RcsF, a proposed auxiliary regulator of the regulation of capsule synthesis (rcs) phosphorelay system, is a key element for understanding the RcsC-D-A/B signaling cascade, which is responsible for ...the regulation of more than 100 genes and is involved in cell division, motility, biofilm formation, and virulence. The RcsC-D-A/B system is one of the most complex bacterial signal transduction pathways, consisting of several membrane-bound and soluble proteins. RcsF is a lipoprotein attached to the outer membrane and plays an important role in activating the RcsC-d-A/B pathway. The exact mechanism of activation of the rcs phosphorelay by RcsF, however, remains unknown. We have analyzed the sequence of RcsF and identified three structural elements: 1) an N-terminal membrane-anchored helix (residues 3–13), 2) a loop (residues 14–48), and 3) a C-terminal folded domain (residues 49–134). We have determined the structure of this C-terminal domain and started to investigate its interaction with potential partners. Important features of its structure are two disulfide bridges between Cys-74 and Cys-118 and between Cys-109 and Cys-124. To evaluate the importance of this RcsF disulfide bridge network in vivo, we have examined the ability of the full-length protein and of specific Cys mutants to initiate the rcs signaling cascade. The results indicate that the Cys-74/Cys-118 and the Cys-109/Cys-124 residues correlate pairwise with the activity of RcsF. Interaction studies showed a weak interaction with an RNA hairpin. However, no interaction could be detected with reagents that are believed to activate the rcs phosphorelay, such as lysozyme, glucose, or Zn2+ ions.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Cropland abandonment is a widespread land-use change, but it is difficult to monitor with remote sensing because it is often spatially dispersed, easily confused with spectrally similar land-use ...classes such as grasslands and fallow fields, and because post-agricultural succession can take different forms in different biomes. Due to these difficulties, prior assessments of cropland abandonment have largely been limited in resolution, extent, or both. However, cropland abandonment has wide-reaching consequences for the environment, food production, and rural livelihoods, which is why new approaches to monitor long-term cropland abandonment in different biomes accurately are needed. Our goals were to 1) develop a new approach to map the extent and the timing of abandoned cropland using the entire Landsat time series, and 2) test this approach in 14 study regions across the globe that capture a wide range of environmental conditions as well as the three major causes of abandonment, i.e., social, economic, and environmental factors. Our approach was based on annual maps of active cropland and non-cropland areas using Landsat summary metrics for each year from 1987 to 2017. We streamlined per-pixel classifications by generating multi-year training data that can be used for annual classification. Based on the annual classifications, we analyzed land-use trajectories of each pixel in order to distinguish abandoned cropland, stable cropland, non-cropland, and fallow fields. In most study regions, our new approach separated abandoned cropland accurately from stable cropland and other classes. The classification accuracy for abandonment was highest in regions with industrialized agriculture (area-adjusted F1 score for Mato Grosso in Brazil: 0.8; Volgograd in Russia: 0.6), and drylands (e.g., Shaanxi in China, Nebraska in the U.S.: 0.5) where fields were large or spectrally distinct from non-cropland. Abandonment of subsistence agriculture with small field sizes (e.g., Nepal: 0.1) or highly variable climate (e.g., Sardinia in Italy: 0.2) was not accurately mapped. Cropland abandonment occurred in all study regions but was especially prominent in developing countries and formerly socialist states. In summary, we present here an approach for monitoring cropland abandonment with Landsat imagery, which can be applied across diverse biomes and may thereby improve the understanding of the drivers and consequences of this important land-use change process.
