An image-based Pathogen Box screen identifies new compounds with anti-Giardia activity and highlights the importance of assay choice in phenotypic drug discovery

Tiash, Snigdha; Saunders, Jake; Hart, Christopher J; Ryan, John H.; Riches, Andrew G.; Skinner‐Adams, Tina S.

doi:10.1016/j.ijpddr.2020.03.002

Cited by 6 publications

(4 citation statements)

References 64 publications

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“…For controls, we often found too much information, and these were thus excluded from further consideration. The remaining 374 compounds ( Table S1 ) are ones that have reported activity in published phenotypic screens for neglected tropical disease, with the majority being antimalarial or antimycobacterial compounds(36–38) ( Fig. 2B ).…”

Section: Resultsmentioning

confidence: 99%

CACTI: An in-silicodrug-target prediction tool through the integration of chemogenomic data and clustering analysis

Godinez-Macias,

Winzeler

2024

Preprint

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It is well-accepted that knowledge of a small molecule’s target can accelerate optimization. Although chemogenomic databases are helpful resources for predicting or finding compound interaction partners, they tend to be limited and poorly annotated. Furthermore, unlike genes, compound identifiers are often not standardized, and many synonyms may exist, especially in the biological literature, making batch analysis of compounds difficult. Here, we constructed an open-source annotation and target prediction tool that explores some of the largest chemical and biological databases, mining these for both common name, synonyms, and structurally similar molecules. We used this Chemical Analysis and Clustering for Target Identification (CACTI) tool to analyze the Pathogen Box collection, an open-source set of 400 drug-like compounds active against a variety of microbial pathogens. Our analysis resulted in 4,315 new synonyms, 35,963 pieces of new information and target prediction hints for 58 members.Scientific ContributionWith the employment of this tool, a comprehensive report with known evidence, close analogs and drug-target prediction can be obtained for large-scale chemical libraries that will facilitate their evaluation and future target validation and optimization efforts.

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Section: Resultsmentioning

confidence: 99%

CACTI: An in-silicodrug-target prediction tool through the integration of chemogenomic data and clustering analysis

Godinez-Macias,

Winzeler

2024

Preprint

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“…Components of such collections have been specifically chosen to stimulate infectious disease drug discovery. Various drugs of interest with potential giardicidal effects are only coded so far, like, e.g., MMV676358 and MMV028694 [ 152 ].…”

Section: Treatment Attempts Alternative To the “5-nitroimidazoles Standard”mentioning

confidence: 99%

Antimicrobial resistance of the enteric protozoon Giardia duodenalis – A narrative review

Loderstädt

Frickmann

2021

EuJMI

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IntroductionAs therapy-refractory giardiasis is an emerging health issue, this review aimed at summarizing mechanisms of reduced antimicrobial susceptibility in Giardia duodenalis and strategies to overcome this problem.MethodsA narrative review on antimicrobial resistance in G. duodenalis was based upon a selective literature research.ResultsFailed therapeutic success has been observed for all standard therapies of giardiasis comprising nitroimidazoles like metronidazole or tinidazole as first line substances but also benznidazoles like albendazole and mebendazole, the nitrofuran furazolidone, the thiazolide nitazoxanide, and the aminoglycoside paromomycin. Multicausality of the resistance phenotypes has been described, with differentiated gene expression due to epigenetic and post-translational modifications playing a considerable bigger role than mutational base exchanges in the parasite DNA. Standardized resistance testing algorithms are not available and clinical evidence for salvage therapies is scarce in spite of research efforts targeting new giardicidal drugs.ConclusionIn case of therapeutic failure of first line nitroimidazoles, salvage strategies including various options for combination therapy exist in spite of limited evidence and lacking routine diagnostic-compatible assays for antimicrobial susceptibility testing in G. duodenalis. Sufficiently powered clinical and diagnostic studies are needed to overcome both the lacking evidence regarding salvage therapy and the diagnostic neglect of antimicrobial resistance.

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“…A quantitative study of the microbial life cycle is crucial to understand ecological and evolutionary processes that can be used to control parasites and pathogens [1][2][3][4] . However, defining the structure of a microbial life cycle traditionally relies on partial microscopy observations or genetic and theoretical evidence 5 .…”

Section: Introductionmentioning

confidence: 99%

Deep learning-driven imaging of cell division and cell growth across an entire eukaryotic life cycle

Ramakanth,

Kennedy,

Yalcinkaya

et al. 2024

Preprint

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The life cycle of biomedical and agriculturally relevant eukaryotic microorganisms involves complex transitions between proliferative and non-proliferative states such as dormancy, mating, meiosis, and cell division. New drugs, pesticides, and vaccines can be created by targeting specific life cycle stages of parasites and pathogens. However, defining the structure of a microbial life cycle often relies on partial observations that are theoretically assembled in an ideal life cycle path. To create a more quantitative approach to studying complete eukaryotic life cycles, we generated a deep learning-driven imaging framework to track microorganisms across sexually reproducing generations. Our approach combines microfluidic culturing, life cycle stage-specific segmentation of microscopy images using convolutional neural networks, and a novel cell tracking algorithm, FIEST, based on enhancing the overlap of single cell masks in consecutive images through deep learning video frame interpolation. As proof of principle, we used this approach to quantitatively image and compare cell growth and cell cycle regulation across the sexual life cycle of Saccharomyces cerevisiae. We developed a fluorescent reporter system based on a fluorescently labeled Whi5 protein, the yeast analog of mammalian Rb, and a new High-Cdk1 activity sensor, LiCHI, designed to report during DNA replication, mitosis, meiotic homologous recombination, meiosis I, and meiosis II. We found that cell growth preceded the exit from non-proliferative states such as mitotic G1, pre-meiotic G1, and the G0 spore state during germination. A decrease in the total cell concentration of Whi5 characterized the exit from non-proliferative states, which is consistent with a Whi5 dilution model. The nuclear accumulation of Whi5 was developmentally regulated, being at its highest during meiotic exit and spore formation. The temporal coordination of cell division and growth was not significantly different across three sexually reproducing generations. Our framework could be used to quantitatively characterize other single-cell eukaryotic life cycles that remain incompletely described. An off-the-shelf user interface Yeastvision provides free access to our image processing and single-cell tracking algorithms.

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An image-based Pathogen Box screen identifies new compounds with anti-Giardia activity and highlights the importance of assay choice in phenotypic drug discovery

Cited by 6 publications

References 64 publications

CACTI: An in-silicodrug-target prediction tool through the integration of chemogenomic data and clustering analysis

CACTI: An in-silicodrug-target prediction tool through the integration of chemogenomic data and clustering analysis

Antimicrobial resistance of the enteric protozoon Giardia duodenalis – A narrative review

Deep learning-driven imaging of cell division and cell growth across an entire eukaryotic life cycle

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