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Supplementary Material 11: Subset of populations of wild relatives of lentils in Europe selected through the Calibration Method of the Predictive Characterization technique and potentially resistant to lentil rust (Uromyces vicia-fabae (Pers.) Schröt)., Crop wild relatives are species related to cultivated plants, whose populations have evolved in natural conditions and confer them valuable adaptive genetic diversity, that can be used in introgression breeding programs. Targeting four wild lentil taxa in Europe, we applied the predictive characterization approach through the filtering method to identify populations potentially tolerant to drought, salinity, and waterlogging. In parallel, the calibration method was applied to select wild populations potentially resistant to lentil rust and broomrape, using, respectively, 351 and 204 accessions evaluated for these diseases. An ecogeographic land characterization map was used to incorporate potential genetic diversity of adaptive value. We identified 13, 1, 21, and 30 populations potentially tolerant to drought, soil salinity, waterlogging, or resistance to rust, respectively. The models targeting broomrape resistance did not adjust well and thus, we were not able to select any population regarding this trait. The systematic use of predictive characterization techniques may boost the efficiency of introgression breeding programs by increasing the chances of collecting the most appropriate populations for the desired traits. However, these populations must still be experimentally tested to confirm the predictions., Peer reviewed

Supplementary information. Peer Review File. Source Data, Peer reviewed

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Genomic architectures of the plant viruses, detection by RT-qPCR, additional results of Cas12a-based detection, nuclease expression and purification scheme, additional results of Cas13a/d-based detection, nucleic acid sequences, and numerical data., Peer reviewed

The dataset contains 6 growing seasons of a local winter wheat cultivar ‘Wakanui’ at two farms located in the Canterbury Region of New Zealand. The data of the experiment was used in the AgMIP-Wheat Phase 4 project to evaluate the performance of an ensemble of 29 crop models to predict the effect of changing sowing dates and rates on yield and yield components, in a high-yielding environment. The treatments were managed for non-stress conditions. Data include local daily weather, soil characteristics and initial soil N conditions, crop measurements (anthesis and maturity dates, total above-ground biomass, final grain yield, and yield components), and cultivar information. Simulations include both daily in-season and end-of-season results from 29 wheat crop models., Peer reviewed

S1 Data. Raw data. It collects the data from the surveys. No processing is included in this set. Data on gender, age, location and authors of the interview were considered potentially identifying information by the publisher, so they have been removed from the dataset provided with the article. All the answers of 284 respondents are included. S2 Data. Cleaned data. First day surveys are excluded. Data on gender, age, location and authors of the interview were considered potentially identifying information by the publisher, so they have been removed from the dataset provided with the article. Data were cleaned without removing any relevant information. S3 Data. Data with variables included in the analysis. The inputs (SDE variables) and output (Risk Indices) used for the analysis. S4 Data. Data with the variables appearing in the final model. This dataset contains only the variables appearing in the model with the binarized risk indices., S1 File. IRB results. Result of Lehigh University’s Institutional Review Board evaluation. S2 File. Consent statement of participants: Informed consent statement that was distributed to all the survey participants, in English and Krio. S3 File. Survey instrument: Survey questions and all possible answers, in English and Krio. S4 File. PLOS’ questionnaire on inclusivity in global research: A complete copy of PLOS’ questionnaire on inclusivity in global research in our manuscript. S5 File. Inclusivity in global research., S1 Fig. Results of the Xgboost algorithm. S2 Fig. Results of the UMAP analysis. S3 Fig. Results of the principal component analysis (PCA)., Peer reviewed

This Supplementary Material contains some details related to the Experimental Section, including Reagents, Experimental Methods and Instrumentation. Concerning the Results and Discussion section, different Figures and Tables are included showing experimental results related to optimizations of the immunocytochemistry assay, the analysis of HRPEsv cells by LA-ICP-MS and the characterisation of HRPEsv cells@AuNCs standards., Peer reviewed

Appendix S1. Details of data sets included in the EVA-W arable weed vegetation subset of the European Vegetation Archive. Appendix S2. Details of data sets included in Arable Weeds and Management in Europe data collection (as of 2021). Appendix S3. Record numbers presented in Figure 5 of the main paper. Appendix S4. Distribution of records over time in two data collections., Peer reviewed

