Efficient Microscale Basic Reverse Phase Peptide Fractionation for Global and Targeted Proteomics

Analysis of small biological samples would benefit from an efficient microscale fractionation strategy that minimizes sample handling, transfer steps, and accompanying losses. Here we describe a microscale basic reverse phase liquid chromatographic (bRPLC) fractionation method that offers high reproducibility and efficiency for peptide mixtures from small (5–20 μg) samples. We applied our platform to detect differentially expressed proteins from lung tumor cell lines that are sensitive (11–18) and resistant (11–18R) to the tyrosine kinase inhibitor erlotinib. Label-free analyses of 5–20 μg samples yielded identifications of approximately 3,200 to 4,000 proteins with coefficients of variation of 1.9–8.9% in replicate analyses. iTRAQ analyses produced similar protein inventories. Label-free and iTRAQ analyses displayed high concordance in identifications of proteins differentially expressed in 11–18 and 11–18R cells. Micro-bRPLC fractionation of cell proteomes increased sensitivity by an average of 4.5-fold in targeted quantitation using parallel reaction monitoring for three representative receptor tyrosine kinases (EGFR, PDGFRA, and BMX), which are present at low abundance in 11–18 and 11–18R cells. These data illustrate the broad utility of micro-bRPLC fractionation for global and targeted proteomic analyses. Data are available through Proteome eXchange Accession PXD003604

Efficient Microscale Basic Reverse Phase Peptide Fractionation for Global and Targeted Proteomics

Analysis of small biological samples would benefit from an efficient microscale fractionation strategy that minimizes sample handling, transfer steps, and accompanying losses. Here we describe a microscale basic reverse phase liquid chromatographic (bRPLC) fractionation method that offers high reproducibility and efficiency for peptide mixtures from small (5–20 μg) samples. We applied our platform to detect differentially expressed proteins from lung tumor cell lines that are sensitive (11–18) and resistant (11–18R) to the tyrosine kinase inhibitor erlotinib. Label-free analyses of 5–20 μg samples yielded identifications of approximately 3,200 to 4,000 proteins with coefficients of variation of 1.9–8.9% in replicate analyses. iTRAQ analyses produced similar protein inventories. Label-free and iTRAQ analyses displayed high concordance in identifications of proteins differentially expressed in 11–18 and 11–18R cells. Micro-bRPLC fractionation of cell proteomes increased sensitivity by an average of 4.5-fold in targeted quantitation using parallel reaction monitoring for three representative receptor tyrosine kinases (EGFR, PDGFRA, and BMX), which are present at low abundance in 11–18 and 11–18R cells. These data illustrate the broad utility of micro-bRPLC fractionation for global and targeted proteomic analyses. Data are available through Proteome eXchange Accession PXD003604

Distinct Protein Expression Profiles of Solid-Pseudopapillary Neoplasms of the Pancreas

Solid-pseudopapillary neoplasm (SPN) is an uncommon pancreatic tumor with mutation in <i>CTNNB1</i> and distinct clinical and pathological features. We compared the proteomic profiles of SPN to mRNA expression. Pooled SPNs and pooled non-neoplastic pancreatic tissues were examined with high-resolution mass spectrometry. We identified 329 (150 up-regulated and 179 down-regulated) differentially expressed proteins in SPN. We identified 191 proteins (58.1% of the 329 dysregulated proteins) with the same expression tendencies in SPN based on mRNA data. Many overexpressed proteins were related to signaling pathways known to be activated in SPNs. We found that several proteins involved in Wnt signaling, including DKK4 and β-catenin, and proteins that bind β-catenin, such as FUS and NONO, were up-regulated in SPNs. Molecules involved in glycolysis, including PKM2, ENO2, and HK1, were overexpressed in accordance to their mRNA levels. In summary, SPN showed (1) distinct protein expression changes that correlated with mRNA expression, (2) overexpression of Wnt signaling proteins and proteins that bind directly to β-catenin, and (3) overexpression of proteins involved in metabolism. These findings may help develop early diagnostic biomarkers and molecular targets

Quantitative Profiling Identifies Potential Regulatory Proteins Involved in Development from Dauer Stage to L4 Stage in <i>Caenorhabditis elegans</i>

