Mining Deeper into the Proteome: Computational Strategies for Improving Depth and Breadth of Coverage in High-Throughput Protein Identification Studies.

The proteomics field is driven by the need to develop increasingly high-throughput methods for the identification and characterization of proteins. The overall goal of this research is to improve the success rate of modern high-throughput proteomics studies. The focus is on developing computational strategies for increasing the number of identifications as well as improving the ability to distinguish new forms of proteins and peptides. Several studies are presented, addressing different points in the proteomics analysis pipeline. At the most fundamental data analysis level, methods for using modern machine learning algorithms to improve the ability to distinguish correct from incorrect peptide identifications are presented. These techniques have the potential to minimize the need for manual curation of results, providing a significant increase in throughput in addition to increased identification confidence. Non-standard types of mass spectrometry data are being generated in specific contexts. Specifically, phosphoproteomics often involves the generation of MS3 spectra. These spectra alleviate problems associated with MS2 fragmentation of phosphopeptides, but utilizing the additional information contained in these spectra requires novel informatics. Several strategies for accommodating this additional information are presented. A statistical model is developed for translating the information contained in the coupling of consecutive MS2 and MS3 spectra into a more accurate peptide identification probability score. Also, methods for combining MS2 and MS3 data are explored. A newer mass spectrometry methodology useful for phosphoproteomics has recently been introduced as well, termed multistage activation (MSA). A comparative study of this and other methods is presented aimed at determining an optimal method for generating phosphopeptide identifications, focusing not only on data analysis techniques, but also on the mass spectrometry methodologies themselves. A dataset is presented from a differential study of a human cell line infected with the dengue virus. The study explores the complementarity of different fractionation methods in generating more unique protein identifications. A discussion of a statistical mixture model that utilizes relative quantification information to classify identified peptides into two categories based on their membrane topology is given in the final chapter. Finally, a comment on utilizing pI information to enrich for phosphopeptides is provided.Ph.D.BioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/58496/1/pulintz_1.pd

A comparative phosphoproteomic analysis of a human tumor metastasis model using a label-free quantitative approach

Alterations in cellular phosphorylation patterns have been implicated in a number of diseases, including cancer, through multiple mechanisms. Herein we present a survey of the phosphorylation profiles of an isogenic pair of human cancer cell lines with opposite metastatic phenotype. Phosphopeptides were enriched from tumor cell lysates with titanium dioxide and zirconium dioxide, and identified with nano-LC-MS/MS using an automatic cross-validation of MS/MS and MS/MS/MS (MS2+MS3) data-dependent neutral loss method. A spectral counting quantitative strategy was applied to the two cell line samples on the MS2-only scan, which was implemented successively after each MS2+MS3 scan in the same sample. For all regulated phosphopeptides reported by spectral counting analysis, sequence and phosphorylation site assignments were validated by a MS2+MS3 data-dependent neutral loss method. With this approach, we identified over 70 phosphorylated sites on 27 phosphoproteins as being differentially expressed with respect to tumor cell phenotype. The altered expression levels of proteins identified by LC-MS/MS were validated using Western blotting. Using network pathway analysis, we observed that the majority of the differentially expressed proteins were highly interconnected and belong to two major intracellular signaling pathways. Our findings suggest that the phosphorylation of isoform A of lamin A/C and GTPase activating protein binding protein 1 is associated with metastatic propensity. The study demonstrates a quantitative and comparative proteomics strategy to identify differential phosphorylation patterns in complex biological samples.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/75766/1/1842_ftp.pd

Identification of autophosphorylation sites in eukaryotic elongation factor-2 kinase

eEF2K [eEF2 (eukaryotic elongation factor 2) kinase] phosphorylates and inactivates the translation elongation factor eEF2. eEF2K is not a member of the main eukaryotic protein kinase superfamily, but instead belongs to a small group of so-called α-kinases. The activity of eEF2K is normally dependent upon Ca2+ and calmodulin. eEF2K has previously been shown to undergo autophosphorylation, the stoichiometry of which suggested the existence of multiple sites. In the present study we have identified several autophosphorylation sites, including Thr348, Thr353, Ser366 and Ser445, all of which are highly conserved among vertebrate eEF2Ks. We also identified a number of other sites, including Ser78, a known site of phosphorylation, and others, some of which are less well conserved. None of the sites lies in the catalytic domain, but three affect eEF2K activity. Mutation of Ser78, Thr348 and Ser366 to a non-phosphorylatable alanine residue decreased eEF2K activity. Phosphorylation of Thr348 was detected by immunoblotting after transfecting wild-type eEF2K into HEK (human embryonic kidney)-293 cells, but not after transfection with a kinase-inactive construct, confirming that this is indeed a site of autophosphorylation. Thr348 appears to be constitutively autophosphorylated in vitro. Interestingly, other recent data suggest that the corresponding residue in other α-kinases is also autophosphorylated and contributes to the activation of these enzymes [Crawley, Gharaei, Ye, Yang, Raveh, London, Schueler-Furman, Jia and Cote (2011) J. Biol. Chem. 286, 2607–2616]. Ser366 phosphorylation was also detected in intact cells, but was still observed in the kinase-inactive construct, demonstrating that this site is phosphorylated not only autocatalytically but also in trans by other kinases

Pediatric emergency department use by Afghan refugees at a temporary housing facility

