How proteomics reveals potential biomarkers in brain diseases

The brain is complex, and so are the proteomics studies of brain tissue and its diseases, including Alzheimer's Disease, Parkinson's Disease and schizophrenia. In this review, general considerations and strategies of proteomics technologies, the advantages and challenges as well as the special needs for brain tissue are described and summarized. In addition, the results of the first studies are presented including a quality evaluation of the candidate proteins for these diseases. A paragraph is dedicated to the efforts of standardization in this field.     

How proteomics reveals potential biomarkers in brain diseases

The brain is complex, and so are the proteomics studies of brain tissue and its diseases, including Alzheimer’s Disease, Parkinson’s Disease and schizophrenia. In this review, general considerations and strategies of proteomics technologies, the advantages and challenges as well as the special needs for brain tissue are described and summarized. In addition, the results of the first studies are presented including a quality evaluation of the candidate proteins for these diseases. A paragraph is dedicated to the efforts of standardization in this field.     

IsobariQ: software for isobaric quantitative proteomics using IPTL, iTRAQ, and TMT

Isobaric peptide labeling plays an important role in relative quantitative comparisons of proteomes. Isobaric labeling techniques utilize MS/MS spectra for relative quantification, which can be either based on the relative intensities of reporter ions in the low mass region (iTRAQ and TMT) or on the relative intensities of quantification signatures throughout the spectrum due to isobaric peptide termini labeling (IPTL). Due to the increased quantitative information found in MS/MS fragment spectra generated by the recently developed IPTL approach, new software was required to extract the quantitative information. IsobariQ was specifically developed for this purpose; however, support for the reporter ion techniques iTRAQ and TMT is also included. In addition, to address recently emphasized issues about heterogeneity of variance in proteomics data sets, IsobariQ employs the statistical software package R and variance stabilizing normalization (VSN) algorithms available therein. Finally, the functionality of IsobariQ is validated with data sets of experiments using 6-plex TMT and IPTL. Notably, protein substrates resulting from cleavage by proteases can be identified as shown for caspase targets in apoptosis.     

Aberrant Mucin5B expression in lung adenocarcinomas detected by iTRAQ labeling quantitative proteomics and immunohistochemistry

Background

Lung cancer is the number one cause of cancer-related deaths in the United States and worldwide. The complex protein changes and/or signature of protein expression in lung cancer, particularly in non-small cell lung cancer (NSCLC) has not been well defined. Although several studies have investigated the protein profile in lung cancers, the knowledge is far from complete. Among early studies, mucin5B (MUC5B) has been suggested to play an important role in the tumor progression. MUC5B is the major gel-forming mucin in the airway. In this study, we investigated the overall protein profile and MUC5B expression in lung adenocarcinomas, the most common type of NSCLCs.

Methods

Lung adenocarcinoma tissue in formalin-fixed paraffin-embedded (FFPE) blocks was collected and microdissected. Peptides from 8 tumors and 8 tumor-matched normal lung tissue were extracted and labeled with 8-channel iTRAQ reagents. The labeled peptides were identified and quantified by LC-MS/MS using an LTQ Orbitrap Velos mass spectrometer. MUC5B expression identified by iTRAQ labeling was further validated using immunohistochemistry (IHC) on tumor tissue microarray (TMA).

Results

A total of 1288 peptides from 210 proteins were identified and quantified in tumor tissues. Twenty-two proteins showed a greater than 1.5-fold differences between tumor and tumor-matched normal lung tissues. Fifteen proteins, including MUC5B, showed significant changes in tumor tissues. The aberrant expression of MUC5B was further identified in 71.1% of lung adenocarcinomas in the TMA.

Discussions

A subset of tumor-associated proteins was differentially expressed in lung adenocarcinomas. The differential expression of MUC5B in lung adenocarcinomas suggests its role as a potential biomarker in the detection of adenocarcinomas.     

Investigation of plasma biomarkers in HIV-1/HCV mono- and coinfected individuals by multiplex iTRAQ quantitative proteomics

The analysis of plasma samples from HIV-1/HCV mono- and coinfected individuals by quantitative proteomics is an efficient strategy to investigate changes in protein abundances and to characterize the proteins that are the effectors of cellular functions involved in viral pathogenesis. In this study, the infected and healthy plasma samples (in triplicate) were treated with ProteoMiner beads to equalize protein concentrations and subjected to 4-plex iTRAQ labeling and liquid chromatography/mass spectrometry (LC-MS/MS) analysis. A total of 70 proteins were identified with high confidence in the triplicate analysis of plasma proteins and 65% of the proteins were found to be common among the three replicates. Apolipoproteins and complement proteins are the two major classes of proteins that exhibited differential regulation. The results of quantitative analysis revealed that APOA2, APOC2, APOE, C3, HRG proteins were upregulated in the plasma of all the three HIV-1 mono-, HCV mono-, and coinfected patient samples compared to healthy control samples. Ingenuity pathway analysis (IPA) of the upregulated proteins revealed that they are implicated in the hepatic lipid metabolism, inflammation, and acute-phase response signaling pathways. Thus, we identified several differentially regulated proteins in HIV-1/HCV mono and coinfected plasma samples that may be potential biomarkers for liver disease.     