Supplementary data to this article: Supplementary Document 1. Detailed description of the databases construction. Supplementary Figure 1. Processing and construction workflow of reference databases. The final databases used in the comparison are highlighted in red. Supplementary Figure 2. Determination of the optimal number of clusters for average abundance clustering analysis. The Elbow method was used to set the optimal number of clusters for the k-means algorithm at genus (A) and species (B) level. Supplementary Figure 3. Determination of the optimal number of clusters for z-scored average abundance patterns analysis. The Elbow method was used to set the optimal number of clusters for the k-means algorithm at genus (A) and species (B) level. Supplementary Figure 4. Bracken results comparison between the enriched filtered-MAG and original NT databases. From top to bottom, the performance of the databases was examined in terms of classification capacity for all samples as a whole, by host lifestyles and geographic origin at genus (A) and species (B) level. In the box plots, the black line within the box marks the median and the red triangle the mean, outliers are presented as red dots. Supplementary Figure 5. Relative abundances of top gut microbiota at the genus level. Only the 25 most abundant taxa in all samples were represented. Taxa are sorted by average relative abundances and their NCBI’s taxID is indicated in parentheses. In the box plots, the black line within the box marks the median and the red triangle the mean. Supplementary Figure 6. Universal core species. (A) Intersections between cores of interest. (B) Taxonomic relationships between universal core species. The number of core taxa assigned to a particular level is indicated inside the square brackets. (C) Prevalence-Abundance Heatmap. Taxa are sorted by average relative abundances and their NCBI’s taxID is indicated in parentheses. Supplementary Figure 7. Taxonomic relationships between different intersect core species in a prevalence gradient. Additional intersect cores were computed defining soft and medium prevalence species cores, which were compared to the corresponding universal species core, working in a prevalence range between 0.5 and 1. Supplementary Figure 8. Relative abundances of top gut microbiota at the species level. Only the 25 most abundant taxa in all samples were represented. Taxa are sorted by average relative abundances and their NCBI’s taxID is indicated in parentheses. In the box plots, the black line within the box marks the median and the red triangle the mean. Supplementary Figure 9. Relative abundances of individuals for the universal core taxa. Abundances are shown for the genus (A) and species (B) core taxa. Groups and taxa were clustered using the Complete method and Euclidean distances. In the case of the groups, this was done for every single group combination, and each taxon NCBI’s taxID is indicated in parentheses. Supplementary Figure 10. Abundance patterns of individuals for the universal core taxa. Z-scored abundances are shown for the genus (A) and species (B) core taxa. Groups and taxa were clustered using the Complete method and Euclidean distances. In the case of the groups, this was done for every single group combination, and each taxon NCBI’s taxID is indicated in parentheses. Supplementary Table 1. Summary information for the microbiome project genomes used in the construction of the enriched databases. Supplementary Table 2. Summary information for the metagenome studies used in this work. Supplementary Table 3. Summary of statistics for samples quality control. The different results generated by FastQC were aggregated with MultiQC for each study. Supplementary Table 4. Metadata for each human gut metagenome sample. Supplementary Table 5. Core genera membership for the different cores of interest. Presence-absence data is presented for the different cores of interest at 0.9, 0.7, and 0.5 prevalence thresholds. Prevalence values for the intersect core taxa at 1e−4 relative abundance threshold are also shown. NCBI’s taxIDs are indicated in parentheses. Supplementary Table 6. Core species membership for the different cores of interest. Presence-absence data is presented for the different cores of interest at 0.9, 0.7, and 0.5 prevalence thresholds. Prevalence values for the intersect core taxa at 1e−4 relative abundance threshold are also shown. NCBI’s taxIDs are indicated in parentheses. Supplementary Table 7. Universal core genera information and known characteristics., Peer reviewed

