Background: Starch serves as a temporal storage of carbohydrates in plant leaves during day/night cycles. To study transcriptional regulatory modules of this dynamic metabolic process, we conducted gene regulation network analysis based on small-sample inference of graphical Gaussian model (GGM).Results: Time-series significant analysis was applied forArabidopsisleaf transcriptome data to obtain a set of genes that are highly regulated under a diurnal cycle. A total of 1,480 diurnally regulated genes included 21 starch metabolic enzymes, 6 clock-associated genes, and 106 transcription factors (TF). A starch-clock-TF gene regulation network comprising 117 nodes and 266 edges was constructed by GGM from these 133 significant genes that are potentially related to the diurnal control of starch metabolism. From this network, we found that β-amylase 3 (b-amy3: At4g17090), which participates in starch degradation in chloroplast, is the most frequently connected gene (a hub gene). The robustness of gene-to-gene regulatory network was further analyzed by TF binding site prediction and by evaluating global co-expression of TFs and target starch metabolic enzymes. As a result, two TFs, indeterminate domain 5 (AtIDD5: At2g02070) and constans-like (COL: At2g21320), were identified as positive regulators of starch synthase 4 (SS4: At4g18240). The inference model of AtIDD5-dependent positive regulation ofSS4gene expression was experimentally supported by decreasedSS4mRNA accumulation inAtidd5mutant plants during the light period of both short and long day conditions. COL was also shown to positively controlSS4mRNA accumulation. Furthermore, the knockout of AtIDD5 and COL led to deformation of chloroplast and its contained starch granules. This deformity also affected the number of starch granules per chloroplast, which increased significantly in both knockout mutant lines.Conclusions: In this study, we utilized a systematic approach of microarray analysis to discover the transcriptional regulatory network of starch metabolism inArabidopsisleaves. With this inference method, the starch regulatory network ofArabidopsiswas found to be strongly associated with clock genes and TFs, of which AtIDD5 and COL were evidenced to controlSS4gene expression and starch granule formation in chloroplasts.

In order to improve our understanding of air quality in Southeast Asia, the anthropogenic emissions inventory must be well represented. In this work, we apply different anthropogenic emission inventories in the Weather Research and Forecasting Model with Chemistry (WRF-Chem) version 3.3 using Model for Ozone and Related Chemical Tracers (MOZART) gas-phase chemistry and Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) aerosols to examine the differences in predicted carbon monoxide (CO) and ozone (O₃) surface mixing ratios for Southeast Asia in March and December 2008. The anthropogenic emission inventories include the Reanalysis of the TROpospheric chemical composition (RETRO), the Intercontinental Chemical Transport Experiment-Phase B (INTEX-B), the MACCity emissions (adapted from the Monitoring Atmospheric Composition and Climate and megacity Zoom for the Environment projects), the Southeast Asia Composition, Cloud, Climate Coupling Regional Study (SEAC4RS) emissions, and a combination of MACCity and SEAC4RS emissions. Biomass-burning emissions are from the Fire Inventory from the National Center for Atmospheric Research (NCAR) (FINNv1) model. WRF-Chem reasonably predicts the 2 m temperature, 10 m wind, and precipitation. In general, surface CO is underpredicted by WRF-Chem while surface O₃ is overpredicted. The NO₂ tropospheric column predicted by WRF-Chem has the same magnitude as observations, but tends to underpredict the NO₂ column over the equatorial ocean and near Indonesia. Simulations using different anthropogenic emissions produce only a slight variability of O₃ and CO mixing ratios, while biomass-burning emissions add more variability. The different anthropogenic emissions differ by up to 30% in CO emissions, but O₃ and CO mixing ratios averaged over the land areas of the model domain differ by ~4.5% and ~8%, respectively, among the simulations. Biomass-burning emissions create a substantial increase for both O₃ and CO by ~29% and ~16%, respectively, when comparing the March biomass-burning period to the December period with low biomass-burning emissions. The simulations show that none of the anthropogenic emission inventories are better than the others for predicting O₃ surface mixing ratios. However, the simulations with different anthropogenic emission inventories do differ in their predictions of CO surface mixing ratios producing variations of ~30% for March and 10–20% for December at Thai surface monitoring sites.

Mahakkanukrauh P, et al. Anat Cell Biol. 2015 Dec;48(4):275-283. http://dx.doi.org/10.5115/acb.2015.48.4.275