11 results on '"Sánchez-López, Nuria"'
Search Results
2. Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission
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Duncanson, Laura, Kellner, James R., Armston, John, Dubayah, Ralph, Minor, David M., Hancock, Steven, Healey, Sean P., Patterson, Paul L., Saarela, Svetlana, Marselis, Suzanne, Silva, Carlos E., Bruening, Jamis, Goetz, Scott J., Tang, Hao, Hofton, Michelle, Blair, Bryan, Luthcke, Scott, Fatoyinbo, Lola, Abernethy, Katharine, Alonso, Alfonso, Andersen, Hans-Erik, Aplin, Paul, Baker, Timothy R., Barbier, Nicolas, Bastin, Jean Francois, Biber, Peter, Boeckx, Pascal, Bogaert, Jan, Boschetti, Luigi, Boucher, Peter Brehm, Boyd, Doreen S., Burslem, David F.R.P., Calvo-Rodriguez, Sofia, Chave, Jérôme, Chazdon, Robin L., Clark, David B., Clark, Deborah A., Cohen, Warren B., Coomes, David A., Corona, Piermaria, Cushman, K.C., Cutler, Mark E.J., Dalling, James W., Dalponte, Michele, Dash, Jonathan, de-Miguel, Sergio, Deng, Songqiu, Ellis, Peter Woods, Erasmus, Barend, Fekety, Patrick A., Fernandez-Landa, Alfredo, Ferraz, Antonio, Fischer, Rico, Fisher, Adrian G., García-Abril, Antonio, Gobakken, Terje, Hacker, Jorg M., Heurich, Marco, Hill, Ross A., Hopkinson, Chris, Huang, Huabing, Hubbell, Stephen P., Hudak, Andrew T., Huth, Andreas, Imbach, Benedikt, Jeffery, Kathryn J., Katoh, Masato, Kearsley, Elizabeth, Kenfack, David, Kljun, Natascha, Knapp, Nikolai, Král, Kamil, Krůček, Martin, Labrière, Nicolas, Lewis, Simon L., Longo, Marcos, Lucas, Richard M., Main, Russell, Manzanera, Jose A., Martínez, Rodolfo Vásquez, Mathieu, Renaud, Memiaghe, Herve, Meyer, Victoria, Mendoza, Abel Monteagudo, Monerris, Alessandra, Montesano, Paul, Morsdorf, Felix, Næsset, Erik, Naidoo, Laven, Nilus, Reuben, O’Brien, Michael, Orwig, David A., Papathanassiou, Konstantinos, Parker, Geoffrey, Philipson, Christopher, Phillips, Oliver L., Pisek, Jan, Poulsen, John R., Pretzsch, Hans, Rüdiger, Christoph, Saatchi, Sassan, Sanchez-Azofeifa, Arturo, Sanchez-Lopez, Nuria, Scholes, Robert, Silva, Carlos A., Simard, Marc, Skidmore, Andrew, Stereńczak, Krzysztof, Tanase, Mihai, Torresan, Chiara, Valbuena, Ruben, Verbeeck, Hans, Vrska, Tomas, Wessels, Konrad, White, Joanne C., White, Lee J.T., Zahabu, Eliakimu, and Zgraggen, Carlo
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- 2022
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3. Crown-Level Structure and Fuel Load Characterization from Airborne and Terrestrial Laser Scanning in a Longleaf Pine (Pinus palustris Mill.) Forest Ecosystem.
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Rocha, Kleydson Diego, Silva, Carlos Alberto, Cosenza, Diogo N., Mohan, Midhun, Klauberg, Carine, Schlickmann, Monique Bohora, Xia, Jinyi, Leite, Rodrigo V., de Almeida, Danilo Roberti Alves, Atkins, Jeff W., Cardil, Adrian, Rowell, Eric, Parsons, Russ, Sánchez-López, Nuria, Prichard, Susan J., and Hudak, Andrew T.
