Computational characterization of differential kinase activity from phosphoproteomics datasets is critical for correctly inferring cellular circuitry and how signaling cascades are altered in drug ...treatment and/or disease. Kinase-Substrate Enrichment Analysis (KSEA) offers a powerful approach to estimating changes in a kinase's activity based on the collective phosphorylation changes of its identified substrates. However, KSEA has been limited to programmers who are able to implement the algorithms. Thus, to make it accessible to the larger scientific community, we present a web-based application of this method: the KSEA App. Overall, we expect that this tool will offer a quick and user-friendly way of generating kinase activity estimates from high-throughput phosphoproteomics datasets.
the KSEA App is a free online tool: casecpb.shinyapps.io/ksea/. The source code is on GitHub: github.com/casecpb/KSEA/. The application is also available as the R package "KSEAapp" on CRAN: CRAN.R-project.org/package=KSEAapp/.
mark.chance@case.edu.
Supplementary data are available at Bioinformatics online.
Protein footprinting mediated by mass spectrometry has evolved over the last 30 years from proof of concept to commonplace biophysics tool, with unique capabilities for assessing structure and ...dynamics of purified proteins in physiological states in solution. This review outlines the history and current capabilities of two major methods of protein footprinting: reversible hydrogen-deuterium exchange (HDX) and hydroxyl radical footprinting (HRF), an irreversible covalent labeling approach. Technological advances in both approaches now permit high-resolution assessments of protein structure including secondary and tertiary structure stability mediated by backbone interactions (measured via HDX) and solvent accessibility of side chains (measured via HRF). Applications across many academic fields and in biotechnology drug development are illustrated including: detection of protein interfaces, identification of ligand/drug binding sites, and monitoring dynamics of protein conformational changes along with future prospects for advancement of protein footprinting in structural biology and biophysics research.
The process of parturition involves the transformation of the quiescent myometrium (uterine smooth muscle) to the highly contractile laboring state. This is thought to be driven by changes in gene ...expression in myometrial cells. Despite the existence of multiple myometrial gene expression studies, the transcriptional programs that initiate labor are not known. Here, we integrated three transcriptome datasets, one novel (NCBI Gene Expression Ominibus: GSE80172) and two existing, to characterize the gene expression changes in myometrium associated with the onset of labor at term. Computational analyses including classification, singular value decomposition, pathway enrichment, and network inference were applied to individual and combined datasets. Outcomes across studies were integrated with multiple protein and pathway databases to build a myometrial parturition signaling network. A high-confidence (significant across all studies) set of 126 labor genes were identified and machine learning models exhibited high reproducibility between studies. Labor signatures included both known (interleukins, cytokines) and unknown (apoptosis,
, cell proliferation/differentiation) pathways while cyclic AMP signaling and muscle relaxation were associated with non-labor. These signatures accurately classified and characterized the stages of labor. The data-derived parturition signaling networks provide new genes/signaling interactions to understand phenotype-specific processes and aid in future studies of parturition.
Hydroxyl radical footprinting (HRF) of proteins with mass spectrometry (MS) is a widespread approach for assessing protein structure. Hydroxyl radicals react with a wide variety of protein side ...chains, and the ease with which radicals can be generated (by radiolysis or photolysis) has made the approach popular with many laboratories. As some side chains are less reactive and thus cannot be probed, additional specific and nonspecific labeling reagents have been introduced to extend the approach. At the same time, advances in liquid chromatography and MS approaches permit an examination of the labeling of individual residues, transforming the approach to high resolution. Lastly, advances in understanding of the chemistry of the approach have led to the determination of absolute protein topologies from HRF data. Overall, the technology can provide precise and accurate measures of side-chain solvent accessibility in a wide range of interesting and useful contexts for the study of protein structure and dynamics in both academia and industry.
NF-κB, a central coordinator of immune and inflammatory responses, must be tightly regulated. We describe a NF-κB regulatory pathway that is driven by reversible lysine methylation of the p65 ...subunit, carried out by a lysine methylase, the nuclear receptor-binding SET domain-containing protein 1 (NSD1), and a lysine demethylase, F-box and leucine-rich repeat protein 11 (FBXL11). Overexpression of FBXL11 inhibits NF-κB activity, and a high level of NSD1 activates NF-κB and reverses the inhibitory effect of FBXL11, whereas reduced expression of NSD1 decreases NF-κB activation. The targets are K218 and K221 of p65, which are methylated in cells with activated NF-κB. Overexpression of FBXL11 slowed the growth of HT29 cancer cells, whereas shRNA-mediated knockdown had the opposite effect, and these phenotypes were dependent on K218/K221 methylation. In mouse embryo fibroblasts, the activation of most p65-dependent genes relied on K218/K221 methylation. Importantly, expression of the FBXL11 gene is driven by NF-κB, revealing a negative regulatory feedback loop. We conclude that reversible lysine methylation of NF-κB is an important element in the complex regulation of this key transcription factor.