•We propose an approach to map cropland abandonment using all available Landsat images.•A novel method is developed to generate training dataset semi-automatically.•Annual cropland maps are generated using Landsat spectral-temporal metrics.•Our approach is successful in most of 14 study regions across the globe.•Strong spatial and temporal variations exist in cropland abandonment.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Addressing global declines in biodiversity requires accurate assessments of key environmental attributes determining patterns of species diversity. Spatial heterogeneity of vegetation strongly ...affects species diversity patterns, and measures of vegetation structure derived from lidar and satellite image texture analysis correlate well with species richness. Our goal here was to gain a better understanding of why image texture explains bird richness, by linking field-based measures of vegetation structure directly with both image texture and bird richness. In addition, we asked how image texture compares with lidar-based canopy height variability, and how sensor resolution affects the explanatory power of image texture. We generated texture metrics from 30 m (Landsat 8) and 10 m (Sentinel-2) resolution Enhanced Vegetation Index (EVI) imagery from 2017 to 2019. We compared textures with vegetation metrics and bird richness data from 27 National Ecological Observatory Network (NEON) terrestrial field sites across the continental US. Both 30 and 10 m resolution texture metrics were strongly correlated with lidar-based canopy height variability (|r| = 0.64 and 0.80, respectively). Texture was moderately correlated with field-based metrics, including variability of vegetation height and tree stem diameter, and foliage height diversity (range |r| = 0.31–0.52). Generally, 10 m resolution texture had stronger correlations with lidar and field-based metrics than 30 m resolution texture. In univariate linear models of total bird richness, 10 m resolution texture metrics also had higher explanatory power (up to R2adj = 0.45), than 30 m texture metrics (up to R2adj = 0.31). Among all metrics evaluated, the 10 m homogeneity texture was the best univariate predictor of total bird richness. In multivariate bird richness models that combined texture with lidar-based canopy height variability and field-based metrics, both 30 m and 10 m resolution texture metrics were selected in top-ranked models and independently contributed explanatory power (up to R2adj = 46%). Lidar-based canopy height variability was also selected in a top-ranked model of total bird richness, but independently contributed only 15% of the variance explained. Our results show satellite image texture characterized multiple features of structural and compositional vegetation heterogeneity, complemented more commonly used metrics in models of bird richness and for some guilds outperformed both lidar-based canopy height variability and field-based vegetation measurements. Ours is the first study to directly link image texture both to specific components of vegetation heterogeneity and to bird richness across multiple ecoregions and spatial resolutions, thereby shedding light on habitat features underlying the strong correlation between image texture and biodiversity.
•Image texture captures heterogeneity in both vegetation structure and composition.•10 m resolution texture outperforms 30 m texture in bird richness models.•Texture metrics outperform lidar canopy height variability in bird richness models.•Image texture has exciting potential for biodiversity research and conservation.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Short linear motifs, known as LC3-interacting regions (LIRs), interact with mactoautophagy/autophagy modifiers (Atg8/LC3/GABARAP proteins) via a conserved universal mechanism. Typically, this ...includes the occupancy of 2 hydrophobic pockets on the surface of Atg8-family proteins by 2 specific aromatic and hydrophobic residues within the LIR motifs. Here, we describe an alternative mechanism of Atg8-family protein interaction with the non-canonical UBA5 LIR, an E1-like enzyme of the ufmylation pathway that preferentially interacts with GABARAP but not LC3 proteins. By solving the structures of both GABARAP and GABARAPL2 in complex with the UBA5 LIR, we show that in addition to the binding to the 2 canonical hydrophobic pockets (HP1 and HP2), a conserved tryptophan residue N-terminal of the LIR core sequence binds into a novel hydrophobic pocket on the surface of GABARAP proteins, which we term HP0. This mode of action is unique for UBA5 and accompanied by large rearrangements of key residues including the side chains of the gate-keeping K46 and the adjacent K/R47 in GABARAP proteins. Swapping mutations in LC3B and GABARAPL2 revealed that K/R47 is the key residue in the specific binding of GABARAP proteins to UBA5, with synergetic contributions of the composition and dynamics of the loop L3. Finally, we elucidate the physiological relevance of the interaction and show that GABARAP proteins regulate the localization and function of UBA5 on the endoplasmic reticulum membrane in a lipidation-independent manner.