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LONGLEAF pine ,AIRBORNE lasers ,OPTICAL scanners ,ALLOMETRIC equations ,FUELWOOD - Abstract
Airborne Laser Scanners (ALS) and Terrestrial Laser Scanners (TLS) are two lidar systems frequently used for remote sensing forested ecosystems. The aim of this study was to compare crown metrics derived from TLS, ALS, and a combination of both for describing the crown structure and fuel attributes of longleaf pine (Pinus palustris Mill.) dominated forest located at Eglin Air Force Base (AFB), Florida, USA. The study landscape was characterized by an ALS and TLS data collection along with field measurements within three large (1963 m
2 each) plots in total, each one representing a distinct stand condition at Eglin AFB. Tree-level measurements included bole diameter at breast height (DBH), total height (HT), crown base height (CBH), and crown width (CW). In addition, the crown structure and fuel metrics foliage biomass (FB), stem branches biomass (SB), crown biomass (CB), and crown bulk density (CBD) were calculated using allometric equations. Canopy Height Models (CHM) were created from ALS and TLS point clouds separately and by combining them (ALS + TLS). Individual trees were extracted, and crown-level metrics were computed from the three lidar-derived datasets and used to train random forest (RF) models. The results of the individual tree detection showed successful estimation of tree count from all lidar-derived datasets, with marginal errors ranging from −4 to 3%. For all three lidar-derived datasets, the RF models accurately predicted all tree-level attributes. Overall, we found strong positive correlations between model predictions and observed values (R2 between 0.80 and 0.98), low to moderate errors (RMSE% between 4.56 and 50.99%), and low biases (between 0.03% and −2.86%). The highest R2 using ALS data was achieved predicting CBH (R2 = 0.98), while for TLS and ALS + TLS, the highest R2 was observed predicting HT, CW, and CBD (R2 = 0.94) and HT (R2 = 0.98), respectively. Relative RMSE was lowest for HT using three lidar datasets (ALS = 4.83%, TLS = 7.22%, and ALS + TLS = 4.56%). All models and datasets had similar accuracies in terms of bias (<2.0%), except for CB in ALS (−2.53%) and ALS + TLS (−2.86%), and SB in ALS + TLS data (−2.22%). These results demonstrate the usefulness of all three lidar-related methodologies and lidar modeling overall, along with lidar applicability in the estimation of crown structure and fuel attributes of longleaf pine forest ecosystems. Given that TLS measurements are less practical and more expensive, our comparison suggests that ALS measurements are still reasonable for many applications, and its usefulness is justified. This novel tree-level analysis and its respective results contribute to lidar-based planning of forest structure and fuel management. [ABSTRACT FROM AUTHOR]- Published
- 2023
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4. Hacking Limnology Workshop and DSOS22: Creating a Community of Practice for the Nexus of Data Science, Open Science, and the Aquatic Sciences
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Meyer, Michael F., Barbosa, Carolina C., Ladwig, Robert, Mesman, Jorrit P., Börekçi, Nahit Soner, Cawley, Kaelin, Feldbauer, Johannes, Oleksy, Isabella A., Pilla, Rachel M., Tran, Patricia Q., Zwart, Jacob A., Schnedler-Meyer, Nicolas Azaña, Andersen, Tobias K., Brousil, Matthew R., Fickas, Kate C., Filazzola, Alessandro, King, Tyler V., Sánchez-López, Nuria, Stengel, Victoria, and Trolle, Dennis
