No abstract

Veratrum mengtzeanum Loes. F. is a medicinal plant belonging to the genus Veratrum (Liliaceae). In the present study, we assembled and characterized the complete chloroplast (cp) genome of this species. The chloroplast genome is 152,051 bp in length, with one large single copy (LSC) region and one small single copy (SSC) region of 82,112 bp and 17,544 bp, respectively; two inverted repeat (IR) regions of 26,198 bp. It contains 131 annotated genes, including 85 protein-coding genes (PCGs), 38 transfer RNA (tRNA) genes, and 8 ribosomal RNA (rRNA) genes. Phylogenetic analysis indicated that V. mengtzeanum was closely related to Veratrum japonicum with 100% bootstrap value.

RNA pseudouridine modification is particularly important in a variety of cellular biological and physiological processes. It plays a significant role in understanding RNA functions, RNA structure stabilization, translation processes, etc. To understand its functional mechanisms, it is necessary to accurately identify pseudouridine sites in RNA sequences. Although some computational methods have been proposed for the identification of pseudouridine sites, it is still a challenge to improve the identification accuracy and generalization ability. To address this challenge, a novel feature fusion predictor, named PsoEL-PseU, is proposed for the prediction of pseudouridine sites. Firstly, this study systematically and comprehensively explored different types of feature descriptors and determined six feature descriptors with various properties. To improve the feature representation ability, a binary particle swarm optimizer was used to capture the optimal feature subset for six feature descriptors. Secondly, six individual predictors were trained by using the six optimal feature subsets. Finally, to fuse the effects of all six features, six individual predictors were fused into an ensemble predictor by a parallel fusion strategy. Ten-fold cross-validation on three benchmark datasets indicated that the PsoEL-PseU predictor significantly outperformed the current state-of-the-art predictors. Additionally, the new predictor achieved better accuracy in the independent dataset evaluation—accuracy which is significantly higher than that of its existing counterparts—and the user-friendly webserver developed by the PsoEL-PseU predictor has been made freely accessible.

Veratrum mengtzeanum is the main ingredient for Chinese folk medicine known as “Pimacao” due to its unique alkaloids. A diverse class of plant-specific metabolites having key pharmacological activities. There are limited studies on alkaloid synthesis and its metabolic pathways in plants. To elucidate the alkaloid pathway and identify novel biosynthetic enzymes and compounds in V. mengtzeanum, transcriptome and metabolome profiling has been conducted in leaves and roots. The transcriptome of V. mengtzeanum leaves and roots yielded 190,161 unigenes, of which 33,942 genes expressed differentially (DEGs) in both tissues. Three enriched regulatory pathways (isoquinoline alkaloid biosynthesis, indole alkaloid biosynthesis and tropane, piperidine and pyridine alkaloid biosynthesis) and a considerable number of genes such as AED3-like, A4U43, 21 kDa protein-like, 3-O-glycotransferase 2-like, AtDIR19, MST4, CASP-like protein 1D1 were discovered in association with the biosynthesis of alkaloids in leaves and roots. Some transcription factor families, i.e., AP2/ERF, GRAS, NAC, bHLH, MYB-related, C3H, FARI, WRKY, HB-HD-ZIP, C2H2, and bZIP were also found to have a prominent role in regulating the synthesis of alkaloids and steroidal alkaloids in the leaves and roots of V. mengtzeanum. The metabolome analysis revealed 74 significantly accumulated metabolites, with 55 differentially accumulated in leaves compared to root tissues. Out of 74 metabolites, 18 alkaloids were highly accumulated in the roots. A novel alkaloid compound viz; 3-Vanilloylygadenine was discovered in root samples. Conjoint analysis of transcriptome and metabolome studies has also highlighted potential genes involved in regulation and transport of alkaloid compounds. Here, we have presented a comprehensive metabolic and transcriptome profiling of V. mengtzeanum tissues. In earlier reports, only the roots were reported as a rich source of alkaloid biosynthesis, but the current findings revealed both leaves and roots as significant manufacturing factories for alkaloid biosynthesis.

Chloroplast is a crucial site for photosynthesis in plants. Determining the location and distribution of proteins in subchloroplasts is significant for studying the energy conversion of chloroplasts and regulating the utilization of light energy in crop production. However, the prediction accuracy of the currently developed protein subcellular site predictors is still limited due to the complex protein sequence features and the scarcity of labeled samples. We propose DaDL-SChlo, a multi-location protein subchloroplast localization predictor, which addresses the above problems by fusing pre-trained protein language model deep learning features with traditional handcrafted features and using generative adversarial networks for data augmentation. The experimental results of cross-validation and independent testing show that DaDL-SChlo has greatly improved the prediction performance of protein subchloroplast compared with the state-of-the-art predictors. Specifically, the overall actual accuracy outperforms the state-of-the-art predictors by 10.7% on 10-fold cross-validation and 12.6% on independent testing. DaDL-SChlo is a promising and efficient predictor for protein subchloroplast localization. The datasets and codes of DaDL-SChlo are available at https://github.com/xwanggroup/DaDL-SChlo.