Analyzing peptides and proteins by mass spectrometry: principles and applications in proteomics

Podeu consultar el llibre complet a: http://hdl.handle.net/2445/32166The study of proteins has been a key element in biomedicine and biotechnology because of their important role in cell functions or enzymatic activity. Cells are the basic unit of living organisms, which are governed by a vast range of chemical reactions. These chemical reactions must be highly regulated in order to achieve homeostasis. Proteins are polymeric molecules that have taken on the evolutionary process the role, along with other factors, of control these chemical reactions. Learning how proteins interact and control their up and down regulations can teach us how living cells regulate their functions, as well as the cause of certain anomalies that occur in different diseases where proteins are involved. Mass spectrometry (MS) is an analytical widely used technique to study the protein content inside the cells as a biomarker point, which describes dysfunctions in diseases and increases knowledge of how proteins are working. All the methodologies involved in these descriptions are integrated in the field called Proteomics

Significance analysis of microarray for relative quantitation of LC/MS data in proteomics

<p>Abstract</p> <p>Background</p> <p>Although fold change is a commonly used criterion in quantitative proteomics for differentiating regulated proteins, it does not provide an estimation of false positive and false negative rates that is often desirable in a large-scale quantitative proteomic analysis. We explore the possibility of applying the Significance Analysis of Microarray (SAM) method (PNAS 98:5116-5121) to a differential proteomics problem of two samples with replicates. The quantitative proteomic analysis was carried out with nanoliquid chromatography/linear iron trap-Fourier transform mass spectrometry. The biological sample model included two <it>Mycobacterium smegmatis </it>unlabeled cell cultures grown at pH 5 and pH 7. The objective was to compare the protein relative abundance between the two unlabeled cell cultures, with an emphasis on significance analysis of protein differential expression using the SAM method. Results using the SAM method are compared with those obtained by fold change and the conventional <it>t</it>-test.</p> <p>Results</p> <p>We have applied the SAM method to solve the two-sample significance analysis problem in liquid chromatography/mass spectrometry (LC/MS) based quantitative proteomics. We grew the pH5 and pH7 unlabelled cell cultures in triplicate resulting in 6 biological replicates. Each biological replicate was mixed with a common ¹⁵N-labeled reference culture cells for normalization prior to SDS/PAGE fractionation and LC/MS analysis. For each biological replicate, one center SDS/PAGE gel fraction was selected for triplicate LC/MS analysis. There were 121 proteins quantified in at least 5 of the 6 biological replicates. Of these 121 proteins, 106 were significant in differential expression by the <it>t</it>-test (<it>p </it>< 0.05) based on peptide-level replicates, 54 were significant in differential expression by SAM with Δ = 0.68 cutoff and false positive rate at 5%, and 29 were significant in differential expression by the <it>t</it>-test (<it>p </it>< 0.05) based on protein-level replicates. The results indicate that SAM appears to overcome the false positives one encounters using the peptide-based <it>t</it>-test while allowing for identification of a greater number of differentially expressed proteins than the protein-based <it>t</it>-test.</p> <p>Conclusion</p> <p>We demonstrate that the SAM method can be adapted for effective significance analysis of proteomic data. It provides much richer information about the protein differential expression profiles and is particularly useful in the estimation of false discovery rates and miss rates.</p

Quantitative Proteomics Identify Novel miR-155 Target Proteins

Background: MicroRNAs are 22 nucleotides long non-coding RNAs and exert their function either by transcriptional or translational inhibition. Although many microRNA profiles in different tissues and disease states have already been discovered, only little is known about their target proteins. The microRNA miR-155 is deregulated in many diseases, including cancer, where it might function as an oncoMir. Methodology/Principal Findings: We employed a proteomics technique called ‘‘stable isotope labelling by amino acids in cell culture’ ’ (SILAC) allowing relative quantification to reliably identify target proteins of miR-155. Using SILAC, we identified 46 putative miR-155 target proteins, some of which were previously reported. With luciferase reporter assays, CKAP5 was confirmed as a new target of miR-155. Functional annotation of miR-155 target proteins pointed to a role in cell cycle regulation. Conclusions/Significance: To the best of our knowledge we have investigated for the first time miR-155 target proteins in the HEK293T cell line in large scale. In addition, by comparing our results to previously identified miR-155 target proteins i

