## 5 Materials and Methods
### Data Analysis and Compound Annotation  
The LC-MS CDF files (raw data) were processed with the 'cosmiq' R package ([[https://doi.org/10.18129/B9.bioc.cosmiq](https://doi.org/10.18129/B9.bioc.cosmiq)]([https://doi.org/10.18129/B9.bioc.cosmiq](https://doi.org/10.18129/B9.bioc.cosmiq)), accessed on 30 May 2022) with the settings of RTinfo = TRUE, rtcombine = c(290,750), mzbin = 0.003, continuum = TRUE, profStep = 1, and SNR.Th = 50. Using the 'MetaboAnalystR' package ([[https://doi.org/10.1093/bioinformatics/bty528](https://doi.org/10.1093/bioinformatics/bty528)]([https://doi.org/10.1093/bioinformatics/bty528](https://doi.org/10.1093/bioinformatics/bty528)), accessed on 30 May 2022), the data were batch corrected using the ComBat algorithm to remove any bias. Missing values were substituted by half of the minimum intensity value found within the data. The data were filtered using the \"mean\" option and normalized by the median. The intensity data were log transformed and auto scaled prior to the multivariate analysis. For each time point, the default parameters of the 'MetaboAnalystR' package were used to calculate the fold change between the control and inoculated samples and to perform the multivariate analysis, a partial least squares discriminant analysis (PLS-DA). Using the 'MetaboShingy' metabolomics analysis package [100], the ions, i.e., the \(m/z\) values detected, were putatively annotated and classified against the KEGG database [101]. When a compound was putatively annotated as a lipid, the LIPID MAPS classification was considered [49; 102; 103]. All the putatively identified ions, i.e., the \(m/z\) values with KEGG identifiers containing KNApSACK [104] equivalencies, were searched for their presence in the Plantae Kingdom. For compounds with multiple annotations, a manual curation was performed, and compounds only belonging to the Plantae Kingdom were considered. The putative compounds identification and annotation for the leaves and roots at all the time points are represented in Supplementary Tables S1 and S2, respectively. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/plants12101929/s1](https://www.mdpi.com/article/10.3390/plants12101929/s1)]([https://www.mdpi.com/article/10.3390/plants12101929/s1](https://www.mdpi.com/article/10.3390/plants12101929/s1)), Figure S1: Combined mass spectra of metabolites from leaves and roots samples of _Phlomis pur pur pururea_ non-challenged and challenged with _Phytoophthora cinnamomi_; Table S1: Metabolites identified in _Phlomis pur pururea_ leaves upon challenge with _Phytophthora cinnamomi_; Table S2: Metabolites identified in _Phlomis pur pururea_ roots upon challenge with _Phytophthora cinnamomi_; Table S3: Differentially accumulated metabolites in _Phlomis purpurea_ leaves upon challenge with _Phytophthora cinnamomi_; Table S4: Differentially accumulated metabolites in _Phlomis purpurea_ roots upon challenge with _Phytophthora cinnamomi_. Funding: A.C.--Fund raising. A.C. and M.S.P.--partnership planning and organization, project organization, research framework conception and design, paper writing and discussion. D.N.--Preparation of the plant material, convective isolates and assays, LC-MSMS analyses. E.L.--Metabolomic analysis by UPLC-MSMS, part of the data analysis, part of manuscript writing and review. A.F. and M.M.--Data analysis, compound annotation and writing of the corresponding results. All authors have read and agreed to the published version of the manuscript.All authors have read and agreed to the published version of the manuscript. This work was supported by the project PTDC/AGR-CFL/100217/2008, the D.N. grant SFRH/BD/66016/2009 and M.M. research contract 2022.07433.CEECIND, all funded by Fundacao para a Ciencia e Tecnologia (FCT). Fundacao Calouste Gulbenkian provided financial support in the frame of the Academic Mobility Program for PhD Students, project reference number 122180. The APC charge was supported by the FCT's UIDB/05183/2020 MED project.