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- 2022
5. S-Nitrosation of E3 Ubiquitin Ligase Complex Components Regulates Hormonal Signalings in Arabidopsis.
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Terrile, Maria Cecilia, Tebez, Nuria Malena, Colman, Silvana Lorena, Mateos, Julieta Lisa, Morato-López, Esperanza, Sánchez-López, Nuria, Izquierdo-Álvarez, Alicia, Marina, Anabel, Calderón Villalobos, Luz Irina A., Estelle, Mark, Martínez-Ruiz, Antonio, Fiol, Diego Fernando, Casalongué, Claudia Anahí, and Iglesias, María José
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UBIQUITIN ligases ,MUTANT proteins ,POST-translational modification ,PHYSIOLOGY ,ARABIDOPSIS thaliana - Abstract
E3 ubiquitin ligases mediate the last step of the ubiquitination pathway in the ubiquitin-proteasome system (UPS). By targeting transcriptional regulators for their turnover, E3s play a crucial role in every aspect of plant biology. In plants, SKP1/CULLIN1/F-BOX PROTEIN (SCF)-type E3 ubiquitin ligases are essential for the perception and signaling of several key hormones including auxins and jasmonates (JAs). F-box proteins, TRANSPORT INHIBITOR RESPONSE 1 (TIR1) and CORONATINE INSENSITIVE 1 (COI1), bind directly transcriptional repressors AUXIN/INDOLE-3-ACETIC ACID (AUX/IAA) and JASMONATE ZIM-DOMAIN (JAZ) in auxin- and JAs-depending manner, respectively, which permits the perception of the hormones and transcriptional activation of signaling pathways. Redox modification of proteins mainly by S-nitrosation of cysteines (Cys) residues via nitric oxide (NO) has emerged as a valued regulatory mechanism in physiological processes requiring its rapid and versatile integration. Previously, we demonstrated that TIR1 and Arabidopsis thaliana SKP1 (ASK1) are targets of S-nitrosation, and these NO-dependent posttranslational modifications enhance protein-protein interactions and positively regulate SCF
TIR1 complex assembly and expression of auxin response genes. In this work, we confirmed S-nitrosation of Cys140 in TIR1, which was associated in planta to auxin-dependent developmental and stress-associated responses. In addition, we provide evidence on the modulation of the SCFCOI1 complex by different S-nitrosation events. We demonstrated that S-nitrosation of ASK1 Cys118 enhanced ASK1-COI1 protein-protein interaction. Overexpression of non-nitrosable ask1 mutant protein impaired the activation of JA-responsive genes mediated by SCFCOI1 illustrating the functional relevance of this redox-mediated regulation in planta. In silico analysis positions COI1 as a promising S-nitrosation target, and demonstrated that plants treated with methyl JA (MeJA) or S-nitrosocysteine (NO-Cys, S-nitrosation agent) develop shared responses at a genome-wide level. The regulation of SCF components involved in hormonal perception by S-nitrosation may represent a key strategy to determine the precise time and site-dependent activation of each hormonal signaling pathway and highlights NO as a pivotal molecular player in these scenarios. [ABSTRACT FROM AUTHOR]- Published
- 2022
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6. The AEMON‐J "Hacking Limnology" Workshop Series & Virtual Summit: Incorporating Data Science and Open Science in Aquatic Research.
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Meyer, Michael F., Ladwig, Robert, Mesman, Jorrit P., Oleksy, Isabella A., Barbosa, Carolina C., Cawley, Kaelin M., Cramer, Alli N., Feldbauer, Johannes, Tran, Patricia Q., Zwart, Jacob A., López Moreira M., Gregorio A., Shikhani, Muhammed, Gurung, Deviyani, Hensley, Robert T., Matta, Elena, McClure, Ryan P., Petzoldt, Thomas, Sánchez‐López, Nuria, Soetaert, Karline, and Thomas, Mridul K.
