Deborah H. Lundgren

Proteomic analysis of active multiple sclerosis lesions reveals therapeutic targets

Understanding the neuropathology of multiple sclerosis (MS) is essential for improved therapies. Therefore, identification of targets specific to pathological types of MS may have therapeutic benefits. Here we identify, by laser-capture microdissection and proteomics, proteins unique to three major types of MS lesions: acute plaque, chronic active plaque and chronic plaque. Comparative proteomic profiles identified tissue factor and protein C inhibitor within chronic active plaque samples, suggesting dysregulation of molecules associated with coagulation. In vivo administration of hirudin or recombinant activated protein C reduced disease severity in experimental autoimmune encephalomyelitis and suppressed Th1 and Th17 cytokines in astrocytes and immune cells. Administration of mutant forms of recombinant activated protein C showed that both its anticoagulant and its signalling functions were essential for optimal amelioration of experimental autoimmune encephalomyelitis. A proteomic approach illuminated potential therapeutic targets selective for specific pathological stages of MS and implicated participation of the coagulation cascade.

Quantitative Phosphoproteomic Analysis of T Cell Receptor Signaling Reveals System-Wide Modulation of Protein-Protein Interactions

Serine-threonine phosphorylation plays a role in regulating the interactions among proteins involved in T cell responses. Further Interactions The binding of antigen to the T cell receptor (TCR) complex triggers a cascade of responses that culminate in T cell activation. Key to the initial stages of this cascade is the phosphorylation of tyrosine residues in proteins proximal to the TCR, which enables the recruitment of other proteins that contain phosphotyrosine-binding domains. Given its importance to TCR signaling, tyrosine phosphorylation of target proteins has received considerable attention. To view protein phosphorylation from a larger perspective, Mayya et al. performed a system-level phosphoproteomics analysis of the events triggered by TCR activation in the human Jurkat T cell line. They found that the status of hundreds of phosphorylation sites was modulated in response to stimulation of the TCR. In addition to identifying previously unknown TCR-responsive phosphorylation events, this analysis also suggests a role for phosphorylated serine and threonine residues in modulating protein-protein interactions between many proteins involved in T cell responses. Protein phosphorylation events during T cell receptor (TCR) signaling control the formation of complexes among proteins proximal to the TCR, the activation of kinase cascades, and the activation of transcription factors; however, the mode and extent of the influence of phosphorylation in coordinating the diverse phenomena associated with T cell activation are unclear. Therefore, we used the human Jurkat T cell leukemia cell line as a model system and performed large-scale quantitative phosphoproteomic analyses of TCR signaling. We identified 10,665 unique phosphorylation sites, of which 696 showed TCR-responsive changes. In addition, we analyzed broad trends in phosphorylation data sets to uncover underlying mechanisms associated with T cell activation. We found that, upon stimulation of the TCR, phosphorylation events extensively targeted protein modules involved in all of the salient phenomena associated with T cell activation: patterning of surface proteins, endocytosis of the TCR, formation of the F-actin cup, inside-out activation of integrins, polarization of microtubules, production of cytokines, and alternative splicing of messenger RNA. Further, case-by-case analysis of TCR-responsive phosphorylation sites on proteins belonging to relevant functional modules together with network analysis allowed us to deduce that serine-threonine (S-T) phosphorylation modulated protein-protein interactions (PPIs) in a system-wide fashion. We also provide experimental support for this inference by showing that phosphorylation of tubulin on six distinct serine residues abrogated PPIs during the assembly of microtubules. We propose that modulation of PPIs by stimulus-dependent changes in S-T phosphorylation state is a widespread phenomenon applicable to many other signaling systems.

Role of spectral counting in quantitative proteomics

Spectral count, defined as the total number of spectra identified for a protein, has gained acceptance as a practical, label-free, semiquantitative measure of protein abundance in proteomic studies. In this review, we discuss issues affecting the performance of spectral counting relative to other label-free methods, as well as its limitations. Possible consequences of modifications, which are commonly applied to raw spectral counts to improve abundance estimations, are considered. The use of spectral counting for different types of quantitation studies is explored and critiqued. Different statistical methods and underlying frameworks that have been applied to spectral count analysis are described and compared, and problem areas that undermine confident statistical analysis are considered. Finally, the issue of accurate estimation of false-discovery rates is addressed and identified as a major current challenge in quantitative proteomics.