When <i>Caenorhabditis elegans</i> encounters unfavorable growth conditions, it enters the dauer stage, an alternative L3 developmental period. A dauer larva resumes larval development to the normal L4 stage by uncharacterized postdauer reprogramming (PDR) when growth conditions become more favorable. During this transition period, certain heterochronic genes involved in controlling the proper sequence of developmental events are known to act, with their mutations suppressing the Muv (multivulva) phenotype in <i>C. elegans.</i> To identify the specific proteins in which the Muv phenotype is highly suppressed, quantitative proteomic analysis with iTRAQ labeling of samples obtained from worms at L1 + 30 h (for continuous development [CD]) and dauer recovery +3 h (for postdauer development [PD]) was carried out to detect changes in protein abundance in the CD and PD states of both N2 and <i>lin-28­(n719)</i>. Of the 1661 unique proteins identified with <i>a</i> < 1% false discovery rate at the peptide level, we selected 58 proteins exhibiting ≥2-fold up-regulation or ≥2-fold down-regulation in the PD state and analyzed the Gene Ontology terms. RNAi assays against 15 selected up-regulated genes showed that seven genes were predicted to be involved in higher Muv phenotype (<i>p</i> < 0.05) in <i>lin-28­(n791)</i>, which is not seen in N2. Specifically, two genes, K08H10.1 and W05H9.1, displayed not only the highest rate (%) of Muv phenotype in the RNAi assay but also the dauer-specific mRNA expression, indicating that these genes may be required for PDR, leading to the very early onset of dauer recovery. Thus, our proteomic approach identifies and quantitates the regulatory proteins potentially involved in PDR in <i>C. elegans</i>, which safeguards the overall lifecycle in response to environmental changes

Quantitative Profiling Identifies Potential Regulatory Proteins Involved in Development from Dauer Stage to L4 Stage in <i>Caenorhabditis elegans</i>

When <i>Caenorhabditis elegans</i> encounters unfavorable growth conditions, it enters the dauer stage, an alternative L3 developmental period. A dauer larva resumes larval development to the normal L4 stage by uncharacterized postdauer reprogramming (PDR) when growth conditions become more favorable. During this transition period, certain heterochronic genes involved in controlling the proper sequence of developmental events are known to act, with their mutations suppressing the Muv (multivulva) phenotype in <i>C. elegans.</i> To identify the specific proteins in which the Muv phenotype is highly suppressed, quantitative proteomic analysis with iTRAQ labeling of samples obtained from worms at L1 + 30 h (for continuous development [CD]) and dauer recovery +3 h (for postdauer development [PD]) was carried out to detect changes in protein abundance in the CD and PD states of both N2 and <i>lin-28­(n719)</i>. Of the 1661 unique proteins identified with <i>a</i> < 1% false discovery rate at the peptide level, we selected 58 proteins exhibiting ≥2-fold up-regulation or ≥2-fold down-regulation in the PD state and analyzed the Gene Ontology terms. RNAi assays against 15 selected up-regulated genes showed that seven genes were predicted to be involved in higher Muv phenotype (<i>p</i> < 0.05) in <i>lin-28­(n791)</i>, which is not seen in N2. Specifically, two genes, K08H10.1 and W05H9.1, displayed not only the highest rate (%) of Muv phenotype in the RNAi assay but also the dauer-specific mRNA expression, indicating that these genes may be required for PDR, leading to the very early onset of dauer recovery. Thus, our proteomic approach identifies and quantitates the regulatory proteins potentially involved in PDR in <i>C. elegans</i>, which safeguards the overall lifecycle in response to environmental changes

gFinder: A Web-Based Bioinformatics Tool for the Analysis of <i>N</i>‑Glycopeptides

Glycoproteins influence numerous indispensable biological functions, and changes in protein glycosylation have been observed in various diseases. The identification and characterization of glycoprotein and glycosylation sites by mass spectrometry (MS) remain challenging tasks, and great efforts have been devoted to the development of proteome informatics tools that facilitate the MS analysis of glycans and glycopeptides. Here we report on the development of gFinder, a web-based bioinformatics tool that analyzes mixtures of native <i>N</i>-glycopeptides that have been profiled by tandem MS. gFinder not only enables the simultaneous integration of collision-induced dissociation (CID) and high-energy collisional dissociation (HCD) fragmentation but also merges the spectra for high-throughput analysis. These merged spectra expedite the identification of both glycans and <i>N</i>-glycopeptide backbones in tandem MS data using the glycan database and a proteomic search tool (e.g., Mascot). These data can be used to simultaneously characterize peptide backbone sequences and possible <i>N</i>-glycan structures using assigned scores. gFinder also provides many convenient functions that make it easy to perform manual calculations while viewing the spectrum on-screen. We used gFinder to detect an additional protein (Q8N9B8) that was missed from the previously published data set containing N-linked glycosylation. For <i>N</i>-glycan analysis, we used the GlycomeDB glycan structure database, which integrates the structural and taxonomic data from all of the major carbohydrate databases available in the public domain. Thus, gFinder is a convenient, high-throughput analytical tool for interpreting the tandem mass spectra of <i>N</i>-glycopeptides, which can then be used for identification of potential missing proteins having glycans. gFinder is available publicly at http://gFinder.proteomix.org/

The functional analysis of 6 k-mean clusters.

