A dynamic phosphoproteomic analysis provides insight into the C4 plant maize (Zea mays L.) response to natural diurnal changes.

As sessile organisms, plants need to respond to rapid changes in numerous environmental factors, mainly diurnal changes of light, temperature, and humidity. Maize is the world's most grown crop, and as a C4 plant it exhibits high photosynthesis capacity, reaching the highest rate of net photosynthesis at midday; that is, there is no "midday depression." Revealing the physiological responses to diurnal changes and underlying mechanisms will be of great significance for guiding maize improvement efforts. In this study, we collected maize leaf samples and analyzed the proteome and phosphoproteome at nine time points during a single day/night cycle, quantifying 7424 proteins and 5361 phosphosites. The new phosphosites identified in our study increased the total maize phosphoproteome coverage by 8.5%. Kinase-substrate network analysis indicated that 997 potential substrates were phosphorylated by 20 activated kinases. Through analysis of proteins with significant changes in abundance and phosphorylation, we found that the response to a heat stimulus involves a change in the abundance of numerous proteins. By contrast, the high light at noon and rapidly changing light conditions induced changes in the phosphorylation level of proteins involved in processes such as chloroplast movement, photosynthesis, and C4 pathways. Phosphorylation is involved in regulating the activity of large number of enzymes; for example, phosphorylation of S55 significantly enhanced the activity of maize phosphoenolpyruvate carboxykinase1 (ZmPEPCK1). Overall, the database of dynamic protein abundance and phosphorylation we have generated provides a resource for the improvement of C4 crop plants.

Uncovering kappa-opioid receptor agonist-induced PAK1/2 phosphorylation by quantitative phosphoproteomics.

Kappa-opioid receptor (KOR) is a member of G-protein coupled receptors (GPCRs) expressed in serotonergic neurons and neuronal terminals. The involvement of KOR ligands in nociception, diuresis, emotion, cognition, and immune system has been extensively studied. Omics-based methods are preferable to understand the signaling cascade after KOR activation in a systematic manner. In this study, an in-depth quantitative phosphoproteomic analysis resulted in 305 phosphosites, which were significantly changed in three KOR-overexpressed cells upon treatment with two KOR agonists. The subsequent substrate-kinase prediction analysis revealed that 18 potential kinases might be activated under stimulation of the agonists. We found that phosphorylation of PAK1/2 (p21-activated kinase 1/2) was induced by KOR agonists, resulting in reduced actin stress fibers and cytoskeletal reorganization. In summary, this quantitative phosphoproteomics-based research studied the downstream phosphorylation events upon KOR activation, which may shed light on the investigations of KOR signaling pathway and targeted therapy for KOR-related diseases.

Integrated proteomic and phosphoproteomic data-independent acquisition data evaluate the personalized drug responses of primary and metastatic tumors in colorectal cancer.

Mass spectrometry (MS)-based proteomics and phosphoproteomics are powerful methods to study the biological mechanisms, diagnostic biomarkers, prognostic analysis, and drug therapy of tumors. Data-independent acquisition (DIA) mode is considered to perform better than data-dependent acquisition (DDA) mode in terms of quantitative reproducibility, specificity, accuracy, and identification of low-abundance proteins. Mini patient derived xenograft (MiniPDX) model is an effective model to assess the response to antineoplastic drugs in vivo and is helpful for the precise treatment of cancer patients. Kinases are favorable spots for tumor-targeted drugs, and their functional completion relies on signaling pathways through phosphorylating downstream substrates. Kinase-phosphorylation networks or edge interactions are considered more credible and permanent for characterizing complex diseases. Here, we provide a workflow for personalized drug response assessment in primary and metastatic colorectal cancer (CRC) tumors using DIA proteomic data, DIA phosphoproteomic data, and MiniPDX models. Three kinase inhibitors, afatinib, gefitinib, and regorafenib, are tested pharmacologically. The process mainly includes the following steps: clinical tissue collection, sample preparation, hybrid spectral libraries establishment, MS data acquisition, kinase-substrate network construction, in vivo drug test, and elastic regression modeling. Our protocol gives a more direct data basis for individual drug responses, and will improve the selection of treatment strategies for patients without the druggable mutation.

Bioinformatics Analysis of Protein Phosphorylation in Plant Systems Biology Using P3DB.

Protein phosphorylation is one of the most pervasive protein post-translational modification events in plant cells. It is involved in many plant biological processes, such as plant growth, organ development, and plant immunology, by regulating or switching signaling and metabolic pathways. High-throughput experimental methods like mass spectrometry can easily characterize hundreds to thousands of phosphorylation events in a single experiment. With the increasing volume of the data sets, Plant Protein Phosphorylation DataBase (P3DB, http://p3db.org ) provides a comprehensive, systematic, and interactive online platform to deposit, query, analyze, and visualize these phosphorylation events in many plant species. It stores the protein phosphorylation sites in the context of identified mass spectra, phosphopeptides, and phosphoproteins contributed from various plant proteome studies. In addition, P3DB associates these plant phosphorylation sites to protein physicochemical information in the protein charts and tertiary structures, while various protein annotations from hierarchical kinase phosphatase families, protein domains, and gene ontology are also added into the database. P3DB not only provides rich information, but also interconnects and provides visualization of the data in networks, in systems biology context. Currently, P3DB includes the KiC (Kinase Client) assay network, the protein-protein interaction network, the kinase-substrate network, the phosphatase-substrate network, and the protein domain co-occurrence network. All of these are available to query for and visualize existing phosphorylation events. Although P3DB only hosts experimentally identified phosphorylation data, it provides a plant phosphorylation prediction model for any unknown queries on the fly. P3DB is an entry point to the plant phosphorylation community to deposit and visualize any customized data sets within this systems biology framework. Nowadays, P3DB has become one of the major bioinformatics platforms of protein phosphorylation in plant biology.

Reconfiguring phosphorylation signaling by genetic polymorphisms affects cancer susceptibility.

Large-scale sequencing has characterized an enormous number of genetic variations (GVs), and the functional analysis of GVs is fundamental to understanding differences in disease susceptibility and therapeutic response among and within populations. Using a combination of a sequence-based predictor with known phosphorylation and protein-protein interaction information, we computationally detected 9606 potential phosSNPs (phosphorylation-related single nucleotide polymorphisms), including 720 known, disease-associated SNPs that dramatically modify the human phosSNP-associated kinase-substrate network. Further analyses demonstrated that the proteins in the network are heavily associated in various signaling and cancer pathways, while cancer genes and drug targets are significantly enriched. We re-constructed four population-specific kinase-substrate networks and found that several inherited disease or cancer genes, such as IRS1, RAF1, and EGFR, were differentially regulated by phosSNPs. Thus, phosSNPs may influence disease susceptibility and be involved in cancer development by reconfiguring phosphorylation networks in different populations. Moreover, by systematically characterizing potential phosphorylation-related cancer mutations (phosCMs) in 12 types of cancers, we observed that both types of GVs preferentially occur in the known cancer genes, while a considerable number of phosphorylated proteins, especially those over-representing cancer genes, contain both phosSNPs and phosCMs. Furthermore, it was observed that phosSNPs were significantly enriched in amplification genes identified from breast cancers and tyrosine kinase circuits of lung cancers. Taken together, these results should prove helpful for further elucidation of the functional impacts of disease-associated SNPs.