Supplementary Table 1 Genes, primer pairs and RT-qPCR parameters. Supplementary Figure 1 Box plots showing median values of relative expression of APX (A), β-1,3-glucanase (B) and BAK1 (C) genes. Data are the average values of the relative gene expression in all plants (n= 15) sampled along the experiment (i.e. from 0 to 15 days after inoculation). Control (non-inoculated) plants are represented in white color and Verticillium dahliae-inoculated plants in light blue for tolerant cultivars and in orange for the susceptible ones. Tukey post hoc test differences (p <0.05) are represented with black letters for control plants and in red letters for inoculated plants. Statistical differences resulted by the ANOVA analysis between control and inoculated plants of each cultivar are indicated by asterisks (level of significance: *, p < 0.05; **, p < 0.01; ***, p < 0.001). Supplementary Figure 2 Box plots showing median values of relative expression of C4H (A), chitinase (B), and DRR2 (C) genes. Data are the average values of the relative gene expression in all plants (n= 15) sampled along the experiment (i.e. from 0 to 15 days after inoculation). Control (non-inoculated) plants are represented in white color and Verticillium dahliae-inoculated plants in light blue for tolerant cultivars and in orange for the susceptible ones. Tukey post hoc test differences (p <0.05) are represented with black letters for control plants and in red letters for inoculated plants. Statistical differences resulted by the ANOVA analysis between control and inoculated plants of each cultivar are indicated by asterisks (level of significance: *, p < 0.05; **, p < 0.01; ***, p < 0.001). Supplementary Figure 3 Box plots showing median values of relative expression of CO-MT (A), POX (B), and WRKY5 (C) genes. Data are the average values of the relative gene expression in all plants (n= 15) sampled along the experiment (i.e. from 0 to 15 days after inoculation). Control (non-inoculated) plants are represented in white color and Verticillium dahliae-inoculated plants in light blue for tolerant cultivars and in orange for the susceptible ones. Tukey post hoc test differences (p <0.05) are represented with black letters for control plants and in red letters for inoculated plants. Statistical differences resulted by the ANOVA analysis between control and inoculated plants of each cultivar are indicated by asterisks (level of significance: *, p < 0.05; **, p < 0.01; ***, p < 0.001). Supplementary Figure 4 Time course of the relative expression of the APX gene. Control (non inoculated) plants are represented in white color while Verticillium dahliae-inoculated plants are shown in light blue color (tolerant cultivars) or in orange color (susceptible varieties). The error bar corresponds to the standard deviation (SD) from the mean of the three biological replicates. Tukey post hoc test differences (p <0.05) are represented with black letters among control plants and in red letters among inoculated plants. The statistical differences resulted by the ANOVA analysis between control and inoculated plants are represented by asterisks (level of significance: *, p < 0.05; **, p < 0.01; ***, p < 0.001). Supplementary Figure 5 Time course of the relative expression of the POX gene. Control (non inoculated) plants are represented in white color while Verticillium dahliae-inoculated plants are shown in light blue color (tolerant cultivars) or in orange color (susceptible varieties). The error bar corresponds to the standard deviation (SD) from the mean of the three biological replicates. Tukey post hoc test differences (p <0.05) are represented with black letters among control plants and in red letters among inoculated plants. The statistical differences resulted by the ANOVA analysis between control and inoculated plants are represented by asterisks (level of significance: *, p < 0.05; **, p < 0.01; ***, p < 0.001). Supplementary Figure 6 Time course of the relative expression of the DRR2 gene. Control (non inoculated) plants are represented in white color while Verticillium dahliae-inoculated plants are shown in light blue color (tolerant cultivars) or in orange color (susceptible varieties). The error bar corresponds to the standard deviation (SD) from the mean of the three biological replicates. Tukey post hoc test differences (p <0.05) are represented with black letters among control plants and in red letters among inoculated plants. The statistical differences resulted by the ANOVA analysis between control and inoculated plants are represented by asterisks (level of significance: *, p < 0.05; **, p < 0.01; ***, p < 0.001)., Verticillium wilt of olive (VWO), caused by the soil-borne vascular pathogen Verticillium dahliae, is one of the most devastating diseases affecting olive, the woody crop. One of the best VWO management measures is the use of tolerant cultivars. Yet, our knowledge about defense mechanisms that operate at the root level to explain tolerance to this disease is incomplete. Moreover, most of the approaches so far followed focus only on a specific mechanistic level (e.g., genetic, physiological, or biochemical) rather than on a holistic/multilevel perspective. In this study, eighteen root functional traits, the time-course expression of nine defense-related genes, the root lignin content, and the root membrane permeability were evaluated in six olive varieties differing in their level of tolerance/susceptibility to VWO. The aim was to find links between the level of tolerance to VWO and specific root defense mechanisms at the structural, genetic, biochemical, and physiological levels. Tolerant and susceptible cultivars showed substantial differences in the root system architecture and root lignin content. VWO-susceptible cultivars presented roots with higher specific length and area, but lower diameter and larger number of forks and tips compared to tolerant varieties that also showed less branched roots, higher root diameter, and larger basal content of lignin. Interestingly, VWO-tolerant varieties significantly increased their root lignin content and root membrane permeability after inoculation with V. dahliae. These results were seldom (or not at all) observed in the susceptible plants. At the genetic level, genes related to defense mechanisms, such as cell wall lignin biosynthesis (C4H and CO-MT), production of hydrolytic enzymes able to degrade the fungal cell wall (β-1.3-glucanase), and activation of innate immunity (BAK1 and WRKY5) increased their expression in tolerant cultivars from early moments after inoculation, in contrast to the susceptible ones. These results showed that differences in the root system architecture and lignin content may greatly determine the performance of olive against colonization and invasion by V. dahliae. Moreover, the increase in root membrane permeability in the presence of the pathogen was a typical response of tolerant cultivars. Finally, VWO-tolerant cultivars were able to mount a more intense and rapid defense-related genetic response to respond to the attack by V. dahliae., Peer reviewed