A noise model for mass spectrometry based proteomics

Motivation: Mass spectrometry data are subjected to considerablenoise. Good noise models are required for proper detection andquantification of peptides. We have characterized noise in bothquadrupole time-of-flight (Q-TOF) and ion trap data, and haveconstructed models for the noise. Results: We find that the noise in Q-TOF data from Applied BiosystemsQSTAR fits well to a combination of multinomial and Poissonmodel with detector dead-time correction. In comparison, iontrap noise from Agilent MSD-Trap-SL is larger than the Q-TOFnoise and is proportional to Poisson noise. We then demonstratethat the noise model can be used to improve deisotoping forpeptide detection, by estimating appropriate cutoffs of thegoodness of fit parameter at prescribed error rates. The noisemodels also have implications in noise reduction, retentiontime alignment and significance testing for biomarker discovery. Contact: pdu{at}us.ibm.com Supplementary information: Supplementary data are availableat Bioinfomatics Online. Associate Editor: Olga Troyanskaya     

Study of nitrate stress in Desulfovibrio vulgaris Hildenborough using iTRAQ proteomics.

The response of Desulfovibrio vulgaris Hildenborough (DvH), a sulphate-reducing bacterium, to nitrate stress was examined using quantitative proteomic analysis. DvH was stressed with 105 mM sodium nitrate (NaNO(3)), a level that caused a 50% inhibition in growth. The protein profile of stressed cells was compared with that of cells grown in the absence of nitrate using the iTRAQ peptide labelling strategy and tandem liquid chromatography separation coupled with mass spectrometry (quadrupole time-of-flight) detection. A total of 737 unique proteins were identified by two or more peptides, representing 22% of the total DvH proteome and spanning every functional category. The results indicate that this was a mild stress, as proteins involved in central metabolism and the sulphate reduction pathway were unperturbed. Proteins involved in the nitrate reduction pathway increased. Increases seen in transport systems for proline, glycine-betaine and glutamate indicate that the NaNO(3) exposure led to both salt stress and nitrate stress. Up-regulation observed in oxidative stress response proteins (Rbr, RbO, etc.) and a large number of ABC transport systems as well as in iron-sulphur-cluster-containing proteins, however, appear to be specific to nitrate exposure. Finally, a number of hypothetical proteins were among the most significant changers, indicating that there may be unknown mechanisms initiated upon nitrate stress in DvH.     

Comparative iTRAQ proteomics revealed proteins associated with horn development in yak

Background

The practice of dehorning yak raises animal safety concerns, which have been addressed by selective breeding to obtain genetically hornless yak. The POLLED locus in yak has been studied extensively; however, little is known regarding the proteins that regulate horn bud development.

Methods

A differential proteomic analysis was performed to compare the skin from the horn bud region of polled yak fetuses and the horn bud tissue of horned yak fetuses using isobaric tags for relative and absolute quantitation (iTRAQ) technology coupled with 2D LC-MS/MS.

Results

One hundred differentially abundant proteins (DAPs) were identified. Of these, 29 were up-regulated and 71 were down-regulated in skin from the horn bud region of polled fetuses when compared to the horn bud tissue of horned fetuses. Bioinformatics analyses showed that the up-regulated DAPs were mainly associated with metabolic activities, while the down-regulated DAPs were significantly enriched in cell adhesion and cell movement activities.

Conclusions

We concluded that some important proteins were associated with cell adhesion, cell motility, keratinocyte differentiation, cytoskeleton organization, osteoblast differentiation, and fatty acid metabolism during horn bud development. These results advance our understanding of the molecular mechanisms underlying horn development.

     

Advances in quantitative proteomics

Large-scale protein quantification has become a major proteomics application in many areas of biological and medical research. During the past years, different techniques have been developed, including gel-based such as differential in-gel electrophoresis (DIGE) and liquid chromatography-based such as isotope labeling and label-free quantification. These quantitative proteomics tools hold significant promise for biomarker discovery, diagnostic and therapeutic applications. They are also important for research in functional genomics and systems biology towards basic understanding of molecular networks and pathway interactions. In this review, we summarize current technologies in quantitative proteomics and discuss recent applications of the technologies.     