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AQUATIC sciences ,DATA science ,LIMNOLOGY ,COMPUTER hacking ,INFORMATION-seeking behavior ,AQUATIC exercises - Abstract
DSOS combined forces with the Aquatic Ecosystem MOdeling Network - Junior (AEMON-J; https://github.com/aemon-j) to host a 4-d "Hacking Limnology" Workshop Series prior to the summit (13-16 July 2021). The AEMON-J "Hacking Limnology" Workshop Series & Virtual Summit: Incorporating Data Science and Open Science in Aquatic Research 2021a. AEMON-J/DSOS archive: "Hacking Limnology" Workshop + virtual summit in Data Science & Open Science In Aquatic Research. doi: 10.17605/OSF.IO/682V5 7 Meyer, M. F., and others. Following the 2020 "Virtual Summit: Incorporating Data Science and Open Science in Aquatic Research" (DSOS; Meyer and Zwart 2020), a grassroots group of scientists convened the 2nd Virtual DSOS Summit on 22-23 July 2021. [Extracted from the article]
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- 2021
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7. A semi-automated LiDAR-GEOBIA methodology for forest even-aged stand delineation based on a two-stage evaluation strategy
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Sánchez López, Nuria, Boschetti, Luigi, Hudak, Andrew Thomas, University of Idaho [Moscow, USA], USDA Forest Service Rocky Mountain Forest and Range Experiment Station, United States Department of Agriculture (USDA), Centre d'Etudes Spatiales de la BIOsphère (CESBIO), Office national d'études et de recherches aérospatiales (ONERA), Espace pour le développement (ESPACE DEV), Société T.E.T.I.S, and Univ, Réunion
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[SDV.EE]Life Sciences [q-bio]/Ecology, environment ,[SDV.EE] Life Sciences [q-bio]/Ecology, environment ,LiDAR ,GEOBIA ,[SDV.SA.SF]Life Sciences [q-bio]/Agricultural sciences/Silviculture, forestry ,Even aged forest stand ,Multiresolution segmentation ,Delineation ,[SDV.SA.SF] Life Sciences [q-bio]/Agricultural sciences/Silviculture, forestry ,Evaluation - Abstract
International audience; Forest stand delineation is rapidly evolving from traditional photointerpretation to semi-automated GEOBIA techniques. To obtain a good correspondence between image objects and geographic objects, GEOBIA techniques require user decisions considering input data, segmentation algorithms, and classification strategies. GEOBIA applications in forestry have relied mostly on optical remotely sensed data, focusing on the spectral properties of vegetation to identify forest stand boundaries. A limitation of this approach is that optical data have limited sensitivity to forest structural parameters which are the main driver of stand boundaries in even-aged forests. Active sensors such as Light Detection and Ranging (LiDAR) are an alternative, providing a direct estimation of forest structure (e.g. height, density) and potentially leading to more accurate stand maps. In this paper, we propose a semi-automated methodology for even-aged stand delineation using LiDAR data and a two-stage GEOBIA evaluation strategy, combining both unsupervised and supervised evaluation methods to select a suitable segmentation output. The study area is located in the Clear Creek, Selway River & Elk Creek watersheds (~ 54,000 ha) in Northern Idaho (USA), where available LiDAR data was collected in 2009 (Clear Creek watershed) and 2012 (Selway River & Elk Creek). Additionally, a reference dataset of stand-replacing disturbances consisting of yearly clearcut maps compiled from timber harvest records were also available from 1950 as part of the US Forest Service FACTS (Forest ACtivity Tracking System). The proposed methodology involves: (1) image segmentation of several airborne LiDAR metrics using the multiresolution segmentation algorithm implemented on the eCognition software varying consistently the scale, compactness and shape parameters; (2) selection of the best set of parameters for segmentation for each tested LiDAR metric, applying an unsupervised evaluation method based on measures of spatial autocorrelation. This stage ensures that the selected segmentation has the highest possible intra-object uniformity and inter-object heterogeneity; (3) selection of the most suitable LiDAR metric for the segmentation, applying a supervised evaluation method based on measures of area-based dissimilarity, selecting the segmentation with the maximum degree of similarity in size and shape to FACTS reference dataset; and (4) validation using as reference data forest stand perimeters independently derived from visual interpretation. The results show good delineation of even-aged forest, including stands harvested more than 60 years ago that are generally challenging to detect with optical data, because the spectral response of forest canopy saturates at high levels of canopy closure. On a methodological level, the proposed two-stage procedure allows not only accurate image objects delineations but also allows to select the most suitable input data that assure that the image objects are spatially matching with the ground objects. This workflow could be implemented in other studies where different segmentation strategies (e.g., different segmentation algorithms, parameters or resolutions), input data (e.g., Landsat data) or target features (e.g., land cove types) need to be assessed.
