Whole-Animal High-Throughput Screens: The C. elegans Model

2009 ◽  
pp. 57-75 ◽  
Author(s):  
Eyleen J. O’Rourke ◽  
Annie L. Conery ◽  
Terence I. Moy
Keyword(s):  
High Throughput ◽  
C Elegans ◽  
High Throughput Screens

scholarly journals High-throughput behavioral screen in C. elegans reveals Parkinson’s disease drug candidates

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Salman Sohrabi ◽  
Danielle E. Mor ◽  
Rachel Kaletsky ◽  
William Keyes ◽  
Coleen T. Murphy
Keyword(s):  
Neural Network ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
High Throughput ◽  
Potential Candidate ◽  
Automated Scoring ◽  
C Elegans ◽  
Drug Candidates ◽  
Branched Chain ◽  
Approved Drugs

AbstractWe recently linked branched-chain amino acid transferase 1 (BCAT1) dysfunction with the movement disorder Parkinson’s disease (PD), and found that RNAi-mediated knockdown of neuronal bcat-1 in C. elegans causes abnormal spasm-like ‘curling’ behavior with age. Here we report the development of a machine learning-based workflow and its application to the discovery of potentially new therapeutics for PD. In addition to simplifying quantification and maintaining a low data overhead, our simple segment-train-quantify platform enables fully automated scoring of image stills upon training of a convolutional neural network. We have trained a highly reliable neural network for the detection and classification of worm postures in order to carry out high-throughput curling analysis without the need for user intervention or post-inspection. In a proof-of-concept screen of 50 FDA-approved drugs, enasidenib, ethosuximide, metformin, and nitisinone were identified as candidates for potential late-in-life intervention in PD. These findings point to the utility of our high-throughput platform for automated scoring of worm postures and in particular, the discovery of potential candidate treatments for PD.

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.

An overview of analytical methods for monitoring bacterial transglycosylation

2014 ◽  
Vol 6 (19) ◽  
pp. 7590-7596 ◽  
Author(s):  
Bart Blanchaert ◽  
Erwin Adams ◽  
Ann Van Schepdael
Keyword(s):  
High Throughput ◽  
Analytical Methods ◽  
High Throughput Screens

This review highlights the fluorescence and radioactively labeled assays and high-throughput screens for the search for antibiotics targeting bacterial transglycosylation.

scholarly journals Generation of RNAi Libraries for High-Throughput Screens

2006 ◽  
Vol 2006 ◽  
pp. 1-7 ◽  
Author(s):  
Julie Clark ◽  
Sheng Ding
Keyword(s):  
High Throughput ◽  
High Throughput Screens

Technology for high-throughput screens: the present and future using zebrafish

2004 ◽  
Vol 15 (6) ◽  
pp. 564-571 ◽  
Author(s):  
Donald R Love ◽  
Franz B Pichler ◽  
Andrew Dodd ◽  
Brent R Copp ◽  
David R Greenwood
Keyword(s):  
High Throughput ◽  
High Throughput Screens

scholarly journals Providing the ‘Best’ Lipophilicity Assessment in a Drug Discovery Environment

2021 ◽  
Author(s):  
george chang ◽  
Nathaniel Woody ◽  
Christopher Keefer
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
High Throughput ◽  
In Silico ◽  
Shake Flask ◽  
Chromatographic Method ◽  
Learning Approach ◽  
Rule Based ◽  
Machine Learning Approach ◽  
High Throughput Screens

Lipophilicity is a fundamental structural property that influences almost every aspect of drug discovery. Within Pfizer, we have two complementary high-throughput screens for measuring lipophilicity as a distribution coefficient (LogD) – a miniaturized shake-flask method (SFLogD) and a chromatographic method (ELogD). The results from these two assays are not the same (see Figure 1), with each assay being applicable or more reliable in particular chemical spaces. In addition to LogD assays, the ability to predict the LogD value for virtual compounds is equally vital. Here we present an in-silico LogD model, applicable to all chemical spaces, based on the integration of the LogD data from both assays. We developed two approaches towards a single LogD model – a Rule-based and a Machine Learning approach. Ultimately, the Machine Learning LogD model was found to be superior to both internally developed and commercial LogD models.<br>

scholarly journals High throughput functional variant screens via in-vivo production of single-stranded DNA

2020 ◽  
Author(s):  
Max G. Schubert ◽  
Daniel B. Goodman ◽  
Timothy M. Wannier ◽  
Divjot Kaur ◽  
Fahim Farzadfard ◽  
...  
Keyword(s):  
Antibiotic Resistance ◽  
High Throughput ◽  
Entire Genome ◽  
Single Stranded Dna ◽  
Functional Variant ◽  
Continuous Generation ◽  
Sources Of Variation ◽  
Targeted Deep Sequencing ◽  
High Throughput Screens

AbstractTremendous genetic variation exists in nature, but our ability to create and characterize individual genetic variants remains far more limited in scale. Likewise, engineering proteins and phenotypes requires the introduction of synthetic variants, but design of variants outpaces experimental measurement of variant effect. Here, we optimize efficient and continuous generation of precise genomic edits in Escherichia coli, via in-vivo production of single-stranded DNA by the targeted reverse-transcription activity of retrons. Greater than 90% editing efficiency can be obtained using this method, enabling multiplexed applications. We introduce Retron Library Recombineering (RLR), a system for high-throughput screens of variants, wherein the association of introduced edits with their retron elements enables a targeted deep sequencing phenotypic output. We use RLR for pooled, quantitative phenotyping of synthesized variants, characterizing antibiotic resistance alleles. We also perform RLR using sheared genomic DNA of an evolved bacterium, experimentally querying millions of sequences for antibiotic resistance variants. In doing so, we demonstrate that RLR is uniquely suited to utilize non-designed sources of variation. Pooled experiments using ssDNA produced in vivo thus present new avenues for exploring variation, both designed and not, across the entire genome.

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