BUSCADOR RECOLECTA

Cyanobacteria are prokaryotic organisms that capture energy from sunlight using oxygenic photosynthesis and transform CO2 into products of interest such as fatty acids. Synechococcus elongatus PCC 7942 is a model cyanobacterium efficiently engineered to accumulate high levels of omega-3 fatty acids. However, its exploitation as a microbial cell factory requires a better knowledge of its metabolism, which can be approached by using systems biology tools. To fulfill this objective, we worked out an updated, more comprehensive, and functional genome-scale model of this freshwater cyanobacterium, which was termed iMS837. The model includes 837 genes, 887 reactions, and 801 metabolites. When compared with previous models of S. elongatus PCC 7942, iMS837 is more complete in key physiological and biotechnologically relevant metabolic hubs, such as fatty acid biosynthesis, oxidative phosphorylation, photosynthesis, and transport, among others. iMS837 shows high accuracy when predicting growth performance and gene essentiality. The validated model was further used as a test-bed for the assessment of suitable metabolic engineering strategies, yielding superior production of non-native omega-3 fatty acids such as α-linolenic acid (ALA). As previously reported, the computational analysis demonstrated that fabF overexpression is a feasible metabolic target to increase ALA production, whereas deletion and overexpression of fabH cannot be used for this purpose. Flux scanning based on enforced objective flux, a strain-design algorithm, allowed us to identify not only previously known gene overexpression targets that improve fatty acid synthesis, such as Acetyl-CoA carboxylase and β-ketoacyl-ACP synthase I, but also novel potential targets that might lead to higher ALA yields. Systematic sampling of the metabolic space contained in iMS837 identified a set of ten additional knockout metabolic targets that resulted in higher ALA productions. In silico simulations under photomixotrophic conditions with acetate or glucose as a carbon source boosted ALA production levels, indicating that photomixotrophic nutritional regimens could be potentially exploited in vivo to improve fatty acid production in cyanobacteria. Overall, we show that iMS837 is a powerful computational platform that proposes new metabolic engineering strategies to produce biotechnologically relevant compounds, using S. elongatus PCC 7942 as non-conventional microbial cell factory., Peer reviewed

Cyanobacteria are prokaryotic organisms that capture energy from sunlight using oxygenic photosynthesis and transform CO2 into products of interest such as fatty acids. Synechococcus elongatus PCC 7942 is a model cyanobacterium efficiently engineered to accumulate high levels of omega-3 fatty acids. However, its exploitation as a microbial cell factory requires a better knowledge of its metabolism, which can be approached by using systems biology tools. To fulfill this objective, we worked out an updated, more comprehensive, and functional genome-scale model of this freshwater cyanobacterium, which was termed iMS837. The model includes 837 genes, 887 reactions, and 801 metabolites. When compared with previous models of S. elongatus PCC 7942, iMS837 is more complete in key physiological and biotechnologically relevant metabolic hubs, such as fatty acid biosynthesis, oxidative phosphorylation, photosynthesis, and transport, among others. iMS837 shows high accuracy when predicting growth performance and gene essentiality. The validated model was further used as a test-bed for the assessment of suitable metabolic engineering strategies, yielding superior production of non-native omega-3 fatty acids such as α-linolenic acid (ALA). As previously reported, the computational analysis demonstrated that fabF overexpression is a feasible metabolic target to increase ALA production, whereas deletion and overexpression of fabH cannot be used for this purpose. Flux scanning based on enforced objective flux, a strain-design algorithm, allowed us to identify not only previously known gene overexpression targets that improve fatty acid synthesis, such as Acetyl-CoA carboxylase and β-ketoacyl-ACP synthase I, but also novel potential targets that might lead to higher ALA yields. Systematic sampling of the metabolic space contained in iMS837 identified a set of ten additional knockout metabolic targets that resulted in higher ALA productions. In silico simulations under photomixotrophic conditions with acetate or glucose as a carbon source boosted ALA production levels, indicating that photomixotrophic nutritional regimens could be potentially exploited in vivo to improve fatty acid production in cyanobacteria. Overall, we show that iMS837 is a powerful computational platform that proposes new metabolic engineering strategies to produce biotechnologically relevant compounds, using S. elongatus PCC 7942 as non-conventional microbial cell factory., Peer reviewed

