Resultados de investigación

The following are available online, spectral and analytical data for hybrid compounds.. NMR spectroscopic and mass-spectrometric data NMR and Mass Spectra: Figure S1: 1H-NMR (500 MHz, CDCl3) Spectrum of Compound 3 Figure S2: 13C-NMR (125 MHz, CDCl3) Spectrum of Compound 3 Figure S3: HRESI-MS Spectrum of Compound 3 Figure S4: ESI-MS Spectrum of Compound 3 Figure S5: 1H-NMR (500 MHz, CDCl3) Spectrum of Compound 6a Figure S6: 13C-NMR (125 MHz, CDCl3) Spectrum of Compound 6a Figure S7: HRESI-MS Spectrum of Compound 6a Figure S8: ESI-MS Spectrum of Compound 6a Figure S9: 1H -NMR (500 MHz, CDCl3) Spectrum of Compound 6b Figure S10: 13C-NMR (125 MHz, CDCl3) Spectrum of Compound 6b Figure S11: HRESI-MS Spectrum of Compound 6b Figure S12: ESI-MS Spectrum of Compound 6b Figure S13: 1H-NMR (500 MHz, CDCl3) Spectrum of Compound 6c Figure S14: 13C-NMR (125 MHz, CDCl3) Spectrum of Compound 6c Figure S15: HRESI-MS Spectrum of Compound 6c Figure S16: ESI-MS Spectrum of Compound 6c 2 Figure S17: 1H-NMR (500 MHz, CDCl3) Spectrum of Compound 6d Figure S18: 13C-NMR (125 MHz, CDCl3) Spectrum of Compound 6d Figure S19: HRESI-MS Spectrum of Compound 6d Figure S20: ESI-MS Spectrum of Compound 6d Figure S21: 1H-NMR (500 MHz, CDCl3) Spectrum of Compound 6e Figure S22: 13C-NMR (125 MHz, CDCl3) Spectrum of Compound 6e Figure S23: HRESI-MS Spectrum of Compound 6e Figure S24: ESI-MS Spectrum of Compound 6e Figure S25: 1H-NMR (500 MHz, CDCl3) Spectrum of Compound 6f Figure S26: 13C-NMR (125 MHz, CDCl3) Spectrum of Compound 6f Figure S27: HRESI-MS Spectrum of Compound 6f Figure S28: ESI-MS Spectrum of Compound 6f Figure S29: 1H-NMR (500 MHz, CDCl3) Spectrum of Compound 6g Figure S30: 13C -NMR (125 MHz, CDCl3) Spectrum of Compound 6g Figure S31: HRESI-MS Spectrum of Compound 6g Figure S32: ESI-MS Spectrum of Compound 6g Figure S33: 1H-NMR (500 MHz, CDCl3) Spectrum of Compound 6h Figure S34: 13C-NMR (125 MHz, CDCl3) Spectrum of Compound 6h Figure S35: HRESI-MS Spectrum of Compound 6h Figure S36: ESI-MS Spectrum of Compound 6h Figure S37: 1H-NMR (500 MHz, CDCl3) Spectrum of Compound 6i Figure S38: 13C-NMR (125 MHz, CDCl3) Spectrum of Compound 6i Figure S39: HRESI-MS Spectrum of Compound 6i Figure S40: ESI-MS Spectrum of Compound 6i Figure S41: 1H-NMR (500 MHz, CDCl3) Spectrum of Compound 6j Figure S42: 13C-NMR (125 MHz, CDCl3) Spectrum of Compound 6j Figure S43: HRESI-MS Spectrum of Compound 6j Figure S44: ESI-MS Spectrum of Compound 6j Figure S45: 1H-NMR (500 MHz, CDCl3) Spectrum of Compound 6k Figure S46: 13C-NMR (125 MHz, CDCl3) Spectrum of Compound 6k Figure S47: HRESI-MS Spectrum of Compound 6k Figure S48: ESI-MS Spectrum of Compound 6k, Peer reviewed

Table of Content 1. Supplemental Figures 2. Supplemental Note a. Research electronic data capture (REDCap) b. Genetic risk score and multinomial regression on severity scale c. Evaluating the associations of leading SNPs in relation with comorbidities d. Measuring genome-specific effects on COVID-19 severity and hospitalization 3. Supplemental references 4. A full list of cohort members and affiliations a. SCOURGE cohort group b. HOSTAGE cohort group c. GR@ACE cohort group, Peer reviewed

Figure S1: 1H-NMR (500 MHz, CDCl3) Spectrum of Compound 1 Figure S2: 13C-NMR (125 MHz, CDCl3) Spectrum of Compound 1 Figure S3: ESI-MS Spectrum of Compound 1 Figure S4: HRHRESI-MS Spectrum of Compound 1 Figure S5: 1H-NMR (500 MHz, CDCl3) Spectrum of Compound 2 Figure S6: 13C-NMR (125 MHz, CDCl3) Spectrum of Compound 2 Figure S7: ESI-MS Spectrum of Compound 2 Figure S8: HRESI-MS Spectrum of Compound 2 Figure S9: 1H-NMR (400 MHz, CDCl3) Spectrum of Compound 3 Figure S10: 13C-NMR (101 MHz, CDCl3) Spectrum of Compound 3 Figure S11: ESI-MS Spectrum of Compound 3 Figure S12: HRESI-MS Spectrum of Compound 3 Figure S13: 1H-NMR (400 MHz, CDCl3) Spectrum of Compound 4 Figure S14: 13C-NMR (101 MHz, CDCl3) Spectrum of Compound 4 Figure S15: ESI-MS Spectrum