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- 2018
8. S-Nitrosylation of Ras Mediates Nitric Oxide-Dependent Post-Injury Neurogenesis in a Seizure Model.
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Santos, Ana Isabel, Carreira, Bruno Pereira, Izquierdo-Álvarez, Alicia, Ramos, Elena, Lourenço, Ana Sofia, Filipa Santos, Daniela, Morte, Maria Inês, Ribeiro, Luís Filipe, Marreiros, Ana, Sánchez-López, Nuria, Marina, Anabel, Carvalho, Caetana Monteiro, Martínez-Ruiz, Antonio, and Araújo, Inês Maria
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- 2018
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9. A spatially explicit model of tree leaf litter accumulation in fire maintained longleaf pine forests of the southeastern US.
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Sánchez-López, Nuria, Hudak, Andrew T., Boschetti, Luigi, Silva, Carlos A., Robertson, Kevin, Loudermilk, E Louise, Bright, Benjamin C., Callaham, Mac A., and Taylor, Melanie K.
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FOREST litter , *LONGLEAF pine , *CROWNS (Botany) , *AIRBORNE lasers , *AIR bases , *DEAD trees - Abstract
• This model is a novel yet simple methodology to quantify the spatially explicit litter loads in frequently burnt longleaf pine forests. • Tree crown objects and estimates of foliage biomass derived from ALS data can be used to characterize patterns of tree leaf litter production and the discontinuity of the litter layer. • Aboveground biomass and YSF are the two major drivers of tree leaf litter accumulation in fire-maintained longleaf pine forests of the southeastern US. The continuity and depth of litter fuelbeds are major drivers of fire spread and fuel consumption. However, no established approach is available for the spatially explicit prediction of litter loads over large areas. Local fuel heterogeneity introduces large uncertainties on estimates derived from field-based models based on the extrapolation of sparse data samples. In fire-maintained pine forests of the southeastern US, litter accumulation and its distribution over the forest floor are mainly driven by vegetation productivity and by the number of years since the last fire (YSF). Some ecological models that simulate fire effects allow for a time-dependent estimation of litter by accounting for the opposing rates of litter deposition and decomposition as a function of YSF at the landscape level, but they do not account for spatial heterogeneity. We developed a conceptually simple approach for wall-to-wall estimation of tree leaf litter loads at high spatial resolution (5 m). The approach involved, first, estimating spatial patterns of tree annual litterfall. We mapped individual tree crowns through segmentation of airborne laser scanning (ALS) data, and we estimated crown foliage biomass using tree inventory data and ALS derived tree crown attributes. Tree annual litterfall was calculated as a fraction of the crown foliage biomass based on leaf turnover rates. We then quantified tree leaf litter accumulation through a spatially explicit implementation of the established Olson (1963) accumulation and negative decay model. We tested and validated our model in several management and research units at Eglin Air Force Base (Florida), Pebble Hill Plantation (Georgia), and Osceola National Forest (Florida), where managers maintain predominantly longleaf pine forests using frequent fire. Pixel-level RMSD and BIAS between tree leaf litter biomass estimated by the proposed model and reference field measurements were 0.21 and 0.01 kg m −2, and area-level RMSD and BIAS were 0.09 and 0.01 kg m −2. We concluded that linking patterns of litterfall and tree leaf litter accumulation to tree crown objects provides a means to characterizing the discontinuity of the litter layer, accounting for spatial heterogeneity largely traceable to tree crown foliage inputs. [ABSTRACT FROM AUTHOR]
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- 2023
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10. Regulation of SCF TIR1/AFBs E3 ligase assembly by S-nitrosylation of Arabidopsis SKP1-like1 impacts on auxin signaling.
