Double Duty Synthons in Epoxide Synthesis and Palladium-Catalyzed Carbonylative Heterocyclizations

The concept of double duty presumes incorporation of two traditionally distinctly reacting parts of a chemical into one transformation. The first example of the double duty reactivity was demonstrated by using haloalkynes as a combination of both acetylide and electrophilic halogen in highly efficient transformations of ketones into density substituted alkynyl epoxides. During the course of research, we were able to demonstrate the double duty reactivity of cyanogen bromide and bromopolyfluoroarenes in highly efficient transformations of enolizable ketones into densely substituted epoxides via an electrophilic α-bromination/nucleophilic addition/nucleophilic substitution cascade reaction. This method allows for synthesis of valuable divergently substituted epoxides. C2-substituted indolizines, particularly those possessing electron-withdrawing groups, are interesting synthetic targets with great potential for biological activity study. However, there are no efficient and general approaches toward these molecules. We successfully developed the palladium-catalyzed synthesis of 2-aroyl indolizines from aryl iodides and readily available propargyl pyridines under carbon monoxide atmosphere. This general method allows for efficient synthesis of divergently substituted 2-aroyl indolizines under benign reaction conditions

Palladium-Catalyzed Carbonylative Cyclization/Arylation Cascade for 2‑Aroylindolizine Synthesis

An efficient synthesis of densely substituted 2-aroylindolizines via the palladium-catalyzed carbonylative cyclization/arylation is reported. This transformation proceeds via the <i>5-endo-dig</i> cyclization of 2-propargylpyridine triggered by an aroyl Pd complex. It produced diversely substituted 2-aroylindolizines in good to excellent yields

Image_1_Identification of molecular pattern and prognostic risk model based on ligand-receptor pairs in liver cancer.tif

IntroductionThe tumor microenvironment of hepatocellular carcinoma is composed of multiple cells, and the interactive communication between cells drives tumor progression and characterizes the tumor. Communication between cells is mainly achieved through signal transduction between receptor ligands, and the rise of single-cell technology has made it possible to analyze the communication network between cells.MethodsWe applied a train of bioinformatic techniques and in vitro experiments. We analyzed the composition of the microenvironment of liver cancer by combining single-cell sequencing data and transcriptome sequencing data from liver cancer to construct molecular typing and risk models for LRs. Then, we analyzed association of it with prognosis, mutation, KEGG, tumor microenvironment (TME), immune infiltration, tumor mutational burden (TMB) and drug sensitivity in liver cancer. qPCR and was used to identify SLC1A5 expression in LIHC cell lines and CCK8, transwell and cell colony formation were performed to validate the function of SLC1A5. Meanwhile, we also performed polarization of macrophages.ResultsIn this experiment, we found that liver cancer tissues are rich in immune and mesenchymal cells, and there is extensive signaling between individual cells, so we constructed molecular typing and risk models for LRs. Combining clinical data revealed significant differences in clinical characteristics, prognosis and mutated genes between the molecular typing of receptor-ligand pairs, as well as in sensitivity to drugs; similarly, there were significant prognostic differences between the risk models. There were also notable differences in activated signaling pathways, infiltrating immune cells and immune subtypes. Subsequently, we used siRNA to knock down SLC1A5 in hepatocellular carcinoma cells and found that cell proliferation, migration and invasion were diminished.ConclusionsIn conclusion, our LRs model may become a marker to guide clinical treatment and prognosis.</p

DataSheet_1_Identification of molecular pattern and prognostic risk model based on ligand-receptor pairs in liver cancer.docx

IntroductionThe tumor microenvironment of hepatocellular carcinoma is composed of multiple cells, and the interactive communication between cells drives tumor progression and characterizes the tumor. Communication between cells is mainly achieved through signal transduction between receptor ligands, and the rise of single-cell technology has made it possible to analyze the communication network between cells.MethodsWe applied a train of bioinformatic techniques and in vitro experiments. We analyzed the composition of the microenvironment of liver cancer by combining single-cell sequencing data and transcriptome sequencing data from liver cancer to construct molecular typing and risk models for LRs. Then, we analyzed association of it with prognosis, mutation, KEGG, tumor microenvironment (TME), immune infiltration, tumor mutational burden (TMB) and drug sensitivity in liver cancer. qPCR and was used to identify SLC1A5 expression in LIHC cell lines and CCK8, transwell and cell colony formation were performed to validate the function of SLC1A5. Meanwhile, we also performed polarization of macrophages.ResultsIn this experiment, we found that liver cancer tissues are rich in immune and mesenchymal cells, and there is extensive signaling between individual cells, so we constructed molecular typing and risk models for LRs. Combining clinical data revealed significant differences in clinical characteristics, prognosis and mutated genes between the molecular typing of receptor-ligand pairs, as well as in sensitivity to drugs; similarly, there were significant prognostic differences between the risk models. There were also notable differences in activated signaling pathways, infiltrating immune cells and immune subtypes. Subsequently, we used siRNA to knock down SLC1A5 in hepatocellular carcinoma cells and found that cell proliferation, migration and invasion were diminished.ConclusionsIn conclusion, our LRs model may become a marker to guide clinical treatment and prognosis.</p