Proteomics Analysis of Human Coronary Atherosclerotic Plaque A Feasibility Study of Direct Tissue Proteomics by Liquid Chromatography and Tandem Mass Spectrometry

Cardiovascular disease presents significant variations in human populations with respect to the atherosclerotic plaque progression, inflammation, thrombosis, and rupture. To gain a more comprehensive picture of the pathogenic mechanism of atherosclerosis and the variations seen in patients, efficient methods to identify proteins from the normal and diseased arteries need to be developed. To accomplish this goal, we tested the feasibility and efficiency of protein identification by a recently developed method, termed direct tissue proteomics (DTP). We analyzed frozen and paraformaldehyde-fixed archival coronary arteries with the DTP method. We also validated the distinct expression of four proteins by immunohistochemistry. In addition, we demonstrated the compatibility of the DTP method with laser capture microdissection and the possibility of monitoring specific cytokines and growth factors by the absolute quantification of abundance method. Major findings from this feasibility study are that 1) DTP can be used to efficiently identify proteins from paraformaldehyde-fixed, paraffin-embedded, and frozen coronary arteries; 2) approximately twice the number of proteins were identified from the frozen sections when compared with the paraformaldehyde-fixed sections; 3) laser capture microdissection is compatible with DTP; and 4) detection of low abundance cytokines and growth factors in the coronary arteries required selective reaction monitoring experiments coupled to absolute quantification of abundance. The analysis of 35 human coronary atherosclerotic samples allowed identification of a total of 806 proteins. The present study provides the first large scale proteomics map of human coronary atherosclerotic plaques.

Janus-like opposing roles of CD47 in autoimmune brain inflammation in humans and mice

CD47 exerts different effects on disease in distinct cell types and locations and during different stages of experimental autoimmune encephalomyelitis.

Systematic Characterization of Nuclear Proteome during Apoptosis A Quantitative Proteomic Study by Differential Extraction and Stable Isotope Labeling

Identification and characterization of the nuclear proteome is important for detailed understanding of multiple signaling events in eukaryotic cells. Toward this goal, we extensively characterized the nuclear proteome of human T leukemia cells by sequential extraction of nuclear proteins with different physicochemical properties using three buffer conditions. This large scale proteomic study also tested the feasibility and technical challenges associated with stable isotope labeling by amino acids in cell culture (SILAC) to uncover quantitative changes during apoptosis. Analyzing proteins from three nuclear fractions extracted from naive and apoptotic cells generated 780,530 MS/MS spectra that were used for database searching using the SEQUEST algorithm. This analysis resulted in the identification and quantification of 1,174 putative nuclear proteins. A number of known nuclear proteins involved in apoptosis as well as novel proteins not known to be part of the nuclear apoptotic machinery were identified and quantified. Consistent with SILAC-based quantifications, immunofluorescence staining of nucleus, mitochondria, and some associated proteins from both organelles revealed a dynamic recruitment of mitochondria into nuclear invaginations during apoptosis.

Protein Identification Using Sorcerer 2 and SEQUEST

Sage‐Ns Sorcerer 2 provides an integrated data analysis system for comprehensive protein identification and characterization. It runs on a proprietary version of SEQUESTR, the most widely used search engine for identifying proteins in complex mixtures. The protocol presented here describes the basic steps performed to process mass spectrometric data with Sorcerer 2 and how to analyze results using TPP and Scaffold. The unit also provides an overview of the SEQUESTR algorithm, along with Sorcerer‐SEQUESTR enhancements, and a discussion of data filtering methods, important considerations in data interpretation, and additional resources that can be of assistance to users running Sorcerer and interpreting SEQUESTR results. Curr. Protoc. Bioinform. 28:13.3.1‐13.3.21.