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Iglesias MJ, Terrile MC, Correa-Aragunde N, Colman SL, Izquierdo-Álvarez A, Fiol DF, París R, Sánchez-López N, Marina A, Calderón Villalobos LIA, Estelle M, Lamattina L, Martínez-Ruiz A, and Casalongué CA
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- Models, Molecular, Nitroso Compounds metabolism, Protein Interaction Maps, Ubiquitin-Protein Ligases metabolism, Arabidopsis metabolism, Arabidopsis Proteins metabolism, F-Box Proteins metabolism, Indoleacetic Acids metabolism, Nitric Oxide metabolism, Receptors, Cell Surface metabolism, SKP Cullin F-Box Protein Ligases metabolism, Signal Transduction
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The F-box proteins (FBPs) TIR1/AFBs are the substrate recognition subunits of SKP1-cullin-F-box (SCF) ubiquitin ligase complexes and together with Aux/IAAs form the auxin co-receptor. Although tremendous knowledge on auxin perception and signaling has been gained in the last years, SCF
TIR1/AFBs complex assembly and stabilization are emerging as new layers of regulation. Here, we investigated how nitric oxide (NO), through S-nitrosylation of ASK1 is involved in SCFTIR1/AFBs assembly. We demonstrate that ASK1 is S-nitrosylated and S-glutathionylated in cysteine (Cys) 37 and Cys118 residues in vitro. Both, in vitro and in vivo protein-protein interaction assays show that NO enhances ASK1 binding to CUL1 and TIR1/AFB2, required for SCFTIR1/AFB2 assembly. In addition, we demonstrate that Cys37 and Cys118 are essential residues for proper activation of auxin signaling pathway in planta. Phylogenetic analysis revealed that Cys37 residue is only conserved in SKP proteins in Angiosperms, suggesting that S-nitrosylation on Cys37 could represent an evolutionary adaption for SKP1 function in flowering plants. Collectively, these findings indicate that multiple events of redox modifications might be part of a fine-tuning regulation of SCFTIR1/AFBs for proper auxin signal transduction., (Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.)- Published
- 2018
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11. Mitochondrial complex I deactivation is related to superoxide production in acute hypoxia.
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Hernansanz-Agustín P, Ramos E, Navarro E, Parada E, Sánchez-López N, Peláez-Aguado L, Cabrera-García JD, Tello D, Buendia I, Marina A, Egea J, López MG, Bogdanova A, and Martínez-Ruiz A
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- Animals, Cattle, Cell Hypoxia, Cells, Cultured, Endothelial Cells cytology, Mitochondria metabolism, Oxidation-Reduction, Protein Kinases metabolism, Signal Transduction, Electron Transport Complex I metabolism, Endothelial Cells metabolism, Superoxides metabolism
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Mitochondria use oxygen as the final acceptor of the respiratory chain, but its incomplete reduction can also produce reactive oxygen species (ROS), especially superoxide. Acute hypoxia produces a superoxide burst in different cell types, but the triggering mechanism is still unknown. Herein, we show that complex I is involved in this superoxide burst under acute hypoxia in endothelial cells. We have also studied the possible mechanisms by which complex I could be involved in this burst, discarding reverse electron transport in complex I and the implication of PTEN-induced putative kinase 1 (PINK1). We show that complex I transition from the active to 'deactive' form is enhanced by acute hypoxia in endothelial cells and brain tissue, and we suggest that it can trigger ROS production through its Na
+ /H+ antiporter activity. These results highlight the role of complex I as a key actor in redox signalling in acute hypoxia., (Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.)- Published
- 2017
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