Cyanobacteria are prokaryotic organisms that capture energy from sunlight using oxygenic photosynthesis and transform CO2 into products of interest such as fatty acids. Synechococcus elongatus PCC 7942 is a model cyanobacterium efficiently engineered to accumulate high levels of omega-3 fatty acids. However, its exploitation as a microbial cell factory requires a better knowledge of its metabolism, which can be approached by using systems biology tools. To fulfill this objective, we worked out an updated, more comprehensive, and functional genome-scale model of this freshwater cyanobacterium, which was termed iMS837. The model includes 837 genes, 887 reactions, and 801 metabolites. When compared with previous models of S. elongatus PCC 7942, iMS837 is more complete in key physiological and biotechnologically relevant metabolic hubs, such as fatty acid biosynthesis, oxidative phosphorylation, photosynthesis, and transport, among others. iMS837 shows high accuracy when predicting growth performance and gene essentiality. The validated model was further used as a test-bed for the assessment of suitable metabolic engineering strategies, yielding superior production of non-native omega-3 fatty acids such as α-linolenic acid (ALA). As previously reported, the computational analysis demonstrated that fabF overexpression is a feasible metabolic target to increase ALA production, whereas deletion and overexpression of fabH cannot be used for this purpose. Flux scanning based on enforced objective flux, a strain-design algorithm, allowed us to identify not only previously known gene overexpression targets that improve fatty acid synthesis, such as Acetyl-CoA carboxylase and β-ketoacyl-ACP synthase I, but also novel potential targets that might lead to higher ALA yields. Systematic sampling of the metabolic space contained in iMS837 identified a set of ten additional knockout metabolic targets that resulted in higher ALA productions. In silico simulations under photomixotrophic conditions with acetate or glucose as a carbon source boosted ALA production levels, indicating that photomixotrophic nutritional regimens could be potentially exploited in vivo to improve fatty acid production in cyanobacteria. Overall, we show that iMS837 is a powerful computational platform that proposes new metabolic engineering strategies to produce biotechnologically relevant compounds, using S. elongatus PCC 7942 as non-conventional microbial cell factory., Peer reviewed

Cyanobacteria are prokaryotic organisms that capture energy from sunlight using oxygenic photosynthesis and transform CO2 into products of interest such as fatty acids. Synechococcus elongatus PCC 7942 is a model cyanobacterium efficiently engineered to accumulate high levels of omega-3 fatty acids. However, its exploitation as a microbial cell factory requires a better knowledge of its metabolism, which can be approached by using systems biology tools. To fulfill this objective, we worked out an updated, more comprehensive, and functional genome-scale model of this freshwater cyanobacterium, which was termed iMS837. The model includes 837 genes, 887 reactions, and 801 metabolites. When compared with previous models of S. elongatus PCC 7942, iMS837 is more complete in key physiological and biotechnologically relevant metabolic hubs, such as fatty acid biosynthesis, oxidative phosphorylation, photosynthesis, and transport, among others. iMS837 shows high accuracy when predicting growth performance and gene essentiality. The validated model was further used as a test-bed for the assessment of suitable metabolic engineering strategies, yielding superior production of non-native omega-3 fatty acids such as α-linolenic acid (ALA). As previously reported, the computational analysis demonstrated that fabF overexpression is a feasible metabolic target to increase ALA production, whereas deletion and overexpression of fabH cannot be used for this purpose. Flux scanning based on enforced objective flux, a strain-design algorithm, allowed us to identify not only previously known gene overexpression targets that improve fatty acid synthesis, such as Acetyl-CoA carboxylase and β-ketoacyl-ACP synthase I, but also novel potential targets that might lead to higher ALA yields. Systematic sampling of the metabolic space contained in iMS837 identified a set of ten additional knockout metabolic targets that resulted in higher ALA productions. In silico simulations under photomixotrophic conditions with acetate or glucose as a carbon source boosted ALA production levels, indicating that photomixotrophic nutritional regimens could be potentially exploited in vivo to improve fatty acid production in cyanobacteria. Overall, we show that iMS837 is a powerful computational platform that proposes new metabolic engineering strategies to produce biotechnologically relevant compounds, using S. elongatus PCC 7942 as non-conventional microbial cell factory., Peer reviewed

