Transcriptome response of wheat Norin 10 to long-term elevated CO_2 under high yield field condition

The increasing atmospheric carbon dioxide concentration, caused by fossil fuel combustion and deforestation, plays an important role in plant growth and development. Wheat, as a major staple crop, adapts to climate change by tuning its inherent molecular mechanism, which is not well understood. The present study employed the RNA-Seq method to generate transcriptome profiles of the wheat Norin 10 in response to elevated CO_2 in comparison with ambient CO_2. The 10 895 787 high-quality clean reads of Norin 10 were assembled de novo using Trinity（without a reference genome） resulting in a total of 18 206 candidate transcripts with significant BLAST matches. GO enrichment analysis of Norin 10 at different CO_2 concentrations showed that some functional genes related to plastids, precursor metabolites, and energy, thylakoid and photosynthesis were apparently enriched at elevated CO_2（550 μmol mol^–1） in contrast to that at ambient CO_2（400 μmol mol^–1）; these findings were further confirmed by RT-PCR analysis. The findings demonstrated the specific effects of elevated CO_2 during long-term period in free air CO_2 enrichment（FACE） on transcriptome response of the high yielding wheat variety, Norin 10, which has a large spike.

Mechanism of Wumei Pill in the Treatment of Non-Erosive reflux disease from the Perspective of Network Pharmacology and Molecular docking

Objective:Based on network pharmacology and molecular docking to explore the mechanism of Wumei Pill in the treatment of non-erosive reflux disease(NERD).Method:We collected the active ingredients and targets of Wumei Pill by Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP),and collected NERD related targets through Genecards,PharmGKB,Drugbank,DisGeNET,OMIM,CTD and TTD databases.Intersection targets of Wumei Pill targets and NERD related targets were the potential targets of Wumei Pill in the treatment of NERD.We imported the intersection targets into the STRING database to obtain the PPI network,and obtained the hub targets.The network diagram of"Drugs-Potential active ingredients-Potential targets"was constructed by Cytoscape 3.7.2 software.We used R software to perform Gene Ontology function enrichment analysis(GO)and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis(KEGG)on hub targets,and then performed molecular docking verification.Results:There were 129 active ingredients and 213 drug targets of Wumei Pill of which 114 were the intersection targets.1587 GO enrichment items were identified(P<0.05),including 1,491 biological processes,11 cell components,and 85 molecular functions.143 KEGG pathways(P<0.05),mainly related to Kaposi sarcoma-associated herpesvirus infection,IL-17 signaling pathway,the TNF signaling pathway,MAPK signaling pathway.Results of molecular docking showed that the potential active ingredients in Wumei Pill had relatively stable binding activity to the key targets.Conclusion:Wumei pill for the treatment of non-erosive reflux disease are main active ingredients quercetin,kaempferol,beta sitosterol,Isocorypalmine,Stigmasterol,rutaecarpine,etc,the main targets is JUN,TP53,AKT1,may inhibit excessive inflammation,antioxidant therapy effect into full play.This provided a certain theoretical basis for clinical application.

Two-dimensional gel electrophoresis-based analysis provides global insights into the cotton ovule and fiber proteomes

Proteomic analysis of upland cotton was performed to profile the global detectable proteomes of ovules and fibers using two-dimensional electrophoresis(2DE).A total of 1,203 independent protein spots were collected from representative 2DE gels,which were digested with trypsin and identified by matrix-assisted laser desorption and ionization-time-offlight/time-of-flight(MALDI-TOF/TOF)mass spectrometry.The mass spectrometry or tandem mass spectrometry(MS or MS/MS)data were then searched against a local database constructed from Gossypium hirsutum genome sequences,resulting in successful identification of 975 protein spots(411 for ovules and 564 for fibers).Functional annotation analysis of the 975identified proteins revealed that ovule-specific proteins were mainly enriched in functions related to fatty acid elongation,sulfur amino acid metabolism and post-replication repair,while fiber-specific proteins were enriched in functions related to root hair elongation,galactose metabolism and D-xylose metabolic processes.Further annotation analysis of the most abundant protein spots showed that 28.96%of the total proteins in the ovule were mainly located in the Golgi apparatus,endoplasmic reticulum,mitochondrion and ribosome,whereas in fibers,27.02%of the total proteins were located in the cytoskeleton,nuclear envelope and cell wall.Quantitative real-time polymerase chain reaction(q RT-PCR)analyses of the ovule-specific protein spots P61,P93 and P198 and fiber-specific protein spots 230,477 and 511 were performed to validate the proteomics data.Protein-protein interaction network analyses revealed very different network cluster patterns between ovules and fibers.This work provides the largest protein identification dataset of 2DE-detectable proteins in cotton ovules and fibers and indicates potentially important roles of tissue-specific proteins,thus providing insights into the cotton ovule and fiber proteomes on a global scale.

Mining Functional Gene Modules Linked with Rheumatoid Arthritis Using a SNP-SNP Network

The identification of functional gene modules that are derived from integration of information from different types of networks is a powerful strategy for interpreting the etiology of complex diseases such as rheumatoid arthritis （RA）. Genetic variants are known to increase the risk of developing RA. Here, a novel method, the construction of a genetic network, was used to mine functional gene modules linked with RA. A polymorphism interaction analy- sis （PIA） algorithm was used to obtain cooperating single nucleotide polymorphisms （SNPs） that contribute to RA disease. The acquired SNP pairs were used to construct a SNP-SNP network. Sub-networks defined by hub SNPs were then extracted and turned into gene modules by mapping SNPs to genes using dbSNP database. We per- formed Gene Ontology （GO） analysis on each gene module, and some GO terms enriched in the gene modules can be used to investigate clustered gene function for better understanding RA pathogenesis. This method was applied to the Genetic Analysis Workshop 15 （GAW 15） RA dataset. The results show that genes involved in func- tional gene modules, such as CD160 （rs744877） and RUNX1 （rs2051179）, are especially relevant to RA, which is supported by previous reports. Furthermore, the 43 SNPs involved in the identified gene modules were found to be the best classifiers when used as variables for sample classification.