Abstract Objectives In August 2021, “Operation Allies Welcome” evacuated 76,000 Afghan refugees to 8 US temporary housing facilities. The impact of refugee influx on local emergency department (ED) use and the resources needed during resettlement are poorly described. We report the frequency of pediatric ED visits and characterize the ED resources needed by pediatric Afghan refugees from 1 temporary housing facility. Methods This single‐center, retrospective cohort study identified participants via a refugee identifier in the medical record. The primary outcome was the frequency and timing of pediatric ED visits; secondary outcomes included resources used during ED evaluation and management. Trained reviewers collected data using a predefined instrument and descriptive statistics are reported. Results This study included 175 pediatric ED visits by Afghan refugees. The highest volumes (n = 73, 42%) occurred 3–5 weeks after evacuation. Common presenting complaints included fever (36%), gastrointestinal (15%), and respiratory (13%). Resources used included radiography (64%), lab testing (63%), and medication (78%). Specialist consultation occurred in 43% of visits; infectious diseases (17%) and neurology (15%) were the most common. Discharge (61%) was more common than admission (39%), though 31% of discharged patients had a repeat ED visit. Only 51% attended a recommended follow‐up appointment. Conclusion In this study, most pediatric ED visits by refugees occurred within 5 weeks of arrival. Most patients were discharged after diagnostic testing, medication, and specialist consultation, but repeat ED visits were common. These patterns have important implications in preparing for future mass displacement events

Investigating MS2/MS3 matching statistics: a model for coupling consecutive stage mass spectrometry data for increased peptide identification confidence

Improvements in ion trap instrumentation have made n-dimensional mass spectrometry more practical. The overall goal of the study was to describe a model for making use of MS(2) and MS(3) information in mass spectrometry experiments. We present a statistical model for adjusting peptide identification probabilities based on the combined information obtained by coupling peptide assignments of consecutive MS(2) and MS(3) spectra. Using two data sets, a mixture of known proteins and a complex phosphopeptide-enriched sample, we demonstrate an increase in discriminating power of the adjusted probabilities compared with models using MS(2) or MS(3) data only. This work also addresses the overall value of generating MS(3) data as compared with an MS(2)-only approach with a focus on the analysis of phosphopeptide data

Protein Profiling of Mouse Livers with Peroxisome Proliferator-Activated Receptor α Activation

Peroxisome proliferator-activated receptor α (PPARα) is important in the induction of cell-specific pleiotropic responses, including the development of liver tumors, when it is chronically activated by structurally diverse synthetic ligands such as Wy-14,643 or by unmetabolized endogenous ligands resulting from the disruption of the gene encoding acyl coenzyme A (CoA) oxidase (AOX). Alterations in gene expression patterns in livers with PPARα activation were delineated by using a proteomic approach to analyze liver proteins of Wy-14,643-treated and AOX(−/−) mice. We identified 46 differentially expressed proteins in mouse livers with PPARα activation. Up-regulated proteins, including acetyl-CoA acetyltransferase, farnesyl pyrophosphate synthase, and carnitine O-octanoyltransferase, are involved in fatty acid metabolism, whereas down-regulated proteins, including ketohexokinase, formiminotransferase-cyclodeaminase, fructose-bisphosphatase aldolase B, sarcosine dehydrogenase, and cysteine sulfinic acid decarboxylase, are involved in carbohydrate and amino acid metabolism. Among stress response and xenobiotic metabolism proteins, selenium-binding protein 2 and catalase showed a dramatic ∼18-fold decrease in expression and a modest ∼6-fold increase in expression, respectively. In addition, glycine N-methyltransferase, pyrophosphate phosphohydrolase, and protein phosphatase 1D were down-regulated with PPARα activation. These observations establish proteomic profiles reflecting a common and predictable pattern of differential protein expression in livers with PPARα activation. We conclude that livers with PPARα activation are transcriptionally geared towards fatty acid combustion

Global Topology Analysis of Pancreatic Zymogen Granule Membrane Proteins *S⃞

The zymogen granule is the specialized organelle in pancreatic acinar cells for digestive enzyme storage and regulated secretion and is a classic model for studying secretory granule function. Our long term goal is to develop a comprehensive architectural model for zymogen granule membrane (ZGM) proteins that would direct new hypotheses for subsequent functional studies. Our initial proteomics analysis focused on identification of proteins from purified ZGM (Chen, X., Walker, A. K., Strahler, J. R., Simon, E. S., Tomanicek-Volk, S. L., Nelson, B. B., Hurley, M. C., Ernst, S. A., Williams, J. A., and Andrews, P. C. (2006) Organellar proteomics: analysis of pancreatic zymogen granule membranes. Mol. Cell. Proteomics 5, 306–312). In the current study, a new global topology analysis of ZGM proteins is described that applies isotope enrichment methods to a protease protection protocol. Our results showed that tryptic peptides of ZGM proteins were separated into two distinct clusters according to their isobaric tag for relative and absolute quantification (iTRAQ) ratios for proteinase K-treated versus control zymogen granules. The low iTRAQ ratio cluster included cytoplasm-orientated membrane and membrane-associated proteins including myosin V, vesicle-associated membrane proteins, syntaxins, and all the Rab proteins. The second cluster having unchanged ratios included predominantly luminal proteins. Because quantification is at the peptide level, this technique is also capable of mapping both cytoplasm- and lumen-orientated domains from the same transmembrane protein. To more accurately assign the topology, we developed a statistical mixture model to provide probabilities for identified peptides to be cytoplasmic or luminal based on their iTRAQ ratios. By implementing this approach to global topology analysis of ZGM proteins, we report here an experimentally constrained, comprehensive topology model of identified zymogen granule membrane proteins. This model contributes to a firm foundation for developing a higher order architecture model of the ZGM and for future functional studies of individual ZGM proteins