Cyanobacteria are prokaryotic organisms that capture energy from sunlight using oxygenic photosynthesis and transform CO2 into products of interest such as fatty acids. Synechococcus elongatus PCC 7942 is a model cyanobacterium efficiently engineered to accumulate high levels of omega-3 fatty acids. However, its exploitation as a microbial cell factory requires a better knowledge of its metabolism, which can be approached by using systems biology tools. To fulfill this objective, we worked out an updated, more comprehensive, and functional genome-scale model of this freshwater cyanobacterium, which was termed iMS837. The model includes 837 genes, 887 reactions, and 801 metabolites. When compared with previous models of S. elongatus PCC 7942, iMS837 is more complete in key physiological and biotechnologically relevant metabolic hubs, such as fatty acid biosynthesis, oxidative phosphorylation, photosynthesis, and transport, among others. iMS837 shows high accuracy when predicting growth performance and gene essentiality. The validated model was further used as a test-bed for the assessment of suitable metabolic engineering strategies, yielding superior production of non-native omega-3 fatty acids such as α-linolenic acid (ALA). As previously reported, the computational analysis demonstrated that fabF overexpression is a feasible metabolic target to increase ALA production, whereas deletion and overexpression of fabH cannot be used for this purpose. Flux scanning based on enforced objective flux, a strain-design algorithm, allowed us to identify not only previously known gene overexpression targets that improve fatty acid synthesis, such as Acetyl-CoA carboxylase and β-ketoacyl-ACP synthase I, but also novel potential targets that might lead to higher ALA yields. Systematic sampling of the metabolic space contained in iMS837 identified a set of ten additional knockout metabolic targets that resulted in higher ALA productions. In silico simulations under photomixotrophic conditions with acetate or glucose as a carbon source boosted ALA production levels, indicating that photomixotrophic nutritional regimens could be potentially exploited in vivo to improve fatty acid production in cyanobacteria. Overall, we show that iMS837 is a powerful computational platform that proposes new metabolic engineering strategies to produce biotechnologically relevant compounds, using S. elongatus PCC 7942 as non-conventional microbial cell factory., Peer reviewed

This dataset is a major update to the quasi-global, high-frequency (at least hourly) sea level dataset known as GESLA (Global Extreme Sea Level Analysis). Versions 1 (released 2009) and 2 (released 2016) of the dataset has been used in many published studies, across a wide range of oceanographic and coastal engineering-related investigations concerned with evaluating tides, storm surges, extreme sea levels and other oceanographic processes. The third version of the dataset (released 2021), presented here, contains twice the number of station-years of data (91021), and nearly four times the number of station records (5119), compared to version 2. The dataset consists of records obtained from 36 sources around the world, including some data archaeology efforts. The oldest record dates from year 1805 (spanning 217 years), followed by a few other stations starting during the 1840s and 1850s. However, the vast majority of records start during the 1950s. Data have been updated until October 2021 whenever possible. We have archived the dataset into two parts. This first part contains the 4527 records that are can be used for both research and consultancy purposes. The higher-frequency sea-level dataset in this first part of GESLA-3 was obtained from 33 international and national data providers, specifically: University of Hawaii Sea level Center, National Oceanic and Atmospheric Administration, Marine Environmental Data Section, United States Geological Survey, Bureau of Meteorology, Rijkswaterstaat, Japan Oceanographic Data Center, Japan Meteorological Agency, Swedish Meteorological and Hydrological Institute, Réseaux de référence des observations marégraphiques (Reference networks for tidal observations), British Oceanographic Data Centre, California Department of Water Resources, Japan Oceanographic Data Center, Japan Coast Guard, Norwegian Hydrographic Service, Japan Oceanographic Data Center, Geospatial Information Authority of Japan, Wasserstraßen-und Schifffahrtsverwaltung des Bundes (Federal Waterway and Shipping Administration), Japan Oceanographic Data Center, Ports and Harbours Bureau, South Florida Water Management District, Instituto Superiore per la Protezione e la Ricerca Ambientale (Higher Institute for Environmental Protection and Research), Instituto Español de Oceanografía (Spanish Istitute of Oceanography), Data archaeology exercise, National Autonomous University of Mexico, Finnish Meteorological Institute, Danish Meteorological Institute, Bundesanstalt Für Gewässerkunde(Federal Institute of Hydrology), Marine Institute (Coastal sites), Coastal Channel Observatory, National Oceanography Centre, North West Florida Water Management Department, European Sea-Level Service, Icelandic Coast Guard Hydrographic and Maritime Safety Department, North Carolina Department of Emergency Management, Marine Institute (River Sites) and the Global Sea Level Observing System. These data are made available under the creative commons CC-BY 4.0 license., Peer reviewed