of Compound 4 Figure S16: HRESI-MS Spectrum of Compound 4 Figure S17: 1H-NMR (400 MHz, CDCl3) Spectrum of Compound 5 Figure S18: 13C-NMR (101 MHz, CDCl3) Spectrum of Compound 5 Figure S19: ESI-MS Spectrum of Compound 5 Figure S20: HRESI-MS Spectrum of Compound 5 2 Figure S21: 1H-NMR (500 MHz, CDCl3) Spectrum of Compound 6 Figure S22: 13C-NMR (126 MHz, CDCl3) Spectrum of Compound 6 Figure S23: ESI-MS Spectrum of Compound 6 Figure S24: HRESI-MS Spectrum of Compound 6 Figure S25: 1H-NMR (400 MHz, CDCl3) Spectrum of Compound 7 Figure S26: 13C-NMR (101 MHz, CDCl3) Spectrum of Compound 7 Figure S27: ESI-MS Spectrum of Compound 7 Figure S28: HRESI-MS Spectrum of Compound 7 Figure S29: 1H-NMR (400 MHz, CDCl3) Spectrum of Compound 8 Figure S30: 13C-NMR (101 MHz, CDCl3) Spectrum of Compound 8 Figure S31: ESI-MS Spectrum of Compound 8 Figure S32: HRESI-MS Spectrum of Compound 8 Figure S33: 1H-NMR (500 MHz, CDCl3) Spectrum of Compound 9 Figure S34: 13C-NMR (126 MHz, CDCl3) Spectrum of Compound 9 Figure S35: ESI-MS Spectrum of Compound 9 Figure S36: HRESI-MS Spectrum of Compound 9 Figure S37: 1H-NMR (500 MHz, CDCl3) Spectrum of Compound 10 Figure S38: 13C-NMR (126 MHz, CDCl3) Spectrum of Compound 10 Figure S39: ESI-MS Spectrum of Compound 10 Figure S40: HRESI-MS Spectrum of Compound 10 Figure S41: 1H-NMR (400 MHz, CDCl3) Spectrum of Compound 11 Figure S42: 13C-NMR (101 MHz, CDCl3) Spectrum of Compound 11 Figure S43: ESI-MS Spectrum of Compound 11 Figure S44: HRESI-MS Spectrum of Compound 11 Figure S45: 1H-NMR (400 MHz, CDCl3) Spectrum of Compound 12 Figure S46: 13C-NMR (101 MHz, CDCl3) Spectrum of Compound 12 Figure S47: ESI-MS Spectrum of Compound 12 Figure S48: HRESI-MS Spectrum of Compound 12 Figure S49: 1H-NMR (400 MHz, CDCl3) Spectrum of Compound 13 Figure S50: 13C-NMR (101 MHz, CDCl3) Spectrum of Compound 13 Figure S51: ESI-MS Spectrum of Compound 13 Figure S52: HRESI-MS Spectrum of Compound 13 Figure S53: 1H-NMR (400 MHz, CDCl3) Spectrum of Compound 14 Figure S54: 13C-NMR (101 MHz, CDCl3) Spectrum of Compound 14 Figure S55: ESI-MS Spectrum of Compound 14 Figure S56: HRESI-MS Spectrum of Compound 14, Peer reviewed

Description of Additional Supplementary Files: File Name: Supplementary Data 1 Description: The file includes all statistics from linear regression model performed in the manuscript, including meqtl analysis. File Name: Supplementary Data 2 Description: Individual collected clinical information., Peer reviewed

Supplemental Table S1. Complete overview of the inclusion and exclusion criteria for this study. Supplemental Table S2. Composition of the EuroFlow B-cell panel and technical information on the reagents for the IMI-2 PERISCOPE BERT study. Supplemental Table S3. Phenotypic descriptions used to define B-cell subsets stained with the EuroFlow B-cell panel by manual analysis. Supplemental Table S4. Baseline distribution of leukocytes, lymphocytes, T cells, and NK cells in donor groups. Supplemental Table S5. Spearman Ranking Correlation between IgG1+ plasma cell and memory B-cell kinetics and vaccine-component-specific serum IgG. Supplemental Table S6. Spearman Ranking Correlation between IgA1+ plasma cell and IgA memory B-cell kinetics and vaccine-component-specific serum IgA. Supplemental Figure S1. No clear over-time postvaccination changes in major populations in any of the donor groups. Supplemental Figure S2. Over-time maturation of total plasma cells. Supplemental Figure S3. No significant changes in IgG1+ memory B-cell subsets upon vaccination. Supplemental Figure S4. Correlation between cellular changes as measured by flow cytometry and ELISpot. Supplemental Figure S5. Correlation between cellular changes and the vaccine-specific serum IgG level postvaccination as determined by Spearman’s Ranking Correlation per age cohort. Supplemental Figure S6. Impact of sex on cellular responses after vaccination in the young adult cohort (all wP-primed). Supplemental Figure S7. IgG1+ and total plasma cell expansion is more prominent in non-age-matched donors after wP priming., Peer reviewed

