Salvia miltiorrhiza and S. grandifolia are rich in diterpenoids and have therapeutic effects on cardiovascular diseases. In this study, the spatial distribution of diterpenoids in both species was analyzed by a combination of metabolomics and mass spectrometry imaging techniques. The results indicated that diterpenoids in S. miltiorrhiza were mainly abietane-type norditerpenoid quinones with a furan or dihydrofuran D-ring and were mainly distributed in the periderm of the roots, e.g. cryptotanshinone and tanshinone IIA. The compounds in S. grandifolia were mainly phenolic abietane-type tricyclic diterpenoids with six- or seven-membered C-rings, and were widely distributed in the periderm, phloem, and xylem of the roots, e.g. 11-hydroxy-sugiol, 11,20-dihydroxy-sugiol, and 11,20-dihydroxy-ferruginol. In addition, the leaves of S. grandifolia were rich in tanshinone biosynthesis precursors, such as 11-hydroxy-sugiol, while those of S. miltiorrhiza were rich in phenolic acids. Genes in the upstream pathway of tanshinone biosynthesis were highly expressed in the root of S. grandifolia, and genes in the downstream pathway were highly expressed in the root of S. miltiorrhiza. Here, we describe the specific tissue distributions and mechanisms of diterpenoids in two Salvia species, which will facilitate further investigations of the biosynthesis of diterpenoids in plant synthetic biology.

Developing a fast and non-destructive methodology to identify the storage years of Coix seed is important in safeguarding consumer well-being. This study employed the utilization of hyperspectral imaging (HSI) in conjunction with conventional machine learning techniques such as support vector machines (SVM), k-nearest neighbors (KNN), random forest (RF), extreme gradient boosting (XGBoost), as well as the deep learning method of residual neural network (ResNet), to establish identification models for Coix seed samples from different storage years. Under the fusion-based modeling approach, the model’s classification accuracy surpasses that of visible to near infrared (VNIR) and short-wave infrared (SWIR) spectral modeling individually. The classification accuracy of the ResNet model and SVM exceeds that of other conventional machine learning models (KNN, RF, and XGBoost). Redundant variables were further diminished through competitive adaptive reweighted sampling feature wavelength screening, which had less impact on the model’s accuracy. Upon validating the model’s performance using an external validation set, the ResNet model yielded more satisfactory outcomes, exhibiting recognition accuracy exceeding 85%. In conclusion, the comprehensive results demonstrate that the integration of deep learning with HSI techniques effectively distinguishes Coix seed samples from different storage years.

Hyperspectral imaging (HSI) technology was combined with chemometrics to achieve rapid determination of tanshinone contents in Salvia miltiorrhiza, as well as the rapid identification of its origins. Derivative (D1), second derivative (D2), Savitzky–Golay filtering (SG), multiplicative scatter correction (MSC), and standard normal variate transformation (SNV) were utilized to preprocess original spectrum (ORI). Partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) models were employed to discriminate 420 Salvia miltiorrhiza samples collected from Shandong, Hebei, Shanxi, Sichuan, and Anhui Provinces. The contents of tanshinone IIA, tanshinone I, cryptotanshinone, and total tanshinones in Salvia miltiorrhiza were predicted by the back-propagation neural network (BPNN), partial least square regression (PLSR), and random forest (RF). Finally, effective wavelengths were selected using the successive projections algorithm (SPA) and variable iterative space shrinkage approach (VISSA). The results indicated that the D1-PLS-DA model performed the best with a classification accuracy of 98.97%. SG-BPNN achieved the best prediction effect for cryptotanshinone (RMSEP = 0.527, RPD = 3.25), ORI-BPNN achieved the best prediction effect for tanshinone IIA (RMSEP = 0.332, RPD = 3.34), MSC-PLSR achieved the best prediction effect for tanshinone I (RMSEP = 0.110, RPD = 4.03), and SNV-BPNN achieved the best prediction effect for total tanshinones (RMSEP = 0.759, RPD = 4.01). When using the SPA and VISSA, the number of wavelengths was reduced below 60 and 150, respectively, and the performance of the models was all very good (RPD > 3). Therefore, the combination of HSI with chemometrics provides a promising method for predicting the active ingredients of Salvia miltiorrhiza and identifying its geographical origins.

Two endophytic fungi Trichoderma afroharzianum (HP-3) and Alternaria alstroemeriae (HP-7) were isolated and purified from the fresh root of Dryopteris crassirhizoma. Chemical investigation of the two fungi resulted in the isolation of two new phenols 2,4-dihydroxy-3-farnesyl-5-methoxy benzoic acid (1) and 2-hydroxyphenethyl 2-phenylacetate (2), together with 22 known compounds. Their structures were elucidated by NMR, UV, IR, HRESIMS, and comparison to the literature data. Compounds 15 and 16 showed significant antibacterial activity against Micrococcus lysodeikticus with MIC value of 6.25 μg/mL, while 8 and 14 displayed moderate inhibitory activities against several plant pathogenic fungi and clinically important bacterial strains. This is the first study to report the isolation, identification, and antimicrobial properties of metabolites from endophytic fungi of D. crassirhizoma. Our findings may provide lead compounds for the development of new antibacterial agents.

Plant stress memory can provide the benefits of enhanced protection against additional stress exposure. Here, we aimed to explore the responses of recurrent and non-recurrent yeast extract (YE) stresses in Sorbus pohuashanensis suspension cells (SPSCs) at metabolomics and transcriptional levels. Biochemical analyses showed that the cell wall integrity and antioxidation capacity of SPSCs in the pretreated group were evidently improved. Metabolic analysis showed that there were 39 significantly altered metabolites in the pretreated group compared to the non-pretreated group. Based on the transcriptome analysis, 219 differentially expressed genes were obtained, which were highly enriched in plant–pathogen interaction, circadian rhythm–plant, oxidative phosphorylation, and phenylpropanoid biosynthesis. Furthermore, the correlation analysis of the transcriptome and metabolome data revealed that phenylpropanoid biosynthesis involved in the production of biphenyl phytoalexins may play a critical role in the memory response of SPSC to YE, and the key memory genes were also identified, including PAL1, BIS1, and BIS3. Collectively, the above results demonstrated that the memory responses of SPSC to YE were significant in almost all levels, which would be helpful for better understanding the adaptation mechanisms of medicinal plants in response to biotic stress, and laid a biotechnological foundation to accumulate favorable antimicrobial drug candidates from plant suspension cells.