Expanding the protein catalogue in the proteome reference map of human breast cancer cells

Pucci‐Minafra, Ida; Cancemi, Patrizia; Fontana, Simona; Minafra, Luigi; Feo, Salvatore; Becchi, Michel; Freyria, Anne‐Marie; Minafra, Salvatore

doi:10.1002/pmic.200500627

Cited by 35 publications

(44 citation statements)

References 87 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Breast cancer cell lines are commonly used as model systems to investigate the pathways that lead to the development of cancer [37][38][39][40][41][42][43][44][45]. In a previous work, we generated a full proteome profile of the MCF-7 cell line using 2DLC-MS/MS technology [35].…”

Section: Resultsmentioning

confidence: 99%

Differential protein expression analysis using stable isotope labeling and PQD linear ion trap MS technology

Armenta

Hoeschele

Lazar

2009

J. Am. Soc. Mass Spectrom.

View full text Add to dashboard Cite

An isotope tags for relative and absolute quantitation (iTRAQ)-based reversed-phase liquid chromatography (RPLC)-tandem mass spectrometry (MS/MS) method was developed for differential protein expression profiling in complex cellular extracts. The estrogen positive MCF-7 cell line, cultured in the presence of 17␤-estradiol (E2) and tamoxifen (Tam), was used as a model system. MS analysis was performed with a linear trap quadrupole (LTQ) instrument operated by using pulsed Q dissociation (PQD) detection. Optimization experiments were conducted to maximize the iTRAQ labeling efficiency and the number of quantified proteins. MS data filtering criteria were chosen to result in a false positive identification rate of Ͻ4%. The reproducibility of protein identifications was ϳ60%-67% between duplicate, and ϳ50% among triplicate LC-MS/MS runs, respectively. The run-to-run reproducibility, in terms of relative standard deviations (RSD) of global mean iTRAQ ratios, was better than 10%. The quantitation accuracy improved with the number of peptides used for protein identification. From a total of 530 identified proteins (P Ͻ 0.001) in the E2/Tam treated MCF-7 cells, a list of 255 proteins (quantified by at least two peptides) was generated for differential expression analysis. A method was developed for the selection, normalization, and statistical evaluation of such datasets. An approximate ϳ2-fold change in protein expression levels was necessary for a protein to be selected as a biomarker candidate. According to this data processing strategy, ϳ16 proteins involved in biological processes such as apoptosis, RNA processing/metabolism, DNA replication/transcription/repair, cell proliferation and metastasis, were found to be up-or down-regulated. R ecently, two-dimensional liquid chromatography (2DLC) with tandem MS detection has emerged as an attractive technology for quantitative proteomic profiling of complex cellular extracts [1][2][3][4]. Stable isotope labeling and label-free quantitation strategies have been explored [4 -13]. Isotope labeling approaches rely on the covalent attachment of stable isotope tags to specific amino acid residues of proteins or peptides during metabolic, enzymatic, or chemical processes. Label-free quantitation methods rely on measuring peak areas, intensities, or spectral counts, and benefit from not having to chemically alter the sample. Throughput, however, is lower, and quantitation errors are higher, as the samples are processed independently. Among chemical labeling techniques, isotope tags for relative and absolute quantitation (iTRAQ) has received much attention [14 -30]. In this approach, peptides are labeled with isobaric tags at the N-terminus and the lysine side chains. MS/MS fragmentation produces signature ions that can be used to obtain quantitative information. Perhaps the most attractive advantage of this approach is that it can be used for the simultaneous quantification (relative or absolute) of up to four/eight different samples. Simplicity, of course, is an added benefit.A...

show abstract

Section: Resultsmentioning

confidence: 99%

Differential protein expression analysis using stable isotope labeling and PQD linear ion trap MS technology

Armenta

Hoeschele

Lazar

2009

J. Am. Soc. Mass Spectrom.

View full text Add to dashboard Cite

show abstract

“…nih.gov/) and UniProtKB/Swiss-Prot (http://www.expasy.ch/) databases, the 44 identified proteins were separated according to their main biological functions into nine classes ( Figure 2): 1) cytoskeleton and associated proteins; 2) proteins with binding function; 3) metabolic enzymes; 4) molecular chaperones/heat shock proteins; 5) detoxification and redox proteins; 6) membrane-associated proteins with multiple activities; 7) cell growth and proliferation regulators; 8) protein degradation, and 9) other functions. These classes were based on a study by Pucci-Minafra et al (2006). In spite of this functional classification, these classes should be treated with caution.…”

Section: Resultsmentioning

confidence: 99%

“…The actin proteins are extremely conserved evolutionarily and are essential for eukaryotic cells. Because they are very similar, distinctions by peptide mass fingerprinting are difficult, and they are commonly identified in a single spot (Dupont et al, 2005;Pucci-Minafra et al, 2006). We identified three spots as actin, cytoplasmatic 1/2 (ACTB/ACTG).…”

