Investigation of the Acetylation Mechanism by GCN5 Histone Acetyltransferase

The histone acetylation of post-translational modification can be highly dynamic and play a crucial role in regulating cellular proliferation, survival, differentiation and motility. Of the enzymes that mediate post-translation modifications, the GCN5 of the histone acetyltransferase (HAT) proteins family that add acetyl groups to target lysine residues within histones, has been most extensively studied. According to the mechanism studies of GCN5 related proteins, two key processes, deprotonation and acetylation, must be involved. However, as a fundamental issue, the structure of hGCN5/AcCoA/pH3 remains elusive. Although biological experiments have proved that GCN5 mediates the acetylation process through the sequential mechanism pathway, a dynamic view of the catalytic process and the molecular basis for hGCN5/AcCoA/pH3 are still not available and none of theoretical studies has been reported to other related enzymes in HAT family. To explore the molecular basis for the catalytic mechanism, computational approaches including molecular modeling, molecular dynamic (MD) simulation and quantum mechanics/molecular mechanics (QM/MM) simulation were carried out. The initial hGCN5/AcCoA/pH3 complex structure was modeled and a reasonable snapshot was extracted from the trajectory of a 20 ns MD simulation, with considering post-MD analysis and reported experimental results. Those residues playing crucial roles in binding affinity and acetylation reaction were comprehensively investigated. It demonstrated Glu80 acted as the general base for deprotonation of Lys171 from H3. Furthermore, the two-dimensional QM/MM potential energy surface was employed to study the sequential pathway acetylation mechanism. Energy barriers of addition-elimination reaction in acetylation obtained from QM/MM calculation indicated the point of the intermediate ternary complex. Our study may provide insights into the detailed mechanism for acetylation reaction of GCN5, and has important implications for the discovery of regulators against GCN5 enzymes and related HAT family enzymes

White oak (Quercus fabri Hance) regenerated stump sprouts show few senescence symptoms during 40 years of growth in a natural forest

Abstract Background The relationship between physiological age of parental trees and lifespan of clonal offspring is unclear. White oak (Quercus fabri Hance) has a high sprouting capability after harvest, with the regenerated sprouts being typical clonal individuals. To determine whether regenerated sprouts undergo rapid senescence compared with the parent, the senescence levels of 5-, 10-, 20- and 40-year-old regenerated stump sprouts in a natural forest were evaluated. The antioxidative abilities and transcriptomes in these leaves and shoots were compared. Results Older regenerated sprouts still had robust antioxidative systems, with 40-year-old sprouts having lower peroxidation product levels but similar antioxidative enzyme activity levels compared with 5-year-old sprouts. Older leaves had greater transcriptional activities in pathways related to cell growth and division than younger leaves. However, older sprouts had some unhealthy characteristics, such as increased base excision repair levels and upregulated phagosome, proteasome and glycerophospholipid metabolism pathways in 40-year-old leaves, which indicates that DNA damage and tissue remodeling occurred more frequently than in younger leaves. Additionally, plant-pathogen interactions and MAPK signals pathways were upregulated in older shoots, which indicates that older shoots suffered from more pathogen-related biotic stress. Conclusions The 40-year-old sprouts still had the same vitality level as the 5-year-old sprouts, although the former had some unhealthy characteristics. We conclude that during their first 40 years of growth, regenerated stump sprouts do not begin to senesce, and that physiological age of parental trees does not significantly affect the lifespan of its clonal offspring

Self-Triggered Model Predictive Control of AC Microgrids with Physical and Communication State Constraints

In this paper, we investigate the secondary control problems of AC microgrids with physical states (i.e., voltage, frequency and power, etc.) constrained in the process of actual control, namely, under the condition of state constraint. On the basis of the primary control (i.e., droop control), the control signals generated by distributed secondary control algorithm are used to solve the problems of voltage and frequency recovery and power allocation for each distributed generators (DGs). Therefore, the model predictive control (MPC) with the mechanism of rolling optimization is adopted in the second control layer to achieve the above control objectives and solve the physical state constraint problem at the same time. Meanwhile, in order to reduce the communication cost, we designed the self-triggered control based on the prediction mechanism of MPC. In addition, the proposed algorithm of self-triggered MPC does not need sampling and detection at any time, thus avoiding the design of observer and reducing the control complexity. In addition, the Zeno behavior is excluded through detailed analysis. Furthermore, the stability of the algorithm is verified by theoretical derivation of Lyapunov. Finally, the effectiveness of the algorithm is proved by simulation

Relationship between Vegetation Index and Forest Surface Fuel Load in UAV Multispectral Remote Sensing

[Objectives] To explore the relationship between vegetation index and forest surface fuel load. [Methods] UAV multispectral remote sensing was used to obtain large-scale forest images and obtain structural data of forest surface fuel load. This experimental area was located in Gaoming District, Foshan City, Guangdong Province. The average surface fuel load of the experimental area was as high as 39.33 t/ha, and the forest surface fuel load of Pinus elliottii was the highest. [Results] The normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) had moderately strong correlation with the forest surface fuel load. The regression model of NDVI (X) and forest surface fuel load (Y) was established: Y = -5.935 4X + 8.466 3, and the regression model of EVI (X) and forest surface fuel load (Y) was established: Y = -5.848 5X + 6.727 1. The study also found that the linear relationship between NDVI and surface fuel load was more significant. [Conclusions] Both NDVI and EVI have moderately strong correlations with forest surface fuel load. NDVI is moderately or strongly correlated with the surface fuel load of Pinus massoniana forest, shrub grassland, broad-leaf forest and bamboo forest, while EVI is only strongly correlated with surface fuel load of broad-leaf forest and bamboo forest. It is expected that the relationship between other vegetation indices and forest surface fuel load can be obtained by the method in this study, so as to find a more universal vegetation index for calculating surface fuel load