Global characterization of macrophage polarization mechanisms and identification of M2-type polarization inhibitors
Lizhi HeJhih-Hua JhongQi ChenKai‐Yao HuangKarin StrittmatterJohannes KreuzerMichael DeRanXu WuTzong-Yi LeeNikolai SlavovWilhelm HaasAlexander G. Marneros
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Macrophages undergoing M1- versus M2-type polarization differ significantly in their cell metabolism and cellular functions. Here, global quantitative time-course proteomics and phosphoproteomics paired with transcriptomics provide a comprehensive characterization of temporal changes in cell metabolism, cellular functions, and signaling pathways that occur during the induction phase of M1- versus M2-type polarization. Significant differences in, especially, metabolic pathways are observed, including changes in glucose metabolism, glycosaminoglycan metabolism, and retinoic acid signaling. Kinase-enrichment analysis shows activation patterns of specific kinases that are distinct in M1- versus M2-type polarization. M2-type polarization inhibitor drug screens identify drugs that selectively block M2- but not M1-type polarization, including mitogen-activated protein kinase kinase (MEK) and histone deacetylase (HDAC) inhibitors. These datasets provide a comprehensive resource to identify specific signaling and metabolic pathways that are critical for macrophage polarization. In a proof-of-principle approach, we use these datasets to show that MEK signaling is required for M2-type polarization by promoting peroxisome proliferator-activated receptor-γ (PPARγ)-induced retinoic acid signaling.Keywords:
Macrophage polarization
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Abstract Protein phosphorylation modulates many biological processes. However, the characterization of the complex regulatory circuits underlying cell response to external and internal stimuli is still limited by our inability to describe the phosphorylation network on a global scale. Modern MS‐based phosphoproteomics allows monitoring tens of thousands of phosphorylation sites in multiple conditions, making the approach ideal to explore signaling pathways mediated by phosphorylation. Here, we review recent advances in phosphoproteomics and discuss some of the computational approaches developed to facilitate extraction of signaling information from these datasets. Finally, this review focuses on approaches that integrate prior literature information with unbiased phosphoproteomics experiments.
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Mass spectrometry (MS)-based quantitative phosphoproteomics has become a key approach for proteome-wide profiling of phosphorylation in tissues and cells. Traditional experimental design often compares a single treatment with a control, whereas increasingly more experiments are designed to compare multiple treatments with respect to a control. To this end, the development of bioinformatic tools that can integrate multiple treatments and visualise kinases and substrates under combinatorial perturbations is vital for dissecting concordant and/or independent effects of each treatment. Here, we propose a hypothesis driven kinase perturbation analysis (KinasePA) to annotate and visualise kinases and their substrates that are perturbed by various combinatorial effects of treatments in phosphoproteomics experiments. We demonstrate the utility of KinasePA through its application to two large-scale phosphoproteomics datasets and show its effectiveness in dissecting kinases and substrates within signalling pathways driven by unique combinations of cellular stimuli and inhibitors. We implemented and incorporated KinasePA as part of the "directPA" R package available from the comprehensive R archive network (CRAN). Furthermore, KinasePA also has an interactive web interface that can be readily applied to annotate user provided phosphoproteomics data (http://kinasepa.pengyiyang.org).
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ABSTRACT Eukaryotic cells create gradients of cAMP across space and time to regulate the cAMP dependent protein kinase (PKA) and, in turn, growth and metabolism. However, how PKA responds to different concentrations of cAMP is unclear. Here, to address this question, we examine PKA signaling in S. cerevisiae in different conditions, timepoints, and concentrations of the chemical inhibitor 1-NM-PP1 using phosphoproteomics. These experiments show that there are numerous proteins that are only phosphorylated when cAMP and PKA activity are at/near their maximum level, while other proteins are phosphorylated even when cAMP levels and PKA activity are low. The data also show that PKA drives cells into distinct growth states by acting on proteins with different thresholds for phosphorylation in different conditions. Analysis of the sequences surrounding the 118 PKA-dependent phosphosites suggests that the phosphorylation thresholds are set, at least in part, by the affinity of PKA for each site.
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The phosphorylation of proteins modulates various functions of proteins and plays an important role in the regulation of cell signaling. In recent years, label-free quantitative (LFQ) phosphoproteomics has become a powerful tool to analyze the phosphorylation of proteins within complex samples. Despite the great progress, the studies of protein phosphorylation are still limited in throughput, robustness, and reproducibility, hampering analyses that involve multiple perturbations, such as those needed to follow the dynamics of phosphoproteomes. To address these challenges, we introduce here the LFQ phosphoproteomics workflow that is based on Fe-IMAC phosphopeptide enrichment followed by strong anion exchange (SAX) and porous graphitic carbon (PGC) fractionation strategies. We applied this workflow to analyze the whole-cell phosphoproteome of the fission yeast Schizosaccharomyces pombe. Using this strategy, we identified 8353 phosphosites from which 1274 were newly identified. This provides a significant addition to the S. pombe phosphoproteome. The results of our study highlight that combining of PGC and SAX fractionation strategies substantially increases the robustness and specificity of LFQ phosphoproteomics. Overall, the presented LFQ phosphoproteomics workflow opens the door for studies that would get better insight into the complexity of the protein kinase functions of the fission yeast S. pombe.
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Protein phosphorylation has long been recognized as an essential mechanism to regulate many important processes of plant life. However, studies on phosphorylation mediated signaling events in plants are challenged with low stoichiometry and dynamic nature of phosphorylated proteins. Significant advances in mass spectrometry based phosphoproteomics have taken place in recent decade, including phosphoprotein/phosphopeptide enrichment, detection and quantification and phosphorylation site localization. This review describes a variety of separation and enrichment methods for phosphoproteins and phosphopeptides, the applications of technological innovations in plant phosphoproteomics, and highlights significant achievement of phosphoproteomics in the areas of plant signal transduction, growth and development.
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It is well known that eukaryotic cells create gradients of cAMP across space and time to regulate the cAMP dependent protein kinase (PKA) and, in turn, growth and metabolism. However, it is unclear how PKA responds to different concentrations of cAMP. Here, to address this question, we examine PKA signaling in Saccharomyces cerevisiae in different conditions, timepoints, and concentrations of the chemical inhibitor 1-NM-PP1, using phosphoproteomics. These experiments show that there are numerous proteins that are only phosphorylated when cAMP and PKA activity are at/near their maximum level, while other proteins are phosphorylated even when cAMP levels and PKA activity are low. The data also show that PKA drives cells into distinct growth states by acting on proteins with different thresholds for phosphorylation in different conditions. Analysis of the sequences surrounding the 118 PKA-dependent phosphosites suggests that the phosphorylation thresholds are set, at least in part, by the affinity of PKA for each site.
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