Resultados de investigación

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

Project World Ocean Circulation (WOC).-- Data acquisition: Scatterometer datasets (ASCAT-A,B,C, OSCAT and OSCAT2) and stress-equivalent ERA5 winds provided by Royal Netherlands Meteorological Institute (KNMI). Filenames: 2020010103-WOC-L4-STRESS_ERAstar_GLO_0125_1H_R20191231T18_09-v2.0-fv1.0.nc. Sensor: ASCAT-A, -B, -C onboard the EUMETSAT Metop satellite series, OSCAT onboard the Oceansat-2 and OSCAT2 onboard SCATSat-1. Spatial resolution: 0.125 degree. Spatial grid: WGS 84 / Regular longitude-latitude, The ERA* stress-equivalent wind (U10S) and stress vector product is a correction of the ECMWF ERA5 output by means of geo-located scatterometer-ERA5 differences over a few days temporal window. ERA* can correct for local, persistent NWP model output errors associated with physical processes that are absent or misrepresented by the model, e.g., strong current effects (such as WBCS, highly stationary), wind effects associated with the ocean mesoscales (SST), coastal effects (land see breezes, katabatic winds), PBL parameterization errors, and large-scale circulation effects, e.g., at the ITCZ, ESA Contract No. 4000130730/20/I-NB, With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), L4 Erastar stress equivalent model wind u component at 10 m, erastar stress equivalent model wind v component at 10 m, erastar eastward wind stress, erastar northward wind stress, era5 stress equivalent model wind u component at 10 m, era5 stress equivalent model wind v component at 10 m, era5 eastward wind stress, era5 northward wind stress, number of scatterometer samples, land sea ice quality flag, Peer reviewed

S1 Fig. Reliability of the dose-response curve. a) Mean of eBFP2 expression as a function of IPTG. b) Mean of sfGFP expression as a function of IPTG. c) Noise of eBFP2 expression as a function of IPTG. d) Noise of sfGFP expression as a function of IPTG. Points correspond to the values of the population shown in the main figures. Error bars correspond to standard errors calculated from four different populations. Solid lines correspond to predictions with the mathematical model. S2 Fig. Numerical simulations of stochastic dynamics. a-d) Stochastic trajectories with time of eBFP2 and sfGFP for two different IPTG concentrations. In red, deterministic trajectories. The initial condition corresponds to the uninduced state in all cases. e-h) Histograms of protein expression computed from long trajectories. The Gamma distributions fitted against the experimental data (blue lines) were also represented. S3 Fig. Sensitivity analysis of the model parameters. Plots of mean and noise of expression as a function of IPTG, where solid lines correspond to the dynamics predicted with the adjusted parameter, dotted lines to the dynamics if the parameter increases 2-fold, and dashed lines to the dynamics if the parameter decreases 2-fold. S4 Fig. Stochastic gene expression described by a Gamma distribution. Histograms of experimental single-cell fluorescence for both a) eBFP2 and b) sfGFP for different induction conditions with IPTG, together with fitted Gamma distributions against the data (blue lines) and predicted Gamma distributions obtained by using the model values of mean and noise (red lines). S5 Fig. Growth curves. Three different populations (blue, red, and green) were monitored with time. Points correspond to absorbance values, while solid lines come from fitted exponential trends. S6 Fig. Relationship between cellular growth rate and volume. a) Schematics to show that as TC increases, cells grow slower and are bigger. b) Scatter plot between the cube of the forward scattering signal (proxy of cellular volume) and the growth rate for the 81 IPTG and TC conditions (colored by TC condition). An exponential trend was adjusted (solid line). S1 Appendix. Stochastic differential equations. Derivation of the mathematical expressions of noise in eBFP2 and sfGFP having followed a Langevin formalism and the mean-field approximation. S2 Appendix. Gamma distribution. Derivation of the Gamma distribution for protein expression from a general stochastic differential equation. S1 Data. Flow cytometry data. Single-cell fluorescence data of eBFP2 and sfGFP for different induction conditions with IPTG and TC after filtering events., Peer reviewed