Comprehensive comparison of iTRAQ and label-free LC-based quantitative proteomics approaches using two Chlamydomonas reinhardtii strains of interest for biofuels engineering

Comprehensive comparisons of quantitative proteomics techniques are rare in the literature, yet they are crucially important for optimal selection of approaches and methodologies that are ideal for a given proteomics initiative. In this study, two LC-based quantitative proteomics approaches--iTRAQ and label-free--were implemented using the LTQ-Orbitrap Velos platform. For this comparison, the model used was the total protein content from two Chlamydomonas reinhardtii strains in the context of alternative biofuels production. The strain comparison includes sta6 (a starch-less mutant of cw15) that produces twice as many lipid bodies (LB) containing triacylglycerols (TAGs) as its parental strain cw15 (a cell wall-deficient C. reinhardtii strain) under nitrogen starvation. Internal standard addition was used to rigorously assess the quantitation accuracy and precision of each method. Results from iTRAQ-4plex labeling using HCD (higher energy collision-induced dissociation) fragmentation were compared to those obtained using a label-free approach based on the peak area of intact peptides and collision-induced dissociation. The accuracy and precision, number of identified/quantified proteins and statistically significant protein differences detected, as well as efficiency of these two quantitative proteomics methods were evaluated and compared. Four technical and three biological replicates of each strain were performed to assess both the technical and biological variation of both approaches. A total of 896 and 639 proteins were identified with high confidence, and 329 and 124 proteins were quantified significantly with label-free and iTRAQ, respectively, using biological replicates. The results showed that both iTRAQ labeling and label-free methods provide high quality quantitative and qualitative data using nano-LC coupled with the LTQ-Orbitrap Velos mass spectrometer, but the selection of the optimal approach is dependent on experimental design and the biological question to be addressed. The functional categorization of the differential proteins between cw15 and sta6 reveals already known but also new mechanisms likely responsible for the production of lipids in sta6 and sets the baseline for future studies aimed at engineering these strains for high oil production.     

Label-free quantitative proteomics reveals differentially regulated proteins in the latex of sticky diseased Carica papaya L. plants

Papaya meleira virus (PMeV) is so far the only described laticifer-infecting virus, the causal agent of papaya (Carica papaya L.) sticky disease. The effects of PMeV on the laticifers' regulatory network were addressed here through the proteomic analysis of papaya latex. Using both 1-DE- and 1D-LC-ESI-MS/MS, 160 unique papaya latex proteins were identified, representing 122 new proteins in the latex of this plant. Quantitative analysis by normalized spectral counting revealed 10 down-regulated proteins in the latex of diseased plants, 9 cysteine proteases (chymopapain) and 1 latex serine proteinase inhibitor. A repression of papaya latex proteolytic activity during PMeV infection was hypothesized. This was further confirmed by enzymatic assays that showed a reduction of cysteine-protease-associated proteolytic activity in the diseased papaya latex. These findings are discussed in the context of plant responses against pathogens and may greatly contribute to understand the roles of laticifers in plant stress responses.     

Identification of key players for colorectal cancer metastasis by iTRAQ quantitative proteomics profiling of isogenic SW480 and SW620 cell lines

This study compared the whole cell proteome profiles of two isogenic colorectal cancer (CRC) cell lines (primary SW480 cell line and its lymph node metastatic variant SW620), as an in vitro metastatic model, to gain an insight into the molecular events of CRC metastasis. Using iTRAQ (isobaric tags for relative and absolute quantitation) based shotgun proteomics approach, we identified 1140 unique proteins, out of which 147 were found to be significantly altered in the metastatic cell. Ingenuity pathway analysis with those significantly altered proteins, revealed cellular organization and assembly as the top-ranked altered biological function. Differential expression pattern of 6 candidate proteins were validated by Western blot. Among these, the low expression level of β-catenin combined with the up-regulation of CacyBP (Calcyclin binding Protein), a β-catenin degrading protein, in the metastatic cell provided a rational guide for the downstream functional assays. The relative expression pattern of these two proteins was further validated in three other CRC cells by Western blot and quantitative immunofluorescence studies. Overexpression of CacyBP in three different primary CRC cell lines showed significant reduction in adhesion characteristics as well as cellular β-catenin level as confirmed by our experiments, indicating the possible involvement of CacyBP in CRC metastasis. In short, this study demonstrates successful application of a quantitative proteomics approach to identify novel key players for CRC metastasis, which may serve as biomarkers and/or drug targets to improve CRC therapy.     

The methods of quantitative proteomics

In modern science proteomic analysis is inseparable from other fields of systemic biology. Possessing huge resources quantitative proteomics operates colossal information on molecular mechanisms of life. Advances in proteomics help researchers to solve complex problems of cell signaling, posttranslational modification, structure and funciotnal homology of proteins, molecular diagnostics etc. More than 40 various methods have been developed in proteomics for quantitative analysis of proteins. Although each method is unique and has certain advantages and disadvantages all these use various isotope labels (tags). In this review we will consider the most popular and effective methods employing both chemical modifications of proteins and also metabolic and enzymatic methods of isotope labeling.     

