cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10581416
title: Cavity effect on NQO1

description: Raw data published in: Pacheco-Garcia JL, Loginov DS, Anoz-Carbonell E, Vankova P, Palomino-Morales R, Salido E, Man P, Medina M, Naganathan AN, Pey AL. Allosteric Communication in the Multifunctional and Redox NQO1 Protein Studied by Cavity-Making Mutations. Antioxidants (Basel). 2022 Jun 2;11(6):1110. doi: 10.3390/antiox11061110. PMID: 35740007; PMCID: PMC9219786.

Data measured using a stopped-flow spectrophotometer from Applied Photophysics (SX.18MV, Applied Photophysics Ltd., Leatherhead, UK) interfaced with a photodiode array detector and under anaerobic conditions, following previously established protocols. Multiple wavelength absorption data in the flavin absorption region (400-900 nm) were collected and processed using the ProData-SX software (Applied Photophysics Ltd.). Time-dependent spectral deconvolution was performed by global analysis and numerical integration methods using Pro-Kineticist (Applied Photophysics Ltd.). Collected data were fitted to either single- or multi-step (A→B→n….→Z) models allowing for estimation of the corresponding observed conversion rate constants at each NADH concentration, as well of the spectra of intermediate and final species.

Experimental section:

Fast hydride- and deuteride-transfer reactions (HT and DT, respectively) were carried out under anaerobic conditions using a stopped-flow spectrophotometer as described [15]. Briefly, the reductive half reaction was measured by mixing NQO1holo variants (7.5 µM) with NADH ranging from 7.5 to 100 µM (these refer to final concentrations). Reactions were performed in 20 mM HEPES-KOH, pH 7.4. Data were collected using either NADH or NADD, but using only one of these reducing species in a given experiment. Multiple-wavelength absorption data in the flavin absorption region were collected and processed as described [15]. Time-dependent spectral deconvolution was performed by global fitting analysis and numerical integration using previously described procedures [15]. This deconvolution procedure was carried out considering sequential and irreversible steps in the context of a two-step mechanism (A⟶B⟶C) and was used to determine observed rate constants (kobs) for these steps as well as the spectroscopic properties of these species (A, B and C). 

 

To determine primary kinetic isotopic effects (KIEs) in the HT process [20], the kobs for HT and DT was determined by mixing NADH/D with NQO1holo using equimolar concentrations of NQO1holo and NADH or [4R-2H]-NADD (7.5 µM of each component again experiments at higher NADH concentrations where limited but reactions becoming too fast for detection upon increasing temperature). These apparent KIEs were determined as the ratio of kobs values using NADH and NADD. Experiments were carried out at temperatures ranging 6–20 °C. Activation parameters (frequency factor, A, and the activation energy, Ea) were determined using the Arrhenius equation as described [15].

Password: nqo1
DOI: 10.5281/zenodo.10581417
title: Cavity effect on NQO1

description: Raw data published in: Pacheco-Garcia JL, Loginov DS, Anoz-Carbonell E, Vankova P, Palomino-Morales R, Salido E, Man P, Medina M, Naganathan AN, Pey AL. Allosteric Communication in the Multifunctional and Redox NQO1 Protein Studied by Cavity-Making Mutations. Antioxidants (Basel). 2022 Jun 2;11(6):1110. doi: 10.3390/antiox11061110. PMID: 35740007; PMCID: PMC9219786.

Data measured using a stopped-flow spectrophotometer from Applied Photophysics (SX.18MV, Applied Photophysics Ltd., Leatherhead, UK) interfaced with a photodiode array detector and under anaerobic conditions, following previously established protocols. Multiple wavelength absorption data in the flavin absorption region (400-900 nm) were collected and processed using the ProData-SX software (Applied Photophysics Ltd.). Time-dependent spectral deconvolution was performed by global analysis and numerical integration methods using Pro-Kineticist (Applied Photophysics Ltd.). Collected data were fitted to either single- or multi-step (A→B→n….→Z) models allowing for estimation of the corresponding observed conversion rate constants at each NADH concentration, as well of the spectra of intermediate and final species.

