Binding of dsDNA by cyclic GMP-AMP (cGAMP) synthase (cGAS) triggers formation of the metazoan second messenger c[G(2′,5′)pA(3′,5′)p], which binds the signaling protein STING with subsequent activation of the interferon (IFN) pathway. We show that human hSTING adopts a “closed” conformation upon binding c[G(2′,5′)pA(3′,5′)p] and its linkage isomer c[G(2′,5′)pA(2′,5′)p], as does mouse mSting on binding c[G(2′,5′)pA(3′,5′)p], c[G(3′,5′)pA(3′,5′)p] and the antiviral agent DMXAA, leading to similar “closed” conformations. Comparing hSTING to mSting, 2′,5′-linkage-containing cGAMP isomers were more specific triggers of the IFN pathway compared to the all-3′,5′-linkage isomer. Guided by structural information, we identified a unique point mutation (S162A) placed within the cyclic-dinucleotide-binding site of hSTING that rendered it sensitive to the otherwise mouse-specific drug DMXAA, a conclusion validated by binding studies. Our structural and functional analysis highlights the unexpected versatility of STING in the recognition of natural and synthetic ligands within a small-molecule pocket created by the dimerization of STING. 2′,5′-linkage-containing cGAMP second messengers bind to human and mouse STING with stronger affinity than 3′,5′ isomers to trigger interferon signaling. A point mutation in human STING may explain the lack of sensitivity to DMXAA, a drug with potent antiviral and antitumorigenic effects in mice.
We report enzymologic, thermodynamic and structural analyses of a series of six clinically derived mutant HIV proteases (PR) resistant to darunavir. We show that even very dramatic changes in PR sequence leading to the loss of hydrogen bonds with the inhibitor could be partially compensated by the entropy contribution due to burial of larger nonpolar surface area of the protein.
First released in 2009, MetaboAnalyst (www.metaboanalyst.ca) was a relatively simple web server designed to facilitate metabolomic data processing and statistical analysis. With continuing advances in metabolomics along with constant user feedback, it became clear that a substantial upgrade to the original server was necessary. MetaboAnalyst 2.0, which is the successor to MetaboAnalyst, represents just such an upgrade. MetaboAnalyst 2.0 now contains dozens of new features and functions including new procedures for data filtering, data editing and data normalization. It also supports multi-group data analysis, two-factor analysis as well as time-series data analysis. These new functions have also been supplemented with: (i) a quality-control module that allows users to evaluate their data quality before conducting any analysis, (ii) a functional enrichment analysis module that allows users to identify biologically meaningful patterns using metabolite set enrichment analysis and (iii) a metabolic pathway analysis module that allows users to perform pathway analysis and visualization for 15 different model organisms. In developing MetaboAnalyst 2.0 we have also substantially improved its graphical presentation tools. All images are now generated using anti-aliasing and are available over a range of resolutions, sizes and formats (PNG, TIFF, PDF, PostScript, or SVG). To improve its performance, MetaboAnalyst 2.0 is now hosted on a much more powerful server with substantially modified code to take advantage the server's multi-core CPUs for computationally intensive tasks. MetaboAnalyst 2.0 also maintains a collection of 50 or more FAQs and more than a dozen tutorials compiled from user queries and requests. A downloadable version of MetaboAnalyst 2.0, along detailed instructions for local installation is now available as well.
Pointillistic based super‐resolution techniques, such as photoactivated localization microscopy (PALM), involve multiple cycles of sequential activation, imaging, and precise localization of single fluorescent molecules. A super‐resolution image, having nanoscopic structural information, is then constructed by compiling all the image sequences. Because the final image resolution is determined by the localization precision of detected single molecules and their density, accurate image reconstruction requires imaging of biological structures labeled with fluorescent molecules at high density. In such image datasets, stochastic variations in photon emission and intervening dark states lead to uncertainties in identification of single molecules. This, in turn, prevents the proper utilization of the wealth of information on molecular distribution and quantity. A recent strategy for overcoming this problem is pair‐correlation analysis applied to PALM. Using rigorous statistical algorithms to estimate the number of detected proteins, this approach allows the spatial organization of molecules to be quantitatively described. Inphotoactivated localization microscopy (PALM) photoactivable fluorescent proteins (PA‐FPs) are stochastically activated, imaged, their localization determined and then bleached. By repeating this cycle with different subsets of PA‐FPs and combining the data, a super‐resolution image is obtained. An overview of latest developments in pair‐correlation analysis is given here.
Here, we speculated that genes of the module 5 were the essential genes which were associated to human osteosarcoma. Together, our findings provided the framework of co‐expression gene modules of human osteosarcoma and furthered the understanding of these modules at functional aspect.