Image_3_Identification of molecular pattern and prognostic risk model based on ligand-receptor pairs in liver cancer.tif

IntroductionThe tumor microenvironment of hepatocellular carcinoma is composed of multiple cells, and the interactive communication between cells drives tumor progression and characterizes the tumor. Communication between cells is mainly achieved through signal transduction between receptor ligands, and the rise of single-cell technology has made it possible to analyze the communication network between cells.MethodsWe applied a train of bioinformatic techniques and in vitro experiments. We analyzed the composition of the microenvironment of liver cancer by combining single-cell sequencing data and transcriptome sequencing data from liver cancer to construct molecular typing and risk models for LRs. Then, we analyzed association of it with prognosis, mutation, KEGG, tumor microenvironment (TME), immune infiltration, tumor mutational burden (TMB) and drug sensitivity in liver cancer. qPCR and was used to identify SLC1A5 expression in LIHC cell lines and CCK8, transwell and cell colony formation were performed to validate the function of SLC1A5. Meanwhile, we also performed polarization of macrophages.ResultsIn this experiment, we found that liver cancer tissues are rich in immune and mesenchymal cells, and there is extensive signaling between individual cells, so we constructed molecular typing and risk models for LRs. Combining clinical data revealed significant differences in clinical characteristics, prognosis and mutated genes between the molecular typing of receptor-ligand pairs, as well as in sensitivity to drugs; similarly, there were significant prognostic differences between the risk models. There were also notable differences in activated signaling pathways, infiltrating immune cells and immune subtypes. Subsequently, we used siRNA to knock down SLC1A5 in hepatocellular carcinoma cells and found that cell proliferation, migration and invasion were diminished.ConclusionsIn conclusion, our LRs model may become a marker to guide clinical treatment and prognosis.</p

Image_2_Identification of molecular pattern and prognostic risk model based on ligand-receptor pairs in liver cancer.tif

IntroductionThe tumor microenvironment of hepatocellular carcinoma is composed of multiple cells, and the interactive communication between cells drives tumor progression and characterizes the tumor. Communication between cells is mainly achieved through signal transduction between receptor ligands, and the rise of single-cell technology has made it possible to analyze the communication network between cells.MethodsWe applied a train of bioinformatic techniques and in vitro experiments. We analyzed the composition of the microenvironment of liver cancer by combining single-cell sequencing data and transcriptome sequencing data from liver cancer to construct molecular typing and risk models for LRs. Then, we analyzed association of it with prognosis, mutation, KEGG, tumor microenvironment (TME), immune infiltration, tumor mutational burden (TMB) and drug sensitivity in liver cancer. qPCR and was used to identify SLC1A5 expression in LIHC cell lines and CCK8, transwell and cell colony formation were performed to validate the function of SLC1A5. Meanwhile, we also performed polarization of macrophages.ResultsIn this experiment, we found that liver cancer tissues are rich in immune and mesenchymal cells, and there is extensive signaling between individual cells, so we constructed molecular typing and risk models for LRs. Combining clinical data revealed significant differences in clinical characteristics, prognosis and mutated genes between the molecular typing of receptor-ligand pairs, as well as in sensitivity to drugs; similarly, there were significant prognostic differences between the risk models. There were also notable differences in activated signaling pathways, infiltrating immune cells and immune subtypes. Subsequently, we used siRNA to knock down SLC1A5 in hepatocellular carcinoma cells and found that cell proliferation, migration and invasion were diminished.ConclusionsIn conclusion, our LRs model may become a marker to guide clinical treatment and prognosis.</p

Table_1_Identification of molecular pattern and prognostic risk model based on ligand-receptor pairs in liver cancer.xlsx

