Cargando…

Predicting antimicrobial mechanism-of-action from transcriptomes: A generalizable explainable artificial intelligence approach

To better combat the expansion of antibiotic resistance in pathogens, new compounds, particularly those with novel mechanisms-of-action [MOA], represent a major research priority in biomedical science. However, rediscovery of known antibiotics demonstrates a need for approaches that accurately ident...

Descripción completa

Detalles Bibliográficos
Autores principales: Espinoza, Josh L., Dupont, Chris L., O’Rourke, Aubrie, Beyhan, Sinem, Morales, Pavel, Spoering, Amy, Meyer, Kirsten J., Chan, Agnes P., Choi, Yongwook, Nierman, William C., Lewis, Kim, Nelson, Karen E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8031737/
https://www.ncbi.nlm.nih.gov/pubmed/33780444
http://dx.doi.org/10.1371/journal.pcbi.1008857
_version_ 1783676176847339520
author Espinoza, Josh L.
Dupont, Chris L.
O’Rourke, Aubrie
Beyhan, Sinem
Morales, Pavel
Spoering, Amy
Meyer, Kirsten J.
Chan, Agnes P.
Choi, Yongwook
Nierman, William C.
Lewis, Kim
Nelson, Karen E.
author_facet Espinoza, Josh L.
Dupont, Chris L.
O’Rourke, Aubrie
Beyhan, Sinem
Morales, Pavel
Spoering, Amy
Meyer, Kirsten J.
Chan, Agnes P.
Choi, Yongwook
Nierman, William C.
Lewis, Kim
Nelson, Karen E.
author_sort Espinoza, Josh L.
collection PubMed
description To better combat the expansion of antibiotic resistance in pathogens, new compounds, particularly those with novel mechanisms-of-action [MOA], represent a major research priority in biomedical science. However, rediscovery of known antibiotics demonstrates a need for approaches that accurately identify potential novelty with higher throughput and reduced labor. Here we describe an explainable artificial intelligence classification methodology that emphasizes prediction performance and human interpretability by using a Hierarchical Ensemble of Classifiers model optimized with a novel feature selection algorithm called Clairvoyance; collectively referred to as a CoHEC model. We evaluated our methods using whole transcriptome responses from Escherichia coli challenged with 41 known antibiotics and 9 crude extracts while depositing 122 transcriptomes unique to this study. Our CoHEC model can properly predict the primary MOA of previously unobserved compounds in both purified forms and crude extracts at an accuracy above 99%, while also correctly identifying darobactin, a newly discovered antibiotic, as having a novel MOA. In addition, we deploy our methods on a recent E. coli transcriptomics dataset from a different strain and a Mycobacterium smegmatis metabolomics timeseries dataset showcasing exceptionally high performance; improving upon the performance metrics of the original publications. We not only provide insight into the biological interpretation of our model but also that the concept of MOA is a non-discrete heuristic with diverse effects for different compounds within the same MOA, suggesting substantial antibiotic diversity awaiting discovery within existing MOA.
format Online
Article
Text
id pubmed-8031737
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-80317372021-04-14 Predicting antimicrobial mechanism-of-action from transcriptomes: A generalizable explainable artificial intelligence approach Espinoza, Josh L. Dupont, Chris L. O’Rourke, Aubrie Beyhan, Sinem Morales, Pavel Spoering, Amy Meyer, Kirsten J. Chan, Agnes P. Choi, Yongwook Nierman, William C. Lewis, Kim Nelson, Karen E. PLoS Comput Biol Research Article To better combat the expansion of antibiotic resistance in pathogens, new compounds, particularly those with novel mechanisms-of-action [MOA], represent a major research priority in biomedical science. However, rediscovery of known antibiotics demonstrates a need for approaches that accurately identify potential novelty with higher throughput and reduced labor. Here we describe an explainable artificial intelligence classification methodology that emphasizes prediction performance and human interpretability by using a Hierarchical Ensemble of Classifiers model optimized with a novel feature selection algorithm called