Identification of differentially regulated secretome components during skeletal myogenesis.

Myogenesis is a well-characterized program of cellular differentiation that is exquisitely sensitive to the extracellular milieu. Systematic characterization of the myogenic secretome (i.e. the ensemble of secreted proteins) is, therefore, warranted for the identification of novel secretome components that regulate both the pluripotency of these progenitor mesenchymal cells, and also their commitment and passage through the differentiation program. Previously, we have successfully identified 26 secreted proteins in the mouse skeletal muscle cell line C2C12 (1). In an effort to attain a more comprehensive picture of the regulation of myogenesis by its extracellular milieu, quantitative profiling employing stable isotope labeling by amino acids in cell culture was implemented in conjunction with two parallel high throughput online reverse phase liquid chromatography-tandem mass spectrometry systems. In summary, 34 secreted proteins were quantified, 30 of which were shown to be differentially expressed during muscle development. Intriguingly, our analysis has revealed several novel up- and down-regulated secretome components that may have critical biological relevance for both the maintenance of pluripotency and the passage of cells through the differentiation program. In particular, the altered regulation of secretome components, including follistatin-like protein-1, osteoglycin, spondin-2, and cytokine-induced apoptosis inhibitor-1, along with constitutively expressed factors, such as fibulin-2, illustrate dynamic changes in the secretome that take place when differentiation to a specific lineage occurs.

iTRAQ-multidimensional liquid chromatography and tandem mass spectrometry-based identification of potential biomarkers of oral epithelial dysplasia and novel networks between inflammation and premalignancy.

Chronic exposure of the oral mucosa to carcinogens in tobacco is linked to inflammation and development of oral premalignant lesions (OPLs) with high risk of progression to cancer; there is currently no clinical methodology to identify high-risk lesions. We hypothesized that identification of differentially expressed proteins in OPLs in relation to normal oral tissues using proteomic approach will reveal changes in multiple cellular pathways and aid in biomarker discovery. Isobaric mass tags (iTRAQ)-labeled oral dysplasias and normal tissues were compared against pooled normal control by online liquid chromatography and tandem mass spectrometry. Verification of biomarkers was carried out in an independent set of samples by immunohistochemistry, immunoblotting, and RT-PCR. We identified 459 nonredundant proteins in OPLs, including structural proteins, signaling components, enzymes, receptors, transcription factors, and chaperones. A panel of three best-performing biomarkers identified by iTRAQ analysis and verified by immunohistochemistrystratifin (SFN), YWHAZ, and hnRNPKachieved a sensitivity of 0.83, 0.91, specificity of 0.74, 0.95, and predictive value of 0.87 and 0.96, respectively, in discriminating dysplasias from normal tissues, thereby confirming their utility as potential OPL biomarkers. Pathway analysis revealed direct interactions between all the three biomarkers and their involvement in two major networks involved in inflammation, signaling, proliferation, regulation of gene expression, and cancer. In conclusion, our work on determining the OPL proteome unraveled novel networks linking inflammation and development of epithelial dysplasia and their key regulatory proteins may serve as novel chemopreventive/therapeutic targets for early intervention. Additionally, we identified and verified a panel of OPL biomarkers that hold promise for large-scale validation for ultimate clinical use.

Discovery and verification of head-and-neck cancer biomarkers by differential protein expression analysis using iTRAQ labeling, multidimensional liquid chromatography, and tandem mass spectrometry.

Multidimensional LC-MS/MS has been used for the analysis of biological samples labeled with isobaric mass tags for relative and absolute quantitation (iTRAQ) to identify proteins that are differentially expressed in human head-and-neck squamous cell carcinomas (HNSCCs) in relation to non-cancerous head-and-neck tissues (controls) for cancer biomarker discovery. Fifteen individual samples (cancer and non-cancerous tissues) were compared against a pooled non-cancerous control (prepared by pooling equal amounts of proteins from six non-cancerous tissues) in five sets by on-line and off-line separation. We identified 811 non-redundant proteins in HNSCCs, including structural proteins, signaling components, enzymes, receptors, transcription factors, and chaperones. A panel of proteins showing consistent differential expression in HNSCC relative to the non-cancerous controls was discovered. Some of the proteins include stratifin (14-3-3sigma); YWHAZ (14-3-3zeta); three calcium-binding proteins of the S100 family, S100-A2, S100-A7 (psoriasin), and S100-A11 (calgizarrin); prothymosin alpha (PTHA); L-lactate dehydrogenase A chain; glutathione S-transferase Pi; APC-binding protein EB1; and fascin. Peroxiredoxin2, carbonic anhydrase I, flavin reductase, histone H3, and polybromo-1D (BAF180) were underexpressed in HNSCCs. A panel of the three best performing biomarkers, YWHAZ, stratifin, and S100-A7, achieved a sensitivity of 0.92 and a specificity of 0.91 in discriminating cancerous from non-cancerous head-and-neck tissues. Verification of differential expression of YWHAZ, stratifin, and S100-A7 proteins in clinical samples of HNSCCs and paired and non-paired non-cancerous tissues by immunohistochemistry, immunoblotting, and RT-PCR confirmed their overexpression in head-and-neck cancer. Verification of YWHAZ, stratifin, and S100-A7 in an independent set of HNSCCs achieved a sensitivity of 0.92 and a specificity of 0.87 in discriminating cancerous from non-cancerous head-and-neck tissues, thereby confirming their overexpressions and utility as credible cancer biomarkers.

