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A glimpse into the fungal metabolomic abyss: Novel network analysis reveals relationships between exogenous compounds and their outputs
Fungal specialized metabolites are a major source of beneficial compounds that are routinely isolated, characterized, and manufactured as pharmaceuticals, agrochemical agents, and industrial chemicals. The production of these metabolites is encoded by biosynthetic gene clusters that are often silent...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10581544/ https://www.ncbi.nlm.nih.gov/pubmed/37854706 http://dx.doi.org/10.1093/pnasnexus/pgad322 |
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author | Gopalakrishnan Meena, Muralikrishnan Lane, Matthew J Tannous, Joanna Carrell, Alyssa A Abraham, Paul E Giannone, Richard J Ané, Jean-Michel Keller, Nancy P Labbé, Jesse L Geiger, Armin G Kainer, David Jacobson, Daniel A Rush, Tomás A |
author_facet | Gopalakrishnan Meena, Muralikrishnan Lane, Matthew J Tannous, Joanna Carrell, Alyssa A Abraham, Paul E Giannone, Richard J Ané, Jean-Michel Keller, Nancy P Labbé, Jesse L Geiger, Armin G Kainer, David Jacobson, Daniel A Rush, Tomás A |
author_sort | Gopalakrishnan Meena, Muralikrishnan |
collection | PubMed |
description | Fungal specialized metabolites are a major source of beneficial compounds that are routinely isolated, characterized, and manufactured as pharmaceuticals, agrochemical agents, and industrial chemicals. The production of these metabolites is encoded by biosynthetic gene clusters that are often silent under standard growth conditions. There are limited resources for characterizing the direct link between abiotic stimuli and metabolite production. Herein, we introduce a network analysis-based, data-driven algorithm comprising two routes to characterize the production of specialized fungal metabolites triggered by different exogenous compounds: the direct route and the auxiliary route. Both routes elucidate the influence of treatments on the production of specialized metabolites from experimental data. The direct route determines known and putative metabolites induced by treatments and provides additional insight over traditional comparison methods. The auxiliary route is specific for discovering unknown analytes, and further identification can be curated through online bioinformatic resources. We validated our algorithm by applying chitooligosaccharides and lipids at two different temperatures to the fungal pathogen Aspergillus fumigatus. After liquid chromatography–mass spectrometry quantification of significantly produced analytes, we used network centrality measures to rank the treatments’ ability to elucidate these analytes and confirmed their identity through fragmentation patterns or in silico spiking with commercially available standards. Later, we examined the transcriptional regulation of these metabolites through real-time quantitative polymerase chain reaction. Our data-driven techniques can complement existing metabolomic network analysis by providing an approach to track the influence of any exogenous stimuli on metabolite production. Our experimental-based algorithm can overcome the bottlenecks in elucidating novel fungal compounds used in drug discovery. |
format | Online Article Text |
id | pubmed-10581544 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-105815442023-10-18 A glimpse into the fungal metabolomic abyss: Novel network analysis reveals relationships between exogenous compounds and their outputs Gopalakrishnan Meena, Muralikrishnan Lane, Matthew J Tannous, Joanna Carrell, Alyssa A Abraham, Paul E Giannone, Richard J Ané, Jean-Michel Keller, Nancy P Labbé, Jesse L Geiger, Armin G Kainer, David Jacobson, Daniel A Rush, Tomás A PNAS Nexus Biological, Health, and Medical Sciences Fungal specialized metabolites are a major source of beneficial compounds that are routinely isolated, characterized, and manufactured as pharmaceuticals, agrochemical agents, and industrial chemicals. The production of these metabolites is encoded by biosynthetic gene clusters that are often silent under standard growth conditions. There are limited resources for characterizing the direct link between abiotic stimuli and metabolite production. Herein, we introduce a network analysis-based, data-driven algorithm comprising two routes to characterize the production of specialized fungal metabolites triggered by different exogenous compounds: the direct route and the auxiliary route. Both routes elucidate the influence of treatments on the production of specialized metabolites from experimental data. The direct route determines known and putative metabolites induced by treatments and provides additional insight over traditional comparison methods. The auxiliary route is specific for discovering unknown analytes, and further identification can be curated through online bioinformatic resources. We validated our algorithm by applying chitooligosaccharides and lipids at two different temperatures to the fungal pathogen Aspergillus fumigatus. After liquid chromatography–mass spectrometry quantification of significantly produced analytes, we used network centrality measures to rank the treatments’ ability to elucidate these analytes and confirmed their identity through fragmentation patterns or in silico spiking with commercially available standards. Later, we examined the transcriptional regulation of these metabolites through real-time quantitative polymerase chain reaction. Our data-driven techniques can complement existing metabolomic network analysis by providing an approach to track the influence of any exogenous stimuli on metabolite production. Our experimental-based algorithm can overcome the bottlenecks in elucidating novel fungal compounds used in drug discovery. Oxford University Press 2023-09-29 /pmc/articles/PMC10581544/ /pubmed/37854706 http://dx.doi.org/10.1093/pnasnexus/pgad322 Text en © The Author(s) 2023. Published by Oxford University Press on behalf of National Academy of Sciences. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Biological, Health, and Medical Sciences Gopalakrishnan Meena, Muralikrishnan Lane, Matthew J Tannous, Joanna Carrell, Alyssa A Abraham, Paul E Giannone, Richard J Ané, Jean-Michel Keller, Nancy P Labbé, Jesse L Geiger, Armin G Kainer, David Jacobson, Daniel A Rush, Tomás A A glimpse into the fungal metabolomic abyss: Novel network analysis reveals relationships between exogenous compounds and their outputs |
title | A glimpse into the fungal metabolomic abyss: Novel network analysis reveals relationships between exogenous compounds and their outputs |
title_full | A glimpse into the fungal metabolomic abyss: Novel network analysis reveals relationships between exogenous compounds and their outputs |
title_fullStr | A glimpse into the fungal metabolomic abyss: Novel network analysis reveals relationships between exogenous compounds and their outputs |
title_full_unstemmed | A glimpse into the fungal metabolomic abyss: Novel network analysis reveals relationships between exogenous compounds and their outputs |
title_short | A glimpse into the fungal metabolomic abyss: Novel network analysis reveals relationships between exogenous compounds and their outputs |
title_sort | glimpse into the fungal metabolomic abyss: novel network analysis reveals relationships between exogenous compounds and their outputs |
topic | Biological, Health, and Medical Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10581544/ https://www.ncbi.nlm.nih.gov/pubmed/37854706 http://dx.doi.org/10.1093/pnasnexus/pgad322 |
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