Quantitative Phosphoproteomics of CXCL12 (SDF-1) Signaling

CXCL12 (SDF-1) is a chemokine that binds to and signals through the seven transmembrane receptor CXCR4. The CXCL12/CXCR4 signaling axis has been implicated in both cancer metastases and human immunodeficiency virus type 1 (HIV-1) infection and a more complete understanding of CXCL12/CXCR4 signaling pathways may support efforts to develop therapeutics for these diseases. Mass spectrometry-based phosphoproteomics has emerged as an important tool in studying signaling networks in an unbiased fashion. We employed stable isotope labeling with amino acids in cell culture (SILAC) quantitative phosphoproteomics to examine the CXCL12/CXCR4 signaling axis in the human lymphoblastic CEM cell line. We quantified 4,074 unique SILAC pairs from 1,673 proteins and 89 phosphopeptides were deemed CXCL12-responsive in biological replicates. Several well established CXCL12-responsive phosphosites such as AKT (pS473) and ERK2 (pY204) were confirmed in our study. We also validated two novel CXCL12-responsive phosphosites, stathmin (pS16) and AKT1S1 (pT246) by Western blot. Pathway analysis and comparisons with other phosphoproteomic datasets revealed that genes from CXCL12-responsive phosphosites are enriched for cellular pathways such as T cell activation, epidermal growth factor and mammalian target of rapamycin (mTOR) signaling, pathways which have previously been linked to CXCL12/CXCR4 signaling. Several of the novel CXCL12-responsive phosphoproteins from our study have also been implicated with cellular migration and HIV-1 infection, thus providing an attractive list of potential targets for the development of cancer metastasis and HIV-1 therapeutics and for furthering our understanding of chemokine signaling regulation by reversible phosphorylation

Quantitative phosphoproteomics of cytotoxic T cells to reveal Protein Kinase D 2 regulated networks

The focus of the present study was to characterize the phosphoproteome of cytotoxic T cells and to explore the role of the serine threonine kinase PKD2 (Protein Kinase D2) in the phosphorylation networks of this key lymphocyte population. We used Stable Isotope Labeling of Amino acids in Culture (SILAC) combined with phosphopeptide enrichment and quantitative mass-spectrometry to determine the impact of PKD2 loss on the cytotoxic T cells phosphoproteome. We identified 15,871 phosphorylations on 3505 proteins in cytotoxic T cells. 450 phosphosites on 281 proteins were down-regulated and 300 phosphosites on 196 proteins were up-regulated in PKD2 null cytotoxic T cells. These data give valuable new insights about the protein phosphorylation networks operational in effector T cells and reveal that PKD2 regulates directly and indirectly about 5% of the cytotoxic T-cell phosphoproteome. PKD2 candidate substrates identified in this study include proteins involved in two distinct biological functions: regulation of protein sorting and intracellular vesicle trafficking, and control of chromatin structure, transcription, and translation. In other cell types, PKD substrates include class II histone deacetylases such as HDAC7 and actin regulatory proteins such as Slingshot. The current data show these are not PKD substrates in primary T cells revealing that the functional role of PKD isoforms is different in different cell lineages

Phosphorylation of the ribosomal protein RPL12/uL11 affects translation during mitosis

Emerging evidence indicates that heterogeneity in ribosome composition can give rise to specialized functions. Until now, research mainly focused on differences in core ribosomal proteins and associated factors. The effect of posttranslational modifications has not been studied systematically. Analyzing ribosome heterogeneity is challenging because individual proteins can be part of different subcomplexes (40S, 60S, 80S, and polysomes). Here we develop polysome proteome profiling to obtain unbiased proteomic maps across ribosomal subcomplexes. Our method combines extensive fractionation by sucrose gradient centrifugation with quantitative mass spectrometry. The high resolution of the profiles allows us to assign proteins to specific subcomplexes. Phosphoproteomics on the fractions reveals that phosphorylation of serine 38 in RPL12/uL11, a known mitotic CDK1 substrate, is strongly depleted in polysomes. Follow-up experiments confirm that RPL12/uL11 phosphorylation regulates the translation of specific subsets of mRNAs during mitosis. Together, our results show that posttranslational modification of ribosomal proteins can regulate translation