Clairvoyance; collectively referred to as a CoHEC model. We evaluated our methods using whole transcriptome responses from Escherichia coli challenged with 41 known antibiotics and 9 crude extracts while depositing 122 transcriptomes unique to this study. Our CoHEC model can properly predict the primary MOA of previously unobserved compounds in both purified forms and crude extracts at an accuracy above 99%, while also correctly identifying darobactin, a newly discovered antibiotic, as having a novel MOA. In addition, we deploy our methods on a recent E. coli transcriptomics dataset from a different strain and a Mycobacterium smegmatis metabolomics timeseries dataset showcasing exceptionally high performance; improving upon the performance metrics of the original publications. We not only provide insight into the biological interpretation of our model but also that the concept of MOA is a non-discrete heuristic with diverse effects for different compounds within the same MOA, suggesting substantial antibiotic diversity awaiting discovery within existing MOA. Public Library of Science 2021-03-29 /pmc/articles/PMC8031737/ /pubmed/33780444 http://dx.doi.org/10.1371/journal.pcbi.1008857 Text en © 2021 Espinoza et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Espinoza, Josh L.
Dupont, Chris L.
O’Rourke, Aubrie
Beyhan, Sinem
Morales, Pavel
Spoering, Amy
Meyer, Kirsten J.
Chan, Agnes P.
Choi, Yongwook
Nierman, William C.
Lewis, Kim
Nelson, Karen E.
Predicting antimicrobial mechanism-of-action from transcriptomes: A generalizable explainable artificial intelligence approach
title Predicting antimicrobial mechanism-of-action from transcriptomes: A generalizable explainable artificial intelligence approach
title_full Predicting antimicrobial mechanism-of-action from transcriptomes: A generalizable explainable artificial intelligence approach
title_fullStr Predicting antimicrobial mechanism-of-action from transcriptomes: A generalizable explainable artificial intelligence approach
title_full_unstemmed Predicting antimicrobial mechanism-of-action from transcriptomes: A generalizable explainable artificial intelligence approach
title_short Predicting antimicrobial mechanism-of-action from transcriptomes: A generalizable explainable artificial intelligence approach
title_sort predicting antimicrobial mechanism-of-action from transcriptomes: a generalizable explainable artificial intelligence approach
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8031737/
https://www.ncbi.nlm.nih.gov/pubmed/33780444
http://dx.doi.org/10.1371/journal.pcbi.1008857
work_keys_str_mv AT espinozajoshl predictingantimicrobialmechanismofactionfromtranscriptomesageneralizableexplainableartificialintelligenceapproach
AT dupontchrisl predictingantimicrobialmechanismofactionfromtranscriptomesageneralizableexplainableartificialintelligenceapproach
AT orourkeaubrie predictingantimicrobialmechanismofactionfromtranscriptomesageneralizableexplainableartificialintelligenceapproach
AT beyhansinem predictingantimicrobialmechanismofactionfromtranscriptomesageneralizableexplainableartificialintelligenceapproach
AT moralespavel predictingantimicrobialmechanismofactionfromtranscriptomesageneralizableexplainableartificialintelligenceapproach
AT spoeringamy predictingantimicrobialmechanismofactionfromtranscriptomesageneralizableexplainableartificialintelligenceapproach
AT meyerkirstenj predictingantimicrobialmechanismofactionfromtranscriptomesageneralizableexplainableartificialintelligenceapproach
AT chanagnesp predictingantimicrobialmechanismofactionfromtranscriptomesageneralizableexplainableartificialintelligenceapproach
AT choiyongwook predictingantimicrobialmechanismofactionfromtranscriptomesageneralizableexplainableartificialintelligenceapproach
AT niermanwilliamc predictingantimicrobialmechanismofactionfromtranscriptomesageneralizableexplainableartificialintelligenceapproach
AT lewiskim predictingantimicrobialmechanismofactionfromtranscriptomesageneralizableexplainableartificialintelligenceapproach
AT nelsonkarene predictingantimicrobialmechanismofactionfromtranscriptomesageneralizableexplainableartificialintelligenceapproach