1 Supplementary Figures and Table: Fig. S1 Demodex is maternally inherited -Fig. S2 Functions of rapidly evolving gene families of Demodex and Acariformes -Fig. S3 Mitochondrial genome has polycistrons -Fig. S4 Distribution of the RELAX parameter K across significant selection tests - Fig. S5 Host association determines extend of AT-bias of invertebrate and vertebrate- animals -Tab. S1 Intergenic distances and introns -Fig. S6 Endopolyploidy in Demodex 2 Supplementary Items (SI): -SI 1 Benchmarking Universal Single Copy Orthologs -Fig. SI 2 Length distribution of proteins in parasitic/endosymbiotic Demodex and freeliving/plant-parasitic Tetranychus -Tab. SI 1 Probably pseudogenes of Demodex arranged according to Demodex AA length -Tab. SI 2 Orthogroups of Demodex rapidly expanding -Tab. SI 3 Minimal genome sizes and number of coding genes per taxonomic clade in the Pan-Arthropoda -Tab. SI 4 Species used in genome analysis -Tab. SI 5 Repeat content of mite genomes -Tab. SI 6 Intergenic distances and introns -Fig. SI 3 Host association determines extend of AT-bias of Acariformes -Fig. SI 4 Genome-wide codon usage bias in Acariformes -Fig. SI 5 Mutation spectrum for Demodex demonstrates an AT-mutational bias -Tab. SI 7 Orthogroups (OGs) of arthropod species -Tab. SI 8 Computational analysis of gene family evolution model parameters -Fig. SI 6 Computational Analysis of Gene Family Evolution Ihtest histogram -Fig. SI 7 Alternative IQtree topologies -Tab. SI 9 Gene families showing rapid contraction in Demodex -Tab. SI 10 Gene families showing rapid contraction in the Acariformes -Tab. SI 11 Gene families showing rapid expansion in the Acariformes -Tab. SI 12 Gene family loss in Demodex -Tab. SI 13 Gene family loss in the Acariformes -Tab. SI 14 RELAX test significant orthogroups -Tab. SI 15 Relaxed selection enrichment -Tab. SI 16 Intensified selection enrichment -Tab. SI 17 Counting of nuclei in Drosophila melanogaster -Tab. SI 18 Histidine pathway genes., Peer reviewed

Supplementary Figure 1 | Evaluation of the toxicity and knockdown efficiency for the esiRNAs used in the primary screen. (A) The relative viability of cells transfected with esiRNAs targeting cellular proteostasis components was compared to that of cells transfected with non-targeting esiRNA. (B) The expression of the targeted genes following esiRNA transfection was compared to that in cells transfected with non-targeting esiRNA. Supplementary Table 1 | Description of the genes selected for evaluation. Supplementary Table 2 | Results of the toxicity, knockdown efficiency, and SSMD for evaluated genes. Supplementary Table 3 | esiRNA information for screened genes. Supplementary Table 4 | Primer sequences for evaluation of gene knockdown using qPCR. Supplementary Table 5 | Significant hits from the primary and secondary screen RNAi screens., Peer reviewed

Figure S1: Correspondence Analysis of all the potentially deleterious SNPs, Figure S2: Unsupervised analysis of the deletion data of the genes included in iEK1011 2.0 genome-scale metabolic model, Figure S3: Principal Component Analysis of FBA flux distributions of the lineage-specific genome-scale metabolic models, Figure S4: Principal Component Analysis of the sampled solution space of each one of the lineage-specific genome-scale metabolic models, Figure S5: OPLS-DA loadings for the significantly altered subsystems between animal- and human-associated sampled models, Figure S6: Difference of exchange fluxes between sampled models and FBA flux distribution of metabolites in Middlebrock 7H9 OADC + cholesterol for each lineage’s model, Table S1: Illumina genomes information, Table S2: iEK1011 2.0 reaction information, Table S3: Removed reactions per lineage model, Table S4: OPLS-DA variable coefficients, Table S5: Statistically significant reaction fluxes between samples of animal- and human-associated models, File S1: Description of the genes removed from iEK1011 2.0 to generate lineage-specific GEMs, File S2: Lineage-specific genome scale metabolic models., Peer reviewed

Supplementary Data S1. Supplementary Raw Research Data., Peer reviewed