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.
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
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
SKP1 (ASK1) are targets of S-nitrosation, and these NO-dependent posttranslational modifications enhance protein-protein interactions and positively regulate SCF
complex assembly and expression of auxin response genes. In this work, we confirmed S-nitrosation of Cys140 in TIR1, which was associated
to auxin-dependent developmental and stress-associated responses. In addition, we provide evidence on the modulation of the SCF
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 SCF
illustrating the functional relevance of this redox-mediated regulation
.
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.
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 m2 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.
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, SCFTIR1/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.
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•ASK1 adaptor protein of the SCFTIR1/AFB E3 ligase complex is redox regulated.•NO regulates ASK1 function by S-nitrosylation in Cys37 and Cys118 residues.•NO enhances ASK1-CUL1 and ASK1-TIR1/AFB2 protein-protein interactions required for SCFTIR1/AFB2 assembly in vitro and in vivo.•S-nitrosylated residues in ASK1 are essential for activation of auxin signaling pathway in plants.
•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.
Mapping with LiDAR data is not a standardized practice, though LiDAR databasesare increasing in all countries in Europe. We develop and test a simple method forautomated land-cover mapping. The study ...area was a farm located at a natural parkof southern Spain. It comprises 502 ha covered by Mediterranean forestagroecosystems, like dehesa (a very open woodland of scattered evergreen treesused by grazing animals), woodland and scrubland, and transitions among them,composing a heterogeneous landscape. This heterogeneity is caused by variationsin holm and cork oak tree density and a sclerophyllous shrub cover, i.e., 3Dstructure of woody vegetation. Using aerial photographs digitization, Landsatimage classification, and image segmentation of tree crowns, land-cover maps weregenerated. Besides, other maps were produced from LiDAR-derived canopy coverand height of tree vegetation and shrub stratum. These 3D variables allowed to awall-to-wall characterization of woody vegetation land-cover classes in the studyarea, that was completed with a NDVI assessment. The results show that automatedmapping with LiDAR is reliable and accurate enough in comparison with othermapping techniques. It outperforms them because its higher spatial resolution, andcan be combined with other remote sensing methods to provides an improvedunderstanding of forest landscapes.
Regional scale maps of homogeneous forest stands are valued by forest managers and are of interest for landscape and ecological modelling. Research focused on stand delineation has substantially ...increased in the last decade thanks to the development of Geographic Object Based Image Analysis (GEOBIA). Nevertheless, studies focused on even-age dominated forests are still few and the proposed approaches are often heuristic, local, or lacking objective evaluation protocols. In this study, we present a two-stage evaluation strategy combining both unsupervised and supervised evaluation methods for semi-automatic delineation of forest stands at regional scales using Light Detection and Ranging (LiDAR) raster summary metrics. The methodology is demonstrated on two contiguous LiDAR datasets covering more than 54,000 ha in central Idaho, where clearcuts were a common harvesting method during the twentieth century. Results show good delineation of even-aged forests and demonstrate the ability of LiDAR to discriminate stands harvested more than 50 years ago, that are generally challenging to discriminate with optical data. The two-stage strategy reduces the reference data required within the supervised evaluation and increases the scope of a reliable semi-automatic delineation to larger areas. This is an objective and straightforward approach that could potentially be replicated and adapted to address other study needs.
The 2nd Aquatic Ecosystem Modeling-Junior (AEMON-J) Hacking Limnology Workshop and 3rd Virtual Summit: Incorporating Data Science and Open Science in the Aquatic Sciences (DSOS) took place on 25–29 ...July 2022. These virtual events were developed to bring together researchers from diverse backgrounds to share developments in data-intensive research in the aquatic sciences and train participants in cutting-edge data analysis methods related to remote sensing, data pipelines, and modeling of aquatic ecosystems.
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