PXD067833 is an
original dataset announced via ProteomeXchange.
Dataset Summary
| Title | Insights into the biodegradation of two persistent fluorinated fungicides by coupling metabolic modelling with metaproteogenomics |
| Description | Epoxiconazole (EPO) and fludioxonil (FLU) are fluorinated fungicides characterized by their extremely high environmental persistence and ecotoxicity. Given their decades-old use in the agrochemical sector, EPO and FLU have become frequent pollutants of terrestrial and aquatic ecosystems. And yet, not much is known regarding how these pesticides biodegrade in the natural environment and/or how to develop suitable bioremediation approaches capable of tackling their inherent recalcitrance. As a result, this work focused on providing new insights into the bacterial degradation of EPO and FLU, by surveying the catabolic activity of a previously obtained EPO-enriched bacterial consortium through chemical and metaproteogenomic analyses guided by different metabolic modelling tools. The bacterial consortium was capable of extensively degrading EPO and FLU in 21 days, with fungicide removals of over 90 % and defluorination efficiencies of 55 to 80 %, but none of the subproducts predicted for either pesticide were identified by ESI-LC-MS/MS. Despite this, the combination of metabolic modelling tools and metaproteogenomic surveys suggested that EPO and FLU were first attacked in their N-heterocyclic moieties and that the targets of defluorination were the resulting aromatic fluorinated intermediates. This catabolic cascade is consistent with the experimental data gathered in this study and with the existing literature on this topic. Also, the degrading consortium remained surprisingly stable at the taxonomical and functional levels, highlighting its catabolic plasticity in biodegrading and defluorinating two chemically distinct fluorinated compounds. This work offers a conceptual framework with novel observations that can guide future efforts to further elucidate the pathways of microbial transformation of these pesticides, ultimately contributing for better environmental risk management practices for these pollutants. |
| HostingRepository | PRIDE |
| AnnounceDate | 2026-01-23 |
| AnnouncementXML | Submission_2026-01-23_03:13:59.869.xml |
| DigitalObjectIdentifier | |
| ReviewLevel | Peer-reviewed dataset |
| DatasetOrigin | Original dataset |
| RepositorySupport | Unsupported dataset by repository |
| PrimarySubmitter | Diogo Alexandrino |
| SpeciesList | scientific name: metagenomes; NCBI TaxID: NEWT:408169; |
| ModificationList | No PTMs are included in the dataset |
| Instrument | Q Exactive HF |
Dataset History
| Revision | Datetime | Status | ChangeLog Entry |
| 0 | 2025-08-28 05:54:36 | ID requested | |
| ⏵ 1 | 2026-01-23 03:14:00 | announced | |
Publication List
| 10.1038/s41598-025-31941-y; |
| Alexandrino DAM, Semedo M, Cao W, Azevedo J, Magalh, ã, es C, Os, ó, rio H, Jia Z, Campos A, Mucha AP, Almeida CMR, Carvalho MF, Insights into the biodegradation of two persistent fluorinated fungicides by coupling metabolic modelling with metaproteogenomics. Sci Rep, 16(1):2126(2026) [pubmed] |
Keyword List
| submitter keyword: Epoxiconazole |
| Fludioxonil |
| Metagenomics |
| Metaproteomics |
| Defluorination |
Contact List
| Diogo Alexandrino |
| contact affiliation | Microbial Biodegradation and Bioprospection Research Group, Interdisciplinary Centre of Marine and Environmental Research (CIIMAR), University of Porto, Terminal de Cruzeiros do Porto de Leixões, Avenida General Norton de Matos s/n, 4450-208, Matosinhos, Portugal |
| contact email | dalexandrino@ciimar.up.pt |
| lab head | |
| Diogo Alexandrino |
| contact affiliation | CIIMAR - University of Porto; Department of Environmental Health, P.Porto |
| contact email | dalexandrino@ciimar.up.pt |
| dataset submitter | |
Full Dataset Link List
Dataset FTP location
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| PRIDE project URI |
Repository Record List
[ + ]
[ - ]
- PRIDE
- PXD067833
- Label: PRIDE project
- Name: Insights into the biodegradation of two persistent fluorinated fungicides by coupling metabolic modelling with metaproteogenomics