Emerging evidence indicates that gene products implicated in human cancers often cluster together in "hot spots" in protein-protein interaction (PPI) networks. Additionally, small sub-networks within ...PPI networks that demonstrate synergistic differential expression with respect to tumorigenic phenotypes were recently shown to be more accurate classifiers of disease progression when compared to single targets identified by traditional approaches. However, many of these studies rely exclusively on mRNA expression data, a useful but limited measure of cellular activity. Proteomic profiling experiments provide information at the post-translational level, yet they generally screen only a limited fraction of the proteome. Here, we demonstrate that integration of these complementary data sources with a "proteomics-first" approach can enhance the discovery of candidate sub-networks in cancer that are well-suited for mechanistic validation in disease. We propose that small changes in the mRNA expression of multiple genes in the neighborhood of a protein-hub can be synergistically associated with significant changes in the activity of that protein and its network neighbors. Further, we hypothesize that proteomic targets with significant fold change between phenotype and control may be used to "seed" a search for small PPI sub-networks that are functionally associated with these targets. To test this hypothesis, we select proteomic targets having significant expression changes in human colorectal cancer (CRC) from two independent 2-D gel-based screens. Then, we use random walk based models of network crosstalk and develop novel reference models to identify sub-networks that are statistically significant in terms of their functional association with these proteomic targets. Subsequently, using an information-theoretic measure, we evaluate synergistic changes in the activity of identified sub-networks based on genome-wide screens of mRNA expression in CRC. Cross-classification experiments to predict disease class show excellent performance using only a few sub-networks, underwriting the strength of the proposed approach in discovering relevant and reproducible sub-networks.
Measurements from hydroxyl radical footprinting (HRF) provide rich information about the solvent accessibility of amino acid side chains of a protein. Traditional HRF data analyses focus on comparing ...the difference in the modification/footprinting rate of a specific site to infer structural changes across two protein states, e.g., between a free and ligand-bound state. However, the rate information itself is not fully used for the purpose of comparing different protein sites within a protein on an absolute scale. To provide such a cross-site comparison, we present a new, to our knowledge, data analysis algorithm to convert the measured footprinting rate constant to a protection factor (PF) by taking into account the known intrinsic reactivity of amino acid side chain. To examine the extent to which PFs can be used for structural interpretation, this PF analysis is applied to three model systems where radiolytic footprinting data are reported in the literature. By visualizing structures colored with the PF values for individual peptides, a rational view of the structural features of various protein sites regarding their solvent accessibility is revealed, where high-PF regions are buried and low-PF regions are more exposed to the solvent. Furthermore, a detailed analysis correlating solvent accessibility and local structural contacts for gelsolin shows a statistically significant agreement between PF values and various structure measures, demonstrating that the PFs derived from this PF analysis readily explain fundamental HRF rate measurements. We also tested this PF analysis on alternative, chemical-based HRF data, showing improved correlations of structural properties of a model protein barstar compared to examining HRF rate data alone. Together, this PF analysis not only permits a novel, to our knowledge, approach of mapping protein structures by using footprinting data, but also elevates the use of HRF measurements from a qualitative, cross-state comparison to a quantitative, cross-site assessment of protein structures in the context of individual conformational states of interest.
Following its tyrosine phosphorylation, STAT3 is methylated on K140 by the histone methyl transferase SET9 and demethylated by LSD1 when it is bound to a subset of the promoters that it activates. ...Methylation of K140 is a negative regulatory event, because its blockade greatly increases the steady-state amount of activated STAT3 and the expression of many (i.e., SOCS3) but not all (i.e., CD14) STAT3 target genes. Biological relevance is shown by the observation that overexpression of SOCS3 when K140 cannot be methylated blocks the ability of cells to activate STAT3 in response to IL-6. K140 methylation does not occur with mutants of STAT3 that do not enter nuclei or bind to DNA. Following treatment with IL-6, events at the SOCS3 promoter occur in an ordered sequence, as shown by chromatin immunoprecipitations. Y705-phosphoryl-STAT3 binds first and S727 is then phosphorylated, followed by the coincident binding of SET9 and dimethylation of K140, and lastly by the binding of LSD1. We conclude that the lysine methylation of promoter-bound STAT3 leads to biologically important down-regulation of the dependent responses and that SET9, which is known to help provide an activating methylation mark to H3K4, is recruited to the newly activated SOCS3 promoter by STAT3.
Hydroxyl radical protein footprinting (HRPF) using synchrotron radiation is a well-validated method to assess protein structure in the native solution state. In this method, X-ray radiolysis of water ...generates hydroxyl radicals that can react with solvent accessible side chains of proteins, with mass spectrometry used to detect the resulting labeled products. An ideal footprinting dose provides sufficient labeling to measure the structure but not so much as to influence the results. The optimization of hydroxyl radical dose is typically performed using an indirect Alexa488 fluorescence assay sensitive to hydroxyl radical concentration, but full evaluation of the experiment's outcome relies upon bottom-up liquid chromatography mass spectrometry (LC-MS) measurements to directly determine sites and extent of oxidative labeling at the peptide and protein level. A direct evaluation of the extent of labeling to provide direct and absolute measurements of dose and “safe” dose ranges in terms of, for example, average numbers of labels per protein, would provide immediate feedback on experimental outcomes prior to embarking on detailed LC-MS analyses. To this end, we describe an approach to integrate intact MS screening of labeled samples immediately following exposure, along with metrics to quantify the extent of observed labeling from the intact mass spectra. Intact MS results on the model protein lysozyme were evaluated in the context of Alexa488 assay results and a bottom-up LC-MS analysis of the same samples. This approach provides a basis for placing delivered hydroxyl radical dose metrics on firmer technical grounds for synchrotron X-ray footprinting of proteins, with explicit parameters to increase the likelihood of a productive experimental outcome. Further, the method directs approaches to provide absolute and direct dosimetry for all types of labeling for protein footprinting.
•Intact MS was used to directly assess hydroxyl radical labeling outcome.•An oxidation events per protein (OEP) metric was defined to analyze MS spectra.•Classical bottom-up MS analysis validates the intact MS results.•Intact MS and OEP analysis can be used to optimize X-ray footprinting experiments.
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