C.-H. Lin ◽  
R. Damoiseaux ◽  
J. Nikolic ◽  
D. L. Black
Keyword(s):  
Alternative Splicing ◽  
High Throughput ◽  
High Throughput Screening ◽  
Screening Strategy

scholarly journals High Throughput Phenotypic Screening of The Human Spermatozoon

Reproduction ◽  
2021 ◽  
Author(s):  
Zoe Claire Johnston ◽  
Franz S Gruber ◽  
Sean Brown ◽  
Neil R Norcross ◽  
Jason R Swedlow ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Large Scale ◽  
Hormonal Contraceptives ◽  
Diagnostic Tools ◽  
Sperm Function ◽  
Phenotypic Screening ◽  
Environmental Chemical ◽  
Potential Applications

Despite recent advances in male reproductive health research, there remain many elements of male (in)fertility where our understanding is incomplete. Consequently, diagnostic tools and treatments for men with sperm dysfunction, other than medically assisted reproduction, are limited. On the other hand, the gaps in our knowledge of the mechanisms which underpin sperm function have hampered the development of male non-hormonal contraceptives. The study of mature spermatozoa is inherently difficult. They are a unique and highly specialised cell type which does not actively transcribe or translate proteins and cannot be cultured for long periods of time or matured in vitro. One, large scale, approach to both increasing understanding of sperm function, and the discovery and development of compounds that can modulate sperm function, is to directly observe responses to compounds with phenotypic screening techniques. These target agnostic approaches can be developed into high-throughput screening platforms with the potential to drastically increase advances in the field. Here we discuss the rationale and development of high-throughput phenotypic screening platforms for mature human spermatozoa, and the multiple potential applications these present, as well as the current limitations and leaps in our understanding and capabilities needed to overcome them. Further development and use of these technologies could lead to the identification of compounds which positively or negatively affect sperm cell motility or function, or novel platforms for toxicology or environmental chemical testing among other applications. Ultimately, each of these potential applications is also likely to increase understanding within the field of sperm biology.

scholarly journals Inductive Transfer Learning for Molecular Activity Prediction: Next-Gen QSAR Models with MolPMoFiT

2020 ◽  
Author(s):  
Xinhao Li ◽  
Denis Fourches
Keyword(s):  
Transfer Learning ◽  
High Throughput Screening ◽  
Structure Prediction ◽  
Large Scale ◽  
High Reliability ◽  
Structural Features ◽  
Fine Tuning ◽  
Qsar Modeling ◽  
Chemical Structures ◽  
Effective Transfer

<p>Deep neural networks can directly learn from chemical structures without extensive, user-driven selection of descriptors in order to predict molecular properties/activities with high reliability. But these approaches typically require large training sets to learn the endpoint-specific structural features and ensure reasonable prediction accuracy. Even though large datasets are becoming the new normal in drug discovery, especially when it comes to high-throughput screening or metabolomics datasets, one should also consider smaller datasets with challenging endpoints to model and forecast. Thus, it would be highly relevant to better utilize the tremendous compendium of unlabeled compounds from publicly-available datasets for improving the model performances for the user’s particular series of compounds. In this study, we propose the <b>Mol</b>ecular <b>P</b>rediction <b>Mo</b>del <b>Fi</b>ne-<b>T</b>uning (<b>MolPMoFiT</b>) approach, an effective transfer learning method based on self-supervised pre-training + task-specific fine-tuning for QSPR/QSAR modeling. A large-scale molecular structure prediction model is pre-trained using one million unlabeled molecules from ChEMBL in a self-supervised learning manner, and can then be fine-tuned on various QSPR/QSAR tasks for smaller chemical datasets with specific endpoints. Herein, the method is evaluated on four benchmark datasets (lipophilicity, FreeSolv, HIV, and blood-brain barrier penetration). The results showed the method can achieve strong performances for all four datasets compared to other state-of-the-art machine learning modeling techniques reported in the literature so far. <br></p>

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