Photosynthetic carbon and nitrogen metabolism of Camellia oleifera Abel during acclimation to low light conditions

An integration of physiology, transcriptomics, and proteomics reveals carbon and nitrogen metabolism responses in alfalfa (Medicago sativa L.) exposed to titanium dioxide nanoparticles

Nanoparticle (NP) pollution has negative impacts and is a major global environmental problem. However, the molecular response of alfalfa (Medicago sativa L.) to titanium dioxide nanoparticles (TiO2 NPs) is limited. Herein, the dual effects of TiO2 NPs (0-1000 mg L-1) on carbon (C) and nitrogen (N) metabolisms in alfalfa were investigated. The results showed that 500 mg L-1 TiO2 NPs (Ti-500) had the highest phytotoxicity in the C/N metabolizing enzymes; and it significantly increased total soluble sugar, starch, sucrose, and sucrose-phosphate synthase. Furthermore, obvious photosynthesis responses were found in alfalfa exposed to Ti-500. By contrast, 100 mg L-1 TiO2 NPs (Ti-100) enhanced N metabolizing enzymes. RNA-seq analyses showed 4265 and 2121 differentially expressed genes (DEGs) in Ti-100 and Ti-500, respectively. A total of 904 and 844 differentially expressed proteins (DEPs) were identified in Ti-100 and Ti-500, respectively. Through the physiological, transcriptional, and proteomic analyses, the DEGs and DEPs related to C/N metabolism, photosynthesis, chlorophyll synthesis, starch and sucrose metabolism, and C fixation in photosynthetic organisms were observed. Overall, TiO2 NPs at low doses improve photosynthesis and C/N regulation, but high doses can cause toxicity. It is valuable for the safe application of NPs in agriculture.

A comprehensive assessment of photosynthetic acclimation to shade in C4 grass (Cynodon dactylon (L.) Pers.)

BACKGROUND

Light deficit in shaded environment critically impacts the growth and development of turf plants. Despite this fact, past research has predominantly concentrated on shade avoidance rather than shade tolerance. To address this, our study examined the photosynthetic adjustments of Bermudagrass when exposed to varying intensities of shade to gain an integrative understanding of the shade response of C4 turfgrass.

RESULTS

We observed alterations in photosynthetic pigment-proteins, electron transport and its associated carbon and nitrogen assimilation, along with ROS-scavenging enzyme activity in shaded conditions. Mild shade enriched Chl b and LHC transcripts, while severe shade promoted Chl a, carotenoids and photosynthetic electron transfer beyond QA- (ET0/RC, φE0, Ψ0). The study also highlighted differential effects of shade on leaf and root components. For example, Soluble sugar content varied between leaves and roots as shade diminished SPS, SUT1 but upregulated BAM. Furthermore, we observed that shading decreased the transcriptional level of genes involving in nitrogen assimilation (e.g. NR) and SOD, POD, CAT enzyme activities in leaves, even though it increased in roots.

CONCLUSIONS

As shade intensity increased, considerable changes were noted in light energy conversion and photosynthetic metabolism processes along the electron transport chain axis. Our study thus provides valuable theoretical groundwork for understanding how C4 grass acclimates to shade tolerance.

Maturity Grading and Identification of Camellia oleifera Fruit Based on Unsupervised Image Clustering

Maturity grading and identification of Camellia oleifera are prerequisites to determining proper harvest maturity windows and safeguarding the yield and quality of Camellia oil. One problem in Camellia oleifera production and research is the worldwide confusion regarding the grading and identification of Camellia oleifera fruit maturity. To solve this problem, a Camellia oleifera fruit maturity grading and identification model based on the unsupervised image clustering model DeepCluster has been developed in the current study. The proposed model includes the following two branches: a maturity grading branch and a maturity identification branch. The proposed model jointly learns the parameters of the maturity grading branch and maturity identification branch and used the maturity clustering assigned from the maturity grading branch as pseudo-labels to update the parameters of the maturity identification branch. The maturity grading experiment was conducted using a training set consisting of 160 Camellia oleifera fruit samples and 2628 Camellia oleifera fruit digital images collected using a smartphone. The proposed model for grading Camellia oleifera fruit samples and images in training set into the following three maturity levels: unripe (47 samples and 883 images), ripe (62 samples and 1005 images), and overripe (51 samples and 740 images). Results suggest that there was a significant difference among the maturity stages graded by the proposed method with respect to seed oil content, seed soluble protein content, seed soluble sugar content, seed starch content, dry seed weight, and moisture content. The maturity identification experiment was conducted using a testing set consisting of 160 Camellia oleifera fruit digital images (50 unripe, 60 ripe, and 50 overripe) collected using a smartphone. According to the results, the overall accuracy of maturity identification for Camellia oleifera fruit was 91.25%. Moreover, a Gradient-weighted Class Activation Mapping (Grad-CAM) visualization analysis reveals that the peel regions, crack regions, and seed regions were the critical regions for Camellia oleifera fruit maturity identification. Our results corroborate a maturity grading and identification application of unsupervised image clustering techniques and are supported by additional physical and quality properties of maturity. The current findings may facilitate the harvesting process of Camellia oleifera fruits, which is especially critical for the improvement of Camellia oil production and quality.