Leaf Transcriptome and Weight Gene Co-expression Network Analysis Uncovers Genes Associated with Photosynthetic Efficiency in Camellia oleifera

Genomic and genetic advances of oiltea-camellia (Camellia oleifera)

Oiltea-camellia (C. oleifera) is a widely cultivated woody oil crop in Southern China and Southeast Asia. The genome of oiltea-camellia was very complex and not well explored. Recently, genomes of three oiltea-camellia species were sequenced and assembled, multi-omic studies of oiltea-camellia were carried out and provided a better understanding of this important woody oil crop. In this review, we summarized the recent assembly of the reference genomes of oiltea-camellia, genes related to economic traits (flowering, photosynthesis, yield and oil component), disease resistance (anthracnose) and environmental stress tolerances (drought, cold, heat and nutrient deficiency). We also discussed future directions of integrating multiple omics for evaluating genetic resources and mining key genes of important traits, and the application of new molecular breeding and gene editing technologies to accelerate the breeding process of oiltea-camellia.

Integration of mRNA and miRNA analysis reveals the differentially regulatory network in two different Camellia oleifera cultivars under drought stress

Camellia oleifera Abel. (C. oleifera) is an edible oil tree species that provide an important guarantee for targeted poverty alleviation strategy in China. Severe difficulties in irrigation leading to drought stress have become a major obstacle to the development of the C. oleifera planting industry. Breeding of drought-tolerant cultivars is the main idea for solving the problem of water shortage stress in C. oleifera cultivation. The photosynthetic physiology traits of C. oleifera cultivars 'Xianglin No.1' and 'Hengdong No.2' were affected by drought stress to different degrees, which demonstrated that the two cultivars suffered different degrees of damage. In the present study, we applied mRNA-seq and miRNA-seq to analyze the difference in molecular responses between drought stress and control, drought-tolerant and -sensitive cultivars, at mRNA and miRNA levels. The differentially expressed genes (DEGs) involved in photosynthesis-related, porphyrin, and chlorophyll metabolism, circadian rhythm system, and plant hormone signal transduction pathways were identified that might be candidates for drought stress tolerance genes. Subsequently, the miRNA-mRNA regulatory networks connected the differentially expressed miRNAs (DEMs) to their predicted target genes were established. miR398 and miR408-3p in C. oleifera showed that associated with the response to drought stress by negatively regulating genes encoding Downy Mildew Resistance 6 (DMR6) and Enhanced Disease Resistance 2 (EDR2), respectively, which might further improve drought tolerance via crosstalk between different stress-responsive pathways. The quantitation results of miRNA and mRNA were validated by quantitative real-time PCR (qRT-PCR). In summary, the integrated mRNA-seq and miRNA-seq analysis deepen our understanding of the regulatory network response to drought stress and variety-specific responses improving drought tolerance in C. oleifera.

Identification of Oil Tea (Camellia oleifera C.Abel) Cultivars Using EfficientNet-B4 CNN Model with Attention Mechanism

Cultivar identification is a basic task in oil tea (Camellia oleifera C.Abel) breeding, quality analysis, and an adjustment in the industrial structure. However, because the differences in texture, shape, and color under different cultivars of oil tea are usually inconspicuous and subtle, the identification of oil tea cultivars can be a significant challenge. The main goal of this study is to propose an automatic and accurate method for identifying oil tea cultivars. In this study, a new deep learning model is built, called EfficientNet-B4-CBAM, to identify oil tea cultivars. First, 4725 images containing four cultivars were collected to build an oil tea cultivar identification dataset. EfficientNet-B4 was selected as the basic model of oil tea cultivar identification, and the Convolutional Block Attention Module (CBAM) was integrated into EfficientNet-B4 to build EfficientNet-B4-CBAM, thereby improving the focusing ability of the fruit areas and the information expression capability of the fruit areas. Finally, the cultivar identification capability of EfficientNet-B4-CBAM was tested on the testing dataset and compared with InceptionV3, VGG16, ResNet50, EfficientNet-B4, and EfficientNet-B4-SE. The experiment results showed that the EfficientNet-B4-CBAM model achieves an overall accuracy of 97.02% and a kappa coefficient of 0.96, which is higher than that of other methods used in comparative experiments. In addition, gradient-weighted class activation mapping network visualization also showed that EfficientNet-B4-CBAM can pay more attention to the fruit areas that play a key role in cultivar identification. This study provides new effective strategies and a theoretical basis for the application of deep learning technology in the identification of oil tea cultivars and provides technical support for the automatic identification and non-destructive testing of oil tea cultivars.

Biological Parts for Plant Biodesign to Enhance Land-Based Carbon Dioxide Removal

A grand challenge facing society is climate change caused mainly by rising CO2 concentration in Earth's atmosphere. Terrestrial plants are linchpins in global carbon cycling, with a unique capability of capturing CO2 via photosynthesis and translocating captured carbon to stems, roots, and soils for long-term storage. However, many researchers postulate that existing land plants cannot meet the ambitious requirement for CO2 removal to mitigate climate change in the future due to low photosynthetic efficiency, limited carbon allocation for long-term storage, and low suitability for the bioeconomy. To address these limitations, there is an urgent need for genetic improvement of existing plants or construction of novel plant systems through biosystems design (or biodesign). Here, we summarize validated biological parts (e.g., protein-encoding genes and noncoding RNAs) for biological engineering of carbon dioxide removal (CDR) traits in terrestrial plants to accelerate land-based decarbonization in bioenergy plantations and agricultural settings and promote a vibrant bioeconomy. Specifically, we first summarize the framework of plant-based CDR (e.g., CO2 capture, translocation, storage, and conversion to value-added products). Then, we highlight some representative biological parts, with experimental evidence, in this framework. Finally, we discuss challenges and strategies for the identification and curation of biological parts for CDR engineering in plants.