Mass spectrometry investigation of glycosylation on the NXS/T sites in recombinant glycoproteins

Mass Spectrometric (MS) Analysis of Proteins and Peptides

The human genome is sequenced and comprised of ~30,000 genes, making humans just a little bit more complicated than worms or flies. However, complexity of humans is given by proteins that these genes code for because one gene can produce many proteins mostly through alternative splicing and tissue-dependent expression of particular proteins. In addition, post-translational modifications (PTMs) in proteins greatly increase the number of gene products or protein isoforms. Furthermore, stable and transient interactions between proteins, protein isoforms/proteoforms and PTM-ed proteins (protein-protein interactions, PPI) add yet another level of complexity in humans and other organisms. In the past, all of these proteins were analyzed one at the time. Currently, they are analyzed by a less tedious method: mass spectrometry (MS) for two reasons: 1) because of the complexity of proteins, protein PTMs and PPIs and 2) because MS is the only method that can keep up with such a complex array of features. Here, we discuss the applications of mass spectrometry in protein analysis.

Bottlenecks in Proteomics: An Update

Mass spectrometry (MS) is the core for advanced methods in proteomic experiments. When effectively used, proteomics may provide extensive information about proteins and their post-translational modifications, as well as their interaction partners. However, there are also many problems that one can encounter during a proteomic experiment, including, but not limited to sample preparation, sample fractionation, sample analysis, data analysis & interpretation and biological significance. Here we discuss some of the problems that researchers should be aware of when performing a proteomic experiment.

Role of Mass Spectrometry in Investigating a Novel Protein: The Example of Tumor Differentiation Factor (TDF)

Better understanding of central nervous system (CNS) molecules can include the identification of new molecules and their receptor systems. Discovery of novel proteins and elucidation of receptor targets can be accomplished using mass spectrometry (MS). We describe a case study of such a molecule, which our lab has studied using MS in combination with other protein identification techniques, such as immunohistochemistry and Western Blotting. This molecule is known as tumor differentiation factor (TDF), a recently-found protein secreted by the pituitary into the blood. TDF mRNA has been detected in brain; not heart, placenta, lung, liver, skeletal muscle, or pancreas. Currently TDF has an unclear function, and prior to our studies, its localization was only minimally understood, with no understanding of receptor targets. We investigated the distribution of TDF in the rat brain using immunohistochemistry (IHC) and immunofluorescence (IF). TDF protein was detected in pituitary and most other brain regions, in specific neurons but not astrocytes. We found TDF immunoreactivity in cultured neuroblastoma, not astrocytoma. These data suggest that TDF is localized to neurons, not to astrocytes. Our group also conducted studies to identify the TDF receptor (TDF-R). Using LC-MS/MS and Western blotting, we identified the members of the Heat Shock 70-kDa family of proteins (HSP70) as potential TDF-R candidates in both MCF7 and BT-549 human breast cancer cells (HBCC) and PC3, DU145, and LNCaP human prostate cancer cells (HPCC), but not in HeLa cells, NG108 neuroblastoma, or HDF-a and BLK CL.4 cells fibroblasts or fibroblast-like cells. These studies have combined directed protein identification techniques with mass spectrometry to increase our understanding of a novel protein that may have distinct actions as a hormone in the body and as a growth factor in the brain.

Proteomics analysis of human breast milk to assess breast cancer risk

Detection of breast cancer (BC) in young women is challenging because mammography, the most common tool for detecting BC, is not effective on the dense breast tissue characteristic of young women. In addition to the limited means for detecting their BC, young women face a transient increased risk of pregnancy-associated BC. As a consequence, reproductively active women could benefit significantly from a tool that provides them with accurate risk assessment and early detection of BC. One potential method for detection of BC is biochemical monitoring of proteins and other molecules in bodily fluids such as serum, nipple aspirate, ductal lavage, tear, urine, saliva and breast milk. Of all these fluids, only breast milk provides access to a large volume of breast tissue, in the form of exfoliated epithelial cells, and to the local breast environment, in the form of molecules in the milk. Thus, analysis of breast milk is a non-invasive method with significant potential for assessing BC risk. Here we analyzed human breast milk by mass spectrometry (MS)-based proteomics to build a biomarker signature for early detection of BC. Ten milk samples from eight women provided five paired-groups (cancer versus control) for analysis of dysregulatedproteins: two within woman comparisons (milk from a diseased breast versus a healthy breast of the same woman) and three across women comparisons (milk from a woman with cancer versus a woman without cancer). Despite a wide range in the time between milk donation and cancer diagnosis (cancer diagnosis occurred from 1 month before to 24 months after milk donation), the levels of some proteins differed significantly between cancer and control in several of the five comparison groups. These pilot data are supportive of the idea that molecular analysis of breast milk will identify proteins informative for early detection and accurate assessment of BC risk, and warrant further research. Data are available via ProteomeXchange with identifier PXD007066.