Mass spectrometric-based approaches in quantitative proteomics

Classically, experiments aimed at studying changes in protein expression have always followed a small set of proteins. This focused approach was necessary since tools to efficiently analyze large numbers of proteins were simply not available. Large-scale quantitative proteomics promises to produce reams of data that previously would have taken decades to measure with classical methods. Mass spectrometry is already a well-established protein identification tool and recent methodological developments indicate that it can also be successfully applied to extract quantitative data of protein abundance. From the first reports 4 years ago, numerous schemes to take advantage of stable isotope nuclei incorporation in proteins and peptides have been developed. Here we review the benefits and pitfalls of some of the most commonly used protocols, focusing on a procedure now being used extensively in our laboratory, stable isotope labeling with amino acids in cell culture (SILAC). The basic theory, application, and data analysis of a SILAC experiment are discussed. The emerging nature of these techniques and the rapid pace of technological development make forecasting the directions of the field difficult but we speculate that SILAC will soon be a key tool of quantitative proteomics.     

Systematic and quantitative comparison of digest efficiency and specificity reveals the impact of trypsin quality on MS-based proteomics

Trypsin is the most frequently used proteolytic enzyme in mass spectrometry-based proteomics. Beside its good availability, it also offers some major advantages such as an optimal average peptide length of ~ 14 amino acids, and typically the presence of at least two defined positive charges at the N-terminus as well as the C-terminal Arg/Lys, rendering tryptic peptides well suited for CID-based LC-MS/MS. Here, we conducted a systematic study of different types of commercially available trypsin in order to qualitatively and quantitatively compare cleavage specificity, efficiency as well as reproducibility and the potential impact on quantitation and proteome coverage. We present a straightforward strategy applied to complex digests of human platelets, comprising (1) digest controls using a monolithic column HPLC-setup, (2) SCX enrichment of semitryptic/nonspecific peptides, (3) targeted MRM analysis of corresponding full cleavage/missed cleavage peptide pairs as well as (4) LC-MS analyses of complete digests with a three-step data interpretation. Thus, differences in digest performance can be readily assessed, rendering these procedures extremely beneficial to quality control not only the trypsin of choice, but also to effectively compare as well as optimize different digestion conditions and to evaluate the reproducibility of a dedicated digest protocol for all kinds of quantitative proteome studies.     

Identification of prosaposin and transgelin as potential biomarkers for gallbladder cancer using quantitative proteomics

Gallbladder cancer is an uncommon but lethal malignancy with particularly high incidence in Chile, India, Japan and China. There is a paucity of unbiased large-scale studies investigating molecular basis of gallbladder cancer. To systematically identify differentially regulated proteins in gallbladder cancer, iTRAQ-based quantitative proteomics of gallbladder cancer was carried out using Fourier transform high resolution mass spectrometry. Of the 2575 proteins identified, proteins upregulated in gallbladder cancer included several lysosomal proteins such as prosaposin, cathepsin Z and cathepsin H. Downregulated proteins included serine protease HTRA1 and transgelin, which have been reported to be downregulated in several other cancers. Novel biomarker candidates including prosaposin and transgelin were validated to be upregulated and downregulated, respectively, in gallbladder cancer using tissue microarrays. Our study provides the first large scale proteomic characterization of gallbladder cancer which will serve as a resource for future discovery of biomarkers for gallbladder cancer.     

ABA-dependent and -independent G-protein signaling in Arabidopsis roots revealed through an iTRAQ proteomics approach

Heterotrimeric G-proteins are important signal transducers in all eukaryotes. The plant hormone abscisic acid (ABA) has emerged as a key regulator of G-protein-mediated signaling pathways in plants. ABA-regulation of G-protein signaling involves both conventional and novel mechanisms. We have utilized the null mutant of the Arabidopsis G-protein α subunit gpa1 to evaluate to what extent ABA-dependent changes in the proteome are regulated by G-proteins. We used Arabidopsis root tissue as both ABA and G-proteins, individually and in combination, affect root growth and development. We identified 720 proteins, of which 42 showed GPA1-dependent and 74 showed ABA-dependent abundance changes. A majority of ABA-regulated proteins were also GPA1-dependent. Our data provide insight into how tissue specificity might be achieved in ABA-regulated G-protein signaling. A number of proteins related to ER body formation and intracellular trafficking were altered in gpa1 mutant, suggesting a novel role for GPA1 in these pathways. A potential link between ABA metabolism and ABA signaling was also revealed. The comparison of protein abundance changes in the absence of ABA offers clues to the role of GPA1 in ABA-independent signaling pathways, for example, regulation of cell division. These findings substantially contribute to our knowledge of G-protein signaling mechanisms in plants.