Overexpression of Phosphoserine Aminotransferase (PSAT)-Enhanced Cadmium Resistance and Accumulation in Duckweed (Lemna turionifera 5511).

Cadmium (Cd) hampers plant growth and harms photosynthesis. Glutamate (Glu) responds to Cd stress and activates the Ca2+ signaling pathway in duckweed, emphasizing Glu's significant role in Cd stress. In this study, we overexpressed phosphoserine aminotransferase (PSAT), a crucial enzyme in Glu metabolism, in duckweed. We investigated the response of PSAT-transgenic duckweed to Cd stress, including growth, Glu metabolism, photosynthesis, antioxidant enzyme activity, Cd2+ flux, and gene expression. Remarkably, under Cd stress, PSAT-transgenic duckweed prevented root abscission, upregulated the expression of photosynthesis ability, and increased Chl a, Chl b, and Chl a + b levels by 13.9%, 7%, and 12.6%, respectively. Antioxidant enzyme activity (CAT and SOD) also improved under Cd stress, reducing cell membrane damage in PSAT-transgenic duckweeds. Transcriptomic analysis revealed an upregulation of Glu metabolism-related enzymes in PSAT-transgenic duckweed under Cd stress. Moreover, metabolomic analysis showed a 68.4% increase in Glu content in PSAT duckweed exposed to Cd. This study sheds novel insights into the role of PSAT in enhancing plant resistance to Cd stress, establishing a theoretical basis for the impact of Glu metabolism on heavy metal tolerance in plants.

The Antimicrobial Potential and Aquaculture Wastewater Treatment Ability of Penaeidins 3a Transgenic Duckweed.

With the development of aquaculture, wastewater treatment and diseases have been paid more and more attention. The question of how to improve the immunity of aquatic species, as well as treat aquaculture wastewater, has become an urgent problem. In this study, duckweed with a high protein content (37.4%) (Lemna turionifera 5511) has been employed as a feedstock for aquatic wastewater treatment and the production of antimicrobial peptides. Penaeidins 3a (Pen3a), from Litopenaeus vannamei, were expressed under the control of CaMV-35S promoter in duckweed. Bacteriostatic testing using the Pen3a duckweed extract showed its antibacterial activity against Escherichia coli and Staphylococcus aureus. Transcriptome analysis of wild type (WT) duckweed and Pen3a duckweed showed different results, and the protein metabolic process was the most up-regulated by differential expression genes (DEGs). In Pen3a transgenic duckweed, the expression of sphingolipid metabolism and phagocytosis process-related genes have been significantly up-regulated. Quantitative proteomics suggested a remarkable difference in protein enrichment in the metabolic pathway. Pen3a duckweed decreased the bacterial number, and effectively inhibited the growth of Nitrospirae. Additionally, Pen3a duckweed displayed better growth in the lake. The study showed the nutritional and antibacterial value of duckweed as an animal feed ingredient.

SuHAN: Substructural hierarchical attention network for molecular representation.

Recently, molecular representation and property exploration, with the combination of neural network, play a critical role in the field of drug design and discovery for assisting in drug related research. However, previous research in molecular representation relies heavily on artificial extraction of features based on biological experiments which may result in a manually introduced noise of molecular information with high cost in time and money. In this paper, a novel method named Substructural Hierarchical Attention Network (SuHAN) is proposed to discover inherent characteristics of molecules for representation learning. Specifically, SuHAN is composed of the cascaded layer: atom-level layer and substructure-level layer. Molecule in the SMILES format is divided into several substructural fragments by predefined partition rules, and then they are fed into atom-level layer and substructure-level layer successively to obtain feature representation from different perspective: atomic view and substructural view. In this way, the prominent structural features that may be omitted in global extraction are excavated from a fine-grained viewpoint and fused to reconstruct representative pattern in an overall view. Experiments on biophysics and physiology datasets demonstrate that our model is competitive with a significant improvement of both accuracy and stability in performance. We confirmed that the substructural segments and progressive hierarchical networks lead to an effective molecular representation for downstream tasks. These results provide a novel perspective about reconstructing overall pattern through local prominent structure.