Comparative two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) of human milk to identify dysregulated proteins in breast cancer

Breast cancer (BC) remains a major cause of mortality, and early detection is considered important for reducing BC-associated deaths. Early detection of BC is challenging in young women, due to the limitations of mammography on the dense breast tissue of young women. We recently reported results of a pilot proteomics study, using one-dimensional polyacrylamide gel electrophoresis (1D-PAGE) and mass spectrometry (MS) to investigate differences in milk proteins from women with and without BC. Here, we applied two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) and MS to compare the protein pattern in milk from the breasts of a single woman who was diagnosed with BC in one breast 24 months after donating her milk. Statistically different gel spots were picked for protein digestion followed by nanoliquid chromatography tandem MS (nanoLC-MS/MS) analysis. The upregulated proteins in BC versus control are alpha-amylase, gelsolin isoform a precursor, alpha-2-glycoprotein 1 zinc isoform CRA_b partial, apoptosis-inducing factor 2 and vitronectin. Several proteins were downregulated in the milk of the breast later diagnosed with cancer as compared to the milk from the healthy breast, including different isoforms of albumin, cholesterol esterase, different isoforms of lactoferrin, different proteins from the casein family and different isoforms of lysozyme. Results warrant further studies to determine the usefulness of these milk proteins for assessing risk and detecting occult disease. MS data is available via ProteomeXchange with identifier PXD009860.

CCTOP: a Consensus Constrained TOPology prediction web server

The Consensus Constrained TOPology prediction (CCTOP; http://cctop.enzim.ttk.mta.hu) server is a web-based application providing transmembrane topology prediction. In addition to utilizing 10 different state-of-the-art topology prediction methods, the CCTOP server incorporates topology information from existing experimental and computational sources available in the PDBTM, TOPDB and TOPDOM databases using the probabilistic framework of hidden Markov model. The server provides the option to precede the topology prediction with signal peptide prediction and transmembrane-globular protein discrimination. The initial result can be recalculated by (de)selecting any of the prediction methods or mapped experiments or by adding user specified constraints. CCTOP showed superior performance to existing approaches. The reliability of each prediction is also calculated, which correlates with the accuracy of the per protein topology prediction. The prediction results and the collected experimental information are visualized on the CCTOP home page and can be downloaded in XML format. Programmable access of the CCTOP server is also available, and an example of client-side script is provided.

Expediting topology data gathering for the TOPDB database

The Topology Data Bank of Transmembrane Proteins (TOPDB, http://topdb.enzim.ttk.mta.hu) contains experimentally determined topology data of transmembrane proteins. Recently, we have updated TOPDB from several sources and utilized a newly developed topology prediction algorithm to determine the most reliable topology using the results of experiments as constraints. In addition to collecting the experimentally determined topology data published in the last couple of years, we gathered topographies defined by the TMDET algorithm using 3D structures from the PDBTM. Results of global topology analysis of various organisms as well as topology data generated by high throughput techniques, like the sequential positions of N- or O-glycosylations were incorporated into the TOPDB database. Moreover, a new algorithm was developed to integrate scattered topology data from various publicly available databases and a new method was introduced to measure the reliability of predicted topologies. We show that reliability values highly correlate with the per protein topology accuracy of the utilized prediction method. Altogether, more than 52 000 new topology data and more than 2600 new transmembrane proteins have been collected since the last public release of the TOPDB database.

Mass spectrometry for proteomics-based investigation using the zebrafish vertebrate model system

The zebrafish (Danio rerio) is frequently being used to investigate the genetics of human diseases as well as resulting pathologies. Ease of both forward and reverse genetic manipulation along with conservation of vertebrate organ systems and disease causing genes has made this system a popular model. Many techniques have been developed to manipulate the genome of zebrafish producing mutants in a vast array of genes. While genetic manipulation of zebrafish has progressed, proteomics have been under-utilized. This review highlights studies that have already been performed using proteomic techniques and as well as our initial proteomic work comparing changes to the proteome between the ascl1a-/- and WT intestine.