Section: Cytoskeleton and Associated Proteinsmentioning

confidence: 99%

“…In general, cytoskeletal proteins polymerize to form long chains, giving the appearance of strand-like or fibrous structures. It is directly or indirectly involved in several important cellular processes, including cell polarity, adhesion, intracellular transport, signal transduction, and cell movement (Pucci-Minafra et al, 2006), which in turn play important roles in embryogenesis, wound healing, inflammation, and cancer metastasis (Eriksson et al, 2009). There are three general classes of cytoskeletal fibers: 1) microtubules, 2) intermediate filaments, and 3) actin filaments.…”

Section: Cytoskeleton and Associated Proteinsmentioning

confidence: 99%

“…A first step towards achieving this goal will be the definition of the phenotypes that exist in the normal breast (Lopez-Garcia et al, 2010). In order to contribute to the knowledge of the non-tumoral profile, we gathered data on six samples; 44 proteins were identified from 70 spots and grouped into categories based on their major biological functions (Pucci-Minafra et al, 2006): cytoskeleton and associated proteins, proteins with binding function, metabolic enzymes, molecular chaperones/heat shock proteins, detoxification and redox proteins, membrane-associated proteins with multiple activities, cell growth and proliferation regulators, protein degradation, and other functions.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Proteomic analysis of non-tumoral breast tissue

Costa¹,

Kaviski²,

Souza³

et al. 2011

Genet. Mol. Res.

View full text Add to dashboard Cite

ABSTRACT. Breast cancer is a complex and heterogeneous disease. In spite of the advances made in recent decades, a better understanding of the intrinsic mechanisms of this disease is crucial. The development of new biomarkers is absolutely necessary to improve diagnosis and prognosis. Research using the proteomic approach has generated interesting results; however, the complexity of the mammary gland and of breast tumors remains a major limitation to the development of new markers. An initial step is to characterize non-tumoral human breast tissue. We present data from classical proteomic analysis based on 2-D electrophoresis and peptide mass fingerprinting identification, which were performed on six non-tumoral samples from patients with invasive ductal breast carcinomas. Forty-four different proteins from 70 spots were identified and classified according to their biological function. Cytoskeleton and associated proteins represent the largest class (30%) followed by the proteins with binding function (27%). Several of the proteins have been described in breast tumors, such as vimentin, endoplasmin, small heat shock beta-6, disulfide isomerase ©FUNPEC-RP www.funpecrp.com.br Genetics and Molecular Research 10 (4) 2430-2442 (2011) Proteomics of mammary tissue 2431and some cell growth, and proliferation regulators, suggesting the importance of including data on the characterization of non-tumoral breast and to studies on differential expression in cancer tissue.

show abstract

The effect of serum withdrawal on the protein profile of quiescent human dermal fibroblasts in primary cell culture

Boraldi

Annovi

Paolinelli-deVincenzi

et al. 2007

Proteomics

View full text Add to dashboard Cite

The effect of serum deprivation on proliferating cells is well known, in contrast its role on primary cell cultures, at confluence, has not been deeply investigated. Therefore, in order to explore the response of quiescent cells to serum deprivation, ubiquitous mesenchymal cells, as normal human dermal fibroblasts, were grown, for 48 h after confluence, in the presence or absence of 10% FBS. Fibroblast behaviour (i.e. cell morphology, cell viability, ROS production and elastin synthesis) was evaluated morphologically and biochemically. Moreover, the protein profile was investigated by 2-DE and differentially expressed proteins were identified by MS. Serum withdrawal caused cell shrinkage but did not significantly modify the total cell number. ROS production, as evaluated by the dihydroethidium (DH2) probe, was increased after serum deprivation, whereas elastin synthesis, measured by a colorimetric method, was markedly reduced in the absence of serum. By proteome analysis, 41 proteins appeared to significantly change their expression, the great majority of protein changes were related to the cytoskeleton, the stress response and the glycolytic pathway. Data indicate that human dermal fibroblasts in primary cell culture can adapt themselves to environmental changes, without significantly altering cell viability, at least after a few days of treatment, even though serum withdrawal represents a stress condition capable to increase ROS production, to influence cell metabolism and to interfere with cell behaviour, favouring the expression of several age-related features.

show abstract

Expanding the protein catalogue in the proteome reference map of human breast cancer cells

Cited by 35 publications

References 87 publications

Differential protein expression analysis using stable isotope labeling and PQD linear ion trap MS technology

Differential protein expression analysis using stable isotope labeling and PQD linear ion trap MS technology

Proteomic analysis of non-tumoral breast tissue

The effect of serum withdrawal on the protein profile of quiescent human dermal fibroblasts in primary cell culture

Contact Info

Product

Resources

About