The metabolite composition of strawberry floral organs (i.e. sepals, petals, stamen, pistil and receptacle) has been studied by UPLC-qTOF-MS and MS/MS analysis. Various classes of metabolites, including ellagitannins, proanthocyanidins, flavonols, terpenoids, and spermidine derivatives were detected that showed differential accumulation between the floral organs. These results allude to spatially-restricted production of secondary metabolite classes and specialized derivatives in flowers that take part in implementing the unique program of individual organs in the floral life cycle. Formation of flower organs and the subsequent pollination process require a coordinated spatial and temporal regulation of particular metabolic pathways. In this study a comparison has been made between the metabolite composition of individual flower organs of strawberry ( × ) including the petal, sepal, stamen, pistil and the receptacle that gives rise to the strawberry fruit. Non-targeted metabolomics analysis of the semi-polar secondary metabolites by the use of UPLC-qTOF-MS was utilized in order to localize metabolites belonging to various chemical classes (e.g. ellagitannins, proanthocyanidins, flavonols, terpenoids, and spermidine derivatives) to the different flower organs. The vast majority of the tentatively identified metabolites were ellagitannins that accumulated in all five parts of the flower. Several metabolite classes were detected predominantly in certain flower organs, as for example spermidine derivatives were present uniquely in the stamen and pistil, and the proanthocyanidins were almost exclusively detected in the receptacle and sepals. The latter organ was also rich in terpenoids (i.e. triterpenoid and sesquiterpenoid derivatives) whereas phenolic acids and flavonols were the predominant classes of compounds detected in the petals. Furthermore, we observed extensive variation in the accumulation of metabolites from the same class in a single organ, particularly in the case of ellagitannins, and the flavonols quercetin, kaempferol and isorhamnetin. These results allude to spatially-restricted production of secondary metabolite classes and specialized derivatives in flowers that take part in implementing the unique program of individual organs in the floral life cycle.
With the completion of the Arabidopsis genome sequencing project, the next major challenge is the large-scale determination of gene function. As a model organism for agricultural biotechnology, Arabidopsis presents the opportunity to provide key insights into the way that gene function can affect commercial crop production. In an attempt to aid in the rapid discovery of gene function, we have established a high throughput phenotypic analysis process based on a series of defined growth stages that serve both as developmental landmarks and as triggers for the collection of morphological data. The data collection process has been divided into two complementary platforms to ensure the capture of detailed data describing Arabidopsis growth and development over the entire life of the plant. The first platform characterizes early seedling growth on vertical plates for a period of 2 weeks. The second platform consists of an extensive set of measurements from plants grown on soil for a period of ∼2 months. When combined with parallel processes for metabolic and gene expression profiling, these platforms constitute a core technology in the high throughput determination of gene function. We present here analyses of the development of wild-type Columbia (Col-0) plants and selected mutants to illustrate a framework methodology that can be used to identify and interpret phenotypic differences in plants resulting from genetic variation and/or environmental stress.
MicroScope is an integrated platform dedicated to both the methodical updating of microbial genome annotation and to comparative analysis. The resource provides data from completed and ongoing genome projects (automatic and expert annotations), together with data sources from post-genomic experiments (i.e. transcriptomics, mutant collections) allowing users to perfect and improve the understanding of gene functions. MicroScope (http://www.genoscope.cns.fr/agc/microscope) combines tools and graphical interfaces to analyse genomes and to perform the manual curation of gene annotations in a comparative context. Since its first publication in January 2006, the system (previously named MaGe for Magnifying Genomes) has been continuously extended both in terms of data content and analysis tools. The last update of MicroScope was published in 2009 in the Database journal. Today, the resource contains data for >1600 microbial genomes, of which similar to 300 are manually curated and maintained by biologists (1200 personal accounts today). Expert annotations are continuously gathered in the MicroScope database (similar to 50 000 a year), contributing to the improvement of the quality of microbial genomes annotations. Improved data browsing and searching tools have been added, original tools useful in the context of expert annotation have been developed and integrated and the website has been significantly redesigned to be more user-friendly. Furthermore, in the context of the European project Microme (Framework Program 7 Collaborative Project), MicroScope is becoming a resource providing for the curation and analysis of both genomic and metabolic data. An increasing number of projects are related to the study of environmental bacterial (meta) genomes that are able to metabolize a large variety of chemical compounds that may be of high industrial interest.
To complete the metabolic map for an entire class of compounds, it is essential to identify gene-metabolite correlations of a metabolic pathway. We used liquid chromatography-mass spectrometry (LC-MS) to identify the flavonoids produced by Arabidopsis thaliana wild-type and flavonoid biosynthetic mutant lines. The structures of 15 newly identified and eight known flavonols were deduced by LC-MS profiling of these mutants. Candidate genes presumably involved in the flavonoid pathway were delimited by transcriptome coexpression network analysis using public databases, leading to the detailed analysis of two flavonoid pathway genes, UGT78D3 (At5g17030) and RHM1 (At1g78570). The levels of flavonol 3-O-arabinosides were reduced in ugt78d3 knockdown mutants, suggesting that UGT78D3 is a flavonol arabinosyltransferase. Recombinant UGT78D3 protein could convert quercetin to quercetin 3-O-arabinoside. The strict substrate specificity of UGT78D3 for flavonol aglycones and UDP-arabinose indicate that UGT78D3 is a flavonol arabinosyltransferase. A comparison of flavonol profile in RHM knockout mutants indicated that RHM1 plays a major role in supplying UDP-rhamnose for flavonol modification. The rate of flavonol 3-O-glycosylation is more affected than those of 7-O-glycosylation by the supply of UDP-rhamnose. The precise identification of flavonoids in conjunction with transcriptomics thus led to the identification of a gene function and a more complete understanding of a plant metabolic network.