Dendrobium is the second biggest genus in the Orchidaceae family, some of which have both ornamental and therapeutic values. Alkaloids are a group of active chemicals found in Dendrobium plants. Dendrobine has emerged specific pharmacological and therapeutic properties. Although Dendrobium alkaloids have been isolated and identified since the 1930s, the composition of alkaloids and their biosynthesis pathways, including metabolic intermediates, alkaloid transporters, concrete genes involved in downstream pathways, and associated gene clusters, have remained unresolved scientific issues. This paper comprehensively reviews currently identified and tentative alkaloids from the aspect of biogenic pathways or metabolic genes uncovered based on the genome annotations. The biosynthesis pathways of each class of alkaloids are highlighted. Moreover, advances of the high-throughput sequencing technologies in the discovery of Dendrobium alkaloid pathways have been addressed. Applications of synthetic biology in large-scale production of alkaloids are also described. This would serve as the basis for further investigation into Dendrobium alkaloids.

Salt stress is a constraint on crop growth and productivity. When exposed to high salt stress, metabolic abnormalities that disrupt reactive oxygen species (ROS) homeostasis result in massive oxygen radical deposition. Dendrobium huoshanense is a perennial orchid herb that thrives in semi-shade conditions. Although lots of studies have been undertaken on abiotic stresses (high temperature, chilling, drought, etc.) of model plants, few studies were reported on the mechanism of salt stress in D. huoshanense. Using a label-free protein quantification method, a total of 2,002 differential expressed proteins were identified in D. huoshanense. The Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment indicated that proteins involved in vitamin B6 metabolism, photosynthesis, spliceosome, arginine biosynthesis, oxidative phosphorylation, and MAPK signaling were considerably enriched. Remarkably, six malate dehydrogenases (MDHs) were identified from deferentially expressed proteins. (NAD+)-dependent MDH may directly participate in the biosynthesis of malate in the nocturnal crassulacean acid metabolism (CAM) pathway. Additionally, peroxidases such as superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT), as well as antioxidant enzymes involved in glutathione biosynthesis and some vitamins biosynthesis were also identified. Taken together, these results provide a solid foundation for the investigation of the mechanism of salt stress in Dendrobium spp.

ObjectiveThe aim of this study was to develop and validate a deep learning-based radiomic (DLR) model combined with clinical characteristics for predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer. For early prediction of pCR, the DLR model was based on pre-treatment and early treatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data.Materials and methodsThis retrospective study included 95 women (mean age, 48.1 years; range, 29–77 years) who underwent DCE-MRI before (pre-treatment) and after two or three cycles of NAC (early treatment) from 2018 to 2021. The patients in this study were randomly divided into a training cohort (n=67) and a validation cohort (n=28) at a ratio of 7:3. Deep learning and handcrafted features were extracted from pre- and early treatment DCE-MRI contoured lesions. These features contribute to the construction of radiomic signature RS1 and RS2 representing information from different periods. Mutual information and least absolute shrinkage and selection operator regression were used for feature selection. A combined model was then developed based on the DCE-MRI features and clinical characteristics. The performance of the models was assessed using the area under the receiver operating characteristic curve (AUC) and compared using the DeLong test.ResultsThe overall pCR rate was 25.3% (24/95). One radiomic feature and three deep learning features in RS1, five radiomic features and 11 deep learning features in RS2, and five clinical characteristics remained in the feature selection. The performance of the DLR model combining pre- and early treatment information (AUC=0.900) was better than that of RS1 (AUC=0.644, P=0.068) and slightly higher that of RS2 (AUC=0.888, P=0.604) in the validation cohort. The combined model including pre- and early treatment information and clinical characteristics showed the best ability with an AUC of 0.925 in the validation cohort.ConclusionThe combined model integrating pre-treatment, early treatment DCE-MRI data, and clinical characteristics showed good performance in predicting pCR to NAC in patients with breast cancer. Early treatment DCE-MRI and clinical characteristics may play an important role in evaluating the outcomes of NAC by predicting pCR.

With the rapid advancement of high-throughput sequencing technology, it is now possible to identify individual gene families from genomes on a large scale in order to study their functions. WRKY transcription factors are a key class of regulators that regulate plant growth and abiotic stresses. Here, a total of 74 WRKY genes were identified from Dendrobium officinale Kimura et Migo genome. Based on the genome-wide analysis, an in-depth analysis of gene structure and conserved motif was performed. The phylogenetic analysis indicated that DoWRKYs could be classified into three main groups: I, II, and III, with group II divided into five subgroups: II-a, II-b, II-c, II-d, and II-e. The sequence alignment indicated that these WRKY transcriptional factors contained a highly conserved WRKYGQK heptapeptide. The localization analysis of chromosomes showed that WRKY genes were irregularly distributed across several chromosomes of D. officinale. These genes comprised diverse patterns in both number and species, and there were certain distinguishing motifs among subfamilies. Moreover, the phylogenetic tree and chromosomal location results indicated that DoWRKYs may have undergone a widespread genome duplication event. Based on an evaluation of expression profiles, we proposed that DoWRKY5, 54, 57, 21, etc. may be involved in the transcriptional regulation of the JA signaling pathway. These results provide a scientific reference for the study of DoWRKY family genes.