Global Survey of Human T Leukemic Cells by Integrating Proteomics and Transcriptomics Profiling

A global protein survey is needed to gain systems-level insights into mammalian cell signaling and information flow. Human Jurkat T leukemic cells are one of the most important model systems for T cell signaling study, but no comprehensive proteomics survey has been carried out in this cell type. In the present study we combined subcellular fractionation, multiple protein enrichment methods, and replicate tandem mass spectrometry analyses to determine the protein expression pattern in a single Jurkat cell type. The proteome dataset was evaluated by comparison with the genome-wide mRNA expression pattern in the same cell type. A total of 5381 proteins were identified by mass spectrometry with high confidence. Rigorous comparison of RNA and protein expression afforded removal of the false positive identifications and redundant entries but rescued the proteins identified by a single high scoring peptide, resulting in the final identification of 6471 unique gene products among which 98% of the corresponding transcripts were detected with high probability. Using hierarchical clustering of the protein expression patterns in five subcellular fractions (cytosol, light membrane, heavy membrane, mitochondria, and nuclei), the primary subcellular localization of 2241 proteins was assigned with high confidence including 792 previously uncharacterized proteins. This proteome landscape can serve as a useful platform for systems-level understanding of organelle composition and cellular functions in human T cells.

Differential Protein Expression Profiles in Estrogen Receptor–Positive and –Negative Breast Cancer Tissues Using Label-Free Quantitative Proteomics

Identification of the proteins that are associated with estrogen receptor (ER) status is a first step towards better understanding of the hormone-dependent nature of breast carcinogenesis. Although a number of gene expression analyses have been conducted, protein complement has not been systematically investigated to date. Because proteins are primary targets of therapeutic drugs, in this study, we have attempted to identify proteomic signatures that demarcate ER-positive and -negative breast cancers. Using highly enriched breast tumor cells, replicate analyses from 3 ERα+ and 3 ERα- human breast tumors resulted in the identification of 2,995 unique proteins with ≥2 peptides. Among these, a number of receptor tyrosine kinases and intracellular kinases that are abundantly expressed in ERα+ and ERα- breast cancer tissues were identified. Further, label-free quantitative proteome analysis revealed that 236 proteins were differentially expressed in ERα+ and ERα- breast tumors. Among these, 141 proteins were selectively up-regulated in ERα+, and 95 proteins were selectively up-regulated in ERα- breast tumors. Comparison of differentially expressed proteins with a breast cancer database revealed 98 among these have been previously reported to be involved in breast cancer. By Gene Ontology molecular function, dehydrogenase, reductase, cytoskeletal proteins, extracellular matrix, hydrolase, and lyase categories were significantly enriched in ERα+, whereas selected calcium-binding protein, membrane traffic protein, and cytoskeletal protein were enriched in ERα- breast tumors. Biological process and pathway analysis revealed that up-regulated proteins of ERα+ were overrepresented by proteins involved in amino acid metabolism, proteasome, and fatty acid metabolism, while up-regulated proteins of ERα- were overrepresented by proteins involved in glycolysis pathway. The presence and relative abundance of 4 selected differentially abundant proteins (liprin-α1, fascin, DAP5, and β-arrestin-1) were quantified and validated by immunohistochemistry. In conclusion, unlike in vitro cell culture models, the in vivo signaling proteins and pathways that we have identified directly from human breast cancer tissues may serve as relevant therapeutic targets for the pharmacological intervention of breast cancer.

Discovery of putative pancreatic cancer biomarkers using subcellular proteomics

Pancreatic cancer (PC) is a highly aggressive disease that frequently remains undetected until it has progressed to an advanced, systemic stage. Successful treatment of PC is hindered by the lack of early detection. The application of proteomic analysis to PC combined with subcellular fractionation has introduced new possibilities in the field of biomarker discovery. We utilized matched pairs of pancreas tumor and non-tumor pancreas from patients undergoing tumor resection. The tissues were treated to obtain cellular protein fractions corresponding to cytosol, membrane, nucleus and cytoskeleton. The fractions were then separated by molecular weight and digested with trypsin, followed by liquid chromatography and tandem mass spectrometry. The spectra obtained were searched using Sequest engine and combined into a single analysis file to obtain a semi-quantitative number, spectral count, using Scaffold software. We identified 2393 unique proteins in non-tumor and cancer pancreas. Utilizing PLGEM statistical analysis we determined 104 proteins were significantly changed in cancer. From these, we further validated four secreted proteins that are up-regulated in cancer and have potential for development as minimally-invasive diagnostic markers. We conclude that subcellular fractionation followed by gel electrophoresis and tandem mass spectrometry is a powerful strategy for identification of differentially expressed proteins in pancreatic cancer.