BUSCADOR RECOLECTA

Figure S1. Photographs of the experimental rig for the synthesis of the ZIF-8 layer on the SWCNT buckypaper (A). The buckypaper placed between the disc and the ring (B). ZIF-8 layer being synthesized on the buckypaper (C) at room temperature; Figure S2. Arrhenius model linear fit for the calculation of the apparent activation energy of permeance in the Pebax®/ZIF-8/SWCNT-bp-SC for CO2 (A) and N2 (B). R2 fitting parameter is 0.97 and 0.94, respectively for CO2 and N2; Figure S3. CO2/N2 bound defined in GPU at 35 °C, adapted from the data published in Robeson [7]; Table S1. Gas permeation and selectivity results at 35 °C and 3 bar feed pressure., Peer reviewed

6 pages. -- Supplementary Figure 1. Current mean maximum summer temperature (average 𝑇!"# """""" for the period 1980-2005) across potential seagrass distribution. -- Supplementary Figure 2. Difference between current mean maximum summer temperature ( 𝑇!"# """""" ) and the Tlimit as a function of latitude. Negative and positive latitude values for southern and northern hemispheres, respectively. -- Supplementary Figure 3. Uncertainty associated to the time (in years) for mean maximum summer temperature to reach seagrass upper thermal limit (Tlim) at the warming rates projected under the RCP8.5 scenario around potential seagrass sites. -- Supplementary Figure 4. Time (in years) for mean maximum summer temperature to reach the upper thermal limits (Tlim) of temperate and tropical affinity seagrass flora at the warming rates projected under the RCP8.5 scenario around potential seagrass sites in the Mediterranean Sea and Queensland (Australia) coastal areas. -- Supplementary Figure 5. The time (in years) to reach Tlimit at the warming rates predicted under the RCP4.5 scenario around potential seagrass sites. -- Supplementary Figure 6. Time (in years) for mean maximum summer temperature to reach the upper thermal limits (Tlim) of temperate and tropical affinity seagrass flora at the warming rates projected under the RCP4.5 scenario around potential seagrass sites in the Mediterranean Sea and Queensland (Australia) coastal areas., Seagrasses have experienced major losses globally mostly attributed to human impacts. Recently they are also associated with marine heat waves. The paucity of information on seagrass mortality thermal thresholds prevents the assessment of the risk of seagrass loss under marine heat waves. We conducted a synthesis of reported empirically- or experimentally-determined seagrass upper thermal limits (Tlimit) and tested the hypothesis that they increase with increasing local annual temperature. We found that Tlimit increases 0.42± 0.07°C per°C increase in in situ annual temperature (R2 = 0.52). By combining modelled seagrass Tlimit across global coastal areas with current and projected thermal regimes derived from an ocean reanalysis and global climate models (GCMs), we assessed the proximity of extant seagrass meadows to their Tlimit and the time required for Tlimit to be met under high (RCP8.5) and moderate (RCP4.5) emission scenarios of greenhouse gases. Seagrass meadows worldwide showed a modal difference of 5°C between present Tmax and seagrass Tlimit. This difference was lower than 3°C at the southern Red Sea, the Arabian Gulf, the Gulf of Mexico, revealing these are the areas most in risk of warming-derived seagrass die-off, and up to 24°C at high latitude regions. Seagrasses could meet their Tlimit regularly in summer within 50-60 years or 100 years under, respectively, RCP8.5 or RCP4.5 scenarios for the areas most at risk, to more than 200 years for the Arctic under both scenarios. This study shows that implementation of the goals under the Paris Agreement would safeguard much of global seagrass from heat-derived mass mortality and identifies regions where actions to remove local anthropogenic stresses would be particularly relevant to meet the Target 10 of the Aichi Targets of the Convention of the Biological Diversity., Peer reviewed