Experimental section:

Fast hydride- and deuteride-transfer reactions (HT and DT, respectively) were carried out under anaerobic conditions using a stopped-flow spectrophotometer as described [15]. Briefly, the reductive half reaction was measured by mixing NQO1holo variants (7.5 µM) with NADH ranging from 7.5 to 100 µM (these refer to final concentrations). Reactions were performed in 20 mM HEPES-KOH, pH 7.4. Data were collected using either NADH or NADD, but using only one of these reducing species in a given experiment. Multiple-wavelength absorption data in the flavin absorption region were collected and processed as described [15]. Time-dependent spectral deconvolution was performed by global fitting analysis and numerical integration using previously described procedures [15]. This deconvolution procedure was carried out considering sequential and irreversible steps in the context of a two-step mechanism (A⟶B⟶C) and was used to determine observed rate constants (kobs) for these steps as well as the spectroscopic properties of these species (A, B and C). 

 

To determine primary kinetic isotopic effects (KIEs) in the HT process [20], the kobs for HT and DT was determined by mixing NADH/D with NQO1holo using equimolar concentrations of NQO1holo and NADH or [4R-2H]-NADD (7.5 µM of each component again experiments at higher NADH concentrations where limited but reactions becoming too fast for detection upon increasing temperature). These apparent KIEs were determined as the ratio of kobs values using NADH and NADD. Experiments were carried out at temperatures ranging 6–20 °C. Activation parameters (frequency factor, A, and the activation energy, Ea) were determined using the Arrhenius equation as described [15].

Password: nqo1
DOI: 10.5281/zenodo.10264337
title: Base datos segundo y tercer año 13_15_Teoría de la Educación EDU2012-32725.sav

description: Base de datos de 426 sujetos, formada por tres grupos de alumnos (los grupos vienen recogidos en la primera variable de la base de datos). El primer grupo (grupo 1), correspondiente al curso académico 2012-13, de 65 sujetos, está formado, a su vez, por dos subgrupos; el primer grupo corresponde al profesor A y el segundo al profesor B (los profesores se recogen en la cuarta de las variables). El segundo (grupo 2), correspondiente al curso académico 2013-14, de 181 sujetos, está formado, a su vez, por tres subgrupos de alumnos; el primer grupo corresponde al profesor A , el segundo al profesor B y el tercero al profesor C (los profesores se recogen en la cuarta de las variables). El tercero (grupo 3), correspondiente al curso académico 2014-15, de 180 sujetos, también está formado por tres subgrupos de alumnos; el primer grupo corresponde al profesor A , el segundo al profesor B y el tercero al profesor C (los profesores se recogen en la cuarta de las variables).
La muestra, de la asignatura de Teoría de la Educación, cursada en 1º curso de los grados de Educación Social y Pedagogía de la Universidad de Valencia, se usó para analizar el impacto de la aplicación de métodos centrados en el aprendizaje, sobre los enfoques de aprendizaje de los alumnos de estos grupos y sobre sus capacidades y su percepción del entorno de aprendizaje articulado por sus profesores.
Los datos correspondientes al curso 2012-13 se tomaron de los alumnos en situación de postest, una vez acabada la docencia de la materia en cuestión, usando en ella metodología más bien tradicional, para tener puntuaciones de anclaje de cara a los siguientes cursos, En los dos cursos siguientes (2013-14 y 2014-15) se aplicaron a los tres grupos de la materia Teoría de la Educación métodos centrados en el aprendizaje y se pasó, cada uno de los cursos, pretest y postest, con un diseño transversal, ya que los alumnos eran diferentes cada curso.
En este trabajo se quería valorar la influencia de los métodos centrados en el aprendizaje sobre los enfoques de aprendizaje y sobre diversas capacidades/habilidades de tres grupos de alumnos (426 sujetos) de 1º curso de los grados de Pedagogía y Educación Social de la Universidad de Valencia en la asignatura de Teoría de la Educación. Se utilizó un diseño cuasiexperimental de grupo de control no equivalente, con medidas de postest en el grupo de control y de pretest y postest en los grupos experimentales, evaluando los enfoques de aprendizaje mediante  el cuestionario  CPE y las capacidades del alumno mediante el cuestionario SEQ. 
La base de datos, en SPSS 17.0, está integrada por 50 variables, siendo las cuatro primeras nominales y las restantes escalares, con valores que van de 1 (máximo desacuerdo) a 5 (máximo acuerdo). De la variable 5 a la 16 recogen resultados del cuestionario CPE, que evalúa los enfoques de aprendizaje del alumnado. 
En el fichero que se ha subido, en txt, junto a la base de datos, se explican las variables y otras cuestiones relativas a la base de datos.
DOI: 10.5281/zenodo.7947708
title: Macroalgal genomics illuminate three disparate paths to multicellularity - Supplementary Data - Tables - Data S4

