Project description:Untargeted-metabolomics LC-MS/MS analysis of commercial natural products pool, analyzed with different DDA settings with the objective to find the best one.
Project description:Natural products exhibit potential as candidates for developing multi-target agents for Alzheimer's disease treatment. The aim of this study is to utilize network-based medicine to identify novel natural products for Alzheimer's disease, and investigate their efficacy and mechanisms of action. In this study, we identified (-)-Vestitol and Salviolone as new potential natural products for treating Alzheimer's disease via an Alzheimer's disease-related pathway-gene network. Both natural products improved the cognition of APP/PS1 transgenic mice, reduced Aβ deposition, and lowered soluble toxic Aβ levels in the brain. Notably, a synergistic effect was observed when the two natural products were combined. Transcriptomic analysis and qRT-PCR experiments revealed that the synergistic mechanism of (-)-Vestitol and Salviolone combination is associated with the regulation of a broader range of AD-related pathways and genes, particularly the neuroactive ligand-receptor interaction pathway and calcium signaling pathway.
Project description:SDF-1 has been reported to trigger ADAMTS4,5 overexpression through activating CXCR4 signaling in chondrocytes. Here we described the transcriptional changes of SDF-1-treatment as well as natural products CXCR4 antagonists treatment.
Project description:We integrated quantitative proteomics and activity-based protein profiling (ABPP) approach to systematically elucidate the involved pathways and the covalent targets of natural products.
Project description:Data-independent acquisition (DIA) is a promising method for quantitative proteomics. Library-based DIA database searching against project-specific data-dependent acquisition (DDA) spectral libraries is the gold standard. These libraries are constructed using material-consuming pre-fractionation two dimensional DDA analysis. The alternative to this is library-free DIA analysis. Limited sample amounts restrict the use of fractionation to build spectral libraries for post-translational modifications (PTMs) DIA analysis. We present the use of gas-phase fractionation (GPF) DDA data to improve the depth of library-free DIA identification for the phosphoproteome, called GPF-DDA hybrid DIA. This method fully utilizes the remnants of samples post-DIA analysis and leverages both library-based and -free DIA database searching. GPF-DDA hybrid DIA analyzes phosphopeptides surplus sample after DIA analysis using a number of DDA injections with each scanning different mass-to-charge (m/z) windows, instead of preforming traditional off-line fractionation-based DDA. The GPF-DDA data is integrated into the library-free DIA database search to create a hybrid library, enhancing phosphopeptide identification. Two GPF-DDA injections proved to increase 18 % phosphopeptide and 13 % phosphosite identification in HEK293 cell lines, while five injections resulted in up to 28 % phosphopeptide and 21 % phosphosite increases compared to library-free DIA analysis alone. We used GPF-DDA hybrid DIA phosphoproteomics to characterize lung tissue upon direct (smoke induced) and indirect (sepsis induced) acute lung injury (ALI) in mice. The differentially expressed phosphosites (DEPsites) in direct ALI were found in proteins related to mRNA processing and RNA. DEPsites in indirect ALI were enriched in proteins related to microtubule polymerization, positive regulation of microtubule polymerization and fibroblast migration. This study demonstrates that GPF-DDA hybrid DIA analysis workflow can indeed promote depth of DIA analysis of phosphoproteome and could be extended to DIA analysis of other PTMs.