CiberAMP is an R package that uses differential expression analyses to stablish accurate correlations between specific SCNVs and changes in expression in the genes affected by them. The algorithm has been designed to be an easy-to-access tool for the TCGA, the largest database in the world with genomic and transcriptomic data ofr more than 10,000 samples of 33 different human cancers. Unlike other methods, CiberAMP can yield information on: (i) SCNV-DEGs (somatic copy number variations associated differentially expressed genes) in a cohort of TCGA tumor samples (ii) The type of copy number variation associated with each SCNV-DEG in terms of expression pattern and genomic context (iii) Insights on the potential functional relevance of each identified SCNV-DEG, Peer reviewed

The promoter‐less CRISPR‐Trap system efficiently the specific integration of the gene trap donor in BCR/ABL but is not a robust reporter system to select corrected cells. As a first approach, we generate a dsDNA donor by PCR amplification containing a SAT2A‐ Venus interfering cassette (Figure S1A). The K562 cell line was divided into three experimental groups according to the conditions for their subsequent electroporation: a) with the donor dsDNA (donor), b) with the donor DNA and Cas9 nuclease without sgRNA (Cas9 + donor) and c) with the donor DNA, Cas9 nuclease and the specific BCR/ABL sgRNA (CRISPR/Cas9 + donor). Twenty‐four hours after electroporation, no fluorescent cells (Venus + cells) were observed in any of the three groups (Figure S1B). The result of the site‐unspecific PCR (In 5´F/Venus R, Table 1) which amplify a region contained entirely in the donor HDR dsDNA, showed a band of the expected size in the Cas9 + donor and CRISPR/Cas9 + donor groups, but not in the donor group (Figure S1C), implying that all donor HDR dsDNA not integrated in the genome was fully degraded. In contrast, the sitespecific PCR (Out 5´F/Venus R, Table 1) corroborated the correct insertion of the donor HDR dsDNA at the BCR/ABL target sequence only in the CRISPR/Cas9 + donor group, with no sitespecific integration detected in any of the controls (Figure S1C). These results were corroborated by Sanger sequencing of the PCR products, supporting the HDR‐mediated insertion of the donor dsDNA into the CRISPR/Cas9 + donor group., Peer reviewed

The following are available online at https://www.mdpi.com/article/10.3390/cancers14020449/s1. Supplementary Figures. Figure S1: Distribution of pathological CSF samples (without healthy ones) among each phase of study and the different groups according to the incidence of the pathology, depending on the infiltration (CSF +/− LM) and the primary tumor (hematologic and solid tumor). Figure S2: Quality control images of the planar protein microarrays generated. Figure S3: A quantile normalization in planar protein microarrays. Figure S4: Coomasie gels which indicate protein distribution across samples. Figure S5: Venn diagrams of total identified proteins with LC-MS/MS. Figure S6: Plots showing the functional proteins using the Reactome for different conditions. Figure S7: Differential protein profiles within CSF + LM according to primary tumor (Lymphoma) by protein microarrays. Figure S8: Differential protein profiles within CSF + LM according to primary tumor (Leukemia) by protein microarrays. Figure S9: Differential protein profiles within CSF + LM according to primary tumor (Lymphoma) by affinity proteomics. Figure S10: Differential protein profiles within CSF + LM according to primary tumor (Leukemia) by affinity proteomics. Figure S11: Summary of the multipronged proteomics characterization among the different phases of study. Supplementary Tables. Table S1: Table of clinical-biological characteristics from the whole CSF samples used in the study. Table S2: