⮝ Full datasets listing
PXD037686
PXD037686 is an original dataset announced via ProteomeXchange.
Dataset Summary
| Title | A light into the fungal metabolomic abyss guided by data-driven science: Revealing the relationships between exogenous compounds and their outputs using network analysis |
| Description | Fungal secondary metabolites include a plethora of bioactive compounds with potential usage as pharmaceuticals, agrochemical agents, and industrial chemicals. Exploring and discovering novel fungal metabolites is critical to combat antimicrobial resistance encountered in various fields including medicine and agriculture. Yet, identifying the conditions or treatments that will trigger the production of secondary 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 identify the production of known and putative secondary metabolites and unknown analytes triggered by various exogenous compounds. We use bipartite networks to quantify the relationship between the metabolites and the treatments triggering their production through two routes. The first called directed route, is used to determine the cause and production of known and putative secondary metabolites induced by a treatment and the second called discovery route, is specific for unknown analytes. We demonstrate the two routes by applying chitooligosaccharides and lipids at two different temperatures to the opportunistic human fungal pathogen Aspergillus fumigatus. We use various network centrality measures to rank the treatments based on their ability to trigger a broad range of secondary metabolites. The secondary metabolites are ranked based on their receptivity to be triggered by 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 fungal metabolites. |
| HostingRepository | MassIVE |
| AnnounceDate | 2026-07-14 |
| AnnouncementXML | Submission_2026-07-14_10:01:34.309.xml |
| DigitalObjectIdentifier | |
| ReviewLevel | Non peer-reviewed dataset |
| DatasetOrigin | Original dataset |
| RepositorySupport | Unsupported dataset by repository |
| PrimarySubmitter | Tomas Allen Rush |
| SpeciesList | scientific name: Aspergillus fumigatus Af293; NCBI TaxID: 330879; |
| ModificationList | unknown modification |
| Instrument | instrument model |
Dataset History
| Revision | Datetime | Status | ChangeLog Entry |
|---|---|---|---|
| 0 | 2022-10-24 08:46:47 | ID requested | |
| ⏵ 1 | 2026-07-14 10:01:34 | announced |
Publication List
| no publication |
Keyword List
| submitter keyword: Metabolomic space, data-driven algorithm, network analysis, fungal metabolites, drug discovery, chitooligosaccharides, lipids |
Contact List
| Tomas Allen Rush | |
|---|---|
| contact affiliation | Oak Ridge National Laboratory |
| contact email | rushta@ornl.gov |
| lab head | |
| Tomas Allen Rush | |
| contact affiliation | Oak Ridge National Laboratory |
| contact email | rushta@ornl.gov |
| dataset submitter | |
Full Dataset Link List
| MassIVE dataset URI |
| Dataset FTP location NOTE: Most web browsers have now discontinued native support for FTP access within the browser window. But you can usually install another FTP app (we recommend FileZilla) and configure your browser to launch the external application when you click on this FTP link. Or otherwise, launch an app that supports FTP (like FileZilla) and use this address: ftp://massive-ftp.ucsd.edu/v05/MSV000090575/ |
