Based on plasma metabonomics and network analysis to research the mechanisms of Chaigui granules for treating depression / 药学学报

The purpose of this study was to systematically analyze the antidepressant mechanism of Chaigui granules from the perspective of biological metabolic network by using integrated metabolomics and biological network analysis tools. The model of chronic unpredictable mild stress (CUMS) depression rat was established, and LC-MS-based plasma metabolomics was used to identify the key metabolites and analyze metabolic pathways underlying the antidepressant effects of Chaigui Granules. The key metabolites regulated by Chaigui granules was integrated with biological network analysis tools to further focus on the key metabolic pathways and explore the potential targets of the antidepressant effect of Chaigui granules. The results showed that there were significant differences in the plasma levels of 20 metabolites in the model group compared with the control group (P < 0.05), Chaigui granules significantly regulated 12 metabolites including docosatrienoic acid, 3-hydroxybutyric acid, 4-hydroxybenzaldehyde, chenodeoxycholic acid, cholic acid, L-glutamine, glycocholic acid, linoleyl carnitine, L-tyrosine, N-acetylvaline, palmitoylcarnitine, arachidonic acid. Further network analysis of the key metabolites regulated by Chaigui granules indicated that plasma arachidonic acid metabolism might be the core pathway for the antidepressant effect of Chaigui granules, with 10 proteins were potential targets for the antidepressant effect of Chaigui granules, including CYP2B6, CYP2E1, CYP2C9, CYP2C8, PLA2G6, PTGS2, ALOX15B, PTGS1, ALOX12 and ALOX5. The animal experimental operations involved in this paper was followed the regulations of the Animal Ethics Committee of Shanxi University and passed the animal experimental ethical review (Approval No. SXULL2020028).

Analysis of chickpea gene co-expression networks and pathways during heavy metal stress

Pseudomonas putida is widely used as a biocontrol agent, however, mechanisms by which it initiates the plants’ defenseresponse remains obscure. To gain an insight into the molecular changes that occur in plants upon plant growth-promotingrhizobacteria colonization, root transcriptome analysis by using a microarray was performed in rice using P. putida RRF3 (arice rhizosphere isolate). Data analysis revealed a differential regulation of 61 transcripts (48 h post-treatment), of which,majority corresponded to defense response, cell wall modification and secondary metabolism. Seven genes encodingsalicylic acid (SA) responsive pathogenesis-related proteins were up-regulated significantly (fold change ranges from 1 to4), which suggests that RRF3 has a profound impact on a SA-mediated defense signaling mechanism in rice. Investigationsperformed at later stages of RRF3 colonization by real-time polymerase chain reaction and high-performance liquidchromatography (HPLC) analysis confirmed the above results, demonstrating RRF3 as a potent biocontrol agent. Further,the impact of RRF3 colonization on root exudation, in particular, exudation of SA was investigated by HPLC. However,analysis revealed RRF3 to have a negative impact on root exudation of SA. Overall, this study shows that P. putida RRF3immunizes the rice plants by re-organizing the root transcriptome to stimulate plant defense responses (‘priming’), andsimultaneously protects itself from the primed plants by altering the rhizosphere chemical constituents.

Use of Graph Database for the Integration of Heterogeneous Biological Data

Understanding complex relationships among heterogeneous biological data is one of the fundamental goals in biology. In most cases, diverse biological data are stored in relational databases, such as MySQL and Oracle, which store data in multiple tables and then infer relationships by multiple-join statements. Recently, a new type of database, called the graph-based database, was developed to natively represent various kinds of complex relationships, and it is widely used among computer science communities and IT industries. Here, we demonstrate the feasibility of using a graph-based database for complex biological relationships by comparing the performance between MySQL and Neo4j, one of the most widely used graph databases. We collected various biological data (protein-protein interaction, drug-target, gene-disease, etc.) from several existing sources, removed duplicate and redundant data, and finally constructed a graph database containing 114,550 nodes and 82,674,321 relationships. When we tested the query execution performance of MySQL versus Neo4j, we found that Neo4j outperformed MySQL in all cases. While Neo4j exhibited a very fast response for various queries, MySQL exhibited latent or unfinished responses for complex queries with multiple-join statements. These results show that using graph-based databases, such as Neo4j, is an efficient way to store complex biological relationships. Moreover, querying a graph database in diverse ways has the potential to reveal novel relationships among heterogeneous biological data.

Research methods and applications in network pharmacology / 药学实践杂志

Recently ,network pharmacology was a pop emerging pharmacology branch .The theory of "multi-gene , multi-target"about network pharmacology was consistent with treatment of complex disease .Different from the traditional ex-perimental methods about pharmacology ,the research methods of network pharmacology uniquely obtain the information of drugs and relative targets with less time and less money .In this paper ,according to the searching of China Knowledge Re-source Integrated Database and PubMed ,three aspects about research methods of network pharmacology together with its ap-plications were introduced :network construction ,network analysis and experimental validation .The introductions of the meth-ods gave us a new vision in researching the field of pharmacology deeply .

Review of Biological Network Data and Its Applications

Studying biological networks, such as protein-protein interactions, is key to understanding complex biological activities. Various types of large-scale biological datasets have been collected and analyzed with high-throughput technologies, including DNA microarray, next-generation sequencing, and the two-hybrid screening system, for this purpose. In this review, we focus on network-based approaches that help in understanding biological systems and identifying biological functions. Accordingly, this paper covers two major topics in network biology: reconstruction of gene regulatory networks and network-based applications, including protein function prediction, disease gene prioritization, and network-based genome-wide association study.