Antibodies list used in Planar Protein Microarrays. Table S3: Antibodies list used in Beads Suspension Microarrays. Table S4: Protein identification with LC-MS/MS among the different strategies and their emPAI quantification. Table S5: Boxplots of the protein identified in validation and confirmation phases, respectively, comparing the different groups of study. Table S6: Intensity data results from Planar Protein Microarrays. Table S7: Intensity data results from Beads Suspension Microarrays. Table S8: ROC analysis list of potential biomarker panel on CSF +/− LM and the different comparisons by protein arrays and affinity proteomics., Peer reviewed

List of Supplementary Tables 1. Supplementary Table 1: Metadata and WGS/WES specifications a. Supplementary Table 1a: Metadata b. Supplementary Table 1b: WGS/WES specifications c. Supplementary Table 1c: Sex and age at CLL diagnosis 2. Supplementary Table 2: Immunoglobulin gene rearrangements and oncogenic translocations determined by IgCaller 3. Supplementary Table 3: Mutations (SNV and indels) 4. Supplementary Table 4: Copy number alterations a. Supplementary Table 4a: List of copy number alterations b. Supplementary Table 4b: Candidate driver genes affected by copy number alterations 5. Supplementary Table 5: Structural variants 6. Supplementary Table 6: DNA methylation analyses a. Supplementary Table 6a: Metadata for samples with DNA methylation data b. Supplementary Table 6b: Differentially methylated CpGs between CLL and RT 7. Supplementary Table 7: Bulk ChIP-seq of H3K27ac and transcription factor analysis a. Supplementary Table 7a: Samples and metadata b. Supplementary Table 7b: Number of changes c. Supplementary Table 7c: Richter-specific common changes d. Supplementary Table 7d: Annotated differential expression genes in Richter-specific common regions e. Supplementary Table 7e: Transcription factors (expressed in RT) f. Supplementary Table 7f: Transcription factors (differentially expressed between RT and CLL) 8. Supplementary Table 8: Bulk ATAC-seq analyses a. Supplementary Table 8a: Samples and metadata b. Supplementary Table 8b: Number of changes c. Supplementary Table 8c: Richter-specific common changes d. Supplementary Table 8d: Annotated differential expression genes in Richter-specific common regions 9. Supplementary Table 9: Coding mutations in CLL and RT 10. Supplementary Table 10: CLL and lymphoma driver genes according to previous literature a. Supplementary Table 10a: Driver gene list b. Supplementary Table 10b: Regions considered for driver genes 11. Supplementary Table 11: Bulk RNA-seq analyses a. Supplementary Table 11a: Samples and metadata b. Supplementary Table 11b: Differentially expressed genes between RT and CLL c. Supplementary Table 11c: GSEA using hallmark gene sets d. Supplementary Table 11d: GSEA using curated C2 canonical pathways e. Supplementary Table 11e: Gene Ontology (GO) analysis 12. Supplementary Table 12: SNV identified in 147 CLL samples from the ICGC cohort and in 27 CLL posttreatment samples 13. Supplementary Table 13: Extraction and assignment of genome-wide mutational signatures a. Supplementary Table 13a: Single base substitution signatures extracted by HDP b. Supplementary Table 13b: Assignment of signatures extracted by HDP c. Supplementary Table 13c: Single base substitution signatures extracted by SignatureAnalyzer d. Supplementary Table 13d: Assignment of signatures extracted by SignatureAnalyzer e. Supplementary Table 13e: Single base substitution signatures extracted by SigProfiler f. Supplementary Table 13f: Assignment of signatures extracted by SigProfiler g. Supplementary Table 13g: Single base substitution signatures extracted by sigfit h. Supplementary Table 13h: Assignment of signatures extracted by sigfit i. Supplementary Table 13i. Comparison of SBS-RT with known signatures 14. Supplementary Table 14: Extraction and assignment of mutational signatures leading to