Label-free and spike-in standard-free mass spectrometry in the proteomic analysis of plasma membrane proteins and membrane-associated protein networks

Mass spectrometry is the primary technology of proteomics. For the analysis of complex proteomes, protein identities and quantities are inferred from their peptides that are generated by cleaving all proteins with the endopeptidase trypsin. But there is one major disadvantage that is due to biophysical differences, different peptides cause different intensities. Miscellaneous approaches have been developed to circumvent this problem based on the chemical or metabolic introduction of heavy stable isotopes. This enables to monitor protein abundance differences of two or more samples on the same tryptic peptides that differ in mass only. Absolute quantification can be achieved similar by spiking-in synthetic isotopical labeled counterparts of a sample’s tryptic peptides. However, labeling technics suffer from high prices, introduced biases, need for extensive manual control, laborious implementation and implementation restrictions. Therefore, a multiplicity of label-free approaches have been developed that profit from instrumental improvements targeting reliability of identifications and reproducibility of quantitative values. No extensive systematic comparison of label-free quantitative parameters has been published so far presumably because of the laborious implementation. An analysis of primary label-free parameters and associated normalization methods is presented here that compares dynamic and linear ranges and accuracies in the estimation of protein amounts. This facilitated the establishment of label-free procedures addressing three fundamental questions in proteomics: what is a sample’s composition, are proteins that share a specific property enriched and what are the differences between two (or more) samples. A new mathematic model is presented that defines and elucidates enrichment. The procedures were applied first to analyze and compare stem cell plasma membrane proteomes. This is an ambitious model for proteomics because of only small amounts of arduous to analyze, partial hydrophobic proteins in a complex proteomic and chemical background. It is of scientific relevance, as membrane proteins are the cell’s communication interface that enable cell type specific processes and hence can be used to define, isolate and quantify those. The success of cell surface proteome enrichment, the quantitative composition of the proteome and the proteomic difference between stem cells isolated from the dental pulp and cultivated in different media is shown. Secondly, the procedures were applied to the analysis of transient protein networks that assemble onto proteo-liposomes in a newly designed recruitment assay that fully recapitulates membrane sorting as seen in vivo. All transmembrane proteins need to be trafficked to other organelles’ membranes by vesicular trafficking. Sorting signals within the cytosolic regions of the protein cargos trigger the formation of trafficking complexes around those. The transient membrane complexes additionally recognize organelle or organelle-domain specific membrane lipids, such as phosphatidylinositol phosphates. Different trafficking ways are characterized by different trafficking complexes. The elucidation of trafficking complexes that form around a transmembrane protein of interest discloses its trafficking routes and involved signaling processes. The synthetic proteo-liposomes were prepared from chemically defined lipids and heterologous expressed cytosolic domains of type I or type II membrane receptors. The proteomic analyses of such samples are challenging because of huge proteomic backgrounds of proteins binding to the liposomes irrespective of the receptor and relatively small amounts and numbers of receptor-specific binders. Though the basic idea is to elucidate sorting machineries and study membrane trafficking processes, such experiments are untargeted and miscellaneous discoveries were achieved. We elucidated that the apical determinant crumbs 2 is a cargo of the retromer complex. This revealed a fameless level of control for the establishment of cell polarity. We found retromer along with the adapter complexes AP 4 and AP 5 trafficking the beta amyloid precursor protein APP. This confirmed recent publications and yielded new insights. Moreover, many more proteins and complexes appeared to associate with the cytosolic part of APP (AICD) in a membrane context-dependent or -independent manner. Among those, some were so far unknown to interact with AICD, like mTORC1 and the PIKFyve complex