This dataset is a major update to the quasi-global, high-frequency (at least hourly) sea level dataset known as GESLA (Global Extreme Sea Level Analysis). Versions 1 (released 2009) and 2 (released 2016) of the dataset has been used in many published studies, across a wide range of oceanographic and coastal engineering-related investigations concerned with evaluating tides, storm surges, extreme sea levels and other oceanographic processes. The third version of the dataset (released 2021), presented here, contains twice the number of station-years of data (91021), and nearly four times the number of station records (5119), compared to version 2. The dataset consists of records obtained from 36 sources around the world, including some data archaeology efforts. The oldest record dates from year 1805 (spanning 217 years), followed by a few other stations starting during the 1840s and 1850s. However, the vast majority of records start during the 1950s. Data have been updated until mid-2021 whenever possible. We have archived the dataset into two parts. This second part contains the 592 records that are can be used for research purposes, but not consultancy. The higher-frequency sea-level dataset in this second part of GESLA-3 was obtained from 3 international and national data providers, specifically: City of Venice, Tide Forecasts and Reporting Center, University of Zagreb and the Copernicus Marine Environment Monitoring Service. These data are made available under the creative commons BY-NC 4.0 license., Peer reviewed

Figure S1: (a) Valve connected to the biodigester with the respective rings and screw nut; (b) Bottle lid showing the hose pipe connected after glue (T-Rex Flex the universal adhesive sealant, Soudal). Figure S2: Filter system assembly schematic. Figure S3: Air chamber and the valve core. Figure S4: Flame system with the air inlet open (a) and closed (b). Figure S5: Color changing of NaOH solution during the absorption process. Table S1: Material list and their respective images involved in the construction of the DIY biodigester. Video S1: Building a mini biodigester for schools., Peer reviewed

Table S1. Characteristics of the 11 studied ST274-Pseudomonas aeruginosa recovered from different origins; Table S2. Characteristics of the genomes of 14 reference ST274-P. aeruginosa strains and control strains PAO1 and PA14 downloaded from the NCBI database; Table S3. Single-nucleotide polymorphisms (number of SNPs) in the core genome among the 11 studied ST274-P. aeruginosa, the 14 reference ST274-P. aeruginosa genomes, and the two control genomes; Table S4. Pangenome determined using Roary in all 25 ST274-P. aeruginosa strains and the control strains PAO1 and PA14; Table S5. Acquired genes in ST274-P. aeruginosa with Resfinder prediction; Table S6. Dataset and function of the 170 antimicrobial resistance genes analyzed to determine the mutational resistome of the ST274-P. aeruginosa strains; Table S7. Mutational resistome of the 25 studied ST274-P. aeruginosa strains with respect to PAO1 genome; Table S8. Detection of alterations in the mutome genes (involved in the mutator phenotype) of the 11 studied ST274-P. aeruginosa strains and the 14 reference ST274-P. aeruginosa genomes, with respect to PAO1 genome; Table S9. Dataset and functions of the 250 virulence genes analyzed to determine the virulome of the ST274-P. aeruginosa strains; Table S10. Amino acid changes in virulome of the 25 studied ST274-P. aeruginosa strains with respect to the PAO1 genome., Peer reviewed

Additional file 1: Table S1. Requested and simulated transcript models and read counts for ONT and PacBio datasets used in SQANTI-SIM validation. Table S2 Simulated cDNA ONT and PacBio datasets used for pipeline benchmarking. Additional file 2: Figure S1. Number of detected true (TP) and false positives (FP) for different types of novelty (ISM, NIC, and NNC). Figure S2. Relationship between true positives (TP), false negatives (FN), and false positives (FP) with (a) transcript length, (b) number of exons, and (c) simulated expression level. Figure S3. SQANTI-SIM characterization of CAGE peak data. Additional file 3: Supplementary methods and code. Additional file 4: Peer review history., Peer reviewed