clustered mutations a. Supplementary Table 14a: Single base substitution signatures extracted by HDP b. Supplementary Table 14b: Assignment of signatures extracted by HDP c. Supplementary Table 14c: Single base substitution signatures extracted by SignatureAnalyzer d. Supplementary Table 14d: Assignment of signatures extracted by SignatureAnalyzer e. Supplementary Table 14e: Single base substitution signatures extracted by SigProfiler f. Supplementary Table 14f: Assignment of signatures extracted by SigProfiler g. Supplementary Table 14g: Single base substitution signatures extracted by sigfit h. Supplementary Table 14h: Assignment of signatures extracted by sigfit 15. Supplementary Table 15: Fitting of genome-wide mutational signatures a. Supplementary Table 15a: Fitting of mutational signatures per sample (CLL/RT cohort) b. Supplementary Table 15b: Fitting of mutational signatures per sample (147 cases from the ICGCCLL cohort) c. Supplementary Table 15c: Fitting of mutational signatures per sample (27 CLL post-treatment) d. Supplementary Table 15d: Presence of SBS-melphalan in CLL/RT samples (mSigAct) e. Supplementary Table 15e: Fitting of mutational signatures per clone 16. Supplementary Table 16: Characterization of SBS-RT a. Supplementary Table 16a: SBS-RT in coding gene mutations b. Supplementary Table 16b: Activity of mutational processes on specific chromatin states (RTprivate mutations) c. Supplementary Table 16c: Enrichment of SBS-RT on specific chromatin states (RT-specific mutations) d. Supplementary Table 16d: Activity of mutational processes on early/late replication regions (RTprivate mutations) e. Supplementary Table 16e: Enrichment of SBS-RT on early-late replication (RT-private mutations) f. Supplementary Table 16f: Replication strand bias analysis in RT- private mutations g. Supplementary Table 16g: Transcriptional strand bias analysis in RT- private mutations 17. Supplementary Table 17: Subclonal reconstruction from WGS a. Supplementary Table 17a: MCMC sampler details and tolerated errors b. Supplementary Table 17b: Clusters identified c. Supplementary Table 17c: Abundance of clusters in each time point 18. Supplementary Table 18: High-coverage, UMI-based NGS analysis a. Supplementary Table 18a: Metadata b. Supplementary Table 18b: Targeted mutations c. Supplementary Table 18c: Design of the amplicon-based NGS panel d. Supplementary Table 18d: Results 19. Supplementary Table 19: Fitting of clustered mutational signatures a. Supplementary Table 19a: Kataegis identified in the ICGC-CLL cohort b. Supplementary Table 19b: Kataegis identified in the initial CLL (#1) and RT subclones c. Supplementary Table 19c: Fitting of mutational signatures in kataegis 20. Supplementary Table 20: Single-cell DNA-seq a. Supplementary Table 20a: Samples and metadata b. Supplementary Table 20b: Studied genes c. Supplementary Table 20c: Mutations identified by scDNA-seq (from Tapestri Insights) d. Supplementary Table 20d: Allele dropout and doublet rates e. Supplementary Table 20e: Count matrices (based on infSCITE) 21. Supplementary Table 21: Characterization of immunoglobulin heavy chain gene rearrangements using high-coverage NGS a. Supplementary Table 21a: Samples and summary (Lymphotrack, DNA-based) b. Supplementary Table 21b: IGH subclones identified in case 3495 at time point 1 (Lymphotrack) c. Supplementary Table 21c: IGH subclones identified in case 3495 at time point 2 (Lymphotrack) d. Supplementary Table 21d: IGH subclones identified in case 12 at time point 1 (Lymphotrack) e. Supplementary Table 21e: Samples and results (RNA-based) 22. Supplementary Table 22: Single-cell RNA-seq: metadata, QC, clusters, and marker genes a. Supplementary Table 22a: Samples and metadata b. Supplementary Table 22b: Marker genes for clusters of case 12 c. Supplementary Table 22c: Marker genes for clusters of case 19 d. Supplementary Table 22d: Marker genes for clusters of case 63 