description: Macroalgae are multicellular, aquatic autotrophs that play vital roles in global climate maintenance and have diverse applications in biotechnology and eco-engineering, which are directly linked to their multicellularity phenotypes. However, their genomic diversity and the evolutionary mechanisms underlying multicellularity in these organisms remain uncharacterized. Here, we sequenced 112 macroalgal genomes from diverse climates and phyla, identifying key genomic features that distinguish them from their microalgal relatives. We found that macroalgae have expanded gene families related to cellular adhesion, extracellular matrix formation, cytoskeletal organization and signaling pathways. We discovered that many of these genes have viral origins and are lineage-specific or conserved among the three major macroalgal phyla: Rhodophyta (red algae), Chlorophyta (green algae) and Ochrophyta (brown algae). Our work reveals genetic determinants of convergent and divergent evolutionary trajectories that have shaped morphological diversity in macroalgae and provides genome-wide frameworks to understand photosynthetic multicellular evolution in marine environments. <strong>Table S1. Functional annotation and metadata for macroalgal species. </strong>This is a multi-sheet Excel workbook containing the PFAM count matrix for decontaminated assemblies and their strain metadata, contamination estimates, and source data for the ternary analysis. Related to Figs. 2 and 3. <strong>Table S2. GO enrichment in macroalgal-specific genes. </strong>This is a multi-sheet Excel workbook containing enriched GO terms in macroalgal-specific PFAMs conserved in Rhodophyta, Phaeophyta, and Chlorophyta and response screening results comparing means in PFAM counts between divisions. Related to Fig. 3. <strong>Table S3.</strong> <strong>Comparative genomics of micro- and macroalgae. </strong>This is a multi-sheet Excel workbook containing response screening tables comparing PFAM and GO variation in micro- compared to macroalgae. Related to Fig. 4. <strong>Table S4. Unraveling the macroalgal adhesome. </strong>This is a multi-sheet Excel workbook containing macroalgal adhesome atlas and response screens among macroalgal phyla and between micro- and macroalgae. Related to Fig. 4. <strong>Table S5. Endogenous viral elements in macroalgae. </strong>This is a multi-sheet Excel workbook containing VFAM count matrix and response screening results for comparisons of macroalgal VFAM counts by climate and habitat. This table also includes the EVOP matrix and response screening results comparing EVOPs found in the macroalgal genomes among climates and macroalgal tORFs with EsV-1-7 domains and their codomains. Related to Fig. 5.
DOI: 10.5281/zenodo.8412535
title: Macroalgal genomics illuminate three disparate paths to multicellularity - Supplementary Data - Tables - Data S4

description: Macroalgae are multicellular, aquatic autotrophs that play vital roles in global climate maintenance and have diverse applications in biotechnology and eco-engineering, which are directly linked to their multicellularity phenotypes. However, their genomic diversity and the evolutionary mechanisms underlying multicellularity in these organisms remain uncharacterized. Here, we sequenced 112 macroalgal genomes from diverse climates and phyla, identifying key genomic features that distinguish them from their microalgal relatives. We found that macroalgae have expanded gene families related to cellular adhesion, extracellular matrix formation, cytoskeletal organization and signaling pathways. We discovered that many of these genes have viral origins and are lineage-specific or conserved among the three major macroalgal phyla: Rhodophyta (red algae), Chlorophyta (green algae) and Ochrophyta (brown algae). Our work reveals genetic determinants of convergent and divergent evolutionary trajectories that have shaped morphological diversity in macroalgae and provides genome-wide frameworks to understand photosynthetic multicellular evolution in marine environments.


 


Table S1. Functional annotation and metadata for macroalgal species. This is a multi-sheet Excel workbook containing the PFAM count matrix for decontaminated assemblies and their strain metadata, contamination estimates, and source data for the ternary analysis. Related to Figs. 2 and 3.


