Identification of P450 Candidates Associated with the Biosynthesis of Physalin-Class Compounds in Physalis angulata.

The Physalis genus has long been used as traditional medicine in the treatment of various diseases. Physalins, the characteristic class of compounds in this genus, are major bioactive constituents. To date, the biogenesis of physalins remains largely unknown, except for the recently established knowledge that 24-methyldesmosterol is a precursor of physalin. To identify the genes encoding P450s that are putatively involved in converting 24-methyldesmosterol to physalins, a total of 306 P450-encoding unigenes were retrieved from our recently constructed P. angulata transcriptome. Extensive phylogenetic analysis proposed 21 P450s that might participate in physalin biosynthesis. To validate the candidates, we developed a virus-induced gene silencing (VIGS) system for P. angulata, and four P450 candidates were selected for the VIGS experiments. The reduction in the transcripts of the four P450 candidates by VIGS all led to decreased levels of physalin-class compounds in the P. angulata leaves. Thus, this study provides a number of P450 candidates that are likely associated with the biosynthesis of physalin-class compounds, forming a strong basis to reveal the unknown physalin biosynthetic pathway in the future.

MuLHiTA: A Novel Multiclass Classification Framework With Multibranch LSTM and Hierarchical Temporal Attention for Early Detection of Mental Stress.

Mental stress is an increasingly common psychological issue leading to diseases such as depression, addiction, and heart attack. In this study, an early detection framework based on electroencephalogram (EEG) data is developed for reducing the risk of these diseases. In existing frameworks, signals are often segmented into smaller sections prior to being input to a deep neural network. However, this approach ignores the fundamental nature of EEG signals as a carrier of valuable information (e.g., the integrity of frequency and phase, and temporal fluctuations of EEG components). As such, this type of segmenting may lead to information loss and a failure to effectively identify mental stress levels. Thus, we propose a novel multiclass classification framework termed multibranch LSTM and hierarchical temporal attention (MuLHiTA) for the early identification of mental stress levels. It specifically focuses on not only intraslice (within each slice) but also interslice (between different slices) samples in parallel. This was achieved by including two complementary branches, each of which integrated a specifically designed attention module into a bidirectional long short-term memory (BLSTM) network, enabling extraction of the most discriminative features from interslice and intraslice EEG signals simultaneously. The outputs of attention modules were then summed to obtain a feature representation that contributes to reduce overfitting and more effective multiclass classification. In addition, electrode positions were optimized using neural activity areas under high-stress conditions, thereby reducing computational costs by minimizing the number of critical electrodes. MuLHiTA was evaluated across one private [Montreal imaging stress task (MIST)] and two publicly available EEG datasets [EEG during mental arithmetic tasks (DMAT) and Simultaneous task EEG workload (STEW)]. These were divided into training and test sets using an 8:2 ratio, and the training data were further divided into training and validation sets using a fivefold cross-validation (CV) method, in which the model with the highest accuracy among the five was selected. The model was trained once more with the full training set, and the test data were then used to evaluate its performance. This approach achieved average classification accuracies of 93.58%, 91.80%, and 99.71% for the MIST, STEW, and DMAT datasets, respectively. Experimental results showed MuLHiTA was superior to state-of-the-art algorithms, including EEGNet, BLSTM, EEGLearn, convolutional neural network (CNN)-long short-term memory (LSTM), and convolutional recurrent attention model (CRAM), for multiclass classification. This demonstrates the viability of MuLHiTA for the early detection of mental stress.

Molecular Cloning and Functional Characterization of a Sterol 3-O-Glucosyltransferase Involved in Biosynthesis of Steroidal Saponins in Trigonella foenum-graecum.

Fenugreek (Trigonella foenum-graecum), a pharmacologically important herb, is widely known for its antidiabetic, hypolipidemic, and anticancer effects. The medicinal properties of this herb are accredited to the presence of bioactive steroidal saponins with one or more sugar moieties linked to the C-3 OH position of disogenin or its C25-epimer yamogenin. Despite intensive studies regarding pharmacology and phytochemical profiles of this plant, enzymes and/or genes involved in synthesizing the glycosidic part of fenugreek steroidal saponins are still missing so far. This study reports the molecular cloning and functional characterization of a key sterol-specific glucosyltransferase, designated as TfS3GT2 here, from fenugreek plant. The recombinant TfS3GT2 was purified via expression in Escherichia coli, and biochemical characterization of the recombinant enzyme suggested its role in transferring a glucose group onto the C-3 hydroxyl group of diosgenin or yamogenin. The functional role of TfS3GT2 in the steroidal saponin biosynthesis was also demonstrated by suppressing the gene in the transgenic fenugreek hairy roots via the RNA interference (RNAi) approach. Down-regulation of TfS3GT2 in fenugreek generally led to reduced levels of diosgenin or yamogenin-derived steroidal saponins. Thus, Tf3SGT2 was identified as a steroid-specific UDP-glucose 3-O-glucosyltransferase that appears to be involved in steroidal saponin biosynthesis in T. foenum-graecum.