Browsing by Author "Thompson, JW"

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    Analysis of oxygen/glucose-deprivation-induced changes in SUMO3 conjugation using SILAC-based quantitative proteomics.
    (Journal of proteome research, 2012-02) Yang, W; Thompson, JW; Wang, Z; Wang, L; Sheng, H; Foster, MW; Moseley, MA; Paschen, W
    Transient cerebral ischemia dramatically activates small ubiquitin-like modifier (SUMO2/3) conjugation. In cells exposed to 6 h of transient oxygen/glucose deprivation (OGD), a model of ischemia, SUMOylation increases profoundly between 0 and 30 min following re-oxygenation. To elucidate the effect of transient OGD on SUMO conjugation of target proteins, we exposed neuroblastoma B35 cells expressing HA-SUMO3 to transient OGD and used stable isotope labeling with amino acids in cell culture (SILAC) to quantify OGD-induced changes in levels of specific SUMOylated proteins. Lysates from control and OGD-treated cells were mixed equally, and HA-tagged proteins were immunoprecipitated and analyzed by 1D-SDS-PAGE-LC-MS/MS. We identified 188 putative SUMO3-conjugated proteins, including numerous transcription factors and coregulators, and PIAS2 and PIAS4 SUMO ligases, of which 22 were increased or decreased more than ±2-fold. In addition to SUMO3, the levels of protein-conjugated SUMO1 and SUMO2, as well as ubiquitin, were all increased. Importantly, protein ubiquitination induced by OGD was completely blocked by gene silencing of SUMO2/3. Collectively, these results suggest several mechanisms for OGD-modulated SUMOylation, point to a number of signaling pathways that may be targets of SUMO-based signaling and recovery from ischemic stress, and demonstrate a tightly controlled crosstalk between the SUMO and ubiquitin conjugation pathways.
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    Human genetic and metabolite variation reveals that methylthioadenosine is a prognostic biomarker and an inflammatory regulator in sepsis
    (Science Advances, 2017-03-08) Wang, L; Ko, ER; Gilchrist, JJ; Pittman, KJ; Rautanen, A; Pirinen, M; Thompson, JW; Dubois, LG; Langley, RJ; Jaslow, SL; Salinas, RE; Rouse, DC; Moseley, MA; Mwarumba, S; Njuguna, P; Mturi, N; Williams, TN; Scott, JAG; Hill, AVS; Woods, CW; Ginsburg, GS; Tsalik, EL; Ko, DC
    Sepsis is a deleterious inflammatory response to infection with high mortality. Reliable sepsis biomarkers could improve diagnosis, prognosis, and treatment. Integration of human genetics, patient metabolite and cytokine measurements, and testing in a mouse model demonstrate that the methionine salvage pathway is a regulator of sepsis that can accurately predict prognosis in patients. Pathway-based genome-wide association analysis of nontyphoidal Salmonella bacteremia showed a strong enrichment for single-nucleotide polymorphisms near the components of the methionine salvage pathway. Measurement of the pathway’s substrate, methylthioadenosine (MTA), in two cohorts of sepsis patients demonstrated increased plasma MTA in nonsurvivors. Plasma MTA was correlated with levels of inflammatory cytokines, indicating that elevated MTA marks a subset of patients with excessive inflammation. A machine-learning model combining MTA and other variables yielded approximately 80% accuracy (area under the curve) in predicting death. Furthermore, mice infected with Salmonella had prolonged survival when MTA was administered before infection, suggesting that manipulating MTA levels could regulate the severity of the inflammatory response. Our results demonstrate how combining genetic data, biomolecule measurements, and animal models can shape our understanding of disease and lead to new biomarkers for patient stratification and potential therapeutic targeting.
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    Latent protein trees
    (Annals of Applied Statistics, 2013-06-01) Henao, R; Thompson, JW; Moseley, MA; Ginsburg, GS; Carin, L; Lucas, JE
    Unbiased, label-free proteomics is becoming a powerful technique for measuring protein expression in almost any biological sample. The output of these measurements after preprocessing is a collection of features and their associated intensities for each sample. Subsets of features within the data are from the same peptide, subsets of peptides are from the same protein, and subsets of proteins are in the same biological pathways, therefore, there is the potential for very complex and informative correlational structure inherent in these data. Recent attempts to utilize this data often focus on the identification of single features that are associated with a particular phenotype that is relevant to the experiment. However, to date, there have been no published approaches that directly model what we know to be multiple different levels of correlation structure. Here we present a hierarchical Bayesian model which is specifically designed to model such correlation structure in unbiased, label-free proteomics. This model utilizes partial identification information from peptide sequencing and database lookup as well as the observed correlation in the data to appropriately compress features into latent proteins and to estimate their correlation structure. We demonstrate the effectiveness of the model using artificial/benchmark data and in the context of a series of proteomics measurements of blood plasma from a collection of volunteers who were infected with two different strains of viral influenza. © Institute of Mathematical Statistics, 2013.
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    Mass spectrometry-based thermal shift assay for protein-ligand binding analysis.
    (Anal Chem, 2010-07-01) West, GM; Thompson, JW; Soderblom, EJ; Dubois, LG; Moseley, MA; Fitzgerald, MC
    Described here is a mass spectrometry-based screening assay for the detection of protein-ligand binding interactions in multicomponent protein mixtures. The assay utilizes an oxidation labeling protocol that involves using hydrogen peroxide to selectively oxidize methionine residues in proteins in order to probe the solvent accessibility of these residues as a function of temperature. The extent to which methionine residues in a protein are oxidized after specified reaction times at a range of temperatures is determined in a MALDI analysis of the intact proteins and/or an LC-MS analysis of tryptic peptide fragments generated after the oxidation reaction is quenched. Ultimately, the mass spectral data is used to construct thermal denaturation curves for the detected proteins. In this proof-of-principle work, the protocol is applied to a four-protein model mixture comprised of ubiquitin, ribonuclease A (RNaseA), cyclophilin A (CypA), and bovine carbonic anhydrase II (BCAII). The new protocol's ability to detect protein-ligand binding interactions by comparing thermal denaturation data obtained in the absence and in the presence of ligand is demonstrated using cyclosporin A (CsA) as a test ligand. The known binding interaction between CsA and CypA was detected using both the MALDI- and LC-MS-based readouts described here.