Predicting host dependency factors of pathogens in Drosophila melanogaster using machine learning

Aromolaran, Olufemi; Beder, Thomas; Adedeji, Eunice O.; Ajamma, Yvonne Ukamaka; Oyelade, Jelili; Adebiyi, Ezekiel; Koenig, Rainer

doi:10.1016/j.csbj.2021.08.010

Cited by 4 publications

(2 citation statements)

References 92 publications

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“…In this study we provide a comprehensive and robust machine learning-based host factor identification strategy (36) using HDF screens performed for SARS-CoV-2 (16–19) and a drug screen (22). HDF screens of human or African green monkey cells infected with SARS-CoV-2 showed limited overlap in their hits.…”

Section: Discussionmentioning

confidence: 99%

Machine learning on large scale perturbation screens for SARS-CoV-2 host factors identifies β-catenin/CBP inhibitor PRI-724 as a potent antiviral

Kelch

Vera-Guapi

Beder

et al. 2023

Preprint

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Expanding antiviral treatment options against SARS-CoV-2 remains crucial as the virus evolves rapidly and drug resistant strains have emerged. Broad spectrum host-directed antivirals (HDA) are promising therapeutic options, however the robust identification of relevant host factors by CRISPR/Cas9 or RNA interference screens remains challenging due to low consistency in the resulting hits. To address this issue, we employed machine learning based on experimental data from knockout screens and a drug screen. As gold standard, we assembled perturbed genes reducing virus replication or protecting the host cells. The machines based their predictions on features describing cellular localization, protein domains, annotated gene sets from Gene Ontology, gene and protein sequences, and experimental data from proteomics, phospho-proteomics, protein interaction and transcriptomic profiles of SARS-CoV-2 infected cells. The models reached a remarkable performance with a balanced accuracy of 0.82 (knockout based classifier) and 0.71 (drugs screen based classifier), suggesting patterns of intrinsic data consistency. The predicted host dependency factors were enriched in sets of genes particularly coding for development, morphogenesis, and neural related processes. Focusing on development and morphogenesis-associated gene sets, we found β-catenin to be central and selected PRI-724, a canonical β-catenin/CBP disruptor, as a potential HDA. PRI-724 limited infection with SARS-CoV-2 variants, SARS-CoV-1, MERS-CoV and IAV in different cell line models. We detected a concentration-dependent reduction in CPE development, viral RNA replication, and infectious virus production in SARS-CoV-2 and SARS-CoV-1-infected cells. Independent of virus infection, PRI-724 treatment caused cell cycle deregulation which substantiates its potential as a broad spectrum antiviral. Our proposed machine learning concept may support focusing and accelerating the discovery of host dependency factors and the design of antiviral therapies.

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Section: Discussionmentioning

confidence: 99%

Machine learning on large scale perturbation screens for SARS-CoV-2 host factors identifies β-catenin/CBP inhibitor PRI-724 as a potent antiviral

Kelch

Vera-Guapi

Beder

et al. 2023

Preprint

Self Cite

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“…The fruit fly Drosophila melanogaster has been recognized as an outstanding model to study host-pathogen interactions and immunity ( Lemaitre and Hoffmann, 2007 ; Buchon et al, 2014 ; Galenza and Foley, 2019 ; Aromolaran et al, 2021 ). Over the years, several infection models have been evaluated in the fruit fly, so a deep understanding of the molecular mechanisms taking place in the host after infection has been gained [Extensively reviewed lately in Younes et al (2020) and Michael Harnish et al (2021) ].…”

Section: Introductionmentioning

confidence: 99%

Drosophila melanogaster as a model to study innate immune memory

et al. 2022

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Over the last decades, research regarding innate immune responses has gained increasing importance. A growing body of evidence supports the notion that the innate arm of the immune system could show memory traits. Such traits are thought to be conserved throughout evolution and provide a survival advantage. Several models are available to study these mechanisms. Among them, we find the fruit fly, Drosophila melanogaster. This non-mammalian model has been widely used for innate immune research since it naturally lacks an adaptive response. Here, we aim to review the latest advances in the study of the memory mechanisms of the innate immune response using this animal model.

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A Review on Proteomic Function Prediction in Pathogenic Bacterial Organism Using Machine Learning

Vijay

Tiwari

Sharma

2022

Proceedings of Seventh International Congress on Information and Communication Technology

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Predicting host dependency factors of pathogens in Drosophila melanogaster using machine learning

Abstract: Graphical abstract

Cited by 4 publications

References 92 publications

Machine learning on large scale perturbation screens for SARS-CoV-2 host factors identifies β-catenin/CBP inhibitor PRI-724 as a potent antiviral

Machine learning on large scale perturbation screens for SARS-CoV-2 host factors identifies β-catenin/CBP inhibitor PRI-724 as a potent antiviral

Drosophila melanogaster as a model to study innate immune memory

A Review on Proteomic Function Prediction in Pathogenic Bacterial Organism Using Machine Learning

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