Verification of endometrial tissue biomarkers previously discovered using mass spectrometry-based proteomics by means of immunohistochemistry in a tissue microarray format.

Verification of candidate protein biomarkers is a necessary step in moving from the initial discovery to application. Here, we report results of a verification exercise involving six candidate endometrial cancer biomarkers previously discovered using mass-tagging and multidimensional liquid chromatography/tandem mass spectrometry (DeSouza L., et al. J. Proteome Res. 2005, 4, 377-386) on a cohort of 148 patient samples by means of immunohistochemistry on a tissue microarray format. A panel of the three best-performing biomarkers, chaperonin 10, pyruvate kinase M2, and alpha-1-antitrypsin, achieved a sensitivity of 0.85, specificity of 0.93, predictive value of 0.90, and positive predictive value of 0.88 in discriminating malignant from benign endometrium. The ruggedness of this panel of biomarkers was verified in a 2/3-training-set-1/3-test-set cross-validation analysis by randomly splitting the cohort in 10 ways. The roles of chaperonin 10 and pyruvate kinase M2 in tumorigenesis confirm them as credible cancer biomarkers.

Identification of candidate biomarker proteins released by human endometrial and cervical cancer cells using two-dimensional liquid chromatography/tandem mass spectrometry.

Candidate biomarker proteins, including chaperonin 10 and pyruvate kinase, previously discovered and identified using mass-tagging reagents with multidimensional liquid chromatography and tandem mass spectrometry (DeSouza, L.; et al. J. Proteome Res. 2005, 4, 377-386) have been identified in serum-free media of cultured endometrial cancer (KLE and HEC-1-A) and cervical cancer (HeLa) cells. These and other cancer-associated proteins were released by the cultured cells within 24 h of growth. A total of 203 proteins from the KLE cells, 86 from HEC-1-A, and 161 from HeLa are reported.

Endometrial carcinoma biomarker discovery and verification using differentially tagged clinical samples with multidimensional liquid chromatography and tandem mass spectrometry.

The utility of differentially expressed proteins discovered and identified in an earlier study (DeSouza, L., Diehl, G., Rodrigues, M. J., Guo, J., Romaschin, A. D., Colgan, T. J., and Siu, K. W. M. (2005) Search for cancer markers from endometrial tissues using differentially labeled tags iTRAQ and cleavable ICAT with multidimensional liquid chromatography and tandem mass spectrometry. J. Proteome Res. 4, 377-386) to discriminate malignant and benign endometrial tissue samples was verified in a 40-sample iTRAQ (isobaric tags for relative and absolute quantitation) labeling study involving normal proliferative and secretory samples and Types I and II endometrial cancer samples. None of these proteins had the sensitivity and specificity to be used individually to discriminate between normal and cancer samples. However, a panel of pyruvate kinase, chaperonin 10, and alpha1-antitrypsin achieved the best results with a sensitivity, specificity, predictive value, and positive predictive value of 0.95 each in a logistic regression analysis. In addition, three new potential markers were discovered, whereas two other proteins showed promising trends but were not detected in sufficient numbers of samples to permit statistical validation. Differential expressions of some of these candidate biomarkers were independently verified using immunohistochemistry.

Global landscape of protein complexes in the yeast Saccharomyces cerevisiae.

Identification of protein-protein interactions often provides insight into protein function, and many cellular processes are performed by stable protein complexes. We used tandem affinity purification to process 4,562 different tagged proteins of the yeast Saccharomyces cerevisiae. Each preparation was analysed by both matrix-assisted laser desorption/ionization-time of flight mass spectrometry and liquid chromatography tandem mass spectrometry to increase coverage and accuracy. Machine learning was used to integrate the mass spectrometry scores and assign probabilities to the protein-protein interactions. Among 4,087 different proteins identified with high confidence by mass spectrometry from 2,357 successful purifications, our core data set (median precision of 0.69) comprises 7,123 protein-protein interactions involving 2,708 proteins. A Markov clustering algorithm organized these interactions into 547 protein complexes averaging 4.9 subunits per complex, about half of them absent from the MIPS database, as well as 429 additional interactions between pairs of complexes. The data (all of which are available online) will help future studies on individual proteins as well as functional genomics and systems biology.