A guidance into the fungal metabolomic abyss: Network analysis for revealing relationships between exogenous compounds and their outputs

Meena, M. G.; Lane, M. J.; Tannous, J.; Carrell, A. A.; Abraham, P. E.; Giannone, R. J.; Ane, J.-M.; Keller, N. P.; Labbe, J. L.; Kainer, D.; Jacobson, D. A.; Rush, T. A.

doi:10.1101/2022.08.11.503656

bioRxiv · 10.1101/2022.08.11.503656

A guidance into the fungal metabolomic abyss: Network analysis for revealing relationships between exogenous compounds and their outputs

Meena, M. G.·Lane, M. J.·Tannous, J.·Carrell, A. A.·Abraham, P. E.·Giannone, R. J.·Ane, J.-M.·Keller, N. P.·Labbe, J. L.·Kainer, D.·Jacobson, D. A.·Rush, T. A.

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Abstract

AO_SCPLOWBSTRACTC_SCPLOWFungal specialized metabolites include many bioactive compounds with potential applications as pharmaceuticals, agrochemical agents, and industrial chemicals. Exploring and discovering novel fungal metabolites is critical to combat antimicrobial resistance in various fields, including medicine and agriculture. Yet, identifying the conditions or treatments that will trigger the production of specialized metabolites in fungi can be cumbersome since most of these metabolites are not produced under standard culture conditions. Here, we introduce a data-driven algorithm comprising various network analysis routes to characterize the production of known and putative specialized metabolites and unknown analytes triggered by different exogenous compounds. We use bipartite networks to quantify the relationship between the metabolites and the treatments stimulating their production through two routes. The first, called the direct route, determines the production of known and putative specialized metabolites induced by a treatment. The second, called the auxiliary route, is specific for unknown analytes. We demonstrated the two routes by applying chitooligosaccharides and lipids at two different temperatures to the opportunistic human fungal pathogen Aspergillus fumigatus. We used various network centrality measures to rank the treatments based on their ability to trigger a broad range of specialized metabolites. The specialized metabolites were ranked based on their receptivity to various treatments. Altogether, our data-driven techniques can track the influence of any exogenous treatment or abiotic factor on the metabolomic output for targeted metabolite research. This approach can be applied to complement existing LC/MS analyses to overcome bottlenecks in drug discovery and development from fungi. NoticeThis manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). Author summaryTriggering silent biosynthetic gene clusters in fungi to produce specialized metabolites is a tedious process that requires assessing various environmental conditions, applications of epigenetic modulating agents, or co-cultures with other microbes. We provide a data-driven solution using network analysis, called "direct route", to characterize the production of known and putative specialized metabolites triggered by various exogenous compounds. We also provide a "auxiliary route" to distinguish unique unknown analytes amongst the abundantly produced analytes in response to these treatments. The developed techniques can assist researchers to identify treatments or applications that could positively influence the production of a targeted metabolite or recognize unique unknown analytes that can be further fractionated, characterized, and screened for their biological activities and hence, discover new metabolites.

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Meena, M. G., Lane, M. J., Tannous, J., Carrell, A. A., Abraham, P. E., Giannone, R. J., Ane, J.-M., Keller, N. P., Labbe, J. L., Kainer, D., Jacobson, D. A., Rush, T. A.. 2022-08-14. A guidance into the fungal metabolomic abyss: Network analysis for revealing relationships between exogenous compounds and their outputs. https://doi.org/10.1101/2022.08.11.503656

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