IntroductionThe tumor microenvironment of hepatocellular carcinoma is composed of multiple cells, and the interactive communication between cells drives tumor progression and characterizes the tumor. Communication between cells is mainly achieved through signal transduction between receptor ligands, and the rise of single-cell technology has made it possible to analyze the communication network between cells.MethodsWe applied a train of bioinformatic techniques and in vitro experiments. We analyzed the composition of the microenvironment of liver cancer by combining single-cell sequencing data and transcriptome sequencing data from liver cancer to construct molecular typing and risk models for LRs. Then, we analyzed association of it with prognosis, mutation, KEGG, tumor microenvironment (TME), immune infiltration, tumor mutational burden (TMB) and drug sensitivity in liver cancer. qPCR and was used to identify SLC1A5 expression in LIHC cell lines and CCK8, transwell and cell colony formation were performed to validate the function of SLC1A5. Meanwhile, we also performed polarization of macrophages.ResultsIn this experiment, we found that liver cancer tissues are rich in immune and mesenchymal cells, and there is extensive signaling between individual cells, so we constructed molecular typing and risk models for LRs. Combining clinical data revealed significant differences in clinical characteristics, prognosis and mutated genes between the molecular typing of receptor-ligand pairs, as well as in sensitivity to drugs; similarly, there were significant prognostic differences between the risk models. There were also notable differences in activated signaling pathways, infiltrating immune cells and immune subtypes. Subsequently, we used siRNA to knock down SLC1A5 in hepatocellular carcinoma cells and found that cell proliferation, migration and invasion were diminished.ConclusionsIn conclusion, our LRs model may become a marker to guide clinical treatment and prognosis.</p

Table_2_Identification of molecular pattern and prognostic risk model based on ligand-receptor pairs in liver cancer.xlsx

IntroductionThe tumor microenvironment of hepatocellular carcinoma is composed of multiple cells, and the interactive communication between cells drives tumor progression and characterizes the tumor. Communication between cells is mainly achieved through signal transduction between receptor ligands, and the rise of single-cell technology has made it possible to analyze the communication network between cells.MethodsWe applied a train of bioinformatic techniques and in vitro experiments. We analyzed the composition of the microenvironment of liver cancer by combining single-cell sequencing data and transcriptome sequencing data from liver cancer to construct molecular typing and risk models for LRs. Then, we analyzed association of it with prognosis, mutation, KEGG, tumor microenvironment (TME), immune infiltration, tumor mutational burden (TMB) and drug sensitivity in liver cancer. qPCR and was used to identify SLC1A5 expression in LIHC cell lines and CCK8, transwell and cell colony formation were performed to validate the function of SLC1A5. Meanwhile, we also performed polarization of macrophages.ResultsIn this experiment, we found that liver cancer tissues are rich in immune and mesenchymal cells, and there is extensive signaling between individual cells, so we constructed molecular typing and risk models for LRs. Combining clinical data revealed significant differences in clinical characteristics, prognosis and mutated genes between the molecular typing of receptor-ligand pairs, as well as in sensitivity to drugs; similarly, there were significant prognostic differences between the risk models. There were also notable differences in activated signaling pathways, infiltrating immune cells and immune subtypes. Subsequently, we used siRNA to knock down SLC1A5 in hepatocellular carcinoma cells and found that cell proliferation, migration and invasion were diminished.ConclusionsIn conclusion, our LRs model may become a marker to guide clinical treatment and prognosis.</p

Synthesis and Direct Visualization of Dumbbell-Shaped Molecular Brushes

Dumbbell-shaped triblock molecular brushes were synthesized by ring-opening metathesis polymerization (ROMP) of poly­(lactide) macromonomers with terminal norbornene groups (NB-PLA) in a sequential addition manner. By changing the macromonomer size and the feed ratio of Grubbs’ catalyst to macromonomer, the dimensions of the “ball” and “bar” of the dumbbell-shaped molecular brushes were controlled. The growth and production of well-defined structures were verified by gel permeation chromatography (GPC), and the final dumbbell-shaped architectures were visualized by atomic force microscopy (AFM). This synthetic methodology represents a rapid and convenient route to unique macromolecular topologies

List of protein spots with differential expressions between superior and inferior spikelets in 3 grain-filling stages.

<p>Note:</p>a<p>proteins number,</p>b<p>protein spots number correspond to those on 2-DE gels shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0089140#pone-0089140-g003" target="_blank">Fig. 3</a>;</p>c<p>accession number;</p>d<p>theoretical MW (kDa) and pI.</p>e<p>match peptides;</p>f<p>changes on protein spots in superior spikelets compared to inferior spikelets; UR: protein upregulated in inferior spikelets as compared to superior spikelets; DR: protein downregulated in inferior spikelets as compared to superior spikelets; EGS: early grain-filling stage, MGS: mid-grain-filling stage, LGS: late grain-filling stage;</p>g<p>protein spot identified by MALDI-TOF-MS;</p>h<p>protein spot identified by LC-ESI-MS/MS.</p