Table S2. GO enrichment in macroalgal-specific genes. This is a multi-sheet Excel workbook containing enriched GO terms in macroalgal-specific PFAMs conserved in Rhodophyta, Phaeophyta, and Chlorophyta and response screening results comparing means in PFAM counts between divisions. Related to Fig. 3.


Table S3. Comparative genomics of micro- and macroalgae. This is a multi-sheet Excel workbook containing response screening tables comparing PFAM and GO variation in micro- compared to macroalgae. Related to Fig. 4.


Table S4. Unraveling the macroalgal adhesome. This is a multi-sheet Excel workbook containing macroalgal adhesome atlas and response screens among macroalgal phyla and between micro- and macroalgae. Related to Fig. 4.


Table S5. Endogenous viral elements in macroalgae. This is a multi-sheet Excel workbook containing VFAM count matrix and response screening results for comparisons of macroalgal VFAM counts by climate and habitat. This table also includes the EVOP matrix and response screening results comparing EVOPs found in the macroalgal genomes among climates and macroalgal tORFs with EsV-1-7 domains and their codomains. Related to Fig. 5.
DOI: 10.5281/zenodo.10404855
title: Macroalgal genomics illuminate three disparate paths to multicellularity - Supplementary Data - Tables - Data S4

description: Macroalgae are multicellular, aquatic autotrophs that play vital roles in global climate maintenance and have diverse applications in biotechnology and eco-engineering, which are directly linked to their multicellularity phenotypes. However, their genomic diversity and the evolutionary mechanisms underlying multicellularity in these organisms remain uncharacterized. Here, we sequenced 112 macroalgal genomes from diverse climates and phyla, identifying key genomic features that distinguish them from their microalgal relatives. We found that macroalgae have expanded gene families related to cellular adhesion, extracellular matrix formation, cytoskeletal organization and signaling pathways. We discovered that many of these genes have viral origins and are lineage-specific or conserved among the three major macroalgal phyla: Rhodophyta (red algae), Chlorophyta (green algae) and Ochrophyta (brown algae). Our work reveals genetic determinants of convergent and divergent evolutionary trajectories that have shaped morphological diversity in macroalgae and provides genome-wide frameworks to understand photosynthetic multicellular evolution in marine environments.

Table S1. Functional annotation and metadata for macroalgal species. This is a multi-sheet Excel workbook containing the PFAM count matrix for decontaminated assemblies and their strain metadata, assembly metrics, and contamination estimates. Related to Figs. 2 and 3.

Table S2. GO enrichment in macroalgal-specific genes. This is a multi-sheet Excel workbook containing the ternary analysis data, enriched GO terms in macroalgal-specific PFAMs conserved in Rhodophyta, Phaeophyta, and Chlorophyta and response screening results comparing means in PFAM counts between divisions. Related to Fig. 3.

Table S3. Comparative genomics of micro- and macroalgae. This is a multi-sheet Excel workbook containing response screening tables comparing PFAM and GO variation in micro- compared to macroalgae. Related to Fig. 4.

Table S4. Unraveling the macroalgal adhesome. This is a multi-sheet Excel workbook containing macroalgal adhesome atlas and response screens among macroalgal phyla and between micro- and macroalgae. Related to Fig. 4.

Table S5. Endogenous viral elements in macroalgae. This is a multi-sheet Excel workbook containing VFAM count matrix and response screening results for comparisons of macroalgal VFAM counts by climate and habitat. This table also includes the EVOP matrix and response screening results comparing EVOPs found in the macroalgal genomes among climates and macroalgal tORFs with EsV-1-7 domains and their codomains. Related to Fig. 5.
DOI: 10.5281/zenodo.10405017
title: Macroalgal genomics illuminate three paths to multicellularity - Supplementary Data - Tables - Data S4

description: Macroalgae are multicellular, aquatic autotrophs that play vital roles in global climate maintenance and have diverse applications in biotechnology and eco-engineering, which are directly linked to their multicellularity phenotypes. However, their genomic diversity and the evolutionary mechanisms underlying multicellularity in these organisms remain uncharacterized. Here, we sequenced 112 macroalgal genomes from diverse climates and phyla, identifying key genomic features that distinguish them from their microalgal relatives. We found that macroalgae have expanded gene families related to cellular adhesion, extracellular matrix formation, cytoskeletal organization and signaling pathways. We discovered that many of these genes have viral origins and are lineage-specific or conserved among the three major macroalgal phyla: Rhodophyta (red algae), Chlorophyta (green algae) and Ochrophyta (brown algae). Our work reveals genetic determinants of convergent and divergent evolutionary trajectories that have shaped morphological diversity in macroalgae and provides genome-wide frameworks to understand photosynthetic multicellular evolution in marine environments.

