Identification of multi-target anti-cancer agents from TCM formula by in silico prediction and in vitro validation / 中国天然药物

Cancer is a complex disease associated with multiple gene mutations and malignant phenotypes, and multi-target drugs provide a promising therapy idea for the treatment of cancer. Natural products with abundant chemical structure types and rich pharmacological characteristics could be ideal sources for screening multi-target antineoplastic drugs. In this paper, 50 tumor-related targets were collected by searching the Therapeutic Target Database and Thomson Reuters Integrity database, and a multi-target anti-cancer prediction system based on mt-QSAR models was constructed by using naïve Bayesian and recursive partitioning algorithm for the first time. Through the multi-target anti-cancer prediction system, some dominant fragments that act on multiple tumor-related targets were analyzed, which could be helpful in designing multi-target anti-cancer drugs. Anti-cancer traditional Chinese medicine (TCM) and its natural products were collected to form a TCM formula-based natural products library, and the potential targets of the natural products in the library were predicted by multi-target anti-cancer prediction system. As a result, alkaloids, flavonoids and terpenoids were predicted to act on multiple tumor-related targets. The predicted targets of some representative compounds were verified according to literature review and most of the selected natural compounds were found to exert certain anti-cancer activity in vitro biological experiments. In conclusion, the multi-target anti-cancer prediction system is very effective and reliable, and it could be further used for elucidating the functional mechanism of anti-cancer TCM formula and screening for multi-target anti-cancer drugs. The anti-cancer natural compounds found in this paper will lay important information for further study.

Research Progress of Micro-PIV in Biomedical Engineering Field / 医用生物力学

Blood is an important component of living organism which is responsible for material transportation. The microenvironment of blood flow plays an important role in physiological and pathological processes of angiogenesis and cardiac development, erythrocytes aggregation and blood viscosity, tumor metastasis and atherosclerosis. Besides, micro-fluid environment significantly affects drug delivery, cell screening, and artificial organ design. Thus, the measurement and quantitative analysis of micro-fluid contribute to the biomedical engineering filed. Micro-particle imaging velocimetry (Micro-PIV) combines conventional PIV with microscopy technique. Correlation analysis is conducted in two groups of images captured by high speed camera at different time intervals, and the velocity profiles in micro-fluid environment are successfully measured. Compared with other velocity measurement methods, Micro-PIV has high temporal resolution and spatial resolution. The main setup of Micro-PIV and its principle analysis method were introduced in this review. Recent studies of Micro-PIV applications in biomedical engineering field were then summarized. Moreover, the drawbacks of Micro-PIV technique and prospect of its applications were discussed.

Comprehensive transcriptomics and proteomics analyses of rice stripe virus-resistant transgenic rice

Stable transgenic rice line (named KRSV-1) with strong resistance against rice stripe virus was generated using the genesequence of disease-specific protein by RNA interference. Comprehensive safety assessment of transgenic plants has turnedinto a significant field of genetic modification food safety. In this study, a safety assessment of KRSV-1 was carried out in astepwise approach. The molecular analysis exhibited that KRSV-1 harbored one copy number of transgene, which wasintegrated into the intergenic non-coding region of chromosome 2 associated with inter-chromosomal translocations of 1.6-kb segments of chromosome 8. Then, transcriptomics and proteomics analyses were carried out to detect the unintendedeffects as a result of the integration of the transgene. Although 650 dramatically differentially expressed genes (DDEGs)and 357 differentially expressed proteins were detected between KRSV-1 and wild-type (WT) by transcriptomics andproteomics analyses, no harmful members in the form of toxic proteins and allergens were observed. Encouragingly, thenutritional compositions of seeds from KRSV-1 were comparable with WT seeds. The results of this entire study ofmolecular analysis, transcriptome and proteome profile of KRSV-1 revealed that no detrimental changes in the form of toxicproteins and allergens were detected in the transgenic rice line due to the integration of the transgene.

Network pharmacology-based analysis of Chinese herbal Naodesheng formula for application to Alzheimer's disease / 中国天然药物

Naodesheng (NDS) formula, which consists of Rhizoma Chuanxiong, Lobed Kudzuvine, Carthamus tinctorius, Radix Notoginseng, and Crataegus pinnatifida, is widely applied for the treatment of cardio/cerebrovascular ischemic diseases, ischemic stroke, and sequelae of cerebral hemorrhage, etc. At present, the studies on NDS formula for Alzheimer's disease (AD) only focus on single component of this prescription, and there is no report about the synergistic mechanism of the constituents in NDS formula for the potential treatment of dementia. Therefore, the present study aimed to predict the potential targets and uncover the mechanisms of NDS formula for the treatment of AD. Firstly, we collected the constituents in NDS formula and key targets toward AD. Then, drug-likeness, oral bioavailability, and blood-brain barrier permeability were evaluated to find drug-like and lead-like constituents for treatment of central nervous system diseases. By combining the advantages of machine learning, molecular docking, and pharmacophore mapping, we attempted to predict the targets of constituents and find potential multi-target compounds from NDS formula. Finally, we built constituent-target network, constituent-target-target network and target-biological pathway network to study the network pharmacology of the constituents in NDS formula. To the best of our knowledge, this represented the first to study the mechanism of NDS formula for potential efficacy for AD treatment by means of the virtual screening and network pharmacology methods.

Network pharmacology-based analysis of Chinese herbal Naodesheng formula for application to Alzheimer's disease / 中国天然药物

Naodesheng (NDS) formula, which consists of Rhizoma Chuanxiong, Lobed Kudzuvine, Carthamus tinctorius, Radix Notoginseng, and Crataegus pinnatifida, is widely applied for the treatment of cardio/cerebrovascular ischemic diseases, ischemic stroke, and sequelae of cerebral hemorrhage, etc. At present, the studies on NDS formula for Alzheimer's disease (AD) only focus on single component of this prescription, and there is no report about the synergistic mechanism of the constituents in NDS formula for the potential treatment of dementia. Therefore, the present study aimed to predict the potential targets and uncover the mechanisms of NDS formula for the treatment of AD. Firstly, we collected the constituents in NDS formula and key targets toward AD. Then, drug-likeness, oral bioavailability, and blood-brain barrier permeability were evaluated to find drug-like and lead-like constituents for treatment of central nervous system diseases. By combining the advantages of machine learning, molecular docking, and pharmacophore mapping, we attempted to predict the targets of constituents and find potential multi-target compounds from NDS formula. Finally, we built constituent-target network, constituent-target-target network and target-biological pathway network to study the network pharmacology of the constituents in NDS formula. To the best of our knowledge, this represented the first to study the mechanism of NDS formula for potential efficacy for AD treatment by means of the virtual screening and network pharmacology methods.