Abbreviations: ATG: AuTophaGy-related; EGFP: enhanced green fluorescent protein; GABARAP: GABA-type A receptor-associated protein; ITC: isothermal titration calorimetry; KO: knockout; LIR: LC3-interacting region; MAP1LC3/LC3: microtubule associated protein 1 light chain 3; NMR: nuclear magnetic resonance; RMSD: root-mean-square deviation of atomic positions; TKO: triple knockout; UBA5: ubiquitin like modifier activating enzyme 5
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BFBNIB, GIS, IJS, KISLJ, NUK, PNG, UL, UM, UPUK
The Rcs (regulator of capsule synthesis) signalling complex comprises the membrane-associated hybrid sensor kinases RcsC and RcsD, the transcriptional regulator RcsB and the two co-inducers RcsA and ...RcsF. Acting as a global regulatory network, the Rcs phosphorelay controls multiple cellular pathways including capsule synthesis, cell division, motility, biofilm formation and virulence mechanisms. Signal-dependent communication of the individual Rcs domains showing histidine kinase, phosphoreceiver, phosphoryl transfer and DNA-binding activities is characteristic and essential for the modulation of signal transfer. We have analysed the structures of core elements of the Rcs network including the RcsC-PR (phosphoreceiver domain of RcsC) and the RcsD-HPt (histidine phosphotransfer domain of RcsD), and we have started to characterize the dynamics and recognition mechanisms of the proteins. RcsC-PR represents a typical CheY-like alpha/beta/alpha sandwich fold and it shows a large conformational flexibility near the active-site residue Asp(875). NMR analysis revealed that RcsC-PR is able to adopt preferred conformations upon Mg(2+) co-ordination, BeF(3)(-) activation, phosphate binding and RcsD-HPt recognition. In contrast, the alpha-helical structure of RcsD-HPt is conformationally stable and contains a recognition area in close vicinity to the active-site His(842) residue. Our studies indicate the importance of protein dynamics and conformational exchange for the differential response to the variety of signals perceived by complex regulatory networks.
Grassland ecosystems cover up to 40% of the global land area and provide many ecosystem services directly supporting the livelihoods of over 1 billion people. Monitoring long‐term changes in ...grasslands is crucial for food security, biodiversity conservation, achieving Land Degradation Neutrality goals, and modeling the global carbon budget. Although long‐term grassland monitoring using remote sensing is extensive, it is typically based on a single vegetation index and does not account for temporal and spatial autocorrelation, which means that some trends are falsely identified while others are missed. Our goal was to analyze trends in grasslands in Eurasia, the largest continuous grassland ecosystems on Earth. To do so, we calculated Cumulative Endmember Fractions (annual sums of monthly ground cover fractions) derived from MODIS 2002–2020 time series, and applied a new statistical approach PARTS that explicitly accounts for temporal and spatial autocorrelation in trends. We examined trends in green vegetation, non‐photosynthetic vegetation, and soil ground cover fractions considering their independent change trajectories and relations among fractions over time. We derived temporally uncorrelated pixel‐based trend maps and statistically tested whether observed trends could be explained by elevation, land cover, SPEI3, climate, country, and their combinations, all while accounting for spatial autocorrelation. We found no statistical evidence for a decrease in vegetation cover in grasslands in Eurasia. Instead, there was a significant map‐level increase in non‐photosynthetic vegetation across the region and local increases in green vegetation with a concomitant decrease in soil fraction. Independent environmental variables affected trends significantly, but effects varied by region. Overall, our analyses show in a statistically robust manner that Eurasian grasslands have changed considerably over the past two decades. Our approach enhances remote sensing‐based monitoring of trends in grasslands so that underlying processes can be discerned.
We studied trends in grasslands in Eurasia using Cumulative Endmember Fractions (annual sums of monthly ground cover fractions) derived from MODIS 2002–2020 time series, and analyzed using a new statistical approach PARTS that explicitly accounts for temporal and spatial autocorrelation in trends. We show in a statistically robust manner how Eurasian grasslands have changed over the past two decades.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
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