Using breast milk to assess breast cancer risk: the role of mass spectrometry-based proteomics

Although mammography and treatment advances have led to declines in breast cancer mortality in the United States, breast cancer remains a major cause of morbidity and mortality. Breast cancer in young women is associated with increased mortality and current methods of detecting breast cancers in this group of women have known limitations. Tools for accurately assessing personal breast cancer risk in young women are needed to identify those women who would benefit the most from earlier intervention. Proteomic analysis of breast milk could identify biomarkers of breast cancer risk and provide a tool for identifying women at increased risk. A preliminary analysis of milk from four women provides a proof of concept for using breast milk to assess breast cancer risk.

Mass spectrometry for proteomics-based investigation

Within the past years, we have witnessed a great improvement in mass spectrometry (MS) and proteomics approaches in terms of instrumentation, protein fractionation, and bioinformatics. With the current technology, protein identification alone is no longer sufficient. Both scientists and clinicians want not only to identify proteins but also to identify the protein's posttranslational modifications (PTMs), protein isoforms, protein truncation, protein-protein interaction (PPI), and protein quantitation. Here, we describe the principle of MS and proteomics and strategies to identify proteins, protein's PTMs, protein isoforms, protein truncation, PPIs, and protein quantitation. We also discuss the strengths and weaknesses within this field. Finally, in our concluding remarks we assess the role of mass spectrometry and proteomics in scientific and clinical settings in the near future. This chapter provides an introduction and overview for subsequent chapters that will discuss specific MS proteomic methodologies and their application to specific medical conditions. Other chapters will also touch upon areas that expand beyond proteomics, such as lipidomics and metabolomics.

Cancer secretomes and their place in supplementing other hallmarks of cancer

The secretome includes all macromolecules secreted by cells, in particular conditions at defined times, allowing cell-cell communication. Cancer cell secretomes that are altered compared to normal cells have shown significant potential for elucidating cancer biology. Proteins of secretomes are secreted by various secretory pathways and can be studied using different methods. Cancer secretomes seem to play an important role in known hallmarks of cancers such as excessive proliferation, reduced apoptosis, immune invasion, angioneogenesis, alteration in energy metabolism, and development of resistance against anti-cancer therapy [1, 2]. If a significant role of an altered secretome can be identified in cancer cells, using advanced mass spectrometry-based techniques, this may allow researchers to screen and characterize the secretome proteins involved in cancer progression and open up new opportunities to develop new therapies. We aim to elaborate upon recent advances in cancer cell secretome analysis using different proteomics techniques. In this review, we highlight the role of the altered secretome in contributing to already recognized and emerging hallmarks of cancer and we discuss new challenges in the field of secretome analysis.

Application of mass spectrometry to characterize localization and efficacy of nanoceria in vivo

In vivo study of nanomaterials is complicated by the physical and chemical changes induced in the nanomaterial by exposure to biological compartments. A diverse array of proteins can bind to the nanomaterial, forming a protein corona which may alter the dispersion, surface charge, distribution, and biological activity of the material. Evidence suggests that unique synthesis and stabilization strategies can greatly affect the composition of the corona, and thus, the in vivo properties of the nanomaterial. Protein and elemental analyses techniques are critical to characterizing the nature of the protein corona in order to best predict the in vivo behavior of the nanomaterial. Further, as described here, inductively coupled mass spectroscopy (ICP-MS) can also be used to quantify nanomaterial deposition in tissues harvested from exposed animals. Elemental analysis of ceria content demonstrated deposition of cerium oxide nanoparticles (CeNPs) in various tissues of healthy mice and in the brains of mice with a model of multiple sclerosis. Thus, ICP-MS analysis of nanomaterial tissue distribution can complement data illustrating the biological, and in this case, therapeutic efficacy of nanoparticles delivered in vivo.

Mass spectrometry for the study of autism and neurodevelopmental disorders

Mass spectrometry (MS) has been increasingly used to study central nervous system disorders, including autism spectrum disorders (ASDs). The first studies of ASD using MS focused on the identification of external toxins, but current research is more directed at understanding endogenous protein changes that occur in ASD (ASD proteomics). This chapter focuses on how MS has been used to study ASDs, with particular focus on proteomic analysis. Other neurodevelopmental disorders have been investigated using this technique, including genetic syndromes associated with autism such as fragile X syndrome and Smith-Lemli-Opitz syndrome.