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High-throughput functional annotation of natural products by integrated activity profiling
Determining mechanism of action (MOA) is one of the biggest challenges in natural products discovery. Here, we report a comprehensive platform that uses Similarity Network Fusion (SNF) to improve MOA predictions by integrating data from the cytological profiling high-content imaging platform and the...
Autores principales: | , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
National Academy of Sciences
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9894231/ https://www.ncbi.nlm.nih.gov/pubmed/36449542 http://dx.doi.org/10.1073/pnas.2208458119 |
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author | Hight, Suzie K. Clark, Trevor N. Kurita, Kenji L. McMillan, Elizabeth A. Bray, Walter Shaikh, Anam F. Khadilkar, Aswad Haeckl, F. P. Jake Carnevale-Neto, Fausto La, Scott Lohith, Akshar Vaden, Rachel M. Lee, Jeon Wei, Shuguang Lokey, R. Scott White, Michael A. Linington, Roger G. MacMillan, John B. |
author_facet | Hight, Suzie K. Clark, Trevor N. Kurita, Kenji L. McMillan, Elizabeth A. Bray, Walter Shaikh, Anam F. Khadilkar, Aswad Haeckl, F. P. Jake Carnevale-Neto, Fausto La, Scott Lohith, Akshar Vaden, Rachel M. Lee, Jeon Wei, Shuguang Lokey, R. Scott White, Michael A. Linington, Roger G. MacMillan, John B. |
author_sort | Hight, Suzie K. |
collection | PubMed |
description | Determining mechanism of action (MOA) is one of the biggest challenges in natural products discovery. Here, we report a comprehensive platform that uses Similarity Network Fusion (SNF) to improve MOA predictions by integrating data from the cytological profiling high-content imaging platform and the gene expression platform Functional Signature Ontology, and pairs these data with untargeted metabolomics analysis for de novo bioactive compound discovery. The predictive value of the integrative approach was assessed using a library of target-annotated small molecules as benchmarks. Using Kolmogorov–Smirnov (KS) tests to compare in-class to out-of-class similarity, we found that SNF retains the ability to identify significant in-class similarity across a diverse set of target classes, and could find target classes not detectable in either platform alone. This confirmed that integration of expression-based and image-based phenotypes can accurately report on MOA. Furthermore, we integrated untargeted metabolomics of complex natural product fractions with the SNF network to map biological signatures to specific metabolites. Three examples are presented where SNF coupled with metabolomics was used to directly functionally characterize natural products and accelerate identification of bioactive metabolites, including the discovery of the azoxy-containing biaryl compounds parkamycins A and B. Our results support SNF integration of multiple phenotypic screening approaches along with untargeted metabolomics as a powerful approach for advancing natural products drug discovery. |
format | Online Article Text |
id | pubmed-9894231 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | National Academy of Sciences |
record_format | MEDLINE/PubMed |
spelling | pubmed-98942312023-02-03 High-throughput functional annotation of natural products by integrated activity profiling Hight, Suzie K. Clark, Trevor N. Kurita, Kenji L. McMillan, Elizabeth A. Bray, Walter Shaikh, Anam F. Khadilkar, Aswad Haeckl, F. P. Jake Carnevale-Neto, Fausto La, Scott Lohith, Akshar Vaden, Rachel M. Lee, Jeon Wei, Shuguang Lokey, R. Scott White, Michael A. Linington, Roger G. MacMillan, John B. Proc Natl Acad Sci U S A Biological Sciences Determining mechanism of action (MOA) is one of the biggest challenges in natural products discovery. Here, we report a comprehensive platform that uses Similarity Network Fusion (SNF) to improve MOA predictions by integrating data from the cytological profiling high-content imaging platform and the gene expression platform Functional Signature Ontology, and pairs these data with untargeted metabolomics analysis for de novo bioactive compound discovery. The predictive value of the integrative approach was assessed using a library of target-annotated small molecules as benchmarks. Using Kolmogorov–Smirnov (KS) tests to compare in-class to out-of-class similarity, we found that SNF retains the ability to identify significant in-class similarity across a diverse set of target classes, and could find target classes not detectable in either platform alone. This confirmed that integration of expression-based and image-based phenotypes can accurately report on MOA. Furthermore, we integrated untargeted metabolomics of complex natural product fractions with the SNF network to map biological signatures to specific metabolites. Three examples are presented where SNF coupled with metabolomics was used to directly functionally characterize natural products and accelerate identification of bioactive metabolites, including the discovery of the azoxy-containing biaryl compounds parkamycins A and B. Our results support SNF integration of multiple phenotypic screening approaches along with untargeted metabolomics as a powerful approach for advancing natural products drug discovery. National Academy of Sciences 2022-11-30 2022-12-06 /pmc/articles/PMC9894231/ /pubmed/36449542 http://dx.doi.org/10.1073/pnas.2208458119 Text en Copyright © 2022 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by/4.0/This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY) (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Biological Sciences Hight, Suzie K. Clark, Trevor N. Kurita, Kenji L. McMillan, Elizabeth A. Bray, Walter Shaikh, Anam F. Khadilkar, Aswad Haeckl, F. P. Jake Carnevale-Neto, Fausto La, Scott Lohith, Akshar Vaden, Rachel M. Lee, Jeon Wei, Shuguang Lokey, R. Scott White, Michael A. Linington, Roger G. MacMillan, John B. High-throughput functional annotation of natural products by integrated activity profiling |
title | High-throughput functional annotation of natural products by integrated activity profiling |
title_full | High-throughput functional annotation of natural products by integrated activity profiling |
title_fullStr | High-throughput functional annotation of natural products by integrated activity profiling |
title_full_unstemmed | High-throughput functional annotation of natural products by integrated activity profiling |
title_short | High-throughput functional annotation of natural products by integrated activity profiling |
title_sort | high-throughput functional annotation of natural products by integrated activity profiling |
topic | Biological Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9894231/ https://www.ncbi.nlm.nih.gov/pubmed/36449542 http://dx.doi.org/10.1073/pnas.2208458119 |
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