Appendix A. Supplementary data: Multimedia component 1., Peer reviewed

Document S1. Figures S1–S7 and Data S2. Data S1. Table summarizing several information on TE-derived peptides from immunopeptidomics or on predicted peptides by NetMHCpan, related to Figure 3. Document S2. Article plus supplemental information., Peer reviewed

1 table., Additional file 5: Table S4. Expression data of defense-related genes in leaves of Control and High-Fe plants during M. oryzae infection (48 hpi)., Consejo Superior de Investigaciones Cientificas (CSIC), Peer reviewed

1 table., Additional file 6: Table S5. Expression data (FPKM and Log2FC) of genes involved in the biosynthesis of diterpene phytoalexins and sakuranetin in leaves of Control and High-Fe plants (M. oryzae-infected and mock-inoculated plants)., Consejo Superior de Investigaciones Cientificas (CSIC), Peer reviewed

1 table., Additional file 7: Table S6. Expression data (FPKM and Log2FC) of genes involved in Fe homeostasis in leaves of Control and High-Fe plants (M. oryzae-infected and mock-inoculated plants)., Consejo Superior de Investigaciones Cientificas (CSIC), Peer reviewed

1 table., Additional file 8: Table S7. Statistics for RNAseq analysis of leaves from Control and High-Fe plants, mock-inoculated and M. oryzae-inoculated (48 hpi)., Consejo Superior de Investigaciones Cientificas (CSIC), Peer reviewed

1 table., Additional file 9: Table S8. List of oligonucleotides used in this study., Consejo Superior de Investigaciones Cientificas (CSIC), Peer reviewed