Table S1. Functional annotation and metadata for macroalgal species. This is a multi-sheet Excel workbook containing the PFAM count matrix for decontaminated assemblies and their strain metadata, assembly metrics, and contamination estimates. Related to Figs. 2 and 3.

Table S2. GO enrichment in macroalgal-specific genes. This is a multi-sheet Excel workbook containing the ternary analysis data, enriched GO terms in macroalgal-specific PFAMs conserved in Rhodophyta, Phaeophyta, and Chlorophyta and response screening results comparing means in PFAM counts between divisions. Related to Fig. 3.

Table S3. Comparative genomics of micro- and macroalgae. This is a multi-sheet Excel workbook containing response screening tables comparing PFAM and GO variation in micro- compared to macroalgae. Related to Fig. 4.

Table S4. Unraveling the macroalgal adhesome. This is a multi-sheet Excel workbook containing macroalgal adhesome atlas and response screens among macroalgal phyla and between micro- and macroalgae. Related to Fig. 4.

Table S5. Endogenous viral elements in macroalgae. This is a multi-sheet Excel workbook containing VFAM count matrix and response screening results for comparisons of macroalgal VFAM counts by climate and habitat. This table also includes the EVOP matrix and response screening results comparing EVOPs found in the macroalgal genomes among climates and macroalgal tORFs with EsV-1-7 domains and their codomains. Related to Fig. 5.
DOI: 10.5281/zenodo.10581453
title: Macroalgal genomics illuminate three paths to multicellularity - Supplementary Data - Tables - Data S4

description: Macroalgae are multicellular, aquatic autotrophs that play vital roles in global climate maintenance and have diverse applications in biotechnology and eco-engineering, which are directly linked to their multicellularity phenotypes. However, their genomic diversity and the evolutionary mechanisms underlying multicellularity in these organisms remain uncharacterized. Here, we sequenced 112 macroalgal genomes from diverse climates and phyla, identifying key genomic features that distinguish them from their microalgal relatives. We found that macroalgae have expanded gene families related to cellular adhesion, extracellular matrix formation, cytoskeletal organization and signaling pathways. We discovered that many of these genes have viral origins and are lineage-specific or conserved among the three major macroalgal phyla: Rhodophyta (red algae), Chlorophyta (green algae) and Ochrophyta (brown algae). Our work reveals genetic determinants of convergent and divergent evolutionary trajectories that have shaped morphological diversity in macroalgae and provides genome-wide frameworks to understand photosynthetic multicellular evolution in marine environments.

Table S1. Functional annotation and metadata for macroalgal species. This is a multi-sheet Excel workbook containing the PFAM count matrix for decontaminated assemblies and their strain metadata, contamination estimates, assembly metrics including BUSCO and N50 scores, and source data for the ternary analysis. Related to Figs. 2 and 3.

Table S2. GO enrichment in macroalgal-specific genes. This is a multi-sheet Excel workbook containing enriched GO terms in macroalgal-specific PFAMs conserved in Rhodophyta, Ochrophyta, and Chlorophyta and response screening results comparing means in PFAM counts between divisions. Related to Fig. 3.

Table S3. Comparative genomics of micro- and macroalgae. This is a multi-sheet Excel workbook containing response screening tables comparing PFAM and GO variation in micro- compared to macroalgae. Related to Fig. 4.

Table S4. Unraveling the macroalgal adhesome. This is a multi-sheet Excel workbook containing macroalgal adhesome atlas and response screens among macroalgal phyla and between micro- and macroalgae. Related to Fig. 4.

Table S5. Endogenous viral elements in macroalgae. This is a multi-sheet Excel workbook containing VFAM count matrix and response screening results for comparisons of macroalgal VFAM counts by climate and habitat. This table also includes the EVOP matrix and response screening results comparing EVOPs found in the macroalgal genomes among climates and macroalgal tORFs with EsV-1-7 domains and their codomains. Related to Fig. 5.