Autophosphorylation-based calcium (Ca2+) sensitivity priming and Ca2+/Calmodulin inhibition of Arabidopsis thaliana Ca2+-dependent protein kinase 28 (CPK28)

Plant calcium (Ca2+) dependent protein kinases (CPKs) are composed of a dual specificity (Ser/Thr and Tyr) kinase domain tethered to a Calmodulin-like domain (CLD) via an autoinhibitory junction (J) and represent the primary Ca2+-dependent protein kinase activities in plant systems. While regulation of CPKs by Ca2+ has been extensively studied, the contribution of autophosphorylation in the control of CPK activity is less well understood. Furthermore, whether Calmodulin (CaM) contributes to CPK regulation, as is the case for Ca2+/CaM-dependent protein kinases (CaMKs) outside the plant lineage, remains an open question. We screened a subset of plant CPKs for CaM-binding and found that CPK28 is a high-affinity Ca2+/CaM-binding protein. Using synthetic peptides and native gel electrophoresis, we coarsely mapped the CaM-binding domain to a site within the CPK28 J domain that overlaps with the known site of intramolecular interaction between the J domain and CLD. Peptide kinase activity of fully dephosphorylated CPK28 was Ca2+-responsive and inhibited by Ca2+/CaM. Using in situ autophosphorylated protein, we expand on the known set of CPK28 autophosphorylation sites, and demonstrate that, unexpectedly, autophosphorylated CPK28 had enhanced activity at physiological concentrations of Ca2+ compared to dephosphorylated protein, suggesting that autophosphorylation functions to prime CPK28 for Ca2+-activation. Furthermore, CPK28 autophosphorylation substantially reduced sensitivity of the kinase to Ca2+/CaM inhibition. Overall, our analyses uncover new complexities in the control of CPK28 and provide mechanistic support for Ca2+ signaling specificity through Ca2+ sensor priming

Influence of Land Use and Topographic Factors on Soil Organic Carbon Stocks and Their Spatial and Vertical Distribution

Soil organic carbon (SOC) plays a critical role in major ecosystem processes, agriculture, and climate mitigation. Accurate SOC predictions are challenging due to natural variation, as well as variation in data sources, sampling design, and modeling approaches. The goal of this study was to (i) understand SOC stock distribution due to land use (restored prairie grass—PG; lawn grass—LG; and forest—F), and local topography, and (ii) assess the scalability of SOC stock predictions from the study site in North Carolina (Lat: 36°7′ N; Longitude: 80°16′ W) to the geographic extension of the Fairview soil series based on the US Soil Survey Geographic (gSSURGO) database. Overall, LG had the highest SOC stock (82 Mg ha−1) followed by PG (79 Mg ha−1) and forest (73.1 Mg ha−1). SOC stock decreased with the depth for LG and PG, which had about 60% concentrated on the surface horizon (0–23 cm), while forest had only 40%. The differences between measured SOC stocks and those estimated by gSSURGO and modeled based on land use for the Fairview series extent were comparable. However, subtracting maps of the uncertainty predictions based on the 90% confidence interval (CI) derived from the measured values and estimated gSSURGO upper and lower values (an estimated CI) resulted in a range from −17 to 41 Mg ha−1 which, when valued monetarily, varied from USD 33 million to USD 824 million for the Fairview soil series extent. In addition, the spatial differences found by subtracting the gSSURGO estimations from measured uncertainties aligned with the county administrative boundaries. The distribution of SOC stock was found to be related to land use, topography, and soil depth, while accuracy predictions were also influenced by data source