He, Jingjing | Search NASA

DOE OSTI2023

Diel dynamics of multi-omics in elkhorn fern provide new insights into weak CAM photosynthesis

Crassulacean acid metabolism (CAM) has high water-use efficiency (WUE) and is widely recognized to have evolved from C 3 photosynthesis. Different plant lineages have convergently evolved CAM, but the molecular mechanism that underlies C 3 -to-CAM evolution remains to be clarified. Platycerium bifurcatum (elkhorn fern) provides an opportunity to study the molecular changes underlying the transition from C 3 to CAM photosynthesis because both modes of photosynthesis occur in this species, with sporotrophophyll leaves (SLs) and cover leaves (CLs) performing C 3 and weak CAM photosynthesis, respectively. Here, we report that the physiological and biochemical attributes of CAM in weak CAM-performing CLs differed from those in strong CAM species. We investigated the diel dynamics of the metabolome, proteome, and transcriptome in these dimorphic leaves within the same genetic background and under identical environmental conditions. We found that multi-omic diel dynamics in P. bifurcatum exhibit both tissue and diel effects. Our analysis revealed temporal rewiring of biochemistry relevant to the energy-producing pathway (TCA cycle), CAM pathway, and stomatal movement in CLs compared with SLs. We also confirmed that PHOSPHOENOLPYRUVATE CARBOXYLASE KINASE (PPCK) exhibits convergence in gene expression among highly divergent CAM lineages. Gene regulatory network analysis identified candidate transcription factors regulating the CAM pathway and stomatal movement. Taken together, our results provide new insights into weak CAM photosynthesis and new avenues for CAM bioengineering.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

iPNHOT: a knowledge-based approach for identifying protein-nucleic acid interaction hot spots

The interaction between proteins and nucleic acids plays pivotal roles in various biological processes such as transcription, translation, and gene regulation. Hot spots are a small set of residues that contribute most to the binding affinity of a protein-nucleic acid interaction. Compared to the extensive studies of the hot spots on protein-protein interfaces, the hot spot residues within protein-nucleic acids interfaces remain less well-studied, in part because mutagenesis data for protein-nucleic acids interaction are not as abundant as that for protein-protein interactions. In this study, we built a new computational model, iPNHOT, to effectively predict hot spot residues on protein-nucleic acids interfaces. One training data set and an independent test set were collected from dbAMEPNI and some recent literature, respectively. To build our model, we generated 97 different sequential and structural features and used a two-step strategy to select the relevant features. The final model was built based only on 7 features using a support vector machine (SVM). The features include two unique features such as ΔSASsa 1/2 and esp3, which are newly proposed in this study. Based on the cross validation results, our model gave F1 score and AUROC as 0.725 and 0.807 on the subset collected from ProNIT, respectively, compared to 0.407 and 0.670 of mCSM-NA, a state-of-the art model to predict the thermodynamic effects of protein-nucleic acid interaction. The iPNHOT model was further tested on the independent test set, which showed that our model outperformed other methods. Here, by collecting data from a recently published database dbAMEPNI, we proposed a new model, iPNHOT, to predict hotspots on both protein-DNA and protein-RNA interfaces. The results show that our model outperforms the existing state-of-art models. Our model is available for users through a webserver: http://zhulab.ahu.edu.cn/iPNHOT/.

59 BASIC BIOLOGICAL SCIENCES↗