e. Supplementary Table 22e: Marker genes for clusters of case 365 f. Supplementary Table 22f: Marker genes for clusters of case 3299 g. Supplementary Table 22g: Number of cells per time point and cluster 23. Supplementary Table 23: Single-cell RNA-seq: patient-specific DEA and GSEA a. Supplementary Table 23a: DEA for case 12 (RT vs CLL) b. Supplementary Table 23b: DEA for case 19 (RT vs CLL) c. Supplementary Table 23c: DEA for case 63 (RT vs CLL) d. Supplementary Table 23d: DEA for case 365 (RT vs CLL) e. Supplementary Table 23e: DEA for case 3299 (RT vs CLL) f. Supplementary Table 23f: GSEA for case 12 (RT vs CLL) g. Supplementary Table 23g: GSEA for case 19 (RT vs CLL) h. Supplementary Table 23h: GSEA for case 63 (RT vs CLL) i. Supplementary Table 23i: GSEA for case 365 (RT vs CLL) j. Supplementary Table 23j: GSEA for case 3299 (RT vs CLL) 24. Supplementary Table 24: Respirometry assays in intact CLL and RT cells a. Supplementary Table 24a: Samples and metadata b. Supplementary Table 24b: Measurements c. Supplementary Table 24c: Summary 25. Supplementary Table 25: BCR signaling and cell growth assays in CLL and RT cells a. Supplementary Table 25a: Samples and metadata b. Supplementary Table 25b: Mean fluorescent ratio Indo-1(violet)/Indo-1(blue) c. Supplementary Table 25c: Flow cytometry gating strategy and proliferation results, Peer reviewed

Innate myeloid cell (IMC) populations form an essential part of innate immunity. Flow cytometric (FCM) monitoring of IMCs in peripheral blood (PB) has great clinical potential for disease monitoring due to their role in maintenance of tissue homeostasis and ability to sense micro-environmental changes, such as inflammatory processes and tissue damage. However, the lack of standardized and validated approaches has hampered broad clinical implementation. For accurate identification and separation of IMC populations, 62 antibodies against 44 different proteins were evaluated. In multiple rounds of EuroFlow-based design-testing-evaluation-redesign, finally 16 antibodies were selected for their non-redundancy and separation power. Accordingly, two antibody combinations were designed for fast, sensitive, and reproducible FCM monitoring of IMC populations in PB in clinical settings (11-color; 13 antibodies) and translational research (14-color; 16 antibodies). Performance of pre-analytical and analytical variables among different instruments, together with optimized post-analytical data analysis and reference values were assessed. Overall, 265 blood samples were used for design and validation of the antibody combinations and in vitro functional assays, as well as for assessing the impact of sample preparation procedures and conditions. The two (11- and 14-color) antibody combinations allowed for robust and sensitive detection of 19 and 23 IMC populations, respectively. Highly reproducible identification and enumeration of IMC populations was achieved, independently of anticoagulant, type of FCM instrument and center, particularly when database/software-guided automated (vs. manual “expert-based”) gating was used. Whereas no significant changes were observed in identification of IMC populations for up to 24h delayed sample processing, a significant impact was observed in their absolute counts after >12h delay. Therefore, accurate identification and quantitation of IMC populations requires sample processing on the same day. Significantly different counts were observed in PB for multiple IMC populations according to age and sex. Consequently, PB samples from 116 healthy donors (8-69 years) were used for collecting age and sex related reference values for all IMC populations. In summary, the two antibody combinations and FCM approach allow for rapid, standardized, automated and reproducible identification of 19 and 23 IMC populations in PB, suited for monitoring of innate immune responses in clinical and translational research settings., Peer reviewed