Resultados de investigación

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

1 Overview of methods included in MultiBaC package. 2 MultiBaC use case. 3 Comparison of ARS y Nbacto other BECAs. 3.1 Qualitative comparison. 3.2 Performance comparison. 4 Anote on MultiBaC scalability., Peer reviewed

Bilevel problem reformulation, lycopene and naringenin biosynthetic pathway, model reduction, and impact of OptDesign parameters on biochemical production (PDF). Comparison between in silico predictions and in vivo manipulations for nine compounds; knockout and regulation candidates for succinate, lycopene, and naringenin; impact of reference flux vectors (XLSX)., Peer reviewed

Peer reviewed

Supplementary S1: Yield and related traits in bread wheat. Table S1: Examples of genomic regions, candidate and cloned genes for yield and related traits in bread wheat. Supplementary S2: Drought tolerance. Table S2: Examples of genomic regions and candidate genes for drought tolerance. Supplementary S3: Heat tolerance. Table S3. Examples of genomic regions and candidate genes for heat tolerance. Supplementary S4: salinity tolerance in bread wheat. Table S4. Examples of genomic regions and candidate genes for salinity tolerance in bread wheat. Supplementary S5: Frost tolerance. Supplementary S6: Disease resistance. Table S5. Examples of genomic regions, candidate and cloned genes mapped for disease resistance in wheat species. Supplementary S7 insect and mite resistance. Table S6. Examples of genomic regions and candidate genes mapped for insect and mite resistance. Supplementary S8: Quality traits. Table S7. Examples of genomic regions, candidate and cloned genes for quality traits., Recent technological advances in next-generation sequencing (NGS) technologies have dramatically reduced the cost of DNA sequencing, allowing species with large and complex genomes to be sequenced. Although bread wheat (Triticum aestivum L.) is one of the world’s most important food crops, efficient exploitation of molecular marker-assisted breeding approaches has lagged behind that achieved in other crop species, due to its large polyploid genome. However, an international public–private effort spanning 9 years reported over 65% draft genome of bread wheat in 2014, and finally, after more than a decade culminated in the release of a gold-standard, fully annotated reference wheat-genome assembly in 2018. Shortly thereafter, in 2020, the genome of assemblies of additional 15 global wheat accessions was released. As a result, wheat has now entered into the pan-genomic era, where basic resources can be efficiently exploited. Wheat genotyping with a few hundred markers has been replaced by genotyping arrays, capable of characterizing hundreds of wheat lines, using thousands of markers, providing fast, relatively inexpensive, and reliable data for exploitation in wheat breeding. These advances have opened up new opportunities for marker-assisted selection (MAS) and genomic selection (GS) in wheat. Herein, we review the advances and perspectives in wheat genetics and genomics, with a focus on key traits, including grain yield, yield-related traits, end-use quality, and resistance to biotic and abiotic stresses. We also focus on reported candidate genes cloned and linked to traits of interest. Furthermore, we report on the improvement in the aforementioned quantitative traits, through the use of (i) clustered regularly interspaced short-palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9)-mediated gene-editing and (ii) positional cloning methods, and of genomic selection. Finally, we examine the utilization of genomics for the next-generation wheat breeding, providing a practical example of using in silico bioinformatics tools that are based on the wheat reference-genome sequence., Peer reviewed

Supplementary Table 1 and Table 2. Supplementary Figures: Supplementary Figure 1. Schematic representation of improvements in PaintOmics 4. Supplementary Figure 2. Supplementary Figure 3. STATegra multi-layered networks., Peer reviewed

The supplementary material contains supplementary data on molecular assays using ELISA kits, the supplementary Table S1 of Analytical parameters of individual ELISA analysis, the Supplementary Table S2 describing common oral treatments in both control subjects and AMD patients, and the Supplementary Table S3 of Concentrations of proteins and elements determined in the tear film of AMD patients and control subjects without considering patients with dyslipidemia., Peer reviewed

Supplementary Figure 1 | Dendrograms obtained as a result of HCA for cosym dataset. The analyses have been performed for each translational subprocess (legend A) considering clades (legend B). Supplementary Material 1 | Genomes and cosymbionts of the study (XLSX file). “Genomes” sheet: List of organisms with the abbreviation code used in this study. “Consortia” sheet: list of cosymbionts (symbiotic consortia) used as unique genome entity. Supplementary Material 2 | Translational genes set (XLSX file). List of genes with translational functions used as universe in this study. Supplementary Material 3 | Matrix of presence/absence of genes in the 92 genomes of the study (XLSX file). Zero values indicate absence of the gene. Supplementary Material 4 | Comparison among cosym, bcc, ssz, JCVI-syn3.0, and DEG datasets (Escherichia coli and Bacillus subtilis) used in the study (XLSX file). The abbreviation code is the same as in Supplementary Material 1., Peer reviewed

The dataset is made available under the Open Database License. Any rights in individual contents of the database are licensed under the Database Contents License. Please, read the full ODbL 1.0 license text for the exact terms that apply. Users of the dataset are free to: Share: copy, distribute and use the database, either commercially or non-commercially. Create: produce derivative works from the database. Adapt: modify, transform and build upon the database. Under the following conditions: Attribution: You must attribute any public use of the database, or works produced from the database. For any use or redistribution of the database, or works produced from it, you must make clear to others the license of the original database. Share-Alike: If you publicly use any adapted version of this database, or works produced from an adapted database, you must also offer that adapted database under the ODbL., This work was supported by PROMETEO/2018/066 from ‘Conselleria d’Educació’ (Generalitat Valenciana, Comunitat Valenciana, Spain), grant PID2021-125858OB-100 and the Severo Ochoa Excellence Program CEX2021-001189-S funded by MCIN/AEI/ 10.13039/501100011033 and by “ERDF A way of making Europe”. EM-G was recipient of a predoctoral grant FPU18/02019 funded by MCIN/AEI/ 10.13039/501100011033 and by “ESF Investing in your future”. SG holds a ‘Juan de la Cierva Incorporación’ grant (IJC2020-042749-I) from the Spanish ‘Ministerio de Ciencia e Innovación’, funded by The European Union–NextGenerationEU., Fig 1: Controlled fungal infection assays on orange fruits -- Fig.2: P. chrysogenum Terb resistant mutants on PDA + PDA terb -- Fig.3: Controlled fungal infection assays on orange fruits -- Fig.4: Measurement of luciferase and nanoluciferase luminescence signals for transformants expressing luciferase under different promoters after 2 days of growth in PDB -- Fig. 5: Measurement of luciferase and nanoluciferase luminescence signals for transformants expressing luciferase under different inducible promoters after 4 days of growth in PdMM – Fig. 6: Measurement of luciferase and nanoluciferase luminescence signals for Penicillium digitatum transformants expressing luciferase under dCas9-regulated GB_SynP synthetic promoter and the corresponding dCas9 activation system (samples named as "+dcas9"), after 2 days of growht in PDB., Peer reviewed