<p>This list of proteins was employed to identify significantly activated pathways by comparing their functional annotations according to the PANTHER classification systems. Proteins involved in nucleic acid binding, protein synthesis, and signaling (particularly, proteins involved in integrin signaling) were enriched in the downregulated protein clusters (clusters 1, 2, and 3), whereas the upregulated proteins in clusters 4, 5, and 6 were enriched in proteins involved in cytoskeleton structure.</p

Proteogenomic Analysis of Human Chromosome 9‑Encoded Genes from Human Samples and Lung Cancer Tissues

The Chromosome-centric Human Proteome Project (C-HPP) was recently initiated as an international collaborative effort. Our team adopted chromosome 9 (Chr 9) and performed a bioinformatics and proteogenomic analysis to catalog Chr 9-encoded proteins from normal tissues, lung cancer cell lines, and lung cancer tissues. Approximately 74.7% of the Chr 9 genes of the human genome were identified, which included approximately 28% of missing proteins (46 of 162) on Chr 9 compared with the list of missing proteins from the neXtProt Master Table (2013-09). In addition, we performed a comparative proteomics analysis between normal lung and lung cancer tissues. On the basis of the data analysis, 15 proteins from Chr 9 were detected only in lung cancer tissues. Finally, we conducted a proteogenomic analysis to discover Chr 9-residing single nucleotide polymorphisms (SNP) and mutations described in the COSMIC cancer mutation database. We identified 21 SNPs and four mutations containing peptides on Chr 9 from normal human cells/tissues and lung cancer cell lines, respectively. In summary, this study provides valuable information of the human proteome for the scientific community as part of C-HPP. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium with the data set identifier PXD000603

Proteogenomic Analysis of Human Chromosome 9‑Encoded Genes from Human Samples and Lung Cancer Tissues

The Chromosome-centric Human Proteome Project (C-HPP) was recently initiated as an international collaborative effort. Our team adopted chromosome 9 (Chr 9) and performed a bioinformatics and proteogenomic analysis to catalog Chr 9-encoded proteins from normal tissues, lung cancer cell lines, and lung cancer tissues. Approximately 74.7% of the Chr 9 genes of the human genome were identified, which included approximately 28% of missing proteins (46 of 162) on Chr 9 compared with the list of missing proteins from the neXtProt Master Table (2013-09). In addition, we performed a comparative proteomics analysis between normal lung and lung cancer tissues. On the basis of the data analysis, 15 proteins from Chr 9 were detected only in lung cancer tissues. Finally, we conducted a proteogenomic analysis to discover Chr 9-residing single nucleotide polymorphisms (SNP) and mutations described in the COSMIC cancer mutation database. We identified 21 SNPs and four mutations containing peptides on Chr 9 from normal human cells/tissues and lung cancer cell lines, respectively. In summary, this study provides valuable information of the human proteome for the scientific community as part of C-HPP. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium with the data set identifier PXD000603

Proteogenomic Analysis of Human Chromosome 9‑Encoded Genes from Human Samples and Lung Cancer Tissues

The Chromosome-centric Human Proteome Project (C-HPP) was recently initiated as an international collaborative effort. Our team adopted chromosome 9 (Chr 9) and performed a bioinformatics and proteogenomic analysis to catalog Chr 9-encoded proteins from normal tissues, lung cancer cell lines, and lung cancer tissues. Approximately 74.7% of the Chr 9 genes of the human genome were identified, which included approximately 28% of missing proteins (46 of 162) on Chr 9 compared with the list of missing proteins from the neXtProt Master Table (2013-09). In addition, we performed a comparative proteomics analysis between normal lung and lung cancer tissues. On the basis of the data analysis, 15 proteins from Chr 9 were detected only in lung cancer tissues. Finally, we conducted a proteogenomic analysis to discover Chr 9-residing single nucleotide polymorphisms (SNP) and mutations described in the COSMIC cancer mutation database. We identified 21 SNPs and four mutations containing peptides on Chr 9 from normal human cells/tissues and lung cancer cell lines, respectively. In summary, this study provides valuable information of the human proteome for the scientific community as part of C-HPP. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium with the data set identifier PXD000603