13 pages. -- Supplementary Figure 1. Breeding scheme used in the marked-assisted backcross introgression of the Saltol QTL from FL478 (indica) into the background of the rice variety OLESA (temperate japonica rice). -- Supplementary Figure 2. Polymorphism obtained with the SKC10 SSR marker visualized by agarose gel analysis. (A) Saltol QTL region showing the SKC10 SSR marker and relevant salt-related genes positions. (B) PCR products obtained from the Saltol donor (FL478), the recurrent (OLESA) parent and 4 representative introgression lines derived from FL478 x OLESA crosses (BC2F1). C-, negative control, He, heterozygous, Ho, homozygous. Primers are indicated in Supplementary Table 2. -- Supplementary Figure 3. Graphical representation of the genotypes of the Saltol-introgressed rice lines (BC3F3). Genotyping was carried out by KASPar analysis. SNPs are indicated in columns according to their chromosomal location (in mega base-pairs, Mb). Introgression lines (IL1 to IL31) are clustered in four groups (I to IV) depending on the BC3F1 parent from which they derive. The Saltol QTL location (and length) is indicated in the upper part. Homozygous donor (FL478) and recurrent (OLESA) alleles are depicted in blue and white, respectively. The KASPar markers used in this study are listed in Supplementary Tables 1 and 2. -- Supplementary Figure 4. SES score of parental lines (FL478, OLESA) hydroponically grown in modified Yoshida solution containing different NaCl concentrations (60 mM, 80 mM and 100 mM) for 14 days. Box plots show the distribution of SES scores in each line and condition (15 plants/genotype each experiment; T-test, * P < 0.05). Values above each box indicate the mean SES score . -- Supplementary Figure 5. Characterization of salt tolerant introgression lines. (A) Standard evaluation system (SES) scores of visual salt injury of the 30 ILs. Evaluation was performed after 14 days of salt treatment (80 mM NaCl). SES scores are shown as the percentage of plants at each score value. 1, highly tolerant; 3, tolerant; 5, moderately tolerant; 7, sensitive; 9, highly sensitive. ILs are clustered in four groups (I to IV) depending on the BC3F1 parent used. ILs were evaluated in successive rounds, with 5 plants and 10 plants in control and salt conditions respectively in each experiment, and most salt-sensitive ILs were discarded in the following assays. A total of six independent experiments were carried out with the most salt-tolerant ILs. (B) Representative images of IL22 and IL13 plants and parental lines in control and salt conditions after 14 days of treatment. -- Supplementary Figure 6. Plant growth of parental lines (FL478, OLESA) and IL22 plants hydroponically grown in modified Yoshida solution containing 80 mM NaCl. Control plants were not supplemented with NaCl. The leaf number of each genotype at different times of salt treatment is indicated. At least 6 plants per genotype and condition were assayed. -- Supplementary Figure 7. Samples analysed by RNASeq, and comparisons of data sets from each genotype (IL22, OLESA) and condition (control, salt-treated). -- Supplementary Figure 8. Singular enrichment analysis of introgressed indica genes (chromosome 1, blocks 1 and 2, and chromosome 3) using AgriGO (Tian et al., 2017). For a full list of gene IDs, see Supplementary Table 5. -- Supplementary Figure 9. Singular enrichment analysis of japonica genes up-regulated in IL22 plants at 24 h of salt treatment (80 mM NaCl) using AgriGO (Tian et al., 2017). For a full list of gene IDs, see Supplementary Table 8. -- Supplementary Figure 10. Singular enrichment analysis of japonica genes up-regulated in OLESA plants at 24 h of salt treatment (80 mM NaCl) using AgriGO (Tian et al., 2017). For a full list of gene IDs, see Supplementary Table 8. -- Supplementary Figure 11. Singular enrichment analysis of japonica genes down-regulated in IL22 plants at 24 h of salt treatment (80 mM NaCl) using AgriGO (Tian et al., 2017). For a full list of gene IDs, see Supplementary Table 8. -- Supplementary Figure 12. Singular enrichment analysis of japonica genes down-regulated in OLESA plants at 24 h of salt treatment (80 mM NaCl) using AgriGO (Tian et al., 2017). For a full list of gene IDs, see Supplementary Table 8. -- Supplementary Figure 13. Mapman analysis of japonica genes up- and down-regulated in IL22 and OLESA plants at 24 h of salt treatment (80 mM NaCl). Regulation overview, stress and transport schemes are shown. Color scale (yellow to blue) represents the log2 fold change of salt vs. control conditions., Rice is the most salt sensitive cereal crop and its cultivation is particularly threatened by salt stress, which is currently worsened due to climate change. This study reports the development of salt tolerant introgression lines (ILs) derived from crosses between the salt tolerant indica rice variety FL478, which harbors the Saltol quantitative trait loci (QTL), and the salt-sensitive japonica elite cultivar OLESA. Genotyping-by-sequencing (GBS) and Kompetitive allele specific PCR (KASPar) genotyping, in combination with step-wise phenotypic selection in hydroponic culture, were used for the identification of salt-tolerant ILs. Transcriptome-based genotyping allowed the fine mapping of indica genetic introgressions in the best performing IL (IL22). A total of 1,595 genes were identified in indica regions of IL22, which mainly located in large introgressions at Chromosomes 1 and 3. In addition to OsHKT1;5, an important number of genes were identified in the introgressed indica segments of IL22 whose expression was confirmed [e.g., genes involved in ion transport, callose synthesis, transcriptional regulation of gene expression, hormone signaling and reactive oxygen species (ROS) accumulation]. These genes might well contribute to salt stress tolerance in IL22 plants. Furthermore, comparative transcript profiling revealed that indica introgressions caused important alterations in the background gene expression of IL22 plants (japonica cultivar) compared with its salt-sensitive parent, both under non-stress and salt-stress conditions. In response to salt treatment, only 8.6% of the salt-responsive genes were found to be commonly up- or down-regulated in IL22 and OLESA plants, supporting massive transcriptional reprogramming of gene expression caused by indica introgressions into the recipient genome. Interactions among indica and japonica genes might provide novel regulatory networks contributing to salt stress tolerance in introgression rice lines. Collectively, this study illustrates the usefulness of transcriptomics in the characterization of new rice lines obtained in breeding programs in rice., Peer reviewed